Passive microwave remote sensing of snow constrained by hydrological simulations

This paper describes a snow parameter retrieval algorithm from passive micr owave remote sensing measurements. The three components of the retrieval al gorithm include a dense media radiative transfer (DMRT) model, which is bas ed on the quasicrystalline approximation (QCA) with the sticky particle ass umption, a physically-based snow hydrology model (SHM) that incorporates me teorological and topographical data, and a neural network (NN) for computat ional efficient inversions. The DMRT model relates physical snow parameters to brightness temperatures. The SHM simulates the mass and heat balance an d provides initial guesses for the neural network. The NN is used to speed up the inversion of parameters. The retrieval algorithm can provide speedy parameter retrievals for desired temporal and spatial resolutions. Four cha nnels of brightness temperature measurements: 19V, 19H, 37V, and 37H are us ed. The algorithm was applied to stations in the northern hemisphere. Two s ets of results are shown. For these cases, we use ground-truth precipitatio n data, and estimates of snow water equivalent (SWE) from SEM give good res ults. For the second set, a weather forecast model is used to provide preci pitation inputs for SHM. Additional constraints in grain size and density a re used. We show that inversion results compare favorably with ground truth observations.