SNOW WETNESS ESTIMATES OF VEGETATED TERRAIN FROM SATELLITE PASSIVE MICROWAVE DATA

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (T_Bs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the T_B signature. SSM/I T_Bs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover.     

Determination of snow water equivalent over the eastern part of Turkey using passive microwave data

Information on regional snow water equivalent (SWE) is required for the management of water generated from snowmelt. Modeling of SWE in the mountainous regions of eastern Turkey, one of the major headwaters of Euphrates–Tigris basin, has significant importance in forecasting snowmelt discharge, especially for optimum water usage. An assimilation process to produce daily SWE maps is developed based on Helsinki University of Technology (HUT) model and AMSR‐E passive microwave data. The characteristics of the HUT emission model are analyzed in depth and discussed with respect to the extinction coefficient function. A new extinction coefficient function for the HUT model is proposed to suit models for snow over mountainous areas. Performance of the modified model is checked against the original, other modified cases and ground truth data covering the 2003–2007 winter periods. A new approach to calculate grain size and density is integrated inside the developed data assimilation process. An extensive validation was successfully performed by means of snow data measured at ground stations during the 2008–2010 winter periods. The root mean square error of the data set for snow depth and SWE between January and March of the 2008–2010 periods compared with the respective AMSR‐E footprints indicated that errors for estimated snow depth and predicted SWE values were 16.92 cm and 40.91 mm, respectively, for the 3‐year period. Validation results were less satisfactory for SWE less than 75.0 mm and greater than 150.0 mm. An underestimation for SWE greater than 150 mm could not be resolved owing to the microwave signal saturation that is observed for dense snowpack. Copyright © 2012 John Wiley & Sons, Ltd.     

Analysis of snowpack dynamics during the spring melt season for a sub‐alpine site using point measurements and numerical modeling

Snowpack dynamics through October 2014–June 2017 were described for a forested, sub‐alpine field site in southeastern Wyoming. Point measurements of wetness and density were combined with numerical modeling and continuous time series of snow depth, snow temperature, and snowpack outflow to identify 5 major classes of distinct snowpack conditions. Class (i) is characterized by no snowpack outflow and variable average snowpack temperature and density. Class (ii) is characterized by short durations of liquid water in the upper snowpack, snowpack outflow values of 0.0008–0.005 cm hr^?1, an increase in snowpack temperature, and average snow density between 0.25–0.35 g cm^?3. Class (iii) is characterized by a partially saturated wetness profile, snowpack outflow values of 0.005–0.25 cm hr^?1, snowpack temperature near 0 °C, and average snow density between 0.25–0.40 g cm^?3. Class (iv) is characterized by strong diurnal snowpack outflow pattern with values as high as 0.75 cm hr^?1, stable snowpack temperature near 0 °C, and stable average snow density between 0.35–0.45 g cm^?3. Class (v) occurs intermittently between Classes (ii)–(iv) and displays low snowpack outflow values between 0.0008–0.04 cm hr^?1, a slight decrease in temperature relative to the preceding class, and similar densities to the preceding class. Numerical modeling of snowpack properties with SNOWPACK using both the Storage Threshold scheme and Richards' equation was used to quantify the effect of snowpack capillarity on predictions of snowpack outflow and other snowpack properties. Results indicate that both simulations are able to predict snow depth, snow temperature, and snow density reasonably well with little difference between the 2 water transport schemes. Richards' equation more accurately simulates the timing of snowpack outflow over the Storage Threshold scheme, especially early in the melt season and at diurnal timescales.     

Estimation of snow water equivalent using microwave radiometry over Arctic first‐year sea ice

The magnitude and spatial distribution of snow on sea ice are both integral components of the ocean–sea‐ice–atmosphere system. Although there exists a number of algorithms to estimate the snow water equivalent (SWE) on terrestrial surfaces, to date there is no precise method to estimate SWE on sea ice. Physical snow properties and in situ microwave radiometry at 19, 37 and 85 GHz, V and H polarization were collected for a 10‐day period over 20 first‐year sea ice sites. We present and compare the in situ physical, electrical and microwave emission properties of snow over smooth Arctic first‐year sea ice for 19 of the 20 sites sampled. Physical processes creating the observed vertical patterns in the physical and electrical properties are discussed. An algorithm is then developed from the relationship between the SWE and the brightness temperature measured at 37 GHz (55°) H polarization and the air temperature. The multiple regression between these variables is able to account for over 90% of the variability in the measured SWE. This algorithm is validated with a small in situ data set collected during the 1999 field experiment. We then compare our data against the NASA snow thickness algorithm, designed as part of the NASA Earth Enterprise Program. The results indicated a lack of agreement between the NASA algorithm and the algorithm developed here. This lack of agreement is attributed to differences in scale between the Special Sensor Microwave/Imager and surface radiometers and to differences in the Antarctic versus Arctic snow physical and electrical properties. Copyright © 2003 John Wiley & Sons, Ltd.     

