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Geographically weighted regression (GWR), was used here for spatial interpolation of two selected cases of the urban heat island measured in Wroclaw (SW Poland). The GWR algorithm was extended with spatial interpolation of the local regression residuals (GWRK). It was shown that the GWRK outperforms GWR, as well as other widely used interpolation procedures, including multiple regression models and residual kriging.
The r.sun model was applied to assess the spatial and temporal changes in incoming total (beam + diffused) real sky radiation. Presented approach accounts for aerosol, precipitable water content and cloudiness effects on attenuation of solar radiation. The results are compared with the measurements gathered at Polish Polar Station, showing good agreement.
Our study focuses on the application of a static and dynamic ammonia emissions based on a Europe-wide default setting into the weather research and forecasting chemistry model (WRF-Chem), and the influence on the simulated ammonia concentrations and overall model performance. The WRF-Chem model was run twice for all of Europe at a spatial resolution of 36 x 36 km for the year 2012. In the first simulation we used a static emissions approach (the “BASE” simulation) and in the second simulation we used dynamic ammonia emissions (the “DYNAMIC” simulation). Both simulations underestimate measured concentrations of NH₃ for all seasons, have similar NMGE (about 0.7 μg m⁻³), and model hourly ammonia peaks that shift toward the afternoon hours if compared with measurements. However, for all temporal resolutions, normalised mean gross error in winter and summer is lower for DYNAMIC than for BASE. The DYNAMIC simulation also generally gives worse performance in spring for each temporal resolution. For further improvement of the modelled ammonia concentrations with WRF-Chem we suggest using a nested approach with higher spatial resolution, which will lead to better separation of the ammonia source regions from surrounding areas and take into account national practices and regulations in the emissions model, eventually only in the nested model domain.
In this study we analyzed daily pollen concentrations of Alnus spp. and Betula spp. from Worcester, UK and Wrocław, Poland. We analyzed seasonality, annual pollen index and footprint areas for the observed pollen concentrations by using the trajectory model hybrid single particle Lagrangian integrated trajectory (HYSPLIT). We examined 10 years of data during the period 2005–2014 and found substantial differences in the seasonality, pollen indices and footprint areas. For both genera, concentrations in Wrocław are in general much higher, the seasons are shorter and therefore more intense than in Worcester. The reasons appear to be related to the differences in overall climate between the two sites and more abundant sources in Poland than in England. The footprint areas suggest that the source of the pollen grains are mainly local trees but appear to be augmented by remote sources, in particular for Betula spp. but only to a small degree for Alnus spp. For Betula spp., both sites appear to get contributions from areas in Germany, the Netherlands and Belgium, while known Betula spp. rich regions in Russia, Belarus and Scandinavia had a very limited impact on the pollen concentrations in Worcester and Wrocław. Substantial and systematic variations in pollen indices are seen for Betula spp. in Wrocław with high values every second year while a similar pattern is not observed for Worcester. This pattern was not reproduced for Alnus spp.
The Global Navigation Satellite System (GNSS) can be used to determine accurate and high-frequency atmospheric parameters, such as Zenith Total Delay (ZTD) or Precipitable Water Vapour (PW), in all-weather conditions. These parameters are often assimilated into Numerical Weather Prediction (NWP) models and used for nowcasting services and climate studies. The effective usage of the ZTDs obtained from a ground-based GNSS receiver’s network in a NWP could fill the gap of insufficient atmospheric water vapour state information. The supply of such information with a latency acceptable for NWP assimilation schemes requires special measures in the GNSS data processing, quality control and distribution. This study is a detailed description of the joint effort of three institutions – Wrocław University of Environmental and Life Sciences, Wrocław University, and the Institute of Meteorology and Water Management – to provide accurate and timely GNSS-based meteorological information. This paper presents accuracy analyses of near real-time GNSS ZTD validated against reference ZTD data: the International GNSS Service (IGS) from a precise GNSS solution, Weather Research and Forecasting (WRF) model, and radiosonde profiles. Data quality statistics were performed for five GNSS stations in Poland over a time span of almost a year (2015). The comparison of near real-time ZTD and IGS shows a mean ZTD station bias of less than 3 mm with a related standard deviation of less than 10 mm. The bias between near real-time ZTD and WRF ZTD is in the range of 5-11 mm and the overall standard deviation is slightly higher than 10 mm. Finally, the comparison of the investigated ZTD against radiosonde showed an average bias at a level of 10 mm, whereas the standard deviation does not exceed 14 mm. Considering the data quality, we assess that the NRT ZTD can be assimilated into NWP models.
The objective of this paper is to present the concept of a novel system, known as HydroProg, that aims to issue flood warnings in real time on the basis of numerous hydrological predictions computed using various models. The core infrastructure of the system is hosted by the University of Wrocław, Poland. A newly-established computational centre provides in real time, courtesy of the project Partners, various modelling groups, referred to as “project Participants”, with hydrometeorological data. The project Participants, having downloaded the most recent observations, are requested to run their hydrologic models on their machines and to provide the HydroProg system with the most up-to-date prediction of riverflow. The system gathers individual forecasts derived by the Participants and processes them in order to compute the ensemble prediction based on multiple models, following the approach known as multimodelling. The system is implemented in R and, in order to attain the above-mentioned functionality, is equipped with numerous scripts that manipulate PostgreSQL- and MySQL-managed databases and control the data quality as well as the data processing flow. As a result, the Participants are provided with multivariate hydrometeorological time series with sparse outliers and without missing values, and they may use these data to run their models. The first strategic project Partner is the County Office in Kłodzko, Poland, owner of the Local System for Flood Monitoring in Kłodzko County. The experimental implementation of the HydroProg system in the Nysa Kłodzka river basin has been completed, and six hydrologic models are run by scientists or research groups from the University of Wrocław, Poland, who act as Participants. Herein, we shows a single prediction exercise which serves as an example of the HydroProg performance.
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