Two kinds of Bayesian-based cost functions (i.e., the unconstrained cost function and parameter-constrained cost function) are investigated for retrieving the sea surface salinity (SSS). In low SSS regions, we have an...Two kinds of Bayesian-based cost functions (i.e., the unconstrained cost function and parameter-constrained cost function) are investigated for retrieving the sea surface salinity (SSS). In low SSS regions, we have analyzed the sensitivity of the two cost functions to geophysical parameters. The results show that the unconstrained cost function is valid for retrieving several parameters (including SSS, wind speed and significant wave height), and the constrained cost function, which largely depends on the accuracy of reference values, may lead to large retrieval biases. Furthermore, as a retrieval parameter, the sea surface temperature (SST) can re-sult in the divergence of other geophysical parameters in an unconstrained cost function due to the strong sensitivity of brightness temperature to SST. By using the unconstrained cost function and the simulated brightness temperature TB with white noises, the retrieval biases of SSS are discussed with the following two procedures. Procedure a): the simulated TB values are first averaged, and then SSS is retrieved. Procedure b): the SSS is directly retrieved from the simulated TB , and then the retrieved SSS values are aver-aged. The results indicate that, for low SSS and SST distributions, the SSS retrieval by procedure a) has less biases compared with that by procedure b), while the two procedures give almost the same retrieval results for high SSS and SST sea regions.展开更多
Two monthly datasets of sea surface temperature (SST),TMI SST retrieved from satellite observations by Remote Sensing System and HadISST1 (Hadley Centre Sea-ice and Sea-surface Temperature Data Set Version 1) derived ...Two monthly datasets of sea surface temperature (SST),TMI SST retrieved from satellite observations by Remote Sensing System and HadISST1 (Hadley Centre Sea-ice and Sea-surface Temperature Data Set Version 1) derived from in situ measurements by Hadley Centre,were compared on climatologic multiple time scales over tropical and subtropical areas from 1998 to 2006.Results indicate that there is a good consistency in the horizontal global distribution,with 1.0° resolution on multi-year and multi-season mean scales between the two datasets,and also in the time series of global mean SST anomalies.However,there are still some significant differences between the datasets.Generally,TMI SST is relatively higher than HadISST1.In addition,the differences between the two datasets show not only remarkable regionality,but also distinct seasonal variations.Moreover,the maximum departure occurs in summer,while theminimum takes place in autumn.For all seasons,over 30% of the regions in the Tropical and Subtropical areas have a difference of more than 0.3°C.EOF analysis of the SST anomaly field also shows that there are differences between the two datasets,where HadISST1 has more significant statistical characteristics than TMI SST.On the other hand,results show that the difference between the two datasets is related to the vertical structure of ocean temperatures,as well as other simultaneously retrieved parameters in TMI products,such as wind speed,water vapor,liquid cloud water and rain rates.In addition,large biases between HadISST1 and TMI SST are found in coastal regions,where TMI SST cannot be accurately retrieved because of polluted microwave signals.展开更多
基金supported by the National Natural Science Foundation of China (Grant No. 40876094)the National 863 Project of China (Grant Nos. 2009AA09Z102 and 2008AA09A403)
文摘Two kinds of Bayesian-based cost functions (i.e., the unconstrained cost function and parameter-constrained cost function) are investigated for retrieving the sea surface salinity (SSS). In low SSS regions, we have analyzed the sensitivity of the two cost functions to geophysical parameters. The results show that the unconstrained cost function is valid for retrieving several parameters (including SSS, wind speed and significant wave height), and the constrained cost function, which largely depends on the accuracy of reference values, may lead to large retrieval biases. Furthermore, as a retrieval parameter, the sea surface temperature (SST) can re-sult in the divergence of other geophysical parameters in an unconstrained cost function due to the strong sensitivity of brightness temperature to SST. By using the unconstrained cost function and the simulated brightness temperature TB with white noises, the retrieval biases of SSS are discussed with the following two procedures. Procedure a): the simulated TB values are first averaged, and then SSS is retrieved. Procedure b): the SSS is directly retrieved from the simulated TB , and then the retrieved SSS values are aver-aged. The results indicate that, for low SSS and SST distributions, the SSS retrieval by procedure a) has less biases compared with that by procedure b), while the two procedures give almost the same retrieval results for high SSS and SST sea regions.
基金supported by the National Basic Research Program of China(Grant No.2010CB428601)the Special Funds for Public Welfare of China(Grant Nos.GYHY200906002,GYHY200906003)+2 种基金the Science and Technology Special Basic Research of the Ministry of Science and Technology(Grant No.2007FY110700)the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant Nos.KZCX2-YW-Q11-04,KZCX2-EWQN507,KJCX2-YW-N25)the National Natural Science Foundation of China(Grant Nos.40730950,40805008)
文摘Two monthly datasets of sea surface temperature (SST),TMI SST retrieved from satellite observations by Remote Sensing System and HadISST1 (Hadley Centre Sea-ice and Sea-surface Temperature Data Set Version 1) derived from in situ measurements by Hadley Centre,were compared on climatologic multiple time scales over tropical and subtropical areas from 1998 to 2006.Results indicate that there is a good consistency in the horizontal global distribution,with 1.0° resolution on multi-year and multi-season mean scales between the two datasets,and also in the time series of global mean SST anomalies.However,there are still some significant differences between the datasets.Generally,TMI SST is relatively higher than HadISST1.In addition,the differences between the two datasets show not only remarkable regionality,but also distinct seasonal variations.Moreover,the maximum departure occurs in summer,while theminimum takes place in autumn.For all seasons,over 30% of the regions in the Tropical and Subtropical areas have a difference of more than 0.3°C.EOF analysis of the SST anomaly field also shows that there are differences between the two datasets,where HadISST1 has more significant statistical characteristics than TMI SST.On the other hand,results show that the difference between the two datasets is related to the vertical structure of ocean temperatures,as well as other simultaneously retrieved parameters in TMI products,such as wind speed,water vapor,liquid cloud water and rain rates.In addition,large biases between HadISST1 and TMI SST are found in coastal regions,where TMI SST cannot be accurately retrieved because of polluted microwave signals.