Relative influence upon microwave emissivity of fine-scale stratigraphy,internal scattering,and dielectric properties

Summary The microwave emissivity of relatively low-loss media such as snow, ice, frozen ground, and lunar soil is strongly influenced by fine-scale layering and by internal scattering. Radiometric data, however, are commonly interpreted using a model of emission from a homogeneous, dielectric halfspace whose emissivity derives exclusively from dielectric properties. Conclusions based upon these simple interpretations can be erroneous. Examples are presented showing that the emission from fresh or hardpacked snow over either frozen or moist soil is governed dominantly by the size distribution of ice grains in the snowpack. Similarly, the thickness of seasonally frozen soil and the concentration of rock clasts in lunar soil noticeably affect, respectively, the emissivities of northern latitude soils in winter and of the lunar regolith. Petrophysical data accumulated in support of the geophysical interpretation of microwave data must include measurements of not only dielectric properties, but also of geometric factors such as finescale layering and size distributions of grains, inclusions, and voids.     

Radiance data assimilation for operational snow and streamflow forecasting

Estimation of seasonal snowpack, in mountainous regions, is crucial for accurate streamflow prediction. This paper examines the ability of data assimilation (DA) of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center (NWSRFC) are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the ensemble Kalman filter (EnKF) and the particle filter (PF), is made using a coupled SNOW17 and the microwave emission model for layered snow pack (MEMLS) model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the advanced microwave scanning radiometer-earth observing system (AMSR-E) at the 36.5 GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting.     

On the winter evolution of snow thermophysical properties over land‐fast first‐year sea ice

The geophysical, thermodynamic and dielectric properties of snow are important state variables that are known to be sensitive to Arctic climate variability and change. Given recent observations of changes in the Arctic physical system (Arctic Climate Impact Assessment, 2004), it is important to focus on the processes that give rise to variability in the horizontal, vertical and temporal dimensions of the life‐history of snow on sea ice. The objectives in this study are to present these ‘state’ variables and to investigate the processes that govern variability in the vertical, horizontal and temporal dimension by using a case study over land‐fast first‐year sea ice for the period December 2003 to June 2004. Results from two sampling areas (thin and thick snowpacks) show that differences in snowpack thickness can substantially change the vertical and temporal evolution of snow properties. During the late fall and early winter (cooling period) we measured no significant changes in the physical properties, except for thin snow‐cover salinity, which decreased throughout the period. Fall‐snow desalination was only observed under thin snowpacks with a rate of ?0·12 ppt day^?1. Significant changes occurred in the late winter and early spring (warming period), especially for snow grain size. Snow grain kinetic growth of 0·25–0·48 mm·day^?1 was measured coincidently with increasing salinity and wetness for both thin and thick snowpacks. Copyright © 2006 John Wiley & Sons, Ltd.     

Subgrid variability of snow water equivalent at operational snow stations in the western USA

