Annual Interagency Weather Research Review and Coordination Meeting

1. A Decision Support System for Diagnosing and Nowcasting Oceanic Convection for Oceanic Aviation Use Cathy Kessinger, HuaqingCai, WiebkeDeierling, Nancy Rehak, Daniel Megenhardt, Matthias Steiner National Center for Atmospheric Research, Boulder, Colorado Richard Bankert, Jeffrey Hawkins Naval Research Laboratory, Monterey, CA Michael Donovan and Earle Williams MIT Lincoln Laboratory, Lexington, MA Annual Interagency Weather Research Review and Coordination Meeting Boulder, CO 30 November – 2 December 2010 Sponsored by NASA ROSES

2. NASA ROSES Grants for Oceanic Convection • “Oceanic Convection Diagnosis and Nowcasting” • “Global Atmospheric Turbulence Decision Support System for Aviation” • “Characteristics of Oceanic Convective Storms for Inclusion in Aviation Decision Support Systems” • “Short-term Storm Forecasting over the Gulf of Mexico by Blending Satellite-based Extrapolation Forecasts with Numerical Weather Prediction Results”

3. Motivation – Aviation SafetyAir France Flight 447 Airbus A330 flying from Rio de Janeiro, Brazil to Paris, France on 1 June 2009 216 passengers/12 crew died Flew through a large, deep storm that likely contained severe turbulence Storm features, including overshooting tops, are readily diagnosed from GOES and Meteosat Flight Data Recorders have not been found; fourth attempt to start next year (approx.) Last ACARS message, 0212 UTC Cloud Top Height (CTOP) (approx.) Last verbal contact, 0133 UTC + (approx.) Last ACARS message, 0212 UTC Convective Diagnosis Oceanic (CDO) (approx.) Last verbal contact, 0133 UTC +

4. Uplink Message - Simulation(valid 0130 UTC 1 June 2009) Aircraft-specific, uplink product that could have been sent to Air France Flight 447 using today’s methodology ~1 hr look-ahead It is unknown whether this would have made a difference; the cause of the accident remains undetermined FAA Weather Technology in the Cockpit (WTIC) examining inflight display concepts Cockpit simulation Demonstration to follow? /EXP CLOUD TOP FI AF447/AN NXXXAF 01 Jun 09 -- '/' Cloud tops 30,000 to 40,000 ft//////CCC///// 'C' Cloud tops above 40,000 ft///////////CC///// *4.0N,30.0W///////// *////////C////////// //*//CC///CCC///////// ///*CCCC/C/CC////////// ////*CCCCCCC//C///////// ///CC*CCCCC///CC//////// ///CCC*CCCCC///////////// //CCCCC*CCCC////////////// //CCCCCCC*CCC////////////// /CCCCCCCCC*CCC///// / //CCCCCCCCCC*CC///// // /// //CCCCCCCCCCC*C///////// ////// /////CCCCCCCCCC*C// // // ////// //////CCCCCCCC//*/ // / ////// CC//////CCCCCCC//* ///////// CCC////////////// * ///////// /C//////////// *1.3N,31.4W //// /////// */ *// / /*/// // /*/// / /*// /*// * // * /// * // // * //// * ///// * ////// * ///// /// * /// Pos Rpt / // * / 0133 // X 1.4S,32.8W // Valid for // / 0130-0200z // Pilot feedback at url: http://[site deleted] Route C=>40kft /=30-39kft Way Point Air France position at 0133 UTC Text-based view for ACARS printer

5. CTop CClass GCD Oceanic Convection Diagnosis & Nowcasting System Convective Nowcasting Oceanic (CNO) makes 1-hr and 2-hr nowcasts of storm location using an object tracker CNO Polygons CNO-G Gridded Nowcast CNO-G makes gridded 1-8 hrnowcasts that more closely resemble storm structures Convective Diagnosis Oceanic (CDO) identifies convective cells CTop CClass GCD CDO Interest CNO-G produces hourly forecasts to 8 hr with 30 min regional updates and 3 hrly global updates (Regional = greater Gulf of Mexico/Caribbean) CDO Binary Product Gulf of Mexico domain

