A range of in situ, satellite and reanalysis products on a common daily 1°×1°latitude/longitude grid were extracted from the Frequent Rainfall Observations on Grids database to help facilitate intercomparison and analysis of precipitation extremes on a global scale. 22 products met the criteria for this analysis, namely that daily data were available over global land areas from 50°S to 50°N since at least 2001. From these daily gridded data, 10 annual indices that represent aspects of extreme precipitation frequency, duration and intensity were calculated. Results were analysed for individual products and also for four cluster types: (i) in situ, (ii) corrected satellite, (iii) uncorrected satellite and (iv) reanalyses. Climatologies based on a common 13-year period (2001-2013) showed substantial differences between some products. Timeseries (which ranged from 13 years to 67 years) also highlighted some substantial differences between products. A coefficient of variation showed that the in situ products were most similar to each other while reanalysis products had the largest variations. Reanalyses however agreed better with in situ observations over extra-tropical land areas compared to the satellite clusters, although reanalysis products tended to fall into 'wet' and 'dry' camps overall. Some indices were more robust than others across products with daily precipitation intensity showing the least variation between products and days above 20 mm showing the largest variation. In general, the results of this study show that global space-based precipitation products show the potential for climate scale analyses of extremes. While we recommend caution for all products dependent on their intended application, this particularly applies to reanalyses which show the most divergence across results. OPEN ACCESS RECEIVED
Despite the availability of several observationally constrained data sets of daily precipitation based on rain gauge measurements, remote sensing, and/or reanalyses, we demonstrate a large disparity in the quasi‐global land mean of daily precipitation intensity. Surprisingly, the magnitude of this spread is similar to that found in the Coupled Model Intercomparison Project Phase 5 (CMIP5). A weakness of reanalyses and CMIP5 models is their tendency to over simulate wet days, consistent with previous studies. However, there is no clear agreement within and between rain gauge and remotely sensed data sets either. This large discrepancy highlights a shortcoming in our ability to characterize not only modeled daily precipitation intensities but even observed precipitation intensities. This shortcoming is partially reconciled by an appreciation of the different spatial scales represented in gridded data sets of in situ precipitation intensities and intensities calculated from gridded precipitation. Unfortunately, the spread in intensities remains large enough to prevent us from satisfactorily determining how much it rains over land.
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.
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