Context: At the recent COP28 of UNFCCC, NASA and IBM announced that an Artificial Intelligence (AI) tool called watsonx.ai would be available on the open-source AI platform Hugging Space. 

Watsonx.AI: An AI tool for Weather forecasting

• Watsonx.ai is a collaborative AI tool developed by IBM and NASA has the potential to enhance the ability to predict hurricanes, droughts, and other severe weather events with increased precision.
• Utilising NASA’s store of valuable of data and IBM’s AI technology, the model can help scientists estimate the past and future extent of wildfires, floods, and urban heat maps.
• Watsonx.ai will help users monitor the Earth from space, measuring environmental changes that have already happened while also making predictions.
• Effort aims to widen access to NASA earth science data for geospatial intelligence and accelerate climate-related discoveries.
• The Indian government is testing AI to build climate models to improve weather forecasting as torrential rains, floods, and droughts proliferate across the country. 

Must Read: Global Partnership On Artificial Intelligence – GPAI

About Weather Forecasting

• Definition of Weather: It simply refers to the state of the atmosphere at a particular place and time as regards heat, cloudiness, dryness, sunshine, wind, rain, etc. 
• Definition of Weather Forecasting: The prediction of the weather through application of the principles of physics, supplemented by a variety of statistical and empirical techniques. 
  • It is a continuous, data-intensive, multidimensional, dynamic and chaotic process. 
• Meteorology: Meteorology is the science of weather. Knowledge of meteorology forms the basis of scientific weather forecasting, often made by collecting quantitative data about the current state of the atmosphere and using scientific understanding of atmospheric processes to project how the atmosphere will evolve in future. 
• Types of Weather Predictions: There are two types of weather predictions, viz., short-range and long-range forecasts. 
  • Short-range forecasts range from 1 to 14 days. 
  • Long-range forecasts are predictions over 14 days. 
  • As predictive models attempt to forecast farther into the future, the predictions become less accurate.
• Forecasts in India: The India Meteorological Department (IMD) provides forecasts based on mathematical models using supercomputers. Using AI with an expanded observation network could help generate higher-quality forecast data at lower cost.

Significance of Using AI for Weather Forecasts

• Improve Accuracy: AI software can make the weather prediction process more effective as one of the key strengths of AI is its ability to work with large sets of data. 
  • This can be attributed to AI machine learning capabilities. AI can be programmed to analyze multiple data sets of weather information to provide more accurate forecast maps.
• Improved Efficiency: Recently, Nvidia, Google DeepMind, and Huawei have introduced machine-learning methods that can predict the weather at least as accurately as conventional methods and much more quickly. 
  • Researchers at DeepMind have unveiled a cutting-edge weather prediction program named “GraphCast,” leveraging machine learning to forecast weather variables up to 10 days in advance, all within a one-minute timeframe.
  • Predictions for extreme weather events such as thunderstorms can be more complicated to predict compared to cyclones since these events develop over a very short period of time and also dissipate equally fast. AI and machine learning have the potential to speed up these predictions. 
• Combating Climate Change: Changing climate conditions have increased the frequency of severe storms, floods, heat waves, and more enormous wildfires. As a result, scientists are using AI techniques for more accurate forecasts that help to minimize damage and save lives.
  • For example, ClimateAi use deep learning, a form of inductive reasoning, for seasonal forecasting.

• Continuous Adaptation Process: In the case of weather prediction, AI programs don’t need additional input from human operators to make predictions, they can continuously observe data without rest. This infinite data analysis allows the models to learn more about patterns and how to predict them more precisely than a human could.
  • In addition, if radar or satellite observes sudden changes in weather patterns, AI programs can adapt more quickly to the new data than traditional computer systems.
• Preparedness: With increased accuracy and efficiency of predictions, AI tool can predict when and where a natural disaster might occur. Such predictions would give populated areas in the path of a disaster enough time to take precautions, saving countless lives.
  • For example, Huawei’s Pangu-Weather Model, is said to be capable of predicting global weather a week in advance which would allow meteorologists to better study weather patterns in real-time.

What are the challenges associated with weather forecasts?

• Lack of Quality Data: Access to that data is an issue as climate data sets are massive and take significant time to collect. 
  • Further, AI models require large amounts of high-quality historical data for training which may be scarce or incomplete for certain regions, hindering the effectiveness of AI models.
• Complex Atmospheric Processes: Weather systems are highly complex and small changes in initial conditions can lead to significant variations in outcomes, making it challenging to develop accurate predictive models. While current weather forecasting systems are quite advanced, they are not good at adapting to sudden changes.
• Local Variations: AI models need to account for microclimates and local geographical features that can significantly influence weather conditions. Accurate predictions at a local level may be challenging due to the diversity of terrain and local atmospheric conditions. 
  • Without having high-resolution data in space and time, no AI model for location-specific magnification of existing model forecasts is feasible.
  • For example, in a tropical environment like the Florida Keys, the weather doesn’t change much from day to day, so researchers had to manually look at variations in the atmosphere that the algorithms don’t always take into account.
• Lack of Interpretability: Some advanced AI models, particularly deep learning models, are often considered black boxes, meaning it can be challenging to understand how they arrive at specific predictions. 
  • This can be a barrier to gaining trust in the predictions and may limit the model’s usefulness in critical applications.
• Accessibility: Training and running sophisticated AI models for weather forecasting require access to high-performance computing infrastructure which may be limited, particularly in developed regions.

Way Forward

• Improving Data Quality and Quantity: There is a need to explore alternative data sources, such as satellite imagery, remote sensing, and crowd-sourced data, to supplement traditional weather station data.
• Enhanced Computational Resources: Investing in high-performance computing infrastructure to support the training and deployment of sophisticated AI models. 
  • Further exploring cloud computing solutions can provide scalable and flexible computational resources.
• Real-time Adaptability: Design AI models that can adapt quickly to changing conditions by incorporating real-time data assimilation techniques while continuously updating data on new observations can improve their performance over time.
• Integration with Traditional Methods: Foster collaboration between meteorologists and data scientists to develop hybrid forecasting approaches that leverage the strengths of both AI and traditional methods which can improve their accuracy.

Conclusion:

Watsonx.ai, a joint effort by IBM and NASA, uses AI to improve weather forecasting, offering better accuracy and preparedness. 

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