Mariusz Szymanowski

Local regression models for spatial interpolation of urban heat island—an example from Wrocław, SW Poland

Geographically weighted regression algorithm (GWR) has been applied to derive the spatial structure of urban heat island (UHI) in the city of Wrocław, SW Poland. Seven UHI cases, measured during various meteorological conditions and characteristic of different seasons, were selected for analysis. GWR results were compared with global regression models (MLR), using various statistical procedures including corrected Akaike Information Criterion, determination coefficient, analysis of variance, and Moran’s I index. It was found that GWR is better suited for spatial modeling of UHI than MLR models, as it takes into account non-stationarity of the spatial process. However, Monte Carlo and F3 tests for spatial stationarity of the independent variables suggest that for several spatial predictors a mixed GWR–MLR approach is recommended. Both local and global models were extended by the interpolation of regression residuals and used for spatial interpolation of the UHI structure. The interpolation results were evaluated with the cross-validation approach. It was found that the incorporation of the spatially interpolated residuals leads to significant improvement of the interpolation results for both GWR and MLR approaches. Because GWR is better justified in terms of statistical specification, the combined GWR + interpolated regression residuals (GWR residual kriging; GWRK) approach is recommended for spatial modeling of UHI, instead of widely applied MLR models.

Land Surface Temperature Patterns in the Urban Agglomeration of Krakow (Poland) Derived from Landsat-7/ETM+ Data

The aim of this study was to identify typical and specific features of land surface temperature (LST) distribution in the city of Krakow and its surroundings with the use of Landsat/ETM+ data. The paper contains a detailed description of the study area and technical properties of the Landsat program and data, as well as a complete methodology of LST retrieval. Retrieved LST records have been standardized in order to ensure comparability between satellite images acquired during different seasons. The method also enables identification of characteristic thermal regions, i.e. areas always colder and always warmer than a zonal mean LST value for Krakow. The research includes spatial analysis of the standardized LST with regard to different land cover types. Basic zonal statistics such as mean standardized LST and percentage share of hot and cold regions within 10 land cover types were calculated. GIS was used for automated data processing and mapping. The results confirmed the most obvious dependence of the LST on different land cover types. Some more factors influencing the LST were recognized on the basis of detailed investigation of the LST pattern in the urban agglomeration of Krakow. The factors are: emission of anthropogenic heat, insolation of the surfaces depending first of all on land relief and shape of buildings, seasonal changes of vegetation and weather conditions at the time of satellite image acquisition.

Determination of Ventilation Channels In Urban Area: A Case Study of Wrocław (Poland)

Urban areas are among the roughest landscapes in the Earth and its aerodynamical properties are responsible for a lot of processes and phenomena of urban climate, such as surface drag and pollutant dispersion. These properties can be quantitatively expressed by various parameters, with zero plane displacement height (zd) and roughness length (z0) as the most frequently applied. Based on remotely gathered (LIDAR scan) height data and morphometric methods of roughness calculations, the comprehensive procedure to determine ventilation channels in urban area is proposed and implemented on the example from Wrocław, Poland. Morphometric analysis of urban structure allowed establishing a proper database of aerodynamic parameters of the city. Then a series of maps of the city showing the distribution of two roughness parameters were prepared. GIS tools were used to carry out the analysis of roughness data, assuming various directions of wind flow. It enabled to determine the locations of potential ventilation paths in the city which, if combined, form large ventilation channels. They may have a significant role in improving air quality and be a valuable source of information for local government responsible for the appropriate development of the city.

Influence of Selected Meteorological Variables on the Questing Activity of Ixodes ricinus Ticks in Lower Silesia, SW Poland

ABSTRACT: The relationship between climate data and tick questing activity is crucial for estimation of the spatial and temporal distribution of the risk of ticks and tick-borne diseases. This study establishes correlations between selected meteorological variables provided by the Weather Research and Forecasting model (WRF) and the questing activity of Ixodes ricinus nymphs and adults on a regional scale across Lower Silesia, Poland. Application of Generalized Linear Mixed Models (GLMM), built separately for adults and nymphs, showed that solar radiation, air temperature, and saturation deficit appeared to be the meteorological variables of prime importance, whereas the wind speed was less important. However, the effect of meteorological parameters was different for adults and nymphs. The adults are also more influenced by forest cover and the percentage of forest type if compared to nymphs. The WRF model providing meteorological variables separately for each location and day of tick sampling can be useful in studies of questing activity of ticks on a regional scale.

