Abstract:
An artificial neural network (ANN) is presented for the purpose of estimating passive microwave (PMW) brightness temperatures over snow covered land in North America. The NASA Catchment Land Surface Model (Catchment) is used to define snowpack properties; the Catchment-based ANN is then trained with PMW measurements acquired by the Advanced Microwave Scanning Radiometer (AMSR-E). A comparison of ANN output against AMSR-E measurements not used during training activities as well as a comparison against independent PMW measurements collected during airborne surveys demonstrates the predictive skill of the ANN. When averaged over the study domain for the 9-year study period, computed statistics (relative to AMSR-E measurements not used during training) for multiple frequencies and polarizations yielded a near-zero bias, a root mean squared error less than 10K, and a time series anomaly correlation coefficient of approximately 0.7. The ANN demonstrates skill at reproducing brightness temperatures during the ablation phase when the snowpack is ripe and relatively wet. The ANN demonstrates even greater skill during the accumulation phase when the snowpack is relatively dry. Overall, the results suggest the ANN could serve as a computationally efficient measurement operator for data assimilation at the continental scale.