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Global urban population is projected to double by 2050. This rapid urbanization is the driver of economic growth but has environmental challenges. To that end, there is an urgent need to understand, simulate, and disseminate information about extreme events, routine city operations, and long-term planning decisions. This paper describes an effort underway in India involving an interdisciplinary community of meteorology, hydrology, air quality, and computer science from national and international institutes. The urban collaboratory is a system of systems for simulating weather, hydrology, air quality, health, energy, transport, and economy and its interactions. Study and prediction of urban events involve multiscale observations and cross-sector models, heterogeneous data management, and enormous computing power. The consortia program (NSM_Urban) is part of “weather ready cities,” under the aegis of India’s National Supercomputing Mission. The ecosystem “Urban Environment Science to Society” (UES2S) builds on the integrated cyberinfrastructure with a science gateway for community research and end-user service with modeling and interoperable data. The collaboratory has urban computing, stakeholder participation, and a coordinated means to scaffold projects and ideas into operational tools. It discusses the design and the utilization of high-performance computing (HPC) as a science cloud platform for bridging urban environment and data science, participatory stakeholder applications, and decision-making. The system currently integrates models for high-impact urban weather, flooding, air quality, and simulating street- and building-scale wind flow and dispersion. The program with the work underway is ripe for interfacing with regional and international partners, and this paper provides an avenue toward that end.

This paper presents the lightning activity, aerosol optical depth (AOD) and climatic parameters (Bowen ratio, relative humidity, rainfall, maximum surface temperature and maximum updraft speed) over wet (Northeast India—NEI) and dry (Northwest India—NWI) land surfaces in a comparative analysis. The analyses are performed on flash counts and weather data of 17 years (1998–2014) retrieved from the Tropical Rainfall Measuring Mission (TRMM) lightning imaging sensors (LIS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), respectively. The first higher peak for flash counts during pre-monsoon (April–May) and second lower peak during September over wet NEI and dry NWI regions indicate the development of strong electrified storms during pre-monsoon and weakly electrified clouds during the withdrawal phase of the southwest summer monsoon. The monthly means of flash counts, Bowen ratio, maximum surface temperature, AOD and maximum updraft speed are higher by 27, 81, 16, 50 and 16%, respectively, for NWI than those of NEI. The relative humidity and rainfall are higher by 12% and 73% for NEI than those of NWI, respectively. The Pearson’s correlation coefficients of lightning activity with climatic parameters and AOD demonstrate the distinctive orographic lifting, moisture content and vertical wind shear in the upper troposphere in the regional climatic zone of dry NWI in comparison to that of wet NEI.

This paper presents the analysis of the frequency of lightning strikes associated with thunderstorm and precipitation distributions over smooth oceanic surface relative to that over solid earth surface in the tropical region. Long-term (1998–2014) data retrieved from the Lightning Imaging Sensors (LIS) of the Tropical Rainfall Measuring Mission (TRMM) satellite shows lightning flash counts over Indian landmass found to be 9.1 times more than those over the smooth oceanic surface of the Arabian Sea and the Bay of Bengal. On the other hand, the annual variation of rainfall-to-lightning ratio (RLR) is found to be 0.8 over Indian landmass, whereas it is 10 over the oceanic surfaces. We discuss the convective strength of thunderstorm distributions over land and oceanic regions by examining the relationships of RLR to the Bowen ratio, sea surface temperature (SST), and maximum air temperature over land, maximum updraft speed, and Aerosol Optical Depth (AOD) and cloud ice water content. The RLR shows high positive Pearson’s correlations with the Bowen ratio, maximum updraft speed, AOD, and cloud ice water content over land region relative to those measured over the oceanic region. The RLR also shows negative correlations with SST, maximum updraft speed, AOD over the oceanic region. The results are applicable in understanding of the convective characteristics of thunderstorm distributions and lightning flashes over the tropical regions of the world.

This paper presents a relationship of lightning activity with maximum air temperature, Bowen ratio, rainfall, cloud ice contents and Aerosol Optical Depth (AOD) over India during transition period from dry to wet seasons. Lightning flash count data and weather parameters are retrieved from the Tropical Rainfall Measuring Mission (Lightning Imaging Sensor - LIS) and the Moderate Resolution Imaging Spectroradiometer (MODIS) satellites for the period of 17 years (1998–2014). The Pearson correlation between lightning flash count and Bowen ratio is found to be coefficient of R = 0.95 for both dry and wet seasons. For dry season, the Pearson correlation of lightning flash count with surface maximum air temperature is found to be coefficient of R = 0.97 whereas, that for wet season negative coefficient of R = −0.89. The comparative analysis of Pearson correlations of lightning flash counts with AOD, rainfall and cloud ice content are found to be coefficients higher by 20%, 28% and 34% for dry season than those of during wet season, respectively. The results of lightning activity and weather parameters in comparative analyses over the Indian region may be useful for better understanding the differential convection characteristics during transition period of dry to wet season. The results are also important for estimations of impact associated with lightning strikes to ground during dry and wet seasons.

Change in land-use and land-cover (LULC) at local, regional, and global scales is one of the fundamental causes of global climate change. The present study aims to quantify the impact of LULC changes that occurred in a decade on surface meteorology and the urban heat island intensity over northwest India using the Weather Research and Forecasting (WRF) model. The analysis focuses on eight major metropolitan cities of northwest India, which witnessed the highest expansion of urban areas. Satellite-based LULC over northwest India suggests a significant increase in built-up/urban settlement areas over all the growing cities in the decade. Simulations of two months (January and May) for the year 2016 using LULC corresponding to 2016 and 2006 are used to estimate the impact of change in LULC over the urban locations during winter and summer months. Results showed a significant increase in near-surface temperature (3-5 °C) and decreased humidity (up to 10%) over the newly formed urban areas with more substantial variations during winter. Besides, there is an increase in the areas of higher temperature zones due to urban expansion. Night-time averaged UHI intensity is in the range of 2.4–5 °C during winter and 0.6–2 °C during 2016 over the cities considered.