Estimating Sea Surface Temperature Measurement Methods Using Characteristic Differences in the Diurnal Cycle

Estimating Sea Surface Temperature Measurement Methods Using Characteristic Differences in the Diurnal Cycle

Lack of reliable observational metadata represents a key barrier to understanding sea surface temperature (SST) measurement biases, a large contributor to uncertainty in the global surface record. We present a method to identify SST measurement practice by comparing the observed SST diurnal cycle from individual ships with a reference from drifting buoys under similar conditions of wind and sol...

Multi-element sea surface temperature measurement using coral substructures

DIURNAL BEHAVIOR OF LAND SURFACE TEMPERATURE IN LUT DESERT

analyses and thermal infrared channels 4 and 5 NOAA- AVHRR images. This research was carried out in Lut Desert located in center of sub-tropical divergence dominant in' Iranian plateau. The physical and thermal properties of the various desert surfaces have been determined using correlation relationships between the desert variables including: dark sand, normal sand, marl, saline soil, soil dep...

The pattern determination of sea surface temperature distribution and chlorophyll a in the Southern Caspian Sea using SOM Model

Remote sensing has changed modern oceanography by proving synoptic periodic data which can be processed. Since the satellite data are usually too much and nonlinear, in most cases, it is difficult to distinguish the patterns from these images. In fact, SOM (Self-Organizing Maps) model is a type of ANN (Artificial Neural Network) that has the ability to distinguish the efficient patterns from th...

The pattern determination of sea surface temperature distribution and chlorophyll a in the Southern Caspian Sea using SOM Model

Remote sensing has changed modern oceanography by proving synoptic periodic data which can be processed. Since the satellite data are usually too much and nonlinear, in most cases, it is difficult to distinguish the patterns from these images. In fact, SOM (Self-Organizing Maps) model is a type of ANN (Artificial Neural Network) that has the ability to distinguish the efficient patterns from th...