The Mountain Climate Generator, MCLIGEN, is a useful tool for resource professionals and scientists requiring high resolution weather data in the mountainous west. Potential applicaions for such data are ecosystem modeling, small scale and watershed hydrologic modeling, forest growth modeling, assessments of forest health, and climate change effects.

OVERVIEW

MCLIGEN consists of a set of nested forecast models to reduce synoptic weather grids to 20-km grids, a stochastic weather sequence model to generate stochastic sequences at any grid point or an existing weather station, a microclimate model to adjust to site specific topography, and vegetation at points not on the grid, and a snow melt model. The initial data base was developed by the European Center for Medium Range Weather Forecasting (ECMWF), which includes 15 years of weather observations on a 300-km grid covering the earth. This information is processed by the National Center for Atmospheric Research (NCAR) at Boulder, CO., as part of the global climate change research program now in progress. Global Circulation Model outputs reflecting altered climates may be used in place of the ECMWF data to look at effects of changing climates on ecosystems at a small scale.

The 300-km synoptic grid is analyzed by NCAR's mesoscale forecast program, RegCM2, to combine location, elevation, aspect, vegetation, and hydrology to predict weather on a 50km grid. RegCM2 has been modified by Utah State University (USU) and used a second time to predict weather for a 20-km grid nested within the 50 km grid (RegCM2MR). (One township is 10 km by 10 km.)

The 20-km grid weather is statistically analyzed to develop a set of probability density functions for the weather parameters. The statistics are within a package called STOCHASTIC.

A detailed point analysis can be done by a specific location simulator entitled ZOOM. ZOOM predicts a climate sequence at a desired point within any 20-km cell, based on a climate sequence for the nearest 20-km grid point, topography, hydrology, and vegetation. ZOOM can be used to deveiop a predicted weather sequence from the 15 years of grid data, or with STOCHASTIC to generate simulated weather sequences for many potential weather scenarios.

An energy balance model has been developed to model snowmelt. Energy from radiation, air and latent sources are combined with canopy to predict snowmelt rate. User input will be required to specify effects of drifting in snow accumulation.

OTHER CONSIDERATIONS

The MCLIGEN project is a cooperative agreement between RWU4702 of the Intermountain Research Station and the Utah Water Resources Research Laboratory of USU. Additional funding has come from the Washington Office (WO) Staffs of Watershed and Air, Engineering, and Timber Management. USU is in the final year of a three year agreement to develop MCLIGEN.

Figure 1. Diagram of the relationships between the MCLIGEN grid scales.