Warn-on-Forecast Capabilities and Possible Contributions by CAPS

By Ming XueCenter for Analysis and Prediction of Storms and

School of MeteorologyUniversity of Oklahoma

mxue@ou.eduFebruary, 2010

ARPS Simulated Tornado

Capabilities

Storm-scale model development Data assimilation system development Ensemble forecasting Thunderstorm/tornado dynamics and

predictability studies High-performance computing Pre- and post-processing/analysis

Storm-scale model development ARPS model – a complete system optimized to storm-scale applications.

Thunderstorm dynamics, tornadogenesis, and physics process studies

Part of storm-scale multi-model ensemble

Initial test system for integrated scalable peta-scale EnKF/LETKF systems

Prediction tool in CASA realtime forecasting demonstrations

WRF ARW and NMM

Capabilities to initialize the models using storm-scale radar assimilation capabilities

Part of a multi-model ensemble framework

Using ARW in our FAA project for RR and HRRR

Model physics/dynamical/computational frameworks

Advanced microphysics (e.g., multi-moment scheme) and their interaction with radar DA (e.g., dual-pol DA and microphysics parameter estimation)

Parallel pre- and post-processing tools, QC

Ensemble forecasting A multi-model, multi-physics, perturbed IC/LBC

storm-scale ensemble prediction framework and evaluation/demonstration via HWT spring forecast experiments

Research on optimal configuration/perturbation generation techniques (e.g., ETKF)

Ensemble post-processing/calibration

Thunderstorm/tornado-scale ensemble forecasting using EnKF

Data assimilation Experience and expertise in variational data assimilation

ARPS 3DVAR/cloud analysis system, and 4DVAR

Knowledge and experience with GSI 3DVAR system

Experience and expertise in developing and applying ensemble-DA systems

ARPS EnKF system with sophisticated radar data assimilation capabilities (Vr, Z, dual-pol data, parameter estimation, coupling with two-moment microphysics scheme, multi-scale data sources, parallel capabilities)

Developing an EnKF system for RR/HRRR (with ESRL)

Developing an ensemble-var hybrid system based on GSI (with NCEP and ESRL)

NSF Peta-Apps grant to develop a scalable ensemble DA system for peta-scale computers

Opportunities to test these capabilities in realtime via HWT and in CASA.

Thunderstorm/tornado dynamics and predictability studies

Involved in VORTEX-2 NSF tornado dynamics/DA grant

CASA – tornado prediction/process studies/tornado vortex characterization

Convective initiation studies with IHOP cases Sensitivity/predictability studies

High-performance computing Infrastructure development from LEAD

Complete pre-processing/DA/prediction/post-processing capabilities scalable up to 10,000 processors and beyond

NSF Peta-Apps project to develop a scalable ensemble DA system collaborating with CS scientists/supercomputing centers

Completely portable/multi-platform workflow control system for complex realtime forecasting

Access to national supercomputing resources for research and realtime experiments

Experience/capability to develop/optimize scalable parallel systems

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.

With radar

no radar

12 km NAM

With radar

no radar12 km NAM

Comparison of CAPS 4 km Cn/C0 2008 Forecasts with McGill 2-km MAPLE Nowcasting System

and Canadian 15-km GEM Model

Correlation for reflectivity CSI for 0.2 mm/h

Courtesy of Madalina Surcel of McGill U. (Surcel et al. 2009 Radar Conf.)

4km with radar

4km with radar

4km no radar

MAPLE

BIAS for 1 h precip of 2009

≥0.1 inch/h

12 h fcst of 1 h precip. ≥ 0.1in

50-m Grid Forecast v.s. Observation (Movie)

Forecast Low-level Reflectivity Observed Low-level Reflectivity

Movie

43 minute forecast Used ARPS 3DVAR/Cloud analysis DA

Short-Range Radar Initialized Prediction of Thunderstorms, Strong Winds, Gust Fronts,

Downbursts, and Tornadoes using NWP ModelUsing 3DVAR/Cloud Analysis DA

Observed Damage Track v.s Predicted Surface Wind Swaths

Dx = 250 m > 1 hour long track

3 May 1999 F5-Tornado Outbreak in Central Oklahoma

With 3-moment microphysics

Required 3-moment microphysics for the best results

Movie

Anx at2155 UTC

Obs at 0.48° Of OKC radar2155 UTC

40 min fcstat 2235 UTC

Obs at 0.48° of OKC radar2235 UTC

70 x 70 km

ARPS EnKF Results for the May 8, 2003 tornadic case (Lei et al. 2009)

OKC TDWR v.s. 500m Grid 15-min Fcst

Low-level reflectivity from OKC TDWR radar at 2208 UTC, 8 May 2003.

500m forecast Z, Vort and Vectorsat Z= 1km , 2210 UTC, 8 May 2003.

No uniform storm-environment, mesoscale perturbations and mesonet data important

© Patrick Marsh

7:21pm (0021Z)Lawton Tornado

Minco Tornado 10:54pm (0354Z)

Tornadoes of 8-9 May 2007 El Reno tornado

Lawton tornado

Union City tornado

CASA X-band Radar Network – 30 km range

Predictions at z=2km for May 9, 2007

0400UTC, 2h fcst

Minco tornado at 0354Z

0420UTC, 2h 20min fcst 0440UTC, 2h 40min fcst

El Reno tornado at 0443ZUnion City tornado at 0426Z

Vorticity contours from ensemble predictions at z=2km

0400UTC, 2h fcst

Minco tor. at 0354Z

0420UTC, 2h 20min fcst 0440UTC, 2h40min fcst

El Reno tor. at 0443ZUnit City tor. at 0426Z

May 9, 2007 – Ensemble/Probabilistic Forecasting

CNTL case -- CASA and WSR-88D data assimilated using EnKF from 1:00Z to 2:00Z at 5 minute intervals.

Observed tornado location (reported at approximately 3:54Z) is indicated by the red triangle.

(Snook et al. 2010a,b – being submitted)

Planned CASA Forecast Experiment for Spring 2010

(Hour-long forecasts every 10 minutes)

0110 0120 01300100 0140 0150 0200

Rapidly updated forecasts

0210 0220 0230

DA: 3DVAR and later EnKF

Results using ARPS 3DVAR/Cloud Analysis

GFS IC

Results assimilating data from two coastal radars using EnKF for Hurricane Ike (2008)

RR v.s. HRRR for an MCS case – hourly cycling with GSI and radar data

Red: 3 km HRRR, Blue: 13 km RR Using DDFI with radar

Tests with HRRR configuration with hourly updated GSI and radar data

HRRR_DFIRAD - self-cycled HRRR with RR DDFI

RR_DFIRAD –

using RR fcst background and RR DDFI

RR_DFI – using RR background and standard WRF DFI

RR_NoDFI – using RR background but no DFI

EnKF analysis using 2-moment microphysics for May 24, 2004 tornadic thunderstorm case

KOUN Observation Analysis using KTLX data

Reflectivity Z

Diff. Ref. Zdr

Specific diff. phase

Other results

WRF Hybrid-DA system applied to Ike radar DA problem

WRF Hybrid-DA for Ike over ocean

How can CAPS best contribute?

Main areas: Development, testing and inter-comparison of

VAR/EnKF/hybrid DA systems/methods

Assimilation of dual-pol data in combination with advanced microphysics

Design and testing of optimal WoF ensemble forecasting capabilities

Dynamics/process/predictability/sensitivity studies

Realtime forecasting demonstration/evaluation