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  • Author or Editor: Christopher A. Hiemstra x
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Glen E. Liston
and
Christopher A. Hiemstra

Abstract

A methodology for assimilating ground-based and remotely sensed snow data within a snow-evolution modeling system (SnowModel) is presented. The data assimilation scheme (SnowAssim) is consistent with optimal interpolation approaches in which the differences between the observed and modeled snow values are used to constrain modeled outputs. The calculated corrections are applied retroactively to create improved fields prior to the assimilated observations. Thus, one of the values of this scheme is the improved simulation of snow-related distributions throughout the entire snow season, even when observations are only available late in the accumulation and/or ablation periods. Because of this, the technique is particularly applicable to reanalysis applications. The methodology includes the ability to stratify the assimilation into regions where either the observations and/or model has unique error properties, such as the differences between forested and nonforested snow environments. The methodologies are introduced using synthetic data and a simple simulation domain. In addition, the model is applied over NASA’s Cold Land Processes Experiment (CLPX), Rabbit Ears Pass, Colorado, observation domain. Simulations using the data assimilation scheme were found to improve the modeled snow water equivalent (SWE) distributions, and simulated SWE displayed considerably more realistic spatial heterogeneity than that provided by the observations alone.

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Glen E. Liston
,
Christopher A. Hiemstra
,
Kelly Elder
, and
Donald W. Cline

Abstract

The Cold Land Processes Experiment (CLPX) had a goal of describing snow-related features over a wide range of spatial and temporal scales. This required linking disparate snow tools and datasets into one coherent, integrated package. Simulating realistic high-resolution snow distributions and features requires a snow-evolution modeling system (SnowModel) that can distribute meteorological forcings, simulate snowpack accumulation and ablation processes, and assimilate snow-related observations. A SnowModel was developed and used to simulate winter snow accumulation across three 30 km × 30 km domains, enveloping the CLPX mesocell study areas (MSAs) in Colorado. The three MSAs have distinct topography, vegetation, meteorological, and snow characteristics. Simulations were performed using a 30-m grid increment and spanned the snow accumulation season (1 October 2002–1 April 2003). Meteorological forcing was provided by 27 meteorological stations and 75 atmospheric analyses grid points, distributed using a meteorological model (MicroMet). The simulations included a data assimilation model (SnowAssim) that adjusted simulated snow water equivalent (SWE) toward ground-based and airborne SWE observations. The observations consisted of SWE over three 1 km × 1 km intensive study areas (ISAs) for each MSA and a collection of 117 airborne gamma observations, each integrating area 10 km long by 300 m wide. Simulated SWE distributions displayed considerably more spatial heterogeneity than the observations alone, and the simulated distribution patterns closely fit the current understanding of snow evolution processes and observed snow depths. This is the result of the MicroMet/SnowModel’s relatively finescale representations of orographic precipitation, elevation-dependant snowmelt, wind redistribution, and snow–vegetation interactions.

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Glen E. Liston
,
Daniel L. Birkenheuer
,
Christopher A. Hiemstra
,
Donald W. Cline
, and
Kelly Elder

Abstract

This paper describes the Local Analysis and Prediction System (LAPS) and the 20-km horizontal grid version of the Rapid Update Cycle (RUC20) atmospheric analyses datasets, which are available as part of the Cold Land Processes Field Experiment (CLPX) data archive. The LAPS dataset contains spatially and temporally continuous atmospheric and surface variables over Colorado, Wyoming, and parts of the surrounding states. The analysis used a 10-km horizontal grid with 21 vertical levels and an hourly temporal resolution. The LAPS archive includes forty-six 1D surface fields and nine 3D upper-air fields, spanning the period 1 September 2001 through 31 August 2003. The RUC20 dataset includes hourly 3D atmospheric analyses over the contiguous United States and parts of southern Canada and northern Mexico, with 50 vertical levels. The RUC20 archive contains forty-six 1D surface fields and fourteen 3D upper-air fields, spanning the period 1 October 2002 through 31 September 2003. The datasets are archived at the National Snow and Ice Data Center (NSIDC) in Boulder, Colorado.

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