Parametrizing horizontally-averaged wind and temperature profiles in the urban roughness sublayer

Tower-based measurements from within and above the urban canopy in two cities are used to evaluate several existing approaches that parametrize the vertical profiles of wind speed and temperature within the urban roughness sublayer (RSL). It is shown that current use of Monin–Obukhov similarity theory (MOST) in numerical weather prediction models can be improved upon by using RSL corrections when modelling the vertical profiles of wind speed and friction velocity in the urban RSL using MOST. Using anisotropic building morphological information improves the agreement between observed and parametrized profiles of wind speed and momentum fluxes for selected methods. The largest improvement is found when using dynamically-varying aerodynamic roughness length and displacement height. Adding a RSL correction to MOST, however, does not improve the parametrization of the vertical profiles of temperature and heat fluxes. This is expected since sources and sinks of heat are assumed uniformly distributed through a simple flux boundary condition in all RSL formulations, yet are highly patchy and anisotropic in a real urban context. Our results can be used to inform the choice of surface-layer representations for air quality, dispersion, and numerical weather prediction applications in the urban environment

Persistent cloud cover over mega-cities linked to surface heat release

Urban areas are a hotspot for the interactions between the built environment, its inhabitants, and weather. Unlike the impact of temperatures through the well-known urban heat island effect, urban effects on cloud formation remain unknown. In this study we show observational evidence of a systematic enhancement of cloud cover in the afternoon and evening over two large metropolitan areas in Europe (Paris and London). Long-term measurements in and around London show that during late-spring and summer, even though less moisture is available at the surface and the atmosphere is drier, low clouds can persist longer over the urban area as vertical mixing of the available moisture is maintained for a longer period of time, into the evening transition. Our findings show that urban impacts on weather extend beyond temperature effects. These prolonged clouds over the city might enhance the urban heat island via night-time radiative forcing

Cool city mornings by urban heat

The urban heat island effect is a phenomenon observed worldwide, i.e. evening and nocturnal temperatures in cities are usually several degrees higher than in the surrounding countryside. In contrast, cities are sometimes found to be cooler than their rural surroundings in the morning and early afternoon. Here, a general physical explanation for this so-called daytime urban cool island (UCI) effect is presented and validated for the cloud-free days in the BUBBLE campaign in Basel, Switzerland. Simulations with a widely evaluated conceptual atmospheric boundary-layer model coupled to a land-surface model, reveal that the UCI can form due to differences between the early morning mixed-layer depth over the city (deeper) and over the countryside (shallower). The magnitude of the UCI is estimated for various types of urban morphology, categorized by their respective local climate zones

Urban Multi-scale Environmental Predictor - an extensive tool for climate services in urban areas

The city based climate service tool UMEP (Urban Multi-scale Environmental Predictor) is a coupled modelling system that combines models essential for urban climate processes and is developed as an extensive QGIS plugin. An application is presented to illustrate its potential, specifically of the identification of heat waves and cold waves in cities. The tool has broad utility for applications related to outdoor thermal comfort, urban energy consumption, climate change mitigation etc. It includes tools to: enable users to input atmospheric and surface data from multiple sources, prepare meteorological data for use in urban areas, undertake simulations and consider scenarios, and compare and visualize different combinations of climate indicators

Urban Multi-scale Environmental Predictor (UMEP) : An integrated tool for city-based climate services

UMEP (Urban Multi-scale Environmental Predictor), a city-based climate service tool, combines models and tools essential for climate simulations. Applications are presented to illustrate UMEP's potential in the identification of heat waves and cold waves; the impact of green infrastructure on runoff; the effects of buildings on human thermal stress; solar energy production; and the impact of human activities on heat emissions. UMEP has broad utility for applications related to outdoor thermal comfort, wind, urban energy consumption and climate change mitigation. It includes tools to enable users to input atmospheric and surface data from multiple sources, to characterise the urban environment, to prepare meteorological data for use in cities, to undertake simulations and consider scenarios, and to compare and visualise different combinations of climate indicators. An open-source tool, UMEP is designed to be easily updated as new data and tools are developed, and to be accessible to researchers, decision-makers and practitioners. (C) 2017 The Authors. Published by Elsevier Ltd.Peer reviewe

WUDAPT: an urban weather, climate and environmental modeling infrastructure for the Anthropocene

WUDAPT is an international community-based initiative to acquire and disseminate climate relevant data on the physical geographies of cities for modeling and analyses purposes. The current lacuna of globally consistent information on cities is a major impediment to urban climate science towards informing and developing climate mitigation and adaptation strategies at urban scales. WUDAPT consists of a database and a portal system; its database is structured into a hierarchy representing different levels of detail and the data are acquired using innovative protocols that utilize crowdsourcing approaches, Geowiki tools, freely accessible data, and building typology archetypes. The base level of information (L0) consists of Local Climate Zones (LCZ) maps of cities; each LCZ category is associated with range of values for model relevant surface descriptors (e.g. roughness, impervious surface cover, roof area, building heights, etc.). Levels 1 (L1) and 2 (L2) will provide specific intraurban values for other relevant descriptors at greater precision, such as data morphological forms, material composition data and energy usage. This article describes the status of the WUDAPT project and demonstrates its potential value using observations and models. As a community-based project, other researchers are encouraged to participate to help create a global urban database of value to urban climate scientists

Validation of wind farm parameterisation in Weather Forecast Model HARMONIE-AROME: Analysis of 2019

