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The GOESR Risk Reduction Project (GOES-R3)

2017 through 2020 Participants, Projects/Proposals, and Reports

 Participants

Contacts

Projects  

Project Titles 2016 – 2019

January-June 2017 Reports July-December 2017 Reports January-June 2018 Reports July-December 2018 Reports   January-June 2019 Reports   July-December 2019 Reports   January-June 2020 Reports
 CoRP 2016 2017
CIMSS StAR Mike Pavolonis Mike.Pavolonis@noaa.gov 301 VOLCAT Development for GOES-R: Volcanic Ash and SO2 Report Report Report Report Report Report Final Report
Mike Pavolonis Mike.Pavolonis@noaa.gov 302 Fog and Low Stratus Report Report FinalReport
Tony Wimmers Wimmers@ssec.wisc.edu 303   Evaluation of Turbulence-Detection Methods on Himawari-8 Report Report Report Report Report Report FinalReport
Brad Pierce Brad.Pierce@noaa.gov Chris Velden chrisv@ssec.wisc.edu Megan Bela megan.bela@noaa.gov 308 CIMSS Support to GOES-R High Impact Weather Risk Reduction Report Report Report Report Report Report Report
Mike Pavolonis Mike.Pavolonis@noaa.gov 421 ProbSevere: Upgrades and Adaptation to Offshore Thunderstorms CIMSS ReportCIRAReport Report Report Report Report Final Report
Jun Li Jun.Li@ssec.wisc.edu 439 Improving the Assimilation of High-Resolution GOES-16 Water Vapor Variables and Atmospheric Motion Vectors in the HWRF Model Report Report Report Report Report FinalReport

Claire Pettersen Claire.Pettersen@ssec.wisc.edu Mark Kulie Mskulie@wisc.edu

480 An Enhanced Lake Effect Nowcasting Tool Using Synergistic GOES-R, NEXRAD, and Ground-Based Snowfall Microphysics Observations Report Report Report Report Report FinalReport
Satya Kalluri Satya.Kalluri@noaa.gov 488 Upgrading the GOES ET and Drought (GET-D) Product System for GOES-16 and 17 Report Report Report Report Report Report Final Report
CIRA StAR Chris Kummerow Christian.Kummerow@colostate.edu Steve Miller Steven.Miller@colostate.edu Kyle Hilburn Kyle.Hilburn@colostate.edu 307 Connecting GOES-R High Resolution Temporal Information with Rapid Updating Models Report Report Report Report Report FinalReport
Milija.Zupanski Milija.Zupanski@colostate.edu 410 Data assimilation of GLM observations in HWRF/GSI system Report Report Report Report Report FinalReport

Lewis Grasso Lewis.Grasso@colostate.edu

420 GOES-R ABI channel differencing used to reveal cloud-free zones of ‘precursors of convective initiation’ Report Report Report Report Report FinalReport
John Forsythe John.Forsythe@colostate.edu 444 Using the New Capabilities of GOES-R to Improve Blended, Multisensor Water Vapor Products for Forecasters Report Report Report Report Report Report
Steve Miller Steven.Miller@colostate.edu 476 Developing an Environmental Awareness Repertoire of ABI Imagery (‘DEAR-ABII’) to Advise the Operational Weather Forecaster Report Report Report Report Report Final Report
John Haynes John.Haynes@colostate.edu 479 Improving the ABI Cloud Layers Product for Multiple Layer Cloud Systems and Aviation Forecast Applications Report Report Report Report Report Report
CIRA CIMMS Kristin Calhoun Kristin.Kuhlman@noaa.gov 477 Integration of the Geostationary Lightning Mapper with ground-based lightning detection systems for National Weather Service Operations Report Report Report Report Report FinalReport
    Other
George Mason University Sanmei Li Slia@masonlive.gmu.edu 402 Integration of GOES-R/ABI data in Flood Mapping Software for Flood Monitoring and Forecasting Report Report Report Report Report FinalReport
University of Oklahoma Pierre Kirstetter Pierre.Kirstetter@noaa.gov 424 Probabilistic precipitation rate estimates from GOES-R for hydrologic applications Report Report Report Report Report Final Report
University of Oklahoma Xuguang Wang Xuguang.Wang@ou.edu 449 Assimilation of high resolution GOES-R ABI infrared water vapor and cloud sensitive radiances using the GSI-based hybrid ensemble-variational data assimilation system to improve convection initiation forecast Report Report Report Report Report Final Report
University of Alabama-Huntsville Phillip Bitzer phillip.bitzer@nsstc.uah.edu 450 Bayesian Merging of GLM data with Ground-Based Networks Report Report Report Report
Report
Final Report
NASA MSFC Christopher Schultz Christopher.J.Schultz@nasa.gov 460 Utilizing Sub-Flash Properties of GLM to Monitor Convective Intensity with Probabilistic Guidance

Report Poster

Report Poster Report Report
Report
FinalReport
CAPS University of Oklahoma Ming Xue Mxue@ou.edu 473 Assimilation of High-Frequency GOES-R Geostationary Lightning Mapper (GLM) Flash Extent Density Data in GSI-Based EnKF and Hybrid for Improving Convective Scale Weather Predictions Report Report Report Report
Report
Report

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