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Improving Cloud Simulation in Weather Research and Forecasting
(WRF) Through Assimilation of GOES Satellite Observations
Andrew WhiteAdvisor: Dr. Arastoo Pour Biazar
Dr. Richard McNider, Dr. Kevin Doty, Dr. Bright Dornblaser
Motivation Goal: To improve the simulated clouds fields in the Air
Quality model system. WRF, Sparse Matrix Operator Kernel Emissions (SMOKE),
Community Multi-scale Air Quality (CMAQ). Clouds greatly impact tropospheric chemistry by altering
dynamics and chemical processes. Regulate photochemical reaction rates Impact boundary-layer development and vertical mixing Impact surface insolation and temperature leading to changes in
biogenic emissions Wet removal Generation of NOx by lightning
Background Errors in simulated clouds is a particular area of concern in State
Implementation Plan (SIP) modeling where the best representation of the physical atmosphere is necessary. Model how an emission control strategy will lead to attainment of the
National Ambient Air Quality Standards (NAAQS) Previous attempts at using satellite data to insert cloud water have
had limited success. Studies have indicated that adjustment of dynamics and thermodynamics
is necessary to support insertion of cloud liquid water in models (Yucel, 2003).
Jones et al., 2013, assimilated cloud water path in WRF and realized that the maximum error reduction is achieved within the first 30 minutes of the forecast.
Assimilation of radar observations miss non-precipitating clouds.
Assimilation Technique Approach: Create a dynamic environment in the WRF that is
supportive of cloud formation and removal through the use of GOES observations.
Makes use of GOES derived cloud albedos to determine where WRF under-predicts and over-predicts clouds.
Developed an analytical technique for determining maximum vertical velocities necessary to create and dissipate clouds within WRF.
Use a 1D-VAR technique similar to O’Brien (1970) to minimally adjust divergence fields to support the determined maximum vertical velocity. Inputs for 1D-VAR: target maximum vertical velocity (Wtarget), target height
for the maximum vertical velocity (Ztarget), bottom adjustment height (ADJ_BOT), top adjustment height (ADJ_TOP)
Description of Over-Prediction Method
Objective: Create subsidence within the model to evaporate cloud droplets.
Determine the model layer with the maximum amount of cloud liquid water (CLW).
Determine the location that a parcel located at ZMaxCLW can be pushed down to so that it evaporates.
1D-VAR Inputs:
ADJ_TOP = Zctop + 1000. [m] ADJ_BOT = Zpar_mod – 1000 [m]
Zctop
Zbase
Zparcel_mod
ADJ_TOP
ADJ_BOT
Ztarget
∆Z
Description of Under-Prediction Method
Objective: Lift a parcel to saturation. Use GOES derived cloud top
temperature and cloud albedo to estimate the location and thickness of the observed cloud.
Use this estimated cloud thickness to determine the minimum height a parcel at a given model location needs to be lifted to reach saturation.
1D-VAR Inputs:
ADJ_TOP = + Cloud DepthADJ_BOT = Zpar_mod – 1000 [m]
ZSaturation
Zparcel_mod
ADJ_TOP
ADJ_BOT
∆Z
WRF Configuration Domain 1 Domain 2
Running Period August, 2006
Horizontal Resolution 36 km 12 km
Time Step 90s 30s
Number of Vertical Levels
42
Top Pressure of the Model
50 hPa
Shortwave Radiation Dudhia
Longwave Radiation RRTM
Surface Layer Monin-Obukhov
Land Surface Layer Noah (4-soil layer)
PBL YSU
Microphysics LIN
Cumulus physics Kain-Fritsch (with Ma and Tan 2009 trigger function)
Grid Physics Horizontal Wind
Meteorological Input Data
EDAS
Analysis Nudging Yes
U, V Nudging Coefficient
3 x 10-4
T Nudging Coefficient 3 x 10-4
Q Nudging Coefficient 1 x 10-5
Nudging within PBL Yes for U and V, NO for q and T
Agreement Index for Determining Model Performance
= 67.3%
CLOUDY CLEARCLOUDY A BCLEAR C D
Model
GOES
August 12th, 2006 at 17UTCUnderprediction
Overprediction
36 km Results8/
18/
28/
38/
48/
58/
68/
78/
88/
98/
108/
118/
128/
138/
148/
158/
168/
178/
188/
198/
208/
218/
228/
238/
248/
258/
268/
278/
288/
298/
308/
31
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
Agreement Index [36km]36km.CNTRL 36km.Assim
Date
Agreem
ent Ind
ex
8/1
8/2
8/3
8/4
8/5
8/6
8/7
8/8
8/9
8/10
8/11
8/12
8/13
8/14
8/15
8/16
8/17
8/18
8/19
8/20
8/21
8/22
8/23
8/24
8/25
8/26
8/27
8/28
8/29
8/30
8/31
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
Percent Change [36km]
Date
Percen
t Cha
nge
Based on agreement index, the assimilation technique improved agreement between model and GOES observations.
The daily average percentage change over the August 2006 time period was determined to be 14.79%.
