The Status of NOAA/NESDIS Precipitation Algorithms and Products

Transcription

1 The Status of NOAA/NESDIS Precipitation Algorithms and Products Ralph Ferraro NOAA/NESDIS College Park, MD USA S. Boukabara, E. Ebert, K. Gopalan, J. Janowiak, S. Kidder, R. Kuligowski, H. Meng, M. Sapiano, H. Semunegus, T. Smith, A. Sudradjat, D. Vila, N Y. Wang, F. Weng, L. Zhao October th IPWG Hamburg, Germany 1

2 Operational products GOES based products POES based products Blended products Validation efforts Non NOAA products Climate products Summary and Future Outline October th IPWG Hamburg, Germany 2

3 NESDIS Operational Precipitation Products Applications Current Capability Future Capability MSPPS MIRS Hydro Estimator AMSU Rain Rate, TPW, CLW, etc. (NOAA 15*, 16, 18, 19 and Metop A) AMSU Rain Rate, TPW, CLW, etc. (NOAA 18, 19, DMSP F16, Metop A) Instantaneous, 1 hr, 3 hr, 6 hr and 24 hr rainfall estimate over CONUS (GOES 11 and 13) Extended to include Metop B MIRS will be the upgrade of MSPPS as NOAA enters to NPP, JPSS, and GPM era. AMSU/MHS Rain Rate (DMSP F18, F19, Metop B) ATMS Rain Rate (NPP, JPSS) GPM Rain Rate (M T, GMI) Extended to include multi day rainfall estimate. Extended coverage from CONUS to global ScaMPR will be the upgrade of HE (GOES R) SCaMPR Under development 1 hour, 6 hour and 24 hour rainfall total over CONUS (GOES R) Blended Hydro etrap Courtesy of L. Zhao Blended TPW and TPW percentage of normal products (NOAA 15, 16, 17, 18, 19, Metop A, GPS, GOES, DMSP F13*, F14*, F15*) Blended RR products (NOAA 15, 16, 18, 19, Metop A, DMSP F16, F17) Deterministic rain amount QPFs and probabilistic POP forecasts for each of four 6h time periods (e.g., 00 06h, 06 12h, 12 18h, 18 24h) as well as the 24 hour cumulative time period. (Rain Rate from NOAA 15, 16, 18, 19 and Metop A, TRMM TMI, Aqua AMSR E) Blended TPW and TPW percentage of normal (Extended to include: DMSP F16, F17, F18 NPP, M T, GCOM W, JPSS, GOES R) Blended RR products (Extended to include: DMSP F18, NPP, M T, GCOM W, GMI, JPSS) Extended to include: F16, F17, F18, HE, NPP, M T, GCOM W, JPSS, GMI October th IPWG Hamburg, Germany 3 V Jan 2010

4 GOES based Short Term Rainfall Products Courtesy of R. Kuligowski Current: Hydro Estimator IR only plus adjustments using NWP model data Operational over CONUS; global experimentally Experimental SCaMPR Multi spectral IR calibrated against MW Currently CONUS only GOES R (2016+) Era: Rainfall Rate Modification of SCaMPR with additional spectral bands 0 3 h Rainfall Potential Extrapolation based nowcast 0 3 h Probability of Rainfall Conditional probabilities based on rainfall nowcasts Hydro Estimator Rainfall Rate SCaMPR Rainfall Potential Rainfall Probability October th IPWG Hamburg, Germany 4

5 POES based L2 and L3 products Courtesy of L. Zhao, S. Boukabara, H. Meng, D. Vila Current: MSPPS Heritage AMSU algorithms utilizing high frequency and H2O channels Snowfall identification over land Some fixes for aging sensors Other products like TPW, CLW, etc. Global L2 and L3 products MIRS 1DVAR scheme T, RH, hydrometeor profiles, TPW, CLW, emissivity, etc. Portable to variety of sensors Global Primary POES + DMSP JPSS era: MIRS for NPP/ATMS, JPSS/ATMS MSPPS will be phased out GCOM AMSR 2? MSPPS Rain Rate MSPPS Climatology MIRS Rain Rate MSPPS TPW MIRS WV Profiles October th IPWG Hamburg, Germany 5

6 AMSU/MHS Snowfall Rate Algorithm Retrieve Ice Water Path from passive microwave measurements from AMSU/MHS and RTM Calculate cloud depth from NWP T and V profiles Derive snowfall rate Image sequence of the US Mid Atlantic snowstorm on Feb 5 6, 2010 (left: satellite retrieval; right: NEXRAD reflectivity) Courtesy of H. Meng SFR (mm/hr) October th IPWG Hamburg, Germany 6

7 Blended Hydrological Products To support weather forecasters (AWIPS), NOAA moving towards integrated products Better products that are transparent to forecaster Makes forecaster more efficient Optimizes computer resources Two primary products TPW SSMI/SSMIS; AMSU; (AMSR E and TMI) Precipitation Rate SSMI/SSMIS and AMSU Developing synergy with CMORPH/QMORPH Data latency is key driver Courtesy of S. Kidder and L. Zhao October th IPWG Hamburg, Germany 7

