28 December 2007

**NPOESS Preparatory Project**

**Advanced Technology Microwave Sounder (ATMS)**

**Postlaunch** **Calibration and** **Validation** **Plan**

**(publicly accessible version)**

National Aeronautics and Space Administration (NASA)

Goddard Space Flight Center (GSFC)

Greenbelt, Maryland 20771

Submitted by:

NPP ATMS Science Team

  ----------------------- ----------------------- -----------------------
                          TABLE OF CONTENTS       

  ----------------------- ----------------------- -----------------------

[1](#__RefHeading___Toc132717924)

Purpose [1](#__RefHeading___Toc132717925)1

[2](#__RefHeading___Toc132717926) 1. ATMS Description
[2](#atms-description)2

1.1 ATMS Hardware Overview [2](#atms-hardware-overview)2

[2](#__RefHeading___Toc132717929) 1.2 Scan Profiles
........................................................................................................................[3](#__RefHeading___Toc132717930)3

1.3 Science Objectives [3](#__RefHeading___Toc132717931)4

2\. on-orbit performance evaluation [4](#__RefHeading___Toc132717944)5

2.1 Passive Telemetry Assessment [4](#__RefHeading___Toc132717945)5

[4](#__RefHeading___Toc132717946) [4](#__RefHeading___Toc132717947)
[4](#__RefHeading___Toc132717948) [4](#__RefHeading___Toc132717949)
[4](#__RefHeading___Toc132717950) [4](#__RefHeading___Toc132717951)
[4](#__RefHeading___Toc132717952) [4](#__RefHeading___Toc132717953)
[4](#__RefHeading___Toc132717954) [4](#__RefHeading___Toc132717955)
[4](#__RefHeading___Toc132717956) [4](#__RefHeading___Toc132717957)
[5](#__RefHeading___Toc132717958) 2.2 Health and Status Telemetry
[5](#__RefHeading___Toc132717959)5

2.3 Engineering (HotCal) Telemetry [5](#__RefHeading___Toc132717960)5

[5](#__RefHeading___Toc132717961) [5](#__RefHeading___Toc132717962)
[5](#__RefHeading___Toc132717965) 2.4 Scan Angles [5](#scan-angles)5

[5](#__RefHeading___Toc132717967) 2.5 Dynamic Range
[5](#__RefHeading___Toc132717968)5

2.6 Radiometric Sensitivity [6](#radiometric-sensitivity)6

[7](#__RefHeading___Toc132717971)2.7 Functional
Check-out..........................................................................................................\....
7

2.8 Dwell
Test..........................................................................................................................
7

2.9 Lunar Contamination in Space
View............................................................................................
8

2.10 NPP Roll Maneuver 1 \-- Cross Track Scan
Check............................................................................
8

2.11 NPP Roll Maneuver 2 \-- Imaging of Earth\'s
Limb...........................................................................
.. 9

2.12 NPP Pitch
Maneuver.........................................................................................................\...\.....
9

2.13 Functional Test after Roll/Pitch
Maneuver......................................................................................
9

2.14 ATMS Geolocation in Stare Mode
..........................................................................................\.....
9

2.15 ATMS Warm Load Analysis
....................................................................................................
10

2.16 NPP Roll Maneuver 3 -- Imaging of
Moon....................................................................................
10

2.17 ATMS Radiometric Environmental Characterization
.........................................................................10

2.18 Nonlinearity Correction and Validation Plan
.................................................................................
10

[11](#__RefHeading___Toc132717972) 3. comprehensive performance
evaluation [12](#__RefHeading___Toc132717973)12

3.1 ATMS RFI Check
.................................................................................................................
12

3.2 ATMS RFI Correction (if needed)
.............................................................................................
12

3.3 ATMS Geolocation Check
......................................................................................................
12

3.4 ATMS Ascending/Descending Brightness Temperature Comparison
.................................................... 13

3.5 ATMS RDR Gross Anomaly Identification
..................................................................................
13

3.6 ATMS Scan Uniformity Analysis and Scan Bias Analysis
................................................................. 13

3.7 ATMS Resampling to AMSU FOV Verification
............................................................................
13

3.8 ATMS Parameter Trending
.....................................................................................................
14

3.9 ATMS Trending of Blackbody and cold Space Count
...................................................................... .
14

3.10 ATMS SDR Center Frequency Stability
......................................................................................
14

3.11 ATMS Resampling to CrIS FOR Verification
...............................................................................
14

3.12 ATMS High-altitude Aircraft Underflights
...................................................................................
14

3.13 ATMS SDR Comparison with Model Fields
.................................................................................
15

3.14 ATMS NWP Radiance Validation
.............................................................................................
15

3.15 ATMS SDR Comparison with Radiosonde Observations (RAOBs)
....................................................... 15

4\. Abbreviations and acronyms [16](#abbreviations-and-acronyms)16

  ----------------------- ----------------------- -----------------------
                          FIGURES                 

                                                  
  ----------------------- ----------------------- -----------------------

Figure 1‑1 ATMS Instrument [2](#__RefHeading___Toc132717734)2

Figure 1‑2 Beam Position Geometry [3](#__RefHeading___Toc132717736)3

[5](#__RefHeading___Toc132717741) [12](#__RefHeading___Toc132717742)

  ----------------------- ----------------------- -----------------------
                          TABLES                  

  ----------------------- ----------------------- -----------------------

Table 1‑1 Cold Calibration Angle Start Positions
[3](#__RefHeading___Toc132717743)3

Table 2‑1 Sensitivity Requirements [7](#__RefHeading___Toc132717756) 7

## Purpose {#purpose .unnumbered}

This document contains plans for post-launch calibration and validation
checkout of the Advanced Technology Microwave Sounder (ATMS). The
calibration and validation checkout described here shall be performed
during the activation, checkout phase and operational phase of the
National Polar-orbiting Environmental Satellite System (NPOESS)
Preparatory Project (NPP) mission. Note that most of the ATMS instrument
information are adopted from Northrop Grumman Report 13676.

