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

Paper:

Carminati, F., Migliorini, S., Ingleby, B., Bell, W., Lawrence, H., Newman, S., Hocking, J., and Smith, A.: Using reference radiosondes to characterise NWP model uncertainty for improved satellite calibration and validation, Atmos. Meas. Tech., 12, 83-106, https://doi.org/10.5194/amt-12-83-2019, 2019.

Carminati, F., B. Candy, W. Bell, and N. Atkinson, 2018: Assessment and assimilation of FY-3 humidity sounders and imager in the UK Met Office global model. Adv. Atmos. Sci., 35 (8), 942–954, https://doi.org/10.1007/s00376-018-7266-8.

Public report:

Fabien Carminati, Jacqueline Goddard, Heather Lawrence, Stuart Newman: Calibration/validation study of GPM GMI, 2017, http://www.gaia-clim.eu/system/files/document/d4.6.pdf, D4.6, pages 22-75

Fabien Carminati, Nigel Atkinson, Qifeng Lu: Preliminary assessment of FY-3D microwave instruments towards their use in NWP systems, 2019, Forecasting Research Technical Report 634, https://digital.nmla.metoffice.gov.uk/digitalFile_b41cba5a-f328-44d7-9b19-af4cf6e57801/

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.

  • Berg, W., R. Kroodsma, C. D. Kummerow, and D. S. McKague, "Fundamental climate data records of microwave brightness temperatures," Remote Sens., 10(8), pp. 1306, 2018, doi:10.3390/rs10081306.

  • 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.

  • Colton, M. C. and G. A. Poe, 1999: Intersensor calibration of DMSP SSM/I's: F-8 to F-14, 1987-1997, IEEE Trans. Geosci. Remote Sens., 37(1), 418-439.
  • Jones, W.L., J. Park, S. Soisuvarn, L. Hong, P. Gaiser, and K. St. Germain, "Deep-Space Calibration of WindSat Radiometer", IEEE Trans. GeoSci. Rem. Sens. , vol. 44, no. 3, Mar. 2006. (DOI: 10.1109/TGRS.2005.862499)
  • 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.

  • Kroodsma, R., S. Bilanow, and D. McKague, "TRMM Microwave Imager (TMI) alignment and along-scan bias corrections," J. Atmos. Oceanic Technol., 35(7), pp. 1457-1470, Jul. 2018.
  • Okuyama, A. and K. Imaoka, 2015: Intercalibration of Advanced Microwave Scanning Radiometer-2 (AMSR2) brightness temperature, IEEE Trans. Geosci. Rem. Sens., 53(8), 4568-4577.
  • 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.

  • Yan, B. and F. Weng, 2008: Intercalibration between special sensor microwave imager/sounder and special sensor microwave imager, IEEE Trans. Geosci. Remote Sens., 46(4), 984-995.
  • 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.

  • Yang S., F. Weng, B. Yan, N. Sun, and M. Goldberg, 2011: Special Sensor Microwave Imager (SSM/I) intersensor calibration using a simultaneous conical overpass technique, J. Appl. Meteor. Climatol., 50, 77-95.

SSMI TMI AMSR-E WindSat SSMIS AMSR-2 MADRAS GMI MWRI
SSMI

Colton and Poe, 1999


Yang et al., 2011

Sapiano et al., 2013
Yan and Weng, 2008
TMI
AMSR-E

Wilheit, 2013
WindSat

Wilheit, 2013

Biswas et al., 2013

Kroodsma et al., 2017
SSMIS
AMSR-2

Alsweiss et al., 2013

Okuyama and Imaoka, 2015

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.
  • Burgdorf, M.J., Imke Hans, Marc Prange, Theresa Lang, and Stefan A. Buehler: Inter-channel uniformity of a microwave sounder in space, Atmos. Meas. Tech., 11, 4005–4014, 2018. doi.org/10.5194/amt-11-4005-2018
  • Burgdorf, M.J., Buehler, S.A., Hans, I., Prange, M., Meng, Z.: Disk-Integrated Lunar Brightness Temperatures between 89 and 190 GHz, Advances in Astronomy, 2019
  • 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.
  • 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.

