Ensemble of Data-Driven Weather Prediction Models for Sub-Seasonal Forecasting

Core Concepts

Data-driven ensemble models improve sub-seasonal forecasting accuracy.

Abstract

Abstract: Presents an operations-ready multi-model ensemble weather forecasting system. Outperforms ECMWF extended-range ensemble for 2-meter temperature predictions. Demonstrates near-state-of-the-art subseasonal-to-seasonal forecasts using data-driven models. Introduction: Early toy models evolved into accurate data-driven weather prediction systems. Limitations include lack of testing on extended-range forecasting and focus on deterministic forecasts. Methodology: Trained on ECMWF ERA5 reanalysis dataset with various weather parameters. Utilizes five trained models based on different architectures for predictions. Results: Compares performance with ECMWF ensemble, showing competitive results. Bias correction improves ECMWF results more than the data-driven ensemble. Combination of data-driven and NWP forecasts leads to better probabilistic forecasts. Discussion: Data-driven models show promise but face challenges in extreme event forecasting. Multi-model approach leverages different model strengths for improved forecasts.

Stats

We present an operations-ready multi-model ensemble weather forecasting system which uses hybrid data-driven weather prediction models coupled with the European Centre for Medium-range Weather Forecasts (ECMWF) ocean model to predict global weather at 1-degree resolution for 4 weeks of lead time. For predictions of 2-meter temperature, our ensemble on average outperforms the raw ECMWF extended-range ensemble by 4-17%, depending on the lead time. However, after applying statistical bias corrections, the ECMWF ensemble is about 3% better at 4 weeks. For other surface parameters, our ensemble is also within a few percentage points of ECMWF’s ensemble. We demonstrate that it is possible to achieve near-state-of-the-art subseasonal-to-seasonal forecasts using a multi-model ensembling approach with data-driven weather prediction models. Many of the models have not been tested on extended-range forecasting in the two- to six-week subseasonal-to-seasonal (S2S) time scales. Despite the importance of these time scales for a variety of applications such as agriculture and risk management, the models cannot be expected to generalize. While some data-driven models, including the DLWP ensemble of [Weyn et al., 2021], FuXi-S2S [Chen et al., 2023c], the SFNO [Bonev et al., 2023], and NeuralGCM [Kochkov et al., 2024], have been run at S2S or longer time scales, some key considerations, including the need for post-processing, that have not been fully addressed. The use of regression losses in training data-driven weather models has resulted in deterministic forecasts which overly smooth fine-scale features, limiting the models’ ability to capture the full range of weather phenomena. It also “games” the target metrics such as root-mean-squared-error (RMSE), which are optimized by ensemble-mean-like forecasts, making it difficult to benchmark the actual skill of different models (some models perform particularly well on RMSE at longer lead times because of this smoothing). Our goal here is to broadly demonstrate the performance of our ensemble of data-driven weather prediction models. To compare with the latest 100-member ECMWF IFS extended-range ensemble, we limit our test set to forecasts issued on Mondays and Thursdays at 00 UTC time starting on July 3, 2023 until Dec 31, 2023. All verification data are from... Using a multi-model ensembling approach, we have shown that near-state-of-the-art sub-seasonal-to-seasonal forecasts are possible using data-driven weather forecasting models. Our ensemble is competitive with... Our results show promise for operationalizing ensembles of data-driven weather forecasting models. However,...

Quotes

"We present an operations-ready multi-model ensemble weather forecasting system." "Our goal here is to broadly demonstrate the performance..." "Using a multi-model ensembling approach..."

Key Insights Distilled From

An ensemble of data-driven weather prediction models for operational sub-seasonal forecasting

by Jonathan A. ... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15598.pdf

Deeper Inquiries

How can these findings impact industries reliant on accurate sub-seasonal forecasting?

The findings presented in the research paper on ensemble data-driven weather prediction models for sub-seasonal forecasting have significant implications for industries that heavily rely on accurate weather forecasts. Industries such as agriculture, energy, transportation, and disaster management require precise sub-seasonal forecasts to make informed decisions and mitigate risks effectively. Agriculture: Farmers can benefit from more accurate predictions of temperature, precipitation, and other weather parameters over longer lead times. This information helps optimize planting schedules, irrigation plans, and pest control strategies. Energy: Energy companies can better anticipate demand fluctuations based on expected weather conditions. For example, knowing about upcoming heatwaves or cold spells enables them to adjust energy production accordingly. Transportation: Airlines and shipping companies rely on weather forecasts to plan routes efficiently and avoid disruptions due to adverse weather conditions like storms or heavy snowfall. Disaster Management: Emergency response agencies can prepare for natural disasters such as hurricanes or floods well in advance with improved sub-seasonal forecasts. Evacuation plans and resource allocation can be optimized based on this information. By leveraging more accurate sub-seasonal forecasts generated by advanced ensemble modeling techniques, these industries can enhance operational efficiency, reduce costs associated with unexpected events caused by inclement weather conditions, improve safety measures for employees and customers alike.

What are potential drawbacks or limitations when relying solely on data-driven forecast modeling?

While data-driven forecast modeling offers numerous advantages in terms of accuracy and efficiency compared to traditional numerical weather prediction models (NWP), there are several drawbacks and limitations that need consideration: Overfitting: Data-driven models may overfit the training data if not properly regularized or validated against diverse datasets. This could result in poor generalization performance when applied to unseen data. Limited Interpretability: Deep learning models used in data-driven forecasting often lack interpretability compared to traditional physical-based NWP models where meteorological principles guide predictions. Data Quality Issues: The accuracy of data-driven models heavily relies on the quality of input datasets; any biases or errors present in the training dataset could propagate through the model's predictions. Incorporating Physical Constraints: Data-driven models may struggle with incorporating physical constraints like conservation laws governing atmospheric processes which are inherently embedded within traditional NWP methods. Computational Resources: Training complex deep learning architectures requires substantial computational resources which might be a limitation for some organizations with constrained computing capabilities.

How might advancements in probabilistic forecast methods influence decision-making processes beyond meteorology?

Advancements in probabilistic forecast methods have far-reaching implications beyond meteorology across various domains: 1-Risk Management: In finance & insurance sectors: Probabilistic forecasts provide insights into uncertain events allowing businesses to manage risk exposure effectively. 2-Supply Chain Optimization: Retailers & manufacturers use probabilistic forecasting to optimize inventory levels based on anticipated demand variability. 3-Healthcare Planning: Hospitals utilize probabilistic disease outbreak predictions for resource allocation during epidemics ensuring timely responses. 4-Environmental Impact Assessment: Urban planners leverage probabilistic climate change projections while designing infrastructure resilient against extreme climatic events. 5-Marketing Strategies: Businesses tailor marketing campaigns using probabilistic consumer behavior predictions enhancing targeting precision. Probabilistic forecast advancements empower decision-makers across diverse sectors enabling proactive planning under uncertainty fostering resilience amid dynamic environmental & market conditions