#  

# 

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# 1. 

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#  

# ATMS Description

## ATMS Hardware Overview

## 

The ATMS is a 22-channel mm-wave radiometer, shown in Figure 1-1. The
ATMS will measure upwelling radiances in six frequency bands centered at
23 GHz, 31 GHz, 50-58 GHz, 89 GHz, 166 GHz, and 183 GHz. The ATMS is a
total-power radiometer, with "through-the-antenna" radiometric
calibration. Radiometric data is collected by a pair of antenna
apertures, scanned by rotating flat-plate reflectors. Scanning is
performed cross-track to the satellite motion from sun to anti-sun,
using the \"integrate-while-scan\" type data collection. The scan period
is 8/3 second, synchronized to the Cross-track Infrared Sounder (CrIS)
using a spacecraft-provided scan synchronization pulse.

![](media/image2.wmf)

[]{#__RefHeading___Toc132717734 .anchor}Figure 1‑1 ATMS Instrument

1.2 Scan Profiles

The ATMS has four selectable Scan Profiles. The difference between the
Scan profiles is the start angle of the cold calibration positions.
Figure 1-1Figure 1-2 depicts the beam position geometry. Table 1-1 shows
the start angle for each of the cold calibration positions.

[]{#__RefHeading___Toc132717736 .anchor}Figure 1‑2 Beam Position
Geometry

  ------------------------------- ---------------------------------------
  Scan Profile                    Cold Cal Angle

  1                               6.66°

  2                               8.33°

  3                               10.00°

  4                               13.33°
  ------------------------------- ---------------------------------------

[]{#__RefHeading___Toc132717743 .anchor}Table 1‑1 Cold Calibration Angle
Start Positions

###  {#section-7 .unnumbered}

### 

### 

ATMS Instrument-to-Spacecraft Interface Characteristics

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##  {#section-10 .unnumbered}

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##  {#section-52 .unnumbered}

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## 1.3 Science Objectives {#science-objectives .unnumbered}

Mission data obtained from the ATMS and the CrIS will be used to produce
three of the NPOESS Environmental Data Records (EDRs):

-   Atmospheric Temperature Profiles

-   Atmospheric Moisture Profiles

-   Atmospheric Pressure Profiles

The primary roles of ATMS are to obtain data during overcast conditions,
to provide corrections for cloud effects in partly cloudy conditions,
and to provide a "first-guess" for iterative physical retrievals. The
ATMS data will also support generation of the following EDRs:

-   Cloud liquid water content

-   Precipitation rate

-   Snow cover

##  {#section-56 .unnumbered}

## 

## 

## 

Redundancy Configurations

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Diagnostics capabilities summary

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#   {#section-60 .unnumbered}

Passive Analog Temperature Telemetry

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SPA Critical Telemetry

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Mode Word Operational Status

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##### 

##### 

On-Orbit Performance Evaluation

The procedures for on-orbit instrument checkout and the functional
evaluations described below may be performed over a period of several
orbits, in order to cover the full range of thermal and EMI
environments.

##  Passive Telemetry Assessment

The Passive Analog Temperature (PAT) telemetry points shall be monitored
prior to initial instrument turn-on to ensure key temperatures are
within allowable operational limits. The temperatures should be within
Yellow (safe) limits prior to instrument turn-on. These temperatures are
monitored by the Spacecraft and reported in the Spacecraft Telemetry.

### 2.2 Health and Status Telemetry  {#health-and-status-telemetry .unnumbered}

After the Instrument Activation is completed, verify that all the
telemetry items are within the Yellow Limits. Depending on the
spacecraft interface temperature the PRT temperatures could take several
orbits to reach their operating values.

[]{#__RefHeading___Toc132717741 .anchor}

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2.3 Engineering (HotCal) Telemetry

After the Instrument Activation is completed, verify that the hot
calibration load temperatures, derived from the 4-wire PRT data in the
Engineering HotCal Temperatures Data Packets (APID=212~16~ ), are within
the expected operational range of 260 K to 330 K, and that all the PRTs
within each load are in agreement within 1C.

Determine if there are any PRT outliers (w.r.t. T/V good data), which
could indicate a bad PRT or a thermal gradient across the calibration
target, and identify PRT temperature difference for each warm load unit.
In this test, we need to have each black body PRT temperature, in normal
scan mode, for 24 hours. Then determine the minimum and maximum PRT
temperatures for each warm load unit for each scan. Compare the PRT
temperature difference (max -- min) for each warm load unit. Identify
any individual PRTs that are out of bounds, using T/V data as a
benchmark. Also, we should analyze PRTs during thermal stress, such as
while passing over Antarctica during winter (June). However, it would
also be of value to do this test at other times.

1.  
2.  
3.  
4.  
5.  
6.  
7.  