  • Moradi, I., Beauchamp, J., and Ferraro, R.: Radiometric correction of observations from microwave humidity sounders, Atmos. Meas. Tech., 11, 6617-6626, https://doi.org/10.5194/amt-11-6617-2018, 2018.
  • 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.

  • Yang, J. X., H. Yang, 2018, Radiometry Calibration with High-Resolution Profiles of GPM: Application to ATMS 183 GHz Water Vapor Channels and Comparison against NWP Profiles, IEEE Transactions on Geoscience and Remote Sensing
  • 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

For MSU:

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., M. Gao, M.Goldberg, 2009, Error structure and atmospheric temperature trends in observations from the Microwave Sounding Unit, J. Climate, 22, 1661-1681, DOI: 10.1175/2008JCLI2233.1

Zou, C.-Z., and W. Wang, 2013: MSU/AMSU radiance fundamental climate data record derived from integrated microwave inter-calibration approach, 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

For AMSU:

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., W. Wang, and X. Hao, 2016: AMSU Brightness Temperature –NOAA and AMSU Brightness Temperature –NOAA Gridded, Climate Algorithm Theoretical Basis Document (C-ATBD), Revision 2, NOAA/NESDIS, Available from https://www1.ncdc.noaa.gov/pub/data/sds/cdr/CDRs/AMSU%20Brightness%20Temperatures/AlgorithmDescription_01B-18_18a.pdf

For ATMS:

Zou, C.-Z., M. Goldberg and X. Hao, 2018: New Generation of US Microwave Sounder Achieves High Radiometric Stability Performance for Reliable Climate Change Detection, Science Advances, Vol. 4, No 10, eaau0049, DOI: 10.1126/sciadv.aau0049

For SSU:

Zou, C.-Z., H. Qian, W. Wang, L. Wang, and C. Long (2014), Recalibration and merging of SSU observations for stratospheric temperature trend studies, J. Geophys. Res. Atmos.,119,13,180-13,205, doi:10.1002/2014JD021603

Hu Yang, Ninghai Sun, Kent Anderson, Quanhua Liu, Ed Kim, 2018, “Developing vicarious calibration for microwave sounding instruments using lunar radiation”, IEEE Transactions on Geoscience and Remote Sensing, 56 (11), 6723-6733

Hu Yang and Fuzhong Weng, Kent Anderson, 2016, "Estimation of ATMS Antenna Emission from Cold Space Observations”, , IEEE Geoscience and Remote sensing, 10.1109/TGRS.2016.2542526”

Hu Yang and Xiaolei Zou, X, 2014. Optimal ATMS remapping algorithm for climate research. Geoscience and Remote Sensing, IEEE Transactions on Geoscience and Remote Sensing, 52(11), 7290-7296.

Hu Yang and Fuzhong Weng, 2016, “On-Orbit ATMS Lunar Contamination Corrections”, IEEE Transactions on Geoscience and Remote Sensing, Vol. 54 Issue: 4, page(s): 1-7
  • 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
Jun Zhou, Hu Yang, Kent Anderson, 2019, “SNPP ATMS On-orbit Geolocation Error Evaluation and Correction Algorithm”, IEEE Transactions on Geoscience and Remote Sensing, 57(6), 3802-3812.

John Xun Yang, Hu Yang, 2018, “Radiometry Calibration With High-Resolution Profiles of GPM: Application to ATMS 183-GHz Water Vapor Channels and Comparison Against Reanalysis Profiles”, IEEE Transactions on Geoscience and Remote Sensing, 57(2), 829-838

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

Intercomparson with Intrusions of the Moon in the Deep Space View

By Martin Burgdorf, August 2019

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.

