Calculation of Sea Surface Temperature Forward Radiative Transfer Model Approach Alec Bogdanoff, Florida State University Carol Anne Clayson and Brent Roberts GHRSST Users Symposium May 28, 2009

Background  Empirically based retrievals are biased towards those areas where in situ measurements are abundant.  GOAL: Increases confidence in Sea Surface Temperatures (SSTs) in areas with few in situ data points with a minimum computing effort.  Use technique of Merchant and LeBorgne (2004), which provides a more physical approach to the derivation of Brightness Temperatures (BT) coefficients. 2

Background con’t.  Using a full Radiative Transfer Model (RTM) for Sea- Surface Temperature (SST) retrievals is computationally exhaustive.  We use RTTOV, a fast RTM.  Neural Networks have the ability to increase computational efficiency by times depending on atmospheric profile. Chavallier et. al. (1998) 3

Distribution of MDB 4 Match-up Database from Pierre Le Borgne and EUMETSAT's Ocean and Sea Ice Satellite Application Facility

Quick Introduction to Neural Networks  Type of nonlinear regression  Inputs are weighted and transformed nonlinearly. These nonlinearly transformed values are weighted and linearly combined to produce the output.  For neural networks, one uses standard nonlinear optimization techniques such as steepest-descent  No guarantee that a global minimum will always be found and so it sometimes requires a bit of trial and error 5

Neural Network Example 6

Neural Networks  Used 100,000 randomly selected data (of 236, 704) to train the networks  Using night-time measurements only  Buoy to skin SST correction of -0.2 K  Inputs of NWP and observed BTs 7

Skill of Neural Network Approach 8 NN – No RTMNN – RTM O&SI SAF AlgorithmMLR

Skill of Neural Network Approach 9 NN – No RTMNN – RTM O&SI SAF AlgorithmMLR

Neural Network emulation of O&SI SAF Nocturnal SST Algorithm 10 MLP – NO RTM MLP – RTM MLRO&SI SAF MLP – O&SI RMS Mean Bias Neural Net emulated O&SI SAF Algorithm

Can the Neural Network emulate the Radiative Transfer Model? 11

Neural Network Emulation of RTTOV μ m 10.8 μ m 12.0 μ m RMS Mean Bias μm 10.8 μm 12.0 μm

Neural Network Emulation of RTTOV Jacobian: dBT/dTCWV 3.7 μ m 10.8 μ m 12.0 μ m RMS Mean Bias μm10.8 μm 12.0 μm

Comparison to GHRSST Products  This technique will be compared to GHRSST’s L2P AVHRR products, once expanded beyond MetOp-A.  After optimization, the dataset will be compared to the closest spatial and temporal L4 data point, specifically those products that use AVHRR. 14

Conclusions  As a first guess model, the neural networks do an exceptional job, with the inclusion of the RTM increasing the accuracy of the modeled approach.  Will see much greater impact from the Radiative Transfer approach in cloudy sky and/or daytime condition. 15

Future Work  Many more runs are required to determine the best regression. Further optimization of the networks will increase the accuracy of the approach.  Expand beyond clear sky at night. A RTM approach will be greater during daytime and cloudy sky conditions  Expand beyond MetOp-A satellite to allow for better global coverage of satellite derived SSTs.  The error characteristics of the resulting SST datasets will be compared to GHRSST products. 16

Acknowledgements  Brent Roberts and Carol Anne Clayson  Pierre Le Borgne of Météo-France (MDB)  Chris Merchant of the University of Edinburgh (RTM)  The dataset is from EUMETSAT's Ocean and Sea Ice Satellite Application Facility  NASA PO Program for their support of this research 17

References  Chevallier, F., F. Chéruy, N. A. Scott, and A. Chédin, 1998: A neural network approach for a fast and accurate computation of a longwave radiative budget. J. Appl. Meteor., 37, 1385–1397.  Merchant, C. J. and P. Le Borgne, 2004: Retrieval of Sea Surface Temperature from Space, Based on Modeling of Infrared Radiative Transfer: Capabilities and Limitations. J. Atmos. Oceanic Technol., 21,  _____, P. Le Borgne, A. Marsouin, and H. Roquet, 2008: Optimal estimation of sea surface temperature from split-window observations. Remote Sens. Environ., 112, , doi: /j.rse  Ocean & Sea Ice SAF Project Team, 2009: MetOp/AVHRR Sea Surface Temperature Product User Manual Version 1.5. O&SI SAF Product User Manual. SAF/OSI/CDOP/M-F/TEC/MA/127, 48 pp.  Rumelhart, D. E., G. E. Hinton, and R. J. Williams, 1986: Learning internal representations by error propagation. Parallel Distributed Processing: Exploration in the Macrostructure of Cognition 1, D. E. Rumelhart and McClelland, Eds., MIT Press,  Walton, C. C., W. G. Pichel, J. F. Sapper, and D. A. May, 1998: The development and operational application of nonlinear algorithm for the measurement of sea surface temperatures with the NOAA polar-orbiting environmental satellites. J. Geophys. Res., 103(C12),