### 2.4  {#section-63 .unnumbered}

###  {#section-64 .unnumbered}

###  {#section-65 .unnumbered}

###  {#section-66 .unnumbered}

### Scan Angles {#scan-angles .unnumbered}

Compare the scan angle readouts in the Science Data Packets
(APID=210~16~) with the specified scan table. Verify that the errors
within the Earth-View sector are no greater than 0.035 degrees, and that
the calibration samples are within the specified sector 2.25 degrees.
Verify also the following items: (1) The scan angles in all scan
positions are denoted correctly in the RDR packets; (2) The scan angles
in all scan positions are repeatable; (3) The motor encoder position
readings do not drift appreciably for any of the scan positions. This
shall be done both for all operational scan profiles. All data required
is available while the instrument is in operational mode.

### 

### 2.5 Dynamic Range and Gain Analysis  {#dynamic-range-and-gain-analysis .unnumbered}

The purpose of this evaluation is to verify that the radiometric counts
in the Science Packet (APID=210~16~) do not exceed the specified maximum
allowable for the instrument Analog-to-Digital conversion. The ATMS
shall be in the operational mode for this evaluation. The Hot
Calibration counts (APID=212~16~) (beam position 102 or 103) shall be
converted to an equivalent value at 300 K brightness temperature, using
the following equation:

$C_{\text{300}} = C_{\text{Hi}} + G_{i}(\text{300} - T_{H})$,

where C~Hi~ is the count value for the ith scan, G~i~ is the channel
gain, and T~H~ is the physical temperature of the target. A running
average of this derived counts value shall be computed, over 100 scans.
This data should be accumulated for at least one orbit. The maximum
allowable average value, for any channel, is 45,150 counts.

Determine if the radiometric gain and radiometric output are as
expected. These analyses should be performed on T/V measurements as well
as on-orbit data, and the results should be compared. First, T/V data
should be analyzed to determine radiometric gain and its dependence on
instrument temperatures - for each channel. Also, a table of nominal
warm-cal count values for a range of nominal warm-cal target
temperatures should be generated. The on-orbit analysis consists of a)
comparing warm-cal counts with T/V values for the corresponding
combination of temperatures and b) using the orbital variation of
warm-cal counts due to variation in instrument temperatures (i.e. minus
the estimated variation due to target temperature variation) to estimate
the gain and its sensitivity to instrument temperatures and compare with
T/V values.

### 2.6 Radiometric Sensitivity {#radiometric-sensitivity .unnumbered}

The purpose of this test is to evaluate the radiometer sensitivity
(NET). This test will be performed with the instrument in full
operational scan mode. Radiometric data provided in the Science data
packets (APID=210~16~) will be processed to derive system gain and NET
for all 22 channels. In addition to NEΔT, the receiver noise temperature
and noise figure should be determined for each channel and compared with
those derived from T/V data.

One simple methodology for the calculation of receiver noise temperature
and noise figure, yet there are other ways as well, could be: Consider
100 temperature-normalized (as opposed to calibrated) observations of
the internal target, fit a third-order polynomial, calculate the
standard deviation of the difference between the observed brightness
temperatures and those from the polynomial fit, multiply by the
square-root of the product of effective bandwidth and integration time
and subtract the average (over the 100 observations) of the physical
temperature of the internal target.

The equations to determine Gain and NET are as follows:

.

$\text{NE}\Delta T = \sqrt{\frac{1}{n_{\text{sc}}}\sum_{i = 1}^{n_{\text{sc}}}\text{SD}_{i}^{2}}\text{   }$

> Where:
> $\text{   SD}_{i}^{2} = \frac{G_{i}^{2}}{N_{s}}\sum_{j = 1}^{N_{s}}{(C_{\text{ij}}} - \overline{C_{i}})^{2}$,
>
> And:
> $\overline{C_{i}} = \frac{1}{N_{s}}\sum_{j = 1}^{N_{s}}C_{\text{ij}}$
> ,
>
> And:
> $G_{i} = \frac{T_{\text{hi}} - T_{\text{ci}}}{M_{i} - N_{i}}\text{  }$

where i = scan index

j = sample index

n~sc~ = number of scans (100)

N~s~ = number of samples per scan (4)

C~ij~ = Hot Target counts for given scan and sample within scan

T~hi~ = Physical temperature of the warm load derived from the PRT data
in

the Hot Cal Packet (APID=212~16~)

T~ci~ = Physical temperature of the cold calibration

M~i~ = Average of the radiometric readings in counts in the Science
Packet (APID=210~16~) viewing the warm load, beam positions 101-104 (4
samples per scan, for 8 scans)

> N~i~ = Average of the radiometric readings in counts Science Packet
> (APID=210~16~) viewing the cold calibration sector, beam positions
> 97-100 (4 samples per scan, for 8 scans).

The results shall be trended during the full stabilization (the
orbit-to-orbit variation of all four receiver shelf PRT readings is less
than 2^○^ C) time interval. The NET requirements are shown in Table 2-1.

