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Abstract Bhubaneswar, Odisha, experiences an increasing trend of heavy rainfall events (HREs). This study aims to configure the WRF mesoscale model configuration at a hectometre scale and undertakes numerical experiments at a 0.5 km grid spacing. The experiments simulate HREs and assess the various physical parameterization schemes to identify suitable combinations for the region. Sensitivity experiments with various physical parametrization options identified the top eight combinations based on rainfall statistics. Their performance was further evaluated by simulating an additional four HREs over Bhubaneswar. A novel rank analysis approach based on statistical techniques to determine the rank of each configuration. The Noah-MP; Ferrier; Multi-Scale Kain-Fritsch (MFS), Noah-MP;Ferrier; Kain-Fritsch (MFK), as well as Noah; Lin;No cumulus (NLN), and Noah; Ferrier; No cumulus (NFN) emerged as the top performers in simulating precipitation. The study also tested eight parameterization combinations for simulating air temperature, relative humidity, and wind speed. The top configurations change when a different variable is used as a reference. However, a broad choice of MFS, MFK, and Noah-MP; Ferrier; No cumulus (MFN) merged as the top configurations in simulating HRE characteristics. These model configurations were independently tested and yielded good performance in simulating the atmospheric pre-storm environment and storm characteristics. Broadly stated the choice of Noah-MP instead of the Noah land model, with Ferrier and Multi-Scale Kain-Fritsch schemes could yield good results- though there is no singular best potential. These findings help establish the computational framework for studying and improving the understanding of heavy rainfall, enhance weather hazard preparedness, and offer an optimized WRF model for forecasting HRE in cities. 

Abstract This study investigates the influence of land surface processes on short-spell monsoonal heavy rainfall events under varying soil wetness conditions in India, using the Weather Research and Forecasting Model coupled with two land surface schemes: Noah and SLAB. To represent contrasting soil conditions, four rainfall events are chosen, two in onset (June) and two in active (August) months, during the Indian summer monsoon season. The results indicate that rainfall sensitivity differs notably between onset and active cases. Specifically, in onset, the SLAB overpredicts rainfall to the north of the storm compared to the Noah. The northward displacement of rainfall is attributed to the sensitivity of evapotranspiration to the preferential soil moisture regime in onset. Furthermore, the higher surface air saturation deficit in onset favors plant transpiration, resulting in increased boundary layer moisture. This contributes to enhanced moist static energy, thereby affecting potential vorticity and precipitation. In contrast, evapotranspiration sensitivity is modest during active months, under wet soil and lower surface air saturation deficit conditions. The study reveals the distinct soil moisture feedback mechanisms during the onset and active phases, through variations in evapotranspiration sensitivity. Variations in soil moisture and surface air saturation deficit in these phases play a crucial role in modulating evapotranspiration, which in turn affects precipitation. These findings underscore the importance of land surface initialization and land data assimilation in land–atmosphere interaction studies. 

Urbanization has accelerated dramatically across the world over the past decades. Urban influence on surface temperatures is now being considered as a correction term in climatological datasets. Although prior research has investigated urban influences on precipitation for specific cities or selected thunderstorm cases, a comprehensive examination of urban precipitation anomalies on a global scale remains limited. This research is a global analysis of urban precipitation anomalies for over one thousand cities worldwide. We find that more than 60% of the global cities and their downwind regions are receiving more precipitation than the surrounding rural areas. Moreover, the magnitude of these urban wet islands has nearly doubled in the past 20 y. Urban precipitation anomalies exhibit variations across different continents and climates, with cities in Africa, for example, exhibiting the largest urban annual and extreme precipitation anomalies. Cities are more prone to substantial urban precipitation anomalies under warm and humid climates compared to cold and dry climates. Cities with larger populations, pronounced urban heat island effects, and higher aerosol loads also show noticeable precipitation enhancements. This research maps global urban rainfall hotspots, establishing a foundation for the consideration of urban rainfall corrections in climatology datasets. This advancement holds promise for projecting extreme precipitation and fostering the development of more resilient cities in the future.