Satellite Precipitation Estimation and Nowcasting Plans for the GOES-R Era Robert J. Kuligowski NOAA/NESDIS Center for Satellite Applications and Research.

Slides:

Advertisements

Similar presentations

SPoRT Products in Support of the GOES-R Proving Ground and NWS Forecast Operations Andrew Molthan NASA Short-term Prediction Research and Transition (SPoRT)

Advertisements

SPoRT Activities in Support of the GOES-R and JPSS Proving Grounds Andrew L. Molthan, Kevin K. Fuell, and Geoffrey T. Stano NASA Short-term Prediction.

1 1. FY09 GOES-R3 Project Proposal Title Page Title: Trace Gas and Aerosol Emissions from GOES-R ABI Project Type: GOES-R algorithm development project.

Calibration of GOES-R ABI cloud products and TRMM/GPM observations to ground-based radar rainfall estimates for the MRMS system – Status and future plans.

Transitioning unique NASA data and research technologies to operations GOES-R Proving Ground Activities at the NASA Short-term Prediction Research and.

1 GOES-R Precipitation Products July 27, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR Thanks to: Richard Barnhill, Yaping Li, and Zhihua Zhang.

Convective Initiation Studies at UW-CIMSS K. Bedka (SSAI/NASA LaRC), W. Feltz (UW-CIMSS), J. Sieglaff (UW-CIMSS), L. Cronce (UW-CIMSS) Objectives Develop.

1 GOES Users’ Conference October 1, 2002 GOES Users’ Conference October 1, 2002 John (Jack) J. Kelly, Jr. National Weather Service Infusion of Satellite.

UW-CIMSS/UAH MSG SEVIRI Convection Diagnostic and Nowcasting Products Wayne F. Feltz 1, Kristopher M. Bedka 1, and John R. Mecikalski 2 1 Cooperative Institute.

Xin Kong, Lizzie Noyes, Gary Corlett, John Remedios, Simon Good and David Llewellyn-Jones Earth Observation Science, Space Research Centre, University.

An artificial neural networks system is used as model to estimate precipitation at 0.25° x 0.25° resolution. Two different networks are being developed,

Cooperative Institute Director’s Meeting 2-3 June 2005 Al Powell Deputy Director, ORA.

Intercomparing and evaluating high- resolution precipitation products M. R. P. Sapiano*, P. A. Arkin*, S. Sorooshian +, K. Hsu + * ESSIC, University of.

Analysis of High Resolution Infrared Images of Hurricanes from Polar Satellites as a Proxy for GOES-R INTRODUCTION GOES-R will include the Advanced Baseline.

Introduction Land surface temperature (LST) measurement is important for understanding climate change, modeling the hydrological and biogeochemical cycles,

1 GOES-R AWG Hydrology Algorithm Team: Rainfall Probability June 14, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.

GOES-R highlights Bernie Connell Cooperative Institute for Research in the Atmosphere Colorado State University December 2013.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 CLOUD MASK AND QUALITY CONTROL FOR SST WITHIN THE ADVANCED CLEAR SKY PROCESSOR.

1 GOES-R AWG Hydrology Algorithm Team: Rainfall Potential June 14, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.

GOES-R Risk Reduction New Initiative: Storm Severity Index Wayne M. MacKenzie John R. Mecikalski John R. Walker University of Alabama in Huntsville.

GOES-R for the Americas Luiz A. T. Machado INPE-CPTEC

Remote sensing of aerosol from the GOES-R Advanced Baseline Imager (ABI) Istvan Laszlo 1, Pubu Ciren 2, Hongqing Liu 2, Shobha Kondragunta 1, Xuepeng Zhao.

GOES-R ABI PROXY DATA SET GENERATION AT CIMSS Mathew M. Gunshor, Justin Sieglaff, Erik Olson, Thomas Greenwald, Jason Otkin, and Allen Huang Cooperative.

User Input from past GOES Users’ Conferences Jim Gurka Steve Goodman NOAA/NESDIS GOES-R Program Office Tim Schmit NOAA/NESDIS/ STAR 7 th GOES Users’ Conference.

GOES-R Risk Reduction Annual Meeting, September 2011, Huntsville SPoRT GOES-R Proving Ground Activities GOES-R Risk Reduction Annual Meeting 22 September.

The GOES-R Algorithm Working Group (AWG) program requests a high quality of proxy data for algorithm developments, testing and assessments. The central.

1 The GOES-R Rainfall Rate / QPE Algorithm Status May 1, 2011 Presented By: Bob Kuligowski NOAA/NESDIS/STAR.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Image: MODIS Land Group, NASA GSFC March 2000 Precipitation and Flash Flood.

PLANS FOR THE GOES-R SERIES AND COMPARING THE ADVANCED BASELINE IMAGER (ABI) TO METEOSAT-8 UW-Madison James J Gurka, Gerald J Dittberner NOAA/NESDIS/OSD.

1 1. FY08 GOES-R3 Project Proposal Title Page  Title: Hazards Studies with GOES-R Advanced Baseline Imager (ABI)  Project Type: (a) Product Development.

1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)

1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)

GOES and GOES-R ABI Aerosol Optical Depth (AOD) Validation Shobha Kondragunta and Istvan Laszlo (NOAA/NESDIS/STAR), Chuanyu Xu (IMSG), Pubu Ciren (Riverside.

Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental Satellite (GOES)-R platform. The sensor.