Table 2‑1 Sensitivity **NET** Requirements at 300 K, CP=5 C

  ---------------- -------------------------------- ------------------ ------------------
  **Channel**      **Frequency (GHz)**              **3dB BW (deg)**   **Req. NET (K)**

  1                **23.8**                         **5.2**            0.50

  2                **31.4**                         **5.2**            0.60

  3                **50.3**                         **2.2**            0.70

  4                **51.76**                        **2.2**            0.50

  5                **52.8**                         **2.2**            0.50

  6                **53.596+/-0.115**               **2.2**            0.50

  7                **54.40**                        **2.2**            0.50

  8                **54.94**                        **2.2**            0.50

  9                **55.50**                        **2.2**            0.50

  10               **57.290344**                    **2.2**            0.75

  11               **57.290344+/-0.217**            **2.2**            1.00

  12               **57.290344+/-0.3222+/-0.048**   **2.2**            1.00

  13               **57.290344 +/-0.3222+/-0.022**  **2.2**            1.50

  14               **57.290344 +/-0.3222+/-0.010**  **2.2**            2.20

  15               **57.290344 +/-0.3222+/-0.0045** **2.2**            3.60

  16               **89.45+/-2.45^1^**              **2.2**            0.30

  17               **165.5+/-1.5^1^**               **1.1**            0.60

  18               **183.31+/-7**                   **1.1**            0.80

  19               **183.31+/-4.5**                 **1.1**            0.80

  20               **183.31+/-3**                   **1.1**            0.80

  21               **183.31+/-1.8**                 **1.1**            0.80

  22               **183.31+/-1**                   **1.1**            0.90
  ---------------- -------------------------------- ------------------ ------------------

NOTE: 1 Maximum allowable bandwidth

###  

### 

### 

### 

### 

### 

### 

### 

### 

##  Functional Check-out

This test begins after ATMS is activated and fully functional. The test
set will consist of 12 minutes of radiometric and H&S data taken while
staring continuously (i.e., in diagnostic mode 148 radiometric samples
per 8/3 second) at the internal (warm) and cold space targets
alternately in 40-second steps. Namely, the total staring time for
internal and cold target would be 6 minutes (135 scans), respectively.

The 2048-point FFT will be computed for each 40-second staring data, and
this will be used to characterize the receiver 1/f noise. These data
will be compared with ATMS T/V data. The analysis will focus on the
gain, offset, NEDT, and 1/f characteristics.

Note that the above 12 minutes stare test should be repeated monthly for
first six months after launch and whenever there is a chance (e.g., no
science data collection times) afterwards for trending purpose.

##  Dwell Test

Set ATMS in diagnostic/point-and-stare mode for 5 minutes with
continuous sampling enabled at the on-board calibration target (OBCT)
position and optimal cold space view, respectively. Generate a time
series of 16,384 data points separated by 0.018 seconds. Calculate the
noise power spectrum, comparing it with T/V tests. NPP science team
would assist in performing full statistical analysis of the sensor
performance. The dwell data series should also be used to compute the
average and standard deviation of warm-cal counts over N points, where N
ranges from 1 to the length of the series. The purpose is to determine
the N where the standard deviation is minimum and compare that with the
power spectrum analysis. Any difference should be reconciled and the
result used to optimize the calibration algorithm (i.e. update the
calibration parameters).

##  Lunar Contamination in Space View

This task will serve as a check of lunar appearance predictions. To
prevent the SV from the worst lunar contamination, we can change the SV
sector (scan profile) in advance of predicted lunar contamination.

Note that switching between different scan profiles back and forth
requires each scan profile being characterized properly over a period of
time (e.g., a week). This could be a good test for processing lunar
contamination. Moreover, switching between different scan profiles will
happen only in the first 90 days post launch. This would help us to
determine the characteristics of each scan profiles in dealing with
lunar contamination.

Based on the current experiences with AMSU-B, when the Moon appears in
the (optimal) SV sector, only 2 out of 4 pixels would be contaminated.
For ATMS, at least one to three clean SV pixels are still good for
regular science calibrations. This is an important opportunity to verify
this issue (in terms of on-orbit operations and the SDR processing) and
to verify if we don't need to apply software fix for the lunar
contaminations.

Specifically, either with the optimal SV sector or with the less
contaminated SV sector, the contamination will be measured for each
space view and will be ignored if above some threshold.  This approach
preserves contaminated observations for which noise pushes data below
threshold.  An alternative is to know the beam pointing to better than
(say) 0.5 degrees and eliminate space calibration views predicted to be
contaminated.  All calibration data could be preserved to determine,
perhaps weekly or seasonally, the biases that result from this strategy
so that we might continually update the calibration algorithm. The
current SDR algorithm implementation uses the \"alternative\" method
described above. It calculates the lunar contamination for each SV pixel
based on the Moon position and the beam pointing information. The pixel
is ignored if the predicted contamination is above a tunable threshold.
The SDR calibration will proceed to use only \"good\" pixels which have
predicted contamination below the threshold.

During early orbit we need to compare values from each of the space
views to see if there are any side lobe differences that need to be
accounted for in the ground code.  We would expect that the C3 (Command,
Control, Communications) system is responsible for calculating
occurrences of the moon going through the space view and command the
instrument to a space view collection that will not (or least) be
contaminated in that orbital pass.  The ground software needs to look at
the space view selection to see if an adjustment is necessary for
differences between space views.

## NPP Roll Maneuver 1 -- Cross Track Scan Check 

**All proposed spacecraft maneuvers are under study.** Were this
maneuver to occur, the following description would apply:

The goal of this maneuver is to characterize the cross-track scan bias
dependence and determine at least part of any asymmetries that may
exist. The roll maneuver involves moving the ATMS full-scan field of
regard completely off the Earth and enables ATMS to collect radiometric
data while looking at deep space and to compare those results with the
instrument's cold reference. This maneuver enables a characterization of
any side-lobe contamination in the ATMS.

