A new method for deriving ocean surface specific humidity and air temperature: an artificial neural network approach

摘要

A new methodology for deriving monthly averages of surface specific humidity (Q_a) and air temperature (T_a) is described. Two main aspects characterize the new approach. First, remotely sensed parameters, total precipitable water (W), and sea surface temperature (SST) are used to derive Q_a and T_a. Second. artificial neural networks (ANN) are employed to find transfer functions relating the input (W, SST) and output (Q_a and T_a) parameters. Input data consist of nearly six years (January 1988-November 1993) of monthly averages of total precipitable water from Special Sensor Microwave/lmager data and sea surface temperature analysis from the National Centers for Environmental Prediction. Surface marine observations of Q_a and T_a are used to develop and evaluate the new methodology. The performance of the algorithm is measured with surface marine observations not used in the development phase. Higher seasonally dependent discrepancies between Q_a and T_a derived from the new method and in sim data are observed in regions such as the Kuroshio and Gulf Stream currents. After removal of systematic biases. the new method indicates that the combination of W and SST as input parameters and the ANN algorithm provides an interesting alternative for deriving monthly averaged surface parameters. The global mean bias in Q_a is 0.010 +- 0.23 g kg~(-1) over most oceanic areas, whereas root-mean-square (rms) differences are 0.77+- 0.39 g kg~(-1). Likewise. the global mean bias and rms in T_a are on the order of -7.3 X 10~(-5) +- 0.27 deg C and 0.72 +- 0.38 deg C, respectively.