A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making
- URL: http://arxiv.org/abs/2409.05866v1
- Date: Mon, 9 Sep 2024 17:59:54 GMT
- Title: A Framework for Evaluating PM2.5 Forecasts from the Perspective of Individual Decision Making
- Authors: Renato Berlinghieri, David R. Burt, Paolo Giani, Arlene M. Fiore, Tamara Broderick,
- Abstract summary: Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks.
We evaluate several existing forecasts of fine particular matter (PM2.5) within the continental United States in the context of individual decision-making.
- Score: 15.759815530683465
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Wildfire frequency is increasing as the climate changes, and the resulting air pollution poses health risks. Just as people routinely use weather forecasts to plan their activities around precipitation, reliable air quality forecasts could help individuals reduce their exposure to air pollution. In the present work, we evaluate several existing forecasts of fine particular matter (PM2.5) within the continental United States in the context of individual decision-making. Our comparison suggests there is meaningful room for improvement in air pollution forecasting, which might be realized by incorporating more data sources and using machine learning tools. To facilitate future machine learning development and benchmarking, we set up a framework to evaluate and compare air pollution forecasts for individual decision making. We introduce a new loss to capture decisions about when to use mitigation measures. We highlight the importance of visualizations when comparing forecasts. Finally, we provide code to download and compare archived forecast predictions.
Related papers
- Enhancing multivariate post-processed visibility predictions utilizing CAMS forecasts [0.0]
Weather forecasts increasingly incorporate ensemble predictions of visibility.
Post-processing is recommended to enhance the reliability and accuracy of predictions.
Our study confirms that post-processed forecasts are substantially superior to raw and climatological predictions.
arXiv Detail & Related papers (2024-06-20T09:57:49Z) - Urban Air Pollution Forecasting: a Machine Learning Approach leveraging Satellite Observations and Meteorological Forecasts [0.11249583407496218]
Air pollution poses a significant threat to public health and well-being, particularly in urban areas.
This study introduces a series of machine-learning models that integrate data from the Sentinel-5P satellite, meteorological conditions, and topological characteristics to forecast future levels of five major pollutants.
arXiv Detail & Related papers (2024-05-30T10:02:53Z) - Back to the Future: GNN-based NO$_2$ Forecasting via Future Covariates [49.93577170464313]
We deal with air quality observations in a city-wide network of ground monitoring stations.
We propose a conditioning block that embeds past and future covariates into the current observations.
We find that conditioning on future weather information has a greater impact than considering past traffic conditions.
arXiv Detail & Related papers (2024-04-08T09:13:16Z) - ExtremeCast: Boosting Extreme Value Prediction for Global Weather Forecast [57.6987191099507]
We introduce Exloss, a novel loss function that performs asymmetric optimization and highlights extreme values to obtain accurate extreme weather forecast.
We also introduce ExBooster, which captures the uncertainty in prediction outcomes by employing multiple random samples.
Our solution can achieve state-of-the-art performance in extreme weather prediction, while maintaining the overall forecast accuracy comparable to the top medium-range forecast models.
arXiv Detail & Related papers (2024-02-02T10:34:13Z) - FengWu-GHR: Learning the Kilometer-scale Medium-range Global Weather
Forecasting [56.73502043159699]
This work presents FengWu-GHR, the first data-driven global weather forecasting model running at the 0.09$circ$ horizontal resolution.
It introduces a novel approach that opens the door for operating ML-based high-resolution forecasts by inheriting prior knowledge from a low-resolution model.
The hindcast of weather prediction in 2022 indicates that FengWu-GHR is superior to the IFS-HRES.
arXiv Detail & Related papers (2024-01-28T13:23:25Z) - Air Quality Forecasting Using Machine Learning: A Global perspective
with Relevance to Low-Resource Settings [0.0]
Air pollution stands as the fourth leading cause of death globally.
This study proposes a novel machine learning approach for accurate air quality prediction using two months of air quality data.
arXiv Detail & Related papers (2024-01-09T05:52:02Z) - Streaming Motion Forecasting for Autonomous Driving [71.7468645504988]
We introduce a benchmark that queries future trajectories on streaming data and we refer to it as "streaming forecasting"
Our benchmark inherently captures the disappearance and re-appearance of agents, which is a safety-critical problem yet overlooked by snapshot-based benchmarks.
We propose a plug-and-play meta-algorithm called "Predictive Streamer" that can adapt any snapshot-based forecaster into a streaming forecaster.
arXiv Detail & Related papers (2023-10-02T17:13:16Z) - GraphCast: Learning skillful medium-range global weather forecasting [107.40054095223779]
We introduce a machine learning-based method called "GraphCast", which can be trained directly from reanalysis data.
It predicts hundreds of weather variables, over 10 days at 0.25 degree resolution globally, in under one minute.
We show that GraphCast significantly outperforms the most accurate operational deterministic systems on 90% of 1380 verification targets.
arXiv Detail & Related papers (2022-12-24T18:15:39Z) - Interpretable and Transferable Models to Understand the Impact of
Lockdown Measures on Local Air Quality [5.273501657421094]
COVID-19 related lockdown measures offer a unique opportunity to understand how changes in economic activity and traffic affect ambient air quality.
We estimate pollution reduction over the lockdown period by using the measurements from ground air pollution monitoring stations.
We show that our models achieve state-of-the-art performance on the data from air pollution measurement stations in Switzerland and in China.
arXiv Detail & Related papers (2020-11-19T23:09:30Z) - A generative adversarial network approach to (ensemble) weather
prediction [91.3755431537592]
We use a conditional deep convolutional generative adversarial network to predict the geopotential height of the 500 hPa pressure level, the two-meter temperature and the total precipitation for the next 24 hours over Europe.
The proposed models are trained on 4 years of ERA5 reanalysis data from 2015-2018 with the goal to predict the associated meteorological fields in 2019.
arXiv Detail & Related papers (2020-06-13T20:53:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.