USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland

1. USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH Joel Susskind University of Maryland May 12, 2005

2. USE OF AIRS/AMSU DATA FOR WEATHER AND CLIMATE RESEARCH AIRS/AMSU/HSB launched on EOS Aqua May 5, 2002 AIRS is a multi-detector array grating spectrometer 2378 channels between 650 cm-1 and 2760 cm-1 Channel spacing (0.25 cm-1 - 1.1 cm-1) Resolving power (0.5 cm-1 - 2.2 cm-1) Footprint 13 km at nadir 3 x 3 array within AMSU A footprint - collocated with HSB One sounding produced per AMSU A footprint HSB failed on February 5, 2003

3. OBJECTIVESOF AIRS/AMSU Provide data to improve operational weather forecasting Required global accuracy in up to 80% cloud cover: 1 K RMS error in 1 km layer mean tropospheric temperature 20% RMS error in tropospheric 1 km layer precipitable water Provide long-term global coverage of surface and atmospheric parameters Monitor climate variability and trends Study processes affecting climate change Extend 25 year TOVS Pathfinder data set

5. AIRS/AMSU PRODUCTS Surface and Atmospheric Products - one set per FOR (45 km) Sea/land skin temperature Temperature profile T(p) to 1 mb Water vapor profile q(p) to 100 mb O3, CO, CH4 profiles Cloud Cleared Radiances Cloud Products - one set per FOV (13 km) Effective cloud fraction for up to two cloud layers is geometric fractional cloud cover is cloud emissivity at 11 m Cloud top pressure for up to two cloud layers OLR Computed using and retrieved parameters Clear Sky OLR Computed using retrieved parameters with Each product has a quality flag

9. PROPERTIES OF CHANNELS = effective average temperature within weighting function At night radiance is weighted value (brightness temperature) is weighted average between and As decreases, decreases because see less of warm surface and more of . Also decreases as peak of weighting function rises, but increases in stratosphere increases brightness temperature, primarily for Brightness temperatures can be higher than physical temperature

13. OVERVIEW OF AIRS/AMSU RETRIEVAL METHODOLOGY Physically based system Independent of GCM except for surface pressure Uses cloud cleared radiances to produce solution represents what AIRS would have seen in the absence of clouds Basic steps Microwave product parameters – solution agrees with AMSU A radiances Initial cloud clearing using microwave product: produces AIRS regression guess parameters based on cloud cleared radiances Update cloud clearing using AIRS regression guess parameters: produces Sequentially determine surface parameters, T(p), q(p), O3(p), CO(p), CH4(p), using Apply quality control Select retrieved state - coupled AIRS/AMSU or AMSU only retrieval parameters Determine cloud parameters consistent with retrieved state and observed radiances Compute OLR, CLR sky OLR from all parameters via radiative transfer

15. QUALITY FLAGS Order of increasing difficulty to pass Stratospheric Temperature Test - temperature profile good above 200 mb 90% and cloud clearing passes minimal quality control Use coupled AIRS/AMSU retrieval state if Stratospheric Temperature Test is passed Constituent profile test Slightly more stringent cloud clearing quality control Mid-tropospheric Temperature Test - T(p) good above 3 km Tighter quality control on cloud clearing and T(p) convergence flagged good Lower Troposphere Temperature Test - T(p) good above surface SST test - for non frozen ocean only 6) Tight SST test - for non frozen ocean only

25. USE OF AIRS OBSERVATIONS FOR DATA ASSIMILATION Could be (used by ECMWF, NCEP) or T(p),q(p) (used by Bob Atlas) Accuracy of T(p),q(p) degrades slowly with increasing cloud fraction There is a trade-off between accuracy and spatial coverage ECMWF, NCEP uses radiances “unaffected by clouds” Passes internal threshold tests They should try assimilating clear column radiances “unaffected by clouds” Bob Atlas assimilated T(p) for all levels flagged as good Treats AIRS T(p) as radiosonde reports

26. AIRS EXPERIMENTS WITH FVSSI Global data assimilation system used: fvSSI: fvGCM - Resolution: 1x1.25 SSI (NCEP) analysis-T62 Period of assimilation: 1 January - 31 January, 2003 Experiments: Control: All Conventional Data + ATOVS Radiance (NOAA-14, 15, 16) + CTW + SSM/I TPW+ SSM/I Wind Speed + QuikScat + SBUV Ozone Control + AIRSRetrieved Temperature Profiles (Global, passing level quality control) Forecasts: 25 forecasts run every day beginning on January, 6 2003

28. AIRS LEVEL 3 PRODUCTS Different geophysical parameters are gridded according to different tests Stratospheric Temperature Test T(p) 200 mb and above Constituent Profile Test q(p), O3(p), CO(p) at all p Mid-Tropospheric Temperature Test T(p) beneath 200 mb, MSU2R/MSU4, land (including ice and coasts) surface skin temperature (and emissivity) Sea Surface Temperature Test Non-frozen ocean surface skin temperature (and emissivity) Cloud parameters, OLR and clear sky OLR use all AIRS cases

38. Interannual Differences of T(P) AIRS AIRS-ECMWF AIRS-TOVS Jan 2004-2003 Jan 2004-2003 Jan 2004-2003 mean STD mean STD correlation mean STD correlation Interannual Difference of MSU2R/MSU4 AIRS AIRS-Spencer-Christy AIRS-TOVS

43. DATA AVAILABILITY Results shown are based on the AIRS Version 4.0 algorithm We (SRT) currently have results for January 2003 and January, August, September 2004 Goddard DAAC began analyzing AIRS/AMSU data near real time April 1, 2004 DAAC is also processing backwards from March 31, 2004 at 5 days per day Level 1B (radiances), Level 2 (spot by spot retrievals), and Level 3 (gridded) data is available Level 3 is 1°x 1° daily, 8 day mean, and monthly mean Ascending (1:30 pm local time) and descending (1:30 am local time) data are separate To order data to go http://daac.gsfc.nasa.gov/data/datapool/AIRS/index.html Use collection 003 (Version 4.0) Earlier DAAC products (collection 002) used AIRS Version 3.0 Do not use earlier results