The Urban Wind Island (UWI), a small but persistent positive mean boundary-layer wind anomaly over the city as a whole, has previously been revealed using a simplified conceptual model of the convective atmospheric boundary layer. This study extends the UWI research into less idealised cases by using the three-dimensional WRF mesoscale model for Amsterdam (The Netherlands) and its surroundings, at 500 m grid spacing. Two summers of forecast results for in total 173 days are used to identify whether the UWI persists in a three-dimensional modelling environment, and which conditions are optimal for its formation and persistence. In order to focus only on wind modified by surface processes, large-scale influences which modify wind speed, such as frontal passages, are identified and eliminated from the dataset. We then find that a positive UWI is present roughly half the time, with an order of magnitude that is similar to the previous work (∼ 0.2–0.5 ms⁻¹). In addition we find an evening UWI that is caused by the delayed onset of the transition from an unstable to a stable or a neutral boundary layer in the urban area, while the rural area is already stable and calm.

Ongoing urbanization highlights the need for a better understanding and high resolution modelling of the urban climate. In this study, we combine rural observations by WMO surface stations, weather radar data and urban crowd-sourced observations with very fine-scale modelling efforts for Amsterdam, The Netherlands. As a model, we use the Weather Research and Forecasting (WRF) mesoscale model with 3D variational data assimilation at a 100-m resolution in the innermost model domain. In order to enable the assimilation of observations within the urban canopy, we develop a scheme to reduce urban temperature biases by adjusting urban fabric temperatures. The scheme is tested against independent urban observations for the summer month of July 2014 and specifically for a hot period and an extreme precipitation event. We find data assimilation reduces biases in temperature and wind speed. Within the city, the most significant improvement is the reduction of negative temperature biases during clear nights, which implies a better prediction of the Urban Heat Island (UHI). Concerning precipitation, the fractional skill score improves incrementally when additional observations are assimilated, and the largest impact is seen from the assimilation of weather radar observations.

In this paper, we revisit a well-known formulation of temperature structure parameter (CT2), originally proposed by V. I. Tatarskii. We point out its limitations and propose a revised formulation based on turbulence variance and flux budget equations. Our formulation includes a novel physically-based outer length scale which can be estimated from routine meteorological data.

In this study, the stability dependence of turbulent Prandtl number (Pr_t) is quantified via a novel and simple analytical approach. Based on the variance and flux budget equations, a hybrid length scale formulation is first proposed and its functional relationships to well-known length scales are established. Next, the ratios of these length scales are utilized to derive an explicit relationship between Pr_t and gradient Richardson number. In addition, theoretical predictions are made for several key turbulence variables (e.g., dissipation rates, normalized fluxes). The results from our proposed approach are compared against other competing formulations as well as published datasets. Overall, the agreement between the different approaches is rather good despite their different theoretical foundations and assumptions.

High-resolution, regularly gridded air-temperature maps are frequently used in climatology, hydrology, and ecology. Within the Netherlands, 34 official automatic weather stations (AWSs) are operated by the National Met Service according to World Meteorological Organization (WMO) standards. Although the measurements are of high quality, the spatial density of the AWSs is not sufficient to reconstruct the temperature on a 1-km-resolution grid. Therefore, a new methodology for daily temperature reconstruction from 1990 to 2017 is proposed, using linear regression and multiple adaptive regression splines. The daily 34 AWS measurements are interpolated using eight different predictors: diurnal temperature range, population density, elevation, albedo, solar irradiance, roughness, precipitation, and vegetation index. Results are cross-validated for the AWS locations and compared with independent citizen weather observations. The RMSE of the reference method ordinary kriging amounts to 2.6 °C whereas using the new methods the RMSE drops below 1.0 °C. Especially for cities, a substantial improvement of the predictions is found. Independent predictions are on average 0.3 °C less biased than ordinary kriging at 40 high-quality citizen measurement sites. With this new method, we have improved the representation of local temperature variations within the Netherlands. The temperature maps presented here can have applications in urban heat island studies, local trend analysis, and model evaluation.

In this article an analysis of urban rainfall from hourly to seasonal scales is conducted for the Netherlands, with a focus on its capital, Amsterdam. In addition, the potential of synoptic weather types and local wind directions to categorize extreme rainfall in Amsterdam is assessed. An analysis of gauge-adjusted daily radar rainfall retrievals with 1 km spatial resolution for 10 years shows that rainfall is enhanced over Dutch cities compared to their rural surroundings, with a maximum of a 14.2% increase over the largest cities in winter. The annual cumulative rainfall in Amsterdam appears to be significantly higher compared to its surroundings. This is due both to the higher frequency of occurrence of urban rainfall and to the higher hourly mean intensities. Extreme hourly rainfall rates appear to be affected by urban areas only in summer. Diurnal and weekly rainfall cycles do not reveal any significant urban influence. A wind direction analysis reveals that extreme rainfall events can primarily be attributed to westerly and next to southerly air masses. An analysis of the Jenkinson and Collinson (JC) and the German Weather Service (Deutscher Wetterdienst, DWD) weather types with rainfall and extreme rainfall events reveals that the JC weather types are more indicative of situations associated with rainfall extremes, whereas the DWD weather types are more indicative of situations resulting in higher accumulated rainfall amounts.

In this study, we improve the Weather Research and Forecasting mesoscale model (WRF) performance by incorporating observations of a variety of sources using data assimilation and nudging techniques on a resolution up to 100 meter for urban areas. Our final goal is to create a 15 year climatological urban re-analysis data archive of (hydro)meteorological variables for Amsterdam which is named ERA-urban. This will enable us to trace trends in thermal comfort and extreme precipitation.