Abstract. With the aim of reducing the uncertainty of simulations, data assimilation methodology is increasingly being applied in operational purposes. This study aims to investigate the performance of genetic particle filter which used as snow data assimilation scheme, designed to assimilate ground-based snow depth measurements across different snow climates. We employed the default parameterization scheme combination within Noah-MP model as model operator in the snow data assimilation system. And the feasibility of genetic particle filter used as snow data assimilation scheme was investigated at different sites, at the same time, the impact of measurement frequency, particle number on the filter updating of the snowpack state were also evaluated. The results demonstrated that the genetic particle filter can be used as snow data assimilation scheme and obtain satisfactory assimilation results across different snow climates. We found the particle number is not the crucial factor to impact the filter performance and one hundred particles can sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the performance of filter updating and a dense ground-based snow observational data always can dominate the accuracy of assimilation results. Finally, we concluded that the genetic particle filter is a suitable candidate approach to snow data assimilation and appropriate for different snow climates.
Passive microwave remote sensing is a valuable tool for snow depth estimation. However, accurate retrieval is limited by nonlinear relationships between the snow depth and passive microwave brightness temperature (TB) that are caused by snow physical properties, underlying surface type, and topographical factors. Our study aims to enhance snow depth estimation in Northern Xinjiang (NX), China, utilizing Advanced Microwave Scanning Radiometer 2 TB data (with a resolution of 0.1 deg) and fractional snow cover products through a combination of wavelet transform and two artificial neural network (ANN) models: feedforward neural network (FFNN) and generalized regression neural network (GRNN). The hybrid models were trained and validated using in situ snow depth observations from 44 stations across NX. Results indicate that applying wavelet transform reduces the root-mean-square error (RMSE) by 28.88% for FFNN. In the snow season of 2013 to 2014, Wavelet-GRNN (RMSE: 7.36 cm, NSE: 0.59, R: 0.78, bias: 1.68 cm) outperforms Wavelet-FFNN (RMSE: 8.26 cm, NSE: 0.48, R: 0.75, bias: 1.69 cm) by 10.90%. However, Wavelet-FFNN exhibits superior performance, up to 13.78% than Wavelet-GRNN in complex topographic areas like Xiaoquzi station. In addition, spatial–temporal estimations demonstrate that the hybrid models surpass three well-known snow depth products and alleviate issues of excessively high or low values in NX. These findings underscore the effectiveness of hybrid models combining wavelet transform and ANNs, integrating passive microwave remote sensing and auxiliary data, for accurate snow depth estimation in mountainous regions.
Snow cover plays a crucial role in the surface energy balance and hydrology and serves as a key indicator of climate change. In this study, we conducted an ensemble simulation comprising 48 members generated by randomly combining the parameterizations of five physical processes within the Noah-MP model. Utilizing the variance-based Sobol total sensitivity index, we quantified the sensitivity of regional-scale snow depth simulations to parameterization schemes. Additionally, we analyzed the spatial patterns of the parameterization sensitivities and assessed the uncertainty of the multi-parameterization scheme ensemble simulation. The results demonstrated that the differences in snow depth simulation results among the 48 scheme combinations were more pronounced in mountain regions, with melting mechanisms being the primary factor contributing to uncertainty in ensemble simulation. Contrasting mountain regions, the sensitivity index for the physical process of partitioning precipitation into rainfall and snowfall was notably higher in basin areas. Unexpectedly, the sensitivity index of the lower boundary condition of the physical process of soil temperature was negligible across the entire region. Surface layer drag coefficient and snow surface albedo parameterization schemes demonstrated meaningful sensitivity in localized areas, while the sensitivity index of the first snow layer or soil temperature time scheme exhibited a high level of sensitivity throughout the entire region. The uncertainty of snow depth ensemble simulation in mountainous areas is predominantly concentrated between 0.2 and 0.3 m, which is significantly higher than that in basin areas. This study aims to provide valuable insights into the judicious selection of parameterization schemes for modeling snow processes.
Atmospheric disturbance, sensor malfunctions, and other factors can cause serious gap pixels in MODIS normalized difference snow index (NDSI) products. In this paper, MODIS NDSI gap pixels are reconstructed in a highly heterogeneous area with drastic snow accumulation and melting changes using a long short-term memory (LSTM) network. Three LSTM-based MODIS NDSI gap pixel reconstruction schemes, i.e., forward, backward, and bidirectional LSTM networks that separately use earlier, subsequent, and integrated earlier and subsequent timestamp information, are developed. NDSI information for the gap pixel is restored using the long-term spatiotemporal information for this pixel and its adjacent pixels. A case study of NDSI reconstruction in the source area of the Yellow River, northwestern China, during the 2018–2019 snow season, demonstrates that all three LSTM-based schemes can reliably generate spatiotemporally continuous NDSI data with an accuracy comparable to that of the original MODIS NDSI products under clear-sky conditions. The bidirectional LSTM-based scheme, which has the best performance, can achieve a desirable overall accuracy of 89.93%, with an omission error of 3.82% and a commission error of 6.25%, in terms of dichotomous evaluation based on in situ snow depth observations. The R2, average RMSE, overestimation error, and underestimation error are 0.95, 5.13%, 5.39, and 6.40%, respectively, in terms of the continuous value assessment based on the gap pixels assumption. Our results demonstrate the reliability and feasibility of the LSTM-based schemes in recovering the missing values in MODIS NDSI products by deeply excavating the spatial continuity and long-time series dependence of the snow cover.
