SGC-120011

GCOM-W1 "SHIZUKU"
Data Users Handbook

First Edition
January 2013

Preface
Global environment change has become a worldwide concern in recent years. JAXA is
conducting a project known as “GCOM (Global Change Observation Mission)”. GCOM
aims to construct, use, and verify systems that enable continuous global-scale observations(for 10 to 15 years) of effective geophysical parameters for elucidating global climate change,
weather forecasting, water circulation mechanisms and for providing fishery information to
the fishing industry.
GCOM has two series of observation satellites: the GCOM-W series for observing the water
cycle and the GCOM-C series for observing climate change. In order to carry out
observations continuously for at least 10 years, six satellites are to be launched in three phases.
The GCOM-W1 is the first generation of the GCOM-W satellite.
GCOM-W1 was launched by the H-IIA Launch Vehicle No. 21 (H-IIA F21) at 1:39 a.m. on
May 18th, 2012 (Japan Standard Time, JST) from the Tanegashima Space Center, andinserted into a planned position on the “A-Train” orbit(*).
* The Afternoon Constellation, or “A-Train”, is an Earth observation satellite constellation
run by NASA, and consists of multiple satellites orbiting the Earth in close proximity at analtitude of about 700km, crossing the equator at around 1:30 p.m. mean solar time.
GCOM-W1 is also slated to join the A-Train, with the goal of further expanding scientificresearch by using data from AMSR2.
GCOM-W1 is equipped with an Advanced Microwave Scanning Radiometer 2 (AMSR2).
AMSR2 is the successor of the Advanced Microwave Scanning Radiometer (AMSR) onboard
the Advanced Earth Observing Satellite II (ADEOS-II) and the Advanced Microwave
Scanning Radiometer for EOS Aqua (AMSR-E) on board Aqua, NASA’s Earth observation
satellite. AMSR2 takes measurements at multiple frequencies and multiple polarizations of
weak electromagnetic waves in the microwave band radiated from the Earth’s surface and theatmosphere. It is designed to estimate a variety of geophysical parameters, particularly thoseconnected to water. The observation data obtained by AMSR2 is received at groundstations(domestic stations in Japan, foreign station in Norway (SvalSat/KSAT)). Then, thereceived data is transmited to JAXA/Tsukuba Space Center via internet and processed into
data products and become ready for distribution to users.
AMSR2 will continue global observations with high spatial resolution and at multiple
frequencies and multiple polarizations, including low frequency channels by AMSE/AMSR-E.
This is made to know its geographical distribution and seasonal and/or yearly changes. The
observation data will enable the creation of long-term trustworthy data sets of global physicalamount (Total Precipitable Water, Cloud Liquid Water, Pricipitation, Sea Surface Wind, Sea

Surface Temperature, Sea Ice Concentration, Snow Depth, Soil Moisture Content).
AMSR2 Level1 products will be released in January 2013, and further, Level2 and Level3
products will be released in May 2013 from GCOM-W1 Data Providing Service(https://gcom-w1.jaxa.jp/).
This handbook intends to make the GCOM-W1 data be acknowledge by users in general andprovides the users with information about the GCOM-W1 products as well as the backgroundknowledge of the spacecraft, sensors, ground systems, and so on; thereby promoting a wideuse of the data products and serving users with convenience. We hope AMSR2 products as
described in this handbook contribute to studies on global environment change monitoring,preservation, and so on.
January 2013
Japan Aerospace Exploration Agency
Team of GCOM Project

GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition
Contents

GCOM-W1 "SHIZUKU" Data Users Handbook
Contents

Chapter 1 Introduction .................................................................................................................... 1-1
1.1 Purpose of this Document ......................................................................................................... 1-1
1.2 Scope of this Document ............................................................................................................. 1-2
1.3 Overview of the AMSR2 Mission .............................................................................................. 1-2
Chapter 2 Overview of AMSR2 ........................................................................................................ 2-1
2.1 Overview of GCOM-W1............................................................................................................. 2-1
2.2 Advanced Microwave Scanning Radiometer 2 (AMSR2) ........................................................ 2-3
2.3 A-Train ..................................................................................................................................... 2-28
Chapter 3 Overview of the GCOM-W1 Ground System ................................................................. 3-1
3.1 Overall System .......................................................................................................................... 3-1
3.2 GCOM-W1 Mission Operation System .................................................................................... 3-2
3.3 Receiving and Recording Equipment ....................................................................................... 3-5
3.4 Overview of Ground System Operations ................................................................................. 3-8
Chapter 4 AMSR2 Products ............................................................................................................. 4-1
4.1 Product Definitions ................................................................................................................... 4-1
4.2 Level 1 Processing Algorithms ................................................................................................. 4-8
4.3 Higher Level Processing Algorithms...................................................................................... 4-16
4.4 Project Formats ....................................................................................................................... 4-33
4.5 Data Calibration and Verification .......................................................................................... 4-33
Chapter 5 Data Providing Service ................................................................................................... 5-1
5.1 Product Storage ......................................................................................................................... 5-1
5.2 Product Provision Policy ........................................................................................................... 5-2
5.3 User Tools ................................................................................................................................ 5-10iGCOM-W1 "SHIZUKU" Data Users Handbook
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Contents

List of Tables and Figures
Table 1-1: Geophysical parameters observed by GCOM-W1 ........................... 1-5
Table 2-1: Main GCOM-W1 specifications ........................................................ 2-3
Table 2-2: Main AMSR2 specifications ............................................................. 2-4
Table 2-3: AMSR series comparisons ............................................................... 2-5
Table 2-4: AMSR2 architecture ......................................................................... 2-9
Table 2-5: Main performance values of the antenna ........................................2-11
Table 2-6: Main performance values of the HTS ............................................ 2-12
Table 2-7: Main performance values of the CSM ............................................ 2-13
Table 2-8: Main performance values of the receiver ....................................... 2-14
Table 2-9: Main performance values of the antenna drive assembly .............. 2-15
Table 2-10: Main performance values of the signal processor........................ 2-16
Table 2-11: Main performance values of the power distribution unit ............... 2-17
Table 2-12: AMSR2 temperature resolutions (specification values) ................ 2-21
Table 2-13: Beam width and footprint ............................................................. 2-26
Table 2-14: Overlap ratios (under nominal design conditions) ........................ 2-27
Table 3-1: Ground station satellite operations .................................................. 3-8
Table 3-2: Definitions of mission data processing ........................................... 3-10
Table 3-3: Product delivery to operational institutions (for JMA) ..................... 3-12
Table 3-4: Product delivery to operational institutions (for JAFIC) .................. 3-13
Table 4-1: AMSR2 product definitions............................................................... 4-1
Table 4-2: AMSR2 scene definitions ................................................................. 4-2
Table 4-3: Observation center positions ........................................................... 4-2
Table 4-4: Data specifications for AMSR2 Level 1 products ............................. 4-5
Table 4-5: List of AMSR2 Level 2 products ....................................................... 4-7
Table 4-6: List of AMSR2 Level 3 products ....................................................... 4-8
Table 4-7: Coefficients used in scanning bias corrections .............................. 4-13
Table 4-8: Frequencies selected for resampling ............................................. 4-16
Table 4-9: Sea temperature dependency of coefficients a and b .................... 4-25
Table 4-10: AMSR2 Level 3 product projection methods and lattice grid sizes................................................................................................................ 4-33
Table 4-11: Overall AMSR2 schedule ............................................................. 4-34
Table 4-12: Designed accuracy for each AMSR2 geophysical parameter ...... 4-38
Table 4-13: Product descriptions .................................................................... 4-39
Table 5-1: List of supplied data ......................................................................... 5-1
Table 5-2: Delivery of products for general researchers ................................... 5-2ii

GCOM-W1 "SHIZUKU" Data Users Handbook
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Table 5-3: User categories................................................................................ 5-3
Table 5-4: Service contents .............................................................................. 5-3
Figure 1-1: Overview of the GCOM mission ..................................................... 1-3
Figure 1-2: JAXA’s climatic and environmental observation satellite missions . 1-3
Figure 1-3: Relationships between geophysical parameters and AMSR2
observation frequencies ............................................................................ 1-6
Figure 1-4: Examples of hydrological cycle observations ................................. 1-7
Figure 1-5: Examples of North Pole sea ice distribution observations .............. 1-8
Figure 1-6: Example of observations of El Nino and La Nina trends ................ 1-8
Figure 1-7: Example of soil moisture content observations .............................. 1-9
Figure 1-8: Example of a fishing industry application ..................................... 1-10
Figure 2-1: Exterior views of GCOM-W1 .......................................................... 2-2
Figure 2-2: Exterior view of AMSR2 ................................................................. 2-4
Figure 2-3: AMSR2: Principle of operation ....................................................... 2-7
Figure 2-4: Exterior schematic of AMSR2....................................................... 2-10
Figure 2-5: AMSR2 mode transitions .............................................................. 2-20
Figure 2-6: Exterior view of the CAL ............................................................... 2-22
Figure 2-7: Illustration of AMSR2’s geometric performance (off-nadir andincidence angle) ...................................................................................... 2-23
Figure 2-8: Layout of the feed-horn assembly ................................................ 2-24
Figure 2-9: Illustration of AMSR2’s geometric performance (sampling interval,
beam width, footprint, and overlap) ......................................................... 2-25
Figure 2-10: View of the A-Train ..................................................................... 2-29
Figure 3-1: Overview of the GCOM-W1 ground system ................................... 3-1
Figure 3-2: Overview of AMSR2 data processing ............................................. 3-3
Figure 3-3: Home window of the GCOM-W1 data providing service ................ 3-5
Figure 3-4: View of Katsuura Station ................................................................ 3-6
Figure 3-5: View of Tsukuba Station ................................................................. 3-6
Figure 3-6: View of KSAT / Svalbard Station (taken from the KSAT website) ... 3-7
Figure 3-7: View of KSAT / Troll Station (taken from the KSAT website) .......... 3-9
Figure 4-1: Relationship between the AMSR2 scene definitions and the position
directly under the satellite .......................................................................... 4-3
Figure 4-2: Relationship between the scan center observation point location andthe number of data points .......................................................................... 4-3
Figure 4-3: Definition of the Northern polar stereo projection [TB/SIC] ............ 4-4
Figure 4-4: Definition of the Southern polar stereo projection [TB/SIC] ............ 4-4iii

GCOM-W1 "SHIZUKU" Data Users Handbook
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Figure 4-5: Definition of the Northern polar stereo projection [TB/SND] ........... 4-5
Figure 4-6: Level 1A product processing sequence .......................................... 4-9
Figure 4-7: Processing sequence for calculating the observation points .......... 4-9
Figure 4-8: Sketch of calculating the longitude/latitude of observation point .. 4-10
Figure 4-9: Processing sequence for calculating the antenna temperature
conversion coefficients .............................................................................4-11
Figure 4-10: Processing sequence for the Level 1B product .......................... 4-12
Figure 4-11: Processing sequence for Level 1R products .............................. 4-14
Figure 4-12: Overview of the Level 1R process.............................................. 4-15
Figure 4-13: Method of correcting S36 for anisotropy .................................... 4-25
Figure 5-1: Level 1 / Level 2 granule ID assignment rules................................ 5-5
Figure 5-2: Level 3 granule ID assignment rules .............................................. 5-5
Figure 5-3: Search window (guest user) ......................................................... 5-10
Figure 5-4: User tool acquisition methods (1 / 2) .............................................5-11
Figure 5-5: User tool acquisition methods (2 / 2) .............................................5-11

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Chapter 1 Introduction

Chapter 1

Introduction

The expanding range of human activities in pursuit of abundance and richness is
beginning to have a profound impact on the planet’s environment, such as climate
warming and widening ozone holes, on a scale that cannot be ignored. Given thisreality, the abundance and richness the human race should pursue from this point
forward should encompass more than our daily abundance; it should include asound human society and a healthy global environment that we can pass on
without guilt to our children’s and grandchildren’s generations. To attain andpreserve a future healthy global environment, it is imperative that we understandthe current state of the global environment and its underlying mechanisms, forecast
future trends, and connect this knowledge and prescience to meaningfulenvironmental preservation and improvement measures.
Essentially, we have two means of observing and measuring our planet’s
environment at our disposal. One is direct measurement of observablephenomenon on land and at sea. The second is remote sensing from satellites andother remote platforms via visible and infrared light, microwaves, and otherelectromagnetic waves. The latter method, although requiring various algorithms
and processing to derive observables, is nonetheless essential to observing at
once phenomena on a global scale.
The Japan Aerospace Exploration Agency (JAXA) manages the Global Change
Observation Mission (GCOM), a mission that uses satellites to observe the global
environment. In effect, this mission’s role is like a health check of the Earth fromspace, as it observes global hydrological mechanisms and climate changes over along time period.
The first stage of the GCOM-W series is the Global Change Observation Mission
1st-Water “Shizuku” (GCOM-W1) satellite, which observes hydrologicalmechanisms. GCOM-W1 carries the Advanced Microwave Scanning Radiometer 2(AMSR2), which is capable of measuring, among other phenomenon, precipitation,water vapor, sea surface wind speeds, sea surface temperatures, soil moisture
content, and snow depths.
1.1

Purpose of this Document

This document provides users who obtain AMSR2 data sets publically released by
JAXA with the information needed to make effective use of these data sets. It also
gives information related to both standard products and other products, theGCOM-W1 satellite, the onboard AMSR2 sensor, and the ground system.
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1.2

Scope of this Document

This document provides information needed by AMSR2 data users as well as brief
descriptions of the satellite, its sensor, the ground system, the products, and thedata providing service. The document includes information users need to accessthe data providing service in order to obtain data sets. Furthermore, the annex
gives details on each product format. The document’s overall structure is as below:
Chapter 1 — Preface
Chapter 2 — Overview of the AMSR2
Chapter 3 — Overview of the GCOM-W1 Ground System
Chapter 4 — AMSR2 Products
Chapter 5 — Data Providing Service
Annex — List of Abbreviations, Related Information, AMSR2 Product Formats
1.3

Overview of the AMSR2 Mission

AMSR2 is the GCOM-W1 satellite’s onboard sensor system. This section gives a
description of the GCOM concept as a background to the measurement sensor,
followed by an explanation of the AMSR2 itself.
1.3.1

GCOM Concept

GCOM consists of two series: Global Change Observation Mission – Water(GCOM-W) and Global Change Observation Mission – Climate (GCOM-C).
GCOM-W makes observations connected with water and energy cycles on a global
scale, including the polar regions, using an onboard microwave scanningradiometer. GCOM-C is responsible for long-term continuous observations ofparameters connected with the Earth’s radiant energy budget and with the state ofvegetation growth, particularly as it relates to food production and the carbon cycle.
These parameters are associable to the balance of the Earth’s overall climate.
GCOM-C carries an onboard multi-spectral optical radiometer to make theseobservations.
Through long-term observations (10 to 15 years) of the entire planet, GCOM is
expected to monitor hydrological cycles and climate changes and to clarify theirmechanisms. To carry out continuous observations for more than 10 years, eachsatellite series is divided into three stages, meaning that a total of six satellites areplanned to be launched. Subsequent satellites will overlap the on-orbit operation
period of the former satellites by approximately one year to allow for comparisons
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Chapter 1 Introduction

and calibrations of both observation sensors to ensure observation data continuity.

Figure 1-1: Overview of the GCOM mission

Given the sheer size of our Earth, it is impossible for a single country to make all
the observations necessary to gauge the Earth’s environmental changes. Instead,various countries proceed with their own satellite plans for climatic andenvironmental observations within a worldwide framework known as the Global
Earth Observation System of Systems (GEOSS), which facilitates international
collaborations. Japan has three priority fields: global warming and carbon cyclechanges, hydrological cycle changes and climate changes, and natural disasters.
GCOM’s mission contributes to GEOSS, and GCOM is intended to makeinternational contributions in coordination with other domestic and international
programmes, such as the Joint Polar Satellite System (JPSS), belonging to theUnited States’ National Oceanic and Atmospheric Administration (NOAA).

DPR/GPM with NASA

GCOM-C
CPR/Earth Care with ESA

GCOM-W

GOSAT

Figure 1-2: JAXA’s climatic and environmental observation satellite missions

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Chapter 1 Introduction

1.3.2
1.3.2.1

Overview of the AMSR2 mission
Importance of observing hydrological changes

The water on the Earth’s surface plays a very important role in redistributing solar
energy streaming down on the surface and is intimately connected tometeorological phenomena and climate change. Water in the oceans and on land,in both liquid and solid states, is evaporated or sublimated by solar energy andcarried as water vapor from the Earth’s surface to the atmosphere.
The water vapor transported into the atmosphere after a time is cooled and reverts
back to liquid or solid water in the form of clouds or precipitation. The energy
present in the gas-phase water vapor heats the atmosphere, becoming a driving
force causing all kinds of meteorological phenomena. At the same time, water thathas changed to liquid or solid form in the atmosphere returns to the Earth’s surface
as rainfall or snowfall. This water either flows back to the oceans via rivers or isstored inland as soil moisture, snow accumulations, or frozen soil. The water stored
in land functions as a sort of climate memory, because it stores the current state fora certain period of time. Thus, water stored in land is equal in importance to theoceans, and the tremendous volume of water they hold, in medium and long termchanges to the Earth’s intricate systems.
This circulation of water on the Earth is indispensible to our lives. Rain is importantfor maintaining ecosystems, and it brings about many blessings to human society.
On the other hand, typhoons, hurricanes, and torrential rains can, on occasions,cause massive damage to social infrastructure and claim human lives. Droughts,
too, damage ecosystems and inflict direct harm to agricultural crops. There areconcerns that further temperature increases will unleash more frequent extreme
weather phenomena, such as hurricanes, torrential rains, and droughts. At issue inthese transformations is ensuring effective use of rainfall as a water resource while
minimizing damage caused by natural disasters. This is what makes monitoring thecurrent state of the Earth’s hydrological cycle so important, along with
understanding the underlying mechanisms of the hydrological cycle using
accumulated observation data sets and predicting the behavior of transformationsin the short, medium, and long term.

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Table 1-1: Geophysical parameters observed by GCOM-W1
Geophysical Parameter1.3.2.2

Observation Region

Total Precipitable Water

Global oceans

Integrated cloud liquid water

Global oceans

Precipitation

From Tropical to Temperate zones

Sea surface temperature

Global oceans

Sea surface wind speed

Global oceans

Sea ice concentration

High-latitude regions

Snow depth

Land

Soil moisture content

Land

Spatial Resolution(approximate value)

15 km
15 km
15 km
15 km
15 km
50 km
30 km
50 km

Applications microwave observations and data

GCOM-W1 uses a microwave scanning radiometer to observe water in various
forms connected to the hydrological cycle with great accuracy on a global scale.
Microwave scanning radiometers take advantage of microwaves’ sensitivity towater in order to measure microwave energy that contains what is in effect “water
data.” Because the Earth’s surface and particles in the atmosphere are theradiation sources and, thus, this method does not need sunlight, it is particularly
well suited for making measurements at night and at the poles in winter. Also, withmicrowaves, how the object of interest appears changes depending on theobserved frequency range. For example, high frequencies with shorter
wavelengths are sensitive to clouds and precipitation, whereas low frequencies
with longer wavelengths pass through clouds for observations of the Earth’s
surface. GCOM-W1 measures phenomena over seven frequency bands, from the
6.9 GHz band to the 89 GHz band, in order to take advantage of this property andmake quantitative observations of geophysical parameters related to thehydrological cycle.
The measured data include precipitation and water vapor, important components of
the atmosphere state, the area of North Pole sea ice, where there are concerns theice pack is shrinking, soil moisture content, which helps our understanding ofdroughts and floods and may have applications in the agriculture sector, and sea
surface temperatures in all weather conditions, which are particularly helpful for
oceanographic observations of the Kuroshio and other currents. Measured data areprovided to researchers in various fields as satellite products.
This measurement data are becoming indispensible to numeric weather forecasting,the fishing industry, and other current applications. At the present time, theJapanese Meteorological Agency, the Japan Fisheries Information Service Center,and other institutions routinely use data from the Advanced Microwave Scanning
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Chapter 1 Introduction

Radiometer for EOS (AMSR-E) for weather predictions, and particularly rainfall
predictions related to typhoons and torrential rains, as well as for creating
information on fishing and oceanographic conditions to help understanding offishing grounds. AMSR2, onboard the GCOM-W1, has a number of improvements
over AMSR-E and is expected to deliver data with even higher utility value.

Figure 1-3: Relationships between geophysical parameters and AMSR2 observation
frequencies

Figure 1-4 provides global distributions of four geophysical parameters averaged
for the month of April 2004 measured by AMSR-E, a previous-generation sensor to
the AMSR2. The geophysical parameters are sea surface temperature (upper left),
Total Precipitable Water (upper right), sea surface wind speed (lower left), andprecipitation (lower right, monthly aggregate value).
April 2004 was comparatively close to normal seasonal values, as neither El Nino
nor La Nina was active. In this state, the sea surface temperatures off the coast ofPeru in the eastern Pacific Ocean near the equator are cooler than theirsurroundings due to upwelling, as shown in the upper left figure. The lower rightfigure of precipitation distributions shows a distinctive belt of heavy rains thatstretches just north of the western equator. This is the area known as theintertropical convergence zone, where lower strata winds converge. A similar
convergence zone is seen in the southern hemisphere. In the tropics at this time of
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Chapter 1 Introduction

year, the Total Precipitable Water distribution (upper right), which indicates theamount of water vapor in the atmosphere, agrees closely with the precipitation
distribution. In the Antarctic Ocean region, the latitudes have been dubbed the
Roaring Forties, the Furious Fifties, and the Screaming Sixties for the ferocity of
their winds and waves. The truth in these sayings is borne out by sea surface windspeed observations (lower left) of extremely strong winds around Antarctica.
In similar fashion, GCOM-W1 will be able to measure, almost simultaneously,
multiple sets of hydrological cycle data on a global scale.

Figure 1-4: Examples of hydrological cycle observations

1.3.2.3 Data Application Example 1 — Observations of sea ice distribution at
the North Pole
Figure 1-5 provides sea ice distributions at the North Pole observed by AMSR-E.
From the left, these figures show the mid-September sea ice distribution in 2002,
2007, and 2010. In 2007, the smallest sea ice coverage ever in the history ofsatellite observations was recorded; an area of sea ice equivalent to 2.8-times thesize of Japan’s total land mass had disappeared since 2005, the year in which theprevious smallest sea ice coverage had been recorded. Sea ice had evendisappeared from northern Canadian islands, where in normal years the sea ice
never melts, opening up a long Arctic Ocean sea route joining the Atlantic andPacific.
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Chapter 1 Introduction

Sept. 8, 2002

Sept. 8, 2007

Sept. 8, 2010

Figure 1-5: Examples of North Pole sea ice distribution observations

1.3.2.4 Data Application Example 2 — Observations of El Nino and La Nina
trendsFigure 1-6 provides an example of AMSR-E observations of El Nino and La Nina
trends. The top figure, which illustrates the sea surface temperature distribution at
the start of September 2010, shows cold water rising from the deep sea being
carried westward along the equator beginning off the Peru coast. The bottom figure
shows a time series of deviations from normal sea surface temperatures in the sea
area where El Nino is monitored (shown by the black box in the top figure). From
this figure, you can see that sea surface temperatures are in a negative deviation
as of September 2010, indicating a La Nina episode.