The spatial distribution of snow water equivalent (SWE) is a key variable in many regional‐scale land surface models. Currently, the assimilation of point‐scale snow sensor data into these models is commonly performed without consideration of the spatial representativeness of the point data with respect to the model grid‐scale SWE. To improve the understanding of the relationship between point‐scale snow measurements and surrounding areas, we characterized the spatial distribution of snow depth and SWE within 1‐, 4‐ and 16‐km² grids surrounding 15 snow stations (snowpack telemetry and California snow sensors) in California, Colorado, Wyoming, Idaho and Oregon during the 2008 and 2009 snow seasons. More than 30 000 field observations of snowpack properties were used with binary regression tree models to relate SWE at the sensor site to the surrounding area SWE to evaluate the sensor representativeness of larger‐scale conditions. Unlike previous research, we did not find consistent high biases in snow sensor depth values as biases over all sites ranged from 74% overestimates to 77% underestimates. Of the 53 assessments, 27 surveys indicated snow station biases of less than 10% of the surrounding mean observed snow depth. Depth biases were largely dictated by the physiographic relationship between the snow sensor locations and the mean characteristics of the surrounding grid, in particular, elevation, solar radiation index and vegetation density. These scaling relationships may improve snow sensor data assimilation; an example application is illustrated for the National Operational Hydrologic Remote Sensing Center National Snow Analysis SWE product. The snow sensor bias information indicated that the assimilation of point data into the National Operational Hydrologic Remote Sensing Center model was often unnecessary and reduced model accuracy. Copyright © 2012 John Wiley & Sons, Ltd.     

One‐year‐long runoff forecast by a single snowpack evaluation

A degree‐day‐based model is presented for a 1 year ahead runoff forecast, with 1 day time steps. The input information is a single snowpack evaluation collected at the beginning of the snowmelt season. The snow‐cover dynamics, the key information for long‐term snowmelt forecast, are described by the snow‐line dynamics, i.e. by the movements of the downhill snowpack limit. The snowmelt volume, estimated by the snow‐line dynamics, is the exogenous input of an autoregressive transformation model. The model is calibrated by a least‐squares procedure on the basis of observed daily runoff data and the corresponding measurements of the snowpack volume (one measurement per year). A real‐world case study on the Alto Tunuyan River basin (2380 km², Argentinean Andes) is presented. The 1 year ahead Alto Tunuyan River runoff patterns, computed for both calibration and validation periods, reveal high agreement with observed streamflows. Copyright © 2004 John Wiley & Sons, Ltd.     

Global Snow Mass Measurements and the Effect of Stratigraphic Detail on Inversion of Microwave Brightness Temperatures

Snow provides large seasonal storage of freshwater, and information about the distribution of snow mass as snow water equivalent (SWE) is important for hydrological planning and detecting climate change impacts. Large regional disagreements remain between estimates from reanalyses, remote sensing and modelling. Assimilating passive microwave information improves SWE estimates in many regions, but the assimilation must account for how microwave scattering depends on snow stratigraphy. Physical snow models can estimate snow stratigraphy, but users must consider the computational expense of model complexity versus acceptable errors. Using data from the National Aeronautics and Space Administration Cold Land Processes Experiment and the Helsinki University of Technology microwave emission model of layered snowpacks, it is shown that simulations of the brightness temperature difference between 19 and 37 GHz vertically polarised microwaves are consistent with advanced microwave scanning radiometer-earth observing system and special sensor microwave imager retrievals once known stratigraphic information is used. Simulated brightness temperature differences for an individual snow profile depend on the provided stratigraphic detail. Relative to a profile defined at the 10-cm resolution of density and temperature measurements, the error introduced by simplification to a single layer of average properties increases approximately linearly with snow mass. If this brightness temperature error is converted into SWE using a traditional retrieval method, then it is equivalent to ±13 mm SWE (7 % of total) at a depth of 100 cm. This error is reduced to ±5.6 mm SWE (3 % of total) for a two-layer model.     

Prediction of temperature variation within a snowpack in open areas and under different canopy covers

Snow temperature is a major component of many physical processes in a snowpack. The temperature and the change in temperature across a layer have a dominant effect on physical properties of snow grains as well as its hardness, strength, and failure resistance. In this study, temperature and snow cover thickness were measured during the snow season of 2007–2008 in 11 elevation classes and in three different sampling locations, one in an open area and two under different forest canopy covers for each class along Kartalkaya road, Bolu. Each sampling site was visited 44 times to collect data including snow depth, snow surface temperature, ground temperature, and temperature within snowpack at 20‐cm intervals. Seven different models are developed to determine snowpack temperature variations under forest canopy covers and in an open area with different leaf area index values. All models were performed using a multilayer perceptron (MP) method for the Bolu–Kartalkaya area, Turkey. MP approach constitutes a standard form of neural network modeling and can modify two‐layer linear perceptron methods using three and more layers. The ability of MP is to handle complex nonlinear interactions, which ease the natural process of modeling. This method can overcome complex computations using neuron networks, and they can easily nonlinearly link input and output variables. The predictive errors are determined on the basis of mean absolute error and mean square error criteria. The Nash–Sutcliffe sufficiency score showing compliance between observed and predicted values is also calculated. According to the mean absolute error, the mean square error, and the Nash–Sutcliffe sufficiency score criteria, the predictive errors are within reasonable error intervals, justifying the use of the developed MP models for engineering applications. Copyright © 2012 John Wiley & Sons, Ltd.     