The antenna pattern measurements for ATMS are imperfect (near G band
they are substantially imperfect), and we don't know the impact of the
NPP spacecraft structure on the antenna patterns. Therefore, further
characterization of antenna pattern characteristics using on-orbit
testing is essential.

Angle-dependent brightness temperature biases introduced by antenna
sidelobes pose the highest risk to ATMS mission success with respect to
climate studies for which data records with long-term accuracies
approaching tenths of a degree Kelvin are desired. The tasks outlined in
NPP maneuvers are expected to provide direct measurements of integrated
antenna sidelobe levels with accuracies substantially greater than those
of the pre-launch antenna pattern measurements and will therefore lower
the risk of CDR degradation due to antenna sidelobe effects.

## NPP Roll Maneuver 2 -- Imaging of Earth's Limb 

**All proposed spacecraft maneuvers are under study.** Were this
maneuver to occur, the following description would apply:

In this maneuver, the spacecraft and, by inference, the ATMS field of
view is rolled to enable ATMS to scan down the Earth's limb. This
enables a characterization of any side-lobe contamination in the ATMS.

The sidelobes of the innermost beams would not be tested here because
their main beams would not leave the earth. Not being able to scan
further in this direction forces us to assume symmetry in certain
untested antenna parameters, but how serious this is will probably not
be fully known until the experiments are performed.

## NPP Pitch Maneuver 

**All proposed spacecraft maneuvers are under study.** Were this
maneuver to occur, the following description would apply:

In this maneuver, the spacecraft is pitched completely off the Earth to
enable ATMS to acquire full scans of deep space, permitting the
uniformity of the ATMS field of view to be characterized. When the
earth\'s disk lies totally outside the beams and their larger sidelobes,
then there should be good sensitivity to any anomalies introduced by
obstacles near the spacecraft itself. This maneuver will also allow us
to establish the baseline radiometer output (counts) for pure cold
space. A pitch maneuver (35 min) would yield good sidelobe information
fore and aft, but less for left and right sidelobe levels. 

## Functional Test after Roll/Pitch Maneuver

Repeat the same procedures as those in Sec. 2.7.

###  {#section-77 .unnumbered}

## ATMS Geolocation in Stare Mode

The nadir stare mode is a very important early on-orbit checkout test,
since it is by far the quickest and most accurate way of determining
pitch and roll pointing in a short time (i.e. without having to wait to
accumulate a large amount of data sufficient to form a high fidelity
surface image). This test is described in the paper on co-alignment of
the AIRS system (IEEE TGRS, vol 41, 343-351, 2003) -- see the last
section. The method was developed after the Aqua launch to verify the
pointing of both AMSU-A and HSB, and the method works very well and is
very accurate. It is necessary for ATMS to stay in stare mode for 24 --
48 hours, to ensure that a sufficient number of unobscured coastline
crossings with suitable crossing geometry can be obtained.

In the stare mode, ATMS geolocation accuracy is obtained in two steps:
(1) Use perpendicular coastline cross to calculate the along-track
accuracy. (2) Use the oblique cross case to calculate the cross-track
accuracy. Moreover, the FOV and antenna beam pointing accuracy are also
derived.

The stare mode analysis will determine any pitch and roll offsets that
need to be applied to the geolocation processing and used routinely
thereafter.

Yaw offsets can be determined either by (a) going into swath-edge stare
mode for a while (if that is possible), or (b) accumulating enough data
to form high fidelity coastal images that can then be used to determine
offsets between the observations and a map. The latter method can be
used routinely, to monitor pointing during the mission, but it takes a
lot of data and rather sophisticated analysis.

## ATMS Warm Load Analysis

This test is partly a verification of the expected best spacecraft
interface (cold) plate temperature. It should be made part of on-orbit
performance verification tests. Determine the relationship between the
warm load temperature and ATMS spacecraft interface (cold) plate
temperature and that between the warm load and the shelf (or MUX)
temperatures. Average the 4 warm load view measurements for each scan.
Tabulate and plot the averaged warm load measurements against cold plate
and shelf (or MUX) temperature data, scan by scan, for a 24-hour period.
Compare these results with the ground test data. If there are no issues,
we would only need to use nominal data to perform this analysis.

Since the on-orbit thermal environment will be different vs. T/V, if
there is a significant discrepancy between T/V performance test results
and flight test data, and/or if thermal gradients are too large, then a
calibration correction may be needed. (Analyze PRT data with this test).
Moreover, if the difference versus T/V is significant, we would need to
make on-orbit performance measurement with ATMS stabilized at each cold
plate temperature, in order to find out which temperature works best. In
this case, analyze also the counts and temperature of cold space for
optimal space view position versus different cold plate temperatures.
Note that changing of the interface plate temperature also involves
waiting for ATMS internal temperatures to settle to new equilibrium
values, and running performance evaluations at the new cold plate
temperature(s) & may have implications to s/c operations.

## 2.16 NPP Roll Maneuver 3 -- Imaging of Moon  {#npp-roll-maneuver-3-imaging-of-moon .unnumbered}

**All proposed spacecraft maneuvers are under study.** Were this
maneuver to occur, the following description would apply:

Lunar views may also be used to determine the extent and magnitude of
the sidelobe response of both low and high frequency ATMS channels. When
the Moon appears in the ATMS SV sector we perform this maneuver by
scanning across the Moon with EV pixels. We could examine the scan
contaminations with respect to different view angles.

Note that this maneuver would be performed if it is required for other
NPP sensors.

