International Space Station (ISS)Japanese Experiment Module - Exposed Facility (JEM-EF)
Superconducting Submillimerter-Wave Limb-Emission Sounder (SMILES)

JEM/SMILES Mission Plan

Version 2.1
November 15, 2002

SMILES Science Team
SMILES Mission TeamPREFACE to Version 2
In almost two years since the first version was issued, SMILES has seen significant
progress in designs of mission instruments and investigations on data analysis. What
we expected on SMILES capability has been getting reality through detailed designs andtesting results of engineering models. Estimation of data accuracy is also improved bycarrying out retrieval simulations, part of which has been conducted in a multi-national
collaboration. Scientific discussions have been more focused on potential advantages of
SMILES data. This version of SMILES Mission Plan is issued to cover those progresses.
Nevertheless, we admit the contents remain short of full satisfaction. Any critical comments will be invaluable for us to make our ways toward the launching of SMILES.
Further information about the SMILES mission including the updated version of thisdocument can be found at the following URL.
http://smiles.tksc.nasda.go.jp/indexe.shtml
November 15, 2002

Masato Shiotani
Leader, SMILES Science Team
Kyoto University

Harunobu Masuko
Principal Investigator, SMILES Mission TeamNational Space Development Agency
Communications Research LaboratoryiPREFACE to Version 1
A proposal of Superconducting Submillimeter-wave Limb-emission Sounder (SMILES)
was approved in April 1997 as an experiment on the JEM Exposed Facility. The SMILES
experiment has twofold mission objectives: to demonstrate new technology of submillimeterwave sensor for sounding the middle atmosphere, and to provide scientists with global dataof trace gases that are deeply connected to the ozone depletion. The SMILES mission team,which was formed as a joint collaborative group between the National Space Development
Agency of Japan (NASDA) and Communications Research Laboratory (CRL), has been
concentrating on the technical development of critical components and the design of thewhole SMILES system. Although there still remain several unresolved technical problems,
the SMILES system is taking shape and seems promising for the mission objectives.
Meanwhile, Japanese researchers in the field of atmospheric sciences have been collaborating with the mission team on critical discussions concerning the mission capabilities
for maximizing scientific returns. The discussion has been performed in a working group,
the SMILES Science Team, under the Committee for Earth Observation Systems, Earth
Science and Technology Forum. This report is a summary of the discussion, which clarifies the fundamental requirements on the SMILES mission, specifically from the scientificviewpoints. Now we believe that SMILES could be a powerful probe for atmosphericresearches, if these requirements are met.
We hope this report is read by many people including atmospheric scientists worldwide.
Any critical comments on the contents of this report will be appreciated. We will continue
our efforts for improving the SMILES instrumental specifications and maximizing thescientific outputs of this challenging mission.
Further information about the SMILES mission including the updated version of thisdocument can be found at the following location.
http://smiles.tksc.nasda.go.jp/indexe.shtml
The activity is sponsored by Earth Science & Technology Organization (ESTO) undera research contract with NASDA.

February 19, 2001

Masato Shiotani
Leader, SMILES Science Team
Committee for Earth Observation Systems
Earth Science and Technology Forum

Harunobu Masuko
Principal Investigator, SMILES Mission TeamNational Space Development Agency
Communications Research Laboratoryii

Contents
Contents

iii

List of Figuresx

List of Tablesxi

1 Executive Summary
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.2 Atmospheric Submillimeter Observations from Space . . . . . . . . . . . . .
1.3 Objectives of the JEM/SMILES Mission . . . . . . . . . . . . . . . . . . . .
1.3.1 Demonstration of New Technology for Submillimeter-wave Limbemission Sounder . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.3.2 Demonstration of High-quality Data for Atmospheric Trace Gases .
1.3.3 Delivery of Useful Database for Atmospheric Researches . . . . . . .
1.4 Overview of the JEM/SMILES Mission . . . . . . . . . . . . . . . . . . . .
1.4.1 The ISS, JEM, and Measurement Coverage of SMILES . . . . . . .
1.4.2 Objective Species and Measurement Frequency . . . . . . . . . . . .
1.4.3 SMILES Payload . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1.4.4 Data Processing and Distribution . . . . . . . . . . . . . . . . . . . .
1.4.5 Calibration and Validation . . . . . . . . . . . . . . . . . . . . . . .
1.4.6 Data Retrieval Method . . . . . . . . . . . . . . . . . . . . . . . . .
1.5 Development Schedule and Operation Plan . . . . . . . . . . . . . . . . . .
1.6 Mission Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

5
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15

2 Scientific Objectives
2.1 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3 Radical Species in the Stratosphere . . . . . . . . . . . . . . . . . . . . . . .
2.3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.2 Chlorine and Bromine Chemistry . . . . . . . . . . . . . . . . . . . .
2.3.3 HOx Chemistry . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.3.4 Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4 Observation of Ozone Isotopes . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
2.4.2 Atmospheric Observations and Laboratory and Theoretical Studies .
2.4.3 Targets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

18
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3 Instrumental Capabilities
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1 ISS and JEM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.1 Overall Structure . . . . . . . . . . . . . . . . . . . . . . .
3.1.1.2 Orbit and Attitude . . . . . . . . . . . . . . . . . . . . . .
3.1.1.3 ISS Operational Modes . . . . . . . . . . . . . . . . . . . .
3.1.1.4 Data Link between JEM/SMILES and ground facility . . .
3.1.2 SMILES Payload . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.1.2.1 Payload Configuration . . . . . . . . . . . . . . . . . . . . .
3.1.2.2 Dimension, Mass, Power, and Data Rate . . . . . . . . . .
3.1.2.3 Launching and Installation to JEM . . . . . . . . . . . . .
3.1.3 Mission Instruments . . . . . . . . . . . . . . . . . . . . . . . . . . .

31
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33
33
33
34
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iii

1
1
4
5

3.2

3.3

3.1.3.1 Submillimeter Antenna (ANT) . . . . . . . . . . . . . . . .
3.1.3.2 Submillimeter Receiver (SRX) . . . . . . . . . . . . . . . .
3.1.3.3 IF Amplification Section (IFA) . . . . . . . . . . . . . . . .
3.1.3.4 Radio Spectrometer (AOS) . . . . . . . . . . . . . . . . . .
3.1.3.5 Star Tracker (STT) . . . . . . . . . . . . . . . . . . . . . .
3.1.3.6 Data Processing and Control Section (DPC) . . . . . . . .
Field of View (FOV) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1 Altitude Scanning of the Antenna . . . . . . . . . . . . . . . . . . .
3.2.1.1 Scanning Profile . . . . . . . . . . . . . . . . . . . . . . . .
3.2.1.2 Range of Tangent Point Altitude . . . . . . . . . . . . . . .
3.2.1.3 Spatial Sampling Rate and Altitude Resolution . . . . . . .
3.2.1.4 Reference-sky Calibration . . . . . . . . . . . . . . . . . . .
3.2.1.5 Continuity of Observations . . . . . . . . . . . . . . . . . .
3.2.1.6 Effects of Atmospheric Refraction . . . . . . . . . . . . . .
3.2.2 Global Coverage . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.2.1 Global Coverage of Atmospheric Limb Scanning . . . . . .
3.2.2.2 Horizontal Resolution . . . . . . . . . . . . . . . . . . . . .
3.2.2.3 Effects of ISS Attitude and Altitude on Global Coverage .
3.2.3 Interference with FOV . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.3.1 Interference by the Sun . . . . . . . . . . . . . . . . . . . .
3.2.3.2 Interference by the Solar Paddle . . . . . . . . . . . . . . .
3.2.4 Antenna Response Pattern . . . . . . . . . . . . . . . . . . . . . . .
3.2.4.1 Response Pattern Model . . . . . . . . . . . . . . . . . . .
3.2.4.2 Effective Response Pattern for Atmospheric Limb Scanning
3.2.4.3 Beam Efficiency . . . . . . . . . . . . . . . . . . . . . . . .
3.2.5 ISS Orbit and Attitude Variations . . . . . . . . . . . . . . . . . . .
3.2.5.1 Orbit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.2.5.2 Attitude Control . . . . . . . . . . . . . . . . . . . . . . . .
3.2.6 Tangent Height Calibration . . . . . . . . . . . . . . . . . . . . . . .
3.2.6.1 Calibration Scheme . . . . . . . . . . . . . . . . . . . . . .
3.2.6.2 Calibration Accuracy . . . . . . . . . . . . . . . . . . . . .
Spectral Regions and Sensitivity . . . . . . . . . . . . . . . . . . . . . . . .
3.3.1 Observation Frequency Bands . . . . . . . . . . . . . . . . . . . . . .
3.3.2 Frequency Down-conversion . . . . . . . . . . . . . . . . . . . . . . .
3.3.2.1 Intermediate Frequency System . . . . . . . . . . . . . . .
3.3.2.2 Sideband Separation . . . . . . . . . . . . . . . . . . . . . .
3.3.3 Acousto-optic Spectrometer . . . . . . . . . . . . . . . . . . . . . . .
3.3.3.1 Frequency Characteristics . . . . . . . . . . . . . . . . . . .
3.3.3.2 Frequency Calibration . . . . . . . . . . . . . . . . . . . . .
3.3.3.3 Noise Dynamic Range . . . . . . . . . . . . . . . . . . . . .
3.3.3.4 Differential Non-Linearity . . . . . . . . . . . . . . . . . . .
3.3.4 Sensitivity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.4.1 Radiometric Noise . . . . . . . . . . . . . . . . . . . . . . .
3.3.4.2 Gain Stability . . . . . . . . . . . . . . . . . . . . . . . . .
3.3.4.3 Standing Waves . . . . . . . . . . . . . . . . . . . . . . . .
3.3.4.4 Differential Non-linearity of A/D Converter . . . . . . . . .
3.3.4.5 Electromagnetic Interference . . . . . . . . . . . . . . . . .
3.3.4.6 Overall Sensitivity . . . . . . . . . . . . . . . . . . . . . . .
3.3.5 Brightness Temperature Calibration . . . . . . . . . . . . . . . . . .
3.3.5.1 Calibration Scheme . . . . . . . . . . . . . . . . . . . . . .
iv

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36
37
38
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39
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39
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40
41
41
42
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44
46
46
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48
48
48
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62
68
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69
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78

3.4

3.3.5.2 Calibration Accuracy . . . . . . . . . . . . . . . . . . . . . 81
Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83

4 Retrieval– Principle and Method
86
4.1 Retrieval Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.1.1 Principle of Limb Sounding Instruments . . . . . . . . . . . . . . . . 86
4.1.2 Retrieval Method and Basic Error Estimation . . . . . . . . . . . . . 87
4.2 Simulation of Observation Spectra . . . . . . . . . . . . . . . . . . . . . . . 88
4.2.1 ARTS Forward Model . . . . . . . . . . . . . . . . . . . . . . . . . . 88
4.2.1.1 Atmospheric Part . . . . . . . . . . . . . . . . . . . . . . . 89
4.2.1.2 Sensor Part . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
4.2.1.3 Weighting Function . . . . . . . . . . . . . . . . . . . . . . 90
4.2.2 SMOCO Forward Model . . . . . . . . . . . . . . . . . . . . . . . . . 90
4.2.2.1 Atmospheric Part . . . . . . . . . . . . . . . . . . . . . . . 90
4.2.2.2 Sensor Part . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
4.2.2.3 Weighting Function . . . . . . . . . . . . . . . . . . . . . . 92
4.2.3 Forward Model Intercomparison . . . . . . . . . . . . . . . . . . . . 92
4.2.4 Observational Spectra Simulated by SMOCO . . . . . . . . . . . . . 95
4.3 Molecular Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
4.3.1 Standard Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.3.2 Research Products . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100
4.3.3 Ozone Isotopes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.3.4 Mesospheric Products . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.4 Temperature and Pointing Retrieval . . . . . . . . . . . . . . . . . . . . . . 105
4.4.1 Temperature Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.4.1.1 Information Content . . . . . . . . . . . . . . . . . . . . . . 105
4.4.1.2 With Hydrostatic Equilibrium . . . . . . . . . . . . . . . . 106
4.4.1.3 Without Hydrostatic Equilibrium . . . . . . . . . . . . . . 107
4.4.2 Pointing Offset Retrieval . . . . . . . . . . . . . . . . . . . . . . . . . 114
4.4.2.1 Information Content . . . . . . . . . . . . . . . . . . . . . . 114
4.4.2.2 With Hydrostatic Equilibrium . . . . . . . . . . . . . . . . 116
4.4.2.3 Without Hydrostatic Equilibrium . . . . . . . . . . . . . . 116
4.5 Spectroscopic Data for SMILES . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.5.1 Spectroscopic Data Required for the JEM/SMILES Experiment . . 117
4.5.2 Laboratory Submillimeter-wave Spectrometer System . . . . . . . . 118
4.5.3 Available Molecular Parameters up to Present . . . . . . . . . . . . 119
4.5.4 Plans for Further Improvement of Data . . . . . . . . . . . . . . . . 120
5 Ground Data System
123
5.1 Objectives of JEM/SMILES Ground Data System . . . . . . . . . . . . . . 123
5.2 JEM Operation System . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
5.2.1 Operation Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
5.2.2 Commanding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
5.2.3 House Keeping Telemetry Data Checking . . . . . . . . . . . . . . . 125
5.3 Functions and Data Flow between ISS and SMILES ground data system . . 126
5.3.1 JEM/SMILES to Receiving Stations . . . . . . . . . . . . . . . . . . 126
5.3.2 Receiving Stations to JEM Operation System at NASDA/TKSC . . 127
5.3.3 JEM Operation Center to SMILES Ground Data System . . . . . . 127
5.4 Functions of SMILES Ground Data System . . . . . . . . . . . . . . . . . . 128
5.5 Consideration on Computation in the JEM/SMILES L2 Data Processing . 129v

6 Validation Plan
132
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.2 The Basic Concepts of Validation for JEM/SMILES Data Products . . . . . 132
6.3 Correlative Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
6.3.1 The Needs of Correlative Measurements . . . . . . . . . . . . . . . . 132
6.3.2 Plan of Correlative Measurements . . . . . . . . . . . . . . . . . . . 133
6.3.2.1 The Use of Existing Data . . . . . . . . . . . . . . . . . . . 133
6.3.2.2 Field Campaign . . . . . . . . . . . . . . . . . . . . . . . . 134
6.3.3 Activities in JEM/SMILES Team for Validation . . . . . . . . . . . 135
6.3.3.1 Development of a Balloon Borne Submillimeter Spectrometer135
6.3.3.2 Laboratory Measurement of Spectroscopic Parameters . . . 135
6.3.3.3 Use of Observation Sites in Japan . . . . . . . . . . . . . . 135
6.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
Appendix: Estimation of Retrieval Errors for SMILES Major Species
137
A.1 Radical Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
A.2 Isotope Observations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
A.3 Mesospheric Observation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
List of Acronyms

156

vi

List of Figures
1.1

Schematic of the north to south variation in ozone depletion from 1979 to
1997. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Prediction of combined abundance of stratospheric chlorine and bromine. . 2
1.3 The method of limb-emission sounding from the ISS. . . . . . . . . . . . . 3
1.4 Altitude coverage of the JEM/SMILES data estimated from preliminary
result of simulation studies. . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.5 Artistic view of the International Space Station (ISS). . . . . . . . . . . . . 7
1.6 Japanese Experiment Module (JEM) and Exposed Facility (EF). . . . . . . 7
1.7 World map of orbit and measurement positions of JEM/SMILES. . . . . . 8
1.8 Arrangement of IF bands. . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
1.9 JEM/SMILES data transmission, processing and distribution. . . . . . . . 13
1.10 Schedule of JEM/SMILES. . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
2.1 Estimate of the mean trend using all four measurement systems at northern
mid-latitudes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.2 Time series of 20- 22 km layer averaged water vapor mixing ratio over Boulder. 20
2.3 Observed and calculated total ozone anomalies from 1979 to 1997 at 45◦ N. 20
2.4 Dynamical aspects of stratosphere-troposphere exchange. . . . . . . . . . . 21
2.5 UARS MLS measurements of ClO and O3 for February 20, 1996 for thenorthern hemisphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
2.6 Ozone depletion reaction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.7 Profile of BrO mixing ratios and ClO mixing ratios. . . . . . . . . . . . . . 24
2.8 Gas phase reaction of HOx in the stratosphere (Based on Brasseur [2000]). . 25
2.9 Comparison of published results of measurements of heavy ozone enrichment. 28
3.1 Space Station reference coordinate system. . . . . . . . . . . . . . . . . . . . 32
3.2 Configuration of Japanese Experiment Module (JEM). . . . . . . . . . . . . 32
3.3 Block diagram of JEM/SMILES payload. . . . . . . . . . . . . . . . . . . . 35
3.4 External view of the SMILES payload. . . . . . . . . . . . . . . . . . . . . . 35
3.5 Antenna scanning pattern of SMILES with the associated operation of aswitching mirror for radiometric calibration. . . . . . . . . . . . . . . . . . . 40
3.6 Effects of atmospheric refraction on limb-sounding line-of-sight paths. . . . 43
3.7 Antenna scanning step interval and vertical half-power beamwidth (HPBW)
at tangent point for exponential model atmosphere. . . . . . . . . . . . . . . 43
3.8 Horizontal half-power beamwidth (HPBW) at tangent point for exponential
model atmosphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.9 The SMILES observation tangent-point coverage for an ISS one-day orbit. . 45
3.10 The SMILES observation tangent-points coverage viewed from the north. . 45
3.11 Trajectory of tangent points in a vertical plane for single limb scanning. . . 46
3.12 The horizontal resolution of SMILES observation. . . . . . . . . . . . . . . . 47
3.13 The local time of the SMILES measurements and solar/solar-paddle interference with the SMILES beam. . . . . . . . . . . . . . . . . . . . . . . . . . 49
3.14 Top view of the ISS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.15 The probabilty of the interference. . . . . . . . . . . . . . . . . . . . . . . . 50
3.16 Calculated near-axis antenna response pattern. . . . . . . . . . . . . . . . . 51
3.17 Approximate response pattern. . . . . . . . . . . . . . . . . . . . . . . . . . 52
3.18 Effective antenna response pattern. . . . . . . . . . . . . . . . . . . . . . . . 54
3.19 Beam efficiency. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.20 Geometrical definition of tangent-point altitude . . . . . . . . . . . . . . . . 58
3.21 Flow chart of STT data analysis . . . . . . . . . . . . . . . . . . . . . . . . 59
vii

3.22 Atmospheric emission profiles expected for Band-A. . . . . . . . . . . . . .
3.23 Atmospheric emission profiles expected for Band-B. . . . . . . . . . . . . . .
3.24 Atmospheric emission profiles expected for Band-C. . . . . . . . . . . . . .
3.25 Frequency conversion in SMILES. . . . . . . . . . . . . . . . . . . . . . . . .
3.26 Schematic configuration of SSB filtering and local injection. . . . . . . . . .
3.27 The function of the SSB filter in detail. . . . . . . . . . . . . . . . . . . . .
3.28 Coupling coefficient Kij for signal transmission and image rejection of the
SSB filter designed for SMILES. . . . . . . . . . . . . . . . . . . . . . . . .
3.29 The effect of the image contributions in Band-A. . . . . . . . . . . . . . . .
3.30 The effect of the image contributions in Band-B. . . . . . . . . . . . . . . .
3.31 The effect of the image contributions in Band-C. . . . . . . . . . . . . . . .
3.32 Spectral line-width versus atmospheric height with respect to two importantlines of the SMILES mission. . . . . . . . . . . . . . . . . . . . . . . . . . .
3.33 Frequency response function of the SMILES/AOS. The typical resolution
bandwidth is 1.35 MHz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.34 Residuals from the frequency fit with comb generator. 1 Ch. corresponds
to approximately 0.8 MHz. . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.35 RF noise, read-out noise and the variance ratio of them . . . . . . . . . . .
3.36 Simulated effects of the differential non-linearity on the spectrum for 1 shot,
and for 2, 3, and 60 accumulations. . . . . . . . . . . . . . . . . . . . . . . .
3.37 Dependence of the DNL effect on the difference of brightness temperature
between the limb atmosphere and the reference sky. . . . . . . . . . . . . .
3.38 Estimated amplitude of standing waves as a function of atmospheric brightness temperature. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.39 ISS environmental field. . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.40 Shield performance. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.41 Overall Sensitivity. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
3.42 Calibration accuracy of the brightness temperatur. . . . . . . . . . . . . . .
4.1 Illustration of limb sounding geometry. . . . . . . . . . . . . . . . . . . . . .
4.2 Schematic display of the geometry for remote sensing of the atmosphere. . .
4.3 Comparison between ARTS and SMOCO calculations of the absorption
spectra in the frequency range around the H2 O line at 22.23 GHz for different atmospheric levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.4 Comparison between ARTS and SMOCO calculations of the absorption
spectra in the frequency range around the O3 line at 625.37 GHz for different
atmospheric levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.5 Comparison between ARTS and SMOCO calculations of the absorption
spectra in the frequency range around the HCl line at 625.9 GHz for different
atmospheric levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.6 Comparison between ARTS and SMOCO calculations of the absorption
spectra in the frequency range around the ClO line at 649.45 GHz for different atmospheric levels. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.7 Comparison between ARTS and SMOCO calculations of brightness temperature spectra seen by a limb sounding instrument. . . . . . . . . . . . .
4.8 SMILES spectra for a tangent height of 40 km simulated with the 45◦ N
model atmosphere. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
4.9 Comparison of two spectra, one expected with Tsys = 500 K and the otherwith 3000K. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

viii

61
61
62
63
64
64
65
66
67
67
68
69
70
71
72
72
75
76
77
79
82
86
87

94

94

95

96
96
98
99

4.10 The a priori molecular height profiles used in the retrieval of the SMILES
standard product species. The abscissa is the volume mixing ratio (VMR)
of each species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.11 The ratio of the retrieval error to the retrieved height profile for the SMILES
standard product species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.12 The a priori molecular height profiles used in the retrieval of the SMILES
research product species. The abscissa is the volume mixing ratio (VMR)
of each species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.13 The ratio of the total retrieval error to the retrieved height profile for the
SMILES research product species. . . . . . . . . . . . . . . . . . . . . . . . 102
4.14 The a priori molecular height profiles used in the retrieval of the SMILES
research product species. The abscissa is the volume mixing ratio (VMR)
of each species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
4.15 The ratio of the total retrieval error to the retrieved height profile for the
SMILES research product species. . . . . . . . . . . . . . . . . . . . . . . . 102
4.16 The a priori molecular height profiles used in the retrieval of the SMILES
ozone isotopes. The abscissa is the volume mixing ratio (VMR) of eachspecies. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.17 The ratio of the retrieval error to the retrieved height profile for the SMILES
ozone isotopes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
4.18 The a priori molecular height profiles used in the retrieval of the SMILES
mesospheric species. The abscissa is the volume mixing ratio (VMR) of
each species. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.19 The ratio of the total retrieval error to the retrieved height profile for the
SMILES mesospheric species. . . . . . . . . . . . . . . . . . . . . . . . . . . 104
4.20 “Information content” on the atmospheric temperature in Band-A. . . . . . 106
4.21 Temperature retrieval in Band A with hydrostatic equilibrium. . . . . . . . 108
4.22 Temperature retrieval in Band B with hydrostatic equilibrium. . . . . . . . 109
4.23 Temperature retrieval in Band C with hydrostatic equilibrium. . . . . . . . 110
4.24 Temperature retrieval in Band A without hydrostatic equilibrium. . . . . . 111
4.25 Temperature retrieval in Band B without hydrostatic equilibrium. . . . . . 112
4.26 Temperature retrieval in Band C without hydrostatic equilibrium. . . . . . 113
4.27 “Information content” of the measurement on the pointing offset. . . . . . . 115
4.28 A schematic diagram of a double modulation submillimeter-wave system at
Ibaraki University. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118
4.29 A least square fitting of the BrO spectrum to derive the pressure broadening
parameter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
5.1 JEM Operation System and its relation to JEM Experiment users. . . . . 124
5.2 Concept of operation planning for JEM. . . . . . . . . . . . . . . . . . . . . 125
5.3 SMILES data flow. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.4 Data handshaking between JEM Operation System and SMILES Ground
System. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
A.1 Retrieval simulation results for single-scan data for normal O3 in Band-A. . 138
A.2 Retrieval simulation results for single-scan data for H37 Cl in Band-A. . . . . 139
A.3 Retrieval simulation results for 30-scan averaged data for HNO3 in Band-A. 140
A.4 Retrieval simulation results for 30-scan averaged data for HOCl in Band-A. 141
A.5 Retrieval simulation results for 30-scan averaged data for H2 O2 in Band-A. 142
A.6 Retrieval simulation results for 30-scan averaged data for CH3 CN in Band-A.143
A.7 Retrieval simulation results for single-scan data for H35 Cl in Band-B. . . . . 144
A.8 Retrieval simulation results for single-scan data for ClO in Band-C. . . . . . 145
ix

A.9 Retrieval simulation results for 30-scan averaged data for HO2 in Band-C. . 146
A.10 Retrieval simulation results for 30-scan averaged data for BrO in Band-C. . 147
A.11 Retrieval simulation results for 30-scan averaged data for asymmetric-17 O3
in Band-C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
A.12 Retrieval simulation results for 30-scan averaged data for asymmetric-18 O3
in Band-C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150
A.13 Retrieval simulation results for 30-scan averaged data for symmetric-17 O3
in Band-C. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
A.14 Retrieval simulation results for 30-scan averaged data in BAND-A for the
standard profiles of normal O3 in the mesosphere. . . . . . . . . . . . . . . . 153
A.15 Retrieval simulation results for 30-scan averaged data in BAND-B for the
standard profiles of H35 Cl in the mesosphere. . . . . . . . . . . . . . . . . . 154
A.16 Retrieval simulation results for 30-scan averaged data in BAND-C for the
standard profiles of HO2 in the mesosphere. . . . . . . . . . . . . . . . . . . 155
clearpage

List of Tables
1.1

Objective trace gases and their observation frequencies considered for JEM/
SMILES. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.2 Major design parameters of the SMILES payload. . . . . . . . . . . . . . . 11
1.3 JEM/SMILES Project Key Personnel. . . . . . . . . . . . . . . . . . . . . 16
3.1 Configuration of SMILES Payload. . . . . . . . . . . . . . . . . . . . . . . . 34
3.2 Requirements for SMILES-ANT . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.3 Requirements for SMILES-SRX . . . . . . . . . . . . . . . . . . . . . . . . . 37
3.4 Requirements for SMILES-IFA . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.5 Requirements for SMILES-AOS . . . . . . . . . . . . . . . . . . . . . . . . . 38
3.6 Requirements for SMILES-STT . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.7 Expected range of tangent point altitudes for several ISS conditions. . . . . 41
3.8 Highest latitudes sampled by SMILES limb scanning. . . . . . . . . . . . . . 47
3.9 Antenna Specification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
3.10 Model coefficients. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.11 Characteristics of the ISS Orbit . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.12 ISS Orbital Elements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.13 Characteristics of the ISS Attitude (TEA:Torque Equilibrium Attitude) . . 57
3.14 Residual Errors in the tangent height after calibration with the STT data . 60
3.15 Model parameters m, α, f0 of power coupling coefficients Kij for leastsquares-fit model of SSB filter and simplified model for SMILES optics. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.16 Description of SMILES Mission Instruments . . . . . . . . . . . . . . . . . . 83
3.17 Summary of SMILES Instrumental Capabilities . . . . . . . . . . . . . . . . 84
4.1 Selected molecular species transitions for the absorption coefficients intercomparison. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
4.2 Molecular transition frequencies of JEM/SMILES species. . . . . . . . . . . 97
4.3 Retrieval precision, measurement error and measurement response for the
retrieved pointing offset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116
4.4 Retrieval precision, measurement error and measurement response for the
retrieved pointing offset. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
4.5 Pressure broadening parameters . . . . . . . . . . . . . . . . . . . . . . . . . 120
5.1 SMILES data sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124
x5.2

Estimated performance requirements (GFlops and memory size) for SMILES
Data System compared to those of EOS/MLS program. . . . . . . . . . . . 130

xi

Contributors to the JEM/SMILES Mission Plan
Name
Amano, TakayoshiBühler, Stefan
Habara, Hideta
Inatani, Junji
Kasai, Yasuko
Manabe, Takeshi
Masuko, Harunobu
Miura, Takeshi
Nagahama, Tomoo
Nishibori, Toshiyuki
Ochiai, Satoshi
Ozeki, Hiroyuki
Shibasaki, Kazuo
Shiotani, Masato
Shirai, Tomoko
Suzuki, Makoto
Takahashi, Chikako
Takahashi, Kenshi
Tsujimaru, Sho
Verdes, Carmen

CRL :TKSC :EORC :STEL :FIP :Kyoto Univ. :Ibaraki Univ. :Nagoya Univ. :IUP :E-mailAffiliation
Chapter∗amano@mito.ipc.ibaraki.ac.jp
Ibaraki Univ.
4sbuehler@uni-bremen.de
IUP
4
habara@mtg.biglobe.ne.jp
TKSC, Ibaraki Univ. 4
inatani.junji@nasda.go.jp
TKSC
3
ykasai@crl.go.jp
CRL
2, 4manabe@crl.go.jp
CRL
3masuko@crl.go.jp
CRL
1
miura.takeshi@nasda.go.jp
TKSC
3nagahama@a.phys.nagoya-u.ac.jp Nagoya Univ.
2nishibori.toshiyuki@nasda.go.jp
TKSC
3
ochiai@crl.go.jp
CRL
3, 4ozeki.hiroyuki@nasda.go.jp
TKSC
2, 3
sibasaki@eorc.nasda.go.jp
EORC
6
shiotani@kurasc.kyoto-u.ac.jp
Kyoto Univ.
2shirai@eorc.nasda.go.jp
EORC
2
suzuki@eorc.nasda.go.jp
EORC
5takahashi.c@fip.co.jp
FIP
4
kent@stelab.nagoya-u.ac.jp
STEL
2
sho@eorc.nasda.go.jp
EORC
4
cverdes@uni-bremen.de
IUP
4∗ Boldface indicates the editor of the chapter.
Communications Research Laboratory
Koganei, Tokyo 184-8795, Japan
National Space Development Agency of Japan
Tsukuba Space Center
Tsukuba 305-8505, Japan
National Space Development Agency of Japan
Earth Observation Research Center
Harumi, Chuo-ku, Tokyo 104-6023, Japan
Solar-Terrestrial Environment Laboratory
Nagoya University
Honohara, Toyokawa 442-8507, Japan
Fujitsu FIP Corporation
Aomi, Tokyo 135-1612, Japan
Kyoto University
RASC, Gokasho, Uji, Kyoto 611-0011, Japan
Ibaraki University
Bunkyo, Mito 310-8512, Japan
Nagoya University
Furo-cho, Chikusa-ku, Nagoya 464-8601, Japan
Institute of Environmental Physiks
University of Bremen, FB1
PO Box 330440
28334 Bremen, Germanyxii

How Severe Is the Ozone Depletion Now?

1

Executive Summary

Stratospheric ozone depletion, caused by increasing
sponsible for the formation of the Antarctic ozone hole.
This chapter
outlines the mission
planhasof inthe Superconducting
Submillimeter-wave
concentrationsof human-produced
chemicals,
Furthermore, theoverwhelming weightLimbof evidence indiemission
Sounder(SMILES),
which
is
to
be
accommodatedbytheExposedFacilitycreased since the 1980s. The springtime loss incates that those same
compounds
also (EF)play a major role
Antarctica
is thelargest depletion.
Currently,
in nonpolar
the ozone depletion
the Arctic
and Theat midlatitudes.
of theJapanese
Experiment
Module(JEM) on thein International
SpaceinStation(ISS).
regions,JEM/SMILES
the ozone layer mission
has beenwas
depleted
up to several
approved
in 1997, and is currently under development jointly
percentby
comparedwith that of two Research
decades ago.
In addition
these long-termtransient effects
the Communications
Laboratory (CRL)
and toNational
Space changes,
Development
have also been observed in the stratospheric ozone layer
Agency of Japan (NASDA).
As the figure below indicates, the magnitude of ozone
following major volcanic eruptions such as Mt. Pinatubo
depletion varies between the regions of the Earth. For
in 1991. During 1992 and 1993, ozone in many locations
1.1thereIntroduction
example,has been little or no ozone depletion in
dropped to record low values. For example, springtime
the tropics (about 20 degrees north and south of thedepletions exceeded 20% in some populated northernTracegases suchof as
BrO,alsoHO
depletingregions,ozone andin thethelevels
stratospheric.
2 have beenmidlatitude
equator).
The magnitude
theClO,
depletion
depends
in the Antarctic ozone
The origins
such
tracethegases
are human-madechlorofluorocarbons(CFCs),
halogenon the season.
From of1979
to 1997,
observedlosseshole
fell to the lowest values
everrecorded. Thesein the amountof ozoneoverheadmethane,
have totaled
5- Figureunusuallylarge,thebut short-term,ozone
decreases of 1992
containing
substances,
andabout
others.
1.1 showsnorth-south
variations
6% for ofnorthern
midlatitudes
in winterand 1979
spring,about
and 1993
are believedOrganization,
to be related in1998].
part to the large
the ozone
depletion
betweenand1997 [World
Meteorological
3% for Afternorthern
midlatitudes
insummer
and
fall,
and
amountsofvolcanic
particles
injected
intostratosphere,
the Montreal Protocol [United Nations Environmental Programme, 1987] and its
about 5% year round for southern midlatitudes. Since
which temporarily increased the ozone depletion caused
amendments, the total combined abundance of ozone-depleting compounds in the lower
the early 1980s, the ozone hole has formed overby human-produced chlorine and bromine compounds,
atmospherepeakedaroundHemisphere1994, whichspringis now slowly
combinedclouds
abundance
Antarctica
during every
Southernmuchdeclining.
as polar The
stratosphericincrease theseof
thestratosphericchlorineandbrominewas
expected
to
peak
before
the
year
2000,
as regions.
(September to November), in which up to 60% of thechemicals’ effect on ozone depletionin polarshown
Figure Since
1.2 [World
MeteorologicalIts decrease
indispenstotal ozone
is indepleted.
the early
1990s, ozoneOrganization,
Because 1998].
these particles
settleisoutof the stratosphere
depletionhasbeen observed
over theArctic,with report
withina few years,
the ozone concentrations
MeteorologicalOrganization,have largely
able
foralsorecoveryof the ozone
layer.
A WMO[Worldthe ozone
from
JanuaryMarch
returnedtheemissions
depleted oflevelsconsistent with the1998]loss
says,
even
if therethroughwere tolate
be animmediatestop totoall
human-madetypically being 20-25% in most of the recent years. All
downward trend observed before the Mt. Pinatubo
ozone-depleting substances, including those currently in use, stratospheric halogen loadof these decreases are larger than known long-term
eruption. Should a similar eruption occur in the coming
would not return to the 1980 levels by 2033. decade,
Therefore,
it losses
is needed
tosamecontinuously
naturaling
variations.
ozoneof themagnitude might bemonitor the stratospheric chlorine and bromine foranalyses
of
the
ozone
trend.
expected, because the chlorine levels in the stratosphereozone
will affectthe Earth’s
andbechanges
will stillhigh. in climate and meteoroThe large The
increasein changes
atmosphericconcentrationsof climate,human-made chlorine and bromine compounds is re-

Schematic of the North-to-South Ozone Depletion: 1979-1997

Figure 1.1 Schematic of the north to south ozone depletion from 1979 to 1997[World
32
Meteorological Organization, 1998].
1

d countries and 2010 in
2 Copenhagen Amendan to 1996 in developedn ozone-depleting subn in Vienna (1995) andpounds and a full global compliance with the international
regulations on their emissions will have eliminated what
would have been, as the figure illustrates, a major
deterioration of the Earth’s protective ultraviolet shield.

st and projected stratoand bromine without theoriginal provisions, ands. Without the Montreal
ts, continuing use ofd other ozone-depleting
ased the stratosphericmine tenfold by the mid0 amounts. Such highces would have caused
h would have been far
ed at present.

Effect of the International Agreements on OzoneDepleting Stratospheric Chlorine/Bromine

ternational agreements
an-caused emissions of
tropospheric concentracontaining compounds
cause 3 to 6 years are
he troposphere to theabundances of chlorine
nt level and will slowly
pliance, the internationaleliminate most of thee-depleting gases. Alle ozone layer would be
Figure 1.2 Prediction of combined abundance of stratospheric chlorine and bromine,
which is based on each protocol and amendment [World Meteorological Organization,
1998]. 33

logical conditions will affect the ozone depletion, because they share a number of common
physical and chemical processes [World Meteorological Organization, 1998]. Stratosphericozone losses have caused a cooling of the lower stratosphere and a global negative radiative
forcing to the climate system. The future behavior of ozone also will be affected by changing abundances of methane (CH4 ), nitrous oxide (N2 O), water vapor (H2 O), and sulfate
aerosol. Understanding of the relationships between the ozone trend and climate change
is connected to chemical processes within trace gases and their coupling with dynamic
processes.
This situation requires three-dimensional and simultaneous measurements of ozone
and trace gases in the stratosphere and upper troposphere. Also important is global
accurate data with high resolutions in time and location. Submillimeter limb-emission
sounding from space will have several advantages of meeting these requirements [Waters,
1993]. Figure 1.3 schematically shows the method of limb-emission sounding. Many
species among trace gases have spectral emissions in submillimeter ranges. The heterodyne
spectroscopy has a high spectral resolution that is needed for precise retrieval of tracegases. Emission measurements, which are independent of sunlight in any region, arepreferable for complete global observations and essential for understanding diurnal cycles ofchemical processes. The limb-emission sounding also has another advantage of reasonable2

limb path s

dh

z

h

satellite

z
ho
R

earth

atmospheric layers

Figure 1.3 The method of limb-emission sounding from the ISS.

altitude resolution. Since a mechanically scanning antenna is used to sweep the limb
atmosphere vertically, the altitude resolution is basically determined by the beam size at
the atmosphere. Furthermore, submillimeter molecular emissions are less dependent on
atmospheric temperatures, and no aerosols will affect submillimeter measurements, which
also makes the submillimeter observations preferable.
On the other hand, recent progress in submillimeter receiver technology has enabled
us to drastically improve the quality of limb-emission sounding data. A 4 K-cooled superconductive mixer receiver will detect molecular spectra with an order-of-magnitude higher
signal-to-noise ratio than the conventional ambient-temperature semiconductor receiver.
This improvement is essential to produce global distribution maps of trace gases, whose
emissions are generally weak because of thermal excitation at ambient temperatures of
the atmosphere. Submillimeter waves around the wavelength of 0.5 mm or less are profitable for observing various trace gases up to high altitudes with reasonable signal-to-noise
ratios. For simultaneous observations of a dozen of trace gases as well as atmospheric temperatures and pressures, we need a receiver with a wide instantaneous bandwidth, and awide-band radio spectrometer with high frequency resolution. Single-sideband (SSB) observation with the submillimeter mixer is preferable for producing reliable quantitativedata.
In a bid to demonstrate new submillimeter technology in space and to conduct limbemission sounding for a group of trace gases, the CRL and NASDA, with a technical support of the National Astronomical Observatory (NAO), submitted an experiment proposalof Superconducting Submillimeter-wave Limb-emission Sounder (SMILES), in response to
the announcement of opportunity (AO) for the use of the Exposed Facility (EF) of the
ISS-JEM. The proposal was accepted in March 1997, and the instrumental development
started from the fiscal year 1998 under the permission of the Space Development Boardof Japan.
For full understanding of atmospheric environment, more comprehensive and simultaneous measurements of chemical and physical processes are needed for a wide range of theatmosphere. For this purpose, an atmospheric chemistry and dynamics mission, ATMOSC, has been proposed in Japan [ATMOS-C1 Team, 1997]. An extended version of SMILES
with several bands from 300 GHz to 2.5 THz to measure ClO, BrO, H2 O, OH, atmospherictemperature, etc. is a candidate sensor proposed for ATMOS-C. JEM/SMILES is therefore
regarded as a pre-phase experiment to ensure the feasibility of ATMOS-C/SMILES.

3

1.2

Atmospheric Submillimeter Observations from Space

The first of millimeter-wave limb-emission sounding from space was made by the Microwave Limb Sounder (MLS) on the Upper Atmosphere Research Satellite (UARS), which
was launched in September 1991 by NASA [Waters, 1993]. UARS-MLS was developed by
the Jet Propulsion Laboratory (JPL), in collaboration with the Rutherford Appleton Laboratory, Heriot-Watt University, and Edinburgh University in the United Kingdom. The
UARS-MLS instrument carries ambient-temperature double-sideband radiometers operating at 63 GHz, 183 GHz, and 205 GHz, which enable simultaneous observations of theatmospheric temperatures, pressures (about 30–60 km), H2 O (about 15–85 km), O3 (about
15–80 km), and ClO (about 15–45 km) in the stratosphere. UARS-MLS is still in operation. The system noise temperature for the 205 GHz band is 990 K (double sideband),
which provides zonal mean measurements of ClO profiles with a sensitivity of about 0.1
ppbv and each limb-scan measurement of ClO with a sensitivity of 0.5 ppbv. Sensitivities to O3 and H2 O in the middle stratosphere are a few percent in each limb scan. The
UARS-MLS observations have produced a variety of atmospheric researches until today[Waters et al., 1999], such as findings of the correlations between ozone losses and ClO
enhancement in polar regions.
The Max-Planck Institute for Aeronomy, University of Bern, and Naval Research Laboratory conducted three short-period observations in March 1992, August 1993, and November 1994 with the Millimeter-wave Atmospheric Sounder (MAS) on the Atmospheric Laboratory for Applications and Science (ATLAS) mission. The frequency bands used for
ATLAS/MAS are almost the same as those of UARS-MLS.
The Swedish Space Corporation (SSC) has launched an aeronomy-and-astronomy joint
mission, Odin, in February 2001, in collaboration with France, Finland, and Canada. The
objectives in aeronomy are to measure the height profiles of trace species in the stratosphere and mesosphere. The Odin millimeter and submillimeter limb-emission sounder isoperated at 119 GHz for temperatures and pressures, at 495 GHz for H2 O and HNO3 , andat 561 GHz for H2 O isotope, NO, N2 O, NO2 , H2 O2 , ClO, CO, and HO2 . The radiometers
use single-sideband (SSB) Schottky-diode mixers that are cooled to 50-80 K by a closedcycle Stirling refrigerator. The noise temperatures (SSB) of the receiver are around 500
K at 119 GHz, and 2,000 K for the submillimeter ranges.
NASA is to launch the Earth Observing System (EOS) Chemistry, now called “Aura”,
as a follow-on mission of UARS in 2004. EOS-Aura has a sun-synchronous orbit witha height of 705 km. It will carry an improved version of UARS-MLS [Waters, 1999].
An intense objective of Aura-MLS is to measure the upper-troposphere (UT) and lowerstratosphere (LS) for global change monitoring. It will observe submillimeter transitions
for chemical studies of the stratosphere. The Aura-MLS adopts ambient-temperature
double-sideband mixers operational at 118 GHz for temperatures and pressures, at 190
GHz for H2 O and HNO3 , at 240 GHz for ozone and CO, at 640 GHz for ClO, HCl, BrO,
HO2 , and N2 O, and at 2.5 THz for OH. The antenna scans the atmosphere from 2 km to
60 km. The size of the primary reflector is the same as that of UARS-MLS (except 2.5
GHz), which provides a vertical resolution of 1.5 km in the 640 GHz band.
Other new proposals of submillimeter limb-emission sounder are discussed also in ESA
and European countries. Future missions will obviously utilize submillimeter waves thatenable simultaneous observations of many trace species. In particular, high sensitivity
will be essential for those missions to clarify trends of trace gases in mid-latitudes and to
detect dynamical features of the atmosphere. It requires further developments toward more
extended submillimeter measurements technology, including superconductive receivers ofhigh sensitivity, and wide-band spectrometers of high resolution.

