All-Weather Sounding of Moisture and Temperature From Microwave Sensors Using a Coupled Surface/Atmosphere Inversion Algorithm

Friday, 19 December 2014: 4:00 PM
Kevin Garrett, Riverside Technology Inc., Fort Collins, CO, United States; RTi@NOAA/NESDIS/STAR, JCSDA, College Park, MD, United States and Sid Ahmed Boukabara, NOAA/NESDIS/STAR, JCSDA, College Park, MD, United States
A one-dimensional variational retrieval system has been developed, capable of producing temperature and water vapor profiles in clear, cloudy and precipitating conditions. The algorithm, known as the Microwave Integrated Retrieval System (MiRS), is currently running operationally at the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite Data and Information Service (NESDIS), and is applied to a variety of data from the AMSU-A/MHS sensors on board the NOAA-18, NOAA-19, and MetOp-A/B polar satellite platforms, as well as SSMI/S on board both DMSP F-16 and F18, and from the NPP ATMS sensor. MiRS inverts microwave brightness temperatures into atmospheric temperature and water vapor profiles, along with hydrometeors and surface parameters, simultaneously. This atmosphere/surface coupled inversion allows for more accurate retrievals in the lower tropospheric layers by accounting for the surface emissivity impact on the measurements. It also allows the inversion of the soundings in all-weather conditions thanks to the incorporation of the hydrometeors parameters in the inverted state vector as well as to the inclusion of the emissivity in the same state vector, which is accounted for dynamically for the highly variable surface conditions found under precipitating atmospheres. The inversion is constrained in precipitating conditions by the inclusion of covariances for hydrometeors, to take advantage of the natural correlations that exist between temperature and water vapor with liquid and ice cloud along with rain water. In this study, we present a full assessment of temperature and water vapor retrieval performances in all-weather conditions and over all surface types (ocean, sea-ice, land, and snow) using matchups with radiosonde as well as Numerical Weather Prediction and other satellite retrieval algorithms as references. An emphasis is placed on retrievals in cloudy and precipitating atmospheres, including extreme weather events, to assess the quality of soundings in these conditions. We will also assess the potential added value of considering the coupled inversion approach.