Accurate precipitation data is vital to sustainable municipal water management, efficient agricultural and corporate supply chains, and gaining a better understanding of global environmental 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 synthesize 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 satellite 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 Meteorological Organization (WMO) is the primary source for rain gauge 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; Meyer-Christoffer et al. 2018). UDEL-TS provides global monthly data from 1900-2017 at 0.5 degree resolution. UDEL-TS data has recently been featured in investigations of vegetation variability and climate in the La Plata River Basin (Chug and Dominguez 2019), asylum application to European Union Nations (Missirian and Schlenker 2017), and the climatology of the summer Shamal wind (Yu et al. 2016).
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. 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. 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 inter-annual variability, 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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Chug, Divyansh, and Francina Dominguez. 2019. “Isolating the Observed Influence of Vegetation Variability on the Climate of La Plata River Basin.” Journal of Climate 32 (14): 4473–90. https://doi.org/10.1175/JCLI-D-18-0677.1.
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.
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.
Meyer-Christoffer, Anja, 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.
Missirian, Anouch, and Wolfram Schlenker. 2017. “Asylum Applications Respond to Temperature Fluctuations.” Science 358 (6370): 1610–4. https://doi.org/10.1126/science.aao0432.
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.
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.
Yu, Yan, Michael Notaro, Olga V. Kalashnikova, and Michael J. Garay. 2016. “Climatology of Summer Shamal Wind in the Middle East.” Journal of Geophysical Research: Atmospheres 121 (1): 289–305. https://doi.org/10.1002/2015JD024063.