1. Satellite Precipitation Estimation and Nowcasting Plans for the GOES-R Era Robert J. Kuligowski NOAA/NESDIS Center for Satellite Applications and Research (STAR) Camp Springs, MD USA Third Workshop of the International Precipitation Working Group 23 October 2006

2. Background: GOES-R The next generation of NOAA GOES begins with deployment of GOES-R in December 2014 The next generation of NOAA GOES begins with deployment of GOES-R in December 2014 The GOES-R Advanced Baseline Imager (ABI) will feature: The GOES-R Advanced Baseline Imager (ABI) will feature: Increased spectral capability: 16 bands in the visible and infrared Increased spectral capability: 16 bands in the visible and infrared Enhanced spatial resolution: 0.5 km VIS, 2 km IR Enhanced spatial resolution: 0.5 km VIS, 2 km IR Enhanced temporal resolution: full- disk scan in 5 min instead of 30 Enhanced temporal resolution: full- disk scan in 5 min instead of 30 The GOES-R Lightning Mapper (GLM) will produce hourly full- disk lightning imagery The GOES-R Lightning Mapper (GLM) will produce hourly full- disk lightning imagery

3. Background: AWG The GOES-R Algorithm Working Group (AWG) has been established in order to: The GOES-R Algorithm Working Group (AWG) has been established in order to: develop, demonstrate and recommend end-to-end capabilities for the GOES-R Ground Segment develop, demonstrate and recommend end-to-end capabilities for the GOES-R Ground Segment provide sustained post-launch validation, and product enhancements provide sustained post-launch validation, and product enhancements The AWG will pursue numerous avenues in order to perform these functions, including: The AWG will pursue numerous avenues in order to perform these functions, including: Proxy Dataset Development Proxy Dataset Development Algorithm and Application Development Algorithm and Application Development Product Demonstration Systems Product Demonstration Systems Development of Cal/Val Tools Development of Cal/Val Tools Sustained Product Validation Sustained Product Validation Algorithm and application improvements Algorithm and application improvements

4. GOES-R PROGRAM OFFICE GOES-R Program Manager GOES-R Contract Representative Algorithm Working Group ORA Senior Management System Prime - Provide algorithm recommendations - Directions to the System Prime - Recommend algorithms - Provide recommendations on System Prime alternatives - Algorithm acceptance - Provide alternative solution or recommendation - Review System Prime alternatives Notional GOES-R Product & Algorithm Process

5. GOES-R Product Generation Development Exploratory Operational Product Development Operational Demonstration Operational Transition Operational Production GOES-R Risk Reduction AWG System Prime OSDPD AWG will continue to develop and improve algorithms over the life cycle of GOES-R

6. Application Teams Each Application Team will Each Application Team will review candidate algorithms and identify algorithm deficiencies review candidate algorithms and identify algorithm deficiencies establish priorities and suggest solutions to resolve algorithm deficiencies, establish priorities and suggest solutions to resolve algorithm deficiencies, formulate, oversee, and participate in algorithm intercomparisons formulate, oversee, and participate in algorithm intercomparisons recommend algorithm for GOES-R recommend algorithm for GOES-R The GOES-R Application Teams support the AWG by providing recommended, demonstrated and validated algorithms for processing GOES-R observations into user-required products which satisfy requirements. The GOES-R Application Teams support the AWG by providing recommended, demonstrated and validated algorithms for processing GOES-R observations into user-required products which satisfy requirements.

7. Application Teams Radiances Land Surface Soundings Ocean Color Imagery Ocean SST WindsCryosphere Clouds Radiation Budget AviationLightning Aerosols / Air Quality / Atmospheric Chemistry Space Environment Simulation and Proxy Data Sets Hydrology

8. Members: Members: Bob Kuligowski, NESDIS/STAR, Chair Bob Kuligowski, NESDIS/STAR, Chair Phil Arkin, ESSIC Phil Arkin, ESSIC Ralph Ferraro, NESDIS/STAR Ralph Ferraro, NESDIS/STAR John Janowiak, NWS/CPC John Janowiak, NWS/CPC Andy Negri, NASA-GSFC Andy Negri, NASA-GSFC Soroosh Sorooshian /Kuo-lin Hsu, UC-Irvine Soroosh Sorooshian /Kuo-lin Hsu, UC-Irvine Responsible for 3 GOES-R Environmental Data Records (EDR’s): Responsible for 3 GOES-R Environmental Data Records (EDR’s): 3.4.6.1, “Probability of Rainfall” 3.4.6.1, “Probability of Rainfall” 3.4.6.2, “Rainfall Potential” 3.4.6.2, “Rainfall Potential” 3.4.6.3, “Rainfall Rate / QPE” 3.4.6.3, “Rainfall Rate / QPE” Hydrology Algorithm Team