Regression-based air temperature spatial prediction models: an example from Poland

A Geographically Weighted Regression – Kriging (GWRK) algorithm, based on the local Geographically Weighted Regression (GWR), is applied for spatial prediction of air temperature in Poland. Hengl’s decision tree for selecting a suitable prediction model is extended for varying spatial relationships between the air temperature and environmental predictors with an assumption of existing environmental dependence of analyzed temperature variables. The procedure includes the potential choice of a local GWR instead of the global Multiple Linear Regression (MLR) method for modeling the deterministic part of spatial variation, which is usual in the standard regression (residual) kriging model (MLRK). The analysis encompassed: testing for environmental correlation, selecting an appropriate regression model, testing for spatial autocorrelation of the residual component, and validating the prediction accuracy. The proposed approach was performed for 69 air temperature cases, with time aggregation ranging from daily to annual average air temperatures. The results show that, irrespective of the level of data aggregation, the spatial distribution of temperature is better fitted by local models, and hence is the reason for choosing a GWR instead of the MLR for all variables analyzed. Additionally, in most cases (78%) there is spatial autocorrelation in the residuals of the deterministic part, which suggests that the GWR model should be extended by ordinary kriging of residuals to the GWRK form. The decision tree used in this paper can be considered as universal as it encompasses either spatially varying relationships of modeled and explanatory variables or random process that can be modeled by a stochastic extension of the regression model (residual kriging). Moreover, for all cases analyzed, the selection of a method based on the local regression model (GWRK or GWR) does not depend on the data aggregation level, showing the potential versatility of the technique.

Influence of diversified relief on the urban heat island in the city of Kraków, Poland

In cities located in concave landforms, urban heat island (UHI) is an element of a complicated thermal structure and occurs due to the common impact of urban built-up areas and orography-induced processes like katabatic flows or air temperature inversions. Kraków, Poland (760,000 inhabitants) is located in a large valley of the river Vistula. In the years 2009–2013, air temperature was measured with the 5-min sampling resolution at 21 urban and rural points, located in various landforms. Cluster analysis was used to process data for the night-time. Sodar and synoptic data analysis provided results included in the definition of the four types of night-time thermal structure representing the highest and the lowest spatial air temperature variability and two transitional types. In all the types, there are three permanent elements which show the formation of the inversion layer, the cold air reservoir and the UHI peak zone. As the impact of land use and relief on air temperature cannot be separated, a concept of relief-modified UHI (RMUHI) was proposed as an alternative to the traditional UHI approach. It consists of two steps: (1) recognition of the areal thermal structure taking into consideration the city centre as a reference point and (2) calculation of RMUHI intensity separately for each vertical zone.

The role of domestic dogs and cats in the zoonotic cycles of ticks and pathogens. Preliminary studies in the Wroclaw Agglomeration (SW Poland).

The collection of 729 tick specimens (Ixodes ricinus, 88.6%; Ixodes hexagonus, 9.2%; Dermacentor reticulatus, 2.2%) removed from 373 dogs and 78 cats, along with 201 ticks from vegetation (I. ricinus, 75.6%; D. reticulatus, 24.4%), allows one to say that pets play an important role in maintaining tick life cycles in different urban area. It shows the lack of statistical differences between tick intensity in high-impact anthropogenic areas (HIAA), low-impact anthropogenic areas (LIAA) and mixed areas designed, in an objective way, by GIS techniques. The comparable (statistically insignificant) level of infection with Borrelia spp. of I. ricinus from pets (22.5%) and vegetation (24.8%), shows that dogs and cats do not have zooprophylactic competence for Borrelia spp. in different urban areas. Moreover, Borrelia spp. was detected in I. hexagonues (1.5%) collected from pets, and in D. reticulatus (2%) obtained from vegetation. The presence of D. reticulatus in the Wrocław Agglomeration confirms its expansion and the distribution range in Poland.

