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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:.

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Goes 12 sounder sfov sounding improvement

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
Gauss-Newton Iteration

Classical Gauss-Newton iteration:

Ma’s iteration:

Jun Li’s iteration:

retrieval precision

Calculation efficiency

(Convergence, stability)


Analyze the iteration equation
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


Possible improvements
Possible improvements

  • First guess (regression)

  • Covariance matrix of state variables

  • Measured Radiances

    - Noise

  • Calculated Radiances (RTM)

    - Bias


First guess
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


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


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)


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


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


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


Analysis of rmse and bias hourly and seasonally summer
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


Analysis of rmse and bias hourly and seasonally winter
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


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)


Summaries
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
Future work

  • New covariance matrices for dry and wet cases

  • GPS TPW as extra “channel”

  • Time continuity, Kalman Filter



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