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.
Screenshot or Representative Figure
Dataset Contact Information:Kenji Matsuura, University of Delaware
Cort Willmott, University of Delaware
Physical Science Division- Data Management, NOAA/ESRL/PSD
Cort Willmott & Kenji Matsuura of the University of Delaware have created a series of gridded temperature and precipitation data sets. More information can be found regarding precipitation here and about temperature here These data sets draw on the GHCN2 (Global Historical Climate Network), and from the archives of Legates and Willmott. The result us the Terrestrial Air Temperature: 1900-2017 Gridded Monthly Time Series (Version 5.01) and Terrestrial Precipitation: 1900-2016 (Version 5.01). These datasets contribute a monthly climatology of precipitation and air temperature, both at the surface, and a time series spanning 1900 to 2010 of monthly mean surface air temperatures and monthly total precipitation. Users should be aware that each value of precipitation or temperature is a local grid-point estimate, not grid-cell average or raster data. Also, the precipitation data are not corrected for rain-gauge undercatch, and the accuracy of the gridded fields depends on the station density
- West Bounding Coordinate: 0
- East Bounding Coordinate: 360
- North Bounding Coordinate: 90
- South Bounding Coordinate: -90
Spatial Reference Information:
- Coordinate System: Geographic
- Resolution: 0.5
- Units: degree
- Geodetic Model: WGS1984
Time Period Information:
- Beginning Date: 1900
- Ending Date: 2017
- Resolution: monthly