Editorial Type:
Article
Article Type:
Research Article

Operational Retrieval of Atmospheric Temperature, Moisture, and Ozone from MODIS Infrared Radiances

Suzanne W. Seemann Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Jun Li Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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W. Paul Menzel Office of Research and Applications, NOAA/NESDIS, Madison, Wisconsin

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Liam E. Gumley Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin—Madison, Madison, Wisconsin

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Print Publication:
01 Aug 2003
DOI:
https://doi.org/10.1175/1520-0450(2003)042<1072:OROATM>2.0.CO;2
Page(s):
1072–1091
Restricted access

Abstract

The algorithm for operational retrieval of atmospheric temperature and moisture distribution, total column ozone, and surface skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) longwave infrared radiances is presented. The retrieval algorithm uses clear-sky radiances measured by MODIS over land and ocean for both day and night. The algorithm employs a statistical retrieval with an option for a subsequent nonlinear physical retrieval. The synthetic regression coefficients for the statistical retrieval are derived using a fast radiative transfer model with atmospheric characteristics taken from a dataset of global radiosondes of atmospheric temperature, moisture, and ozone profiles. Evaluation of retrieved total precipitable water vapor (TPW) is performed by a comparison with retrievals from the Geostationary Operational Environmental Satellite (GOES) sounder, radiosonde observations, and data from ground-based instrumentation at the Atmospheric Radiation Measurement (ARM) Program Cloud and Radiation Test Bed (CART) in Oklahoma. Comparisons over one and one-half years show that the operational regression-based MODIS TPW agrees with the microwave radiometer (MWR) TPW at the ARM CART site in Oklahoma with an rmse of 4.1 mm. For moist cases, the physical retrieval improves the retrieval performance. For dry atmospheres (TPW less than 17 mm), both physical and regression-based retrievals from MODIS radiances tend to overestimate the moisture by 3.7 mm on average. Global maps of MODIS atmospheric-retrieved products are compared with the Special Sensor Microwave Imager (SSM/I) moisture and Total Ozone Mapping Spectrometer (TOMS) ozone products. MODIS retrievals of temperature, moisture, and ozone are in general agreement with the gradients and distributions from the other satellites, and MODIS depicts more detailed structure with its improved spatial resolution.

Corresponding author address: Suzanne Wetzel Seemann, CIMSS/SSEC, University of Wisconsin—Madison, Madison, WI 53706. swetzel@ssec.wisc.edu

Abstract

The algorithm for operational retrieval of atmospheric temperature and moisture distribution, total column ozone, and surface skin temperature from the Moderate Resolution Imaging Spectroradiometer (MODIS) longwave infrared radiances is presented. The retrieval algorithm uses clear-sky radiances measured by MODIS over land and ocean for both day and night. The algorithm employs a statistical retrieval with an option for a subsequent nonlinear physical retrieval. The synthetic regression coefficients for the statistical retrieval are derived using a fast radiative transfer model with atmospheric characteristics taken from a dataset of global radiosondes of atmospheric temperature, moisture, and ozone profiles. Evaluation of retrieved total precipitable water vapor (TPW) is performed by a comparison with retrievals from the Geostationary Operational Environmental Satellite (GOES) sounder, radiosonde observations, and data from ground-based instrumentation at the Atmospheric Radiation Measurement (ARM) Program Cloud and Radiation Test Bed (CART) in Oklahoma. Comparisons over one and one-half years show that the operational regression-based MODIS TPW agrees with the microwave radiometer (MWR) TPW at the ARM CART site in Oklahoma with an rmse of 4.1 mm. For moist cases, the physical retrieval improves the retrieval performance. For dry atmospheres (TPW less than 17 mm), both physical and regression-based retrievals from MODIS radiances tend to overestimate the moisture by 3.7 mm on average. Global maps of MODIS atmospheric-retrieved products are compared with the Special Sensor Microwave Imager (SSM/I) moisture and Total Ozone Mapping Spectrometer (TOMS) ozone products. MODIS retrievals of temperature, moisture, and ozone are in general agreement with the gradients and distributions from the other satellites, and MODIS depicts more detailed structure with its improved spatial resolution.

Corresponding author address: Suzanne Wetzel Seemann, CIMSS/SSEC, University of Wisconsin—Madison, Madison, WI 53706. swetzel@ssec.wisc.edu

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Cover Journal of Applied Meteorology and Climatology
Journal of Applied Meteorology and Climatology

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