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Presentation by Laura Read and Fernando Salas May 5, 2009. Simulating rainfall in the Chehalis river basin using the WRF model. Presentation Outline. Motivation for modeling heavy rainfall Introduction to Weather Research & Forecasting model The Chehalis River Basin and storm event - PowerPoint PPT Presentation
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Presentation by Laura Read and Fernando SalasMay 5, 2009
Presentation Outline Motivation for modeling heavy rainfall Introduction to Weather Research &
Forecasting model The Chehalis River Basin and storm
event Experimental design and methods Model validation Results and conclusions Future work
Motivation: Places Flood Adjusted Flood Damage Costs in the U.S. from 1900-2007
Flood Forecasting System
Weather Research & Forecast Model
(WRF)
HEC-HMS
Flooding
Courtesy of John E. Strack et al.
Climate ChangeEvidence for more rain, stronger storms
IPCC report on regional climate conditions
National recognition among the NRC, NOAA
Uncertainty in current projections
Higher temperatures = More moisture flux and convergence in the atmosphere
A call to action
The Weather Research & Forecasting Model
Who, What, Where, When Simulation Process Developed by NCAR,
NOAA, NCEP, FAA, Air Force and Navy Research
Regional version of a GCM—finer resolution, set boundaries and initializations
Uses physical equations to conduct research, forecast weather (numeric weather prediction)
WRF Post-Processing
ARWpost
http://www.dtcenter.org/wrfnmm/users/docs/user_guide/V3/users_guide_nmm_chap3_files/image001.gif
Storm Event Justification Why Washington?
31 of 43 Disaster Declarations are flood related
NEXRAD storm database lists heavy precipitation events and its associated damage costs
http://www4.ncdc.noaa.gov/cgi-win/wwcgi.dll?wwEvent~Storms
The Chehalis River Basin Basin Area: 2,660 sq
mi Length: 115 mi Annual Average
Discharge: 11,208 cfs200 in/yr
45 in/yr
http://www.chehalislandtrust.org/_borders/MAP1.JPG
http://www.ngdc.noaa.gov/mgg/topo/img/wa.jpg
Chehalis River Basin USGS collects
continuous observational stream-flow data nationwide
Nov 3rd storm event eclipsed flood stage in Chehalis River
2007 and 2008 both had record breaking floods in the South-Central region…is this a trend? http://waterdata.usgs.gov/wa/nwis/dv/?
dd_cd=02_00065_00003&format=img_default&site_no=12027500&set_logscale_y=0&begin_date=20061103&end_date=20061109
Storm Event: Nov 3, 2006 to Nov 9, 2006
http://water.weather.gov/
Storm Event: Nov 3, 2006 to Nov 9, 2006
Storm Event: Nov 3, 2006 to Nov 9, 2006
Storm Event: Nov 3, 2006 to Nov 9, 2006
Storm Event: Nov 3, 2006 to Nov 9, 2006
Storm Event: Nov 3, 2006 to Nov 9, 2006
Storm Event: Nov 3, 2006 to Nov 9, 2006
WRF Domain
43º N
52º N
128º W 112º W
Courtesy of Google Earth
Experimental Design
ParameterizationsCode Cumulus (cu_physics) Code Microphysics (mp_physics)
1 Kain-Fritsch (new eta) 1 Kessler scheme
2 Betts-Miller-Janjic 2 Lin et al
3 Grell-Devenyi ensemble 3 WSM 3-class simple ice
5 New Grell 4 WSM 5-class
5 Ferrier (new Eta)
6 WSM 6-class graupel
Simulation Combinations
Grid Cell Resolution Time Step (sec) Microphysics Schemes Cumulus Schemes Simulation Duration
32 km 180 Kessler, Lin, WSM3, Eta KF, BMJ, GD, NG 5 days
16 km 30 Lin, WSM5, WSM6, Eta KF, BMJ 2.5 days
16 km 60 Lin, WSM5, WSM6, Eta KF, BMJ 2 days, 2.5 days, 3 days
Model Validation
Atmospheric – 32 km NARR Geo-potential height Specific Humidity/Water vapor mixing
ratio Wind and wind direction
Precipitation – 4 km NEXRAD Accumulated rainfall Temporal Distribution Spatial Distribution
Geo-Potential Height: 120 W Over TimeWRF: 01_02 Simulation NARR
Wind and Wind Speed: 700 mb
NARRWRF: 01_02 Simulation at 00z7nov2006
Specific Humidity: Constant Latitude01_02 Simulation:
00z7nov2006 NARR
Pres
sure
(mb)
Pres
sure
(mb)
Pres
sure
(mb)
NEXRAD ValidationProcess of Importing Our
Simulations
Model Validation with NEXRAD: Daily