## 2.17 ATMS Radiometric Environmental Characterization  {#atms-radiometric-environmental-characterization .unnumbered}

The purpose of this task is to characterize the radiometric
contributions originating outside the standard scan profile, including,
for example, the angles between the earth limbs and the spacecraft. This
exercise can also be performed when the moon is near the cold space
views so that the angular effect on each channel can be characterized.

18. **Nonlinearity Correction and Validation Plan**

The nonlinearity correction lookup table resulting from the PFM T/V
measurements will be derived. On-orbit, without the benefit of known
calibration sources at intermediate brightness temperatures (such as
were available during T/V testing), it is not possible to directly
measure the nonlinearity. But, the nonlinearity correction is a function
of the physical temperature of the detectors.  So, on-orbit, we will
rely on temperature readings from temperature sensors located near the
detectors to determine the correction values to apply.

The correspondence between these temperature sensor readings and
detector nonlinearity will come from the T/V test results.  And while
the on-orbit correspondence is expected to be different, we do not
expect dramatic differences.  After the instrument has reached a steady
operational state on -orbit, a new on-orbit correspondence must be
derived based on several orbits\' worth of internal temperature
readings, and running NG\'s and/or NASA's thermal model(s) of the
instrument to better understand the appropriate weighting of sensor
readings.

Depending on the magnitude of the estimated nonlinearity and it\'s rate
of change, a decision can be made regarding how often the correction
value needs to be updated.  This has potential implications for climate
data records.

# 

# 

# 

# 

# 

#  

# comprehensive Performance evaluation 

##  ATMS RFI Check

Determine ATMS RFI susceptibility. Any change in the RF environment will
affect the RDR data if the sensor is susceptible to RF interference. Two
types of RFI can be investigated. One is RFI from onboard transmitters
or instruments and the other is RFI from ground sources. We need to
investigate the radiometric count difference at the space and OBCT
(OnBoard Calibration Target) views before and after an RF device or
instrument is turned on. A big jump in radiometric counts indicates ATMS
may be susceptible to RFI at that particular channel. For ground based
RFI sources, we can only record the source locations for further
reference. The onboard instruments will be CrIS, OMPS, and VIIRS. This
test may be covered during the BATC Satellite I&T campaign with the
Flight sensors when they do a self compatibility test.

A carefully sequenced power-up of the various on-board instruments would
be valuable for locating local (on board) sources of potential RFI.
Depending on the flexibility of the in-flight software there may have
some means for mitigating local RFI.

The identification of RFI from ground sources requires the instrument to
be operated in normal imaging mode for at least several (ideally at
least several dozen) overpasses of every major continental area, along
with a statistical analysis of this data that accounts for expected
temperature and moisture variations. SDRs are need for such an analysis.
Based on current market penetration of automobile collision avoidance
radars using the 23.6-24.0 GHz band it seems likely that some RFI will
be seen by ATMS. Unfortunately, not much can be done to compensate for
this type of RFI post launch since it is highly variable.

The post-launch RFI identification procedure could include at least one
follow-on airborne survey of the general area(s) of RFI-contaminated
pixels so as to unambiguously determine the source(s) of interference.
Without such source determination it may be impossible to mitigate if
the transmissions are illegal. Also, we would perform a detailed study
of atmospheric-compensated imagery with the goal of characterizing the
statistics of the SDRs on a spatial basis, and particularly comparing
such statistics between populated and unpopulated areas.

##  ATMS RFI Correction (if needed)

This test would be needed if RFI from onboard sources is detected.
During the RFI check, if there is RFI from an onboard source, then a RFI
correction factor should be derived. To check the RFI from on board
transmitter, first we have to identify the RFI source. Then we check the
radiometric counts before and after each transmitter is turned on. The
temporal stability of any correction factor would need to be determined
by examining its value during many orbits over enough time that all
normal operating conditions of the offending source are experienced by
ATMS.

**3.3** **ATMS Geolocation Check**

Determine ATMS geolocation and navigation accuracy. From the window
channels, ATMS maps geographical features, i.e., an island, lake or
coastline. By comparing the position of known geographical features in
the image with higher resolution ground truth (e.g., Landsat data), the
ATMS geolocation accuracy can be determined. Plan to fly the NPP over a
long coast line and to collect the scene brightness data. From the
geolocation data and brightness temperature, we can determine the ATMS
geolocation accuracy. The navigation accuracy will be derived by
convolving land-sea masks with ATMS brightness temperature images. By
varying the spatial position of the footprints and using knowledge of
the sea surface temperature and homogeneous land temperature near coast
line, the navigation accuracy can be determined. Note that for the
non-window channels, 50 -- 58 GHz, we would depend on those derived
either from the pre-launch tests and/or from NPP Roll Maneuver 2 --
Imaging of Moon.

**3.4** **ATMS Ascending/Descending Brightness Temperature Comparison**

Determine pointing, navigation, and asymmetry errors. Global SDR
brightness temperatures will be binned and averaged in 0.5 deg lat-lon
boxes separately for ascending and descending nodes. These values will
then be differenced. Any systematic pointing or navigation errors will
be revealed by shadows on opposite sides of continental coastlines.
Perhaps we could find the geolocation differences during day/night
measurements. This could be used as a sanity check!

