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Joanna Joiner Donald Frank, Paul Poli, Arlindo da Silva

Assimilation of satellite data at DAO: cloud- and land-affected sounding data, kilo-channel sounders, and other new data types. Joanna Joiner Donald Frank, Paul Poli, Arlindo da Silva. Outline. Introduction

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Joanna Joiner Donald Frank, Paul Poli, Arlindo da Silva

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  1. Assimilation of satellite data at DAO: cloud- and land-affected sounding data,kilo-channel sounders, and other new data types Joanna Joiner Donald Frank, Paul Poli, Arlindo da Silva

  2. Outline • Introduction • Experiments with TOVS data in next-generation finite-volume data assimilation system • NASA/NOAA collaboration • New satellite data types for assimilation • AIRS radiance data • GPS radio occultation • MISR and MODIS cloud-track winds • Summary Joanna Joiner, MTR presentation, Sat. Data Assim.

  3. 1978-1998: TOVS instrument band channels HIRS IR/VIS 20 MSU microwave 4 SSU IR 3 1998-present: ATOVS instrument band channels HIRS/3 IR/VIS 20 AMSU -A microwave 15 AMSU-B microwave 5 2002 and beyond: Aqua, etc. instrument band channels AIRS IR/VIS 2300+ AMSU -A microwave 15 HSB microwave 4 Joanna Joiner, MTR presentation, Sat. Data Assim.

  4. DAOTOVS: What is it and what makes it different from other NWP centers? • 1 dimensional variational (1DVAR) radiance assimilation system • Uses raw level 1b data • Assimilate information about temperature, humidity • Variational cloud-clearing (Joiner and Rokke, 2000); • Use land-emissivity model from CERES science team • Other centers do not use cloud or land-affected data • Bias correction (tuning) using collocated radiosondes (not forecast model). Joanna Joiner, MTR presentation, Sat. Data Assim.

  5. What is cloud-clearing and why is it important? • Clouds affect over 80% of HIRS pixels. • Estimate clear radiance from adjacent pixels • We use 3 FOVs to get information about 2 cloud formations • Extrapolation procedure: adds noise • Must have variability in cloud content in FOVs • Need estimate of clear radiance (we use accurate estimate from forecast model) Joanna Joiner, MTR presentation, Sat. Data Assim.

  6. Monthly-mean zonal winds from FVDAS(impact of DAOTOVS) Joanna Joiner, MTR presentation, Sat. Data Assim.

  7. Independent validation:Monthly-mean radiosonde observations – 6 hr forecastzonal wind(blue in bottom panel = improvement with 1DVAR) Joanna Joiner, MTR presentation, Sat. Data Assim.

  8. 1 hPa Temperatures: impact of AMSU/DAOTOVS Joanna Joiner, MTR presentation, Sat. Data Assim.

  9. Forecast experiments: impact of cloud-clearing red: cloud-cleared,blue: no cloudy; RMS errors of 500 hPa height Joanna Joiner, MTR presentation, Sat. Data Assim.

  10. NASA/NOAA collaborations:Joint Center for Satellite Data Assimilation • Forward radiative transfer algorithm: computes radiance from geophysical fields (OPTRAN – developed by NESDIS, currently in operations at NCEP, extending and improving for AIRS) • AIRS real-time data (compressed and subsetted) distribution (provided by NESDIS) • Assimilation: AIRS, GPS, other new satellite data Joanna Joiner, MTR presentation, Sat. Data Assim.

  11. New Sensors: Atmospheric InfraRed Sounder (AIRS) • High-spectral resolution spectrometer with over 2300 channels (compared with 20 on current operational sounder) • Launch in 2002 on NASA Earth Observing System (EOS) Aqua satellite • Flying with Advanced Microwave Sounding Unit (AMSU-A) and the Humidity Sounder for Brazil (HSB) Joanna Joiner, MTR presentation, Sat. Data Assim.

  12. AIRS initial channel selection Joanna Joiner, MTR presentation, Sat. Data Assim.

  13. Channel selection based on retrieved cloud height Joanna Joiner, MTR presentation, Sat. Data Assim.

  14. Channel selection based on retrieved cloud height Joanna Joiner, MTR presentation, Sat. Data Assim.

  15. New Sensors – GPS Occultation Satellite motion provides vertical scanning Rays bent by atmosphere Derive information about temperature and humidity Joanna Joiner, MTR presentation, Sat. Data Assim.

  16. GPS Update • Completed off-line retrieval of temperature, humidity, surface pressure using FVDAS as first guess (Poli et al., 2001) • Temperature retrievals at tropopause and stratosphere showed good accuracy and vertical resolution as expected • Humidity and surface pressures not as good as expected due to 1) first guess bias 2) forward model limitations in areas of large gradients • New receivers, capable of handling encryption, have been launched on 2 satellites, with more planned launches, data to be provided soon by JPL • Will soon be starting assimilation experiments Joanna Joiner, MTR presentation, Sat. Data Assim.

  17. MISR and MODIS cloud track winds • MISR: 8 cameras at image simultaneously at different angles. Multiple views of same area yield accurate cloud-heights and wind estimates • MODIS cloud-track winds available at high latitudes • Covers higher latitudes where Geo is not available • DAO is working with MISR/MODIS science teams to assimilate their products Joanna Joiner, MTR presentation, Sat. Data Assim.

  18. Summary and Future Work • DAOTOVS has positive impact, especially on stratospheric temperatures, winds, and upper tropospheric humidity • Cloud-cleared data has positive impact on forecasts (6hrs-5 days), land-affected data smaller positive impact • Prepare for AIRS with NCEP and NESDIS: e.g. OPTRAN, dynamic channel selection • Implemented GPS retrievals, soon ready for full data assimilation experiments • Working with MISR/MODIS science teams to assimilate new cloud-track wind products Joanna Joiner, MTR presentation, Sat. Data Assim.

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