Satellite Snow Cover Research Articles

Assimilating Satellite-Derived Snow Cover and Albedo Data to Improve 3-D Weather and Photochemical Models

During wintertime temperature inversion episodes, ozone in the Uinta Basin sometimes exceeds the standard of 70 ppb set by the US Environmental Protection Agency. Since ozone formation depends on sunlight, and less sunlight is available during winter, wintertime ozone can only form if snow cover and albedo are high. Researchers have encountered difficulties replicating high albedo values in 3-D weather and photochemical transport model simulations for winter episodes. In this study, a process to assimilate MODIS satellite data into WRF and CAMx models was developed, streamlined, and tested to demonstrate the impacts of data assimilation on the models’ performance. Improvements to the WRF simulation of surface albedo and snow cover were substantial. However, the impact of MODIS data assimilation on WRF performance for other meteorological quantities was minimal, and it had little impact on ozone concentrations in the CAMx photochemical transport model. The contrast between the data assimilation and reference cases was greater for a period with no new snow since albedo appears to decrease too rapidly in default WRF and CAMx configurations. Overall, the improvement from MODIS data assimilation had an observed enhancement in the spatial distribution and temporal evolution of surface characteristics on meteorological quantities and ozone production.

Improving glacio-hydrological model calibration and model performance in cold regions using satellite snow cover data

Hydrological modeling realism is a central research question in hydrological studies. However, it is still a common practice to calibrate hydrological models using streamflow as a single hydrological variable, which can lead to large parameter uncertainty in hydrological simulations. To address this issue, this study employed a multi-variable calibration framework to reduce parameter uncertainty in a glacierized catchment. The current study employed multi-variable calibration using three different calibration schemes to calibrate a glacio-hydrological model (namely the FLEXG) in northern Sweden. The schemes included using only gauged streamflow data (scheme 1), using satellite snow cover area (SCA) derived from MODIS data (scheme 2), and using both gauged streamflow data and satellite SCA data as references for calibration (scheme 3) of the FLEXG model. This study integrated the objective functions of satellite-derived SCA and gauged streamflow into one criterion for the FLEXG model calibration using a weight-based approach. Our results showed that calibrating the FLEXG model based on solely satellite SCA data (from MODIS) produced an accurate simulation of SCA but poor simulation of streamflow. In contrast, calibrating the FLEXG model based on the measured streamflow data resulted in minimum error for streamflow simulation but high error for SCA simulation. The promising results were achieved for glacio-hydrological simulation with acceptable accuracy for simulation of both streamflow and SCA, when both streamflow and SCA data were used for calibration of FLEXG. Therefore, multi-variable calibration in a glacierized basin could provide more realistic hydrological modeling in terms of multiple glacio-hydrological variables.

Dynamics of Spring Snow Cover Variability over Northeast China

Spring snow cover variability over Northeast China (NEC) has a profound influence on the local grain yield and even the food security of the country, but its drivers remain unclear. In the present study, we investigated the spatiotemporal features and the underlying mechanisms of spring snow cover variability over NEC during 1983–2018 based on the satellite-derived snow cover data and atmospheric reanalysis products. The empirical orthogonal function (EOF) analysis showed that the first EOF mode (EOF1) explains about 50% of the total variances and characterizes a coherent snow cover variability pattern over NEC. Further analyses suggested that the formation of the EOF1 mode is jointly affected by the atmospheric internal variability and the sea surface temperature (SST) anomaly at the interannual timescale. Specifically, following a negative phase of the atmospheric teleconnection of the Polar–Eurasian pattern, a prominent cyclonic circulation appears over NEC, which increases the snowfall over the east of NEC by enhancing the water vapor transport and decreases the air temperature through reducing the solar radiation and intensifying the cold advection. As a result, the snow cover has increased over NEC. Additionally, the tripole structure of the North Atlantic spring SST anomaly could excite a wave-train-type anomalous circulation propagating to NEC that further regulates the snow cover variability by altering the atmospheric dynamic and thermodynamic conditions and the resultant air temperature and snowfall. Our results have important implications on the understanding of the spring snow cover anomaly over NEC and the formulation of the local agricultural production plan.