**3.5** **ATMS RDR** **Gross Anomaly Identification**

Identification of radiometric outliers using simple statistical tools
(e.g., histograms and scatterplots). Examples of sensor anomalies that
would result in statistical outliers in the radiometric count data
include RFI and A/D converter malfunction. Two problems that have been
encountered on previous operational microwave sounders are RFI and A/D
malfunction ("stuck bits"). These problems can escape detection during
pre-launch testing because the on-orbit radiometric environment, as well
as the sensor hardware characteristics, can be markedly different than
that observed during pre-launch testing. In such cases, simple
statistical tools can be used to quickly identify sensor anomalies.

The general idea is to amass a representative ensemble of radiometric
data over a variety of scene types, and plot various statistical metrics
derived from these data ensembles -- examples include histograms of
radiometric counts for each channel and scatterplots of the radiometric
counts for channel X versus channel Y. These histograms and scatterplots
can then be compared with those derived from model-generated data. Any
gross discrepancies generally indicate a sensor anomaly.

Use 24 hours of radiometric (count) data and generate histograms and
scatterplots as described above. Identify any gaps or discontinuities.
The results of this task would be used to develop flags & action lists
for operators---i.e., abnormal conditions to look for in the operational
telemetry stream & instructions for operators when anomalies are
detected. All engineering parameters should be analyzed for dependence
on lat/lon, local time, sun angle, solar illumination of instrument
surfaces.

**3.6** **ATMS Scan Uniformity Analysis and Scan Bias Analysis**

Radiometer data will be collected for a long period of time and analyzed
for potential field of view intrusion (by spacecraft or other sensors)
and to characterize any asymmetry in the cross track scan pattern. To
examine the brightness temperature imagery with high sensitivity
requires averaging a relatively large data set of reasonably homogeneous
scenes, e.g., rain free ocean or desert, to reduce the effects of
geophysical fluctuations within the scene. To help determine the
intrusion of the spacecraft, the spacecraft layout should be available
for reference. Each ATMS channel will be analyzed for scan uniformity.
Average and standard deviation will be computed. The ascending and
descending pass will be analyzed separately. Plot the data as a function
of scan position, channel by channel. The results derived from this
analysis are expected to be comparable to those from NPP Roll Maneuver 1
-- Cross Track Scan Check.

**3.7** **ATMS Resampling to AMSU FOV Verification**

To verify that if the resampled ATMS data is comparable to AMSU. In
polar regions, cloud-free ATMS data are convolved to AMSU fields of view
using Backus-Gilbert method. The synthesized AMSU brightness
temperatures derived from ATMS are then plotted against the AMSU
brightness temperatures, and the mean and standard deviation of the
difference is computed as function of scan angle. The AMSU brightness
temperatures will also be compared to the "nearest neighbor" ATMS
brightness temperature (i.e., no Backus-Gilbert processing). The
Backus-Gilbert reprocessing of the ATMS footprints to AMSU footprints
should yield lower RMS differences to actual AMSU data than the simple
nearest-neighbor comparison. If this is not the case, the Backus-Gilbert
coefficients should be reevaluated.

Note that it is possible that ATMS obs vs. calcs may differ from AMSU
obs vs. calcs due to differences in the reflector/shroud geometries
(i.e., they will have different sidelobe artifacts).

**3.8** **ATMS Parameter Trending**

Trend NEΔT, telemetry, gain and offset through ATMS on-orbit lifetime.
We would keep a time record of the various parameters for the duration
of the ATMS lifetime. The key parameters are NEΔT, telemetry, gain, and
offset. Compute and record the parameter values at various time scales
(minutes to seasons). Also calculate down-track (cross track) versus
along track differences (to identify RFI) as well as left-right scan
asymmetry. Verify or update prelaunch calibration and dynamic range.
Monitor on an orbital to seasonal time scale. Identify and understand
correlation between instrument response function and spacecraft
telemetry (such as temperature) or state (such as transmitter state).
Verify RDR, including blackbody and cold space calibration analysis.
Verify PRT to temperature conversion. Trend RDR engineering parameters
(voltages, currents, temperatures).

**3.9** **ATMS Trending of Blackbody and Cold Space Count**

Trend warm load counts vs. time and cold space view counts vs. time. To
collect ATMS long term data for blackbody and cold space radiometric
counts. From these long term data sets, perform statistical analysis and
the trend for each data. From ATMS regular telemetry data, save the warm
load counts, cold space view counts, and time of each data point that
was collected. Save the results to special data files for analysis.

**3.10** **ATMS SDR Center Frequency Stability**

Verify the frequency stability of the opaque 57.29-GHz channels. Long
term trending of the brightness temperatures observed near 57.29 GHz as
compared to synthesized 57.29-GHz brightness temperatures using CrIS
stratospheric Q-branch channels near 667 cm^-1^. The CrIS stratospheric
channels will be regressed against the ATMS stratospheric channels (near
57.29 GHz) using model-data (for example, upper atmospheric data derived
from MLS measurements which are then input to a radiative transfer
model). Long-term drifts would most likely (but not necessarily) be due
to a drift of the 57.29 GHz LO. If drifts are detected, more detailed
analysis of both the CrIS and ATMS stratospheric channels would be
undertaken by using high-fidelity ground truth (MLS, for example).

Drifts exceeding 0.2K will be flagged. The stratospheric temperature
should be horizontally homogeneous, and CrIS and ATMS should therefore
agree very well (the difference in their viewing geometries is
negligible due to the atmospheric homogeneity). We can use this fact as
a simple check for any systematic drift between the two sensors.

