Severe storm forecast guidance based on explicit identification of convective phenomena in WRF-model forecasts
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1 Severe storm forecast guidance based on explicit identification of convective phenomena in WRF-model forecasts Ryan Sobash 10 March 2010 M.S. Thesis Defense 1
2 Motivation When the SPC first started issuing probabilistic convective outlooks for severe weather (circa 1999), forecasters needed some basis for calibration. Given a field of severe reports, What is the best possible forecast? Brooks et al. (1998) applied a twodimensional Gaussian smoother to generate practically perfect probabilistic forecasts 2
3 Motivation Can this concept be applied to produce real forecasts based on severe phenomena predicted by a convection-allowing model (or ensemble of models)?? Valid 01 UTC Valid 12 UTC-12 UTC 3
4 Motivation How do we obtain model reports? Attempt to use model fields as surrogates for the severe events the SPC is trying to predict? Tornadoes Large hail High winds Mesocyclone Updraft strength? Mesocyclone Updraft strength Reflectivity? Mesocyclone 10-m wind speed Downdraft strength? 4
5 Previous work Identifying mesocyclones Early studies identified mesocyclones using an updraft/vertical vorticity correlation threshold [Davies-Jones (1984), Weisman and Klemp (1984), Droegemeier (1993), Elmore et al. (2002)]. More recently, an integrated vertical helicity diagnostic parameter has been used during the NSSL/SPC Spring Experiments, as documented by Kain et al. (2008). This updraft helicity diagnostic is computed by integrating w*, traditionally between 2 km and 5 km AGL. Shaded: UH > 25 m 2 s -2 Kain et al. (2008) 5
6 Previous work Spring Experiment 2008 As a proof-of-concept, during the 2008 NSSL/SPC Spring Experiment severe convection was identified in a convection-allowing ensemble to produce guidance for forecasters. Along with UH, 10-m wind speed was used as a surrogate field. 1. Large updraft helicity 2. Strong low-level wind speed 3. Moderately strong low-level winds colocated with linear reflectivity segments Subjectively determined thresholds The grid points that met any of these criteria over a 24 hour period were flagged (using hour model forecasts), to match SPC outlook valid times. 6
7 Previous work Spring Experiment 2008 A Gaussian smoother was applied following the procedure outlined in Brooks et. al. (1998) to produce a practically perfect Convective Outlook given a distribution reports. Surrogate severe weather reports Practically perfect forecast Subjective interpretations indicated this technique routinely captured the areas of severe reports. 7
8 Current Study: Model Configuration Although an ensemble was used during SE2008, it was uncalibrated. In this work, we begin the calibration process by looking at one deterministic member. NSSL-WRF Configuration: > WRF-ARW V2.2 > Initialization time: 00 UTC > Forecast length: 36 hours > Horizontal resolution: 4 km > Physics: MYJ BL, WSM6 MP Domain 8
9 Current Study: Surrogate Fields 5 model fields were chosen as surrogates in the NSSL-WRF output UH Updraft helicity (computed between 2 km and 5 km) [m 2 s -2 ] UU 10 m wind speed [m s -1 ] RF 1 km AGL simulated reflectivity [dbz] UP Max. column updraft (below 400 hpa) [m s -1 ] DN Max. column downdraft (below 400 hpa) [m s -1 ] To capture intra-hourly convective-scale variations, the maximum value of each field within an hour was recorded. 9
10 Current Study: Threshold Selection A reasonable UH threshold was determined during SE2008, but was unknown for other surrogate fields. Thus, 10 thresholds were chosen from each field s frequency distribution, approximately centered on the percentile of the original subjective UH threshold. 1: : : UH [m 2 s -2 ] thresholds UU [ms -1 ] thresholds RF [dbz] thresholds UP [ms -1 ] thresholds DN [ms -1 ] thresholds As in SE2008, grid points where the field exceeded the threshold within the hour forecasts (12 UTC to 12 UTC) were flagged and are referred to as surrogate severe weather reports (SSRs). 10
11 Surrogate Report Climatology SE2008 Number of days with UH SSR during SE2008 (18 Apr 8 Jun) UH 34 m 2 s -2 78,838 SSRs *produced w/ lowest threshold 11
12 Surrogate Report Climatology SE2008 Number of days with UU SSR during SE2008 (18 Apr 8 Jun) UU 19 ms -1 64,829 SSRs *produced w/ lowest threshold 12
13 Surrogate Report Climatology SE2008 Number of days with RF SSR during SE2008 (18 Apr 8 Jun) RF 53 dbz 77,116 SSRs *produced w/ lowest threshold 13
14 Surrogate Report Climatology SE2008 Number of days with UP SSR during SE2008 (18 Apr 8 Jun) UP 20 ms -1 81,394 SSRs *produced w/ lowest threshold 14
15 Surrogate Report Climatology SE2008 Number of days with DN SSR during SE2008 (18 Apr 8 Jun) DN -5 ms -1 79,543 SSRs *produced w/ lowest threshold 15
16 Surrogate Report Climatology SE2008 Number of days with OSR during SE2008 (18 Apr 8 Jun) OBS 8,017 OSRs 16
17 Surrogate Report Climatology SE2008 The model is capable of producing surrogate reports every time step and at every grid point (a perfect observation network ). This is not the case for observed reports. Since there exists a disparity in SSRs and OSRs totals, both reports were placed on an 80 km grid to produce biases closer to 1. SSR4s SSR80s UH 78,838 2,349 UU 64,829 2,114 RF 77,116 3,018 UP 81,394 4,218 DN 79,543 3,825 OBS 8,017 2,691 17
