Abstract
Wind speed and direction are fundamental data for many application fields, such as power generation and hydrological modelling. Wind measurements are usually few and sparse; hence, spatial interpolation of wind data is required. However, in mountainous areas with complex orography, accurate interpolation of wind data should consider topographic effects. Due to computational constraints, fully physically based methods that solve thermodynamic and mass conservation equations in three dimensions cannot be applied for long-time simulations or very large areas, while fast empirical methods seem more suitable. The aim of this work is to compare fast empirical methods to interpolate wind speed against a physically based full atmospheric model in order to assess the impact of the introduced approximation in estimating the wind field and the potential evapotranspiration. Comparison is carried out over the area of the upper Po River basin, a predominantly alpine region located in northern Italy. Results show that empirical topographic correction can increase accuracy of interpolated wind speed in areas with complex topography, but it requires about 50% more computational time than simpler empirical methods that do not consider topography.
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References
Allen RG, Pereira LS, Raes D, Smith M (1998) Crop evapotranspiration – guidelines for computing crop water requirements – FAO Irrigation and Drainage Paper 56. FAO, 1998. ISBN 92-5-104219-5
Buzzi A, Davolio S, Malguzzi P, Drofa O, Mastrangelo D (2014) Heavy rainfall episodes over Liguria of autumn 2011: numerical forecasting experiments. Nat Hazard Earth Syst Sc 14:1325–1340
Cheng F-Y, Georgakakos KP (2011) Wind speed interpolation in the vicinity of the Panama Canal. Meteorol Appl 18:459–466
Ching J, Rotunno R, LeMone M, Martilli A, Kosovic B, Jimenez PA, Dudhia J (2014) Convectively induced secondary circulations in fine-grid mesoscale numerical weather prediction models. Mon Weather Rev 142:3284–3302
Corbari C, Ravazzani G, Martinelli J, Mancini M (2009) Elevation based correction of snow coverage retrieved from satellite images to improve model calibration. Hydrol Earth Syst Sc 13(5):639–649
Davolio S, Henin R, Stocchi P, Buzzi A (2017) Bora wind and heavy persistent precipitation: atmospheric water balance and role of air-sea fluxes over the Adriatic Sea. Q J R Meteorol Soc 143:1165–1177. https://doi.org/10.1002/qj.3002
Ercolani G, Gorlé C, García-Sánchez C, Corbari C, Mancini M (2015) RAMS and WRF sensitivity to grid spacing in large eddy simulations of the dry convective boundary layer. Comput Fluids 123:54–71
Forthofer JM, Butler BW, Wagenbrenner NS (2014) A comparison of three approaches for simulating fine-scale surface winds in support of wildland fire management. Part I. model formulation and comparison against measurements. Int J Wildland Fire 23:969–931. https://doi.org/10.1071/WF12089
González-Longatt F, Medina H, Serrano González J (2015) Spatial interpolation and orographic correction to estimate wind energy resource in Venezuela. Renew Sust Energ Rev 48:1–16
Hartkamp AD, De Beurs K, Stein A, White e J.W (1999) Interpolation techniques for climate variables. Mexico, DF (Mexico), CIMMYT
Helbig N, Mott R, van Herwijnen A, Winstral A, Jonas T (2017) Parameterizing surface wind speed over complex topography. J Geophys Res Atmos 122:651–667. https://doi.org/10.1002/2016JD025593
Liston GE, Elder K (2006) A meteorological distribution system for high-resolution terrestrial modeling (MicroMet). J Hydrometeorol 7:217–234
Liston GE, Sturm M (1998) A snow-transport model for complex terrain. J Glaciol 44(148):498–516
Luo W, Taylor MC, Parker SR (2008) A comparison of spatial interpolation methods to estimate continuous wind speed surfaces using irregularly distributed data from England and Wales. Int J Climatol 28:947–959
Lussana C, Tveito OE, Uboldi F (2018) Three-dimensional spatial interpolation of 2 m temperature over Norway. Q J R Meteorol Soc 144:344–364. https://doi.org/10.1002/qj.3208
Malguzzi P, Grossi G, Buzzi A, Ranzi R, Buizza R (2006) The 1966 “century” flood in Italy: a meteorological and hydrological revisitation. J Geophys Res 111:D24106. https://doi.org/10.1029/2006JD007111
Palomino I, Martin F (1995) A simple method for spatial interpolation of the wind in complex terrain. J Appl Meteorol 34:1678–1693
Ravazzani G, Corbari C, Morella S, Gianoli P, Mancini M (2012) Modified Hargreaves-Samani equation for the assessment of reference evapotranspiration in alpine river basins. J Irrig Drain Eng 138:592–599
Ravazzani G, Ghilardi M, Mendlik T, Gobiet A, Corbari C, Mancini M (2014) Investigation of climate change impact on water resources for an alpine basin in northern Italy: implications for evapotranspiration modeling complexity. PLoS One 9(10):e109053. https://doi.org/10.1371/journal.pone.0109053
Rotach MW, Gohm A, Lang MN, Leukauf D, Stiperski I, Wagner JS (2015) On the vertical exchange of heat, mass, and momentum over complex, mountainous terrain. Front Earth Sci 3:76. https://doi.org/10.3389/feart.2015.00076
Ryan BC (1977) A mathematical model for diagnosis and prediction of surface winds in mountainous terrain. J Appl Meteorol 16(6):571–584
Seaman NL, Gaudet BJ, Stauffer DR, Mahrt L, Richardson SJ, Zielonka JR, Wyngaard JC (2012) Numerical prediction of submesoscale flow in the nocturnal stable boundary layer over complex terrain. Mon Weather Rev 140:956–977
Shepard D (1968) A two-dimensional interpolation function for irregularly-spaced data. Proceedings of the 1968 ACM National Conference. 517–524. https://doi.org/10.1145/800186.810616
Thiessen AH (1911) Precipitation for large areas. Mon Weather Rev 39:1082–1084
Trini Castelli E Bisignano A, Donateo A, Landi T, Martano P, and Malguzzi P (2019) Evaluation of the turbulence parameterisation in the MOLOCH meteorological model. Q. J. R. Meteorol Soc, accepted, https://doi.org/10.1002/qj.3661
Wagenbrenner NS, Forthofer JM, Lamb BK, Shannon KS, Butler BW (2016) Downscaling surface wind predictions from numerical weather prediction models in complex terrain with WindNinja. Atmos Chem Phys 16:5229–5241. https://doi.org/10.5194/acp-16-5229-2016
Acknowledgements
We thank Prof. Glen E. Liston for kindly providing the original Fortran code of the MicroMet model from which the program used in this analysis was derived.
Funding
This research was funded by Italian Ministry of University and Research within the project “Reconciling precipitation with runoff: the role of understated measurement biases in the modelling of hydrological processes” (http://www.precipitation-biases.it/).
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Ravazzani, G., Ceppi, A. & Davolio, S. Wind speed interpolation for evapotranspiration assessment in complex topography area. Bull. of Atmos. Sci.& Technol. 1, 13–22 (2020). https://doi.org/10.1007/s42865-019-00001-5
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DOI: https://doi.org/10.1007/s42865-019-00001-5