Spatial distribution and controls of snowmelt runoff in a sublimation-dominated environment in the semiarid Andes of Chile

Abstract. Sublimation is the main ablation component of snow in the upper areas of the semiarid Andes (∼ 26 to ∼ 32∘ S and ∼ 69 to ∼ 71∘ W). This region has elevations up to 6000 m, is characterized by scarce precipitation, high solar radiation receipt, and low air humidity, and has been affected by a severe drought since 2010. In this study, we suggest that most of the snowmelt runoff originates from specific areas with topographic and meteorological features that allow large snow accumulation and limited mass removal. To test this hypothesis, we quantify the spatial distribution of snowmelt runoff and sublimation in a catchment of the semiarid Andes using a process-based snow model that is forced with field data. Model simulations over a 2-year period reproduce point-scale records of snow depth (SD) and snow water equivalent (SWE) and are also in good agreement with an independent SWE reconstruction product as well as satellite snow cover area and indices of winter snow absence and summer snow persistence. We estimate that 50 % of snowmelt runoff is produced by 21 %–29 % of the catchment area, which we define as “snowmelt hotspots”. Snowmelt hotspots are located at mid-to-lower elevations of the catchment on wind-sheltered, low-angle slopes. Our findings show that sublimation is not only the main ablation component: it also plays an important role shaping the spatial variability in total annual snowmelt. Snowmelt hotspots might be connected with other hydrological features of arid and semiarid mountain regions, such as areas of groundwater recharge, rock glaciers, and mountain peatlands. We recommend more detailed snow and hydrological monitoring of these sites, especially in the current and projected scenarios of scarce precipitation.

DSRSS-Net: Improved-Resolution Snow Cover Mapping from FY-4A Satellite Images Using the Dual-Branch Super-Resolution Semantic Segmentation Network

The Qinghai–Tibet Plateau is one of the regions with the highest snow accumulation in China. Although the Fengyun-4A (FY4A) satellite is capable of monitoring snow-covered areas in real time and on a wide scale at high temporal resolution, its spatial resolution is low. In this study, the Qinghai–Tibet Plateau, which has a harsh climate with few meteorological stations, was selected as the study area. We propose a deep learning model called the Dual-Branch Super-Resolution Semantic Segmentation Network (DSRSS-Net), in which one branch focuses with super resolution to obtain high-resolution snow distributions and the other branch carries out semantic segmentation to achieve accurate snow recognition. An edge enhancement module and coordinated attention mechanism were introduced into the network to improve the classification performance and edge segmentation effect for cloud versus snow. Multi-task loss is also used for optimization, including feature affinity loss and edge loss, to obtain fine structural information and improve edge segmentation. The 1 km resolution image obtained by coupling bands 1, 2, and 3; the 2 km resolution image obtained by coupling bands 4, 5, and 6; and the 500 m resolution image for a single channel, band 2, were inputted into the model for training. The accuracy of this model was verified using ground-based meteorological station data. Snow classification accuracy, false detection rate, and total classification accuracy were compared with the MOD10A1 snow product. The results show that, compared with MOD10A1, the snow classification accuracy and the average total accuracy of DSRSS-Net improved by 4.45% and 5.1%, respectively. The proposed method effectively reduces the misidentification of clouds and snow, has higher classification accuracy, and effectively improves the spatial resolution of FY-4A satellite snow cover products.

Multi-criteria evaluation for parameter uncertainty assessment and ensemble runoff forecasting in a snow-dominated basin

Abstract The increase in global temperatures undesirably affects the ever-growing world population and reveals the significance of hydrology science. Hydrological models might estimate spatial and temporal variability in hydrological components at the basin scale, which is critical for efficient water resource management. Satellite data sets with enhanced snow mapping with high spatial and temporal resolutions have been developed. The potential of these satellite data sets is evaluated in this study for multi-criteria evaluation of a conceptual hydrological model to improve model performance and reduce uncertainty. The upstream part of the transboundary Coruh River is selected for this study because snowmelt contributes a significant portion of the streamflow feeding major reservoirs during the spring and early summer months. The region’s snow cover dynamic has been analyzed using a combination of two satellite products. Hydrologic modeling is performed using the HBV model for the 2003–2015 water years (01 Oct–30 Sep). The Monte Carlo method is used for multi-criteria optimization exploiting satellite snow cover data besides runoff data. The sensitivity and uncertainty analysis on the model parameters indicate that multi-criteria calibration effectively reduces the uncertainty of the parameters and increases the model performance. Moreover, ensemble runoff forecasts are generated with several best model parameters using 1-day and 2-day lead time numerical weather prediction data for the snowmelt period (March–June) of the 2015 water year. The results indicate that the use of multiple remote sensing products in combination better represents the snow-covered area for the region. Additionally, including these data sets into hydrological models enhances the representation of hydrological components while reducing runoff prediction uncertainty.