La‐Nina
El‐NinoLa‐Nina
El‐NinoFigure 1-6: Example of observations of El Nino and La Nina trends

1.3.2.5

Data Application Example 3 — Observations of soil moisture content

A massive drought occurred in Russia in the summer of 2010, causing extensive
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Chapter 1 Introduction

damage to crops. The top figure in Figure 1-7 shows in red the areas in Russia withdrier soil than normal values taken from AMSR-E soil moisture content
observations in July 2010. The bottom figure shows changes in AMSR-E soil
moisture observations between March and August for the area marked by the bluebox in the top figure. The black line indicates normal values, and the red line
indicates 2010 readings.

Figure 1-7: Example of soil moisture content observations

1.3.2.6

Actual Data Application Example — Use in the fishing industry

Information such as ocean conditions and the distribution of fish stocks are key toefficient fishing industry operations. One of the most basic pieces of information toascertain these conditions is sea temperatures. Fish are sensitive to thesurrounding water temperatures, and certain water temperatures are conducive tocertain species. For example, fish distributed in warm currents prefer warmer water,
and fish distributed in cold currents prefer cooler water.
Figure 1-8 plots on AMSR-E/SST images taken over October 19 – 21, 2003 (topfigure) and October 19 – 21, 2004 (bottom figure) the locations of fishing grounds at
the respective time. Sardine and mackerel roll-net fishing grounds are marked inyellow, bonito trolling fishing grounds are marked in blue, tuna and other trawl line
fishing grounds are marked in red, and saury stick-held dip net fishing grounds aremarked in green. What is particularly noteworthy is the formation of saury fishinggrounds offshore in 2003, as the Kurile current spread widely to the south. In 2004,
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Chapter 1 Introduction

however, the fishing grounds formed along the coast as a warm water mass was
distributed along eastern Hokkaido. Bonito fishing grounds also formed offshore
during this season in 2003.
Fishing grounds can be identified by sea water temperatures because fishinggrounds form where the water temperatures suit the specific species. Because
satellite sensors like AMSR2 that use microwaves are only slightly affected bycloud cover, it is possible to obtain continuous sea surface temperature data thatare useful in determining fishing ground locations.

Figure 1-8: Example of a fishing industry application

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Chapter 2 Overview of AMSR2

Chapter 2

Overview of AMSR2

The first satellite of the GCOM-W series, GCOM-W1, carries the Advanced
Microwave Scanning Radiometer 2 (AMSR2). AMSR2 is the successor to the
AMSR on the Advanced Earth Observation Satellite-II (ADEOS-II) and theAdvanced Microwave Scanning Radiometer for EOS (AMSR-E) on the Aqua
satellite. The sensor takes measurements at multiple frequencies and multiple
polarizations of weak electromagnetic waves in the microwave band radiated fromthe Earth’s surface and the atmosphere. It is designed to estimate a variety ofgeophysical parameters, particularly those connected to water.
AMSR2 will continue AMSR/AMSR-E global observations with high spatial
resolution and at multiple frequencies and multiple polarizations, including low
frequency channels. This will enable the creation of long-term trustworthy data sets(including sea ice concentrations, sea surface temperatures, sea surface windspeeds, water vapor content, precipitation, and marine flux) that will help us
understand, monitor, and predict climate changes.
2.1

Overview of GCOM-W1

GCOM-W1, the first satellite of the GCOM-W series, carries AMSR2, themission-critical device that supplants AMSR-E. The satellite unit has the following
reliability and survivability characteristics to ensure it can definitely execute its
development and mission.
Reliability and survivability(1) The design is highly reliable with redundant systems for nearly every onboard
device.
(2) The satellite’s survivability has been increased in the event that one of the twosolar paddles fails by equipping it with a graceful degeneracy mode so that thesatellite can operate on reduced power.
(3) In preparation for on-orbit contingencies, the satellite is equipped withautonomous functions in which the satellite autonomously makes various
controls while on orbit.
(4) Highly reliable devices and components were selected based on on-orbit
performance in past satellites. Furthermore, costs have been kept down by using
a common bus design for GCOM-W1 and GCOM-C1, the first generation of
GCOM-C series satellites that will be launched after GCOM-W1.

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Chapter 2 Overview of AMSR2

Accessibility and serviceability(5) The satellite bus consists of the bus module, mission module, and propulsion
module. The satellite bus’s structure is such that the three modules can be
developed and serviced in parallel. Even after assembly, the propulsion module
can be dismounted from the satellite bus module relatively easily for better
accessibility and serviceability.
Figure 2-1 provides exterior views of GCOM-W1, and Table 2-1 lists its main
specifications.

Figure 2-1: Exterior views of GCOM-W1

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Chapter 2 Overview of AMSR2Table 2-1: Main GCOM-W1 specifications
H-IIA

Launch vehicle
LaunchLaunch site

Tanegashima Space Center
FY 2012

Launch timing

Sun-synchronous sub-recurrent orbit (frozen orbit)
Altitude (above the equator)

699.6 kmOrbit
98.186°

Inclination
Local sun time at ascending node

WeightLaunch mass

13:30  15 min.

1991 kg max.
(including the propellant)

Box shape with two solar array paddles

Shape

Satellite body section

Approx. 2.2 x 2.1 x 1.8 m

Solar array paddle

Approx. 7.7 x 2.1 m

AMSR2 (after deployment)

Approx. 2.2 x 4.9 x 2.0 m

Design life

5 years

Life
Probability of survival

2.2

0.8 min., 5 years after launchPower generated

3,880 W min. (end of life)

Mission equipment

Advanced Microwave Scanning Radiometer 2

Advanced Microwave Scanning Radiometer 2 (AMSR2)

AMSR2 was developed to be the successor to AMSR on the ADEOS-II andAMSR-E on NASA’s Aqua Earth observation satellite. AMSR2’s antenna diameterhas been enlarged to 2.0 meters from the AMSR-E’s 1.6 meters to give a higher
spatial resolution. For more accurate observations, a new 7.3 GHz channel has
been added to eliminate electromagnetic-wave interference, and the calibration
system has been upgraded for better calibration accuracy.
The objective of the GCOM mission is long-term observations over a 10-year to
15-year span through three generations of satellites. After the first-generation’s
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Chapter 2 Overview of AMSR2AMSR2, successors to the AMSR2 are planned to be developed for the second
and third generation satellites.
Figure 2-2 provides exterior views of AMSR2, Table 2-2 lists its main specifications,and Table 2-3 compares the AMSR series’ sensors.

Figure 2-2: Exterior view of AMSR2Table 2-2: Main AMSR2 specifications

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Chapter 2 Overview of AMSR2Table 2-3: AMSR series comparisonsSensorAMSR

AMSR-EAMSR2Operation dates

Dec. 14, 2002 toOct. 25, 2003

May 4, 2002 toOct. 4, 2011

From May 18,
2012 onSatellite

ADEOS-II

Aqua

GCOM-W1

Major
performancechanges fromprevious AMSR
series sensors

n/aObservation
frequencies(GHz)• Enlarged thedeployed antenna• Reduced thediameter to 2
antenna diameter meters to boost(from 2.0 meters
spatial resolution
to 1.6 meters) due • Upgraded theto limitations ofcalibration system
the payload faring, for more reliable
used an antenna
calculations of
expansionbrightness
deployment
temperatures
method, and• Added a 7.3
eliminated the 50 GHz band
GHz bandchannel to lower
channelsinterference fromterrestrial radio
signals
Spatial resolution (km)6.925

40 x 70 (HV)

43 x 75 (HV)

35 x 62 (HV)

7.3

－

－

35 x 62 (HV)

10.65

27 x 46 (HV)

29 x 51 (HV)

24 x 42 (HV)

18.7

14 x 25 (HV)

16 x 27 (HV)

14 x 22 (HV)

23.8

17 x 29 (HV)

18 x 32 (HV)

15 x 26 (HV)

36.5

8 x 14 (HV)

8.2 x 14.4 (HV)

7 x 12 (HV)

50.3

6 x 10 (V)

－

－

52.8

6 x 10 (V)－－

89 (A channel)

3 x 6 (HV)

3.7 x 6.5 (HV)

3 x 5 (HV)

89 (B channel)

3 x 6 (HV)

3.5 x 5.9 (HV)

3 x 5 (HV)

Note: HV: horizontal and vertical polarization, V: vertical polarization

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Chapter 2 Overview of AMSR2AMSR2 consists of a sensor unit — essentially a rotating scanner — and a fixed
control unit as well as a momentum wheel B system carried by the satellite mission
chassis. The AMSR2’s antenna unit that picks up microwaves from Earth rotates
once every 1.5 seconds, creating a conical scan pattern covering a width of about
1,450 kilometers on the Earth’s surface each rotation. This scanning method canmake one day and one night observation of more than 99 percent of the Earth’s
surface in two days. During one rotation, the sensor collects surface observation
data over a 61 degree range centered on the flight direction and calibrator data(from a high-temperature noise source and a cold sky mirror) at all other angles.
The high-temperature noise source is a microwave absorber temperature
controlled to about 300 Kelvin, and the cold sky mirror is a reflector oriented toward
deep space. The data obtained from these two measurements are used for
two-point calibration purposes. The scanning rotational speed must be kept
constant to collect in sequence low-temperature calibration data, surface
observation data, and high-temperature calibration data at the correct rotational
angles. The rotational speed is controlled using feedback from a speed signal fromthe rotational drive unit.
Furthermore, a momentum wheel offsets the large angular momentum that thescanning unit produces. The momentum wheel’s rotational speed is controlled to
track the scanning unit’s angular momentum and its design is such that the angularmomentum has no effect on the overall satellite system.
2.2.1

AMSR2: Principle of Operation

AMSR2 is a microwave scanning radiometer that captures both horizontally andvertically polarized near-surface radiation brightness data as antenna equivalentnoise temperatures in seven frequency bands, ranging from the 6.9 GHz band to
the 89 GHz band.
Figure 2-3 illustrates how the AMSR2 operates once in orbit. The AMSR2 sensor
unit is located at the satellite’s forward apex and rotates counterclockwise about theZ-axis while in flight, creating a conical scan pattern in which the antenna beamtranscribes an arc of approximately 1,660 kilometers in diameter on the Earth’s
surface.

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Rotation (40rpm)

Incidence Angle: 55deg

Off-Nadir Angle: 47.5deg

Figure 2-3: AMSR2: Principle of operation

The antenna beam maintains an off-nadir angle of 47.5 degrees with the Z-axis,
resulting in an incidence angle of 55 degrees at the Earth’s surface due to the
Earth’s curvature.
Because microwave scanning radiometry is strongly dependent on the incidence
angle, favorable characteristics were assured by employing a conical scan patternwith a constant surface incidence angle independent of the observation point. The
55-degree incidence angle was selected because this angle is fairly immune to theeffects of sea surface winds and because it produces a large differential between
horizontally and vertically polarized signals.
The signal processor is specified to capture surface observation data over anangular sector of more than 75 degrees about the satellite’s X-direction. Not all of
this data is valid, however, due to main beam interference by the high-temperaturenoise source and cold-sky mirror. The design ensures an interference-free angularsector between -61 degrees and +58 degrees in the 6.9 GHz band and between
61 degrees in the other frequency bands. Consequently, valid surface observation
data are the data captured within the scanning arc of 61 degrees about the
X-direction. This provides a 1450-kilometer observation swath of the Earth’s
surface.
To achieve a 10-kilometer sampling interval along the satellite’s track, the nominal
conical scan period is set to 1.5 seconds, so that the sensor unit rotates once every
10 surface kilometers travelled, given the ground speed of the GCOM-W1, which
has a flight altitude of 700 kilometers. The 1.5-second scan period corresponds to a
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rotational speed of 40 rpm. And to ensure a sampling interval of 10 kilometers inthe scanning direction, the sampling period (integration period) in the scanning
direction is set to 2.6 milliseconds.
The 89 GHz band, however, has a separate beam with an offset between the twochannels of approximately 15 kilometers about the sub-satellite track. Therefore, anominal sampling interval along the sub-satellite track of 5 kilometers is used tomatch up data every three rotations. Furthermore, setting the sampling period in
the scanning direction to 1.3 milliseconds gives a nominal sampling interval of 5
kilometers in the scanning direction.
The antenna beam’s instantaneous field of view (IFOV) (footprint, defined as thehalf-power (3 dB) beam-width) projects as an ellipse on the Earth’s surface with its
major axis along the sub-satellite track. Because the antenna’s effective aperturesize is 2.0 meters in diameter, its IFOV is approximately 3 km x 5 km (along scan xalong track) in the 89 GHz band. Furthermore, the spatial resolution (distance
between pixel centers) is the measurement of an area approximately 10 kilometers
long in the scanning direction at an integration period of approximately 2.6
milliseconds (1.3 milliseconds for the 89 GHz band). Underlapping is the condition
when no overlap occurs between scan lines or between adjacent pixels in the scanline direction. AMSR2 is designed to ensure no underlapping occurs.
To observe seven frequency bands, the antenna unit contains six primaryradiometers (feed horns) arranged in a row. Because the beam axis of eachradiometer points at a different part of the main reflector, the angle necessary topoint each beam simultaneously at the surface from the main reflector is different
for each frequency. Therefore, the feed horns for each frequency are spaced apartin the direction of the sub-satellite track so that the beam axis of each frequency
and each polarization travels along the identical scan line. Furthermore, themeasurement for each frequency occurs at a slightly different time to compensate
for displacements in the scanning direction. In this way, the design ensures the
footprint for each frequency measures the identical point on the Earth’s surface.
The relative positional differences in footprints between frequencies are part of thefrequency registration.
2.2.2

System Architecture and External Appearance

AMSR2 consists of a sensor unit and a control unit. This section describes
AMSR2’s system architecture and external appearance.

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2.2.2.1

System architecture

Table 2-4 lists the subsystems and components that make up AMSR2.
Table 2-4: AMSR2 architecture
Unit
Sensor unit
(SENS
UNIT)

Component
Antenna

Abbreviation
ANT
CAL

Calibrator
RX

ReceiverAntenna drive
assembly (Antenna
drive mechanism)

ADA (ADM)

SPS
Signal processor
sensor unit

Thermal controller
sensor unit
Power distributor unit
sensor unit
Disturbance controller(Orbital balancing
mechanism/Orbitalbalancing electronics)
Sensor unit structure
Deployment
mechanism
Integrator unit
Control unit
(CONS
UNIT)

Antenna drive
assembly (Antenna
drive electronics)

TCS
PDUS

OBM/OBESTRSDEPADA (ADE)

SPC

Signal processor
control unit

Thermal controller
control unit

TCC

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Primary Functions
Directs microwave emissions from the Earth to
the receiver.
Consists of a cold sky mirror and ahigh-temperature noise source to maketemperature calibrations of observation data.
After passing the microwaves from the antenna
through a low-noise amplifier, the receiverapplies bandwidth filtering to the signal to pass
only the observation bandwidth, followed byadditional amplification, demodulation, andintegration, before outputting the signal to thesignal processor sensor unit. The receiver also
performs temperature monitoring needed forobservation data calibration.
Rotates the AMSR2’s antenna, receiver, andother components at a constant angular speed.
Performs A/D conversions of observation datafrom the receiver units, controls calibration of
the receiver, controls components in the sensor
unit according to commands from the SPC,
obtains telemetry data from within the sensor,
and outputs signals to the SPC.
Performs thermal controls within the sensor unit
based on commands from the SPS.
Acts as the power-supply interface with the
PDUC and distributes power to all the sensor
unit components.
Regulates the mass balance of the sensor
unit’s rotation part in orbit.

Securely fastens the sensor unit components inplace.
Stows the antenna’s main reflector during thelaunch phase and deploys the main reflector on
orbit.
Electrically and mechanically integrates thecomponents.
The drive mechanism (motor, bearings, etc.)that calibrates the antenna drive mechanism islocated in the sensor unit, and the antenna
drive electronics are housed in the control unit.
Adds satellite telemetry and other telemetry
data to the observation data and telemetry datafrom the SPS, edits the data into a preset
format, and outputs them to the satellite
system. Receives commands from the satellite
system and controls components in the control
unit and sends sensor unit commands to the
SPS. It also controls the rotation / disturbance
response of the ADA and MWA.
Performs thermal controls within the control unit
based on commands from the SPC.

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Chapter 2 Overview of AMSR2
Unit

Component
Power distributor unit
control unit

Abbreviation
PDUC

MWA
Momentum wheel
assembly
STRC

Control unit structure-

Integrator unit

2.2.2.2

Primary Functions
Acts as the primary power-supply interface withthe satellite system and distributes power to thecontrol unit and the PDUS.
To offset the angular momentum generated by
the scanning rotation, the momentum wheel
assembly rotates in the opposite direction to thescanner unit with same angular momentum.
Securely fastens the control unit components inplace.
Electrically and mechanically integrates thecomponents.

External appearance

Figure 2-4 provides an external view of AMSR2.
Antenna main reflector

Cold sky mirror(low-temperature calibration source)

High-temperature noise source

Antenna drive mechanism

Motor axis

Rotation part
Stationary part

Sensor unit
when deployed on orbit

Figure 2-4: Exterior schematic of AMSR2

2.2.3

Component Descriptions

This section describes the structure, primary functions, and performance of each
AMSR2 component. Note that specification values are given for the performancevalues listed here. These values may be revised later through equipment
verifications after launch.
2.2.3.1

Antenna(1) Structure
The AMSR2 antenna consists of a main reflector and a feed-horn assembly. The
feed-horn cluster consists of a shared 6/7 GHz horn, a 10 GHz horn, a shared
18/23 GHz horn, a 36 GHz horn, and two 89 GHz band horns. It also haspolarization splitters for separating vertical and horizontal polarized waves. Note
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Chapter 2 Overview of AMSR2that the splitters in the shared 6/7 GHz and 18/23 GHz feed horns are equippedwith band-rejection filters and high-pass filters to separate both frequencies.
(2) Functions
The primary functions of the antenna are as follows:a. Direct microwave emissions from the Earth to the receiver.
b. Maintain a beam width that can deliver the required spatial resolution.
c. Maintain a beam efficiency that can deliver sufficient observation accuracy.
d. Separate vertical polarized waves and horizontal polarized waves and direct eachto the receiver for each polarization.
(3) Performance
Table 2-5 lists the antenna’s main performance values.
Table 2-5: Main performance values of the antenna

Parameter

Performance Value

Centerfrequency
[GHz]

6.925

7.3

10.65

18.7

23.8

36.5

89.0
(A)

89.0
(B)

Bandwidth
[MHz]

350

350

100

200

400

1000

3000

3000

Beam width[deg.]

1.8

1.8

1.2

0.65

0.75

0.35

0.15

0.15

1

1

Cross
polarization

No more than -20dB

Main beam
efficiency

No less than 90%

Polarization

Vertical and horizontal

No. of feedhorns
2.2.3.2

1

1

1

1

Calibrator(1) Structure
To calibrate observation data, the AMSR2 calibrator consists of a cold sky mirror(CSM), which captures the brightness temperature of deep space (approximately
2.7 Kelvin) and a high-temperature noise source (HTS), which is controlled to
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approach the ambient temperature.
(2) Functions
The primary functions of the calibrator are as follows:a. The high-temperature noise source (HTS) is a microwave absorber with aconstant temperature that emits microwaves used as the calibration reference thatare fed into the feed-horn assembly.
b. The cold sky mirror (CSM), used for low-temperature calibration, directs
microwave background emissions from deep space (at 2.7 Kelvin) into thefeed-horn assembly.
(3) Performance
Table 2-6 and Table 2-7 list the main performance values of the HTS and CSM
respectively.
Table 2-6: Main performance values of the HTS

Parameter

Performance Value

Center frequency[GHz] (nominal)

6.925

7.3

10.65

18.7

23.8

36.5

89.0

Bandwidth [MHz](nominal)

350

350

100

200

400

1000

3000

Reflection
characteristics

No more than -30dB

Temperature control
range

20C  10C

Variance in surface
temperature
distribution

No more than 2.5Cp-p

Temperature
measurement
accuracy

Bias component 0.3C
Random component: within 0.4 (3) (within 0.1 isthe target)(The platinum sensor itself, temperature differences
between the microwave absorber’s surface
temperature and the temperature sensor, and errors
in the temperature measurement circuit)

Temperature
measurement range
Polarization-35C to +35C
Vertical and horizontal
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Chapter 2 Overview of AMSR2Table 2-7: Main performance values of the CSM

Parameter

Performance Value

Center frequency[GHz] (nominal)

6.925

7.3

10.65

18.7

23.8

36.5

89.0

Bandwidth [MHz](nominal)

350

350

100

200

400

1000

3000

Reflector shape

Offset parabola

Beam width

No more than 10 degrees

Polarization

Vertical and horizontal

2.2.3.3

Receiver(1) Structure
The AMSR2 receiver has independent receiver units for each horizontal polarizedwave and vertical polarized wave at each observation frequency.
(2) Functions
The primary functions of the receiver are as follows:a. After passing the microwaves from the antenna through a low-noise amplifier, thereceiver applies bandwidth filtering to the signal to pass only the observation
bandwidth, followed by additional amplification, demodulation, and integration,
before outputting the signal to the signal processor sensor unit.
b. The receiver also performs temperature monitoring needed for observation datacalibration.
c. It has auto gain control (AGC), which adjusts the gain according to control signals
from the signal processor sensor unit.
d. The receiver has a DC/DC converter that provides a voltage reference to stabilize
the primary power source.
(3) Performance
Table 2-8 lists the receiver’s main performance values.

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Chapter 2 Overview of AMSR2Table 2-8: Main performance values of the receiverParameter

Performance Value

Centerfrequency [GHz]

6.925

7.3

10.65

18.7

23.8

36.5

89.0

IF center
frequency [GHz]

-

-

2.5

2.5

2.2

2.5

4.5

Bandwidth [MHz]

350

350

100

200

400

1000

3000

Noise factor [dB]

1.25

2.0

1.52

2.30

2.70

4.40

6.70

Integration
period [ms]

2.6

1.3

2.2.3.4 Antenna drive assembly(1) Structure
The AMSR2 antenna drive assembly consists of the antenna drive mechanism andthe antenna drive electronics. The antenna drive mechanism consists of thefollowing main components. Note that the antenna drive electronics are housed in
the control unit.
1.Motor
2.Bearings
3.Slip ring
4.Encoder
5.Commentator(2) Functions
The primary functions of the antenna drive assembly are as follows:a. Make conical scans of the Earth’s surface by rotating the AMSR2 antenna,
receiver, and other components at a constant angular speed.
b. The fixed axle’s bearings support the rotation part of the sensor unit.
c. It has a slip ring and related parts in order to pass power and signals bidirectionally
between components in the rotation part of the sensor unit and components in thestationary part.
d. The encoder functions to monitor the angular position and speed of the rotation
part.
(3) Performance
Table 2-9 lists the antenna drive assembly’s main performance values.
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Chapter 2 Overview of AMSR2Table 2-9: Main performance values of the antenna drive assembly

Parameter

2.2.3.5

Performance Value

Scanning
angularspeed

40 rpm  1%(forobservations)

Direction ofrotation

Rotatescounterclockwise about
the +Z axis

Remarks

The functionality exists
nominal to obtain correct
observation data andcalibration data forrotational speeds
anywhere between 30
rpm and 40 rpm.

