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Abstract Amplified rates of urban convective systems pose a severe peril to the life and property of the inhabitants over urban regions, requiring a reliable urban weather forecasting system. However, the city scale's accurate rainfall forecast has constantly been a challenge, as they are significantly affected by land use/ land cover changes (LULCC). Therefore, an attempt has been made to improve the forecast of the severe convective event by employing the comprehensive urban LULC map using Local Climate Zone (LCZ) classification from the World Urban Database and Access Portal Tools (WUDAPT) over the tropical city of Bhubaneswar in the eastern coast of India. These LCZs denote specific land cover classes based on urban morphology characteristics. It can be used in the Advanced Research version of the Weather Research and Forecasting (ARW) model, which also encapsulates the Building Effect Parameterization (BEP) scheme. The BEP scheme considers the buildings' 3D structure and allows complex land–atmosphere interaction for an urban area. The temple city Bhubaneswar, the capital of eastern state Odisha, possesses significant rapid urbanization during the recent decade. The LCZs are generated at 500 m grids using supervised classification and are ingested into the ARW model. Two different LULC dataset, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS) and WUDAPT derived LCZs and initial, and boundary conditions from NCEP GFS 6-h interval are used for two pre-monsoon severe convective events of the year 2016. The results from WUDAPT based LCZ have shown an improvement in spatial variability and reduction in overall BIAS over MODIS LULC experiments. The WUDAPT based LCZ map enhances high-resolution forecast from ARW by incorporating the details of building height, terrain roughness, and urban fraction. 

Taking the examples of Hurricane Florence (2018) over the Carolinas and Hurricane Harvey (2017) over the Texas Gulf Coast, the study attempts to understand the performance of slab, single‐layer Urban Canopy Model (UCM), and Building Environment Parameterization (BEP) in simulating hurricane rainfall using the Weather Research and Forecasting (WRF) model. The WRF model simulations showed that for an intense, large‐scale event such as a hurricane, the model quantitative precipitation forecast over the urban domain was sensitive to the model urban physics. The spatial and temporal verification using the modified Kling‐Gupta efficiency and Method for Object based Diagnostic and Evaluation in Time Domain suggests that UCM performance is superior to the BEP scheme. Additionally, using the BEP urban physics scheme over UCM for landfalling hurricane rainfall simulations has helped simulate heavy rainfall hotspots.

 

This study evaluates the impact of land surface models (LSMs) and urban heterogeneity [using local climate zones (LCZs)] on air temperature simulated by the Weather Research and Forecasting model (WRF) during a regional extreme event. We simulated the 2017 heatwave over Europe considering four scenarios, using WRF coupled with two LSMs (i.e., Noah and Noah‐MP) with default land use/land cover (LULC) and with LCZs from the World Urban Database and Access Portal Tools (WUDAPT). The results showed that implementing the LCZs significantly improves the WRF simulations of the daily temperature regardless of the LSMs. Implementing the LCZs altered the surface energy balance partitioning in the simulations (i.e., the sensible heat flux was reduced and latent heat flux was increased) primarily due to a higher vegetation feedback in the LCZs. The changes in the surface flux translated into an increase in the simulated 2‐m relative humidity and 10‐m wind speed as well as changed air temperature within cities section and generated a temperature gradient that affected the temperatures beyond the urban regions. Despite these changes, the factor separation analysis indicated that the impact of LSM selection was more significant than the inclusion of LCZs. Interestingly, the lowest bias in temperature simulations was achieved when WRF was coupled with the Noah as the LSM and used WUDAPT as the LULC/urban representation.

 

This study investigates the impact of direct versus indirect initialization of soil moisture (SM) and soil temperature (ST) on monsoon depressions (MDs) and heavy rainfall simulations over India. SM/ST products obtained from high‐resolution, land data assimilation system (LDAS) are used in the direct initialization of land surface conditions in the ARW modeling system. In the indirect method, the initial SM is sequentially adjusted through the flux‐adjusting surface data assimilation system (FASDAS). These two approaches are compared with a control experiment (CNTL) involving climatological SM/ST conditions for eight MDs at 4‐km horizontal resolution. The surface fields simulated by the LDAS run showed the highest agreement, followed by FASDAS for relatively dry June cases, but the error is high (~15–30%) for the relatively wet August cases. The moisture budget indicates that moisture convergence and local influence contributed more to rainfall. The surface‐rainfall feedback analysis reveals that surface conditions and evaporation have a dominant impact on the rainfall simulation and these couplings are notable in LDAS runs. The contiguous rain area (CRA) method indicates better performance of LDAS for very heavy rainfall distribution, and the location (ETS > 0.2), compared to FASDAS and CNTL. The pattern error contributes the maximum to the total rainfall error, and the displacement error is more in August cases' rainfall than that in June cases. Overall analyses indicated that the role of land conditions is significantly high in the drier month (June) than a wet month (August), and direct initialization of SM/ST fields yielded improved MD and heavy rain simulations.