Efficient Spatial Simulation of Extreme High-Resolution Radar Precipitation Data Using Integrated Nested Laplace Approximations (INLA)

แนวคิดหลัก

A computationally efficient framework for modelling and simulating extreme spatial precipitation using high-resolution radar data, capturing both marginal and tail dependence properties.

บทคัดย่อ

The authors develop a methodology for modelling and simulating high-dimensional spatial precipitation extremes, focusing on both their marginal distributions and tail dependence structures. The spatial distribution of precipitation occurrences is modelled with four competing models, while the spatial distribution of nonzero extreme precipitation intensities are modelled with a latent Gaussian version of the spatial conditional extremes model. Nonzero precipitation marginal distributions are modelled using latent Gaussian models with gamma and generalised Pareto likelihoods. Fast inference is achieved using integrated nested Laplace approximations (INLA).
The key highlights and insights are:

The authors propose novel empirical diagnostics and parametric models for choosing components of the spatial conditional extremes model, allowing for better data utilisation through a lower extremal threshold.

A new method is proposed for modelling precipitation zeros within the spatial conditional extremes framework, by separately modelling precipitation occurrences and intensities. Four competing models are used to describe the spatial distribution of conditional precipitation occurrences.

The marginal distributions of nonzero precipitation are modelled by merging two latent Gaussian models, with a gamma likelihood and a generalised Pareto likelihood, respectively.

The spatial conditional extremes model is implemented as a latent Gaussian model, enabling fast high-dimensional inference using INLA.

The framework is applied to simulate spatial precipitation extremes using a data set of high-resolution hourly precipitation data from a weather radar in Norway. Inference on this high-dimensional data set is achieved within hours, and the simulations capture the main trends of the observed precipitation well.

สถิติ

The 95%-quantile of hourly precipitation intensity varies between 24-36 mm/h over the summer weeks from 2010-2022.
The median of precipitation threshold exceedances varies between 0.0-2.5 mm/h over the summer weeks from 2010-2022.

คำพูด

"Aiming to deliver improved precipitation simulations for hydrological impact assessment studies, we develop a methodology for modelling and simulating high-dimensional spatial precipitation extremes, focusing on both their marginal distributions and tail dependence structures."
"Tail dependence is crucial for assessing the consequences of extreme precipitation events, yet most stochastic weather generators do not attempt to capture this property."

ข้อมูลเชิงลึกที่สำคัญจาก

Fast spatial simulation of extreme high-resolution radar precipitation data using INLA

by Sili... ที่ arxiv.org 10-01-2024

https://arxiv.org/pdf/2307.11390.pdf

สอบถามเพิ่มเติม

How could the proposed framework be extended to model and simulate spatio-temporal precipitation extremes?

The proposed framework for modeling and simulating spatial precipitation extremes can be extended to a spatio-temporal context by incorporating time as an additional dimension in the modeling process. This can be achieved by adapting the existing spatial conditional extremes model to account for temporal dependencies. One approach is to define a spatio-temporal random field that captures both spatial and temporal correlations in precipitation data.
To implement this, the framework could utilize a combination of latent Gaussian models that incorporate temporal smoothing splines alongside spatial random fields. By extending the model to include temporal dynamics, one could define a joint distribution for precipitation at multiple locations over time, allowing for the assessment of how extreme precipitation events evolve spatially and temporally.
Moreover, the integration of time-varying thresholds could enhance the model's ability to capture the changing nature of extreme precipitation events, particularly in the context of climate change. This would involve developing a dynamic threshold selection mechanism that adjusts based on historical data trends and future projections.
Finally, the simulation process could be adapted to generate spatio-temporal realizations of extreme precipitation by sequentially simulating precipitation occurrences and intensities over time, ensuring that the generated scenarios reflect realistic temporal patterns and dependencies.

What are the potential limitations of the spatial conditional extremes model in capturing the full complexity of high-resolution precipitation data, and how could these be addressed in future research?

While the spatial conditional extremes model offers a robust framework for capturing the extremal dependence of precipitation, several limitations may hinder its ability to fully represent the complexity of high-resolution precipitation data.
One limitation is the assumption of Gaussianity in the latent field, which may not adequately capture the heavy-tailed nature of extreme precipitation events. Future research could explore alternative distributions that better reflect the characteristics of precipitation data, such as using generalized extreme value (GEV) distributions or other heavy-tailed models.
Another potential limitation is the model's reliance on a fixed threshold for defining extremes. This approach may overlook the variability in extreme events across different spatial and temporal contexts. Future studies could investigate adaptive thresholding techniques that dynamically adjust based on local conditions or historical data, allowing for a more nuanced understanding of what constitutes an extreme event in varying contexts.
Additionally, the model may struggle with capturing the spatial heterogeneity inherent in precipitation data, particularly in complex terrains or urban environments. Incorporating more sophisticated spatial correlation structures, such as non-stationary models or hierarchical Bayesian approaches, could enhance the model's ability to account for local variations in precipitation patterns.
Lastly, the computational demands of high-dimensional inference in the spatial conditional extremes model may limit its applicability to larger datasets. Future research could focus on developing more efficient computational techniques, such as parallel processing or advanced approximation methods, to facilitate the analysis of extensive high-resolution datasets.

What other applications beyond hydrological impact assessment could benefit from the realistic extreme precipitation scenarios generated by the proposed modelling framework?

The realistic extreme precipitation scenarios generated by the proposed modeling framework have a wide range of applications beyond hydrological impact assessment.

Urban Planning and Infrastructure Design: The generated scenarios can inform the design and resilience of urban infrastructure, such as drainage systems, roads, and buildings. By understanding potential extreme precipitation events, urban planners can develop strategies to mitigate flooding risks and enhance the resilience of critical infrastructure.

Disaster Risk Management: Emergency management agencies can utilize the scenarios to improve disaster preparedness and response strategies. By simulating extreme precipitation events, agencies can better understand potential impacts on communities and develop effective evacuation plans and resource allocation strategies.

Agricultural Management: Farmers and agricultural planners can benefit from the insights provided by the modeling framework. Understanding the likelihood and intensity of extreme precipitation events can help in planning irrigation, crop selection, and soil management practices, ultimately enhancing food security.

Climate Change Research: The framework can contribute to climate change studies by providing insights into how extreme precipitation patterns may evolve under different climate scenarios. This information is crucial for understanding the broader implications of climate change on water resources and ecosystem health.

Insurance and Risk Assessment: The insurance industry can leverage the generated scenarios to assess risk and set premiums for properties in flood-prone areas. By quantifying the likelihood of extreme precipitation events, insurers can better manage their portfolios and develop more accurate risk models.

Ecological Studies: Researchers studying the impacts of precipitation extremes on ecosystems can use the scenarios to model potential changes in habitat conditions, species distributions, and ecosystem services, aiding in conservation efforts and biodiversity management.

In summary, the proposed modeling framework for extreme precipitation scenarios has the potential to inform a diverse array of fields, enhancing decision-making processes and improving resilience to extreme weather events across various sectors.