Signal processor(1) Structure
The AMSR2 signal processor consists of the signal processor sensor unit (SPS)
and the signal processor control unit (SPC). The SPS contains the disturbance
controller’s electronic circuitry. The SPC and its interface have a redundant
structure.
(2) Functions
The primary functions of the signal processor are as follows:a. Performs A/D conversions of observation data from the receiver units, edits
calibration data and telemetry data into a preset format, and outputs them to thesatellite system.
b. Controls the receiver gain and controls the offset.
c. Controls the receiver’s integrator discharge timing and the receiver’s hold timing.
d. Measures the temperature of the sensor unit and control unit.
e. Processes commands from the satellite system and sends them to other AMSR2
components. Also edits telemetry data collected from other AMSR2 components
and sends them to the satellite system.
f. It has a DC/DC converter that it provides a voltage reference to stabilize theprimary power source.
(3) Performance
Table 2-10 lists the signal processor’s main performance values.

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Chapter 2 Overview of AMSR2Table 2-10: Main performance values of the signal processor

Parameter

2.2.3.6

Performance Value

A/D conversion
resolution of receiversignals

12 bitNo. of temperature
sensor channels

SPS: 46 channels
SPC: 31 channels

Temperature
measurement cycle

1.5 seconds (during regularobservations)

Thermal controller(1) Structure
The AMSR2 thermal controller consists of the thermal controller sensor unit (TCS)
and the thermal controller control unit (TCC).
(2) Functions
The primary functions of the thermal controller are as follows:a. The TCS controls the temperature within the sensor unit with an on/off control in
response to commands from the signal processor sensor unit.
b. The TCC controls the temperature within the control unit and the sensor unit withan on/off control in response to commands from the signal processor control unit.
2.2.3.7 Power distribution unit
(1) Structure
The AMSR2 power distribution unit consists of the power distribution unit sensor
unit (PDUS) and the power distribution unit control unit (PDUC).
(2) Functions
The primary functions of the power distribution unit are as follows:a. The PDUS acts as the interface with the PDUC and distributes power to all the
sensor unit components. It also has a capacitor bank.
b. The PDUC acts as the primary power-supply interface with the satellite systemand distributes power to the AMSR2 control unit and the PDUS. It also has acapacitor bank.

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Chapter 2 Overview of AMSR2(3) Performance
Table 2-11 lists the power distribution unit’s main performance values.
Table 2-11: Main performance values of the power distribution unit

Parameter
Max.
power
2.2.3.8

Performance Value

distributed Sensor unit: no less than 533 W
Control unit: no less than 900 W

Disturbance controller(1) Structure
The AMSR2 disturbance controller consists of the disturbance controller sensor
unit and the disturbance controller control unit. The disturbance controller sensor
unit consists of the orbital balancing mechanism (OBM) and the orbital balancing
electronics (OBE), and the disturbance controller control unit consists of the
momentum wheel and the momentum wheel control circuit. The disturbance
controller electronics are contained in the signal processor control unit.
(2) Functions
The primary functions of the disturbance controller are as follows:a. To offset the angular momentum generated by the scanning rotation, thedisturbance controller’s momentum wheel assembly rotates in the opposite
direction to the scanner unit with the same angular momentum.
b. The active OBM regulates the mass balance of the sensor unit’s rotation partwhen in orbit.
c. The momentum wheel and the OBM are controlled with commands from thesatellite system.
d. The controller runs up and runs down the rotation as necessary so as to minimize
any influence on the satellite’s attitude.
2.2.3.9

Deployment mechanism(1) Structure
The AMSR2 deployment mechanism consists of the following main components:
1. Supports
2. Hinges
3. Joints
4. Damper
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5. Latch(2) Functions
The primary function of the deployment mechanism is as follows:a. Stows the antenna’s main reflector during the launch phase and deploys the mainreflector on orbit.
(3) Performance
The primary performance values of the deployment mechanism are as follows:a. Has sufficient torque to deploy the antenna’s main reflector on orbit.
b. Is able to maintain the prescribed alignment accuracy after deployment of theantenna’s main reflector.
2.2.4

Operational Modes

2.2.4.1

Mode definitions

AMSR2 has six operational modes, as described below.
(1) Science ModeIn this mode, AMSR2 collects and edits observation data and outputs the data as
mission data to the satellite. All devices are on, including the receivers. In the
Observation Mode, mission data and HK telemetry data are output to the satellite.
This mode includes a standby state that starts after the heater for the receivers andthe high-temperature noise source (HTS) is turned on and that continues until thestart of observation data collections.
(2) Safe 1 ModeIn this mode, observations are stopped. The antenna drive mechanism (ADM) andthe momentum wheel assembly (MWA) continue to rotate at the regular angularspeed. The receiver’s power supply is on, and the thermal settings are active tomaintain all devices within their permitted temperature ranges. AMSR2 transitions
from the Science Mode to this mode in response when the satellite goes into its
Safe Mode.
(3) Survival ModeIn this mode, devices are maintained within their operating temperature ranges.
The ADM and the MWA are stopped, and the survival thermal settings are active to
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maintain all devices within their permitted temperature ranges. In this mode, thesignal processor handles only telemetry data and command processing.
(4) Off ModeIn this mode all devices are off. This mode is active when launching, when
deploying the antenna, and when releasing the sensor unit.
(5) Run Up
This mode runs up the rotational speed of the ADM and the MWA from a stationary
state to the regular rotational speed. AMSR2 remains in this mode until a stablerotation is achieved. The acceleration behavior and a series of operations andstates, such as thermal setting switchovers, after the ADM heater is turned on areused as the acceleration reference.
The rotational speed is increased in a number of stages to keep the resulting
disturbance to the satellite within the permissible range. Where necessary, the
MWA rotational speed is corrected and the sensor unit’s mass balance is adjustedwith the OBM. Note that the ADM’s regular rotational speed is 40 rpm, and the
MWA’s regular rotational speed is approximately 2,000 rpm (when two wheels arein operation).
(6) Run Down
This mode runs down the rotational speed of the ADM and the MWA from theregular rotational speed to a stationary state. The deceleration is regulated by thedeceleration behavior and a series of operations and states, such as thermal
setting switchovers. In normal on-orbit operation of AMSR2, this mode is not used.
2.2.4.2

Mode transitions

AMSR2 is launched in the All Off Mode, and the Survival Mode maintains device
temperatures until antenna deployment and sensor unit release. During antenna
deployment and sensor unit release, it goes into the All Off Mode, which shuts
down all devices temporarily, after which percussion caps trigger the deployment
and release.
AMSR2 moves into the Survival Mode after antenna deployment and sensor unit
release and then commences the run-up according to the run-up schedule. Whennecessary, the OBM adjusts the sensor unit’s mass balance during the run-up.
Once the ADM and the MWA reach their regular rotational speeds, AMSR2 moves
first into the Sleep Mode and then finally into the Normal Mode by powering on the
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receivers and making the Normal Mode thermal settings.
Figure 2-5 illustrates the AMSR2 mode transitions after antenna deployment andsensor unit release.
Note that AMSR2, in regular operation, runs in Normal Mode, in which normal
observations are made. It can, when necessary, transition into the Sleep Mode,
Run Down Mode, or Survival Mode.

When an LLM command is received while the disturbancesystem is processing an anomaly, the disturbance system’s
anomaly processing sequence is given priority.

Figure 2-5: AMSR2 mode transitions

2.2.5
2.2.5.1

Radiometric Performance
Observation frequencies and polarizations

The AMSR2 observation frequencies and polarizations are as given in Table 2-2.
The main difference from AMSR-E is the addition of the 7.3 GHz frequency, whichis intended to reduce radio-frequency interference.
2.2.5.2

Main beam efficiency

The main beam efficiency is the fraction of the total desired polarized energy
received within an angle 2.5 times the beam width. The main beam efficiency is
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expressed as an average value within a given observation band. The design
requirement for AMSR2 is a main beam efficiency of no less than 90 percent in all
observation frequency bands.
2.2.5.3

Temperature resolution

Table 2-12 gives the temperature resolution for each AMSR2 observation
frequency band.
Table 2-12: AMSR2 temperature resolutions (specification values)

Center Frequency[GHｚ]

Temperature Resolution [K](nominal)

2.2.5.4

6.925

No more than 0.34 (target is no more than
0.3)

7.3

No more than 0.43

10.65

No more than 0.7 (target is no more than 0.6)

18.7

No more than 0.7 (target is no more than 0.6)

23.8

No more than 0.6 (target is no more than
0.55)

36.5

No more than 0.7 (target is no more than
0.65)

89.0

No more than 1.2 (target is no more than 1.1)

Dynamic range

The AMSR2 dynamic range is 2.7 to 340 Kelvin. The dynamic range is maintainedby adjusting the gain / offset of receivers so that the high-temperature andlow-temperature calibration outputs are contained within the given output range.
2.2.5.5 Linearity
The AMSR2 linearity (specification value) is 1 percent (RMS).
2.2.6

Calibration

AMSR2 collects low-temperature and high-temperature calibration data each
1.5-second scanning cycle that are used to correct receiver gain fluctuations.
Figure 2-6 provides an exterior view of the CAL in order to show the positional
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Chapter 2 Overview of AMSR2

relationship of the high-temperature noise source (HTS) and the cold sky mirror(CSM).

Figure 2-6: Exterior view of the CAL

2.2.6.1

Low-temperature calibration

Low-temperature calibration takes place by observing deep space (with abrightness temperature of 2.7 Kelvin) using a cold sky mirror (CSM). The
calibration brightness temperature, however, rises above 2.7 Kelvin due to theeffect of receiving radiation other than deep space background radiation.
There are many error factors affecting the low-temperature calibration brightness
temperature: Earth radiation, moon radiation, radio frequency interference fromstationary satellites, solar contamination, radiation from the satellite structure itself,and Earth radiation projecting on the main reflector. The largest of these are Earthradiation, moon radiation, radio frequency interference, and solar effects. Theseerrors are corrected in the Level 1A processing stage.
2.2.6.2

High-temperature calibration

High-temperature calibration takes place by observing the high-temperature noise
source (HTS), a microwave absorber that is controlled at a temperature of 300
Kelvin.
The high-temperature calibration brightness temperature may contain errors due to
HTS non-uniformities or discrepancies with the platinum sensor measurement
temperature. These errors are corrected in the Level 1A processing stage. (See
Section 4.2.1.)
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2.2.7

Geometric Performance

The AMSR2’s geometric performance must be gauged by accounting for the
performance of numerous factors, including the off-nadir angle, the Earth incidence
angle, the scanning arc, the observation swath, the beam width associated with the
instantaneous field of view, the footprint — which considers the instantaneous fieldof view and the integration period, and the pointing.
2.2.7.1

Off-nadir angle and Earth incidence angle

The off-nadir angle and the Earth incidence angle are two geometrical parameters
related to the Earth’s curvature and the satellite’s altitude. In the AMSR2’s
hardware design, these parameters are dependent on the design of the beam
direction relative to the AMSR2’s coordinate axis. Figure 2-7 illustrates therelationship of these parameters.
For the AMSR2’s design, the flight altitude is assumed to be 700 kilometers and theEarth’s radius is assumed to be 6,378 kilometers. The off-nadir angle is set to givean Earth incidence angle of 55 degrees. These factors result in a nominal off-nadir
angle of 47.5 degrees.
AMSR2
off-nadirangle
オフナディア角
θ1Figure 2-7: Illustration of AMSR2’s geometric performance (off-nadir and incidence angle)

For the 89 GHz band, however, the sensor uses two beams, 89 GHz A and 89 GHz
B, with a nominal offset of 15 kilometers along the sub-satellite track. Setting the 89
GHz B beam’s off-nadir angle to 47.0 degrees gives a nominal offset between 89
GHz A and 89 GHz B of 14.98 kilometers on the Earth’s surface. The change in theoff-nadir angle produces a nominal incidence angle of 54.5 degrees for the 89 GHz
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B beam.
Note that AMSR2 uses a method by which six feed horns simultaneously use themain reflector, as shown in Figure 2-8. The orientation and position of each feedhorn is offset geometrically to give a sensor off-nadir angle of 47.5 degrees.

Figure 2-8: Layout of the feed-horn assembly

2.2.7.2

Scanning arc and observation swath

With the AMSR2’s off-nadir angle set to 47.5 degrees, the movable portion that
includes the antenna rotates counterclockwise about the Earth-center axis (theZ-axis) to produce a conical scan pattern in which the beam center, as seen relative
to the AMSR2’s stationary part, transcribes an arc 1,660 kilometers in diameter on
the Earth’s surface.
When a scanning arc of 61 degrees about the sub-satellite track (X-axis) is
maintained, a nominal observation swath of 1,450 kilometers can be guaranteed as
the Earth projection swath covered by the scanning arc.
To ensure this scanning arc, the requirements on the AMSR2’s hardware design
are: (1) there are no obstacles in the main beam’s path between the feed horns andthe main reflector and between the main reflector and the Earth surface, and (2) thesignal processor captures and processes data that wholly contains the prescribedscan arc.
The design ensures the main beam is interference-free over an angular sector
between -61 degrees and +58 degrees in the 6.9 GHz band and between 61
degrees in the other frequency bands. Furthermore, the signal processor is
specified to capture observation data over an angular sector of more than 75
degrees. By meeting both requirements, the scanning arc is sufficiently ensured.
Even though some field-of-view interference is expected in the +58 degree to +61
degree range in the 6.9 GHz band, no problems are expected in terms of gain,
beam efficiency, cross polarization, or signal strength. Therefore, the design
ensures an effective scanning arc of no less than 61 degrees and an observation
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swath of no less than 1,450 kilometers for all frequencies.
2.2.7.3

Rotational speed, scan period, and sampling interval

The rotational speed, scan period, and sampling interval are related through the
AMSR2’s geometric imaging principles. The sampling interval in the flight direction
corresponds to the distance traveled on the surface along the sub-satellite track
during the time (the scan period) between two consecutive data captures by the
AMSR2’s rotation part when rotating at a constant rotational speed. Figure 2-9
illustrates this relationship.
Assuming a flight altitude of 700 kilometers and an Earth radius of 6,378 kilometers,
the GCOM-W1 ground speed is 676 kilometers per second. If the AMSR2’srotational part rotates at a nominal speed of 40 rpm, a scan period of 1.5 seconds
corresponds exactly to one revolution, resulting in a nominal sampling interval in
the flight direction of 10.14 kilometers.

Figure 2-9: Illustration of AMSR2’s geometric performance (sampling interval, beam width,
footprint, and overlap)

2.2.7.4

Sampling period, integration period, and sampling interval

The AMSR2’s beam center transcribes an arc 1,660 kilometers in diameter on the
Earth’s surface. Given that the AMSR’s rotational part rotates at a nominal 40 rpm,
setting the sampling period to 2.6 milliseconds gives a nominal sampling interval in
the scanning direction of 8.87 kilometers. The sampling period for the 89 GHz bandis set to 1.3 milliseconds, resulting in a nominal sampling interval in the scanning
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direction of 4.44 kilometers.
2.2.7.5 Beam width and footprint
The beam width is set based on the antenna characteristics. Table 2-13 gives thebeam width design values for the antenna.
The main beam’s shape and distance on the Earth surface (called the “footprint”)
are dependent on the flight altitude, the off-nadir angle, and the beam width. The
footprint’s shape is determined by the Earth’s curvature.
As Figure 2-9 indicates, the footprint in the flight direction is determinedgeometrically as the Earth incidence arc between the upper and lower limits of thebeam width in the flight direction. Table 2-13 also lists the footprint corresponding to
the nominal beam width assuming a flight altitude of 700 kilometers and an Earthradius of 6,378 kilometers.
Table 2-13: Beam width and footprint

2.2.7.6

CenterFrequency

Beam Width(nominal)

Footprint(along scan x along
track)

6.925 GHz

1.8  15%

35 x 61 km

7.3 GHz1.8  15%

35 x 61 km

10.65 GHz

1.2  15%

24 x 41 km

18.7 GHz

0.65  15%

13 x 22 km

23.8 GHz

0.75  15%

15 x 26 km

36.5 GHz

0.35  15%

7 x 12 km

89.0 GHz A

0.15  15%

3 x 5 km89.0 GHz B

0.15  15%

3 x 5 km

Remarks
Assuming aflight altitude of
700 kilometers
and an Earthradius of 6,378
kilometers

Overlap and underlap

The overlap in the flight direction is the ratio of the flight direction distance thatconsecutive scan-period footprints overlap. Conversely, underlapping is thecondition when consecutive footprints do not overlap.
The overlap in the scanning direction is the ratio of the scanning direction distancethat two footprints, covering consecutive integration periods in the scanning
direction, overlap. Conversely, underlapping is the condition when these
consecutive footprints do not overlap.
Table 2-14 provides the overlap ratios in both the flight direction and scanning
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direction for each observation frequency assuming the nominal design conditions.
Table 2-14: Overlap ratios (under nominal design conditions)Overlap Ratio

2.2.7.7

Frequency Band

Scanning
Direction

FlightDirection

6.925 GHz

82.6%

86.6%

10.65 GHz

76.3%

80.3%

18.7 GHz

62.9%

63.0%

23.8 GHz

66.2%

67.8%

36.5 GHz

46.5%

29.5%

89GHz A/B

42.5%

22.6%

Frequency registration

Frequency registration accounts for the following error sources in the flight direction
and the scanning direction.
- Scanning direction• Antenna beam orientation error• Imaging timing discrepancies(Errors in time correction calculations for each frequency to collect data fromthe same surface point)- Flight direction• Antenna beam orientation error• Flight effects(Time differences for each frequency to collect data from the same surface
point)The initial antenna beam orientation error and the imaging timing discrepancies areerror factors that emerge in the initial design, assembly, or production. The flighteffects and antenna beam orientation fluctuations based on thermal distortions areerror factors that emerge after launch.
Strictly speaking, the error factors in the flight direction actually affect both the flight
direction and scanning direction, but the extent of their effect is maximized in the
AMSR2’s flight direction when making observations directly along the flight
direction (when the scan passes through the X-Z plane). Consequently, the effects
were evaluated under this condition. The effects in the scanning direction were
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found to be substantially negligible.
The design accounted for all these error factors and has the center of the beamwidth for each frequency and each polarization pass along the same scan line,
except for the 89 GHz band B channel, with an error in the flight direction within
0.16 degrees.
The sampling center error in the scanning direction is within 0.26 degrees
measured from the sampling start position of the 89 GHz band A channel.
2.2.7.8

Pointing

The bore-sight misalignment of the AMSR2’s sensor’s antenna beams is estimatedby aggregating the various tilt-angle error sources in the scanning direction andflight direction when the antenna beam is pointed along the sub-satellite track (inthe center of the scanning arc), using the AMSR2’s coordinate system with its origin
at the center of the ADM installation plate. The error sources can be divided into afixed error component that is a constant error independent of time, and a variableerror component that is time-dependent. Because the fixed error component isconstant, it can potentially be corrected by improving the angular error recognition
accuracy, which includes assessments using observation data. The variable
component, however, will be included in the observation data as an error.
2.3

A-Train

The Afternoon Constellation (A-Train) is a NASA-directed constellation of Earthobservation satellites in a sun-synchronous orbit at an altitude of approximately 700
kilometers that crosses the equator each day at about 13:30.
Because the observation orbit of A-Train satellites is closely regulated, different
satellites are able to observe the same surface point less than ten minutes apart.
The A-Train currently consists of four orbiting satellites — Aqua, Cloudsat,
CALIPSO, and Aura. GCOM-W1 will join the A-Train with the aim of furtheringscientific research using AMSR2 data.

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Entry position of the Shizuku

Figure 2-10: View of the A-Train

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Chapter 3 Overview of the GCOM-W1 Ground System

Chapter 3
3.1

Overview of the GCOM-W1 Ground System

Overall System

The GCOM-W1 ground system comprises three systems: the satellite control
system, the mission operation system, and the analysis and research system. In
general, the satellite control system operates the satellite; the mission operation
system is responsible for data processing; and the analysis and research system isused for algorithm development and calibration verifications. The mission operation
system, which we will look at in detail here, is divided into three parts: the dataprocessing system, which processed satellite observation data; the EORC
information system, which stores and manages processes products and thesatellite’s operational data as well as transfers data within the system and tooutside entities; and the data provision subsystem, which supplies data to generalusers.
The tracking network system makes use of the ground network system to receive
telemetry data, send commands, and conduct ranging over the S band. Receiving
and recording equipment at the domestic and international stations receives dataover the X band, demodulates the observation data, and prepares APID sorted
data (ASD).
The satellite control system both establishes the mission operation plan and thesatellite operation plan and processes telemetry data / commands via the tracking
network system and the Svalbard station.

Figure 3-1: Overview of the GCOM-W1 ground system

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Chapter 3 Overview of the GCOM-W1 Ground System

The mission operation system processes observation data received over the X
band by the receiving and recording equipment at the domestic and international
stations. Data processing begins with the input of ASD and proceeds in three steps:
Level 0, Level 1, and Higher Level Processing (Level 2 and Level 3). The system
also takes care of data storage management and user services. And the systemprocesses near-real time products and provides them to institutional users.
The analysis and research system takes Level 0 data and Level 1 products fromthe mission operation system for validating the algorithms and parameters used inprocessing and calibration.
3.2

GCOM-W1 Mission Operation System

The GCOM-W1 mission operation system takes as its input mission data received
and demodulated at the X-band receiving stations and, after processing the
AMSR2 data, stores and manages the data as an archive. The system also
provides near-real time products to institutional users and acts as a service contact
point for general users.
3.2.1

GCOM-W1 Data Processing System

The GCOM-W1 data processing system applies Level 0, Level 1, and Higher Level
Processing to interfaced mission data (ASD) from the X-band receiving stations
and provides the processing results to the EORC information system. The system
also does near-real time processing for institutional users. This system contains thefollowing subsystems.
3.2.1.1

Processing controller

This subsystem establishes the processing plan for GCOM-W1 mission data andmanages the plan’s status, controls processing functions according to the plan(such as calling external functions), and regulates the processing computer loads.
And when a processing algorithm is updated, this subsystem reprocesses the dataand governs, as above, the series of operations from processing planestablishment to data processing.
3.2.1.2

Individual processors

This subsystem executes (provides) processing functions (Level 0, Level 1,
Higher-level, and near-real time processing) for GCOM-W1 / AMSR2 data based
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Chapter 3 Overview of the GCOM-W1 Ground Systemon the processing controller’s processing plan. The figure below illustrates theoverall data processing scheme.