4

1.3

Objectives of the JEM/SMILES Mission

The JEM/SMILES mission has both engineering and scientific objectives as follows. While
technical developments for SMILES are a challenging task that we believe will open new
possibilities for future missions, emphasis is also put on scientific productivity of SMILES
in atmospheric researches.
1.3.1

Demonstration of New Technology for Submillimeter-wave Limb-emission
Sounder

JEM/SMILES will demonstrate the effectiveness of a highly sensitive instrument for submillimeter limb-emission sounding. Its high sensitivity is attributed to a low noise receiver with superconductor-insulator-superconductor (SIS) mixers operating in cryogenically cooled conditions (at 4.5 K). In order to realize a compact SIS receiver in space
without a huge and massive storage tank of liquid helium, we have developed a mechanical 4 K cooler. Neither a superconductivity sensor nor a 4 K mechanical cooler has everbeen flown in space until today. This is the most critical technology of the SMILES experiment. However, that is not all we need. Several other key techniques are needed, such asa high-precision antenna, submillimeter optics, submillimeter signal source, cryogenicallycooled IF amplifier, and acousto-optic spectrometer, for all of which we have quite limited
precedents of space use. The SMILES mission has an engineering objective to establish
these key techniques toward a high-sensitivity submillimeter limb-emission sounder. The
engineering usefulness will not be limited within applications to atmospheric sciences.
They also will be utilized for future space science missions.
1.3.2

Demonstration of High-quality Data for Atmospheric Trace Gases

High sensitivity with the SMILES observation data will drastically reduce the errors in
atmospheric volume mixing ratios retrieved for various molecular species. Figure 1.4
summarizes the result of simulation studies described in Chapter 4. With respect toozone and HCl, the single-scan data that is available in every 53 seconds will be sufficient
to retrieve their mixing ratios with errors significantly less than 5 percent (ozone) or 10
percent (HCl) at any latitudes. As for ClO, which is the most important ozone destruction
species, the retrieval error levels estimated with the single-scan data are less than 30
percent at low and middle latitudes, and less than 10 percent for the ClO enhancement in
the lower stratosphere at high latitudes.
For other less abundant species such as CH3 CN, HOCl, HO2 , HNO3 , and BrO, scientific
data will be obtained from a half-day zonal mean for a 5-degree width in latitude, which is
produced with 30 pieces of single-scan data. The retrieval errors estimated with such zonal
mean are less than 10 percent for CH3 CN at low latitudes, and for HOCl globally. The
error levels will be less than 30 percent for HO2 in the equator and for HNO3 at middle
and high latitudes. Those for BrO remain at 50 percent levels at any latitudes, which
however will be improved with observation data that is averaged over a longer period.
A new aspect of the SMILES data is the capability to measure ozone isotopes including
18 OOO and 17 OOO. For these two species, the retrieval errors are estimated to be lessthan 3 percent with respect to a model profile of their abundance based on the isotopic
ratio of oxygen atoms in the ocean water.
1.3.3

Delivery of Useful Database for Atmospheric Researches

The third objective is to demonstrate scientific values of SMILES sensitive measurements
by shedding new light on chemical processes and regional interactions, particularly in530-scan30

30-scan30-scan50
40Error < 50 %Error < 2K

Error < 20 %30-scanError < 5 %30-scan

Altitude [km]6030-scan30-scan1-scan30-scan1-scan30-scan30-scan1-scan70

Error < 2 %

20
10
HNO3

BrO Temp.

Temp.

HOCl

BrO *

HO2

HNO3 *OOO CH3CN

HOCl *

17

HO2

OOO

CH3CN

18

O3-17(a)

ClO*

ClO

HCl

HCl

O3

O3

O3-18(a)0Figure 1.4 Altitude coverage of the JEM/SMILES data estimated from preliminary results of simulation studies assuming 0◦ N standard profile for each molecular species except
for ClO for which the standard profile for polar region is assumed. Refer to Chapter 4 formore details.

the equatorial and in the northern high-latitude regions. SMILES data will enable us toinvestigate the chlorine and bromine chemistry and the HOx chemistry around the polar
vortex region and over the equatorial and mid-latitude regions. The SMILES mission also
provides a database for ozone variations in time and position around the upper troposphereand lower stratosphere (UT/LS). SMILES’ wide-band and high-resolution spectroscopic
data also enables us to investigate the isotopic compositions of ozone. The enrichment ofrare isotopes in altitude distribution is reported and expected to reflect some unknown
atmospheric processes. Chapter 2 will describe SMILES scientific objectives in detail.

1.4
1.4.1

Overview of the JEM/SMILES Mission
The ISS, JEM, and Measurement Coverage of SMILES

Figures 1.5 and 1.6 show the configurations of the ISS and JEM, respectively. JEM is
attached to the front side of the ISS. Scientific experiments will be conducted both in thePressurized Module (PM) and on the Exposed Facility (EF). JEM-EF has 10 interface
ports to accept experiment payloads, four of which are on the front side of the ISS. The
use of the interface ports is shared between Japan and the United States. The AO towhich the SMILES proposal was submitted was for the first use of four Japanese interface
ports. Maximum envelope specified for a JEM-EF experiment unit is 0.8 m (W) × 1.0 m(H) × 1.85 m (L). Maximum allowable mass is 500 kg. Services such as electricity, liquid
coolant, and data communications including the Ethernet are to be supplied through the
EF interface ports.
The ISS has a circular orbit with an inclination angle of 51.6◦ . Most scientific experiments will be conducted while the ISS is in the inertial flight condition to meet microgravity
requirements. It results in a steady decrease in altitude, and re-boosting is needed periodically. The optimum operational altitude of the ISS and the re-boosting period depends
6

Figure 1.5 Artistic view of the International Space Station (ISS).

JEM/SMILES Payload

Japanese Experiment
Module (JEM)

Experiment
Logistic
ModuleRemote Manipulator
SystemExposed Facility

Pressurized Module

Figure 1.6 Japanese Experiment Module (JEM) and Exposed Facility (EF).

7

Figure 1.7 World map of orbit and measurement positions of JEM/SMILES.

on the solar activity, which affects the Earth atmosphere.
With respect to the attitude of the ISS, a set of Control Moment Gyros (CMG) will
work to keep the ISS in the Torque Equilibrium Attitude (TEA). No thrusters are operated while the ISS is in the microgravity mode. Maximum range of operational attitude,
which is defined by deviations from the Local-Vertical-Local-Horizontal (LVLH) attitude,
is specified as −20◦ to +15◦ in pitch, and within ±15◦ both in yaw and roll. Changes
in the operational attitude during the orbital period of 93 minutes can be approximatedby sinusoidal oscillations with amplitude less than 2◦ (peak-to-peak) in each axis [Treder,
1999].
For limb-emission sounding, variations in yaw are not critical, but those in pitch androll have to be canceled by adjusting the initial angle of the antenna scanning. The initial
angle will be calculated on the ground and commanded to SMILES, based on the predicted
attitude of the ISS (and of SMILES). The ISS attitude estimation accuracy is described
as 0.5◦ (3σ) at the ISS navigation base, and 3.0◦ (3σ) at attached payloads (see Table
3.6). These are knowledge errors in determination of absolute attitude. With its own
Star Tracker (STT) in SMILES, we will be able to predict the SMILES attitude with theaccuracy of 0.5◦ (3σ). This error corresponds to about 20 km at the atmosphere (for the
ISS altitude of 407 km). Therefore, the mechanical adjustment of the initial angle is not
sufficient to ensure the antenna to scan an exact height range of the atmosphere. Current
design of the antenna scan pattern (see Section 3.2.1) includes a margin of +35 km, and−30 km to ensure observations for 10 km to 60 km.
Another critical issue is stability of the ISS attitude while a single scan is performed in
53 s. The attitude change rate is specified as ±0.002◦ s−1 . This corresponds to a deviationof around 0.1◦ in a single scan, which is the same size as the half-power beam-width of theantenna. There is no hardware to compensate this deviation on board, although the STT
measures it every second. Some necessary compensation should be done in data processing
on the ground.
In order to measure high-latitude regions, the antenna beam is tilted 45◦ left fromthe direction of orbital motion. This design enables SMILES to observe latitudes from38◦ S to 65◦ N (for cases when yaw attitude is nominal). Figure 1.7 shows measurement
positions along the ISS orbit. In this case, however, the antenna beam is interfered witha rotating solar paddle of the ISS in every orbit. The latitude of observation points where8Table 1.1 Objective trace gases and their observation frequencies considered for
JEM/SMILES. Local frequency is taken at 637.32 GHz.
Species624.32 -625.52 GHz
Band-A
O3
625.37ClO
HCl
624.98 (37 Cl)
HOCl
625.07
81 BrO624.77HO2
H2 O2
625.04HNO3
624.48, 624.78
SO2
624.89CH3 CN 624.8
Mode-1: Band-A and Band-B
Mode-2: Band-A and Band-C
Mode-3: Band-B and Band-C

625.12 - 626.32 GHz
Band-B
625.37625.92 (35 Cl)
625.84, 626.17-

649.12 - 650.32 GHz
Band-C649.45650.18649.70650.28649.24-the interference occurs moves according to the solar angle. Average unobservable duration
at each local time and position is estimated to be less than 10 %.
1.4.2

Objective Species and Measurement Frequency

Many of important stratospheric trace species, such as ozone, ClO, HCl, HOCl, BrO,
HO2 , O3 -isotopes, have emission spectra in a submillimeter region around 640 GHz, andthe atmospheric opacity is comparatively lower around this region. On the other hand,a millimeter region around 300 GHz is better for measurements of ozone, water vapor,and oxygen in the lower stratosphere (LS) and upper troposphere (UT), because of lower
opacity in the millimeter region. But high sensitivity is not critical for detection of theseabundant gases. Therefore, we have prioritized the submillimeter measurements of stratospheric trace gases for the SMILES mission. Table 1.1 shows trace gases observed in the
640 GHz region, which is divided into three frequency bands. Two bands are located in thelower sideband (Band-A and Band-B) and the other is in the upper-sideband (Band-C) in
respect of a single submillimeter local oscillator fixed at 637.32 GHz. Both sidebands aredetected separately by means of a single sideband (SSB) filter. Two of the three bands
are selected at a time and delivered to the Acousto-optic Spectrometer (AOS). The firstIF is selected to be 11–13 GHz. The IF instantaneous bandwidth is much narrower thanthat required to completely involve all of important species in the 640 GHz region. Thisis unavoidable for protecting the IF band against strong environmental electromagnetic
fields that are anticipated in the ISS and JEM.
Figure 1.8 shows the arrangements we have made for SMILES observation bands.
There are some disadvantages in this frequency design. For example, we cannot include
an N2 O emission line at 652.83 GHz within our measurement band due to the restricted IF
bandwidth mentioned above. In addition, no suitable emission lines for measuring water
vapor are available around 640 GHz. Since water vapor measurements in the LS/UT region
are particularly important in the tropics, we are investigating the possibility of estimating
9

ARRANGEMENT OF IF BANDS
LO 637.32GHz

H2 O

625.0GHz 625.7GHz

H2 O

650GHz660GHz

H2 O
12.4GHz(11.8-13.0GHz)

12.0GHz( 13.0-11.0GHz)
620GHz649.7GHz

630GHz640GHz1.2GHz1.2GHz

LSB2 1.2GHz

LSB1 1.2GHz
621GHz622GHz624GHz623GHz625GHzH2 O

626GHzHCl
HNO3 SO2
BrO
17GHz

627GHz

O3

16GHz

15GHz

14GHz

USB 1.2GHz
649GHz650GHzHCl SO2

HOCL12GHz
H2 O2

13GHz

651GHz628GHzN2 O + O1 8 O

11GHz

652GHz10GHz

653GHz654GHz

N2 O

HNO3

CLO

12GHz

HO2

HNO3
BrO
14GHz

13GHz

15GHz

16GHz

Figure 1.8 Arrangement of IF bands.

water vapor by means of continuum emissions around 640 GHz. Furthermore, no suitable
oxygen-molecule line is available in the SMILES measurement band, which is usually used
for measuring atmospheric temperatures and pressures. Nonetheless, our simulation has
shown that the temperatures and pressures could be derived from the retrieval process ofmeasured species (see Chapter 4 in detail).
1.4.3

SMILES Payload

The SMILES payload is composed of the Submillimeter Antenna (ANT), Submillimeter
Receiver (SRX), IF Amplification Section (IFA), Radio Spectrometer (AOS), Star Tracker(STT), Data Processing and Control Section (DPC), and Payload Bus (BUS).
The ANT is an offset Cassegrain antenna with an elliptical reflector of 40 cm x 20
cm, which gives an elliptical beam with a half-power beam-width (HPBW) of 0.08◦ inelevation and 0.17◦ in azimuth. The antenna scans the atmosphere vertically at the rateof 0.1125◦ s−1 , and the atmospheric signals are accumulated within the AOS in every
consecutive 0.5 s. This gives a sampling interval of 0.056◦ (corresponding to 2.1-4.1 km at
the tangent point). The effective vertical resolution to the atmospheric layer is calculated
as 0.096◦ (corresponding to 3.5-4.1 km), which includes the effects of the antenna runningin 0.5 s and the broader horizontal response of the antenna. Surface accuracy of the mainreflector is designed to be 15 µm, which gives the beam efficiency larger than 90 % for a
2.5-times HPBW area.
The SRX is composed of the Ambient Temperature Optics (AOPT), Cryo-electronics
Unit (CRE), Ambient Temperature Amplifiers (AAMP), Helium Gas Compressors (HECP),
Submillimeter LO Controller (SLOC), CRE Control Electronics (CREC), and Stirling &JT Drive Electronics (SJTD). The AOPT combines quasi-optically the submillimeter signals from the antenna and local oscillator (LO), while terminating the image band to the10

2.7 K cosmic microwave background. Sideband separation, higher than 20 dB, is madeby a new-type of Martin-Puplett interferometer. The submillimeter LO is generated by afrequency tripler and doubler associated with a phase-locked Gunn-diode oscillator operated at 106.22 GHz. A quasi-optical circular polarizer, which is composed of a mirror andwire-grid, is integrated in the AOPT to reduce spectral baseline ripples due to standing
waves.
The CRE includes two SIS mixers and four IF amplifiers. The SIS mixers are based on
Nb/AlOx/Nb devices and equipped with a corrugated feed horn. They are integrated into
a block of submillimeter optics cooled at 4.5 K. Two SIS mixers, by detecting perpendicular polarizations each other, observe the upper sideband and lower sideband separately.
Cryogenically cooled IF amplifiers are based on high-electron-mobility transistor (HEMT)
devices. Two of them are put at 20 K stage, and another two at 100 K stage. They areoperated at 11–13 GHz with a noise temperature of about 15 K (for amplifiers cooled to 20K) and 40 K (for amplifiers cooled to 100 K). The noise temperature of the whole SMILES
system is mainly determined by the performance of the SRX. The noise temperature of
the SRX is estimated around 500 K for single sideband measurements.
The SIS mixers and IF amplifiers are cooled by a 4 K mechanical cooler, which iscomposed of a two-stage Stirling cycle and Joule-Thomson cooler. The former has acooling capacity of 1 W at 100 K, and 200 mW at 20 K. The latter has a capacity of
20 mW at 4.5 K. The total power consumption is about 300 W, including a conversion
loss in the power supply. The total mass of the cooler is 90 kg, including the cryostat,

Table 1.2 Major design parameters of the SMILES payload.
RF Frequency (LSB)

624.32 - 626.32 GHz

RF Frequency (USB)649.12 - 650.32 GHz

System Noise Temperature

Less than 700 K (SSB)

Integration Time

0.5 sec for each observation point

Input Signal Intensity

0–300 K in brightness temperature

Spectral Resolution

1.8 MHz (FWHM)

Spectral Coverage

1,200 MHz × 2

Antenna Aperture

0.4 m (vertical) × 0.2 m (horizontal)

Effective Antenna Beam-width

0.096◦ (HPBW, elevation)

Instrumental Height Resolution

3.5 km – 4.1 km (nominal)

Instrumental Error in Tangent Height0.76 km (rms, bias)
0.34 km (rms, random)

Sensitivity in Brightness Temperature(for each scan)

about 0.7 K (rms) for Tb < 20 K
about 1.0 K (rms) for Tb > 20 K

Accuracy in Brightness Temperature(for each scan)

about 1 K (rms) for Tb < 20 K
about 3% (rms) for Tb > 20 K

Data Rate

Less than 200 kbps

Measurement Height

10 − 60 km

Observation Latitudes

65◦ N - 38◦ S (nominal)

Mission Life

1 yearPower Consumption

about 800 W including payload bus

Payload Weightless than 500 kg

Payload Size

0.8 m (W) × 1 m (H) × 1.85 m (L)11helium gas compressors, and power supply. The cryostat, which includes three different
temperature stages (4.5 K, 20 K, and 100 K), cold-head of the two-stage Stirling cycle,
heat exchangers for the Joule-Thomson cooler, and sophisticated structures for thermal
isolation, is designed to be about 500 mm long and 350 mm in diameter.
The submillimeter signal of the atmosphere in Band-A, Band-B, and Band-C is converted to the first IF at 11–13 GHz. It is further down-converted to the second IF at
1.55–2.75 GHz, and delivered to the Radio Spectrometer (AOS). The AOS has two units ofsignal analyzers; each has a frequency coverage of 1.2 GHz over 1500 channels. The spectral
resolution is 1.8 MHz (FWHM) while the channel separation is about 0.8 MHz/channel.
SMILES observation data combined with housekeeping and attitude information arecontinuously transmitted via satellite link with a data rate of about 200 kbps. The total
power consumption and mass, including all components of the payload bus, are estimatedto be about 800 W and less than 500 kg, respectively. Table 1.2 shows major design
parameters of the SMILES payload.
1.4.4

Data Processing and Distribution

SMILES raw data including atmospheric spectra and housekeeping items are transmitted
to the Tsukuba Space Center (TKSC), NASDA, via the Data Relay and Test Satellite(DRTS), a Japanese relay satellite. At first, the raw data are edited to generate Level 0
data, which are packed into units each corresponding to a certain portion of an orbit.
The data will be checked for overlaps and losses, and necessary flags will be added. The
quantity of Level 0 data is estimated to be 2 GB/day. Secondly, the Level 0 data will
be converted to Level 1 data, which comprise engineering values, spectral temperatures,calibration data, and measurement parameters including estimated tangent heights andpositions. The size of Level 1 data will be 4 GB/day. In the third step, the height profilesof volume mixing ratios are generated along the orbital trajectory for all target gases,
which are processed from the Level 1 data and ancillary information by means of a retrieval algorithm. This product is called Level 2 data, and the size will be 94 MB/dayor 23.5 GB/year for an operation rate of 70 %. All of the first data processing will bemade at NASDA/TKSC within the experimental period. The data is transmitted to
the Communications Research Laboratory (CRL), the Earth Observation Research Center (EORC) of NASDA, and the University of Bremen, a partner of the JEM/SMILES
project. These three cooperative institutions will respectively produce Level 3 data: averaging over a gridded area of latitude and longitude in every week or month; collocation
with other measurements such as EOS-Aura; combination with model-derived temperatures and pressures; combination with assimilation study data set. After the SMILES
operation is completed, all of the data will be archived in the CRL, and reprocessing will
be made if necessary. Figure 1.9 shows the SMILES data transmission, processing anddistribution. Users in prior registration can access the data provided at the institution’ssite via electronic lines. The data stored in CD-ROMs will be distributed to all users onrequest.
1.4.5

Calibration and Validation

In-orbit calibrations will be made for the intensity and frequency of submillimeter signals
in every scan of the atmosphere. The atmospheric intensity is calibrated by comparison
with reference data for “hot load” and “cold sky”. For this purpose a carefully designed“hot load” is used, which can be regarded as a perfect submillimeter blackbody. Potential
frequency shift of AOS is measured also in every scan with a reference signal delivered froma well-stabilized comb generator. This frequency calibration gives a real relation between
12

!!

""
##

$$ %% ""&&''(() ) " " * *+ + ' ' ' '

. /
$

, '

)

% "

- )

Figure 1.9 JEM/SMILES data transmission, processing and distribution.

the channel number of AOS and frequency. However, these are minimum processes toestablish the accuracy of SMILES observation data. There remain several other factors toaffect the absolute accuracy of retrieved quantities for each species, including optimizationof detail processes in retrieval algorithm. Cross-calibration with other satellite data, suchas EOS-Aura-MLS, is therefore very important. Furthermore, scientist groups supportedby NASDA and the CRL would contribute to validation studies for the SMILES data.
Validation studies will be made mainly by correlative measurements from the ground,balloons, aircraft, and satellites.
• Ground-based observations will be made at existing sites including; Fairbanks in
Alaska (CRL, University of Alaska), Tsukuba (National Institute for Environmental Studies: NIES), Rikubetsu (NIES, Solar-Terrestrial Environment Laboratory of
Nagoya University), and Indonesia (NASDA, Japanese universities) . Observations
will be conducted with ozonesondes, lidars, Fourier Transform spectrometers, andshort millimeter-wave radiometers. Measurements by international networks, suchas NDSC stations, will also be valuable.
• Field campaign experiments will be planned in a high-latitude area and in an equatorial area, by synthesizing ground-based sensors, sondes, balloon-borne sensors,
and airborne sensors. The CRL is developing a balloon-borne submillimeter limbemission sounder, and the University of Bremen is operating an airborne submillimeter sounder.

13• Cross-validation with other satellite sensors, such as EOS-Aura, will also be considered.
1.4.6

Data Retrieval Method

Rodgers’ optimal estimation method [Rodgers, 1976; Rodgers, 1990] will be adopted as aretrieval algorithm of SMILES data. In applying the method to SMILES, there are thefollowing items to be considered.
• Elimination of background emissions due to thermal emissions and the sidelobe response of the antenna.
• Elimination of effects of residual standing waves.
• Compensation for potential attitude irregular variations.
• Estimation of accurate tangent height.
• Nonlinear effect on the radiation transfer of abundant species.
In order to estimate measurement errors for target species, and to find what factors are
critical to them, the CRL have conducted simulation studies since 1999. The simulator
consists of three parts, i.e. Forward Model I, Forward Model II, and Inversion Model.
The Forward Model I gives the antenna temperature by calculating the radiative transfer
formula with assumed atmospheric conditions and antenna response pattern. The Forward Model II simulates the output signal of the system, using the antenna temperature
calculated by the Forward Model I, and including the effects of system noise temperature,
optical losses, standing waves, image bands, AOS response function, calibration, digitization, etc. After the Level 0-to-Level 1 simulation processing, the Inversion Model retrieves
height profiles based on Rodgers’ optimal estimation method. The simulator enables us
to estimate the measurement capability of SMILES, the degree of errors caused by various conditions of the atmosphere and instrument, and the speed of data processing. The
simulator is also useful in developing and checking the data retrieval algorithm. It also
can be used to estimate post-flight calibrations and validations.
The University of Bremen has also developed its own retrieval algorithm and conductedthorough comparisons with the CRL simulator. The comparisons have shown the twosystems are generally consistent with minor differences to be improved. Furthermore, theUniversity of Bremen has proposed the feasibility to retrieve atmospheric temperaturesand pressures successfully from SMILES data. The details are described in Chapter 4.
Accurate spectroscopic information, such as frequency, spectral intensity, pressure
broadening parameters, and their temperature dependence, is essential for decreasing errors in the retrieval processing. A spectral catalogue published by the Jet Propulsion
Laboratory (JPL) [Pickett et al., 1992] gives the parameters. However, as far as submillimeter transitions are concerned, the accuracy of those parameters is not experimentally
established yet. The exact values of the parameters are to be measured by laboratory
experiments. This is also indispensable part of the SMILES project, and dedicated experiments are being conducted at the Ibaraki University and Fukui University.

1.5

Development Schedule and Operation Plan

JEM/SMILES is to be carried to the ISS in 2007 aboard the H-II Transfer Vehicle (HTV)that is launched by a boost-up version of H-IIA rocket from the NASDA launch site in the
Tanegashima Island, Japan. Figure 1.10 shows the schedule of the SMILES development
14

Calendar Year

1996
Milestone

Design1997*

Conceptual
Design

1998

1999

2000

**

2001

2002

2003

2004

PDR

Basic Design

2005
CDR

Detailed Design

2006

2007

2008

2009

LAUNCH

Maintenance
DesignFlight Instrument
BBM
EM
EFM
Integration and Test
Ground System
Simulator
System Design
System Development
Argorithm Development
Integration and Test
Experiment in Orbit
Operation and Data Processing
Validation

Figure 1.10 Schedule of JEM/SMILES.

and experiment. In order to shorten development period and to decrease cost, simpler andeffective ways have been pursued in all areas of the project: such as the EFM approachin which some parts of the engineering model (EM) be used as the flight model (FM)after refurbishing. Although instrumental development is generally making progress inline with the schedule, the actual launch of SMILES is dependent on many programmatic
issues including the future plans of the ISS, JEM, and HTV.
The official mission duration of SMILES is one year in space. However, if the next
mission is not ready and SMILES is still good enough to continue its operation, there
could be a chance to extend the mission beyond one year. SMILES should be operational
continuously during the whole mission period except for the initial phase of mission checkout. But in reality, we foresee several cases that may hamper continuous observations.
The ISS altitude decreases gradually during its inertial flight, and the ISS is re-boosted
periodically. In this case, the usual stability of the ISS attitude is not guaranteed. Secondly, the ISS attitude will change largely while the Space Shuttle is moored at the ISS,
which makes the SMILES antenna look at beyond the limb atmosphere. Thirdly, some
restrictions could be imposed on SMILES concerning available amount of resources fromthe ISS or JEM, specifically in electricity and data communications. This also could affectnormal observations of SMILES.

1.6

Mission Organization

The National Space Development Agency of Japan (NASDA) and Communications Research Laboratory (CRL) are main organizations cooperatively responsible for the overall
SMILES experiment. The Space Utilization Research Program (SURP) and Space Utilization Research Center (SURC), both in NASDA/TKSC, and the CRL are responsible
for the payload development and flight operation of SMILES. The Earth Observation

15

Table 1.3 JEM/SMILES Project Key Personnel.
PERSONNEL
Masuko, Harunobu
masuko@crl.go.jp
Inatani, Junjiinatani.junji@nasda.go.jp
Satoh, Ryouta
satoh.ryouta@nasda.go.jp
Manabe, Takeshi
manabe@crl.go.jp
Suzuki, Makoto
suzuki@eorc.nasda.go.jp
Shiotani, Masato
shiotani@kurasc.kyoto-u.ac.jp
Shibasaki, Kazuo
sibasaki@eorc.nasda.go.jp
Klaus Künzi
kunzi@physik.uni-bremen.de
Amano, Takayoshi
amano@mito.ipc.ibaraki.ac.jp

AFFILIATIONRESPONSIBILITY

CRL, NASDA

Principal Investigator

NASDA/TKSC

Hardware development

NASDA/TKSC

Project management

CRL

Hardware development

NASDA/EORC

Ground system development

Kyoto University

Chief scientist

NASDA, Kokugakuin University

Validation program

University of Bremen

Scientific cooperation

Ibaraki University

Spectroscopic measurements

Research Center (EORC) and Earth Observation Program Department (EOPD), both in
NASDA, share development of the ground system, data processing, and distribution of theLevel 1, Level 2, and Level 3 data, with the CRL and NASDA/TKSC. They also supportscience teams for validation and data analysis. The CRL also conducts development of
the ground system, data processing, and distribution of another type of Level 3 data, in
addition to archiving of the whole SMILES products. The CRL also participates in thevalidation activities. In addition, the Nobeyama Radio Observatory (NRO), in the National Astronomy Observatory of Japan (NAO) technically supports the development of
the SIS mixers. The Ibaraki University supports the project by laboratory measurementsof spectroscopic parameters. The University of Bremen, in Germany, is an international
collaborator participating in simulation studies, algorithm development, validation studies, and science. The University of Bern, in Switzerland, is collaborating with the CRL todevelop the submillimeter optics for the SMILES receiver. Table 1.3lists the key personnelof the SMILES project.

References
ATMOS-C1 Team, ATMOS-C1 Mission Proposal, Earth Observation Committee/Earth Science& Technology Forum (forum@hq.esto.or.jp), Earth Science and Technology Organization(ESTO) 97OA1-D003A, 1997.
Pickett, H., R. Poynter, and E. Cohen, Submillimeter, Millimeter and Microwave Spectral Line
Catalog: Revision 3, Tech. Rep. 80-23, Jet Propulsion Laboratory, California Institute ofTechnology, Pasadena, CA, USA, 1992.

16

Rodgers, C. D., Retrieval of atmospheric temperature and composition from remote measurementsof thermal radiation, Rev. Geophys. and Space. Phys.,14, 609-624, 1976.
Rodgers, C. D., Charactrization and error analysis of profiles retrieved from remote sensing measurements, J. Geophys. Res., 95 (D5), 5587-5595, 1990.
Treder, A. J., Space Station GN&C Overview for Payloads, Space Technology and Application
International Forum, 1999.
United Nations Environmental Programme, The 1987 Montreal Protocol on Substances That Deplete the Ozone Layer, 1987.
Waters, J. W., Microwave Limb Sounding, in Atmospheric Remote Sensing by Microwave Radiometry, edited by M. A. Janssen, John Wiley and Sons, Inc., 383-496, 1993.
Waters, J. W., W. G. Read, L. Froidenvaux, R. F. Jarnot, R. E. Cofield, D. A. Flower, G. K. Lau,
H. M. Pickett, M. L. Santee, D. L. Wu, M. A. Boyles, J. R. Burke, R. R. Lay, M. S. Loo, N.
J. Livesey, T. A. Lungu, G. L. Manney, L. L. Nakamura, V. S. Perun, B. P. Ridenoure, Z.
Sippony, P. H. Siegel, R. P. Thurstans, R. S. Harwood, H. C. Pumphery, and M. J. Filipiak,
The UARS and EOS Microwave Limb Sounder (MLS) Experiments, J. Atmospheric Science,
56 (2), 194-218, 1999.
Waters, J. W., An Overview of the EOS MLS Experiment, JPL D-15475, Version 1.1,
(http://mls.jpl.nasa.gov/), October 1999.
World Meteorological Organization (WMO), Scientific Assessment of Ozone Depletion: 1998,
Global Ozone Research and Monitoring Project – Report No. 44, 1998.

17

2

Scientific Objectives

2.1

Summary

One of the most unique characteristics of the JEM/SMILES observation is its high sensitivity in detecting atmospheric limb emission of the submillimeter wave range. Molecularspecies to be observed by JEM/SMILES are: normal O3 , isotope O3 , ClO, HCl, HOCl,
BrO, HO2 , H2 O2 and HNO3 (See Section 4.2.4). Thus, the JEM/SMILES mission is distinguished as that focusing on the detailed halogen chemistry related to ozone destruction.
Spatial coverage is near global, but there is no observational chance at polar latitudes (See
Section 3.2.2). In spite of this limitation, we expect high possibility to make measurements
within the elongated polar vortex in the northern hemisphere. Vertical resolution is supposed to be about 3 km. Detectable range for each of the molecular species with height
will be discussed in detail in Section 4.
In view of the above-mentioned status, we focus on the following scientific targets for
the JEM/SMILES mission.
• Variation and its impact of radical species• Isotopic composition of ozone
JEM/SMILES will make measurements on several radical species crucial to the ozone
chemistry. Some of them have never be seen by any other satellite measurements, andthe JEM/SMILES mission will be the first to detect them such as BrO. It would be very
interesting to see the space-time variations in the polar vortex and even in the backgroundsituation. Overall observations of these radical species will provide us further insight into
the ozone chemistry in the middle atmosphere.
Another possible scientific objective using the JEM/SMILES data is on the isotopic
composition of ozone. Unusually high enrichment in most of the heavy ozone isotopomers
has been puzzling problem and we still don’t have a clear answer for this. The JEM/SMILES
observation will provide us important findings to work out the puzzle of ozone isotope
anomaly.
In the following, the background and detailed description for the JEM/SMILES scientific targets will be discussed.

2.2

BackgroundThe earth system consisting of the atmosphere, the ocean, and the biosphere has been
affected by natural and artificial perturbations. Resulting changes in the earth system
become obvious in several places and in several parameters. Among those the middle
atmosphere is one of the good places to sense such changes, since it has been known that
there are some sensitive atmospheric parameters and minor species. In recent years wehave found several important trends in the middle atmosphere, and now we need thesynthetic understanding of these variations to project the future state of the earth system.
After the discovery of the Antarctic ozone hole in the mid 1980s, numerous observations
from the ground, aircraft and satellites have been conducted to clarify the mechanism. Oneof the characteristic features is that the ozone hole is a typical phenomenon in the southernhemisphere where the polar vortex is stronger and the resulting temperature is colder than
the northern hemisphere. However, in the 1990s, it has been reported that the late-winterozone amounts in the Arctic have decreased significantly [Newman et al., 1997].
Surrounding the Arctic ozone depletion, a decrease of lower stratospheric ozone at
mid latitudes in the northern hemisphere has been noticed recently. In WMO Scientific18Figure 2.1 Estimate of the mean trend using all four measurement systems at northern
mid-latitudes (heavy solid line). Combined uncertainties are also shown as 1 σ (light solidline) and 2 σ (dashed line) (After [WMO, 1999]]).

Assessment of Ozone Depletion [1999, vertical distribution of the mean trend in the northern mid latitude is presented (Figure 2.1). This figure shows the lower stratospheric trendwith 7.5 % decrease for decade, in addition to the upper stratospheric trend which isdriven by the accumulation of anthropogenic chlorine. The lower stratospheric trend isalso supposed to be contributed by the increased chlorine and bromine loading. However,the quantitative understanding about this mid latitude decrease is still uncertain. At thesame time a cooling trend in the lower stratosphere has been observed. It is supposed tobe due to the ozone decrease in the lower stratosphere [WMO, 1999]. The cooling trend
may also contribute to the decreasing trend in ozone, but quantitative argument is notsatisfactory yet.
One of the most highlighted trends in the stratosphere is an increasing one in water
vapor, though it is not directly related to the anthropogenic perturbation (SPARC Assessment of Upper Tropospheric and Stratospheric Water Vapor [2000]). The observations
show that water vapor has increased at a rate of about 1 %/year (Figure 2.2). This increase may be attributed to an increase in methane entering the stratosphere, because
water vapor in the stratosphere is produced by the methane oxidation. The increase inmethane, however, is found to be far below what we expect from the observational increase in water vapor. Another idea to explain the water vapor trend is a control by
the equatorial tropopause temperature, which can affect the concentration of water vapor
entering the stratosphere, but this does not seem the case because the observations show
the cooling trend in the equatorial tropopause temperature. Thus the mechanism of thewater vapor trend has not been explained yet. Dvortsov and Solomon [2001] conducted amodel calculation by taking account of the water vapor increase. They found that it cancontribute to the cooling trend in temperature from the radiative aspect of water vapor
and to the decreasing trend in ozone from the chemical role of it.
Associated with the mid latitude ozone depletion, the lower stratospheric ozone issensitive to the volcanic aerosols. The Mt. Pinatubo eruption is a good natural experiment19

Figure 2.2 Time series of 20- 22 km layer averaged water vapor mixing ratio over Boulder.
(After [SPARC, 2000]).

to learn its effect on the atmospheric dynamics and chemistry. After the eruption of Mt.
Pinatubo in June 1991, significant total ozone decrease was observed. The eruption could
modify the thermal structure of the atmosphere, then the general circulation, throughthe radiative effect. At the same time the increase of sulfuric aerosols may promote theheterogeneous chemistry around the tropopause region. Figure 2.3 shows a result fromthe two dimensional model with or without temperature variations and the aerosol effects

Figure 3.30.

Time series of 2 km

layer average water vapour mixing

volume (ppmv) over Boulder,
stratosphere

over

Boulder,

Colorado,(Middle)seasonal variation, and (Bottom)

for

(Top)

20-22 km24-26 km.

ratio

16-18 km

which

is

an

in

parts

which
altitude

The data are obtained

chilled-mirror hygrometers that are launched approximately monthly.

per

is

the

above

from

millionbylowermostthestrongballoon-borne,

The solid

line

is

a

least squares fit to the data and m is the slope with the 95% confidence interval.

Figure 2.3 Observed and calculated total ozone anomalies from 1979 to 1997 at 45◦ N.
(From [Solomon et al., 1997].)

20

[Solomon et al., 1997]. This suggests that chemical effects owing to the aerosol loadingplay a significant role in perturbing the ozone anomalies.
From a dynamical point of view, understanding the meridional circulation in the stratosphere is also very important. Most long-live trace species are emitted in the troposphereas natural or anthropogenic perturbations. These are transported into the stratosphereand are deformed there. Methane is oxidized in the upper stratosphere and produces water
vapor which is an important minor species to affect radiative balance of the atmosphere.
CFCs are also destructed in the upper stratosphere, resulting in the production of chlorine
species which affect the ozone layer especially in the lower stratosphere polar region during
the spring time. Therefore, investigation of the atmospheric meridional circulation is a
key issue to know temporal and spatial distributions of the minor species and their effects
on the atmosphere.
Particularly the upper troposphere and lower stratosphere (UT/LS) is an importantregion for the dynamical processes such as transport and mixing, because the photochemical time scale of ozone in UT/LS is comparable or longer than that of the dynamical
time scale. Of course the chemical processes such as heterogeneous reactions on PSCs
are crucial to understand the ozone distribution and variation in the lower stratospherein association with the ozone depletion. It has recently been recognized that the meridional circulation around UT/LS should be understood from the global view of dynamics(Figure 2.4). Holton et al. [1995] presented an idea that the upward motion at equatorial

10

Wave-driven
30

extratropical
Pressure (mb)Large-scale ascent
Large-scale
subsidence

100"pump"
400
380
Some
cumulonimbus
cloudspenetrate
stratosphere

350
330

300
300
Two-way exchange
blocking anticyclones
cut-off cyclones
tropopause folds

1000

Pole

Equator
LatitudeFigure 2.4 Dynamical aspects of stratosphere-troposphere exchange. The tropopause
is shown by the thick line. Thin lines are isentropic or constant potential temperaturesurfaces labeled in kelvins. In detail, see Holton et al. [1995].

21

latitudes and the downward motion at mid- and high latitudes in the lower stratosphere
are mainly driven by planetary wave induced torque. The meridional circulation is controlled by the strength of wave driving in the both hemispheres as the season goes by.
At equatorial latitudes a modulation of the meridional circulation produces changes inthe tropopause properties such as the tropopause height and the tropopause temperature.
The manifestation of these combination is a recent finding of the so-called tape recordersignal in water vapor over the equator [Mote et al., 1996].
All these factors affecting the earth’s atmosphere system are closely connected witheach other, but our recognition on these processes has just started; we don’t know detailed inter-relation between the trends. We need comprehensive understanding of thesevariations to construct reliable models for the future earth system.

2.3
2.3.1

Radical Species in the Stratosphere
Introduction

Recently it has been widely recognized that ozone abundance in the lower stratosphere has
crucially decreased. The Antarctic ozone hole has been observed since 1980’s, the Arctic
ozone depletion is another one over the past decade, and the mid latitude decrease trend
in the northern lower stratosphere is slowly but certainly emerging. Current observational
evidences and their understandings are summarized in detail in WMO Scientific Assessment of Ozone Depletion [1999]. What we have learned about the scenario of the ozone
depletion is that the chlorine chemistry is crucial even in the lower stratosphere when thechemical processes such as heterogeneous reactions on PSCs occur.
One of the most successful results from the satellite was brought by the Microwave
Limb Sounder (MLS) onboard the Upper Atmosphere Research Satellite (UARS). The
MLS measurements provided the first global maps of stratospheric ClO, which should beobserved when ozone is destructed by reactive chlorine chemistry. Figure 2.5 shows suchenhancement of ClO and low abundance of O3 even in the northern hemisphere of thelower stratosphere.

465 K

465 K

ClO
0.25

0.75

1.25

1.75

(ppbv)

O3

2.25

1.2

(ppmv)
1.6

2.0

2.4

2.8

3.2

Figure 2.5 UARS MLS measurements of ClO and O3 for February 20, 1996 for thenorthern hemisphere (After Manney et al. [1996]).

22

The decrease of ozone in the upper stratosphere has also been noticed on the basis ofrecent satellite observations (e.g. Figure 2.1). This decreasing trend is also understood interms of the accumulation of anthropogenic chlorine. Especially in the upper stratospherenot only ClOx cycle, but also NOx , BrOx and HOx cycles become very important.
Here we will briefly see such reactions in association with the ozone destruction processes.
2.3.2

Chlorine and Bromine ChemistryOver the Earth’s lifetime, natural processes have regulated the balance of ozone in thestratosphere. In the past two decades, however, human production of halogen-containing
compounds, such as chlorofluoro-carbons (CFCs), has brought an additional force to destroy ozone. Once in the stratosphere, the CFC molecules break up due to UV radiation
and release their chlorine atoms. The free chlorine atoms then react with ozone molecules
taking one oxygen atom to form chlorine monoxide as shown in Eq. (2.1).
Cl + O3 → ClO + O2
ClO + O → Cl + O2
net O + O3 → O2 + O2

(2.1)

The reaction cycle happens over and over again as called catalytic reaction, allowing
a single atom of chlorine to destroy many of ozone. Fortunately, chlorine atoms do not
remain in the stratosphere forever. When a free chlorine atom reacts with gases such as
methane (CH4 ), it produces molecule such as hydrogen chloride (HCl). This Chlorine
cycle is illustrated in Figure 2.6 (right).

Cl compounds

Br compounds

5%, 103s220%, 10 s
HOBr
hv
H2CO, HO2

1%, 104s
HBr

hv

Br

5%, 1s
O3

OH, O
hv, O

BrCl

30%, 105s
ClONO2hv

20%, 102s
BrONO2
O, ClO

HOCl

HO2

NO2

BrO

CH4, HO2

50%, 10s
HClOH

50%, 10s
ClO

hv

NO2
O, NO

Cl

0.01%
O3

M

ClOO

0.5%, 102s

HO2

Br
hv

ClO
15%, 102s
ClO(ClO)2

1 dimensional simulation
height = 30km, mid latitude spring, Time = 12:00

Figure 2.6 Ozone depletion reaction (% and lifetime) (After Lary [1996, 1997]).
Bromine-related chemistry in the stratosphere has become an important issue since
the halons and methyl bromide have been suspected as ozone-depleting substances more
powerful than the CFCs. Bromine is estimated to be about 50 times more efficient than Cl
in destroying stratospheric ozone on a per-atom basis. Methyl bromide, which represents
23

Figure 2.7 Profile of BrO mixing ratios (left panel); ClO mixing ratios from Pierson etal. [1997] (After [McKinney et al., 1997]).

the major source of bromine in the stratosphere, is used as an agricultural fumigant, andis an interesting compound in the context of stratospheric ozone depletion because it has
significant natural sources and also has a very short lifetime in the troposphere.
Br photo-chemistry is believed to be basically similar to that of Cl, but the lifetime ofradicals and reservoirs is completely different from that of Cl. In the Br chemistry, HBr
and BrONO2 do not work as reservoirs, as shown in Figure 2.6. In contrast to chlorine
species, there have been few measurements of bromine compounds in the stratosphere.
No observation has been reported for the thermal emission in the frequency region of theinfrared and sub-millimeter. Almost the only in situ observation of BrO in the stratosphereis plotted in Figure 2.7 [McKinney et al., 1997].
2.3.3

HOx Chemistry

Reactions of HOx family (OH, HO2 , H2 O2 ) are also considered to play an important rolein the whole stratosphere. In spite of the fundamental importance of HOx family, theobservational data remains one of the poorest in the atmosphere. Global measurements of
the latitudinal, seasonal, and diurnal variation in the HOx family and related species, ClOx ,BrOx , H2 O, CH4 , H2 and O3 are needed to address this deficiency. The JEM/SMILES
observation will enable us to obtain the global data for HO2 , H2 O2 , HOCl, HOBr, ClO,
BrO and O3 . Figure 2.8 summarizes ozone loss mechanism by the HOx catalytic cycle,
and we will see details in the following.
(a) HOx reactions destroying the odd oxygens
The odd oxygen, O and O3 , are destructed by the activated hydrogen through a varietyof catalytic cycles. In Figure 2.8, the reactions destroying the odd hydrogen in a directmanner are represented by the straight blue line. Water vapor molecule can be transformed
into two OH radicals via the reaction of water vapor with the first electronically excitedoxygen atom.
H2 O + O(1 D) → 2OH
OH radical reacts with O3 to form HO2 , and HO2 radical reacts with O to produce OH
24

1 dimensional simulation
height = 25km and in the global 24h averageHOx catalytic reaction

hv 30

concentration (x10 cm-3)
fluxes (x 103cm-3s-1)
6

H2O

CO,H2OO(1D)

3.8

4.1 x 106

1.1

6.4

hv 2.0OH

NO 2.0hv

hv

O3 ClO 110

0.14

2.4

4.7

NO2
6.3

O2

4.3

O3 O,NO 110
hv
HO2

0.7
HONO

H

0.04

6100 HNO3

HO2

60

NO2

27 0.18
H2O2
ClO

2.0

2.4

5.7
HOCl

BrO

1.7

HOBr

1.3

H2O
4.1 x 106

1.1

HNO4
1800

Figure 2.8 Gas phase reaction of HOx in the stratosphere (Based on Brasseur [2000]).

radical. The sum of reactions is written as follows.
OH + O3 → HO2 + O2
HO2 + O → OH + O2
net O + O3 → O2 + O2

(2.2)

It should be noticed that the net effect of the reactions is simply a conversion of two odd
oxygens into two O2 molecules.
(b) Reservoir molecules
The reservoir molecules are able to remove HOx through catalytic reactions. In Figure 2.8, reservoir molecules are written by using green letters (i.e., H2 O, HNO3 , and
HNO4 ). These molecules are produced by the reaction such as,
OH + HO2 → H2 O + O2
OH + NO2 + M→ HNO3 + M

HO2 + NO2 + M→ HNO4 + M

As these reservoir molecules are stable chemical compounds and have very long lifetimes,
they are transported into the troposphere. They have an important role of removing HOx
from the stratosphere altogether.
(c) Reactions in the upper stratosphere
In the upper stratosphere, where O atoms are relatively plentiful, the HOx catalytic
cycle outlined above is fairly effective. Two other reactions are also important in theupper stratosphere. The first one involves a free H (hydrogen) atom as an intermediate
compound. This reaction is described in the upper part of Figure 2.8. Two O atoms are25

converted into a single diatomic oxygen molecule.
H + O2 + M → HO2 + M
HO2 + O → OH + O2
OH + O → H + O2
net 2O → O2

(2.3)

The second reaction involves the loss of two odd oxygens. The two odd oxygens areconverted into two diatomic oxygen molecules.
OH + O3 → HO2 + O2
HO2 + O → OH + O2
net O3 + O → 2O2(2.4)(d) Reactions in the lower stratosphere
The HOx cycles interacting with the chlorine or bromine cycles are important in thelower stratosphere. This is the most complicated one of the catalytic cycles, but yields
the similar catalytic destruction of odd oxygen as we have seen so far. In these reactions,
X can be either chlorine or bromine:
XO + HO2 → HOX + O2
HOX + hν → OH + X
OH + O3 → HO2 + O2
X + O3 → XO + O2
net 2O3 → 3O2(2.5)

These two cycles are shown in the lower part of Figure 2.8.
2.3.4

Targets

JEM/SMILES will make measurements on several radical species crucial to the ozone
chemistry. Some of them have never seen by any other satellite measurements in thestratosphere, and the JEM/SMILES mission will be the first to detect them such as BrO.
Especially in the lower stratosphere it would be very interesting to see the space-time
variations of these species. Though there is a limitation of latitudinal coverage of the
JEM/SMILES observations, we expect high possibility to make measurements within theelongated polar vortex in the northern hemisphere. Also we hope JEM/SMILES has acapability to observe such species even in the background situation.
The upper stratosphere is another interesting field to investigate from the viewpointof the ozone budget. By the use of the JEM/SMILES data we may try to estimate thedecreasing trend of ozone affected by the anthropogenic chlorine and bromine loading.
As to the HOx reaction cycle, for example, the volume mixing ratios of HO2 and H2 O2
become large in the upper stratosphere and the mesosphere. This region is still unknown
considering the distribution of HOx species.

