Tags: Agriculture, IoT, Computational Modeling

Localized Weather Modeling

Weather prediction is limited by the “resolution” of the computational weather models (the granularity of the grid of points in the model).

IoT researchers have suggested a hybrid forecasting model where local weather data is collected on a finer grid and then a local forecasting model is integrated with a larger, coarser-grain forecasting model, with the result being a new ability to accurately interpret the large scale forecast into a higher resolution local forecast. For example, additional weather instrumentation could be installed in the wine country in Northern California and then the integrated local model could be used to forecast the weather in the individual micro-climates of various vineyards.

NTT network researchers have worked with JAMSTEC (the Japan Agency for Marine-Earth Science and Technology) to integrate IoT collected, additional, fine-grain data with JAMSTEC’s multi-scale weather model.  Edge computers aggregate the IoT data and provides a local forecast based on the larger forecast.

Industry: Agriculture

Business Need: Refine existing weather models to produce detailed forecasts for local micro-climates.

Key Enabling Technologies: IoT, Computational Modeling

Industry Perspective

Modern IoT and cloud computing make most of this solution readily available and a pragmatic way to improve important weather forecasting use cases.

Edge Need: The mobile edge offers the possibility of edge services for the local calculation without the burden of procuring and operating a server which could be quite challenging for rural agricultural applications.

Ease of Incorporation: A weather solution is most likely if an IoT-based agricultural instrumentation system is already in place (e.g., for irrigation and fertilization optimization).

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