Simulating glacier mass balance and its contribution to runoff in Northern Sweden

Glaciers are one of the main sources of freshwater in cold regions. The glacier melting process can significantly impact the glacier mass balance (GMB) and contribute a large amount of runoff in cold regions. This study applied the recently developed semi-distributed glacio-hydrological conceptual model (FLEXG) to understand the glacier melting process and the effect of topography on GMB in the Torne River basin, northern Sweden. The study simulated glacier and snow accumulation and ablation, as well as runoff from the glacier and non-glacier areas of the basin using the FLEXG model for the time period 1989–2018. The FLEXG model considers the influence of topography on runoff generation, and in this study the basin was classified into 143 zones depending on elevation and aspect. In order to gain a comprehensive view of the performance of the FLEXG model, the classical lumped hydrological model HBV was used and compared with the FLEXG model in simulating total streamflow and peak runoff at the outlet of the basin. Our results revealed that the FLEXG model performed well in reproducing the streamflow (also better than the HBV model) with metric Kling-Gupta Efficiency (KGE) of 0.80 and 0.71 for the calibration and validation periods, respectively. We also found that the FLEXG model performs better in peak runoff simulation than the HBV model. The FLEXG simulated snow cover area proportion agreed well with the MODIS satellite snow cover product (R2 = 0.60 and RMSE = 28%). The GMB in different elevation zones was simulated, and a downward trend was found for GMB changes during the study period because of climate change.

The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites

Abstract. The recent advances in remote sensing provide opportunities for estimating the parameters of conceptual hydrologic models more reliably. However, the question of whether and to what extent the use of satellite data in model calibration may assist in transferring model parameters to ungauged catchments has not been fully resolved. The aim of this study is to evaluate the efficiency of different methods for transferring model parameters obtained by multiple-objective calibrations to ungauged sites and to assess the model performance in terms of runoff, soil moisture, and snow cover predictions relative to existing regionalization approaches. The model parameters are calibrated to daily runoff, satellite soil moisture (Advanced Scatterometer – ASCAT), and snow cover (Moderate Resolution Imaging Spectroradiometer – MODIS) data. The assessment is based on 213 catchments situated in different physiographic and climate zones of Austria. For the transfer of model parameters, eight methods (global and local variants of arithmetic mean, regression, spatial proximity, and similarity) are examined in two periods, i.e., the period in which the model is calibrated (2000–2010) and an independent validation period (2010–2014). The predictive accuracy is evaluated by the leave-one-out cross-validation. The results show that the method by which the model is calibrated in the gauged catchment has a larger impact on runoff prediction accuracy in the ungauged catchments than the choice of the parameter transfer method. The best transfer methods are global and local similarity and the kriging approach. The performance of the transfer methods differs between lowland and alpine catchments. While the soil moisture and snow cover prediction efficiencies are higher in lowland catchments, the runoff prediction efficiency is higher in alpine catchments. A comparison of the model transfer methods, based on parameters calibrated to runoff, snow cover, and soil moisture with those based on parameters calibrated to runoff, only indicates that the former outperforms the latter in terms of simulating soil moisture and snow cover. The performance of simulating runoff is similar, and the accuracy depends mainly on the weight given to the runoff objective in the multiple-objective calibrations.

