Sea surface air temperature and humidity estimated by artificial neural networks

Sea surface air temperature (Ta) and relative humidity (RH) have been the main parameters of climate studies. In the past, the data can be obtained from observations, but the observations are sparse, especially over ocean. Now we can get the aid of satellites, but it is impossible to estimate them from satellites directly so far. This paper presents a new method to derive monthly averaged Ta and RH from satellite data using artificial neural networks (ANN). For Ta estimation, four inputs are needed: wind speed, cloud liquid water, total precipitable water from SSM/I and sea surface temperature (SST) from AVHRP, the data to develop and train the methodology are offered by Tropical Atmosphere Ocean (TAO) project and National Database Buoy Center (NDBC). For RH estimation, the methodology is similar with the method of Ta estimation, except adding the parameter of rain rate (from SSM/I) as the fifth inputs. Comparison with independent validation samples in the Pacific and Atlantic Oceans indicate the result of Ta and RH estimated from satellite data is reasonable well. The root mean square (RMS) and the correlation between estimated and measured air temperature are about 0.94 degrees C and 0.98, respectively. The RMS and the correlation of relative humidity are about 3.74 and 0.64, respectively. The simple statistical formula is also obtained in this paper. Compared with ANN methodology, the statistical formula is intuitionistic and the result is reasonable accepted.