Abstract
The regional HydroMeteorological DataBase (HMDB) was designed for easy access to climate data via the Internet. It contains data on various climatic parameters (temperature, precipitation, pressure, humidity, and wind strength and direction) from 190 meteorological stations in Russia and bordering countries for a period of instrumental observations of over 100 years. Open sources were used to ingest data into HMDB. An analytical block was also developed to perform the most common statistical analysis techniques.
Highlights
Analysis of the reactions of biodiversity parameters to climate fluctuations and the monitoring of common trends of vegetation changes are important tasks in modern ecological research in Northern Russian. Meteorological observations in this region reveal a steady trend of warming since the 1970s, with a peak in the 1990s—the modern ‘warming’ of climate (Anisimov & Belolutskaya, 2003; Pavlov, 2003). To accurately assess this climate change, it has been highly important to use instrumental observations such as temperature, precipitation, and other climate characteristics collected by weather stations
Vast numbers of such data are available from these weather stations, and the average duration of observations is 100–120 years or more
The developed HydroMeteorological DataBase (HMDB) † contains information from 190 weather stations located in Russia and its bordering countries (Figure 1), where the data used to fill the database were obtained from the following openly accessible portals: http://aisori.meteo.ru/ClimateR and http://rp5.ru
Summary
Meteorological observations in this region reveal a steady trend of warming since the 1970s, with a peak in the 1990s—the modern ‘warming’ of climate (Anisimov & Belolutskaya, 2003; Pavlov, 2003). To accurately assess this climate change, it has been highly important to use instrumental observations such as temperature, precipitation, and other climate characteristics collected by weather stations. We had the following objectives: (1) to develop the database structure and to construct the user interface; (2) to implement the most frequently used data-processing algorithms (generation of average monthly and annual characteristics, sums of temperature and precipitation, sliding means, wind roses, etc.); (3) to query the climate data using an open source search engine; and (4) to fill the database and to develop a simple method for export of data and data analysis results (tables and images) in different formats
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