Prediction of Active Microwave Backscatter Over Snow-Covered Terrain Across Western Colorado Using a Land Surface Model and Support Vector Machine Regression.

The main objective of this article is to develop a physically constrained support vector machine (SVM) to predict C-band backscatter over snow-covered terrain as a function of geophysical inputs that reasonably represent the relevant characteristics of the snowpack. Sentinel-1 observations, in conjunction with geophysical variables from the Noah-MP land surface model, were used as training targets and input datasets, respectively. Robustness of the SVM prediction was analyzed in terms of training targets, training windows, and physical constraints related to snow liquid water content. The results showed that a combination of ascending and descending overpasses yielded the highest coverage of prediction (15.2%) while root mean square error (RMSE) ranged from 2.06 to 2.54 dB and unbiased RMSE ranged from 1.54 to 2.08 dB, but that the combined overpasses were degraded compared with ascending-only and descending-only training target sets due to the mixture of distinctive microwave signals during different times of the day (i.e., 6 A.M. versus 6 P.M. local time). Elongation of the training window length also increased the spatial coverage of prediction (given the sparsity of the training sets), but resulted in introducing more random errors. Finally, delineation of dry versus wet snow pixels for SVM training resulted in improving the accuracy of predicted backscatter relative to training on a mixture of dry and wet snow conditions. The overall results suggest that the prediction accuracy of the SVM was strongly linked with the first-order physics of the electromagnetic response of different snow conditions.

Evaluating the uncertainty of terrestrial water budget components over High Mountain Asia.

This study explores the uncertainties in terrestrial water budget estimation over High Mountain Asia (HMA) using a suite of uncoupled land surface model (LSM) simulations. The uncertainty in the water balance components of precipitation (P), evapotranspiration (ET), runoff(R), and terrestrial water storage (TWS) is significantly impacted by the uncertainty in the driving meteorology, with precipitation being the most important boundary condition. Ten gridded precipitation datasets along with a mix of model-, satellite-, and gauge-based products, are evaluated first to assess their suitability for LSM simulations over HMA. The datasets are evaluated by quantifying the systematic and random errors of these products as well as the temporal consistency of their trends. Though the broader spatial patterns of precipitation are generally well captured by the datasets, they differ significantly in their means and trends. In general, precipitation datasets that incorporate information from gauges are found to have higher accuracy with low Root Mean Square Errors and high correlation coefficient values. An ensemble of LSM simulations with selected subset of precipitation products is then used to produce the mean annual fluxes and their uncertainty over HMA in P, ET, and R to be 2.11±0.45, 1.26±0.11, and 0.85±0.36 mm per day, respectively. The mean annual estimates of the surface mass (water) balance components from this model ensemble are comparable to global estimates from prior studies. However, the uncertainty/spread of P, ET, and R is significantly larger than the corresponding estimates from global studies. A comparison of ET, snow cover fraction, and changes in TWS estimates against remote sensing-based references confirms the significant role of the input meteorology in influencing the water budget characterization over HMA and points to the need for improving meteorological inputs.

Estimating snow mass in North America through assimilation of AMSR-E brightness temperature observations using the Catchment land surface model and support vector machines.

To estimate snow mass across North America, brightness temperature observations collected by the Advanced Microwave Scanning Radiometer from 2002 to 2011 were assimilated into the Catchment model using a support vector machine (SVM) as the observation operator and a one-dimensional ensemble Kalman filter. The performance of the assimilation system is evaluated through comparisons against ground-based measurements and reference snow products. In general, there are no statistically significant skill differences between the domain-averaged, model-only ("open loop", or OL) snow estimates and assimilation estimates. The assessment of improvements (or degradations) in snow estimates is difficult because of limitations in the measurements (or products) used for evaluation. It is found that assimilation estimates agree slightly better in terms of root-mean-square error (RMSE) and Nash-Sutcliffe model efficiency with ground-based snow depth measurements than OL estimates in 82% (56 out of 62) of pixels that are colocated with at least two ground-based stations. Assimilation estimates tend to agree slightly better in terms of mean difference with reference snow products over tun-dra snow, alpine snow, maritime snow, and sparsely-vegetated, snow covered pixels. Changes in snow mass via assimilation translate into improvements (e.g.,by 22% on average in terms of RMSE, relative to OL) in cumulative runoff estimates when compared against discharge measurements in 11 out of 13 snow-dominated basins in Alaska. These results suggest that a SVM can potentially serve as an effective observation operator for snow mass estimation within a radiance assimilation system, but a better observational baseline is required to document a statistically significant improvement.

Assimilation of MODIS Snow Cover Fraction Observations into the NASA Catchment Land Surface Model.

The NASA Catchment land surface model (CLSM) is the land model component used for the Modern-Era Retrospective Analysis for Research and Applications (MERRA). Here, the CLSM versions of MERRA and MERRA-Land are evaluated using snow cover fraction (SCF) observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Moreover, a computationally-efficient empirical scheme is designed to improve CLSM estimates of SCF, snow depth, and snow water equivalent (SWE) through the assimilation of MODIS SCF observations. Results show that data assimilation (DA) improved SCF estimates compared to the open-loop model without assimilation (OL), especially in areas with ephemeral snow cover and mountainous regions. A comparison of the SCF estimates from DA against snow cover estimates from the NOAA Interactive Multisensor Snow and Ice Mapping System showed an improvement in the probability of detection of up to 28% and a reduction in false alarms by up to 6% (relative to OL). A comparison of the model snow depth estimates against Canadian Meteorological Centre analyses showed that DA successfully improved the model seasonal bias from -0.017 m for OL to -0.007 m for DA, although there was no significant change in root-mean-square differences (RMSD) (0.095 m for OL, 0.093 m for DA). The time-average of the spatial correlation coefficient also improved from 0.61 for OL to 0.63 for DA. A comparison against in situ SWE measurements also showed improvements from assimilation. The correlation increased from 0.44 for OL to 0.49 for DA, the bias improved from -0.111 m for OL to -0.100 m for DA, and the RMSD decreased from 0.186 m for OL to 0.180 m for DA.

Evaluation and enhancement of permafrost modeling with the NASA Catchment Land Surface Model.

Besides soil hydrology and snow processes, the NASA Catchment Land Surface Model (CLSM) simulates soil temperature in six layers from the surface down to 13m depth. In this study, to examine CLSM's treatment of subsurface thermodynamics, a baseline simulation produced subsurface temperatures for 1980-2014 across Alaska at 9-km resolution. The results were evaluated using in situ observations from permafrost sites across Alaska. The baseline simulation was found to capture the broad features of inter- and intra-annual variations in soil temperature. Additional model experiments revealed that: (i) the representativeness of local meteorological forcing limits the model's ability to accurately reproduce soil temperature, and (ii) vegetation heterogeneity has a profound influence on subsurface thermodynamics via impacts on the snow physics and energy exchange at surface. Specifically, the profile-average RMSE for soil temperature was reduced from 2.96°C to 2.10°C at one site and from 2.38°C to 2.25°C at another by using local forcing and land cover, respectively. Moreover, accounting for the influence of soil organic carbon on the soil thermal properties in CLSM leads to further improvements in profile-average soil temperature RMSE, with reductions of 16% to 56% across the different study sites. The mean bias of climatological ALT is reduced by 36% to 89%, and the RMSE is reduced by 11% to 47%. Finally, results reveal that at some sites it may be essential to include a purely organic soil layer to obtain, in conjunction with vegetation and snow effects, a realistic "buffer zone" between the atmospheric forcing and soil thermal processes.