1. Convective-Scale Numerical Weather Prediction and Data Assimilation Research At CAPS
A few examples of heavy precipitation forecast
Ming Xue
Director
Center for Analysis and Prediction of Storms and School of Meteorology
University of Oklahoma
September, 2010
ARPS Simulated Tornado

2. Future of NWP – also what’s happening in research and experimental realtime forecast mode at CAPS
Global models running at < 10 km grid spacing,
Continental-scale regional models and their ensembles running at ~1 km grid spacing, resolving individual thunderstorms and localized phenomena, and providing probabilistic information for decision making and response.
Localized nested ensemble prediction systems running at < 1 km grid spacing, for tornado, turbulence, city-scale forecasts.
Typhoon/hurricane track and intensity forecasts are much improved at convection-resolving resolutions
Observations from radar, satellite and in-situ platforms are effectively assimilated into NWP models
Need Peta-flop+ supercomputers!

3. Storm-Scale Convection-Allowing Ensemble and Convection-Resolving Deterministic Forecasting
CAPS/OU has been carrying out a project since 2007 to develop, conduct and evaluate realtime high-resolution ensemble and deterministic forecasts for convective-scale hazardous weather. Forecasts were directly fed to the NOAA HWT (Hazardous Weather Testbed) and evaluated in realtime by forecasters and researchers in an organized effort.
Goals: To determine the optimal design, configurations, and post-processing of storm-scale ensemble prediction, and to provide the products for evaluation by forecasters and researchers, and test storm-scale data assimilation methods.
Spring 2010: 26-member 4-km ensemble and one 1-km forecasts for full CONUS domain. 30-hourly daily forecasts over 7 weeks. Assimilation of data from 120+ radars. Multi-model (WSR-ARW, WRF-NMM and ARPS), multi-physics, perturbed IC and LBC (from SREF).

4. June 14, 2010 OKC Flooding

5. 13h
13Z
hourly accumulated precipitation (mm)
Probability-matched ensemble mean
Max=151mm
Max=71%
Raw probability of hourly
precipitation >0.5 inch
14h
14Z
Max=125mm
Max=71%
Left: Probability-matched ensemble mean hourly accumulated precipitation (mm) derived from the 26-member 4-km ensemble. Forecast range between 13 and 15 hours.
Right: Probability of hourly precipitation >0.5 inch derived from the 4 km ensemble.
15h
15Z
Max=141mm
Max=64%
June 14, 2010 OKC Flooding

6. 1 km WRF-ARW forecasts of composite reflectivity
13h
13Z
Observed radar mosaic reflectivity
14h
14Z
15h
15Z

7. 12–18Z accumulated precipitation: 18h (June 14, 2010 – OKC Flood Day)
SSEF mean
SSEF Prob match
QPE
SREF mean
SREF Prob match
NAM
A major OKC flooding event
SSEF: CAPS 4 km Storm Scale Ensemble Forecasting System
SSEF Probability matched – pattern is based on ensemble mean, magnitude is based on values found in the ensemble forecasts.
SREF: NCEP Short Range ensemble forecasting system with average grid spacing of about 30 km.
NAM: 12 km operational North American Mesocale model
QPE: Precpitation estimate
HWT images

8. 18–0Z accumulated precipitation: 24h (June 14, 2010 – OKC Flood Day)
SSEF mean
SSEF Prob match
QPE
SREF mean
SREF Prob match
NAM
HWT images

9. ETS for 3-hourly Precip. ≥ 0.5 in
2008 (32-day)
2009 (26-day)
With radar
With radar
no radar
no radar
12 km NAM
12 km NAM
Probability-matched score generally better than any ensemble member
2 km score no-better than the best 4-km ensemble member – may be due to physics
1-km score better than any 4-km member and than the 4 km PM score.
Radar data clearly improves precipitation forecasts, up to 12 hours.
High-resolution forecasts clearly consistently better than 12 km NAM.

