High resolution modelling of severe wind patterns over the north-west Black Sea Region
The research studies the performance of the convective-permitting Harmonie model in reproducing mesoscale features of the wind regime over the north-western part of the Black Sea. It allowed establishing the optimum configuration, projection and geometry of the model's high spatial resolution area over Ukrainian part of the Black Sea, preparing a digital format for a coastline based on the resolution and conducting numerical experiments to verify informativeness and stability of computations. It also presents a detailed description of the forecasting procedure which includes a data flow from the Meteorological Archiving and Retrieving System (MARS) of the European Center, creating boundary conditions, forecast computations and a model output composition for the particular region and domain resolution.
The results have shown that the Harmonie model with the 2.5 km spatial resolution and the 60 second time step is able to reproduce detailed spatial variability of a near-surface wind field and its evolution to the corresponding scales. In particular, the model is able to simulate mesoscale circulation features of approximately ten km over a homogeneous sea surface and to track their evolution; to monitor the position of convergence zones; to highlight the spatial characteristics of a lee-side wind attenuation band along the coast line when wind blows from the shore; to specify mesoscale wind patterns in bays and along the coastline with complex orography; to reproduce the weakening of a wind velocity over an urban area due to increased surface roughness.
Two operational forecasting systems, GFS-WRF and ARPEGE/IFS-Harmonie are compared by the following components: numerical solvers, sub-grid parameterizations, efficiency of computer resources and intellectual potential. The GFS model output with the 25 km spatial resolution is able to correctly reproduce over the region only large-scale atmospheric patterns. However, for rapid changes in the atmospheric circulation accompanied by changes in the wind direction to the opposite and wind increase, the model simulations are delayed in terms of wind field evolution. Additionally, because of crude spatial resolution, the GFS model is unable to describe mesoscale atmospheric features due to differences in surface types, orography, thermal contrasts, etc. Comparison of the both model outputs versus observations from Odesa, Chornomorsk and Yuzhnyi port during severe wind conditions has shown that the discrepancy between the models and observations within the allowable error value (5 m/s) occurred only for Odesa port with regard to the Harmonie model for weak wind velocity. The difference partially increases for moderate wind from the shore, while for strong wind from east and south directions indicates disagreement between the model results and observations and achieves critical values of 20-25 m/s. Such values are mainly determined by the discrepancy in wind direction (up to 180°). The comparison results clearly indicate the doubtful representativeness of wind observations at Chornomorsk and Yuzhnyi stations in general, and at Odesa station in particular.
Lind, P., Lindstedt, D., Kjellström, E. et al. (2016). Spatial and temporal characteristics of summer precipitation over central Europe in a suite of high-resolution climate models. J. Climate, 29, pp. 3501–3518. https://doi.org/10.1175/JCLI-D-15-0463.1.
Malardel S. & Ricard, D. (2015). An alternative cell-averaged departure point reconstruction for pointwise semi-Lagrangian transport schemes. Quart. J. Roy. Meteor. Soc., 141, pp.2114–2126. https://doi.org/10.1002/qj.2509.
Baldauf, M., Seifert, A., Furstner, J. et. al. (2011). Operational Convective-Scale Numerical Weather Prediction with the COSMO Model: Description and Sensitivities. Monthly Weather Review, 139(12), pp. 3887-3905.
Vil’fand, R.M., Rivin, G.S., Rozinkina, I.A. (2010). Mesoscale weather short-range forecasting at the Hydrometcenter of Russia, on the example of COSMO-RU. Russian Meteorology and Hydrology, 35, pp. 1-9.
Rontu, L., Wastl C. & Niemelä, S. (2016). Influence of the details of topography on weather forecast-Evaluation of HARMONIE experiments in the Sochi Olympics domain over the Caucasian Mountains. Front. Earth Sci., 4, p.13, https://doi.org/10.3389/feart.2016.00013.
Baklanov, A., Tijm, S., Rontu, L. (2011). HIRLAM/HARMONIE-Atmospheric Chemical Transport Models. In: Baklanov, A., Mahura, A., Sokhi, R. (eds). Integrated Systems of Meso-Meteorological and Chemical Transport Models. Springer, pp. 215-228.
Braun, A., Milton, S., Cullen, M. et al. Unified modelling and prediction of weather and climate: A 25 year Journey. Bull. Amer. Meteor. Soc., 2012, № 93(12), pp. 1865-1877.
Davies, T., Cullen, M.J.P., Malcolm, A.J. et al. (2005). A new dynamical core for the Met Office's global and regional modelling of the atmosphere. Quart. J. Roy. Meteorol. Soc., 131, pp. 1759-1768.
Fillion, L., Tanguay, M., Lapalme, E. et al. (2010). The Canadian Meteorological Center Data Assimilation and Forecasting System. Weather and Forecasting, 25(6), pp. 1645–1669.
Yang, D., Ritchie, H., Desjardins, S. et al. (2010). High-Resolution GEM-LAM Application in Marine Fog Prediction: Evaluation and Diagnosis. Weather and Forecasting, 25(2), pp. 727-748.
Efimov, V.V., Barabanov, V.S., Krupin, A.V.(2012). Modelirovanie mezomasshtabnykh osobennostey atmosfernoy cirkulyatsii v Krymskom regione Chernogo morya [Modeling of mesoscale features of the atmospheric circulation in the Crimean Black Sea region]. Morskoy gidrofizicheskiy zhurnal [Physical Oceanography J.], 1, pp. 64–74. (In Russ.).
Bengtsson, L. et al. (2017). The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System. Mon.Wea.Rev., 145, pp. 1919-1935.
Ivanov, S.V., Ruban, I.G., Tuchkovenko, Y.S. (2018). Advantages of using the Harmonie atmospheric mesoscale model for simulating water dynamics in offshore area. Ukr. gìdrometeorol. ž. [Ukrainian hydrometeorological journal], 1, 22, рр. 107-114. https://doi.org/10.31481/uhmj.22.2018.10
ECMWF, Operational implementation 12 May 2015. Part III: Dynamics and numerical procedures. European Centre for Medium-Range Weather Forecasts IFS Doc. Cy41r1, 2015a.. Available at: http://www.ecmwf.int/en/elibrary/9210-part-iii-dynamics-and-numerical-procedures (Accessed 20.05.2019)
ECMWF, Operational implementation 12 May 2015. Part IV: Physical processes. European Centre for Medium-Range Weather Forecasts IFS Doc. Cy41r1, 2015b. Available at: http://www.ecmwf.int/sites/default/files/elibrary/2016/16648-part-iv-physical-processes.pdf (Accessed 18.03.2019)
Mas̆ek, J., Geleyn, J.-F., Broz̆ková, R., et al. (2016). Single interval shortwave radiation scheme with parameterized optical saturation and spectral overlaps. Quart. J. Roy. Meteor. Soc., 142, pp. 304–326. https://doi.org/10.1002/qj.2653.
Müller, M. et al. (2017). AROME-MetCoOp: A Nordic convective-scale operational weather prediction model. Wea.Forecasting, 32, pp: 609–627. https://doi.org/10.1175/WAF-D-16-0099.1
Brousseau, P., Seity, Y., Ricard D. et al. (2016). Improvement of the forecast of convective activity from the AROME-France system. Quart. J. Roy. Meteor. Soc., 142, pp. 2231–2243. https://doi.org/10.1002/qj.2822.
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