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1 Advancements toward Fused Satellite, Radar, NWP, and in-situ Aviation Weather Decision Support Wayne F. Feltz, Tony Wimmers, John Williams #, Robert.

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Presentation on theme: "1 Advancements toward Fused Satellite, Radar, NWP, and in-situ Aviation Weather Decision Support Wayne F. Feltz, Tony Wimmers, John Williams #, Robert."— Presentation transcript:

1 1 Advancements toward Fused Satellite, Radar, NWP, and in-situ Aviation Weather Decision Support Wayne F. Feltz, Tony Wimmers, John Williams #, Robert Sharman #, Haig Iskenderian *, Bill Smith Jr. ^, John Mecikalski +, Valliappa Lakshmanan @, and numerous other UW-CIMSS contributors University of Wisconsin – Madison CIMSS/SSEC, Madison,WI # NCAR/UCAR, Boulder, CO * MIT/Lincoln Labs, Boston, MA ^ NASA LaRC, Hampton, VA + University of Alabama, Huntsville, AL @ University of Oklahoma, Norman, OK

2 Satellite-based Decision Support Fusion Overview  New satellite-based hazardous weather decision support advancements  Current and Future NOAA Operational Testbed Activities  Fusion Pathways  Conclusions

3 2010-2012 GOES-R Warning Product Set The following list of products offers opportunity for near-real time Warning Related utility within various fusion pathways: Baseline Products: Volcanic Ash: detection & Height Cloud and Moisture Imagery Hurricane Intensity Lightning Detection: Events, Groups & Flashes Rainfall Rate / QPE Total Precipitable Water Fire/Hot Spot Characterization Cloud phase/temperature/height/snow discrimination Option 2 Products: Aircraft Icing Threat Convective Initiation Turbulence Enhanced “V” / Overshooting Top Detection Low Cloud and Fog SO 2 Detection

4 4 Satellite PG Testbed Demonstrations HWT Spring Experiment at SPC/NSSL (2009 - ) HWT Spring Experiment at SPC/NSSL (2009 - ) Aviation Weather Center (2011 - ) Aviation Weather Center (2011 - ) Ocean Prediction Center/Hydrological Prediction Center and NESDIS Satellite Analysis Branch (2011 -) Ocean Prediction Center/Hydrological Prediction Center and NESDIS Satellite Analysis Branch (2011 -) Pacific Region Demonstration (2011 - ) Pacific Region Demonstration (2011 - ) Hurricane testbed (2010 - ) Hurricane testbed (2010 - ) High Latitude Testbed and Alaska Region (2010 - ) High Latitude Testbed and Alaska Region (2010 - ) NWS Operational Testbed (TBD, John Ogren) NWS Operational Testbed (TBD, John Ogren) Local WFO Demonstrations Local WFO Demonstrations Future field campaigns (DC3, Brazil Precipitation, …. ) Future field campaigns (DC3, Brazil Precipitation, …. )

5 Weather Decision Support Fusion Occurs at Multiple Levels Satellite decision support primarily not “stand- alone”, transition from GOES-R option 2 Satellite decision support primarily not “stand- alone”, transition from GOES-R option 2 Algorithm developers – NWP/in-situ used within satellite-based algorithms for turbulence, low cloud/fog, etc Algorithm developers – NWP/in-situ used within satellite-based algorithms for turbulence, low cloud/fog, etc Subject matter multi-agency scientific experts – Satellite-based decision support flows to OU- CIMMS, MIT, and NCAR as additional input Subject matter multi-agency scientific experts – Satellite-based decision support flows to OU- CIMMS, MIT, and NCAR as additional input Operational forecasters – use AWIPS, N- AWIPS, and soon AWIPS-2 to fuse data Operational forecasters – use AWIPS, N- AWIPS, and soon AWIPS-2 to fuse data

6 Fusion pathway for satellite turbulence-based inference decision support in NOAA AWC and NextGen: GTG-N ( NCAR Sharman/Williams PI ) NextGen 4D cube Tropopause folding Mountain wave Overshooting Top Rapid Anvil, Transverse banding Cloud top cooling Future, in development

7 Flow Chart for Global GTG Geo-, Leo- Satellites In Situ Observations (tuning) Convection Diag. & Now. Turb. Detection (trop. folding) Mountain Wave GFS Model CIT/CAT Diagnoses Global Graphical Turbulence Guidance Credit: J. Williams (NCAR)

8 Global GTG Demonstration at NCAR Global GTG World Wide Web Synthetic SIGMETs Pilots ATM / Dispatchers Credit: J. Williams (NCAR)

9 Future NEXTGen Use of Global GTG Global GTG NEXTGen 4-D Data Cube World Wide Web WAFC SIGMETs Human over the Loop Pilots ATM / Dispatchers Credit: J. Williams (NCAR)

10 GOES-R FIT Algorithm Demonstration Icing Severity from Current GOES 10 Prototype Icing Threat Nov. 8, 2008 (1745 UTC) PIREPS confirm significant icing Note: One severe icing report in Ohio Credit: W. Smith Jr. (NASA LaRC)

11 CIP without Satellite Products Warmer colors imply higher icing potential Better definition of the field gives more options to pilots! Results validated against research aircraft flights from NASA GRC Improved CIP is on pathway to AWC and NextGen (M. Polotivitch) Courtesy of Julie Haggerty, NCAR GOES Cloud Product Fusion Test with FAA-NCAR Current Icing Potential (CIP) Product CIP with Satellite Cloud Water Path Credit: W. Smith Jr. (NASA LaRC)

