No abstract
<p>Land use information is crucial in weather modelling as it determines the energy partitioning of the land surface. Based on the partitioning heating of near surface air and moisture supply of the planetary boundary layer is determined. These processes affect the general calculation of temperature, but it also has substantial effect on precipitation formation, especially on convective precipitation.</p><p>In this study the CORINE 44 categories are integrated into the WRF model. Usually the 44 land cover types are recategorized into a standard USGS or MODIS land use types. Here we present a dataset and application with the complete integration of the 44 types.</p><p>One-year runs are created with the CORINE land cover compared to the standard USGS dataset. Along with the new land cover types vegetation parameters had be defined as well. Four runs refer to a USGS-reference, CORINE2USGS converted, CORINE-USGS parameter, CORINE-newparameters where the effect of land cover and parameter change is analyzed. The modelled area covers the whole European region with 50 km resolution using the WRF 4.2 model. Regionally, on a monthly average 5-30% difference in precipitation and around 1 &#176;C differences occur.</p><p>The research was supported by the Hungarian National Research, Development and Innovation Office, Grant No. FK132014. Hajnalka Breuer's work was additionally financed by the J&#225;nos Bolyai Research Scholarship of the Hungarian Academy of Sciences.</p>
<p>The CORINE land cover dataset provides a more realistic dataset for various scientific applications. In this study we incorporated almost all (38 instead of the original 44 classes, as water classes are not dividable in the WRF routines) CORINE classes to the WRF model and created 1-year long 5 km resolution simulations to check its effects. The simulated area covers the Central European region.</p><p>On a closer look, compared to the USGS dataset the first observable differences are the decreased number of non-irrigated croplands that are described as forests. The secondary difference comes in the coverage of urban areas. In some cases, a city previously covered by 2 grid points are now covered by 20, which causes a more pronounced urban heat island (UHI) effect.</p><p>Our goal is to analyse the effect of land cover differences on urban heat island intensity by only relying on the NoahMP provided heat flux calculations. For regional climate simulations the application of urban parameterisation could be more computationally demanding. Therefore, we try to assess the feasibility of UHI analysis at such a scale without urban parameterisation.</p><p>Urban areas are selected similarly to radar cell tracking, fitting a circle around the urban land cover classes and the rural regions are selected from a double size circle radius if there are no large altitude differences between the urban average altitude and the rural grid point. According to the preliminary results an annual average 1.5 &#176;C UHI can be simulated without urban parameterisation. Principal component analysis shows that the main driver of the UHI is the annual variation of leaf area index in the rural regions. The secondary driver is either the precipitation or the snow cover, depending on the spring and wintertime weather.</p>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.