A Meteorological Distribution System for High-Resolution Terrestrial Modeling (MicroMet)

Abstract

An intermediate-complexity, quasi-physically based, meteorological model (MicroMet) has been developed to produce high-resolution (e.g., 30-m to 1-km horizontal grid increment) atmospheric forcings required to run spatially distributed terrestrial models over a wide variety of landscapes. The following eight variables, required to run most terrestrial models, are distributed: air temperature, relative humidity, wind speed, wind direction, incoming solar radiation, incoming longwave radiation, surface pressure, and precipitation. To produce these distributions, MicroMet assumes that at least one value of each of the following meteorological variables are available for each time step, somewhere within, or near, the simulation domain: air temperature, relative humidity, wind speed, wind direction, and precipitation. These variables are collected at most meteorological stations. For the incoming solar and longwave radiation, and surface pressure, either MicroMet can use its submodels to generate these fields, or it can create the distributions from observations as part of a data assimilation procedure. MicroMet includes a preprocessor component that analyzes meteorological data, then identifies and corrects potential deficiencies. Since providing temporally and spatially continuous atmospheric forcing data for terrestrial models is a core objective of MicroMet, the preprocessor also fills in any missing data segments with realistic values. Data filling is achieved by employing a variety of procedures, including an autoregressive integrated moving average calculation for diurnally varying variables (e.g., air temperature). To create the distributed atmospheric fields, spatial interpolations are performed using the Barnes objective analysis scheme, and subsequent corrections are made to the interpolated fields using known temperature-elevation, wind-topography, humidity-cloudiness, and radiation-cloud-topography relationships.

Analysis and Predictability of the Hydrological Response of Mountain Catchments to Heavy Rain on Snow Events: A Case Study in the Spanish Pyrenees

“…This has been observed in many other mountain regions. For example Liston and Elder [72] use a seasonally variable linear gradient, where precipitation increases with altitude, according to work in the western US by Thornton et al [73]. Closer to the Pyrenees, in the Alps Lehning et al [74] have noted the problems of deriving a reliable precipitation gradient, either because of lack of measurements at high altitude [75,76], or because the difficulty of measuring solid precipitation at high elevation and with strong winds [77].…”

Snowmelt Water Alters the Regime of Runoff in the Arid Region of Northwest China

“…The observed meteorological data was interpolated into 10 km × 10 km grids based on the meteorological distribution system for high-resolution terrestrial modeling (MicroMet) [37]. The potential evaporation was estimated using the Penman-Monteith equation [38] for each grid cell.…”

Parameterizing sub-surface drainage with geology to improve modeling streamflow responses to climate in data limited environments

“…One potential source of these errors would be errors in estimation of meteorologic inputs. Interpolation of both temperature and precipitation in mountain environments is a well-documented source of error in hydrologic models (Liston and Elder, 2006). Here we use a relatively simple approach where point meteorologic measurements of temperature are scaled using a constant environmental lapse rate of temperature with elevation, and precipitation is scaled based on long-term mean patterns derived from PRISM (Daly et al, 1994).…”