Transcript Document 7285998

Science Mission Directorate
National Aeronautics and Space Administration
The Use of AIRS Profiles in Short-term
Weather Forecasts: A Case for Enhanced
Quality Indicators
Gary Jedlovec
NASA / Marshall Space Flight Center
Bill Lapenta – NASA/MSFC (detailed to HQs)
Brad Zavodsky - Univ. of Alabama
Shih-hung Chou – NASA/MSFC
AIRS Science Team - September 2005
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NASA’s Short-term Prediction and Research
Transition (SPoRT) Center
Mission: Apply NASA measurement systems and unique
Earth science research to improve the accuracy of shortterm (0-24 hr) weather prediction at the regional and local
scale
(http://weather.msfc.nasa.gov/sport/)
Transition research capabilities to operations
o real-time MODIS data and products to 6 NWS forecast offices
twice daily WRF model output (initialized with MODIS SSTs)- operational
o convective initiation / lightning products for nowcasting severe weather
o
Development of new products and capabilities for transition
MODIS SST composites
o radiance data assimilation w/ filtered radiances (NASA Fellowship student)
o
o
assimilation of AIRS profiles into SPoRT WRF
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How We Operate
NASA/MSFC Earth and Planetary Sciences
Branch collocated with UAH and the
Huntsville NWS Forecast Office at the
NSSTC – regular interactions facilitate a
test-bed environment
SMD funded with supporting Applications program initiatives
Problem driven rapid proto-typing and
transitional activity
o provide real-time data and products to
meet NWS forecaster needs
o operational WRF output with MODIS SSTs
o training – product modules, science
sharing with NASA / UAH
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AIRS Data Assimilation in WRF
Establish assimilation methodology and demonstrate short
term weather forecast improvement with AIRS profiles
Initial case studies over SEUS – relevant to SPoRT WFOs
o LAPS (previous experience with surface fields)
AIRS Vers.3.6 un-validated soundings (mainly over land)
o Limited quality flags
o
Previous work: Limited impact (mainly upper level temperature)
Recent initiative – west coast US winter-time storm system
(14-16 January 2004)
o ADAS (flexibility, tunable for unique datasets)
o
o
AIRS Vers.4.0 validated soundings – T & q ocean profiles only
T - quality flags important for proper data assimilation
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January 14-16, 2004 Case Study
Slow moving synoptic system off west coast – in-adequate
forecasts with conventional models
Case selection
o weather system over ocean
varied cloud cover
coverage from AIRS – multiple
assimilation times
o availability of AIRS version 4.0
profiles
o applicable to SPoRT SEUS
situations (data void over Gulf)
o
o
Infrared image on 14 January 2004
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SPoRT Research WRF for AIRS Assimilation
30km domain with 37 vertical levels
Dynamics and Physics
o Eulerian mass core
o Dudhia SW radiation
o RRTM LW radiation
o YSU PBL, Noah LSM
o Ferrier microphysics
o Kain-Fritsch
Initialized with NCEP 1° GFS grids,
with 6-h forecasts used as LBC
WRF Forecast Domain
Validation region
Assimilation / forecast
ADAS
4h WRF
Forecast
18 UTC
ADAS
2h WRF
Forecast
22 UTC
48h WRF Forecast initialized from
ADAS analysis at 00 UTC
00 UTC
00 UTC
Validation every 12 h
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WRF Forecasts with AIRS Profiles
AIRS improves WRF short-term forecasts of temperature and moisture
Temperature Bias
Initial case studies indicate positive
impact of AIRS T / q at most levels
for 12-48h forecasts
Full and surface flag retrievals
150
200
250
300
AIRS
400
Based on full and sfc
flagged retrievals
ctl
500
700
850
temperature o
o
925
0.2-1.0K improvement in bias - most levels
0.5K reduction in RMS
moisture -
-8
-6
-4
-2
0
2
4
6
8
uncertain performance in lowest levels
10
Temperature RMSE
Temperatur
e improved
at most
levels
150
o improvement varied
o
-10
200
250
300
AIRS
400
Performance varies with quality of AIRS
profiles used in ADAS
ctl
500
700
850
925
0
1
2
3
4
5
24h forecast
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AIRS Data – January 14-16, 2004
Temperature and moisture profiles
Retrieval QA Flags (Vers. 4.0)
o ~ 50km spacing
o
o
profiles assigned quality values
by science team
V4.0 temperature quality flags
Quality indicators
o identify retrieval process
o
layer quality checks
Full and surface flag retrievals
Distribution of AIRS profiles by QI
inside domain
Full retrieval
627
Sfc failed
1122
Sfc+Bot failed
518
Sfc+Bot+Mid failed
751
All levels failed
1275
% of total
15
26
12
17
30
Full retrieval
Sfc+Bot+Mid flagged
Sfc flagged
All flagged
Sfc+Bot flagged No retrieval
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AIRS Data Quality Indicators
Quality indicators
o identify retrieval process
o layer quality checks
RED = Full Retrieval
GREEN = SFC+B+M
flagged
BLUE = All flagged
Variations in retrieval “quality”
based on QI flags can be at times
subtle, other times more significant
Reduced quality of profiles seems to
be related to the presence of
overcast conditions
Separate moisture quality indicators
are needed
Retrievals w/in
100km of FULL
Retrieval variations based on QI
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 x (k  1)
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ADAS Bratseth Method
Used iteratively to update a first-guess (or background) field provided by a
model forecast. The correction,  , at each grid point is given by
 x (k  1)   x (k )   xi iobs  i (k )
nobs
i 1
where x(k+1) is the analysis for the kth iteration,
x(k) is the analysis value at the grid point (background value if k =1),
[iobs - i(k)] is the value of the innovations (obs. - bckgrd), and
xi is the weighting function.
The xi is a function of
observation and background error variances (error tables),
distance of the observations from the grid point
 r 
  z 
and is proportional to




