#Daily rainfall totals series#

Radar estimates provide high spatial and temporal resolution estimates of rainfall over local According to Lorenz and Kunstmann ( 2012) even the state-of-the-art reanalyses are unsuitable for climate trend and long-term water budget analysis. Reanalyses also contain temporal inhomogeneities due to the changing amount of assimilated observations over time ( Compo et al., 2006). This is evidenced by the classification of precipitation as the least reliable class by Kalnay et al. Furthermore, none of the reanalysis products assimilate surface precipitation observations (MERRA2 however incorporates satellite infrared and microwave measurements) and as such are not representative of reality. They are often misused as observations but in fact inherit issues from the incomplete observations and imperfect models and are based on complex assimilation techniques. Reanalysis products assimilate observations and models to create a synthesised estimate of the state of the earth system. However, alternatives to gauge-based measurements such as satellite observations, model reanalysis products and radar-based observations have additional limitations. Hence in some ways gridded observations of the past also help us to better plan for and adapt to these changes in the future.Īll observations have errors – for example, gauge-based precipitation measurements are subject to undercatch, wind related errors, evaporation loss, wetting loss, splash in/out errors and tipping errors (see McMillan et al., 2012 for details). Reliable observations are necessary to understand these short- and long-term changes and to evaluate climate models which help understand the processes driving these changes. In addition to changes in precipitation due to climate change, precipitation is also characterised by strong variability in many regions. Theoretical expectations are that the global hydrological cycle would intensify in a warmer climate, associated with increases in mean and extreme precipitation (whereby mean and total precipitation would increase at lower rate than extreme precipitation due to energetic constraints Allen and Ingram, 2002). These changes in precipitation are strongly linked to social, economic and environmental prosperity due to the role precipitation plays in global food production and maintaining biodiversity. Here we document the development of the dataset and guidelines for best practices for users with regards to the two datasets.Įarth's climate is changing, leading to spatial and temporal variations in precipitation. 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. 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. We also provide a quality mask based on these uncertainty measures. Besides the daily precipitation amounts, fields of standard deviation, kriging error and number of stations are also provided. Remaining values were interpolated to create area-average estimates of daily precipitation for global land areas on a 1 ∘ × 1 ∘ latitude–longitude resolution.

#Daily rainfall totals series#

The station time series were quality-controlled using strict criteria and flagged values were removed. This resulted in an unprecedented station density compared to existing datasets.

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). 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.