6. CTop CClass GCD Convection Diagnosis Oceanic (CDO) Product Manual TRMM Validation of CDO 1817 cells analyzed 12-22 Aug 2007 Hurricane Dean • Satellite-based detection of deep convection for remote regions • Data fusion of algorithms creates interest field • Cloud top height • Cloud classification (regional only) • Global convective diagnosis • Threshold applied for binary product • TRMM provides validation (Donovan et al., 2009) • Implemented over regional and global domains CTop CClass GCD CDO Interest CDO Binary Product Gulf of Mexico domain

7. Enhancements to CDO Day Terminator Night • Partition by time of day • Differing performance by one CDO input algorithm for day/terminator/night regions • Using sun zenith angle, CDO calculated for each region separately • Three regions merged • Compute inferred lightning, L, from TRMM Microwave Imager TB 37 & 85 GHz • TB(37GHz) = 330 – 45log(L) • TB(85GHz) = 330 – 70log(L) where L>20 grps/min (Blyth et al. 2001) • Input into CDO • Validation is next step Merged CDO Interest CDO – No Ltg CDO – Ltg

8. Convection Nowcasting Oceanic (CNO) Product CNO Titan Nowcast CNO-G Gridded Nowcast • Using CDO, nowcast location of deep convection. • Two extrapolation methods tested (Cai et al. 2010) • TITAN (object tracking to 8hr, growth/decay) • Gridded Nowcast (gridded nowcasts w/ stepwise Lagrangian scheme to 8hr, no explicit growth/decay) • Gridded Nowcast is best overall method • Implemented in regional and global domains CNO-Gridded validation of 1-8 hr nowcasts Storms from Sep 1-30, 2009 used, within Gulf of Mexico domain = Hurricane Dean validation

9. Sinking of Sailing Vessel Concordia • 17 Feb 2010, Concordia encountered a microburst outflow, capsized at 1722 UTC, sank ~20 min later • ~500km SSE of Rio de Janeiro • 48 Class Afloat students and 16 crew - No fatalities CDO Validation CNO-G 1hr 1609 GenTime 1709 ValidTime • CNO-G 1 hr and 6 hr forecasts predict storms near ship location • Good performance during storm’s mature stage • Convection initiation and explicit growth/decay methodologies needed The Canadian Press/Andrew Vaughan CDO Validation CNO-G 6hr 1109 GenTime 1709 ValidTime

10. Global Convection: CDO and CNO-G • Global Atmospheric Turbulence DSS will use global convection as indicator for convectively-induced turbulence (CIT) • Global data set from 2007-2008, CDO (3hr update) & CNO-G (1-8hr) shown • GOES-W and GOES-E for real time system 20080207

11. Storm Characterization Vol. of Reflectivity >35 dBZ • Examining relationships between lightning occurrence, storm lifecycle and turbulence production • Geo-Lightning Mapper application • Relationships could be applied within CNO-G to better predict storm intensity • Data sets: • NSSL NEXRAD mosaic • National Lightning Detection Network (NLDN) • NEXRAD Turbulence Detection Algorithm (NTDA) eddy dissipation rate • At and above -10°C level, as appropriate • Suggests correlation during storm evolution – examination of additional cases in progress NLDN 10min CG Strikes Vol. of ε1/3>0.15 Moderate Light Maximum ε1/3

12. Blending Satellite-based Extrapolation Forecasts with Numerical Weather Prediction Results Domain of the NOAA GSD Rapid Refresh (RR) model is shown with the CoSPA domain (red box) and the Gulf of Mexico blending domain i(orange box). • Blending of observational nowcasts with Global Forecast System (GFS) model forecasts • Gulf of Mexico domain • Will produce 1-12 hr convective forecasts that blend: • GOES-POES blended precipitation rate with GFS precipitation rate • GOES cloud top height with GFS-derived cloud top height • No results yet CoSPA Domain Blending Domain

13. Thank you! Web site: http://www.rap.ucar.edu/projects/ocn Cathy Kessinger National Center for Atmospheric Research Research Applications Laboratory 3450 Mitchell Lane, Boulder CO 80301Email: kessinge@ucar.edu Voice: (303)497-8481 This research is supported by NASA, primarily under Grants No. NNX09AM77G, NNA07CN14A and NNX08AL89G. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Aeronautics and Space Administration.