Reliable estimation, forecasting and analysis of rainfall and related hydro-meteorological variables are essential for effective water resources and weather risk management. Data are increasingly available from a variety of sources and with varying quality –from traditional to novel ground and remote sensors, and even citizen-generated data-. Together with advances in statistical methods and computing technologies, this offers a wealth of opportunities for improving the monitoring, analysis and management of water systems. However, it also enhances the problem of how to handle and make best use of these datasets to drive effective decision making. Rain++ offers off-the-shelf tools and customised solutions to address these challenges, ultimately improving livelihoods and helping build a more sustainable world.
Core techniques: Based upon cutting-edge stochastic methods, geo-statistics and hydro-meteorological research, and making use of advanced computing technologies, Rain++ offers four core techniques for handling and enhancing rainfall estimates and forecasts:
Our geostatistical methods integrate the advantages of data from multiple sensors.
Our stochastic methods generate rainfall estimates at the required scales.
Our tools, based on computer vision and machine learning, provide reliable forecasts.
Our stochastic tools, combined with the latest climate projections, shed light upon future rainfall.
These techniques can be deployed as stand-alone or web-based services both for offline as well as real-time applications. Alternatively, they can be customised and embedded within the client’s operational system to meet operational requirements.
Bespoke solutions: At Rain++ we are happy to help you tackle other hydrometeorological challenges requiring specialised statistical, hydro-meteorological and computational skills. We have experience in projects ranging from weather insurance product design to operational system implementation. Check out our projects for more information.
Rain++ consists of experts in engineering, mathematics and computing from academia and beyond, coordinated by our core team:
Hydrometeorologist and Hydraulic Modeller
Hydrologist and Hydraulic Network Modeller