Innovations of the spatially representative climate data series for Hungary

A more accurate understanding of climate and its changes requires spatially representative climate databases. However, weather stations are not evenly distributed, consequently, the station network consists of both densely and sparsely covered subregions. In order to estimate the values of meteorological variables at points where no measurements are available, a spatial interpolation method must be used. Our gridded climate datasets are generated using the MISH (Meteorological Interpolation based on Surface Homogenized Data Basis) method (MISHv1.03 software). Using the MISH interpolation, we have spatially representative climate database.

In general, the spatial distribution of meteorological elements depends to a large extent on topography, e.g. altitude. For this reason, if a more accurate gridded database is to be created, then a more accurate elevation model is required. For Hungary, the elevation model used for MISH modelling was unchanged for the last more than 20 years. This year, a new and more accurate elevation model has been introduced, which resulted in a complete renewal of the climate statistical parameters per meteorological element for MISH interpolation. At the same time, new model variables have been introduced in addition to those previously used for modelling, further improving our gridded climate data series. In this presentation, we will present the results obtained for temperature and precipitation interpolation and, as it is particularly important for decision-makers to have as detailed information as possible on a given area, we will also present the ~1 km resolution climate database for Hungary.

 

Acknowledgements:

The development presented was carried out within the framework of the Széchenyi Plan Plus Program with the support DIMOP Plusz-2.3.1-23-2023-00001 project, and the EKÖP-KDP-24 University Excellence Cooperative Doctoral Program of the Ministry for Culture and Innovation from the source of the National Research, Development and Innovation fund.