A Constrained Stochastic Weather Generator for Daily Mean Air Temperature and Precipitation

Feifei Pan,Samuel F Atkinson,Marty O’Neill,Steve Wolverton,Lisa Nagaoka,Timothy A Kohler

doi:10.3390/atmos12020135

Feifei Pan, Samuel F Atkinson + Show 4 more

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https://doi.org/10.3390/atmos12020135

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Abstract

A constrained stochastic weather generator (CSWG) for producing daily mean air temperature and precipitation based on annual mean air temperature and precipitation from tree-ring records is developed and tested in this paper. The principle for stochastically generating daily mean air temperature assumes that temperatures in any year can be approximated by a sinusoidal wave function plus a perturbation from the baseline. The CSWG for stochastically producing daily precipitation is based on three additional assumptions: (1) In each month, the total precipitation can be estimated from annual precipitation if there exists a relationship between the annual and monthly precipitations. If that relationship exists, then (2) for each month, the number of dry days and the maximum daily precipitation can be estimated from the total precipitation in that month. Finally, (3) in each month, there exists a probability distribution of daily precipitation amount for each wet day. These assumptions allow the development of a weather generator that constrains statistically relevant daily temperature and precipitation predictions based on a specified annual value, and thus this study presents a unique method that can be used to explore historic (e.g., archeological questions) or future (e.g., climate change) daily weather conditions based upon specified annual values.

Highlights

The impact of climate change on agricultural productivity is as important to understanding prehistoric subsistence as it is to today’s economic landscape
Achieving this research goal is equivalent to addressing the following question: if annual mean temperature and annual precipitation at a site are specified for a given year based on proxy indicators, what could the daily weather conditions be in that year? That is what are the likely daily weather scenarios that occurred within years? To answer this question, we have developed a constrained stochastic weather generator (CSWG) that we refer to as the Daily Weather Generator Constrained by Specified Annual Mean Temperature and Precipitation
The CSWG developed in this study contains two functions: (1) stochastically generating daily mean air temperature based on annual mean air temperature, and (2) stochastically generating daily precipitation based on annual precipitation

Summary

Introduction

The impact of climate change on agricultural productivity is as important to understanding prehistoric subsistence as it is to today’s economic landscape. Researchers studying potential yield of modern crops use a variety of climate variables, such as temperature, precipitation, solar radiation, etc. Data for these variables are often recorded as daily measurements This level of precision is important because conditions vary and uncertainty can grow across time and result in disparate effects. What is lacking in comparison to modern data is an understanding of how temperature or precipitation varies within a growing season. Without finer-scale temporal resolution, it is difficult to develop and test hypotheses about comparatively precise, within-year shifts in temperature and precipitation that were likely important in early farming societies. A finer temporal resolution can be achieved by modeling daily temperature and precipitation using a stochastic weather generator (SWG).

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