2.4
2.4.1

Observation of Ozone Isotopes
Introduction

A problem in molecular and atmospheric physics has puzzled scientists for almost two
decades: the unusually high enrichments in most of the heavy isotopomers of ozone have26

been observed in the troposphere and stratosphere [Thiemens, 1999]. In general, by combining the observed isotopic compositions with knowledge of the rates and isotope effectof each of the process producing and removing a given molecule in a system, one should be
able to validate them with each other. Alternatively, if the isotopic composition and either
the isotope specific production or loss rates are known, the other may be inferred from theavailable observations using a simple model on the basis of the corresponding chemistryof the molecule. Despite the extensive laboratory studies and atmospheric observations
have been made, a convincing physical explanation of the processes to result in the heavyozone isotope enrichment is still missing.
The oxygen isotope distribution in the Earth’s atmosphere is well defined. The threestable isotopes have the following abundance: 16 O = 0.9976, 17 O = 0.00037, and 18 O
= 0.00204. Besides the dominant form of ozone, 48 O3 , the isotopomers produced bysingle replacement of 16 O to 17 O or 18 O are of any significance in the atmosphere, namely
49 O and 50 O , respectively. The abundance ratios of these isotopomers are statistically33calculated to be 900 and 163 for 48 O3 /49 O3 and 48 O3 /50 O3 , respectively. The degree ofthe ozone isotope enrichment is defined as δ M O = (M Robs /M Rstd − 1) × 1000 [h], whereMR
obs is the observed abundance ratio of the heavy isotopomer with mass number M , andMR
std is the standard ratio described above [Kaye, 1987]. Isotope fractionation in chemical
reaction is usually explained in terms of its mass-dependence. For example, the isotope
ratios in a three-isotope system are an approximate linear function of the differences in thereciprocal masses of the isotopic species. In the case of ozone isotopes, mass-dependent
fractionation is given by δ 17 O ≈ 0.5δ 18 O. Thus, 2 h enrichment in 18 O is accompaniedby 1 h fractionation of 17 O. The “mass-independent isotopic compositions,” or simply“isotope anomaly” are now known to be pervasive in a range of atmospheric molecules,
including O3 , CO2 , N2 O and sulfate aerosols, which awaits for thorough understanding of
the mechanism.
2.4.2

Atmospheric Observations and Laboratory and Theoretical Studies

The first observational evidence of the ozone isotope anomaly was found during a balloon flight using an in-situ airborne mass spectrometer (AMS) in 1981 [Mauersberger,1981]. Their study showed the stratospheric ozone is greatly enriched in 50 O3 , as high asδ ≈ 400 h at 32 km, with approximately equal δ 17 O and δ 18 O enrichments. Hereafter,
heavy isotope enrichments in stratospheric ozone have been confirmed by various techniques as reviewed by Johnson et al. [2000] (Figure 2.9). The first ground-based FT-IR
measurements were made in 1985 and could discriminate between the two isotopomersof 50 O3 while mass spectrometric measurements cannot. The higher column enrichment
in asymmetric isotopomer of 18 O16 O16 O than symmetric one 16 O18 O16 O was observed.
The Atmospheric Trace Molecule Spectroscopy (ATMOS) Grille Spectrometer has flown
four times on the Space Shuttle since 1985. The zonal mean profiles of 18 O16 O16 O and16 O18 O16 O retrieved showed no statistically significant variation with latitude, altitude,or time. The FIRS-2 FTIR spectrometer also made measurements during seven balloon
flights, which took place between 1989 and 1997.
The extent of the enrichments observed are in good agreement except that AMS measurements showed systematically larger enrichments in 18 O compared to cryogenic sampling (CS), ATOMS, and FIRS results. Meanwhile, observed variation of the enrichmentswith latitude, altitude, or time are not consistent with each other where the statisticallysignificant variability was seen in AMS and CS measurements while it was not seen inspectroscopic observations. One possible explanation of the discrepancy is that the small
variability may not be detected with the limited precision of the remote sensing mea-

27

Figure 2.9 Comparison of published results of measurements of heavy ozone enrichment.
(a) Enrichment for 50 O3 . Enrichment for 49 O3 . (From Johnson et al. [2000]).

surements. Very recently, Mauersberger et al. [2001] have made a critical review for the
stratospheric ozone isotope data, whose samples were cryogenically collected in the past.
They concluded that the data indicated 50 O3 enrichment ranging from 70 to 90 h in the
middle stratosphere, in which the enrichments were in good agreement with results fromlaboratory studies, and suggested to abandon several measurements showing very highenrichments due to unidentified measurement errors.
Since pioneering work of atmospheric observations by Mauersberger [1981], the dedicated laboratory experiments have emerged to explain the ozone isotope anomaly occurredin the stratosphere. The relative recombination rate coefficients for ozone production
from various combinations of atomic and molecular oxygen isotopes have been evaluatedthrough mass spectrometric and tunable diode laser spectroscopic experiments [for example Mauersberger et al., 1999]. Those studies suggest that, for example, the rate coefficientof the recombination 16 O +18 O18 O is about 50 % larger than that of the standard 48 O3
formation. Such laboratory data have been applied to model the enrichments of symmetric and asymmetric 50 O3 and 49 O3 in the stratosphere [Johnson et al, 2000]. Laboratory
experiments also motivated quantum chemical treatment of the kinetic isotope effects in
the recombination processes [Gao and Marcus, 2001].
2.4.3

Targets

The inability to account for the stratospheric ozone isotope anomaly and variability represents a significant inadequacy in our understanding of the chemical transformation and/ortransport of stratospheric and tropospheric ozone. The magnitude of the heavy isotope
enrichment at times far exceeds that expected on the basis of laboratory measurements
and theoretical studies as shown in Figure 2.9. Further improvement of precision, spatial and temporal coverage of the measurement are required to get a firm picture of theenrichments in stratospheric 50 O3 and 49 O3 , which will provide vital information for the28

sound understanding of the mechanism of the isotopic fractionation. Global and quantitative measurements may elucidate hitherto unobserved chemical processes related to theheavy isotope enrichment of ozone, by making a comparison with the experimental andtheoretical studies. The JEM/SMILES observations will cover polar, mid latitude andtropical region (from 38◦ S to 65◦ N) in the latitude range between 10 and 60 km with all 5
isotopes of mono-substituted ozone; 16 O16 O16 O, 16 O18 O16 O, 18 O16 O16 O, 16 O17 O16 O, and17 O16 O16 O. The first simultaneous observation of ozone isotopic species reduces systematic errors due to the difference of the observational methods as previously. We proposethat the new tool JEM/SMILES observation will provide us important findings to workout the puzzle of ozone isotope anomaly
It is worth while commenting that ozone molecule is believed to be the principal
source of mass-independent fractionation in other atmospheric molecules and its anomaly
can transfer to other species either through direct reaction or via reaction with the ozone
photolysis products. For example, precise measurements of the ozone isotopomer distributions are crucial to investigate the oxygen isotope distributions of stratospheric andmesospheric CO2 by means of a photochemical model calculation [Barth and Zahn, 1997].
This is because the CO2 isotope anomaly is thought to be related to the ozone isotope
anomaly through the reaction of CO2 with O(1 D) generated via UV photolysis of ozone.
More recently a photochemical equilibrium model for short-lived radical species such as
ClO, HOx , and NOx , has been constructed to calculate their amounts of mass-independent
fractionations under mid-latitude conditions [Lyons, 2001]. Thus the JEM/SMILES observations of ozone isotopomer would also be incorporated in the studies of isotope anomaliesof other atmospheric species.

References
Barth, V. and A. Zahn, Oxygen isotope composition of carbon dioxide in the middle atmosphere,
J. Geophys. Res., 102, 12995–13007, 1997.
Brasseur, G. P., Lecture talk on The Chemistry of Atmospheric Ozone, at Hokkaido University,
Sapporo, Japan, July, 2000.
Dvortsov, V. L., and S. Solomon, Response of the stratospheric temperatures and ozone to pastand future increases in stratospheric humidity, J. Geophis. Res., 106, 7505–7514, 2001.
Gao, Y. Q. and R. A. Marcus, On the theory of the strange and unconventional isotopic effects inozone formation J. Chem. Phys., 116, 137–154, 2002.
Holton, J. R., P. H. Haynes, M. E. McIntyre, A. R. Duglass, R. B. Rood, and L. Pfister,
Stratosphere-troposphere exchange, Rev. Geophys., 33, 403–439, 1995.
Johnson, D. G., K. W. Jucks, W. A. Traub, and K. V. Chance, Isotopic composition of stratosphericozone, J. Geophys. Res., 105, 9025–9031, 2000.
Kaye, J. A., Mechanisms and observations for isotope fractionation of molecular species in planetaryatmosphere, Rev. Geophys., 25, 1609–1658, 1987.
Lary, D. J., Gas phase atmospheric bromine photochemistry, J. Geophys. Res., 101, 1505–1516,
1996.
Lary, D. J., Catalytic destruction of stratospheric ozone, J. Geophys. Res., 102, 21515–21526, 1997.
Lyons, J. R., Transfer of mass-independent fractionation in ozone to other oxygen-containg radicals
in the atmosphere, Geophys. Res. Lett. , 28, 3231–3234, 2001.
Manney, G. L., M. L. Santee, L. Froidevaux, J. W. Waters, and R. W. Zurek, Polar vortex conditions during the 1995–96 Arctic winter: Meteorology and MLS ozone. Geophys. Res. Lett.,
23, 3203–3206, 1996.
Mauersberger, K., Measurement of heavy ozone in the stratosphere, Geophys. Res. Lett., 8, 935–
937, 1981.
Mauersberger, K., B. Erbacher, D. Krankowsky, J. Gunther, and R. Nickel, Ozone isotope enrichment: Isotopomer-specific rate coefficients, Science, 283, 370–372, 1999.
Mauersberger, K., P. Laemmerzahl, and D. Krankowsky, Stratospheric ozone isotope enrichments29

– revisited, Geophys. Res. Lett. , 28, 3155–3158, 2001.
McKinney, K. A., J. M. Pierson, and D. W. Toohey, A wintertime in situ profile of BrO between
17 and 27 km in the Arctic vortex. Geophys. Res. Lett., 24, 853–856, 1997.
Mote, P. W., K. H. Rosenlof, M. E. McIntyre, E. S. Carr, J. C. Gille, J. R. Holton, J. S. Kinnersley,
J. M. Pumphrey, J. M. Russell III, and J. W. Waters, An atmospheric tape recorder: The
imprint of tropical tropopause temperatures on stratospheric water vapor, J. Geophys. Res.,
101, 3989–4006, 1996.
Newman, P. A., J. F. Gleason, R. D. McPeters, and R. S. Stolarski, Anomalously low ozone over
the Arctic, Geophys. Res. Lett., 24, 2689–2692, 1997.
Pierson, J. M., K. A. McKinney, D. W. Toohey, A. Engel, and P. A. Newman, In situ balloon
measurement and modeling studies of ClO in the Arctic Vortex, J. Atm. Chem., 1997.
Solomon, S., S. Borrowmann, R. R. Garcia, R. Portmann, L. Thomason, L. R. Poole, D. Winker,
and M. P. McCormick, Heterogeneous chlorine chemistry in tropopause region. J. Geophys.
Res., 102, 21411–21429, 1997.
SPRC, SPARC Assessment of Upper Tropospheric and Stratospheric Water Vapor, SPARC Report
No. 2, Ed. by D. Kley, J. M. Russell, and C. Phillips, WCCRP No 113, WMO/TD-No 1043,
December 2000.
Thiemens, M. H., Mass-independent isotope effects in planetary atmospheres and the early solar
system Science, 283, 341–345, 1999.
WMO (World Meteorological Organization), Scientific Assessment of Ozone Depletion: 1994,
Global Ozone Research and Monitoring Project – Report No. 44, Geneva, 1999.
Yung, Yuk L., W. B. Demore, and Joseph P. Pinto, Isotope exchange between carbon dioxide andozone via O(1 D) in the stratosphere, Geohpys. Res. Lett., 18, 13–16, 1991.

30

3

Instrumental Capabilities

3.1

Introduction

3.1.1

ISS and JEM

3.1.1.1

Overall Structure

The Japanese Experiment Module (JEM), also known by a Japanese nickname Kibo,is the segment of ISS developed by the National Space Development Agency (NASDA) of
Japan for supporting research and development experiments in the ISS orbit. The JEM
consists of manned Pressurized Module (PM), Experiment Logistic Module-Pressurized
Section (ELM-PS), and Exposed Facility (EF). The JEM Exposed Facility (EF), an unpressurized pallet structure exposed to the space environments, will be located just outsideof the JEM Pressurized Module (PM) placed at the ram-port side of the ISS. The SMILES
payload will be attached to the ram side of the JEM-EF at the location EFU #1 or EFU#3 as shown in Figure 3.1 and Figure 3.2.
3.1.1.2

Orbit and Attitude

The ISS will be in a nearly circular orbit at a nominal altitude of approximately 400
km with an inclination of 51.6 degrees to the equator, and will usually maintain a torqueequilibrium attitude (TEA) during the microgravity mode of its operation. It is predictedthat the ISS altitude will vary between 350 km and 460 km. In order to keep this altitude
range, the ISS will be periodically reboosted approximately every 10 to 45 days by using
onboard thrusters. During this Reboost Mode, the ISS attitude will be yaw-maneuveredby 180 degrees.
The ISS flight attitude (orientation relative to the plane of its orbit) is referenced to
local-vertical/local-horizontal (LVLH) axes fixed with respect to the ISS’s near-circularorbit [NASA, 1999]. The torque equilibrium attitude (TEA), which balances the averageexternal torque disturbances such as aerodynamic torque and gravity gradient, will varyaround the local-vertical/local-horizontal (LVLH) coordinate as shown in Figure 3.1. The
x-axis points along the orbital velocity vector (the ram direction) and the z-axis points
radially toward the Earth’s center (or toward the nadir direction.) The ISS attitude
variation will never exceed ±15 degrees in roll and yaw angles and +15 and −20 degrees
in pitch angle from the nominal LVLH attitude. The detailed descriptions of the orbit andattitude variations will be found in 3.2.5.
3.1.1.3

ISS Operational Modes

The ISS will be operated according to a number of specific modes of operation, each ofwhich has a specified set of conditions and capabilities. Currently defined operation modes
include Standard mode, Reboost mode, Microgravity mode, Survival mode, ProximityOperations mode, Assured-Safe-Crew-Return mode, and External-Operations mode. Although most modes support research payload operations at some level, there are somemodes for which payloads operations may be sharply curtailed or discontinued. Microgravity and Standard modes are the primary modes of operation during which full support
for research payload will be provided. During Microgravity mode the ISS must be operated
so as to meet a stringent set of requirements for its microgravity environment maintaining

31

Figure 3.1 Space Station reference coordinate system. The x-axis points along the orbital
velocity vector (the ram direction), the z-axis points radially toward the Earth’s center(or toward the nadir direction).

Experiment Logistics
Module-Pressurized Section(ELM-PS)

Remote Manipulator System(JEM RMS)

Exposed Facility(EF)
Pressurized Module(PM)

EFU #1
EFU #3

Experiment Logistics
Module-Exposed Section(ELM-ES)

Figure 3.2 Configuration of Japanese Experiment Module (JEM). SMILES will be attached at the location EFU #1 or EFU #3

its attitude by non-propulsion means. Standard mode is similar to Microgravity mode
except that microgravity environment will not be guaranteed.
In Reboost mode, drastic variations of altitude and attitude due to maneuvering will
be expected. A periodic reboosting of the ISS will be carried out approximately every 10
to 45 days as described in 3.1.1.2. During Survival mode which will be invoked for the caseof contingency, support and commanding for research payload are precluded. In these two32

modes of operation, atmospheric observation by SMILES cannot be carried out.
In the remaining three modes of operation, Proximity-Operations, Assured-Safe-CrewReturn, and External-Operations, it is still not certain to what extent the SMILES atmospheric observations can be properly performed.
3.1.1.4

Data Link between JEM/SMILES and ground facility

Three types of data communications links are provided by the ISS for onboard data
communications [NASA, 1999]: 1) a MIL-STD-1553B Payload Bus, 2) an IEEE 802.3
Ethernet, and 3) a fiber-optic High-Rate Data Link (HRDL). Among them, SMILES
will employ the Payload Bus #2 for commanding and transmission of health-and-status(H&S) and house-keeping (HK) data of instruments, and the Ethernet for transmission ofobservation data as well as the instrument HK data.
There are two data links either through the U.S. Tracking and Data Relay Satellite
System (TDRSS) or through the Japanese Data Relay Test Satellite System (DRTSS) areavailable for command uplink and data downlink between the ISS and the ground facilities
at Tsukuba Space Center (TKSC) [NASDA, 1998]. Considering the resource allocation
among International Partners for the TDRSS link, SMILES will only use the Japanese
DRTSS link for which NASDA is allocated 50 % of link capacity. Although two DRTSs(DRTS-W and DRTS-E) are planned, only one DRTS (DRTS-W) will be available at thetime of SMILES launch. Since the DRTS is an geostationary satellite, the DRTS link
is secured only for a limited percentage of time during which it is visible from the ISS.
During the loss-of-sight period referred to as Zone of Exclusion (ZOE), downlink data
will be temporally stored in the High Rate Data Recorder (HRDR) with a capacity of 20
Gbits of the Interorbit Communication System (ICS) of JEM. Currently, downlink data
rate available to SMILES is estimated to be 200 kbps at the most.
3.1.2
3.1.2.1

SMILES Payload
Payload Configuration

SMILES payload instrument consists of a number of sections as listed in Table 3.1.
Figure 3.3 shows a block-diagram of SMILES payload instrument. The Submillimeter
Antenna (ANT), Submillimeter Receiver (SRX), Intermediate Frequency Amplification
Section (IFA), and Radio Spectrometer (AOS) comprises the main part of the payload.
The Star Tracker (STT) has a function of detection and calibration of the SMILES payload
attitude.
The SMILES is equipped with a heterodyne superconductor-insulator-superconductor(SIS) receiver to be operated in the 625/650-GHz band as a limb-emission sounding radiometer. The SMILES has a mechanically scanning elliptical offset-Cassegrain antenna
with diameters of 40 cm × 20 cm to achieve an altitude resolution of about 3.5—4.1 km
at the tangential altitude ranging from upper troposphere (10 km) to lower mesosphere(60 km) from the orbit of the ISS. The atmospheric limb emission collected by the ANT
is directed to the SRX, where the received submillimeter-wave signal is combined witha reference signal (637.32 GHz) from a submillimeter-wave local oscillator (SLO), and isdirected to SIS mixers through quasioptics consisting of focusing mirrors, wire-grids, anda sideband filter. Two SIS mixers, one of which for upper sideband (USB: 649.12 GHz –650.32 GHz) and the other for lower sideband (LSB: 624.32 GHz – 626.32 GHz), are operated for simultaneously down-converting both sidebands into the intermediate frequency
33

Table 3.1 Configuration of SMILES Payload.
Subsystem Components
Antenna Reflectors (REF)
Antenna Mounting Structure (MNT)
Beam Transfer Section (TRN)
Submillimeter Antenna (ANT)
Cold-sky Terminator (CST)
Antenna Drive Electronics (ADE)
Calibration Hot Load (CHL)
Ambient Temperature Optics (AOPT)
Cryo-electronics Unit (CRE)
Ambient Temperature Amplifiers (AAMP)
Submillimeter Receiver (SRX) Helium Gas Compressors (HECP)
Submillimeter LO Controller (SLOC)CRE Control Electronics (CREC)
Stirling & JT Drive Electronics (SJTD)
IF Amplification Section (IFA)AOS Analyzer Unit (AU)Radio Spectrometer (AOS)
AOS Control & Video Unit (CVU)
STT Camera Units (CAM)
Star Tracker (STT)STT Camera Controller (CAMC)
Data Processing and Control
Section (DPC)
Electric Power System (EPS)
Mainframe Structure (MFS)
Payload Bus (BUS)
JEM Interface Mechanism (JIF)
Thermal Control System (TCS)
Section(IF) band ranging between 11 GHz and 13 GHz. The IF signals are further down-converted
and amplified in the IFA, and then analyzed by two acousto-optical spectrometers in the
AOS. Further details of the functions of each subsystem will be described in the following
sections.
3.1.2.2

Dimension, Mass, Power, and Data Rate

Figure 3.1.2.1 shows an external view of the SMILES payload. The envelope of the
SMILES payload mainframe structure has a dimension of 1.85 m × 1 m × 0.8 m. The
total mass of the payload is less than 500 kg. The electrical power consumption of the
payload is less than 900 W for normal operation.
As a data communications interface for commanding and telemetry, SMILES will use
the MIL-STD-1553B Payload Bus #2 and the IEEE 802.3 Ethernet. Commands from theground and the ancillary data such as the reference time data are transmitted to the DPC
through the Payload Bus #2 via the JEM Control Processor (JCP) and the Payload Data
Handling Unit (PDH) and are distributed to SMILES subsystems [NASDA, 1998].
The Health and Status (H&S) data and the experimental data of the subsystem instruments are collected by the DPC and are downlinked to the ground through the 1553B
Payload Bus #2 via the PDH and the JCP. The instrument experimental data are down34

Submillimeter Antenna(ANT)
Submm-Wave
Main Reflector
Limb-Emission Signal
Calibration
Hot Load (CHL)
Cold Sky
Terminator (CST)SSB Filter

Star Tracker(STT)Star Camera
Units (CAM)

Payload Bus (BUS)

Star Camera
Controller(CAMC)

ThermalControl System
(TCS)

SLO
Controller(SLOC)

Submm Local Osc.
(SLO) 637.32 GHz

IF Amplification Section (IFA)BPF IF Amp

D/C
11.8 - 13.0 GHz

HEMT
Amp.

HEMT
Amp.

Amp.

HEMT
Amp.

Amp.

SW

IF: 11GHz - 13GHz

1.55 - 2.75 GHz
Band C

4K

HEMT
Amp.

Switching
Network
CVU

1.55 - 2.75 GHz
Band B

SW
11.8 - 13.0 GHz

Cryostat Cryo-Electronics Unit (CRE) 100K
Helium Gas
Compressor (HECP)

Data Processing&
Control Section
(DPC)

AU-1

14.55 GHz

20K

Stirling & JT
Drive. Elec.
(SJTD)JEM Interface
Mechanism(JIF)

Amb. Temp. Optics (AOPT)USB
SIS Mixer

LSB
SIS Mixer

Electric Power
System
(EPS)

11.0 - 12.2 GHz

CRE Control
Electronics(CREC)

9.45 GHz

Submillimeter Receiver (SRX)

1.55 - 2.75 GHz
Band A

Comb
Generator

AU-2

Radio
Spectrometer(AOS)

Figure 3.3 Block diagram of JEM/SMILES payload.

Figure 3.4 External view of the SMILES payload.

linked through the Ethernet along with the atmospheric observation data yielded by the
AOS. The downlink 1553B data rate is estimated to be about 18 kbps which includes the
H&S data of about 90 bps. The downlink Ethernet data rate is estimated to be less than35

200 kbps.
3.1.2.3

Launching and Installation to JEM

SMILES will be launched around in 2007 by a Japanese H-IIA rocket from Tanegashima
Space Center of NASDA in Japan. The SMILES payload will be transported to the orbitof the ISS by H-II Transfer Vehicle (HTV) and attached either to the JEM-EF Unit (EFU)#1 or #3. According to the current plan, on-orbit experiment mission period is scheduled
to be one year. If there is no succeeding mission payload that takes place of the EFU
position occupied by SMILES, the mission period of SMILES may be prolonged as longas the payload instruments are alive.
After the completion of its on-orbit mission, SMILES will be detached from the JEMEF and thrown away into the atmosphere by HTV.
3.1.3

Mission Instruments

Mission instruments whose functions and performances are decisive to SMILES experiments comprise a submillimeter antenna, submillimeter receiver, intermediate frequency(IF) amplification section, radio spectrometer, star tracker, and data processing and control section. The following sections will give explanations and basic requirements for those
instruments.
3.1.3.1

Submillimeter Antenna (ANT)

SMILES needs a submillimeter antenna that has a reasonable size and high degree of accuracy. Large mirror is preferable from the viewpoint of achieving a higher spatial resolution,
particularly in the vertical direction. A kind of trade-off is, however, unavoidable over the
spatial resolution and a given envelope for JEM payloads. Our choice is an elliptical mirror
with dimensions of 40 cm in vertical direction and 20 cm in horizontal. The mirror is not
so large, but it must be highly accurate in shape for generating a high-quality beam at
submillimeter wavelengths. One important figure of merit is the beam efficiency, which
represents how much radiation is collected in the main beam in comparison with inevitablesidelobe contributions. We regard a reasonable target for the beam efficiency is 90 % for
the antenna alone, which means the efficiency in the case the antenna is illuminated by an
ideal feed, and 85 % for the whole integrated SMILES system. In addition to these radio(optical) qualities, we need mechanical accuracies to determine the beam direction while
the antenna is continuously driven along the atmosphere scan path. Since the verticalbeam size is 0.09◦ (HPBW), the angular accuracy of the antenna driving mechanism must
be better than 0.01◦ . The basic requirements on the antenna performance are summarizedin Table 3.2.
3.1.3.2

Submillimeter Receiver (SRX)

A superconducting low-noise receiver is a key instrument of SMILES experiment. With SIS
mixers associated with cryogenically cooled HEMT amplifiers, it will reduce receiver noise
by a factor of 20 in comparison with the conventional semiconductor diode-mixer receiver.
The SMILES receiver noise temperature will be about 500 K (SSB). Two SIS mixers, which
are pumped by a single submillimeter LO source, will receive the atmospheric signal in theupper sideband (USB) and lower sideband (LSB) separately: one for 649.12—650.32 GHz,and the other for 624.32—626.32 GHz. The LO frequency will be stabilized to 10−7 by36

Table 3.2 Requirements for SMILES-ANT
Items
Requirements
Observation frequency
624.32 — 650.32 GHz
Main reflector aperture size
400 mm (El) × 200 mm (Az)Main beam width
0.09◦ (El) × 0.18◦ (Az)Main beam efficiency
Higher than 90 %Reflection loss
Higher than 50 dB
Elevation angle range−40◦ to +5◦
Elevation angle control accuracy±0.01◦

means of a phase-locked loop. A quasi-optical sideband filter, based on a modified MartinPuplett interferometer, makes each SIS mixer sensitive to the atmospheric emissions only
in one sideband, with suppression of more than 15 dB over the other sideband. A 4Kelvin class mechanical cooler is another key component of SMILES. A combined systemof a two-stage Stirling cooler and Joule-Thomson circuit will cool SIS mixers and theirassociated optics to 4.5 K, first HEMT amplifiers to 20 K and second HEMT amplifiers
to 100 K. All the cooled components are contained in a cryostat that is thermally isolated
from the ambient temperatures. The coolers are continuously operated with pressurized
He gas supplied by compressors. The cooler lifetime will be determined by either residual
mechanical abrasion or He gas deterioration due to outgassing. Current estimate of life is
one year or longer. Basic requirements on the submillimeter receiver are summarized inTable 3.3.
Table 3.3 Requirements for SMILES-SRX
Items
Requirements
Input Frequency
624.32—626.32 GHz (channel-T)
649.12—650.32 GHz (channel-R)
Image Rejection Ratio
More than 15 dB
LO Frequency
637.32 GHz
LO Frequency Accuracy±64 kHzLO Phase Noise
Lower than −75 dBc/Hz (offset 1 MHz)
Output Frequency
11.00—13.00 GHz (channel-T)
11.80—13.00 GHz (channel-R)
Gain Linearity±1% or less
Gain Variation
3%p−p or less in 1 minuteReceiver Noise Temperature
500 K or less3.1.3.3

IF Amplification Section (IFA)IFA has functions of frequency down conversion and signal transmission from SRX to
AOS. Since SMILES has three RF observation bands (Band-A and Band-B in LSB, andBand-C in USB), and two AOS units, it makes simultaneous observations only for twobands among three. So IFA should have functions of flexible combinations among these
inputs and outputs, which is required from operational points of view as well as to preparea kind of redundancy for reliability. IFA also contains functions of adjusting the output
37

power level and of generating reference signals, a comb generator, for calibrating the AOS
frequency drift. The basic requirements are given in Table 3.4.
Table 3.4 Requirements for SMILES-IFA
Items
Requirements
Input Frequency
11.00—13.00 GHz (Band-A & B)
11.80—13.00 GHz (Band-C)
Output Frequency
1.55—2.75 GHz
LO Frequency Drift
100 kHz or less
Gain Ripple
2.0 dBp−p or less in a signal band
Gain Variation
0.04 dBp−p deg−1 or less
Comb Generator Frequency Step
100 MHz
Comb Generator Frequency Drift
17 kHz or less3.1.3.4

Radio Spectrometer (AOS)

SMILES adopts the acousto-optical spectrometer (AOS) for analyzing the atmosphericsignal and detecting its power spectrum. AOS will have two analyzing units, each ofwhich has a frequency coverage of 1.55—2.75 GHz with a resolution of 1.8 MHz (FWHM)
and each power spectrum is obtained with a CCD array of 1728 channels. The CCD data
is digitized, accumulated and sent to DPC in every 1 sec. The basic requirements are
given in Table 3.5.
Table 3.5 Requirements for SMILES-AOS
Items
Requirements
Input Frequency
1.55—2.75 GHz
Frequency Resolution
1.8 MHz or less (FWHM)
CCD: Number of Channels
1728 channels / each CCD
CCD: Channel Separation
0.8 MHz or less
Noise Dynamic Range
9 dB or more (for 10 % additional noise)
A/D Conversion Accuracy
12 bits
A/D Differential Nonlinearity±1/2 LSB or less
Data Accumulation
500 ms
Output Data Length
16 bits
Output Data Rate
108 kbps

3.1.3.5

Star Tracker (STT)

Since the ISS or JEM attitude is assumed to be unstable, SMILES will be equipped with
two STT cameras to determine its real time attitude. Each camera will work unless the suncomes within an angular distance of 30◦ . STT will determine the attitude of the SMILES
mainframe structure with respect to the stellar inertia coordinate. It is then converted tothe attitude with respect to the coordinate fixed to the Earth to calculate the position of
the tangential point of the SMILES beam. The basic requirements for STT are listed inTable 3.6.

38

Table 3.6 Requirements for SMILES-STT
Items
Requirements
Stellar Detection Limit
6.5 mag.
Attitude Determination Accuracy
0.003◦ (rms) for pitch, yaw
0.03◦ for roll±0.03◦ for bias component
Attitude Refresh Period
0.96 sAllowable Attitude Change Rate
0.6◦ s−1
Solar Avoidance Angle30◦ (separation from the STT axis)
Field of View
18.4◦ × 13.4◦
Reference Stellar Catalogue
Hipparcos Catalogue

3.1.3.6

Data Processing and Control Section (DPC)

DPC will have the following functions:• to control the antenna scan, which is to be synchronized with IFA, AOS and STT,
• to acquire observation data from AOS and STT,
• to acquire telemetry data from almost all components of SMILES,• to send necessary commands to SMILES components,• to have data interfaces with ISS/JEM.

3.2

Field of View (FOV)

3.2.1
3.2.1.1

Altitude Scanning of the Antenna
Scanning Profile

The antenna scanning profile of SMILES is shown in Figure 3.5. Although the altitude
and attitude of the ISS (and also SMILES) will vary during a mission life, a scanningprofile of the antenna can be defined in terms of the relative elevation angles. A repetition
period of the antenna scan is designed to be 53 seconds, the first 29.5 seconds of which areallocated to observations toward limb directions. The rest of the scan period is dedicatedto calibrations for the system gain and the AOS frequency. The former is done firstly bydirecting the main reflector to a reference sky, at the altitude higher than 160 km, andsecondly by inserting a calibration hot load (CHL) into the antenna submillimeter optics.
A switching mirror is used for this operation. Frequency scale of the AOS is calibrated
with a set of carrier signals injected into the IF band. The antenna scanning and the data
accumulation in the AOS have to be synchronized by a SYNC pulse provided from the
SMILES Data Processing and Control Section (DPC) with the accuracy of 1 ms.
3.2.1.2

Range of Tangent Point Altitude

The height of tangent point for a line-of-sight depends on the antenna elevation angle aswell as the altitude and attitude of the ISS/SMILES. Precise direction of the submillimeterbeam will be determined with a reference to the Star Tracker (STT) data in the Level-1
data processing on the ground. In orbit, however, the prediction errors and fluctuations of
39

2
0
30
0
0

Atmospheric Observation

Hot load calibration

4

Frequency calibartion

Main Antenna
Reference sky observation

Relative Scan Angle (degree)

6

Switching mirror

10

20

30

40

50

Time (s)Figure 3.5 Antenna scanning pattern of SMILES with the associated operation of aswitching mirror for radiometric calibration.

the ISS attitude will bring a large uncertainty in the tangent point altitude. For simplicityof the system, we have designed the ascending phase wide enough to cover the altitude
range of 10 to 60 km with a sufficient margin. Initial tangent point altitude of every scan
is settled to a nominal value of −35 km on the basis of predicted information of the ISS
orbit and attitude. An expected range of real observational altitudes is listed in Table 3.7
for several representative altitudes and attitudes of the ISS.
3.2.1.3

Spatial Sampling Rate and Altitude Resolution

During atmospheric observations in the scanning period, the antenna ascends at a stepping rate of 12 Hz with a unit angle of 0.009375◦ . The emissions from the limb-atmosphere
are accumulated, or averaged, for 500 ms to generate a unit output data. Strictly speaking, six different emissions originating from six consecutive altitudes contribute to each
spectrum data. The effective beam-size for such output spectrum is calculated to be 0.096◦
in HPBW. Both the beam size and sampling interval, expressed in altitude at the tangent
point, depend on the altitude and attitude of the ISS. They also vary along the antennascan, because the distance between the tangent point and the ISS decreases as we go to thehigher tangent point altitude. Figures 3.7 and 3.8 shows how the beam size and samplinginterval will change while the antenna is ascending in a single scan.

40

Table 3.7 Expected range of tangent point altitudes for several ISS conditions.
Roll
Pitch
Yaw ISS Altitude Obs. Range Reference Sky(deg.) (deg.) (deg.)
(km)
(km)
(km)
0
0
0
350
−35 - 84
168
0
0
0
400
−35 - 92.5
184
0
0
0
460
−35 - 102201
∗1 0.5
−8.8
−2.0
350
−35 - 83167
∗1 0.5
−8.8
−2.0
400
−35 - 91.5
182
∗1 0.5
−8.8
−2.0
460
−35 - 101200
∗2 −15−20−15350−35 - 82.5
166
∗2 −15−20−15400−35 - 91181
∗2 −15−20−15460−35 - 100
198
∗3 15
15
15
350
−35 - 83.5
167
∗3 15
15
15
400
−35 - 92
183
∗3 15
15
15
460
−35 - 101200
*1: Expected ISS attitude after FLT 15A (Rev.D) SMILES will be installedaround this event. *2 and *3 : The worst case of the ISS attitude.

3.2.1.4

Reference-sky Calibration

Onboard calibration of the radiometric gain will be done with two noise references
at submillimeter wavelengths; the hot-load (CHL) that is virtually a black-body at theambient temperature, and the reference sky that can be regarded as the microwave cosmic
background. The two references are measured by the SMILES receiver in every antenna
scan. The reference sky is measured with the main reflector of the antenna that is set at
6.13◦ upward with respect to its initial position. As far as the initial antenna position isadjusted in every scan at the nominal altitude of −35 km, which is to be programmed
based on the prediction of the ISS altitude and attitude, the real height of the reference
sky will be higher than 160 km. Since SMILES observes at around 640 GHz, the brightness
temperature, which usually expressed in the Rayleigh-Jeans temperature, of the cosmic
background is virtually zero Kelvin.
For the hot load calibration, a switching mirror is inserted into the antenna optics for
the SMILES receiver to view the CHL. The physical temperature of the CHL is measured
with the accuracy of 0.1◦ every 10 seconds. Since the reflectivity of the CHL is well
suppressed to −55 dB or less, its brightness temperature is also accurate. In the scan periodof 53 seconds, we allocate 4 seconds each to the hot load and reference sky calibrations.
This method makes the increase of spectral noise, which is attributed to the subtraction
between the atmospheric and the reference sky data, to be less than 10 %.
3.2.1.5

Continuity of Observations

Once SMILES is launched and attached to the JEM-EF Unit, the first several weeks
will be reserved for initial checkout of the instruments. During this period, electrical performance of the radiometer is optimized and the antenna pointing accuracy is established.
During a mission life, several engineering tests will be scheduled to confirm operating conditions of the instruments. In addition to the engineering tests, several factors can prevent
continuous observations:41

1. Acute attitude changes of the ISS
Among the ISS operational modes, Microgravity Mode is only suitable for limb
sounding from the viewpoint of attitude stability. More than 30 successive days in
one time and more than 180 days in total per year are planned to be in Microgravity
Mode after completion of the ISS construction. During the construction phase of
the ISS, the length of this mode will be shortened due to more flights accessing to
the ISS. Observations can be continued during Standard Mode, if attitude changes
are found not severe. Dynamical analysis of the SMILES attitude is still in progress.
2. Poor environmental conditions on the ISS
RF electromagnetic fields radiated from transmitters on the ISS may deteriorate
the quality of the observed spectra. Of course, in its current design, the SMILES
instruments are well shielded against such harmful radiations by means of completely
enclosed panels. The submillimeter receiver is also protected by the cryostat. In spiteof that, some interference may remain on the observed spectra in the worst cases.
The increase of the background pressure around the JEM also may limit normal
operation of SMILES, because we have to protect cryogenically cooled components
from contamination. This situation can occur in the events such as water dump fromthe Space Shuttle orbiter, the ISS boosting by a propulsion module, or ventilationfrom the pressurized module of the JEM.
3. Limitations on the ISS resources
The ISS is a multi-purpose facility that accommodates many experiments in thesame period. SMILES will be provided from the ISS-JEM with resources such as
electric power, coolant, and means of data communications. When a large amountof electric power is needed for some other experiment on board, SMILES may berequested to reduce its power consumption. It is however impossible for SMILES
to continue atmospheric observations with a reduced level of power consumption.
On the other hand, heavy traffic in the ISS data link system may force SMILES to
reduce its data generation. This also means that SMILES has to be dormant.
3.2.1.6

Effects of Atmospheric Refraction

So far the effects of refraction due to Earth’s atmosphere on the limb-sounding field-of-view
are ignored. For submillimeter-waves propagated in the actual Earth’s atmosphere, thepropagation path is deviated from the geometrical straight line due to refraction caused
by vertical gradient of the refractive index which depends on the atmospheric pressure,temperature, and humidity.
Figure 3.6 shows the effects of refractive bending on the limb-sounding line-of-sightpaths for two typical reference atmospheres, i.e., low-latitude annual and high-latitudewinter atmospheres1 . It is found in Figure 3.6 that the refractive bending of the pathgives rise to an appreciable deviation from the geometrically calculated one for the paths
with tangent points below 20 km.
A significant deviation are found for the actual atmosphere below 15 km in tangent
point altitude compared with the case of no refraction (Fig. 3.6). However, variations
among different models of atmospheres, which representing seasonal changes, are almost
negligible for altitudes above 2 km [Manabe, 2000]. The simple exponential model [International Telecommunication Union, 1997], in which the atmospheric refractivity n is given
1

Further detailed discussions on the effects of atmospheric refraction on JEM/SMILES limb-sounding
and their dependence on atmospheric models and on radiowave frequency, etc., are found elsewhere [Manabe, 1999; Manabe, 2000].

42

6400
30
6390

6380
10

[km]

[km]

20

6370
0
6360
-200

-100

0

100
200
[km]

300

400

500

Step Interval or HPBW in Altitude at Tangent Point [km]Figure 3.6 Effects of atmospheric refraction on limb-sounding line-of-sight paths forgeometrical tangent height without refraction of every 2 km from 0 km to 26 km. Solid anddashed curves are calculated for the low-latitude-annual and high-latitude-winter reference
atmospheres [Manabe, 2000; International Telecommunication Union, 1997], respectively,
while the dotted line represents the geometrical ray without refraction. Although theordinate is exaggerated about 10 times as the abscissa, geometrical rays without refraction
are represented by straight lines. For the sake of clarity, nominal tangent points forrefraction-free rays are aligned on x = 0 km. The SMILES orbital altitude is assumed to
be 400 km.

6
ISS Altitude = 460 km
ISS Altitude = 400 km
ISS Altitude = 350 km
5

HPBW in Altitude at Tangent Point

4

3
Step Interval in Altitude at Tangent Point
2

1
0

20

40
60
Tangent Point Altitude [km]

80

100

Figure 3.7 Antenna scanning step interval and vertical half-power beamwidth (HPBW)
at tangent point for exponential model atmosphere for ISS orbital altitudes of 350 km,
400 km, and 460 km
43HPBW in Azimuth at Tangent Point Altitude [km]8
ISS Altitude = 460 km
ISS Altitude = 400 km
ISS Altitude = 350 km

7.5
7
6.5
6
5.5
5
4.5
4
0

20

40
60
Tangent Point Altitude [km]

80

100

Figure 3.8 Horizontal half-power beamwidth (HPBW) at tangent point for exponential
model atmosphere for ISS orbital altitudes of 350 km, 400 km, and 460 km
by a simple exponential function of altitude h [km] asn(h) = 1 + 315 × 10−6 exp(−h/7.35),(3.1)

is found to be a good approximation to a wide range of actual atmosphere.
As a result of the refractive bending of the limb-sounding path, the spatial resolution
defined by the antenna beamwidth at the tangent point becomes slightly worse than is
expected by ignoring atmospheric refraction. Figures 3.7 and 3.8 show the effects ofatmospheric refraction on the spatial resolution at the tangent point calculated for thecases of ISS orbital altitude of 350 km, 400 km, and 460 km by assuming the exponentialreference atmosphere whose altitude profile of atmospheric refractivity is given by Eq.
(3.1) and the spherical Earth with a radius of 6378.136 km.
3.2.2
3.2.2.1

Global Coverage
Global Coverage of Atmospheric Limb Scanning

Since the ISS orbit is a circular orbit with an inclination of 51.6◦ to the equator, thehighest latitude reached by the ISS orbit is 52◦ in north and south. In order to extend thelatitudinal coverage to the north polar region where the ozone depletion is significant, thefield-of-view of the SMILES antenna is deflected 45◦ to ram port side from the ISS orbital
plane. SMILES limb-sounding observation with this field-of-view deflection provides alatitudinal coverage 65◦ N to 38◦ S on each orbit.
In Figure 3.9, red segments depict the ground projections of the tangent points forthe altitude of 10 km to 60 km in the antenna scanning scheme described in 3.2.1. The
associated one-day orbit, shown by a blue curve, is assumed to start from the ascending
node in the prime meridian. Each red segment corresponds each limb scan from 10 km
to 60 km in altitude. In this calculation, it is assumed that the Earth is spherical witha radius of 6378 km, the ISS travels at a speed of 7.669 km/s in a circular orbit with an
44

Figure 3.9 The SMILES observation tangent-point coverage (red dots) for an ISS oneday orbit starting from the ascending node corresponding to the prime meridian (bluecurve). Only the tangent points within the altitude range between 10 km and 60 km areplotted. The earth is assumed to be spherical with a radius of 6378.136 km. The ISS
orbital altitude is assumed to be 400 km. (Cylindrical equidistant projection)

Figure 3.10 The SMILES observation tangent-points coverage (red dots) viewed fromthe north. (Azimuthal equidistant projection)

45

70
60

Altitude [km]

50
40
30
20
10
0
-10

0

10

20

30
40
50
60
70
Horizontal Distance [km]

80

90

100

Figure 3.11 Trajectory of tangent points in a vertical plane for single limb scanning.

orbital altitude of 400 km. An along-track separation of about 360 km between adjacentlimb scans, which corresponds to about 105 limb scans per orbit, can be achieved by the
SMILES antenna scanning scheme described in 3.2.1. Figure 3.10 shows the coverage of
the SMILES observation tangent points around the Arctic Circle. It is found in Figure
3.10 that the limb sounding by SMILES samples most densely the northern high latitude
region around the 65◦ N parallel of latitude around which the polar vortex may appear.
Figure 3.11 shows the trajectory of the tangent points in the vertical plane for a limb
scanning from 10 km to 60 km. For each limb scanning, the tangent point travels about
100 km in horizontal direction as its altitude increases from 10 km to 60 km2
3.2.2.2

Horizontal Resolution

In the retrieval of scientific parameters, the atmosphere should be assumed to behomogeneously stratified, i.e., the atmospheric properties depend only on the altitude. In
Figure 3.12, the area where the horizontal homogeneity should be assumed is depicted bya right blue triangle for a single limb scan from 10 km to 60 km. The size of this triangle
area is considered to be a measure of the horizontal resolution for single limb scanning.
The base of this triangular area is about 1550 km irrespective of the ISS orbital position
while its altitude varies within a range between 50 km and 57 km depending on the ISS
longitude.
3.2.2.3

Effects of ISS Attitude and Altitude on Global Coverage

So far the attitude of ISS is assumed to be in the nominal LVLH attitude ignoring
attitude variation. If the attitude variation of ±15◦ in yaw angle is taken into account,the latitudinal coverage should be between 68◦ N and 35◦ S for a −15◦ yaw deviation, andbetween 61◦ N and 42◦ S for a +15◦ yaw deviation (the yaw angle of the ISS is defined2

It should be noted that the effects of atmospheric refraction is ignored here. If the effects of atmosphericrefraction are taken into account, horizontal extent of the tangent points for a shingle limb scanningbecomes appreciably longer than 100 km. See http://www.crl.go.jp/ck/ck321/smiles/refrac/refrac.html,
and http://www.crl.go.jp/ck/ck321/smiles/refrac/refrac.pdf.

