Context
The recent calamities that happen due to unfavourable climates have raised the importance of weather prediction. Best fixed models of forecasting show their weakness when it comes to predicting complicated weather occurrences. It could be argued that the practical ways of weather forecast improvement hinge upon the modernization of related technologies and methods.
About
- Bringing out the next generation of weather forecasting requires introducing new data, better and more efficient algorithms and finally powerful computing equipment.
- The planned approach is to enhance the precision and lead time of positive outlooks and conflicts in forecasts.
- Advanced models are fittest for intricate weather operations and fine-tuned for exact locations.
Description
- High-Resolution Models: Enhance the topography of land use, and soil moisture information to increase the forecast accuracy results.
- Ensemble Forecasting: Join several models to produce another model that will give a more accurate prediction.
- Data Assimilation: Data input of real-time from such assets as radar, satellites, and weather stations.
- Artificial Intelligence (AI) and Machine Learning (ML): Explain how Forecast accuracy & pattern recognition can be enhanced by the use of advanced AI and ML.
Significance
- Enhancing the accuracy of these forecasts therefore means saving lives and reducing both economic losses, as well as also improving on decision making.
- Alternatively, modernised models can offer valuable inputs concerning climatic conditions as well as for research and policy purposes.
- The improvement in forecasting capabilities, therefore, is a key to the UN sustainable development and environmental management.
Pros
- These features can be summarised as enhanced precision and better ability to supply data relevant to a given territory.
- Improved lead time as well as resolution
- Having detailed to represent the multi-faceted weather occurrence
- Climate modelling and research assists friendly political institution
- Tools that will enable Hospitals to make better economic decisions due to the reduced losses.
Cons
- Expensive computational costs and necessarily substantial physical infrastructure
- Difficulties in data heterogeneity and complexity of the applied models
- Lack of required independent skilled resources in AI, ML, and data analytics
- Possibility of technology dependency
Challenges
- Data Quality and Availability:Some of the challenges are; Providing the end user with high quality real-time data.
- Model Complexity: Balancing the overall model complexity to integrate multiple models and the proposed data sources.
- Interpretation and Communication: Implementation of successful communication approaches regarding the degree of uncertainty and the level of complexity of the forecasts.
- Infrastructure and Resources: About the basic concern of providing sufficient computing power and facilities.
Conclusion/Way Forward
- The method of weather forecasting needs to be improved in order to step up its efficiency and contribute to the goals of sustainable development.
- It is possible to achieve effective modernization of clinical research through addressing the problems and furthering technology and data analysis.
- The policy makers, the researchers, and other stakeholders should come together in order to fashion new and well adapted bleeding edge weather forecasting models.
Source: The Hindu
UPSC Mains Practice Question
Q. The accuracy of weather forecasting is crucial for mitigating the impacts of extreme weather events. Discuss the need for modernising weather forecasting models, highlighting the significance, pros, and cons of incorporating advanced technologies like Artificial Intelligence (AI) and Machine Learning (ML)