GOES-R Risk Reduction Annual Meeting 2011 Christian Kummerow CIRA Director 22 September 2011 1.

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Presentation transcript:

GOES-R Risk Reduction Annual Meeting 2011 Christian Kummerow CIRA Director 22 September

CIRA Product Development Interest 2 Tropical cyclone genesis Severe weather forecasting Data Assimilation for maximum data use Solar energy forecasts Data fusion for cloud properties Precipitation, orographically forced precipitation Snow & snow melt

Risk Reduction Research GOES Improved Measurements and Product Assurance Plan (GIMPAP) Product System Development and Implementation (PSDI) Program Algorithm Working Groups Proving Ground Testbed Operational SatellitesFuture Satellites/Sensors Time line Year 4-5 Year 6-7 Fundamental Research Year 1-3 Product Implementation Program 3 WFO Boulder Forecaster Chad Gimmestad Studying GeoColor WRF-based Synthetic ABI 10.35µm Imagery (02/27/2011) TC Dora (2011) ABI Natural Color Imagery

GOES-R Risk Reduction Project: CIRA produces their first synthetic GOES-R ABI imagery (ABI bands 7-16) for a severe weather event over Kansas which occurred in 2003 First Algorithm Working Group project: Simulated ABI Fire Proxy Datasets Case 1: Multiple fire hotspots with differing characteristics were inserted into the Kansas data set. Case 2: Simulated ABI agricultural fire proxy datasets located in Central America were created based on real GOES observational data. Example: CIRA GOES-R Synthetic Imagery Projects Timeline – Future Product Implementation Program: Synthetic forecast model data will most likely be displayed at WFOs and National Centers as part of the AWIPS II product palette. 4 Additional AWG Fire Proxy Dataset projects: Base on real fire events, 5 additional fire proxy datasets of varying locations and different types of fires were produced over a five-year time span: California wildfires, British Columbia forest fires, Arkansas agricultural fires, etc ?

GOES-R Risk Reduction Project: CIRA produces their first synthetic GOES-R ABI imagery (ABI bands 7-16) for a severe weather event over Kansas which occurred in 2003 First Algorithm Working Group project: Simulated ABI Fire Proxy Datasets Case 1: Multiple fire hotspots with differing characteristics were inserted into the Kansas data set. Case 2: Simulated ABI agricultural fire proxy datasets located in Central America were created based on real GOES observational data. 5-year CIRA GOES-R Synthetic Imagery Project Timeline Future Product Implementation Program: Synthetic forecast model data will most likely be displayed at WFOs and National Centers as part of the AWIPS II product palette. 5 Additional AWG Fire Proxy Dataset projects: Base on real fire events, 5 additional fire proxy datasets of varying locations and different types of fires were produced over a five-year time span: California wildfires, British Columbia forest fires, Arkansas agricultural fires, etc ? Risk reduction research on simulated GOES-R ABI datasets including the production of simulated ABI datasets for different type of weather and fire events.

6 Collaborative Computing Environment (CCE) Oper. Product R&D (Alg. Working Groups) Exp. Product R&D (Risk Reduction) Proving Grounds & Testbeds Satellite Algorithm Test Bed (SATB) Satellite Operational Algorithm Readiness (SOAR) A paradigm for multi-sensor fusion product development Common environment for rapid R2O transition R&D Coupled to End-User Needs Evolution of STAR  SOAR must ‘fly above’ any single satellite program and provide an environment for integrated product development.

Thematic Approach (Example) 7 Solar Energy Forecasting NOAA Models Satellite Resources Partners GOES Satellite Program GIMPAP Solar Energy Forecasting HRRR, LAPS, GFS, etc.GOES, GOES-R, JPSS, International NREL, Industry A big-picture approach provides broader connection between NOAA resources, provides latitude to define research approach optimally, and ties research to end-user throughout the development process. GOES Product Ongoing program, sustained student involvement 1-2 year project Satellite/Model Fusion Products NSOF Operations Operational Transitions to Vested Users Stovepipe Linear ModelParallel Model