1. A Comparison of Two Microwave Retrieval Schemes in the Vicinity of Tropical Storms Jack Dostalek Cooperative Institute for Research in the Atmosphere, Colorado State University Galina Chirokova and Kate Musgrave Cooperative Institute for Research in the Atmosphere, Colorado State University Mark DeMaria National Hurricane Center 1

2.  CIRA has developed tropical cyclone (TC) products using remotely sensed data.  Microwave data is particularly helpful in tropical cyclones due to insensitivity to most clouds  Temperature retrievals derived from Advanced Microwave Sounding Unit (AMSU) data have been extensively used Flies aboard NOAA-15 (1998), -16, -17, -18, -19, MetOp-A, MetOp-B, Aqua satellites  The temperature retrievals can be used to derive the height field which can be used to derive a non-divergent wind field  Estimate values of interest to TC forecasters such as: Minimum pressure Intensity (maximum wind speed) Radius of maximum winds Radius of 34-, 50-, 64-kt winds 2 Background

3. AMSU Wind Analysis Initial Estimates (Minimum Pressure, Intensity, Radius of Maximum Winds) Statistical Fitting for Improved Estimates Multiplatform Satellite Surface Wind Analysis Surface Wind Estimate Data AMSU Temperature Profiles GFS for Boundary Conditions Operational Fix Entry 3 + +

4.  AMSU temperature profiles for the TC products currently come from a statistical retrieval technique > 10 years old  NESDIS’ current operational microwave retrieval algorithm is the Microwave Integrated Retrieval System (MIRS) 1D VAR retrieval scheme Flexible, can use with AMSU, SSM/I, or ATMS Also retrieves vertical profile of water vapor mixing ratio 4 An Issue How does the quality of the initial estimates of the TC products compare when the temperature profiles come from the statistical technique vs. MIRS? A Question

5.  How do the MIRS temperature retrievals compare to the statistical retrievals with respect to collocated dropsondes? MIRS data first available in 2012 Dropsondes from 2012 Atlantic season (Ernesto, Isaac, Sandy) Statistical and MIRS retrievals from AMSU aboard NOAA-18 Collocation: retrievals must be within 50 km and 1 hour of dropsonde 5 But First

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8.  Do the higher quality soundings translate into better first estimates of minimum pressure, intensity, and radius of maximum wind? 33 matchups from the 2012 Atlantic hurricane season (Chris, Debby, Ernesto, Gordon, Isaac, Leslie, Sandy) Comparisons are made with respect to the best track The bias and rmse in the following plots are “large”, but in the flow of the processing, these estimates are of the first guess. Improvement from the statistical technique to the MIRS algorithm is what is sought 8  Overall the MIRS temperatures are closer to the dropsonde temperatures than the statistical technique Results of Temperature Comparison Back to the Main Question

9. 9  Both have high bias  Lower bias with MIRS  Lower RMSE with MIRS

10. 10  Both have low bias, consistent with high bias on minimum pressure (assuming similar environmental surface pressures)  MIRS bias smaller in magnitude  RMSE same

11. 11  Both have very high bias – again these values are refined later on  Larger bias with MIRS  Lower RMSE with MIRS

12. 12 Summary  MIRS temperature soundings are an improvement over the older, statistical technique  This improvement does translate into an (overall) improvement in the initial estimates of minimum pressure, intensity, and radius of maximum winds Future Work  More cases  Temperature profiles – Why isn’t the MIRS better at all levels?  Check quality of operational fix entries (definitely need more cases for this step)  Include moisture effects (MIRS also gives water vapor mixing ratio profile)  Other parameters (MPI, vertical velocity profile)