Relationships between Satellite-derived Snow Cover Extent in the Northern Hemisphere and Surface Air Temperature

Relationships between Satellite-derived Snow Cover Extent in the Northern Hemisphere and Surface Air Temperature

Glacier Runoff Variation Since 1981 in the Upper Naryn River Catchments, Central Tien Shan

Water resources in Central Asia strongly depend on glaciers, which in turn adjust their size in response to climate variations. We investigate glacier runoff in the period 1981–2019 in the upper Naryn basin, Kyrgyzstan. The basins contain more than 1,000 glaciers, which cover a total area of 776 km2. We model the mass balance and runoff contribution of all glaciers with a simplified energy balance melt model and distributed accumulation model driven by ERA5 LAND re-analysis data for the time period of 1981–2019. The results are evaluated against discharge records, satellite-derived snow cover, stake readings from individual glaciers, and geodetic mass balances. Modelled glacier volume decreased by approximately 6.7 km3 or 14%, and the majority of the mass loss took place from 1996 until 2019. The decreasing trend is the result of increasingly negative summer mass balances whereas winter mass balances show no substantial trend. Analysis of the discharge data suggests an increasing runoff for the past two decades, which is, however only partly reflected in an increase of glacier melt. Moreover, the strongest increase in discharge is observed in winter, suggesting either a prolonged melting period and/or increased groundwater discharge. The average runoff from the glacierized areas in summer months (June to August) constitutes approximately 23% of the total contributions to the basin’s runoff. The results highlight the strong regional variability in glacier-climate interactions in Central Asia.

Sensitivities of Hydrological Processes to Climate Changes in a Central Asian Glacierized Basin

This study used the WASA (Water Availability in Semi-Arid Environments) hydrological model to simulate runoff generation processes and glacier evolution in the Ala-Archa basin in Central Asia. Model parameters were calibrated by observations of streamflow, satellite snow cover area (SCA) and annual glacier mass balance (GMB). Temperature and precipitation change scenarios were set up by perturbations of the reference measurements in a 20-year period of 1997 to 2016. Seven temperature warming scenarios with an increment of +1°C and six precipitation change scenarios ranging from 70 to 130% of the reference precipitation were used to investigate the sensitivities of hydrological processes to climate changes in the study basin. Results indicate that: (1) Annual runoff increased with rising temperature (T) and precipitation (P) at rates of 76 mm/+1°C and 62 mm/+10%P, respectively. Glacier area was more sensitive to T changes than to P changes. The total glacier area in the basin decreased with T warming at a rate of −0.47 km2/+1°C, whilst increasing with rising P at a rate of 0.16 km2/+10%P. (2) The basin runoff switched from rainfall and groundwater-dominated to ice melt-dominated with warming T, while the dominance of rainfall and groundwater were strongly enhanced by rising P. Proportion of rainfall in the total water input for runoff generation decreased with T warming at a rate of −0.5%/+1°C, while increasing with P increases at a rate of 1.2%/+10% P. Ice melt proportion changed with T and P increases at rates of 4.2%/+1°C and −1.8%/+10%P, respectively. Groundwater contribution to total runoff decreased by −2.8% per T warming of 1°C, but increased by 1.5% per P increase of 10%. (3) The maximum P changes (±30%) could only compensate the effects of T warming of 0.5 to 2.5°C. Increase of annual runoff forced by T warming lower than 2.2°C could be compensated by decrease caused by the maximum P decrease of −30%. Decrease of glacier area caused by 1°C warming cannot be compensated by the maximum P increase of +30%. The combined input of 20% increase of P and T warming of 6°C resulted in 90% increase of annual runoff, and 8% reduction of glacier area. The results inform understandings of the hydrological responses to potential climate changes in glacierized basins in Central Asia.

Reducing hydrological modelling uncertainty by using MODIS snow cover data and a topography-based distribution function snowmelt model

This work introduces a general multi-objective parameter estimation framework to exploit MODIS-based snow cover maps to reduce predictive streamflow uncertainty in snow-dominated catchments. The well-known GLUE methodology is applied with a multi-objective approach, combining streamflow observations recorded at the outlet section and satellite-derived snow cover maps, aggregated to fractional values of the catchment area. The hydrological model used in this study includes a snowpack routine which exploits a statistical representation of the distribution of clear sky potential solar radiation - a significant advantage when parameter sensitivity and uncertainty estimation procedures are carried out. The study provides an assessment of this approach based on operational quality data from two medium-size mountainous basins (a nested one included in a larger parent basin) located in the eastern Italian Alps. The nested basin is considered as ungauged, thus allowing a spatial assessment of the multi-objective approach. Results show a positive feedback between streamflow and snow cover area likelihoods, highlighted by means of the Pareto plot. Moreover, a better identifiability of the parameters driving snowmelt rate is found and consequently a shrink of the predictive streamflow uncertainty is observed. A containing ratio of 0.54 and a mean sharpness of 0.11 are found at the outlet of the parent basin, while a containing ratio equal to 0.65 and a mean sharpness equal to 0.17 are estimated at the nested basin, used as a validation test. These results confirm the potential of MODIS snow cover maps as additional data to inform hydrological models leading to more reliable and sharper streamflow simulations. This approach might be also appealing when streamflow simulations are required for ungauged basins.

