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Recent and Future Advancements in Convective-Scale Storm Prediction with the High- Resolution Rapid Refresh (HRRR) Forecast System NOAA/ESRL/GSD/AMB Curtis.

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Presentation on theme: "Recent and Future Advancements in Convective-Scale Storm Prediction with the High- Resolution Rapid Refresh (HRRR) Forecast System NOAA/ESRL/GSD/AMB Curtis."— Presentation transcript:

1 Recent and Future Advancements in Convective-Scale Storm Prediction with the High- Resolution Rapid Refresh (HRRR) Forecast System NOAA/ESRL/GSD/AMB Curtis Alexander, David Dowell, Steve Weygandt, Stan Benjamin, Tanya Smirnova, Ming Hu, Patrick Hofmann, Eric James, John Brown, and Brian Jamison 26 th Conference on Severe Local Storms 06 November 2012

2 13km Rapid Refresh (RAP) (mesoscale) 13km RUC (mesoscale) 3km HRRR (storm-scale) High-Resolution Rapid Refresh Experimental 3km nest inside RAP, hourly 15-h fcst Replaced RUC at NCEP 05/01/12 WRF, GSI, RUC features Hourly Updated NWP Models AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 2

3 Radar Reflectivity Data Assimilation Model Forecasts (WRF-ARW) Reflectivity = Diagnostic variable ESTIMATE Hydrometeors are prognostic variables in microphysics scheme(s) Radar Observations (WSR-88D) Reflectivity = SUM(Rain, Snow, Hail, etc…) Not a direct observation of individual hydrometeors and distributions Reflectivity observation type does not correspond to a state variable Options: A.Specification of hydrometeors using some assumptions B.Use implicit condensate formation as a model forcing function C.Combination of A and B D.Others AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 3

4 Latent heating (LH) estimated from radar observed condensate (3-dimensional) Applied during forward step of DFI within 13-km model forecast (RAP) 1.If outside radar coverage use existing model LH 2.If observed reflectivity ≤ 0 dBZ set LH = 0 to suppress convection or precipitation 3.If observed reflectivity > 0 dBZ Replace model LH with value derived from obs reflectivity Don’t apply in planetary boundary layer (PBL) Radar-derived LH applied over forward DFI period LH rate based on 10 min. convective time-scale (we have evaluated 5 min, 20 min, and 30 min) Radar Reflectivity Data Assimilation AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 4

5 3-km Interp Hourly HRRR Initialization from RAP GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst 15 hr fcst 3-km Interp GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst 15 hr fcst 3-km Interp GSI 3D-VAR Cloud Anx Digital Filter 18 hr fcst 15 hr fcst 13 km RAP 3 km HRRR 13z 14z 15z

6 Reflectivity Convergence Cross-Section RAP HRRR RADAR RAP HRRR no radar 00z 12 Aug 2011 00z init

7 Convergence Cross-Section Reflectivity 01z 12 Aug 2011 +1h fcst RAP HRRR RADAR RAP HRRR no radar

8 Eastern US, Reflectivity > 25 dBZ 11-21 August 2011 3km HRRR forecasts improve upon RAP 13km forecasts, especially at coarser scales much better upscaled skill Radar DDFI adds skill at both 13km and 3km CSI 13 km CSI 40 km Radar Reflectivity Verification AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 8

9 3-km Interp Experiment #0: DDFI at 3 km GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst 15 hr fcst 3-km Interp GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst 3-km Interp GSI 3D-VAR Cloud Anx Digital Filter 18 hr fcst 3 km HRRR Digital Filter 15 hr fcst Digital Filter 15 hr fcst Digital Filter 13z 14z 15z 13 km RAP

10 DDFI in 13-km RAP (parent model) AND in 3-km HRRR Eastern US, Reflectivity > 25 dBZ 14-24 July 2010 Application of DDFI at both scales results in spurious convection Significant loss of skill 2x Latent heating rate in RAP 1x Latent heating rate in RAP 1/3x Latent heating rate in RAP 1x Latent heating in RR and HRRR CSI 40 km BIAS 03 km Optimal 2x Latent heating rate in RAP 1x Latent heating rate in RAP 1/3x Latent heating rate in RAP 1x Latent heating in RR and HRRR HRRR Reflectivity Verification AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 10

11 3-km Interp Experiment #1: Cycled Refl at 3 km GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst 3-km Interp GSI 3D-VAR Cloud Anx Digital Filter 1 hr fcst 18 hr fcst GSI 3D-VAR Cloud Anx Digital Filter 18 hr fcst 3 km HRRR 13z 14z 15z 13 km RAP 15 hr fcst 1 hr fcst Refl Obs

12 Experiment #1: LH Specification -60 -45 -30 -15 0 -60-45-30-15 0 Model Pre-Forecast Time (min) Temperature Tendency (i.e. LH) = f(Observed Reflectivity) LH specified from reflectivity observations in four 15-min periods The observations are valid at the end of each 15-min period 1-hr older mesoscale observations Latency reduced to ~1 hr NO digital filtering AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 12 Equation for LH = f(dBZ)?

