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Convection-Permitting Ensemble Forecasts at CAPS for Hazardous Weather Testbed (HWT). Ming Xue Center for Analysis and Prediction of Storms and School of Meteorology University of Oklahoma [email protected] August, 2010. ARPS Simulated Tornado. NOAA Hazardous Weather Testbed (HWT). - PowerPoint PPT Presentation
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Convection-Permitting Ensemble Forecasts at CAPS for Hazardous Weather Testbed (HWT)
Ming XueCenter for Analysis and Prediction of Storms
and School of MeteorologyUniversity of Oklahoma
[email protected], 2010
ARPS Simulated Tornado
NOAA Hazardous Weather Testbed (HWT)
HWT is a facility jointly managed by NSSL, SPC, and NWS Norman WFO
To accelerate the transition of promising new technologies into forecasting and warning for hazardous weather.
HWT organizes annual Spring Experiment that attracts about 100 researchers and forecasters each year.
Provides forecasters with a first-hand look at the latest research concepts and potential future products, and immerses researchers in the challenges, needs, and constraints of forecasters.
HWT Spring Experiment Daily Discussions(pictures from spring 2007)
Storm-Scale Convection-Permitting Ensemble and Convection-Resolving Deterministic Forecasting
CAPS/OU provided CONUS-scale 4-km ensemble & 1-2 km high-res forecasts for HWT Spring Experiment since 2007.
NSSL, EMC, NCAR, and GSD provided additional 3-4 km deterministic forecasts.
Scientific Issues to Address The values and cost-benefits of storm-scale versus coarser-
resolution short-range ensembles versus even-higher-resolution deterministic forecast;
Suitable perturbation methods for storm-scale ensemble, e.g., IC, physics, and model perturbations;
Proper handling and use of boundary perturbations;
The value and impact of assimilating high-res data including those from radars;
The most effective ensemble post-processing and most useful products at the convective scales;
The impact of such unique products on forecasting and warning.
Forecast Configurations of Four Years Spring 2007: 10-member WRF-ARW, 4 km, 33 h, 21Z start time, NAM+SREF
ICs. 5 members physics perturbations only, 5 with Phy+IC+LBC perturbations. Single 2 km grid. 2/3 CONUS (Xue et al.; Kong et al.; 2007 NWP conf.)
Spring 2008: larger domain, 00Z start, Phy+IC+LBC pert for all. Radar Vr and Z data assimilation for 4 and 2 km grids! (Xue et al.; Kong et al. 2008 SLS Conf.)
Spring 2009: 20 members, 4 km, 3 models (ARW, NMM, ARPS), mixed physics/IC/LBCs. Single 1 km grid. Radar DA on native grids. 30 h forecasts from 0Z (Xue et al.; Kong et al. 2009 NWP Conf.)
Spring 2010: 26 4-km and one 1-km forecasts. Full CONUS domain. Some members with physics difference only, and 3 with storm-scale and mesoscale IC perturbations only for studying error growth and predictability.
About 1.5 months each spring season from mid-April through early June
http://forecast.caps.ou.edu.
Configuration of 2007 Ensemble
WRF ARW Model at 4 kmWRF ARW Model at 4 km
Average Domain Total Precipitation
Stage II Obs
Physics only membersAll members
Ferrier/MYJ
Thompson/YSU
WSM6/YSU
Native grids
(Schwartz et al. 2009a,b)(Schwartz et al. 2009a,b)
Areal Coverages
Ferrier/MYJ
Thompson/YSU
WSM6/YSU
Native grids
Domain-mean ensemble spread
- averaged over 38 forecast dates from April 18 to June 7
Local Standard Time: 18 00 06 12 18 00
(Kong et al. 2007)(Kong et al. 2007)
Key Findings from 2007 Experiment Ferrier/MYJ schemes are associated with greater average precipitation
YSU PBL scheme seems to be associated with relatively less precipitation, on average, in combination with WSM6 or Thompson microphysics
Physics only members are under-dispersive for large-scale fields
For precipitation, physics perturbations seem to generate as much spread as IC/LBC perturbations
There is significant high bias for most members especially on the second day
Convective-allowing ensemble clearly out-performs convection-parameterization ensemble in propagation, ETS, statistical consistency, ROC, etc. (Adam Clark’s talk)
2 km forecasts didn’t seem to provide much more value than 4 km forecasts for the second day guidance.