12 21 Sept 2011 Today Where we are headed… VIS 3.9 µm 6.5 µm 10.7 µm 12.0 µm 13.3 µm GOES t = t 3 t = t 2 t = t 1 t = 30-60 min SATCAST is a satellite product, yet is constrained by NWP/RUC CAPE to limit CI Nowcasts to where there is instability SATCAST 0-1 hr Nowcast CCMCCM MAMVMAMV OTOT Data Fusion: Towards a product that uses multiple sensors to solve a challenging forecast problem. GOES NWP CI GOES-R NWP NEXRAD-DP Enhanced CI GOES-R Storm intensity NEXRAD-DP GLM, GPM AI methods NWP CCMCCM MAMVMAMV OTOT SATCAST w/NWP Radar/ Dual-Pol + = Boundaries, storm morphology, life cycle GPM RUC, HRRR, WRF A-Train J Mecikalski Sept 2011

13 Fusion of Satellite, Radar, and NWP Data for Convective Initiation Forecasts in the FAA’s CIWS System CI Interest Fuzzy Logic CI Nowcast Model RUC/STMAS Instability NEXRAD Radar Growth GOES Solar Angle # of SATCAST CI Indicators CIWS Forecast Engine Credit: Haig Iskenderian MIT Lincoln Laboratory

14 CIWS 1 Hour Convective Initiation Forecast 24 June 2011 1 Hour Truth CI Credit: Haig Iskenderian MIT Lincoln Laboratory

15 Satellite-based Object Tracking Motivation – Why? WDSS-II – OU-CIMMS/UW-CIMSS 1) As a validation tool (Satellite-based convective decision support vs Radar) 2) Fusion of any number of data fields from any sensor type (satellite, satellite algorithm output, radar, lightning, NWP model, surface and upper air observations, etc.) into a single framework for 0-1 hour nowcasting relationships All these quantities are used in cloud object space, not single pixel space, allowing for easy monitoring of temporal as well as spatial trends A continuously (not discrete) changing field is key, in this case: Top of Troposphere Cloud Emissivity (Pavolonis, 2010) A continuously (not discrete) changing field is key, in this case: Top of Troposphere Cloud Emissivity (Pavolonis, 2010)

16 CIMSS Seminar August 18, 2011

17 GOES R – Why are WFO forecasters excited? Ability to sync satellite, radar, lightning, profiler, and ASOS/mesonet observations every 5 minutes (regional with highest fidelity resolution data) Ability to sync satellite, radar, lightning, profiler, and ASOS/mesonet observations every 5 minutes (regional with highest fidelity resolution data) Potential to change analysis standard (RTMA) from hourly to 5 minute by 2020? (Draft of NWS S&T Roadmap) Potential to change analysis standard (RTMA) from hourly to 5 minute by 2020? (Draft of NWS S&T Roadmap) Credit: Jeff Craven SOO Milwaukee/Sullivan -WFO

18 GOES-R/JPSS satellite-based weather decision support are or will fused systematically at developmental, subject matter expert, and operational venues: Satellite-based future capability science team Turbulence (T. Wimmers CIMSS)  AWC/NextGen Graphical Turbulence Guidance – N (John Williams/Bob Sharman NCAR/RAL PI) Icing (William Smith Jr NASA Langley)  Cloud Icing Product (Marcia Politovich NCAR/RAL PI) Convective initiation (J. Mecikalski UAH)  AWC/FAA/NextGen CoSPA (Haig Iskendarian MIT PI) National/Regional/ and WFO operational level Satellite-based Future Capabilities Fusion Bottom-line

19 19 Satellite-based Future Capabilities Fusion Bottom-line GOES-R algorithm development has fostered new decision support applications with current imager technology (expected launch of GOES-R in 2015) and new JPSS PG: GOES-R algorithm development has fostered new decision support applications with current imager technology (expected launch of GOES-R in 2015) and new JPSS PG: Convective Initiation, Overshooting-top, Volash, SO2, Fog/low cloud, Cloud typing, Fires Convective Initiation, Overshooting-top, Volash, SO2, Fog/low cloud, Cloud typing, Fires Vested interest in fused satellite-based decision support products (AWG to Proving Ground connections) Vested interest in fused satellite-based decision support products (AWG to Proving Ground connections) Very exciting interaction and process has shown new ways to uses Polar and Geostationary data fusion for NOAA decision support guidance and will occur at multiple stages within research to operations process Very exciting interaction and process has shown new ways to uses Polar and Geostationary data fusion for NOAA decision support guidance and will occur at multiple stages within research to operations process

20 NPP Launch JPSS will be particularly valuable over northern (Alaska) and polar regions where multiple overpasses should provide near geostationary-like capabilities and therefore compliment GOES-R JPSS will be particularly valuable over northern (Alaska) and polar regions where multiple overpasses should provide near geostationary-like capabilities and therefore compliment GOES-R VIIRS will provide low-light imaging capability, new uniform nadir to limb horizontal resolution and improved temporal latency, all important for aviation applications VIIRS will provide low-light imaging capability, new uniform nadir to limb horizontal resolution and improved temporal latency, all important for aviation applications CrIS will provide an additional hyperspectral platform for monitoring atmospheric stability, flight level temperature for fuel conservation purposes, polar wind information, and improved high latitude volcanic ash monitor (US to China routes are expected to increase rapidly in the near future) CrIS will provide an additional hyperspectral platform for monitoring atmospheric stability, flight level temperature for fuel conservation purposes, polar wind information, and improved high latitude volcanic ash monitor (US to China routes are expected to increase rapidly in the near future) Reference: National Polar-orbiting Operational Environmental Satellite System (NPOESS): New Capabilities for Supporting Aviation Applications, NASA Tech Report 2008, Wayne Feltz and David Johnson

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