Multi-Sensor Convection Analysis Kristopher Bedka

1. Multi-Sensor Convection Analysis Kristopher Bedka Cooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison John R. Mecikalski Atmospheric Sciences Department, University of Alabama-Huntsville

2. Special Thanks To Collaborators UW-CIMSS Wayne Feltz – ASAP/MURI Coordination and Mesoscale Wind Validation Jun Li and Chian-Yi Liu – Simulated ABI Imagery and Hyperspectral Retrievals Tom Rink - Hydra Visualization UW-CIMSS Winds Group - Support for CI Nowcasting and Winds Processing Ralph Petersen - Mesoscale Wind Validation UAH Todd Berendes - Convective Cloud Classification Simon Paech - Radar/Lightning Data Processing and CI Nowcasting Analysis NASA John Murray - ASAP Coordination and Financial Support

3. Talk Outline • Current Generation Satellite Technology • ASAP Initiative Overview • Assessing Relative Accuracy of Mesoscale Winds (AMV’s) • GOES-Based Convective Storm Nowcasting • Preparation for Next Generation Technology • Cloud Electrification Studies/Lightning Nowcasting • Simulated Hyperspectral IHOP Convective Case, Visualization/Nowcasting • GIFTS/HES Hyperspectral Stability Fields, IHOP vs AtREC

4. ASAP Background • ASAP = Advanced Satellite Aviation-weather Products initiative • A partnership between NASA and the FAA to infuse high-resolution satellite data into aviation weather products for ground and airborne users • Collaboration currently occurring between SSEC/CIMSS, UAH, MIT, NASA, and the FAA AWRP PDTs to evaluate and implement satellite aviation weather products into operations • UW-CIMSS and UAH actively involved in producing satellite-based convective weather, volcanic ash, turbulence, and flight-level wind diagnostic and prognostic products • This talk will address 3 of the 4 CIMSS/UAH ASAP research areas • Turbulence (wind shear), Flight-level Wind = Mesoscale AMVs • Convective Weather = Satellite convective/lightning initiation nowcasting • ASAP Phase II will be focused on using next-generation, hyperspectral instrument data to develop aviation-weather products

5. Evaluating Relative Accuracy of Mesoscale AMVs • High-density “mesoscale” AMVs produced using the UW-CIMSS algorithm currently used in convective storm nowcasting applications - Cloud features are tracked over 30 min periods to identify convective cloud growth rates - Weakened the numerical model (NOGAPS) background motion constraint to allow ageostrophic (convective) cloud motions to be identified • Visible features tracked throughout the troposphere, compared to 600 mb in operations • Reduced size of wind targeting boxes such that small-scale features (i.e. pre-CI cumulus) can be tracked over time • Greater emphasis placed on cross-correlation feature tracking to identify high-resolution cloud and water vapor motions • These procedures greatly increase the number of vectors (~20-fold for complex flow), but can also introduce a greater number of errant vectors • Errant vector impact minimized through QC checks in Cu nowcast apps

6. Relative Accuracy of Mesoscale AMVs (cont’d) • To use vectors as a stand-alone product (i.e. ASAP flight-level winds, turbulence), we must understand error characteristics • Two ways to evaluate the quality and utility of mesoscale vectors 1) Use the vectors within a larger framework (NWP assimilation, nowcasting model), evaluate improvements over control run 2) Compare vectors to another wind observing system with known error characteristics (radiosonde, wind profiler) • Important to understand ability of current generation satellite AMV algorithms to depict mesoscale flow - We need to identify areas for future improvement in preparation for GOES-R ABI and advanced NWP model assimilation

7. 6 7 Satellite AMVs, Mesoscale vs Operational AMVs Using Operational Settings (152 vectors) 1000-700 mb 700-400 mb 400-100 mb Mesoscale AMVs (only 20% shown, 3516 total vectors) Bedka & Mecikalski (JAM, 2005)

8. Mesoscale AMVs: Cumulus Growth Estimation Using “Operational” AMVs 30 Min Cloud-Top Cooling (by Human Expert) Using “Mesoscale” AMVs

9. High-Density AMVs vs NOAA Wind Profiler Comparison • Profiler-GOES Matchup Criteria • AMVs within .25 ° of 23 NOAA wind profiler sites are collected over the NWS Southern Region • Profiler levels converted to pressure (RUC model), must be within 10 mb of the satellite AMV height assignment • 6 min profiler data used, all profiler winds within a 30 min period (i.e. a 3-image GOES sequence) are averaged and compared to GOES • Only “good” profiler data used, all operational QC checks passed • NOTE: Errors in GOES height assignment cannot be investigated here • One must utilize “truth” cloud heights to get best profiler/GOES comparison

