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ASAP Convective Weather Research at NCAR Matthias Steiner and Huaqing Cai Rita Roberts, John Williams, David Ahijevych, Sue Dettling and David Johnson.

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Presentation on theme: "ASAP Convective Weather Research at NCAR Matthias Steiner and Huaqing Cai Rita Roberts, John Williams, David Ahijevych, Sue Dettling and David Johnson."— Presentation transcript:

1 ASAP Convective Weather Research at NCAR Matthias Steiner and Huaqing Cai Rita Roberts, John Williams, David Ahijevych, Sue Dettling and David Johnson NASA Applied Sciences Weather Program Review 18 - 19 November 2008 in Boulder, Colorado

2 12 9 6 3 Height (km) Satellite Detection 12 9 6 3 Time Radar Detection Different pieces of information revealed: cloud properties (satellite) precipitation (radar) electric activity (lightning) environment (surface obs, sounding) Satellite added value: earlier detection of new echoes spatial coverage for data sparse region (e.g., oceans, complex terrain) Time Satellite Radar Lightning Surface Obs Monitoring Present

3 Forecasting Time Monitoring Satellite Radar Lightning Surface Obs Present NWP Model Forecasting beyond 1-2 hours: data assimilation numerical weather prediction (NWP) blending of heuristic & NWP forecasts satellite CI Heuristic nowcasting: extrapolation of existing echoes growth & decay of echoes initiation of new echoes rapidly decreasing forecast skill CoSPA AutoNowcaster Oceanic Wx

4 DFW CoSPA ASAP ASAP Evaluations (1)Subjective evaluation of ASAP fields in real-time for Dallas/Fort Worth (DFW) (2)Case study-based evaluation of ASAP fields over parts of CoSPA domain (3)Objective statistical analysis of ASAP fields using Random Forest technique over parts of CoSPA domain approx. $40 K per year support through ASAP highly leveraged with other NCAR efforts, such as CoSPA (FAA), Dallas/Fort Worth (NWS), and Oceanic Weather (NASA ROSES)

5 (1) Dallas/Fort Worth Real-Time Evaluations ingested ASAP CI fields into NCAR AutoNowcaster subjective evaluation of ASAP CI nowcasts and box-averaged rate-of-change (ROC) fields Findings: data latency of approx. 25 min (15 min satellite data latency & 8-10 min processing by CIMMS) => reduction in data latency highly desirable to increase value of ASAP products daytime product only => 24 hour coverage desired (i.e., develop a nighttime product)

6 (2) Case Study Evaluations over CoSPA Domain Several cases selected from summer 2007: 7 & 8 June, 12 June, 18 & 19 June, 27 & 28 June, 4 & 5 July Examination of range of predictor fields from - NASA ASAP: CuMask; brightness temperature difference; box-average rate-of-change - MIT/LL: PeakyField (VIS differencing); WxClass; LowLevel Ci; ConvInit - NCAR: IR rate-of-change - Verification: WSI reflectivity and NSSL VIL Methodology: - analysis of predictor time series relative to occurrence of new convection (>35 dBZ) - assessment of relative lead time

7 19 June 2007 16:30 UTC 17:30 UTC 21:15 UTC 21:20 UTC 17:30 UTC 19 June 2007 16:30 UTC 35 dBZ-60 min 18 June 2007 35 dBZ-60 min 27 June 2007

8 Satellite predictor fields are fairly steady (reliable) with time NCAR Rate of Change and MIT/LL Peaky fields, and to a lesser extent, the NASA ASAP Box-averaged Rate of Change field, are the best satellite-based fields for predicting convection initiation The Rate of Change and Peaky fields provide the largest forecast lead times (30-60 min) These predictor fields will have great impact when used in conjunction with other predictors, e.g, - Combining best attributes of the Rate of Change Peaky, and Box Averaged fields to minimize false alarms, and - using cloud-type fields (NRL, CuMask), stability masks (STMASK), and convergence boundary interest fields (BdryGrid, WxClass, Frontal Likelihood) to mask out areas of low interest Results:

9 (3) Statistical Analyses Using Random Forest over CoSPA Domain Objective evaluation of ASAP product value for convective initiation based on 1 July to 27 August 2007 ASAP data Use of random forest technique as tool of choice for objective statistical analyses Random forest technique based on lots of decision trees & associated confidence votes for an “event” to happen (=> invited talk by John Williams & Haig Iskenderian) Preprocessing includes generation of additional predictors based on - maximum, minimum, average & standard deviation filters with 5, 10, 20, 40 & 80 km radius of influence - distance of ASAP CI nowcasts from pixel values exceeding 5, 6, 7 & 8 => ASAP experiments based on ~90 satellite-only predictors Random forest technique used for objective assessment of ~300 predictors for CoSPA development

10 4 July 2007 at 1702 UTC - ASAP CI nowcast1800 UTC - Radar verification 1745 UTC - ASAP CI nowcast1900 UTC - Radar verification

11 Original ASAP CI nowcastWith 5 km max spatial filtering With 10 km max spatial filteringWith 20 km max spatial filtering

12 Satellite-only basic predictor fields, plus many derived & enhanced fields Basic SatelliteCloud TypeOther Products VisNCAR cloud typeIR 11 Rate of Change IR 13.3NRL cloud typeMIT/LL Peaky IR 11NRL cloud type interestASAP CI Nowcast IR 6.7 IR 3.9 Relative importance ranking for top 18 predictors

13 Satellite-based ASAP products can help sharpen location of convective initiation when properly combined with other relevant pieces of information Spatial filtering of ASAP products may yield enhanced prediction value Data latency should be minimized Aim for 24 hour product coverage Continue objective statistical analyses of ASAP fields using random forest to optimize predictor value (e.g., for inclusion in CoSPA forecast system) Summary

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