Microwave Sounders

NASA and NOAA Advanced Technology Microwave Sounder. ( Photo courtesy of NASA and Northrop Grumman Electronic Systems)

The initial driver to manifest passive microwave instruments on operational weather satellites was remote sensing of atmospheric temperature and moisture profiles for the purpose of numerical weather prediction. The first generation of these operational “Microwave Sounders” was the 4-channel Microwave Sounding Unit (MSU) flown on several NOAA Polar-orbiting Operational Environmental Satellite (POES) missions between 1979 and 2006. Since then several additional channels have been added to take advantage of all the unique remote sensing qualities of microwave instruments that are not captured by visible/near-infrared or infrared instruments.

The strongest atmospheric absorption bands in the microwave region are the 60 GHz and 118 GHz oxygen, and the 183 GHz water vapor, absorption bands. The remaining regions from 20 GHz to 200 GHz are characterized by much weaker, but increasing, sensitivity to moisture and cloud liquid water and ice as frequency increases. These characteristics make microwave sounding instruments well equipped to sense atmospheric layer temperature in the presence of non-precipitating clouds.

Most microwave sounders strongly utilize the 60 GHz oxygen absorption band and 183 GHz water vapor absorption band to sense thermal and moisture structure of the atmosphere, respectively. They also use low-frequency window regions such as near 23 GHz and 37 GHz to sense water vapor burden, surface characteristics and rain over ocean. A channel near 88 GHz is also often used because of this channels’ sensitivity to ice scattering, which helps to sense surface snowpack and convective cloud ice.

Microwave sounding instruments are characterized by measurements taken at varying scan and local zenith angles. Typically these instruments have antennas mounted on a rotating scan assembly that have an axis of rotation opposite of spacecraft motion, which allows cross-track measurements to be build up into an image over time. On-board calibration of total power microwave sounding radiometers – e.g., the NOAA Advanced Microwave Sounding Unit-A (AMSU-A), JPSS Advanced Technology Microwave Sounder (ATMS), and CMA FY-3 Microwave Temperature Sounder Model II (MWTS-2) and Microwave Humidity Sounder Model II (MWHS-2) - 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 sounder data effectiveness. Besides trending instrument engineering and housekeeping data, microwave radiometer Ta measurement monitoring has also been carried out using the Simultaneous Nadir Overpass (SNO) and the Radiative Transfer Model (RTM) Background Simulation (BS) methods. The latter method uses numerical weather prediction and/or global navigation satellite system radio-occultation soundings as input to radiative transfer models.

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.

• AMSU-A
  • NOAA KLM User's Guide
• AMSU-B
  • NOAA KLM User's Guide
• ATMS
  • NOAA STAR ATMS Information Site
  • ATMS Algorithm Theoretical Basis Document
  • JPSS Science Documents
  • JPSS Common Algorithm Data Dictionary
  • ATMS Data Dictionary
• MHS
  • NOAA KLM User's Guide
• MSU
  • Data Extraction and Calibration of TIROS-N/NOAA Radiometers
  • NOAA Polar Orbiter Data User's Guide
• MWTS
• MWHS
• SAPHIR
  • MEGHA-TROPIQUES_HANDBOOK_final_SEP2015.pdf
  • CNES Instrument Information
  • CNES Instrument Photos
• SSMT
  • DMSP Instrument and Data Information
• SSMT/2
  • DMSP Instrument and Data Information

KEY REFERENCES

1. Kim, E., Lyu, C.H.J., Anderson, K., Vincent, L.R., and Blackwell, W.G., S-NPP ATMS instrument prelaunch and on-orbit performance evaluation, J. Geopys. Res. Atmos., 119(9), 5653-5670, 2014. doi:10.1002/2013JD020483.

Future

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.

Monitoring Pages

Microwave sounder monitoring pages have been designed to monitor and trend instrument house-keeping and telemetry parameters, and 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.

Fengyun-3

• FY-3B and 3C

Joint Polar Satellite System (JPSS)

• NOAA-20 ATMS

Suomi-NPP (S-NPP)

• S-NPP ATMS

Initial Joint Polar System (IJPS)

• MetOp-C AMSU-A
• MetOp-C MHS
• MetOp-B AMSU-A
• MetOp-B MHS
• MetOp-A AMSU-A
• MetOp-A MHS

NOAA Polar Operational Environmental Satellite (POES)

• NOAA-19 AMSU-A
• NOAA-19 MHS
• NOAA-18 AMSU-A
• NOAA-18 MHS

Megha-Tropiques Mission

• SAPHIR

Methodologies

Microwave sounder 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.

Calibration Knowledge Sharing

• Calibration Assessment and Correction
  • Radiometric
    • Lunar Intrusion in Cold Space
  • Geometric
    • Geolocation Error Monitoring
• Science Product Assessment and Monitoring to Infer Calibration Quality and Stability
  • Radiative Transfer Model (RTM) Background Simulation (BS) Method
    • Using NWP output atmospheric soundings
    • Using GNSS Radio-Occultation atmospheric profiles
  • Vicarious Calibration
    • Lunar Calibration

Inter-calibration Knowledge Sharing

• Simultaneous Nadir Overpass (SNO) Method
• Double Difference Method

In-orbit Microwave Reference Records

KEY REFERENCES

These are key microwave sounder papers not captured with the separate methodology pages - e.g., SNO Method, RTM BS Method, and In-orbit Microwave Reference Records.

1. 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
2. Hans, I., M. Burgdorf and S. Buehler, Onboard radio frequency interference as the origin of inter-satellite biases for microwave humidity sounders. Remote Sens. , 11(7), 866, 2019, https://doi.org/10.3390/rs11070866.
3. 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.
4. 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.
5. 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.
6. 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.
7. Yang, Hu, Fuzhong Weng, Kent Anderson, 2016, "Estimation of ATMS Antenna Emission from Cold Space Observations”, IEEE Geoscience and Remote sensing, 10.1109/TGRS.2016.2542526.
8. Yang, John Xun, 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
9. Zhou, Jun, 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.

-- RobbieIacovazzi - 13 Apr 2020