Using Python as a tool for processing forecast climate data

The Python scripting language is increasingly being use in the scientific community for modeling and analysis. With a great number of libraries and cross-platform compatibility, Python is an ideal platform for building a remote sensing application that preforms data storage and retrieval and implements atmospheric and/or crop models. This application can be used to access, extract and analyze geophysical data provided by organizations such as NASA and forecast data from the National Weather Service. In this work, we present a Python application that is used for extracting and pre-processing climate forecast data to be used in crop simulation models and remote sensing evapotranspiration algorithms. This is a standalone program that parses the hourly climate forecast data information about temperature, wind speed, and humidity and computes solar radiation and reference evapotranspiration for a given position for a specified time range. The program output data is formatted to be used as a driver file for different computational models. We will showcase the extracting of time series forecast data using this application and compare the data to measure climate station data for the same time series.