13 Experiment #1: LH Specification Observed Radar Reflectivity (3-D) Based Latent Heating Model Microphysics Latent Heating 0 1 -60-45-30-150 Model Pre-Forecast Time (min) 0 1 -60-45-30-150 Model Pre-Forecast Time (min) Experiment 1a “Fixed” Experiment 1b “Ramp” 0 Multiple options for weighting LH specification vs model LH Two approaches including: (a)100% specification for the entire pre-forecast hour and (b) Time-varying with linear ramp down to 0% specification at 1 hr Option (b) permits more “free model” behavior for additional DA AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 13

14 0-h fcst without 3-km radar DA Obs 0000 UTC 11 June 2011 0-h fcst with 3-km radar DA (fixed) 0-h fcst with 3-km radar DA (ramp) Benefit from pre-forecast 3-km model integration

15 1-h fcst without 3-km radar DA Obs 0100 UTC 11 June 2011 1-h fcst with 3-km radar DA (fixed) 1-h fcst with 3-km radar DA (ramp) Convective systems more mature even by 1-hr

16 3-day retrospective period June 2011 Forecasts every 2 hours > 25 dBZ Composite Reflectivity Eastern half of US Upscaled to 40-km grid With 3-km fixed radar DA With 3-km ramp radar DA Without 3-km radar DA Bias = 1.0 Native 3-km grid Greatly improved CSI and BIAS between 0-1 fcst hr Benefit persists until 4 hrs Very similar skill at longer lead times HRRR Reflectivity Verification

17 14-day retrospective period June 2011 (160 runs) Forecasts every 2 hours > 25 dBZ Composite Reflectivity Eastern half of US Upscaled to 40-km grid With 3-km ramp radar DA Without 3-km radar DA Bias = 1.0 Native 3-km grid Greatly improved CSI and BIAS between 0-1 fcst hr Benefit persists until 4 hrs Very similar skill at longer lead times HRRR Reflectivity Verification

18 Experiment #2: Full-Cycling at 3 km Cloud Anx 3 km HRRR 13z 14z 15z GSI 3D-VAR 1 hr fcst Cloud Anx GSI 3D-VAR 15 hr fcst Cloud Anx GSI 3D-VAR 15 hr fcst 1 hr fcst AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 18

19 0-h fcst without 3-km radar DA Obs 1800 UTC 29 May 2011 0-h fcst with 3-km radar DA (ramp) Benefit from pre-forecast 3-km model integration 0-h fcst with fully cycled 3-km DA

20 6-h fcst without 3-km radar DA Obs 0000 UTC 30 May 2011 6-h fcst with 3-km radar DA (ramp) 6-h fcst with fully cycled 3-km DA Fully Cycled 3-km DA shows improved structures

21 1-day retrospective period May 2011 (6 runs) Forecasts every 2 hours > 25 dBZ Composite Reflectivity Eastern half of US Upscaled to 40-km grid Bias = 1.0 Native 3-km grid Small sample size Fully-cycled 3-km DA appears promising Runtime for 3-km DA is O(20 min) With fully cycle 3-km DA With 3-km ramp radar DA Without 3-km radar DA AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 21 HRRR Reflectivity Verification

22 Experiment #3: Partial-Cycling at 3km 3 km HRRR 13z 14z 15z 13 km RAP 3-km Interp 1 hr fcst Cloud Anx GSI 3D-VAR 1 hr fcst Cloud Anx GSI 3D-VAR 15 hr fcst Cloud Anx GSI 3D-VAR 15 hr fcst 1 hr fcst

