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Transcript Transplant ARPS soil-vegetation package to COAMPS
IMPROVING VERY-SHORT-TERM STORM
PREDICTIONS BY ASSIMILATING RADAR AND
SATELLITE DATA INTO A MESOSCALE NWP MODEL
Allen Zhao1, John Cook1, Qin Xu2, and Paul Harasti3
1 Naval Research Laboratory, Monterey, California, USA
2
National Severe Storms Laboratory, Norman. Oklahoma, USA.
3
University Corporation for Atmospheric Research, Boulder, Colorado, USA.
Phone: (831) 656-4700
Fax:
(831) 656-4769
[email protected]
Nowcasting and Data Assimilation
Mesoscale NWP models provide a practical means for nowcasting
•
A physical-based approach
•
Provide all atmospheric parameters for nowcasting convective storms and
other hazardous atmospheric conditions (e.g., low ceiling & visibility)
•
Smooth transition from nowcasting (0-6h) to forecasting (6-72h)
0-6 hour represents a hard period for mesoscale NWP models
•
Inaccurate initial conditions due to the lack of (or poor) observational data
and inadequate data assimilation procedures
•
Imperfectness in model dynamics & physical parameterization
Recent developments in high-resolution data assimilation pave the
way to use NWP models for nowcasting
•
More and more high-resolution data are available from radars, satellites and
other sensors
•
New techniques, such as variational methods and ensemble-based
approaches, have been developed for mesoscale data assimilation
Objective: To study the opportunity and capability of improving 0-6
hour NWP forecasts by assimilation of high-resolution
observational data
Data Assimilation Procedures
Satellite Radar
data reflectivity
Conventional
Observations
3D Cloud
Analysis
NAVDAS
or
qv, qc, qi, qr, qs, qg
Blending
COAMPS®
Forecast
3D Wind
Analysis
COAMPS
Forecast
T, P, Z, U, V, qv
U, V, W, T, P
Radar radial
velocity
is a registered trademark of the Naval Research Laboratory
COAMPS
A Convective Storm Case
A strong convective storm system on 9 May 2003 was
moving southward along the east coast of the United States
The storm system entered the study area at about 1800 UTC
and reached its mature stage at about 2300 UTC
Data from three WSR-88D radars in that area were collected
every 5-minutes
GOES-12 IR and vis data were also collected every 30
minutes
Radar Radius = 150 km
Norfolk, VA
(KAKQ)
Raleigh, NC
(KRAX)
Height (km)
23:08 UTC May 09, 2003
3-D radar reflectivity on COAMPS® grid
(Isosurface = 20 dBZ)
20
18
16
14
12
10
8
6
4
2
0
South – North (600 km)
10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0 50.0 55.0 60.0
Morehead City, NC
(KMHX)
Model domain (100x100, 6km)
is a registered trademark of the Naval Research Laboratory
COAMPS
Experiment Design
Forecast from 12 UTC 9 May
Forecast
CNTL
No Data
Assimilation
Forecast from
12 UTC 9 May
1-hour
forecast
1-hour
forecast
1-hour
forecast
Forecast
CLD
Satellite IR and vis data
19 UTC
Forecast from
12 UTC 9 May
20 UTC
1-hour
forecast
21 UTC
1-hour
forecast
22 UTC
1-hour
forecast
Forecast
CLD+PR
Satellite IR and vis data, Radar Reflectivity
19 UTC
Forecast from
12 UTC 9 May
20 UTC
1-hour
forecast
21 UTC
1-hour
forecast
22 UTC
1-hour
forecast
Forecast
WIND
Radar radial velocity
19 UTC
Forecast from
12 UTC 9 May
20 UTC
1-hour
forecast
21 UTC
1-hour
forecast
22 UTC
1-hour
forecast
Forecast
ALL
Satellite IR and vis, Radar reflectivity and radial velocity
19 UTC
20 UTC
21 UTC
22 UTC
Five experiments have been
conducted:
• CNTL: no radar data assimilation
• CLD: Cloud fields from satellite
observations are assimilated hourly
• CLD+PR: Cloud fields from satellite
observations and precipitations from
radar reflectivity data are assimilated
hourly
• WIND: Radar radial velocity data are
assimilated hourly
• ALL: All these fields are assimilated
hourly
12-hour forecasts were made starting
at 22 UTC 9 May 2003 in all five
experiments
Correlation coefficients and RMS errors of 1-hour forecast radial velocity
(Vrf) verified against radar observations of all scans
(Raleigh radar station, 23:00 UTC 9 May 2003)
0.89
CLD
WIND
CNTL
CLD+PR
ALL
RMS Errors (m/s)
Correlation Scores
CNTL
CLD+PR
ALL
0.84
CLD
WIND
6
5
0.79
0.48 1.49 2.37 3.38 4.26 5.31 6.24 7.47 8.65 9.97 13.9716.6919.46
4
0.48 1.49 2.37 3.38 4.26 5.31 6.24 7.47 8.65 9.97 13.97 16.6919.46
Radar Elevation Angles (degree)
Radar Elevation Angle (degree)
Wind Forecast Improvements with Forecast Time
RMS Error (m1s-1)
Correlation Coefficient
0.95
CNTL
CLD+PR
ALL
CLD
WIND
9
0.85
Ele. Angle
=
0.75
2.37o
7
0.65
CNTL
CLD+PR
ALL
0.55
1
2
3
0.95
4
5
CNTL
CLD
CLD+PR
WIND
5
1
2
3
CLD
WIND
4
5
11
ALL
0.8
9
Ele. Angle
= 1.49o
0.65
7
CNTL
CLD
CLD+PR
ALL
WIND
5
0.5
1
2
3
Forecast Hour
4
5
1
2
3
Forecast Hour
4
5
Conclusions
The data assimilations affected all dynamical and hydrological fields.
The effects of the implicit latent heat from the assimilated satellite and radar
reflectivity data were seen in the temperature changes and affected the wind
fields significantly.
The data assimilation impacts remained in the forecasts of winds, temperature
and water vapor for several hours, but decreased rapidly in the precipitation
fields as the storm system weakened.
Radar radial velocity assimilation led to the biggest improvement in wind
forecast, while reflectivity assimilation was the major cause of the
improvement in storm location and strength prediction.
The combined data assimilation did not have the best results in each individual
field forecast, but was the best in overall improvement.