Implementation and Evaluation of WSR-88D Reflectivity Data Assimilation for WRF-ARW via GSI and Cloud Analysis. Ming Hu University of Oklahoma
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1 Implementation and Evaluation of WSR-88D Reflectivity Data Assimilation for WRF-ARW via GSI and Cloud Analysis Ming Hu University of Oklahoma
2 1. Previous work and Goal of visiting Previous work: Radar reflectivity in convective-scale assimilation and mode forecast system Analyzed through a complex cloud analysis procedure Horizontal resolution: 3-km or 1-km Critical for capturing the behavior of individual cells in shortterm forecast (2-3 hours) Within ARPS system My visiting: Impacts of radar reflectivity in operational analysis and forecast system Cloud analysis is used with GSI and WRF-ARW model 9-km horizontal grid Relatively larger scale weather system, such as storm cluster
3 2. Introduction to cloud analysis package Cloud analysis can Build up hydrometers fields (cloud water, cloud ice, rain, snow, and hail) Adjust in-cloud moisture and temperature in initial fields reduce spin-up problem in short-term forecast Cloud and precipitation information surface cloud observations (GOS, METARs) radar reflectivity data satellite data Background ( temperature, moisture)
4 2.Introduction to cloud analysis package - in-cloud temperature adjustment Latent heat scheme: Based on latent heat Δ θ = β θ Lv( Δ qc +Δqi) c π p Moisture adiabatic scheme: Based on a moist-adiabatic temperature profile Heating is applied only in updraft regions The effect of entrainment is considered
5 3. Systems used in experiment GSI Analyze conventional data and provide background fields for cloud analysis WRF-ARW Forecast WRF2ARPS and ARPS2WRF Exchange fields between WRF-ARW grid and ARPS grid 88D2ARPS Process radar data: quality control and mapping reflectivity from radar coordinate into ARPS grid
6 4. 23 May 2005 Central Plains Storm Cluster Case Initiation stage: 03Z Mature stage: 04~11Z Decay stage : after 11Z Dissipated by 13Z
7 23 May 2005 Central Plains Storms -Environment 500 hpa at 0000 Z May 23, 2005 Surface map at 04:19 Z May 23, 2005
8 23 May 2005 Central Plains Storms -Environment (continued) SRH shear at 0000 Z May 23, 2005 CAPE/CINH at 0000 Z May 23, 2005
9 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :02 Z
10 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :04 Z
11 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :01 Z
12 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :03 Z
13 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :00 Z
14 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :01 Z
15 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :03 Z
16 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :00 Z
17 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :02 Z
18 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :03 Z
19 23 May 2005 Central Plains Storms -Radar base reflectivity 05/23/ :32 Z
20 5. Experimental Design
21 Grid, domain, and initial time Horizontal 9 km and vertical 30 levels Domain Background and boundary condition come from NAM model forecast Analysis conducted at 0600 UTC May 23, 2005 Computation domain study domain Background available Storm are well captured by radars around
22 Radar base reflectivity at 0603 Z with radar data Reflectivity from 5 radars were used in the cloud analysis KVNX KSGF Only WSR-88D LEVEl-II data were used in the cloud analysis KTLX KINX KSRX
23 Three Experiments Background Interpolated from NAM ARW forecast background GSI Analysis with conventional observations background Cloud analysis with radar reflectivity 06 Z 12 Z
24 6.Experiment Results Initial condition Forecast
25 Effects of Cloud Analysis on Initial Conditions - observed reflectivity (km) (km) Observed composite reflectivity from radars around storm cluster Cross-section of observed reflectivity at 0600 Z at 0600 UTC
26 Effects of Cloud Analysis on Initial Conditions -temperature, hydrometers analysis 12 Ref qc MIN=0.0 MAX=1.7 inc=0.4 Cloud water qi Cloud ice MIN=0.0 MAX=8.8 inc= ptprt (K) qs qr Rain 0.8 MIN=0.0 MAX=1.3 inc= Δθ MIN=-1.7 MAX=8.0 inc= Snow MIN=0.0 MAX=8.6 inc= qh Hail MIN=0.0 MAX=1.3 inc=
27 Evolution of Predicted Storms -composite reflectivity at half hour intervals from 06 Z to 12 Z Observation Cloud Analysis GSI only Background
28 1 hour forecast (07 Z) Observation Cloud Analysis GSI only Background
29 2 hour forecast (08 Z) Observation Cloud Analysis GSI only Background
30 3 hour forecast (09 Z) Observation Cloud Analysis GSI only Background
31 4 hour forecast (10 Z) Observation Cloud Analysis GSI only Background
32 5 hour forecast (11 Z) Observation Cloud Analysis GSI only Background
33 6 hour forecast (12 Z) Observation Cloud Analysis GSI only Background
34 5 hour forecast valid at 11 Z -Individual Cells comparison Predicted low level reflectivity Observed low level reflectivity
35 7. Summary Successfully build up storms in initial fields Significantly reduce spin-up problem and improve short-term forecast for storm cluster Predicted storm cluster sustains longer than observation Need tuning cloud analysis to fit the larger scale system ARW seems to keep storms lasting longer The explicit microphysics was used in forecast
36 8 Work since visiting Cooperation with GSD scientists closely to incorporate both RUC and ARPS cloud analysis packages into GSI Building GSI framework outside original GSI to add cloud analysis packages Generating backgrounds for RUC and APRS DATA prepare NESDIS products, NSSL mosica reflectivity, METAR from prepbufr Add RUC cloud analysis within GSI framework Add ARPS cloud analysis within GSI framework
37 Plan in near future A generalized cloud analysis based on both RUC and ARPS cloud analysis for GSI analysis New schemes Adjusting in-cloud temperature to suppress fake storms Using cloud and precipitation type observations from Metar data Lightning data
38 Acknowledgment The results of this presentation is fully supported by DTC. GSD s provide computer source (JET), GSI systems and helpful discussions.
39 Comments?
Implementation and evaluation of cloud analysis with WSR-88D reflectivity data for GSI and WRF-ARW
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