Bartosz Czernecki

A Cloud-to-Ground Lightning Climatology for Poland

AbstractThis research focuses on the climatology of cloud-to-ground (CG) lightning flashes based on PERUN lightning detection network data from 2002 to 2013. To present various CG lightning flash characteristics, 10 km × 10 km grid cells are used, while for estimating thunderstorm days, circles with radii of 17.5 km in the 1 km × 1 km grid cells are used. A total of 4 328 892 CG lightning flashes are used to analyze counts, density, polarity, peak current, and thunderstorm days. An average of 151 days with thunderstorm (appearing anywhere in Poland) occurs each year. The annual number of days with thunderstorms increases southeasterly from the coast of the Baltic Sea (15–20 days) to the Carpathian Mountains (30–35 days). The mean CG lightning flash density varies from 0.2 to 3.1 flashes km−2 yr−1 with the highest values in the southwest–northeast belt from Krakow-Czestochowa Upland to the Masurian Lake District. The maximum daily CG lightning flash density in this region amounted to 9.1 km−2 day−1 (3 July...

Changes in spring and summer phenology in Poland - Responses of selected plant species to air temperature variations

ABSTRACT Many studies on the changes in the rhythm of nature (animate and inanimate) revealed that plants are good indicators of climate change, because they are responsive to air temperature variations. There is a clear trend towards earlier onset of plant development in Poland, especially for spring phenological events as flowering and leafing. The main objective of this study was to identify phenological tendencies of selected plant indicator species in Poland and to explore the statistical relationship between mean phenological onset date against mean monthly air temperature. Phenological observations of 9 plant species: Corylus avellana L., Tussilago farfara L., Padus avium L., Taraxacum officinale F.H. Wiggers coll., Betula pendula Roth, Syringa vulgaris L., Aesculus hippocastanum L., Robinia pseudoacacia L., Tilia cordata Mill. from 25 locations acquired in the period 1951–1990 were used. The results proved that phenological changes were the reaction of plants to air temperature variations. For spring and summer phenophases, the mean correlation coefficient was negative — higher air temperature was related to earlier onset dates. The advance of spring and summer phenology was estimated at -2.2 days per 1°C. Majority of phenological events were the most significantly correlated with mean monthly air temperature of the preceding months. Phenological trends observed in Poland were mainly negative (73% of analysed stations), significant (22%) but diversified between the selected species. Linear regression analysis of the onset dates against year (including regression coefficient) showed the highest values for Corylus avellana flowering: -0.24 days per year (20% significant) and Betula pendula leaf unfolding: -0.18 days per year (33% significant). Robinia pseudoacacia showed significant advances (72% of negative trends were significant) in a summer season with an average rate of -0.3 days per year. Whereas in case of Tilia cordata no tendencies to advance has been proved.

Sounding-Derived Parameters Associated with Convective Hazards in Europe

AbstractObserved proximity soundings from Europe are used to highlight how well environmental parameters discriminate different kind of severe thunderstorm hazards. In addition, the skill of parameters in predicting lightning and waterspouts is also tested. The research area concentrates on central and western European countries and the years 2009–15. In total, 45 677 soundings are analyzed including 169 associated with extremely severe thunderstorms, 1754 with severe thunderstorms, 8361 with nonsevere thunderstorms, and 35 393 cases with nonzero convective available potential energy (CAPE) that had no thunderstorms. Results indicate that the occurrence of lightning is mainly a function of CAPE and is more likely when the temperature of the equilibrium level drops below −10°C. The probability for large hail is maximized with high values of boundary layer moisture, steep mid- and low-level lapse rates, and high lifting condensation level. The size of hail is mainly dependent on the deep layer shear (DLS) i...

The variability of PM10 and PM2.5 concentrations in selected Polish agglomerations: the role of meteorological conditions, 2006–2016

Abstract The research focuses on the analysis of PM10 and PM2.5 concentrations variability at 11 stations in selected urbanized areas of Poland (Tricity, Poznań, Łódź, Kraków). Methods comprised: the analysis of basic statistical characteristics in yearly/monthly/daily/hourly scale and threshold exceedance frequencies. Also, correlations between PM10 and meteorological variables were investigated. GEV distribution analysis allowed the estimation of the return levels of monthly maxima of PM10 and PM2.5. Results show that in Tricity there are fewer than 5 % of days with PM10 and PM2.5 threshold exceedance. In Kraków, the standards are only met during summer and the frequency of daily PM limit exceedance in winter was around 65–90 %. GEV analysis indicates that 10y return level of PM10 monthly maximum daily average do not usually exceed 250 μg/m3 at most of the stations (Kraków agglomeration is an exception here). In winter, the meteorological conditions unfavourable to the pollutant’s dispersion comprise: high-pressure systems, stable equilibrium in the atmosphere and limited turbulence occur quite often together with low wind speed and reduced height of the planetary boundary layer.