Forecast Configurations of Four Years Spring 2007: 10-member WRF-ARW, 4 km, 33 h, 21Z start time, NAM+SREF ICs.
5 members physics perturbations only, 5 with Phy+IC+LBC perturbations. Single 2 km grid. 2/3 CONUS (Xue et al.; Kong et al.; 2007 NWP conf.)
Spring 2008: larger domain, 00Z start, Phy+IC+LBC pert for all. Radar Vr and Z data assimilation for 4 and 2 km grids! (Xue et al.; Kong et al. 2008 SLS Conf.)
Spring 2009: 20 members, 4 km, 3 models (ARW, NMM, ARPS), mixed physics/IC/LBCs. Single 1 km grid. Radar DA on native grids. 30 h forecasts from 0Z (Xue et al.; Kong et al. 2009 NWP Conf.)
Spring 2010: 26 4-km and one 1-km forecasts. Full CONUS domain. Some members with physics difference only, and 3 with storm-scale and mesoscale IC perturbations only for studying error growth and predictability (Xue et al. 2010; Kong et al. 2010 SLS conf.).
About 1.5 months each spring season from mid-April through early June
http://forecast.caps.ou.edu.
4 km ensemble and 2 km high-res domains
3600 x 2688 km
Movie of 2 km forecast v.s. observations5 minute time intervals
Configuration of 4-km Ensemble
1-h accumulated precipitation
2008
2007
2008
2007
≥ 0.1in, t=12 h≥ 0.1in, t=12 h ≥ 0.01in, t=24 h≥ 0.01in, t=24 h
BIAS comparison 1-h accumulated precipitation ≥ 0.1 in
2008 2007
Bias correction based on first 12 days’ biasbased on ranks for each hour
(a) Sorted 1 h accumulated precipitation, and (b) differences between
members and observation (bias) for the 24 h forecast, averaged over
a 12-day period from April 16 to May 7, 2008.
(a) Sorted 1 h accumulated precipitation, and (b) differences between
members and observation (bias) for the 24 h forecast, averaged over
a 12-day period from April 16 to May 7, 2008. (Kong et al. 2008 SLS)(Kong et al. 2008 SLS)
Bias corrected (for the later 15 days)
Probability matching(Ebert 2001)Probability matching(Ebert 2001)
>0.01 in/h>0.01 in/h >0.1 in/h>0.1 in/h
>0.5 in/h>0.5 in/h > 1.0 in/h> 1.0 in/h
Rank histogram of 1 h accumulated precipitation for 18 h, and 24 h, averaged over 15 days of bias corrected
dates
18 h 24h18 h 24h
(not much improvement to the reliability though)(not much improvement to the reliability though)
ETS comparison 1-h accumulated precipitation ≥0.1 in
2008
2007
Forecast Configurations of Four Years Spring 2007: 10-member WRF-ARW, 4 km, 33 h, 21Z start time, NAM+SREF
ICs. 5 members physics perturbations only, 5 with Phy+IC+LBC perturbations. Single 2 km grid. 2/3 CONUS (Xue et al.; Kong et al.; 2007 NWP conf.)
Spring 2008: larger domain, 00Z start, Phy+IC+LBC pert for all. Radar Vr and Z data assimilation for 4 and 2 km grids! (Xue et al.; Kong et al. 2008 SLS Conf.)
Spring 2009: 20 members, 4 km, 3 models (ARW, NMM, ARPS), mixed physics/IC/LBCs. Single 1 km grid. Radar DA on native grids. 30 h forecasts from 0Z (Xue et al.; Kong et al. 2009 NWP Conf.)
Spring 2010: 26 4-km and one 1-km forecasts. Full CONUS domain. Some members with physics difference only, and 3 with storm-scale and mesoscale IC perturbations only for studying error growth and predictability.
