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In this study, a comparative analysis of three satellite precipitation products including Tropical Rainfall Measuring Mission (TRMM-3B43 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS V2) with ground-measured Indian Meteorological Department (IMD) precipitation data were performed to estimate the meteorological drought in the Bundelkhand region of Central India. The high-resolution CHIRPS data showed the closest agreement with the IMD precipitation and well captured the drought characteristics. The Standardized Precipitation Index (SPI) identified seven major droughts events during the period of 1981 to 2016. Appropriate calibration and validation were performed for drought forecasting using the Auto-Regressive Integrated Moving Average (ARIMA) model. The forecasting result showed a reasonably good agreement with the observed datasets with the one-month lead time. The outcomes of this study have policy level implications for drought monitoring and preparedness in this region. 

The WUDAPT (World Urban Database and Access Portal Tools project goal is to capture consistent information on urban form and function for cities worldwide that can support urban weather, climate, hydrology and air quality modeling. These data are provided as urban canopy parameters (UCPs) as used by weather, climate and air quality models to simulate the effects of urban surfaces on the overlying atmosphere. Information is stored with different levels of detail (LOD). With higher LOD greater spatial precision is provided. At the lowest LOD, Local Climate Zones (LCZ) with nominal UCP ranges is provided (order 100 m or more). To describe the spatial heterogeneity present in cities with great specificity at different urban scales we introduce the Digital Synthetic City (DSC) tool to generate UCPs at any desired scale meeting the fit-for-purpose goal of WUDAPT. 3D building and road elements of entire city landscapes are simulated based on readily available data. Comparisons with real-world urban data are very encouraging. It is customized (C-DSC) to incorporate each city's unique building morphologies based on unique types, variations and spatial distribution of building typologies, architecture features, construction materials and distribution of green and pervious surfaces. The C-DSC uses crowdsourcing methods and sampling within city Testbeds from around the world. UCP data can be computed from synthetic images at selected grid sizes and stored such that the coded string provides UCP values for individual grid cells.