A little over two years ago, I dove into the world of machine learning in weather forecasting.

I was convinced that machine learning could revolutionize this field.

Forecasting the weather saves lives, saves money and keeps people happy, but there are SO many challenges!

Imagine this: weather forecasts that could accurately predict the path of a hurricane, provide precise details on the formation of thunderstorms, and anticipate localized events like flash floods.

These were the ambitious goals we set out to improve with machine learning.

Turns out ML in weather forecasting is hard

But as we embarked on this journey, the hurdles grew taller, and the challenges more daunting.

What I discovered was that applying machine learning to weather forecasting is akin to navigating a labyrinth of complexity.

The traditional weather models we relied on for decades are grounded in physics, a solid foundation that was then enhanced by capturing the intricacies of atmospheric phenomena.

Machine learning, with its promise of pattern recognition and data-driven insights, seemed like the perfect antidote.

However, the transition wasn't without its complications.

Despite these challenges, the potential benefits were too good to ignore.

Addressing some challenges in data-driven weather forecasting

A purely data-driven weather forecasting system could provide forecasts with unprecedented accuracy, speed, and flexibility, offering critical information for disaster preparedness, agriculture, and a wide range of industries dependent on weather insights.

But we have to address some issues along the way:

Data Quality and Quantity

We rely on datasets like the ERA5 dataset prepared at the ECMWF, which offers consistent historical weather data over the last 80 years globally.

The quality and quantity of data are foundational pillars in the realm of data-driven weather forecasting.

Without robust datasets that encompass extensive historical records, it becomes challenging to impossible to train machine learning models effectively.

For machine learning, the ERA5 dataset, with its rich history of weather data, provides a comprehensive foundation for modelling long-term weather patterns and variations. Additionally, the dataset's global coverage is invaluable in capturing weather phenomena on a planetary scale, allowing us to build models that can offer accurate predictions for diverse regions and climates.

Ensuring data quality and quantity is paramount, as it underpins the accuracy and reliability of forecasts, especially in the face of an ever-changing climate landscape.

Complex Atmospheric Processes

We have to build sophisticated models that can capture multi-scale processes accurately, ensuring accurate predictions for even the most intricate weather events.

Among these complex atmospheric processes, phenomena occurring across multiple scales present particularly daunting challenges. From the grand movements of global weather systems to the specific details of local weather events, the atmosphere operates on interconnected scales.

Data-driven weather forecasting, with its capacity for pattern recognition and adaptability, offers a promising avenue for modeling these complex atmospheric processes in ways that may previously have been unattainable.

Through the integration of machine learning and advanced analytics, we can add another tool to our toolbelt and delve deeper into the intricate web of atmospheric processes, enabling more precise and comprehensive weather predictions, from regional thunderstorms to global climate trends.

Limited Spatial and Temporal Resolution

When I was in Singapore, we talked about how most global weather models can't resolve their entire country, due to the sheer lack of spatial resolution.

We're generating high-resolution training data that captures the fine-grained details necessary for localized event forecasting.

However, one significant challenge in data-driven weather forecasting lies in the limited spatial and temporal resolution. Weather events often unfold with dynamics at various scales, from small-scale localized phenomena like thunderstorms to large-scale systems like atmospheric cells.

In traditional weather forecasting, this is also known as sub-grid processes.

By addressing limited spatial and temporal resolution, data-driven approaches aim to bridge this gap and deliver forecasts that can account for the nuanced and rapidly changing conditions that occur over different geographic areas and time frames.

This improved resolution is especially vital for predicting localized events, or even model Singapore as more than a single cell in a model, as it enables forecasts that are more tailored to specific regions and their unique weather challenges.

Enormous Computational Requirements

Making datasets and computational resources accessible through cloud infrastructure, along with sharing model weights, enables researchers to harness the power of advanced forecasting methods.

(Check out “pip install AI-models”, for example!)

The computational demands of training data-driven weather forecasting are enormous.

Weather systems are inherently complex, spanning vast spatial and temporal scales, necessitating extensive computational resources to process and model the vast amount of data involved.

