Machine Learning Weather Model

By combining computational fluid dynamics with machine learning and meteorological data-sources, this system steadily improves the ability and accuracy to forecast both weather and natural incidents with far-reaching results for individuals and businesses.
Technology Life Cycle

Technology Life Cycle


Marked by a rapid increase in technology adoption and market expansion. Innovations are refined, production costs decrease, and the technology gains widespread acceptance and use.

Technology Readiness Level (TRL)

Technology Readiness Level (TRL)

Prototype Demonstration

Prototype is fully demonstrated in operational environment.

Technology Diffusion

Technology Diffusion

Early Majority

Adopts technologies once they are proven by Early Adopters. They prefer technologies that are well established and reliable.

Machine Learning Weather Model

By combining computational fluid dynamics with machine learning and meteorological data-sources, a predictive weather system provides a more accurate alternative to current weather prediction models. This dynamic and fluid forecast software aggregates a multitude of climate and weather-related data. This data is later rated, ranked, and weighted by machine learning to process immediate comparisons between historical data and weather forecasts. Used in this way, weather models could more efficiently take into account inaccuracies, such as overestimated rain predictions or even to perform more accurate predictions.

Apart from developing rankings, machine-learning models that use deep learning show promising results in predicting cyclones, atmospheric rivers, weather fronts, and forest fires, and can even assess deforestation. With better predictions of climate, it may be possible to modify decisions to decrease undesirable impacts and take better advantage of expected favorable conditions.

Farmers, for instance, could be better prepared to make decisions regarding crop management with the inclusion of the ideal window for sowing, fertilizing, weeding, and harvesting. In the case of urban planners, simulation models can be useful to adapt both existing projects and upcoming developments in order to make them more responsive to the consequences of climate change (for instance, reducing heat islands and adaptation of areas prone to flood), as well as more accountable to the measurements and policies that regulate emissions.

Future Perspectives

Gathering and processing high-quality data of potential environmental risks will continuously be a need for both future and present-day society. Improving accuracy in weather prediction can have far-reaching implications, not just for disaster mitigation, but also for businesses that rely on commodity futures trading or other weather-based factors. Due to the amount of data that needs to be analyzed, advancements in the quantum computing area should directly impact the accuracy of these forecasting systems.

Also, by combining this software with new infrared remote atmospheric sounding systems with cloud-penetrating microwave radiometers, space-borne radar, light detection and ranging (LiDAR), as well as remote sensing observations from the ground, it could create a sensor web around the globe for future views of the Earth. These new and improved weather and climate products are also leading to new industries, like “weather derivatives.” Weather derivatives are financial solutions that help weather-sensitive companies spread out and insure themselves against their inherent risks.

Image generated by Envisioning using Midjourney

As the planet continues to warm, climate change impacts are worsening. In 2016, there were 772 weather and disaster events, triple the number that occurred in 1980. Twenty percent of species currently face extinction, and that number could rise to 50 percent by 2100. And even if all countries keep their Paris climate pledges, by 2100, it’s likely that average global temperatures will be 3˚C higher than in pre-industrial times.
Improving public understanding of the links between human activity, climate change and extreme weather events is vital.
Climate change is undoubtedly one of the biggest problems in the 21st century. Currently, however, most research efforts on climate forecasting are based on mechanistic, bottom-up approaches such as physics-based general circulation models and earth system models. In this study, we explore the performance of a phenomenological, top-down model constructed using a neural network and big data of global mean monthly temperature. By generating graphical images using the monthly temperature data of 30 years, the neural network system successfully predicts the rise and fall of temperatures for the next 10 years. Using LeNet for the convolutional neural network, the accuracy of the best global model is found to be 97.0%; we found that if more training images are used, a higher accuracy can be attained. We also found that the color scheme of the graphical images affects the performance of the model. Moreover, the prediction accuracy differs among climatic zones and temporal ranges. This study illustrated that the performance of the top-down approach is notably high in comparison to the conventional bottom-up approach for decadal-scale forecasting. We suggest using artificial intelligence-based forecasting methods along with conventional physics-based models because these two approaches can work together in a complementary manner.
Scientific American is the essential guide to the most awe-inspiring advances in science and technology, explaining how they change our understanding of the world and shape our lives.
Predicting the weather is notoriously difficult, but many experts hope new machine learning techniques could help better sort the sunshine from the sleet. In a new research paper, Google describes how it uses AI to create speedy rainfall forecasts in the US. The work has not yet been integrated into any commercial systems, but looks promising, especially in a world increasingly effected by climate change and unpredictable weather patterns.
By actually using less data than existing forecasting techniques, Google believes it can give us accurate, timely weather predictions.
As companies like IBM and Monsanto begin to employ AI in weather forecasting, could this be a game changer for meteorologists?
Read chapter 3. The Impact of Weather and Climate on Society and a Vision for the Future: This report addresses the transition of research satellites, ins...
The "Information Portal Climate Adaptation in Cities" (INKAS) is a web-based advisory tool for those engaged in urban development, stakeholders and other interested persons.
A not-for-profit organization, IEEE is the world's largest technical professional organization dedicated to advancing technology for the benefit of humanity.© Copyright 2018 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
Weather forecasting and prediction are tricky for meteorologists. But AI can help conquer such issues using machine learning and neural networks. Google, IBM are already leading this circuit.
DWD scientists have developed a series of simulation models, which can be used to simulate the impact of planned changes on the local or regional climate.
ARMONK, NY - The Weather Company, an IBM (NYSE: IBM) Business, today announced plans to advance the precision and accuracy of weather forecasting by combining hyper-local, short-term custom forecasts developed by IBM Research with The Weather Company's global forecast model. The powerful combination of the two models will be called Deep Thunder, and will also use historical weather data to train machine learning models that will help businesses predict the actual impact of weather.
Weather and water extreme events—including droughts, hurricanes, tornadoes, flooding and wild fires—cause US$11 billion in damages each year in the United States.

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