How effective are hyperlocal weather forecasting models in predicting extreme weather events on roads?
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Hyperlocal weather forecasting models have shown effectiveness in predicting extreme weather events on roads. These models utilize advanced techniques like Knowledge-driven Attribute-augmented Attention Spatiotemporal Graph Convolutional Networks to forecast urban road traffic under extreme weather conditions, such as wind and rain during tropical cyclones. Additionally, the study of local predictability limits using methods like the backward nonlinear local Lyapunov exponent has provided insights into the predictability of extreme weather events, showing that extreme warm events are generally more predictable than extreme cold events. By leveraging nonlocal data and regions of maximum transfer entropy, these models can enhance forecasting accuracy, allowing for greater warning times of at least twice the time horizon predicted from local data.
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| Papers (5) | Insight |
|---|---|
| Hyperlocal weather forecasting models using nonlocal data can effectively predict extreme events, offering greater warning times compared to local data, as demonstrated in forecasting spatiotemporally chaotic microcavity laser behavior. | |
| Hyperlocal weather forecasting models using nonlocal data can effectively predict extreme events, offering greater warning times compared to local data, as demonstrated in forecasting spatiotemporally chaotic microcavity laser behavior. | |
| Hyperlocal weather forecasting models can effectively predict extreme weather events' local predictability limits, influenced by factors like initial error size and attractor structure, aiding in road weather forecasting. | |
15 May 2023 | The paper proposes a Knowledge-driven Attribute-augmented Attention Spatiotemporal Graph Convolutional Network to predict urban road traffic under extreme weather, enhancing predictability significantly by incorporating disaster-related factors. |
| Hyperlocal weather forecasting models using nonlocal data can effectively predict extreme events, offering greater warning times compared to local data, as demonstrated in forecasting spatiotemporally chaotic microcavity laser behavior. |
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