A Rapid Prototyping Capability Experiment to
Evaluate CrIS / ATMS Observations for
a Mesoscale Weather Event
Robert Moorhead, Valentine G. Anantharaj, Xingang Fan,
Christopher M. Hill, Patrick J. Fitzpatrick, and Yongzuo Li
(Mississippi State University)
Michiko Masutani and Lars-Peter Riishojgaard
(Joint Center for Satellite Data Assimilation)
March 2, 2009
1
General Overview
• National Polar-orbiting Operational Environmental Satellite System
(NPOESS) to be launched in 2013
• preceding NPOESS Preparatory Project (NPP) to be launched in 2010
• component sensors of NPOESS and NPP to include:
– Advanced Technology Microwave Sounder (ATMS)
• across track scanning microwave radiometer
• cross-track resolution: 15.0 km
– Cross-track Infrared Sounder (CrIS)
• Fourier transform spectrometer
• cross-track resolution: 14.0 km
• vertical resolution:
~ 3.0 km
• Combination of ATMS / CrIS should improve profiles of T and q
• Mesoscale OSSE needed to assess the impact of ATMS / CrIS
2
2
NWP OSSE Methodology
ECMWF
(ATMS / CrIS)
adapted from NCEP model of OSSE
3
3
Mesoscale Case event identified from ECMWF T799 NR
May 2 - 4: squall line affecting all points along US Gulf coast
3-hour total of precipitation (mm)
12 UTC 2 May
18 UTC 2 May
00 UTC 3 May
mean-sea-level barometric pressure (hPa)
4
MSU OSSE Approach
• Objective: investigate the impact of new satellite data observations on
the numerical model representation of a regional weather event
• Case event: squall line system of thunderstorms affecting the Gulf
Coast
• Regional Scale Nature Run (RSNR) using MM5 model
– one-way nested grid set, with ICs and BCs from ECMWF NR data
• Sensitivity experiments
– use second model (WRF) to avoid similarity in model error
(fraternal twin problem)
– WRF model calibrated with background errors
• Steps involved in data assimilation experiments
1) Cold start simulation (no data assimilation)
2) First assimilate synthetic surface, rawinsonde observations
3) Then assimilate data representing ATMS / CrIS
• use RTM to simulate sensor-observed radiance, assimilate
derived T and q profiles
5
Region Scale Nature Run (RSNR)
ECMWF NR grid (global)
MM5 NR grid (regional)
L
forecast hour-15
surface temperature
•
•
The MM5 simulates the virtual
atmosphere, based on the ECMWF NR,
within a limited area
forecast hour-15
1-hour precipitation
MM5 RSNR performed over duration of
the weather event, or about 72 hours
6
Map image adopted from Shuttle Radar Topography Mission
Data Assimilation Experiments (1)
•
ECMWF NR grid (global)
boundary conditions (BCs) restrain
the solution of the inner model
MM5 NR grid (regional)
MM5 NR
grid
MM5 nest grid
L
MM5
nest
ECMWF NR
grid
unperturbed BC
unperturbed BC
WRF
nest
WRF nest grid
•
perturbed BC
•
As the MM5 nest simulates the virtual
atmosphere, the WRF nest simulates a
realistically imperfect solution of the atmosphere
•
Simulation of the nest grids provides greater detail of the weather event, which is
necessary for creating synthetic satellite observations from MM5 and assimilating
these observations into the WRF model
BCs from ECMWF NR and MM5
RSNR are modified to prevent
“contamination” of WRF results
7
Map image adopted from Shuttle Radar Topography Mission
Data Assimilation Experiments (2)
ECMWF NR grid (global)
MM5 NR grid (regional)
MM5 nest grid
L
MM5 forecast hour-15
1-hour precipitation
WRF nest grid
•
The assimilation of ATMS/CrIS data should
improve the WRF solution and reduce
forecasting error relative to MM5 results (truth)
•
Specific fields, such as precipitation, will be evaluated for impact from the synthetic
ATMS/CrIS dataset through model experiments that include, and exclude, this
dataset
WRF forecast hour-15
1-hour precipitation
8
Map image adopted from Shuttle Radar Topography Mission
Case event from ECMWF T799 nature run
Simulated squall line is focus of our regional nature run
(T799 NR)
9
Regional Scale Nature Run (RSNR) with MM5
•
•
•
9-km domain 521 × 553 × 30
3-km domain 511 × 661 × 30
One-way nesting from 9- to 3-km grid
•
Initial and boundary conditions from
ECMWF T799 NR
•
Integration period:
00 UTC 02 May to 00 UTC 04 May
MM5 9 km
MM5 /WRF
3 km
• Parameterizations:
–
–
–
–
simple ice microphysics
Blackadar PBL
Kain-Fritsch cumulus (9-km domain)
no cumulus scheme (3-km domain)
10
Simulation Results of MM5 9-km grid of RSNR
10-m wind
hourly precipitation
11
Simulation Results of MM5 3-km nest grid of RSNR
10-m wind
Precipitation
12
Sensitivity experiments with WRF
•
3-km domain 511 × 661 × 30
•
Initial and boundary conditions from
MM5 9-km [ regional nature run ]
•
One-way nesting from MM5 9-km grid
•
Integration period (cold start run):
00 UTC 02 May to 00 UTC 04 May
MM5 9 km
MM5 /WRF
3 km
• Parameterizations:
– no cumulus scheme
– simple ice microphysics
– YSU PBL
•
Synthetic observations assimilated using
WRF-VAR during 00 UTC to 12 UTC
02 May
13
Model background error calculation
•
background errors were calculated between successive 24-h WRF simulations
initialized with ECMWF T799 data from April 12 – May 14
•
errors of a quantity are computed between different forecast cycles
(Parrish & Derber 1992)
•
background errors used to calibrate the experimental WRF simulations
12 UTC (Day 1)
00 UTC (Day 2)
12 UTC (Day 2)
00 UTC (Day 3)
00 UTC (Day 1)
12h forecast
24h forecast
-
-
12 UTC (Day 1)
-
12h forecast
24h forecast
-
00 UTC (Day 2)
-
24h - 12h error
12h forecast
24h forecast
12 UTC (Day 2)
-
-
24h - 12h error
12h forecast
00 UTC (Day 3)
-
-
-
24h - 12h error
15
WRF 3DVAR Cycling and Simulation
Synthetic observations are extracted from MM5 3-km grid simulation.