Response of snow processes to climate change: spatial variability in a small basin in the Spanish Pyrenees

In this study, the Cold Regions Hydrological Modelling platform was used to create an alpine snow model including wind redistribution of snow and energy balance snowmelt to simulate the snowpack over the period 1996–2009 in a small (33 ha) snow‐dominated basin in the Spanish Pyrenees. The basin was divided into three hydrological response units (HRUs), based on contrasting physiographic and aerodynamic characteristics. A sensitivity analysis was conducted to calculate the snow water equivalent regime for various combinations of temperature and precipitation that differed from observed conditions. The results show that there was large inter‐annual variability in the snowpack in this region of the Pyrenees because of its marked sensitivity to climatic conditions. Although the basin is small and quite homogeneous, snowpack seasonality and inter‐annual evolution of the snowpack varied in each HRU. Snow accumulation change in relation to temperature change was approximately 20% for every 1 °C, and the duration of the snowpack was reduced by 20–30 days per °C. Melting rates decreased with increased temperature, and wind redistribution of snow was higher with decreased temperature. The magnitude and sign of changes in precipitation may markedly affect the response of the snowpack to changes in temperature. There was a non‐linear response of snow to individual and combined changes in temperature and precipitation, with respect to both the magnitude and sign of the change. This was a consequence of the complex interactions among climate, topography and blowing snow in the study basin. Copyright © 2012 John Wiley & Sons, Ltd.     

A temperature‐precipitation‐based model of thirty‐year mean snowpack accumulation and melt in Oregon,USA

High‐resolution, spatially extensive climate grids can be useful in regional hydrologic applications. However, in regions where precipitation is dominated by snow, snowmelt models are often used to account for timing and magnitude of water delivery. We developed an empirical, nonlinear model to estimate 30‐year means of monthly snowpack and snowmelt throughout Oregon. Precipitation and temperature for the period 1971–2000, derived from 400‐m resolution PRISM data, and potential evapotranspiration (estimated from temperature and day length) drive the model. The model was calibrated using mean monthly data from 45 SNOTEL sites and accurately estimated snowpack at 25 validation sites: R² = 0·76, Nash‐Sutcliffe Efficiency (NSE) = 0·80. Calibrating it with data from all 70 SNOTEL sites gave somewhat better results (R² = 0·84, NSE = 0·85). We separately applied the model to SNOTEL stations located < 200 and ≥ 200 km from the Oregon coast, since they have different climatic conditions. The model performed equally well for both areas. We used the model to modify moisture surplus (precipitation minus potential evapotranspiration) to account for snowpack accumulation and snowmelt. The resulting values accurately reflect the shape and magnitude of runoff at a snow‐dominated basin, with low winter values and a June peak. Our findings suggest that the model is robust with respect to different climatic conditions, and that it can be used to estimate potential runoff in snow‐dominated basins. The model may allow high‐resolution, regional hydrologic comparisons to be made across basins that are differentially affected by snowpack, and may prove useful for investigating regional hydrologic response to climate change. Published in 2011 by John Wiley & Sons, Ltd.     

Climate variability,snow, and physiographic controls on storm hydrographs in small forested basins,western Cascades,Oregon

