Download - The Use of AIRS Profiles in Short-term Weather Forecasts: A Case for Enhanced Quality Indicators
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
The Use of AIRS Profiles in Short-term Weather Forecasts: A Case for Enhanced
Quality Indicators
Gary JedlovecNASA / 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
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
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 short-term (0-24 hr) weather prediction at the regional and local scale (http://weather.msfc.nasa.gov/sport/)
Transition research capabilities to operationso real-time MODIS data and products to 6 NWS forecast offices
o twice daily WRF model output (initialized with MODIS SSTs)- operationalo convective initiation / lightning products for nowcasting severe weather
Development of new products and capabilities for transitiono MODIS SST compositeso radiance data assimilation w/ filtered radiances (NASA Fellowship student)o assimilation of AIRS profiles into SPoRT WRF
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
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
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
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 WFOso LAPS (previous experience with surface fields)o AIRS Vers.3.6 un-validated soundings (mainly over land)o Limited quality flags
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 AIRS Vers.4.0 validated soundings – T & q ocean profiles onlyo T - quality flags important for proper data assimilation
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
January 14-16, 2004 Case Study
Slow moving synoptic system off west coast – in-adequate forecasts with conventional models
Infrared image on 14 January 2004
2141 UTC
2329 UTC
Case selectiono weather system over oceano varied cloud covero coverage from AIRS – multiple assimilation timeso availability of AIRS version 4.0 profileso applicable to SPoRT SEUS situations (data void over Gulf)
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
4h WRF Forecast
SPoRT Research WRF for AIRS Assimilation
ADASADAS
18 UTC 00 UTC 00 UTC
2h WRF Forecast
22 UTC
30km domain with 37 vertical levels
Dynamics and Physicso Eulerian mass coreo Dudhia SW radiationo RRTM LW radiationo YSU PBL, Noah LSMo Ferrier microphysicso Kain-Fritsch
Initialized with NCEP 1° GFS grids, with 6-h forecasts used as LBC
Assimilation / forecast
2329UTC2141UTC
WRF Forecast Domain
Validation region
Validation every 12 h
48h WRF Forecast initialized from ADAS analysis at 00 UTC
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
WRF Forecasts with AIRS Profiles
Temperature RMSE
0 1 2 3 4 5
925
850
700
500
400
300
250
200
150
AIRS
ctl
Temperature Bias
-10 -8 -6 -4 -2 0 2 4 6 8 10
925
850
700
500
400
300
250
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150
AIRS
ctl
Initial case studies indicate positive impact of AIRS T / q at most levels for 12-48h forecasts
Full and surface flag retrievals
temperature -o 0.2-1.0K improvement in bias - most levelso 0.5K reduction in RMS
moisture - o improvement variedo uncertain performance in lowest levels
Performance varies with quality of AIRS
profiles used in ADAS
AIRS improves WRF short-term forecasts of temperature and moisture
Temperature improved
at most levels
Temperature improved
at most levels
Based on full and sfc flagged retrievals
24h forecast
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
AIRS Data – January 14-16, 2004
Retrieval QA Flags (Vers. 4.0)
Sfc+Bot+Mid flaggedAll flaggedNo retrieval
Full retrievalSfc flaggedSfc+Bot flagged
Temperature and moisture profileso ~ 50km spacingo profiles assigned quality values by science teamo V4.0 temperature quality flags
Quality indicators o identify retrieval processo layer quality checks
Full and surface flag retrievals
inside domain % of totalFull retrieval 627 15Sfc failed 1122 26Sfc+Bot failed 518 12Sfc+Bot+Mid failed 751 17All levels failed 1275 30
Distribution of AIRS profiles by QI
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
AIRS Data Quality Indicators
Quality indicators
o identify retrieval processo layer quality checks
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
Retrieval variations based on QI
RED = Full RetrievalGREEN = SFC+B+M flaggedBLUE = All flagged
Retrievals w/in 100km of FULL
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
obsn
ii
obsixixx kkk
1
)()()1(
ADAS Bratseth Method
)1( kx
Used iteratively to update a first-guess (or background) field provided by a model forecast. The correction, , at each grid point is given by
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), [i
obs - 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
and is proportional to
where rij and Δzij - horizontal / vertical distances between obs. and grid R and Rz - horizontal and vertical scaling factors.
2
2
2
2
expexpz
ijij
R
z
R
r
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
ADAS and AIRS Data Example Assimilation
AIRS analysis
ADAS Background
Impact of DA
Bckgrd+AIRS+MADIS
AIRS assimilated 850mb T at 2200UTC on 14 January 2004 - 4h WRF as backgroundAn ADAS example:
AIRS data assimilated with 4h WRF forecast as backgroundo AIRS in first two iterations with coarse vertical and horizontal influence factorso other data (mainly ACARS, sfc, and few special raobs) assimilated in other iterationso AIRS error tables with realistic vertical variations and more influence than background
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
ADAS Horizontal and Vertical Resolution Factors
Data Assimilated Vert. Scale (m) Horiz. Scale (km)Pass 1 AIRS, RAOB, WPF 750 180Pass 2 AIRS 750 120Pass 3 RAOB, ACARS, WPF 400 100Pass 4 ACARS, BUOY, METAR, SAO N/A 80Pass 5 BUOY, METAR, SAO N/A 60
Resolution factors can control influence of AIRS data on resulting assimilated field
o select factors consistent with AIRS vertical and horizontal resolutiono relative magnitude w.r.t other assimilated data is important
ADAS Resolution Factors used with AIRS Profiles
Influence of AIRS varies with ADAS constraints
ADAS converges towards AIRS data
Vertical Resolution Factor Changes
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
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 vertical resolution and accuracy of AIRS – varies between T, qo interplays with vertical/horizontal influence factors
Temperature Moisture
AIRS values taken from V4.0 validation results
Data source weights used in ADAS – no raob
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
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
o 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
Can we adjust assimilation to minimize negative - maximize positive impact?
WRF forecast verification @ 24h by AIRS data type
Mixing Ratio RMSE - 04011600_W115
0 0.2 0.4 0.6 0.8 1 1.2 1.4 1.6 1.8 2
925
850
700
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200
150
OPTIMAL OPT_FULL_SFC OPT_FULL_SFC_BOT
QI sfc and bottom degrade
forecast
Temperature RMSE - 04011600_W115
0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6
925
850
700
500
400
300
250
200
150
OPTIMAL OPT_FULL_SFC OPT_FULL_SFC_BOT
QI sfc and bottom improve
mid-level forecast
OPTIMAL –full retrievals
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
Temperature
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 valueso separate quality indicators for temperature and moisture
Example error profile for ADAS for AIRS data flagging low-level temperature check
Sfc+Bot+Mid flaggedAll flaggedNo retrieval
Full retrievalSfc flaggedSfc+Bot flagged
Science Mission DirectorateNational Aeronautics and Space Administration
transitioning unique NASA data and research technologies to the NWS
SummaryPreliminary 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 smoothingo 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 qualityo maximize use of all AIRS retrievals
Need more quality indicators, especially for moisture
Future work:o refine use of profiles in ADAS based on AIRS quality indicators (v5.0?)o forecast improvement – basic parameters and skill scoreso additional case studies are being selected – Gulf coast