The Status of NOAA/NESDIS Precipitation Algorithms and Products

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The Status of NOAA/NESDIS Precipitation Algorithms and Products Ralph Ferraro NOAA/NESDIS College Park, MD USA Limin Zhao, Sid Boukabara, Tanvir Islam, Stan Kidder, Bob Kuligowski, Huan Meng, Patrick

1 The Status of NOAA/NESDIS Precipitation Algorithms and Products Ralph Ferraro NOAA/NESDIS College Park, MD USA Limin Zhao, Sid Boukabara, Tanvir Islam, Stan Kidder, Bob Kuligowski, Huan Meng, Patrick Meyers, Isaac Moradi, Brian Nelson, Scott Rudlosky, Tom Smith, JJ Wang, Nai-Yu Wang, Yalei You, Jerry Zhan (and some others that I have unintentionally left off ) November th IPWG - Tsukuba, Japan 1

2 Satellite Status Operational products GOES-based products POES-based products Including non-noaa satellites Blended products Other activities Outline Climate Products/Data Records New Algorithms and Approaches Future Enterprise Product Systems at NOAA Summary November th IPWG - Tsukuba, Japan 2

3 GMI#2/Windsat 2018? February 2017? November th IPWG - Tsukuba, Japan

4 17-21 November th IPWG - Tsukuba, Japan 4

5 January November th IPWG - Tsukuba, Japan 6

6 NESDIS Operational Products of Interest MSPPS Microwave Surface and Precipitation Products System MiRS Microwave Integration Retrieval System GHE Global HydroEstimator btpw Blended Total Precipitable Water brr Blended Rain Rate etrap - Ensemble Tropical Rainfall Potential (etrap) Courtesy of Limin Zhao Algo Products Satellites/Sensors Res Type Formats MSPPS Rainfall rate, Snowfall rate, TPW, CLW, Snow Cover, Sea Ice, etc NOAA-18&NOAA-19&Metop-A & Metop-B /AMSU- A&MHS 16 km Level-2, Level-3 HDF-EOS, McIDAS area, PNG MiRS Rainfall rate, TPW, CLW, Snow Cover, Sea Ice, etc NOAA-18 & NOAA-19 & Metop-A & Metop-B /AMSU- A&MHS; DMSP F17&F18/SSMIS, NPP/ATMS, M-T/SAPHIR, GPM/GMI Varies (Lo and Hi Res) Level-2, Level-3 HDF-EOS,netCDF4, McIDAS area, PNGs GHE Rainfall rate, multi-hours and multi-days rainfall total GOES-E & GOES-W & MTSAT & Meteosat-7 & Meteosat- 10 IR Imager 4 km Level-3 netcdf4, McIDAS area, GRIB1/GRIB2, GIFs btpw Global Total Precipitable Water Map NOAA-18, NOAA-19, Metop-A and Metop-B /AMSU- A&MHS, GOES-W/-E, GPS-Met, DMSP F17&F18/SSMIS, NPP/ATMS, GPM/GMI, GCOM AMSR-2 16 km Level-4 HDF-EOS, McIDAS area, AWIPS, PNGs brr Global Rainfall Rate Map NOAA-18, NOAA-19, Metop-A and Metop-B /AMSU- A&MHS, DMSP 17&F18/SSMIS, NPP/ATMS, GPM/GMI, GCOM-AMSR-2 16 km Level-4 HDF-EOS, McIDAS area, AWIPS, PNGs etrap Prob-matched QPF, Probability NOAA-18, NOAA-19, Metop-A and Metop-B /AMSU- A&MHS, GOES-W/-E, DMSP F17, F18/SSMIS, NPP/ATMS, GPM/GMI, GCOM-AMSR-2 4 km Level-3 ASCII, McIDAS area, GIFs November th IPWG - Tsukuba, Japan

7 Upcoming New Satellite Capabilities MiRS Microwave Integration Retrieval System GPROF Goddard Profiling Algorithm GPDS - GCOM-W Processing and Distribution System SCaMPR - Self-Calibrating Multivariate Precipitation Retrieval Courtesy of Limin Zhao Satellites/ Sensors Algorithm Products Planned Schedule GCOM- W1/AMSR-2 GPDS/GPROF2010V2 Rainfall Rate, TPW, CLW, Snow Cover, Sea Ice, Soil Moisture, etc Experimental now; full operational Jan 2015 M-T/SAPHIR MiRS Rainfall Rate, TPW Imminent GPM/GMI GPROF/MiRS Rainfall Rate, TPW, etc. Dec 2014; Feb 2016 GOES-R/ABI (GLM) SCaMPR Probability of Rainfall, Rainfall Potential, QPE Jan ~ Mar November th IPWG - Tsukuba, Japan 7

POES-based L2 and L3 products Courtesy of L. Zhao, H. Meng, X.