46

Figure 3.12 The horizontal resolution of SMILES observation depicted by a blue triangle
defined as the area where the horizontal homogeneity should be assumed for limb sounding.
Red segments are the ground projection of the trajectory of tangent points within a range
between 10 km and 60 km. Broken line is a sub-orbital track of the ISS.

in Fig. 3.1) The effects of pitch and roll variations are trivial since the range of antenna
elevation scanning has some margin for short-term small variations as described in 3.2.1.2
and can be offset for long-term large variations.
If the ISS altitude is within a range between 350 km and 460 km, its variation has
smaller effects on the latitudinal coverage than the yaw variation. Highest latitudes coveredby SMILES limb observations are summarized in Table 3.8 for several different combinations of the ISS altitude and yaw deviation. The highest latitude is defined here as the
one reached by the 10-km altitude tangent point by SMILES limb observation.
Table 3.8 Highest latitudes sampled by SMILES limb scanning. Highest latitudes aredefined as the highest latitude of the tangent point for altitude of 10 km reached by
SMILES limb scanning(a) Northern Hemisphere(b) Southern Hemisphere
+15◦
0◦
−15◦
+15◦
0◦
−15◦
460 km 61.9◦ N 66.2◦ N 69.6◦ N
460 km 41.3◦ S 37.0◦ S 33.6◦ S◦◦◦
400 km 61.2 N 65.3 N 68.4 N
400 km 42.0◦ S 37.8◦ S 34.8◦ S
350 km 60.6◦ N 64.4◦ N 67.4◦ N
350 km 42.6◦ S 38.8◦ S 35.8◦ S

47

3.2.33.2.3.1

Interference with FOVInterference by the Sun

Since the ISS orbit is sun-asynchronous, the incidence of solar radiation can be from any
direction except that of the Earth. SMILES does not have an azimuthal drive of the mainreflector, so that its main beam inevitably makes an occasional encounter with the sun. In
that case, the SMILES receiver will suffer from about 10 times larger signal than the normal
operation. SMILES will use a switching mirror to protect critical devices such as the Radio
Spectrometer (AOS). Besides the main reflector, SMILES has the Cold-sky Terminator(CST) that sees the zenith. The CST is used as a low-temperature submillimeter-wave
source for the image sideband of SIS mixers, so that the signal from the CST is always fed
to the SMILES receiver on almost equal weight of intensity with the signal from the mainreflector. Coaxial switches, denoted as ‘sw’ in Fig. 3.3, in the IF Amplification Section(IFA) will be turned off while solar radiation interferes with the CST beam.
The Data Processing and Control Section (DPC) will control these switching operations, following a programmed sequence. Because of the uncertainty of the ISS attitude,
the duration of the switch-off mode must be longer than that expected only from theantenna beam-width. The frequency of interference by the sun in the main beam and the
CST beam depends on the ISS orbit, season, latitude, and local time. The sun hits themain beam in the morning of a particular season depending on the latitude, while the suncomes to the zenith when the ISS flies over low latitudes around its local noon.
In Figure 3.13, the local times of the SMILES observation points are plotted withgreen dots in the latitude range of 0 - 10◦ N, 30 - 40◦ N, and 50 - 60◦ N. In this figure, redcrosses show where the sun comes within ±2◦ from the line-of-sight of the main beam.
The solar interference with the main beam occurs around 6 o’clock of local time in theseason near equinox. In the same figure, the interference with the CST beam is shownwith orange boxes. The points where the sun comes within ±3◦ from the ISS zenith aremarked. This interference occurs at the local noon occasionally. Refer the caption of the
figure for assumptions used in this calculation.
3.2.3.2

Interference by the Solar Paddle

While SMILES has an ability of dense observation in Arctic region, its main beam is
occasionally interfered by the ISS solar paddles. The SMILES main beam views 45◦ northfrom the ISS orbital plane. The huge photovoltaic arrays, which rotate around the axisof the ISS main truss, will intersect the SMILES main beam twice an orbital rotation of
the ISS (Figure 3.14). Blue circles in Fig. 3.13 plot the points where the photovoltaic
arrays come within ±0.1◦ from the SMILES main beam. Averaged probabilities of theinterference are 1.2%, 0.8%, and 0.5% in the latitude range of 0 - 10◦ N, 30 - 40◦ N, and
50 - 60◦ N, respectively. If we assume the main beam is interfered when the photovoltaic
arrays come within ±1◦ , the probabilities increase. The probabilities of the occurrenceof interference are shown in Fig. 3.15 for three different types of interferences describedabove. Local times when the interferences occur are unevenly distributed, in particular
local noons have the higher probabilities of the interference as shown in Fig. 3.15.
3.2.4

Antenna Response Pattern

SMILES has a mechanically-scanning offset-Cassegrain antenna of 400 mm × 200 mm indiameter, which is designed to generate an elliptical beam with an altitude resolution ofaround 3.2-4.4 km at tangent altitudes ranging from 10 km to 60 km, while keeping thephysical size small enough to be compatible with the payload envelope. The Half-power48

Latitude range of 0-10(a)

Apr May Jun

Jul

Aug Sep Oct Nov Dec Jan Feb

24

Local time

18

12

6

0
11

41

72

102 133 164 194 225 255 286 317 345
Day from equinox
Latitude range of 30-40(b)

Apr May Jun

Jul

Aug Sep Oct Nov Dec Jan Feb

24

Local time

18

12

6

0
11

41

72

102 133 164 194 225 255 286 317 345
Day from equinox
Latitude range of 50-60(c)

Apr May Jun

Jul

Aug Sep Oct Nov Dec Jan Feb

24

Local time

18

12

6

0
11

41

72

102 133 164 194 225 255 286 317 345
Day from equinox

Figure 3.13 The local time of the SMILES measurements. The local times are shown(green dots) with respect to the day from the vernal equinox. The SMILES single scan
is assumed to last 53 seconds. Green dots are replaced with other marks when one of
the measurements in the scan has the solar interference with the main beam (red verticalcrosses), or the CST beam (orange boxes), or the solar paddle interference with the main
beam (blue circles). Three latitudinal zones are calculated; (a) 0 - 10◦ N, (b) 30 - 40◦ N,
and (c) 50 - 60◦ N. Assumptions are; a circular orbit of the ISS, a spherical earth, theISS decreasing altitude from 400 km to 386 km, a simple rotation of the ISS solar paddle
pointing toward the sun, and the SMILES location of EFU #3 on JEM-EF. It is judged
to be interfered when the interior angle between the sun and the main beam, the sun and
the CST beam, and the nearest point of the solar paddles and the main beam are lower
than 2◦ , 3◦ , and 0.1◦ , respectively.

49

The ISS orbital velocity
vector direction

The SMILES main beamline-of-sight

Figure 3.14 Top view of the ISS. The rotating solar paddle will occasionally intercept
the SMILES main beam (yellow arrow).
(a)

Solar paddle intereference
Solar interference with CST beam
Solar interference with main beam

Probability of interference [%]

14
12
10
8
6
4
2
0
-40

(b)

-20

40

60

80

Solar paddle intereference
Solar interference with CST beam

14
Probability of interference [%]020
Latitude [degree]

12
10
8
6
4
2
0
-40

-20

0
20
Latitude [degree]

40

60

80

Figure 3.15 The probability of the interference versus latitude. Plotted are the ratiosof the numbers of scan which include interfered measurements by the sun with the main
beam (filled triangles) or with the CST beam (open triangles) or by the solar paddle withthe main beam (circles) to the total number of scan. The ratios are calculated from (a)whole one year scans and (b) one year scans corresponding to local times between 9:00
and 13:00.
50

Table 3.9 Antenna Specification. ∗ Frequency: 637.32 GHz. FOV beam efficiency isdefined as the relative radiated power involved within a 2.5-time HPBW area.
Item
Antenna shape
Aperture diameters
Main reflector material
Surface roughness of reflectors
Surface accuracy of reflectors
Aperture edge-level
Beam width (HPBWAz )Beam width (HPBWEl )FOV beam efficiency

Characteristic
Elliptical-beam offset Cassegrain
400 mm × 200 mm
Aluminum with a sandblast treatment
5 µm rms
15 µm rms
−20 dB∗
0.173◦∗
0.083◦∗
Higher than 90 %∗specification of the SMILES antenna is summarized in Table 3.9.
A calculated near-axis response pattern of the SMILES antenna is shown in Figure 3.16
(designed by MELCO). The surface shape of the main- and sub-reflectors has been op-

60

Gain [dBi]

40
20
0
-20
0.5
Ele
0
vat
ion
[de -0.5
g]

0.5
0
]
[deg
-0.5
h
t
u
Azim

Figure 3.16 Calculated near-axis antenna response pattern.
(designed by MELCO)

51

timized for achieving low sidelobe levels and high beam efficiency. Such quality of thesubmillimeter beam is essential to reduce additional noise contributions from the earth
and the SMILES payload itself. The actual response pattern of the antenna will be evaluated with a near-field phase-retrieval measurement on the ground.
3.2.4.1

Response Pattern Model

In order to evaluate the effect of the antenna response pattern on atmospheric limb
scanning, we introduce an approximate response pattern model. An antenna response
function is described as P (Az, El).
The model is defined in four regions and consists of three functions as shown in Figure 3.17. Region A is an area to define the FOV beam efficiency. Region B represents
a main beam area. Region A is within region B. A response pattern in regions A and B(Pmain ) is approximated by a gaussian function as following:1
Pmain (Az, El) = 10 log a0 + a1 exp −
2



Az
a2

2



+

El
a3

2 !!!

[dB]

(3.2)

Normalized amplitude [dB]

where Az and El is the azimuth and elevation angles in a coordinate fixed on the mainreflector. Region C describes an error pattern area. In region C, a response pattern (Perror )Region A

0

Region B-20

Region C-40

Region D

-60
-80
2

0
Ele
vat
-2
ion
[de
g]

-2

0

2

eg]
th [d
u
m
i
Az

Figure 3.17 Approximate response pattern. Region A is an area to define the FOV beam
efficiency. Region B represents the main beam area. The response pattern in region B
is approximated by a gaussian function. Region C describes an error pattern area. The
response pattern in region C is described by second order polynomials of azimuth andelevation angles in decibel values. Region D is a wide angle pattern area. Its response
function is described by a constant.

52

Table 3.10 Response pattern model coefficients for several cases in FOV beam efficiency.
Coefficients a0 ∼ a3 and b1 ∼ b5 are common for all cases. Coefficients b0 and c0 in eachcase are different.

a0-9.2377 ×10−5
b1
0.92124(a) Common coefficientsa1
a2
a3
1.0026
7.1774 ×10−2 3.4227 ×10−2
b2
b3
b4-0.23690-11.084-0.45984(b) Different coefficients
FOV beam efficiency
96 %
90 %
85 %
b0
-44.210 -29.042 -26.021
c0-80.242 -79.963 -79.535

b5-18.48580 %-24.133-79.254

is described by second order polynomials of azimuth and elevation angles as
Perror (Az, El) = b0 + b1 × Az + b2 × El + b3 × Az 2 + b4 × Az × El + b5 × El2 [dB]. (3.3)
Region D is a wide angle pattern area. Its response function (Pwide ) is described by aconstant as following:
Pwide (Az, El) = c0 (= constant) [dB].
(3.4)
Then, the response function is determined by a maximum value among the three functionsas
P (Az, El) = max[Pmain (Az, El), Perror (Az, El), Pwide ].
(3.5)
The FOV efficiency for the ideal response pattern is 96.1 %. However, the real FOV
efficiency may deteriorate due to several causes, such as thermal distortion and mechanicalmisalignment. It is considered that these factors cause a decrease of main beam gain andan increase of power level in the error pattern region. Following this assumption, the model
coefficients are determined. We find model coefficients for four cases in FOV efficiency as
shown in Table 3.10.
3.2.4.2

Effective Response Pattern for Atmospheric Limb Scanning

The antenna response pattern is usually expressed with a radiation characteristic in acertain cut plane such as Az=0. For the atmospheric limb scanning, however, an effective
response pattern in the height direction is more important. Since the atmosphere is wellstratified, the vertical response pattern Pvert (El) is obtained from the following equation:
Pvert (El) =

Z Az+
Az−

P (Az, El) cos(El)d(Az),(3.6)

where P (Az, El), Az+ , and Az− represent the antenna response function, and the azimuth
boundary angles of the FOV. Based on the actual extent of the open window for theantenna, we assume that Az+ and Az− is +99◦ and -5◦ , respectively. As previously
mentioned in 3.2.1.3, atmospheric emissions from six consecutive tangent point altitudes
53

Calculated cut pattern (Az=0)
Model cut patternPvert(El) of pattern model
Peff(El) of pattern model0

Normalized gain [dB]

-10
-20
-30
-40
-50
-60

-0.8

-0.6

-0.4

-0.2

0.0

0.2

0.4

0.6

0.8

Antenna elevation angle [deg]Figure 3.18 Effective antenna response pattern.

will be collected and integrated in each unit of the output data. Therefore, the effective
response pattern Pef f (El) is calculated by averaging Pvert (El) for six consecutive altitudes
as the following:j=5

Pef f (El) =1X
Pvert (El + (j − 2.5)∆El),
6 j=0

(3.7)

where ∆El is a unit stepping angle of the SMILES antenna, 0.009375◦ , which is driven by
the Antenna Drive Electronics (ADE).
Figure 3.18 shows the vertical and the effective antenna responses for the patternmodel in comparison with cut-responses at Az = 0 for the designed pattern and the model
pattern.
All patterns are normalized by each antenna gain. The result shows the sidelobe levelsof the vertical and the effective antenna response pattern are approximately 9 dB higher
than those of the cut response pattern. It means that SMILES has a higher sensitivity
to the atmospheric emissions at adjacent altitudes than expected simply from the verticalcut response pattern.
The HPBW of the vertical response pattern is similar to that of the cut response
pattern of the model. They are 0.080◦ . The effective beam size is 0.090◦ in HPBW, which
is 12 % larger than that for the cut response pattern of the model.
3.2.4.3

Beam Efficiency

In the SMILES antenna specification, the FOV beam efficiency is defined by the ratioof the power radiated into an elliptical cone with a flare-angle of 2.5-time HPBW to thetotal power radiated by a feed horn. The feed horn adopted for SMILES is a so-called“back-to-back horn,” which is placed at the interface point between the SMILES antenna
and the Ambient Temperature Optics (AOPT). It is a circular corrugated waveguide with
54

100

Beam efficiency [%]

90
80
70
60

FOV beam efficiency:
96%
90%
85%
80%

50
40
0.0

0.1
0.2
0.3
BWEl (Beam-width in Elevation) [deg]0.4

Figure 3.19 Beam efficiency.

a taper in one end, which will feed the antenna. This optical approach is adopted tomake electro-magnetic shielding for the sensitive submillimeter receiver against high-levelartificial electric fields anticipated on the ISS. To understand the relationship between
the beam efficiency and a degradation of the beam quality, we have calculated a beam
efficiency as a function of a beam width in the elevation angle, BWEl . Such function isdefined by
Z BWEl /2

ηbeam (BWEl ) =−BWEl /2

Pvert (El)d(El)Ptotal,(3.8)

where Ptotal is the total power and BWEl is the full extent of the elevation angle to define
the beam efficiency.
The calculated results are shown in Figure 3.19. We have assumed that the FOV
beam efficiencies are 96 %, 90 %, 85 %, and 80 %. A decrease in the FOV beam efficiency
represents a decrease in the main beam power level and the increase in a error pattern
power level. When the FOV beam efficiency is higher than 90 %, the beam efficiency willapproximately saturate around the definition area of the FOV beam efficiency, which isthe 2.5 times as wide as the HPBWEl .
We will discuss the effect of the FOV beam efficiency on the brightness temperature
in detail in 3.3.5.
3.2.5
3.2.5.1

ISS Orbit and Attitude Variations
Orbit

The ISS is in a nearly circular orbit with an altitude of 350 – 460 km and an inclinationof 51.6◦ to the equator. Major characteristics of the orbit are listed in Table 3.11 [NASA,2000]. The orbit will decay due to atmospheric drag on the ISS, which is enlarged with its

55low altitude and large cross section. The lower limit of the ISS altitude, for its existencein orbit, is taken as 278 km. The operational altitude must be kept higher than 350 km,
where the orbital decay takes 180 days to reach the lower limit. Periodic boosting byusing onboard thrusters will be carried out to maintain the ISS altitude within 350 – 460
km. The orbit control accuracy is specified as 305 m (3σ) in terms of the semi-major axis.
The orbit determination accuracy is 305 m (3σ) for the semi-major axis, and 914 m (3σ)
for the position vector. The ISS orbit prediction data will be found on the NASA website at
http://spaceflight.nasa.gov/realdata/sightings/SSapplications/Post/JavaSSOP/orbit/ISS/SVPOST.html, and orbit determination data will be provided from the NORAD data
website at http://celestrak.com/NORAD/elements/.
Table 3.11 Characteristics of the ISS Orbit
Parameters
Characteristics
Nominal Orbit
Altitude : 407 km
Inclination : 51.6◦

Website
NASA

NORAD

3.2.5.2

Range of Operational Altitude

350 km to 460 km

Orbit Control Accuracy

Semi-major axis : 305 m (3σ)

Orbit Determination Accuracy(Knowledge Accuracy)

Semi-major axis : 305 m (3σ)
Position vector : 914 m (3σ)

Orbit Prediction Accuracy

TBD

Table 3.12 ISS Orbital Elements
Prediction Data
Coordinates
Description
Notes
M50 cartesian
position, velocity
M50 : Inertial mean of year
1950 frame of reference
M50 keplerian
6 orbital elements
M50 cartesian
position, velocity
J2000 : Inertial mean of year
2000 frame of reference
J2000 cartesian position, velocity
KeplerianTLE (5 orbital ele- TLE : Two-lines elements
ments, mean mortion)
Keplerian
AMSAT (5 orbital elements, mean mortion)
Determination Data
Keplerian
NORAD TLE (5 orbital
elements, mean mortion)

Attitude Control

The ISS will fly in the so-called “Local Vertical Local Horizontal (LVLH)” attitude.
This means that the ISS attitude is nominally fixed in a reference coordinate that is defined56

by a local horizontal plane and a local vertical direction (nadir). Since the ISS attitudeis affected by perturbation torque due to aerodynamic drag and gravity gradient, it iscontrolled by a set of onboard Control Moment Gyros (CMGs) to maintain the TorqueEquilibrium Attitude (TEA). Major characteristics of the ISS attitude are shown in Table
3.13 [NASA, 2000; Treder, 1999]. Absolute tilt angles are allowed up to ±15◦ in roll andyaw, and −20/+15◦ in pitch. These angles depend on the mass characteristics of the whole
ISS and atmospheric changes due to the solar activity. Its stability during a cycle periodof orbit is specified to be within 3.5◦ per axis, and the rate of attitude change is within±0.002◦ /sec per axis. This attitude change rate is formally applied to the pressurized
modules during microgravity operations. During a unit scan of the SMILES antenna,
which lasts 53 seconds, the track of the scanning beam can deviate from an ideal straightascending line by about the same size as its HPBW. We have to determine the real trackwith a help of the star tracker that is equipped in SMILES.
Table 3.13 Characteristics of the ISS Attitude (TEA:Torque Equilibrium Attitude)
Parameters
Nominal Attitude

Characteristics
LVLH attitude

Range of Operational Attitude

Roll, Yaw : +15◦ to −15◦
Pitch : +15◦ to −20◦

Attitude Control Accuracy±5◦ per axis
(compared with the commanded values)±3.5◦ per axis
(controlling to TEA)

Attitude Change Rate

Within ± 0.002◦ /sec per axis
(during microgravity operations, in pressurized labs)

Attitude Estimation Accuracy(Knowledge Accuracy)

0.5◦ per axis (3σ)
0.01◦ /sec per axis (3σ)(at the NASA navigation base)3.0◦ per axis (3σ)(at attached payload)Attitude Prediction Accuracy

TBD

Continuous Period in the TEA

30 days

3.2.6
3.2.6.1

Tangent Height Calibration
Calibration Scheme

By comparing observed stellar images with an installed star catalog, the Star Tracker(STT) will calculate the orientation of the mechanical framework of the STT camera in
the heliocentric inertial equatorial reference coordinate system (HCS) which is an inertial
coordinate system with its origin located at the sun and its equatorial plane in the earth
orbital plane. The STT on SMILES provides the output in the quaternion representation.
It is a method to express a rigid-body rotation by means of the direction cosines (e1 , e2 ,e3 ) of an axis and a rotation (Φ) around the axis. Four parameters of the quaternion is57

e(ant,GCS)

φ el
Sub-mm beam

θ ant
ISS

h

θ bore-P(ISS)

Re

0

Figure 3.20 Geometrical definition of tangent-point altitudedefined asΦ,2Φ≡ e2 sin ,2
Φ≡ e3 sin ,2Φ≡ cos .
2

q1 ≡ e1 sin

(3.9)

q2

(3.10)

q3
q4(3.11)(3.12)

With these four parameters, we can express any rigid-body rotation by the following
matrix.
q12 − q22 − q32 + q422(q1 q2 + q3 q4 )2(q1 q3 − q2 q4 )


2
2
2
2
−q1 + q2 − q3 + q4
2(q2 q3 + q1 q4 )M [q] =  2(q1 q2 − q3 q4 ).
2(q1 q3 + q2 q4 )2(q2 q3 − q1 q4 )−q12 − q22 + q32 + q42(3.13)

If a unit vector in the STT-based coordinate is represented by e(stt), the direction of
the antenna mounting basis (a reference direction corresponding to the antenna elevation
angle of 0◦ ) that is evaluated in the geodetic coordinate system (GCS), which is an earthbased coordinate with its origin at the earth center and its equatorial plane in the earth
equator, e(ant, GCS), is calculated bye(ant, GCS) = M2 · M1 · K(ant, stt) · e(stt),(3.14)

where M1 and M2 are coordinate transformation matrices to express the conversion fromthe STT-based coordinate to the heliocentric inertial equatorial coordinate system (HCS),
and that from the HCS to the GCS, respectively. The matrix elements of M1 are expressedwith the quaternion components of the STT outputs. Matrix K(ant, stt) denotes a vector
58e(stt)

M_1

K

M_2

Calculation of
Tangent Point
AltitudeTangent
Point
Altitude

Unit vector
in STT coordinate
Transformation between
HCS to GCS

Aligment Data

Quaternion between
ANT-RCS and STT-RCS

STT Data

Antenna Drive
Angle

ISS Orbit Parameters,
Earth Elliptical Model

Quaternion between
STT-RCS and HCS

Figure 3.21 Flow chart of STT data analysis

rotation from the unit vector of the STT-based coordinate to the axis of the antennamounting basis. Matrix M2 includes transformation in time system. A difference between
the UT1 and UTC should be taken into account (UT1 represents the rotation of the earth
while UTC are determined on the base of atomic time).
The tangent-point altitude for the limb-sounding observations is derived from e(ant, GCS)
and information such as the ISS position in orbit, scanning angle of the antenna, and surface model of the Earth. The STT is mechanically fixed to the antenna-mounting basis,
while the main reflector of the antenna will rotate around the elevation angle (φel ). If theISS position is determined with a position vector P (ISS) in the GCS, the angle between
the line-of-sight of the antenna (mechanical axis) and P (ISS) is calculated asθbore = arccos

−P (ISS)e(ant, GCS)
− φel .
| P (ISS) |(3.15)The tangent-point altitude is given by
h =| P (ISS) | sin θbore − Re ,(3.16)

where Re represent the Earth radius at the tangent point.
3.2.6.2

Calibration Accuracy

Several factors that affect the accuracy of tangent point height are listed in Table 3.14.
They include errors both in the attitudes of the SMILES antenna and in the ISS orbital
altitude. Among the attitudinal errors are the squint of the submillimeter beam fromthe mechanical axis of the antenna reflector, offset angle in the driving mechanism of theantenna, thermal bending of the antenna structure with respect to the STT, alignment
errors between the antenna and STT, measurement errors in the STT, and errors due toimperfect time synchronization between the ISS and STT. On the other hand, errors in
the ISS orbit determination will affect the knowledge of tangent point height. Expected
error values for those factors are given in two columns for bias and random errors. Bias
error means its effect is applied in common to all of the data acquired in a unit scan of
the antenna (in 53 sec). The relative altitude distance between any two points is notaffected by the bias errors. But random error occurs independently for each point. While59the current estimation of those error values are very tentative, we expect that the beamsquint will be the largest factor in bias errors. It should be calibrated in orbit by means ofobserving a reference object such as the moon. The overall altitudinal error without thebeam squint is estimated about 0.76 km in bias error, and 0.34 km in random error (bothin 1σ).
Table 3.14 Residual Errors in the tangent height after calibration with the STT data
Error Factors
Attitudinal Errors:
Sub-mm beam offset from the REF axis
Mechanical driving errors of REF
Thermal bending of REF against STT
Mechanical alignment of REF against STT
STT measurement errors
Time synchronization errors

0.13◦
0.007◦
0.003◦
0.007◦
0.007◦
0.20 sec

5.02 km0.27 km0.12 km0.27 km0.27 km0.50 km–0.007◦––
0.0015◦0.07 sec–
0.27 km––0.06 km0.18 km

Altitudinal Error:
Determination errors of the ISS position–

0.3 km

–0.1 kmOverall(rss) with the beam offset
Overall(rss) without the beam offset

Bias Error@1σ

5.07 km
0.76 km

Random error@1σ

0.34 km0.34 km

REF means the main and sub reflectors of the antenna. STT is the Star Tracker. Altitudinalsensitivity to the attitude error is 38.6 km/deg, and that to the error in time is 2.52 km/sec forthe nominal orbital height of 400 km.

3.3

Spectral Regions and Sensitivity

3.3.1

Observation Frequency BandsThree observation frequency bands have been selected in line with the SMILES scientificobjectives toward chlorine-related processes in the stratosphere. Most important moleculartransitions included in these bands are 649.45 GHz (ClO), 625.92 GHz (H35 Cl) and 624.98
GHz (H37 Cl), as well as 625.37 GHz and others for O3 . This selection is a result of
optimization between scientific targets and instrumental restrictions, such as the numberof SIS mixers and down-converters. The first intermediate frequency (IF) is determined to
avoid potential interference by high-level electromagnetic fields in the ISS environments. A
spectral range around 650 GHz and the other around 625 GHz are simultaneously observedin the upper sideband (USB) and lower sideband (LSB), respectively, of the submillimeter
mixing with a single local oscillator (LO) at 637.32 GHz. In Figures 3.22-3.24, expected
emission profiles are calculated based on a standard model of the atmosphere (Rutherford
Appleton Laboratory). The brightness temperature and line-width of the atmosphericemissions are widely varied, depending on the tangent altitude of the observing line-ofsight. Frequency coverage is 624.32 - 625.52 GHz in Band-A, 625.12 – 626.32 GHz in BandB, and 649.12 – 650.32 GHz in Band-C. As SMILES has only two units of spectrometers,
only two among these three bands are observed at a time. Nevertheless, Band-A and BandB are partially overlapped to keep observations of O3 at 625.37 GHz being conducted inany cases. HCl lines are observed in both Band-A and Band-B, while they are attributed
to different isotopes of chlorine. The natural abundance of 35 Cl and 37 Cl is 75.78 percentsand 24.22 percents respectively. ClO is observed only in Band-C. There are two 81 BrO
60

Brightness Temperature [K]

16 km
20 km
30 km
40 km
50 km
60 km

100
10
1
0.1
0.01
0.001
624.32624.52624.72624.92625.12
RF Frequency [GHz]625.32625.52

O3
HCl
18

OOO

O18OO
17

OOO
CH3CN
HO2
HOCl
HNO3
BrO
H2O2
OClO
SO2

Brightness Temperature [K]Figure 3.22 Atmospheric emission profiles expected for Band-A.
16 km
20 km
30 km
40 km
50 km
60 km

100
10
1
0.1
0.01
0.001
625.12625.32625.52625.72625.92626.12626.32

RF Frequency [GHz]
O3
HCl
18

OOO

17

OOO

O17OO
CH3CN
HO2
HOCl
HNO3
H2O2
OClO
SO2

Figure 3.23 Atmospheric emission profiles expected for Band-B.

61

Brightness Temperature [K]

16 km
20 km
30 km
40 km
50 km
60 km

100
10
1
0.1
0.01
0.001
649.12649.32649.52649.72649.92
RF Frequency [GHz]650.12650.32

O3
ClO
18

OOO

17

OOO

O17OO
CH3CN
HO2
HNO3
BrO
H2O2
OClO
SO2

Figure 3.24 Atmospheric emission profiles expected for Band-C.

lines, at 624.77 GHz and 650.18 GHz, included in Band-A and Band-C.
3.3.2
3.3.2.1

Frequency Down-conversion
Intermediate Frequency System

The three submillimeter-wave bands are converted to the first IF by two SIS mixers
operated at 4.5 K in a cryostat [Fujii et al., 2000; Inatani et al., 1997]. By means ofa quasi-optical single-sideband (SSB) filter, the USB and LSB emissions are separately
converted to the first IF, which is 11.8 – 13.0 GHz for the USB and 11.0 – 13.0 GHz for
the LSB. Band-A and Band-B are two different portions inside the LSB, while Band-C isin the USB. These are further down-converted to the second IF in 1.55 – 2.75 GHz, andspectral features in each band are analyzed by the Acousto-optic Spectrometer (AOS).
When a microwave mixer is sensitive to both its USB and LSB, the sideband of interestis called a “signal band,” and the other undesirable sideband is an “image band.” Even
with an SSB filter, its performance to reject an image band is not perfect. This situation
makes it important to know how the image band is superimposed on the signal band.
Figure 3.25 illustrates how the three submillimeter-wave observation bands are converted
down to the second IF.
3.3.2.2

Sideband Separation

62

Lower Sideband

Upper Sideband

624.32625.52Band-A [Signal]649.12650.32Band-A [Image]625.12626.32Band-B [Signal]648.32649.52Band-B [Image]624.32625.52Band-C [Image]649.12650.32Band-C [Signal]
LO

SIS mixer
13.00

11.80

637.32

12.20

Band-A

11.00

SIS mixer
11.80

Band-B

13.00
Band-C

14.55

9.45
1.55

2.75

2.75

Band-A

1.55

14.55
2.75

Band-B

1.55
Band-C

Selection Switches

AOS AU-1

AOS AU-2

unit: GHz

Figure 3.25 Frequency conversion in SMILES: Both signal and image bands are converted
to the first IF, while the image is suppressed by an SSB filter. The frequency conversion
to the second IF is made completely in a single sideband mode.
Figure 3.26 illustrates the schematic configuration of SSB filtering and local injectionin the receiver optics of SMILES. To separate the two sidebands, SMILES is equipped witha quasi-optical SSB filter in its submillimeter optics. The SSB filter, which is a modified
Martin-Puplett interferometer [Inatani et al., 1999] newly proposed for SMILES, has theability to reject the image band by 20 dB or more in the SMILES observation bands.
This proposed SSB filter is composed of a pair of frequency-selective polarizers (FSP1 and
FSP2) each of which consists of a free-standing wire-grid backed by a flat mirror, and isintended to achieve a function as a dual-beam interferometer equivalent to that of theconventional type Martin-Puplett interferometer (MPI) [Martin, 1982].
The functional blockdiagram of the sideband separation in the SMILES submillimeterwave optics is shown in Figure 3.27. The SIS mixer-1 is well coupled to the antenna (ANT)in the USB, but is not in the LSB. In the LSB, it is coupled to the Cold-sky Terminator(CST), which is a reference port terminated to the 2.7-K cosmic microwave background.
Since the CST is free from the atmospheric emissions, this method allows the SIS mixer1 to selectively down-convert the USB atmospheric emissions into the first IF. In thesame way, the SIS mixer-2 down-converts the LSB atmospheric emissions into the first
IF. In addition to sideband separation, the SSB filter coupled with the wire-grids RG1and LG1 has a function of injecting the local signal from the submillimeter-wave local

63

SIS-MIX-1+45 o pol.

SIS-MIX-2
CG1-45 opol.

45 o Wire-Grido

90 Wire-Grid

Mirror45 o Wire-Grid
ANT Signal

RG1

FSP 2

+45 o pol.

ANTCST Signal-45 opol.
90 o Wire-Gridd2

Mirror

CST

LG1

FSP 1

32.08 o Wire-Grid

d1

SLO

Figure 3.26 Schematic configuration of SSB filtering and local injection in the SMILES
receiver optics

Figure 3.27 The function of the SSB filter in detail. The antenna port is coupled withthe mixer-1 in the USB, but with the mixer-2 in the LSB. CST stands for the Cold-sky
Terminator, which is free from the atmospheric emissions. Solid lines mean an efficient
coupling, and dotted lines a suppressed coupling.

64

oscillator (SLO) to both LSB (SIS-MIX-1) and USB (SIS-MIX-2) mixers for frequency
down-conversion.
The sideband separation characteristics of the modified Martin-Puplett interferometerare determined by the grid-mirror spacings, d1 and d2 , of two FSPs. In the design of the
SMILES submillimeter-wave opticas, a combination of the spacings, d1 = 1.545 mm andd2 = 1.572 mm, is adopted as a reasonable choise by which we can achieve image rejection
better than 15 dB both for LSB and USB of SMILES observation bands while keeping thedifference in local-signal power couplings to USB and LSB mixers less than 0.5 dB. The
pass-band transmission and image rejection characteristics calculated around the SMILES
obeservation bands by an exact theory for the two FSP combination are shown by red
symbols in Fig. 3.28. Although the exact theoretical calculations are rather involved,
the transmission and rejection characteristics around the SMILES observation bands arefound to be well approximated by simple functions describing power coupling coefficientsfor the SIS mixer-i to the ANT port, Ki,a , and to the CST port, Ki,c , given by
Ki,j (f ) =1 + α2 + 2α cos( mπff0 )(3.17)(i = 1 or 2, j = a or c).

4

A nonlinear least-squares fitting gave an exellent fit of (3.17) to the exact theoretical
calculation as shown in Fig. 3.28 with the values of parameters, m, f0 and α, listed in the0

0

Exact Calculation
Least-Squares Fit (m=24, f0=624.624 GHz, a=-0.973366)
Proposed Specification (m=24, f0=624.65 GHz, a=0.94)

-0.05

-0.1
Transmission [dB]

Transmission [dB]

-0.1
-0.15
-0.2
-0.25

-0.15
-0.2
-0.25

-0.3

-0.3

-0.35

-0.35

-0.4
623

Exact Calculation
Least-Squares Fit (m=28, f0=648.954 GHz, a=0.973846)
Proposed Specification (m=28, f0=648.95 GHz, a=0.94)

-0.05

623.5

624

624.5
625
Frequency [GHz]

625.5

626

-0.4
647.5

626.5

648

648.5

649
649.5
Frequency [GHz]

650

650.5

651

(a) Transmission Characteristics in LSB. (b) Transmission characteristics in USB.
-15

Exact Calculation
Least-Squares Fit (m=24, f0=624.65 GHz, a=-0.9843)
Proposed Specification (m=24, f0=624.65 GHz, a=-0.94)

-20

-20

-25

-25

Transmission [dB]

Transmission [dB]

-15

-30

-35

-40

-45
623

Exact Calculation
Least-Squares Fit (m=28, f0=648.944 GHz, a=-0.983511)
Proposed Specification (m=28, f0=648.95 GHz, a=-0.94)

-30

-35

-40

623.5

624

624.5
625
Frequency [GHz]

625.5

626

626.5

-45
647.5

648

648.5

649
649.5
Frequency [GHz]

650

650.5

651

(c) Image rejection characteristics in LSB. (d) Image rejection characteristics in USB.
Figure 3.28 Coupling coefficient Kij for signal transmission and image rejection of the
SSB filter designed for SMILES. +: Exact theretical calculation for SSB filter; green
curves: least-squares fit to the exact calculation; blue curves: simplified model for SSB
characteristics of SMILES optics.

65

Table 3.15 Model parameters m, α, f0 of power coupling coefficients Kij for least-squaresfit model of SSB filter and simplified model for SMILES optics

Band
LSB Transmission
Rejection
USB Transmission
Rejection

Ki,jK1,a , K2,c
K2,a , K1,c
K2,a , K1,c
K1,a , K2,cLeast-Squares Fit
f0
α
624.624 GHz
0.973366
624.65 GHz−0.9843648.954 GHz
0.973846
648.944 GHz −0.983511

m
24
24
28
28

Proposed Specificationmf0
α
24 624.65 GHz
0.94
24 624.65 GHz −0.94
28 648.95 GHz
0.94
28 648.95 GHz −0.94

columns labeled as Least-Squares Fit. By considering imperfectness of optical components
not only in the SSB filter but also in whole optics, we propose to use a more conservativemodel given by (3.17) with the values of parameters, m, f0 and α, listed in the columns
labeled as Proposed Specification in Table 3.15. The power coupling coefficients given by
this Proposed Specification is also shown in Fig. 3.28
With this Simple Model, the effects of the image band on the atmospheric observations
are calculated in Figures 3.29 – 3.30. The image contribution suppressed by the SSB
filter is superimposed on the signal spectra in the first IF. More precisely, we have to takeaccount of the difference in the SIS mixer conversion losses for the USB and LSB, which
could be up to 2 dB depending on particular device of the SIS mixer. But this effect is
not included in Figures 3.29 – 3.31.

11.8

O3

12

IF Frequency [GHz]
12.4

12.2

Brightness Temperature [K]

100
10

12.6

H2O2
O3
HOCl

1

13

h = 16 km
h = 30 km
h = 50 km

H37Cl

O3(v2)
O3(v2)

12.8

HNO3

0.1

O3

0.01

HNO3
+ BrO

0.001
0.0001625.52625.32625.12624.92624.72
RF Frequency [GHz]624.52624.32

Figure 3.29 The effect of the image contributions in Band-A. Solid curves denote thesignal intensities and dashed curves the image contributions.

66

11

11.2

11.4

IF Frequency [GHz]
11.6

11.8

H35Cl

Brightness Temperature [K]

100
10
HO2
O3

1

O3

12
O3
h = 16 km
h = 30 km
h = 50 km

12.2

O3(v2)
O3(v2)O3

O3(v3)

0.1
0.01
0.001
0.0001626.32626.12625.92625.72625.52
RF Frequency [GHz]625.32625.12

Figure 3.30 The effect of the image contributions in Band-B.
11.8

12

12.6

ClO

100
O3
Brightness Temperature [K]12.2

IF Frequency [GHz]
12.4

10

12.8

13

h = 16 km
h = 30 km
h = 50 km

O3

HO2
O3-isotopes O3 + BrO

1
0.1
0.01
0.001
0.0001649.12649.32649.52649.72649.92
RF Frequency [GHz]650.12

Figure 3.31 The effect of the image contributions in Band-C.

67

650.32

3.3.3

Acousto-optic Spectrometer3.3.3.1

Frequency Characteristics

Two acousto-optic spectrometers (AOS) give us a powerful means of obtaining spectral
images of various chemical species in a required range of altitudes, while constituting thesystem with a single type of spectrometer (see Figure 3.32). Each AOS has 1500 spectral
channels to cover the bandwidth of 1200 MHz. Two units of AOS can be combined by
selecting a mode in the IF switching network, which enables us to view a limb spectrumwith a 2000-MHz bandwidth over 3000 frequency channels. Channel separation is typically
0.8 MHz, but the channel number is not always linearly related with its corresponding
frequency. In addition, the relation can be influenced by the change of the environmental
temperature. Some calibration process is needed on board, which is described in theremaining.

100
0.001
1E-3

Height (km)

80

0.01
0.1

60

1
40
10
20

Pressure (hPa)625GHz HCl
649GHz ClO

100
1000

0
1

10

100

1000

FWHM (MHz)Figure 3.32 Spectral line-width versus atmospheric height with respect to two importantlines of the SMILES mission.
Spectrometer’s response function is obtained with the input of a carrier signal, thetypical shape of which is depicted in Figure 3.33. Any input spectra to the AOS are
convoluted with this kind of frequency response function, the full-width at half-maximum(FWHM) of which is often called as “resolution bandwidth” of the AOS. The FWHM
for the AOS on SMILES will be 1.4 MHz or less. With this frequency response function,
we can estimate the influence of some strong emissions at neighbor frequencies on theemission profile of interest.
On the other hand, when we deal with noise characteristic of the AOS, we should use
the noise bandwidth, which represents the width of such an equivalent boxcar filter thatgives the same fluctuations on the output spectra. The noise bandwidth for the AOS on
SMILES is derived to be 2.5 MHz. With this value, we can estimate the standard deviationof the spectral fluctuations on the AOS output data. Each output spectrum is obtainedin every 500 ms. If the system noise temperature is 500 K, the standard deviation for the68

Figure 3.33 Frequency response function of the SMILES/AOS. The typical resolution
bandwidth is 1.35 MHz.

snap shot spectrum will be approximately 0.5 K.
3.3.3.2

Frequency Calibration

Frequency calibration of the spectrometer will be accomplished by using a frequency
reference with the absolute accuracy of about 10 kHz over the whole mission life. It is
installed in the IFA section, generating a series of carrier signals with an interval of 100
MHz. The output of this comb generator will be injected to the AOS once in every scanof the antenna (53 seconds). Peak positions of each picket are derived against channel
numbers, and the relation between frequency and channel number will be established bya polynomial fitting. Residuals of such fitting will be typically less than 30 kHz as shownin the Figure 3.34, which gives the error in frequency of the AOS spectra.
3.3.3.3

Noise Dynamic Range

Radio signal is converted to optical one in the AOS, and it is detected by a CCD
array. The read-out voltages, or the number of collected charges in each cell of the CCD
array, should be proportional to the input RF power levels, but they are usually modified
by several additional factors in reality. The variance of the original RF noise at CCD isexpressed by the following well-known expression:
2
NRF
=

Q2
,
B·t

(3.18)

where Q,B, and t represent the number of collected charges at CCD, noise band-width,
and the integration time for collecting charges, respectively. Potential contributors to69

Figure 3.34 Residuals from the frequency fit with comb generator. 1 Ch. corresponds toapproximately 0.8 MHz.

additional noise are photon shot noise, CCD dark noise, quantization noise, and detection
circuit noise:
2
2
2
2
2
Nadd
= Nphoton+ NCCD+ NAD+ NDetection−circuit.

(3.19)The read-out noise of the AOS is the sum of the RF noise and the additional ones:2
2
2
Nread−out= NRF+ Nadd
.

