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Satellite QPE: Science Issues, New Capabilities and Perspectives

Satellite QPE: Science Issues, New Capabilities and Perspectives. Bob Kuligowski NOAA/NESDIS Office of Research and Applications. Current Operational Practices. Hydro-Estimator (HE): NESIDS’ operational algorithm

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Satellite QPE: Science Issues, New Capabilities and Perspectives

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  1. Satellite QPE: Science Issues, New Capabilities and Perspectives Bob Kuligowski NOAA/NESDIS Office of Research and Applications

  2. Current Operational Practices • Hydro-Estimator (HE): NESIDS’ operational algorithm • Rain rates based on 10.7-µm Tb’s; corrections based on NAM/GFS moisture, temperature, wind fields • 1-h totals distributed hourly via AWIPS (increase to 15-min updates in late 2005); instantaneous rates, and 1-, 3- and 6-h totals globally via Web, plus 24-h totals at 1200 UTC • Full-CONUS (4-km AWIPS grid) coverage on AWIPS; global coverage on Web • Offline ORA archive in McIDAS AREA file format • Real-time verification over CONUS (Kuligowski, Janowiak) and Australia (Ebert)

  3. Current Operational Practices • GOES Multi-Spectral Rainfall Algorithm (GMSRA) • Rain rates based on GOES 10.7-µm brightness temperatures calibrated in real-time by rain gauge-corrected radar • Other channels used in rain/no rain identification • Same update frequency as HE on Web, but CONUS coverage only; available on AWIPS with same schedule as HE by end of 2006 • Graphic online archive only • Validation over CONUS (Janowiak, Kuligowski) • Other IR algorithms also, but mostly experimental rather than operational

  4. Current Operational Practices • Microwave Rainfall Rates • More accurate instantaneous rates than IR-based, but available much less frequently (so less accurate totals) • Produced operationally from • SSM/I and SSMIS (statistical algorithms) at NRL—4 instruments total (change to SSMIS for F-18) • AMSU and HMS (physical algorithms) at NESDIS/ORA (MSPPS)—4 instruments total (change to HMS for NOAA-18) • AMSR-E (GPROF) at NASA—1 instrument • TMI (GPROF) at NASA—1 instrument • Global coverage 2x/day from each instrument except TMI (35°S-35°N coverage at irregular intervals)

  5. Current Operational Practices • IR/microwave blending techniques: • Effort to combine relative accuracy of microwave with sampling and resolution of IR • Numerous techniques: • NRL (probability matching) • MPA (NASA; error minimization) • PERSIANN (UC-Irvine; neural networks) • CMORPH (CPC; time interpolation using IR) • SCaMPR (ORA; multi-predictor regression) • …and others too numerous to mention • CMORPH performs best in Australia and CONUS validation

  6. Outstanding Science / R2O Issues • Still many challenges: • Stratiform precipitation • Orographically-enhanced precipitation • Snowfall • Optimal use of new IR bands/hyperspectral data? • Optimal use of lightning data • Optimal combination of IR and MW data and blending with other data sets (gauges, radar) given different characteristics • Calibration: fixed vs. variable (i.e., calibrated in real time vs. a target data set) • Format: how to account for uncertainty in a concise but useful manner (e.g., probability distributions?) • Error analysis—a CRITICAL missing piece for assimilation into NWP models

  7. New / Emerging Solutions • Algorithms: • CMORPH—uses IR as a basis for interpolating between MW measurements to address sampling issue • SCaMPR—flexible framework for calibrating IR and other data against any target data (satellite, radar, gauge)

  8. New / Emerging Solutions • Instruments and Platforms: • GOES-R ABI: 16 spectral bands (instead of 5) including bands useful for cloud-top phase and particle size retrieval; Meteosat-8 SEVIRI offers many of these channels • GOES-R lightning mapper—spaceborne lightning of all types (IC, CC, CG) • GPM: effort to provide 3-hourly microwave-based data via coordinated multinational instrumentation—next generation of spaceborne (dual-frequency) radar on main platform for calibration

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