Using a neural networking (NN) approach, we developed an algorithm primarily based upon sea surface temperature (SST) and chlorophyll (Chla) to estimate the partial pressure of carbon dioxide (pCO2) at the sea surface in the northern South China Sea (NSCS). Randomly selected in situ data collected from May 2001, February and July 2004 cruises were used to develop and test the predictive capabilities of the NN based algorithm with four inputs (SST, Chla, longitudes and latitudes). The comparison revealed a high correlation coefficient of 0.98 with a root mean square error (RMSE) of 6.9 μatm. We subsequently applied our NN algorithm to satellite SST and Chla measurements, with associated longitudes and latitudes, to obtain surface water pCO2. The resulting monthly mean pCO2 map derived from the satellite measurements agreed reasonably well with the in situ observations showing a generally homogeneous distribution in the offshore regions. The pCO2 exerts a very dynamic feature in nearshore regions, especially in the coastal upwelling and estuarine plume regions. We identified three low pCO2 zones (<330 μatm), two of which are influenced by coastal upwelling: off Hainan island in the western part of the NSCS; and off Guangdong province in the eastern part of the NSCS. The path of the Pearl River plume on the shelf was another zone with low pCO2. For the monthly mean pCO2variations estimated based on the MODIS‐SST and ‐Chla values, an RMSE of ∼6 μatm may be attributable to the measurement errors associated with MODIS measurements. As a first order estimation, we used the same sampling periods of remote sensing and in situ measurements, and were able to estimate pCO2 with an accuracy of 12.05 μatm for onshore regions and 13.0 μatm for offshore regions, but with combined uncertainties associated with the NN Testing algorithm and MODIS SST and Chla measurements.

where the coordinate system is the same as the geographic coordinate system; u and v are the zonal and meridional components of the wind field.

No abstract

Subsurface coherent vortices in the North Atlantic, whose saline water originates from the Mediterranean Sea and which are known as Mediterranean eddies (meddies), have been of particular interest to physical oceanographers since their discovery, especially for their salt and heat transport properties into the North Atlantic Ocean. Many studies in the past have been successful in observing and studying the typical properties of meddies by probing them with in situ techniques. The use of remote sensing techniques would offer a much cheaper and easier alternative for studying these phenomena, but only a few past studies have been able to study meddies by remote sensing, and a reliable method for observing them remotely remains elusive. This research presents a new way of locating and tracking meddies in the North Atlantic Ocean using satellite altimeter data. The method presented in this research makes use of ensemble empirical mode decomposition (EEMD) as a means to isolate the surface expressions of meddies on the ocean surface and separates them from any other surface constituents, allowing robust meddies to be consistently tracked by satellite. One such meddy is successfully tracked over a 6-month time period (2 November 2005 to 17 May 2006). Results of the satellite tracking method are verified using expendable bathythermographs (XBT).

Abstract. We examine a group of wave-like cloud patterns that occurred along the coast of Texas on a National Oceanic and Atmospheric Administration satellite advanced very high resolution radiometer IR images taken on January 22, 1999. These wave-like cloud patterns were interpreted to be signatures of a coastal lee wave packet on the basis of simultaneous field observations and theories developed by Zheng et al. [1998a]. The wave packet contains 13 waves with crest lines generally parallel to the coastline. The lengths of leading wave crest lines are longer than 500 km. The average wavelength is 9.5 km, ranging from 6.2 to 14.7 km. The width of the horizontal distribution band of the wave packet is as wide as 113 km. This case represents the most energetic coastal lee wave packet that has ever been reported.