10. High-Density AMVs vs NOAA Wind Profiler: Lamont, OK VIS, IR, WV Vectors, All Heights V-Comp RMS = 5.63 m/s V-Comp Bias = .14 m/s U-Comp RMS = 4.91 m/s U-Comp Bias = .06 m/s Vector RMS = 7.47 m/s Profiler RMS (Martner, 1993) = 6.82 m/s (low alt), 7.45 m/s (high alt)

11. High-Density AMVs vs NOAA Wind Profiler: Lamont, OK

12. Remote Sensing Observations and Nowcasting of Convective Storm & Lightning Initiation

13. Why Is Convective/Lightning Initiation Important ? • Thunderstorms are a serious threat to aviation interests (strong updrafts, hail, lightning) • Knowing the particular cumulus that will evolve into a thunderstorm before it appears on radar imagery can save aviation interests a lot of $ by reducing fuel usage and avoiding crew/passenger injuries • Dickinson, ND (1997): Pilot avoiding 2 large thunderstorms, but flies directly over new convective initiation, 22 injuries. Vertically propagating gravity waves believed to produce severe turbulence OBJECTIVES • Use geostationary satellite imagery to classify convectively-induced clouds • Recognize recent signs of rapid vertical growth for immature and “towering” cumulus • Use static IR diagnostics and growth rate estimates to nowcast robust convective storm initiation & development up to 1 hour in the future • Provide these convective growth/nowcast products to Convective Weather PDT AutoNowcaster expert system via ASAP

14. GOES-Based Convective Initiation Nowcasting Convective Initiation (CI): The transition of a convective cloud from below to above 35 dBz WSR-88D reflectivity (Roberts and Rutledge, 2003) Lightning Initiation (LI): First detection of lightning discharge from a convective cloud as detected by the N. Alabama Lightning Mapping Array (LMA) Nowcasting of CI and LI Through Use Of: 1) GOES VIS- and IR-based convective cloud classification 2) 10.7 μm cumulus cloud-top temperatures as proxy for glaciation 3) IR band differencing for height relative to tropopause, cloud-microphysics 4) Mesoscale AMVs used to determine time trends of cloud-top temperature and IR band differencing…identification of growing cumulus 5) Current and future gridded, co-located WSR-88D reflectivity and LMA source counts for product quality assessment and basic research

15. Northern Alabama LMA Data Example Courtesy of the NASA MSFC Lightning Group • Source counts: VHF radio signals associated with charge neutralization in a lightning channel. • Higher source counts=Stronger electrification

16. Northern Alabama LMA Data Example WSR-88D Composite Reflectivity: 2030 UTC WSR-88D Composite Reflectivity: 0300 UTC

17. GOES-Based CI Interest Fields (IFs) • Studied numerous real-time and archived convective events with diverse mesoscale forcing regimes and thermodynamic environments (continental (U.S. Great Plains) to sub-tropical (S. Florida)) • Identified GOES IR TB and multi-spectral technique thresholds and time trends present before convective storms begin to precipitate • Leveraged upon documented satellite studies of convection/cirrus clouds (Schmetz et al. (1997), Velden et al. (1997, 1998), Rabin et al. (2003), Roberts and Rutledge (2003)) • All IFs given equal weight…non-optimal use of these parameters

18. Convective Cloud Classification • Multi-spectral GOES-12 data for can be used to classify the various cloud features present within a scene using an unsupervised classification algorithm • Features highlighted here represent 1) small, immature cumulus2) mid-level cumulus3) deep convection4) thick cirrus anvil5) thin clouds

19. 2000 UTC 2030 UTC 2100 UTC GOES Convection/Lightning Nowcasting

20. 45 Minutes Later GOES Convection/Lightning Nowcasting • Looks good visually, but how good are these nowcasts in terms of POD and FAR

21. Red: CI Nowcast Pixels, Blue: Radar dBZ > 35, Grey: Mature Cu/Cirrus Convection/Lightning Initiation Statistical Analysis • Remap GOES data to 1 km gridded radar reflectivity data • Correct for parallax effect by obtaining cloud height through matching the 10.7 μm TB to standard atmospheric T profile • Identify 1 km radar/lightning pixels that have undergone CI/LI at t+30 mins • Advect pixels forward using low-level satellite wind field to find their approximate location 30 mins later • Determine what has occurred between imagery at time t, t-15, and t-30 mins to force CI/LI to occur in the future (t+30 mins) • Collect database of IR interest fields (IFs) for these CI/LI pixels • Through multiple regression analysis, identify POD and relative contribution of each IF toward a good nowcast • Use optimal combination of IFs to improve CI/LI nowcasting skill Warm (Cool) = Lower (Upper) Level Winds

