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:

))]())([()( 01111

001 xxKxRREKKEKSxx iicmTT

i

Ma’s iteration:)}()]())([({)( 00

1111001 xxxxKxRREKIKEKSxx iiiicm

Ti

Ti

Jun Li’s iteration:))())([()( 0

1111001 xxKxRREKKEKSxx iicm

TTi

retrieval precision Calculation efficiency

(Convergence, stability)

Analyze the iteration equationClassical Gauss-Newton iteration:

))]())([()( 01111

001 xxKxRREKKEKSxx iicmTT

i

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 guessTemperature 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

GOESSounder

• 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 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) T (K)

Cou

nts

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 FilteringTemporal Filtering

Better Handle Clouds

OptimalRadiances

Optimal RTA BiasRAOB/GOES

Sounder Matchup data

Improved SFOV

Moisture Products

Temporal

Spatial

Spectral

Objective

GPS

Sample # = 3041Pure 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 # = 31253x3 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 LegacyRMS: < <

• 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)

Phy1Phy2 legacy

• 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

Reg Phy1 Fcst Phy2

ORGN+NO GPS 1.67 1.45 2.13 1.20

ORGN+GPS 1.29 1.21 2.13 1.17

3x3+NO GPS 1.57 1.31 2.15 1.23

3x3+GPS 1.29 1.22 2.15 1.33

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:

2005359 00Z to 2005360 00Z

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