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

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

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  1. 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

  2. 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 operations • real-time MODIS data and products to 6 NWS forecast offices • twice daily WRF model output (initialized with MODIS SSTs)- operational • convective initiation / lightning products for nowcasting severe weather • Development of new products and capabilities for transition • MODIS SST composites • radiance data assimilation w/ filtered radiances (NASA Fellowship student) • assimilation of AIRS profiles into SPoRT WRF

  3. 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 • provide real-time data and products to meet NWS forecaster needs • operational WRF output with MODIS SSTs • training – product modules, science sharing with NASA / UAH

  4. 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 • LAPS (previous experience with surface fields) • AIRS Vers.3.6 un-validated soundings (mainly over land) • Limited quality flags • Previous work: Limited impact (mainly upper level temperature) • Recent initiative – west coast US winter-time storm system (14-16 January 2004) • ADAS (flexibility, tunable for unique datasets) • AIRS Vers.4.0 validated soundings – T & q ocean profiles only • T - quality flags important for proper data assimilation

  5. January 14-16, 2004 Case Study Slow moving synoptic system off west coast – in-adequate forecasts with conventional models 2141 UTC • Case selection • weather system over ocean • varied cloud cover • coverage from AIRS – multiple • assimilation times • availability of AIRS version 4.0 • profiles • applicable to SPoRT SEUS • situations (data void over Gulf) 2329 UTC Infrared image on 14 January 2004

  6. SPoRT Research WRF for AIRS Assimilation 30km domain with 37 vertical levels Dynamics and Physics • Eulerian mass core • Dudhia SW radiation • RRTM LW radiation • YSU PBL, Noah LSM • Ferrier microphysics • Kain-Fritsch Initialized with NCEP 1° GFS grids, with 6-h forecasts used as LBC Assimilation / forecast WRF Forecast Domain 2141UTC 2329UTC Validation region 48h WRF Forecast initialized from ADAS analysis at 00 UTC ADAS ADAS 4h WRF Forecast 2h WRF Forecast 18 UTC 22 UTC 00 UTC 00 UTC Validation every 12 h

  7. WRF Forecasts with AIRS Profiles AIRS improves WRF short-term forecasts of temperature and moisture Initial case studies indicate positive impact of AIRS T / q at most levels for 12-48h forecasts Full and surface flag retrievals • temperature - • 0.2-1.0K improvement in bias - most levels • 0.5K reduction in RMS • moisture - • improvement varied • uncertain performance in lowest levels • Performance varies with quality of AIRS profiles used in ADAS Based on full and sfc flagged retrievals Temperature improved at most levels Temperature improved at most levels 24h forecast

  8. Distribution of AIRS profiles by QI Sfc+Bot+Mid flagged All flagged No retrieval Full retrieval Sfc flagged Sfc+Bot flagged AIRS Data – January 14-16, 2004 • Temperature and moisture profiles • ~ 50km spacing • profiles assigned quality values • by science team • V4.0 temperature quality flags • Quality indicators • identify retrieval process • layer quality checks Full and surface flag retrievals Retrieval QA Flags (Vers. 4.0)

  9. RED = Full Retrieval GREEN = SFC+B+M flagged BLUE = All flagged Retrievals w/in 100km of FULL AIRS Data Quality Indicators • Quality indicators • identify retrieval process • 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

  10. 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 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 xiis 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.

  11. Bckgrd+AIRS+MADIS ADAS Background AIRS analysis Impact of DA ADAS and AIRS Data Example Assimilation AIRS assimilated 850mb T at 2200UTC on 14 January 2004 - 4h WRF as background • An ADAS example: • AIRS data assimilated with 4h WRF forecast as background • AIRS in first two iterations with • coarse vertical and horizontal • influence factors • other data (mainly ACARS, sfc, • and few special raobs) • assimilated in other iterations • AIRS error tables with realistic • vertical variations and more • influence than background

  12. ADAS Horizontal and Vertical Resolution Factors • Resolution factors can control influence of AIRS data on resulting assimilated field • select factors consistent with AIRS • vertical and horizontal resolution • relative magnitude w.r.t other • assimilated data is important ADAS converges towards AIRS data Vertical Resolution Factor Changes Influence of AIRS varies with ADAS constraints ADAS Resolution Factors used with AIRS Profiles

  13. Temperature Moisture 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 • vertical resolution and accuracy of AIRS – varies between T, q • interplays with vertical/horizontal influence factors Data source weights used in ADAS – no raob AIRS values taken from V4.0 validation results

  14. QI sfc and bottom improve mid-level forecast QI sfc and bottom degrade forecast WRF forecast verification @ 24h by AIRS data type 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) • improved performance in middle and upper levels with additional (lower • quality) profiles • Can we adjust assimilation to minimize negative - maximize positive impact? OPTIMAL –full retrievals

  15. Sfc+Bot+Mid flagged All flagged No retrieval Full retrieval Sfc flagged Sfc+Bot flagged Vary AIRS Error Tables with Quality Indicators • Can we adjust assimilation to minimize negative - maximize positive impact? YES! • need to assign AIRS profiles with different QI flags with different • (more appropriate) error table values • separate quality indicators for temperature and moisture Temperature Example error profile for ADAS for AIRS data flagging low-level temperature check

  16. 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) • vertical and horizontal smoothing • relative weights of AIRS versus other data sources (and background) • Use of AIRS quality indicators • vary weights in assimilation system based on variation in • AIRS quality • 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?) • forecast improvement – basic parameters and skill scores • additional case studies are being selected – Gulf coast

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