Instrument Properties Relevant to Moon Intrusions

From the duration, timing, and maximum counts of the light curve of the Moon one can learn about the
  • Beam pattern: The time it takes for the Moon to move through the DSV contains information about the beam width in the along-track 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 along-track direction. From a comparison of the light curves from the different DSVs it is possible to derive the pointing error in the across-track direction, albeit with lower accuracy (Bonsignori, 2018).
  • Photometric stability: The light curve of a Moon intrusion can usually be well fitted 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. It is therefore desirable to compare intrusions, where the Moon came close to the centre of the beam and where it had similar phase angles.

Results

Some findings of the analyses of Moon intrusions in the DSV are
  • Fitting a Gaussian to the light curve of a Moon intrusion allows in the best cases the calculation of the maximum signal with an accuracy better than 0.3%:
***INSERT FIGURE 1**

  • The comparison of the light curves from different DSV positions makes the identification of Moon intrusions close to the centre of the field of view possible. In the following example the blue line represents the signal in position 1 and the red line the signal in position 3 of the DSV. As the two are almost equal, the Moon must have gone right through the centre of the DSV in position 2 (black line). This conclusion can be drawn from the light curves only, without the additional information about the angular separation calculated with AAPP and displayed in the top panel.
***INSERT FIGURE 2 ****
  • The DSV direction describes a circle in the sky during one orbit of 100 minutes duration. The angular diameter of this circle is determined by the distance of the DSV from nadir. With this information and the width of the Gaussian fitted to the light curve, it is possible to calculate the beam diameter of each channel of a microwave sounder. A comparison with the values from ground tests for MHS on NOAA-18 https://www.eumetsat.int/website/home/Satellites/CurrentSatellites/Metop/MetopDesign/MHS/index.html
revealed significant discrepancies. This is reflected in the following table, where the values in brackets include a correction for the fact that the Moon is an extended source and therefore somewhat broadens the width of the light curve. The beam efficiencies were also taken from the ground tests; their value for channel H2 is probably in error as well.

  • Instruments on satellites with orbit drift get to see the Moon at quite different phase angles. It is therefore possible to use their Moon intrusions to determine its disk-integrated brightness temperature as a function of phase angle. These calculations require knowledge of the exact beam size and produce realistic values for all channels only with our new values, not the beam diameters determined on ground. It became apparent from our investigation that the phase gap between full Moon and the time of maximum microwave emission is too small in several models of the lunar emission. This can be seen in the following figure, where the measurements from Moon intrusions in MHS on NOAA-18 are shown in red, and the other lines represent the predictions from various models.
***INSERT FIGURE 3*****

All Figures taken from Burgdorf et al. (2019)

Suggested Future Tasks

Work might progress in the following steps:
  1. 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. 2. Confirm or correct the assumptions about beam pattern and pointing accuracy made until now by analysing Moon intrusions with high signal-to-noise ratio.
  3. 3. Create a thermal model of the Moon that reproduces its brightness temperatures as measured with microwave sounders (AMSU-B and MHS on different satellites) for a wide variety of phase angles. Such a model will be useful to check with Moon intrusions the photometric stability over long time periods and even for intercomparison of instruments that were operational at different times, because it can normalize all measurements to the same phase angle with high accuracy.
  4. 4. Repeat steps 1 – 3 mutatis mutandis with an infrared sounder, e. g. HIRS.

References

Bonsignori, R., “In-orbit verification of microwave humidity sounder spectral channels coregistration using the moon,” J. Appl. Remote Sens. 12, 025013, doi: 10.1117/1.JRS.12.025013, 2018.

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.

Burgdorf, M., et al., “Inter-channel uniformity of a microwave sounder in space”, Atmospheric Measurement Techniques, 11, 4005-4014, doi:10.5194/amt-11-4005-2018, 2018.

Burgdorf, M., et al., “Disk-Integrated Lunar Brightness Temperatures between 89 and 190 GHz”, Advances in Astronomy, 2019, doi:10.1155/2019/2350476, 2019.

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: r38 - 13 Aug 2019, RalphFerraro
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