In the next few decades climate mitigation efforts will transform the North Sea into one of the most important energy sources. The present wind energy capacity on the North Sea is expected to increase by almost a factor 5 in 2030 and almost a factor 10 in 2050. It is therefore of paramount importance to know how wind farms influence the atmosphere. Wind farms extract kinetic energy from the atmosphere and in doing so decrease the wind speed and increase turbulence levels. More turbulence means more mixing of vertical layers in the atmosphere and a change in humidity and temperature profiles. This may lead to cloud forming or dissipation. Wind farms are also an obstacle to the flow, which is what is called the blockage effect, as opposed to the wake effect behind the wind farm. This report is about the wake effect, mainly on wind, but we also analysed temperature and humidity profiles. In order to assess and quantify the wake effect, we compared two high resolution re-analyses for the year 2019 on a 2000 by 2000 km North Sea domain. The high resolution re-analyses with a 2.5 km horizontal grid spacing is based on global re-analysis ERA5 and downscaled with mesoscale weather model HARMONIE-AROME which is used operationally at KNMI. One of the re-analyses is without the effect of wind farms (referred to as control or HarmCY43-CTL in this report) and one with the Fitch wind farm parametrization that was recently incorporated in HARMONIE-AROME (HarmCY43-WFP). From the differences between the two we can isolate the wind speed deficits, or wakes, from the wind farms.Earlier validation studies have shown that a previous version of the HARMONIE-AROME model (HarmCY40) produces accurate wind climatology for undisturbed wind fields (period 2008-2018) and validates well against disturbed tower, aircraft and lidar measurements from 2016. In these studies the wind climatology is not validated for different stability regimes. In this study we do make that distinction and use measurements from 2019 for validation of HarmCY43-CTL and HarmCY43-WFP. * Generally HarmCY43-WFP outperforms HarmCY43-CTL in wake areas. HarmCY43-WFP even seems to capture the wind in wind farms reasonably well, although the WFP is not designed for that.* The selection criterion that we used to select disturbed (in wakes) and undisturbed wind directions (outside wakes) seems to work well: the WFP reduces the wind speed bias for disturbed winds significantly, but hardly affects undisturbed winds. * Our results confirm earlier studies that wakes are strongest for situations with stable stratification: we observed wake lengths as long as about 50 km. We can conclude that HarmCY43-CTL tends to underestimate the wind speed for stable stratification and overestimate the wind speed for weakly stable and unstable stratification, mainly for the lidar measurements. As expected HarmCY43-WFP reduces the wind speed in the wake. This means that HarmCY43-WFP validates better against measurements for weakly stable and unstable stratification. However, for stable stratification HarmCY43-WFP makes the underestimation of the measurements worse (note that this does not imply the wake deficits are biased). This could even become worse if wind turbines are not performing according to the power curve or are not turning at all because of maintenance or legislation, the WFP will not be aware of that and will extract too much energy, overestimate the wake effect and underestimate the wind speed. * Earlier studies have shown that HarmCY40-CTL captures the diurnal cycle well. HarmCY43-CTL does as well and including the WFP does not seem to affect that. The results of this study give us confidence that the present HARMONIE-AROME model configuration, including the Fitch WFP, can be used to assess the influence of the anticipated wind farm infrastructure in 2050 on the wind climatology.<br/

Interactions between the nocturnal low-level jets and the urban boundary layer: a case study over London

Understanding the physical processes that affect the turbulent structure of the nocturnal urban boundary layer (UBL) is essential for improving forecasts of air quality and the air temperature in urban areas. Low-level jets (LLJs) have been shown to affect turbulence in the nocturnal UBL. We investigate the interaction of a mesoscale LLJ with the UBL during a 60-h case study. We use observations from two Doppler lidars and results from two high-resolution numerical-weather-prediction models (Weather Research and Forecasting model, and the Met Office Unified Model for limited-area forecasts for the U.K.) to study differences in the occurrence frequency, height, wind speed, and fall-off of LLJs between an urban (London, U.K.) and a rural (Chilbolton, U.K.) site. The LLJs are elevated (≈ 70 m) over London, due to the deeper UBL, while the wind speed and fall-off are slightly reduced with respect to the rural LLJ. Utilizing two idealized experiments in the WRF model, we find that topography strongly affects LLJ characteristics, but there is still a substantial urban influence. Finally, we find that the increase in wind shear under the LLJ enhances the shear production of turbulent kinetic energy and helps to maintain the vertical mixing in the nocturnal UBL

Understanding London's summertime cloud cover

Cities are a source of complex land--atmosphere interactions. Spatial differences in the energy balance and enhanced surface roughness interact with the atmosphere to alter clouds and precipitation. Here, we explore how London (UK) alters cloud formation during the spring and summer. The Met Office's high-resolution operational forecasts predict enhanced cloud cover over the city as found in observations, but underpredicts the intensity. During low wind-speeds, cloud enhancement over the city is strongest and linked to an urban induced thermal circulation. These circulations advect moist air from the city edge inwards, transporting it upwards with a large moisture convergence over the urban area. At around 1000 m above the surface, the turbulent moisture flux takes over the moisture transport to the cloud layer. A relative-humidity budget shows the moisture flux in the upper boundary layer to be the largest contribution to the urban-rural differences in relative humidity

A diagnostic equation for the daily maximum urban heat island effect for cities in northwestern Europe

The urban heat island (UHI) effect, defined as the air temperature difference between the urban canyon and the nearby rural area, is investigated. Because not all cities around the world are equipped with an extensive measurement network, a need exists for a relatively straightforward equation for the UHI effect. Here, we derive a simple, diagnostic equation for the UHI using dimensional analysis. This equation provides a first-order estimation of the daily maximum UHI based on routine meteorological observations and straightforward urban morphological properties. The equation is tested for 14 cities across northwestern Europe and appears to be robust. The comprehensiveness of this analytical equation allows for applications beyond urban meteorological studies