scholarly journals Large scale snow water equivalent status monitoring: comparison of different snow water products in the upper Colorado Basin

2013 ◽  
Vol 17 (12) ◽  
pp. 5127-5139 ◽  
Author(s):  
G. A. Artan ◽  
J. P. Verdin ◽  
R. Lietzow
Keyword(s):  
Energy Balance ◽  
Water Content ◽  
Snow Water Equivalent ◽  
Weather Forecast ◽  
Snow Accumulation ◽  
Passive Microwave ◽  
Data Sets ◽  
Snow Water ◽  
Trmm Precipitation ◽  
Colorado Basin

Abstract. We illustrate the ability to monitor the status of snow water content over large areas by using a spatially distributed snow accumulation and ablation model that uses data from a weather forecast model in the upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) data-sets; remaining meteorological model input data were from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a region of the western US that covers parts of the upper Colorado Basin. We also compared the SWE product estimated from the special sensor microwave imager (SSM/I) and scanning multichannel microwave radiometer (SMMR) to the SNODAS and SNOTEL SWE data-sets. Agreement between the spatial distributions of the simulated SWE with MPE data was high with both SNODAS and SNOTEL. Model-simulated SWE with TRMM precipitation and SWE estimated from the passive microwave imagery were not significantly correlated spatially with either SNODAS or the SNOTEL SWE. Average basin-wide SWE simulated with the MPE and the TRMM data were highly correlated with both SNODAS (r = 0.94 and r = 0.64; d.f. = 14 – d.f. = degrees of freedom) and SNOTEL (r = 0.93 and r = 0.68; d.f. = 14). The SWE estimated from the passive microwave imagery was significantly correlated with the SNODAS SWE (r = 0.55, d.f. = 9, p = 0.05) but was not significantly correlated with the SNOTEL-reported SWE values (r = 0.45, d.f. = 9, p = 0.05).The results indicate the applicability of the snow energy balance model for monitoring snow water content at regional scales when coupled with meteorological data of acceptable quality. The two snow water contents from the microwave imagery (SMMR and SSM/I) and the Utah Energy Balance forced with the TRMM precipitation data were found to be unreliable sources for mapping SWE in the study area; both data sets lacked discernible variability of snow water content between sites as seen in the SNOTEL and SNODAS SWE data. This study will contribute to better understanding the adequacy of data from weather forecast models, TRMM, and microwave imagery for monitoring status of the snow water content.

scholarly journals Spatio-temporal variability of snow water equivalent in the extra-tropical Andes Cordillera from distributed energy balance modeling and remotely sensed snow cover

2016 ◽  
Vol 20 (1) ◽  
pp. 411-430 ◽  
Author(s):  
E. Cornwell ◽  
N. P. Molotch ◽  
J. McPhee
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Snow Accumulation ◽  
Model Assessment ◽  
Continental Divide ◽  
Average Maximum ◽  
Snow Water ◽  
Andes Cordillera

Abstract. Seasonal snow cover is the primary water source for human use and ecosystems along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution (500 m), daily distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow-covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically based energy balance model in order to obtain melt-season melt fluxes that are then aggregated to estimate the end-of-winter (or peak) snow water equivalent (SWE). Peak SWE estimates show an overall coefficient of determination R2 of 0.68 and RMSE of 274 mm compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. The spatial distribution of peak SWE shows that the 4000–5000 m a.s.l. elevation band is significant for snow accumulation, despite having a smaller surface area than the 3000–4000 m a.s.l. band. On average, maximum snow accumulation is observed in early September in the western Andes, and in early October on the eastern side of the continental divide. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

scholarly journals Microwave Measurements of Snowpack Properties

1981 ◽  
Vol 12 (3) ◽  
pp. 143-166 ◽  
Author(s):  
W.H. Stiles ◽  
F.T. Ulaby ◽  
A. Rango
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Snow Water Equivalent ◽  
Liquid Water Content ◽  
Microwave Measurements ◽  
Data Sets ◽  
Microwave Response ◽  
Passive Data ◽  
Snow Water ◽  
Snowpack Properties

Prior microwave measurements of snow water equivalent and liquid water content and conceptualizations of emission and backscattering models are reviewed. The results of an experiment designed to collect simultaneous passive and active microwave data to be used in interpreting and analyzing the sensitivity of the microwave spectrum to changing snowpack properties are reported. Both the scattering coefficient, σ°, and the apparent radiometric temperature, Tap, were found to be sensitive to changes in snow water equivalent and liquid water content. The σ° data exhibit an exponential-like increase with increasing water equivalent, whereas, the Tap data exhibit an exponential-like decrease. For both the active and passive data, the snow water equivalent at which the microwave response begins to saturate decreases as the wavelength decreases. Increasing liquid water in the snowpack causes a decrease in σ° and an increase in the Tap. Diurnal data sets show the greatest σ° and Tap variation in response to snowmelt at 35 and 37 GHz with correspondingly less variation at the lower frequencies. Based on research results to date, immediate formulation of a comprehensive microwave and snow research program is recommended.

scholarly journals Large scale snow water status monitoring: comparison of different snow water products in the upper Colorado basins

2013 ◽  
Vol 10 (3) ◽  
pp. 3629-3664
Author(s):  
G. A. Artan ◽  
J. P. Verdin ◽  
R. Lietzow
Keyword(s):  
Large Scale ◽  
Snow Water Equivalent ◽  
Water Status ◽  
Microwave Radiometer ◽  
Tropical Rainfall Measuring Mission ◽  
Snow Accumulation ◽  
Meteorological Model ◽  
Model Input ◽  
Snow Water ◽  
Colorado Basin