Meyers MSPPS Heritage AMSU algorithms Snowfall rates over land

Retirement in 2015 MIRS 1DVAR scheme T, RH, hydrometeor

SFR merger (MSPPS retirement Portable to variety of sensors

rain rates Heritage NASA (Bayesian) approach V2 uses

8 POES-based L2 and L3 products Courtesy of L. Zhao, H. Meng, X. Zhan, P. Meyers MSPPS Heritage AMSU algorithms Snowfall rates over land N18, N19, MOA, MOB Other EDR s like TPW, CLW, etc. Retirement in 2015 MIRS 1DVAR scheme T, RH, hydrometeor profiles, TPW, CLW, emissivity, etc. SFR merger (MSPPS retirement Portable to variety of sensors N18, N19, MOA, MOB, S-NPP, M-T GPDS GCOM AMSR-2 GPROF2010V2 for rain rates Heritage NASA (Bayesian) approach V2 uses climatological screens Other EDR s like TPW, CLW, etc. MSPPS Rain Rate MIRS Rain Rate GCOM Composite MSPPS TPW MIRS WV Profiles November th IPWG - Tsukuba, Japan 8

9 Super-Typhoon Haiyan GCOM/AMSR-2 Courtesy of P. Meyers November th IPWG - Tsukuba, Japan 9

ATMS Snowfall Rate Product Courtesy of H.

utilizes a new and more advanced algorithm The snowfall rate component is an improved physical model from the MHS SFR algorithm A new cold climate

comparable or better performance than MHS SFR The CMORPH team has shown that adding ATMS SFR to MHS SFR enhances the performance of CMORPH Snow.

10 ATMS Snowfall Rate Product Courtesy of H. Meng An ATMS snowfall rate (SFR) algorithm has been developed building on the operational MHS SFR algorithm The snowfall detection (SD) component utilizes a new and more advanced algorithm The snowfall rate component is an improved physical model from the MHS SFR algorithm A new cold climate extension was developed recently which drastically increases the retrieval areas under cold conditions Validation of ATMS SFR shows that it has comparable or better performance than MHS SFR The CMORPH team has shown that adding ATMS SFR to MHS SFR enhances the performance of CMORPH Snow. NASA SPoRT is leading an ATMS/MHS SFR assessment project for this coming winter. Several NWS Weather Forecast Offices (WFOs) will participate, including Alaska. No Cold Extension W/ Cold Extension Radar Reflectivity November th IPWG - Tsukuba, Japan 16

GOES-based Short-Term Rainfall Products Courtesy of R.

using NWP model data Operational over CONUS; global

calibrated against MW Currently CONUS-only GOES-R (2016+)

spectral bands 0-3 h Rainfall Potential Extrapolation-based

probabilities based on rainfall nowcasts Hydro-Estimator

11 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 November th IPWG - Tsukuba, Japan 11

Other Non-NOAA Satellite Related Products DMSP SSMI/SSMIS

developed and delivered to NRL TRMM TMI TRMM V7 2A12 (Land)

land Developing new methods for possible use by GPM Courtesy of

12 Other Non-NOAA Satellite Related Products DMSP SSMI/SSMIS Legacy EDR s developed at NESDIS Improved legacy algorithm developed and delivered to NRL TRMM TMI TRMM V7 2A12 (Land) AMSR-E GPROF2010V2 GPM GPM Contributed to GMI prelaunch over land Developing new methods for possible use by GPM Courtesy of N-Y. Wang, P. Meyers, D. Vila November th IPWG - Tsukuba, Japan 12

13 Megha-Tropiques Recent Results (Rain - T. Islam, CIRA; TPW I.Moradi, CICS) MiRS SAPHIR only TPW (Ocean L, C; Land R) November th IPWG - Tsukuba, Japan 13

Blended TPW (btpw) Product http://www.osdpd.noaa.gov/btpw/index.

available data sources into a seamless product for use by the NWS forecaster in AWIPS format Ocean Satellite MW Land

given location for extended period Connection from (sub)tropics to mid and high latitudes Product is useful to weather

