Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

Abstract

Abstract. The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERAInterim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979-2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparing initial and bias-adjusted ERA-Interim data against gridded observational fields.

Downwelling surface solar irradiance in the tropical Atlantic Ocean: a comparison of re-analyses and satellite-derived data sets to PIRATA measurements

“…Other data sets are available that cover the tropical Atlantic Ocean and may be assessed against the PIRATA measurements to gain knowledge on their limitations and confidence in their use. Examples are the satellite-derived OSI-SAF (http://www.osi-saf.org, last access: 1 September 2018) or the Japanese 55-year re-analysis (JRA 55; Kang and Ahn, 2015;Kobayashi et al, 2015).…”

Version 4 of the CRU TS monthly high-resolution gridded multivariate climate dataset

“…Precipitation, for example, is not usually assimilated. There are many examples of gridded observations being used to 'bias correct' reanalyses, a selection that used CRU TS are described in 30,31 , and 13 . CRU TS is also used as independent assessment of other datasets, such as satellite-derived data for recent decades 32 , highlighting the continuing need for a dataset based only on in-situ direct observations.…”

Evaluation of Four Reanalysis Datasets against Radiosonde over Southwest Asia

“…This is highly related to the dataset itself rather than to the weather conditions over the region, as the other RDs did not display such an issue. Biases and differences among RDs at different timescales and subregional levels are due to several reasons, including differences in the assimilated input observations, assimilation methods, resolutions (horizontal and vertical) [9,35] and differences in local atmospheric processes and topography [7,15]. Differences in input observations are a key reason [9,36], as different reanalysis systems rely on different subsets of inputs to assimilate the atmospheric state.…”

“…This mainly implies that when an atmospheric phenomenon is studied at a local/subregional scale using RDs, the reliability of RDs should be evaluated at the phenomenon level. In the literature, increasing effort is being made to evaluate RDs at the phenomenon and application levels (e.g., [12,28,35,37]). The results of this work also have important implications for planning future weather and climate monitoring networks and evaluating the effectiveness of models within the region.…”