With advancements in computational technology, data assimilation techniques, high-resolution remote sensing, and complex climate models, numerous precipitation products are available with different spatiotemporal resolutions; however, their evaluation, especially in the Himalayan region, is unexplored. Therefore, this study attempts to assess four sources (gridded observation dataset, reanalysis, satellite, and numerical weather prediction models) of precipitation through hydrological modelling for the catastrophic 2013 floods of Uttarakhand, India. The Upper Ganga Basin located in Western Himalayas is selected as the study area consisting of Alaknanda and Bhagirathi streams in the eastern and western parts. The Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) is employed for rainfall-runoff modelling. The rainfall from IMD, ERA-5, GPM-IMERG-Final, and WRF model outputs are forced into the calibrated HEC-HMS model for assessing their performance in hydrological simulations. The correlation coefficient of IMD, ERA-5, GPM-IMERG-Final, and WRF simulations with respect to the observed flow is 0.89, 0.88, 0.55, and 0.89, respectively, whereas their corresponding Modified Kling-Gupta Efficiency (KGE) is 0.66, 0.72, 0.48, and 0.71. Flash flood prioritization of the sub-watersheds based on morphometric characteristics suggests that the Alaknanda basin is relatively more vulnerable to flash floods due to their elongated nature, highest relative relief, and high mean slope.

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.

Herein, we introduce a novel methodology to generate urban morphometric parameters that takes advantage of deep neural networks and inverse modeling. We take the example of Chicago, USA, where the Urban Canopy Parameters (UCPs) available from the National Urban Database and Access Portal Tool (NUDAPT) are used as input to the Weather Research and Forecasting (WRF) model. Next, the WRF simulations are carried out with Local Climate Zones (LCZs) as part of the World Urban Data Analysis and Portal Tools (WUDAPT) approach. Lastly, a third novel simulation, Digital Synthetic City (DSC), was undertaken where urban morphometry was generated using deep neural networks and inverse modeling, following which UCPs are re-calculated for the LCZs. The three experiments (NUDAPT, WUDAPT, and DSC) were compared against Mesowest observation stations. The results suggest that the introduction of LCZs improves the overall model simulation of urban air temperature. The DSC simulations yielded equal to or better results than the WUDAPT simulation. Furthermore, the change in the UCPs led to a notable difference in the simulated temperature gradients and wind speed within the urban region and the local convergence/divergence zones. These results provide the first successful implementation of the digital urban visualization dataset within an NWP system. This development now can lead the way for a more scalable and widespread ability to perform more accurate urban meteorological modeling and forecasting, especially in developing cities. Additionally, city planners will be able to generate synthetic cities and study their actual impact on the environment.

Green Roofs (GRs) are one of the measures considered for Urban Heat Island (UHI) mitigation. The cooling effects of GRs are well studied in the literature. However, previous work has not addressed the impacts of GRs on heavy rainfall in cities. This study develops and tests the hypothesis that incorporating green roofs in urban areas enhances the magnitude of rain for heavy rainfall events. To test this, examples of heavy rainfall events over three different years are examined over Mumbai, India, one of the megapoleis that continues to witness heavy rains and urban flooding. The heavy rain events are simulated using Weather Research and Forecasting (WRF) model for different green roof fraction (GF) scenarios (10%, 25%, 50%, 75%, and 100%) over the urban area. The GF simulations are compared to the ‘no GF’ simulation (control run). The results indicate a consistent increase (1%–72%) in the total accumulated precipitation in all GF scenarios. Additional moisture and increased equivalent potential temperature aided the formation and sustenance of localized pockets of enhanced rain occurrences, contributing to the total amount of rainfall for the rain events for the domain. The increase in rainfall amounts leads to higher runoff and can increase the risk of flash floods. Thus, it is necessary to account for this rainfall-based feedback of GR before adopting it as a mitigation option. The results of this work may be helpful in effective urban planning and managing the urban climate extremes.

Differences in land surface characteristics across a city produce great spatial and temporal variability in air temperature. This fact is particularly pronounced between urban and surrounding rural areas giving rise to the canopy-layer urban heat island (CL-UHI) phenomenon. In the present study, we apply the dimensional analysis technique to develop a simple semi-empirical equation to map daily maximum CL-UHI (UHI_max) intensities during nighttime over the city of Singapore for specific weather conditions. By adopting the methodology proposed by Theeuwes et al., but selecting meteorological and morphological parameters that affect UHI_max intensity most for Singapore, evaluation of the developed equation shows good agreement with observations (RMSE = 1.13 K and IOA = 0.76). Model performance depends strongly on wind conditions and is best during weak winds when ‘ideal’ conditions for UHI development are approached (RMSE = 0.65 K and IOA = 0.85). Results using the simple equation developed to map UHI_max intensities in Singapore under dry weather conditions are comparable to those obtained from more sophisticated numerical models, which demand significant computational resources, and the complex parameterizations involved require expertise to carry out the simulations. The resulting maps of the present study can be used to investigate less favorable thermal conditions and assess population vulnerability to a certain temperature excess, as well as provide insights for urban planning strategies of mitigation measures according to the land cover and morphology of a location.

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.