High-resolution simulations and machine learning models require substantial computational power for training and at least some for running forecasts.

Oftentimes, cloud infrastructure has become indispensable in meeting these computational requirements, as it provides the scalability needed to handle large datasets and run massive model training efficiently.

Moreover, the practice of sharing model weights and resources among researchers fosters collaboration and collective progress, ensuring that the field continually benefits from the latest advancements in computational capabilities.

Rapid Model Degradation

Training on "rollouts" that evaluate multiple model steps helps mitigate accuracy degradation and stabilise over time.

Rapid model degradation is a phenomenon that can plague the effectiveness of machine learning models in weather forecasting.

Over time, these models can experience a loss of accuracy and performance, particularly when facing unpredictable weather patterns or shifts in climate. This degradation can be especially problematic for systems designed to provide continuous, real-time forecasts.

Training on rollouts represents a proactive solution to this issue, ensuring that models are stable in their predictive power and adapt to the evolving dynamics of the Earth's atmosphere over multiple timesteps.

Incorporating Non-Meteorological Data

Collaborating with local communities and considering factors beyond meteorology aids in building more holistic and accurate data-driven weather models.

Incorporating non-meteorological data is an essential aspect of modern data-driven weather forecasting.

While meteorological data forms the foundation of our understanding, integrating information from various sources, such as social and environmental data, enriches our models and leads to more comprehensive predictions.

For instance, local communities can provide valuable insights into regional weather patterns and unique factors that impact their area.

Factors like urbanization, land use, and even human behavior can influence local microclimates. By accounting for these non-meteorological elements, we create a more nuanced and precise picture of the weather, improving forecasts that cater to specific regions and their distinct needs.

This collaborative approach aligns with the inclusive values of weather forecasting, ensuring that the benefits of accurate predictions are accessible to all.

Communication of Uncertainty

Employing ensemble forecasts and effective communication strategies helps convey the inherent uncertainty in weather predictions.

The ability to communicate uncertainty is a cornerstone of responsible weather forecasting.

While we aim to provide accurate and timely predictions, the complexity of the atmosphere and the inherent limitations of any forecasting model introduce an element of unpredictability.

Ensemble forecasts, which involve running multiple variations of a forecast, provide a valuable range of possible outcomes.

Effective communication strategies ensure that the public, emergency services, and various industries receive not just the forecast itself but also an understanding of the confidence level associated with it. This transparency is essential for making informed decisions, especially when it comes to potentially life-altering weather events, and is a key factor in building trust within the community.

Climate Change

Continuous model retraining, monitoring, and exploring models that capture underlying physical connections can enhance resilience to changing climate-based degradation of machine learning models.

In the context of climate change, this resilience is of paramount importance.

The Earth's climate is undergoing rapid and unpredictable transformations, resulting in shifting weather patterns, altered climatic phenomena, and an increased frequency of extreme events.

Machine learning models employed in weather forecasting must adapt to these evolving conditions to maintain their predictive accuracy and effectiveness.

As global temperatures rise and climate systems become more complex, the ability to retrain models with up-to-date data and integrate new insights into their algorithms becomes a critical factor in ensuring that weather forecasts remain reliable and valuable in the face of a changing world.


In conclusion, the journey to applying machine learning to weather forecasting is riddled with challenges, but the promise of more accurate and timely forecasts keeps us moving forward.

By addressing these obstacles and embracing innovative solutions, we can make strides toward a reliable data-driven weather prediction to benefit communities and industries worldwide.

This journey is a testament to the resilience and ingenuity of scientists and researchers in the field.

As we navigate the complex web of atmospheric data and computational intricacies, we remain driven by the profound impact that enhanced weather forecasting can have on our lives.

The potential for saving lives, preserving resources, and bolstering countless industries is a powerful motivator. While the path ahead may still be challenging, the journey is a testament to human perseverance and the endless quest for knowledge, ultimately leading us to a future where weather predictions are not just forecasts but beacons of certainty in an ever-changing world.

The ECMWF has just announced the public alpha of the AIFS data-driven weather forecasting system. What an exciting time!