Large-Scale High-Resolution Modeling of Microwave Radiance of a Deep Maritime Alpine Snowpack

Abstract

Applying passive microwave (PM) remote sensing to estimate mountain snow water equivalent (SWE) is challenging due in part to the large PM footprints and the high subgrid spatial variability of snow properties. In this paper, we linked the land surface model Simplified Simple Biosphere version 3.0 (SSiB3) with the radiative transfer model Microwave Emission Model of Layered Snowpacks, and we forced the coupled model with the disaggregated North American Data Assimilation System phase 2 (NLDAS-2) meteorological data to simulate the snow properties and the 36.5-GHz microwave brightness temperature (T b ) at a spatial resolution of 90 m. The modeled SWE and T b were used to interpret the radiance observed by the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and to explore the impact of snow spatial variability on the microwave radiance in a mountain environment. The modeling was carried out over the Upper Kern Basin, Sierra Nevada. We developed new methods for modeling the effect of large snowfall events on the snow grain size. We aggregated the modeled radiance to the satellite scale using the AMSR-E 36.5-GHz antenna sampling pattern. The methods were calibrated for water years (WYs) 2004-2006 and validated for WYs 2003, 2007, and 2008.The coefficient governing the grain growth rate was also calibrated. The modeling results showed that the new snow grain estimation scheme reduced the error in the modeled radiance by 55.2% during the calibration period. The T b root-mean-square error was 3.1 K during the snow accumulation season for the validation period. The modeling results showed that, in the study area, the microwave signal saturated for SWE values between 0.3 and 0.5 m. It was found that the subfootprint-scale SWE variability has a significant impact on the saturation of spaceborne PM observations. The experiments demonstrate that this modeling system improves the accuracy of the radiance modeling, which is critical for estimating the mountain SWE via PM remote sensing either for informing direct retrieval algorithms or for data assimilation. We plan to use the modeling framework in future radiance assimilation studies.

Estimating snow water equivalent in a Sierra Nevada watershed via spaceborne radiance data assimilation

“…Snow grain size significantly impacts the snowpack microwave radiative transfer properties [ Mätzler and Wiesmann , ]. During the accumulation season, grain growth and snowfall affect T b in different ways; the rate of grain growth impacts the rate at which T b decreases in between snowfall events, while snowfall tends to create a systematic change in T b [ Li et al ., , ]. A pragmatic solution is to use just a single parameter to govern grain growth rate (for every pixel and every year), to estimate values of the grain growth rate parameter that cause the modeled T b to match the rate of change of observed T b time series in between snowfall events, and then treat this grain growth rate as an uncertain input to the assimilation scheme.…”

“…In this study, the Jordan dynamic grain size metamorphism model [ Jordan , ] that includes a calibratable grain size growth rate parameter was integrated with SSiB3 to simulate the snow microstructure. The Jordan model has been successfully applied in several studies to estimate snow grain size [e.g., Huang et al ., ; Li et al ., ], and has outperformed more complex physical grain size model in some cases [ Huang et al ., ]. The details of the Jordan model can be found in Jordan [, section IV].…”

“…The grain size growth coefficient utilized in this study was the value determined in Li et al . [] via calibration; the grain growth coefficient can be estimated essentially by comparing modeled and observed T b time series, even for the prior model which has an incorrect SWE. This approach is discussed in section 5.4, and the detailed calibration method can be found in Li et al .…”

“…This approach is discussed in section 5.4, and the detailed calibration method can be found in Li et al . []. In this study, the same value was used for each water year and each pixel in the study area.…”

“…[, ], in order to resolve spatial patterns of radiation, energy balance and grain growth [ Li et al ., ]. A model forcing disaggregation model (FDM) is integrated in the modeling framework to downscale all the forcing data to 90 m resolution [ Girotto et al ., ]; we have used this framework for characterizing the details of the SWE and radiance patterns in mountain terrains [ Li et al ., , ]. We utilize the Ensemble Kalman Batch Smoother (EnBS) scheme in which an entire time series of observations is utilized, rather than sequentially assimilating one observation at a time using an Ensemble Kalman Filter.…”