36 hours
00Z02 03Z02 06Z02 09Z03 12Z02
WRF 3DVAR cycling
00Z 04
WRF simulation
From 00Z 02 to 12Z 02 WRF 3DVAR cycling is made to assimilate “true data” of synthetic
observation from MM5 3-km.
16
Data Assimilation – Synthetic Observations
Synthetic observations are taken from MM5 3-km grid.
Synthetic satellite coverage
02 May
02 May
02 May
02 May
02 May
00 UTC
03 UTC
06 UTC
09 UTC
12 UTC
SFC
UA
SFC
SFC
SFC
SAT
SFC
UA
WRF 3DVAR cycling every 3 hrs
Sites of synthetic UA soundings
5-minute
ATMS / CrIS swath
Sites of synthetic surface obs
17
In Progress: Satellite data assimilation
Synthetic satellite observations of T and Td are taken from MM5 3-km grid.
Synthetic satellite coverage
02 May
02 May
02 May
02 May
02 May
00 UTC
03 UTC
06 UTC
09 UTC
12 UTC
SFC
UA
SFC
SFC
SFC
SAT
SFC
UA
WRF 3DVAR cycling every 3 hrs
5-minute
ATMS / CrIS swath
Procedure:
Synthetic ATMS / CrIS data available within 3-km grid area
from 0730 UTC to 1000 UTC on 02 May
Synthetic ATMS / CrIS data are thinned to
closely match MM5 3-km grid points
MM5 T and Td are matched to ATMS / CrIS swath points across the
MM5 model grid, and matched to MM5 vertical levels
New synthetic satellite observation dataset is assimilated at
0900 UTC in WRF 3DVAR, with appropriate errors added:
T (± 1K)
Td (± 2K)
18
Comparison of MM5 and WRF Simulations
A comparison of simulation results from MM5, WRF with no data assimilation
(cold start), and WRF with assimilation of synthetic surface and upper-air
observations is made in this section. Only subtle differences are seen between
the MM5 and WRF results.
Hourly precipitation, sea level pressure, and 2-meter dew point temperature
are compared in following slides.
19
MM5
WRF 3DVAR
WRF
18 UTC 02 May:
The locations of precipitation areas simulated by WRF and MM5
are close. However, the patterns of the precipitation areas differ
between WRF and MM5 over the Gulf of Mexico.
22
MM5
WRF 3DVAR
WRF
21 UTC 02 May:
WRF and MM5 each simulate precipitation along the Louisiana
coast. Differences in the precipitation pattern exist over the
Gulf of Mexico and over Tennessee.
23
MM5
WRF 3DVAR
WRF
00 UTC 03 May:
Precipitation fields of MM5 and WRF are close. Rainfall band of
MM5 moves a little faster than that of WRF. The pattern and
position of precipitation area from WRF DA run is more similar to
the WRF cold start.
25
MM5
WRF 3DVAR
WRF
03 UTC 03 May:
Precipitation fields of MM5 and WRF are close. Rainfall band of
MM5 still moves a little faster than that of WRF. The pattern and
position of precipitation area from WRF DA run is more similar
to the WRF cold start.
26
Summary of Results & Discussions
• Successful and fruitful collaboration with JCSDA and the International Joint
OSSE Working Group (J-OSSE WG)
– Access to ECMWF nature runs
– Periodic WG meetings and discussions, including 2 formal presentation about
the ATMS/CrIS OSSE and RSNR to the J-OSSE WG
– 3 conference publications so far
• Design, configuration, and synthesis of RSNR
– New approach with many uncertainties; approach seems to be promising; and
adds to the body of knowledge
• Many challenges in using WRF 3DVAR being addressed systematically
• Satisfactory level of progress
• Still many uncertainties in adopting WRF radiance assimilation; code
release behind schedule at NCAR. Will be ideal to use NCEP GSI; but
requires steep learning curve & beyond scope (recommended for suture
effort).
NASA RPC Review (3/2/09)
27
Summary of Progress
• Completed Tasks
–
–
–
–
–
Mesoscale OSSE experimental design
Regional Scale Nature Run (RSNR - synthetic truth) using MM5
Retrieval of temperature (T) & dew point (Td) profiles
Control run using WRF
Data assimilation experiments using WRF-3DVAR
• WRF background error estimation, necessary for 3DVAR assimilation
• Assimilation of (synthetic) surface and upperair observations
• Preliminary DA run using WRF-3DVAR
• Final Steps (in progress)
– Additional DA runs and validation of DA run(s)
– Error analysis
– Final evaluation (document and publish)
NASA RPC Review (3/2/09)
28
Thank You!
Contact: Valentine Anantharaj <vga1@msstate.edu>
29