Accurate precipiation data is vital to sustainable municipal water management, efficient agricultural and corporate supply chains, and gaining a better understanding of global environental change. Although there are numerous global precipitation data sets, they generally come in 3 types: 1) gridded interpolated data sets produced from point estimates of a global collection of rain gauges, 2) remotely sensed products that infer precipitation from either visible or infrared spectrums, passive sensors, or active sensors, and 3) reanalysis products that synthesyzse multiple geo-physical and climatological sources of data to produce high resolution and spatially uniform global precipitation estimates and forecasts.
Despite their seemingly crude technology, gridded rain guage data products remain extremely popular with academic research, production workflows, and resource management. Their greatest advantage over remotely sensed data is a large historical record. Popular rain gauge data sets have monthly records dating back to 1900, whereas even the oldest satellite precipitation data is limited to 1979. A detailed historical record paramount to global environmental change research and establishing baselines for practitioners and resource managers. In comparison to sattelite data, rain gauges are sufficiently accurate and cost effective, however, to leverage their full potential systems must be in place to centralize their data and perform quality control. With approximately 100,000-250,000 rain gauges in existence this can be an extraordinary logistical challenge (Kidd et al. 2016). The World Meteorlogical Organization (WMO) is the primary source for rain guage data organization through maintenance of the WMO Global Telecommunication System and co-sponsorship of the Global Climate Observing System.
Three of the most widely employed and heavily cited rain gauge precipitation data products are the University of Delaware’s Terrestrial Precipitation Gridded Time Series (UDEL-TS; Willmott and Matsuura 1995), the Climate Research Unit of the University of East Anglia’s Gridded Time Series dataset (CRU-TS v4.04; Harris et al. 2020), and the Global Precipitation Climatology Centre (GPCC; Anja Meyer-Christoffer et al. 2018). GPCC provides global monthly data from 1891-2016 at 0.5 degree resolution in addition to multiple other precipitation statistics. GPCC data has recently been featured in investigations of the effects of anthropogenic warming on California droughts (Williams et al. 2015), high resolutions climatologies for the earth’s land surfaces (Karger et al. 2017), and the effects of pervasive drought in forest ecosystems (Anderegg et al. 2015).
The GPCC and CRU-TS provide nearly identical temporal coverage and resolution, although they rely more on national meteorological agencies, the WMO, and the Food and Agriculture Organization for rain gauge sources. The UDEL-TS utilizes gauges from the Global Historical Climatology Network, Daily Global Historical Climatology Network, Atmospheric Environment Service archive, Hydrometeorological Institute, GC-Net, the Global Surface Summary of Day, and several other regional and global rain gauge networks. In contrast to UDEL-TS and CRU-TS that implement cross validation and outlier detection, GPCC requires a minimum of 10 uninterrupted years for each station to be included in the dataset.
It’s important to review spatial and temporal coverage when deciding which gridded rain gauge data product to incorporate into your research or other workflow. A recent review of precipitation data sets found that UDEL-TS, CRU-TS, and GPCC generally exhibit consistent interannual variablity, however, differences can be as great as 100mm (Sun et al. 2018). The three popular gauge data sets track exceptionally well in tropical zones, but provide divergent estimates in areas with low population and complex topography such as northern Agrica, northern North America, eastern Russia. Conversely, differences in seasonal precipitation estimates between UDEL-TS, CRU-TS, and GPCC were negligible.
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Anderegg, W. R. L., C. Schwalm, F. Biondi, J. J. Camarero, G. Koch, M. Litvak, K. Ogle, et al. 2015. “Pervasive Drought Legacies in Forest Ecosystems and Their Implications for Carbon Cycle Models.” Science 349 (6247): 528–32. https://doi.org/10.1126/science.aab1833.
Anja Meyer-Christoffer, Andreas Becker, Peter Finger, Udo Schneider, and Markus Ziese. 2018. “GPCC Precipitation Climatology Version 2018 at 0.5°: Monthly Land-Surface Precipitation Climatology for Every Month and the Total Year from Rain-Gauges Built on GTS-Based and Historic Data: Globally Gridded Monthly Totals.” Global Precipitation Climatology Centre (GPCC). https://doi.org/10.5676/DWD_GPCC/CLIM_M_V2018_050.
Harris, Ian, Timothy J. Osborn, Phil Jones, and David Lister. 2020. “Version 4 of the CRU TS Monthly High-Resolution Gridded Multivariate Climate Dataset.” Scientific Data 7 (1, 1): 1–18. https://doi.org/10.1038/s41597-020-0453-3.
Karger, Dirk Nikolaus, Olaf Conrad, Jürgen Böhner, Tobias Kawohl, Holger Kreft, Rodrigo Wilber Soria-Auza, Niklaus E. Zimmermann, H. Peter Linder, and Michael Kessler. 2017. “Climatologies at High Resolution for the Earth’s Land Surface Areas.” Scientific Data 4 (1, 1): 1–20. https://doi.org/10.1038/sdata.2017.122.
Kidd, Chris, Andreas Becker, George J. Huffman, Catherine L. Muller, Paul Joe, Gail Skofronick-Jackson, and Dalia B. Kirschbaum. 2016. “So, How Much of the Earth’s Surface Is Covered by Rain Gauges?” Bulletin of the American Meteorological Society 98 (1): 69–78. https://doi.org/10.1175/BAMS-D-14-00283.1.
Sun, Qiaohong, Chiyuan Miao, Qingyun Duan, Hamed Ashouri, Soroosh Sorooshian, and Kuo-Lin Hsu. 2018. “A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons.” Reviews of Geophysics 56 (1): 79–107. https://doi.org/10.1002/2017RG000574.
Williams, A. Park, Richard Seager, John T. Abatzoglou, Benjamin I. Cook, Jason E. Smerdon, and Edward R. Cook. 2015. “Contribution of Anthropogenic Warming to California Drought During 2012–2014.” Geophysical Research Letters, January, 6819–28. https://doi.org/10.1002/2015GL064924@10.1002/(ISSN)1944-8007.CALDROUGHT1.
Willmott, Cort J., and Kenji Matsuura. 1995. “Smart Interpolation of Annually Averaged Air Temperature in the United States.” Journal of Applied Meteorology 34 (12): 2577–86. https://doi.org/10.1175/1520-0450(1995)034<2577:SIOAAA>2.0.CO;2.