## 1. Introduction

[2] Requirements for spatial and temporal resolutions of near-real-time sea surface temperature(SST) product are 100 km and 1 day for numerical weather prediction (WMO World Weather Watch 4th Long Term Plan, 1996–2005) and 10 km and less than 1 day (diurnal cycle resolved) for ocean data assimilation [*Le Traon et al.*, 2001].

[3] Satellite observations offer great advantages, especially in terms of spatial and temporal coverage, to develop SST products suitable to the above requirements. Using microwave and infrared SST measurements, *Guan and Kawamura* [2004] produced, through an objective analysis, a test product of high-resolution cloud-free SST, which is called New Generation SST version 1.0 (hereafter NGSST ver. 1.0).

[4] Objective mapping or optimal interpolation was first introduced by [*Gandin*, 1963] to produce a systematic procedure for the production of gridded maps of meteorological parameters. Oceanographic application of this method was provided by *Bretherton et al.* [1976]. The method has been widely used for mapping of water temperature fields [e.g., *Reynolds and Smith*, 1994; *White*, 1995], sea surface height anomaly [e.g., *Le Traon et al.*, 1998; *Ducet et al.*, 2000]. If the covariance used in the objective mapping is that of the data field, then it is optimal in the sense that it minimizes the mean square error of the objective estimates.

[5] The method requires knowledge of the signal and noise variance and of the spatial-temporal autocorrelation function for the fields of interest. Because early efforts to estimate these statistics of SST and subsurface temperature fields [*Reynolds and Smith*, 1994; *White*, 1995] specifically addressed relatively long-term variability (annual to interannual), the spatial and temporal scales they obtained are larger than 500 km and a few months. Adequate statistics of SST fields that address short-term variability (intra-seasonal) should be used for production of a high-resolution SST dataset. For producing the NGSST ver. 1.0, *Guan and Kawamura* [2004] used a homogeneous and isotropic autocorrelation function for all seasons and regions from trial and error. Since their definition is only a technical solution and does not have theoretical basis, it is necessary to derive the autocorrelation function from data. In addition, it is expected that the SST variability depends on thermal conditions in the upper layer of ocean and on the oceanic and atmospheric disturbances, which changes their features seasonally and regionally. Therefore, it is necessary to discuss seasonality and regionality of the statistics of SST. We have estimated the decorrelation scale of SST variability in the Kuroshio region south of Japan (K. Hosoda and H. Kawamura, Seasonal variation of space/time statistics of short-term sea surface temperature variability in the Kuroshio region, submitted to *Journal of Oceanography*, 2004, hereinafter referred to as Hosoda and Kawamura, submitted manuscript, 2004), identifying the regional and seasonal change of scales. Here, we extend the analysis of decorrelation scale globally, using SST data observed by Advanced Microwave Scanning Radiometer for EOS (AMSR-E) aboard Aqua. In this study, it is assumed that climatological SST is used as the first guess of the optimum interpolation using the statistics derived here.

[6] AMSR-E is a multi-frequency microwave radiometer that detects microwave emissions from the earth's surface and atmosphere. Microwaves penetrate clouds with little attenuation, giving an uninterrupted view of the ocean surface such as SST-cooling produced by hurricanes observed by Tropical Rainfall Measuring Mission/TRMM Microwave Imager (TRMM/TMI) [*Wentz et al.*, 2000]. Therefore, microwave measurements provides a high-availability SST data compared with the infrared measurements which are limited by cloud presence [*Guan and Kawamura*, 2003]. *Shibata et al.* [1999] pointed out that TRMM/TMI has a problem for low temperature observation since its measurements at 10 GHz has low sensitivity toward SST if it is less than 10°C. Since 6 GHz measurements of AMSR-E improve the SST estimation at low temperature, it is appropriate for estimating statistics of SST globally. A. Shibata (SST algorithm developments—Removal of ocean wind effect, submitted to *Italian Journal of Remote Sensing*, 2004) described the algorithm of deriving SST from AMSR-E data and showed that root mean square of difference between buoy observation and AMSR-derived SSTs is 0.59 K. While the spatial resolution of microwave measurements is sparse, our previous study on the decorrelation scales in the Kuroshio region (Hosoda and Kawamura, submitted manuscript, 2004) revealed that the scales are mainly determined by large-scale atmospheric forcings. Therefore, it is expected that the seasonal/regional characteristics could be derived from the analysis using AMSR-E.