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.