Sea surface temperature (SST) fronts are determined for the 2001–2004 time period from Geostationary Operational Environmental Satellites (GOES) data in the California Current System (CCS). The probability of detecting a SST front at an individual pixel location in the CCS is presented as a bi‐monthly climatology. Fronts clearly indicate the seasonal evolution of coastal upwelling, as well as meanders and filaments that are often linked with irregularities in coastline geometry. Winter is characterized by low frontal activity along the entire coast. Fronts first appear close to the coast during spring, particularly south of Cape Blanco, where upwelling favorable winds are already persistent. The area of high frontal activity continues to increase during summer, especially between Monterey Bay and Cape Blanco, extending more than 300 km from the coast. The region with high frontal activity widens at ∼2.6 km day−1. Off northern Baja California, a band with persistent fronts is found close to the coast year‐round, but there is no evidence of a seasonal widening of the area of higher activity. During fall, the weakening of upwelling favorable winds leads to a gradual decrease in frontal activity. An empirical orthogonal function (EOF) decomposition reveals the development of SST fronts associated with seasonal upwelling for locations north of Monterey Bay, with less summer intensification to the south. The first appearance of fronts close to the coast during spring and the occurrence of the fronts offshore later in the season are represented by additional statistically significant EOF modes.
Until recently, quantitative measurements of the circulation of the California Current were limited to hydrographic determinations [See figure in the PDF file] of temperature and salinity. This information is now being augmented by satellite data. Clouds permitting, satellite scanner systems can locate major ocean frontal boundaries if they are associated with even quite weak horizontal sea-surface temperature gradients. The satellite data are most usefully interpreted in a region such as that encompassing the California Current, where the surface and main thermocline temperature distributions bear some relation to each other. In such a region, it is possible to make interpretations of circulation based on satellite-derived sea-surface temperature patterns. The correctness of these interpretations depends heavily on the availability of historical and present-day subsurface data, collected by conventional methods from ships and aircraft. Satellite infrared scanners, in addition to providing information on circulation with vastly increased spatial resolution, have the potential (with cooperative weather) for providing increased time resolution. These improvements in resolution have permitted us to see that much of the spatial variation in the California Current takes place along welldefined fronts and to observe the evolution of one particular meander.
A single, extended‐range neural network (SER NN) has been developed to model the transfer function for special sensor microwave imager (SSM/I) surface wind speed retrievals. Applied to data sets used in previous SSM/I wind speed retrieval studies, this algorithm yields a bias of 0.05 m/s and an rms difference of 1.65 m/s, compared to buoy observations. The accuracy of the SER NN for clear (low moisture) and cloudy (higher moisture/light rain) conditions equals the accuracy of NNs trained separately for each of these cases. A new moisture retrieval criterion based on a single, physically interpretable parameter, cloud liquid water, is proposed in conjunction with the SER NN. Using this retrieval criterion, (1) a moisture retrieval threshold for cloud liquid water of 0.5 kg/m2 was estimated, and (2) 40% of the data rejected by previous rain flags could be recovered. When the SER NN was trained using this retrieval criterion, a bias of 0.03 m/s and an rms value of 1.58 m/s were obtained and only 2% of the data were rejected. Also, a slight improvement in retrieval accuracy for cloudy conditions was achieved (∼10%) by including SSM/I brightness temperatures at 85 GHz. Finally, the limitations of NN algorithms are discussed in light of the present application.
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