Multi-Sensor Convection Analysis

Kristopher BedkaCooperative Institute for Meteorological Satellite Studies (CIMSS), UW-Madison

John R. MecikalskiAtmospheric Sciences Department, University of Alabama-Huntsville

Special Thanks To CollaboratorsUW-CIMSSWayne Feltz ASAP/MURI Coordination and Mesoscale Wind ValidationJun Li and Chian-Yi Liu Simulated ABI Imagery and Hyperspectral RetrievalsTom Rink - Hydra VisualizationUW-CIMSS Winds Group - Support for CI Nowcasting and Winds ProcessingRalph Petersen - Mesoscale Wind Validation

UAHTodd Berendes - Convective Cloud ClassificationSimon Paech - Radar/Lightning Data Processing and CI Nowcasting Analysis

NASAJohn Murray - ASAP Coordination and Financial Support

Current Generation Satellite TechnologyASAP Initiative Overview

Assessing Relative Accuracy of Mesoscale Winds (AMVs)

GOES-Based Convective Storm Nowcasting

Preparation for Next Generation TechnologyCloud Electrification Studies/Lightning Nowcasting

Simulated Hyperspectral IHOP Convective Case, Visualization/Nowcasting

GIFTS/HES Hyperspectral Stability Fields, IHOP vs AtREC Talk Outline

ASAP BackgroundASAP = 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

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

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 assimilationRelative Accuracy of Mesoscale AMVs (contd)

1/120th of vectors shownOceanic Satellite AMVs Meso-scale satellite AMVs provide detailed depictions of flow near convective cloud features

Satellite AMVs, Mesoscale vs OperationalAMVs Using Operational Settings (152 vectors)Mesoscale AMVs (only 20% shown, 3516 total vectors)1000-700 mb700-400 mb400-100 mbBedka & Mecikalski (JAM, 2005)

Mesoscale AMVs: Cumulus Growth EstimationUsing Operational AMVsUsing Mesoscale AMVs30 Min Cloud-Top Cooling (by Human Expert)

High-Density AMVs vs NOAA Wind Profiler ComparisonProfiler-GOES Matchup CriteriaAMVs within .25 of 23 NOAA wind profiler sites are collected over the NWS Southern RegionProfiler levels converted to pressure (RUC model), must be within 10 mb of the satellite AMV height assignment6 min profiler data used, all profiler winds within a 30 min period (i.e. a 3-image GOES sequence) are averaged and compared to GOESOnly good profiler data used, all operational QC checks passed

NOTE: Errors in GOES height assignment cannot be investigated hereOne must utilize truth cloud heights to get best profiler/GOES comparison

High-Density AMVs vs NOAA Wind Profiler: Lamont, OKVIS, IR, WV Vectors, All HeightsU-Comp RMS = 4.91 m/sU-Comp Bias = .06 m/sV-Comp RMS = 5.63 m/sV-Comp Bias = .14 m/sVector RMS = 7.47 m/sProfiler RMS (Martner, 1993) = 6.82 m/s (low alt), 7.45 m/s (high alt)

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

Remote Sensing Observations and Nowcasting of Convective Storm & Lightning Initiation

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 injuriesDickinson, ND (1997): Pilot avoiding 2 large thunderstorms, but flies directly over new convective initiation, 22 injuries. Vertically propagating gravity waves believed to produce severe turbulenceOBJECTIVES 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

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 classification2) 10.7 m cumulus cloud-top temperatures as proxy for glaciation3) IR band differencing for height relative to tropopause, cloud-microphysics4) Mesoscale AMVs used to determine time trends of cloud-top temperature and IR band differencingidentification of growing cumulus5) Current and future gridded, co-located WSR-88D reflectivity and LMA source counts for product quality assessment and basic researchGOES-Based Convective Initiation Nowcasting

Parallax-corrected 15 min GOES-12 1 km VIS & 4-8 km IR imagery and mesoscale AMVs, remapped to 1 km radar grid by McIDAS

2) NWP model temperature profiles to properly height assign AMVs to nearby pixels which do not have a motion vector

3) Gridded 1 km resolution WSR-88D from KHTX below the freezing level (to eliminate bright-band effect)

5 min accumulated total lightning data (intra-cloud, cloud-to-cloud, cloud-to-ground, cloud-to-air) from the Northern Alabama LMAGOES-Based Convective Initiation Nowcasting Datasets