9. Algorithm Evaluation Strategy: QPE Provide ABI proxy and ground validation data to algorithm providers to adapt their algorithms for ABI capabilities Provide ABI proxy and ground validation data to algorithm providers to adapt their algorithms for ABI capabilities Evaluating four QPE algorithms: Evaluating four QPE algorithms: CPC IRFREQ (CPC—Janowiak / Joyce) CPC IRFREQ (CPC—Janowiak / Joyce) NRL-Blended (NRL—Joe Turk) NRL-Blended (NRL—Joe Turk) PERSIANN (UC-Irvine—Hsu and Sorooshian) PERSIANN (UC-Irvine—Hsu and Sorooshian) SCaMPR (NESDIS/STAR—Kuligowski) SCaMPR (NESDIS/STAR—Kuligowski) Provide independent ABI proxy for evaluation— developers provide output QPE to Algorithm Team for evaluation and selection of recommended algorithm Provide independent ABI proxy for evaluation— developers provide output QPE to Algorithm Team for evaluation and selection of recommended algorithm

10. Algorithm Evaluation Strategy: QPF Evaluating three nowcasting frameworks: Evaluating three nowcasting frameworks: Hydro-Nowcaster (NESDIS/STAR—Kuligowski) Hydro-Nowcaster (NESDIS/STAR—Kuligowski) K-Means (NSSL—Lakshmanan) K-Means (NSSL—Lakshmanan) TITAN (NCAR—Dixon) TITAN (NCAR—Dixon) Provide ABI proxy and ground validation data to algorithm providers to adapt their algorithms for ABI capabilities Provide ABI proxy and ground validation data to algorithm providers to adapt their algorithms for ABI capabilities Provide independent ABI proxy for evaluation— developers provide output QPE to Algorithm Team for evaluation and selection of recommended algorithm Provide independent ABI proxy for evaluation— developers provide output QPE to Algorithm Team for evaluation and selection of recommended algorithm

11. Algorithm Evaluation Strategy: QPF Final rainfall potential algorithm will combine the selected nowcasting framework with the recommended QPE algorithm Final rainfall potential algorithm will combine the selected nowcasting framework with the recommended QPE algorithm Final PoP algorithm will be produced by calibrating the nowcasting algorithm with ground validation data to produce an unbiased algorithm Final PoP algorithm will be produced by calibrating the nowcasting algorithm with ground validation data to produce an unbiased algorithm

12. Proxy and Ground Validation Data METEOSAT Second Generation (MSG) Spinning Enhanced Visible and InfRared Imager (SEVIRI) data will be used to create ABI proxy channels METEOSAT Second Generation (MSG) Spinning Enhanced Visible and InfRared Imager (SEVIRI) data will be used to create ABI proxy channels Ground validation data will be used for: Ground validation data will be used for: Brazil (1-h, 3-h, and daily gauge data from CPTEC) Brazil (1-h, 3-h, and daily gauge data from CPTEC) Ethiopia (daily gauge data) Ethiopia (daily gauge data) South Africa (daily ¼-degree gauge analysis) South Africa (daily ¼-degree gauge analysis) UK (NIMROD radar and MIDAS gauge data) UK (NIMROD radar and MIDAS gauge data)

13. Intercompare nowcasting frameworks in terms of skill at identifying, tracking, and extrapolatingrainfall features Select final nowcasting framework Estimation (QPE)Nowcasting (PoP, QPF) Intercompare QPE algorithms Select final QPE algorithm Calibrate PoP algorithm Produce ATBD, operational version 1 of code, and code documentation Produce final QPF algorithm Produce ATBD, operational version 1 of code, and code documentation Invite participation by algorithm developers: MPA (Huffman)SCaMPR (Kuligowski) NRL (Turk)PERSIANN (Sorooshian) Select and obtain proxy ABI and “ground truth” rainfall data Perform QPE algorithm modification to incorporate ABI capabilities Invite participation by algorithm developers: TITAN (NCAR)HN (Kuligowski) WDSSII (Laksmanan)CIMMS (Rabin) MP Nowcaster (Kitzmiller) Define criteria for final algorithm selection Select and obtain required GOES and “ground truth” rainfall data Define criteria for final framework selection Select algorithms for evaluation Define criteria for initial algorithm selection Select algorithms for evaluation Modify nowcasting frameworks as needed to accept ABI input data

14. Rough Schedule Spring 2007: algorithm modification Spring 2007: algorithm modification Summer / Fall 2007: algorithm intercomparison and selection Summer / Fall 2007: algorithm intercomparison and selection Fall 2007-Summer 2008: algorithm demonstration; finalize version 1 operational code and documentation Fall 2007-Summer 2008: algorithm demonstration; finalize version 1 operational code and documentation Fall 2008-on: improvements to operational algorithms Fall 2008-on: improvements to operational algorithms

15. Questions?