(3.20)The relative weight of the additional noise will increase as the RF power decreases. So
2
2 to be less than 1.21,
if a criterion is settled so that the noise variance ratio Nread−out/NRF
then we can define a range of RF power levels, for which the additional noise is less than
10%. We call this the “noise dynamic range” of the AOS. As far as the observed spectra
lie within the noise dynamic range, the increase of the system noise due to the AOS canbe negligible.
Alternatively, we can calculate an increase of noise at a given signal level. Its coefficientis a function of the signal level, and expressed as f (D), where D = Qsat /Q, and
ef f
org
Tsys
= Tsys· f (D),(3.21)

f (D) can be calculated with the AOS instrumental parameters:
v
u
u
B·t
f (D) = t1 +·D+B·t2
q02 + qef
f

Q2sat

Qsat

1
+ 2n
2 · 12!

· D2 ,(3.22)

where, q0 , Qsat , n, and qef f are dark current noise, amount of the saturation charge,
quantization bit of the A/D converter, and equivalent amount of charge of the detection70

circuit noise, respectively. A typical behavior of f (D) is depicted in Figure 3.35. In
SMILES/AOS, the noise dynamic range of 9 dB is foreseen for a criterion of additional
noise of 10%.

1.6
B=2.5MHz, t=5ms, Qsat=1.3Me-/p,

10000

2

-

q eff=450 e rms, n=12

Noise (e-)

8000

RF noise
Read-out Noise

6000

Variance Ratio

1.5
1.4
1.3

4000

1.2

2000

1.1

0

0

Noise Variance Ratio : f(D)

2

12000

1.0
1 2 3 4 5 6 7 8 9 10 11Signal Level from Saturation in dB (10 logD)

Figure 3.35 RF noise, read-out noise and the variance ratio of them3.3.3.4

Differential Non-Linearity

Another concern with the AOS in obtaining the limb spectrum is “differential non-linearity(DNL)” of the analog to digital converter, which was first claimed by R. Schieder of the
University of Cologne. The DNL is irregularities in the “monotonic character” of theconversion from input analog voltages to digitized values. Noise due to this effect is nonstatistical, because it is reproducible if we repeat measurements under the same conditions.
It is troublesome that this effect is never visible as far as the signal and the reference areof the same power level, which is usually the case when a long-term integration test is
performed in a laboratory. The Allan variance test also can never be an indicator ofthis effect. For atmospheric observations, however, the input power levels are largely
separated between the signal and the reference. This makes it indispensable for us to
pay an extra attention to this issue. It is difficult to predict the exact effect of the DNL
for the SMILES/AOS, because the characteristics of A/D converters are varied amongindividual products. Nevertheless, we can simulate the atmospheric observation based on
the characteristics of the A/D converter of the same type as planned to be used for the
SMILES/AOS.
Figure 3.36 shows the effect of the differential non-linearity on the observed spectra.
∆T denotes the difference between the digitized and the analog values of the brightness
temperature. We see that a specific undulation of the spectrum appears, and it remains
unchanged even after 60 times of accumulation of the spectrum, which corresponds to a
one-day average for a zonal map with a latitudinal interval of 5◦ . The magnitude of theundulation depends heavily on the temperature of the limb spectrum. If the brightness
temperature of the limb spectrum is not so different from the reference sky, this effect is71∆T = T

0.4

digital-T

analog60 ACCM.

Temperature (K)

0.3
0.2

3 ACCM.

0.1

2 ACCM.

0.0

1 SHOT

-0.1
0

100

200 300 400
Spectral Channel

500

600

Figure 3.36 Simulated effects of the differential non-linearity on the spectrum for 1 shot,
and for 2, 3, and 60 accumulations. The latter three spectra are shifted upward by 0.1,
0.2 and 0.3 K, respectively. The difference in brightness temperature between the limb
atmosphere and the reference sky is taken as 20 K, and no gain variation is assumed ineach integration period.

1 shot60 accm. without gain var.
60 accm. with 1% gain var.

RMS of ∆ T (mK)

40
30
20
10
0
0.1

1
10
TLimb - TRef (K)100

Figure 3.37 Dependence of the DNL effect on the difference of brightness temperature
between the limb atmosphere and the reference sky.

72

much suppressed. On the contrary, the DNL effect cannot be ignored for low altitudes(high brightness temperatures).
Though the DNL is troublesome in measuring the atmospheric spectra, it is found thata small variation of the system gain has a possibility to smooth out this effect efficiently.
Figure 3.37 shows the DNL effect against the difference in the input power levels between
the two spectra. The lowest plot in the figure depicts an improvement of the sensitivity
by including a “random” gain variation of 1% over 60 observational opportunities. Thisphenomenon will be rationalized by the shift of the “operational point” due to the gain
variation. This plot tells us that if a gain variation occurs randomly, the DNL effect will be
removed by accumulation. In the real observational conditions, the result will be between
the two plots in the figure.
3.3.4
3.3.4.1

Sensitivity
Radiometric NoiseThe sensitivity of SMILES to detect molecular emissions is mainly determined by thesystem noise temperature, Tsys , which is expressed by the following equation for the upper
sideband observation (see Figure 3.27):


u
Tsys

Al



u +T
Tant
amb · (Lant − 1)  Tl + T
amb · (Lant − 1) 
1 Al C u C l ·  ant=u Tcst + Tamb · (Lcst − 1) 
l +T
Tcst
amb · (Lcst − 1)


Lant
Lu
dsb
· u ,
+ Trx
· 1+
Ll
Kant
l
u
l
Kant
Kcst
Kcst
Lu
Lant
Lant Lu
u
l
=
,
C
=
,
C
=
·
.
·
·
·
u
u
u
Kant Ll
Kant Lcst
Kant Lcst Ll(3.23)(3.24)

Here Tant and Tcst are input power levels to the Submillimeter Antenna (ANT) and Coldsky Terminator (CST), expressed in brightness temperatures. Superscripts u and l indicatethat the quantity is evaluated in the USB and LSB, respectively. Tamb is the brightness
temperature for a black-body at the ambient temperature. Lant and Lcst are propagation
dsb is the receiver noise temperature evaluated in the double
losses in the ANT and CST. Trx
sideband (DSB) mode. This is the most dominant factor of the system noise temperature,and it is mainly determined by the performance of the submillimeter mixer inside thereceiver. SMILES adopts cryogenically-cooled superconductive SIS mixers, instead of the
dsb . L andconventional semiconductor diode mixers, to drastically reduce the value of Trx
u
Ll are mixer conversion losses in the USB and LSB. These values are also much reduced
in SIS mixers compared with the diode mixers. Kant and Kcst are the mixer’s power
coupling coefficients with the ANT and CST, which are determined by the performanceu , the receiver output power correspondingof the quasi-optical SSB filter. With this Tsys
to an input signal ∆T to the antenna is expressed asuuTout= (∆T + Tsys
)·

u G
Kant
if
.
Lant Lu(3.25)The minimum detectable temperature (1-sigma) is evaluated by the radiometer equation,
Tsys
Trms = √,B·τ
73(3.26)

where B is a detection noise bandwidth and τ is an integration time. Tsys of SMILES
is about 500 K, for cases where the noise contribution from the atmosphere itself can beneglected. With B=2.5 MHz, τ =0.47 sec, the above equation gives a value of 0.46 K(rms).
3.3.4.2

Gain Stability

The second factor that can affect sensitivity is gain variation of amplifiers in the receiver system. Since the receiver gain is calibrated in every 53 seconds, our concern isvariations in this short time scale. They will occur due to changes in the physical temperature of amplifiers. They can be separated into two parts: (1) a change in the relative
frequency characteristics of the gain (we call this “gain fluctuation”in this chapter), and(2) a parallel shift of gain levels while the relative frequency characteristics are kept unchanged. The former will affect the sensitivity to spectrum, but the latter will be related
only to the absolute calibration accuracy of brightness temperatures. The former amplitude is considered to be much smaller than the latter one. In order that the former
fluctuation should not affect the spectral sensitivity, a requirement:


∆G
G

2



1
,
Bτ

(3.27)

has to be satisfied in the data integration time of τ (0.5 sec in SMILES). This requires
the gain fluctuation of this kind be smaller than 0.03 % for the whole SMILES receiver.
On the other hand, the gain variation of the second kind has to be less than 1 percent
in a period of 53 seconds in order to achieve a 1-percent accuracy in the calibration ofbrightness temperatures.
3.3.4.3

Standing Waves

The third factor that can deteriorate the actual spectral sensitivity is standing waves
in the submillimeter optics. The SMILES observations are performed in three successive
steps with three different submillimeter inputs to the receiver: atmospheric emissions atnumbers of consecutive tangent altitudes, reference-sky emissions at the altitude of 160
km or higher, and thermal emissions of the Calibration Hot-load (CHL). The AOS output
voltages for these steps are described as follows:
Vatm = Gsys · {Ta (atm) · Mobs (atm) + Tsys } ,(3.28)

Vref(3.29)= Gsys · {Ta (ref ) · Mobs (ref ) + Tsys } ,Vhot = Gsys · {Ta (hot) · Mcal + Tsys } .

(3.30)

Here Ta is the antenna temperature evaluated at the input port of the receiver, and Tsysis the system noise temperature. Gsys is the system gain. Mobs (atm), Mobs (ref ) andMcal are power modulation factors due to standing waves. They are generated by apair of reflections; one at the input port of the receiver and the other at some point in
the submillimeter optics. The power modulation is due to the interference between an
original incident wave and an additional one superimposed on it after a return trip along
the optical path.
Mobs =| 1 + robs · exp(−j · 2kLobs ) |2 .

74

(3.31)

Here robs represents the relative strength of the superimposed wave, and Lobs is the distance
between the two ends of the return trip (k = 2π/λ). On the other hand, the standing
waves while the hot-load is observed are described by
Mcal =| 1 + robs · exp(−j · 2kLobs ) + rcal · exp(−j · 2kLcal ) |2 .

(3.32)

Here rcal represents the relative strength of a superimposed wave that is returned from the
hot-load, and Lcal is the distance between the input port of the receiver and the hot-load.
The observed antenna temperature is derived fromTaobs (atm) =Vatm − Vref
· {Ta (hot) − Ta (ref )} + Ta (ref ).
Vhot − Vref

(3.33)

Since Mobs and Mcal are frequency-dependent, this observed antenna temperature is alsofrequency-dependent, which makes a baseline ripple in the frequency spectrum.
There are several factors to determine the amplitude of standing waves. One is adifference (time variation) between Mobs (atm) and Mobs (ref ). This difference can begenerated by a thermal change of the physical distance between the two reflecting points inthe optics. Keeping such temperature drift as small as 0.5 deg in 53 seconds could minimize
this effect. The other factor originates from the insertion of the hot-load itself. The
amplitude of this standing wave is mainly determined by the reflectivity of the hot-load.
It has to be less than −50 dB to make rcal lower than −60 dB. Recent experiments havedemonstrated that the CHL meets such requirement (A.Murk, personal communication).
The third factor is the variation of the system gain. Without such variations, the standing
waves would decrease for lower atmospheric temperatures. The gain variation, however,makes the subtraction of Tsys incomplete in Eq. (3.33), which leaves some level of standing
waves even for lower atmospheric temperatures.

Standing waves (RMS) [K]10
1 deg / 40 dB
1 deg / 60 dB
0.5 deg / 40 dB
0.5 deg / 60 dB

1

dG/G=0.02

0.1

0.01
0.1

1

10

100

1000

Atmospheric Brightness Temperature [K]Figure 3.38 Estimated amplitude of standing waves as a function of atmospheric brightness temperature. Two cases for thermal stability in 53 seconds, and two cases for thestrength of superimposed waves are calculated. The variation of the system gain is assumed to be 2 percent in 53 seconds.

75

The estimated amplitudes of standing waves are shown in Fig. 3.38. Two different
cases are assumed with respect to thermal stability (1 deg or 0.5 deg in 53 seconds) andthe relative level of superimposed waves (−40 dB or −60 dB for robs ). rcal is fixed at −60
dB, and the gain variation is assumed to be 2 percent. When rcal is −60 dB, the amplitude(rms) of spectral ripples is less than 0.5 K for Ta (atm) of 300 K, and linearly decreases forlower atmospheric temperatures, but remains around 0.02 K for temperatures lower than
10 K.
3.3.4.4

Differential Non-linearity of A/D Converter

The fourth factor we have to concern is differential non-linearity (DNL) of the analogto-digital converter in the AOS. The incremental analog voltage, needed to give a step ofsmallest digital bit, is not exactly constant over the dynamic range. When a particularfeature of this deviation is coupled with the frequency characteristic of the receiver gain,
it results in a modulation of the signal spectra. This issue has been discussed in detail in
section 3.3.3. With a measured feature of the DNL for the same type of analog-to-digitalconverter that is to be used for the AOS in SMILES, we have found this effect remains
relatively small as shown in 3.41. But we have to keep it in mind that a temporal gain
variation of 1 % is assumed there to estimate the DNL effect for the integrated data for aday.
3.3.4.5

Electromagnetic Interference

The fifth factor that could deteriorate the sensitivity of SMILES is electromagnetic interference. All the payloads aboard the International Space Station are requested to becompatible with the electromagnetic conditions specified in the requirement document(SSP-30237) [NASA, 1999]. Among categories with respect to the electromagnetic com-

1000

JEM-ICS

RS03(SSP30237E)
161 V/m
2.2 GHz

Electric Field [V/m]284 V/m
25.5 - 27.5 GHz

250 V/m
13.7 - 15.2 GHz
79 V/m
8.5 GHz100
60 V/m0.2 - 8.0 GHz

20 V/m
8 - 10 GHz

10
2nd IF1.55 - 2.75 GHz

1st IF image
6.7 - 7.9 GHz

1st IF11 - 13 GHz

1st IF image
16.1 - 17.3 GHz

SMILES IF Bands
1
0

10

20

30

Frequency [GHz]

Figure 3.39 ISS environmental field. All the payloads aboard the ISS are requested tobe compatible with the electromagnetic conditions specified in the requirement document(SSP-30237) [NASA, 1999].

76

Main frame panel (Low frequency measurement)
Main frame panel (High frequency measurement)
Receiver cryostat

100

Shield performance [dB]

90
80
70
60
50
40
30
20
5

10
Frequency [GHz]15

20

Figure 3.40 Shield performance. Thr measurement result demonstrates that the shielding
performance of 20 dB and 50 dB has been achieved for the SMILES’ structural model andthe engineering model of the cryostat, respectively.

patibility (EMC) specifications, radiated susceptibility (RS) is most critical for SMILES
to achieve its designed sensitivity. Figure 3.39 depicts radiated field specification levels aswell as SMILES intermediate frequency (IF) band allocations. High electric fields must
be anticipated at the SMILES’ second IF band and a pair to the image bands according
to the specification, while the first IF band (11 - 13 GHz) is not cited. To minimize theinterference in such an environments, immunities of RF components shall be improvedby paying special attention for their packaging. Our design target is not to observe, or
recognize any spurious signal in a ’snap shot’ (0.5 sec. integration) spectrum due to theelectromagnetic interference. However, the ISS electromagnetic field specification is so
harmful for the SMILES instrument to exhibit its full performance by itself. It is therefore essential for the payload instruments of SMILES being completely enclosed with anelectromagnetic shield except for the antenna section. In particular, the most sensitive
components, such as the SIS mixers and HEMT amplifiers, are to be tightly shielded by
the cryostat. Figure 3.40 shows the shielding performance of the SMILES’ structural panel
and cryostat up to 26.5 GHz, demonstrating that the shielding performance of 20 dB and
50 dB have been achieved for the SMILES’ structural model and the engineering modelof the cryostat, respectively[Miura et al., 2002; Seta et al., 2002]. The submillimeter-wave
signal from the antenna must be introduced to the cryostat without degrading the shielding capability. This will be accomplished by letting the submillimeter-wave beam passthrough a small metallic tube (corrugated circular waveguide) that works as a cutoff filter
against undesirable microwave field at 27 GHz or less.
Consolidating all the available data concerning the immunity of the instruments against
the electromagnetic field radiation as well as the shielding performance so far achieved,
we conclude that any spurious will not be observed in a snap shot spectrum under the77

specified EMC conditions. As for the first IF frequency region, we estimate the fieldstrength around the SMILES payload must be less than 2 V/m.
This frequency region is not cited in the EMC specification document because notransmitters emitting at this frequency are so far registered. Installation of new RFtransmitter on the ISS can be a potential risk to prevent SMILES from appreciating its
full sensitivity. Since this shall be approved through the evaluation of the EMC of theexistent instruments aboard the ISS, we are requesting a control for 11-13 GHz emissions.
3.3.4.6

Overall Sensitivity

Overall sensitivity of SMILES is calculated in Figure 3.41 with the above factors except
for the electromagnetic interference. For a unit data obtained in 53 seconds, the standard
deviation of the noise in the spectral profile is expected to be about 0.7 K for brightness
temperatures less than 20 K, but it increases gradually for higher temperatures due tostanding waves. It will be about 1 K (rms) for brightness temperatures around 200 K,
if the actual standing waves in orbit are of the level described above. In data processing
on the ground, we will produce latitudinal zone-averaged spectral profiles to find thealtitude-latitude relation of each species. The sensitivities of such zonal averaged data,
generated by integrating unit data over all longitudes with a width of 5◦ in latitude, arealso estimated for an integration period of 1 day and 1 month. The standard deviations
will be reduced to 0.1 K for 1 day, and 0.03 K for 1 month, both for low brightness
temperatures. They also increase for higher brightness temperatures due to the standing
waves, which are not reduced by averaging unit data. Note that the sensitivity calculated
here is for the spectral profiles with a frequency resolution of 2.5 MHz. If another kind of
averaging, over several channels of the AOS, is available, the sensitivity will be improved
even for a unit data at the price of lower frequency resolution.
3.3.5

Brightness Temperature Calibration

3.3.5.1

Calibration Scheme

If we take into account antenna responses in its wide-angle error pattern and thermal
losses at antenna mirrors, the AOS output voltages are expressed by the next equations,corresponding to the three steps of data acquisition in a unit cycle (53 seconds).

Vatm /Caos (Gsys + ∆Gsys )Vref /Caos Gsys
Vhot /Caos Gsys

Tatm Terr + ∆Terr Tmir + ∆Tmir
Terr
Tmir
=  Tref
Thot
Thot
Thot




K/L
Tsys + ∆Tsys  (1 − K)/L  . (3.34)
Tsys
 (L − 1)/L Tsys
1

It is assumed that some variations occur in Gsys , Terr , Tmir , and Tsys in a period between
the time of atmospheric observations and that of intensity calibration. The meaning ofeach parameter is as follows:

78

10

Radio(unit)
DNL(unit)
OA(1-day)

GFlct(unit)
DNL(>day)OA(1-month)SW
OA(unit)Sensitivity (RMS) [K]

1

0.1

0.01

0.001
0.1

1

10

100

1000

Atmospheric Brightness Temperature [K]Figure 3.41 Overall Sensitivity: “Radio”; radiometric noise, “GFlct”; gain fluctuation,“SW”; standing waves, “DNL”; differential non-linearity of the analog-to-digital converter,“OA”; overall sensitivity obtained by rss (root-sum-squares). “unit” means the unit dataobtained in 53 seconds. Standing waves are not reduced by averaging.

Vatm , Vref , Vhot

;

Caos
Gsys
K
L

:
:
:
:

Tatm , Tref

;

Terr
Tmir

:
:

Thot
Tsys

:
:

Output voltages at the AOS corresponding to the atmosphere,
reference sky, and hot-load, respectively (the zero-point offset isomitted for simplicity),
Power-to-voltage conversion factor at the AOS,
Total gain of the system that is from the calibrator to the AOS
Beam efficiency for the FOV defined by a 2.5×HPBW area,
Thermal losses at the antenna mirrors located before the calibrator,
Brightness temperatures integrated in the FOV corresponding to
the atmosphere and reference sky, respectively,
Brightness temperature integrated outside the FOV,
Brightness temperature of the antenna mirrors located before thecalibrator, when they are assumed to be a black-body,
Brightness temperature of the Calibration Hot-load (CHL),
System noise temperature (SSB) evaluated at the calibrator.

With three observable voltages, Vatm , Vref , and Vhot , we obtain an observable quantity,
V CAL, that is basically free from the particular frequency characteristics in Caos , Gsys ,79and the zero-point offset at the AOS.
V CAL =

Vatm − Vref.
Vhot − Vref

(3.35)

With the measured value of V CAL, we obtain the atmospheric brightness temperature
Tatm that is integrated in the FOV. However, there are several kinds of errors that willaffect the derived value of Tatm .
At first, let us consider a simple case where no variations occur in Gsys , Terr , Tmir ,and Tsys . We have
Tatm − Tref.
1−K
(Thot − Tref ) + K (Thot − Terr ) + L−1
K (Thot − Tmir )V CAL =

(3.36)In this equation, the parameters have their real values at the time of observations. If weknow and assume the values of Tref , Terr , Tmir , Thot , K, and L, then we can derive Tatm
from this same equation. With these assumed values indicated with an asterisk, we obtain
V CAL = 

1−K
∗ − T∗
Thot
ref + K ∗obs − T ∗
Tatm
ref
∗



∗

L −1
∗ − T∗∗ − T∗
Thot
Thot
err + K ∗mir.

(3.37)In this case, knowledge errors for these parameters will limit the accuracy of the derived
obs . This knowledge error can be evaluated byvalue of Tatm
knw
obs
∆Tatm
= Tatm− Tatm = (κ − 1) · Tatmκ =∗ + 1−K ∗ (T ∗ − T ∗ ) + L∗ −1 (T ∗ − T ∗ )
Thot
err
mir
hot
hot
K∗
K∗
,
1−K
L−1
Thot + K (Thot − Terr ) + K (Thot − Tmir )(3.38)(3.39)∗
where Tref and Tref
are assumed to be zero. Actually the antenna looks at the cosmic
microwave background for the reference submillimeter input. Since the frequency of 640
GHz is in Wien’s region of its 2.7-K spectrum, the brightness temperature expressed inRayleigh-Jeans approximation is virtually zero.
As a second step, let us consider the effects of time variations of parameters between
the time of atmospheric observations and that of intensity calibration. In this case, V CAL
is expressed as

V CAL =

var
(Tatm − Tref ) + ∆Tatm.
L−1(Thot − Tref ) + 1−K
K · (Thot − Terr ) + K · (Thot − Tmir )(3.40)The error of derived Tatm due to variations is evaluated by
var
∆Tatm
=

1−K
L−1
L
L
Vatm
∆Gsys
· ∆Terr +· ∆Tmir +· ∆Tsys +··. (3.41)KK
K
K Caos Gsys Gsys

Since the values of K and L are close to unity, and ∆Terr and ∆Tmir are likely of theorder of 10 K, the first two terms should be smaller than the third and fourth terms.
var
∆Tatm
≈ ∆Tsys + (Tatm + Tsys )

∆Gsys
.
Gsys

(3.42)

This error is therefore the combination of a shift of the baseline in the frequency spectrum
and a modification of the observed value of Tatm .
The third error factor on the derived Tatm is a residual nonlinear behavior of thesystem gain. The system gain is actually measured in orbit as a ratio of the difference of80

two resultant output voltages to the difference of two input power levels: the hot-load andreference sky. The gain non-linearity is defined as a deviation of the actual output voltage
from the expected value given by linear interpolation between the two measured points.
This effect will be approximated by
GN L
∆Tatm
= Tatm ·

L
∆GGN
sys
.
Gsys

(3.43)

This effect is therefore a modification of the observed value of Tatm .
For clarification, Tatm is not the real brightness temperature, Tb (θatm ), of the limb
atmosphere exactly at a certain tangent altitude, but the one averaged in the FOV witha weighting function expressed by a near-axis response pattern of the antenna, P (θ).
R

Tatm =

f ov Tb (θatm + θ) · P (θ)dθR

f ov P (θ)dθ.

(3.44)The role of the Level-1 data processing is to derive this averaged brightness temperaturethat should be as free as possible from system parameters and effects of the wide-angle
response pattern. On the other hand, the effect of the near-axis response pattern isstrongly coupled with the altitude distribution of the molecular mixing ratio itself, whichshould be properly resolved in the Level-2 processing.
3.3.5.2

Calibration Accuracy

We estimate the calibration accuracy of Tatm in the following. Error factors to be
estimated are the knowledge error, gain variation, gain non-linearity, standing waves,differential non-linearity, and radiometer noise. To estimate the effect of knowledge errors
quantitatively, let us assume reasonable values and the extent of knowledge errors for theparameters used in Eq. (3.38).
K:
L:
Terr :
Tmir :
Thot :

0.92
0.5 dB
100 K
270 K
300 K

± 0.02± 0.2 dB
± 30 K± 10 K±1K(to be evaluated by the ground measurements)(to be evaluated by the ground measurements)(to be evaluated by the ground measurements)(to be measured in orbit)(to be measured precisely in orbit)

Expected error in the derived value of Tatm , calculated by Eq. (3.38), is shown in
Figure 3.42 as a function of its assumed value. This error is proportional to the brightness
temperature, and its relative value is less than 3 percent (rms). The knowledge error isthe largest one among various error factors considered here.
Secondly, the calibration accuracy is affected by temporal variation of the system gain
and system noise temperature. It is expressed by Eq. (3.42). If we can remove the effectof the baseline shift (uncertainty) from the original observed data, the calibration error of
the brightness temperature will be proportional to its value. In Figure 3.42, it is shown as”GTV”, which is about 1 percent (rms) corresponding to the gain variation of ±2 percent
in maximum amplitude.
Thirdly, the calibration accuracy is affected by the gain non-linearity. The effectexpressed by Eq. (3.43) is depicted as “GNL” in Figure 3.42 for the case of residual
non-linearity of ±1 percent in maximum amplitude.
Errors due to other factors such as standing waves, differential non-linearity, and radio
noise are also included in Figure 3.42, which are shown a“SW”, “DNL”, and “Radio”. In
81

Calibration Error (RMS) [K]10

KnowError
SW
Radio(unit)

GTV
DNL(unit)
OA(unit)GNL
DNL(1-day)
OA(1-day)

1

0.1

0.01

0.001
0.1

1

10

100

1000

Atmospheric Brightness Temperature [K]Figure 3.42 Calibration accuracy of the brightness temperature: “KnowError”; knowledge error, “GTV”; time variation of the system gain, “GNL”; non-linearity of the systemgain, “SW”; standing waves, “DNL”; differential non-linearity of the analog-to-digitalconverter of the AOS, “Radio”; radiometer noise, and “OA”; overall error.

conclusion, the overall error is about 0.8 K (rms) for atmospheric brightness temperatures
lower than 20 K, and about 3 percent (rms) for higher temperatures. The radiometer noise
dominates in lower temperatures, and the knowledge error does in higher temperatures.
For the zonal averaged data in 1 day, the overall calibration error will decrease as theradiometer noise decreases for atmospheric temperatures lower than 20 K. But this is also
limited by the knowledge error for temperatures between 2 K and 20 K.
We have to keep in mind that Figure 3.42 does not include the uncertainty of thespectral baseline, i.e. the level of zero emissions. As shown in Eq. (3.42), time variationsof the system gain and system noise temperature can contribute to a large shift of thebaseline. It is difficult to evaluate its amount precisely, but it may reach up to ±20 K, if
both ∆Tsys /Tsys and ∆Gsys /Gsys are ±2 percent. This uncertainty of the baseline will
not affect the accuracy of the observed emissions with a line-width that is narrow enoughcompared with the bandwidth (1.2 GHz) of the AOS. However, this uncertainty will limit
the capability of SMILES to derive some parameters, such as concentration of water vapor
in the atmosphere, from the absolute level of its brightness temperature, because of its
flat spectrum.

82

3.4

Conclusion

Chapter 3 has dealt with instrumental capabilities of SMILES. Emphasis is put on the
aspects of those that are directly coupled to the quality of observational data. We haveinvestigated SMILES performances with respect to the field of view as well as the spectral
regions and sensitivity. To conclude this chapter, we put two tables for description of
SMILES mission instruments (Table 3.16) and for a summary of those capabilities (Table
3.17). Note that some figures in the latter table are still under investigation and subject
to future revisions.
Table 3.16 Description of SMILES Mission Instruments
Instruments
Submillimeter AntennaTypeSize of main reflector
AzimuthElevation
Intensity calibration

Description
Offset Cassegrain antenna shaped for an elliptical beam
400 mm (Vertical) × 200 mm (Horizontal)
Fixed to 45-deg north with respect to the ISS forward direction
Movable from −40◦ to +5◦With an ambient-temperature load and cold sky

Sumillimeter ReceiverType
Submillimeter mixer
LO sourceFirst-stage IF amplifier
Cryogenics
Sideband separation

Cryogenically cooled heterodyne receiver
SIS mixer with Nb/AlOx/Nb junctions operating at 4.5 K
Gunn diode oscillator and frequency multiplier
HEMT amplifier operating at 20 K
Two-stage Stirling cooler plus Joule-Thomson cooler
Modified Martin-Puplett interferometer

IF Amplification Section
Type
Switching
Reference frequency

Down conversion and amplification
Switching to control signal flow into AOS units
Reference CW generation for AOS frequency calibration

AOS
Type
Light deflector
Laser
Detector
Star Tracker
Type
Field of view
Star catalogue

Radio spectrometer based on acouto-optical interaction
LiNbO3-based Bragg’s cell
Laser diode emitting at 785 nm
CCD linear array

Image mapping and comparison with a star catalogue
18.4◦ × 13.4◦
Hipparcos data containing 12200 stars

83

Table 3.17 Summary of SMILES Instrumental Capabilities
Parameters
Spatial Coverage
Tangent-point Height
Latitude
Data Sampling Interval
Data Sampling Speed
Orbital Period

Characteristics−10 km to 60 km (minimum)
65◦ N to 38◦ S (nominal)
Around 360 km along the orbit
105 sets of data per orbit
About 93 minutes

Accuracy of Height Determination
Bias Error
Random Error

About 0.76 km (rms)
About 0.34 km (rms)[after calibrated for a beam-offset error]

Resolution of Tangent-point Height
Effective Beam-width (HPBW)
Sampling Interval

0.096◦ (3.5-4.1 km in height scale)
0.056◦ (2.1-2.4 km in height scale)[for the ISS altitude of 407 km]

Frequency Coverage
Band-A
Band-B
Band-C624.32 — 625.52 GHz
625.12 — 626.32 GHz
649.12 — 650.32 GHz

Major Molecular Species to be Measured(*)
Band-AO3 , O3 -isotopes, H37 Cl, H2 O2 ,HO35 Cl, HNO3 , SO2 , 81 BrO
O3 , O3 -isotopes, H35 Cl, HO2 , HNO3 ,SO2 , O35 ClO
35
ClO, O3 -isotopes, HO2 , HNO3 , SO2 ,81
BrO

Band-B
Band-C

Sensitivity for Atmospheric Brightness Temp.
For Unit Data
(Acquired in every 0.5 sec)
For Daily 5-deg Zonal Avg. Data
(Integrated over all longitudes)

About 0.7 K (rms) for Tb < 20 K
About 1.0 K (rms) for Tb = 200 K
About 0.1 K (rms) for Tb < 20 K
About 0.3 K (rms) for Tb = 200 K

Accuracy for Atmospheric Brightness Temp.
For Unit Data

About 1 K (rms) for Tb < 20 K
About 3 % (rms) for Tb > 20 K
About 0.1 K (rms) for Tb < 2 K
About 3 % (rms) for Tb > 2 K

For Daily 5-deg Zonal Avg. Data

Baseline Uncertainty

Less than ±20 K

Spectrometer Capabilities
Frequency Resolution (FWHM)
Channel Separation
Number of Channels*Only two bands are operational at a time.

1.8 MHz
0.8 MHz per channel
1728 channels per a CCD unit

84

References
Fujii, Y., K. Kikuchi, J. Inatani, Y. Irimajiri, M. Seta, S. Ochiai, T. Manabe, H. Masuko, T.
Noguchi, K. Narasaki, S. Tsunematsu, and T. Shirota, Space-borne 640-GHz SIS Receiver
Based on 4-K Mechanical Cooler, UV, Optical, and IR Space Telescope and Instruments,
Proceedings of SPIE, 4013, 90—99, 2000.
Inatani, J., T. Noguchi, S. C. Shi. K. Miyazawa, H. Masuko. S. Ochiai, Y. Irimajiri, M. Kyoya,
K. Narasaki, S. Tsunematsu, M. Murakami, and D. Okamoto, A Submillimeter SIS Receiver
Cooled by a Compact Stirling-JT Refrigerator, Eighth International Symposium on Space
Terahertz Technology, 273—280, 1997.
Inatani, J., S. Ochiai, T. Manabe, M. Seta, R. Wylde, and D. H. Martin, A New Configuration of theMartin-Puplett Interferometer with Low-Reflection, IEEE Seventh International Conference
on Terahertz Electronics Proceedings, 260—263, 1999.
International Telecommunication Union, ITU-R Reccomendations, Rec. ITU-R 835-2, 1997 P
Series-Part I, Geneve, 1997.
Manabe, T., Effects of Atmospheric Refraction on Limb Sounding from JEM/SMILES, available
at http://www.crl.go.jp/ck/ck321/smiles/refrac.pdf, 1999.
Manabe, T., Effects of Atmospheric Refraction on Limb Sounding from JEM/SMILES –Part II–,
available at http://www.crl.go.jp/ck/ck321/smiles/refrac/refrac.html, 2000.
Miura, T., T. Nishibori, J. Inatani, and JEM/SMILES Mission Team, Measurement of Shield
Performance of JEM/SMILES Payload, Proceedings of the 2002 IEICE General Conference,(Communications 1), 396 (B-4-47), 2002 (in Japanese).
Martin, D. H., Polarizing (Martin-Puplett) interferometric spectrometers for the near- and submillimeter spectra, in Infrared and Millimeter Waves, vol. 6, K. J. button, Ed. New York:
Academic Press, 1982, pp. 65—148.
NASDA, Introductory Guidebook for JEM Exposed Facility Potential Users, JBX-98079, National
Space Development Agency of Japan, 1998.
NASA, International Space Station User’s Guide Release 2.0, National Aeronautics and Space
Administration, available at http://spaceflight.nasa.gov/station/reference/issug/ISSUG111.pdf, http://spaceflight.nasa.gov/station/reference/issug/ISSUG12-34.pdf,
http://spaceflight.nasa.gov/station/reference/issug/ISSUG35-50.pdf.
NASA, Space Station Electromagnetic Emission and Susceptivility Requirements, SSP 30237, Revision E, September, 1999.
NASA, System Specification for the International Space Station, SSP41000R, March 20, 2000.
Ozeki, H., Y. Kasai, S. Ochiai, S. Tsujimaru, J. Inatani, H. Masuko, C. Takahashi, L. Mazuray, and
C. Rosolen, Submillimeter-wave Spectroscopic Performance of JEM/SMILES, SPIE Second
International Asia-Pacific Symposium on Remote Sensing of the Atmosphere, Environment,
and Space, Sendai, Japan, October 9–12, 2000.
Seta, M., T. Miura, T. Manabe, S. Tsunematsu, T. Nishibori, and J Inatani, ’Evaluation ofElectromagnetic Shielding Capability of a Spaceborne Cryostat for a Sensitive Receiver,’
Proceedings of the 2002 IEICE General Conference, (Communications 1),397 (B-4-48), 2002
(in Japanese).
Treder, A. J., Space Station GN&C Overview for Payloads, Space Technology and Applications
International Forum-1999, 49-57, 1999.

85

4

Retrieval– Principle and Method

4.1

Retrieval Algorithm

4.1.1

Principle of Limb Sounding Instruments

Microwave limb sounding instruments obtain remote measurements of atmospheric parameters by observing thermal emission at millimeter and sub-millimeter-wavelength as
the instrument field of view (FOV) is scanned through the atmosphere. Limb soundingmeans that the atmosphere is observed tangentially, as schematically shown in Figure 4.1.

limb path s

dh

z

h

satellite

z
ho
R

earth

atmospheric layers

Figure 4.1 Illustration of limb sounding geometry. The satellite is in orbit and looks
tangentially through the atmosphere. The beam is lowest at the tangent point at analtitude h.
The intensity detected by satellite observation contains implicit information about theparameters of interest. The extraction of these parameters from the satellite measurement
requires knowledge of the radiative transfer in the atmosphere. The radiative transfer
equation, introduced by Chandrasekhar [Chandrasekhar, 1960], describes the way in which
the intensity is affected by extinction and emission of radiation. The intensity, I, is definedas the power of radiation per unit area, per unit solid angle, and per unit frequency interval(measured in Wm−2 sr−1 Hz−1 ). In the case of a non-scattering atmosphere (clear sky),
for an arbitrary slant path, the intensity I at frequency ν received by an instrument at
position s = 0 is given by :R

I(ν) = I∞ (ν)ζ(∞, ν) + 0∞ k(s, ν)B(ν, T (s))ζ(s, ν)ds

(4.1)

where k is the total absorption coefficient (the SI unit for k is m−1 ), ζ is the transmittance,
B is the Planck function (which is derived under thermodynamic equilibrium condition),and I∞ is the intensity at the limit of the atmosphere, which is cosmic background radiation or emission from the ground. The meaning of these quantities is explained below.
Equation 4.1 is schematically shown in Figure 4.2.
For a mixture of gases the total absorption coefficient (summed over all the species) is
given by:
X
k=
ni σi
(4.2)
i

where ni is the number density of species i ( measured in m−3 ), and σi is the absorption
cross section (measured in m2 ).
The optical depth (or opacity) τ , between an initial point s0 and a final point s isdefined as:
Z sτ (s, ν) =k(s, ν)ds(4.3)
s0

and the transmittance ζ is defined as:ζ(s, ν) = exp(−τ (s, ν))
86

(4.4)

The Planck function describes the intensity which a perfect blackbody at temperature
T emits, and is defined as:
B(ν, T ) =2hν 3c2 (exp(hν/kB T ) − 1)

(4.5)

where h is Planck’s constant, kB is Boltzmann’s constant, and c is the speed of light.
B(ν, T ) has the same units as I(ν, T ), i.e. Wm−2 sr−1 Hz−1 .

s

Ι (ν)
ο
ο

receiverk(s, ν)Ι (ν)
Figure 4.2 Schematic display of the geometry for remote sensing of the atmosphere.
The atmospheric radiative transfer is simulated by a computer model, called forward
model F, using a limited number of parameters as input. The calculation of the absorption
coefficients (Equation (4.2)) and the integration of the radiative transfer equation (Equation (4.1)) are performed for a single frequency and a pencil beam, respectively, whileboth the spectrometer channels and antenna pattern are not infinitely narrow. Therefore
a convolution between the signal from the atmosphere as a function of frequency and thespectrometer channel shape, and between the signal as a function of viewing direction andthe antenna pattern, is required. Thus, the observed signal becomes a vector y, which canbe expressed as:
y = F(x, b) + (4.6)
where (x, b) gives a total description of both the atmospheric and sensor states, and  isthe measurement noise. The x vector contains all the parameter to be retrieved, while thevector b contains the model parameters. For a heterodyne instrument, as is the case of
the SMILES instrument, the most important sensor parts are the antenna, the mixer, thesideband filter and the spectrometer.
4.1.2

Retrieval Method and Basic Error Estimation

Including the measurement noise  = T (1 , 2 , · · · , m ), the observation equation is expressed by
y = F(x) + .
(4.7)
Here we omit the model parameters appearing in the forward model for simplicity.
In general the forward function F is nonlinear with respect to the state vector but thelinear approximation is expected to be reasonable for most target gases of SMILES. In
this assumption, it is natural to expand the forward function around the a priori valuexa ,y ≈ F(xa ) + Kx (x − xa ) + ,
(4.8)
Kx =

∂F(x)
.
∂x x
a

87

(4.9)

The retrieval method which we adopt is the optimal estimation method [Rodgers, 1976].
The optimal linear solution can be given byx̂ = xa + Dy [y − F(xa )] ,(4.10)

where the contribution function is−1 −1 T −1
Dy = (KTx S−1
 Kx + Sa ) Kx S ,=Sa KTx (Kx Sa KTx + S )−1 ,(4.11)(4.12)and S and Sa are the measurement error covariance matrix and the a priori error covariance matrix, respectively. It is also possible to express the retrieval in terms of theaveraging kernel matrix∂ x̂
A≡= Dy Kx ,(4.13)∂x
as follows:(4.14)x̂ = (I − A)xa + Ax + Dy .
Finally, the retrieval error covariance is given by
S = (A − In )Sa (A − In )T + Dy S DTy ,−1 −1= (KTx S−1
 Kx + Sa ) .

(4.15)(4.16)

The comprehensive error analysis including the model parameters is found in the originalliterature [Rodgers, 1990].

4.2

Simulation of Observation Spectra

We have two simulators available for analyses of the SMILES observations. One is SMOCO(SMILES Observation retrieval Code) developed by the CRL, and the other is ARTS (Atmospheric Radiative Transfer Simulator) by the Institute of Remote Sensing, Universityof Bremen, Germany, and the Chalmers University, Göteborg, Sweden. These simulators
are capable to produce emission spectra of the SMILES observations by the use of atmospheric and instrumental knowledge. They can also calculate the weighting functions.
These are essential quantities used to expand the observation equation into a linear relation in the optimal estimation method. Since the simulation model is the basis for thecorrect retrieval of atmospheric species from the limb sounding measurements, we conducted intercomparisons between the two simulation models. SMOCO and ARTS havebasically generated the same spectra. The results are also given in this section.
4.2.1

ARTS Forward Model

Forward models consist of either empirically determined relationships or numerical counterparts of the physical relationships needed to describe the radiative transfer and sensor
effects. The ARTS is mainly of the latter type, but some parts are more based on empiricalinvestigations, such as the parameterizations of continuum absorption.
The ARTS forward model is separated into two parts: atmospheric part and sensor
part. Thus, the state vector x and forward model parameter vector b are expressed as:
x=

xr
xs

88

!

(4.17)

and
b=

br
bs

!

