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



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



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



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



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)



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



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

200

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



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



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



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



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



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



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



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

500

400

300

250

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



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



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

Documents

The Use of AIRS Profiles in Short-term Weather Forecasts: A Case for Enhanced Quality Indicators