This study explores the use of a support vector machine (SVM) as the observation operator within a passive microwave brightness temperature data assimilation framework (herein SVM-DA) to enhance the characterization of snow water equivalent (SWE) over High Mountain Asia (HMA). A series of synthetic twin experiments were conducted with the NASA Land Information System (LIS) at a number of locations across HMA. Overall, the SVM-DA framework is effective at improving SWE estimates (~70% reduction in RMSE relative to the Open Loop) for SWE depths less than 200 mm during dry snowpack conditions. The SVM-DA framework also improves SWE estimates in deep, wet snow (~45% reduction in RMSE) when snow liquid water is well estimated by the land surface model, but can lead to model degradation when snow liquid water estimates diverge from values used during SVM training. In particular, two key challenges of using the SVM-DA framework were observed over deep, wet snowpacks. First, variations in snow liquid water content dominate the brightness temperature spectral difference (ΔT_B) signal associated with emission from a wet snowpack, which can lead to abrupt changes in SWE during the analysis update. Second, the ensemble of SVM-based predictions can collapse (i.e., yield a near-zero standard deviation across the ensemble) when prior estimates of snow are outside the range of snow inputs used during the SVM training procedure. Such a scenario can lead to the presence of spurious error correlations between SWE and ΔT_B, and as a consequence, can result in degraded SWE estimates from the analysis update. These degraded analysis updates can be largely mitigated by applying rule-based approaches. For example, restricting the SWE update when the standard deviation of the predicted ΔT_B is greater than 0.05 K helps prevent the occurrence of filter divergence. Similarly, adding a thin layer (i.e., 5 mm) of SWE when the synthetic ΔT_B is larger than 5 K can improve SVM-DA performance in the presence of a precipitation dry bias. The study demonstrates that a carefully constructed SVM-DA framework cognizant of the inherent limitations of passive microwave-based SWE estimation holds promise for snow mass data assimilation.

An airborne gamma-ray remote-sensing technique provides a strong potential to estimate a reliable snow water equivalent (SWE) in forested environments where typical remote-sensing techniques have large uncertainties. This study explores the utility of assimilating the temporally (up to four measurements during a winter period) and spatially sparse airborne gamma SWE observations into a land surface model (LSM) to improve SWE estimates in forested areas in the northeastern US. Here, we demonstrate that the airborne gamma SWE observations add value to the SWE estimates from the Noah LSM with multiple parameterization options (Noah-MP) via assimilation despite the limited number of measurements. Improvements are witnessed during the snow accumulation period, while reduced skills are seen during the snowmelt period. The efficacy of the gamma data is greater for areas with lower vegetation cover fraction and topographic heterogeneity ranges, and it is still effective at reducing the SWE estimation errors for areas with higher topographic heterogeneity. The gamma SWE data assimilation (DA) also shows a potential to extend the impact of flight-line-based measurements to adjacent areas without observations by employing a localization approach. The localized DA reduces the modeled SWE estimation errors for adjacent grid cells up to 32 km distance from the flight lines. The enhanced performance of the gamma SWE DA is evident when the results are compared to those from assimilating the existing satellite-based SWE retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) for the same locations and time periods. Although there is still room for improvement, particularly for the melting period, this study shows that the gamma SWE DA is a promising method to improve the SWE estimates in forested areas.

Prior soil moisture data assimilation (DA) efforts to incorporate human management features such as agricultural irrigation has only shown limited success. This is partly due to the fact that observational rescaling approaches for bias correction used in soil moisture DA systems are less effective when unmodeled processes such as irrigation are the dominant source of systematic biases. In this article, we demonstrate an alternative approach, i.e. anomaly correction for overcoming this limitation. Unlike the rescaling approaches, the proposed method does not scale remote sensing soil moisture retrievals to the model climatology, but it extracts the temporal variability information from the retrievals. The study demonstrates this approach through the assimilation of soil moisture retrievals from the Soil Moisture Active Passive mission into the Noah land surface model. The results demonstrate that DA using the anomaly correction method can better capture the effect of irrigation on soil moisture in agricultural areas while providing comparable performance to the DA integrations using rescaling approaches in non-irrigated areas. These findings emphasize the need to reduce inconsistencies between remote sensing and the models so that assimilation methods can employ information from remote sensing more directly to develop representations of unmodeled processes such as irrigation.

This study investigates the potential benefit of assimilating soil moisture (SM) data retrieved from Soil Moisture Active Passive (SMAP) in improving global SM estimates and enhancing the weather forecast skill of the Korean Integrated Model (KIM). The 36‐km SMAP L2 SM retrievals are assimilated into the Noah land surface model (LSM) using the ensemble Kalman filter scheme through the National Aeronautics and Space Administration Land Information System (LIS) that is weakly coupled to KIM. A suite of cycling experiments of the KIM–LIS system that includes land and atmospheric data assimilation (DA) and five‐day weather forecasts are conducted over the global domain from March to July 2022. In the SMAP SM DA, two different SM bias correction methods, namely the cumulative distribution function matching and anomaly‐based bias correction, are applied to correct systematic biases between SMAP and Noah‐LSM before assimilation. The global triple collocation analysis reveals that compared to the control case (without SM DA), significant improvements in the global SM estimates are achieved by assimilating the SMAP data, especially by employing the anomaly‐based bias correction. Notably, the improved SM initial conditions lead to an improved screen‐level specific humidity and air temperature analysis and forecasts when the results are compared against the European Centre for Medium Range Weather Forecasts‐Integrated Forecasting System analysis. The beneficial impacts of the SMAP DA on the atmospheric variables extend up to an atmospheric level of 700 hPa. Prominent improvements in the KIM forecast skill by the SMAP DA applying the anomaly‐based bias correction are observed in the northern part of Africa and West and Central Asia with stronger impacts for longer forecast lead time. This paper demonstrates the feasibility of assimilating the SMAP data within KIM–LIS to enhance the KIM weather forecasts in the lower atmosphere when a proper SM bias correction method is applied.