Figure 3-2: Overview of AMSR2 data processing

3.2.2

EORC Information System (for GCOM-W1)

The EORC information system acts as the primary interface between the mission
operation system and the X-band receiving stations and other projects (equipment)at the Tsukuba Space Center. It also has the equipment for archiving mission dataand supplying user services. This system contains the following subsystems.
3.2.2.1

Data transfer subsystem

This subsystem provides the data interface between the mission operation systemand external mission operation systems that are associated with GCOM-W1 (suchas the satellite control system, the tracking network system (including the domestic
and international stations), and the analysis and research system).
Data transfers via this subsystem between external mission operation systems are
permitted if the facility infrastructure allows for it.
3.2.2.2

Data management subsystem

This subsystem provides storage, management, and other functions for AMSR2Level 0 data and Level 1 and Higher-level processed products created by the
GEOSS data processing system (for GCOM-W1). It also stores and manages
auxiliary data needed for data processing (such as orbital information and global
objective analysis data).
Note that the most recent generation of Level 1 products is stored and managed in
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accordance with the data storage policy for GCOM-W1 standard products. For
higher-level processed products, the most recent generation and the previous
generation are stored and managed.
3.2.2.3

Catalog management subsystem

This subsystem stores in a database catalog information on GCOM-W1 / AMSR2
standard products (Level 1 and higher-level processed products) and provides
archived products in response to requests from users who receive products via thedata provision subsystem described below.
3.2.2.4

Remote monitoring function

This function provides remote monitoring of the status of GCOM-W1 mission
operation system’s computers and equipment running the system’s interfaces aswell as the state of mission data reception, processing, and distribution. Remotemonitoring is done from the satellite control system (in the operations room), which
operates 24 hours a day.
3.2.3

GCOM-W1 Data Provision Subsystem

The GCOM-W1 data provision subsystem belongs to the GCOM-W1 mission
operation system, maintains an interface with the EORC information system, andhas the following functions to provide online data providing services to general
researchers.
• Client information management function for general researchers (users)• Search function for product archives• Online product provision function• Online tool provision function• Status / history management function
Note that this subsystem provides the same search and provision functions for
ADEOS-II / AMSR and Aqua / AMSR-E products, which belong to the same sensor
family as AMSR2.

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Figure 3-3: Home window of the GCOM-W1 data providing service

3.3

Receiving and Recording Equipment

The receiving and recording equipment (X-band receiving stations) used for
GCOM-W1 operations are located at the Katsuura Station, Tsukuba Station, and
KSAT (Svalbard Station). The domestic Katsuura Station and Tsukuba Station
generally receive real-time visible signals and the Svalbard Station receives global
observation data. The following sections give a brief explanation of each station.
3.3.1

Katsuura Station (primary domestic station)

Katsuura Station’s receiving and recording equipment receives and records
observation data collected at domestic antenna installations. The equipment
provides ASD to the mission operation system. The main purpose of Katsuura
Station’s normal X-band operations is the reception and transfer of real-timeobservation data as well as real-time HK telemetry data. The received real-time HK
telemetry data serve as a backup to the S-band real-time telemetry data.
Katsuura Station’s equipment includes the following subsystems.
• RF subsystem (performs frequency conversions and demodulation)• Baseband subsystem (performs CCSDS packet processing)• Recording subsystem (that processes data as far as APID sorting)

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Chapter 3 Overview of the GCOM-W1 Ground System

Figure 3-4: View of Katsuura Station

3.3.2

Tsukuba Station (backup domestic station)

The KSAT conformance testing equipment located at the Tsukuba Space Centerhas been updated after conformance testing and has been outfitted to run the
Tsukuba Station’s X-band operations. Like the Katsuura Station, this equipment
provides ASD to the mission operation system. The main purpose of the Tsukuba
Station’s X-band operations is the reception and transfer of real-time observation
data as well as real-time HK telemetry data. The received real-time HK telemetry
data serve as a backup to the S-band real-time telemetry data. Tsukuba Station
operates as the backup domestic station when the primary Katsuura Station is
offline because of contention with other satellites or for regular maintenance
operations.

Figure 3-5: View of Tsukuba Station

Tsukuba Station’s equipment includes the following subsystems.

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Chapter 3 Overview of the GCOM-W1 Ground System• Antenna subsystem• X-band modem subsystem• Data acquisition subsystem• S-band TT&C subsystem• M&C subsystem
3.3.3International Station

GCOM-W1 satellite operations make use of the Svalbard Station (located at 78
North latitude), operated by the Norwegian company, Kongsberg Satellite Services
AS (KSAT).
3.3.3.1

KSAT / Svalbard Station

The KSAT / Svalbard Station is responsible for S-band TT&C operations (telemetry
and commands) and X-band mission data transfer operations. During initial
functional check-out operations, the station will confirm the ranging functions andperformance and will conduct ranging operations if needed because of a satellite
anomaly, etc. It also creates ASD from received X-band observation data and HK
telemetry data, which it transfers to the mission operation system at the TsukubaSpace Center. The main purpose of the KSAT / Svalbard Station’s normal X-band
operations is the reception and transfer of observation data stored on the satellite’sdata recorder as well as stored HK telemetry data. The received real-time HK
telemetry data serve as a backup to the S-band real-time telemetry data.

Figure 3-6: View of KSAT / Svalbard Station (taken from the KSAT website)

The KSAT / Svalbard Station maintains fully redundant receiving equipment. In
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most reception failure cases, the equipment can be restored by the next pass even
if the observation data transfer operation does not function temporarily. Therefore,
the normal operation plan is to downlink two passes worth of data (the current passand the previous pass) each downlink cycle.
The station monitors S-band real-time HK telemetry data in correspondence with
the X-band reception pass. Commands are given for operations during anomalies,
etc.
3.4
3.4.1

Overview of Ground System Operations
Data Reception

During normal operations, the KSAT / Svalbard Station is the downlink station for
mission data, the Katsuura Station’s purpose is to acquire observation data forJapan and vicinity, and the Tsukuba Station acts as a backup station. Downlinks to
the KSAT / Svalbard Station, in principle, cover all visible passes. To cope with any
short-term problems at the ground station or reception errors, each downlink cycle
retrieves observation data from the last two passes. This station also operates the
S band (TT&C).
Table 3-1 summarizes the satellite operations at each ground station.

StationKatsuura
Station(X band)
Tsukuba
Station(X band)
KSAT /
Svalbard
Station(S/X
band)
KSAT /
Troll
Station(S band)
JAXA
new GN
Station(S band)

Table 3-1: Ground station satellite operations
TT&C Operations (SMission Data Transfer Operations (X band)
band)
Command Real-Time
GlobalReal-TimeStoredReal-Time
Ranging/ Stored
Observation
Observation
Telemetry
Telemetry
Telemetry
Data
Data
Yes
－
－
Yes
Yes
Yes
(Japan andvicinity)
Yes
－
－
Yes
Yes
Yes
(Japan andvicinity)
Yes

Yes

Yes

Yes

Yes*2
(North Poleand vicinity)

－

Yes

－

－

－

－

Yes

Yes

－

－

－

－

Yes

*1

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Chapter 3 Overview of the GCOM-W1 Ground System*1. Svalbard Station undertakes ranging operations only when necessary.
*2. As there are no plans to use real-time observation data from the North Poleand vicinity, this data will be received by the Svalbard Station but the data will not
distributed.
In addition to the above, real-time observation data will be distributed directly fromthe satellite by X-band transmissions to surface stations (direct domestic andinternational receiving stations) that request it. Transmissions to direct domestic
and international receiving stations operate on the basis of an agreement between
JAXA and the corresponding domestic or international institution.
Note that if the Svalbard Station cannot be used for a long period, transmission
operations will be performed using backup international stations (Kiruna Station
and Fairbanks Station).
The KSAT / Troll Station is used to receive S-band telemetry data (real-time andstored telemetry data). Reception of S-band telemetry data at the Troll Station isconducted within the range of downlinks established by the Troll Station’s antenna(7.3 meter diameter).

Figure 3-7: View of KSAT / Troll Station (taken from the KSAT website)

3.4.2
3.4.2.1

Normal Processing
Processing by the mission operation system

Observation data received at the KSAT / Svalbard Station and the Katsuura Station
are transferred as ASD to the mission operation system at the Tsukuba Space
Center, where the data undergo Level 0, Level 1, and Higher Level Processing, as
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Chapter 3 Overview of the GCOM-W1 Ground Systemdefined in Table 3-2. The orbit and attitude information used in Level 1 processing
is based on the use of the onboard GPS receiver’s orbit and attitude values
contained in the sensor packet’s payload correction data (PCD). (When the GPS is
not locked on, the GPS lock-off flag is raised and the attitude and orbit control
system (AOCS) orbit propagation value is input in the PCD.) Note that inreprocessing, it is possible to also use the orbit generation values created with theunified flight dynamics system (uFDS).
Table 3-2: Definitions of mission data processing

Data Set

Details
Data set (CADU) after converting RF

Raw data

signals from the satellite (modulated
waves are demodulated with ademodulator) to a bit-stream digital
signal.

APID sorted data
(ASD)

Data set after sorting CADU by each
VCID, synchronizing the data into
CCSDS-compliant packets, and sortingthem by APID.

Level 0 data

Data set after sorting the previous data
set based on the time and packet
sequence count in the ASD and applying
loss processing.
Data set after taking the Level 0 productas the input and applying the following
processes:

Level 1A product

Level 1B product(standard
product)Scene division
Calculation of radiometriccorrection dataCalculation of geometric data
Addition of packet loss data andquality data

Data set after taking the Level 1A
product as the input and converting
digital output values from the sensor to
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Supplied toOperational
Institutions
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Chapter 3 Overview of the GCOM-W1 Ground System

Data Set

Details

Supplied toOperational
Institutions
JMA
JAFIC

brightness temperatures.

Near-real time
Level 1B product(Japan andvicinity)

The Level 1B product uses real-time
AMSR2 observation data for Japan andvicinity downlinked at the domestic
stations and global AMSR2 observation
data downlinked at the Svalbard Station
to create near-real time data indownlinked units.

Level 1R product(standard
product)

This product reorganizes the AMSR2
L1B product according to the resolution
at each frequency.

Level 2 product(standard
product)

This product calculates various
geophysical parameters related to waterusing the Level 1B or 1R products as
inputs.

Near-real time
Level 2 product(Japan andvicinity)This product provides near-real time
Level 2 geophysical parameters createdusing the Japan and vicinity near-realtime L1 product and global near-realtime L1 product as inputs.

Level 3 product(standard
product)

This product makes ground projections
on the globe and in the North Pole andSouth Pole regions by taking the timeand spatial averages of the Level 1B andLevel 2 standard products. Daily andmonthly statistical products are createdfor both ascending data sets anddescending data sets.

Yes*

Yes*

*See Table 3-3 and Table 3-4 below for details on the products supplied innear-real time.

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3.4.2.2

Delivering products to operational institutions

The Japan and vicinity and global AMSR2 brightness temperature product (L1B)
and the geophysical parameter product generated through Higher Level
Processing (L2) are supplied as given in Table 3-3 and Table 3-4 to the Japan
Meteorological Agency (JMA) and the Japan Fisheries Information Service Center(JAFIC) within the delivery time requirements from observation to product delivery(near-real time processing requirements). Note that global observation data
supplied in near-real time is delivered in the data units received at the KSAT /Svalbard Station.
The number of operational institutions provided with near-real-time products may
expand in the future.
Table 3-3: Product delivery to operational institutions (for JMA)

Delivered Product
AMSR2 Level 1B
Near-real-time
product

Coverage
RegionJapan
andvicinity
Brightness
temperature(TB)

AMSR2 Level 2
Near-real-time
product

Delivery Time
Requirements
No less than 80% within 30
minutes of the observationtime
No less than 95% within 48
minutes of the observation
time

GlobalNo less than 70% within
2.5 hours of theobservation time
No less than 90% within
4.1 hours of theobservation time

Sea iceconcentration Global(SIC)

No less than 70% within
3.0 hours of theobservation time
No less than 90% within
5.0 hours of theobservation time
No less than 95% within
8.0 hours of theobservation time

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Delivered Product
Snow depth(SND)
Soil moisture
content(SMC)

Sea surface
temperature(SST)

Sea surface
wind speed(SSW)

Coverage
Delivery Time
RegionRequirements
GlobalNo less than 90% within
5.0 hours of theobservation time
GlobalNo less than 90% within
5.0 hours of theobservation time

GlobalNo less than 70% within
3.0 hours of theobservation time
No less than 90% within
5.0 hours of theobservation time
No less than 95% within
8.0 hours of theobservation time

Japan
and
vicinity

No less than 80% within
1.0 hours of theobservation time

GlobalNo less than 70% within
3.0 hours of theobservation time

Table 3-4: Product delivery to operational institutions (for JAFIC)

Delivered Product

AMSR2 Level 2
Near-real-time
product

AMSR2 Level 3Standard productCoveredRegionJapan
andvicinity

Delivery Time
Requirements
No less than 95% within 1.5
hours of the observation
time

GlobalNo less than 70% within 3.0
hours of the observationtime
No less than 90% within 5.0
hours of the observationtime

Sea surface Global

Delivered in the generated

Sea surface
temperature
(SST)

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Delivered ProductCoveredRegiontemperature(SST)

3.4.2.3

Delivery Time
Requirements
units
Daily product: delivered
once a day
Monthly product: delivered
once a monthUser interface

Because AMSR2 makes continuous global observations regardless of the sunlight
conditions, there is no input interface for the data user to make requests forobservation operations; rather, observation data is provided from the GCOM-W1
data provision system.
3.4.2.4

Data archive

The mission operation system’s data archive policy is given below.
(1) Raw X-band data received at the international station are stored for seven days
after reception on the international station’s recording equipment and areconverted to ASD and resent in response to resend commands from the mission
operation system.
(2) Raw X-band data received at the primary domestic station are stored for sevendays after reception on the receiving station’s recording equipment and areconverted to ASD and resent in response to resend commands from the mission
operation system. This policy holds for the backup domestic station as well.
(3) The mission operation system treats Level 0 data, created from the observation
data (ASD) transferred from receiving stations, as the master data and creates
long-term archives of standard products, processing results (catalog information),
etc. The master Level 0 data are stored at a backup facility in a location physicallyseparate from the system installation center as well as at the mission operation
system.

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Chapter 4
4.1

AMSR2 Products

Product Definitions

4.1.1

Processing Level Definitions

Table 4-1 provides definitions of each AMSR2 product’s processing level.
Table 4-1: AMSR2 product definitions

Processing
Level
Level 0Description
Data set with quality check flags appended.

Level 1A

A product that adds radiometric correction processing andgeometric correction processing to the Level 0 data set,
calculates antenna temperature conversion coefficients,
antenna temperature count values, etc. and divides thedata set by scene.

Level 1B

A product that converts antenna temperatures output in
Level 1A to brightness temperatures using the conversion
coefficients.

Level 1R

A product that resamples the Level 1B product using
precalculated resampling coefficients to adjust data forlower frequency resolutions.

Level 2

A product that calculates geophysical parameters primarily
related to water from the Level 1B product.

Level 3

A product that averages the Level 1B product and theLevel 2 product spatially and over time with respect topredefined lattice grid points on the Earth surface andmaps the averages on a global scale.

4.1.2
4.1.2.1

Scene Definitions
Level 1A, Level 1B, Level 1R, and Level 2 products

AMSR2 scenes are defined as a half orbit between the North Pole and the South
Pole with respect to the observation position (Table 4-2). The AMSR2 observation
position, as used in this definition, is not the position directly under the satellite, but
the observation position on the Earth surface that is forward of the satellite along
the sub-satellite track. Consequently, the scene is the area shifted approximately
2.5 minutes ahead of the position directly under the satellite (Figure 4-1). The
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position where the observation is made forward of the satellite along the
sub-satellite track is the scan center position and defines the observation point
indicated in Table 4-3 (Figure 4-2).
The number of scans in one AMSR2 scene can be calculated using the interval
between orbits, the total number of passes in one orbit, and the scanning interval.
The number of scans in one scene is equivalent to one half orbit which takes about
49.4 minutes and consists of about 1,979 scans. The AMSR2 Level 1B productdoes not divide the observation data as AMSR/AMSR-E did. For this reason, thenumber of observation points does not change between the Level 1A product andthe Level 1B product.
1978.7 = 86400 [seconds/day] x 16 [days/rec] / 233 [orbits/rec] / 1.5 [seconds/scan]/ 2 [scenes/orbit] + 1 [the other pole]
The number of scans in any one scene, however, fluctuates due to the effects ofdisturbances on the satellite’s attitude, etc.
Table 4-2: AMSR2 scene definitions

Track Direction

Ascending scene

Descending scene

Definition
Half orbit scans from the southern mostpoint to the northern most point(The southern most point is included at
the start of the scene)
Half orbit scans from the northern mostpoint to the southern most point(The northern most point is included at thestart of the scene)

Table 4-3: Observation center positions

Processing Level /Frequency
L1A and
L1B

No. of
Start
CenterObservation
Position Position
PointsAll but 89
GHz

243

1

122

89 GHz

486

1

244

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The North Pole
GCOM-W1/AMSR2

1 Scene (Descending)

1 Scene (Ascending)

about 2.5min

The Earthabout 2.5min

GCOM-W1/AMSR2The South Pole

Figure 4-1: Relationship between the AMSR2 scene definitions and the position directly under
the satellite

Along Track
Scan center
L1A, L1B(89GHz-A): No.244 counts

End of obs ervation
L1A, L1B(89GHz-A):
No. 486 counts

Beginning of observation
L1A, L1B(89GHz-A):
No. 1 counts
75deg

Scanning direction

75deg

AMSR2

*Rotating velocity is calculated from tac ho-pulse

Figure 4-2: Relationship between the scan center observation point location and the numberof data points

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4.1.2.2

Level 3 product

The Level 3 product is a global data set, not scene-divided data.
(1) There are two map projection methods: equi-rectangular (EQR) and polar stereo(PS).
(2) The map lattice grid point interval is 0.25 degrees in the EQR map projection and25 kilometers in the PS map projection.
(3) PS map projections are defined in following figures.
Latitude30.98°
Longitude 168.35°

Latitude31.37°
Longitude 102.34°

Latitude33.92°
Longitude 279.26°

Latitude34.35°
Longitude 350.03°*The latitude and longitude in this figure indicate the position at the edge of the pixel.
Figure 4-3: Definition of the Northern polar stereo projection [TB/SIC]

Latitude -39.23°
Longitude 317.76°

Latitude -39.23°
Longitude 42.24°

Latitude -41.45°
Longitude 225.00°

Latitude -41.45°
Longitude 135.00°*The latitude and longitude in this figure indicate the position at the edge of the pixel.
Figure 4-4: Definition of the Southern polar stereo projection [TB/SIC]
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Latitude21.48°
Longitude 309.80°

Latitude21.39°
Longitude 230.08°

Latitude 14.59°
Longitude 55.63°

Latitude14.52°
Longitude 124.47°*The latitude and longitude in this figure indicate the position at the edge of the pixel.
Figure 4-5: Definition of the Northern polar stereo projection [TB/SND]

4.1.3

Product Definitions

4.1.3.1

Level 1 products

There are three types of AMSR2 Level 1 products, as shown in Table 4-4. One fileis created for each scene in hierarchical data format (HDF).
Table 4-4: Data specifications for AMSR2 Level 1 products

Level

Processing Details

1A• Identifies observation data packets and deletes duplicate
packets• Interpolates missing packets in the mission data withdummy packets• Converts observation data (12-bit) to a two-byte (16-bit)count value• Calculates the antenna temperature conversion coefficients
necessary for Level 1B processing

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Observation
Data
Storage
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Level

Processing Details

Observation
Data
Storage
Format• Calculates the latitude and longitude data, observation
incidence angle and azimuth, and the solar elevation andazimuth for each observation data point• Appends packet loss data and quality data
• Appends land or sea decision flags• Appends the overlap before and after the scene (10 scanseach before and after the scene)• Appends quality information for each pixel (data on radio
frequency interference (RFI))
1B• Converts Level 1A count values to antenna temperatures
and then converts antenna temperatures to brightness
temperatures

Brightness
temperature
after(• AMSR2 does not divide the observation data as was done conversion
with AMSR / AMSR-E.)

1R• Creates the Level 1R product by resampling the Level 1B
product’s brightness temperatures using precalculated
resampling coefficients to adjust for lower frequency
resolutions• Calculates the average altitude over the observation region

4.1.3.2

Higher-level processing products

4.1.3.3

Level 2 products(1) Products that calculate geophysical parameters based on Level 1B data sets
(2) Adds the same geometric data to Level 1B and stores quality data and metadata(orbit number and time for each scan in the TAI-93 (International Atomic Time)format, which is the number of seconds that have elapsed since 1993)

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Table 4-5: List of AMSR2 Level 2 products
Data
Product
Coverage Resolution
Units
Total
Precipitable
Water

Globalseas

15 km

Cloud liquid water

Globalseas

15 km

Precipitation

Tropics
and
15 km
temperatezones

Sea surface
temperature

4.1.3.4

GlobalScene(half orbit) seas

50 km

Sea surface wind
speed

Globalseas

15 km

Sea iceconcentration

High
latitude
seas

15 km

Snow depth

Land
areas

30 km

Soil moisture
content

Land
areas

50 km

Level 3 products(1) Level 3 products use Level 1B brightness temperature (TB) data set and Level 2geophysical parameter data set and average them spatially and over time withrespect to predefined lattice grid points on the Earth surface. There are two time
averages: daily and monthly. The averaged data is mapped globally using both
EQR and PR map projections.
(2) All Level 3 products are global data sets, but there are two types: ascending
global data sets and descending global data sets.

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Table 4-6: List of AMSR2 Level 3 products
Product

Data Unit

Map
Projection

Averages

Resolution

Daily /
EQR

High resolution (0.1) / Low resolution (0.25)Monthly

Brightness

PS northern

Daily /High resolution (10 km) / Low resolution (25temperature

hemisphere

Monthlykm)

PS southern

Daily /High resolution (10 km) / Low resolution (25hemisphere

Monthlykm)

Daily /Total
EQR

Precipitable

Monthly

High resolution (0.1) / Low resolution (0.25)Water
Cloud liquid

Daily /
EQR

water

High resolution (0.1) / Low resolution (0.25)Monthly
Daily /

Precipitation

EQR

Sea surface

High resolution (0.1) / Low resolution (0.25)Monthly

Global(Ascending /Daily /
EQR

temperature

Descending)High resolution (0.1) / Low resolution (0.25)Monthly

Sea surface wind

Daily /
EQR

speed

High resolution (0.1) / Low resolution (0.25)Monthly

PS northern

Daily /High resolution (10 km) / Low resolution (25Sea icehemisphere

Monthlykm)

concentration

PS southern

Daily /High resolution (10 km) / Low resolution (25hemisphere

Monthlykm)

Daily /
EQR

High resolution (0.1) / Low resolution (0.25)Monthly

Snow depth
PS northern

Daily /High resolution (10 km) / Low resolution (25hemisphere

Monthly

km)

Soil moisture

Daily /
EQR

content

4.2
4.2.1

High resolution (0.1) / Low resolution (0.25)Monthly

Level 1 Processing Algorithms
Level 1A Process

The Level 1A process calculates radiometric data sets and geometric data sets, as
given in Figure 4-6, using edited data output from the editing process as inputs.
Note that the mission data loading and checking procedure consists of loading

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AMSR2 mission data and checking the mission data’s loss flags and HW flags.
The time calculation procedure converts the satellite’s GPS time to the TAI-93 time
format and then calculates the observation start time while accounting for leap
seconds.

