Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF-Urban Asymmetric Convective Model (WRF-UACM) for Delhi, India, integrating explicit urban physics with Sentinel-updated USGS land-use and urban morphological parameters derived from the UT-GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF-BACM) using Winter Fog Experiment (WiFEX) data, WRF-UACM significantly improved urban meteorological variables like diurnal variation of 10-meter wind speed, 2-meter air temperature (T2), and 2-meter relative humidity (RH2) on a fog day. UACM also demonstrates improved accuracy in simulating temperature and a significant reduction in biases for RH2 and wind speed under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF-BACM, WRF-UACM reduces the cold bias soon after the sunset, thus improving the fog onset error by ~4 hours. This study underscores the UACM’s potential in enhancing fog prediction, urging further exploration of various fog types and its application in operational settings, thus offering invaluable insights for preventive measures and mitigating disruptions in urban regions.

Accurate fog prediction in densely urbanized cities poses a challenge due to the complex influence of urban morphology on meteorological conditions in the urban roughness sublayer. This study implemented a coupled WRF-Urban Asymmetric Convective Model (WRF-UACM) for Delhi, India, integrating explicit urban physics with Sentinel-updated USGS land-use and urban morphological parameters derived from the UT-GLOBUS dataset. When evaluated against the baseline Asymmetric Convective Model (WRF-BACM) using Winter Fog Experiment (WiFEX) data, WRF-UACM significantly improved urban meteorological variables like diurnal variation of 10-meter wind speed, 2-meter air temperature (T2), and 2-meter relative humidity (RH2) on a fog day. UACM also demonstrates improved accuracy in simulating temperature and a significant reduction in biases for RH2 and wind speed under clear sky conditions. UACM reproduced the nighttime urban heat island effect within the city, showing realistic diurnal heating and cooling patterns that are important for accurate fog onset and duration. UACM effectively predicts the onset, evolution, and dissipation of fog, aligning well with observed data and satellite imagery. Compared to WRF-BACM, WRF-UACM reduces the cold bias soon after the sunset, thus improving the fog onset error by ~4 hours. This study underscores the UACM’s potential in enhancing fog prediction, urging further exploration of various fog types and its application in operational settings, thus offering invaluable insights for preventive measures and mitigating disruptions in urban regions.

Urbanization manifests rainfall anomalies both in and around the city. While prior studies have assessed the effect of urban expansion as well as city size, little is known about the impact of city shape on rainfall. Here, idealized large eddy simulations (LES) coupled with the Weather Research and Forecasting (WRF) model are conducted to bridge the knowledge gap. Results indicate difference in the timing for urban-induced rainfall in the inland versus coastal environment. This is associated with contrasting diurnal cycles of vertical velocity and cloud water mixing ratio driven by the land-sea breeze. The impact of city shape on rainfall is more evident in the coastal environment, as buoyancy flows arise from cities alter the interactions between urban-rural circulation and sea breeze. A circular city show nearly 22.0% larger rainfall accumulation and 78.6% greater rainfall intensity compared to a triangular city over urban surfaces. Results highlight the need for considering city shape for coastal urban planning, as a potential adaptation strategy to manage rainfall under future climate. Results indicate that coarser models which represent impervious surface area but not the right shape would have a higher uncertainty in simulating rainfall changes.

Subseasonal-to-seasonal (S2S) prediction of winter wheat yields is crucial for farmers and decision-makers to reduce yield losses and ensure food security. Recently, numerous researchers have utilized machine learning (ML) methods to predict crop yield using observational climate variables and satellite data. Meanwhile, some studies also illustrate the potential of state-of-the-art dynamical atmospheric prediction in crop yield forecasting. However, the potential of coupling both methods has not been fully exploited. Herein, we aim to establish a skilled ML–dynamical hybrid model for crop yield forecasting (MHCF v1.0), which hybridizes ML and a global dynamical atmospheric prediction system, and apply it to northern China at the S2S time scale. MHCF v1.0 demonstrates that crop yield forecasting with S2S dynamical predictions generally outperforms that with observational climate data. The coupling of ML and S2S dynamical atmospheric prediction provides a useful tool for yield forecasting, which could guide agricultural practices, policy-making and agricultural insurance.

Understanding the response of extreme precipitation (EP) at a city scale to global warming is critical to protect a city from the risks of urban flooding under climate change. Yet, current knowledge on this issue is limited. Here, focusing on an urban agglomeration in the tropics, Singapore, we reveal that future global warming enhances both frequency and intensity of EP, based on simulations with a state-of-the-art convection-permitting regional climate model. EP intensification can reach maximum “super” Clausius-Clapeyron ( +7% per K warming) rate, consistently for both Representative Concentration Pathways (RCPs) 8.5 and 4.5. The intensification is lower for moderate and light precipitation, implying a situation of “wet gets wetter and dry remains dry”. EP enhancement is attributed to the increased atmospheric moisture and, more importantly, the enhanced lifting force, which directly strengthens the precipitation-making processes.