18 Surrogate Report Climatology SE2008 Field Bias UH 0.87 UU 0.79 RF 1.12 UP 1.57 DN 1.42 *produced w/ lowest threshold OBS 18
19 Producing a SSPF The Gaussian smoother from Brooks et al. (1998) applied to the surrogate reports can be interpreted as a surrogate severe probability forecast (SSPF). Applying the smoother turns a binary forecast into a probabilistic forecast based on the spatial distribution of the surrounding surrogates. 19
20 Case: 29 May 2008 UH UU RF UP OBS DN 20
21 Case: 29 May 2008 UH UU RF UP OBS DN 21
22 Research Questions Verification of SSPFs Does the SSPF provide skillful probabilistic guidance? 1. What surrogate fields produce the most skillful SSPFs? 2. For a given field, what thresholds produce the most skillful SSPFs? 3. What forms of guidance are most appropriate for forecasters? 22
23 SSPF Verification Verification of probabilistic forecasts often focuses on assessing two quantities: reliability and resolution. Reliability: forecast system probabilities match observed frequencies. Resolution: forecast system resolves events into subsets with different frequency distributions. Together, reliability and resolution determine the usefulness of a probabilistic forecasting system. 23
24 SSPF Verification ROC (relative operating characteristic) curves > assesses resolution > plot of probability of detection (POD) vs. probability of false detection (aka false alarm rate FAR) > can be summarized with area under ROC curve (AUC) Reliability diagrams Images courtesy Austrailian Bureau of Meteorology Verification website > assesses reliability > ideally, want the forecasts of 30% to occur 30% of the time > can be summarized using reliability component of Brier score (BS rel ) A skillful probabilistic forecasting system produces large ROC AUCs and is highly reliable (small BS rel ). 24
25 SSPF Verification Max ROC areas for each field ROC AUCs 25
26 SSPF Verification SSPF-UH Reliability diagram for SE
27 SSPF Verification SSPF-UU Reliability diagram for SE
28 SSPF Verification SSPF-RF Reliability diagram for SE
29 SSPF Verification SSPF-UP Reliability diagram for SE
30 SSPF Verification SSPF-DN Reliability diagram for SE
31 SSPF Verification The threshold that produces the best forecast resolution is not necessarily the one that is most reliable. UH UU RF UP DN Thresh # Reliability 0.6e-3 2.2e-3 1.5e-3 3.9e-3 1.9e-3 ROC AUC Overall, best resolution/reliability combination occurs with UH. 31
32 SSPF Verification SSPF forecasts were produced with constant sigma = 120 km. Can reliability of the forecasts be improved by changing this smoothing parameter? sigma = 120 km sigma = 160 km sigma = 240 km Too hot Just right? Too cold 32
33 SSPF Verification SSPF-UH Reliability for SE2008 BS rel
34 SSPF Verification: Reliability changes Reliability curves pivot when sigma is changed. Best reliability shown below. Reliability (sigma=120 km) UH UU RF UP DN 6e-4 2.2e-3 1.5e-3 3.9e-3 1.9e-3 Reliability 2.8e-4 3e-4 3e-4 9.2e-4 2.8e-4 Thresh # Sigma Changing sigma can improve forecast reliability. For UH and UU, most reliable threshold decreases (somewhat better resolution). For RF, UP, DN, most reliable threshold still has poor resolution. 34
35 SSPF Verification Summary Overall, UH is the best predictor during SE2008 as indicated by ROC AUC and forecast reliability. Optimal UH SSPF parameters: Threshold ~35-40 m2s-2 Sigma 140 km By changing sigma, other SSPFs have reliability comparable to UH SSPFs, but suffer from poor resolution. 35
36 Future work This work begins to explore guidance from convection-allowing model forecasts. Additional work should focus on the following. Verify SSPFs over a longer time period (~ 2 years) to determine if these findings are applicable under all seasons and regions. Test additional calibration methods. SSPF for specific severe weather types (hail, wind, etc). Apply SSPF procedure to an ensemble of forecasts. Operational implementation. 36
37 Future work Operational applications Hypothetical operational applications include using the SSPF as a first-guess convective outlook or as a summary of where a specific model/ensemble is producing intense convection.? Day1 Surrogate Convective Outlook 29/1200Z 30/1200Z Model: /00Z NSSL-WRF 37
38 Extra Slides 38
39 SSPF Verification SSPF-UU Reliability for SE2008 BS rel 39
40 SSPF Verification SSPF-RF Reliability for SE2008 BS rel 40
41 SSPF Verification SSPF-UP Reliability for SE2008 BS rel 41
42 SSPF Verification SSPF-DN Reliability for SE2008 BS rel 42
43 SSPF Verification Fractions Skill Score > A postively-defined skill score (from 0 to 1) based on the Brier Score that uses both forecast and observed probabilities. > Used to directly compare SSPF to OSPF. SSPF OSPF 43
44 SSPF Verification: FSS Best fraction skill scores (higher is better) for each field UH UU RF UP DN Thresh # FSS UU and RF less skillful than UH, UP, DN (consistent with ROC curve areas). UP and DN maxima occurred with mid-range threshold. 44
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