The Role of Auxiliary Variables in Deterministic and Deterministic-Stochastic Spatial Models of Air Temperature in Poland

Our study examines the role of auxiliary variables in the process of spatial modelling and mapping of climatological elements, with air temperature in Poland used as an example. The multivariable algorithms are the most frequently applied for spatialization of air temperature, and their results in many studies are proved to be better in comparison to those obtained by various one-dimensional techniques. In most of the previous studies, two main strategies were used to perform multidimensional spatial interpolation of air temperature. First, it was accepted that all variables significantly correlated with air temperature should be incorporated into the model. Second, it was assumed that the more spatial variation of air temperature was deterministically explained, the better was the quality of spatial interpolation. The main goal of the paper was to examine both above-mentioned assumptions. The analysis was performed using data from 250 meteorological stations and for 69 air temperature cases aggregated on different levels: from daily means to 10-year annual mean. Two cases were considered for detailed analysis. The set of potential auxiliary variables covered 11 environmental predictors of air temperature. Another purpose of the study was to compare the results of interpolation given by various multivariable methods using the same set of explanatory variables. Two regression models: multiple linear (MLR) and geographically weighted (GWR) method, as well as their extensions to the regression-kriging form, MLRK and GWRK, respectively, were examined. Stepwise regression was used to select variables for the individual models and the cross-validation method was used to validate the results with a special attention paid to statistically significant improvement of the model using the mean absolute error (MAE) criterion. The main results of this study led to rejection of both assumptions considered. Usually, including more than two or three of the most significantly correlated auxiliary variables does not improve the quality of the spatial model. The effects of introduction of certain variables into the model were not climatologically justified and were seen on maps as unexpected and undesired artefacts. The results confirm, in accordance with previous studies, that in the case of air temperature distribution, the spatial process is non-stationary; thus, the local GWR model performs better than the global MLR if they are specified using the same set of auxiliary variables. If only GWR residuals are autocorrelated, the geographically weighted regression-kriging (GWRK) model seems to be optimal for air temperature spatial interpolation.

Observed changes in SAT and GDD and the climatological suitability of the Poland-Germany-Czech Republic transboundary region for wine grapes cultivation

In this study, we show how the climatological suitability of wine grapes cultivation of the transboundary region of Poland, Germany and the Czech Republic has changed over the 1971–2010 period. Strong, positive and statistically significant trend in sum of active temperatures (SAT) and growing degree days (GDD) is observed. The trend is more pronounced in the lowland areas of the study region. The total acreage suitable for more demanding, in terms of SAT and GDD, varieties of wine grapes is increasing, while the opposite trend is observed for less demanding classes. The observed trends reduce the risk for wine grapes cultivation in terms of accumulative SAT and GDD indices. This shows that the transboundary area of Poland, Germany and Czech Republic shifts towards the climate more suitable for viticulture.

Sensitivity Study of Cloud Cover and Ozone Modeling to Microphysics Parameterization

AbstractCloud cover is a significant meteorological parameter influencing the amount of solar radiation reaching the ground surface, and therefore affecting the formation of photochemical pollutants, most of all tropospheric ozone (O3). Because cloud amount and type in meteorological models are resolved by microphysics schemes, adjusting this parameterization is a major factor determining the accuracy of the results. However, verification of cloud cover simulations based on surface data is difficult and yields significant errors. Current meteorological satellite programs provide many high-resolution cloud products, which can be used to verify numerical models. In this study, the Weather Research and Forecasting model (WRF) has been applied for the area of Poland for an episode of June 17th–July 4th, 2008, when high ground-level ozone concentrations were observed. Four simulations were performed, each with a different microphysics parameterization: Purdue Lin, Eta Ferrier, WRF Single-Moment 6-class, and Morrison Double-Moment scheme. The results were then evaluated based on cloud mask satellite images derived from SEVIRI data. Meteorological variables and O3 concentrations were also evaluated. The results show that the simulation using Morrison Double-Moment microphysics provides the most and Purdue Lin the least accurate information on cloud cover and surface meteorological variables for the selected high ozone episode. Those two configurations were used for WRF-Chem runs, which showed significantly higher O3 concentrations and better model-measurements agreement of the latter.