exp
exp
2
ij
 R 2 


2


ij
Rz2


where rij and Δzij - horizontal / vertical distances between obs. and grid
R and Rz - horizontal and vertical scaling factors.
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ADAS and AIRS Data Example Assimilation
An ADAS example:
AIRS assimilated 850mb T at 2200UTC on
14 January 2004 - 4h WRF as background
AIRS data assimilated with 4h
WRF forecast as background
AIRS in first two iterations with
coarse vertical and horizontal
influence factors
o other data (mainly ACARS, sfc,
and few special raobs)
assimilated in other iterations
o AIRS error tables with realistic
vertical variations and more
influence than background
o
ADAS Background
Bckgrd+AIRS+MADIS
AIRS analysis
Impact of DA
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ADAS Horizontal and Vertical Resolution Factors
Resolution factors can control
influence of AIRS data on resulting
assimilated field
o select factors consistent with AIRS
vertical and horizontal resolution
o relative magnitude w.r.t other
assimilated data is important
Vertical Resolution
Factor Changes
ADAS
converges
towards AIRS
data
Influence of
AIRS varies
with ADAS
constraints ADAS Resolution Factors used with AIRS Profiles
Pass
Pass
Pass
Pass
Pass
1
2
3
4
5
Data Assimilated
AIRS, RAOB, WPF
AIRS
RAOB, ACARS, WPF
ACARS, BUOY, METAR, SAO
BUOY, METAR, SAO
Vert. Scale (m) Horiz. Scale (km)
750
180
750
120
400
100
N/A
80
N/A
60
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Influence of Data Type in ADAS
While error variances are useful to quantify data errors,
“representativeness” of the data type is important to establish
relative weights of each data input
o
o
vertical resolution and accuracy of AIRS – varies between T, q
interplays with vertical/horizontal influence factors
Data source weights used in ADAS – no raob
Temperature
Moisture
AIRS values taken from V4.0 validation results
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Correlation of AIRS Quality with Model Impact
Inclusion of AIRS retrievals with varying quality (additional QI flags)
negatively affects performance over control run at specific levels
degraded performance at 925 and 850mb for both temperature and moisture
for the 24h forecast (when additional AIRS soundings are used)
o improved performance in middle and upper levels with additional (lower
quality) profiles
o
Can we adjust assimilation to minimize negative - maximize positive impact?
Temperature RMSE - 04011600_W115
Mixing Ratio RMSE - 04011600_W115
OPTIMAL –full retrievals
150
150
200
200
QI sfc and
bottom improve
mid-level
forecast
250
300
400
500
250
300
500
700
700
850
850
925
925
0
0.5
1
1.5
OPTIMAL
2
2.5
3
OPT_FULL_SFC
3.5
4
4.5
5
QI sfc and
bottom degrade
forecast
400
5.5
OPT_FULL_SFC_BOT
6
0
0.2
0.4
OPTIMAL
0.6
0.8
1
OPT_FULL_SFC
1.2
1.4
1.6
1.8
2
OPT_FULL_SFC_BOT
WRF forecast verification @ 24h by AIRS data type
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Vary AIRS Error Tables with Quality Indicators
Can we adjust assimilation to minimize negative - maximize positive
impact? YES!
o need to assign AIRS profiles with different QI flags with different
(more appropriate) error table values
o separate quality indicators for temperature and moisture
Temperature
Example error profile
for ADAS for AIRS
data flagging low-level
temperature check
Full retrieval
Sfc+Bot+Mid flagged
Sfc flagged
All flagged
Sfc+Bot flagged No retrieval
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Summary
Preliminary results show that the assimilation of AIRS profiles have a
positive impact on 0-48h forecasts from the SPoRT WRF
Performance is dependent on:
Configuration of data assimilation scheme (ADAS)
o vertical and horizontal smoothing
o
relative weights of AIRS versus other data sources (and background)
Use of AIRS quality indicators
o vary weights in assimilation system based on variation in
AIRS quality
o maximize use of all AIRS retrievals
Need more quality indicators, especially for moisture
Future work:
refine use of profiles in ADAS based on AIRS quality indicators (v5.0?)
o forecast improvement – basic parameters and skill scores
o additional case studies are being selected – Gulf coast
o
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