VERIFICATION OF THE RHEA-OROGRAPHIC-PRECIPITATION MODEL

Atmospheric Research, 2010

This paper presents results of four high resolution simulations of annual snowfall over Colorado, U.S.A. The results are verified using SNOTEL data. Sensitivity to model resolution is also explored. The results show that proper spatial and temporal depictions of snowfall adequate for water resource and climate change purposes can be achieved with the appropriate choice of model resolution and physical parameterizations.

Advances in Water Resources, 2006

A spatially distributed snow model procedure for estimating snow melt, snow water equivalent and snow cover area is formulated and tested with data from the American River basin in CaliforniaÕs Sierra Nevada. An adaptation of the operational National Weather Service snow accumulation and ablation model is used for each model grid cell forced by spatially distributed precipitation and temperature data. The model was implemented with 6-hourly time steps on 1 km 2 grid cells for the snow season of 1999-2003. Temperature is spatially interpolated using the prevailing lapse rate and digital terrain elevation data. Precipitation is spatially interpolated using regional climatological analyses obtained from PRISM. Parameters that control snow melt are distributed using ground surface aspect. The model simulations are compared with data from 12 snow-sensors located in the basin and the daily 500-m snow cover extent product from the MODIS/Terra satellite mission. The results show that the distribution of snow pack over the area is generally captured. The snow pack quantity compared to snow gauges is well estimated in high elevations with increasing uncertainty in the snow pack at lower elevations. Sensitivity and uncertainty analyses indicate that the significant input uncertainty for precipitation and temperature is primarily responsible for model errors in lower elevations and near the snow line. The model is suitable for producing spatially resolved realistic snow pack simulations when forced with operationally available observed or predicted data.

Journal of Geophysical Research: Atmospheres, 2014

Correctly modeling snow is critical for climate models and for hydrologic applications. Snowpack simulated by six land surface models (LSM: Noah, Variable Infiltration Capacity, snow-atmosphere-soil transfer, Land Ecosystem-Atmosphere Feedback, Noah with Multiparameterization, and Community Land Model) were evaluated against 1 year snow water equivalent (SWE) data at 112 Snow Telemetry (SNOTEL) sites in the Colorado River Headwaters region and 4 year flux tower data at two AmeriFlux sites. All models captured the main characteristics of the seasonal SWE evolution fairly well at 112 SNOTEL sites. No single model performed the best to capture the combined features of the peak SWE, the timing of peak SWE, and the length of snow season. Evaluating only simulated SWE is deceiving and does not reveal critical deficiencies in models, because the models could produce similar SWE for starkly different reasons. Sensitivity experiments revealed that the models responded differently to variations of forest coverage. The treatment of snow albedo and its cascading effects on surface energy deficit, surface temperature, stability correction, and turbulent fluxes was a major intermodel discrepancy. Six LSMs substantially overestimated (underestimated) radiative flux (heat flux), a crucial deficiency in representing winter land-atmosphere feedback in coupled weather and climate models. Results showed significant intermodel differences in snowmelt efficiency and sublimation efficiency, and models with high rate of snow accumulation and melt were able to reproduce the observed seasonal evolution of SWE. This study highlights that the parameterization of cascading effects of snow albedo and below-canopy turbulence and radiation transfer is critical not only for SWE simulation but also for correctly capturing the winter land-atmosphere interactions.

Advances in Water Resources, 2013

J. Dozier), michele.reba@ars.usda.gov (M. Reba), adam.winstral@ars.usda.gov (A. Winstral).

Journal of Geophysical Research, 2003

This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's SNOTEL network for a 3-year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic negative bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000mm at certain stations. Experiments with the VIC model indicated that most of the bias in SWE is removed by scaling the precipitation by a regional factor based on the regression of the NLDAS and SNOTEL precipitation. Individual station errors may be reduced further still using precipitation scaled to the local station SNOTEL record. Furthermore, the NLDAS air temperature is shown to be generally colder in winter months and biased warmer in spring and summer when compared to the SNOTEL record, although the level of bias is regionally dependent. Detailed analysis at a selected station indicate that errors in the air temperature forcing may cause the partitioning of precipitation into snowfall and rainfall by the models to be incorrect and thus may explain some of the remaining errors in the simulated SWE.