,

(4.18)

respectively, where (xr , br ) refers to the atmospheric part, and (xs , bs ) refers to the sensor
part.
4.2.1.1

Atmospheric Part

The first part describes the atmospheric radiative transfer, independent of the sensor
characteristics, i.e., for pencil beam (infinite spatial resolution) monochromatic (infinite
frequency resolution) signals. The intensity i of the signal received from the atmosphereis expressed by:
i = Fa (xr , br )(4.19)
Practically, to calculate i (by solving the radiative transfer equation), the line of sight(LOS) is divided into n equidistant points (distance noted with ∆l). The absorption
and transmission is assumed to vary linearly between the points. The optical depth τi ,associated with the step i is calculated as:
τi =∆l(ki + ki+1 ) ,
2

1≤i<n

(4.20)

where ki and ki+1 are the absorption coefficients calculated at the ith and (i + 1)th LOS
points, respectively.
The transmission associated with step i is:ζi = exp(−τi ),1≤i<n(4.21)

The absorption coefficients are calculated at the LOS points, from the absorption matrix,
calculated on a logarithmic pressure grid, by linear interpolation. The grid for the calculation of the absorption matrix does not necessarily contain the corresponding grid pointsof the LOS.
The source function Si is assumed to vary linearly between the LOS points, but for
simplicity, a single source function value Si is assigned to each LOS point, i.e.,:Si =Si + Si+1
,
2

1≤i<n

(4.22)The intensity at the point n, i.e. at the end of LOS, is computed using the following
iterative expression:ii+1 = ii ζi + Si (1 − ζi )) ,1≤i<n−1(4.23)

where ii is the intensity reaching point i. The iteration starts by setting i1 to the cosmic
background radiation.
4.2.1.2

Sensor Part

The second part takes into account the sensor characteristics, such as the measurement
vector is given by:
y = Fs (i, xs , bs ) + y
(4.24)
where the i is the vector holding the spectral values of the signal emitted by the atmosphere for a set of considered frequencies, and viewing directions (calculated as described89

above). For a heterodyne instrument, as is the case of millimeter/sub-millimeter limb
sounding instruments, the most important sensor parts are the antenna, the mixer, thesideband filter and the spectrometer. Therefore, the modeling of the sensor part is either asummation of different frequencies (mixer), or it is a convolution of the spectra as a function of frequency (spectrometer) or as a function of viewing direction (antenna) with thecharacteristic instrument response functions of concern. Summation and weighting of thespectral components are both linear operations, and thus it is possible to model the effectsof the different sensor parts as subsequent matrix multiplications of the monochromatic
pencil beam spectrum [Eriksson, 2001]:
y = H n · · · H 2 H 1 i + y

(4.25)

where n is the total number of the considered sensor components, and Hi (1 ≤ i ≤ n) is thesensor matrix corresponding to the ith component of the sensor. Thus, the sensor model
can be implemented as a straightforward matrix multiplication and the measurement
vector is given by:
y = Hi + y
(4.26)
where H = Hn · · · H2 H1 .
The big advantage of this implementation is that the sensor matrix H is constant fora specific instrument, and it can be stored for further calculations.
4.2.1.3

Weighting Function

The separation of the forward model in two parts, the atmospheric part and the sensor
part, is very important for the practical calculation of the weighting function. Thus theweighting function, representing the sensitivity of the measurement to the state vector x,
can be written as:
∂y
∂y ∂i
∂i
Kx =
=
=H
(4.27)∂x∂i ∂x∂x
Thus the new parts needed to calculate atmospheric weighting functions, are function
giving ∂i/∂x, where x is the state vector representing a VMR profile, or temperature
profile, or spectroscopic data, etc.
The weighting functions can be calculated analytically, or numerically. In ARTS theweighting functions are in general calculated analytically (e.g., for the VMR profiles)
but there are some exceptions (e.g., for the atmospheric temperature profile when thehydrostatic equilibrium is assumed, or for the pointing offset) when they are calculated
numerically.
4.2.2

SMOCO Forward Model

SMOCO and ARTS share purposes and basic functions. The SMOCO forward model also
comprises two components, i.e., atmospheric part and sensor one. These are constructedin principle according to the same method as described for ARTS. Therefore, the following
descriptions are focused mainly on aspects characteristic to SMOCO.
4.2.2.1

Atmospheric Part

The atmospheric part of SMOCO calculates molecular absorption coefficients by a line-byline method, and numerically solves radiative transfer equations for the limb atmosphere.
SMOCO has taken emission-line frequencies from the JPL catalog [Pickett et al., http://spec.jpl.nasa.gov/], and parameters for pressure broadening and pressure shifts from90the HITRAN data or other published information. The absorption by water vapor andthe continuum emissions are calculated by Liebe’s millimeter-wave propagation model(MPM93)[Liebe, 1993]. SMOCO takes into account atmospheric refraction when it solves
radiative transfer equations. The refraction is significant for lower altitudes as shownin Section 3.2.1.6. SMOCO is capable to deal with an asymmetric atmosphere that has
different molecular mixing ratios in farther and nearer regions from the tangent point
along the line of sight. This function is effective to improve a horizontal resolution in the
direction if needed.
4.2.2.2

Sensor Part

In the sensor part, SMOCO deals with instrumental effects including the antenna radiation pattern, receiver sidebands, Doppler shift of frequency, and AOS frequency response
function.
Antenna Radiation Pattern
The antenna will receive radiation signals that come to the aperture in an extended
solid angle around the optical axis. Such response is expressed by the antenna radiation
pattern, which is modeled in Section 3.2.4 for the SMILES antenna. The signal powerreceived by the antenna is given by a spatial integration of the atmospheric brightness
distribution weighted with the antenna radiation pattern. The main beam in the radiation pattern is often described by its half-power beam-width (HPBW). The HPBW of the
SMILES antenna will be 0.08◦ (in elevation) and 0.16◦ (in azimuth). The spatial integration will cover the main beam and its surrounding area within a diameter of 2.5 times
the HPBW. We call this area a field of view (FOV) for SMILES observations. However,
we cannot completely neglect the power contribution from the outside of the FOV. The
SMILES design assumes that the integration of the antenna radiation pattern within the
FOV be higher than 85 % of the whole integration over 4π steradian. SMOCO takes into
account these effects when it calculates the signal power received by the antenna.
Receiver Sidebands
Since the submillimeter mixer, including SMILES’ SIS mixer, is sensitive to bothsidebands, the receiver output is proportional to the combined RF power from the upper
sideband (USB) and lower sideband (LSB). This means that the atmospheric emissions
in one sideband (signal band) are superimposed on those from the other sideband (imageband). However, thanks to a single sideband (SSB) filter, SMILES can suppress theimage-band levels lower than 1 percent as shown in Section 3.3.2.2. SMOCO calculates
the frequency down-conversion by the submillimeter receiver associated with the SSB filter.
Doppler Shift of Frequency
SMOCO deals with the Doppler shift of frequency caused by the movement of the ISS
and the rotation of the Earth. The shift due to the ISS motion is some 10 MHz, dependingon yaw deviations in the SMILES attitude. The shift due to the Earth rotation is about
1.5 MHz at most, while the effect is larger when the ISS flies toward the south comparedwith the case toward the north. Since the direction of the frequency shift for the LSB
and USB is opposite in the IF domain, the effect is to be calculated prior to the down
conversion in the forward model. Furthermore, the effect has to be properly taken into91

account when some daily or weekly averaged data are simulated and used in the retrieval
process.
Acousto-Optical Spectrometer (AOS)
First, each channel of the AOS has a Gaussian-like frequency response function asshown in Section 3.3.3.1. By integrating original real spectra with the weighting function,
SMOCO simulates the AOS output that is separated in channels. Secondly, since the exact
frequency of each channel is sensitive to the environment temperatures, SMILES measures
the frequency-channel relation in every 53 seconds with a reference signal onboard. The
relation is to be used in the calculation of the AOS output.

4.2.2.3

Weighting Function

The variation in the brightness temperature observed at a tangent height can be expressedby a linear combination of variations in the molecular volume-mixing ratio, pressure,temperature, and the error in the tangent height determination of each observation.
∆TA =Z zmax (Xzmin)

Ki ∆xi + Kp ∆(lnP ) + KT ∆T

dz + KHT ∆z0 .

(4.28)∂TA,∂z0(4.29)i

Each weighting function is therefore given by
Ki =

∂TA
,
∆z∂xi

KP =

∂TA
,
∆z∂lnP

KT =

∂TA
,
∆z∂T

KHT =

where z0 is the tangent height of the antenna beam axis. SMOCO evaluates the weighting functions by analytic expressions, but does the tangent height errors from numericaldifferences,
KHT =

4.2.3

TA (z0 + ∆z0 ) − TA (z0 ).
∆z0(4.30)

Forward Model Intercomparison

An intercomparison was carried out between the results produced by two forward models,
SMOCO and ARTS. This concerned both the computation of absorption coefficients andintegration of radiative transfer.
The intercomparison calculations were performed using a number of input parameters
predefined by the ARTS developers.
Absorption Coefficient Intercomparison
In order to check the model implementation for line absorption calculations, line-by-line
calculations were carried out. Absorption coefficients were computed for a large numberof frequencies in a variety of spectral ranges where important molecular species have signatures. The considered spectral ranges were about 1 GHz wide, and centered on the setof selected molecular species spectral transitions listed in Table 4.1. A set of spectroscopic
parameters for each considered transition was defined by the ARTS developers, including the line center frequency, the line intensity, the lower state energy, and the pressure
92

species
H2 O
O3
O2
H2 O
HCN
ClO

line center frequency
22.2 GHz
110.83 GHz
118.7 GHz
183.3 GHz
354 GHz
501 GHz

species
N2 O
HNO3
CO
O3
HCl
ClO

line center frequency
502 GHz
544 GHz576 GHz625.37 GHz
625.9 GHz
649.45 GHz

Table 4.1 Selected molecular species transitions for the absorption coefficients intercomparison.

broadening parameters. The provided spectroscopic data was based on the HITRAN catalog [Rothman et al., 1992]. The Voigt lineshape was selected to compute the absorption
coefficients. The polynomial coefficients used to compute the partition function, and theisotopic ratio for each isotope of each target species were also provided. In addition to thespectroscopic data, provided were the atmospheric temperature and pressure profile, andthe VMR profiles of the selected molecular species.
The spectra of the absorption coefficients were calculated for atmospheric levels ranging
from 0 to 60 km, for the full set of considered spectral lines in one run. That means thatthe possible contributions from the strong lines far away from the watched spectral range
are also included.
The results of the intercomparison showed that there is a good agreement between
the two models. With a few exceptions for some spectral ranges the relative differences
between the models are less than 1%. Here we present only some results for a set ofspectral ranges around some transitions mainly of interest for the SMILES study:• H2 O at 22.23 GHz, a transition of great interest for microwave radiometry.
• O3 at 625.37 GHz, the transition observed by the SMILES instrument (in Band A
and Band B).
• HCl at 625.9 GHz, observed by the SMILES instrument (in Band B).
• ClO at 649.45 GHz, a transition of great interest for microwave radiometry . Thisemission will also be observed by the SMILES instrument (in Band C).
The two models are very close to each other. In the frequency range around the H2 O line
emission a relative difference of less than 0.015% is found (Figure 4.3, lower plot). Similar
results are obtained for the spectral range around 625.37 GHz (Figure 4.4, lower plot).
In the spectral range around 626.9 GHz (Figure 4.5, lower plot) the relative differences
between the two models are about 1%. The differences appear to be caused by the pressure
shift, which seems to be not taken into account in the SMOCO model. An offset between
the two calculations appear in the spectral range around the ClO transition at 649.45 GHz(Figure 4.6, lower plot). This offset seems to be caused by a baseline due to other line
species absorption, as at this altitudes the emission signal from ClO is almost negligible(see Figure 4.6, upper plot).
Radiative Transfer Intercomparison
The intercomparison at the stage presented here aimed to investigate the differences between the models due solely to the numerical solution of the radiative transfer equation. A93H20−−−Reference model: ARTS01

−6

10

−8

α [1/m]

10

−10

10

−12

10

−14

10
0.015

relative diff.[%]

0.01
0.005
0

alt =14 km
alt =24 km
alt =33 km
alt =43 km
alt =55 km−0.005−0.01−0.015

21.8

21.9

22

22.1

22.2

22.3

22.4

22.5

22.6

22.7

Frequency [GHz]

Figure 4.3 Upper: ARTS calculations of the absorption spectra in the frequency range
around the H2 O line at 22.23 GHz for different atmospheric levels. Lower: Relative difference between the absorption spectra calculated with the two models SMOCO and ARTS.
The ARTS model was taken as a reference model.
O3−−−Reference model: ARTS01

−4

10

−6

α [1/m]

10

−8

10

−10

10

−12

relative diff.[%]

10
0.15

0.1

alt =14 km
alt =24 km
alt =33 km
alt =43 km
alt =55 km

0.05

0

−0.05
625.1 625.15 625.2 625.25 625.3 625.35 625.4 625.45 625.5 625.55 625.6

Frequency [GHz]

Figure 4.4 Upper: ARTS calculations of the absorption spectra in the frequency range
around the O3 line at 625.37 GHz for different atmospheric levels. Lower: Relative difference between the absorption spectra calculated with the two models SMOCO and ARTS.
The ARTS model was taken as a reference model.

94HCl−−−Reference model: ARTS01

−4

10

−6

α [1/m]

10

−8

10

−10

10

−12

10
1.5

relative diff.[%]

1
0.5
0
−0.5
−1
−1.5

alt =14 km
alt =24 km
alt =33 km
alt =43 km
alt =55 km
625.5

625.6

625.7

625.8

625.9

626

626.1

626.2

626.3

626.4

Frequency [GHz]

Figure 4.5 Upper: ARTS calculations of the absorption spectra in the frequency range
around the HCl line at 625.9 GHz for different atmospheric levels. Lower: Relative difference between the absorption spectra calculated with the two models SMOCO and ARTS.
The ARTS model was taken as a reference model.

standard set of absorption coefficient spectra, calculated on a fine vertical grid, was provide
by the ARTS developers. These absorption spectra were used the provide the brightness
temperature for a set of viewing direction. The other necessary specifications, such as theplatform altitude, were also provided. The brightness temperature spectra seen by a limb
looking instrument under a set of viewing angle, and the absolute difference between the
SMOCO and ARTS calculations are displayed in Figure 4.7. An absolute deviation of
about 1 K appears in the line wings (Figure 4.7, lower plot). The deviations in the line
center are almost negligible.
4.2.4

Observational Spectra Simulated by SMOCO

JEM/SMILES has three frequency bands: Band-A; 624.32 – 625.52 GHz, Band-B; 625.12– 626.32 GHz, and Band-C; 649.12 – 650.32 GHz. Figure 4.8 shows an example of the
SMILES spectra for the three frequency bands at a tangent height of 40 km. These spectra
are expected with an averaging of 30 atmospheric scans in orbit, which correspond to ahalf-day (12 hour) zonal mean averaged over a 5◦ latitude-width in the mid-latitude.
In the above simulations SMOCO was used with settings as follows.
• We used an antenna response pattern including wide-angle sidelobes with a beam
efficiency of 0.85, which is described in section 3.2.4 of this Mission Plan.
• The ISS altitude is assumed to be its nominal value 407 km.
• The model atmospheres at latitudes of 0◦ N, 45◦ N, and 80◦ N are of the Rutherford Appleton Laboratory, which include molecular concentrations, temperatures,pressures from 0 to 120 km.
• The center frequencies of molecules used in SMOCO are shown in Table 4.2.
95

ClO−−−Reference model: ARTS01

−6

10

−8

α [1/m]

10

−10

10

−12

10

−14

10
0.4
alt =14 km
alt =24 km
alt =33 km
alt =43 km
alt =55 km

relative diff.[%]

0.3
0.2
0.1
0
−0.1

649

649.1

649.2

649.3

649.4

649.5

649.6

649.7

649.8

649.9

Frequency [GHz]

Figure 4.6 Upper: ARTS calculations of the absorption spectra in the frequency range
around the ClO line at 649.45 GHz for different atmospheric levels. Lower: Relative
difference between the absorption spectra calculated with the two models SMOCO andARTS. The ARTS model was taken as a reference model.
Reference model: ARTS.Fine250

alt =13 km
alt =19 km
alt =34 km
alt =49 km

200

TB [K]

150
100
50

Absolute diff.[K]

0
1.5

1

0.5

0

−0.5

499

500

501

502

503

504

505

506

Frequency [GHz]

Figure 4.7 Upper: Arts calculations of brightness temperature spectra seen by a limb
sounding instrument. Lower: Absolute difference between brightness temperatures calculated with the two models (SMOCO and ARTS) calculation. The ARTS model was taken
as a reference model.

96

Table 4.2 Molecular transition frequencies of JEM/SMILES species.
BAND A (GHz)BAND B (GHz)BAND C (GHz)625.12 - 626.32 GHz
649.12 - 650.32 GHz
624.32-625.52 GHz
HNO3
624.48CH3 CN
624.50CH3 CN
624.63CH3 CN
624.73
HO81 Br 625.18
81 BrO
18 OOO624.77O3 (v2 ) 625.28
649.14
17 OOO
HNO3
624.78O3 (v2 ) 625.32
649.14
18 OOO
CH3 CN
624.82
HO79 Br 625.33
649.15
17
OClO
624.82
HNO3
625.34
OOO 649.27
17 OOOO18 OO
624.83O3
625.37649.3818
ClONO2624.87
OOO 625.39
ClO
649.45
18 OOO
CH3 CN
624.88625.56(ClO)2 649.51
CH3 CN
624.91O3 (v2 ) 625.61
HO2
649.7017
CH3 CN
624.93HO2
625.66O OO 649.79
17 OOO
H37 Cl624.98OClO
625.78649.9717
H2 O2625.04O3 (v2 ) 625.79
OOO 649.98
17 OOOO3 (v3 )625.05O3 (v3 ) 625.80
650.01
17 OOO
HO35 Cl
625.07
O17 OO 625.87
650.04
18 OOO1735625.09OOO 650.16
H Cl
625.92
17 OOO
HO81 Br625.18650.17
O17 OO 626.09
81 BrOO3 (v2 )625.28650.18
O17 OO 626.20
O3 (v2 +v3 ) 625.30
HNO3
650.28OClO
626.22O3 (v2 )625.32(ClO)2626.32
HO79 Br625.33HNO3
625.34O3
625.37

values including ones to be measured by the SMILES molecular spectroscopy team.
• Clear atmosphere with no cirrus clouds is assumed.
The simulation has revealed several characteristics of molecular spectra involved in thethree frequency bands.
• The ClO spectrum is relatively isolated from other spectra in Band-C.
• The HO2 spectrum is relatively isolated from other spectra in Band-C.
• The baseline level in the lower sideband is higher than that in the upper sideband,
which is due to a strong water vapor transition at 620.701 GHz.
• Band-C is most appropriate among the three bands to observe weak spectra.
• Although the Band-C is not includes normal ozone lines, there is a possibility toacquire some ozone information from the wing emissions of its 650.733 GHz line.
• Two BrO bands are available in the SMILES bands, although the transition around624.77 GHz in Band-A is superimposed on HNO3 transitions, and the other around97

bandA-45n-40km

Brightness Temperature [K]

1000

100

10

1
624.4

624.6

624.8
625
Frequency [GHz]

625.2

625.4

626

626.2

650

650.2

bandB-45n-40km

Brightness Temperature [K]

1000

100

10

1
625.2

625.4

625.6
625.8
Frequency [GHz]bandC-45n-40km

Brightness Temperature [K]

100

10

1
649.2

649.4

649.6
649.8
Frequency [GHz]

Figure 4.8 SMILES spectra for a tangent height of 40 km simulated with the 45◦ N model
atmosphere. The figures show the quality of observation data accumulated for 30 scans inorbit.

650.18 GHz in Band-C is on isotope ozone transitions. The retrieval error of BrO
from the Band-C spectrum is slightly lower than that for the Band-A case.
• Two transitions are available for HO2 , one in Band-B and the other in Band-C.
The HO2 emission in Band-C is relatively isolated from other lines. HO2 is one ofimportant species with few upper stratospheric and mesospheric data, and it is one
98

bandC-45n-30km
22Brightness Temperature [K]20

BrO+17 OOO

18
16
14
12
10
8
650

650.05650.1

650.15650.2
Frequency [GHz]650.25650.3

650.25650.3

bandC-45n-30km-3000K
22

BrO+17 OOOBrightness Temperature [K]

20
18
16
14
12
10
8
650

650.05650.1

650.15650.2
Frequency [GHz]

Figure 4.9 Comparison of two spectra, one expected with Tsys = 500 K and the otherwith 3000K.

of surely productive molecules for the SMILES observation.
SMILES’s high sensitivity would provide new opportunities to observe stratospheric
radical species such as BrO, HO2 which have been only poorly measured so far. The
BrO spectrum that would be obtained by the system with Tsys = 500 K (SIS mixer) withaccumulations of 30 scans is compared with the same spectrum with 3000 K (Schottky
diode mixers) in Figure 4.9.

4.3

Molecular Retrieval

The retrieval of molecular height profiles has been made for SMILES major species by
SMOCO, and the outputs are presented in the Appendix. They include the retrieved heightprofiles, retrieved total errors, and averaging kernels. For the purposes of description, we
divide the SMILES species into the next four groups.
• Standard products: O3 , HCl, and ClO in the stratosphere.
• Research products: HOCl, CH3 CN, HO2 , HNO3 , and BrO in the stratosphere.
99• Ozone isotopes: asymmetric-18 ozone (18 OOO), asymmetric-17 ozone (17 OOO), andsymmetric-17 ozone (O17 OO) in the stratosphere.
• Mesospheric products: O3 , HCl, and HO2 in the mesosphere.
In the optimal estimation method, the retrieved error informs us how much new observation spectrum with the radiance uncertainty covariance is able to decrease the a priori
uncertainty covariance, as shown in Equation (4.16). The error ratio, which is defined as
the ratio of the total retrieval error to the a priori error covariance, is useful to evaluate theretrieval results. We used the error ratio of that sense in the Appendix. The a priori error
covariance is not a unique value, and different from one species to the other because of its
a priori information of the observations, scientific purposes, the intensity of the spectrum
in each tangent height, the radiance noise, and so on. For example, in our studies in the
Appendix, the diagonal component of a priori uncertainty covariance has been assumed to
be proportional to the a priori height profile in the ratio of 0.5 for the standard products
and HNO3 , 1.0 for the research products (except HNO3 ), and 0.1 for the ozone isotopes.
As the retrieval error thus depends on the a priori uncertainty covariance, this value isregarded as an not very good indicator of the measurement capability of an observation
system for the purpose of the comparision of each molecular species. To show the instrumental capability directly in this section, the ”ERROR” in the figures are presented by
the ratio to the a priori height profile, instead of the ratio to the a priori error.
4.3.1

Standard Products

Three molecular species: O3 , HCl and ClO are classified as the standard products, forwhich SMILES is expected to give retrieved height profiles with errors less than 10 percent
by the use of data available from a single atmospheric scan in orbit. The weighting
functions have been calculated with a 2 km step for these species. The ”ERROR” aregiven by their ratios to the retrieved height profiles, which are the same as the a prioriones, in Figure 4.11. The a priori height profiles used for the retrieval are given in Figure
4.10.
The volume mixing ratio of ClO in the model profile of 80◦ N enhanced at around 20
km due to the activation of chlorine inside the polar vortex. The single-scan error for thisenhanced ClO is estimated less than 10 percent around 20 km. SMILES is also capableof observing ClO in its background abundance that is not enhanced by heterogenious
reactions. These are simulated with the atmosphere model height profiles of 45◦ N. The
single-scan error for the case is estimated less than 20 percent for 30 km to 42 km.
With respect to the normal ozone O3 , the retrieval error is estimated less than 5 percent
for 20 km to 45 km, and less than 10 percent for 18 km to 52 km at any latitudes even in
the case of single scan.
The retrieval error of H35 Cl is estimated less than 10 percent for 25 km to 45 km at
any latitudes in the case of single scan. It decreases to 5 percent levels for the atmosphericmodel profiles at 80◦ N.
4.3.2

Research Products

Five molecular species of HNO3 , HOCl, HO2 , CH3 CN, BrO are classified as the research
products, for which SMILES is expected to give retrieved height profiles with errors lessthan 50 percent by the use of 30-scan averaged data. The weighting functions have been
calculated with a 4 km step for these species. The ”ERROR” are given by their ratios tothe retrieved height profiles, which are the same as the a priori ones, in Figure 4.13 and100

Standard Product

Profiles of Standard Products
60

60

50

50

40

40

altitude [km]altitude [km]O3 (0N)HCl (80N)
ClO (80N)

30

20

10
-11
10

30
O3-single-scan
O3-30-scan
HCl-single-scan
HCl-30-scan
ClO-single-scan
ClO-30-scan

20

10

-10

10

-9

10

-8

10

-7

10

-6

10

10
0.01

-5

0.1

1

ERROR

[vmr]

Figure 4.10 The a priori molecularheight profiles used in the retrieval of
the SMILES standard product species.
The abscissa is the volume mixing ratio(VMR) of each species.

Figure 4.11 The ratio of the retrieval
error to the retrieved height profile for
the SMILES standard product species.

Figure 4.15. The a priori height profiles used for the retrieval are given in Figure 4.12 andFigure 4.14.
With respect to the five research product species, we have retrieved the height profiles
from 30-scan averaged data. Among them, HOCl, CH3 CN, and HNO3 are species thatcan be retrieved with errors significantly less than 20 percent. The error levels for HOCl
are less than 20 percent at any latitudes for 26 km to 43 km. The profiles of CH3 CN
are obtained from [Livesey, 2001], not model profiles. The error levels for CH3 CN in theequator are less than 20 percent for 15 km to 38 km. HNO3 is expected to be abundant
at high latitudes, and the retrieval errors are less than 20 percent at 17 km to 30 km withthe atmospheric model profile at 80◦ N.
For other two species, i.e., HO2 and BrO, the error levels are less than 50 percent. Since
HO2 is abundant in the upper stratosphere and mesosphere at low latitude regions, theHO2 retrieval errors decrease to 30-40 percent levels for higher than 30 km in the equator.
The BrO retrieval errors are less than 50 percent at 25 km to 37 km by the atmosphericmodel profile at 80◦ N. BrO could be detectable with errors of 50 percent levels even at
mid-latitudes profile. Note that we are here talking about the retrieval errors we expectfrom the 30-scan averaged data, which correspond to a 5-degree latitudinal zonal mean
available in half a day. Significant error reduction is therefore very hopeful when we deal
with data averaged in much longer periods.
Based on the above investigations, we expect SMILES data will be useful to study theso-called HOx chemistry, including HOCl and HO2 , from aspects of its relations to the
ClO chemistry.

101Research Products 1

Profiles of Reserach Products 1
60

60

50

50

BrO(80N)
HO2 (0N)
H2O2(0N)
HOCl(80N)

40

altitude [km]altitude [km]

40

30

20

10
-14
10

30

20

10

-13

10

-12

10

-11

10

-10

10

-9

10

BrO(80N)
HO2 (0N)
H2O2(0N)
HOCl(80N)

10
0.01

-8

0.1

1

[vmr]

ERROR

Figure 4.12 The a priori molecularheight profiles used in the retrieval of
the SMILES research product species.
The abscissa is the volume mixing ratio(VMR) of each species.

Figure 4.13 The ratio of the total retrieval error to the retrieved height profile for the SMILES research productspecies.

Profiles of Reserach Products 2

Research Products 260

60
CH3CN (0N)
HNO3 (80N)

50

50

40

40

altitude [km]altitude [km]CH3CN (0N)
HNO3 (80N)

30

20

10
-14
10

30

20

10

-13

10

-12

10

-11

10

-10

10

-9

10

10
0.01

-8

0.1

1

[vmr]

ERROR

Figure 4.14 The a priori molecularheight profiles used in the retrieval of
the SMILES research product species.
The abscissa is the volume mixing ratio(VMR) of each species.

Figure 4.15 The ratio of the total retrieval error to the retrieved height profile for the SMILES research productspecies.

1024.3.3

Ozone Isotopes

There are 73 transitions of ozone isotopes in the three frequency bands of SMILES. Amongthem, Band-C is most suitable to observe ozone isotopes, since it includes a lot of relatively
isolated spectra for asymmetric-17 ozone (17 OOO), asymmetric-18 ozone (18 OOO), andsymmetric-17 ozone (O17 OO). The retrieval errors with the 30-scan averaged data aresummarized as follows. The ”ERROR” are given by their ratios to the retrieved heightprofiles, which are the same as the a priori ones, in Figure 4.17. The a priori heightprofiles used for the retrieval are given in Figure 4.16.
• Normal ozone (O3 ): The retrieval errors are less than 2 percent from 21 km to 45
km with a height resolution of 2 km.
• Asymmetric-18 ozone (18 OOO): The retrieval errors are less than 2 percent from 24
km to 39 km with a height resolution of 5 km.
• Asymmetric-17 ozone (17 OOO): The retrieval errors are less than 2 percent from 25
km to 35 km with a height resolution of 5 km.
• Symmetric-17 ozone (O17 OO): The retrieval errors are less than 9 percent from 25
km to 40 km with a height resolution of 5 km.
The retrieved error of isotopes are converted to the error in the isotope enrichment δ.
The degree of the ozone isotope enrichment is defined as δ M O = (M Robs /M Rstd −1)×1000[h], where M Robs is the observed abundance ratio of the heavy isotopomer with mass

60

Ozone isotopes

Profiles of Ozone Isotopes

60
Aym-17 ozone
Asym-17 ozone
Asym-18 ozone
normal ozone

50

50

40

40

altitude [km]altitude [km]sym17
asym17
asym18normal ozone

30

20

10
-12
10

30

20

10
10

-11

10

-10

10

-9

10

-8

10

-7

10

-6

10

-5

0.01

0.1

1

[vmr]

ERROR

Figure 4.16 The a priori molecularheight profiles used in the retrieval of the
SMILES ozone isotopes. The abscissa isthe volume mixing ratio (VMR) of eachspecies.

Figure 4.17 The ratio of the retrieval
error to the retrieved height profile for
the SMILES ozone isotopes.

103

Profiles of Mesospheric observationMesospheric Observations
80
HO2 (0N)
HCl (80N)
O3 (0N)HO2 (0N)
HCl (80N)
O3 (0N)75

altitude [km]altitude [km]

 

 



65



10

70

-12

10

-11

10

-10

10

-9

10

-8

10

-7

10

60
0.01

-6

0.1

1

[vmr]

ERROR

Figure 4.18 The a priori molecularheight profiles used in the retrieval of the
SMILES mesospheric species. The abscissa is the volume mixing ratio (VMR)
of each species.

Figure 4.19 The ratio of the total retrieval error to the retrieved height profile for the SMILES mesospheric species.

number M , and M Rstd is the standard ratio described above [Kaye, 1987] as described in
section 2.4. The error in δ is affected by two retrieval errors: one in the retrieved normalozone, the other in the retrieved isotope ozone. So it is given by the root-square-sum of
the two errors. Since they are estimated as 2 percent for normal ozone and 18 OOO, theerror in δ for 18 OOO is less than 3 percent. The error in δ for 17 OOO is also less than 3
percent. The absolute value of δ is expected to be 10–40 % (100–400 h) for 18 OOO, andaround 10 percent (100 h) for 17 OOO, as described in Chapter 2. Therefore, SMILES
will be able to provide useful data for the ozone isotope enrichment. Furthermore, thedata will be available in every day and night in 5-degree latitudinal zone maps. SMILES
will be the first mission to investigate the global distribution of ozone isotopes.
4.3.4

Mesospheric Products

Among the SMILES spectrum simulation, O3 , HCl and HO2 are expected to have relative strong line intensity in the mesosphere. We therefore classified these species as themesospheric products, and conducted the retrieval with a reduced height resolution of 10km. With the 30-scan averaged data, the retrieval errors are estimated to be less than 50
percent at altitudes of less than 70 km for O3 and HCl at any latitudes and for HO2 in theequator model profiles. Although the formal observation range of SMILES is 10-60 km,
it will also scan the mesosphere up to around 150 km with an increased speed. If all the
spectral data available in the scan period is utilized for the retrieval, then the retrieval
errors will be much reduced for the mesosphere. The ERROR are given by their ratios tothe retrieved height profiles, which are the same as the a priori ones, in Figure 4.19. The

104

a priori height profiles used for the retrieval are given in Figure 4.18.

4.4

Temperature and Pointing Retrieval

The standard approach for microwave limb sounding instruments is to derive the atmospheric temperature profile and instrumental pointing information from oxygen emission
lines [Wehr, 1998; Engeln, 1998; Carlotti, 1999; Engeln, 2000]. The oxygen volume mixing
ratio is constant and therefore, the intensity of the emission line is only a function of theatmospheric temperature and the total pressure. Nevertheless, other emissions, e.g., ozone
emissions, can also convey accurate temperature and pointing information. In that casethe information originates primarily from the fact that the limb path becomes opaquefor certain tangent altitudes and frequencies. In general, the limb path for the center ofthe spectral lines becomes opaque at higher tangent altitudes, whereas the limb path for
the wings of the spectral lines becomes opaque only at lower tangent altitudes. In theopaque case there is a direct relation between the brightness temperature received by the
instrument and the physical temperature of the atmosphere. The width of the spectralline conveys information on the pressure, and thus, information on pointing.
The spectral ranges which will be observed by the SMILES instrument do not include
an oxygen emission line and thus information on the atmospheric temperature and instrumental pointing have to be extracted from non-uniformly mixed species emission lines.
Therefore it is essential to investigate where the information on the two quantities comesfrom.
The atmospheric temperature profile and a first order correction of the instrumental
pointing, the pointing offset, are retrieved simultaneously with the molecular species profiles. The atmospheric temperature profile is retrieved on a logarithmic pressure verticalgrid corresponding to a vertical spacing in altitude of about 3 km. For this, a variance(a priori error) of 5 K was assumed, and no interchannel correlation was considered. The
pointing offset is retrieved as an offset in the antenna viewing direction. For this, a standard deviation of 0.2◦ , corresponding to about 7 km at the tangent point, was considered.
Retrieval simulations were carried out with or without the hydrostatic equilibrium
constraint. The full set of presented results refers to mid-latitude summer atmosphericconditions [Anderson, 1986].
4.4.1

Temperature Retrieval

4.4.1.1

Information Content

Compared with the molecular species profiles, the derivation of the atmospheric temperature profile is a more complicated process. Information on the atmospheric temperature
profile is derived through different mechanisms, as outlined above. To see where the information on the atmospheric temperature comes from the information content in themeasurement with respect to the atmospheric temperature is investigated. The information content is given by the K̃ matrix which is the weighting function matrix Kx scaled by
the variability of the a priori state and the measurement [Rodgers, 2000]:−11K̃ = S 2 Kx Sa2(4.31)In the above equation S is the measurement noise covariance matrix (in this case adiagonal matrix representing only the thermal noise), and Sa is the a priori covariancematrix (in this case a diagonal matrix with the diagonal elements equal with the varianceof the atmospheric temperature profile, i.e., (5 K)2 ).
105

3.5

62
3

Retrieval Altitude [km]

56
50

2.5

44
2

38
1.5

32
26

1

20
0.5

14
8

0

10 12 14 16 18 21 23 25 27 29 31 33 35 37 39 41 44 46 48 50 52 54 56 58 60

Geometric Tangent Altitude [km]Figure 4.20 Band A: “Information content” on the atmospheric temperature. The x axis
displays the measurement vector during one scan. The spectra taken at different tangent
altitudes (in the altitude range of 10 – 60 km, in steps of 2.1 km) are separated by verticaldashed lines. The tangent altitudes are displayed at the middle point of the corresponding
region of the measurement. The y axis displays the retrieval altitude levels. The retrieval
altitudes where there is no information on the temperature (i.e., very low altitudes) arenot shown.

As an example, the results for Band A are shown. Figure 4.20 (“Information content”)
shows the K̃ matrix for the temperature retrieval in Band A. The x axis displays themeasurement vector during one scan (i.e., 25 tangent altitudes, corresponding to altitudes
between 10 and 60 km in steps of 2.1 km), while the y axis displays the retrieval altitudelevels. The geometric tangent altitudes are placed on the x axis at the middle pointof the corresponding part of the measurement. It is seen that the measurement has ahigh information content with respect to atmospheric temperature at altitudes around
11 km, altitudes where saturation effects occur. At even lower altitudes the atmosphereis totally opaque and thus no information on the temperature can be obtained from themeasurement. Similar behavior has been found for the other two bands. In the firsttwo bands, Band A and Band B (not shown), the O3 transition at 625.37 GHz plays
an important role for the temperature profile derivation at higher altitudes. Nevertheless,some information on the temperature comes from spectral features in the frequency ranges
around 624.98 GHz (Band A) and 625.92 GHz (Band B), where the HCl emission lines
are found. Similar analysis carried out for Band C shows that no information on thetemperature is found at higher altitudes, a consequence of the lack of strong spectralfeatures within this spectral band which could provide information on the temperature.
4.4.1.2

With Hydrostatic Equilibrium

The results for the temperature retrieval with hydrostatic equilibrium are displayed in
Figures 4.21 – 4.23. The watched quantities are the retrieval precision, the measurement
error, the error correlation of the temperature retrieval with the other retrieved quantities,
the averaging kernel functions and the vertical resolution given by the full width at half106

maximum (FWHM) of the averaging kernel functions.
Figure 4.21(a), Figure 4.22(a) and Figure 4.23(a) show that the atmospheric temperature profile can be retrieved (in all three spectral bands) with a precision better than 1 K
at altitudes around 11 km, a consequence of the saturation effects. The first two bands,
Band A and Band B, are almost equally suited for the temperature retrieval at most altitudes, a consequence of the fact that the same main source of information, the O3 line at
625.37 GHz, is used in both bands. The atmospheric temperature profile can be retrieved
with a precision of about 1 K up to altitudes of about 40 km in Band A (Figure 4.21(a)),
and up to altitude of about 45 km in Band B (Figure 4.22(a)). In this altitude range
the retrieval precision is dominated by the measurement error, the smoothing error (notshown) is rather small. The retrieval precision becomes closer to the a priori error ataltitudes below 10 km, and above 50 km, a fact which shows that the information on
the temperature relies more on the a priori information than on the information from themeasurement. The temperature retrieval is somewhat correlated with the HCl retrieval (inBand A also with the HNO3 retrieval) at low altitudes, and with the O3 retrieval at highaltitudes (Figure 4.21(b) and Figure 4.22(b) ). A strong correlation of the temperature
retrieval with the pointing offset retrieval is seen at an altitude of 11 km, the altitude
where the saturation effects occur, and at 14 km, the reference altitude for the hydrostatic
equilibrium. An altitude coverage (i.e., a good measurement response) up to 45 km in
Band A (Figure 4.21(c)), and up to 50 km in Band B (Figure 4.22(c)) with a vertical resolution of approximately 3 km (Figure 4.21(d) and Figure 4.22(d)) is achieved. In contrast
to the first two bands, the retrieval of the atmospheric temperature profile in Band C isachieved with a much poorer performance (Figure 4.23), a consequence of the fact thatin this spectral range there are no strong spectral features which could give informationon the atmospheric temperature. Also for this last band, a strong error correlation of
the temperature retrieval with the pointing offset retrieval at 11 km (where the saturation effects occur), and at 14 km (the reference altitude for the hydrostatic equilibrium
calculation) is seen (Figure 4.23(b)).
4.4.1.3

Without Hydrostatic Equilibrium

The results for the retrieved atmospheric temperature profile without hydrostatic equilibrium are displayed in Figures 4.24 – 4.26. It turns out that the retrieved temperature
profile is very little affected by assuming or not assuming hydrostatic equilibrium. A
slight improvement in the retrieval precision of the temperature profile at high altitudes
is seen for the retrieval scenario without hydrostatic equilibrium. However, the differences
are very small. Neither the shape of the averaging kernel functions or the error correlation with other retrieved quantities are changed in a significant way. A slight change inthe error correlation is seen with the retrieved pointing offset, a fact which was alreadyexpected, as these two quantities are not any longer related through the hydrostatic equation. This change in error correlation is most “visible” in Band B (see for comparison
Figure 4.25(b) and Figure 4.22(b)).
It has to be pointed out that the SMILES observation are not done on a vertical line.
For each limb scanning, the tangent altitude travels about 100 km in horizontal direction
as its altitude increases from 10 km to 60 km. That means that the hydrostatic equilibrium
is not fulfilled. However, it is shown that the retrieval performance of the atmospherictemperature profile without hydrostatic equilibrium is comparable – if not better – to one
obtained when the hydrostatic equilibrium is assumed.

107

a)

"Retrieval error"
retrieval prec.
meas. error

60

O3
HNO3
HCl
HOCl
HO2
SO2
BrO
Pointing offset

50

Altitude [km]"Error correlation"

b)

40

30

20

10
0

2

4

−0.5

Error [K]"Averaging kernels"

c)

0

0.5

1

Max/min correlation [−]d)"FWHM"

60

Altitude [km]

50

40

30

20

10
0

0.2

0.4

0.6

0.8

Averaging kernels [−]

1

0

5

10

15

FWHM [km]Figure 4.21 Temperature retrieval in Band A with hydrostatic equilibrium: (a) Retrieval
precision and measurement error; (b) Error correlation with the other retrieved quantities;(c) Averaging kernel functions; (d) FWHM of the averaging kernel functions presented in(c). The measurement response is also displayed in the plot (c) (solid thick line). Displayed
are only those altitudes where a measurement response better than 0.3 is achieved.

108

a)

"Retrieval error"
retrieval prec.
meas. error

60

O3
HO2
HNO3
H2O2
HCl
Pointing offset

50

Altitude [km]"Error correlation"

b)

40

30

20

10
0

2

4

−0.5

Error [K]"Averaging kernels"

c)

0

0.5

1

Max/min correlation [−]d)"FWHM"

60

Altitude [km]

50

40

30

20

10
0

0.2

0.4

0.6

0.8

Averaging kernels [−]

1

0

5

10

15

FWHM [km]Figure 4.22 Temperature retrieval in Band B with hydrostatic equilibrium: (a) Retrieval
precision and measurement error; (b) Error correlation with the other retrieved quantities;(c) Averaging kernel functions; (d) FWHM of the averaging kernel functions presented in(c). The measurement response is also displayed in the plot (c) (solid thick line). Displayed
are only those altitudes where a measurement response better than 0.3 is achieved.

109

a)

"Retrieval error"

40

retrieval prec.
meas. error

O3
HNO3
ClO
BrO
HO2
Pointing offset

35

Altitude [km]"Error correlation"

b)

30
25
20
15
10
0

2

4

−0.5

Error [K]"Averaging kernels"

c)

0

0.5

1

Max/min correlation [−]"FWHM"

d)40

Altitude [km]

35
30
25
20
15
10
0

0.2

0.4

0.6

0.8

Averaging kernels [−]

1

0

2

4

6

8

FWHM [km]Figure 4.23 Temperature retrieval in Band C with hydrostatic equilibrium: (a) Retrieval
precision and measurement error; (b) Error correlation with the other retrieved quantities;(c) Averaging kernel functions; (d) FWHM of the averaging kernel functions presented in(c). The measurement response is also displayed in the plot (c) (solid thick line). Displayed
are only those altitudes where a measurement response better than 0.3 is achieved.

110

a)

"Retrieval error"
retrieval prec.
meas. error

60

O3
HNO3
HCl
HOCl
HO2
SO2
BrO
Pointing offset

50

Altitude [km]"Error correlation"

b)

40

30

20

10
0

2

4

−0.5

Error [K]"Averaging kernels"

c)

0

0.5

1

Max/min correlation [−]d)"FWHM"

60

Altitude [km]

50

40

30

20

10
0

0.2

0.4

0.6

0.8

Averaging kernels [−]

1

0

5

10

15

FWHM [km]Figure 4.24 Temperature retrieval in Band A without hydrostatic equilibrium: (a) Retrieval precision and measurement error; (b) Error correlation with the other retrieved
quantities; (c) Averaging kernel functions; (d) FWHM of the averaging kernel functions
presented in (c). The measurement response is also displayed in the plot (c) (solid thickline). Displayed are only those altitudes where a measurement response better than 0.3 isachieved.

111

a)

"Retrieval error"
retrieval prec.
meas. error

60

O3
HO2
HNO3
H2O2
HCl
Pointing offset

50

Altitude [km]"Error correlation"

b)

40

30

20

10
0

2

4

−0.5

Error [K]"Averaging kernels"

c)

0

0.5

1

Max/min correlation [−]d)"FWHM"

60

Altitude [km]

50

40

30

20

10
0

0.2

0.4

0.6

0.8

Averaging kernels [−]

1

0

5

10

15

FWHM [km]Figure 4.25 Temperature retrieval in Band B without hydrostatic equilibrium: (a) Retrieval precision and measurement error; (b) Error correlation with the other retrieved
quantities; (c) Averaging kernel functions; (d) FWHM of the averaging kernel functions
presented in (c). The measurement response is also displayed in the plot (c) (solid thickline). Displayed are only those altitudes where a measurement response better than 0.3 isachieved.

112

a)

"Retrieval error"
retrieval prec.
meas. error

40

O3
HNO3
ClO
BrO
HO2
Pointing offset

35

Altitude [km]"Error correlation"

b)

30
25
20
15
10
0

2

4

−0.5

Error [K]"Averaging kernels"

c)

0

0.5

1

Max/min correlation [−]d)"FWHM"

40

Altitude [km]

35
30
25
20
15
10
0

0.2

0.4

0.6

0.8

Averaging kernels [−]

1

0

5

10

FWHM [km]Figure 4.26 Temperature retrieval in Band C without hydrostatic equilibrium: (a) Retrieval precision and measurement error; (b) Error correlation with the other retrieved
quantities; (c) Averaging kernel functions; (d) FWHM of the averaging kernel functions
presented in (c). The measurement response is also displayed in the plot (c) (solid thickline). Displayed are only those altitudes where a measurement response better than 0.3 isachieved.

113

4.4.2

Pointing Offset Retrieval

The same steps as for the temperature retrieval were done for the pointing offset retrieval.
That means that the information content in the measurement of the pointing offset isanalyzed, and the retrieval performance is investigated with or without hydrostatic equilibrium constraint.
4.4.2.1

Information Content

Since no analytical expression is known to calculate the weighting function for the pointing offset, this has to be calculated numerically. The most general and also the moststraightforward way to calculate the weighting function is by doing disturbance calculations by repeated use of the forward model. For example, the weighting function for theith component of the state vector can be calculated by:
Kxi =F(xa + ∆xi ii , ba ) − F(xa , ba )∆xi

(4.32)

where ∆xi is a small disturbance of the ith component of the state vector (in this casepointing offset), and ii is a vector of zeros except the ith component that is unity. The
weighting function of the pointing offset reflects the change of the emission for a smallchange in viewing angle. The magnitude of the disturbance ∆xi plays an important role.
Theoretically, the smaller ∆xi , the more accurate the result. However, the value applied
practically must not be too small as this could lead to numerical instabilities. The value
applied here to calculate the weighting function of the pointing offset is 0.001◦ , found tobe small enough the get acceptable results. The information content of the measurementon the pointing offset is given by K̃ (Equation 4.31). As the pointing offset is describedby a single variable, the weighting function matrix has only one column, and so has the K̃matrix therefore.
Figure 4.27 displays the K̃ matrix for the pointing offset, in the three SMILES bands.
The presented results refer to the retrieval scenario with hydrostatic equilibrium constraint. However, retrieval simulations carried out without hydrostatic equilibrium constraint showed similar results. The K̃ matrix is displayed for the measurement taken during
one scan. The tangent altitude for each spectrum is displayed in the middle point of thecorresponding region of the measurement vector.
The emission at upper tropospheric and lower stratospheric altitudes is dominated by
the continuum absorption from O2 , N2 , and H2 O, but also from the line wings of the O3
emission. Therefore the measurement is characterized by a rather flat, with respect tofrequency, information content on the pointing offset. At even lower tangent altitudes theatmosphere is optically thick and the emission is fairly constant explaining the rapid decrease of the measurement information content on the pointing offset at altitudes between
9 and 12 km. These features appear in all there bands. For Band A and Band B, theoptical thickness is especially high around the center frequency of the O3 transition at
625.37 GHz (Figure 4.27, top and middle plots) and so no information on the pointing
offset is obtained at this frequency. At higher altitudes the information comes mainly fromthe O3 line wings. The features of the HCl lines at 624.98 GHz (Band A) and 625.92 GHz(Band B) show weakly up at high altitudes. Looking at Figure 4.27 it can be concluded
that the best altitudes to derive information on the pointing offset in the spectral rangesof Band A and Band B are between 12 and 18 km, due to the continua and O3 absorption, and between 30 and 50 km where the information on the pointing offset is mainly
gained from the wings of the O3 emission line. From these figures one can clearly seethat the pressure broadening linewidth plays an important role in deriving information114

on the pointing offset; the shape of the “Information content” curve for different tangent
altitudes changes according to the width of the O3 emission line. In case of Band C theinformation on the pointing offset is obtained only at low altitudes, due to the continuum
absorption (Figure 4.27, bottom plot). For this band, the best altitudes to retrieve thepointing offset are found between 12 and 16 km.
500

Information Content [−]

400

300

200

100

0

−100

10 12 14 16 18 21 23 25 27 29 31 33 35 37 39 41 44 46 48 50 52 54 56 58 60

Geometric Tangent Altitude [km]500

Information Content [−]

400

300

200

100

0

−100

10 12 14 16 18 21 23 25 27 29 31 33 35 37 39 41 44 46 48 50 52 54 56 58 60

Geometric Tangent Altitude [km]450

Information Content [−]

400
350
300
250
200
150
100
50
0
−50

10 12 14 16 18 21 23 25 27 29 31 33 35 37 39 41 44 46 48 50 52 54 56 58 60

Geometric Tangent Altitude [km]Figure 4.27 “Information content” of the measurement on the pointing offset. Top:
Band A; Middle: Band B; Bottom: Band C. The x axes display the measurement vector
during one scan (i.e., 25 tangent altitudes), while the y axes display the retrieval altitudelevels. The geometric tangent altitudes are placed at the middle point of the corresponding
part of the measurement (on the x axes). The presented calculations have been performed
under the constraint of the hydrostatic equilibrium.

