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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.

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Presentation on theme: "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."— Presentation transcript:

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

2 146 WSR-88Ds 30 Canadian Radars The Multi-Radar/Multi-Sensor System (MRMS)  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  Domain: 20-55°N, °W  Resolution  0.01°lat x 0.01°long  2 min update cycle ~ 9000 hourly gauges ~ 8000 daily gauges  Data Sources  ~180 radars every 4-5min  ~9000 gauges every hour  RAP model hourly 3D analyses  Satellite and lightning: optional  Operational at NCEP: 10/2014 2

3 Flooded Locations And Simulated Hydrographs (FLASH) A CONUS-wide flash-flood forecasting system – Operational at NCEP 2015 Stormscale Rainfall Observations (MRMS) Stormscale Distributed Hydrologic Models Probabilistic Forecast Return Periods and Estimated Impacts June 2010, Albert Pike Rec Area, Arkansas 10” 8”8” 6”6” Hydrograph of Simulated and Observed Discharge Simulated surface water flow 20 fatalities 80% 60% 40% Probability of life-threatening flash flood t=2300 t=0000 t=0100 Q2Q2 Q5Q5 5 hr 3

4 4

5 5 Winter Summer

6 6 Impact of missing data on calibration

7 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/Stratiform Precip 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

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

9 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/Stratiform Precip Type Segregation in data-sparse areas  TRMM LIS available for training period 9

10 MRMS Inputs: Near-surface rainfall properties  Surface precipitation Rainfall Rate Rainfall Rate Precipitation Type (conv/strat/tropical/hail/sno w/BB) Precipitation Type (conv/strat/tropical/hail/sno w/BB) Radar Quality Index Radar Quality Index Quality Control of Precip/No-Precip Quality Control of Precip/No-Precip  3-D Reflectivity Mosaic Vertical Profiles of Reflectivity Vertical Profiles of Reflectivity 10

11 TRMM/GPM Inputs: Vertical structure of precipitation  TRMM Precipitation Radar 2A25 Version 7 vertical profiles of attenuation- corrected reflectivity factor 2A25 Version 7 vertical profiles of attenuation- corrected reflectivity factor Rain Type Rain Type  GPM Dual-Frequency Radar Full CONUS coverage Full CONUS coverage Better characterization of particle size distributions and precipitation phase Better characterization of particle size distributions and precipitation phase  Lightning Imaging Sensor Identification of convective rainfall Identification of convective rainfall 11 (Photo Credit: JAXA/NASA)

12 MRMS Calibrated GOES-R/TRMM Rainfall Preprocessing of Datasets Preprocessing of Datasets a.Remap all inputs to GOES resolution b.Derive S-band-like vertical profiles of reflectivity from Ku-band TRMM PR (Wen et al. 2013) c.Filter by precip type and NEXRAD coverage quality NEXRAD coverage quality Develop Rainfall Model Develop Rainfall Model a.Two versions: TRMM and no-TRMM b.Joint-PDF Bayesian approach approach 12

13 Current Status and Future Plans Year 1: Current work (June 2014) a.Build training archive (1 year initially, up to 3 years available) b.Database entries of all inputs matched to GOES pixel resolution and scan time TRMM pass within 1 hour of GOES scan time TRMM pass within 1 hour of GOES scan time MRMS rain rate >= 10 mm hr -1 MRMS rain rate >= 10 mm hr -1 Radar Quality Index = 1 Radar Quality Index = 1 13 GOES-R Proxy Variables GOES-R Proxy Variables TRMM PR TRMM PR MRMS Matched Pixels MRMS Matched Pixels

14 Current Status and Future Plans Year 2: Product development and MRMS integration a.Dec 2014: Prototype CONUS precipitation rates and types b.Feb 2015: Merge satellite-based rainfall products with MRMS ground- based QPE (RQI-based weighting scheme) 14 Radar Quality Index 1 0 NEXRADQPE SatelliteQPE 1 Weight

15 Current Status and Future Plans Year 3: Validation and FLASH integration a.Evaluate model QPE performance (both with TRMM/GPM and without) b.Test blended MRMS QPE+satellite QPE as input to FLASH in real- time c.After GOES-R launch, adjust model to use real-time GOES-R input (ABI and GLM) 15

16 Thank you!  Contact: Heather Grams  For more info:   16


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