A cross‐checked global monthly weather station database for precipitation covering the period 1901–2010. (26th January 2020)

• Record Type: Journal Article
• Authors: Castellanos‐Acuna, Dante; Hamann, Andreas
• Is Part Of: Geoscience data journal. Volume 7:Number 1(2020)
• Page Start: 27
• Page End: 37
• Publication Date: 2020-01-26
• Subjects: climate -- land surface -- monthly time series -- precipitation -- weather stations; Earth sciences -- Research -- Periodicals; Earth sciences -- Data processing -- Periodicals; Earth sciences -- Documentation -- Periodicals; 550.28557
• DOI: 10.1002/gdj3.88
• Languages: English
• ISSNs: 2049-6060

Abstract

Abstract: Comprehensive monthly weather station databases are the foundation for many gridded climate data products, and they are widely used to characterize regional climate conditions, track climate change and research the impact of climate on natural and managed ecosystems. However, weather station databases are often regional in coverage, and they can have extensive gaps in station coverage over time. They may also contain errors in climate records, station coordinates or elevation. Here, we assemble a comprehensive monthly weather station database for precipitation from multiple reputable data sources. We use digital elevation models and nearby stations to search for inconsistencies in reported station locations and recorded precipitation values. We also estimated missing values in weather station time series using a linear model approach based on interpolated anomaly surfaces. The resulting station records were ranked into ten classes, according to the completeness of records, the reliability of missing value estimations and other criteria. We corrected incomplete or erroneous location and elevation information for 12% of all available station records. A total of 23% of monthly records that had missing values could be estimated with high or moderate confidence. We sub‐sampled our global database of more than 80, 000 stations with various spatial filters, so that only the highest quality station for a given area was retained. Our contribution significantly enhances global data coverage compared to individual databases currently available. Even when accepting only the stations within the top two quality ranks in our combined database, and applying the coarsest spatial filter of one station per approximately 1, 600 km 2, the remaining station count of more than 20, 000 stations exceeds the largest alternative database (without a spatial filter applied) by more than 50%. Abstract : We assemble a comprehensive global monthly precipitation database of 122, 000 weather station locations from multiple reputable data sources. We use various approaches to search for inconsistencies in reported station locations and recorded precipitation values, and we corrected incomplete or erroneous information in 12% of station records. We also estimated missing values in weather station time series for 23% of database entries. With strict quality criteria and spatial filters applied, our precipitation database exceeds the largest alternative databases by approximately 50%.