115

Table 4.3 Retrieval precision, measurement error and measurement response for the
retrieved pointing offset. The retrieval precision and measurement error are given both inantenna viewing angle units [degree] and in the tangent altitude units [m]. The results referto the retrieval scenario with hydrostatic equilibrium and for the atmospheric conditions
specific to mid-latitude summer.
Termabsolute [◦ ]retrievalprecision
absolute [m]◦measurement absolute [ ]errorabsolute [m]measurement
response

4.4.2.2

Band A
9.12 10−547.03 10−53Band B
9.60 10−54
8.78 10−53Band C
2.91 10−4
112.72 10−4
10

1

1

1

With Hydrostatic Equilibrium

Table 4.3 displays the results for the pointing offset retrieval with hydrostatic equilibrium.
Listed are the retrieval precision and the measurement error (given both in antenna angle
units [degrees] and in tangent altitude units [m]) and the measurement response. The
pointing offset can be retrieved with an extremely good precision in all three spectral
bands. A perfect measurement response is achieved in all three bands. The first two bands,
Band A and Band B, are almost equally well suited for the pointing offset retrieval. This isachieved with a precision of about 0.0001◦ in the antenna viewing angle, corresponding toa value of few meters in the tangent altitude. For these two bands the retrieval precision ismainly dominated by the measurement error (Table 4.3). The results obtained for Band C
are slightly poorer.
Generally, the pointing offset retrieval was found to be highly correlated with thetemperature retrieval, and slightly weaker correlated with the O3 retrieval. In case ofBand A and Band B a correlation of the pointing offset retrieval with the HCl retrieval
was also found.
4.4.2.3

Without Hydrostatic Equilibrium

Similar results for the pointing offset retrieval without hydrostatic equilibrium are given
in Tables 4.4. The same as for the temperature retrieval, the results are slightly influenced
by assuming or not assuming the hydrostatic equilibrium. Within one band, the pointing
offset is retrieved with a similar precision for the two retrieval scenarios. Only the levelof error correlation of the retrieved pointing offset with the other retrieved quantities
is slightly changed. This mainly refers to the error correlations with the temperature
retrieval, a fact which was already expected as the two quantities are not any longer
connected through the hydrostatic equilibrium.

116

Table 4.4 Retrieval precision, measurement error and measurement response for the
retrieved pointing offset. The retrieval precision and measurement error are given bothin antenna viewing angle units [degree] and in the tangent altitude units [m]. The results
refer to the retrieval scenario without hydrostatic equilibrium and for the atmosphericconditions specific to mid-latitude summer.
Termabsolute [◦ ]retrievalprecision
absolute [m]◦measurement absolute [ ]errorabsolute [m]measurement
response

4.5
4.5.1

Band A
8.96 10−537.08 10−42Band B
1.09 10−44
1.02 10−43Band C2.38 10−492.82 10−4
3

1

1

1

Spectroscopic Data for SMILES
Spectroscopic Data Required for the JEM/SMILES Experiment

The identification and monitoring of atmospheric molecules as well as interstellar molecules
critically depend on the laboratory measurements of line positions, intensities, and collisional cross sections (pressure broadening parameters). The transition frequencies thatfall into the submillimeter-wave region which the JEM/SMILES aims at have mostly not
been directly measured. Majority of them have been extrapolated from the values measured in the lower frequency regions. As a result, it is not surprising to find discrepancies
of several MHz, sometimes much larger, between the calculated and measured transition
frequencies. Moreover, crucial parameters other than the center frequencies such as thepressure broadening parameters and the pressure line shifts have been, in many cases,
unknown. In particular, information for minor constituents like free radicals and very
reactive species are extremely incomplete, and the detection of these species themselves
constitutes a great challenge for the laboratory spectroscopy.
Therefore a tremendous amount of efforts has been made to create a number of spectroscopic databases: ATMOS [Brown et al., 1987], SAO [Chance et al., 1994], GEISA [Husson et al., 1992; Husson et al., 1994], JPL [Poynter and Pickett, 1985; Pickett et al., http://spec.jpl.nasa.gov/], HITRAN [Rothman et al., 1987; Rothman et al., 1992; Rothman
et al., 1996]. More recently the Cologne Database for Molecular Spectroscopy (CDMS)
is a new addition [Müller et al., http://www.ph1.uni-koeln.de/vorhersagen/]. The JPL
catalog [Poynter and Pickett, 1985] includes line positions and line intensity parameters.
Line broadening and line shift parameters are not listed in this database. Molecules thatare of great importance in the JEM/SMILES mission are listed in Table 4.2. The values
for these minor constituents listed in the known database may not be accurate enough.
A molecular spectroscopy group has been organized to provide and to evaluate important
molecular parameters, especially pressure broadening parameters for the JEM/SMILES
mission. The measurements have mostly taken place at Ibaraki University.
Among the transitions listed in Table 4.2, O3 , HO2 , HOCl, HNO3 (except for 2925,4 −
2825,3 ), other minor isotopic species, and the vibrationally excited states have not beenmeasured. A most urgent task of the laboratory spectroscopy is to confirm and/or improve
the transition frequencies and the pressure broadening parameters of the target molecules,
117

including common molecules such as O3 and HCl. In particular, systematic discrepancies
are often noticed among the pressure broadening parameters obtained at different locations. In this respect, it is almost imperative to check the parameters that are thought
to be well established. One of the goals set by the JEM/SMILES mission is to obtaina global map of BrO. To derive reliable quantitative map by inverting the observed linefeatures, the pressure broadening parameters accurate to a few percent are indispensablefor the BrO transitions. These crucial parameters for the SMILES target molecules, BrO,
HO2 , and, O3 , are now being measured by the molecular spectroscopy team.
4.5.2

Laboratory Submillimeter-wave Spectrometer System

A submillimeter-wave spectrometer system of a wide frequency coverage and high-sensitivity
has been implemented. Figure 4.28 shows a schematic diagram of a system at Ibaraki University. Characteristic features of this instrument are: (1)use of backward-wave oscillators
manufactured by a Russian company(ISTOK) as radiation sources and (2) double modulation detection scheme, whenever it is applicable. An oscillator tube(BWO) is housed
between the pole faces of a permanent magnet of about 1 T of field strength. The free running frequency fluctuation of BWOs reaches over 10 MHz and therefore active frequency
stabilization is essential for high-sensitivity and high-resolution spectroscopy. We achieved
this goal by using a double phase-lock loop: the submillimeter-wave radiation is phaselocked to a harmonics of millimeter-wave radiation from a stabilized Gunn oscillator bymeans of a GaAs harmonic mixer fabricated in G. Winnewisser’s group at Universität zu

Figure 1
Lock-in
AmplifierLock-in
AmplifierrefFrequency
Synthesizer

refFunction
Generator

Pump(12-18 GHz)

Frequencyrefmod Synthesizer

Function
Function
Generator
Generator

Pre-amp.

ComputermodrefPhase-lock(Gunn)

Phase-lock(BWO)

Spectrum
Analyzer

SignalSignalGenerator
Generator

GaAs
Harmonic
Mixer

IF(345 MHz)

Gunn
Osc.

IF monitor

Discharge
SolenoidPower
SupplyPower Supply

GaAs
Harmonic Mixer(80-110 GHz)

MagnetIn-Sb
Detector
Absorption Cell
Discharge
Discharge
Power SupplyIF(50 MHz)

Beam Splitter

Sample inlet

MagnetBWO
Power Supply

Figure 4.28 A schematic diagram of a double modulation submillimeter-wave system at
Ibaraki University.
118

Köln. The Gunn frequency is stabilized to a harmonics of the microwave frequency(12-18
GHz) generated by a microwave synthesizer.
The submillimeter-wave output is focused by a Teflon lens to make it a parallel beam
to direct through the absorption cell. A part of the power is directed to a GaAs mixer
by a wire grid beam splitter. The frequency region of 380-850 GHz can be covered withthree BWOs. The output power is typically a few milliwatts but it varies greatly overits frequency range. The spectrometer functions very well to the level that we initiallyaimed at. The submillimeter-wave lines of HO2 was systematically measured in the rangeof 400-680GHz. Although the majority of transition frequencies listed in the JPL Catalog
agree reasonably well with our measurements, several cases of uncomfortable discrepancy
were encountered.
4.5.3

Available Molecular Parameters up to Present

Table 4.5 shows the pressure broadening parameters, γ0 , and its temperature exponent,T )n . An example of the line profile of BrO is shown inn, defined as γ(T) = γ0 × ( T
0Figure 4.29. It is almost impossible to fit each hyperfine component separately to a Voigt
line profile. To alleviate this difficulty a convolution method proposed by Pickett has
been adopted. The pressure broadening parameters thus determined are indicated in
the table in boldface. The JPL group has also been performed similar measurements.
Their results are compared also in the table. The spectroscopy group of Sciences andTechnologies University of Lille has recently reported the pressure broadening parametersof submillimeter-wave transitions of O3 and BrO. We included their values also in thetable. Other parameters in the table were taken from original papers, Aura reports, or
HITRAN database. These values should be critically evaluated.

"#

#

$

!

%

&

# ' $ (

Figure 4.29 A least square fitting of the BrO spectrum to derive the pressure broadening
parameter119molecule
HNO3

CH3 CN
79 BrO
81 BrO

H37 ClH35 ClH2 O2HO35 Cl
HO2

ClO
O3

18 OOO

Table 4.5 Pressure broadening parameters
frequency [GHz] γ0 [MHz/Torr] n (T0 =294 K)
181.5496
4.362
0.75
369.2584.16
0.70
624.484.339
0.75
624.784.339
0.75
650.28
4.339
0.75
624.8
499.6
3.54(28)0.45(18)624.768
3.01 (10)624.768
3.11 (16)0.66(3)650.179
3.03 (10)650.179
3.03 (15)0.56(3)624.983.51
0.69
625.9188
3.48
0.74
625.04
3.944
0.50
IR
3.94
625.0752.37
0.5
641.6433.16
0.67
641.643
3.70 (5)649.7023.37 (11)649.7023.71 (22)0.2
649.45122.81(8)0.85(9)206.1320
2.890
0.75
498.7
2.859(11)0.740(24)500.4
2.798(57)0.876(80)501.8
2.785(28)0.784(62)625.3702.789
0.76
650.0112.777
0.76

reference
1
2
3
3
3
4
5
9
5
9
3
1
3
6
7
3
5
5
9
8
1
4
4
4
3
3

1 Aura report
2 T. Goyette, W. Guo, F.C.Delucia, and P. Helminger,

J. Quant. Spectrosc. Radiat. Transfer 46, 293 (1991)
3 Hitran 96 database
4 M. Birk, G. Wagner et al. Study on a spectroscopic database for millimeter andsubmillimeter wavelength, Final report
5 This work, measured by M. Yamada, M. Kobayashi, H. Habara,
and T. Amano at Ibaraki Univ.
6 V. Malathy Devi, C.P.Rinsland, M.A.H.Smith, D.Ch.Benner,and B.Fridovitch, Appl. Opt. 25, 1844 (1986)
7 HITRAN default value
8 J.J.Oh and E.A.Cohen, J. Quant. Spectrosc. Radiat. Transfer 52, 151 (1994)
9 E.A.Cohen et al. preliminary results, private communication

4.5.4

Plans for Further Improvement of Data

Preliminary results of the temperature dependence of the pressure broadening parametersfor BrO and HO2 molecules have been obtained. Monitoring the atmospheric abundances120

of the minor isotopic species of O3 is a major scientific objective of the SMILES mission.
However, spectroscopic data of the minor isotopic species of O3 are sparse. Therefore,systematic measurements of these species should be undertaken. The pressure broadening
parameters for HCl and HOCl also seems unsatisfactory. The pressure broadening parameters are prone to systematic errors. Coordinated arrangements to critically check dataobtained at different institutions are highly desirable.
A remaining problem is to experimentally establish the transition intensity. To derive
the precise intensity, the dipole moment should be known. A most commonly used andbest established method to obtain dipole moments is Stark effect measurements. For stablemolecules such as O3 and HCl, the dipole moments are obtained precisely. However, it
is not always the case for unstable molecules. The dipole moments for HO2 and BrO
are known for quite some time. It is, however, imperative to check them again, because
those Stark effect measurements had been performed at a rather early stage of microwave
spectroscopy and have not been checked ever since.

References
Anderson, G. P., and S. A. Clough and F. X.Kneizys, and E. P. Shettle, AFGL atmosphericconstituents (1-120 km), Tech. Rep., Nr. TR-86-0110, 1986.
Brown, L. R., C. B. Framer, C. P. Rinsl, and R. A. Toth., Molecular line parameters for theatmospheric molecule trace spectroscopy experiment, Appl. Optics, 26,5154, 1987.
Bühler, S., A. von Engeln, and K. Künzi and B.-M. Sinnhuber, J. Urban, R. Siddans, B. J.
Kerridge, W. J. Reburn, R. Peter and K. Arzner The Retrieval of Data from Sub–Millimeter
Limb Sounding, Final Report, Contract No 11979/97/NL/CN, Noordwijk, February, 1999.
Carlotti, M., B. Carli and M. Ridolfi, Derivation of temperature and pressure from submillimetriclimb observations, ESTEC, Contract No 10306/93/NL/CN, Final Report, December, 1994.
Chance, K., K. W. Juckes, D. G. Johnson, and W. A. Traub. The Smithsonian AstrophysicalObservatory Database SAO92, J. Quant. Spectrosc. Radia. Transfer, 52, 447, 1994.
Chandrasekhar, S., Radiative transfer, Dover Publications Inc., 393p, New York, 1960.
von Engeln, A., S. Bühler, J. Langen, T. Wehr, and K. Künzi, Retrieval of upper stratosphericand mesospheric temperature profiles from Millimeter-Wave Atmospheric Sounder data, J.
Geophys. Res., Volume 103, pp. 31735-31748, 1998.
von Engeln, A., Satellite based Temperature Profile Determination using Microwave and Radio
Occultation Instruments, PhD. Thesis, University of Bremen, Germany, Logos Verlag Berlin,
2000, ISBN 3-89722-453-4, ISSN 1615-6862.
Eriksson, P., F. Merino, D. Murtagh, P. Baron, and P. Richau and d. l. Noe, Studies for the Odin
sub-millimetre radiometer: 1. Radiative transfer and instrument simulation, To appear in
Canadian Journal of Physics, 2001.
Husson, N., B. Bonnet, N. A. Scott, and A. Chedin. Management and study of spectroscopic
information: GEISA peogram, J. Quant. Spectrosc. Radia. Transfer, 48, 509, 1992.
Husson, N., B. Bonnet, N. A. Scott, A. Chedin, A. A. Churisn, V. F. Golovko, and V. G. Tyuterev.
The GEISA data bank in 1993: A PC/AT compatible computers’ new version, J. Quant.
Spectrosc. Radia. Transfer, 52, 425, 1994.
Liebe, H. J, MPM - An atmospheric millimeter-wave propagation model. Int. J. Infrared Millimeter
Waves, 10, 631-650, 1989.
Liebe, H. J, G.A.Hufford, and M.G.Cotton, Propagation modeling of moist air and suspended
water/ice particles at frequencies below 1000 GHz. in 52nd Specialists’ Meeting of the Electromagnetic Wave Propagation Panel, AGARD, Palma De Mallorca, Spain, 1993.
Livesey, N.J., J.W. Waters, R. Khosravi, G.P. Brasseur, G.S. Tyndall, W.G. Read, Stratospheric
CH3CN from the UARS Microwave Limb Sounder, Geophys. Res. Lett. 28, 779-782, 2001.
Müller, H. S. P., S. Thorwirth, D. A. Roth, and G. Winnewiser., The Cologne Database for
Molecular Spectroscopy, http://www.ph1.uni-koeln.de/vorhersagen/.
Ochiai, S., MAES, in the proceedings of the forward model workshop, Bremen, Apr., 1999.
Pickett, H. M., R. L. Poynter, and E. A. Cohen. JPL Molecular Spectroscopy,

121http://spec.jpl.nasa.gov/.
Poynter, R. L. and H. M. Pickett. Submillimeter, millimeter and microwave spectral line catalog,
Appl. Opt., 24, 2235, 1985.
Reburn, W. J., R. Siddans, B. J. Kerridge, S. Buehler, A. von Engeln, P. Eriksson, T. Kuhn,
K. Kunzi, and C. Verdes, Critical Assesments in Millimetre-Wave Atmospheric Limb Sounding, ESTEC Contract No.: 13348/98/NL/GD, Final Report, September, 2000.
Rodgers, C. D., Retrieval of atmospheric temperature and composition from remote measurementsof thermal radiation, Rev. Geophys. and Space. Phys.,14, 609-624, 1976.
Rodgers, C. D., Charactrization and error analysis of profiles retrieved from remote sensing measurements, J. Geophys. Res., 95 (D5), 5587-5595, 1990.
Rodgers, C. D., Inverse Methods for Atmospheric Sounding: Theory and Practise, World ScientificPubl., Series on Atmospheric, Oceanic and Planetary Physics, 2000.
Rosenkranz, P. W., Water Vapor Microwave Continuum Absorption: A Comparison of Measurements and Models, Radio Science, 33, 919–928,1998.
Rothman, L. S., R. R. Gamache, A. Goldman, L. R. Brown, L. A. Toth, H. M. Pickett, R. L.
Poynter, J-M.Flaud, and C. Camy-Peyret., The HITRAN database: 1986 edition, Appl. Opt.,
26, 4058, 1987.
Rothman, L. S., R. R. Gamache, R. H. Tipping, M. A. H. Smith, D. Ch. Benner, V. Malathy Devi,
J.-M. Flaud, C. Camy-Pyret, A. Perrin, A. Goldman, S. T. Massie, l. R. Brown, and R. A.
Toth., The HITRAN molecular database: editions of 1991 and 1992, J. Quant. Spectrosc.
Radia. Transfer, 48, 469, 1992.
Rothman, L. S., C. P. Rinsland, A. Goldman, S. T. Massie, D. P. Edwards, J.-M. Flaud, A. Perrin, V. Dana, J.-Y. Mandin, J. Schroeder, A. McCann, R. R. Gamache, R. B. Wattson,
K. Yoshino, K. Chance, K. Jucks, L. R. Brown, V. Nemtchinov, and P. Varanasi., The HITRAN spectroscpic database and HAWKS (HITRAN Workstation): 1996 edition, J. Quant.
Spectrosc. Radia. Transfer, 1996.
Verdes, C., S. Bühler, A. von Engeln, P. Eriksson, T. Kuhn, K. Künzi, The Retrieval of Data from
Sub-Millimetre Limb Sounding, Part 1, ESTEC Contract No.: 11979/97/NL/CN, Final Report, September, 2000.
Wehr, T., S. A. Bühler, A. von Engeln, K. Künzi, and J. Langen Retrieval of Stratospheric Temperatures from Space Borne Microwave Limb Sounding Measurements J. Geophys. Res., 103,
25997-26006,1998.

122

5

Ground Data System

5.1

Objectives of JEM/SMILES Ground Data System

JEM/SMILES Ground Data System must have the following functions as similar to otherground data systems for earth observing satellites.
• Operation Planning• Commanding• Housekeeping Telemetry data watching• Data processing, archiving and distribution• Observation prediction and planning (for validation experiments)
The first two functions will have to be developed in close connection with JEM Operation
System. The later two functions will be developed independently of JEM Operation
System. The goal of SMILES Ground Data System is to support SMILES operation
flawlessly, and to provide scientifically good SMILES data to users. Following requirements
will be necessary to proceed to this goal within the limited SMILES experiment period.
• Extensive effort to improve data quality of SMILES product through data quality
check, algorithm development and validation.
• Processing power enough to re-process and to improve data set quality until broad
scientific acceptance.
• Seamless data distribution to potential data users.
It is assumed that the SMILES operation extends only one year (and may be extended
up to two years). Thus, those efforts should be focused on at (and prior to) SMILES
operation. If there will be 3 major version up during 12 months, the required processing
power must be much higher than the equilibrium processing speed, which means data isprocessed just as much as downlinked.
SMILES data will be distributed in L1, L2, and L3 format. Table 5.1 lists characteristics and volume of these data sets. Typical scientific users will require daily gridded L3
data. Several users will compare their own observed data to SMILES L2 vertical profile
data. Several higher, so-called L4, data sets, such as monthly mean data will be processed
as a part of standard products. Data distribution policy of SMILES is as open as possible,
similar to recent other earth observation programs of NASDA and other space agencies.
Distribution of L0 and L1 data might be limited to the groups having agreement with
NASDA, due to huge data volume (and limited SMILES program budget).

5.2

JEM Operation System

Figure 5.1 shows an overview of JEM Operation System and its interface to JEM experiment users. JEM Operation System will be responsible to (1) Mission Data Interface,(2) Data Management, (3) Operation Planning, (4) Commanding, and (5) House Keeping
Telemetry Data Checking. These functions and its relation to JEM users, such as SMILES
team, will be specified in the “Mission Operation Interface Specification (MOIS)” document.
JEM Operation System at Tsukuba Space Center (TKSC) will receive downlinked
data and archive all raw data. JEM Operation System will handle mission data interface
123

Table 5.1 SMILES data sets
Data Type

Description

Potential Users

Raw

Unedited Binary Packets

inhouseL0

edited engineering data file

instrument team

L1

calibrated physical data file
spectral brightness temperature (K)retrieval

L2

vertical profile of species
ppmv or cm−3

validation

L3

grid data of species
ppmv or cm−3

generalVolume/year
120 kbps
1.24 GB/day
317 GB/year
20 MB/file
1.24 GB/day
317 B/year
2.48 GB/day
634 GB/year
2.82 MB/day/species
56.4 MB/day
14.1 GB/year
2-3 MB/day/species
40-60 MB/day
14-20 GB/year

DistributionNAtape etc.
tape etc.
internet
DVD/ROM
internet
DVD/ROM

to SMILES team. SMILES house keeping telemetry data and command plan by SMILES
team will be checked in detail by JEM Experiments Operation Support Client and/or user
system connected to the External User Interface.

Color Printer
CD-ROM
4mm DAT
Floppy Disk

JEM
Operation
SystemJEM
Experiments
Operation
Support Client

JEM Operation
Supporting
Gateway
User NW
100Base-TX• Mission Data IF• Data Management• Operation Planning• Command• Telemetry Check

RS232CStandard
Time Server

Centronix
Parallel IF

External User Interface

User GSE

ISDN
Dial-upUser GSE

User GSE

Figure 5.1 JEM Operation System and its relation to JEM Experiment users.

5.2.1

Operation Planning

JEM Operation System will arrange an operation plan of JEM and JEM experiments
including SMILES, considering (1) request from experiments, (2) resource limitation, such124

as power, downlink capacity, zero gravity condition, astronauts man power, etc, and (3)limitation from space station. Outline of this operation plan will be pre-determined before
the launch of SMILES in the MOIS and the “Mission Operation Implementation Plan
(MOIP)” document. But, details of operation will be revised monthly, weekly, and daily
basis, since the operation of SMILES, JEM, and ISS will be interfered by reasons such as
docking of the Space Shuttle. Figure 5.2 shows a concept of the operation planning for
JEM.

BeforeLaunchLaunchMission Operation Interface Specification&
Mission Operation Implementation Plan
Basic Operation Plan
8 weeks OPLN
8 daysOPLN
After Mission
Operation
Coordination

1
day

Figure 5.2 Concept of operation planning for JEM.

5.2.2

Commanding

JEM Operation System will send command sequence to JEM experiments based upon thedecided JEM operation. This command sequence should be checked carefully everytime,
whether it is not violating commanding rules for all other experiments on JEM, and ISS.
Details of commanding will be described in the “Spacecraft Orbital Operation Handbook(SOOH)”. The SOOH covers background of sensor design in addition to the details ofcommanding rules, housekeeping data and trouble shooting.
5.2.3

House Keeping Telemetry Data Checking

JEM Operation System will receive, archive and real-time check house keeping (HK)telemetry data, and analyze their trend. JEM Operation System will not check instrument information contained within SMILES mission data. SMILES will have a limited
autonomous function, such as system abortion at cooler failure. Other anomalies will behandled by the operator at JEM Operation System based upon the SMILES SOOH.

125

5.3

Functions and Data Flow between ISS and SMILES ground datasystem

Fig 5.3 shows overall data flow of JEM/SMILES mission. Data acquired by SMILES
will be multiplexed with data of other experiments of JEM. The multiplexed data will
be downlinked from International Space Station (ISS) through geostationary data relay
satellites to ground receiving stations. The multiplexed downlinked data are processed at
JEM Operation Center at NASDA/TKSC, to RAW data of each JEM experiments. The
SMILES RAW data will be provided to SMILES Ground Data System. Data transmission
through these data relay system should be carried out without data loss against continuous
operation of SMILES.

Operational
International Space Station (ISS)
Multiplexed RAW Data

DRTS

BackupMultiplexed RAW Data

US Data Relay Satellite

TDRS

JEM/SMILES

Multiplexed
RAW Data

Multiplexed
RAW Data

120 kbps, 1.24 GB/day
317 GB/year
70 % Duty

NASDA
TKSC

Multiplexed
RAW Data
JEM Ground
Operation
SystemSMILES RAW Data
HK Data
SMILESGround System
at TKSC

Datalink
Dedicated for ISS

L2

EORC
CRL

NASA
JSC

(L1, L2) L3
L3

User
Community

Univ. Bremen

Figure 5.3 SMILES data flow.

5.3.1

JEM/SMILES to Receiving Stations

SMILES RAW data consists of HK data (House Keeping data, 16 kbps) and SMILES
Mission Data (about 120 kbps). SMILES Mission Data (and HK data) will use CCSDS
ver 1 packet format. The SMILES RAW data will be multiplexed with data of other
instruments/experiments of JEM. The multiplexed JEM data will be downlinked immediately when data relay satellites are available. When data relay satellites are not available,
the multiplexed data are stored in JEM common data storage, which can store data up
to 60 minutes. Two data relay satellites are planned for downlink of JEM, primarily
Japanese DRTS will be utilized, and US’s TDRS will be utilized as an option. The DRTS
126

will then downlink to NASDA/TKSC in Tsukuba/Japan, and the TDRS will downlink toNASA/JSC in Houston/USA.
5.3.2

Receiving Stations to JEM Operation System at NASDA/TKSC

The downlinked data at JSC will be transferred to JEM Operation System through adata communication line dedicated to the JEM operation. All downlinked data will becollected and archived at JEM Operation System. Archiving of SMILES mission data (120kbps) in JEM/OS will be tentative, and SMILES ground segment will have responsibilityof archiving of SMILES data.
5.3.3

JEM Operation Center to SMILES Ground Data System

JEM RAW data collected at JEM Operation Center are processed and edited to RAW
data of each JEM experiments. Following description on data handshake between the
JEM Operation System and the SMILES Ground Data System (see Figure 5.4) are still
TBD.
TBD number of CCSDS packets of SMILES RAW data are gathered to make a SMILES
RAW data file in JEM Operation Center. The SMILES RAW data file is edited as first in
and first out (FIFO) rule at JEM Operation Center, and there will be no further editingof CCSDS packets, such as sorting packets for observation time and deleting duplexed
packets. The SMILES RAW data files will be stored as computer data files in a data
distribution system of JEM Operation Center.
JEM Operation Center will send a data ready signal as an e-mail to SMILES Ground
Data System and SMILES RAW data files are ready for data transaction. SMILES Ground
Data System will make an ftp transaction to acquire a SMILES RAW data file fromthe data distribution system of JEM Operation Center. After successful ftp transaction,
SMILES Ground Data System will send a data acquisition signal as an e-mail to JEM
Operation Center. SMILES Ground Data System will check the acquired SMILES RAW
data file, whether it was generated and transferred properly. SMILES Ground Data System
will send a data certification signal as an e-mail to JEM Operation Center. JEM Operation
Center will delete the JEM RAW data file in the data distribution system, after TBD daysof the receiving of data certification signal.
Time LineJEM Operation System
Data
Ready
SignalFileTransfer
by FTP

File
Transfer
Complete
SignalSMILES Ground Data System

Figure 5.4 Data handshaking between JEM Operation System and SMILES Ground
System.

127

5.4

Functions of SMILES Ground Data System

SMILES Ground Data System will be established at NASDA/TKSC (TBD). Currently
three groups, NASDA/TKSC SMILES Mission Team, CRL, and NASDA/EORC are responsible for the development of SMILES Ground Data System. By considering physicalcontact with JEM Operation System, SMILES Ground Data System should be establishedat NASDA/TKSC. SMILES Ground Data System will have following functions: (1) operation planning, (2) commanding, (3) House Keeping telemetry data watching, (4) dataprocessing, archiving, and distribution and (5) observation prediction and planning (forvalidation experiments).
Operation Planning and Commanding
Operation of SMILES will be categorized into the following three, (1) Initial check-out period operation, (2) Instrument Performance Check operation and (3) Normal Observation
operation. Operation plan will be decided by SMILES mission team to fulfill the mission
success criteria of SMILES program. Operation plans are examined by SMILES mission
team members and possibly by SMILES operation simulator program (TBD). Commandsequences are then decided and send to JEM Operation System, but they might be revised or interfered by conditions of SMILES, JEM, ISS and ground segment, until actual
real-time operation of SMILES.
House Keeping telemetry data watching
SMILES Ground Data System will have full house keeping capability of SMILES (, JEM
and ISS, if necessary). JEM Operation System will watch only alert condition of SMILES.
JEM Ground Data System will make caution signal when any status of HK and/or mission
data of SMILES reach caution level or show abnormal trend. SMILES Ground Data
System will analyze statistical feature and trend of SMILES instrument status.
Data processing, archiving, and distribution• SMILES RAW Data Processing
SMILES RAW data files will be acquired and processed by SMILES RAW Data Processor
within SMILES TKSC Data System. SMILES RAW Data Processor will have a dedicatedand isolated ethernet connection with JEM Operation Center. SMILES RAW Data Processor will make data transactions with JEM Operation Center only through this ethernet
connection. SMILES RAW Data Processor temporarily stores SMILES RAW data files
until TBD days after successful Level 0 (L0) data production.
If RAW Data transaction and other RAW data processing are not successful, SMILESRAW Data Processor will alarm to some of SMILES mission team members through email and/or SMILES Mission Management Operation System. All log of transactions
and other events of SMILES RAW Data Processor will be stored in one (or multiple)
text format file(s). The log must contain at least following information; time-date of log
creation, name of RAW data file, time-date of RAW data file creation, beginning and endof time-date of receiving within a RAW data file, and a status flag. The log file(s) willconsist of text lines separated by a delimiter of Operating System, and a full set of recordsof transaction must have been written in a single text line, not in multiple text lines. The
records in one text line must be organized, as any information column can be a key ofsorting and/or filtering of commands of Operating System.
• L0 Data Production Processing
The RAW data in the CCSDS packet format will be depacketed and converted into theL0 data arranged in a time series after quality estimation. Continuity and flawlessness
will be checked and information for data loss will be recorded. There may also be overlaps
to be removed, which can be created when the data transfer via satellite is suspended.
The data obtained during a quarter of an orbit operation will be edited into one file. The

128

content of L0 data is the SMILES observation and the instrument HK data in addition to
information about time and date.
• L1 Data Production Processing
The L0 data will be converted into engineering values and processed into calibrated limb
spectral radiances by using the calibration data. Anomalous data will be checked andmarked before processing. The ancillary data such as the tangent altitude and the positionof the observation will be created and added as a part of L1 data products.
• L2 Data Production Processing
The height profiles of geophysical quantities such as composition and temperature andtheir errors are retrieved from the SMILES L1 data using the optimal estimation method.
The data volume is estimated to be 2.82 MB/day/species. The L2 data processing will be
performed in NADA/EORC, CRL and University of Bremen, independently.
• L3 Data Production Processing
The gridded daily, weekly and monthly maps for all species will be produced in CRL,
NASDA/EORC and University of Bremen, respectively. The SMILES data will be archived
and distributed in L1, L2 and L3 format though the distribution of L0 and L1 data might
be limited to those having agreement with NASDA or CRL due to huge data volume.
There remains a security issue, but users can access SMILES L2 and L3 data products
created at CRL, NASDA/EORC and University of Bremen through internet. The datastored in DVD/ROMs will also be distributed to users.
Observation prediction and planning
Since the SMILES data products must be verified through validation activities, timely andaccurate announcement of near future SMILES observation is crucial for good coincidenceof validation measurements. Orbit characteristics, especially attitude of ISS, will change
and fluctuate even few days after the observation prediction. Thus, observation prediction
must be updated frequently, perhaps daily.

5.5

Consideration on Computation in the JEM/SMILES L2 Data Processing

The task of the SMILES L2 data processing algorithm is to produce the geophysicalparameters such as concentration profiles of targeted gases, temperature and pressure frommeasured radiance. As described in the previous chapter, the retrieval method adopted
in the SMILES data simulator is Optimal Estimation Method (OEM) [Rodgers, 1976].
We believe this retrieval strategy for SMILES is similar to the past and the near future
millimeter and sub-millimeter limb-sounding programs [Levesey and Wu, 1999], and itis, thus, well established already. In the following, we briefly discuss the performancerequirements for the SMILES L2 data processing.
The computational efficiency is determined by both the number of matrix operations
and the numerical cost for updating an expected measurement and a weighting function.
In the forward model, an efficient method for calculating the Voigt function is required.
For this purpose we use Hui ’s rational approximation [Hui et al., 1978 ][Suzuki, 1994]. The
test result of forward model calculation (brightness temperature) for 61 rays is currently
77 sec (10 to 60 km, 1 km tangent height interval, using 400 MHz Pentium-III). It looks
easy to improve computation speed to achieve requirements for operational condition, suchas better than 10 sec, by using faster CPU. If these methods for fast computation of Voigt
function are not acceptable in speed, table interpolation using such B-spline method inpressure and temperature will be applied as similar to the systems of ADEOS/ILAS or
ADEOS/IMG.
Let us turn our attention to the matrix operations. Let the size of the state vector be129n ∼ 150 which may be typical for the simplest retrieval including the baseline parameters.
The measurement vector is a set of observed radiance at scanned tangent altitudes. Since
the SMILES limb scan will be done by taking spectral data from 10 to 60 km in the intervalof ∼ 2 km, the size of the measurement vector m becomes 26 times the number of selected
frequency data from AOS’s 1500 channels for a 1.2 GHz band. If 30 ∼ 1500 frequency
data are used in retrieval, the dimension of the weighting function matrix becomes (7.8 ×
102 ) × 150 ∼ (3.9 × 104 ) × 150. As is easily seen from (4.14) in Chapter 4, the number offloating point operations per iteration in the case of m > n is estimated as ∼ (mn2 + n3 ),where it is assumed that the error covariance matrix is diagonal and S−1
a is prepared in
3 comes from theadvance. Note that mn2 corresponds to the operation KT S−1
K
while
n

inversion of the Hessian. Then the number of operations needed to perform a retrieval
turns out to be 2.1 × 107 − 8.8 × 108 , which corresponds to execution time, 0.02 − 0.88 sec,
on a 1 Gflops/sec computer in an ideal situation. If a couple of species or more have to besolved together, the execution time increases mainly according to ∼ mn2 . For example,
for an extreme case n = 1000 and m = 1500, the execution time becomes 40 sec times thenumber of iteration and the weighting function occupies 0.3 GB. Nevertheless OEM still
seems to work as an efficient retrieval algorithm.
Table 5.2 Estimated performance requirements (GFlops and memory size) for SMILES
Data System compared to those of EOS/MLS program.

L0 Processing

L1 Processing

L2 Processing

JEM/SMILESDesktop WS Performance
8 GB
Disk and network I/O limited
Desktop WS Performance
8 GB
Disk and network I/O limited
4 GFlops (peak performance)
20 GFlops (with a margin for reprocessing)> 32 GB
Computation limited

EOS/MLS
Desktop WS Performance
8 GB
Disk and network I/O limited
Desktop WS Performance
8 GB
Disk and network I/O limited
1 GFlops (true performance)> 16 GB
Computation limited

The detail design of SMILES Data Processing is not specified yet. As described above,the most computing intensive part of SMILES retrieval will require 1 GFlops, ’net’ performance. Actual data processing will require 5-6 times performance for re-processing
after version ups etc. One GFlops net performance should be measured by Linpack 100
benchmark since the nature of SMILES retrieval scheme. One GFlops net performanceis usually equal to 3-4 GFlops peak performance. See Table 5.2. Overall computation
requirement is very feasible even from today’s standard, and it will not be expensive at
the time of SMILES operation. Data archiving and distribution look quite feasible, since
the data volume of SMILES is moderate. Data retrieval scheme for the sub-millimeter
limb sounder has been already established for other space programs, and thus we see nodifficulty on development of SMILES data retrieval system.

References
Hui, A. K., B. H. Armstrong, and A. A. Wray, J. Quant. Spectrosc. Radia. Transfer, 19, 509, 1978.
Levesey N. J., and D. L. Wu, EOS MLS Retrieval Processes Algorithm Theoretical Basis, Jet

130Propulsion Laboratory Document D-16159, Version 1.1, 15 October 1999.
Rodgers, C. D., Retrieval of atmospheric temperature and composition from remote measurementsof thermal radiation, Rev. Geophys. and Space. Phys., 14, 609-624, 1976.
Suzuki, M., Comparison of the fast calculation codes for Voigt function, CGER’S Supercomputer
Activity Report 1992, 71-75, 1994.

131

6

Validation Plan

6.1

Introduction

It is well known that satellite borne observation is very useful for getting information in
global scale. The data products derived from these measurements might have literally thesame quality over the globe because they are obtained with one, the same, instrument. But
also well known is the importance of continuous validation of the satellite borne instrument
for maintaining the quality of data through the mission lifetime.
The validation of JEM/SMILES data products should be strictly controlled following
the standardized handling procedures. These procedures will be discussed and determinedin their details by scientists in JEM/SMILES validation team, which is not yet officiallyestablished, and then will be approved later by the members of science team. Anotherimportant point of validation is the responsibility for each data product. A scientist on
the science team who knows well of its scientific use and meanings must be responsible forthe validation of each data product.
As the standardized procedure is not established, the concepts of JEM/SMILES validation as well as those of the correlative measurements are described in this section.

6.2

The Basic Concepts of Validation for JEM/SMILES Data Products

The following items should be included in the standardized procedures:
1. Sensitivity studies including the simulation study to determine the expected precision
and accuracy of each data product2. Inspection of all incoming data, and checking the reasonableness and unexpectedphenomena through the examination of routine analyses products
3. Close examination of the characteristics of ‘radiance residuals’ as functions of many
parameters for checking the artifacts
4. Comparison of the same parameter measured in more than one spectral region as
possible as we can
5. Comparison with other correlative measurements from ground-based, aircraft, andballoon measurements
6. Comparison of JEM/SMILES data with data obtained by other space-borne instruments such as Aura(formerly EOS-Chem)/MLS
Each item is essential to maintain the quality of data product and will need a responsible
scientist as well.

6.3
6.3.1

Correlative Measurement
The Needs of Correlative Measurements

The correlative measurements are inevitable to validate the JEM/SMILES data products.
JEM/SMILES is capable of measuring many atmospheric species, exact identificationsof whose species are dependent on a final specification of the instrument, though. At
present, standard products for JEM/SMILES are not finally determined. Then the needs ofcorrelative measurement are listed below for only major targeted (probable) data products,
respectively.
132

• O3
Total and vertical profiles of O3 are needed. Correlative measurements such as aircraft and balloon measurements of the vertical profile coincident with JEM/SMILES
observation are very valuable.
• ClO
Ground-based measurements of vertical profiles are needed at a various site using themillimeter-wave radiometer. Coincident measurements with JEM/SMILES should
be organized. Aircraft measurements, coincident with JEM/SMILES observations
are also important. Balloon-borne measurement is very useful to compare the vertical profile. Satellite-borne measurements will be comparable with JEM/SMILES
product. at the JEM/SMILES era.
• HNO3 , HCl, HO2
Balloon measurements of the vertical profile with IR spectrometers are needed, especially coincident with JEM/SMILES observations.
• BrO
Ground-based measurements of total column are needed. Aircraft measurements,
coincident with JEM/SMILES observations, in situ, and column amount measurements are particularly valuable. Balloon-borne measurement is useful to compare
the vertical profile. Satellite instruments will also be usable for validation purposeat the JEM/SMILES era.
• H2 O2
Any kind of measurement of atmospheric H2 O2 is valuable.
The followings are not for data products of JEM/SMILES, but these data will also be
valuable for improving the quality of data products.
1. More accurate spectral parameters for target species and H2 O in frequency region of
600 - 700 GHz: Linewidth, center frequency, line strength, air broadening coefficients,
etc.
2. Atmospheric water vapor profiles in the upper troposphere and in the stratosphere
3. Temperature and pressure profiles along the JEM/SMILES observation
6.3.2

Plan of Correlative Measurements

The use of existing data (measurement network), conducting a field validation campaign,and setting a new ground-based instrument to an observation site should be considered in
the planning of validation.

6.3.2.1

The Use of Existing Data

As for O3 , WMO-GAW ozonesonde stations and ground-based Dobson stations routinely
make measurements of vertical profile and total column O3 . These data are available via
WMO. The climatology of ozone distribution field can also be constructed based uponthese data.
There is no measurement site (network) that provides the profile of other species thanO3 routinely without any special contract. However, NDSC primary and complementary133

sites can provide very accurate data of some species, their total and vertical profiles including ClO. Close collaboration with NDSC sites is very valuable for getting accurate
validation data.

