Some local phenomena, such as significant fluctuations in wind speed and direction, are difficult to predict using global meteorological models. These challenges often arise because small obstacles, which can significantly influence local conditions.
By integrating historical data from local stations and measurements with outputs from numerical forecasts, data mining techniques can develop accurate local weather models for short-term prediction.
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Explore our interactive online model for local wind forecasts... |
The modeling of air pollution is a crucial subject today, as the concentration of hazardous particles in the air is strongly influenced by weather conditions.
With the right machine learning algorithms, pollution measurements are combined with weather forecasts and data from road traffic or industrial activities to improve short-term air quality predictions. Additionally, this modeling helps identify influential factors, enabling the proactive formulation of recommendations to reduce pollution, such as scheduling changes in human and industrial activities.
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Climate change is now more than ever a major concern. Data science can be applied across various timescales, for example:
In the short term (a few days), to better predict unusual and extreme weather events,
ver the medium term (several months), to anticipate seasonal anomalies,
ver the medium term (several months), to anticipate seasonal anomalies,
Historical weather reports spanning several decades, and in some cases, centuries, are available for certain stations.
Machine learning techniques can be utilized to correlate this data with human activities - such as industry,
transportation, and deforestation - to enhance our understanding of climate change mechanisms and improve warning systems.
Do you need meteorological information to enhance your application or to conduct a big data analysis? Leverage our advanced technology to automatically access any data stream.
Based on your specific requirements, we can provide customized datasets of various variables, across multiple locations, and at any desired frequency.