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Read about what snow brings to our world and its animals.

The best way to cure the winter blues is to go outside and build a snowman! In this book, beginning readers join a happy family on a trip through the snow to find some winter fun. Rolling snowballs is just the start of things for their snowman friend. Early readers are taken through the step-by-step process with colorful photographs and accessible text, watching as a pile of snow transforms into a friendly winter character complete with its own eyes, hat, and even a carrot for a nose!

In this illustrated poem in honor of the victims of the 2012 shooting in Newtown, Connecticut, falling snowflakes celebrate the uniqueness of life, its precious, simple moments, and the strength of memory.

Snowpack distribution in Arctic and alpine landscapes often occurs in repeating, year‐to‐year patterns due to local topographic, weather, and vegetation characteristics. Previous studies have suggested that with years of observational data, these snow distribution patterns can be statistically integrated into a snow process modeling workflow. Recent advances in snow hydrology and machine learning (ML) have increased our ability to predict snowpack distribution using in‐situ observations, remote sensing data sets, and simple landscape characteristics that can be easily obtained for most environments. Here, we propose a hybrid approach to couple a ML snow distribution pattern (MLSDP) map with a physics‐based, snow process model. We trained a random forest ML algorithm on tens of thousands of snow survey observations from a subarctic study area on the Seward Peninsula, Alaska, collected during peak snow water equivalent (SWE). We validated hybrid model outputs using in‐situ snow depth and SWE observations, as well as a light detection and ranging data set and a distributed temperature profiling sensor data set. When the hybrid results were compared with the physics‐based method, the hybrid method more accurately depicted the spatial patterns of the snowpack, areas of drifting snow, and years when no in‐situ observations were used in the random forest ML training data set. The hybrid method also showed improvements in root mean squared error at 61% of locations where time‐series estimations of snow depth were observed. These results can be applied to any physics‐based model to improve the snow distribution patterning to reflect observed conditions in high latitude and high elevation cold region environments. Plain Language Summary Snowpacks in cold regions often occur in repeated, year‐to‐year patterns, regardless of the amount of snow that has fallen during the winter. This is due to complex landscape and climate interactions, like the shape and angles of the topography, the direction of the prevailing winter winds, and vegetation cover. Previous studies have used snow pattern maps with models to create more accurate estimations of the snowpack. This study uses machine learning (ML) to create the snow pattern maps, along with previously determined methods for integrating those patterns into estimations of snow depth and snow water content. We spent multiple winter fieldwork campaigns taking measurements of the snowpack, including point‐based snow depth and snow water content measurements, and plane‐based measurements of snow depth over large areas. These measurements were used to compare with our simulated snowpack characteristics. The hybrid approach, one that used ML plus physics‐based models, produced better spatial representations of the snowpack than results when ML was not used. Additionally, the hybrid approach generated improvements to modeled snow drift regions in the study area. These results show that ML and physics‐based models can be used together to create better spatial representations of snowpack characteristics in cold regions. Key Points Machine learning, coupled with physics‐based process models, can predict snow patterns with lower error than the process model alone When machine learning and physics‐based process models were used together, drifted snow areas were improved in the model outputs Improvements in root mean squared error were found at 61% of snow depth observation sites throughout the year

A simple introduction to the characteristics of snow leopards.

The lack of accurate information on the spatiotemporal variations of snow water equivalent (SWE) in mountain catchments remains a key problem in snow hydrology and water resources management. This is partly because there is no sensor to measure SWE beyond local scale. At Mt. Zugspitze, Germany, a superconducting gravimeter senses the gravity effect of the seasonal snow, reflecting the temporal evolution of SWE in a few kilometers scale radius. We used this new observation to evaluate two configurations of the Alpine3D distributed snow model. In the default run, the model was forced with meteorological station data. In the second run, we applied precipitation correction based on an 8 m resolution snow depth image derived from satellite observations (Pléiades). The snow depth image strongly improved the simulation of the snowpack gravity effect during the melt season. This result suggests that satellite observations can enhance SWE analyses in mountains with limited infrastructure.

\"A look at snow leopards, [including] their habitats, physical characteristics such as their retractable claws, behaviors, relationships with humans, and ... ability to survive changing climates in the world today\"-- Provided by publisher.

Estimating snow conditions is often done using numerical snowpack evolution models at spatial resolutions of 500 m and greater; however, snow depth in complex terrain often varies on sub‐meter scales. This study investigated how the spatial distribution of simulated snow conditions varied across seven model spatial resolutions from 30 to 1,000 m and over two meteorological data sets, coarser (≈12 km) and finer (4 km). Simulated snow covered area (SCA) was compared to remotely sensed SCA and simulated watershed mean peak snow water equivalent (SWE) was compared to four streamflow statistics representing different water management‐relevant aspects of the hydrograph using non‐parametric correlations. April 1 SWE tended to increase with model resolution, particularly below 4,000 masl. Finer meteorology simulations produced deeper April 1 SWE than coarser meteorology simulations. Finer resolution snow simulations tended to produce longer snowmelt durations and slower snowmelt rates than coarser resolution simulations. Finer resolution simulations had better agreement with SCA for both meteorology data sets, particularly at high and low elevations. However, finer resolution simulations did not generally outperform coarser simulations in snow versus streamflow statistic correlations. Snow versus streamflow correlations were most sensitive to meteorology, watershed properties, and then resolution. Watershed physiographic properties such as wetness index may increase snow versus streamflow metric correlations while elevation and slope may decrease correlations. At watershed scales, these results suggest that simulation resolution and choice of meteorology is less important than the physiographic properties of the watershed; however, if resolving snow distribution across the landscape is important, finer‐resolution simulations are useful. Plain Language Summary Estimating how much snow is in the mountains is usually done with computer models that divide the landscape into square patches measuring about 500 m or greater on a side, but we know that snow depth can change substantially over smaller distances. This study investigated how changes in (a) the size of the squares (bigger squares are coarser scale, smaller squares are finer scale) representing snow in computer models and (b) the weather information used to run the computer model alters the estimates of the amount of mountain snow. We found that coarse scale computer models tended to have deeper snow and that fine scale models had longer snowmelt seasons. Fine scale computer models had better agreement with satellite observations of where snow was covering the landscape. When model estimates of snow amount were compared to streamflow records from 13 watersheds in Colorado, USA we did not find that fine scale models outperformed coarse models. These results suggest that fine scale computer models are useful to put snow in the right place and represent snowmelt in patches on the landscape but coarser models are sufficient for predicting streamflow when using statistical regression methods. Key Points Snow simulations of April 1 snow water equivalent are similar at resolutions at or below ≈150 m Fine‐resolution (30 m) snow simulations agree best with snow covered area observations Simulations with 500 m and 1,000 m spatial resolutions show acceptable performance for interannual correlations with streamflow