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3 results for “Lightning nowcast”
Machine-learning based lightning nowcasting data archive
<p>This data archive contains the derived data supporting the findings of article "Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset". The paper is currently in the preprint version: https://doi.org/10.21203/rs.3.rs-2616886/v1</p> <p>The prediction results in this data archive are generated by various models:</p> <p>1. Current model. The model involves data input of aerosol observations together with meteorological variables and auxiliary datasets, as well as data enrichment by Geostationary Lightning Mapper (GLM). In the demo of the dataset, the year of 2020 is trained and predicted on a cross-validation scheme. </p> <p>2. LMA model. The model acts as the baseline model considering only data label obtained from the ground-based Lightning Mapping Array (LMA), which observes accurate lightning occurrence in limited spatial range.</p> <p>3. No-AOD model. The model acts as the baseline model considering no aerosol observation is utilized during the machine learning process. </p> <p>The model results are demonstrated in a continuous value in 0-1. Trade-offs between Probability of Detection (POD) and False Alarm Ratio (FAR) can be optimized by selection of different thresholds. </p> <p>Other datasets:</p> <p>1. Dataset for training. It is for the public use of machine learning training for the current model and no-AOD model (training input features vary).</p> <p>2. PM2.5 dataset. The real-time spatially continuous and hourly-level PM<sub>2.5</sub> dataset is obtained following a published method by Zeng et al.. In this method, the fundamental in-situ measurements are obtained from Air Quality System (AQS) monitoring network operated by United States Environmental Protection Agency.</p> <p>Reference:</p> <p>Siwei Li, Ge Song, Jia Xing et al. Lightning nowcasting with aerosol-informed machine learning and satellite-enriched dataset, 14 March 2023, PREPRINT (Version 1) available at Research Square [https://doi.org/10.21203/rs.3.rs-2616886/v1]</p> <p>Zeng, Z. et al. Estimating hourly surface PM2. 5 concentrations across China from high-density meteorological observations by machine learning. Atmospheric Research 254, 105516 (2021).</p>
Data archive for "Seamless lightning nowcasting with recurrent-convolutional deep learning"
<p>This dataset contains the machine learning training data files, pretrained model weights and precomputed results for the paper "Seamless lightning nowcasting with recurrent-convolutional deep learning" published in:<br> Leinonen, J., Hamann, U., & Germann, U. (2022). Seamless Lightning Nowcasting with Recurrent-Convolutional Deep Learning, <em>Artificial Intelligence for the Earth Systems</em>, <em>1</em>(4), e220043, doi:<a href="https://doi.org/10.1175/AIES-D-22-0043.1">10.1175/AIES-D-22-0043.1</a>.<br> A preprint of the paper can be found at <a href="https://arxiv.org/abs/2203.10114">https://arxiv.org/abs/2203.10114</a>.</p> <p>The ML code can be found at <a href="https://github.com/MeteoSwiss/c4dl-lightningdl">https://github.com/MeteoSwiss/c4dl-lightningdl</a>. Download all the files here and extract the contents to the following subdirectories in the ML code directory:</p> <ul> <li>Training data (c4dl-patches-*.zip) -> data/2020/</li> <li>Results (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-results-lightningdl.zip?versionId=54046830-4c7e-48c6-af42-d6d5606af86b">c4dl-results-lightningdl.zip</a>) -> results/</li> <li>Pretrained models (<a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-models-lightningdl.zip?versionId=364bca7c-e6ad-4ed9-9264-57c759ea0ac6">c4dl-models-lightningdl.zip</a>) -> models/</li> </ul> <p>Additionally, the file <a href="https://zenodo.org/api/files/d4829f50-55fd-4d86-b875-7f2b91dba74f/c4dl-randomexamples-lightningdl.zip?versionId=426ca113-950f-4a50-8ef0-8e5d12afe697">c4dl-randomexamples-lightningdl.zip</a> contains the randomly selected examples complementing Figs. 7–9 of the paper, and the file <a href="https://zenodo.org/api/files/939609f2-6699-4f56-9428-391ebe78e010/c4dl-inputsamples-lightningdl.zip">c4dl-inputsamples-lightningdl.zip</a> contains figures showing samples of all the input variables for the three cases shown in Figs. 7–9.</p>
A Benchmark Dataset for Lightning Nowcasting in Latin America
<p>In 2023, the <a href="https://wmo.int/" target="_blank" rel="nofollow noreferrer noopener">World Meteorological Organization</a> (WMO) commissioned a pilot project for nowcasting convective weather hazards using artificial intelligence (AI) methods. The project focuses on predicting lightning and quantitative precipitation estimation (QPE) over Latin America, Africa, and southeast Asia. This project is called the AI Nowcasting Pilot Project (AINPP), and is part of WMO's Early Warning for All initiative (<a href="https://wmo.int/activities/early-warnings-all/wmo-and-early-warnings-all-initiative" target="_blank" rel="nofollow noreferrer noopener">EW4All</a>).</p> <p>This benchmark dataset is for evaluating the prediction of lightning in Latin America. The dataset currently consists of 30 days data at 10-minute temporal resolution (5 consecutive days for 6 consecutive months). <br><br>Contents:</p> <ul> <li>mexico_targets.tar.gz: The targets or predictand over Mexico, using 1- and 2-hour accumulations of flash-extent density observed by the GOES-16 Geostationary Lightning Mapper.</li> <li>mexico_example_predictions.tar.gz: Example predictions from the <a href="https://cimss.ssec.wisc.edu/severe_conv/ptlg.html" target="_blank" rel="noopener">NOAA/CIMSS LightningCast model</a>, matching the files in mexico_targets.tar.gz.</li> <li>south_america_targets.tar.gz: The targets or predictand over South America, using 1- and 2-hour accumulations of flash-extent density observed by the GOES-16 Geostationary Lightning Mapper.</li> <li>south_america_example_predictions.tar.gz: Example predictions from the <a href="https://cimss.ssec.wisc.edu/severe_conv/ptlg.html" target="_blank" rel="noopener">NOAA/CIMSS LightningCast model</a>, matching the files in south_america_targets.tar.gz.</li> </ul>
ScienceDex guides
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