**3.11** **ATMS Resampling to CrIS FOR Verification**

Validation of ATMS resampling to CrIS FOR. Cloud-free ATMS data are
convolved to CrIS Field Of Regard using the Backus-Gilbert method. CrIS
brightness temperatures are used to predict ATMS brightness temperatures
using regression analysis. The average brightness temperatures for the
ATMS are then plotted against the CrIS and the mean and standard
differences are computed as a function of scan angle. The weight of
Backus-Gilbert convolution may need to be adjusted if substantial
differences exist between the two instruments. From ATMS measured
antenna patterns and CrIS FOR, the B-G coefficient will be derived. The
effective ATMS FOR and Brightness temperature will be determined. The
average of the ATMS brightness temperature will be plotted against the
CrIS brightness temperature and the difference between should be less
then 1 K.

Based on modeling predictions that without co-alignments between ATMS
and CrIS, the resampled ATMS brightness temperatures would agree with
CrIS FOR, with an error less than 0.1 K. We would compare also the
BG-resampled ATMS to CrIS near coastline crossings, and for more
stressing conditions (clouds, light precip, fronts).

**3.12** **ATMS High-altitude Aircraft Underflights**

Compare high-resolution and high altitude aircraft brightness
temperature images with those from coincident satellite overpasses.
Campaigns will be designed to collect coincidental data under clear
skies and non-precipitating cloudy conditions. This will be especially
useful for cloud-clearing algorithms.

In general, the aircraft instrument must be capable of simulating (or
exceeding where appropriate) the ATMS performance with respect to
channels, scan geometry, footprint size, radiometric performance, and be
capable of operating from high-altitude airborne platforms that will be
available for underflights. Multiple aircraft instruments are available
that meet these requirements.

**3.13** **ATMS SDR Comparison with Model Fields**

ATMS brightness temperatures (both before and after resampling) will be
compared to NCEP GDAS analyses. This would be general consistency check
of ATMS brightness temperatures. We could also identify if there are any
gross biases. There could be several explanations for substantial biases
with respect to the GDAS data. The most likely are probably: 1) a
channel swap (either in hardware or in software), and 2) LO drift.

**3.14** **ATMS NWP Radiance Validation**

To assess ATMS global radiances and the Community Radiative Transfer
Model for ATMS. ATMS radiances will be simulated using the NCEP/EMC
Global Forecast System and a radiative transfer model. The simulated
radiances will be compared with those observed on a global and regional
basis. Statistics will be compared with microwave instruments on
NOAA-15-18.

**3.15** **ATMS SDR Comparison with** **Radiosonde Observations
(RAOBs)**

Validate the calibration of ATMS. Cloud-screened ATMS SDRs collocated
with dedicated RAOB launches will be compared, and any potential ATMS
biases will be identified. Potential RTMs for this task are AER's OSS
model, Rosenkranz's RTA, etc. We need dedicated Raobs (i.e. released to
coincide with sat overpasses). Also need some with accurate water vapor
sensors. We should set something up at the ARM/CART sites as we have
done with AIRS.

Nominal frequency of radiosonde launches: two launches of two raobs at
each of the three ARM sites (tropical, mid-latitude, and high-latitude)
per day (total of 12 raobs launched per day).\
\
We could possibly get by with less than 12 launches per day: For the
tropical site we won\'t get two overpasses every day, and the weather
doesn\'t change that fast, anyway. At the mid-latitude site, we could
get two overpasses, and launch two sondes for each (by the way, double
launches are for the purpose of using Dave Tobin\'s analysis method)
although they didn\'t keep up that high an average for the AIRS
validation effort. At a high-latitude site, we get frequent overpasses,
so the normally scheduled radiosondes may be perfectly adequate (close
enough to overpass times) if the budget is constrained.\
\
The objective is to assemble a \"golden set\" of a few hundred matchups
of RAOBS and CrIMSS FOR\'s in clear-air.  This data set will be used to
help validate the transmittance models and the SDR/EDR algorithm
performance.

In addition to normal validation, dedicated raobs at ARM/CART sites will
be used as climate reference data points in long term monitoring to
determine trends and drifts in the observing system. They will also be
used to determine empirical scan biases, as a backup to the
orbit-maneuver method described in sec. 2.12. These applications are
crucial to the climate mission, and it is extremely important to
maintain regular launches of these dedicated radiosondes (i.e. where the
launches are timed to coincide with satellite overpasses). In this
context, \"operational radiosondes\" (which are launched at fixed UT
times, typically 0Z and 12Z) are of low value and cannot be used as
substitutes for dedicated launches of high quality radiosondes.

# Abbreviations and acronyms

ADC Analog-to-Digital Converter

APID Application Process Identification

ATMS Advanced Technology Microwave Sounder

C&T Command and Telemetry

CBIT Continuous Built-In Test

CrIS Cross-track Infrared Sounder

EDR Environmental Data Record

EMI Electromagnetic Interference

FOV Field of View

IBIT Initiated Built-In Test

KAV K-band, Ka-band, and V-band channels (channels 1-15)

MLI Multi-Layer Insulation

NET Noise Equivalent Delta Temperature

NPOESS National Polar-orbiting Operational Environmental Satellite
System

NPP NPOESS Preparatory Project

PAT Passive Analog Temperature telemetry

PRT Platinum Resistance Temperature sensor

SAW Surface Acoustic Wave filter

SDE Scan Drive Electronics

SDM Scan Drive Mechanism

SPA Signal Processor Assembly

UIID Unique Instrument Interface Document

WG W-band and G-Band channels (channels 16-22)

##  {#section-84 .unnumbered}