The GNSS data assimilation is currently widely discussed in the literature with respect to the various applications for meteorology and numerical weather models. Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models. With this approach, the “best” estimate of current conditions consistent with both information sources is produced. Some approaches also allow assimilating the non-prognostic variables, including remote sensing data from radar or GNSS (global navigation satellite system). These techniques are named variational data assimilation schemes and are based on a minimization of the cost function, which contains the differences between the model state (background) and the observations. The variational assimilation is the first choice for data assimilation in the weather forecast centers, however, current research is consequently looking into use of an iterative, filtering approach such as an extended Kalman filter (EKF). This paper shows the results of assimilation of the GNSS data into numerical weather prediction (NWP) model WRF (Weather Research and Forecasting). The WRF model offers two different variational approaches: 3DVAR and 4DVAR, both available through the WRF data assimilation (WRFDA) package. The WRFDA assimilation procedure was modified to correct for bias and observation errors. We assimilated the zenith total delay (ZTD), precipitable water (PW), radiosonde (RS) and surface synoptic observations (SYNOP) using a 4DVAR assimilation scheme. Three experiments have been performed: (1) assimilation of PW and ZTD for May and June 2013, (2) assimilation of PW alone; PW, with RS and SYNOP; ZTD alone; and finally ZTD, with RS and SYNOP for 5–23 May 2013, and (3) assimilation of PW or ZTD during severe weather events in June 2013. Once the initial conditions were established, the forecast was run for 24 h. The major conclusion of this study is that for all analyzed cases, there are two parameters significantly changed once GNSS data are assimilated in the WRF model using GPSPW operator and these are moisture fields and rain. The GNSS observations improves forecast in the first 24 h, with the strongest impact starting from a 9 h lead time. The relative humidity forecast in a vertical profile after assimilation of ZTD shows an over 20 % decrease of mean error starting from 2.5 km upward. Assimilation of PW alone does not bring such a spectacular improvement. However, combination of PW, SYNOP and radiosonde improves distribution of humidity in the vertical profile by maximum of 12 %. In the three analyzed severe weather cases PW always improved the rain forecast and ZTD always reduced the humidity field bias. Binary rain analysis shows that GNSS parameters have significant impact on the rain forecast in the class above 1 mm h⁻¹.

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.

Integrated assessment modelling has evolved to support policy development in relation to air pollutants and greenhouse gases by providing integrated simulation tools able to produce quick and realistic representations of emission scenarios and their environmental impacts without the need to re-run complex atmospheric dispersion models. The UK Integrated Assessment Model (UKIAM) has been developed to investigate strategies for reducing UK emissions by bringing together information on projected UK emissions of SO2, NOx, NH3, PM10 and PM2.5, atmospheric dispersion, criteria for protection of ecosystems, urban air quality and human health, and data on potential abatement measures to reduce emissions, which may subsequently be linked to associated analyses of costs and benefits. We describe the multi-scale model structure ranging from continental to roadside, UK emission sources, atmospheric dispersion of emissions, implementation of abatement measures, integration with European-scale modelling, and environmental impacts. The model generates outputs from a national perspective which are used to evaluate alternative strategies in relation to emissions, deposition patterns, air quality metrics and ecosystem critical load exceedance. We present a selection of scenarios in relation to the 2020 Business-As-Usual projections and identify potential further reductions beyond those currently being planned.

The Fine Resolution Atmospheric Multi-pollutant Exchange model (FRAME) was applied to model the spatial distribution of reactive nitrogen deposition and air concentration over the United Kingdom at a 1 km spatial resolution. The modelled deposition and concentration data were gridded at resolutions of 1 km, 5 km and 50 km to test the sensitivity of calculations of the exceedance of critical loads for nitrogen deposition to the deposition data resolution. The modelled concentrations of NO₂ were validated by comparison with measurements from the rural sites in the national monitoring network and were found to achieve better agreement with the high resolution 1 km data. High resolution plots were found to represent a more physically realistic distribution of reactive nitrogen air concentrations and deposition resulting from use of 1 km resolution precipitation and emissions data as compared to 5 km resolution data. Summary statistics for national scale exceedance of the critical load for nitrogen deposition were not highly sensitive to the grid resolution of the deposition data but did show greater area exceedance with coarser grid resolution due to spatial averaging of high nitrogen deposition hot spots. Local scale deposition at individual Sites of Special Scientific Interest and high precipitation upland sites was sensitive to choice of grid resolution of deposition data. Use of high resolution data tended to generate lower deposition values in sink areas for nitrogen dry deposition (Sites of Scientific Interest) and higher values in high precipitation upland areas. In areas with generally low exceedance (Scotland) and for certain vegetation types (montane), the exceedance statistics were more sensitive to model data resolution.