Satellite Satellite Precipitation Precipitation

Estimation and Estimation and Nowcasting Plans Nowcasting Plans

for the GOES-R Erafor the GOES-R EraRobert J. KuligowskiRobert J. KuligowskiNOAA/NESDIS Center for NOAA/NESDIS Center for Satellite Applications and Satellite Applications and

Research (STAR)Research (STAR)Camp Springs, MD USACamp Springs, MD USA

Third Workshop of the International Precipitation Working Group 23 October 2006

Background: GOES-RBackground: GOES-R The next generation of NOAA The next generation of NOAA

GOES begins with deployment GOES begins with deployment of GOES-R in December 2014of GOES-R in December 2014

The GOES-R Advanced The GOES-R Advanced Baseline Imager (ABI) will Baseline Imager (ABI) will feature:feature: Increased Increased spectralspectral capability: 16 capability: 16

bands in the visible and infraredbands in the visible and infrared Enhanced Enhanced spatialspatial resolution: 0.5 resolution: 0.5

km VIS, 2 km IRkm VIS, 2 km IR Enhanced Enhanced temporaltemporal resolution: resolution:

full-disk scan in 5 min instead of full-disk scan in 5 min instead of 3030

The GOES-R Lightning Mapper The GOES-R Lightning Mapper (GLM) will produce hourly full-(GLM) will produce hourly full-disk lightning imagerydisk lightning imagery

Background: AWGBackground: AWG The GOES-R Algorithm Working Group The GOES-R Algorithm Working Group

(AWG) has been established in order to:(AWG) has been established in order to: develop, demonstrate and recommend end-to-end develop, demonstrate and recommend end-to-end

capabilities for the GOES-R Ground Segmentcapabilities for the GOES-R Ground Segment provide sustained post-launch validation, and provide sustained post-launch validation, and

product enhancementsproduct enhancements

The AWG will pursue numerous avenues in The AWG will pursue numerous avenues in order to perform these functions, including:order to perform these functions, including: Proxy Dataset Development Proxy Dataset Development Algorithm and Application Development Algorithm and Application Development Product Demonstration SystemsProduct Demonstration Systems Development of Cal/Val ToolsDevelopment of Cal/Val Tools Sustained Product ValidationSustained Product Validation Algorithm and application improvementsAlgorithm and application improvements

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

GOES-R Product GOES-R Product Generation DevelopmentGeneration 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

Application TeamsApplication Teams

Each Application Team will Each Application Team will review candidate algorithms and identify review candidate algorithms and identify

algorithm deficienciesalgorithm deficiencies establish priorities and suggest solutions establish priorities and suggest solutions

to resolve algorithm deficiencies, to resolve algorithm deficiencies, formulate, oversee, and participate in formulate, oversee, and participate in

algorithm intercomparisonsalgorithm intercomparisons recommend algorithm for GOES-Rrecommend algorithm for GOES-R

The GOES-R Application Teams support the The GOES-R Application Teams support the AWG by providing recommended, AWG by providing recommended, demonstrated and validated algorithms for demonstrated and validated algorithms for processing GOES-R observations into user-processing GOES-R observations into user-required products which satisfy required products which satisfy requirements. requirements.

Application TeamsApplication TeamsRadiancesRadiances Land SurfaceLand SurfaceSoundingsSoundings Ocean ColorOcean ColorImageryImagery Ocean SSTOcean SSTWindsWinds CryosphereCryosphereCloudsClouds Radiation BudgetRadiation BudgetAviationAviation LightningLightningAerosols / Air Aerosols / Air Quality / Quality / Atmospheric Atmospheric ChemistryChemistry

Space Space EnvironmentEnvironment

Simulation and Simulation and Proxy Data SetsProxy Data Sets

HydrologyHydrology

Members:Members: Bob Kuligowski, NESDIS/STAR, ChairBob Kuligowski, NESDIS/STAR, Chair Phil Arkin, ESSICPhil Arkin, ESSIC Ralph Ferraro, NESDIS/STARRalph Ferraro, NESDIS/STAR John Janowiak, NWS/CPCJohn Janowiak, NWS/CPC Andy Negri, NASA-GSFCAndy Negri, NASA-GSFC Soroosh Sorooshian /Kuo-lin Hsu, UC-IrvineSoroosh Sorooshian /Kuo-lin Hsu, UC-Irvine

Responsible for 3 GOES-R Environmental Responsible for 3 GOES-R Environmental Data Records (EDR’s):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 Hydrology Algorithm TeamTeam

Algorithm Evaluation Algorithm Evaluation Strategy: QPEStrategy: QPE

Provide ABI proxy and ground validation Provide ABI proxy and ground validation data to algorithm providers to adapt their data to algorithm providers to adapt their algorithms for ABI capabilitiesalgorithms 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—Provide independent ABI proxy for evaluation—developers provide output QPE to Algorithm Team for developers provide output QPE to Algorithm Team for evaluation and selection of recommended algorithmevaluation and selection of recommended algorithm

Algorithm Evaluation Algorithm Evaluation Strategy: QPFStrategy: 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 Provide ABI proxy and ground validation data to algorithm providers to adapt their data to algorithm providers to adapt their algorithms for ABI capabilitiesalgorithms for ABI capabilities

Provide independent ABI proxy for Provide independent ABI proxy for evaluation—developers provide output evaluation—developers provide output QPE to Algorithm Team for evaluation and QPE to Algorithm Team for evaluation and selection of recommended algorithmselection of recommended algorithm

Algorithm Evaluation Algorithm Evaluation Strategy: QPFStrategy: QPF

Final rainfall potential algorithm will Final rainfall potential algorithm will combine the selected nowcasting combine the selected nowcasting framework with the recommended framework with the recommended QPE algorithmQPE algorithm

Final PoP algorithm will be produced Final PoP algorithm will be produced by calibrating the nowcasting by calibrating the nowcasting algorithm with ground validation algorithm with ground validation data to produce an unbiased data to produce an unbiased algorithmalgorithm

Proxy and Ground Proxy and Ground Validation DataValidation Data

METEOSAT Second Generation (MSG) METEOSAT Second Generation (MSG) Spinning Enhanced Visible and InfRared Spinning Enhanced Visible and InfRared Imager (SEVIRI) data will be used to Imager (SEVIRI) data will be used to create ABI proxy channelscreate 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 Brazil (1-h, 3-h, and daily gauge data from

CPTEC)CPTEC) Ethiopia (daily gauge data)Ethiopia (daily gauge data) South Africa (daily ¼-degree gauge South Africa (daily ¼-degree gauge

analysis)analysis) UK (NIMROD radar and MIDAS gauge data)UK (NIMROD radar and MIDAS gauge data)

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

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 dataDefine criteria for final

framework selection

Select algorithms for evaluation

Define criteria for initial algorithm

selection

Define criteria for initial algorithm

selection

Select algorithms for evaluation

Modify nowcasting frameworks as

needed to accept ABI input data

Rough ScheduleRough Schedule Spring 2007: algorithm modificationSpring 2007: algorithm modification Summer / Fall 2007: algorithm Summer / Fall 2007: algorithm

intercomparison and selectionintercomparison and selection Fall 2007-Summer 2008: algorithm Fall 2007-Summer 2008: algorithm

demonstration; finalize version 1 demonstration; finalize version 1 operational code and documentationoperational code and documentation

Fall 2008-on: improvements to Fall 2008-on: improvements to operational algorithmsoperational algorithms

Questions?Questions?