8 Ensemble Tropical Rainfall Potential (etrap) Courtesy of R. Kuligowski and E. Ebert Forecast of 24 hour rainfall potential for tropical systems about to make landfall. Based on extrapolation of microwave derived rainfall rates along predicted storm track. Ensembles improve deterministic forecasts and provide uncertainty information Additional ensemble members (SSMIS, HE) plus orographic, shear, storm rotation adjustments planned Produced worldwide and available via the Internet: 18 UTC / UTC / UTC / UTC / UTC / 24 QPF EM QPF PM P 50 mm P 100 mm P 150 mm P 200 mm October th IPWG Hamburg, Germany 8

9 NESDIS Satellite Product Swath Validation over U. S. Courtesy of J. Janowiak and D. Vila Based on IPWG heritage, NESDIS providing funding to sustain/enhance this activity and gear it towards supporting operational and emerging algorithms MSPPS, MIRS, HE, SCaMPR, etc. Evaluated MIT/Staelin past year Validation is performed on ensembles of swath data against NCEP Stage IV radar/gauge data Swath products matched to within +30 minutes from ground data Going one step beyond Quarterly reports generated and delivered to NESDIS Precipitation Product Oversight Panel October th IPWG Hamburg, Germany 9

10 Non NOAA Related Products Courtesy of N-Y. Wang, K. Gopalan, A. Sudradjat DMSP SSMI/SSMIS Vintage EDR s developed at NESDIS TRMM TMI Leading the GPROF land efforts New V7 algorithm developed Reduces warm season bias compare to PR V6 AMSR E Same role as in TRMM Also prototyping new surface classification methodology Through ancillary data, eliminates Grody Ferraro era static screening methods TMI v6 - PR bias (mm/month) Longitude (deg) TMI regression - PR bias (mm/month) Longitude (deg) October th IPWG Hamburg, Germany 10

11 SSM/I Legacy products for GPCP Transitioning to NCDC SSMIS extension New QC scheme NOAA/NCDC CDR program Climate Products SSMI FCDR s (CSU lead) AMSU FCDR s & TCDR s (NESDIS) Other time series CHOMPS Reconstructions Courtesy of D. Vila, T. Smith, H. Semunegus, M. Sapiano October th IPWG Hamburg, Germany 11

12 Extended and Improved SSM/I Period of Record (Semunegus et al., 2010) 1. Normalized anomaly (z score) or deviations from climatology calculated to determine data quality 2. Temperature and geolocation threshold checks for each footprint antenna temperature calculated 3. Complete 1987-present record and embedded quality flags in netcdf-cf 4. All SSM/I antenna pattern correction coefficients detailed in paper (previously not publicly available) 5. Channel time series analysis for each platform 6. Pre-cursor to NOAA FCDR dataset for customers October th IPWG Hamburg, Germany 12

13 SSM/I useful data period ended in 2009 Need to extend into SSMIS; main difference is 91 vs. 85 GHz Colocated data Establish empirical relationship between 85 and 91 GHz timeframe between F15 & F16 Results indicate that method is adequate for both orbital and monthly scale products Methodology extended to F17 satellite See Vila et al SSMIS Continuity Courtesy of D. Vila SSMI/T/T October th IPWG Hamburg, Germany 13 SSMI/S Freq. (Ghz)./ Polarization. Footprint (km) Freq. (GHz)./ Polarization Footprint (km) / H & V 43 x / H & V 73 x / V 40 x / V 73 x / H & V 28 x / H & V 41 x / H & V 13 x / H &V 14 x 13 (imager)

14 High Resolution Precipitation Analysis: CHOMPS CHOMPS Cooperative Institute of Climate Studies (CICS) Highresolution Optimally interpolated Microwave Precipitation from Satellites All available passive MW satellites used Minimize time of day biases with more sampling Reprocess MW radiances with most up to data algorithm Builds product off of hourly gridded fields for each sensor type Courtesy of T. Smith October th IPWG Hamburg, Germany 14

15 Precipitation Reconstructions Courtesy of T. Smith and M. Sapiano Develop improved historical precipitation reconstruction ( ), merging recently-developed reconstructions based on satellite-era statistics and historical data (see Smith et al J. Clilmate) Merged Reconstruction EOF reconstruction (REOF), fits precipitation anomalies to a set of EOFs REOF(Blend) historical REOF from gauges and updates from REOF(GPCP) in recent years Less sampling in early 20 th century, may make problem with multidecadal signal CCA reconstruction (RCCA) uses SST & SLP historical predictors Multi-decadal change consistent with theoretical AR4 estimate Merge oceanic low-pass RCCA with oceanic high-pass REOF(Blend) October th IPWG Hamburg, Germany 15

16 Summary and Future NOAA generates several operational precipitation products GOES based POES based Emerging blended products NOAA also actively involved in other missions DMSP NASA TRMM, AMSR E NOAA has growing Climate Data Record Program Future GOES SCaMPR/GOES R Enhancements to bring in lightning data POES NPP and JPSS MSPPS to MiRS Blended Products! CDR s October th IPWG Hamburg, Germany 16