Biometeorology for cities.

Improvements in global sustainability, health, and equity will largely be determined by the extent to which cities are able to become more efficient, hospitable, and productive places. The development and evolution of urban areas has a significant impact on local and regional weather and climate, which subsequently affect people and other organisms that live in and near cities. Biometeorologists, researchers who study the impact of weather and climate on living creatures, are well positioned to help evaluate and anticipate the consequences of urbanization on the biosphere. Motivated by the 60th anniversary of the International Society of Biometeorology, we reviewed articles published in the Society's International Journal of Biometeorology over the period 1974-2017 to understand if and how biometeorologists have directed attention to urban areas. We found that interest in urban areas has rapidly accelerated; urban-oriented articles accounted for more than 20% of all articles published in the journal in the most recent decade. Urban-focused articles in the journal span five themes: measuring urban climate, theoretical foundations and models, human thermal comfort, human morbidity and mortality, and ecosystem impacts. Within these themes, articles published in the journal represent a sizeable share of the total academic literature. More explicit attention from urban biometeorologists publishing in the journal to low- and middle-income countries, indoor environments, animals, and the impacts of climate change on human health would help ensure that the distinctive perspectives of biometeorology reach the places, people, and processes that are the foci of global sustainability, health, and equity goals.

Spatially Explicit Correction of Simulated Urban Air Temperatures Using Crowdsourced Data.

Urban climate model evaluation often remains limited by a lack of trusted urban weather observations. The increasing density of personal weather sensors (PWSs) make them a potential rich source of data for urban climate studies that address the lack of representative urban weather observations. In our study, we demonstrate that carefully quality-checked PWS data not only improve urban climate models' evaluation but can also serve for bias correcting their output prior to any urban climate impact studies. After simulating near-surface air temperatures over London and south-east England during the hot summer of 2018 with the Weather Research and Forecasting (WRF) Model and its building Effect parameterization with the building energy model (BEP-BEM) activated, we evaluated the modeled temperatures against 402 urban PWSs and showcased a heterogeneous spatial distribution of the model's cool bias that was not captured using official weather stations only. This finding indicated a need for spatially explicit urban bias corrections of air temperatures, which we performed using an innovative method using machine learning to predict the models' biases in each urban grid cell. This bias-correction technique is the first to consider that modeled urban temperatures follow a nonlinear spatially heterogeneous bias that is decorrelated from urban fraction. Our results showed that the bias correction was beneficial to bias correct daily minimum, daily mean, and daily maximum temperatures in the cities. We recommend that urban climate modelers further investigate the use of quality-checked PWSs for model evaluation and derive a framework for bias correction of urban climate simulations that can serve urban climate impact studies.

Global mapping of urban thermal anisotropy reveals substantial potential biases for remotely sensed urban climates.

Urban thermal anisotropy (UTA) drastically impacts satellite-derived urban surface temperatures and fluxes, and consequently makes it difficult to gain a more comprehensive understanding of global urban climates. However, UTA patterns and associated biases in observed urban climate variables have not been investigated across an adequate number of global cities with diverse contexts; nor is it known whether there are globally measurable factors that are closely related to the UTA intensity (UTAI, quantified as the maximum difference between the nadir and off-nadir urban thermal radiation). Here we investigate the UTAI over more than 5500 cities worldwide using multi-angle land surface temperature (LST) observations from 2003 to 2021 provided by Moderate Resolution Imaging Spectroradiometer (MODIS). The results show that the global mean UTAI can reach 5.1, 2.7, 2.4, and 1.7 K during summer daytime, winter daytime, summer nighttime, and winter nighttime, respectively. Using nadir LST observations as a reference, our analysis reveals that UTA can lead to an underestimation of satellite-based urban surface sensible heat fluxes (H) by 45.4% and surface urban heat island intensity (Is) by 43.0% when using LST observations obtained from sensor viewing zenith angles (VZAs) of ±60°. Practitioners can limit the biases of H and Is within ±10% by using LSTs from sensor VZAs within ±30°. We also find that UTAI is closely related to urban impervious surface percentage and surface air temperature across global cities. These findings have implications for angular normalization of satellite-retrieved instantaneous LST observations across cities worldwide.