Hurricane Intensity Estimation from GOES-R Hyperspectral Environmental Suite Eye Sounding Fourth GOES-R Users’ Conference Mark DeMaria NESDIS/ORA-STAR,

11 Ice Cover and Sea and Lake Ice Concentration with GOES-R ABI Presented by Yinghui Liu Presented by Yinghui Liu 1 Team Members: Yinghui Liu, Jeffrey.

GSFC GOES-R Notional End-To-End Architectures Satellite Direct Readout Conference for the Americas December 9 – 13, 2002 Miami, Florida Sandra Alba Cauffman.

Center for Hydrometeorology and Remote Sensing - University of California, Irvine Precipitation Detection and Estimation Using Multi-Spectral Remotely.

2015 AMS Annual Meeting Non Export-Controlled Information Progress in Development and Integration of the Product Generation Capabilities of the GOES- R.

Improvements of the Geostationary Operational Environmental Satellites (GOES)-R series for Climate Applications GOES-R data and products will support applications.

Active Projects for FY04 Clouds from AVHRR – technical support for OSDPD transition to operations, validation of products and development of better algorithms.

Near-Real-Time Simulated ABI Imagery for User Readiness, Retrieval Algorithm Evaluation and Model Verification Tom Greenwald, Brad Pierce*, Jason Otkin,

GOES-R Recommendations from past GOES Users’ Conference: Jim Gurka Tim Schmit Tom Renkevens NOAA/ NESDIS Tony Mostek NOAA/ NWS Dick Reynolds Short and.

GOES-R Air Quality Proving Ground H. Zhang 1, R. M. Hoff 1, S. Kondragunta 2, A. Huff 3, M. Green 4, S. A. Christopher 5, B. Pierce.

Framework Details  All products may be run from one program  Coordination of input data:  Model Forecast data  Emissivity Data  Instrument Data 

VALIDATION AND IMPROVEMENT OF THE GOES-R RAINFALL RATE ALGORITHM Background Robert J. Kuligowski, Center for Satellite Applications and Research, NOAA/NESDIS,

5-6 June 2008CEOS Precipitation Constellation Workshop – Tokyo, Japan NOAA Status Report to the CEOS Precipitation Constellation Ralph Ferraro Center for.

Update on PG Program Plan & AWG product prioritization Bonnie Reed.

High impact weather studies with advanced IR sounder data Jun Li Cooperative Institute for Meteorological Satellite Studies (CIMSS),

Transitioning research data to the operational weather community Overview of GOES-R Proving Ground Activities at the Short-term Prediction Research and.

R. T. Pinker, H. Wang, R. Hollmann, and H. Gadhavi Department of Atmospheric and Oceanic Science, University of Maryland, College Park, Maryland Use of.

Introduction GOES-R ABI will be the first GOES imaging instrument providing observations in both the visible and the near infrared spectral bands. Therefore.

The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.

 Establishing state-of-the-art coding, documentation, configuration management standards for the AWG.  Software development process comparable with CMMI.

1 Recommendations from the 2 nd GOES-R Users’ Conference: Jim Gurka Tim Schmit NOAA/ NESDIS Dick Reynolds Short and Associates.

Retrieval Algorithms The derivations for each satellite consist of two steps: 1) cloud detection using a Bayesian Probabilistic Cloud Mask; and 2) application.

1 Inter-comparing high resolution satellite precipitation estimates at different scales Phil Arkin and Matt Sapiano Cooperative Institute for Climate Studies.

Real-time Display of Simulated GOES-R (ABI) Experimental Products Donald W. Hillger NOAA/NESDIS, SaTellite Applications and Research (STAR) Regional And.

.. STATUS UPDATE FROM THE GOES-R HYDROLOGY ALGORITHM TEAM AWG Background and Structure Robert J. Kuligowski NOAA/NESDIS Center for Satellite Applications.

4. GLM Algorithm Latency Testing 2. GLM Proxy Datasets Steve Goodman + others Burst Test 3. Data Error Handling Geostationary Lightning Mapper (GLM) Lightning.

1 GOES-R AWG Product Validation Tool Development Hydrology Application Team Bob Kuligowski (STAR)

Overview of Climate Observational Requirements for GOES-R Herbert Jacobowitz Short & Associates, Inc.

User Readiness Issues for GOES-R Jim Gurka Tim Schmit (NOAA/ NESDIS) Dick Reynolds (Short and Associates) 4 th GOES Users’ Conference May 2, 2006 Broomfield.

Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.

NOAA, May 2014 Coordination Group for Meteorological Satellites - CGMS NOAA Activities toward Transitioning Mature R&D Missions to an Operational Status.

Band 14 (11um) Winds Low-Level >700 mb Mid-Level mb High-Level mb   NPP VIIRS Polar Winds Products The GOES-R AWG Derived Motion Winds.

Bob Kuligowski, NOAA/NESDIS/STAR

GOES-R Resources from NOAA, NASA & CIMSS

USING GOES-R TO HELP MONITOR UPPER LEVEL SO2

GOES-R Hyperspectral Environmental Suite (HES) Requirements

Presentation transcript:

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

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

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

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 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

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.

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

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): , “Probability of Rainfall” , “Probability of Rainfall” , “Rainfall Potential” , “Rainfall Potential” , “Rainfall Rate / QPE” , “Rainfall Rate / QPE” Hydrology Algorithm Team

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

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

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

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)

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

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

Questions?