Is snow cover equivalent to mountain snow water in a reanalysis? Can the snow water storage of a region be calculated?

Seasonal snow in mountainous regions plays a critical role in the global energy and water cycle. The unique properties of snow, such as its high albedo and low thermal conductivity, make it an important factor in the lower energy budget of the atmosphere and the Earth’s surface. Seasonal snow conditions also affect local climate and monsoon circulation (Rudisill et al., 2021). In addition, mountains act as natural water reservoirs, often referred to as “water towers,” by storing water in the form of snow and glaciers at high altitudes (Immerzeel et al., 2020). It is estimated that between 50 and 70 percent of annual precipitation falls in the mountainous regions of the western United States (W.S.). It is stored as snow and in the snowpack. During warmer seasons, this stored water is released as snowmelt, which is critical for meeting downstream water demand and sustaining ecosystems. Many major rivers around the world, such as the Colorado (running through the United States and Mexico), the Indus (flowing through the Himalayas), and the Mackenzie (in Canada), rely heavily on mountains.Snowmelt makes such water towers vulnerable to climate and socio-economic changes, which could have negative impacts on the estimated 2 billion people (22% of the world’s population) living downstream (Immerzeel et al. 2020; Mankin et al., 2015). Monitoring and managing water resources from mountain snow requires accurate snow water equivalent (SWE) data. However, recent studies show that there are large discrepancies in the climatology of the magnitude and timing of seasonal SWE in different global datasets (Fang et al., 2023; Liu et al., 2022; Wrzesien et al., 2019; Mudryk et al., 2024; Kim et al., 2021) with high uncertainties, especially in mountainous regions. CCI). High-quality long-term snow cover fraction and SWE datasets to help understand snow in the climate system. In the initial phase, the Snow CCI project focused on generating multisensor time series of daily fractional snow cover. From optical satellite data (Nagler et al., 2022) and SWE derived from passive microwave (PM) satellite assimilation and in situ snow depth, Snow CCI SWE adapted the GlobSnow (v3) algorithm that estimates SWE by combining PM. Brightness temperatures centered at 19 and 37 GHz from the Scanning Multichannel Microwave Radiometer (SMMR), the Special Microwave Sensor/Imager (SSM/I), and the Special Microwave Sensor/Sounder (SMIS) with daily in situ snow depth measured via a Bayesian nonlinearity (20,10). The Snow CCI SWE product (and previous versions of GlobSnow originally described in Takala et al., 2011) do Do not provide data over complex terrain because the coarse grid spacing (12.5–25 km) of the Snow CCI SWE product is inconsistent with the scales of SWE variability over complex terrain. The Snow CCI SWE retrieval method is not suitable for combining satellite passive microwave measurements with surface snow depth measurements for complex terrain. And deep snow is common in mountainous areas because (1) the sensitivity of passive microwave to SWE saturates when SWE exceeds 150 mm (Chang et al., 198, and 198) and the observations are too few to be meaningfully recorded. Elevation and topographic variations in snow depth distribution (Pulliainen, 2006).The Snow CCI SCF dataset is available globally at a spatial resolution of 0.01° (~1 km). The 1 km resolution of the Snow CCI fSCA product is relatively coarse compared to the resolution of existing optical imagery, but is suitable for potential application across all mountainous regions of the Northern Hemisphere to fill the gap in the Snow CCI SWE product. This study uses the MODIS-based Snow CCI Daily SCF product (version 2) available for the period 2000–2020. (http://cci.esa.int/data). This product is based on data from the MODIS sensor on the Terra satellite (MOD021KM and MOD03). While the MODIS sensor provides radiance data at spatial resolutions of 250, 500, and 1000 m, the Snow CCI SCF uses 1 km Level 1B data, which aggregates all radiance data to the largest spatial scale. The processing chain of the Snow CCI SCF product includes (1) satellite data preprocessing, (2) cloud screening, (3) binary snow preclassification based on the normalized snow difference index (NDSI), and (4) SCF retrieval using the adapted SCAmod algorithm (Metsämäki et al., more 201, 201) (2022). The product consists of two SCF datasets: visible snow cover (SCFV), which is the fraction of snow cover in open areas. and snow cover and vegetation on the ground (SCFG), which is the fraction of snow cover in open areas (same as SCFV) and under forest canopies. The SCFV dataset is used in this study because only visible snow cover (i.e. through forest gaps) is captured in the current snow reanalysis framework described in Section 2.3. Snow CCI SCFV This dataset is referred to hereafter as Snow CCI fSCA. The CCI SCF Snow products have an associated uncertainty layer. We tested the possibility of using this layer to provide spatially and temporally varying weights within the assimilation. However, the framework found that the uncertainty layer was not suitable for this implementation. In particular, it does not take into account the effect of the geometry of the viewing angle, which is a significant influence on the MODIS-derived fSCA (Section 2.3.2), so the uncertainty values ​​were too low for the purposes of assimilation of our data, so that the fSCA images were very heavy, which caused degradation. Performance compared to before