Improving ANN-based streamflow estimation models for the Upper Indus Basin using satellite-derived snow cover area

The mountainous catchments often witness contrasting regimes and the limited available meteorological network creates uncertainty in both the hydrological data and developed models. To overcome this problem, remotely sensed data could be used in addition to on-ground observations for hydrological forecasting. The fusion of these two types of data gives a better picture and helps to generate adequate hydrological forecasting models. The study aims at the improvement of ANN-based streamflow estimation models by using an integrated data-set containing, the satellite-derived snow cover area (SCA) with on-ground flow observations. For this purpose, SCA of three sub catchments of Upper Indus Basin, namely Gilgit, Astore and Bunji coupled with their respective gauge discharges is used as model inputs. The weekly stream-flow models are developed for inflows at Besham Qila located just upstream of Tarbela dam. The data-set for modeling is prepared through normalizing all variables by scaling between 0 and 1. A mathematical tool, Gamma test is applied to fuse the inputs, and a best input combination is selected on the basis of minimum gamma value. A feed forward neural network trained via two layer Broyden Fletcher Goldfarb Shanno algorithm is used for model development. The models are evaluated on the basis of set of performance indicators, namely, Nash–Sutcliffe Efficiency, Root Mean Square Error, Variance and BIAS. A comparative assessment has also been made using these indicators for models developed, through data-set containing gauge discharges, only and the data-set fused with satellite-derived SCA. In particular, the current study concluded that the efficiency of ANN-based streamflow estimation models developed for mountainous catchments could be improved by integrating the SCA with the gauge discharges.

Assimilating multi-satellite snow data in ungauged Eurasia improves the simulation accuracy of Asian monsoon seasonal anomalies

Properly initializing land snow conditions with multi-satellite data assimilation (DA) may help tackle the long-standing challenge of Asian monsoon seasonal forecasts. However, to what extent can snow DA help resolve the problem remains largely unexplored. Here we establish, for the first time, that improved springtime snow initializations assimilating the Moderate Spectral Imaging Satellite (MODIS) snow cover fraction and the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage data can improve the simulation accuracy of Asian monsoon seasonal anomalies. Focusing on the western Tibetan Plateau (TP) and mid- to high-latitude Eurasia (EA), two regions where multi-satellite snow DA is critical, we found that DA influences the monsoon circulation at different months depending on the regional snow–atmosphere coupling strengths. For the pre-monsoon season, accurate initialization of the TP snow is key, and assimilating MODIS data slightly outperforms jointly assimilating MODIS and GRACE data. For the peak-monsoon season, accurate initialization of the EA snow is more important due to its long memory, and assimilating GRACE data brings the most pronounced gains. Among all the Asian monsoon subregions, the most robust improvement is seen over central north India, a likely result of the region’s strong sensitivity to thermal forcing. While this study highlights complementary snow observations as promising new sources of the monsoon predictability, it also clarifies complexities in translating DA to useful monsoon forecast skill, which may help bridge the gap between land DA and dynamical climate forecasting studies.

Benefits of Combining Satellite-Derived Snow Cover Data and Discharge Data to Calibrate a Glaciated Catchment in Sub-Arctic Iceland

The benefits of fractional snow cover area, as an additional dataset for calibration, were evaluated for an Icelandic catchment with a low degree of glaciation and limited data. For this purpose, a Hydrological Projections for the Environment (HYPE) model was calibrated for the Geithellnaá catchment in south-east Iceland using daily discharge (Q) data and satellite-retrieved MODIS snow cover (SC) images, in a multi-dataset calibration (MDC) approach. By comparing model results using only daily discharge data with results obtained using both datasets, the value of SC data for model calibration was identified. Including SC data improved the performance of daily discharge simulations by 7% and fractional snow cover area simulations by 11%, compared with using only the daily discharge dataset (SDC). These results indicate that MDC improves the overall performance of the HYPE model, confirming previous findings. Therefore, MDC could improve discharge simulations in areas with extra sources of uncertainty, such as glaciers and snow cover. Since the change in fractional snow cover area was more accurate when MDC was applied, it can be concluded that MDC would also provide more realistic projections when calibrated parameter sets are extrapolated to different situations.