Source counts: VHF radio signals associated with charge neutralization in a lightning channel. Higher source counts=Stronger electrificationCourtesy of the NASA MSFC Lightning GroupNorthern Alabama LMA Data Example

Northern Alabama LMA Data ExampleWSR-88D Composite Reflectivity: 2030 UTCWSR-88D Composite Reflectivity: 0300 UTC

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 weightnon-optimal use of these parameters

CI Interest FieldCritical Value10.7 m TB (1 score)< 0 C10.7 m TB Time Trend (2 scores)< -4 C/15 minsTB/30 mins < TB/15 minsTiming of 10.7 m TB drop below 0 C (1 score)Within prior 30 mins6.5 - 10.7 m difference (1 score)-35 C to -10 C13.3 - 10.7 m difference (1 score)-25 C to -5 C6.5 - 10.7 m Time Trend (1 score)> 3 C/15 mins13.3 - 10.7 m Time Trend (1 score)> 3 C/15 mins

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 cumulus 2) mid-level cumulus 3) deep convection 4) thick cirrus anvil 5) thin clouds

Satellite AMVs: Applications Within NCAR AutoNowcaster Inclusion of satellite convective cloud identification and ASAP satellite AMVs/cloud-top cooling rate information helps to reduce false alarms for CI nowcasts and build confidence in the proper locationsControl AutoNowcaster RunAutoNowcaster Run with ASAP Data

GOES Convection/Lightning Nowcasting

GOES Convection/Lightning Nowcasting Looks good visually, but how good are these nowcasts in terms of POD and FAR

Convection/Lightning Initiation Statistical AnalysisRemap GOES data to 1 km gridded radar reflectivity dataCorrect for parallax effect by obtaining cloud height through matching the 10.7 m TB to standard atmospheric T profileIdentify 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 laterDetermine 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 pixelsThrough multiple regression analysis, identify POD and relative contribution of each IF toward a good nowcastUse optimal combination of IFs to improve CI/LI nowcasting skill

Warm (Cool) = Lower (Upper) Level Winds

Convection/Lightning Initiation Statistical Analysis (contd) 7234 pixels that CIed were analyzed Very preliminary analyses suggest that the 15 min 10.7 m TB and 13.3-10.7 m time trends are the most important IFs- Makes sensecumulus 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 m TB trend is the most useful IF for nowcasting LI GOES-observed cloud-top cooling is a proxy for storm updraft intensitystrong vertical moisture flux produces charge separation and generation of cloud electrification Future proposed work directed toward quantifying this concept

Hyperspectral Convection Analysis

Simulated Hyperspectral Convection: Hydra VisualizationSimulated GIFTS/HES 11 m TBMM5 Radar Reflectivity Estimate Does developing and precipitating convection have a unique signal compared to other scene types in hyperspectral data?

Simulated Hyperspectral Convection: Hydra VisualizationTri-spectral Technique 11-12 m, x-axis 8.5-11 m, y-axis 8.5-11 m Difference

Simulated ABI Convection NowcastingMM5 Reflectivity: 2030 UTCMM5 Reflectivity: 2100 UTCNowcasting Criteria273 K > 11.2 m TB > 253 K11.2 m TB > 273 K at t-15 mins, < 273 K at t=08.5-11.2 m > 015 min 8.5-11.2 m trend > 0-35 K < 7.0-11.2 m < -10 K15 min 11.2 m trend < -4 K

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

Surface MM5-HES DewpointSurface MM5-HES TemperatureSimulated HES CAPEMM5 Truth CAPEAtREC Convective Stability, Physical Retrievals

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 analysisshould 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

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 productioncan be retrieved from the GOES-R instrument suite Intra-cloud microphysics is the last piece of the puzzleNEXRAD dual-polarization radar will provide this information NEXRAD dual-pol upgrade should be complete as GOES-R becomes operationalFuture Work, Preparation for GOES-R

Multi-Sensor Convection/Lightning AnalysisAdapted From NASA material

4) Clearly GOES is not the entire answeralthough cumulus may be growing strongly, it may be resulting from a strong cold pool aloft. Boundary layer moisture may be lacking, thereby limiting precipitation production. Although GOES only nowcasts show skill in robust (severe) storm development, in marginal CI events, other NWP data may be needed