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Heather Grams, Pierre Kirstetter CIMMS/NSSL, Norman, OK J.J. Gourley , Robert Rabin

Calibration of GOES-R ABI cloud products and TRMM/GPM observations to ground-based radar rainfall estimates for the MRMS system – Status and future plans. Heather Grams, Pierre Kirstetter CIMMS/NSSL, Norman, OK J.J. Gourley , Robert Rabin NOAA/NSSL, Norman, OK.

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Heather Grams, Pierre Kirstetter CIMMS/NSSL, Norman, OK J.J. Gourley , Robert Rabin

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  1. Calibration of GOES-R ABI cloud products and TRMM/GPM observations to ground-based radar rainfall estimates for the MRMS system – Status and future plans Heather Grams, Pierre Kirstetter CIMMS/NSSL, Norman, OK J.J. Gourley, Robert Rabin NOAA/NSSL, Norman, OK

  2. The Multi-Radar/Multi-Sensor System (MRMS) • Domain:20-55°N, 130-60°W • Resolution • 0.01°lat x 0.01°long • 2 min update cycle • Data Sources • ~180 radars every 4-5min • ~9000 gauges every hour • RAP model hourly 3D analyses • Satellite and lightning: optional 146 WSR-88Ds 30 Canadian Radars • QPE Products • Instantaneous Precip Type/Rate • Radar QPE (1 hr – 10-day accums) • Gauge QPE • Local gauge-adjusted radar QPE • Gauge + orographic pcp climatology QPE • Operational at NCEP: 10/2014 ~ 9000 hourly gauges ~ 8000 daily gauges

  3. Flooded Locations And Simulated Hydrographs (FLASH) A CONUS-wide flash-flood forecasting system – Operational at NCEP 2015 Probabilistic Forecast Return Periods and Estimated Impacts Stormscale Distributed Hydrologic Models Stormscale Rainfall Observations (MRMS) Hydrograph of Simulated and Observed Discharge Simulated surface water flow 6” 8” 40% 60% 5 hr 10” 80% t=0100 t=0000 Q5 t=2300 Q2 20 fatalities Probability of life-threatening flash flood 10-11 June 2010, Albert Pike Rec Area, Arkansas

  4. Summer Winter

  5. Impact of missing data on calibration

  6. Filling Gaps with Satellite QPE? • Renewed Opportunity with GOES-R • Spatiotemporal resolution improvements for flash flood scale • 15 min  5 min over CONUS • 4 km  2 km for ABI-derived products • Geostationary Lightning Mapper • Convective/StratiformPrecip Type Segregation in data-sparse areas • Expanded list of cloud properties from ABI: • Cloud top height, temperature, phase, pressure; emissivity, effective radius, optical depth, etc. ~ MRMS scale

  7. MRMS Calibrated GOES-R/TRMM Rainfall • Advantages of GOES/GOES-R: • Rapidly updating (5-15 minutes) • Full CONUS coverage and beyond • Complementary Observing Systems • Geostationary Satellites (GOES) • TRMM/GPM PR • MRMS mosaic of ground radars + environment + gauges • Advantages of TRMM/GPM: • Fine-scale vertical profiles through cloud to surface • PR products complementary to NEXRAD • Advantages of MRMS: • Near-surface observations • High spatiotemporal resolution • Precip/No Precip QC

  8. GOES Inputs: Derived cloud properties • GOES-13 Proxy of GOES-R ABI cloud products (parallax-corrected) • Cloud Top Height/Temperature/Pressure • Cloud Top Phase • Cloud Top Emissivity • Optical Depth/Effective Radius (daytime only) • Geostationary Lightning Mapper • Convective/StratiformPrecip Type Segregation in data-sparse areas • TRMM LIS available for training period

  9. MRMS Inputs: Near-surface rainfall properties • Surface precipitation • Rainfall Rate • Precipitation Type (conv/strat/tropical/hail/snow/BB) • Radar Quality Index • Quality Control of Precip/No-Precip • 3-D Reflectivity Mosaic • Vertical Profiles of Reflectivity

  10. TRMM/GPM Inputs: Vertical structure of precipitation • TRMM Precipitation Radar • 2A25 Version 7 vertical profiles of attenuation-corrected reflectivity factor • Rain Type • GPM Dual-Frequency Radar • Full CONUS coverage • Better characterization of particle size distributions and precipitation phase • Lightning Imaging Sensor • Identification of convective rainfall (Photo Credit: JAXA/NASA)

  11. MRMS Calibrated GOES-R/TRMM Rainfall • Preprocessing of Datasets • Remap all inputs to GOES resolution • Derive S-band-like vertical profiles of reflectivity from Ku-band TRMM PR (Wen et al. 2013) • Filter by precip type and NEXRAD coverage quality • Develop Rainfall Model • Two versions: TRMM and no-TRMM • Joint-PDF Bayesian approach

  12. Current Status and Future Plans Year 1: Current work (June 2014) • Build training archive (1 year initially, up to 3 years available) • Database entries of all inputs matched to GOES pixel resolution and scan time • TRMM pass within 1 hour of GOES scan time • MRMS rain rate >= 10 mm hr-1 • Radar Quality Index = 1 TRMM PR MRMS Matched Pixels GOES-R Proxy Variables

  13. Current Status and Future Plans Year 2: Product development and MRMS integration • Dec 2014: Prototype CONUS precipitation rates and types • Feb 2015: Merge satellite-based rainfall products with MRMS ground-based QPE (RQI-based weighting scheme) 1 NEXRAD QPE Satellite QPE Weight 0 Radar Quality Index 1

  14. Current Status and Future Plans Year 3: Validation and FLASH integration • Evaluate model QPE performance (both with TRMM/GPM and without) • Test blended MRMS QPE+satellite QPE as input to FLASH in real-time • After GOES-R launch, adjust model to use real-time GOES-R input (ABI and GLM)

  15. Thank you! • Contact: Heather Grams (heather.moser@noaa.gov) • For more info: • http://mrms.ou.edu • http://flash.ou.edu

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