23 Backgroun d Radar Specification of Hydrometeors Scale at which Latent Heating is applied DimensionalityUpdated 2012 HRRR model initialization 13-km RAPNo13-km3-DHourly 2013 HRRR model initialization 13-km RAPNo 3km in 60min spin-up (also using 13km radar-LH-DFI) 3-DHourly Rapidly Updating Analysis (RUA) 3-km HRRR 1 hr fcst YesNone3-DHourly Real-Time Meso Analysis (RTMA) 3-km HRRR 1 hr fcst No None 2-D15 min Time-Lagged HRRR (HCPF) 3-km HRRR Fcsts NoSame as HRRR2-DHourly 3-km HRRR Products AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 23

24 10-11 hr fcst 09-10 hr fcst 08-09 hr fcst 11-12 hr fcst 10-11 hr fcst 09-10 hr fcst Forecasts valid 21-22z 27 April 2011Forecasts valid 22-23z 27 April 2011 All six forecasts combined to form probabilities valid 22z 27 April 2011 HRRR 11z Init HRRR 12z Init HRRR 13z Init Time-lagged Ensemble Spatial radius 45 km Time radius 1 hr UH threshold 25 m 2 /s 2

25 13z + 09hr fcst Valid 22z 27 April 2011 1630z SPC Tornado Probability 27 April 2011 Storm Reports Tornado = Red Dots Tornadic Storm Probability (%) Example: 27 April 2011 Valid 1630-1200 UTC 28 Apr Valid 1200-1200 UTC 28 Apr GSD Program Review13 March 2012High-Resolution Rapid Refresh 25

26 Tornadic Storm Probability (%) Reflectivity (dBZ) 13z + 09hr fcst Valid 22z 27 April 2011 27 April 2011 Storm Reports Observed Reflectivity 22z 27 April Example: 27 April 2011 Tornado = Red Dots Valid 1200-1200 UTC 28 Apr GSD Program Review13 March 2012High-Resolution Rapid Refresh 26

27 13z + 11hr fcst Valid 00z 23 May 2011 1300z SPC Tornado Probability Example: 22 May 2011 Tornadic Storm Probability (%) 22 May 2011 Storm Reports Tornado = Red Dots AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 27

28 Tornadic Storm Probability (%) Reflectivity (dBZ) 22 May 2011 Storm Reports 13z + 11hr fcst Valid 00z 23 May 2011 Observed Reflectivity 00z 23 May Tornado = Red Dots AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 28 Example: 22 May 2011

29 HRRR Case Studies HRRR GSD Program Review13 March 2012High-Resolution Rapid Refresh 29

30 HRRR Case Studies HRRR GSD Program Review13 March 2012High-Resolution Rapid Refresh 30

31 HRRR Case Studies HRRR Tuscaloosa-Birmingham tornadic supercell GSD Program Review13 March 2012High-Resolution Rapid Refresh 31

32 HRRR Case Studies HRRR Tuscaloosa-Birmingham tornadic supercell GSD Program Review13 March 2012High-Resolution Rapid Refresh 32

33 HRRR Case Studies HRRR Tuscaloosa-Birmingham tornadic supercell GSD Program Review13 March 2012High-Resolution Rapid Refresh 33

34 HRRR Case Studies HRRR Tuscaloosa-Birmingham tornadic supercell GSD Program Review13 March 2012High-Resolution Rapid Refresh 34

35 Current – 1 computer running HRRR – NOAA/ESRL – Boulder – Current reliability: 97% for last 12h months (allowing up to 3h gaps) 2012-14 – 2 computers running HRRR – interim solution – Boulder – computer 1 – Fairmont, WV – computer 2 – Expected reliability to increase further to 98.5-99% via coordination of downtimes for Boulder vs. Fairmont computers 2015 – NCEP running HRRR – NOAA/NCEP computing budget – will allow no increase before 2015 Conclusion: Interim HRRR computing for 2012-14 on 2 sites to provide “real-time experimental” HRRR from NOAA for NWS, FAA, DOE/energy users HRRR Transition to NCEP AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 35

36 Moist bias reduced in 2012 RAP and HRRR – Reduced false alarms, lower precipitation bias – GSI enhancements and WRF upgrade to v3.3.1 – Reflectivity diagnostic consistent with microphysics Science: Focus on 3-km assimilation for 2013 – 3-km variational analysis – 3-km non-variational cloud analysis – 3-km radar reflectivity data assimilation Technical: Reduced latency for 2013 (2-3 hrs now) – Approximate 1-hr reduction in execution time (1-2 hrs) – Faster post-processing with parallelization – Direct GRIB2 generation Summary and Plans AMS 26 th SLS Conf06 Nov 2012High-Resolution Rapid Refresh 36

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