Microwave Sub-Group of GSICS Research Working Group

Work Space for Intercomparison Results

Relevant GSICS Publications

GSICS Quarterly 2014 Q1

GSICS Quarterly 2016 Q4

Microwave Imagers

Key Scientific Papers
  • Alsweiss, S. O., Z. Jelenak, P. S. Chang, J. D. Park, and P. Meyers, 2015: Inter-calibration results of the Advanced Microwave Scanning Radiometer-2 over ocean, IEEE J. Appl. Earth Obser. Remote Sens., 8(9), pp. 4230-4238.

  • Berg, W., S. Bilanow, R. Chen, S. Datta, D. Draper, H. Ebrahimi, S. Farrar, W.L. Jones, R. Kroodsma, D. McKague, and V. Payne, 2016: Intercalibration of the GPM Microwave Radiometer Constellation, J. Atmos. Oceanic Tech., 33(12), pp.2639-2654.

  • Biswas, S. K., S. Farrar, K. Gopalan, A. Santos-Garcia, W. L. Jones, and S. Bilanow, 2013: Intercalibration of microwave radiometer brightness temperatures for the Global Precipitation Measurement mission, IEEE Trans. Geosci. Remote Sens., 51(3), pp. 1465-1477.

  • Kroodsma, R. A., D. S. McKague, and C. S. Ruf, 2017: Vicarious cold calibration for conical scanning microwave imagers. IEEE Trans. Geosci. Remote Sen., 55(2), 816-827.

  • Sapiano, M., W. Berg, D. McKague, and C. Kummerow, 2013: Towards an intercalibrated fundamental climate data record of the SSM/I sensors, IEEE Trans. Geosci. Rem. Sens., 51, pp. 1492-1503.

  • Wilheit, T. T., 2013: Comparing calibrations of similar conically scanning window-channel microwave radiometers, IEEE Trans. Geosci. Remote Sens., 51(3), pp. 1453-1464.

  • Yang, J. X., D. S. McKague, and C. S. Ruf, 2016: Boreal, Temperate and Tropical Forests as Vicarious Calibration Sites for Spaceborne Microwave Radiometry, IEEE Trans. Geosci. Remote Sens., 54(2), 1035-1051.

Key Scientific Presentations

Intercomparison Plots, Tables, etc.

SSMI TMI AMSR-E WindSat SSMIS AMSR-2 MADRAS GMI MWRI
SSMI Sapiano et al., 2013
TMI
AMSR-E

Wilheit, 2013
WindSat

Wilheit, 2013

Biswas et al., 2013

Kroodsma et al., 2017
SSMIS
AMSR-2

Alsweiss et al., 2013

Kroodsma et al., 2017
MADRAS
GMI

Berg et al., 2016

Yang et al., 2016
Berg et al., 2016 Berg et al., 2016
MWRI http://www.nsmc.org.cn/en/NSMC/Contents/Instruments_MWRI.html

Microwave Sounders

Key Scientific Papers
  • Burgdorf, M., et al., “The Moon as a photometric calibration standard for microwave sensors”, Atmospheric Measurement Techniques, 9, 3467-3475, doi:10.5194/amt-9-
    3467-2016, 2016.
  • Mo, T., & Kigawa, S., “A study of lunar contamination and on-orbit performance of the NOAA 18 Advanced Microwave Sounding Unit-A”, Journal of Geophysical Research, 112,
    D20124, doi: 10.1029/2007JD008765, 2007.
  • Yang, H., & Weng, F., “Corrections for On-Orbit ATMS Lunar Contamination”, IEEE Transactions on Geoscience and Remote Sensing, 54, 1918-1924, doi:
    10.1109/TGRS.2015.2490198, 2015
  • Moradi, I., H. Meng, R. Ferraro, S. Bilanow, 2013: Correcting geolocation errors for microwave instruments aboard NOAA satellites. IEEE Transactions on Geoscience and Remove Sensing, 51, 3625 – 3637.

  • Moradi, I., R. Ferraro, P. Eriksson, and F. Weng, 2015: Inter-calibration and validation of observations from ATMS and SAPHIR microwave sounders, IEEE Trans. Geosci. Remote Sens., 53, 5915–5925.

  • Yang, W, H. Meng, R. Ferraro, I. Moradi, and C. Divaraj, 2013: Cross scan asymmetry of AMSU-A window channels: characterization, correction and verification. IEEE Transactions on Geoscience and Remove Sensing, 51, 1514 – 1530.

  • Zou, C.-Z., and W. Wang, 2011: Inter-satellite calibration of AMSU-A observations for weather and climate applications. J.Geophys. Res., 116, D23113, DOI:10.1029/2011JD016205.

