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Croatian wind atlas

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Croatian wind atlas

... is a basis for wind resource estimation in Croatia. It comprises of the mean annual wind speed (m/s) and mean annual power density (W/m2) maps at 10 m and 80 m above ground level.

The presented wind speed and power density are the output of the numerical atmospheric model and represent the average values within the 2 km x 2 km grid box. The local wind speed and power density at the particular location may be lower or higher than the average grid area value. If the presented data is used in decision making, Državni hidrometeorološki zavod (Meteorological and Hydrological Service) is not responsible for possible economic lossess or other consequences that may arise from the use of the data.

More detailed wind resource estimates for a particular location as well as all other additional information may be reached by contacting us at dhmz@cirus.dhz.hr.

Wind mapping methodology

The most accurate mapping of spatial distributions of wind speed and direction is achieved by using a dense network of long-term measurements. However, measured data is representative of a relatively small geographical area around the measurement location due to the large horizontal and vertical variability of near-surface winds in the atmosphere, numerous local effects on the wind flow as well as the thermally-induced local circulations. Because the establishment of a sufficiently dense network of measurements stations is not feasible, the only scientifically justified method for estimation of the climatologically representative wind speed mapping is through the use of numerical atmospheric models.

The mapping of wind speed is commonly preformed by using mesoscale meteorological models, especially over larger areas and in complex terrain, to dynamically regionalize or downscale the global atmospheric model reanalysis or climate data.

To minimize the effect of permanent changes in model formulation (such as physics, dynamics, numerical aspects) in the ever-evolving development of atmospheric models, the basis for dynamical downscaling is the global reanalysis data of the same atmospheric model which uses the same sources of initial data over a long period. Therefore, for this study we used the ERA40 global reanalysis (Kållberg et. al., 2004) of the European Centre for Medium-Range Weather Forecasts – ECMWF (http://www.ecmwf.int).The global reanalysis was performed by using the IFS (Integrated forecasting system) global operational atmospheric model of the ECMWF. The horizontal grid spacing of ERA40 is ~120 km with 60 vertical levels reaching the height of 0.1 hPa (~65 km).

Because the assessment of the representative wind speed and direction climate over an area requires modeling results for at least a 10-year period, wind regionalization was performed by using the last decade of ERA40 reanalysis 1992-2001. Although the global model reanalysis data can be useful for the evaluation of the atmospheric circulation above the planetary boundary layer, the estimation of the near-surface wind in global models is of limited accuracy. This is due to the fact that neither the model formulation nor the lower boundary conditions (terrain, roughness) are designed for simulating mesoscale processes on scales smaller than several hundreds of kilometers and a few hours. Exactly these spatial and temporal scales are, however, of pertinent importance for wind speed climate in complex terrain of Croatia, where mesoscale processes such as bora and jugo as well as local coastal and mountain circulations are very common. Therefore, the global model reanalysis in our area needs to be regionalized and adopted to the higher horizontal resolution to provide more accurate and representative information about the wind speed and direction and their spatial variability.

Due to its severe strength and high frequency, bora (e.g. Smith, 1987; Bajić, 1989) and jugo (Jurčec et al., 1996) are especially important for the estimation of the wind regime in our region. These spatially and temporally highly variable winds are most commonly directly associated with mesoscale cyclones in the Mediterranean and determined by the mesoscale orographic perturbation (Horvath et al., 2008). Therefore, the ability of mesoscale models to simulate the non-linear dynamical and thermal (e.g. static stability) properties of the related air masses is essential.

The dynamical downscaling has been performed by using the ALADIN/HR, a Croatian version of the mesoscale limited-area numerical atmospheric model ALADIN (Aire Limitee Adaptation Dynamique Developement International) (Bubnova et al., 1995). ALADIN is a spectral model with a hybrid η coordinate (Simmons and Burridge, 1981) that uses two-time-level semi-implicit semi-lagrangian integration scheme. Processes parameterized include vertical diffusion (Louis et al., 1982) and shallow convection (Geleyn, 1987). Stratiform and convective precipitation processes are treated separately with the Kessler microphysics that treats the so-called resolved precipitation (Kessler, 1969) and the modified Kuo scheme for deep convection (Geleyn et al., 1982). Radiation is parameterized according to the Geleyn and Hollingsworth (1979) and Ritter and Geleyn (1979) schemes. The vertical transport of soil moisture and heat is parameterized according to the Giard and Bazile (2000) with two levels within the ground.

The ALADIN/HR model was setup in a hydrostatic mode with grid spacing of 8 km and 37 vertical levels which are stretched in vertical to allow for the highest vertical resolution near the ground (the lowest model level is at 17 m above ground level). Due to the nature of the numerical solutions, the computational domain includes the area much larger than Croatia (Ivatek-Šahdan, Tudor, 2004). The lateral and boundary conditions were provided by aforementioned ERA40 reanalysis with a 6-hourly temporal resolution. The direct nesting to the reanalysis data was applied because the use of intermediate domain for dynamical downscaling with the ALADIN model showed similar results (Žagar et al., 2006). Prior to model integration, reanalysis data was interpolated and initialized using digital filter initialization (Lynch and Huang, 1994). The model was initialized daily at 12 UTC and integrated for the 42-hourly forecast range.