About 1.5 months each spring season from mid-April through early June
http://forecast.caps.ou.edu.
ARPS 3DVAR Analysis Grid
WRF ARW (4 and 1 km) and ARPS forecast grid and common post-processing grid
WRF NMM forecast grid
1 km grid: 3603 x 2691 x 51
ETS for 3-hourly Precip. ≥ 0.5 infrom HWT Spring Forecast Experiments
2008 (32-day) 2009 (26-day)
Probability-matched score generally better than any ensemble member2 km score no-better than the best 4-km ensemble member – may be due to physics1-km score better than any 4-km member and than the 4 km PM score.
Probability-matched score generally better than any ensemble member2 km score no-better than the best 4-km ensemble member – may be due to physics1-km score better than any 4-km member and than the 4 km PM score.
With radar
no radar
12 km NAM
With radar
no radar12 km NAM
BIAS for 1 h precip of 2009 (24-day average)≥0.1 inch/h
12 h forecast of 1 h accumulated precip. ≥ 0.1in
Reliability diagram for precipitation probability forecastReliability diagram for precipitation probability forecast
Reliability is improved by using multiple models
Object-Oriented Precipitation forecasts clusters(by Aaron Johnson)
4
ARW ARPS NMMNo
Radar
Microphysics
ARW NMMARPS
PBL
Forecast Configurations of Four Years Spring 2007: 10-member WRF-ARW, 4 km, 33 h, 21Z start time, NAM+SREF
ICs. 5 members physics perturbations only, 5 with Phy+IC+LBC perturbations. Single 2 km grid. 2/3 CONUS (Xue et al.; Kong et al.; 2007 NWP conf.)
Spring 2008: larger domain, 00Z start, Phy+IC+LBC pert for all. Radar Vr and Z data assimilation for 4 and 2 km grids! (Xue et al.; Kong et al. 2008 SLS Conf.)
Spring 2009: 20 members, 4 km, 3 models (ARW, NMM, ARPS), mixed physics/IC/LBCs. Single 1 km grid. Radar DA on native grids. 30 h forecasts from 0Z (Xue et al.; Kong et al. 2009 NWP Conf.)
Spring 2010: 26 4-km and one 1-km forecasts. Full CONUS domain. Some members with physics difference only, and 3 with storm-scale and mesoscale IC perturbations only for studying error growth and predictability.
About 1.5 months each spring season from mid-April through early June
http://forecast.caps.ou.edu.
2010 Spring Experiment Domains – Full CONUS
3DVAR 1200x780
NMM 790x999
ARW, ARPS & verification 1160x720
member IC BC Radar data microphy LSM PBL
arw_cn 00Z ARPSa 00Z NAMf yes Thompson Noah MYJ
arw_c0 00Z NAMa 00Z NAMf no Thompson Noah MYJ
arw_m3 arw_cn + random pert 00Z NAMf yes Thompson Noah MYJ
arw_m4 arw_cn + RF pert 00Z NAMf yes Thompson Noah MYJ
arw_m5 arw_cn + em-p1 + RF pert 21Z SREF em-p1 yes Morrison RUC YSU
arw_m6arw_cn +
em-p1_pert21Z SREF em-p1 yes Morrison RUC YSU
arw_m7 arw_cn + em-p2_pert 21Z SREF em-p2 yes Thompson Noah QNSE
arw_m8 arw_cn – nmm-p1_pert 21Z SREF nmm-p1 yes WSM6 RUC QNSE
arw_m9 arw_cn + nmm-p2_pert 21Z SREF nmm-p2 yes WDM6 Noah MYNN
arw_m10 arw_cn + rsmSAS-n1_pert 21Z SREF rsmSAS-n1 yes Ferrier RUC YSU
arw_m11 arw_cn – etaKF-n1_pert 21Z SREF etaKF-n1 yes Ferrier Noah YSU