Start
Mission data loading /Time calculation
Geometric corrections
Radiometric corrections
Append land / sea flag
End
Figure 4-6: Level 1A product processing sequence

4.2.1.1

Geometric data set calculations(1) Calculate the location of observation points
Figure 4-7 illustrates the processing sequence for calculating the latitude andlongitude of observation points.

Start
Convert to the Antenna patterncoordinate
Convert to the Earth fixed coordinate
Calculate the latitude and longitudeEnd

Figure 4-7: Processing sequence for calculating the observation points

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To calculate the location on earth’s surface, we should know the direction of
AMSR2 antenna in a proper coordinate, the Earth Fixed coordinate. While theantenna direction is defined in the other coordinate, the Antenna Pattern coordinate,
therefore we repeat the coordinate conversion from the Antenna Pattern coordinate
to the Earth Fixed coordinate, via several interim coordinates. After we know thedirection of AMSR2 antenna, we calculate the location, latitude/longitude ofobservation point.
This calculation is just for 89GHz-A channel, because it is the finest sampling. The
location of the other low frequency channels are calculated using “Relative
registration coefficient”. Refer the format description document the detail ofcalculation.
89GHz-B channel observes the different position from the other channels, itcannot calculate using 89GHz-A channels position and Relative registration
coefficient. Therefore we calculate the position of this channel in the same way of
89GHz-A channel and store them in the product independently.
In above calculation, we use WGS84 as the earth ellipsoid model.
The sketch of above calculation is shown in the below figure.

A ntenna Patter n Coodinate

Observation Direction

Earth Fixed Coordinate
Figure 4-8: Sketch of calculating the longitude/latitude of observation point(2) Solar elevation and azimuth and incidence angle and azimuth at the Earth surface
The solar elevation and azimuth relative to the 89 GHz observation position is
calculated using the latitude and longitude of the observation point calculated
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above and the sun’s position, and the incidence angle and azimuth at the Earthsurface from the latitude and longitude.
(3) Setting the land / sea flag for all frequencies
Land / sea flags stored in a database are retrieved using the latitude and longitudeof the observation point calculated above and the results are set in the data set.
4.2.1.2

Radiometric data set calculations

Figure 4-9 illustrates the processing sequence for calculating radiometric data.

Start
Convert to count value（observation data, HTS, CSM）

Correct data
（observation data, HTS, CSM）

Calculate brightness temperature
End

Figure 4-9: Processing sequence for calculating the antenna temperature conversion
coefficients(1) Identifying and averaging equal gain / offset points
Identify the range in which low-temperature and high-temperature correction data
have been obtained at the same auto gain control (AGC) level (gain and offset) andaverage the calibration data obtained at equal gain levels. Data obtained at
differing gains and offsets are not averaged.
(2) Correct the high-temperature noise source / cold sky mirror
The following corrections are made to the high-temperature noise source’s
temperature and the cold sky mirror’s count value.
a. Correct the high-temperature noise source’s temperature (using a correction
algorithm)
b. Correct the cold sky mirror’s temperature (eliminate the effects of the moon, Earthemissions, RFI, and sunlight)
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Chapter 4 AMSR2 Products(3) Calculating the antenna temperature conversion coefficients at all frequencies
The first-order calibration equation coefficients (A, B) are calculated in order toconvert the observation count value (Cobs) to an antenna temperature (TA)T A  A  C obs  B
4.2.2

Level 1B Process

The Level 1B process corrects for scanning bias and receiver nonlinearity after
antenna temperature conversion using the radiometric coefficients calculated in theLevel 1A process and converts the results to brightness temperatures.
Figure 4-10 shows the Level 1B processing sequence.

Figure 4-10: Processing sequence for the Level 1B product4.2.2.1

Scanning bias correction

There is a possibility that observed antenna temperatures contain some bias at theobservation points within a scan due to the effects of blocking, etc., by thecalibration sources. For this reason, the Level 1B process includes functions tocorrect for this bias.
The bias tendency is thought to vary somewhat depending on the frequency andpolarization, so separate coefficients are set for each frequency and polarization.
Furthermore, separate coefficients are set because the coefficients change foreach observation point location.
The conversion from the observation count value to the antenna temperature uses
the equation shown in Section 4.2.1-II(3).
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The scanning bias correction is made with the following equation on converted
observation antenna temperatures. Table 4-7 gives the meaning of each coefficientin the equation.
T'A = Ta×Cg[i] + Co[i]Table 4-7: Coefficients used in scanning bias corrections
Coefficient

Description

T'A

Antenna temperature after correcting for scanning bias-

Ta

Antenna temperature

-

Cg[i]

Gain coefficient for scanning bias correction at eachobservation point (i: observation position)Co[i]observation point

Offset coefficient for scanning bias correction at eachobservation point (i: observation position)

4.2.2.2

Set for each

Set for eachobservation point

Receiver nonlinearity correction

After correcting the antenna temperature for scanning bias, correction is made forreceiver nonlinearity with the equation below.

TA  ALTA'  BLTA' 2  C L
T A : Antenna temperature after correcting for nonlinearity

T A' : Antenna temperature after correcting for scanning bias
AL , BL , C L : Nonlinearity correction coefficients

Before launch the correction coefficients AL , BL , C L are set to AL  1, BL  0, C L  0 .
After launch, the coefficients are determined through a calibration and verification
process.
4.2.2.3

Brightness temperature calculation

To convert the antenna temperature after correcting for receiver nonlinearity
described in Section 4.2.2.2 to a brightness temperature, the equation below isused to account for cross polarization and spillover correction. The coefficients are
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set to precalculated values before launch, which are then readjusted through anassessment after launch.

TBv  AvvTav  AhvTah  Bv
TBh  AvhTav  AhhTah  Bh

Where, TBv , TBh : V polarized and H polarized observation brightness temperatures
TAv , TAh : V polarized and H polarized observation antenna temperatures

Avv , Ahh

: V polarized and H polarized primary polarization contribution ratios

Ahv , Avh

: V polarized and H polarized cross polarization contribution ratios

Bv , Bh

4.2.3: V polarized and H polarized brightness temperature conversion biases

Level 1R Processes

In Level 1R, the process shown in Figure 4-11 is carried out using the Level 1B
products as inputs.

Start
Resampling process

End
Figure 4-11: Processing sequence for Level 1R products

The Level 1R process resamples the Level 1B brightness temperature data inproportion to the low-frequency resolution. Resampling takes place by overlaying
the original brightness temperature data with weighted parameters (resampling
coefficients) calculated using the Backus-Gilbert Method. Figure 4-12 provides an
overview of the resampling process.

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×

輝度温度データ
重みパラ メータ
Brightness
Weightingfactor(重みパラメータのtemperature
data
影響範囲を
(Extractionof
切り出し)
affectedarea ofthe weighting
factor)（ｘ、ｙ）

低周波相当にリサンプリングした
Resampled brightness
輝度温度データ
temperature
data in proportion
to the low-frequenc y resolution

Figure 4-12: Overview of the Level 1R process

4.2.3.1 Frequencies selected for resampling
The following policy determines which frequencies to resample in the Level 1R
process.
(1) Resampling converts the brightness temperature at a higher resolution frequency
to a brightness temperature at a lower resolution frequency.
(2) There are several AMSR2 frequencies with the same or similar resolutions. 6 GHz
and 7 GHz data sets have the same spatial resolution; therefore, a shared set ofresampling data is created. 18 GHz and 23 GHz data sets have almost the same
resolution, but 23 GHz resolution is slightly lower; therefore, one set of resampling
data is created matching the 23 GHz resolution.
(3) 89 GHz data sets consist of data from A and B channel receivers. The processing
method of the two data sets is determined by the resampling coefficient rule (useonly one data set, merge both data sets, etc.). Both data sets are input
simultaneously into the process.
(4) Resampling is done separately for H polarization and V polarization. For example,
when resampling the H polarization of a given target frequency, the H-polarized
L1B brightness temperature data at the other frequency are used, but thecorresponding V-polarized brightness temperature data are not.
Table 4-8 summarizes these policies and lists the resampled frequencies.

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Table 4-8: Frequencies selected for resampling

Target Resolution Frequency

Observation
frequency

6/7 GHz10 GHz23 GHz36 GHz89 GHz

6 GHz

☆

-

-

-

-

7 GHz

☆

-

-

-

-

10 GHz

○

☆

-

-

-

18 GHz

○

○

○

-

-

23 GHz

○

○

☆

-

-

36 GHz

○

○

○

☆

-

89 GHz

○

○

○

○

-

Notes:○ and ☆ indicate frequencies that are processed and stored. ○ frequencies
primarily undergo spatial resolution conversions, whereas ☆ frequencies
primarily undergo processing for center latitude and longitude alignment; spatial
resolution conversion is not the main purpose.
The process resamples the Level 1B brightness temperature data at frequencies
marked by ○ and ☆ in Table 4-8 and outputs the resampled data as the Level
1R product. ○ frequencies primarily undergo spatial resolution conversions,whereas ☆ frequencies primarily undergo processing for center latitude andlongitude alignment; spatial resolution conversion is not the main purpose. The
center latitude and longitude is adjusted to the 89 GHz A channel receiver data.
Note that the complete sets of original 89 GHz A and B channel brightness
temperature data are also stored.
4.3

Higher Level Processing Algorithms

4.3.1

Level 2 Process

The Level 2 process calculates actual geophysical parameters using AMSR2 Level
1 data sets as inputs. Eight geophysical parameters are calculated: Total
Precipitable Water, cloud liquid water, precipitation, sea surface wind speed, sea
surface temperature, sea ice concentration, snow depth, and soil moisture content.
4.3.1.1

Total Precipitable Water(1) Input data
The data inputs to the Total Precipitable Water calculation algorithm are as
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follows:
a. AMSR2 Level 1R products
1) 18.7, 23.8, 36.5, 89.5 GHz brightness temperatures (horizontal and verticalpolarizations)
2) Latitude and longitude
3) Incidence angle at the Earth surface4) Observation time5) Land / sea flags
b. Sea surface temperatures (Japan Meteorological Agency’s Merged satellite and insitu data Global Daily Sea Surface Temperatures in the global ocean (MGDSST) /JMA)
c. 850 hPa temperatures (Japan Meteorological Agency’s Global objective analysis
data (GANAL) / JMA)
d. Sea surface wind speeds (Japan Meteorological Agency’s Global objective
analysis data (GANAL) / JMA)
e. Reference tables
1) Reference table for calculating sea surface emissivity2) Reference table for calculating mean atmospheric temperature
3) Reference table for Total Precipitable Water conversions(2) Description of the algorithm
Water vapor and cloud liquid water emissivity in the atmosphere can be observed
using microwave scanning radiometer measurements in the 19 GHz, 23 GHz, and
37 GHz bands. Radiance in the upper atmosphere is the integrated value of water
vapor in the atmosphere and liquid water content in clouds. Correctly estimating
emissivity from the sea surface is key to retrieving the Total Precipitable Water. Sea
surface emissivity varies in accordance with the observed microwave frequency,
the sea surface temperature, the sea surface wind speed, and the incidence angle
at the Earth surface. This algorithm assumes a single homogenous isothermal
atmosphere above the sea surface to simulate, with a microwave radiative transfer
model, the radiation absorption by water vapor, oxygen, and ozone in theatmosphere and radiation absorption at the sea surface. Because the AMSR2
observation frequencies bands are not very sensitive to salt concentrations, thealgorithm does not account for the effect of salt concentration variances on sea
surface radiation. The algorithm does account for the effect of sea level undulations
caused by wind and for emissivity variations caused by sea surface temperature
differences. The dispersion process of water molecules in the atmosphere is
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complex and takes considerable time to calculate. Therefore, Total Precipitable
Water is not calculated in areas of precipitation.
Because each AMSR2 frequency band has a different sensitivity to water vapor
and cloud liquid water, the Total Precipitable Water is calculated by combining
multiple AMSR2 frequencies and polarizations.
a. Calculating the sea surface emissivity
The sea surface emissivity is found from a reference table using auxiliary sea
surface temperature and sea surface wind speed data. The reference table iscreated by matching vertical atmospheric profile data obtained from sondes,AMSR2 observed brightness temperatures, and auxiliary sea surface temperatureand sea surface wind speed data.
b. Calculating the mean atmospheric radiation temperature and the atmospherictransmittance multiple
An initial value is input for the atmospheric transmittance (Tr). The mean
atmospheric radiation temperature (Ta) is found from the second reference table.
The reference table is created from the atmospheric transmittance (Tr) and theatmospheric temperature at 850 hPa. The reference table is created in advanceusing sonde vertical atmospheric profile data (temperatures and humidity) and a
microwave radiative transfer model.
The parameter  is calculated for each observation frequency and polarization
using Equation 2.
The atmospheric transmittance (Tr) is calculated from the AMSR2 brightness
temperature for each of the two polarizations using Equation 3.
The atmospheric transmittance (Tr) at each of the observation frequencies is
estimated by taking the geometric mean of the transmittance found for bothpolarizations. Through iterations of this calculation, a convergence value for theatmospheric transmittance (Tr) is found.

Tb   1  (1   s )Tr  Ta 


Tr 
2

2



[1]

(TS  Ta )Tr s 2
1  (1   S )Tr [2]

1  Tb 
1  S[3]

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Chapter 4 AMSR2 Productsc. Calculating the precipitable water index and the cloud liquid water index
First the cloud liquid water index (CWI) is calculated from the atmospherictransmittance multiple for the 18.7 GHz and 36.5 GHz bands. Then the precipitablewater index (PWI) is calculated from the constant  and the coefficient , which is
obtained from the reference table that defines the correlation between the CWI, theatmospheric transmittance multiple for the 18.7 GHz and 36.5 GHz bands, and thesea surface temperature. Note that this reference table has been prepared for
maximum correlation between the PWI and the Total Precipitable Water calculatedwith radiosonde data.
PWI   ln(Tr19 HV )  ln(Tr 22V )  CWI
2

[4]CWI  ln(T r19 HV )  ln(T r 37 HV )

[5]

2

2

2

d. Calculating the Total Precipitable Water
The Total Precipitable Water is calculated by converting the PWI to a PWA (kg/m2)
using a reference table adjusted so that the PWI calculated from AMSR2
observation data matches the PWA calculated from radiosonde observation data.
During the conversion, a parameter is used to eliminate the 36.5 GHz band’s windspeed dependency (S36 defined in the AMSR2 wind-speed algorithm).
4.3.1.2

Cloud liquid water(1) Input data
The data inputs to the cloud liquid water calculation algorithm are as follows:a. AMSR2 Level 1R products
1) 18.7, 23.8, 36.5, 89.5 GHz brightness temperatures (horizontal and verticalpolarizations)
2) Latitude and longitude
3) Incidence angle at the Earth surface4) Observation time5) Land / sea flags
b. Sea surface temperatures (Japan Meteorological Agency’s Merged satellite and insitu data Global Daily Sea Surface Temperatures in the global ocean (MGDSST) /JMA)
c. 850 hPa temperatures (Japan Meteorological Agency’s Global objective analysis
data (GANAL) / JMA)
d. Sea surface wind speeds (Japan Meteorological Agency’s Global objective
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analysis data (GANAL) / JMA)
e. Reference tables
1) Reference table for calculating sea surface emissivity2) Reference table for calculating mean atmospheric temperature
3) Reference table for cloud liquid water conversions(2) Description of the algorithm
Water vapor and cloud liquid water emissivity in the atmosphere can be observed
using microwave scanning radiometer measurements in the 19 GHz, 23 GHz, and
37 GHz bands. Radiance in the upper atmosphere is the integrated value of water
vapor in the atmosphere and liquid water content in clouds. Correctly estimating
emissivity from the sea surface is key to retrieving the Total Precipitable Water. Sea
surface emissivity varies in accordance with the observed microwave frequency,
the sea surface temperature, the sea surface wind speed, and the incidence angle
at the Earth surface. This algorithm assumes a single homogenous isothermal
atmosphere above the sea surface to simulate, with a microwave radiative transfer
model, the radiation absorption by water vapor, oxygen, and ozone in theatmosphere and radiation absorption at the sea surface. Because the AMSR2
observation frequencies bands are not very sensitive to salt concentrations, thealgorithm does not account for the effect of salt concentration variances on sea
surface radiation. The algorithm does account for the effect of sea level undulations
caused by wind and for emissivity variations caused by sea surface temperature
differences. The dispersion process of water molecules in the atmosphere iscomplex and takes considerable time to calculate. Therefore, cloud liquid water is
not calculated in areas of precipitation.
Because each AMSR2 frequency band has a different sensitivity to water vapor
and cloud liquid water, the cloud liquid water is calculated by combining multiple
AMSR2 frequencies and polarizations.
a. Calculating the sea surface emissivity
The sea surface emissivity is found from a reference table using auxiliary sea
surface temperature and sea surface wind speed data. The reference table iscreated by matching vertical atmospheric profile data obtained from sondes,AMSR2 observed brightness temperatures, and auxiliary sea surface temperatureand sea surface wind speed data.
b. Calculating the mean atmospheric radiation temperature and the atmospheric
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transmittance multiple
An initial value is input for the atmospheric transmittance (Tr). The mean
atmospheric radiation temperature (Ta) is found from the second reference table.
The reference table is created from the atmospheric transmittance (Tr) and theatmospheric temperature at 850 hPa. The reference table is created in advanceusing sonde vertical atmospheric profile data (temperatures and humidity) and a
microwave radiative transfer model.
The parameter  is calculated for each observation frequency and polarization
using Equation 2.
The atmospheric transmittance (Tr) is calculated from the AMSR2 brightness
temperature for each of the two polarizations using Equation 3.
The atmospheric transmittance (Tr) at each of the observation frequencies is
estimated by taking the geometric mean of the transmittance found for bothpolarizations. Through iterations of this calculation, a convergence value for theatmospheric transmittance (Tr) is found.

Tb   1  (1   s )Tr  Ta 


Tr 
2

2



[1]

(TS  Ta )Tr s 2
1  (1   S )Tr [2]

1  Tb 
1  S[3]c. Calculating the cloud liquid water index
The cloud liquid water index (CWI) is calculated from the atmospherictransmittance multiple for the 18.7 GHz and 36.5 GHz bands.
CWI  ln(Tr19 HV )  ln(Tr 37 HV )
2

2

[4]

d. Calculating the cloud liquid water
Because the calculated CWI is affected by water vapor, the cloud liquid water(CLW) is calculated after applying water vapor correction using the
AMSR2-calculated PWA, in consideration of the dependency on different water
vapor levels.

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4.3.1.3

Precipitation(1) Input data
The data inputs to the precipitation calculation algorithm are as follows:a. AMSR2 Level 1B products
1) 10.65, 18.7, 36.5, 89.0 GHz brightness temperatures (horizontal and verticalpolarizations) and 23.8 GHz bright temperatures (vertical polarization)2) Latitude and longitude
3) Incidence angle at the Earth surface
4) Land / sea flags
b. Air temperatures (Japan Meteorological Agency’s Global objective analysis data
(GANAL) / JMA)
c. Surface temperatures (Japan Meteorological Agency’s Global objective analysis
data (GANAL) / JMA)
d. 0 altitude (Japan Meteorological Agency’s Global objective analysis data
(GANAL) / JMA)
e. Sea surface temperatures (Japan Meteorological Agency’s Merged satellite and insitu data Global Daily Sea Surface Temperatures in the global ocean (MGDSST) /JMA)(2) Description of the algorithm
The special characteristic of this algorithm is that it finds an optimal value for the
precipitation that gives a calculated brightness temperature value that minimizes
the difference with the observed multi-wavelength brightness temperature value.
The algorithm consists of a forward calculation part that creates a look-up table(LUT) and a retrieval part that estimates the precipitation from the LUT and theobserved brightness temperature. The algorithm estimates precipitation in sea
regions, land regions, and coastal regions, excluding regions of sea ice or cold
temperatures and high latitude regions. Separate algorithms are applied for sea
regions, land regions, and coastal regions. Generally speaking, the forecast
accuracy for sea regions is better than that for land regions or coastal regions.
Regions of sea ice or cold temperatures and high latitude regions are determinedwithin the algorithm based on position information, brightness temperature
information, and other data.
a. Forward calculation
A radiation transfer equation is used to form the relationship between precipitation
intensities and rising or falling radiation brightness temperatures in the forward
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calculation part of the algorithm. In other words, a precipitation physics model witha given precipitation intensity (classification of convection precipitation andstratiform precipitation, precipitation type, precipitation profile, raindrop size
distribution, altitude of the melting layer, etc.) is incorporated in the radiationtransfer equation and a calculated value of the radiation brightness temperatureobserved by the satellite is found. Specifically, then, the air temperature, altitude,water vapor, surface wind speed (using Japan Meteorological Agency’s Global
objective analysis data), and sea surface temperature are used as input values andthe radiation brightness temperatures for all observed frequencies are calculatedwith respect to various precipitation intensities based on the assumed precipitation
physics model. A table is then made of the relationship between precipitation
intensities and radiation brightness temperatures.
Creating the LUT first involves making calculations for uniform precipitation,
estimating the non-uniformity of the rain from the initial precipitation intensity
estimate, and then revising the non-uniformity. This correction is necessary when
precipitation in the observed field of view is non-uniform — for example, theradiation brightness temperature varies even through the average precipitation
intensity in the observed field of view is the same.
Another distinction of this algorithm is that it determines the 0 Celsius altitude,
which demarks liquid precipitation from solid precipitation, based on Japan
Meteorological Agency’s Global objective analysis data rather than estimating themelting layer altitude from unreliable observation data.
Note that the LUT is calculated for each 5 x 5 (latitude x longitude) rectangulararea.
b. Retrieval
In the retrieval part of the algorithm, the precipitation intensity is estimated fromobserved brightness temperatures based on the LUT. The retrieval part’s sequenceis as follows: initial precipitation intensity estimate, determination of rainfall overland, sea, or coastlines, non-uniformity correction, dispersion algorithm, andradiation algorithm (over seas only).
4.3.1.4

Sea surface wind speed(1) Input data
The data inputs to the sea surface wind speed calculation algorithm are as follows:a. AMSR2 Level 1B products
1) 10.65 and 36.5 GHz brightness temperatures (horizontal and vertical
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polarizations)2) 6.925 GHz brightness temperatures (horizontal polarization)b. Sea surface temperatures (Japan Meteorological Agency’s Merged satellite and insitu data Global Daily Sea Surface Temperatures in the global ocean (MGDSST) /JMA)
c. Air temperatures (Japan Meteorological Agency’s Global objective analysis data
(GANAL) / JMA)
(2) Description of the algorithm
Sea surface wind speeds are calculated from vertical-polarized andhorizontal-polarized 36.5 GHz brightness temperatures. Brightness temperaturesat higher frequencies are susceptible to atmospheric effects. Consequently,
atmospheric effects are observed using the lower 6 GHz frequency. The
vertical-polarized 6 GHz signal increases with the effect of wind speed; it is zero inareas of light winds under 6 m/s but becomes larger as winds increase. The
horizontal-polarized 6 GHz signal, however, increases even in areas of light winds
and its degree of increase is greater than that for the vertical-polarized 6 GHz
signal at all wind speeds. Another difficulty is the large anisotropy varies according
to the angle between the AMSR2 antenna’s orientation direction and the sea
surface wind direction. In the 6 GHz band, this anisotropy in vertical-polarized
signals reaches its maximum value with crosswinds and reaches its minimum withupwinds and downwinds. This phenomenon holds for the 36 GHz band as well.
Consequently, the algorithm uses 36 GHz data, calculates S36 defined in
Equation 1, and, after removing the anisotropy, converts the results to a windspeed.
a. Calculating S36
S36=(36H-(a×(36V-208)-b))/f + t
f=1-(36V-200)×0.01

[1]
[2]

The coefficients a and b are dependent on the sea temperature. The coefficients forsea temperatures between 0 and 30 Celsius in 5 Celsius intervals are found bycomparing S36 and SeaWinds’ wind speeds.
Table 4-9 lists the coefficient values.

