Michael Mau Fung Wong

The Eastern Mediterranean, notably Cyprus, is a climate change hotspot facing severe heatwaves. Accurate numerical weather prediction of these extremes requires precise land–atmosphere modeling and initial and boundary conditions. This study assesses replacing the default USGS Land-Use and Land-Cover (LULC) dataset with the 10 m ESA WorldCover 2021 dataset in the Weather Research and Forecasting (WRF) model to simulate the 15–29 July 2023 Cyprus heatwave. The updated LULC increased urban representation six-fold. Statistical validations showed significant improvements in 2 m temperature, relative humidity, and 10 m wind speed predictions across 85% of observational sites. Dynamically, it restored urban thermal memory, effectively capturing the daytime Urban Cool Island effect and nocturnal heat release. Furthermore, radiosonde validations showed that the update corrected nocturnal Planetary Boundary Layer Height (PBLH) underestimations and dampened exaggerated daytime convective mixing. However, crucial limitations remain. High-frequency diagnostics indicated the model still suffers from damped thermal inertia, missing the abrupt temperature spikes and rapid nocturnal cooling typical of semi-arid microclimates. Additionally, the updated configuration failed to capture severe atmospheric stagnation during peak heatwave conditions, highlighting that deep-rooted kinetic errors persist within default boundary layer parameterizations despite static surface improvements.

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 such as diurnal variations in 10‐m wind speed, 2‐m air temperature (T2), and 2‐m relative humidity (RH2) during a fog event. UACM also demonstrates improved accuracy in simulating temperature and significantly reducing biases for wind speed and daytime RH2 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 sunset, thus improving the fog onset error by ∼3 hr. This study highlights the UACM's potential to improve fog prediction and its application in operational settings. With further investigation into different fog types, the UACM can provide crucial insights for preventive measures and reducing disruptions in urban areas.

Planetary boundary layer (PBL) modeling is a primary contributor to uncertainties in a numerical weather prediction (NWP) model due to difficulties in modeling the turbulent transport of surface fluxes. The Weather Research and Forecasting model (WRF) has provided many PBL schemes that may feature a non‐local transport component driven by large eddies or a one‐and‐half order turbulence closure model, but few of them possess the two features at once. In the present study, a turbulent kinetic energy (TKE)‐based eddy diffusivity/viscosity method is integrated into the non‐local Asymmetric Convective Model version 2 (ACM2) PBL scheme and implemented in WRF. The original first‐order eddy‐diffusivity term in ACM2 is discarded and an extra prognostic equation for TKE, which considers the tendency of TKE by buoyancy, wind shear, vertical transport, and dissipative processes, is supplied to calculate the diffusivity/viscosity. Non‐local transport is modeled the same as ACM2 using the transilient matrix method. Idealized tests using prescribed surface heat flux and roughness length are performed. TKE‐ACM2 displays advantages over the PBL scheme developed by Bougeault and Lacarrère (hereinafter referred to as Boulac) and ACM2 in the wind speeds (WS) profile because it better matches large‐eddy simulations results in the surface momentum flux. Real case simulations show that TKE‐ACM2 generally outperforms in the diurnal vertical profiles of WS under stable conditions. TKE‐ACM2 also produces a better alignment under moderately unstable conditions in the early nighttime at the urban LiDAR station. However, the model exhibits discrepancies more apparently under a more unstable condition during the winter daytime.

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.

Planetary boundary layer (PBL) modeling is a primary contributor to uncertainties in a numerical weather prediction model due to difficulties in modeling the turbulent transport of surface fluxes. The Weather Research and Forecasting model (WRF) has included many PBL schemes which may feature a non-local transport component driven by super-grid eddies or a one-and-half order turbulence closure model. In the present study, a turbulent kinetic energy (TKE)-based turbulence closure model is integrated into the non-local Asymmetric Convective Model version 2 (ACM2) PBL scheme and implemented in WRF. Non-local transport is modeled the same as ACM2 using the transilient matrix method. The new TKE-ACM2 PBL scheme is evaluated by comparing it with high spatiotemporal Doppler LiDAR observations in Hong Kong over 30 days each for summer and winter seasons to examine its capability in predicting the vertical structures of winds. Scatter plots of measured versus simulated instantaneous wind speeds show that TKE-ACM2 is able to reduce the root mean square error and mean bias and improve the index of agreement, especially at the urban observational site. The diurnal evolution of monthly averaged wind profiles suggests TKE-ACM2 can better match both the magnitudes and vertical gradients, revealing its superiority compared to ACM2 at stable atmospheric conditions. Other meteorological parameters including the potential temperature profiles, PBL heights, and surface wind speeds have also been investigated with references to various sources of observations.

In mesoscale Weather Research and Forecasting (WRF) model, several urban‐modeling options exist, such as Single‐layer Urban Canopy Model, Building Effect Parameterization, and Building Energy Model. However, these models have limitations in terms of the choice of land surface models (LSMs) and planetary boundary layer (PBL) schemes, the associated computational expenses, and other constraints. Recently, Dy et al. (2019, https://doi.org/10.1029/2018JD029333) included the urban‐momentum drag effect for wind‐speed modeling by developing a new multilayer model based on a modified nonlocal Asymmetric Convective Model version‐2 (ACM2) PBL scheme. This urban‐based ACM2 (UACM) has demonstrated a significantly improved ability over the base‐ACM2 (BACM) to predict wind speed near the urban ground surface and shown an inflection point at roof level in the vertical profile of horizontal wind. In this study, urban thermal and moisture components are introduced into the Pleim‐Xiu (PX) LSM and coupled with UACM. The diurnal variation in street, walls, and roof‐surface temperatures is modeled using the two‐layer force‐restore algorithm. Simple radiation treatment is considered to account for shadowing on streets based on the solar zenith angle and building morphology. Heat and moisture flux evolution are considered explicitly on all urban surfaces. The advantages of this novel UACM are simple formulation, more efficient execution, and its requirement for only a few fundamental urban morphological parameters. The idealized and real urban data case WRF simulations are performed over the Pearl River Delta region in southern China to test the new UACM. The computationally efficient UACM is expected to perform faster for operational forecasting runs.