Large floods are often attributed to the melting of snow during a rain event. This study tested how climate variability, snowpack presence, and basin physiography were related to storm hydrograph shape in three small (<1 km²) basins with old‐growth forest in western Oregon. Relationships between hydrograph characteristics and precipitation were tested for approximately 800 storms over a nearly 30‐year period. Analyses controlled for (1) snowpack presence/absence, (2) antecedent soil moisture, and (3) hillslope length and gradient. For small storms (<150 mm precipitation), controlling for precipitation, the presence of a snowpack on near‐saturated soil increased the threshold of precipitation before hydrograph rise, extended the start lag, centroid lag, and duration of storm hydrographs, and increased the peak discharge. The presence of a snowpack on near‐saturated soil sped up and steepened storm hydrographs in a basin with short steep slopes, but delayed storm hydrographs in basins with longer or more gentle slopes. Hydrographs of the largest events, which were extreme regional rain and rain‐on‐snow floods, were not sensitive to landform characteristics or snowpack presence/absence. Although the presence of a snowpack did not increase peak discharge in small, forested basins during large storms, it had contrasting effects on storm timing in small basins, potentially synchronizing small basin contributions to the larger basin hydrograph during large rain‐on‐snow events. By altering the relative timing of hydrographs, snowpack melting could produce extreme floods from precipitation events whose size is not extreme. Further work is needed to examine effects of canopy openings, snowpack, and climate warming on extreme rain‐on‐snow floods at the large basin scale. Copyright © 2008 John Wiley & Sons, Ltd.     

Estimating sublimation of intercepted and sub‐canopy snow using eddy covariance systems

Direct measurements of winter water loss due to sublimation were made in a sub‐alpine forest in the Rocky Mountains of Colorado. Above‐and below‐canopy eddy covariance systems indicated substantial losses of winter‐season snow accumulation in the form of snowpack (0·41 mm d^?1) and intercepted snow (0·71 mm d^?1) sublimation. The partitioning between these over and under story components of water loss was highly dependent on atmospheric conditions and near‐surface conditions at and below the snow/atmosphere interface. High above‐canopy sensible heat fluxes lead to strong temperature gradients between vegetation and the snow‐surface, driving substantial specific humidity gradients at the snow surface and high sublimation rates. Intercepted snowfall resulted in rapid response of above‐canopy latent heat fluxes, high within‐canopy sublimation rates (maximum = 3·7 mm d^?1), and diminished sub‐canopy snowpack sublimation. These results indicate that sublimation losses from the sub‐canopy snowpack are strongly dependent on the partitioning of sensible and latent heat fluxes in the canopy. This compels comprehensive studies of snow sublimation in forested regions that integrate sub‐canopy and over‐story processes. Copyright © 2007 John Wiley & Sons, Ltd.     

Temporal observations of a seasonal snowpack using upward‐looking GPR

An increase of the spatial and temporal resolution of snowpack measurements in Alpine or Arctic regions will improve the predictability of flood and avalanche hazards and increase the spatial validity of snowpack simulation models. In the winter season 2009, we installed a ground‐penetrating radar (GPR) system beneath the snowpack to measure snowpack conditions above the antennas. In comparison with modulated frequency systems, GPR systems consist of a much simpler technology, are commercially available and therefore are cheaper. The radar observed the temporal alternation of the snow height over more than 2·5 months. The presented data showed that with moved antennas, it is possible to record the snow height with an uncertainty of less than 8% in comparison with the probed snow depth. Three persistent melt crusts, which formed at the snow surface and were buried by further new snow events, were used as reflecting tracers to follow the snow cover evolution and to determine the strain rates of underlaying layers between adjacent measurements. The height in two‐way travel time of each layer changed over time, which is a cumulative effect of settlement and variation of wave speed in response to densification and liquid water content. The infiltration of liquid water with depth during melt processes was clearly observed during one event. All recorded reflections appeared in concordance with the physical principles (e.g. in phase structure), and one can assume that distinct density steps above a certain threshold result in reflections in the radargram. The accuracy of the used impulse radar system in determining the snow water equivalent is in good agreement with previous studies, which used continuous wave radar systems. The results of this pilot study encourage further investigations with radar measurements using the described test arrangement on a daily basis for continuous destruction‐free monitoring of the snow cover. Copyright © 2010 John Wiley & Sons, Ltd.     

Seasonal snowpack dynamics and runoff in a cool temperate forest: lysimeter experiment in Niigata,Japan

