Abstract

In this work, we present the results of high-resolution dynamical downscaling of air temperature, relative humidity, wind speed and direction, for the area of Poland, with the Weather Research and Forecasting (WRF) model. The model is configured using three nested domains, with spatial resolution of 45 km × 45 km, 15 km × 15 km and 5 km × 5 km. The ERA-Interim database is used for boundary conditions. The results are evaluated by comparison with station measurements for the period 1981–2010. The model is capable of reproducing the main climatological features of the study area. The results are in very close agreement with the measurements, especially for the air temperature. For all four meteorological variables, the model performance captures seasonal and daily cycles. For the air temperature and winter season, the model underestimates the measurements. For summer, the model shows higher values, compared with the measurements. The opposite is the case for relative humidity. There is a strong diurnal pattern in mean error, which changes seasonally. The agreement with the measurements is worse for the seashore and mountain areas, which suggests that the 5 km × 5 km grid might still have an insufficient spatial resolution. There is no statistically significant temporal trend in the model performance. The larger year-to-year changes in the model performance, e.g. for the years 1982 and 2010 for the air temperature should therefore be linked with the natural variability of meteorological conditions.

Highlights

  • Downscaling is a method used to obtain geographical distribution and time evolution of smallscale features given large-scale coarse-resolution analyses, forecasts or simulations (HONG and KANAMITSU 2014)

  • We present the application of the Weather Research and Forecasting (WRF) model for dynamical downscaling of the ERAInterim data for the area of Poland, with high spatial resolution of 5 km 9 5 km

  • In terms of Index of Agreement, the model is in very close agreement with the measurements for temperature at 2 m (T2), with Index of agreement (IOA) above 0.99 (1.0 means a ‘‘perfect model performance’’)

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Summary

Introduction

Downscaling is a method used to obtain geographical distribution and time evolution of smallscale features given large-scale coarse-resolution analyses, forecasts or simulations (HONG and KANAMITSU 2014). The results of the European Coordinated Regional Climate Downscaling Experiment (EURO-CORDEX) project show the importance of dynamical downscaling for this area and address the uncertainties related with this approach (GIORGI and GUTOWSKI 2015). The demand for meteorological information, available for a long-term period, at high spatial and temporal resolution, and developed homogenously for a large area is increasing. This information is a must for other studies, such as ecology and tick diseases (KIEWRA et al 2014), air quality (WAłASZEK et al 2015; WERNER et al 2011; HERNANDEZ-CEBALLOS et al 2014) or hydrological forecasting (Jeziorska and Niedzielski, this issue).

Study Area
The WRF Model Configuration
Meteorological Measurements
Evaluation of the Model Results
Results
Summary and Conclusions
Full Text
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