Model Validation
Atmospheric – 32 km NARR Geo-potential height Specific Humidity/Water vapor mixing
ratio Wind and wind direction
Precipitation – 4 km NEXRAD Accumulated rainfall Temporal Distribution Spatial Distribution
Example of 6-Hour Error Analysis
6 Hours MAX (mm) Error RANGE (mm) Error MEAN (mm) Error STD (mm) Error SUM (mm) Error Score
NEXRAD 95.25000 N/A 95.25000 N/A 37.40030 N/A 15.35030 N/A 24871.20000 N/A N/A
hi_01_02 73.53860 22.79% 68.93870 27.62% 23.34100 37.59% 12.23360 20.30% 3897.95000 84.33% 11
hi_01_04 60.39740 36.59% 49.22840 48.32% 22.54510 39.72% 9.65613 37.09% 3765.03000 84.86% 16
hi_01_05 59.63400 37.39% 54.94480 42.32% 21.75210 41.84% 11.86330 22.72% 3632.60000 85.39% 16
hi_01_06 62.77250 34.10% 51.44550 45.99% 22.77840 39.10% 9.96768 35.07% 3803.98000 84.71% 16
hi_02_02 76.73080 19.44% 74.95240 21.31% 23.60430 36.89% 13.81840 9.98% 3941.92000 84.15% 4
hi_02_04 60.28520 36.71% 51.83900 45.58% 23.33370 37.61% 10.83900 29.39% 3896.73000 84.33% 16
hi_02_05 60.97900 35.98% 56.89180 40.27% 23.57740 36.96% 13.09710 14.68% 3937.42000 84.17% 12
hi_02_06 63.77300 33.05% 54.76460 42.50% 23.58740 36.93% 11.19840 27.05% 3939.10000 84.16% 13
Legend1st
2nd
3rd
Other
Model Validation with NEXRAD: 6 Hours
Parameterization Results Summary 02_02: Betts-
Miller-Janjic, Lin et al.
01_02: Kain-Fritsch, Lin et al.
02_05: Kain-Fritsch, new Eta
Simulations Overall Score
01_02 47
01_04 78
01_05 77
01_06 70
02_02 39
02_04 79
02_05 49
02_06 76
Lin et al. microphysics scheme produced lowest percent errors compared with NEXRAD
Histogram of Precipitation Distribution
Testing for Statistical SignificanceNEXRAD 01_02 Simulation
Test for Homogeneity and T-Stats
F-Test: Datasets are not homogeneous
T-test: reject the null hypothesis (small p)
Model Validation
Atmospheric – 32 km NARR Geo-potential height Specific Humidity/Water vapor mixing
ratio Wind and wind direction
Precipitation – 4 km NEXRAD Accumulated rainfall Temporal Distribution Spatial Distribution
Temporal Distribution Results Source Day 18z6nov200
6 Error 00z7nov2006 Error 06z7nov200
6 Error 12z7nov2006 Error Score
NEXRAD 100.00% 31.27% N/A 32.60% N/A 17.43% N/A 18.70% N/A N/A
hi_01_02 82.68% 32.77% -4.81% 26.11% 19.90% 23.08% -32.41% 18.03% 3.57% 12
hi_01_04 83.53% 33.54% -7.27% 26.95% 17.35% 22.06% -26.55% 17.45% 6.67% 15
hi_01_05 79.02% 33.60% -7.46% 28.91% 11.32% 21.76% -24.84% 15.73% 15.90% 13
hi_01_06 84.63% 33.42% -6.91% 26.88% 17.55% 21.97% -26.02% 17.73% 5.21% 14
hi_02_02 86.89% 34.67% -10.88% 25.59% 21.50% 21.57% -23.73% 18.17% 2.83% 13
hi_02_04 83.63% 35.01% -11.97% 27.59% 15.38% 20.94% -20.10% 16.79% 10.22% 12
hi_02_05 87.59% 33.56% -7.35% 27.09% 16.89% 21.26% -21.95% 18.08% 3.31% 13
hi_02_06 85.06% 34.90% -11.61% 27.20% 16.57% 20.90% -19.90% 17.00% 9.08% 12
Legend1st
2nd
3rd
Other
Time period with the lowest amount of accumulated rainfall (06z7nov2006) has the highest associated percent error
Total Precipitation: 12z6nov2006 to 12z7nov2006
The simulations do not capture the magnitude or the trend of the rainfall over the 24 hours
**Note scale difference
Model Validation
Atmospheric – 32 km NARR Geo-potential height Specific Humidity/Water vapor mixing
ratio Wind and wind direction
Precipitation – 4 km NEXRAD Accumulated rainfall Temporal Distribution Spatial Distribution
Spatial Results: Percent Error
0102 Simulation 0205 Simulation
Spatial Error Analysis: Percent Error6 Hour 12
Hour
18 Hour
24 Hour
Conclusions
We need to improve our model WRF underestimates rainfall for all
parameterizations tested BMJ and Lin et al. model physics
work best Spatial conclusions Need to learn about the dynamics of
the atmosphere
Complete spatial analysis—error maps
Run simulations at higher resolution
With better results, look into feeding modeled precipitation into a river basin model (HEC-HMS)
How do WRF errors translate into stream-flow errors for flood forecasting?
Questions? Comments?
No Comments?
“Do you like statisticians?” **”Probably”