Satellite Precipitation Estimation and Nowcasting Plans for the GOES-R Era

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 GOES-R Advanced Baseline Imager (ABI) will feature: • Increased spectral capability: 16 bands in the visible and infrared • Enhanced spatial resolution: 0.5 km VIS, 2 km IR • 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

3. Background: AWG • 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 • provide sustained post-launch validation, and product enhancements • The AWG will pursue numerous avenues in order to perform these functions, including: • Proxy Dataset Development • Algorithm and Application Development • Product Demonstration Systems • Development of Cal/Val Tools • Sustained Product Validation • Algorithm and application improvements

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

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

6. Application Teams • 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. • Each Application Team will • review candidate algorithms and identify algorithm deficiencies • establish priorities and suggest solutions to resolve algorithm deficiencies, • formulate, oversee, and participate in algorithm intercomparisons • recommend algorithm for GOES-R

7. Application Teams Hydrology

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

9. Algorithm Evaluation Strategy: QPE • Evaluating four QPE algorithms: • CPC IRFREQ (CPC—Janowiak / Joyce) • NRL-Blended (NRL—Joe Turk) • PERSIANN (UC-Irvine—Hsu and Sorooshian) • SCaMPR (NESDIS/STAR—Kuligowski) • 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

10. Algorithm Evaluation Strategy: QPF • Evaluating three nowcasting frameworks: • Hydro-Nowcaster (NESDIS/STAR—Kuligowski) • K-Means (NSSL—Lakshmanan) • TITAN (NCAR—Dixon) • 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

11. Algorithm Evaluation Strategy: QPF • 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

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 • Ground validation data will be used for: • Brazil (1-h, 3-h, and daily gauge data from CPTEC) • Ethiopia (daily gauge data) • South Africa (daily ¼-degree gauge analysis) • UK (NIMROD radar and MIDAS gauge data)

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

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

15. Questions?