ALBERT

Description: The Weather Research and Forecasting (WRF) model is commonly associated with meteorological data, but its algorithms may also use geographical data. The objective of this paper is to evaluate the impact of the high resolution CORINE Land Cover (CLC) data and the SRTM topography on the estimation accuracy of the weather model parameters in the WRF microscale simulations (200 × 200 m) for Warsaw. In the presented studies, the authors propose their own method of attaching the CLC data to the WRF microscale modeling for the CLC border areas, where first calculational domains reach beyond areas of CLC coverage. As a part of the research, the adaptation of the proposed method was examined by the assessment of the WRF microscale modeling simulations for Warsaw. The modified IGBP MODIS land use/land cover (LULC) and USGS GMTED2010 terrain elevation geographical data (30 arc seconds) was applied for the WRF simulations as default. As higher resolution geographical data (100 m), the LULC from CORINE Land Cover (CLC) 2018 data, and the SRTM topography were adopted. In this study the forecasts of air temperature and relative humidity at 2 m, and wind (speed and direction) at 10 m above ground level obtained using the WRF model for particular simulations were evaluated against measurements made at the Warsaw airports: Chopin (EPWA) and Babice (EPBC). The research has indicated that for microscale calculation fields there are noticeable changes in the meteorological parameter values when the CLC and the SRTM data are integrated into the WRF model, which in most cases yielded more accurate values of temperature and relative humidity at 2 m. This has also proved the correctness of the proposed methodology of the CLC data adoption. The improvement in the forecasted meteorological parameters is different for the particular locations and depends on the degree of the LULC and topography data change after higher resolution data adoption.

Description: Unmanned aerial vehicle (UAV) imagery has been widely used in remote sensing and photogrammetry for some time. Increasingly often, apart from recording images in the red-green-blue (RGB) range, multispectral images are also recorded. It is important to accurately assess the radiometric quality of UAV imagery to eliminate interference that might reduce the interpretation potential of the images and distort the results of remote sensing analyses. Such assessment should consider the influence of the atmosphere and the seasonal and weather conditions at the time of acquiring the imagery. The assessment of the radiometric quality of images acquired in different weather conditions is crucial in terms of improving the interpretation potential of the imagery and improving the accuracy of determining the indicators used in remote sensing and in environmental monitoring. Until now, the assessment of radiometric quality of UAV imagery did not consider the influence of meteorological conditions at different times of year. This paper presents an assessment of the influence of weather conditions on the quality of UAV imagery acquired in the visible range. This study presents the methodology for assessing image quality, considering the weather conditions characteristic of autumn in Central and Eastern Europe. The proposed solution facilitates the assessment of the radiometric quality of images acquired in the visible range. Using the objective indicator of quality assessment developed in this study, images were classified into appropriate categories, allowing, at a later stage, to improve the results of vegetation indices. The obtained results confirm that the proposed quality assessment methodology enables the objective assessment of the quality of imagery acquired in different meteorological conditions.