Hyperspectral Infrared Alone Cloudy Sounding Algorithm Development Objective and Summary To prepare for the synergistic use of data from the high-temporal resolution ABI (Advanced Baseline Imager) on GOES-R and hyperspectral sounders on polar- orbiting satellites a retrieval algorithm has been developed to obtain sounding profiles under all weather conditions. Hyperspectral IR sounding products are important to meet future NWP requirements (e.g. depict moisture variations, improve weather/hurricane forecasting, detection of low level inversions). This poster describes an algorithm to retrieve sounding profiles simultaneously with cloud properties from hyperspectral infrared (IR) alone measurements at single field-of-view (FOV) resolution. The algorithm was first successfully applied on radiances from the Atmospheric Infrared Sounder (AIRS), and preliminary results using IASI (Infrared Atmospheric Sounding Interferometer) data further demonstrate its potential. Evaluation of the products have been conducted using co-located radiosonde observations, ECMWF analysis data, and products from other instruments (e.g. GOES, MODIS). Future tasks include more validation, improvement of regression training set and cloud mask algorithm, and physical retrieval development. Retrieval Methodology The retrieval method is based on eigenvector regression. Clear Radiances for a training set of ~15000 profiles are calculated using SARTA forward model (developed by UMBC) for 11 scanning angles. Cloudy Radiances for a training set of ~2100 (ice clouds) and ~4000 (water clouds) profiles are calculated using a fast cloudy radiative transfer model (developed in collaboration with the Texas A&M University) For both clear and cloudy calculations radiances are computed for 11 scanning angles. Surface pressure and the solar zenith angle are used as additional predictors. In the retrieval step clear sky coefficients are applied if the footprint is identified as clear (using IR cloud mask and phase mask technique), otherwise the cloudy coefficients are applied. The retrieval product includes 101 levels of temperature (T), humidity (Q), and ozone (O3), as well surface skin temperature and eigenvector coefficients of the IR surface emissivity spectrum. Under cloudy conditions cloud top pressure (CTP) is retrieved as well. For optically thick clouds soundings are derived only above the clouds. For GOES comparisons severe weather indices (e.g. LI, CAPE) are computed as well. Comparison with Sondes Comparison IASI with current GOES Sounder Left: AIRS clear-only (black) and cloudy (red) retrieval of temperature and relative humidity compared with two radiosondes (green, blue) at the ARM-SGP site on , with retrieved CTP ~320 hPa. Right: Temperature, dew point temperature and relative humidity from radiosonde (green), IASI retrieval (red) and AIRS retrieval (blue) at the ARM-SGP site on Above the cloud (retrieved CTP from AIRS is shown as thin gray line) both retrievals correspond well to the radiosonde measurement. Differences might also be due to temporal differences between the observations. Comparison with Model Analysis Fields IASI and ECMWF MoistureAIRS and NCEP Temperature CIMSS is preparing real-time computation of various parameters like lifted index (LI), convective available potential energy (CAPE), total precipitable water (TPW), and total column ozone (TCO) under clear skies, as well as CTP under cloudy conditions, from IASI radiances over the continental US to be compared with current GOES sounder products. As an example we show TCO in Dobson Units retrieved from GOES (top, large scale view, from and IASI (bottom) for (17:00 UTC). In general TCO retrieved from GOES and IASI agree well. Left: AIRS temperature retrieval (top) and NCEP gdas1 temperature along one scanline through hurricane’s Isabel eye ( ). Difference between inside the hurricane eye and the surrounding can be as large as 20 K. Right: IASI humidity retrieval (top) and ECMWF analysis for along one line of footprints. Narrow weighting functions due to the high sampling rate correspond to finer vertical resolution in the retrieval and enable the depiction of features of small extent. Acknowledgements. This project is partially supported by NOAA GOES-R programs at CIMSS. Contact. Elisabeth Jun Chian-Yi Hung-Lung and Mitchell D. Goldberg Institute for Meteorological Satellite Studies (CIMSS), University of Wisconsin-Madison &NOAA/NESDIS Center for Satellite Applications and Research AIRS RTV, IASI RTV vs. ARM-SGP RAOBAIRS RTV vs. ARM-SGP RAOBs