AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky

1. AIRS Profile Assimilation - Case Study results Shih-Hung Chou, Brad Zavodsky Gary Jedlovec, and Bill Lapenta

2. Motivation for Profile Assimilation at SPoRT • The SPoRT Center seeks to improve short-term weather forecasts by the use of satellite-based observation. • AIRS data complement traditional upper-air observations in data-sparse regions (both ocean and land) • In contrast to AIRS radiances, profiles provide an easier assimilation method allowing regional and local end users (e.g. HUN WFO) to run NWP systems • Hyperspectral nature of AIRS sounder allows for high-resolution data

3. AIRS Specifications • Aboard Aqua polar orbiter • Early afternoon equator crossing • 2378 spectral channels • 3.7 – 15.4 μm (650 – 2675 cm-1) • 3 x 3 footprints (50 km spatial resolution) • AMSU allows for retrievals in both clear and cloudy scenes • Version 4.0 Error Estimates (Tobin et al. 2006) • 0.6-1.0K over ocean (± 50o latitude) • 0.9-1.3K global ocean and land (in 1 km layers) • < 15% RH (in 2 km layers)

4. AIRS Data Quality Indicators 0700 UTC 20 November 2005 AIRS swath • Quality indicators (QIs) in prototype v5: • each profile contains level-specific QI • level-by-level error estimates for each T and q profile • QIs allow for the maximum amount of quality data to be assimilated • optimal use of QIs should produce an analysis that provides better initial conditions for the WRF

5. Lessons Learned from Previous SAC • 4 January, 2004 • Pacific storm stalled off shore; limited its impact on land • Difficult to evaluate AIRS impact due to insufficient RAOB stations and stage IV precip datafor verification • Mixed results for AIRS impact on forecast

6. L L L Surface analysis 11/20/05 12 UTC Surface analysis 11/22/05 12 UTC Surface analysis 11/22/05 12 UTC Case Study: November 20-22, 2005 Rapidly intensifying storm off the eastern seaboard under forecasted by GFS, NAM, and SPoRT operational WRF Case Selection • relevant to SPoRT interests in SEUS region • ample verification data available over the Eastern US synoptic setting • opportunity to eventually test both over-ocean and over-land AIRS profiles • comparable CONUS domain to other SPoRT WRF for easy transfer to operational applications

7. WRF Domain for November 2005 Case Study L L L Analysis and Forecast Model Configuration WRF Model Configuration • 36km domain with 150x360 grid • 37 vertical levels • Initialized with NAM analysis, LBC updated every 3 h • ADAS Analysis Configuration • Same horizontal domain as WRF • 43 vertical levels separated by 500 m • AIRS profiles are assimilated as RAOBs using QIs to determine highest quality data • use Tobin et al. (2006) for observation error and standard model errors for background Assimilation / Forecast • 7h forecast used as background for ADAS AIRS valid at 0700 UTC 00 UTC ADAS 7h FCST 11/21/05 00 UTC 00 UTC Validation at 00 UTC and 12 UTC 11/20/05 11/22/05

8. 700 hPa Dew Point Difference AIRS data have a major drying off east seaboard Impact of AIRS Profiles on ADAS Analysis 700 hPa Temp Difference AIRS data have an cooling impact over Atlantic, but a warming impact on land

9. AIRS shows cooling in the lower and upper troposphere • AIRS shows drying above 900 hPa Impact of AIRS Profiles on ADAS Analysis 20 November 2005 Wallops Island, VA 07Z BKGD 07Z AIRS 07Z ADAS

10. AIRS shows mid-troposphere cooling • AIRS correctly detects the moistening of 700-500 hPa layer • AIRS shows drying above 500 hPa Impact of AIRS Profiles on Initial Conditions 20 November 2005 Wallops Island, VA 07Z BKGD 07Z AIRS 07Z ADAS 00Z RAOB 12Z RAOB • AIRS shows cooling in the lower and upper troposphere • AIRS shows drying above 900 hPa AIRS can spatially and temporally fill the gap between conventional observations

11. AIRS cools T by as much as 0.5oC (improvement) in much of troposphere; increases q bias at mid-levels • AIRS reduces RMS error in T and q at most levels Temperature and Moisture Impact • Control is too warm and moist at all tropospheric levels

12. 6-h Cumulative Precipitation Impact • CNTL over-forecast over the low center and under forecast over TN/AL • AIRS improves forecast compared to NCEP Stage IV data in region of heaviest precipitation

13. CNTL AIRS 6-h Cumulative Precipitation Impact Qualitative Precipitation Forecast Bias Score • a measure of precip coverage • Precipitation under-forecasted • CNTL better at middle threshold; AIRS better at high Equital Threat Score • a measure of precip loaction • AIRS outperforms CNTL at most threshold; similar at smallest threshold

14. Summary • AIRS Level-2 profiles provide valuable data over regions otherwise devoid of upper-air observations; they also fill the gap in time between the conventional observations • Level-specific QIs for AIRS profiles allow for the assimilation of the largest volume of highest quality data • AIRS data improves forecasts of T, q, and 6 h precip Future plans involving AIRS • Real-time forecasts to evaluate long-term impact • Select new case studies for in-depth analysis