22. Convection/Lightning Initiation Statistical Analysis (cont’d) • 7234 pixels that “CI’ed” were analyzed • Very preliminary analyses suggest that the 15 min 10.7 μm TB and 13.3-10.7 μm time trends are the most important IFs - Makes sense…cumulus that have been recently growing/glaciating are likely to produce greater precipitation rates in the future • When IFs weighted properly, our maximum POD (yes) of CI is 87 % • Database not structured to assess FAR yet, only CI pixels included • Regression analysis of lightning source count data reveals that 15 min 10.7 μm TB trend is the most useful IF for nowcasting LI • GOES-observed cloud-top cooling is a proxy for storm updraft intensity…strong vertical moisture flux produces charge separation and generation of cloud electrification • Future proposed work directed toward quantifying this concept

23. Hyperspectral Convection Analysis

24. Simulated Hyperspectral Convection: Hydra Visualization Simulated GIFTS/HES 11 μm TB MM5 Radar Reflectivity Estimate • Does developing and precipitating convection have a unique signal compared to other scene types in hyperspectral data?

25. Simulated Hyperspectral Convection: Hydra Visualization “Tri-spectral” Technique 11-12 μm, x-axis 8.5-11 μm, y-axis 8.5-11 μm Difference

26. 15 Min 11.2 μm Cooling Rate ABI 11.2 μmTB: 2030 UTC ABI CI Nowcasting 15 Min 11.2 μm Cooling Rate Simulated ABI Convection Nowcasting MM5 Reflectivity: 2030 UTC • Nowcasting Criteria • 273 K > 11.2 μm TB > 253 K • 11.2 μm TB > 273 K at t-15 mins, < 273 K at t=0 • 8.5-11.2 μm > 0 • 15 min 8.5-11.2 μm trend > 0 • -35 K < 7.0-11.2 μm < -10 K • 15 min 11.2 μm trend < -4 K MM5 Reflectivity: 2100 UTC

27. IHOP Convective Stability, Regression Retrievals • Atmospheric stability differs substantially between fields computed from hyperspectral regression-based T/q retrievals and MM5 truth profiles • Surface temperature and mixing ratio far too warm and moist, yielding much higher CAPE values

28. AtREC Convective Stability, Physical Retrievals Simulated HES CAPE MM5 “Truth” CAPE Surface MM5-HES Temperature Surface MM5-HES Dewpoint

29. Conclusions • Mesoscale AMVs show utility in convective storm nowcasting applications and promise for stand-alone usage in flight-level wind and turbulence diagnostics • Refereed journal article forthcoming on profiler/AMV comparison • GOES-based convective storm nowcasting products can provide skillful 30-60 min CI/LI forecasts and have shown to enhance skill of the NCAR AutoNowcaster through accurate depiction of cloud-top cooling rates • Cloud-top cooling/growth shown to be most important IF through regression analysis…should improve overall skill of nowcast system through optimal weighting • CI nowcasting with simulated ABI shows promise through inclusion of cloud glaciation info from the 8.5 um band and will greatly benefit in the future with VIS and 1.6 um reflectance data • Need cloudy AMV info to better capture cloud growth trends • Simulated hyperspectral IHOP Hydra visualization a useful tool for cloud classification applications and for general study of hyperspectral data characteristics • New AtREC hyperspectral retrievals look good where clear, IHOP convective case will serve as a challenge to this algorithm

30. Future Work, Preparation for GOES-R • The coupling of GOES ABI, HES, and GEO Lightning Mapper provides for an exciting synergy of datasets to better understand convective storm initiation and electrification - Preliminary analysis of simulated ABI/HES data shows promise for storm growth detection and assessment of near-storm environmental instability • Storm vertical motion inferred from cloud-top cooling rates (ABI), height/magnitude of near-storm atmospheric instability (HES), and cloud-top microphysics (phase and particle size (ABI, HES))…all relevant for lightning production…can be retrieved from the GOES-R instrument suite • Intra-cloud microphysics is the last piece of the puzzle…NEXRAD dual-polarization radar will provide this information • NEXRAD dual-pol upgrade should be complete as GOES-R becomes operational

31. Multi-Sensor Convection/Lightning Analysis Adapted From NASA material