36 km Results
8/0
1
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
0
0.2
0.4
0.6
0.8
1
Wind Speed Mean Bias
36km.CNTRL
36km.Assim
Date
Mea
n Bias [m
/s]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
-0.8-0.7-0.6-0.5-0.4-0.3-0.2-0.1
0Temperature Mean Bias
36km.CNTRL
36km.Assim
Date
Mea
n Bias [K
]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
-1.2
-1
-0.8
-0.6
-0.4
-0.2
0Mixing Ratio Mean Bias
36km.CNTRL36km.Assim
Date
Mixing Ra
tio [g
/kg]
36 km Results
8/0
1
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
1.71.81.9
22.12.22.3
Wind Speed RMSE
36km.CNTRL36km.Assim
Date
Error [m/s]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
00.5
11.5
22.5
33.5
4Temperature RMSE
36km.CNTRL36km.Assim
Date
Error [K
]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
0
0.5
1
1.5
2
2.5Mixing Ratio RMSE
36km.CNTRL36km.Assim
Date
Error [g/kg]
August 12th, 2006 – 17UTC
CNTRL AI = 67.3% Assim AI = 82.6%
Assimilation technique shows large gains in agreement index. Very effective at both producing and dissipating clouds.
Cloud AlbedoCNTRL GOES
Assim
Better pattern agreement between assimilation simulation and GOES.
InsolationCNTRL GOES
Assim
Better pattern agreement between assimilation simulation and GOES is also observed for insolation.
12 km Results
Based on agreement index, the assimilation technique improved agreement between model and GOES observations.
The daily average percentage change over the August 2006 time period was determined to be 14.12%.
8/1
8/2
8/3
8/4
8/5
8/6
8/7
8/8
8/9
8/10
8/11
8/12
8/13
8/14
8/15
8/16
8/17
8/18
8/19
8/20
8/21
8/22
8/23
8/24
8/25
8/26
8/27
8/28
8/29
8/30
8/31
0.50
0.55
0.60
0.65
0.70
0.75
0.80
0.85
Agreement Index [12km]12km.CNTRL 12km.Assim
Date
Agreem
ent Ind
ex
8/1
8/2
8/3
8/4
8/5
8/6
8/7
8/8
8/9
8/10
8/11
8/12
8/13
8/14
8/15
8/16
8/17
8/18
8/19
8/20
8/21
8/22
8/23
8/24
8/25
8/26
8/27
8/28
8/29
8/30
8/31
0.00%
5.00%
10.00%
15.00%
20.00%
25.00%
Percent Change[12km]
Date
Percen
t Cha
nge
12 km Results
8/0
1
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
00.10.20.30.40.50.60.70.80.9
Wind Speed Mean Bias
12km.CNTRL
12km.Assim
Date
Mea
n Bias [m
/s]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
0
0.2
0.4
0.6
0.8
1
1.2Temperature Mean Bias
12km.CNTRL
12km.Assim
Date
Mea
n Bias [K
]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
-2.5
-2
-1.5
-1
-0.5
0Mixing Ratio Mean Bias
12km.CNTRL
12km.Assim
Date
Mixing Ra
tio [g
/kg]
12 km Results
8/0
1
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
1.5
1.6
1.7
1.8
1.9
2
2.1Wind Speed RMSE
12km.CNTRL
12km.Assim
Date
Error [m/s]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
00.5
11.5
22.5
3Temperature RMSE
12km.CNTRL12km.Assim
Date
Error [K
]
8
/01
8
/02
8
/03
8
/04
8
/05
8
/06
8
/07
8
/08
8
/09
8
/10
8
/11
8
/12
8
/13
8
/14
8
/15
8
/16
8
/17
8
/18
8
/19
8
/20
8
/21
8
/22
8
/23
8
/24
8
/25
8
/26
8
/27
8
/28
8
/29
8
/30
8
/31
00.5
11.5
22.5
33.5
Mixing Ratio RMSE
12km.CNTRL
12km.Assim
Date
Error [g/kg]
August 12th, 2006 – 17UTC
CNTRL AI = 67.8% Assim AI = 78.6%
Assimilation technique shows large gains in agreement index. Very effective at both producing and dissipating clouds.
Cloud AlbedoCNTRL GOES
Assim
Better pattern agreement between assimilation simulation and GOES.
InsolationCNTRL GOES
Assim
Better pattern agreement between assimilation simulation and GOES is also observed for insolation.
Radiative Impacts
1-Aug
2-Aug
3-Aug
4-Aug
5-Aug
6-Aug
7-Aug
8-Aug
9-Aug
10-Aug
11-Aug
12-Aug
13-Aug
14-Aug
15-Aug
16-Aug
17-Aug
18-Aug
19-Aug
20-Aug
21-Aug
22-Aug
23-Aug
24-Aug
25-Aug
26-Aug
27-Aug
28-Aug
29-Aug
30-Aug
31-Aug
8090
100110120130140150160170
Insolation Gross Mean Error [36km]36km.CNTRL 36km.Assim
Date
Error [W/m
2]
1-Aug
2-Aug
3-Aug
4-Aug
5-Aug
6-Aug
7-Aug
8-Aug
9-Aug
10-Aug
11-Aug
12-Aug
13-Aug
14-Aug
15-Aug
16-Aug
17-Aug
18-Aug
19-Aug
20-Aug
21-Aug
22-Aug
23-Aug
24-Aug
25-Aug
26-Aug
27-Aug
28-Aug
29-Aug
30-Aug
31-Aug
8090
100110120130140150160170180
Insolation Gross Mean Error [12km]12km.CNTRL 12km.Assim
Date
Bias [W
/m2]
Summary & Future Work GOES cloud observations were assimilated into WRF for a
simulation over the August 2006 time period. Overall, the assimilation improved model cloud simulation.
Improved the agreement index between the model and GOES observed clouds.
Improved or maintain model statistics with respect to surface observations of wind speed, temperature and mixing ratio.
Improved insolation statistics with respect to GOES observations.
Assess the usefulness of this technique with respect to air quality forecasting.