Climatological Aspects of Convective Parameters over Europe: A Comparison of ERA-Interim and Sounding Data

AbstractWe compare over 1 million sounding measurements with ERA-Interim reanalysis for the 38-yr period from 1979 to 2016. The large dataset allows us to provide an improved insight into the spati...

If not NAO then what?—regional circulation patterns governing summer air temperatures in Poland

The correlation between the daily/monthly North Atlantic Oscillation (NAO) index and the daily/monthly anomalies of the summer (JJA) surface air temperatures in Poland is close to 0.00, being positive in most of the country (but hardly exceeding 0.15) and negative in the southeast. Therefore, the regional scale circulation patterns other than NAO were determined using principal component analysis (PCA) to establish the relationship between the air circulation and the daily anomalies of the mean, maximum and minimum air temperatures in Poland. Regression and correlation analysis were applied to define the strengths and spatial distributions of these relationships. The best results were obtained for the mean and maximum daily temperature anomalies, for which the Pearson correlation coefficient (r) between the modelled and observed values exceeded 0.75 in the vast areas of central and southeastern Poland, while, over the rest of the country, it amounted to 0.7. The weaker influence of the circulation on air temperatures is observed in the northwestern part of the country, along the Baltic Sea shore (r < 0.65). The weakest results were obtained for the anomalies of the daily minimum temperatures (r equals 0.5–0.6 over most of the country and drops below 0.4 in the northwest). Furthermore, the influences of each PCA-based regional circulation pattern on the variabilities of the surface temperatures were analysed. The circulation pattern recognized as the second principal component, distinguished in the positive phase by the high-pressure conditions with the anticyclonic centre located right over central and southeastern Poland, revealed the strongest positive relationship with the air temperature.

Machine learning modeling of plant phenology based on coupling satellite and gridded meteorological dataset

Changes in the timing of plant phenological phases are important proxies in contemporary climate research. However, most of the commonly used traditional phenological observations do not give any coherent spatial information. While consistent spatial data can be obtained from airborne sensors and preprocessed gridded meteorological data, not many studies robustly benefit from these data sources. Therefore, the main aim of this study is to create and evaluate different statistical models for reconstructing, predicting, and improving quality of phenological phases monitoring with the use of satellite and meteorological products. A quality-controlled dataset of the 13 BBCH plant phenophases in Poland was collected for the period 2007–2014. For each phenophase, statistical models were built using the most commonly applied regression-based machine learning techniques, such as multiple linear regression, lasso, principal component regression, generalized boosted models, and random forest. The quality of the models was estimated using a k-fold cross-validation. The obtained results showed varying potential for coupling meteorological derived indices with remote sensing products in terms of phenological modeling; however, application of both data sources improves models’ accuracy from 0.6 to 4.6 day in terms of obtained RMSE. It is shown that a robust prediction of early phenological phases is mostly related to meteorological indices, whereas for autumn phenophases, there is a stronger information signal provided by satellite-derived vegetation metrics. Choosing a specific set of predictors and applying a robust preprocessing procedures is more important for final results than the selection of a particular statistical model. The average RMSE for the best models of all phenophases is 6.3, while the individual RMSE vary seasonally from 3.5 to 10 days. Models give reliable proxy for ground observations with RMSE below 5 days for early spring and late spring phenophases. For other phenophases, RMSE are higher and rise up to 9–10 days in the case of the earliest spring phenophases.

Initial Assessment of the Weather Research and Forecasting Model for Forecasting Bioclimatic Conditions During Breeze Circulation – Case Study of the Słowiński National Park

Abstract Land-sea interaction at the Polish Baltic Coast impacts the specific local climate conditions. Thermally driven circulation, observed mainly in the summer season, causes the advection of the cool sea air over land and influences the local atmospheric environment, including bioclimatic conditions. The aim of this paper is to present the evaluation of the WRF model for forecasting sensitive bioclimatic conditions on a selected day with sea breeze in the vicinity of the Łeba Sandbar (the Słowiński National Park). The results obtained from a numerical weather prediction model were post-processed to calculate the daily variability of two biothermal indices: the Effective Temperature (ET) and the Dry Cooling Power (H). To evaluate the thermal comfort of a person wearing typical clothes, the Michajlow’s, Petrovič and Kacvińsky’s scales were adopted. A detailed analysis performed for 31st July 2010 shows in most cases a satisfactory level of agreement between the simulated data and the in-situ measurements for nested domains with horizontal grid resolution less than 2 km. However, the simulation results tend to underestimate the thermal comfort, especially in the middle part of the Łeba Sandbar due to terrain data misrepresentations, which results in the overestimation of wind speed.