Seasonal snowpack dynamics are described through field measurements under contrasting canopy conditions for a mountainous catchment in the Japan Sea region. Microclimatic data, snow accumulation, albedo and lysimeter runoff are given through the complete winter season 2002–03 in (1) a mature cedar stand, (2) a larch stand, and (3) a regenerating cedar stand or opening. The accumulation and melt of seasonal snowpack strongly influences streamflow runoff during December to May, including winter baseflow, mid‐winter melt, rain on snow, and diurnal peaks driven by radiation melt in spring. Lysimeter runoff at all sites is characterized by constant ground melt of 0·8–1·0 mm day⁻¹. Rapid response to mid‐winter melt or rainfall shows that the snowpack remains in a ripe or near‐ripe condition throughout the snow‐cover season. Hourly and daily lysimeter discharge was greatest during rain on snow (e.g. 7 mm h⁻¹ and 53 mm day⁻¹ on 17 December) with the majority of runoff due to rainfall passing through the snowpack as opposed to snowmelt. For both rain‐on‐snow and radiation melt events lysimeter discharge was generally greatest at the open site, although there were exceptions such as during interception melt events. During radiation melt instantaneous discharge was up to 4·0 times greater in the opening compared with the mature cedar, and 48 h discharge was up to 2·5 times greater. Perhaps characteristic of maritime climates, forest interception melt is shown to be important in addition to sublimation in reducing snow accumulation beneath dense canopies. While sublimation represents a loss from the catchment water balance, interception melt percolates through the snowpack and contributes to soil moisture during the winter season. Strong differences in microclimate and snowpack albedo persisted between cedar, larch and open sites, and it is suggested further work is needed to account for this in hydrological simulation models. Copyright © 2005 John Wiley & Sons, Ltd.     

Incorporating topographic variability into a simple regional snowmelt model

General circulation models (GCMs), or stand‐alone models that are forced by the output from GCMs, are increasingly being used to simulate the interactions between snow cover, snowmelt, climate and water resources. The variation in snowpack extent, and hence albedo, through time in a cell is likely to be substantial, especially in mid‐latitude mountainous regions. As a consequence, the energy budget simulation by a GCM relies on a realistic representation of snowpack extent. Similarly, from a water resource perspective, the spatial extent of the pack is key in predicting meltwater discharges into rivers. In this paper a simple computationally efficient regional snow model has been developed, which is based on a degree‐day approach and simulates the fraction of the model domain covered by snow, the spatially averaged melt rate and the mean snowpack depth. Computational efficiency is achieved through a novel spatial averaging procedure, which relies on the assumptions that precipitation and temperature scale linearly with elevation and that the distribution of elevations in the domain can be modelled by a continuous function. The resulting spatially averaged model is compared with both observations of the duration of snow cover throughout Austria and with results from a distributed model based on the same underlying assumptions but applied at a fine spatial resolution. The new spatially averaged model successfully simulated the seasonal snow duration observations and reproduced the daily dynamics of snow cover extent, the spatially averaged melt rate and mean pack depth simulated by the distributed model. It, therefore, offers a computationally efficient and easily applied alternative to the current crop of regional snow models. Copyright © 2004 John Wiley & Sons, Ltd.     

Passive microwave remote sensing of the historic February 2010 snowstorms in the Middle Atlantic region of the USA

The snowfall in the Baltimore/Washington metropolitan area during the winter of 2009/2010 was unprecedented and caused serious snow‐related disruptions. In February 2010, snowfall totals approached 2 m, and because maximum temperatures were consistently below normal, snow remained on the ground the entire month. One of the biggest contributing factors to the unusually severe winter weather in 2009/2010, throughout much of the middle latitudes, was the Arctic Oscillation. Unusually high pressure at high latitudes and low pressure at middle latitudes forced a persistent exchange of mass from north to south. In this investigation, a concerted effort was made to link remotely sensed falling snow observations to remotely sensed snow cover and snowpack observations in the Baltimore/Washington area. Specifically, the Advanced Microwave Scanning Radiometer onboard the Aqua satellite was used to assess snow water equivalent, and the Advanced Microwave Sounding Unit‐B and Microwave Humidity Sounder were employed to detect falling snow. Advanced Microwave Scanning Radiometer passive microwave signatures in this study are related to both snow on the ground and surface ice layers. In regard to falling snow, signatures indicative of snowfall can be observed in high frequency brightness temperatures of Advanced Microwave Sounding Unit‐B and Microwave Humidity Sounder. Indeed, retrievals show an increase in snow water equivalent after the detection of falling snow. Yet, this work also shows that falling snow intensity and/or the presence of liquid water clouds impacts the ability to reliably detect snow water equivalent. Moreover, changes in the condition of the snowpack, especially in the surface features, negatively affect retrieval performance. Copyright © 2011. This article is a U.S. Government work and is in the public domain in the USA.