arw_m12 arw_cn + etaKF-p1_pert 21Z SREF etaKF-p1 yes WDM6 RUC QNSE
arw_m13 arw_cn – etaBMJ-n1_pert 21Z SREF etaBMJ-n1 yes WSM6 Noah MYNN
arw_m14 arw_cn + etaBMJ-p1_pert 21Z SREF etaBMJ-p1 yes Thompson RUC MYNN
arw_m15 00Z ARPSa 00Z NAMf yes WDM6 Noah MYJ
arw_m16 00Z ARPSa 00Z NAMf yes WSM6 Noah MYJ
arw_m17 00Z ARPSa 00Z NAMf yes Morrison Noah MYJ
arw_m18 00Z ARPSa 00Z NAMf yes Thompson Noah QNSE
arw_m19 00Z ARPSa 00Z NAMf yes Thompson Noah MYNN
ARW member configuration (19)
For all ARW members: ra_lw_physics= RRTM; ra_sw_physics=Goddard; cu_physics=none
NMM member configuration (5)
member IC BCRadar data
mp_phy lw_phy sw-phy sf_phy
nmm_cn 00Z ARPSa 00Z NAMf yes Ferrier GFDL GFDL Noah
nmm_c0 00Z NAMa 00Z NAMf no Ferrier GFDL GFDL Noah
nmm_m3nmm_cn + nmm-
n1_pert21Z SREF nmm-
n1yes Thompson RRTM Dudhia Noah
nmm_m4nmm_cn + nmm-
n2_pert21Z SREF nmm-
n2yes
WSM 6-class
RRTM Dudhia RUC
nmm_m5 nmm_cn + em-n1_pert 21Z SREF em-n1 yes Ferrier GFDL GFDL RUC
member IC BC Radar data Microphy. radiation sf_phy
arps_cn 00Z ARPSa 00Z NAMf yes Lin Chou/Suarez Force-restore
arps_c0 00Z NAMa 00Z NAMf no Lin Chou/Suarez Force-restore
ARPS member configuration (2)
For all NMM members: pbl_physics=MYJ; cu_physics=none
For all ARPS members: no cumulus parameterization
Members in red contribute to the 15-member sub-ensemble for post-processed product
12–18Z accumulated precipitation: 18h(June 14, 2010 – OKC Flood Day)SSEF mean SSEF Prob match
SREF mean SREF Prob match
QPE
NCEP 12 km NAM
HWT images
12–18Z accumulated precipitation: 18h(May 19, 2010)
SSEF mean SSEF Prob match
SREF mean SREF Prob match
QPE
NAM
HWT images
Gilbert Skill Scores (ETSs) for CAPS’s SSEF (4 and 1 km)ESRL/GSD’s 3 km HRRRNCEP 12 km NAM From 2010 spring experiment
Referred publications from the data Schwartz, C., J. Kain, S. Weiss, M. Xue, D. Bright, F. Kong, K. Thomas, J. Levit, and M. Coniglio,
2009: Next-day convection-allowing WRF model guidance: A second look at 2 vs. 4 km grid spacing. Mon. Wea. Rev., 137, 3351-3372.
Schwartz, C. S., J. S. Kain, S. J. Weiss, M. Xue, D. R. Bright, F. Kong, K. W.Thomas, J. J. Levit, M. C. Coniglio, and M. S. Wandishin, 2010: Toward improved convection-allowing ensembles: model physics sensitivities and optimizing probabilistic guidance with small ensemble membership. Wea. Forcasting, 25, 263-280.
Clark, A. J., W. A. Gallus, Jr., M. Xue, and F. Kong, 2009: A comparison of precipitation forecast skill between small near-convection-permitting and large convection-parameterizing ensembles. Wea. and Forecasting, 24, 1121-1140.
Clark, A. J., W. A. Gallus, Jr., M. Xue, and F. Kong, 2010: Growth of spread in convection-allowing and convection-parameterizing ensembles, In press.
Clark, A. J., W. A. Gallus, Jr., M. Xue, and F. Kong, 2010: Convection-allowing and convection-parameterizing ensemble forecasts of a mesoscale convective vortex and associated severe weather. Wea. Forecasting, Accepted.
