Surface air temperatures modelled by ERA-40, ERA-Interim and (NCEP)/(NCAR) reanalysis (NNRP-1) have been compared with observations at 11 synoptic stations in Ireland over the period [1989][1990][1991][1992][1993][1994][1995][1996][1997][1998][1999][2000][2001]. The three reanalysis datasets show good agreement with the observed data and with each other. Slopes of the least-squares line to scatter plots of reanalysis data versus observational data show small differences between the three reanalyses, with ERA-40, ERA-Interim and NNRP-1 slopes ranging between (0.79-1.06) ± 0.01, (0.83-1.01) ± 0.01 and (0.76-0.98) ± 0.01, respectively. Summary statistics and the monthly mean temperatures over the 1989-2001 period showed that the reanalyses were significantly warmer in winter than the observations, which resulted in best fit lines with slopes consistently less than unity. ERA-Interim was slightly better than both ERA-40 and NNRP-1 at modelling winter temperatures and it had higher correlation coefficients with the observations. All three reanalyses use different grid sizes and types. Subsequent regridding of the ERA-Interim and NNRP-1 data to the ERA-40 grid showed that the grid difference had no significant influence on the results. Comparison of ERA-Interim and NNRP-1 data with the air temperatures at four marine buoys around the Irish coast for the period [2001][2002][2003][2004][2005] showed that the reanalyses modelled colder winter temperatures than the observations; resulting in best fit lines with slopes consistently greater than unity. The slopes for NNRP-1 and ERA-Interim at the marine buoys, respectively, averaged 1.09 ± 0.04 and 1.10 ± 0.05 while the slopes at the four land stations over the same period averaged 0.87 ± 0.02 and 0.89 ± 0.02, respectively. We believe that this pattern results from the difference in the treatment of land and sea surfaces in the reanalysis datasets.

The Weather Research and Forecasting model (WRF) is used to downscale interim ECMWF Re-Analysis (ERA-Interim) data for the climate over Europe for the period 1990-95 with grid spacing of 0.448 for 12 combinations of physical parameterizations. Two longwave radiation schemes, two land surface models (LSMs), two microphysics schemes, and two planetary boundary layer (PBL) schemes have been investigated while the remaining physics schemes were unchanged. WRF simulations are compared with Ensemble-Based Predictions of Climate Changes and their Impacts (ENSEMBLES) observations gridded dataset (E-OBS) for surface air temperatures (T2), precipitation, and mean sea level pressure (MSLP) in eight subregions within the model domain to assess the performance of the different parameterizations on widely varying regional climates. This work shows that T2 is modeled well by WRF with high correlation coefficients (0.8 , R , 0.95) and biases less than 48C. T2 shows greatest sensitivity to land surface models, some sensitivity to longwave radiation schemes, and less sensitivity to microphysics and PBL schemes. Precipitation is not well modeled by WRF with low correlation coefficients (0.1 , R , 0.3) and high root-mean-square differences (RMSDs; 8-9 mm day 21 ). Precipitation shows sensitivity to LSMs in summer. No significant bias has been observed in the MSLP modeled by WRF. Correlation coefficients are typically in the range 0.7 , R , 0.8 while RMSDs are in the range 6-10 hPa. MSLP output is sensitive to longwave radiation scheme in summer but is relatively insensitive to either microphysics or the choice of LSM. The optimum combination of parameterizations for all three state variables examined is strongly dependent on subregion and demonstrates the need to carefully select parameterization combinations when attempting to use WRF as a regional climate model.

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This study investigates the sensitivity of the simulated trajectory, intensification, and forward speed of Tropical Cyclone Yasi to initial conditions, physical parameterizations, and sea surface temperatures. Yasi was a category 5 storm that made landfall in Queensland, Australia in February 2011. A series of simulations were performed using WRF-ARW v3.4.1 driven by ERA-Interim data at the lateral boundaries. To assess these simulations, a new simple skill score is devised to summarize the deviation from observed conditions at landfall. The results demonstrate the sensitivity to initial condition resolution Keywords Australia; Weather Research and Forecasting (WRF) model; tropical cyclone; Yasi; initial conditions; cumulus parameterization; sea surface temperature.

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The diurnal cycle of precipitation during the summer season over the contiguous United States is examined in eight distinct regions. These were identified using cluster analysis applied to the diurnal cycle characteristics at 2141 rainfall gauges over the 10-yr period 1991–2000. Application of the clustering technique provides a physically meaningful way of identifying regions for comparison of model results with observations. The diurnal cycle for each region is specified in terms of 1) total precipitation, 2) frequency of precipitation occurrence, and 3) intensity of precipitation per occurrence on an hourly basis averaged over the 10-yr period. The amplitude and phase of each element of the diurnal cycle was obtained from harmonic analysis and has been compared with the results of a 24-member multiphysics ensemble of simulations produced by the Weather Research and Forecast (WRF) Model on a region-by-region basis. Three cumulus schemes, two radiation schemes, two microphysics schemes, and two planetary boundary layer schemes were included in the ensemble. Simulations of total precipitation showed reasonable agreement with observations in regions where the diurnal cycle is directly influenced by solar radiation, (e.g., the U.S. Southeast), but they were less successful in regions where other factors influence the diurnal cycle (e.g., the central United States). The diurnal cycle of precipitation frequency and intensity showed substantial biases in the simulations of all eight regions, namely, overestimation of occurrences and underestimation of intensities. Simulations were sensitive to the cumulus and radiation schemes but were largely insensitive to either microphysics or planetary boundary layer schemes.

A new algorithm is described that can separate precipitation output from convection‐permitting models into three different types of precipitation: (a) convective, (b) stratiform, and (c) orographically enhanced precipitation. The algorithm is based on physical processes that underlie these types of precipitation and it is applicable over both ocean and land surfaces. It is particularly well suited for mountainous areas or other regions exhibiting complex terrain. The algorithm's performance is first demonstrated for a selection of well‐understood weather events and then for a 10‐year convection‐permitting climate simulation over Norway. The algorithm correctly separates convection embedded in frontal systems from stratiform precipitation and also properly identifies orographically enhanced precipitation when the frontal systems interact with local orography. The results suggest that this can be a powerful new tool for investigating characteristics of precipitation in convection‐permitting climate simulations, particularly in a climate change context and as researchers move towards models that explicitly resolve convection and its related processes.