Advanced hurricane analysis and prediction through convection-allowing ensemble assimilation of multi-sensor all-sky satellite radiance observations

BUILDING ON PREVIOUSLY FUNDED NASA SUPPORT THE ULTIMATE GOAL OF THIS RESEARCH IS TO IMPROVE ANALYSIS AND PREDICTION OF TROPICAL CYCLONES AND HEAVY RAINFALL THROUGH INTEGRATED ENSEMBLE-BASED DATA ASSIMILATION OF ALL-SKY BRIGHTNESS TEMPERATURE (BT) OBSERVATIONS INTO THE CONVECTION-PERMITTING WEATHER RESEARCH AND FORECAST (WRF) MODEL USING THE PENN STATE UNIVERSITY (PSU) WRF-BASED ENSEMBLE KALMAN FILTER (WRF-ENKF) HURRICANE ANALYSIS AND FORECAST SYSTEM. OBSERVATIONS TO BE ASSIMILATED INCLUDE ALL-SKY BRIGHTNESS TEMPERATURE OBSERVATIONS FROM BOTH NEW-GENERATION HIGH-RESOLUTION POLAR-ORBITING MICROWAVE (E.G. GPM SSMIS) AND GEOSTATIONARY INFRARED (E.G. GOES-R HIMAWARI 8) SENSORS AS WELL AS OTHER IN SITU AND REMOTELY SENSED DATA IN ADDITION TO ALL AVAILABLE ROUTINE OBSERVATIONS AT THE SURFACE AND FROM SOUNDINGS. A FIRST STEP IN OUR EFFECTIVE ASSIMILATION OF ALL-SKY MICROWAVE RADIANCE OBSERVATIONS SUCH AS FROM GPM WAS THE DEVELOPMENT OF MICROPHYSICS-SPECIFIC FORWARD RADIATIVE TRANSFER CAPABILITIES WITHIN THE COMMUNITY RADIATIVE TRANSFER MODEL (CRTM) USING SCATTERING PROPERTY LOOKUP TABLES WITH BOTH SPHERICAL AND NON-SPHERICAL PARTICLES (SIERON ET AL. 2017 2018). THE PRIMARY FOCUS OF THIS CONTINUATION PROPOSAL WILL BE ON THE ADVANCED ANALYSIS AND PREDICTION OF TROPICAL CYCLONES AND HEAVY RAINFALL THROUGH INTEGRATED ASSIMILATION OF ALL-SKY RADIANCES AND IMPROVED UNDERSTANDING AND REPRESENTATION OF MODEL MICROPHYSICAL PROCESSES AND FORWARD RADIATIVE TRANSFER MODELING. MAJOR OBJECTIVES OF THE PROPOSED RESEARCH INCLUDE: 1) FURTHER DEVELOP AND REFINE MICROPHYSICS-CONSISTENT SCATTERING PROPERTY LOOKUP TABLES BY INCLUDING DIFFERENT REPRESENTATIONS OF PARTICLE SHAPES IN THE RADIATIVE TRANSFER MODELING OF MICROWAVE RADIANCES (IN PARTICULAR FOR THE FORWARD MODEL OF GMI) EXPANDING TO SELECTED MICROPHYSICS PARAMETERIZATION SCHEMES AND ASSESSING THE EFFECTIVENESS CROSS-CHANNEL CONSISTENCY AND POTENTIAL BIASES OF USING DIFFERENT COMBINATIONS OF SPHERICAL AND NON-SPHERICAL PARTICLE SHAPES FOR DIFFERENT HYDROMETEORS. 2) FURTHER OPTIMIZE ADVANCED STATISTICAL AND PHYSICALLY-BASED DATA ASSIMILATION ALGORITHMS IN ASSIMILATING ALL-SKY SATELLITE RADIANCES FROM BOTH GEOSTATIONARY INFRARED AND POLAR-ORBITING MICROWAVE REMOTE SENSING OBSERVATIONS (E.G. FROM ABI ON GOES-R AND GMI ON GPM) VIA IMPROVED CHANNEL SELECTION AND SYNTHETIZATION DATA THINNING AND QUALITY CONTROL ASSIMILATION INTERVALS ADAPTIVE COVARIANCE LOCALIZATION AND INFLATION TECHNIQUES ENSEMBLE SIZE AND MODEL RESOLUTION AND REPRESENTATION OF UNCERTAINTIES IN MODEL PHYSICS. 3) WITH INTEGRATED ASSIMILATION OF ALL-SKY RADIANCES AND ALL OTHER APPLICABLE OBSERVATIONS IN HAND PERFORM SYSTEMATIC DETERMINISTIC AND ENSEMBLE PREDICTIONS TO EXAMINE THE PREDICTABILITY OF TROPICAL CYCLONES IN PARTICULAR THE RAPID INTENSIFICATION PROCESS AND HEAVY RAINFALL EVENTS OVER DIFFERENT OCEAN BASINS MOST OF WHICH ARE NOT COVERED BY ROUTINE AIRCRAFT RECONNAISSANCE. 4) PERFORM SYSTEMATIC ENSEMBLE PARAMETER SENSITIVITY EXPERIMENTS WITH SELECTED MICROPHYSICS SCHEMES TO EXAMINE ASSUMPTIONS AND QUANTIFY UNCERTAINTIES IN KEY PHYSICAL PROCESSES AND PARAMETERS IN PARTICULAR WITH REGARDS TO PARTICLE FALL SPEEDS AND PARTICLE SIZE DISTRIBUTIONS AND TO EXPLORE THE POTENTIAL AND EFFECTIVENESS OF ASSIMILATING ALL-SKY RADIANCES IN ESTIMATING THESE PARAMETERS. THE PROPOSED WORK IS RELEVANT TO NASA S CONSTELLATION OF SATELLITES THAT ACQUIRE CRITICAL ATMOSPHERIC DATA. THE PROPOSED RESEARCH IS IN DIRECT RESPONSE TO CATEGORY 2.3 OF THE NRA METHODOLOGY DEVELOPMENT FOR IMPROVED APPLICATION OF SATELLITE DATA PRODUCTS PRIMARILY UNDER THE ANNOUNCED TOPICS: (1) DEVELOPMENT AND IMPLEMENTATION OF DATA ASSIMILATION TECHNIQUES FOR IMPROVED ANALYSES AND FORECASTS OF SIGNIFICANT WEATHER (E.G. TROPICAL CYCLONES AND FLOODS) ; AND (2) QUANTITATIVE ERROR CHARACTERIZATIONS OF PRECIPITATIONAFFECTED RADIANCES AND INSTANTANEOUS PRECIPITATION RATES AND THEIR USE IN WEATHER FORECASTING AND DATA ASSIMILATION SYSTEMS .