Simulation of the meltwater under different climate change scenarios in a poorly gauged snow and glacier-fed Chitral River catchment (Hindukush region)

Seasonal and annual water supplies of the rivers originating in the Hindukush-Karakoram-Himalaya (HKH) region of Pakistan are important to manage the Indus basin irrigation system for better agricultural production and its dependent agrarian economy. In this study, we simulated the current and future snowmelt runoff in a poorly gauged river basin of the Hindukush region under Representative Concentration Pathways (RCP) climate change scenarios. Snowmelt Runoff Model (SRM) furnished with satellite snow cover maps and hydro-meteorological data were used to simulate the daily river discharge for the period 2000‒2005. The results indicated that SRM has effectually simulated the runoff in Chitral River with Nash-Sutcliffe model efficiency coefficient of 0.85 (0.84) and 0.88 (0.83) in the basin-wide (zone-wise) application during the calibration and validation periods, respectively. The results obtained under future climate change scenario showed ∼14‒19% increase in mean summer discharge under three mid-21st century RCP (2.6, 4.5 and 8.5) scenarios. While an increase of ∼13‒37% is expected under late-21st century RCP scenarios. This study can help water resource managers to plan and manage peak discharges from the Chitral River Basin in the future and can thus prevent major losses due to floods in the area.

Evaluation of snow depth and snow cover over the Tibetan Plateau in global reanalyses using in situ and satellite remote sensing observations

Abstract. The Tibetan Plateau (TP) region, often referred to as the Third Pole, is the world's highest plateau and exerts a considerable influence on regional and global climate. The state of the snowpack over the TP is a major research focus due to its great impact on the headwaters of a dozen major Asian rivers. While many studies have attempted to validate atmospheric reanalyses over the TP area in terms of temperature or precipitation, there have been – remarkably – no studies aimed at systematically comparing the snow depth or snow cover in global reanalyses with satellite and in situ data. Yet, snow in reanalyses provides critical surface information for forecast systems from the medium to sub-seasonal timescales. Here, snow depth and snow cover from four recent global reanalysis products, namely the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 and ERA-Interim reanalyses, the Japanese 55-year Reanalysis (JRA-55) and the NASA Modern-Era Retrospective analysis for Research and Applications (MERRA-2), are inter-compared over the TP region. The reanalyses are evaluated against a set of 33 in situ station observations, as well as against the Interactive Multisensor Snow and Ice Mapping System (IMS) snow cover and a satellite microwave snow depth dataset. The high temporal correlation coefficient (0.78) between the IMS snow cover and the in situ observations provides confidence in the station data despite the relative paucity of in situ measurement sites and the harsh operating conditions. While several reanalyses show a systematic overestimation of the snow depth or snow cover, the reanalyses that assimilate local in situ observations or IMS snow cover are better capable of representing the shallow, transient snowpack over the TP region. The latter point is clearly demonstrated by examining the family of reanalyses from the ECMWF, of which only the older ERA-Interim assimilated IMS snow cover at high altitudes, while ERA5 did not consider IMS snow cover for high altitudes. We further tested the sensitivity of the ERA5-Land model in offline experiments, assessing the impact of blown snow sublimation, snow cover to snow depth conversion and, more importantly, excessive snowfall. These results suggest that excessive snowfall might be the primary factor for the large overestimation of snow depth and cover in ERA5 reanalysis. Pending a solution for this common model precipitation bias over the Himalayas and the TP, future snow reanalyses that optimally combine the use of satellite snow cover and in situ snow depth observations in the assimilation and analysis cycles have the potential to improve medium-range to sub-seasonal forecasts for water resources applications.