6.3.2.2

Field Campaign

Even for the O3 , it will be hard to get coincident data with JEM/SMILES observationfrom routine observation. Therefore several validation (field) campaigns will be planned
during the JEM/SMILES mission.
CAMPAIGN Site
In order to select the campaign site we must consider the geographical coverage of
JEM/SMILES observation and the availability of balloon launching facility. In north polar region one of the candidate site will be Fairbanks in Alaska or Lynn Lake in Canada.
In Fairbanks there exist ground-based instruments: Millimeter wave radiometer, FTIR,and lidar at Poker Flat (http://www.crl.go.jp /team5/index.html). These instruments
are maintained with both CRL and Geophysical Institute of University of Alaska Fairbanks. So ground-based and balloon measurements can both be carried out coincident
with JEM/SMILES observation. But the geographical position of Fairbanks, about 65◦ N,
is located nearly the northern limit of observable region by JEM/SMILES. On the other
hand Lynn Lake is located at around 52◦ N, where balloon observations were carried out
by NSBF, the balloon launching team of the USA. Anyway collaboration with USA or
USA/Canada must be necessary.
In mid-latitude regions several balloon observation sites will be found as a candidate
site such as France, Japan, United States of America, and Australia. All of these countries
have at least one balloon observation site within their home countries. The validation campaign in tropical region is also important and valuable. Christmas Island, where NASDA
operates a tracking station, is one of the candidate site as well as Watukosek, Indonesia,
where NASDA and LAPAN of Indonesia are conducting the ozonesonde measurement andsome ground-based observations.
Balloon borne Instrument
The candidate instruments for balloon observations will be a submillimeter radiometer, FTIR, and UV-Visible spectrometer. Measurements by more than one instrument
will be valuable for validation purpose. So international collaborations must be made.
Daily ozonesonde measurements at campaign site during the campaign period will be theminimum requirement.
Ground-based Instrument
Millimeter radiometer, FTIR, and UV-Visible spectrometer can provide good validation data, total column amounts and profiles for some species. In Poker Flat, Alaska CRL
operates, with GI/UAF, some ground-based instruments usable for validation purpose.
This site is important for supplying validation data continuously. As mentioned before,close collaboration with NDSC will also play a key role to achieve a good validation result.
Also important point is that NASDA should operate at least one ground-based instrumentby itself for checking the quality of JEM/SMILES data constantly.
Candidate period of field campaign
In high latitude summer is good season for validation considering the stability of at-134

mospheric condition. However the enhancement of ClO occurs in winter to spring season.
Ozone depletion event will also be expected in winter season. As for the validation purpose, validation campaign in high latitude will be recommended to be conducted both in
winter and summer seasons. In middle and low latitudes, exception of special ozonesonde
campaign, one field campaign will be planned at anytime in a year.
6.3.3
6.3.3.1

Activities in JEM/SMILES Team for Validation
Development of a Balloon Borne Submillimeter Spectrometer

JEM/SMILES is one of the most advanced instrument, whose signal to noise ratio is thebest among other submillimeter spectrometers of not only space borne but also groundor air- borne ones. Therefore JEM/SMILES will show the best performance to measuresome species such as ClO and BrO. This means that there is no good instrument atpresent to provide validation data for these species. CRL team is developing a balloon
borne submillimeter spectrometer of using super conductor (SIS mixer) as a detector. Thisballoon borne spectrometer will demonstrate the same performance as JEM/SMILES andwill be used for validation purpose for JEM/SMILES. This project of developing theballoon borne SMILES (BSMILES) will play an important role in conducting correlative
measurements, because vertical profiles of ClO and other species can be measured by
BSMILES with the same or better accuracy than JEM/SMILES. Key components of the
BSMILES system were constructed. Performances of those are checked, and good results
have been demonstrated as expected. The BSMILES is in integration phase and will
be scheduled to complete its development by the end of 2002. The first balloon borne
measurement of BSMILES will be carried out in 2003 from SBC (Sanriku Balloon Centerof ISAS, Japan).

6.3.3.2

Laboratory Measurement of Spectroscopic Parameters

As mentioned above, spectroscopic parameters for many atmospheric species are not accurately determined or measured in the frequency range of around 600GHz. In particularan air pressure broadening parameter is one of key parameters to derive the vertical profiles of atmospheric species from limb sounding. CRL and Ibaraki University are jointly
planning the laboratory measurements of spectroscopic parameters for some species. They
already measured some parameters as described in section 4.4. This project of laboratory
measurement will also be one of key projects in validation plan for the success of deriving
good data products from JEM/SMILES observation.

6.3.3.3

Use of Observation Sites in Japan

Within Japan there exist several good observation sites maintained by University andNational Institute. So not only the international collaborations but also the collaborations within Japan are also very important. NIES’s group operates microwave radiometer,
FTIR, and lidar at Tsukuba, Ibaraki and Rikubetu, Hokkaido. STEL of Nagoya University also maintains FTIR , UV-Visible spectrometer and ozonsonde stations at Rikubetu and Moshiri, Hokkaido. These observation sites are very suitable for validation of
JEM/SMILES observation. During the mission lifetime of JEM/SMILES these sites are
considered as main sites for steadily providing validation data.

135

6.4

Summary

Validation campaign as well as the use of existing database for O3 will become the key
for validating JEM/SMILES data. In order to get coincident data with JEM/SMILES
observation, international collaborations play the key role; for example, close collaboration
with NDSC. The use of observation sites in Japan maintained by the National Instituteand the University will also be inevitable for getting validation data constantly. Evenfor getting good validation data of O3 , a kind of special ozonesonde campaign must beplanned.
Ground-based instruments of CRL at Poker Flat, Alaska will provide good validation
data constantly. NASDA/EORC will operate at least one ground-based instrument forchecking the performance of JEM/SMILES. Balloon observation is also essential to getvalidation data. Close collaboration with foreign research bodies as well as with Japanese
researchers must be key point to succeed the balloon campaign.
Cross validation with other space-borne instrument such as Aura/MLS would be useful.

136

Appendix: Estimation of Retrieval Errors for SMILES Major
SpeciesIn this Appendix, the results of error estimations are shown in detail for all the 9 molecularspecies to be observed by SMILES, for ozone isotopes, and for ozone, H35 Cl, and HO2 in themesosphere, using standard atmospheric profiles in the polar, mid-latitude, and equatorial
regions.

A.1 Radical Observation
Simulations are set up as follows. The spectroscopic line parameters, such as line positionand line strength, are taken from the JPL catalog, and pressure broadening parameters
are given by the HITRAN catalog. The height profiles of molecular volume mixing ratios,
temperature, and pressure used in the simulations are the standard profiles for the midlatitude, equatorial, and polar regions given by the Rutherford Appleton Laboratory.
For the instrumental parameters, representative values of the SMILES instrument
are used. The retrieval error in the volume mixing ratio, the error ratio defined as theratio of the retrieval error to the a priori error, and the averaging kernels are calculated
for each molecules in the three SMILES observation bands. The frequency grid for theforward model is 0.8 MHz, and no frequency binning is applied for the retrieval. The
molecules considered in the forward model and retrieval are normal O3 , ozone isotopes,
vibrationally-excited O3 , HCl, ClO, HOCl, H2 O2 , HO2 , HNO3 , BrO, and CH3 CN, as wellas H2 O, N2 , and O2 . The a priori profiles are assumed to coincide with the true profiles
for all the molecular species except ozone isotopes. The a priori standard deviations
are assumed to be 100 percent of the a priori profiles for the research products and 50
percent for the standard products. The measurement√ error covariance matrix S is given
from the measurement noise, σ = (Tsys + Tatm )/ Bτ , where Tsys is the receiver system
noise temperature assumed to be 500 K, and Tatm is the antenna temperature from thespectrum, B is the equivalent noise bandwidth assumed to be 0.8 MHz, τ is the integration
time. The integration time τ is assumed to be 0.5 s for each tangent height for single-scandata, and is assumed to be 15 s for data averaged over 30 scans, which corresponds to a(day-time) half-daily zonal mean for mid-latitude regions. The limb scan step is 2 km fortangent altitudes from 8 to 60 km. The retrieval height grid is set to be 2 km and 4 km
for the standard products and the research products, respectively, in the 8-60 km region.
The results are shown in the figures shown below. The single scan profiles are shown forthe standard products, and the 30-scan averaged profiles, which consist of the 5◦ -averaged
zonal mean profiles, are shown for the research products.

137

O3-normal-bandA-0n-1s

O3-normal-bandA-45n-1s

60

60
last
error
error
a priori=true

60
last
error
error
a priori=true

40
30
20

40
30
20

10
2

4
6
8
Volume Mixing Ratio [ppm]

10

2

0

10
0.0

0.2 0.4 0.6
Averaging Kernel

0.8

30

-0.2

0.0

O3-normal-bandA-0n-1s

0.2 0.4 0.6
Averaging Kernel

0.8

30

10
1.0

-0.2

0.0

O3-normal-bandA-45n-1s

50Altitude[km]Altitude[km]

20

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

single profile

50

30

0.8

O3-normal-bandA-80n-1s

single profile40

0.2 0.4 0.6
Averaging Kernel

60

single profile

0.4
0.6
Error Ratio [ret./apr.]

40

20

60

50

8

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]

40

10
1.0

4
6
Volume Mixing Ratio [ppm]

60

20

60

0.2

2

O3-normal-bandA-80n-1s
8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]Altitude[km]

20

0.0

10

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

30

-0.2

4
6
8
Volume Mixing Ratio [ppm]O3-normal-bandA-45n-1s

60

40

30

10
0

O3-normal-bandA-0n-1s

50

40

20

10
0

last
error
error
a priori=true

50
Altitude[km]50Altitude[km]Altitude[km]50Altitude[km]O3-normal-bandA-80n-1s

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.1 Retrieval simulation results for single-scan data in BAND-A for standard
profiles of normal O3 at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori errors
which are assumed to be 50 % of the a priori volume mixing ratios.
138

H37Cl-bandA-0n-1s

H37Cl-bandA-45n-1s

60

60
last
error
error
a priori=true

60
last
error
error
a priori=true

40
30
20

40
30
20

10
0.2
0.4
0.6
Volume Mixing Ratio [ppb]

0.8

0.2
0.4
0.6
Volume Mixing Ratio [ppb]

100.0

0.2 0.4 0.6
Averaging Kernel

0.8

30

-0.2

0.0

H37Cl-bandA-0n-1s

0.2 0.4 0.6
Averaging Kernel

0.8

30

10
1.0

-0.2

0.0

H37Cl-bandA-45n-1s

50Altitude[km]Altitude[km]

20

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

single profile

50

30

0.8

H37Cl-bandA-80n-1s

single profile40

0.2 0.4 0.6
Averaging Kernel

60

single profile

0.4
0.6
Error Ratio [ret./apr.]

40

20

60

50

1.2

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]

40

10
1.0

0.4 0.6 0.8 1.0
Volume Mixing Ratio [ppb]

60

20

60

0.2

0.2

H37Cl-bandA-80n-1s
8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]Altitude[km]20

Altitude[km]

0.0

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

30

0.0

0.8

H37Cl-bandA-45n-1s

60

40

30

10

0.0

H37Cl-bandA-0n-1s

50

40

20

10

0.0

last
error
error
a priori=true

50
Altitude[km]50Altitude[km]Altitude[km]

50

-0.2

H37Cl-bandA-80n-1s

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.2 Retrieval simulation results for single-scan data in BAND-A for standard
profiles of H37 Cl at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top: Retrieved
volume mixing ratios are shown with rms retrieval error bounds and a priori profiles which
are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval
error ratios defined as the ratio of the retrieval errors to the a priori errors which areassumed to be 50 % of the a priori volume mixing ratios.
139

HNO3-bandA-0n-1d

HNO3-bandA-45n-1d

60

60
last
error
error
a priori=true

60
last
error
error
a priori=true

40
30
20

40
30
20

10
1

2
3
Volume Mixing Ratio [ppb]

4

1

2

8

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
Volume Mixing Ratio [ppb]HNO3-bandA-80n-1d

100.0

0.2 0.4 0.6
Averaging Kernel

0.8

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

40
30
20
10

1.0

-0.2

0.0

HNO3-bandA-0n-1d

0.2 0.4 0.6
Averaging Kernel

0.8

30
20
10

1.0

-0.2

0.0

daily zonal mean

20

50
Altitude[km]Altitude[km]

30

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

daily zonal mean

50

40

0.8

60

daily zonal mean50

0.2 0.4 0.6
Averaging Kernel
HNO3-bandA-80n-1d

60

0.4
0.6
Error Ratio [ret./apr.]

40

HNO3-bandA-45n-1d

60

0.2

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]50Altitude[km]Altitude[km]

20

0.0

7

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

30

-0.2

3 4 5 6
Volume Mixing Ratio [ppb]HNO3-bandA-45n-1d

60

40

30

10
0

HNO3-bandA-0n-1d

50

40

20

10
0

last
error
error
a priori=true

50
Altitude[km]50Altitude[km]Altitude[km]50Altitude[km]HNO3-bandA-80n-1d

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.3 Retrieval simulation results for 30-scan averaged data in BAND-A for standard profiles of HNO3 at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori errors
which are assumed to be 50 % of the a priori volume mixing ratios.
140

HOCl-bandA-0n-1d

HOCl-bandA-45n-1d

60

60
last
error
error
a priori=true

60
last
error
error
a priori=true

40
30
20

40
30
20

10
0.1
0.2
0.3
Volume Mixing Ratio [ppb]

0.4

0.1
0.2
0.3
Volume Mixing Ratio [ppb]

100.0

0.2 0.4 0.6
Averaging Kernel

0.8

30

-0.2

0.0

HOCl-bandA-0n-1d

0.2 0.4 0.6
Averaging Kernel

0.8

40
30
20
10

1.0

-0.2

0.0

HOCl-bandA-45n-1d
daily zonal mean

20

50
Altitude[km]Altitude[km]

30

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

daily zonal mean

50

40

0.8

60

daily zonal mean50

0.2 0.4 0.6
Averaging Kernel
HOCl-bandA-80n-1d

60

0.4
0.6
Error Ratio [ret./apr.]

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]

40

10
1.0

0.4

60

20

60

0.2

0.1
0.2
0.3
Volume Mixing Ratio [ppb]HOCl-bandA-80n-1d

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]Altitude[km]20

Altitude[km]

0.0

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

30

0.0

0.4

HOCl-bandA-45n-1d

60

40

30

10

0.0

HOCl-bandA-0n-1d

50

40

20

10

0.0

last
error
error
a priori=true

50
Altitude[km]50Altitude[km]Altitude[km]

50

-0.2

HOCl-bandA-80n-1d

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.4 Retrieval simulation results for 30-scan averaged data in BAND-A for standard profiles of HOCl at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori errors
which are assumed to be 100 % of the a priori volume mixing ratios.
141

H2O2-bandA-0n-1d

H2O2-bandA-45n-1d

60

60
last
error
error
a priori=true

60
last
error
error
a priori=true

40
30
20

40
30
20

10
0.4
0.6
0.8
Volume Mixing Ratio [ppb]

1.0

0.0

10
0.0

0.2 0.4 0.6
Averaging Kernel

0.8

30

10
1.0

-0.2

0.0

0.2 0.4 0.6
Averaging Kernel

0.8

30

10
1.0

-0.2

0.0

Altitude[km]Altitude[km]

50

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

daily zonal mean

50

20

0.8

H2O2-bandA-80n-1d

daily zonal mean30

0.2 0.4 0.6
Averaging Kernel

60

daily zonal mean

0.4
0.6
Error Ratio [ret./apr.]

40

H2O2-bandA-45n-1d

40

0.5

8
12
16
20
24
28
32
36
40
44
48
52
56
60

20

60

50

0.2
0.3
0.4
Volume Mixing Ratio [ppb]50
Altitude[km]40

H2O2-bandA-0n-1d

0.2

0.1

60

20

60

Altitude[km]0.0H2O2-bandA-80n-1d
8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]Altitude[km]

20

0.0

0.5

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

30

-0.2

0.2
0.3
0.4
Volume Mixing Ratio [ppb]
H2O2-bandA-45n-1d

60

40

30

10
0.1

H2O2-bandA-0n-1d

50

40

20

10
0.2

last
error
error
a priori=true

50
Altitude[km]50Altitude[km]Altitude[km]

50

0.0

H2O2-bandA-80n-1d

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.5 Retrieval simulation results for 30-scan averaged data in BAND-A for standard profiles of H2 O2 at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori errors
which are assumed to be 100 % of the a priori volume mixing ratios.
142

CH3CN-bandA-0n-1d
60
last
error
error
a priori=true

Altitude[km]

50
40
30
20
10
0.00

0.05
0.10
Volume Mixing Ratio [ppb]0.15

CH3CN-bandA-0n-1d
60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

Altitude[km]

50
40
30
20
10
-0.2

0.0

0.2 0.4 0.6
Averaging Kernel

0.8

1.0

CH3CN-bandA-0n-1d60daily zonal mean

Altitude[km]

50
40
30
20
10
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.6 Retrieval simulation results for 30-scan averaged data in BAND-A for the
standard profile of CH3 CN at a latitude of 0◦ N. Top: Retrieved volume mixing ratios areshown with rms retrieval error bounds and a priori profiles which are assumed to coincidewith true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as theratio of the retrieval errors to the a priori errors which are assumed to be 100 % of the apriori volume mixing ratios.
143

H35Cl-bandB-0n-1s

H35Cl-bandB-45n-1s

60

60
last
error
error
a priori=true

60
last
error
error
a priori=true

40
30
20

40
30
20

10
1.0
1.5
2.0
Volume Mixing Ratio [ppb]

2.5

0.0

10
0.0

0.2 0.4 0.6
Averaging Kernel

0.8

60

30

10
1.0

-0.2

0.0

H35Cl-bandB-0n-1s

0.2 0.4 0.6
Averaging Kernel

0.8

30

10
1.0

-0.2

0.0

H35Cl-bandB-45n-1s

2050
Altitude[km]Altitude[km]

30

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

single profile

50

40

0.8

H35Cl-bandB-80n-1s

single profile50

0.2 0.4 0.6
Averaging Kernel

60

single profile

0.4
0.6
Error Ratio [ret./apr.]

40

20

60

0.2

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]

40

20

60

Altitude[km]0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Volume Mixing Ratio [ppb]
H35Cl-bandB-80n-1s

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]Altitude[km]

20

0.0

2.5

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

30

-0.2

1.0
1.5
2.0
Volume Mixing Ratio [ppb]
H35Cl-bandB-45n-1s

60

40

30

10
0.5

H35Cl-bandB-0n-1s

50

40

20

10
0.5

last
error
error
a priori=true

50
Altitude[km]50Altitude[km]Altitude[km]

50

0.0

H35Cl-bandB-80n-1s

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.7 Retrieval simulation results for single-scan data in BAND-B for standard
profiles of H35 Cl at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top: Retrieved
volume mixing ratios are shown with rms retrieval error bounds and a priori profiles which
are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval
error ratios defined as the ratio of the retrieval errors to the a priori errors which areassumed to be 50 % of the a priori volume mixing ratios.
144

ClO-bandC-0n-1s

ClO-bandC-45n-1s

60

60
last
error
error
a priori

30
20
10
0.1
0.2
0.3
Volume Mixing Ratio [ppb]

0.4

40
30

10

10

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8
Volume Mixing Ratio [ppb]

0.0 0.2 0.4 0.6 0.8 1.0 1.2 1.4
Volume Mixing Ratio [ppb]ClO-bandC-45n-1s

ClO-bandC-80n-1s

30
20
10
0.0

0.2 0.4 0.6
Averaging Kernel

0.8

40
30
20
10

1.0

-0.2

0.0

ClO-bandC-0n-1s

0.2 0.4 0.6
Averaging Kernel

-0.2

0.0

20

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

50
Altitude[km]Altitude[km]

30

0.8

single profile50

40

0.2 0.4 0.6
Averaging Kernel
ClO-bandC-80n-1s

single profile

50
Altitude[km]

10
1.0

60

single profile

0.4
0.6
Error Ratio [ret./apr.]

30
20

60

0.2

40

ClO-bandC-45n-1s

60

0.0

0.8

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]

40

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]Altitude[km]

50

30
20

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

40

20

ClO-bandC-0n-1s
60

last
error
error
a priori

50
Altitude[km]

40

0.0

60
last
error
error
a priori

50
Altitude[km]Altitude[km]

50

-0.2

ClO-bandC-80n-1s

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.8 Retrieval simulation results for single-scan data in BAND-C for standard
profiles of ClO at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top: Retrieved
volume mixing ratios are shown with rms retrieval error bounds and a priori profiles which
are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval
error ratios defined as the ratio of the retrieval errors to the a priori errors which areassumed to be 50 % of the a priori volume mixing ratios.
145

HO2-bandC-0n-1d

HO2-bandC-45n-1d

60

60
last
error
error
a priori

60
last
error
error
a priori

40
30
20

40
30
20

10
0.5
1.0
1.5
Volume Mixing Ratio [ppb]

2.0

0.0

20
10
0.0

0.2 0.4 0.6
Averaging Kernel

0.8

40
30
20
10

1.0

-0.2

0.0

0.2 0.4 0.6
Averaging Kernel

0.8

30
20
10

1.0

-0.2

0.0

Altitude[km]Altitude[km]

50

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

daily zonal mean

50

20

0.8

HO2-bandC-80n-1d

daily zonal mean30

0.2 0.4 0.6
Averaging Kernel

60

daily zonal mean

0.4
0.6
Error Ratio [ret./apr.]40

HO2-bandC-45n-1d

40

0.5

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50

60

50

0.2
0.3
0.4
Volume Mixing Ratio [ppb]60

HO2-bandC-0n-1d

0.2

0.1

HO2-bandC-80n-1d
8
12
16
20
24
28
32
36
40
44
48
52
56
60

50

60

Altitude[km]0.0Altitude[km]

30

0.0

1.0

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

Altitude[km]Altitude[km]

0.4
0.6
0.8
Volume Mixing Ratio [ppb]HO2-bandC-45n-1d

60

40

30

10
0.2

HO2-bandC-0n-1d

50

40

20

10

0.0

last
error
error
a priori

50
Altitude[km]50Altitude[km]Altitude[km]

50

-0.2

HO2-bandC-80n-1d

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.9 Retrieval simulation results for 30-scan averaged data in BAND-C for standard profiles of HO2 at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori errors
which are assumed to be 100 % of the a priori volume mixing ratios.
146

BrO-bandC-0n-1d

BrO-bandC-45n-1d

60

60
last
error
error
a priori

60
last
error
error
a priori

40
30
20

40
30
20

10
2

4
6
8
Volume Mixing Ratio [ppt]

10

12

2

4

BrO-bandC-80n-1d

100.0

0.2 0.4 0.6
Averaging Kernel

0.8

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

40
30
20
10

1.0

-0.2

0.0

BrO-bandC-0n-1d

0.2 0.4 0.6
Averaging Kernel

0.8

30
20
10

1.0

-0.2

0.0

daily zonal mean

20

50
Altitude[km]Altitude[km]

30

40
30
20

10
1.0

0.0

40
30
20

10
0.8

1.0

daily zonal mean

50

40

0.8

60

daily zonal mean50

0.2 0.4 0.6
Averaging Kernel
BrO-bandC-80n-1d

60

0.4
0.6
Error Ratio [ret./apr.]

40

BrO-bandC-45n-1d

60

0.2

8
12
16
20
24
28
32
36
40
44
48
52
56
60

50
Altitude[km]50Altitude[km]Altitude[km]

20

0.0

0 2 4 6 8 10 12 14 16 18 20 22
Volume Mixing Ratio [ppt]

60
8
12
16
20
24
28
32
36
40
44
48
52
56
60

30

-0.2

6 8 10 12 14 16
Volume Mixing Ratio [ppt]BrO-bandC-45n-1d

60

40

30

10
0

BrO-bandC-0n-1d

50

40

20

10
0

last
error
error
a priori

50
Altitude[km]50Altitude[km]Altitude[km]50Altitude[km]BrO-bandC-80n-1d

10
0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.10 Retrieval simulation results for 30-scan averaged data in BAND-C for standard profiles of H35 Cl at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are assumed to coincide with true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori errors
which are assumed to be 100 % of the a priori volume mixing ratios.
147

A.2 Isotope Observations
Simulation setup is the same as that for the radical observations for forward model calculations.
The a priori profiles of ozone isotopes are assumed those derived for the standard midlatitude profile of ozone by applying the atomic-oxygen isotope abundances estimatedfrom the SMOW (Standard Mean Ocean Water) isotope ratio. The true profiles are
assumed to be enriched profiles in the simulation. The 11-percent enriched profiles are
given at 8-60 km. The a priori standard deviations are assumed to be 10 percent of
the corresponding a priori profiles. The measurement√ error covariance matrix S is given
from the measurement noise, σ = (Tsys + Tatm )/ Bτ , where Tsys is the receiver system
noise temperature assumed to be 500 K, and Tatm is the antenna temperature, B is theequivalent noise bandwidth assumed to be 0.8 MHz, τ is the integration time. The limb
scan step is 2 km for tangent altitudes from 8 to 60 km, and the retrieval height grid andthe weighting function step are 5 km. The averaging kernels are plotted every 5 km.
From initial retrieval simulations, it is found that for the here investigated 4 ozone
isotopes excluding normal ozone, single-scan spectra of nominal integration time 0.5 s pertangent height have to be averaged over 30 spectra, which correspond to half-daily (daytime ) zonal means at mid-latitudes from the SMILES observation.
There are 73 transitions of the ozone isotopes in the SMILES observation frequency
bands. As relatively isolated spectra of asymmetric-17 O3 (17 OOO) and asymmetric-18 O3(18 OOO), and symmetric-17 O3 (O17 OO) are found in the Band-C, this upper sideband
is therefore better to observe ozone isotopes. The spectral intensity at each transition
frequency of the asymmetric ozone isotopes is less than 20 K both substituted of 18 O and17 O at the tangent height range 10- 60 km at Band-C. Symmetric-17 O , has transitions
3
in all three Bands. As symmetric-18 O3 (O18 OO) has three transitions with very weakintensity in Band-A and B, it should be difficult to observe and to obtain the enrichment in
the daily zonal means. If the monthly mean profiles are obtained with stable instrumental
conditions, the discussion of the symmetric-18 O3 height profiles will be possible.
The retrievals were performed for the 30-scan averaged data, which correspond to halfdaily (12 hour day time) zonal means. The retrieval results and averaging kernels, absolute
error, and relative error are shown for 4 ozone isotopes including normal ozone.

148

O3-asym-17-bandC-0n-1d

O3-asym-17-bandC-45n-1d
60

last
error
error
a priori
true

45

50

40
35
30

45
40
35
30

40
35
30

25

25

20

20

15

15

15

10

10
1

2

3 4 5 6 7
Volume Mixing Ratio [ppb]

8

9

10
0

1

2 3 4 5
Volume Mixing Ratio [ppb]

6

7

0

45
40
35
30
25

50
45
40
35
30
25

50
45
40
35
30
25

20

20

15

15

15

10
-0.2

10
-0.2

0.2 0.4 0.6
Averaging Kernel

0.8

1.0

0.0

O3-asym-17-bandC-0n-1d

0.2 0.4 0.6
Averaging Kernel

0.8

10
-0.2

1.0

daily zonal mean

daily zonal mean

45

45

45

Altitude[km]50Altitude[km]

50

30

40
35
30

40
35
30

25

25

25

20

20

20

15

15

15

10
0.0

10
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

1.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

daily zonal mean

55

50

35

0.8

60

55

40

0.2 0.4 0.6
Averaging Kernel
O3-asym-17-bandC-80n-1d

60

55

0.0

O3-asym-17-bandC-45n-1d

60

6

10
15
20
25
30
35
40
45
50
55
60

55

20

0.0

5

60
10
15
20
25
30
35
40
45
50
55
60

55

Altitude[km]

50

2
3
4
Volume Mixing Ratio [ppb]O3-asym-17-bandC-80n-1d

60
10
15
20
25
30
35
40
45
50
55
60

55

1

O3-asym-17-bandC-45n-1d60Altitude[km]4520

O3-asym-17-bandC-0n-1d

Altitude[km]

50

25

0

last
error
error
a priori
true

55

Altitude[km]Altitude[km]

50

60
last
error
error
a priori
true

55

Altitude[km]55

Altitude[km]60O3-asym-17-bandC-80n-1d

1.0

10
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.11 Retrieval simulation results for 30-scan averaged data in BAND-C for
asymmetric-17 O3 at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top: Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are here assumed to be 90% of true profiles; Middle Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori errors
which are assumed to be 10 % of the a priori volume mixing ratios.
149

O3-asym-18-bandC-0n-1d

O3-asym-18-bandC-45n-1d
60

last
error
error
a priori
true

45

50

40
35
30

45
40
35
30

40
35
30

25

25

20

20

15

15

15

10

10
5 10 15 20 25 30 35 40 45
Volume Mixing Ratio [ppb]

10
0

5

10 15 20 25 30 35 40
Volume Mixing Ratio [ppb]

0

45
40
35
30
25

50
45
40
35
30
25

50
45
40
35
30
25

20

20

15

15

15

10
-0.2

10
-0.2

0.2 0.4 0.6
Averaging Kernel

0.8

1.0

0.0

O3-asym-18-bandC-0n-1d

0.2 0.4 0.6
Averaging Kernel

0.8

10
-0.2

1.0

daily zonal mean

daily zonal mean

45

45

45

Altitude[km]50Altitude[km]

50

30

40
35
30

40
35
30

25

25

25

20

20

20

15

15

15

10
0.0

10
0.0

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

1.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

daily zonal mean

55

50

35

0.8

60

55

40

0.2 0.4 0.6
Averaging Kernel
O3-asym-18-bandC-80n-1d

60

0.2

0.0

O3-asym-18-bandC-45n-1d

60
55

10
15
20
25
30
35
40
45
50
55
60

55

20

0.0

30

60
10
15
20
25
30
35
40
45
50
55
60

55

Altitude[km]50

10 15 20 25
Volume Mixing Ratio [ppb]O3-asym-18-bandC-80n-1d

60
10
15
20
25
30
35
40
45
50
55
60

55

5

O3-asym-18-bandC-45n-1d60Altitude[km]4520

O3-asym-18-bandC-0n-1d

Altitude[km]

50

25

0

last
error
error
a priori
true

55

Altitude[km]Altitude[km]

50

60
last
error
error
a priori
true

55

Altitude[km]55

Altitude[km]60O3-asym-18-bandC-80n-1d

1.0

10
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.12 Retrieval simulation results for 30-scan averaged data in BAND-C for
asymmetric-18 ozone at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are here assumed to be 90% of the true profile; Middle Averaging kernels;
Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori
errors which are assumed to be 10 % of the a priori volume mixing ratios.
150

O3-sym-17-bandC-0n-1dO3-sym-17-bandC-45n-1d
60

last
error
error
a priori
true

45

50

40
35
30

45
40
35
30

40
35
30

25

25

20

20

15

15

15

10

10
1

2
3
4
Volume Mixing Ratio [ppb]

5

10
0

1

2
3
Volume Mixing Ratio [ppb]

4

0

45
40
35
30
25

60
10
15
20
25
30
35
40
45
50
55
60

55
50
Altitude[km]

50

45
40
35
30
25

50
45
40
35
30
25

20

20

15

15

15

10
-0.2

10
-0.2

0.2 0.4 0.6
Averaging Kernel

0.8

1.0

0.0

O3-sym-17-bandC-0n-1d

0.2 0.4 0.6
Averaging Kernel

0.8

10
-0.2

1.0

daily zonal mean

daily zonal mean

45

45

45

Altitude[km]50Altitude[km]

50

30

40
35
30

40
35
30

25

25

25

20

20

20

15

15

15

10
0.0

10
0.0

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

1.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

daily zonal mean

55

50

35

0.8

60

55

40

0.2 0.4 0.6
Averaging Kernel
O3-sym-17-bandC-80n-1d

60

0.2

0.0

O3-sym-17-bandC-45n-1d

60
55

10
15
20
25
30
35
40
45
50
55
60

55

20

0.0

3

O3-sym-17-bandC-80n-1d

60
10
15
20
25
30
35
40
45
50
55
60

55

1
2
Volume Mixing Ratio [ppb]O3-sym-17-bandC-45n-1d60Altitude[km]4520O3-sym-17-bandC-0n-1d

Altitude[km]

50

25

0

last
error
error
a priori
true

55

Altitude[km]Altitude[km]

50

60
last
error
error
a priori
true

55

Altitude[km]55

Altitude[km]60O3-sym-17-bandC-80n-1d

1.0

10
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.13 Retrieval simulation results for 30-scan averaged data in BAND-C forsymmetric-17 ozone at latitudes of 0◦ N (left), 45◦ N (center), and 80◦ N (right). Top:
Retrieved volume mixing ratios are shown with rms retrieval error bounds and a priori
profiles which are here assumed to be 90% of the true profiles; Middle Averaging kernels;
Bottom: Retrieval error ratios defined as the ratio of the retrieval errors to the a priori
errors which are assumed to be 10 % of the a priori volume mixing ratios.
151

A.3 Mesospheric Observation
We performed the error estimation for ozone, HCl, and HO2 in the mesosphere, using
the model atmosphere for the polar, mid-latitude, and equatorial regions, using the same
conditions as those used for the radical analysis except for weighting function height stepand observation height step. The errors for each 10 km partial column is obtained.

152

O3-normal-bandA-0n-1d-60-80km

O3-normal-bandA-45n-1d-60-80km

80

80
last
error
error
a priori=true

80
last
error
error
a priori=true

70

65

70

65

60
0.0

0.5
1.0
1.5
Volume Mixing Ratio [ppm]

0.5
1.0
1.5
Volume Mixing Ratio [ppm]O3-normal-bandA-0n-1d-60-80km

Altitude[km]Altitude[km]Altitude[km]

0.8

70

60
-0.2

1.0

0.0

0.2 0.4 0.6
Averaging Kernel

60
-0.2

1.0

75

70

65

1.0

60
0.0

1.0

daily zonal mean

Altitude[km]Altitude[km]

65

0.8

O3-normal-bandA-80n-1d-60-80km

75

70

0.2 0.4 0.6
Averaging Kernel

80
daily zonal mean

75

0.8

0.0

O3-normal-bandA-45n-1d-60-80km

daily zonal mean

Altitude[km]

0.8

80

0.4
0.6
Error Ratio [ret./apr.]

70

65

O3-normal-bandA-0n-1d-60-80km
80

0.2

60
70
80

75

65

0.2 0.4 0.6
Averaging Kernel

2.0

80
60
70
80

75

65

0.0

0.5
1.0
1.5
Volume Mixing Ratio [ppm]O3-normal-bandA-80n-1d-60-80km

80
60
70
80

70

60
0.0

60
0.0

2.0

O3-normal-bandA-45n-1d-60-80km

80

75

70

65

60
0.0

2.0

last
error
error
a priori=true

75
Altitude[km]75
Altitude[km]Altitude[km]

75

60
-0.2

O3-normal-bandA-80n-1d-60-80km

70

65

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

60
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.14 Retrieval simulation results for 30-scan averaged data in BAND-A for the
standard profiles of normal O3 in the mesosphere at latitudes of 0◦ N (left), 45◦ N (center),and 80◦ N (right). Top: Retrieved volume mixing ratios are shown with rms retrieval
error bounds and a priori profiles which are assumed to coincide with true profiles; Middle
Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors
to the a priori errors which are assumed to be 50 % of the a priori volume mixing ratios.
153

H35Cl-bandB-0n-1d-60-80km

H35Cl-bandB-45n-1d-60-80km

80

80
last
error
error
a priori=true

80
last
error
error
a priori=true

70

65

70

65

60
0.0

0.5
1.0
1.5
Volume Mixing Ratio [ppb]

0.5
1.0
1.5
Volume Mixing Ratio [ppb]

H35Cl-bandB-0n-1d-60-80km

70

65

0.0

0.2 0.4 0.6
Averaging Kernel

0.8

60
-0.2

1.0

0.0

0.2 0.4 0.6
Averaging Kernel

0.8

60
-0.2

1.0

75
Altitude[km]Altitude[km]

65

70

65

1.0

60
0.0

1.0

daily zonal mean

75

70

0.8

H35Cl-bandB-80n-1d-60-80km

daily zonal mean75

0.2 0.4 0.6
Averaging Kernel

80

daily zonal mean

0.8

0.0

H35Cl-bandB-45n-1d-60-80km
80

0.4
0.6
Error Ratio [ret./apr.]

70

65

H35Cl-bandB-0n-1d-60-80km
80

0.2

60
70
80

75
Altitude[km]Altitude[km]Altitude[km]

80
60
70
80

75

65

Altitude[km]H35Cl-bandB-80n-1d-60-80km

80
60
70
80

70

60
0.0

60
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Volume Mixing Ratio [ppb]2.0

H35Cl-bandB-45n-1d-60-80km

80

75

70

65

60
0.0

2.0

last
error
error
a priori=true

75
Altitude[km]75
Altitude[km]Altitude[km]

75

60
-0.2

H35Cl-bandB-80n-1d-60-80km

70

65

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

60
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.15 Retrieval simulation results for 30-scan averaged data in BAND-B for the
standard profiles of H35 Cl in the mesosphere at latitudes of 0◦ N (left), 45◦ N (center),and 80◦ N (right). Top: Retrieved volume mixing ratios are shown with rms retrieval
error bounds and a priori profiles which are assumed to coincide with true profiles; Middle
Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval errors
to the a priori errors which are assumed to be 50 % of the a priori volume mixing ratios.
154

HO2-bandC-0n-1d-60-80km

HO2-bandC-45n-1d-60-80km

80

80
last
error
error
a priori

80
last
error
error
a priori

70

65

70

65

0.5

1.0 1.5 2.0 2.5
Volume Mixing Ratio [ppb]0.5
1.0Volume Mixing Ratio [ppb]HO2-bandC-0n-1d-60-80km

Altitude[km]Altitude[km]Altitude[km]

0.8

70

60
-0.2

1.0

0.0

0.8

60
-0.2

1.0

75

70

65

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

60
0.0

1.0

daily zonal mean

Altitude[km]Altitude[km]

65

0.8

HO2-bandC-80n-1d-60-80km

daily zonal mean

70

0.2 0.4 0.6
Averaging Kernel

80

75

0.2

0.0

HO2-bandC-45n-1d-60-80km

75
Altitude[km]0.2 0.4 0.6
Averaging Kernel

80
daily zonal mean

60
0.0

70

65

HO2-bandC-0n-1d-60-80km
80

1.0

60
70
80

75

65

0.2 0.4 0.6
Averaging Kernel

0.4
0.6
0.8
Volume Mixing Ratio [ppb]

80
60
70
80

75

65

0.0

0.2

HO2-bandC-80n-1d-60-80km

80
60
70
80

70

60
-0.2

60
0.0

1.5

HO2-bandC-45n-1d-60-80km

80

75

70

65

60
0.0

3.0

last
error
error
a priori

75
Altitude[km]75
Altitude[km]Altitude[km]

75

60
0.0

HO2-bandC-80n-1d-60-80km

70

65

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

60
0.0

0.2

0.4
0.6
Error Ratio [ret./apr.]

0.8

1.0

Figure A.16 Retrieval simulation results for 30-scan averaged data in BAND-C for the
standard profiles of HO2 in the mesosphere at latitudes of 0◦ N (left), 45◦ N (center), and80◦ N (right). Top: Retrieved volume mixing ratios are shown with rms retrieval error
bounds and a priori profiles which are assumed to coincide with true profiles; Middle
Averaging kernels; Bottom: Retrieval error ratios defined as the ratio of the retrieval
errors to the a priori errors which are assumed to be 100 % of the a priori volume mixing
ratios.
155

List of Acronyms
A/D
AAMP
ADE
AMS
ANT
ANT-RCS
AO
AOPT
AOS
AOS
ATLAS
ATMOS
AU
BBM
BDR
bps
BSMILES
BUS
BWO
CAM
CAMC
CCD
CCSDS
CD-ROM
CDMS
CDR
CFC
CHL
CMG
CPU
CRE
CREC
CRL
CS
CST
CVU
DNL
DPC
DRTS
DRTS-W
DRTSS
DSB
DVD/ROM

analogue to digital
Ambient Temperature Amplifiers
Antenna Drive Electronics
Airborne Mass Spectrometer
Submillimeter Antennaantenna reference coordinate systemannouncement of opportunity
Ambient Temperature Optics
Radio Spectrometer
acousto-optic (or acousto-optical) spectrometer
Atmospheric Laboratory for Applications and Science
Atmospheric Trace Molecule Spectroscopy
AOS Analyzer Unit
breadboard model
basic design review
bit per second
Balloon-borne SMILES
Payload Bus
backward-wave oscillator
STT Camera Units
STT Camera Controller
charge coupled device
Consultative Committee for Space Data Systemcompact disc - read-only memory
Cologne Database for Molecular Spectroscopy
critical design review
chlorofluorocarbon
Calibration Hot Load
Control Moment Gyro
Central Processing Unit
Cryo-electronics Unit
CRE Control Electronics
Communications Research Laboratory
cryogenic sampling
Cold Sky TerminatorVideo Unit
differential non-linearity
Data Processing and Control Section
Data Relay Test Satellite
Data Relay Test Satellite-West
Data Relay Test Satellite System
double sideband
digital versatile (or video) disc / read-only memory
156

EF
EFM
EFU
ELM-ES
ELM-PS
EM
EOPD
EORC
EOS
EOS-CHEM
EPS
ESA
ESTO
FIFO
FIP
FIRS
FM
FM1
FM2
FOV
FTIR
FWHM
GAW
GB
GCS
GEISA
Gflops
GI/UAF
GIE-CS
GN&C
GSE
H
HCS
HECP
HEMT
HITRAN
HK
HPBW
HRDL
HRDR
HTV
ICS
IF
IFA
ILAS
IMG
I/O
IR
ISDN
ISS

Exposed Facility
engineering flight model
Exposed Facility Unit
Experiment Logistics Module - Exposed Section
Experiment Logistic Module - Pressurized Sectionengineering model
Earth Observation Program Department
Earth Observation Research Center
Earth Observing System
Earth Observing System Chemistry Mission
Electric Power System
European Space Agency
Earth Science & Technology Organization
first in and first out
FACOM Information Processing
Far Infra-Red Survey
flight model
forward model I
forward model II
field of view
Fourier transform infrared spectrometer
full-width at half-maximum
Global Atmospheric Watch
gigabytes
geocentric inertial equatorial coordinate system
Gestion et Êtude des Informations Spectroscopiques Atmosphériques
giga floating operations per second
Geophysical Institute, University of Alaska Fairbanks
geocentric inertial equatorial coordinate system
Guidance, Navigation and Control
ground support equipment
heightheliocentric inertial equatorial reference coordinate system
Helium Gas Compressors
high electron mobility transistor
High-resolution Transmission Molecular Absorption Databasehouse keeping
half-power beamwidths
High-Rate Data Link
High Rate Data Recorder
H-II Transfer Vehicle
Interorbit Communication Systemintermediate frequency
IF Amplification Section
Improved Limb Atmospheric Spectrometer
Interferometric Monitor for Greenhouse Gases
input/output
infraredintegrated services digital network
International Space Station
157

IUP
JCP
JEM
JEM/OSJEMRMS
JIF
JPL
JSC
L
L1
L2
L3
LAPAN
LO
LS
LSB
LVLH
MAES
MAS
MASTERMBMFS
MIL-STD
MLS
MNT
MOIP
MOIS
MPI
MPM
N
NA
NAO
NASA
NASDA
NDSC
NIES
NRO
NSBF
NW
OEM
OPLN
PDH
PDR
PM
PSC
REF
RF
rms
RMS

Institut für Umweltphysik (Institute for Enviromnental Physics)
JEM Control Processor
Japanese Experiment Module
JEM Operation System
JEM Remote Manipulator System
JEM Interface Mechanism
Jet Propulsion Laboratory
Johnson Space Center
lengthlevel 1level 2level 3
Lembaga Penerbangan dan Antariksa Nasional(National Institute of Aeronautics and Space)local oscillator
lower stratosphere
lower sideband
local vertical local horizontal
Millimeter Wave Atmospheric Emission Simulator
Millimeter-wave Atmospheric Sounder
Millimeter-wave Acquisitions of Stratosphere/Troposphere Exchanges Researchmegabytes
Mainframe Structure
Military Standard
Microwave Limb Sounder
Antenna Mounting Structure
Mission Operation Implementation Plan
Mission Operation Interface Specification
Martin-Puplett interferometer
Millimeter-Wave Propagation Modelnorth
not applicable
National Astronomical Observatory
National Aeronautics and Space Administration
National Space Development Agency of Japan
Network for the Detection of Stratospheric Change
National Institute of Environment Study
Nobeyama Radio Observatory
National Scientific Balloon Facility
network
optimal estimation method
operation plan
Payload Data Handling Unit
preliminary design review
JEM Pressurized Module
polar stratospheric cloudAntenna Reflectors
radio frequency
root-mean-squares
root-mean-squares
158

RSS
S
SAO
SBUV
SIKIE
SIS
SJTD
SLO
SLOC
SMILESSOOH
SOPRANO
SRX
SSB
SSC
STE
STEL
STT
STT-RCS
SYNC
TBD
TCS
TDRSS
TEA
TKSC
TOMS
TRN
UARS
US
USA
USB
UT
UT
UTC
UV
W
WGS84
WMO
ZOE

root-sum-squares
southSmithsonian Astrophysical Observatory Line Databasesolar backscatter ultraviolet
symmetry induced kinetic effectsuperconductor-insulator-superconductor
Stirling & JT Drive Electronics
submillimeter-wave local oscillator
Submillimeter LO Controller
Superconducting Submillimeter-Wave Limb-Emission SounderSpacecraft Orbital Operation Handbook
Sub-millimetre Observation of Process in the Absorption, Noteworthy for Ozone
Submillimeter Receiver
single sideband
Swedish Space Corporation
stratosphere-troposphere exchange
Solar-Terrestrial Environment Laboratory
Star Tracker
Star-Tracker reference coordinate system
synchronization
to be determined
Thermal Control System
Tracking and Data Relay Satellite System
Torque Equilibrium Attitude
Tsukuba Space Center
Total Ozone Mapping Spectrometer
Beam Transfer Section
Upper Atmosphere Research Satellite
United States
United States of America
upper sideband
Universal Timeupper troposphere
Coordinated Universal Timeultraviolet
widthWorld Geodetic System 1984
World Meteorological Organization
Zone of Exclusion

159

COMMUNICATIONSRESEARCHLABORATORYNASDA JEM/SMILES:
http://smiles.tksc.nasda.go.jp/
CRL JEM/SMILES:
http://www.crl.go.jp/dk/c214/smiles/