  • Zou, C.-Z., M. Goldberg, Z. Cheng, N. Grody, J. Sullivan, C. Cao, and D. Tarpley, 2006: Recalibration of microwave sounding unit for climate studies using simultaneous nadir overpasses, J. Geophys. Res, 111, D19114, DOI:10.1029/2005JD006798

  • Zou, C.-Z., and W. Wang, 2013: MSU/AMSU radiance fundamental climate data record derived from integrated microwave inter-calibration appraoch, Climate Algorithm Theoretical Basis Document (C-ATBD), NOAA/NESDIS, available from www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AMSU%20Brightness%20Temperatures/AlgorithmDescription.pdf

Key Scientific Presentations

  • Intercomparison Plots, Tables, etc.

MSU AMSU-A AMSU-B MHS SAPHIR
ATMS

SSM/T SSM/T2 MWTS MWHS
MSU Zou et al. 2006, Zou and Wang 2013
AMSU-A Zou and Wang 2011, 2013 See article below - Zou et al.
AMSU-B
MHS
SAPHIR http://www.star.nesdis.noaa.gov/star/mw-calval.php
ATMS
SSM/T
SSM/T2
MWTS http://www.nsmc.org.cn/en/NSMC/Contents/Instruments_MWTS-I.html
MWHS http://www.nsmc.org.cn/en/NSMC/Contents/Instruments_MWHS.html

Intrusions of the Moon in the Deep Space View

By Martin Burgdorf, Theresa Lang, Marc Prange, and Imke Hans

1 Introduction


Microwave sounders in polar orbits around the Earth employ a two-point calibration with the cold reference being deep space, i. e. the cosmic microwave
background (CMB). The region of the sky chosen for this measurement is always close to the orbital axis of the satellite and more than 90° away from the Sun. As
a consequence, the Moon appears occasionally in the deep space view (DSV) and increases the amount of radiation entering the instrument, thereby altering the
flux from the cold reference. Therefore a model of the brightness temperature of the Moon has been developed that makes it possible to correct for its
contribution in the calibration process (Mo & Kigawa, 2007, Yang & Weng 2016). The light curve of a passage of the Moon in the DSV, however, contains also
information about properties of the instrument in flight that is difficult to obtain otherwise.

2 Instrument Properties Relevant to Moon Intrusions

• Beam pattern: The shape of the light curve of the Moon moving through the DSV contains information about the beam pattern in
scan direction. The values of the maxima of these light curves in the different DSVs contain information about the beam pattern
perpendicular to the scan direction.


• Pointing accuracy: AAPP (ATOVS and AVHRR Pre-processing Package) calculates the time of the closest approach between DSV
and the Moon. By calculating the difference to the time of the maximum of the light curve and using the angular velocity of the
DSV as it moves in the sky, one can get the pointing error for the DSV in the scan direction. From a comparison of the light curves
from the different DSVs it is possible to derive the pointing error perpendicular to the scan direction.


• Photometric stability: The light curve of a Moon intrusion can in general be fitted well with a Gaussian, but uncertainties remain in
comparing the signal strength from different events. The Moon will cross the DSV in different distances from the centre of the beam,
and its brightness changes with phase angle and, to a much lesser extent, with libration. It is therefore desirable to compare only
intrusions with similar characteristics.

3 Results


• The light curve of a Moon intrusion in MHS (red line, compared to a Gaussian in blue, channel H1) is sometimes asymmetric, suggesting that
anomalies are present in the beam pattern:


These can be compared to measurements of beam shape and diameter made before launch as a check of instrument properties in flight:


• The uncertainty in calculating the pointing direction of the DSV is 0.3° according to the MHS Level 1 PGS. This is also the maximum
discrepancy found between calculated (with AAPP) and observed Moon position in scan direction in a sample of 43 Moon intrusions
in MHS on MetOp -A in 2015. In other years, however, larger differences were found occasionally, hinting towards a possible
non-compliance with the specification of 0.12° for the pointing accuracy.


• After correction for the changes in the Moon’s phase angle with the model by Mo & Kigawa and its distance from Earth, upper limits of
2 – 3% for the photometric stability of MHS on different satellites could be derived. The maximum signal obtained during a Moon
intrusion, however, can be determined with an accuracy of 0.2 – 0.3%. It seems therefore possible to use Moon intrusions for
testing the long-term stability of the photometric calibration and inter-calibration, if the uncertainties in the actual flux reaching the
receiver that were mentioned above are reduced.

4 Suggested Future Tasks


Work will progress in the following steps:


1. Make a list of Moon intrusions that are particularly useful for characterising instrument properties, which contains the fit parameters
of the light curve and observer quantities of the Moon.


2. Confirm or correct the assumptions about beam pattern and pointing accuracy made until now by analysing Moon intrusions with high signalto-
noise ratio.