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Table 4-9: Sea temperature dependency of coefficients a and b

SST

a

b

0C
5

2.23

132.0

2.20

132.2

10

2.14

131.5

15

2.07

130.7

20

2.06

128.8

25

2.03

127.4

30

2.06

124.2

b. Correcting S36’s anisotropy
Anisotropy that varies according to the relative direction of the wind remains in thecalculated S36 value. The anisotropy can be estimated by combining S36 and 6H*(the remaining value after removing atmospheric effects and sea surface
temperature effects). Figure 4-13 shows the values of S36 and 6H* for upwinds,
crosswinds, and downwinds at wind speeds between 1 m/s and 19 m/s in 2 m/s
intervals. S36 reaches its maximum value with downwinds and its minimum value
with upwinds. 6H* reaches its maximum value with crosswinds and its minimum
value with downwinds and upwinds. Correcting for anisotropy means adjusting thedownwind and upwind S36 values to the crosswind S36 values that match thefitting curve shown in Figure 4-13. The slopes from upwinds to crosswinds and fromdownwinds to crosswinds are different.

Figure 4-13: Method of correcting S36 for anisotropy

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Chapter 4 AMSR2 Productsc. Converting from S36 to wind speeds
The algorithm finally translates the S36 value after removing the anisotropy (thecrosswind-equivalent S36 value) to a wind speed using a conversion curve. The
conversion curve was created referring to 3 x 108 SeaWinds wind speeds over aseven-month span. Because the S36 value occasionally becomes negative, aconversion method for negative values has also been created. The causes ofnegative S36 values are errors in the sea surface temperature calculated fromaveraged sea surface temperatures and AMSR brightness temperature noise. The
error still remaining in S36 after correction is the source of wind speed errors. An
error of 1 Kelvin in S36 represents an error of 1 m/s in a wind speed between 15
m/s and 20 m/s.
4.3.1.5

Sea surface temperature(1) Input data
The data inputs to the sea surface temperature (SST) calculation algorithm are as
follows:
a. AMSR2 Level 1B products
1) 6.925 and 10.65 GHz brightness temperatures (horizontal and verticalpolarizations)2) 23.8 and 36.5 GHz brightness temperatures (vertical polarization)b. Sea surface temperatures (Japan Meteorological Agency’s Merged satellite and insitu data Global Daily Sea Surface Temperatures in the global ocean (MGDSST) /JMA)
c. Air temperatures (Japan Meteorological Agency’s Global objective analysis data
(GANAL) / JMA)
(2) Description of the algorithm
The algorithm calculates SST using 6 GHz vertical-polarized observation data.
Unfortunately, 6 GHz vertical-polarized observation data contain signals from manyother parameters in addition to SST. These include (a) atmospheric effects, (b)wind effects, (c) salt content effects, (d) contamination due to land, (e)contamination due to sea ice, and (f) sun glitter effects. Of the first three influences,the effects of salt content in the sea are very small. The effect of salt content can becorrected for using monthly average sea-salt climate values. Contamination
components from (d) to (f) are much larger, so the algorithm attempts to remove
these areas as much as possible. Losses caused by land effects extend as far as
100 kilometers from coastlines. And losses from sun glitter occur when the angle
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between the AMSR2 incidence angle and the sun’s incidence angle is around 25
degrees or less. For sea ice, the algorithm eliminates pixels identified by AMSR2 as
sea ice. The effects from (a) to (c) and the contamination components from (d) to (f)are inherent to all microwave scanning radiometers. But the algorithm must also
correct for additional errors specific to AMSR2. These are (g) incidence angle
dependencies, (h) scan bias error, and (i) RFI.
The AMSR2 algorithm uses MGDSST supplied by JMA as the initial value whencalculating SST.
The following sections describe three main AMSR2 sea surface temperature
calculation processes: atmosphere correction, sea surface wind speed correction,and conversion to sea surface temperatures.
a. Atmosphere correction
The algorithm uses vertical-polarized observation data in the 23.8 and 36.5 GHz
bands to correct for atmospheric effects in 6.925 and 10.65 GHz band observation
data. However, because 23.8 and 36.5 GHz brightness temperatures vary
according to the sea surface temperature, a table is made providing correction
values for sea surface temperatures over the range 0 to 35 Celsius in 5 Celsius
intervals.
The algorithm must remove observation data affected by rain since the SST
accuracy is degraded in areas of rainfall. This process involves counting
observation data in a specified threshold range within the 6.925 and 10.65 GHz
band’s field of view. In locations where the observation data values outside of thethreshold range are more numerous than valid data values, SST is not calculated.
b. Sea surface wind speed correction
Observed vertical-polarized brightness temperatures are constant at sea surface
wind speeds of less than 7 to 8 m/s, but horizontal-polarized brightness
temperatures increase proportionally to low speed winds. When sea surface windspeeds exceed 7 to 8 m/s, both vertical-polarized and horizontal-polarized
brightness temperatures rise. Based on this relationship, sea surface wind speeds
are corrected independently for each frequency using 6.925 and 10.65 GHz band
vertical-polarized and horizontal-polarized brightness temperatures.
c. Conversion to sea surface temperatures
6V is the value after the all the corrections above are made. 6V is calculated using
the Klein-Swift (1977) complex dielectric constant of the sea surface temperature
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after removing coastal regions, sea ice and adjacent regions, and regions of sun
glitter. 6V is converted to a sea surface temperature using a curve that associates
6V and sea surface temperature. Because there are slight errors in this value, thecalculation is made after adding correction to the curve to minimize differences withbuoy water temperatures.
4.3.1.6

Snow depth(1) Input data
The data inputs to the snow depth calculation algorithm are as follows:a. AMSR2 Level 1 products
1) 89.5 GHz brightness temperatures (vertical polarization)2) 10.65, 18.7, 23.8, 36.5 GHz brightness temperatures (horizontal and verticalpolarizations)
3) Latitude and longitude
4) Incidence angle at the Earth surface
b. Auxiliary data
1) Land / sea / coast / ice masks2) Snow climate data
3) Forest coverage ratios
4) Forest densities(2) Description of the algorithm
The wavelength differences in the strength of snow emissions are dependent on
several physical variables, the temperature of the snow, the snow crystal size, thevolume of the snow, the state of the underlying surface, and vegetation.
On a satellite scale, the ratio of moisture content in the instantaneous field of viewis important.
Dry snow more than five centimeters deep or a snow water equivalent of over 10
millimeters shows up in the microwave scanning radiometer’s readings as isotropic
dispersion caused by the snow crystals.
Snow emissions are almost impossible to detect at frequencies below about 20
GHz, but the emissions are large at frequencies over 20 GHz. The dispersion
property of accumulated snow is the basic principle used to find snow depth.
a. Calculating the surface temperature (T)The algorithm calculates the surface temperature in order to determine thepossibility of accumulated snow in a target pixel. The surface temperature (T)4-28

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calculation uses 18.7, 23.8, and 89.5 GHz vertical-polarized brightness
temperatures and 36.5 GHz horizontal-polarized brightness temperatures.
T  58.08  0.39Tb  1.21Tb  0.37Tb  0.36Tb
18V

22V

36 H

89V

[1]

b. Calculating the snow depth
The algorithm determines if the snow depth is deep (including intermediate levels)or shallow.
There are two criteria when the snow depth found to be deep (includingintermediate levels).
The first criterion is that difference between the non-dispersion channel and thedispersion channel is larger than zero. When the difference between the
non-dispersion channel and the dispersion channel is positive, it indicates thepresence of a dispersion media. AMSR2 uses 10 GHz as the non-dispersion
channel and 36 GHz as the dispersion channel. When the algorithm sees a positive
difference between the horizontal and vertical polarizations at 10 GHz and 36 GHz,
it determines that accumulated snow is present.
The second criterion differentiates between dispersion by rainfall and dispersion bysnow.
Although the physical property used to estimate the presence and amount of snow
is dispersion, dispersion is caused by rainfall as well as by dry snow. Therefore, athreshold is set to differentiate between the two types of dispersion. Rainfall shows
up as a higher value in 36 GHz band brightness temperatures than snow. When the
36 GHz vertical-polarized brightness temperature is less than the threshold of 255
Kelvin and the 36 GHz horizontal-polarized brightness temperature is less than 245
Kelvin, the algorithm determines that accumulated snow is present and not rainfall.
When both of the conditions above are satisfied, the snow depth (SD), the snow
depth in forested regions (SDf), and the snow depth in open regions (SDo) arecalculated using forest coverage ratios (ff) and forest density (fd).
SD  ff (SD f )  (1  ff )  (SD0 )[cm][2]

SDf  1/log10 (Tb36V  Tb36 H )[3] (Tb18V  Tb36V ) /(1  fd  0.6)[cm]

SD0  [1/log10 (Tb36V  Tb36 H )  (Tb10V  Tb36V )] [1/log10 (Tb18V  Tb18 H )  (Tb10V  Tb18V )][cm][4]

When the snow depth is shallow, there is no response in the 36 GHz band.
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Therefore, the surface temperature (T) is found with the 23 GHz vertical andhorizontal polarized brightness temperatures (Tb23V and Tb23H) and the 89 GHz
vertical and horizontal polarized brightness temperatures (Tb89V and Tb89H).
Shallow accumulated snow possibly exists when the surface temperature is lessthan 267 Kelvin. The algorithm uses Equation 5 to confirm the presence of shallow
accumulated snow.
Tb23V > Tb89V & Tb89v < 255K & Tb23H > Tb89H & Tb89H < 255K & T< 267K
[5]
In locations where Equation 5 is satisfied, shallow accumulated snow exists.
Atmospheric effects are minimized by confirming that the 89 GHz brightness
temperature is under 255 Kelvin and that the surface temperature is under 267
Kelvin to ensure the low brightness temperatures are the result of accumulated
snow emissions.
When the algorithm determines with Equation 5 that snow depth is shallow, itcalculates the snow depth (SD) with Equation 6.
[6]

SD  5.0[cm]4.3.1.7

Sea ice concentration(1) Input data
The data inputs to the sea ice concentration calculation algorithm are as follows:a. AMSR2 Level 1 products
1) 6.925, 18.7, 23.8 GHz brightness temperatures (vertical polarization)2) 36.5 GHz brightness temperatures (horizontal and vertical polarizations)
3) Latitude and longitude
4) Incidence angle at the Earth surface
5) Land / sea flags
b. Auxiliary data
1) Sea surface temperature (AMSR2 sea surface temperature product)(2) Description of the algorithma. Selection of tie points, etc.
The algorithm identifies sea surface brightness temperatures and characterizes
areas covered with 100 percent sea ice using the respective combinations of 36.5
GHz vertical-polarized and horizontal-polarized brightness temperatures and of
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18.7 GHz and 36.5 GHz vertical-polarized brightness temperatures.
b. Identifying sea ice regions
The algorithm first identifies sea regions using the land / sea flag. It then identifies
sea ice regions in sea regions. Sea ice regions are identified using four data sets:(a) the 18.7 GHz vertical-polarized brightness temperature and the difference
between 23.8 GHz and 18.7 GHz vertical-polarized brightness temperatures, (b)the 36.5 GHz vertical-polarized and horizontal-polarized brightness temperatures,(c) the 36.5 GHz and 6.925 GHz vertical-polarized brightness temperatures, and(d) the AMSR2 sea surface temperature product.
c. Estimating the sea ice concentration
The sea ice concentration is calculated using the Bootstrap method from the 36.5
GHz vertical-polarized and horizontal-polarized brightness temperatures and the18.7 GHz and 36.5 GHz vertical-polarized brightness temperatures. First, thealgorithm estimates the sea ice concentration in high concentration areas using the36.5 GHz vertical-polarized and horizontal-polarized brightness temperatures. Next,
it estimates concentrations in other areas using the 18.7 GHz and 36.5 GHz
vertical-polarized brightness temperatures.
d. Land region filter
In coastal regions, the effect of land can cause elevated sea ice concentration
estimates. For this reason, the algorithm corrects sea ice concentration estimatesin coastal regions using a land region filter.
4.3.1.8

Soil moisture content(1) Input data
The data inputs to the soil moisture content calculation algorithm are as follows:a. AMSR2 Level 1 products
1) 10.6 GHz brightness temperatures (horizontal and vertical polarizations) and36.5 GHz brightness temperatures (horizontal polarization)2) Latitude and longitude
3) Observation dateb. Auxiliary data
1) Global vegetation coverage ratios

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Chapter 4 AMSR2 Products(2) Description of the algorithm
Vegetation is widely distributed on land. Because vegetation holds a large amountof moisture, it is essential to assess vegetation effects on microwave emission
paths in order to estimate soil moisture content from satellite data. The soil
moisture content algorithm’s method uses the polarization index (PI) and the index
of soil wetness (ISW), which are found from brightness temperatures in the 10 GHz
and 36 GHz bands, and a predefined look-up table (LUT) to estimate the soil
moisture and vegetation moisture simultaneously. PI and ISW are defined asvalues after removing polarization differences or frequency differences,respectively, by averaging using the following equations.

In these equations, TB refers to brightness temperature, the subscripts 10 and 36
refer to the frequency band (10.6 GHz and 36 GHz), and the subscripts v and hrefer to vertical polarization and horizontal polarization. Because these indices arenormalized through averaging to minimize the effect of physical temperatures, thealgorithm omits processing physical temperatures by using a fixed value instead.
The LUT is used to correct, with vegetation coverage ratios, non-uniform vegetative
cover within the footprint.
4.3.2

Level 3 Process

The Level 3 process uses as its inputs one day’s worth of Level 1B data and Level
2 data and calculates, by taking a simple arithmetic mean, the daily statistical meanvalue at each grid point in the specified mapping projection method (either
equi-rectangular or polar stereo). Furthermore, Level 3 processing takes one
month’s worth of each geophysical parameter’s Level 3 daily statistical meanvalues and calculates the monthly statistical mean value at each grid point using asimple arithmetic mean in the same way as the daily statistical mean calculation.
The statistical means are calculated separately for observations along thesatellite’s ascending and descending tracks. Also, two dummy data values are used.
The dummy value -9999 is used to fill in observation data where packets have been
lost for some reason. The dummy value -8888 indicates grid points outside of theobservation range or grid points where map projection data does not exist.

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4.3.2.1

Polar stereo projection mappings

A projection method, either equi-rectangular or polar stereo, is selected for each
AMSR2 Level 3 product. One of three regions can be selected when polar stereo
projection is used. The resolution can also be selected from high resolution (0.1
degrees or 10 kilometers) or low resolution (0.25 degrees or 25 kilometers).
Table 4-10 lists the current projection methods and lattice grid sizes available for
AMSR2 Level 3 products.
Table 4-10: AMSR2 Level 3 product projection methods and lattice grid sizes

High Resolution

Low Resolution(0.1, 10 km)(0.25, 25 km)

PixelsLines

PixelsLines

Projection
method*PN (SIC)

760

1120

304

448

PN (SND)

1080

1435

432

574

PS

790

830

316

332

EQR

3600

1800

1440

720

*PN (SIC): Polar stereo projection of northern latitudes, sea ice
concentration
PN (SND): Polar stereo projection of northern latitudes, snow depth
PS: Polar stereo projection of southern latitudes
EQR: Equi-rectangular projection
4.4

Project Formats

For details of the AMSR2 Level 1, 2, and 3 product formats, see the Annex to thisdocument, which gives the specifications of the following formats.
•
•
•
•
•
4.5

AMSR2 Level 1A format description
AMSR2 Level 1B product format description
AMSR2 Level 1R product format description
AMSR2 Level 2 product format description
AMSR2 Level 3 format description
Data Calibration and Verification

The GCOM-W1 onboard AMSR2 multi-spectral microwave scanning radiometer
makes observations of global water properties. Its 16 channels, covering six
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frequencies between 6.9 GHz and 89 GHz, observe brightness temperatures thatreach the satellite. The radiometer derives geophysical parameter products throughcombinations of multi-channel brightness temperatures, based on scientificknowledge. The GCOM-W1 mission is to generate and supply global brightness
temperatures and geophysical parameter products on a regular and frequent basis,to assess and predict global hydrological cycle changes and long-term climatechanges, and to assist on-site applications. This requires the generation of data
sets that are uniform globally and have stable, long-term accuracy. Another criticalfactor is consistency with geophysical parameters generated from othersatellite-borne microwave scanning radiometers and other sensors operatingconcurrently with AMSR2.
A series of GCOM calibration and verification processes are used to reach the data
quality described above. These processes include pre-launch ground testing andsensor model creation, on-orbit data assessments and calibrations after launch,accuracy assessments of calculated geophysical parameters, and public release of
accuracy assessment results.
Table 4-11 gives the overall schedule of the calibration and verification processes.
Table 4-11: Overall AMSR2 schedule
2012/
May

Milestone

July

AugustLaunch Start AMSR2 Competion ofobservation initial C/O

Initial check out phase2013/
FebruaryMay

Review of initial
cal/val #1

Review of initial
cal/val #2

Calibration/Validation phase

Cal/Val actibity

AMSR2 performance checkparameter tuning
L1 product evaluation
Higher-level productevaluation

Data distribution tointernal user （PI,cooperative organization）

L1 productHigher level product（one month behind）
L1 product

Data distribution（general user）

Higher level product

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4.5.1

Calibrations

4.5.1.1

Description of the calibration plan

The calibration plan includes ground tests and sensor model construction in thedesign and development phase before launch. During the post-launch initial
calibration and verification phase, there are on-orbit calibrations, combined andreciprocity calibrations, assessments of sensor characteristics and data quality, andgeometric calibrations among others. And in the regular assessment stage,
calibration data is monitored regularly.
Table 4-11 gives the calibration schedule.
4.5.1.2

Calibrated parameters(1) Design and development phase
In conjunction with the sensor design and development, this phase identifies
sensor characteristics associated with calibration, reflects those characteristics inthe scope of ground tests and analyses, and examines corrective measures asneeded. The results of these examinations form the crux of the satellite project andare incorporated into the sensor model used for brightness temperature
conversions and geometric conversions, which are inputs to the Level 1 (brightness
temperature) processing algorithm. When the sensor’s ground tests and analyses
are completed, the sensor characteristics and corrective measures are reexaminedbased on the testing/analysis results and the sensor model’s coefficients areworked out. After this, the sensor model that will be used in the initial calibrationand verification phase is settled on and merged into the Level 1 processing
algorithms.
(2) Initial calibration and verification phasea. Brightness temperature calibration
There is no single approach that can realistically and absolutely evaluate thesensor in order to calibrate brightness temperatures. For this reason, the final
calibration is determined after assessing the overall consistency of results from anumber of calibration evaluation measures, described in the following sections.
1) On-orbit calibrations
The basic on-orbit calibration is a two-point calibration using low-temperature andhigh-temperature calibration data. The first step is assessing the calibration source
data in comparison with various kinds of telemetry information and ascertaining the
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sources’ characteristics. When the characteristics are already expressed in thesensor model, the model can be optimized by adjusting the relevant coefficients inthe model. Other confirmed characteristics are evaluated and modeled separately.
Examples of adjustments include eliminating contributions from non-deep space
entities — such as the Earth, the moon, and the satellite fuselage — from the cold
sky mirror data, and modifying the model to calculate the effective brightness
temperature from the high-temperature noise source’s temperature telemetry data.
The brightness temperature obtained as a result of the two-point calibration isassessed together with the results of the sensor reciprocity calibration, described
below. Further correction is added as needed.
2) Combined and reciprocity calibrations
Because no ideal target exists above the Earth for absolute calibration, reciprocity
calibration is carried out with other similar microwave scanning radiometers
operating concurrently. Calibration values are verified through comparisons withearth observation and other sensor observation data and the results of emission
path calculations.
3) Sensor characteristics and data quality assessments
Sensor characteristics, such as intra-scan and inter-scan biases, individual receiverdifferences (89 GHz A + B channels, 6.9 + 7.3 GHz, etc.), temperature resolution,and gain stability, are studied through statistical processing of actual observation,
calibration, and telemetry data. Sensor characteristics are assessed in combination
with deep-space calibrations and combined calibrations. The global distribution andfrequency of outliers caused by RFI and other interference are also found andmethods are determined to adequately eliminate the outliers.
4) Deep-space calibrations (reference)
Deep-space calibrations are generally not done because of the risk of accidents
due to capturing radio waves from stationary and polar orbit satellites. Nevertheless,the functionally is available, and deep-space calibrations can be conducted if
necessary for calibration purposes. Observations of the deep-space temperature
obtained during the deep-space calibration maneuver are used for absolute
low-temperature calibration, intra-scan bias estimates and correction, and spillover
estimates (TBD). These results are assessed in combination with other calibration
results.