Abstract. We illustrate the ability to monitor the status of snowpack over large areas by using a~spatially distributed snow accumulation and ablation model in the Upper Colorado Basin. The model was forced with precipitation fields from the National Weather Service (NWS) Multi-sensor Precipitation Estimator (MPE) and the Tropical Rainfall Measuring Mission (TRMM) datasets; remaining meteorological model input data was from NOAA's Global Forecast System (GFS) model output fields. The simulated snow water equivalent (SWE) was compared to SWEs from the Snow Data Assimilation System (SNODAS) and SNOwpack TELemetry system (SNOTEL) over a~region of the Western United States that covers parts of the Upper Colorado Basin. We also compared the SWE product estimated from the Special Sensor Microwave Imager (SSM/I) and Scanning Multichannel Microwave Radiometer (SMMR) to the SNODAS and SNOTEL SWE datasets. Agreement between the spatial distribution of the simulated SWE with both SNODAS and SNOTEL was high for the two model runs for the entire snow accumulation period. Model-simulated SWEs, both with MPE and TRMM, were significantly correlated spatially on average with the SNODAS (r = 0.81 and r = 0.54; d.f. = 543) and the SNOTEL SWE (r = 0.85 and r = 0.55; d.f. = 543), when monthly basinwide simulated average SWE the correlation was also highly significant (r = 0.95 and r = 0.73; d.f. = 12). The SWE estimated from the passive microwave imagery was not correlated either with the SNODAS SWE or (r = 0.14, d.f. = 7) SNOTEL-reported SWE values (r = 0.08, d.f. = 7). The agreement between modeled SWE and the SWE recorded by SNODAS and SNOTEL weakened during the snowmelt period due to an underestimation bias of the air temperature that was used as model input forcing.

scholarly journals Spatio-temporal variability of snow water equivalent in the extra-tropical Andes cordillera from a distributed energy balance modeling and remotely sensed snow cover

2015 ◽  
Vol 12 (9) ◽  
pp. 8927-8976
Author(s):  
E. Cornwell ◽  
N. P. Molotch ◽  
J. McPhee
Keyword(s):  
Energy Balance ◽  
Snow Cover ◽  
Water Resource ◽  
Snow Water Equivalent ◽  
Remotely Sensed ◽  
Snow Accumulation ◽  
Model Domain ◽  
Continental Divide ◽  
Snow Water ◽  
Andes Cordillera

Abstract. Seasonal snow cover is the primary water resource precursor for human use and environmental sustain along the extratropical Andes Cordillera. Despite its importance, relatively little research has been devoted to understanding the properties, distribution and variability of this natural resource. This research provides high-resolution distributed estimates of end-of-winter and spring snow water equivalent over a 152 000 km2 domain that includes the mountainous reaches of central Chile and Argentina. Remotely sensed fractional snow covered area and other relevant forcings are combined with extrapolated data from meteorological stations and a simplified physically-based energy balance model in order to obtain melt-season peak SWE. Estimates show an overall coefficient of determination R2 of 0.61 compared to observations at 12 automatic snow water equivalent sensors distributed across the model domain, with R2 values between 0.32 and 0.88. Regional estimates of peak SWE accumulation show differential patterns strongly modulated by elevation, latitude and position relative to the continental divide. Average peak SWE increases by nearly 500 mm for every 1000 m in elevation gain for the central and southern sub-regions of the model domain, but this effect is much less pronounced in the northern reaches. The 3000–4000 m a.s.l. elevation band is the most significant accumulation area for most of the northern and central reaches of the domain, although the 4000–5000 m a.s.l. band, despite a smaller contributing area, almost doubles the accumulation amounts estimated for the lower adjacent subdomain. Snow accumulation reaches an earlier peak in the western Andes, and the eastern side of the continental divide shows lower snow accumulation at all elevations except for the southern region represented by the Neuquén River Basin. The results presented here have the potential of informing applications such as seasonal forecast model assessment and improvement, regional climate model validation, as well as evaluation of observational networks and water resource infrastructure development.

scholarly journals Seasonal Estimates and Uncertainties of Snow Accumulation from CloudSat Precipitation Retrievals

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 363
Author(s):  
George Duffy ◽  
Fraser King ◽  
Ralf Bennartz ◽  
Christopher G. Fletcher
Keyword(s):  
Time Scales ◽  
Snow Water Equivalent ◽  
Spatial Scales ◽  
Snow Accumulation ◽  
High Latitudes ◽  
Percentage Points ◽  
Snow Water ◽  
Time Periods ◽  
Predicted Values ◽  
Good Agreement

CloudSat is often the only measurement of snowfall rate available at high latitudes, making it a valuable tool for understanding snow climatology. The capability of CloudSat to provide information on seasonal and subseasonal time scales, however, has yet to be explored. In this study, we use subsampled reanalysis estimates to predict the uncertainties of CloudSat snow water equivalent (SWE) accumulation measurements at various space and time resolutions. An idealized/simulated subsampling model predicts that CloudSat may provide seasonal SWE estimates with median percent errors below 50% at spatial scales as small as 2° × 2°. By converting these predictions to percent differences, we can evaluate CloudSat snowfall accumulations against a blend of gridded SWE measurements during frozen time periods. Our predictions are in good agreement with results. The 25th, 50th, and 75th percentiles of the percent differences between the two measurements all match predicted values within eight percentage points. We interpret these results to suggest that CloudSat snowfall estimates are in sufficient agreement with other, thoroughly vetted, gridded SWE products. This implies that CloudSat may provide useful estimates of snow accumulation over remote regions within seasonal time scales.

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