The Advance Microwave Scanning Radiometer-E (AMSR-E) instrument. (Photo is courtesy of JAXA)
Remote sensing of the Earth’s hydrologic cycle and wind speed were the primary original motivating factors to manufacture microwave imaging instruments and fly them on research and operational satellites. The Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave Imager (SSM/I) (1987), Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI), Global Precipitation Measurement (GPM) Microwave Imager (GMI), and CMA FY-3 Microwave Radiation Imager (MWRI) are examples of these instruments. These instruments have a tendency to use the more transmissive microwave spectrum regions outside the 60 GHz and 118 GHz oxygen, and the 183 GHz water vapor, absorption bands. These regions from 10 GHz to 200 GHz with less atmospheric opacity are characterized as window channels, although they have increasing sensitivity to moisture and cloud liquid water and ice as frequency increases.
Microwave imaging instruments acquire measurements at fixed local zenith angles. They also have antennas mounted on a scan assembly that has a rotation axis that coincides with the satellite nadir axis, which allows cross-track measurements to be build up into an image over time as the spacecraft orbits. On-board calibration of these radiometers is achieved by observing cold space and a well-characterized internal blackbody target during each revolution of the scan reflector antenna. The calibration measurements are used to accurately determine the so-called radiometer transfer function that relates the measured digitized output (i.e., counts) to a radiance, which then can be expressed as radiometric antenna temperature (Ta) through the Planck function.
Routine data quality monitoring and anomaly assessment is important to sustain microwave imager data effectiveness. Besides trending instrument engineering and housekeeping data, microwave radiometer Ta measurement monitoring has also been carried out using the Simultaneous Conical Overpass (SCO) method, vicarious calibration over scenes with stable surface emissivity, and double differencing.
Baseline Information on Sensors
Past and Present
Links to the World Meteorological Organization (WMO) Observing Systems Capability Analysis and Review (OSCAR) Tool. The baseline information presented in this tool includes: Instrument classification, objectives, and brief overview and channel-specified characteristics; Satellites this instrument is flying on; and Tentative evaluation of measurements. Links to other important documents or web sites are given as sub-bullet lists.
User preparations for the next generation of meteorological satellites (GEO and LEO) can be found at the Coordination Group for Meteorological Satellites SATellite User Readiness Navigator (SATURN
) web site.
Microwave imager monitoring pages have been designed to monitor and trend instrument house-keeping and telemetry parameters, and/or state-of-the art cal/val science method results. They mainly serve to detect instrument anomaly events and to facilitate warnings of possible instrument degradation to satellite operators, instrument scientists, and senior program managers; but they also offer a glimpse of overall instrument performance and its trends to the satellite calibration community.
Microwave imager calibration and inter-calibration methodologies can serve important functions to the GSICS community: Quantify and trend instrument and product short- and long-term stability and performance; Improve instrument calibration and data geolocation, and thus science product integrity; and support numerical weather prediction and fundamental climate data record creation. For these reasons, one critical focus of the GRWG Microwave Subgroup is the "Knowledge Sharing" related to these methodologies, which are outlined below.
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.
- 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.
- Carminati, F. 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
- Carminati, F., 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/
- 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.
- Iacovazzi, R., L. Lin, N. Sun, and Q. Liu, 2020: NOAA operational microwave sounding radiometer data quality monitoring and anomaly assessment using COSMIC GNSS radio-occulation soundings. Remote Sensing. 12(5), 828, https://doi.org/10.3390/rs12050828.
- 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, IEEETrans. 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, IEEETrans. 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.
- 13 Apr 2020