After the start of the ALADIN model integration mesoscale processes gradually build-up and the stabilization of results is achieved after the spin-up period of the first few hours. Model output data was archived with a temporal resolution of 60 min, after the 12-hourly spin-up time. This spin-up time was chosen for consistency with somewhat larger nesting ratio between input and output grid spacing, and is sufficient for development of mesoscale energy in the ALADIN model (Žagar et al., 2006).

After the integration, model results from 12-hourly to 35-hourly forecast ranges with a 60-min frequency were dynamically adopted during a 10-yearly period (Žagar and Rakovec, 1999) to the smaller domain with grid spacing of 2 km (229 x 205 grid points). Dynamical adaptation was performed by using 30 time steps, with the reduced number of vertical levels above heights of 1 km above ground level and with all parameterizations withheld apart from the parameterization of vertical diffusion.

A 10-yearly time-series of wind speed data in each grid point was used to calculate the mean annual wind speed. The value of the mean annual wind speed was assigned to the grid point centered within the grid box of size 2 km x 2 km. The resulting data was used for wind speed mapping that is cartographic representation of the mean annual wind speed.

Detailed information on the methodology, results and verification with the measured data can be found in the following papers:

Bajić, A. Ivatek-Šahdan, S. and K. Horvath, 2009: Prostorna razdioba brzine vjetra na području Hrvatske dobivena numeričkim modelom atmosfere ALADIN. Hrvatski meteorološki časopis 42, 66-77.

Horvath, K., A. Bajić, and S. Ivatek-Šahdan, 2011: Dynamical downscaling of wind speed in complex terrain prone to bora-type flows. J. Appl. Meteor. Climatol., 50, 1676-1691.

Contact person: dr. sc. Alica Bajić (tel: +385 1 4565 682; alica.bajic(at)cirus.dhz.hr)

References

Bajić, A., 1989: Severe bora on the northern Adriatic. Part I: Statistical analysis. Rasprave-Papers, 24, 1-9.
Beck A., Ahrens B. and Stadlbacker K., 2004: Impact of nesting strategies in dynamical downscaling of reanalysis data. Geophys. Res. Let., 31, L19101, doi:10.1029/2004GL020115.
Bubnova R., G. Hello, P. Benard, and J-F. Geleyn, 1995: Integration of fully elastic equations cast in the hydrostatic pressure terrain-following coordinate in the framework of ARPEGE/ALADIN NWP system. Mon. Wea. Rev., 123, 515-535.
Geleyn, J-F., 1987: Use of a modified Richardson number for parametrizing the effect of shallow convection. In: Matsuno Z. (ed)., Short and medium range weather prediction, Speciall volume of J. Meteor. Soc. Japan, 141-149.
Geleyn, J-F., C. Girard and J-F. Louis, 1982: A simple parametrization of moist convection for large-scale atmospheric models. Beitr. Phys. Atmos., 55, 325-334.
Geleyn, J-F. and A. Hollingsworth, 1979: An economical analytical method for computation of the interaction between scattering and line apsorption of radiation. Contr. Atmos. Phys., 52, 1-16.
Giard, D. and E. Bazile, 2000: Implementation of a new assimilation scheme for soil and surface variables in a global NWP model. Mon. Wea. Rev., 128, 997-1015.
Horvath, K., A. Bajić and S. Ivatek-Šahdan, 2011: Dynamical downscaling of wind speed in complex terrain prone to bora-type flows. J. Appl. Meteor. Climatol., 50, 1676-1691.
Horvath, K., Y.-L. Lin and B. Ivančan-Picek, 2008: Classification of Cyclone Tracks over Apennines and the Adriatic Sea. Mon. Wea. Rev., 136, 2210-2227.
Ivatek-Šahdan, S. and M. Tudor, 2004: Use of high-resolution dynamical adaptation in operational suite and research impact studies. Meteorol. Z., 13, 99-108.
Jurčec, V., B., Ivančan-Picek, V. Tutiš, and V. Vukičević, 1996: Severe Adriatic Jugo wind. Meteorol. Z., 5, 67-75.
Kållberg P, A. Simmons, S. Uppala, and M. Fuentes, 2004: The ERA-40 archive. ECMWF ERA-40 Project Report Series, 17, 1-35.
Kessler, E., 1969: On distribution and continuity of water substance in atmospheric circulations. Met. Mon. Am. Met. Soc., 10, br. 32, 84 str.
Louis J-F., M. Tiedke, and J-F. Geleyn, 1982: A short history of PBL parametrisation at ECMWF. Proceedings from the ECMWF Workshop on Planetary Boundary Layer Parametrisation, 59 - 79.
Lynch, P. and X. Y. Huang, 1994: Diabatic initialization using recursive filters. Tellus, 46A, 583-597.
Ritter, B. and J-F. Geleyn, 1992: A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon. Wea. Rev., 120, 303-325.
Simmons, A. J. and D. M. Burridge, 1981: An energy and angular momentum conserving vertical finite-difference scheme and hybrid vertical coordinate. Mon. Wea. Rev., 109, 758-766.
Smith, R. B., 1987: Aerial observations of the Yugoslavian bora. J. Atmos. Sci., 44, 269-297.
Žagar N., M. Žagar, J. Cedilnik, G. Gregorič and J. Rakovec, 2006: Validation of mesoscale low-level winds obtained by dynamical downscaling of ERA-40 over complex terrain. Tellus, 58, 445-455.
Žagar M. i J. Rakovec, 1999: Small-scale surface wind prediction using dynamic adaptation. Tellus, 51, 489-504.