An Extended Version of the Richardson Model for Simulating Daily Weather Variables
- 1 May 2000
- journal article
- Published by American Meteorological Society in Journal of Applied Meteorology and Climatology
- Vol. 39 (5), 610-622
- https://doi.org/10.1175/1520-0450-39.5.610
Abstract
The Richardson model is a popular technique for stochastic simulation of daily weather variables, including precipitation amount, maximum and minimum temperature, and solar radiation. This model is extended to include two additional variables, daily mean wind speed and dewpoint, because these variables (or related quantities such as relative humidity) are required as inputs for certain ecological/vegetation response and agricultural management models. To allow for the positively skewed distribution of wind speed, a power transformation is applied. Solar radiation also is transformed to make the shape of its modeled distribution more realistic. A model identification criterion is used as an aid in determining whether the distributions of these two variables depend on precipitation occurrence. The approach can be viewed as an integration of what is known about the statistical properties of individual weather variables into a single multivariate model. As an application, this extended model is fitte... Abstract The Richardson model is a popular technique for stochastic simulation of daily weather variables, including precipitation amount, maximum and minimum temperature, and solar radiation. This model is extended to include two additional variables, daily mean wind speed and dewpoint, because these variables (or related quantities such as relative humidity) are required as inputs for certain ecological/vegetation response and agricultural management models. To allow for the positively skewed distribution of wind speed, a power transformation is applied. Solar radiation also is transformed to make the shape of its modeled distribution more realistic. A model identification criterion is used as an aid in determining whether the distributions of these two variables depend on precipitation occurrence. The approach can be viewed as an integration of what is known about the statistical properties of individual weather variables into a single multivariate model. As an application, this extended model is fitte...Keywords
This publication has 32 references indexed in Scilit:
- Simultaneous stochastic simulation of daily precipitation, temperature and solar radiation at multiple sites in complex terrainAgricultural and Forest Meteorology, 1999
- Multisite generalization of a daily stochastic precipitation generation modelJournal of Hydrology, 1998
- Overdispersion Phenomenon in Stochastic Modeling of PrecipitationJournal of Climate, 1998
- Relationship between weather variables in observed and WXGEN generated data seriesAgricultural and Forest Meteorology, 1998
- Comparison of the WGEN and LARS-WG stochastic weather generators for diverse climatesClimate Research, 1998
- An improved method for estimating surface humidity from daily minimum temperatureAgricultural and Forest Meteorology, 1997
- Bounded Bivariate Distributions with Nearly Normal MarginalsThe American Statistician, 1996
- Stochastic space-time weather models from ground-based dataAgricultural and Forest Meteorology, 1995
- Preparing the erosion productivity impact calculator (EPIC) model to simulate crop response to climate change and the direct effects of CO2Agricultural and Forest Meteorology, 1992
- Space-Time Modelling with Long-Memory Dependence: Assessing Ireland's Wind Power ResourceJournal of the Royal Statistical Society Series C: Applied Statistics, 1989