THREE STEPS FROM RAW CLIMATE DATASET TO SPATIAL DATABASE
For climate change analysis of the past as well as for future projections of climate change, long-term primary datasets from meteorological stations are crucial. However, in many raw data formats such as *.txt, *.dat, *.csv, or any other file format precipitation or temperature datasets are not practical for data analysis. In three steps we provide users with facilities making a huge amount of climate datasets accessible, downloadable, and convertible to a spatial database. From the National Oceanic and Atmospheric Administration's (NOAA) National Climatic Data Center (http://gis.ncdc.noaa.gov) climate data such as daily temperature and daily precipitation rates are available free of charge. One either can download these text files from the website or can use an FTP server for manually downloading larger parts of the available datasets. To make the download procedure more straightforward and enable to download newly available datasets, we provide the users with a Python script to automate these tasks. In the first step data files are downloaded and unzipped to a pre-defined destination. In the second step temperature are converted from degree Fahrenheit to degree Celsius. The header of the text file is removed and space separations are changed to comma. Finally, a connection is established to a PostgreSQL/PostGIS database and tables are created for storing the ID of the stations, the geographical coordinates, the time of recorded data and the values of the precipitation and temperature. As a result, the data can be used for spatial queries but also interpolation of datasets provided as point information. Data obtained from NOAA are used as an example but with small modifications the script can be useful for any other datasets.
Peter Zalavari - Z_GIS - Centre for Geoinformatics
Hermann Klug - Z_GIS - Centre for Geoinformatics
Elisabeth Weinke - Z_GIS - Centre for Geoinformatics