Abstract A two-dimensional version of the Pennsylvania State University mesoscale model has been applied to Winter Monsoon Experiment data in order to simulate the diurnally occurring convection observed over the South China Sea. The domain includes a representation of part of Borneo as well as the sea so that the model can simulate the initiation of convection. Also included in the model are parameterizations of mesoscale ice phase and moisture processes and longwave and shortwave radiation with a diurnal cycle. This allows use of the model to test the relative importance of various heating mechanisms to the stratiform cloud deck, which typically occupies several hundred kilometers of the domain. Frank and Cohen's cumulus parameterization scheme is employed to represent vital unresolved vertical transports in the convective area. The major conclusions are: Ice phase processes are important in determining the level of maximum large-scale heating and vertical motion because there is a strong anvil componen...

Numerical study of convection observed during the Winter Monsoon Experiment using a mesoscale two-dimensional model [presentation]

Utilising green and bluespace to mitigate urban heat island intensity.

The urban heat island effect, its causes, and mitigation, with reference to the thermal properties of asphalt concrete.

Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures

Temporal and spatial variability of urban heat island and thermal comfort within the Rotterdam agglomeration

It is well known that cities increase air and surface temperatures compared to their rural surroundings, the so-called urban heat island (UHI) effect. However, the associated changes in atmospheric humidity (also known as urban dry island, UDI) and convection triggering remain largely unexplored and it is still unclear how urban modifications of the surface energy budget influence the diurnal evolution of temperature and humidity in the Atmospheric Boundary Layer (ABL) and ultimately control the initiation of convective clouds. Here we quantify the impact of different urban settings and free atmospheric conditions on UHI, UDI, and convection triggers by means of a zero-order model of the ABL. Specifically, we derive an approximate solution for urban-rural changes in surface energy fluxes and ABL potential temperature and humidity and we investigate the crossing between the ABL height and the lifting condensation level (LCL) which is a proxy for the triggering of convective clouds. We show that urban areas are generally warmer and drier, thus causing an increase in both ABL and LCL heights. However, the response of the ABL-LCL crossing to surface conditions is non-linear and there exists a range of free atmosphere conditions for which changes in imperviousness can impact convective clouds. 

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An analytical approximation of urban heat and dry islands and their impact on convection triggering

The need to mitigate climate change will boost the demand for renewable energy and lead to more wind turbines both on‐ and offshore. In the near future, the effect these wind farms have on the atmosphere can no longer be neglected. In numerical weather prediction models wind‐farm parameterisations (WFP) can be used to model the effect of wind farms on the atmosphere. There are different modeling approaches, but the parameterization developed by Fitch et al. (2012) is mostly used in previous studies. It models the wind farm as a momentum sink and a source of power production and turbulent kinetic energy. In this paper, we have implemented the Fitch et al. (2012) WFP into HARMONIE‐AROME, the numerical weather prediction model that is currently used by at least 11 national weather services in Europe. We used HARMONIE‐AROME to make year‐long simulations for 2016 with and without the WFP. The results were extensively evaluated using lidar, tower and flight measurements at several locations near wind farms. Including the WFP reduces the model bias for wind speed near offshore wind farms. Wind farms not only affect wind, but also temperature and humidity, especially during stable atmospheric conditions: the enhanced mixing caused by the wind turbines reduces the stratification of temperature and humidity. Including the WFP in HARMONIE‐AROME results in a more realistic representation of the atmosphere near wind farms and makes it a more future‐proof model for weather forecasting.

A One‐Year‐Long Evaluation of a Wind‐Farm Parameterization in HARMONIE‐AROME

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. ∗Corresponding author Email address: fredrikl@gvc.gu.se (Fredrik Lindberg) Submitted to FOSS4G 2017 Conference Proceedings, Boston, USA. September 20, 2017 FOSS4G 2017 Academic Program Urban Multi-scale Environmental Predictor

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

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.

The set‐up and evaluation of fine‐scale data assimilation for the urban climate of Amsterdam

Modeling lake effect snow on December 24, 2001 over Lake Erie using mesoscale models MM5 and WRF - Sensitivity to convection and microphysics schemes and the temperature of Lake Erie

N.E. Theeuwes

Papers

An analytical approximation of urban heat and dry islands and their impact on convection triggering

A One‐Year‐Long Evaluation of a Wind‐Farm Parameterization in HARMONIE‐AROME

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

The set‐up and evaluation of fine‐scale data assimilation for the urban climate of Amsterdam

Modeling lake effect snow on December 24, 2001 over Lake Erie using mesoscale models MM5 and WRF - Sensitivity to convection and microphysics schemes and the temperature of Lake Erie