Generation of Simulated Daily Precipitation and Air and Soil Temperatures

This paper describes a maximum likelihood method using historical weather data to estimate a parametric model of daily precipitation and maximum and minimum air temperatures. Parameter estimates are reported for Brookings, SD, and Boone, IA, to illustrate the procedure. The use of this parametric model to generate stochastic time series of daily weather is then summarized. A soil temperature model is described that determines daily average, maximum, and minimum soil temperatures based on air temperatures and precipitation, following a lagged process due to soil heat storage and other factors.


Issue Date:
2000
Publication Type:
Working or Discussion Paper
PURL Identifier:
http://purl.umn.edu/18503
Total Pages:
33
Series Statement:
CARD Working Paper 00-WP 234




 Record created 2017-04-01, last modified 2017-11-19

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)