Weather and climate prediction rely on inherently different predictors at different time scales. Some of the variability in precipitation on climate time-scales (i.e., interannual) could be attributed to accumulated noise on weather (i.e., daily) time-scales. To determine a lower bound on how much climate variability is due to processes at climate time-scales, weather generator models with no m...

Weather generators (WG) became significant modules of crop models and decision support systems in the past decade. Using a large meteorological database from North America; two basic problems, related to the applicability of WGs in case of short or lacking data series, were investigated in the framework of the Multivariable weather generator (MVWG). First, the minimum data series length, requir...

This paper describes the development of a weather generator for use in climate impact assessments of agricultural and water system management. The generator produces internally consistent series of meteorological variables including: rainfall, temperature, humidity, wind, sunshine, as well as derivation of potential evapotranspiration. The system produces series at a daily time resolution, usin...

Stochastic models of daily precipitation are useful both for characterizing different precipitation climates and for stochastic simulation of these climates in conjunction with agricultural, hydrological, or other response models. A simple stochastic precipitation model is used to downscale— i.e. disaggregate from area-average to individual station—precipitation statistics for 6 groups of 5 U.S...

This work describes the development of a system designed for renewable power generation integration. It continuously acquires wind, solar and temperature data, which is automatically correlated with energy parameters, obtained from renewable energy systems. The developed system was installed in an urban building equipped with photovoltaic cells and wind renewable generation. To validate the dev...