3. Refine the model by Mo & Kigawa by using Moon intrusions from additional instruments (AMSU-B and MHS). Such a model will be useful to
check the photometric stability over long time periods, because the reflectivity of the Moon does not change, and even for inter-calibration of
instruments that were operational at different times. Such measurements will be particularly sensitive with future instruments, whose smaller
beams will be filled almost completely by the Moon.

5 References


Burgdorf, M., et al., “The Moon as a photometric calibration standard for microwave sensors”, Atmospheric Measurement Techniques, 9, 3467-3475, doi:10.5194/amt-9-
3467-2016, 2016.


Mo, T., & Kigawa, S., “A study of lunar contamination and on-orbit performance of the NOAA 18 Advanced Microwave Sounding Unit-A”, Journal of Geophysical Research, 112,
D20124, doi: 10.1029/2007JD008765, 2007.


Yang, H., & Weng, F., “Corrections for On-Orbit ATMS Lunar Contamination”, IEEE Transactions on Geoscience and Remote Sensing, 54, 1918-1924, doi:
10.1109/TGRS.2015.2490198, 2015

In-orbit Microwave Reference Records

By Manik Bali, Cheng-Zhi Zou, Ralph Ferraro, Fuzhong Weng and Lawrence E Flynn

Introduction

In orbit Microwave instruments are often compared to in-situ targets and GPS-RO measurements. However these comparisons get influenced by local weather conditions and usually require a forward model to compute the TOA (Top of Atmosphere) MW reference radiances from the in-situ and GPS-RO measurements. Further, these Inter-comparisons do not reveal the full scale of instrument biases such as scan angle dependence of measurements, temporal trends and temperature dependence of bias. Recently, the MW community (eg. Moradi et al) has suggested that MW instruments be compared with in-orbit stable references ( as done in IR and VIS by using IASI/AIRS/CrIS and Aqua-MODIS) so that the full scale of measurement biases (over a full range of temperature, scan angles, time and spectrum ) of mw instruments is revealed. This would help in fully understanding the in-orbit instrument performance characteristics, compute cross-calibration bias and offset coefficients and use them to re-calibrate the instrument and improve the quality of its observations.

Such an in-orbit reference needs to be several times more stable and accurate than the monitored instrument to be able to reveal the monitored instrument biases ( temperature, scan angle, spectral etc). Using this basic premise IASI-A/B AIRS and CrIS have been routinely used as a reference to monitor in-orbit GEO instruments in the IR bands by the GSICS community. The inter-comparisons have produced cross calibration products and resulted in long time series of monitoring. However it is often felt that the designed stability and accuracy of a reference instrument alone cannot guarantee its in-orbit performance in the long run. Recently the upper spectrum of the IASI-A developed non linearity and became anomalously negatively biased.

For the impacted spectrum, this anomaly lowered the trustworthiness of the IASI-A reference radiances for GSICS type monitoring. This anomaly also initiated discussions in the GSICS community to wonder if such anomalies will occur in other GSICS reference instruments. Flynn and Bali, 2016 (GSICS discussions see here) suggested that GSICS should use reference records ( i.e. trustworthy stable and accurate) instead of using directly L1 radiances produced by reference instruments. The reference records are radiances whose stability accuracy is monitored and corrected and perhaps combined with other stable references. In addition these should satisfy a reference selection matrix suggested by Fuzhong Weng 2016 (See here) and Bali et al 2016.

Reference Selection Matrix ( Weng Matrix)

1. Sensor Record performance stability
2. Field of view (FOV) consistency (ATMS has oversampling FOV and can be B-G to AMSU-A and MSU)
3. Error budgets (prelaunch characterization and postlaunch verification)
4. Geolocation accuracy
5. Data availability

AMSU/MSU FCDR

The re-calibrated AMSU/MSU radiances are a Fundamental Climate Data Record (FCDR) developed by Cheng-Zhi Zou at NOAA/STAR. This FCDR resembles a high quality L1C measurement produced by an in-orbit instrument. It is a corrected L1C radiance that is produced every day from AMSU. The limb corrected version of the FCDR also has scan angle dependence of measurements removed. This correction is similar to a Response Vs Scan Angle ( RVS) technique employed by GSICS references to mitigate the effects of scan angle dependence of measurements. By employing an advanced recalibration technique, the Integrated Microwave Inter-Calibration Approach (IMICA, Zou and Wang 2013), the FCDR also minimizes the temperature dependent biases in the AMSU/MSU L1C. Temporal anomaly trends from AMSU/MSU measurements are also removed. This results in a highly stable L1C long record of MW radiances (spanning 38 years) that can be used as a robust in-orbit MW reference. The FCDR has been validated using direct comparisons with GPS-RO and has shown an accuracy of ( 0.1K-0.2K) and stability of (0.02-0.03K/dec) and hence can be classified as a much more stable and accurate than any existing L1C record produced by direct measurements from in-orbit MW instruments. The FCDR also satisfies all the critical conditions set in the Weng matrix.

