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

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

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

Possible improvements 1.First guess (regression) 2.Covariance matrix of state variables 3.Measured Radiances - Noise 4.Calculated Radiances (RTM) - Bias

First guess Temperature Moisture 1.New regression is better than old one and forecast 2.Better first guess could produce better physical retrieval results 3. This could be wrong if the covariance matrix is not consistent with the first guess

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

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

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

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

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

Analysis of RMSe and Bias hourly and seasonally (summer) Phy1 Phy2 Legacy RMS: < < 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

Analysis of RMSe and Bias hourly and seasonally (winter) Phy1Phy2legacy 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 RMS: < <

RMSe of TPWs (mm) against Raob RegPhy1FcstPhy2 ORGN+NO GPS ORGN+GPS x3+NO GPS x3+GPS Reg: regression Phy1: physical retrieval (regression) Fcst: forecast Phy2: physical retrieval (forecast) Validation 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) Sample size = 34 Time: Z to Z

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)

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

Thanks