A high‐resolution regional reanalysis for Europe. Part 1: Three‐dimensional reanalysis with the regional HIgh‐Resolution Limited‐Area Model (HIRLAM)

Abstract

A regional reanalysis covering the years 1989-2010 has been produced with the HIgh Resolution Limited-Area Model (HIRLAM) forecast model and data assimilation system. Surface and upper-air variables were analysed at 0000, 0600, 1200 and 1800 UTC on a three-dimensional grid-mesh with 22 km spacing covering Europe using conventional in situ observations. Information from the global reanalysis ERA-Interim has been used as a large-scale constraint in the data assimilation and as lateral boundaries in the forecast model integrations.Comparison to the global forcing reanalysis shows good agreement in the largescale structures, as expected given the forcing from the boundaries and in the analysis. Comparison to the observed climatological distribution and a skill score evaluation showed that the HIRLAM reanalysis is better than ERA-Interim at describing extreme values of 2 m temperature and 24 h accumulated precipitation. However, no added value in the HIRLAM reanalysis could be quantified for the wind speed at 10 m over land.The first production run covered the years 1989-2010 and the statistics presented in this paper are based on that dataset. This reanalysis has also been used as input to a two-dimensional surface analysis using the MESoscale ANalysis (MESAN) system which is presented in Part 2. Since then both the two-and three dimensional reanalyses have been extended to cover the years 1979-2014.

Reanalysis of and attribution to near-surface ozone concentrations in Sweden during 1990–2013

“…Within the EURO4M-project HIRLAM was run as forecasts from 6-hourly analyses, composed of three-dimensional variational upper-air analyses and optimal interpolation surface analyses (Dahlgren et al, 2016). Lateral and lower (sea surface temperature and sea ice) boundaries were taken from ERA-Interim (Dee et al, 2011).…”

High‐Resolution Regional Reanalysis in China: Evaluation of 1 Year Period Experiments

“…Constructing a global reanalysis data set requires enormous computational resources to conduct quality control for observation data and run a system mainly consisting of a data assimilation system and a numerical model, which subjects most the global reanalysis to a spatial grid resolution larger than 50 km. Regional reanalysis, which utilizes the high‐resolution limited‐area models with initial and boundary conditions derived from a trustworthy global reanalysis, can provide a more detailed analysis of weather parameters by the inclusion of additional physical parameterizations (e.g., microphysics) and local details (e.g., regional topography and land use) (Dahlgren et al, ; Mesinger, DiMego, et al, ). Several regional reanalysis data sets have been produced over North America, Europe, and the Arctic during last 10 years: North American Regional Reanalysis (NARR) (Mesinger et al, ), the High‐resolution Regional Reanalysis for the Europe (Bollmeyer, Keller, et al, ; Dahlgren et al, ; Renshaw et al, ), and the regional Arctic system reanalysis (Bromwich et al, ).…”

EURODELTA-Trends, a multi-model experiment of air quality hindcast in Europe over 1990–2010

“…The CTMs LOTOS-EUROS and MATCH have been meteorologically forced by ERA-Interim series further downscaled with RACMO2 (van Meijgaard, 2012) and HIRLAM (Dahlgren et al, 2016), respectively. RACMO2, used here, was part of the EuroCordex studies documented in Jacob et al (2013) and Kotlarski et al (2014) and excludes nudging towards Era-Interim.…”