Coniglio, M. C., K. L. Elmore, J. S. Kain, S. Weiss, and M. Xue, 2009: Evaluation of WRF model output for severe-weather forecasting from the 2008 NOAA Hazardous Weather Testbed Spring Experiment. Wea. Forcasting, Accepted.
Kain, J. S., M. Xue, M. C. Coniglio, S. J. Weiss, F. Kong, T. L. Jensen, B. G. Brown, J. Gao, K. Brewster, K. W. Thomas, Y. Wang, C. S. Schwartz, and J. J. Levit, 2010: Assessing advances in the assimilation of radar data within a collaborative forecasting-research environment. Wea. Forecasting, Accepted.
Web links to papers and realtime products
Xue, M., F. Kong, K. W. Thomas, J. Gao, Y. Wang, K. Brewster, K. K. Droegemeier, X. Wang, J. Kain, S. Weiss, D. Bright, M. Coniglio, and J. Du, 2009: CAPS realtime multi-model convection-allowing ensemble and 1-km convection-resolving forecasts for the NOAA Hazardous Weather Testbed 2009 Spring Experiment. 23nd Conf. Wea. Anal. Forecasting/19th Conf. Num. Wea. Pred., Omaha, NB, Amer. Meteor. Soc., Paper 16A.2.
Kong, F. M. Xue, K. W. Thomas, J. Gao, Y. Wang, K. Brewster, K. K. Droegemeier, J. Kain, S. Weiss, D. Bright, M. Coniglio, and J. Du, 2009: A realtime storm-scale ensemble forecast system: 2009 spring experiment. 23nd Conf. Wea. Anal. Forecasting/19th Conf. Num. Wea. Pred., Omaha, NB, Amer. Meteor. Soc., Paper 16A.3.
http://forecast.caps.ou.edu
Resources
$125K/year CSTAR funding!
NSF supercomputers. 18,000-core Cray XT-4 at NICS ~5 hours a day in 2010
All data archived (TBs/day) – need to be fully exploited
Collaboration in analyzing the data welcome.
Future Plan (in CSTAR Renewal Proposal) General direction: more emphasis on aviation weather (e.g., 3 weeks in June + May), more
runs/day, shorter forecast ranges, fine-tuning of ensemble design, Multi-scale IC perturbations, ETKF perturbations, EnKF-based perturbations Land surface perturbations, Possible additional LBC perturbations, More intelligent choices of physics suites Addition of COAMPS
Improved initial conditions via more advanced data assimilation Possible GSI analyses with target HRRR set up and other more experimental configurations/schemes Possible hybrid ensemble-GSI analysis Possible EnKF analysis
Post-analysis and probabilistic products: e.g., calibration, bias removal, detailed performance evaluation, cost-benefit/trade off assessment, effective products for end users (e.g., those for aviation weather, severe storms);
Integration/coordination with national mesoscale ensemble efforts (DTC/DET collaborations).
Probabilistic Warn-on-Forecast for Tornadoes- The ultimate challenge – need ~100 m resolution
Probabilistic tornado guidance: Forecast looks on track, storm circulation (hook echo) is tracking along centerline of
highest tornadic probabilities
Radar and Initial Forecast at 2100 CST Radar at 2130 CST: Accurate Forecast
MostLikelyTornadoPath
T=2120 CST
T=2150
T=2130T=2140
70%
50%
30%
T=2200 CST
Developing thunderstorm
NSSL Warn on Forecast Briefing March 5, 2007
MostLikelyTornadoPath
T=2120 CST
T=2150
T=2130T=2140
T=2200 CST
An ensemble of storm-scale NWP models predict the path of a potentially tornadic supercell during the next 1 hour. The ensemble is used to create probabilistic tornado guidance.
70%
50%
30%
(Stensrud, Xue, et al. BAMS 2009)(Stensrud, Xue, et al. BAMS 2009)
NICS, Kraken (~99K cores)
PSC (4K cores)
The Computers Used
For 2010: Exclusive use of a 18,000-core Cray XT-4 at
NICS/University of Tennessee6 hours a day
Thanks!