Journal of Geophysical Research, 2007

1] Snow cover strongly interacts with climate through snow albedo feedbacks. However, global climate models still are not adequate in representing snow cover fraction (SCF), i.e., the fraction of a model grid cell covered by snow. Through an analysis of the advanced very high resolution radiometer (AVHRR) derived SCF and the Canadian Meteorological Centre (CMC) gridded snow depth and snow water equivalent (SWE), we found that the SCF-snow depth relationship varies with seasons, which may be approximated by variations in snow density. We then added snow density to an existing SCF formulation to reflect the variations in the SCF-snow depth relationship with seasons. The reconstructed SCF with the gridded snow depth and SWE by employing this snow density-dependent SCF formulation agrees better with the AVHRR-derived SCF than other formulations. The default SCF formulation in the National Center for Atmospheric Research community land model (CLM), driven by observed near-surface meteorological forcings, simulates a smaller SCF and a shallower snow depth than observations. Implementation of the new SCF formulation into the NCAR CLM greatly improves the simulations of SCF, snow depth, and SWE in most North American (NA) river basins. The new SCF formulation increases SCF by 20-40%, decreases net solar radiation by up to 20 W m À2 , and decreases surface temperature by up to 4 K in most midlatitude regions in winter and at high latitudes in spring. The new scheme reproduces the observed SCF, snow depth, and SWE in terms of interannual variability and interbasin variability in most NA river basins except for the mountainous Columbia and Colorado River basins. It produces SCF trends similar to that of AVHRR. However, it produces greater decreasing trends in ablation seasons and smaller increasing trends in accumulation seasons than those of the CMC snow depth and SWE.

Journal of Geophysical Research, 2003

This is the second part of a study on the cold season process modeling in the North American Land Data Assimilation System (NLDAS). The first part concentrates on the assessment of model simulated snow cover extent. In this second part, the focus is on the evaluation of simulated snow water equivalent (SWE) from the four land surface models (Noah, MOSAIC, SAC and VIC) in the NLDAS. Comparisons are made with observational data from the Natural Resources Conservation Service's SNOTEL network for a 3-year retrospective period at selected sites in the mountainous regions of the western United States. All models show systematic negative bias in the maximum annual simulated SWE that is most notable in the Cascade and Sierra Nevada regions where differences can approach 1000mm. Comparison of NLDAS precipitation forcing with SNOTEL measurements revealed a large bias in the NLDAS annual precipitation which may be lower than the SNOTEL record by up to 2000mm at certain stations.

Journal of Hydrology, 2008

Prediction of snowmelt has become a critical issue in much of the western United States given the increasing demand for water supply, changing snow cover patterns, and the subsequent requirement of optimal reservoir operation. The increasing importance of hydrologic predictions necessitates that traditional forecasting systems be re-evaluated periodically to assure continued evolution of the operational systems given scientific advancements in hydrology. The National Weather Service (NWS) SNOW17, a conceptually based model used for operational prediction of snowmelt, has been relatively unchanged for decades. In this study, the Snow-Atmosphere-Soil Transfer (SAST) model, which employs the energy balance method, is evaluated against the SNOW17 for the simulation of seasonal snowpack (both accumulation and melt) and basin discharge. We investigate model performance over a 13-year period using data from two basins within the Reynolds Creek Experimental Watershed located in southwestern Idaho. Both models are coupled to the NWS runoff model [SACramento Soil Moisture Accounting model (SACSMA)] to simulate basin streamflow. Results indicate that while in many years simulated snowpack and streamflow are similar between the two modeling systems, the SAST more often overestimates SWE during the spring due to a lack of midwinter melt in the model. The SAST also had more rapid spring melt rates than the SNOW17, leading to larger errors in the timing and amount of discharge on average. In general, the simpler SNOW17 performed consistently well, and in several years, better than, the SAST model. Input requirements and related uncertainties, and to a lesser extent calibration, are likely to

Monthly Weather Review, 2011

An investigation was conducted on the effects of various physics parameterizations on wintertime precipitation predictions using a high-resolution regional climate model. The objective was to evaluate the sensitivity of cold-season mountainous snowfall to cloud microphysics schemes, planetary boundary layer (PBL) schemes, land surface schemes, and radiative transfer schemes at a 4-km grid spacing applicable to the next generation of regional climate models.

Water Resources Research, 2012

Recent observations show a decrease in the fraction of precipitation falling as snowfall in the western United States. In this work we evaluate a historical and future climate simulation over the Colorado River Basin using a 35 km continuous 111 year simulation (1969–2079) of the Weather Research and Forecasting (WRF) regional climate model with boundary forcing from the Hadley Centre for Climate Prediction and Research/Met Office's HadCM3 model with A2 emission scenario. The focus of this work is to (1) evaluate the simulated spatiotemporal variability of snowfall in the historical period when compared to observations and (2) project changes in snowfall and the fraction of precipitation that falls as snow during the 21st century. We find that the spatial variability in modeled snowfall in the historical period (1981–2005) is realistically represented when compared to observations. The trends of modeled snowfall are similar to the observed trends except at higher elevations. Exa...