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Chapter 4 AMSR2 Productsb. Geometric calibration
The geometric accuracy of the sensor is judged by comparing observation datafrom each channel with map information of preselected coastlines and islands. The
results are used to adjust alignment information in the sensor model andregistration parameters between frequencies, thereby correcting the geometricaccuracy of the sensor.
c. Level 1 processing software optimizations
The results obtained from the calibration operations described above areincorporated in the sensor model and Level 1 processing software in order to
optimize the model and software.
(3) Regular assessment phase
The calibration operations done in the initial calibration and verification phase, as
described above, are repeated on a periodic basis. In addition, the long-term data
accuracy and stability are assessed by regular monitoring of telemetry dataassociated with calibrations, calibration data, and brightness temperatures over
specific regions, such as tropical rainforests, ice sheets, and open seas. There arealso situations where trend analyses of geophysical parameters are fed back into
the brightness temperature calibrations.
(4) Collaboration with international calibration and verification frameworks
We carry out efficient reciprocity calibrations and information exchanges by making
use of international frameworks such as CEOS/WGCV and GPM.
4.5.2
4.5.2.1

Verifications
Description of the verification plan

The primary objectives of the AMSR2 verification plan are to define quantitatively
the accuracy of each product, to create products with the required accuracy, and to
improve algorithms when necessary.
Table 4-12 gives the designed accuracy for each AMSR2 geophysical parameter(product).

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Table 4-12: Designed accuracy for each AMSR2 geophysical parameter
SpatialResolut
Observatio
ionn Region(approx. value)*1

Product

Brightness
temperatures(six frequencies,
two polarizations)GlobalTotal
Precipitable
Water

GlobaloceansCloud liquid
water

GlobaloceansPrecipitation

From
tropical totemperate
zones

*2

Accuracy
Release

5–50
km

1.5K

*3

*4

*5

Standard

Target

1.5K

1.0K
0.3K

15 km

3.5 kg/m

2

3.5 kg/m

2

 2.0 kg/m

15 km

0.10 kg/m

0.05 kg/m

0.02 kg/m

15 km

Over seas50%
Over land120%

2Over seas50%
Over land
120%

2

2

Over seas20%Over land80%

2

Observation Range

Remarks

2.7 – 340K

Systematic error (max.
amplitude, 150K equivalent)
Variable error (3, 150K
equivalent)

0 – 702
kg/m

Vertically Total Precipitable
Water, areas of sea ice andactive precipitation are
excluded.

0 – 1.02
kg/m

Vertically integrated cloudliquid water, areas of sea ice
and active precipitation are
excluded.

0 – 20mm/h

Volume of precipitation at the
Earth surface. Accuracy
given as the average relative
error in 50 km (percentage of
RMSE for the average
precipitation intensity).

Geophysical parameters

Sea surface
temperature

Globaloceans

50 km

0.8℃

0.5℃

0.2℃

-2 – 35℃

Areas of sea ice and active
precipitation are excluded.
Target accuracy is themonthly average bias value
for every 10 degrees oflatitude.

Sea surface
wind speed

Globaloceans15 km

1.5 m/s

1.0 m/s

1.0 m/s

0 – 30 m/s

Areas of sea ice and active
precipitation are excluded.

Sea iceconcentration

High-latitud
e regions

15 km

10%

10%

5%

0 – 100%

Accuracy is expressed in sea
ice concentration [%].

10 cmAreas of ice sheets anddense forests are excluded.
Accuracy is expressed in
0 – 100 cm
snow depth and is theaverage absolute value error
in instantaneous values.

5%

Volumetric water content inglobal land regions (including
dry regions and cold regions)where the vegetation
coverage water content
2
equivalent is 2 kg/m or less.
Areas of ice sheets anddense forests are excluded.
Accuracy is the averageabsolute value error in
instantaneous values.

Snow depth

Soil moisture
content

Land

Land

30 km

50 km

20 cm10%

20 cm10%

0 – 40%*1. Brightness temperatures are the most basic observation quantity converted from the sensor’s engineering value
outputs. All other geophysical parameters are derived from brightness temperatures via conversion algorithms.
*2. Unless specifically noted otherwise, the stated accuracies are the root mean square error (RMSE) of instantaneous
values. The accuracy of brightness temperatures is dependent on the accuracy of onboard and ground processing
calibrations. The accuracy of other geophysical parameters is dependent on the brightness temperature accuracy, theconversion algorithm’s performance, and the verification methods.
*3. The minimum accuracy of data that can be released for use in climate change analyses.
*4. A useable and standard level of accuracy based on past experience with AMSR, AMSR-E, etc.
*5. An accuracy level that contains many research components, such as algorithm performance and calibration accuracy
improvements.

4.5.2.2

Parameter descriptions

Table 4-13 provides descriptions of the products.
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Table 4-13: Product descriptions

Product
Total Precipitable
Water(total precipitablewater)
Cloud liquid water

Precipitation

Description

TPW

Total volume of water vapor in theatmosphere integrated in the verticaldirection

CLW

Total volume of cloud liquid water in
the atmosphere integrated in thevertical direction

PRC

Hourly rainfall depth of liquid
precipitation (rain) at the groundsurface
Water temperature several millimeters

Sea surface
temperature

SST

Sea surface wind
speed

SSW

Wind speeds 10 meters above the sea
surface

Sea iceconcentration

SIC

Ratio of the area occupied by sea ice
when sea water and sea ice exist in
the 18 GHz band’s observed field ofview

Snow depth

SND

Depth of accumulated snow

Soil moisture content

SMC

Volumetric water content in surface
soil

4.5.2.3

below the sea surface penetrated by
microwaves (near-skin sea
temperature)

Product verifications

The purpose of verifications is to assess the accuracy of the geophysical parameterproducts. Verifications are made by comparing actual measurements from groundobservations with AMSR2 calculated values. Collaborations with worldwide
meteorological and oceanographic observation institutions and research projects
provide various actual measurement data taken regularly at locations around theworld. These data sets are used effectively to assemble verification data along withobservation experiments for a given geophysical parameter.
Table 4-11 gives the verification schedule.

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Chapter 4 AMSR2 Products(1) Total Precipitable Water
The Total Precipitable Water product stores the total volume of water vapor in theatmosphere integrated in the vertical direction. The Total Precipitable Water
product is primarily verified using the worldwide radiosonde network. If necessary,
terrestrial microwave scanning radiometers installed on outlying islands andland-based global positioning system (GPS) receivers are used for verifications.
Other verifications include reciprocal comparisons with other global precipitation
measurement (GPM) mission satellites’ observations and quality studies using
on-site numerical forecasting systems.
The specific verification methods are described below.
AMSR2 estimated values for Total Precipitable Water are compared with Total
Precipitable Water values calculated from atmospheric temperature, atmosphericpressure, and relative humidity data obtained from radiosondes. The worldwide
radiosonde data used is standard isobaric surface data and data including singularpoints delivered by the global telecommunication system (GTS). Because AMSR2
estimates exist only over sea regions, radiosondes are selected that are free ofland emission effects, such as those installed on islands with small land areas.
Data sets where the radiosondes and the AMSR2 footprint match temporally andspatially are created and verified regularly. Matching data sets for observation data
are also created and verified.
(2) Cloud liquid water
The cloud liquid water product stores the total volume of cloud liquid water in theatmosphere integrated in the vertical direction. The cloud liquid water product isverified with terrestrial (maritime) microwave scanning radiometer observation dataand quality assessments that combine data from visible and infrared sensors. The
problem is cloud liquid water is one of the most difficult geophysical parameters toverify quantitatively. Cloud liquid water can be measured with radiosondes or withaircraft, but the spatial distribution of clouds is discrete and subject to extreme
temporal fluctuations. As well, radiosonde observations are single-point, andaircraft observations are limited in terms of their range and observation frequency.
Furthermore, these observations are not quantitatively perfect. As for estimates ofcloud liquid water with terrestrial microwave radiation observations, althoughindirect remote sensing methods, their observation background is cosmic
background radiation. Therefore, because the observations are stable and uniform,
they are more accurate than estimates from satellite orbits. And by adding verticaldirection information from ceilometers and cloud radar, it is possible to further
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improve the accuracy of cloud liquid water estimates. Reciprocal comparisons are
made with other GPM satellite observations and on-satellite cloud radarobservations. Comparisons are also made with analytic values from on-site
numerical forecasting systems.
The specific verification methods are described below.
a. The cloud liquid water product is assessed and verified with visible and infrared
sensor data, such as the Second-Generation Global Imager (SGLI), the Moderate
Resolution Imaging Spectroradiometer (MODIS), and the Visible Infrared Imaging
Radiometer Suite (VIIRS), or with Level 3 data sets associated with clouds.
b. AMSR2 data is matched and verified with terrestrial microwave scanningradiometers.
c. The cloud liquid water product is verified through comparisons with cloud radarobservations and on-satellite cloud radar observations.
(3) Precipitation
The precipitation product stores hourly rainfall depths of liquid precipitation (rain) at
the ground surface. The monthly product stores monthly accumulated rainfall
depths. Precipitation is a geophysical parameter that is difficult to compare directlywith terrestrial observation data because it is subject to extreme temporal andspatial fluctuations. Basic verifications use existing terrestrial radar networks andterrestrial rain gauge clusters, but in view of the accuracy and uniformity ofterrestrial data itself, it is very important to compare the product with on-satellite
precipitation radar if the operating cycles can be matched.
The specific verification methods are described below.
a. Comparisons with terrestrial radar networks and terrestrial rain gauges
The precipitation product is verified through comparisons with data from terrestrial
radar networks and terrestrial rain gauge clusters located in Japan, the UnitedStates, Europe, and elsewhere. Comparisons with Japan’s radar AMeDAS are
linked to the verification plans of the highly accurate, high-resolution Global
Satellite Mapping of Precipitation (GSMaP) and the GPM’s global precipitation
map.
b. Comparisons with on-satellite precipitation radar
When on-satellite precipitation radar is operating concurrently with AMSR2, suchas precipitation radar on Tropical Rainfall Measurement Mission (TRMM) satellites
or dual-frequency precipitation radar on GPM satellites, verifications will be madethrough regular comparisons with these data sets.
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Chapter 4 AMSR2 Productsc. Participation in international precipitation comparison experiments
Our participation in international precipitation comparison experiments — such as
the Program to Evaluate High Resolution Precipitation Products (PEHRPP) run by
the International Precipitation Working Group (IPWG), a part of the Coordination
Group for Meteorological Satellites (CGMS) — will help in verifying a consistent
precipitation product.
(4) Sea surface temperature
The sea surface temperature product stores water temperature several millimeters
below the sea surface penetrated by microwaves (near-skin sea temperature).
AMSR2 sea surface temperatures are assessed and verified using watertemperature data from fixed and drift buoys deployed worldwide.
The specific verification methods are described below.
GTS fixed and drift buoy data will be retrieved daily online and a matching data set
with AMSR2 data will be created automatically. This matching data set will be used
to evaluate the product using buoy water temperatures as a reference as well as tofind the frequency distribution of AMSR2 sea surface temperatures and theircorrelation with other geophysical parameters. A system should be constructedcapable of updating the daily comparisons with buoy water temperatures anddisplaying and releasing the results. Furthermore, comparisons with sea surface
temperatures from on-satellite infrared radiometers operating concurrently will be
used for global surface verifications especially during the initial observation phaseand for assessments of the global distribution of characteristics and errors in bothdata sets.
(5) Sea surface wind speed
The sea surface wind speed product stores wind speeds 10 meters above the sea
surface. AMSR2 sea surface wind speeds are verified using wind speed data fromfixed buoys deployed worldwide.
The specific verification methods are described below.
During the initial phase (the first six months or so after launch), verifications will use
wind speed data from fixed buoys obtained via GTS. GTS fixed buoy data will be
retrieved daily online and a matching data set with AMSR2 data will be createdautomatically. After the initial phase (from six months to a year after launch),matching data sets will be created using fixed buoy wind speed data from the U.S.
National Data Buoy Center (NDBC), which is available over the Internet. Thesematching data sets will be used to evaluate the product using buoy water
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temperatures as a reference as well as to find the frequency distribution of AMSR2
sea surface wind speeds and their correlation with other geophysical parameters.
Comparisons will also be made with any on-satellite microwave scatterometers
operating concurrently.
(6) Sea ice concentration
The sea ice concentration product stores the ratio of the area occupied by sea ice
when sea water and sea ice exist in the 18 GHz band’s observed field of view. A
large portion of sea ice regions shift daily due to the influence of winds and ocean
currents, while other regions are a mixture of young ice, first-year ice, andmulti-year ice. Whether using the Bootstrap algorithm, the standard AMSR2 sea ice
concentration estimation algorithm, or the NASA Team 2 algorithm, NASA’sstandard AMSR-E algorithm, what is important is setting the algorithm’s parameters
based on the distributions of first-year ice, multi-year ice, and open sea in a given
space using observation data. Because these distributions vary depending on thesensor, the season, and the sea region, it is necessary after launch to create
distributions in specific segments in the South Pole, the North Pole, the Sea of
Okhotsk, and other sea ice regions and then adjust the algorithm’s parameters
based on these distributions. It is also imperative to assess atmospheric and land
influences. These influences are assessed using observation data and thealgorithm parameters are adjusted appropriately. Because atmospheric effects inparticular have large regional differences and seasonal variations, separate
verifications must be made for each sea ice region and for summer and winter. In
polynya and ice fringe regions where thin ice dominates, the estimation accuracy
declines. Detailed observations of sea ice in these regions have not been possible
until now because the emission characteristics are very changeable as the ice
grows. Therefore, it is important to ascertain through observations the emission
characteristics in each frequency band of sea ice in polynya and ice fringe regions
in order to raise the accuracy of concentration estimates.
The specific verification methods are described below.
Sea ice concentrations are verified primarily through comparisons with opticalsensors. The optical sensor data that will be used comes from Aqua / MODIS,
which is part of the same A-Train as GCOM-W1. The presence or absence of sea
ice is determined from MODIS and other high-resolution images. This information isused to create sea ice concentration data closely aligned to the AMSR2
observation regions, which are then compared with the product. A data set of
graded ground truth data is built up using sea ice concentration data from MODIS
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images and from ship and ice flow sea ice observations, aircraft observations, andmultistage observations that synchronize Terra, Aqua, and other satellite
observations. The same process was used for the development of the algorithm.
The presence or absence of sea ice is classified based on the created data set.
Comparisons are made in the Sea of Okhotsk and in the summer and winter North
Pole and South Pole. The algorithm is verified and the effects of land andatmosphere assessed through comparisons with satellite data sets with resolutions
10 or 20 times higher than AMSR2 data. To fully optimize parameters with a large
influence on sea ice concentration estimate accuracy, variations due to the sea
region and season are assessed and used to fine tune the parameters. This
necessitates cooperation with various related institutions to carry out regularongoing verifications in multiple sea regions instead of only a short verification
immediately after launch.
Polynya and ice fringe regions are particularly important in raising the estimation
accuracy. Tie points in each sea ice type will be verified and applied in comparison
with other satellites and in consideration of sea ice emission characteristics
obtained with on-site observations around Monbetsu harbor, Lake Saroma, and sea
ice regions at both poles.
(7) Snow depth
The snow depth product stores the depth of accumulated snow. The verification
plan, which accounts for the following three effects, is essential to verifying thesnow depth estimation algorithm.
a. Verification under various accumulated snow conditions (snow particle size andlayer composition)
Variations in snow particle size and layer composition, caused by changes in thesnow composition, have a huge impact on the microwave emission pathcharacteristics. Consequently, the product must be verified under conditions thatgive these different microwave emission path characteristics.
b. Verification accounting for the microwave characteristics of the surface under thesnow
The microwave emission and dispersion characteristics of the surface under thesnow present a boundary condition for the microwave emission path equation
within the accumulated snow layer. The lower boundary condition in the microwave
emission path equation changes noticeable between regions where the surface is
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thawing even in winter, regions where there is no melted snow under the snow
layer, and regions where accumulated snow has fallen after the soil has frozen.
Consequently, the product must be verified with different underlying surface
conditions and under the various accumulated snow conditions.
c. Verification under various vegetation conditions (amount and type of vegetation,snow in tree canopies, and the non-uniformity of vegetation cover)
How the moisture contained in vegetation is distributed over the accumulated snow
— namely, vegetation biomass, structure of tree branches and trunks, accumulated
snow in tree canopies, and unevenness of vegetation distribution in the footprint —is known to have a large impact on the microwave emission path. Consequently,
the product must be verified with different vegetation conditions as well as underthe various conditions given above.
The specific verification methods are described below.
Verifications 1) and 2) below are combined in order to verify the product under all
the conditions discussed above and verification 3) is run as needed to enhance thealgorithm.
1) Focused verifications and observation tests in areas the size of the satellite’sfootprint
First, a long-term observation system will be built on an accessible 25-kilometersquare of featureless terrain where multiple auto snow depth measurement devices
and auto weather observation devices are installed. This observation data will becompared with the satellite’s product. Other detailed studies will be made withhuman observations of the snow density, particle size, layer composition,
underlying surface temperature, soil moisture, and vegetation from the start of thesnowfall season to the thawing season.
2) Verifications with long-term observation data under various climate conditions
Verification tests will be conducted over a long period under different climates,
vegetation, snow conditions, and soil conditions. Regions described in 1) above
should be selected for their comparative uniformity of conditions so that point
observations are valid over the entire area.
3) Terrestrial microwave scanning radiometer observations and verifications underwell-managed conditions
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We will collect and examine highly accurate observation data using terrestrial
scanning radiometers with the same performance levels as satellite sensors on ascale that permits quantitative measurements of snow conditions, underlying
surface characteristics, and vegetation conditions.
(8) Soil moisture content
The soil moisture content product stores the volumetric water content in surface
soil. The verification plan, which accounts for the following four effects, is essential
to verifying the soil moisture content estimation algorithm.
a. Verifications under soil moisture conditions from arid to damp
In damp conditions, the microwave emission path characteristics are determined by
the surface emissivity (the surface dielectric constant and surface dispersion
characteristics are important; soil moisture is one of the main factors determining
the dielectric constant) and the surface temperature. Conversely, in arid conditions,the microwave emission path characteristics (volume scattering) in the soil layer
and the soil temperature’s vertical profile dominate. Consequently, the productmust be verified under conditions that give rise to these different emission pathcharacteristics.
b. Verifications with different soil types (surface roughness and texture)
The surface dispersion characteristics — largely determined by the surface
roughness, and the volume scattering in the soil layer — largely determined bydifferences in texture, have a big impact on microwave emission pathcharacteristics. Consequently, the product must be verified under different surface
roughness and texture conditions and simultaneously under a broad range of soil
moisture conditions, from arid to damp.
c. Verification under various vegetation conditions (amount and type of vegetationand the non-uniformity of vegetation cover)
How the moisture contained in vegetation is distributed over the surface — namely,
vegetation biomass, structure of leaves and stems, and unevenness of vegetation
distribution in the footprint — is known to have a large impact on the microwave
emission path. Consequently, the product must be verified with different vegetation
conditions as well as under the conditions given in a. and b. above.
d. Verification under conditions of non-uniform soil moisture distributions within the
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footprint
Actual soil moisture distributions are not even, but are known to change and be
distributed unevenly due to rainfall distributions, surface coverage, andmicrotopography. As the satellite footprint is on the order of tens of kilometers, theproduct must be verified in areas of that scale.
The specific verification methods are described below.
Verifications 1) and 2) below are combined in order to verify the product under all
the conditions discussed above and verification 3) is run as needed to enhance thealgorithm. To help promote use of the product, verifications 4) and 5) will be used tojudge the product’s accuracy and quality more rigorously.
1) Long-term data collection in a verification region equivalent in size to the satellite
footprint
First, a long-term observation system will be built on an accessible 10-kilometer orso square of featureless terrain where multiple auto soil moisture measurement
devices and auto weather observation devices are installed. This observation data
will be compared with the satellite’s product.
2) Verifications with long-term observation data under various climate conditions
Verification tests will be conducted over a long period in different climates,
vegetation conditions, and soil types. In order to compare and evaluate various
regions around the world, locations are selected, in addition to the conditions in 1)
above, for their comparative uniformity of conditions so that point observations arevalid over the entire area.
3) Terrestrial microwave scanning radiometer observations and verifications underwell-managed conditions
We will collect highly accurate observation data using terrestrial scanningradiometers with the same performance levels as satellite sensors on a scale that
permits quantitative measurements of wetness conditions, soil characteristics, andvegetation conditions. The observation data is used to examine algorithm
enhancements.
4) Reciprocal verifications with other satellite products and model outputs
We will move ahead with reciprocal verifications with other soil moisture products
and numeric climate model outputs.
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Chapter 4 AMSR2 Products5) Examinations of verification methods of areas on the scale of the footprint
We will examine the definitions of soil moisture in areas on the scale of the footprint
when soil moisture is unevenly distributed and examine more effective satellite
verification methods.
4.5.2.4

Algorithm verifications

Calculation algorithms are improved as needed based on the results of productverifications. See Section 4.3.1 for details on the algorithms.

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Chapter 5 Data Providing Service

Chapter 5

Data Providing Service

Products for general researchers are supplied with the GCOM-W1 data providing
service, 1 which uses the GCOM-W1 Data Provision Subsystem (DPSS). This
chapter details the range of data provided with this system and describes thebasics of the system. For more details about the service, see the guide “How to
Use the GCOM-W1 Data Providing Service for Beginners to Satellite Data Usage”2on the JAXA website.
5.1

Product Storage

The table below lists the data provided through this service. The service supplies
the most recent generation of Level 1 (L1) data and the two most recent
generations of Higher-level processed products (Level 2 (L2) and Level 3 (L3)).
Table 5-1: List of supplied data

Satellite

Data TypeADEOS-IIAMSR standard products

Aqua

AMSR-E
products

GCOM-W1AMSR2 standard products

standard

Associated information(track information, satellite
and sensor operation
information, user toolkit,
etc.)

Observation
Period2003/04/02 to
2003/10/24
2002/06/01 to
2011/10/04
Five years
from2012/5/18―

Generation――
L1: Most recent
generation
L2/L3： Two mostrecentgenerations―

Note: Version information displayed in the service’s catalog details andelsewhere uses letters after reaching Version 9. For example: 1, 2, 3, … 9, A, B,…

1
2

https://gcom-w1.jaxa.jp/auth.html
https://gcom-w1.jaxa.jp/Beginner.pdf
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GCOM-W1 "SHIZUKU" Data Users Handbook
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Chapter 5 Data Providing Service

DPSS stores and manages only the most recent versions of AMSR2 products.
However, older versions of AMSR2 products stored and managed by the mission
operation system can be supplied to users on request. When a user orders an older
product version that is stored only at the mission operation system, the data will be
provided from the mission operation system. DPSS also stores and manages alluser AMSR and AMSR-E products.
This system does not handle research products or test products. Links are provided
to the sites where these data sets are held.
5.2
5.2.1

Product Provision Policy
Data Policy

The GCOM-W1 data providing service supplies products to the public under thefollowing conditions.
(1) Users: all general researchers(2) Contract: no individual contracts (possible to automatically apply and sign up byagreeing to the online Terms of Use). Online sign-up requires the user’s name,affiliation, and purpose of use, and JAXA reserves the right to collect informationon the results of using its data.
(3) Provided products: AMSR, AMSR-E, and AMSR2 observation data (standard
products)(4) Provision method: online(5) Fees: free of charge provided that usage does not incur additional expenses to
JAXA(6) Provision to third parties: not permittedStandard products for general researchers are made available via DPSS within thetimes given in the table below based on processing performance and demand.
Table 5-2: Delivery of products for general researchers

Delivery
Products
Standard
product

Time to Provisiona. Level 1 products: within 12 hours of theobservation start timeb. Level 2 products: within 24 hours of theobservation start time

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GCOM-W1 "SHIZUKU" Data Users Handbook
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Chapter 5 Data Providing Service

Delivery
Products

Time to Provisionc. Level 3 products: within 24 hours of the
final observation time of the products usedin the Level 3 process(L3 monthly averages are available on thefirst day of the following month)

5.2.2

User Categories

The table below indicates the service’s user categories. Users must register andbecome registered users in order to obtain products. Users can register online.
Table 5-3: User categories

Category

Definition

Usage Limitations / Privileges

Guest user

A user who has notcompleted user
registration

Able to search data, but cannot
place orders or obtain products.