14 Blended TPW (btpw) Product btpw algorithm Kidder and Jones, 2007 Histogram matching to common reference The btpw product combines all available data sources into a seamless product for use by the NWS forecaster in AWIPS format Ocean Satellite MW Land Satellite MW and GOES Sounder; GPS Met Most flooding events can be linked to atmospheric rivers high TPW that focus on a given location for extended period Connection from (sub)tropics to mid and high latitudes Product is useful to weather forecasters Timing & magnitude of moisture surges (NWP models might miss) Companion TPW Anomaly (from climatology) Product Blended TPW 21 UTC 1 May Provided by S. Kussleson, NESDIS/SAB

15 Provided by S. Kussleson, NOAA/NESDIS/SAB 18 November 2014 IPWG Training Workshop 15

16 Provided by S. Kussleson, NOAA/NESDIS/SAB 18 November 2014 IPWG Training Workshop 16

Ensemble Tropical Rainfall Potential (etrap) Courtesy of R. Kuligowski and E.

Based on extrapolation of microwave-derived rainfall rates along predicted storm track.

HE) plus orographic, shear, storm rotation adjustments planned Produced worldwide and available via the Internet:

17 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 November th IPWG - Tsukuba, Japan 17

18 Typhoon Halong Landfall 9 Aug 14 Storm Track 24-hr rain Estimate 0600 UTC Probability Of 100 mm Or more 18 November 2014 IPWG Training Workshop 18

19 Climate Products Courtesy of Brian Nelson, Hilawe Semunegus, Huan Meng SSM/I Legacy products for GPCP Now generated by NCDC for SSMIS NOAA/NCDC CDR program SSMI FCDR s (CSU lead) AMSU FCDR s & TCDR s (NESDIS) November th IPWG - Tsukuba, Japan 19

Monthly mean products derived from SSM/I since

Snow cover frequency Sea-ice concentration

liquid water and frequency Ocean surface wind

at NOAA/NCDC Used by NCEP/CPC, JMA, GEWEX/GPCP

20 Monthly mean products derived from SSM/I since July 1987: Precipitation rate and frequency Snow cover frequency Sea-ice concentration Oceanic total precipitable water Oceanic cloud liquid water and frequency Ocean surface wind speed Products are now generated and archived at NOAA/NCDC Used by NCEP/CPC, JMA, GEWEX/GPCP SSMI Climate Time Series 18 November 2014 IPWG Training Workshop

21 New Ideas for GPROF2014/Sounders Courtesy of Y. You and N-Y. Wang Supported by NESDIS contribution to NASA PMM Team ActivitiesYou, R. Ferraro Working with C. Kidd/C. Kummerow Based on NMQ matchups with SSMIS and ATMS Key questions: How best to stratify databases over land? Ancillary data use How many view angle categories needed? NMQ ATMS November th IPWG - Tsukuba, Japan 21

22 Patrick Meyers Univ. of MD/CICS DCLMA Applications Showing the potential synergy from GOES-R ABI & GLM, and GCOM AMSR2 Minute Lightning Density with 2 Long-track Tornados Lightning Flashes Each Second Overlaid on AMSR2 Precipitation 22

23 November th IP Precipitation Enterprise Concept (NOAA/NESDIS GEARS) Courtesy of C. Kondragunta LEO MICROWAVE GPM (DPR&GMI) NOAA-POES (AMSU&MHS) Suomi NPP/JPSS (ATMS) GCOM-W (AMSR-2) MetOP (AMSU&MHS) MEGHA TR. (MADRAS) L1b L1b L1b L1b L1b L1b NOAA GPM PPS (OSPO/NESDIS) NOAA Enterprise Precipitation Processing System : Phase III PPS Products (L1-c & Rain Rate) NUP Gen. & SCaMPR NUPs NWS/RFC/WFO NWS/Centers DoD NESDIS/STAR/ SAB/CLASS JCSDA OAR/Testbeds DMSP (SSMIS) L1b GEO IR & Light. GOES-R (ABI & GLM) L1b Rain Rate Multi-sensor Precipitation Algorithms MPE, CMORPH & MRMS (GAUGE+WSR-88D + GOES) Rain Rate Legend Sensor or satellite GAUGE WSR-88D Processor End User

24 Summary and Future NOAA/NESDIS generates several operational precipitation products GOES-based POES-based, including JPSS Blended products NOAA also actively involved in other missions DMSP NASA TRMM, AMSR-E, GPM JAXA AMSR-2 CNES/ISRO M-T NOAA has maturing Climate Data Record Program Synergy with SCOPE-CM Future GOES-R (ABI, GLM) JPSS (ATMS, VIIRS, etc.) Enterprise solution to products blended products becoming a requirement Maturing CDR s and role with stakeholders November th IPWG - Tsukuba, Japan 24

The Status of NOAA/NESDIS Precipitation Algorithms and Products

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,