How an AI tool can make weather forecasts more accurate and help tackle climate change

Subject : Science and Tech

Section: Awareness in IT

Watsonx.ai:

It is an Artificial Intelligence (AI) tool that will help users monitor the Earth from space, measuring environmental changes that have already happened while also making predictions about the future.
Launched by NASA and IBM at the COP28 summit and available on the open-source AI platform Hugging Space.
It can help scientists estimate the past and future extent of wildfires, floods, and urban heat maps.

How it works?

It is similar to Bing and ChatGPT, it operates on a foundation model but uniquely incorporates NASA’s satellite image datasets instead of text.
IBM developed the model to interpret these visual datasets.
Training Process: To teach the model to understand evolving visual sequences, scientists tasked it with filling in gaps in images, prompting it to reconstruct the missing parts. As it pieced together more images, the model improved in understanding their connections. It underwent adjustments for tasks like photo segmentation and categorization.
It boasts user-friendly simplicity. Users can select a location and date, prompting the model to highlight changes in floodwater levels, reforestation initiatives, and other pertinent factors.

What will be its impact?

IBM’s approach could significantly reduce data cleaning needs and accelerate geographical analysis by three to four times, streamlining processes.
The insights derived from this technology can aid in mitigating flooding effects, infrastructure development, disaster response improvement, and environmental protection.
Future integration of generative AI in weather forecasting may lead to more accurate predictions of hurricanes, droughts, and other catastrophic weather events. It could shed light on how environmental changes, like polar ice melting, impact daily life.
Businesses, disaster response teams, and logistics companies could benefit. This AI could help prepare for housing-related fires, optimize supply chain logistics, and understand broader weather patterns.

How AI has helped in weather forecasting?

Accessing and analyzing the vast and growing climate data sets pose significant hurdles. By 2024, an estimated 250,000 terabytes of climate data will be available, necessitating time-consuming analysis.
The use of AI, particularly deep-learning models like AI emulators,aids in interpreting these complex data sets.
European Center for Medium-Range Weather Forecasting has begun employing AI emulators to generate forecasts based on historical weather patterns. These models can infer weather patterns from data, enabling quicker desktop-based forecasts, and bypassing the time-consuming processes of traditional systems.
AI integration streamlines data interpretation, potentially reducing forecasting time from hours to minutes, offering a promising approach to handle the increasing volume of climate data for informed decision-making in weather forecasting.

AI and Climate Change:

AI-powered vehicles have the potential to minimize energy consumption by identifying optimal routes, thus reducing environmental impact.
AI aids farmers by optimizing crop irrigation, cutting water wastage by 40%, and enhancing harvest productivity, as demonstrated by a 30% increase in yields among AI-equipped peanut farmers in India.
AI’s potential extends to assessing, reducing, and mitigating emissions at both macro and micro levels, potentially cutting an organization’s carbon footprint by 5-10%.