Different Sources of 10‐ to 30‐day Intraseasonal Variations of Autumn Snow over Western and Eastern Tibetan Plateau

AbstractUsing the latest daily Moderate Resolution Imaging Spectroradiometer satellite snow cover data, the present study reveals distinctly different sources of 10‐ to 30‐day intraseasonal snow cover variations over the western and eastern Tibetan Plateau (TP) during September–December. The intraseasonal snow variation over the western TP is related to a midlatitude wave train associated with the Arctic Oscillation and that over the eastern TP is related to a subtropical wave train triggered by the North Atlantic Oscillation. The Rossby wave train in both cases leads to anomalous water vapor convergence and ascending motion, which contributes to snow accumulation and positive snow cover anomalies. For the western TP snow events, the moisture comes from the Caspian Sea. During the eastern TP snow events, the moisture originates from the Bay of Bengal.

Assimilation of Satellite-Based Snow Cover and Freeze/Thaw Observations Over High Mountain Asia.

Toward qualifying hydrologic changes in the High Mountain Asia (HMA) region, this study explores the use of a hyper-resolution (1 km) land data assimilation (DA) framework developed within the NASA Land Information System using the Noah Multi-parameterization Land Surface Model (Noah-MP) forced by the meteorological boundary conditions from Modern-Era Retrospective analysis for Research and Applications, Version 2 data. Two different sets of DA experiments are conducted: (1) the assimilation of a satellite-derived snow cover map (MOD10A1) and (2) the assimilation of the NASA MEaSUREs landscape freeze/thaw product from 2007 to 2008. The performance of the snow cover assimilation is evaluated via comparisons with available remote sensing-based snow water equivalent product and ground-based snow depth measurements. For example, in the comparison against ground-based snow depth measurements, the majority of the stations (13 of 14) show slightly improved goodness-of-fit statistics as a result of the snow DA, but only four are statistically significant. In addition, comparisons to the satellite-based land surface temperature products (MOD11A1 and MYD11A1) show that freeze/thaw DA yields improvements (at certain grid cells) of up to 0.58 K in the root-mean-square error (RMSE) and 0.77 K in the absolute bias (relative to model-only simulations). In the comparison against three ground-based soil temperature measurements along the Himalayas, the bias and the RMSE in the 0–10 cm soil temperature are reduced (on average) by 10 and 7%, respectively. The improvements in the top layer of soil estimates also propagate through the deeper soil layers, where the bias and the RMSE in the 10–40 cm soil temperature are reduced (on average) by 9 and 6%, respectively. However, no statistically significant skill differences are observed for the freeze/thaw DA system in the comparisons against ground-based surface temperature measurements at mid-to-low altitude. Therefore, the two proposed DA schemes show the potential of improving the predictability of snow mass, surface temperature, and soil temperature states across HMA, but more ground-based measurements are still required, especially at high-altitudes, in order to document a more statistically significant improvement as a result of the two DA schemes.

Evaluation of Remotely Sensed Snow Water Equivalent and Snow Cover Extent over the Contiguous United States

Abstract Global snow water equivalent (SWE) products derived at least in part from satellite remote sensing are widely used in weather, climate, and hydrometeorological studies. Here we evaluate three such products using our recently developed daily 4-km SWE dataset available from October 1981 to September 2017 over the conterminous United States. This SWE dataset is based on gridded precipitation and temperature data and thousands of in situ measurements of SWE and snow depth. It has a 0.98 correlation and 30% relative mean absolute deviation with Airborne Snow Observatory data and effectively bridges the gap between small-scale lidar surveys and large-scale remotely sensed data. We find that SWE products using remote sensing data have large differences (e.g., the mean absolute difference from our SWE data ranges from 45.8% to 59.3% of the mean SWE in our data), especially in forested areas (where this percentage increases up to 73.5%). Furthermore, they consistently underestimate average maximum SWE values and produce worse SWE (including spurious jumps) during snowmelt. Three additional higher-resolution satellite snow cover extent (SCE) products are used to compare the SCE values derived from these SWE products. There is an overall close agreement between these satellite SCE products and SCE generated from our SWE data, providing confidence in our consistent SWE, snow depth, and SCE products based on gridded climate and station data. This agreement is also stronger than that between satellite SCE and those derived from the three satellite SWE products, further confirming the deficiencies of the SWE products that utilize remote sensing data.