ATMS SDR - AMSU FCDR Intercomparision

The Advanced Technology Microwave Sounder (ATMS) is the microwave sounder onboard the Sumo-NPP. It is a key JPSS mission that delivers state of the art sounding measurements in Microwave ( window and water sensitive channels). In many ways it is an advanced version of the AMSU-A and has direct overlapping channels with AMSU-A (Blackwell, 2012 See slide-6 ). In order to understand in-orbit performance of the ATMS we made GSICS style Simultaneous Nadir Overpass( SNO) comparisons between ATMS (SDR) with the AMSU FCDR. for the period of 1 Sept 2015- 30 Nov 2015. The ATMS -SDR is antenna corrected observations. Figures below show the ATMS AMSU-A difference.

Capture.JPG

Fig.1 Shows that the temperature dependence of the SATMS-AMSUA bias is very stable. For the 55 GHz this figure indicates that the bias is similar to that computed by Xiaolei Zou and X. Chen ( see here). Fig.2 Shows the scan angle dependence of the ATMS has been well captured by the FCDR since the FCDR scan angle bias has been limb corrected.

Results

Results of the SNO inter-comparisons were used to address points the Weng matrix of reference selection

1. Sensor Record performance stability

->Long term SNO inter-comparisons would be generated. The scan angle dependence of the SATMS- AMSU bias is very similar to the one seen at pre-launch testing of ATMS and validated via RTM models.

2. Field of view (FOV) consistency (ATMS has oversampling FOV and can be B-G to AMSU-A and MSU)

-> Application of Standard Deviation threshold of the ATMS collocated pixels within AMSU FOV resulted in mitigating the effects of difference in FOV between ATMS and AMSU.


3. Error budgets (prelaunch characterization and post-launch verification)

-> Figures 1 and 2 shows the temperature dependence of the ATMS- AMSU bias very similar to the pre-launch bias seen for this channel. In fact the FCDR has the needed accuracy of a reference level because the bias is in-fact 0.1K close to the pre-launch( Weng,2016) for the 55 GhZ channel.
4. Geolocation accuracy

The FCDR geolocation is well characterized.( Ralph please fill in)

5. Data availability

-> AMSU/MSU FCDR data is available on NCEI CDR Program and has a very high uptime.

With key attributes of the Weng matrix satisfied our analysis indicates that the AMSU/MSU FCDR can act as a trustworthy in-orbit reference for monitoring ATMS like instruments and correcting their biases. Since the temperature dependent bias of the SATMS-FCDR is similar to the SATMS bias seen at the time of pre-launch testing, this result also indicates that the ATMS is delivering very high quality of radiances and in fact ATMS_SDR can also be used as an in-orbit reference.

Future Work

There are two main thrust areas to the effort.

1. Extend the SNO collocations and get a long time series to demonstrate the stability of the FCDR and the ATMS

2. Analyze biases for rest of the SATMS channels.

References

Bali, M.(2016): Comparisons of IASI-A and AATSR measurements of top-of-atmosphere radiance over an extended period. , Atmos. Meas. Tech. Discuss., 8, 9785-9821, doi:10.5194/amtd-8-9785-2015

Blackwell, et. al, (2012), The Advanced Technology Microwave Sounder (ATMS): New Capabilities for Atmospheric Sensing. AMSConference, New Orleans, LA, ( http://www.goes-r.gov/downloads/2012-AMS/02/Blackwell.pdf , Last Access: 7/22/2016)

Weng, F.; Yang, H. (2016). Validation of ATMS Calibration Accuracy Using Suomi NPP Pitch Maneuver Observations. Remote Sens., 8, 332.

Zou C-Z (2013) Atmospheric temperature climate data records from satellite microwave sounders.Source: Satellite-based Applications on Climate Change Pages: 107-125 Published: 2013/01/01

DOI: 10.1007/978-94-007-5872-8_8
Topic attachments
I Attachment Action Size Date Who Comment
Capture.JPGJPG Capture.JPG manage 35 K 22 Jul 2016 - 22:02 ManikBali BIAS
GSICS_MW_-_Status_-_October_2014.docxdocx GSICS_MW_-_Status_-_October_2014.docx manage 17 K 17 Oct 2014 - 14:04 RalphFerraro This document was created jointly by Ralph, Tim, Cheng-Zhi and Manik and contains some thoughts as to some of the key items we will need to discuss at a future meeting and also through email. Feel free to send me comments - thanks. Ralph
Topic revision: r33 - 28 Sep 2017, RachaelKroodsma
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