Abstract. With the aim of reducing the uncertainty of simulations, data assimilation methodology is increasingly being applied in operational purposes. This study aims to investigate the performance of genetic particle filter which used as snow data assimilation scheme, designed to assimilate ground-based snow depth measurements across different snow climates. We employed the default parameterization scheme combination within Noah-MP model as model operator in the snow data assimilation system. And the feasibility of genetic particle filter used as snow data assimilation scheme was investigated at different sites, at the same time, the impact of measurement frequency, particle number on the filter updating of the snowpack state were also evaluated. The results demonstrated that the genetic particle filter can be used as snow data assimilation scheme and obtain satisfactory assimilation results across different snow climates. We found the particle number is not the crucial factor to impact the filter performance and one hundred particles can sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the performance of filter updating and a dense ground-based snow observational data always can dominate the accuracy of assimilation results. Finally, we concluded that the genetic particle filter is a suitable candidate approach to snow data assimilation and appropriate for different snow climates.
Abstract. With the aim of reducing the uncertainty of simulations, data assimilation methodology is increasingly being applied in operational purposes. This study aims to investigate the performance of genetic particle filter which used as snow data assimilation scheme, designed to assimilate ground-based snow depth measurements across different snow climates. We employed the default parameterization scheme combination within Noah-MP model as model operator in the snow data assimilation system. And the feasibility of genetic particle filter used as snow data assimilation scheme was investigated at different sites, at the same time, the impact of measurement frequency, particle number on the filter updating of the snowpack state were also evaluated. The results demonstrated that the genetic particle filter can be used as snow data assimilation scheme and obtain satisfactory assimilation results across different snow climates. We found the particle number is not the crucial factor to impact the filter performance and one hundred particles can sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the performance of filter updating and a dense ground-based snow observational data always can dominate the accuracy of assimilation results. Finally, we concluded that the genetic particle filter is a suitable candidate approach to snow data assimilation and appropriate for different snow climates.
Abstract. With the aim of reducing the uncertainty of simulations, data assimilation methodology is increasingly being applied in operational purposes. This study aims to investigate the performance of genetic particle filter which used as snow data assimilation scheme, designed to assimilate ground-based snow depth measurements across different snow climates. We employed the default parameterization scheme combination within Noah-MP model as model operator in the snow data assimilation system. And the feasibility of genetic particle filter used as snow data assimilation scheme was investigated at different sites, at the same time, the impact of measurement frequency, particle number on the filter updating of the snowpack state were also evaluated. The results demonstrated that the genetic particle filter can be used as snow data assimilation scheme and obtain satisfactory assimilation results across different snow climates. We found the particle number is not the crucial factor to impact the filter performance and one hundred particles can sufficient to represent the high dimensionality of the point-scale system. The frequency of measurements can significantly affect the performance of filter updating and a dense ground-based snow observational data always can dominate the accuracy of assimilation results. Finally, we concluded that the genetic particle filter is a suitable candidate approach to snow data assimilation and appropriate for different snow climates.
Studying the dynamics of snowline altitude at the end of the melting season (SLA-EMS) is beneficial in predicting future trends of glaciers and non-seasonal snow cover and in comprehending regional and global climate change. This study investigates the spatiotemporal variation characteristics of SLA-EMS in nine glacier areas of the Himalayas, utilizing Landsat images from 1991 to 2022. The potential correlations between SLA-EMS, alterations in temperature, and variations in precipitation across the Himalayas region glacier are also being analyzed. The results obtained are summarized below: (1) the Landsat-extracted SLA-EMS exhibits a strong agreement with the minimum snow coverage at the end of the melting season derived from Sentinel-2, achieving an overall accuracy (OA) of 92.6% and a kappa coefficient of 0.85. The SLA-EMS can be accurately obtained by using this model. (2) In the last 30 years, the SLA-EMS in the study areas showed an upward trend, with the rising rate ranging from 0.4 m·a−1 to 9.4 m·a−1. Among them, the SLA-EMS of Longbasaba rose fastest, and that of Namunani rose slowest. (3) The SLA-EMS in different regions of the Himalayas in a W-E direction have different sensitivity to precipitation and temperature. However, almost all of them show a positive correlation with temperature and a negative correlation with precipitation.
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