Abstract
Abstract. We present a new global land-based daily precipitation dataset from 1950 using an interpolated network of in situ data called Rainfall Estimates on a Gridded Network – REGEN. We merged multiple archives of in situ data including two of the largest archives, the Global Historical Climatology Network – Daily (GHCN-Daily) hosted by National Centres of Environmental Information (NCEI), USA, and one hosted by the Global Precipitation Climatology Centre (GPCC) operated by Deutscher Wetterdienst (DWD). This resulted in an unprecedented station density compared to existing datasets. The station time series were quality-controlled using strict criteria and flagged values were removed. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1∘ × 1∘ latitude–longitude resolution. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. We also provide a quality mask based on these uncertainty measures. For those interested in a dataset with lower station network variability we also provide a related dataset based on a network of long-term stations which interpolates stations with a record length of at least 40 years. The REGEN datasets are expected to contribute to the advancement of hydrological science and practice by facilitating studies aiming to understand changes and variability in several aspects of daily precipitation distributions, extremes and measures of hydrological intensity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.
Highlights
Earth’s climate is changing, leading to spatial and temporal variations in precipitation
The raw station data for Rainfall estimates on a gridded network (REGEN) has three sources: 1. the Global Precipitation Climatology Centre (GPCC), operated by Deutscher Wetterdienst (DWD), 2. the Global Historical Climatology Network – Daily (GHCN-Daily) version 3.22-upd-2017092104: stations hosted by National Centers for Environmental Information (NCEI) in the USA (Menne et al, 2012) (103 635 stations), and
The quality control procedures used in REGEN were adopted from NCEI, part of National Oceanic and Atmospheric Administration (NOAA) in the USA (Durre et al, 2010)
Summary
Earth’s climate is changing, leading to spatial and temporal variations in precipitation. The Tropical Rainfall Measuring Mission (TRMM) 3B42 (Huffman et al, 2007), Global Precipitation Climatology Projects 1 Degree Daily (GPCP-1DD) (Huffman et al, 2001), Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) (Funk et al, 2015) and the Precipitation Estimates from Remotely Sensed Information using Artificial Neural Networks – Climate Data Record (PERSIANN-CDR) (Ashouri et al, 2014) are some examples of popular satellite-based precipitation products. These satellite-based datasets, use complex algorithms to derive precipitation estimates from indirect radiation measurements, resulting in large uncertainties in precipitation estimates. We describe how uncertainty estimates are calculated and provide guidelines for how to best use (and not use) the dataset
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