Nothing Special   »   [go: up one dir, main page]

skip to main content
10.1145/3659914.3659916acmconferencesArticle/Chapter ViewAbstractPublication PagespascConference Proceedingsconference-collections
research-article
Open access

Scalable GPU-Enabled Creation of Three Dimensional Weather Fronts

Published: 03 June 2024 Publication History

Abstract

Weather fronts play an important role in atmospheric science. Their correlation to severe natural hazards such as extreme precipitation, cyclones or thunderstorms makes localization and understanding of frontal systems an important factor in weather forecasting. Despite their importance weather fronts are mostly studied on horizontal slices, ignoring their three-dimensional characteristics. In this paper we present an efficient GPU-based parallelization for the detection of three-dimensional weather fronts. We achieve comparable skill to our previous CPU-based method, on which we based our algorithm, while being more than two orders-of-magnitude faster. Furthermore, we extend our previous method by providing additional information for warm, cold, occluded, and stationary fronts. Thus, our approach drastically increases the ability to provide statistical evaluations of three-dimensional fronts for different setups. Even faster runtimes can be achieved by using multiple GPUs with linear scaling.

References

[1]
A. A. Beckert, L. Eisenstein, A. Oertel, T. Hewson, G. C. Craig, and M. Rautenhaus. 2023. The three-dimensional structure of fronts in mid-latitude weather systems in numerical weather prediction models. Geoscientific Model Development 16, 15 (2023), 4427--4450.
[2]
J. C. Biard and K. E. Kunkel. 2019. Automated detection of weather fronts using a deep learning neural network. Advances in Statistical Climatology, Meteorology and Oceanography 5, 2 (2019), 147--160.
[3]
Wolfgang Boehm and Andreas Müller. 1999. On de Casteljau's algorithm. Computer Aided Geometric Design 16, 7 (1999), 587--605.
[4]
David Bolton. 1980. The Computation of Equivalent Potential Temperature. Monthly Weather Review 108, 7 (1980), 1046 -- 1053. <1046:TCOEPT>2.0.CO;2
[5]
S. Brüning, S. Niebler, and H. Tost. 2024. Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data. Atmospheric Measurement Techniques 17, 3 (2024), 961--978.
[6]
Jennifer L. Catto and Andrew Dowdy. 2021. Understanding compound hazards from a weather system perspective. Weather and Climate Extremes 32 (2021), 100313.
[7]
J. L. Catto and S. Pfahl. 2013. The importance of fronts for extreme precipitation. Journal of Geophysical Research: Atmospheres 118, 19 (2013), 10,791--10,801.
[8]
Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler Bonfanti, Balázs Kégl, and Claire Monteleoni. 2020. Tropical Cyclone Track Forecasting Using Fused Deep Learning From Aligned Reanalysis Data. Frontiers in Big Data 3 (2020).
[9]
Hans Hersbach, Bill Bell, Paul Berrisford, Shoji Hirahara, András Horányi, Joaquín Muñoz-Sabater, Julien Nicolas, Carole Peubey, Raluca Radu, Dinand Schepers, Adrian Simmons, Cornel Soci, Saleh Abdalla, Xavier Abellan, Gianpaolo Balsamo, Peter Bechtold, Gionata Biavati, Jean Bidlot, Massimo Bonavita, Giovanna De Chiara, Per Dahlgren, Dick Dee, Michail Diamantakis, Rossana Dragani, Johannes Flemming, Richard Forbes, Manuel Fuentes, Alan Geer, Leo Haimberger, Sean Healy, Robin J. Hogan, Elías Hólm, Marta Janisková, Sarah Keeley, Patrick Laloyaux, Philippe Lopez, Cristina Lupu, Gabor Radnoti, Patricia de Rosnay, Iryna Rozum, Freja Vamborg, Sebastien Villaume, and Jean-Noël Thépaut. 2020. The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society 146, 730 (2020), 1999--2049.
[10]