Registereduser

A user who hascompleted theonline user
registration

Able to search and order data.
Data orders are limited to 1.5
GB per order. Possible to obtainproducts directly without placing
an order.

Table 5-4: Service contents

User authentication

User Type
Guest
Registered
Yes
Yes

User registration

Yes

n/a

User info update

No

Yes

News and announcements

Yes

Yes

Product searches

Yes

Yes

Save and load search conditions

No

Yes

View on screen and check catalog

Yes

Yes

Ordering (including instantdownloads)

No

Yes

Satellite and sensor operation

Yes

Yes

Provided Services

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Chapter 5 Data Providing Service

User Type
Guest
Registered

Provided Services
information
No. of registered data records

Yes

Yes

Product descriptions

Yes

Yes

Product version histories

Yes

Yes

User manual (searching andordering instructions)

Yes

Yes

Product version histories

Yes

Yes

Toolkit

Yes

Yes

Direct downloads of standard
products

No

Yes

Email inquiries(Japanese/English)

Yes

Yes

Fax inquiries(Japanese/English)

No

Yes

No

Yes

Help desk access

Telephone inquiries (Japanese)

5.2.3

Process Versions

The purpose of GCOM algorithm development is to conduct R&D into algorithms
that generate geophysical parameters with sufficient precision for end applications,to implement systems, and to continually maintain and improve the algorithms.
Alongside continuing to maintain the algorithm, test processes and verifications are
run using real AMSR2 data and the results are released. Version 0 of a process
algorithm is given when development is complete and Version 1 is given when data
are released. After the first data release, the algorithm version will be updatedroughly every 1.5 years for better product accuracy and for GCOM-W2.
As a result, products with different process versions are created for the same
sensor / period / observation region. Generally, the service provides products
processed with the latest algorithm. Therefore, users must pay attention to theprocess version. The process version can be identified with the product version,algorithm version, and parameter version appended to the file name (granule ID).

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Chapter 5 Data Providing Service

The granule ID assignment rules are given below.
(1) Granule ID assignment rules for AMSR2 Level 1 / Level 2 products
Scene ID＿Product ID
Byte position
1
2
AMSR2
G W
←

3
1

4
A

5
M

6
2

7
＿

8
C

9
C

10
Y

11
Y

12 13 14M M D
Scene ID

15
D

16
H

17
H

18
m

19
m

20
＿

21
P

22
P

23
P

24 25 26
X ＿ L
→
←

27
L

28
x

29
x

30
K

31
K

32
K

＜Example＞ G

1

A

M

2

_

2

0

1

1

1

3

2

3

4

5

_

0

1

2

D

1

S

G

A

D

N

W

1

1

_

L

33 34 35r
dv
Product ID
R

_

1

36
a

37
a

38
a

39
p

40
p

41
p
→

1

0

1

0

0

1

Scene ID

［Satellite ］［Sensor］(Start date of observation) (Path number) (Ascending/Descending)Satellite code
: GW1 (Fixed)
Sensor Code: AM2 (Fixed)
Start date of Obs.
: CCYYMMDDHHmm (Christian year (UT))
Path number: PPP (000～300) *Path number at begining of the observation
Ascending/Descending : X (A：Ascending, D：Descending, B：Downlink unit (Both))

Product ID(Processing level) (Product type) (Product ID) (Resolution) (Developper ID) (Product ver.) (Algorithm ver.) (Processing parameter ver.)
Processing level :LL (L1：level1, L2：level2)
Product type:xx (SG：Standard product, SN：Near realtime processing product(Grobal), SL:Near realtime procesing product(Around
Japan area), RG：Research product(Standard), RN：Research product(Near realtime(Grobal)), RL：Research product(Near realtime(Aroudn Japan area)))
Product ID:
KKK(<L1A> ADN：Digital Number, <L1B>BTB：Brightness Temperature, <L1R>RTB:Brightness Temperature,<L2>CLW:Cloud Liquid Water, TPW：Total Precipitable Water, PRC：Precipitation,
SST：Sea Surface Temperature, SSW：Sea Surface Wind speed, SIC：Sea Ice Concentration,
SND：Snow Depth, SMC：Soil Moisture Content)
Resolution:r(<L1> R:Raw(Fixed), <L2> L:Low (Small number of observation point(243 points)), H：High (Learge number ofobservaion point(486 points)))
Developper ID:d(<L1>_:Underscore(Fixed), <L2>:A-Z)
Version number of Product:v (0-9, a-z）
Version number of algorithm:aaa (000-999)
Version number of Procesing parameter:
ppp (000-999)

Figure 5-1: Level 1 / Level 2 granule ID assignment rules(2) Granule ID assignment rules for AMSR2 Level 3 products
Scene ID＿Product ID
Byte position
1
2
AMSR2
G W
←

3
1

4
A

5
M

6
2

7
＿

8
Y

9
Y

10
Y

11
Y

12 13 14M M D
Scene ID

15
D

16
＿

17
t

18
t

19
t

20
＿

21
P

22
P

23
W

24 25 26
X ＿ L
→
←

27
L

28
x

29
x

30
K

31
K

32
K

＜Example＞ G

1

A

M

2

_

2

0

1

1

1

3

_

0

1

D

_

E

Q

O

D

3

S

G

T

8

9

W

1

1

_

L

33 34 35r
dv
Product ID
H

A

1

36
a

37
a

38
a

39
p

40
p

41
p
→

1

0

1

0

0

1

Scene ID

［Satellite］［Sensor］ (Start date of observation) (Calculating period) (Map projection) (Statistical method) (Ascending/
Descending)Satellite code
: GW1 (Fixed)
Sensor code
: AM2 (Fixed)
Start date of obs.
: YYYYMMDD(Christian year(UT), "DD"＝"00" means monthly data)
Calculating period: ttt (01D:Daily, 01M：Monthly)
Map projection: PP (EQ:Equirectangular, PN:Polar stereo(North hemisphere), PS:Polar stereo(South hemisphere))Calculating method: W (M:Mean, O:Overwrite)
Ascending/Descending: X (A:Ascending, D:Descending)

Product ID(Processing level) (Product type) (Product ID) (Resolution) (Developper ID) (Product ver.) (Algorithm ver.) (Processing parameter ver.)
Processing level:
LL (L3:Level3)
Product type:xx（SG:Standard product, RG:Research product)
Product ID:
KKK (T06：TB 6GHz,T07:TB 7GHz, T10:TB 10GHz, T18:TB 18GHz, T23:TB 23GHz, T36:TB 36GHz, T89:TB 89GHz)
CLW:Cloud Liquid Water, TPW:Total Precipitable Water, PRC:Precipitation,
SST:Sea Surface Temperature, SSW:Sea Surface Wind speed, SIC:Sea Ice Concentration,
SND:Snow Depth, SMC:Soil Moisture Content)
Resolution(lattice):r (L:Low(25km(0.25°)), H:High(10km(0.1°)))Developper ID:
d (A-Z)
Version number of Product:v (0-9, a-z）
Version number of algorithm:aaa (000-999)
Version number of Procesing parameter:
ppp (000-999)

Figure 5-2: Level 3 granule ID assignment rules

5-5GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition

Chapter 5 Data Providing Service

5.2.4

Terms of Use

The following Terms of Use for the service are given on the JAXA website. See theTerms of Use3 under Using This Site for the latest information. (Quoted material isshown in italics.)
The Japan Aerospace Exploration Agency (hereinafter referred to as “JAXA”)
provides free of charge products containing data from three satellite sensors —AMSR2 on the Global Change Observation Mission 1st-Water (GCOM-W1), AMSR
on Midori II (ADEOS-II), and AMSR-E on Aqua — via the GCOM-W1 Data
Providing Service (the “Service”).
These terms stipulate the conditions users must observe when making use of theService. People wishing to use the Service (the “users”) are required to read thefollowing terms and conditions and indicate their agreement before making use of
the Service.
(1) Site Policy
The Site Policy of the Website (https://gcom-w1.jaxa.jp) on which the Service ishostedconforms
to
JAXA’s
Site
Policy
and
Terms
of
Use
(http://www.jaxa.jp/policy_j.html).
(2) User registration
User registration is necessary in order to make use of the Service. The Service’s
login uses an authentication method consisting of an email address (user account)and password. User registration requires the provision of the user’s name, email
address, organization, department, country or region, and purpose of data usage.
(3) Deletion of user registration
Users can delete their user registration by sending an email requesting deletion of
the user registration to the GCOM-W1 Data Providing Service’s Help Desk given at
the end of the Terms of Use.
(4) Privacy protection and handling of private information
JAXA will handle registered user information (name, email address, organization,
department, country or region, and purpose of data usage) in proper accordance
3

http://gcom-w1.jaxa.jp/useagreement.html
5-6

GCOM-W1 "SHIZUKU" Data Users Handbook
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Chapter 5 Data Providing Service

with JAXA’s Rules on Privacy Protection.
For details, please see the JAXA Privacy Policy(http://www.jaxa.jp/about/disclosure/kojin/index_j.html).
JAXA shall use registered user information only for the following purposes:• Determination of data usage• User surveys and questionnaires to improve the Service• Respond to user inquiries
JAXA outsources some parts of its services to other companies. Outsourced
services include system management, user management, and help desk services.
Contracted businesses do make use of user information as needed to carry out theservices above, but user information is not used for any other purpose.
(5) Account and password management
Users are completely responsible for managing their user account and password.
JAXA does not accept any responsibility whatsoever with regard to losses,
damages, etc. that may arise due to the misuse of a user account or password by athird party.
(6) Ownership of data rights, etc.
JAXA is the owner of the copyrights and intellectual property rights of all data,
including all products and image data, provided with the Service. Redistribution tothird parties of any data obtained with this Service is strictly prohibited.
Use of the Service’s data must comply with the terms and conditions set forth in
JAXA’s Site Policy and Terms of Use (http://www.jaxa.jp/policy_j.html).
Users may not use the Service’s data for business activities, commercial activities,
or similar purposes without prior consent of the copyright holder (JAXA). If you wish
to use the Service’s data for business activities or commercial activities, pleasecontact the JAXA Industrial Collaboration and Coordination Center(http://aerospacebiz.jaxa.jp) beforehand.
(7) Cancelation of service usage
JAXA reserves the right to cancel and delete a user registration if the user has
violated the Terms of Use or if the user is otherwise deemed inappropriate.
(8) Service changes
JAXA reserves the right to change or modify the Service’s operating terms and
5-7

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Chapter 5 Data Providing Service

conditions, the Service content, the Terms of Use, etc. at any time. Changes to theTerms of Use will be notified by posting a statement on the Service website. Users
accessing the Service the first time after the notification of the Terms of Usechanges are asked to verify the new Terms of Use before using the Service.
(9) Service termination
JAXA reserves the right to terminate the Service at its own discretion.
JAXA will notify users about any Service termination beforehand by an appropriate
method.
(10) Reporting findings
Please include the following statement in any published articles, reports, etc. thatmake use of products, image data, etc. supplied by the Service.
For reports in Japanese:「本論文にて使用した AMSR2 データ（AMSR-E データおよび、AMSR データ）
は、宇宙航空研究開発機構 GCOM-W1 データ提供サービスより提供を受けました。」
For reports in English:“AMSR2 data (AMSR-E, AMSR data) were supplied by the GCOM-W1 Data
Providing Service, Japan Aerospace Exploration Agency.”
The GCOM-W1 Data Providing Service’s Help Desk collects materials thatreference the Service’s data. Please send a reprint or copy of any articles, reports,
etc. that make use of the Service’s data to GCOM-W1 Data Providing Service’s
Help Desk given at the end of the Terms of Use. We appreciate your cooperation.
You may send reprints or copies by mail, email attachment, fax, or any other meansof your choice.
(11) Limitations on data orders
To ensure fair access to our limited network and computing resources for as manyusers as possible, JAXA limits the amount of data that can be downloaded perorder per user account.
(12) Disclaimer
Although we strive to ensure the quality of all aspects of the Service, JAXA makes
5-8

GCOM-W1 "SHIZUKU" Data Users Handbook
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Chapter 5 Data Providing Service

no warrantee or guarantee about the safety, security, or reliability of provided
information and does not accept any responsibility whatsoever with regard tolosses, damages, etc. that may arise through the user’s use of the Service or theService’s information.
JAXA reserves the right to change, modify, or delete information on the Service andto suspend or terminate the operation of the Service and does not accept any
responsibility whatsoever with regard to losses, damages, etc. that the user may
incur as a result of these actions.
(13) Inquiries
Questions or comments concerning any aspect of the service should be directed tothe help desk below.
Contact
Address::

Officehours:

Telephone :
Fax
:
Email
:
5.2.5

GCOM-W1 Data Providing Service’s Help Desk
Tsukuba Space Center, Japan Aerospace Exploration Agency,
2-1-1 Sengen, Tsukuba, Ibaraki, 305-8505
9:30 – 12:15
13:00 – 17:45 (except Saturdays, Sundays, public holidays,
and year-end holidays (December 29 – January 3))050-3362-6599
029-859-5574
Z-gw1help@jaxa.jp

Data Searches and Orders

There are three different ways to search for and order data available with theservice (Figure 5-3). See the GCOM-W1 Data Providing Service User Manual for
detailed operating instructions.
(1) Select while following directions
Users can search for data specified through a wizard interface. The product,observation period and region, and other detailed search conditions are set while
following directions on a wizard interface. Search conditions can be refined by Step
1: specifying the product by category, Step 2: specifying the period and region, Step
3: specifying other conditions such as version, quality, satellite, sensor, and pass
number.

5-9

GCOM-W1 "SHIZUKU" Data Users Handbook
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Chapter 5 Data Providing Service(2) Select by category / geophysical parameter
Users can search for data after specifying the category or geophysical parameter.
The geophysical parameter, observation period, observation region, and othersearch conditions can be set on the same window.
(3) Select by satellite / sensor name
Users can search for data after specifying the satellite or sensor. The satellite /sensor name, observation period, observation region, and other search conditions
can be set on the same window.

(1) Select while following directions(2) Select by category / geophysical parameter(3) Select by satellite / sensor nameFigure 5-3: Search window (guest user)

5.3

User Tools

The core of the GCOM-W1 Data Providing Service is downloads of user-related
information references and tools intended for general researchers using the service.
User tools are generally distributed free of charge. While online help is available forthe tools, user support is not offered.
Tools can be downloaded by accessing the respective sites given below. Users canobtain the AMSR / AMSR-E tools and the AMSR I/O toolkit with the Download
Toolkit option on the GCOM-W1 Data Providing Service website. Users can obtain
GCOM-W1 track elements and satellite information with the View Satellite / Sensor
Operation Data option.

5-10

GCOM-W1 "SHIZUKU" Data Users Handbook
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Chapter 5 Data Providing Service

Figure 5-4: User tool acquisition methods (1 / 2)

Alternatively, users can obtain the AMSR / AMSR-E tools and the AMSR I/O toolkit
from the Toolkit menu option and GCOM-W1 track elements and satellite
information with the Satellite / Sensor Operation Data menu option from the site
shown below.

Figure 5-5: User tool acquisition methods (2 / 2)

5-11

GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition
Appendix 1

Appendix 1 Acronyms
Acronyms
ADM
AGC
AMSR2
AMSR-E
ASD
A-Train
CALIPSO

:
:
:
:
:
:
:
:

CEOS
CGMS
CLW
CSM
CWI
DPR
DPSS
EQR
GANAL
GCOM
GCOM-CGCOM-W
GCOM-W1
GEOSS
GPM
GPS
GSMaP
GTS
HDF
HTS
IPWG
JAXA
JPSS
KSAT
LUT

:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:

Description
Antenna Drive Mechanism
Auto Gain Control
Advanced Microwave Scanning Radiometer 2
Advanced Microwave Scanning Radiometer for EOS
Apid Sorted Data
The Afternoon Constellation
Cloud-Aerosol Lidar and Infrared Pathfinder Satellite
Observations
Committee on Earth Observation Satellites
Coordination Group for Meteorological Satellites
Cloud Liquid Water
Cold Sky Mirror
Cloud liquid Water Index
Dual-frequency Precipitation Radar
Data Provision SubSystem
Equi-Rectangular Map Projection
Global objective analysis data
Global Change Observation Mission
Global Change Observation Mission - Climate
Global Change Observation Mission - Water
Global Change Observation Mission 1st-Water "Shizuku"
Global Earth Observation System of Systems
Global Precipitation Measurement
Global Positioning System
Global Satellite Mapping of Precipitation
Global Telecommunication System
Hierarchical Data Format
High Temperature Source
International Precipitation Working Group
Japan Aerospace eXploration Agency
Joint Polar Satellite System
Kongsberg Satellite Services AS
Look-Up Table

App. 1-1

GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition
Appendix 1

MGDSST

:

MODIS
MWA
NASA
NDBC
NOAA
OBE
OBM
PDUC
PDUS
PR
PS
PWA
PWI
RFI
RMSE
SGLI
SIC
SMC
SND:
SPC
SPS
SST
SSW
TB
TCC
TCS
TPW
TRMM
VIIRS
WGCV
WGS84

:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:
:

Merged satellite and in situ data Global Daily Sea Surface
Temperatures in the global ocean
MODerate resolution Imaging Spectrometer
Momentum Wheel Assembly
National Aeronautics and Space Administration
National Data Buoy Center
National Oceanic and Atmospheric Administration
Orbital Balancing Electronics
Orbital Balancing Mechanism
Power Distributor Unit Control Unit
Power Distributor Unit Sensor Unit
Precipitation Radar
Polar Stereo Map Projection
Precipitable Water AmountPrecipitable Water Index
Radio-Frequency Interference
Root Mean Square Error
Second generation Global Imager
Sea Ice Concentration
Soil Moisture Content
SNow Depth
Signal Processor Control Unit
Signal Processor Sensor Unit
Sea Surface Temperature
Sea Surface Wind speed
brightness temperature
Thermal Controller Control Unit
Thermal Controller Sensor Unit
Total Precipitable Water
Tropical Rainfall Measuring Mission
Visible and Infrared Imager Radiometer Suite
Working Group on Calibration and Validation
World Geodetic System 1984

App. 1-2

GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition
Appendix 2

Appendix 2 Related Information
Appendix 2.1 Bibliography(1) Global Change Observation Mission Water （GCOM-W1）（Briefing document）(Japanese)（N/A）(2) AMSR2 Level 1Algorithm Description Document (Japanese)（N/A）(3) Earth Observation Satellite Data Archive Policy (Japanese)（MAS-100037）(4) Global Change Observation Mission Water （GCOM-W1）Product Definition
Document (Japanese)（NEB-090029）(5) Global Change Observation Mission Water （ GCOM-W1 ） Validation andVerification Plan (Japanese)（NDX-070017）(6) Global Change Observation Mission Water （ GCOM-W1 ） Verification
Implementation Document (Japanese)（NDX-110017）(7) Algorithm Description of GCOM-W1 AMSR2 Precipitation Algorithm(8) Algorithm Description of GCOM-W1 AMSR2 Snow Depth Algorithm(9) Algorithm Description of GCOM-W1 AMSR2 Sea Ice Concentration Algorithm(10) Algorithm Description of GCOM-W1 AMSR2 Sea Surface Temperature Algorithm(11) Algorithm Description of GCOM-W1 AMSR2 Sea Surface Wind Speed Algorithm(12) Algorithm Description of GCOM-W1 AMSR2 Total Precipitable Water and Cloud
Liquid Water Algorithm(13) GCOM-W1 MISSION OPERATIONS INTERFACE SPECIFICATION (M O I S)
(14) Global Change Observation Mission Water （GCOM-W1） Ground System
Operational Concept (Japanese)（SGC-070079）(15) Global Change Observation Mission Water （GCOM-W1） Routine Operation
Baseline Document (Mission Operation) (Japanese)（N/A）(16) GCOM-W1 Mission Operation system Interface Control Document (Draft)(Japanese)（DSD-800774-B02C）(17) Global Change Observation Mission Water （GCOM-W1）System development
for Data Provision Subsystem and its operation Operational Document (Japanese)
5.3.1.2

Appendix 2.2 Relevant Websites■ JAXA’s site(1) JAXA website
http://www.jaxa.jp/
App. 2-1

GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition
Appendix 2(2) Global Change Observation Mission 1st – Water “SHIZUKU” (GCOM-W1) website
http://www.jaxa.jp/projects/sat/gcom_w/index_e.html(3) GCOM/EORC site
http:// suzaku.eorc.jaxa.jp/GCOM/index.html(4) GCOM-W1 Data Providing Service
https://gcom-w1.jaxa.jp/auth.html(5) ”Aqua” Earth Observation Satellite
http://www.jaxa.jp/projects/sat/aqua/index_e.html(6) Earth Observation Research Center (EORC) website
http://www.eorc.jaxa.jp/en/index.php(7) AMSR/AMSR-E site（JAXA/EORC）
http://sharaku.eorc.jaxa.jp/AMSR/index.html(8) Data Distribution Service for AMSR/AMSR-E
http://www.eorc.jaxa.jp/en/about/distribution/info/aqua.html■ Overseas(9) NASA website
http://www.nasa.gov/(10) Aqua website
http://aqua.nasa.gov/(11) AMSR-E website（NASA/MSFC）
http://wwwghcc.msfc.nasa.gov/AMSR/(12) HDF website
http://www.hdfgroup.org/

App. 2-2

GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition
Appendix 2

Appendix 2.3

Point of Contact◆About this Data Users Handbook
GCOM-W1 Data Providing Service’s Help Desk
Tsukuba Space Center, Japan Aerospace Exploration Agency,
2-1-1 Sengen, Tsukuba, Ibaraki, 305-8505
E-mail：Z-gw1help@jaxa.jp◆About GCOM-W1Data
Please refer 5.2.4 (13) Inquiries.

App. 2-3

GCOM-W1 "SHIZUKU" Data Users Handbook
First Edition
Appendix 3

Appendix 3

AMSR2 Product Format Documents

Appendix 3.1 AMSR2 product format specification(1) AMSR2 level 1 product format specification(SGC-120003)(2) AMSR2 Higher level product format specification(SGC-120005)

App. 3-1