10. Comparisons of reflectivity GSS (ETS) scores of SSEF, HRRR and NAM for Spring 2010
CAPS SSEF Ensemble PM Mean
CAPS SSEF 1 km Model
CAPS SSEF ARW-CN (control w/o radar assimilation)
CAPS SSEF ARW-C0 (control w/o radar assimilation)
HRRR
NAM
Corollary Lesson:
To provide a “fair” comparison
Between CAPS and HRRR, the
01Z and 13Z runs for HRRR should be used
Precipitation ETS/GSS scores produced independently by DTC for Spring 2010 forecasts. Also included are 12-km NAM from NCEP and 3-km HRRR (High-Resolution Rapid Refresh) from GSD.
Similar conclusions can be drawn as the previous years. The benefit of convection-allowing/convection-resolving resolution, radar data assimilation, ensemble processing (ensemble PM mean) are very clear.

11. Comparison of CAPS 4 km Cn/C Forecasts with McGill 2-km MAPLE Nowcasting System and Canadian 15-km GEM Model
4km with radar
MAPLE
4km with radar
4km no radar
The comparison of CAPS 4-km control forecasts with (cn) and without (c0) radar data shows that with radar data assimilation, the NWP model is able to out-perform higher-resolution state-of-the-art nowcasting system MAPLE after 1 hour. The difference in the first hour was mostly due to the verification data set used. MAPLE was initialized the exactly the same data used for verification.
Madalina Surcel of McGill University compared precipitation forecasts of the control members (Cn with radar data and C0 without radar data) of 4 km ensemble of 2008 by CAPS, against those of 15-km Canadia operational regional model GEM, and 2-km resolution forecast by McGill’s Nowcasting system MAPLE, and show that the skill of the nowcasting system falls below the forecast of of radar-initialized 4 km forecast after about 1 hour, and below that of no-radar 4 km forecast after about 2 hours, and after that of GEM after about 3 hours. With the radar data assimilation, the 4 km forecast is superior than the no-radar 4 km and 15-km GEM for nearly 20 hours.
Correlation for reflectivity
CSI for 0.2 mm/h
Courtesy of Madalina Surcel of McGill U. (Surcel et al Radar Conf.)

12. High-Resolution Numerical Simulations of Typhoon Morakot (2009)

13. ARPS 2.5 km Forecast of Composite Reflectivity for Morokat with GFS IC + Radar
By simply initializing the ARPS using GFS global analysis, ARPS is able to predict rainbands assocaited with Typhoon Morakot that is rather close to radar observations.
The assimilation of radar data improves the initial forecasts further (not shown).
obs

14. Hourly Accumulated Precipitation
6 h fcst h fcst h fcst
Radar Precipitation Estimate
observation

15. Total Accumulated Precipitation
6 h fcst h fcst h fcst
1630mm
Radar Precipitation Estimate
24 hour precipitation of 1.63 meter is predicted, compared to 1.53 m observed.
1530mm

16. CAPS Realtime Convection-Allowing-Resolution Hurricane Forecasts
In fall 2010, CAPS is producing experimental single-large-domain 4-km hurricane forecasts over Atlantic
48 hour forecasts twice daily (00 and 12 TC)
Two sets of WRF-ARW forecasts, using GFS and global EnKF analyses and corresponding LBCs. Global EnKF and forecasts produced by Jeff Whitaker of ESRL.
Goals: Assessing convection-resolving model in predicting TC genesis, track, intensity and structure.
Experiment ongoing and systematic evaluation to be performed.

17. 87 h Prediction of Hurricane Earl at 4 km dx
CAPS is producing experiment realtime forecasts for the Atlantic Hurricane season, using a large 4-km grid (
The prediction model is WRF-ARW and the forecasts are initialized using NCEP GFS analysis and experimental global EnKF analyses produced by ESRL.

18. 42 hour forecast valid at 2 pm today
Dx =4 km
1800x900x50 grid