We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions.

The seasonal cycle of Peninsular Florida rainfall is a robust feature that has monsoon like seasonality. We objectively define the onset, demise, and length of the wet seasons over the time period of 1948–2006 in Peninsular Florida based on daily rainfall from the Climate Prediction Center of the National Centers for Environmental Prediction. This definition defines onset (demise) of the wet season of Peninsular Florida as the first day the daily accumulated precipitation anomaly reaches a minimum (maximum) for the year at a given grid point. A discernible linear trend of later onset, earlier demise, and a resulting shorter length of the wet season in urban areas relative to the rural areas is observed. The wet season accumulation of rainfall, however, does not reveal a proportional change in the linear trend implying a perceptible contrast of comparatively higher (lower) linear trend of the daily rain rate of the wet season over urban (rural) areas of Peninsular Florida.

The Ogallala Aquifer is one of the most productive agricultural regions and is referred to as the “breadbasket of the world”. It covers approximately 225,000 square miles beneath the Great Plains region spanning the states of Texas, New Mexico, Oklahoma, Kansas, Nebraska, South Dakota, Wyoming, and Colorado. The aquifer is a major water source for the region, with its use exceeding recharge. Previous studies have documented climate changes and their impacts in the region. However, this is the first study to document temperature and precipitation changes over the entire Ogallala region from 35 General Circulation Models participating in Phase 5 of the Climate Model Intercomparison Project (CMIP5). The main study objectives were (1) to provide estimates of present and future climate change scenarios for the High Plains Aquifer, (2) to translate the temperature and precipitation changes to agro-ecosystem indicator changes for Kansas using scenario funnels, and (3) to make recommendations for water resource and ecosystem managers to enable effective planning for the future availability of ecosystem services. The temperature change ranged from −4 °C to 8 °C, while the precipitation changes were between −50% to +50% over the region. This study improves the understanding of climate change on water resources and agro-ecosystems. This knowledge can be used to evaluate similar resources where the replenishment rate is slow.

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.