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GOES-12 Sounder SFOV sounding improvement

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16 diapositives · 304 Views · Aug 22, 2014

This study presents significant improvements in the GOES-12 Sounder retrieval process by utilizing advanced Gauss-Newton iteration techniques. Key findings indicate that new covariance matrices and optimized first guess methods enhance retrieval precision and stability. The research analyzes classical and modern iteration approaches, emphasizing how improvements in measurement covariance and noise reduction can lead to better physical retrieval results. The new methods have shown increased efficiency in handling moisture products, particularly beneficial in varying atmospheric conditions, indicating a promising direction for future satellite sounding advancements.

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GOES-12 Sounder SFOV sounding improvement
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  1. GOES-12 Sounder SFOV sounding improvement Zhenglong Li, Jun Li, W. Paul Menzel, Timothy J. Schmit and other colleagues Cooperative Institute for Meteorological Satellite Studies University of Wisconsin-Madison

  2. Gauss-Newton Iteration Classical Gauss-Newton iteration: Ma’s iteration: Jun Li’s iteration: retrieval precision Calculation efficiency (Convergence, stability)

  3. Analyze the iteration equation Classical Gauss-Newton iteration: CDRW Covariance matrix of measurements CDR Covariance matrix of state variables Calculated Radiances First guess Measured Radiances Weighting function

  4. Possible improvements • First guess (regression) • Covariance matrix of state variables • Measured Radiances - Noise • Calculated Radiances (RTM) - Bias

  5. First guess Temperature Moisture • New regression is better than old one and forecast • Better first guess could produce better physical retrieval results • This could be wrong if the covariance matrix is not consistent with the first guess

  6. Forecast (Eta) Error GOES Sounder Sounder WV Weighting Functions • New covariance matrices reduce the divergence and instability greatly • New covariance matrices improve the physical retrieval Retrieval (3x3 FOV) Error

  7. Diff btwn obs and cal In New bias estimate: (1) 101-level RTA model is used (2) Surface emissivities are derived from regression based on realistic training Old Bias 14.7 μm 14.7 μm Counts 12.7 μm 12.7 μm • Bias adjustment is needed • Old Bias adjustment is no longer suitable for the new RT model • Noise reduction is needed 7.4 μm 7.4 μm T (K) T (K)

  8. Background and Error Information Traditional Forecast Continue Developing New in this year Error Co-var Objective Forecast Better First Guess Optimal Inverse Algorithm GPS Improved SFOV Moisture Products Temporal Continuity Ecosystem Classified MODIS Emiss Optimal RTA Bias RAOB/GOES Sounder Matchup data Better Handle Clouds Temporal Optimal Radiances Best Validation (RAOB, GPS, MW). Spatial Spectral Radiance Obs Spatial Filtering Temporal Filtering

  9. Validation against microwave-retrieved TPW Legacy algorithm is not optimal for SFOV sounding. New gives good SFOV TPW with reasonable precision. Sample # = 3041 Pure SFOV retrieval Lamont, OK Legacy retrieval Legacy retrieval New:Phy1 New:Phy1 New:Phy2 New:Phy2 Phy1: New physical retrieval with regression as first guess Phy2: New Physical retrieval with forecast as first guess

  10. Validation against microwave-retrieved TPW Sample # = 3125 3x3 SFOV retrieval Lamont, OK Simple 3x3 average helps reducing the RMSe of retrieved TPWs Legacy retrieval Legacy retrieval New:Phy1 New:Phy1 New:Phy2 New:Phy2

  11. Analysis of RMSe and Bias hourly and seasonally(summer) RMS: < < Legacy Phy2 Phy1 • Phy1 has the smallest bias most of the time in the whole day • Phy2 has negative bias at night and positive bias in the day • Legacy has large positive bias at night and small bias in the day

  12. Analysis of RMSe and Bias hourly and seasonally(winter) RMS: < < Phy2 legacy Phy1 • Phy1 has negative bias most of the time in the whole day • Phy2 has small bias • Legacy has small positive bias at night and large positive bias in the day

  13. Validation against RAOB Sample size = 34 Time: 2005359 00Z to 2005360 00Z RMSe of TPWs (mm) against Raob • GPS helps retrieve moisture • Simple 3 by 3 average helps improve first guess • Phy2 has better results than Phy1 • Covariance matrix should match the first guess (better first guess doesn’t guarantee better retrieval) Reg: regression Phy1: physical retrieval (regression) Fcst: forecast Phy2: physical retrieval (forecast)

  14. Summaries • Single FOV sounding retrievals could be improved through the following aspects: • Forecast helps regression • GPS TPW helps regression • Covariance matrix of state variables • 3x3 simple average • New physical retrieval with regression as first guess is good when TPW is large (summer) • New physical retrieval with forecast as first guess is good when TPW is small (winter)

  15. Future work • New covariance matrices for dry and wet cases • GPS TPW as extra “channel” • Time continuity, Kalman Filter

  16. Thanks