T D Hewson. 1997. Objective identification of frontal wave cyclones. Meteorological Applications 4, 4 (1997), 311--315.
[11]
T D Hewson. 1998. Objective fronts. Meteorological Applications 5, 1 (1998), 37--65.
[12]
L. Hoffmann, G. Günther, D. Li, O. Stein, X. Wu, S. Griessbach, Y. Heng, P. Konopka, R. Müller, B. Vogel, and J. S. Wright. 2019. From ERA-Interim to ERA5: the considerable impact of ECMWF's next-generation reanalysis on Lagrangian transport simulations. Atmospheric Chemistry and Physics 19, 5 (2019), 3097--3124.
[13]
IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Vol. In Press. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
[14]
J. Jenkner, M. Sprenger, I. Schwenk, C. Schwierz, S. Dierer, and D. Leuenberger. 2010. Detection and climatology of fronts in a high-resolution model reanalysis over the Alps. Meteorological Applications 17, 1 (2010), 1--18.
[15]
Ryan Lagerquist, Amy McGovern, and David John Gagne II. 2019. Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts. Weather and Forecasting 34, 4 (2019), 1137 -- 1160.
[16]
Mark G. Lawrence. 2005. The Relationship between Relative Humidity and the Dewpoint Temperature in Moist Air: A Simple Conversion and Applications. Bulletin of the American Meteorological Society 86, 2 (2005), 225 -- 234.
[17]
Daisuke Matsuoka, Shiori Sugimoto, Yujin Nakagawa, Shintaro Kawahara, Fumiaki Araki, Yosuke Onoue, Masaaki Iiyama, and Koji Koyamada. 2019. Automatic Detection of Stationary Fronts around Japan Using a Deep Convolutional Neural Network. SOLA 15 (2019), 154--159.
[18]
National Weather Service. 2019. National Weather Service Coded Surface Bulletins, 2003-.
[19]
S. Niebler, A. Miltenberger, B. Schmidt, and P. Spichtinger. 2022. Automated detection and classification of synoptic-scale fronts from atmospheric data grids. Weather and Climate Dynamics 3, 1 (2022), 113--137.
[20]
Stefan Niebler, Bertil Schmidt, Holger Tost, and Peter Spichtinger. 2023. Automated Identification and Location of Three Dimensional Atmospheric Frontal Systems. In Computational Science - ICCS 2023, Jiří Mikyška, Clélia de Mulatier, Maciej Paszynski, Valeria V. Krzhizhanovskaya, Jack J. Dongarra, and Peter M.A. Sloot (Eds.). Springer Nature Switzerland, Cham, 3--17.
[21]
Annika Oertel, Maxi Boettcher, Hanna Joos, Michael Sprenger, Heike Konow, Martin Hagen, and Heini Wernli. 2019. Convective activity in an extratropical cyclone and its warm conveyor belt - a case-study combining observations and a convection-permitting model simulation. Quarterly Journal of the Royal Meteorological Society 145, 721 (2019), 1406--1426. arXiv:https://rmets.onlinelibrary.wiley.com/doi/pdf/10.1002/qj.3500
[22]
P. G. Sansom and J. L. Catto. 2022. Improved objective identification of meteorological fronts: a case study with ERA-Interim. Geoscientific Model Development Discussions 2022 (2022), 1--19.
[23]
Sebastian Schemm, Irina Rudeva, and Ian Simmonds. 2015. Extratropical fronts in the lower troposphere-global perspectives obtained from two automated methods. Quarterly Journal of the Royal Meteorological Society 141, 690 (2015), 1686--1698.
[24]
Sebastian Schemm, Michael Sprenger, and Heini Wernli. 2018. When during Their Life Cycle Are Extratropical Cyclones Attended by Fronts? Bulletin of the American Meteorological Society 99, 1 (2018), 149 -- 165.
[25]
David M. Schultz, Daniel Keyser, and Lance F. Bosart. 1998. The Effect of Large-Scale Flow on Low-Level Frontal Structure and Evolution in Midlatitude Cyclones. Monthly Weather Review 126, 7 (1998), 1767 -- 1791. <1767:TEOLSF>2.0.CO;2
[26]
David M. Schultz and Geraint Vaughan. 2011. Occluded Fronts and the Occlusion Process: A Fresh Look at Conventional Wisdom. Bulletin of the American Meteorological Society 92, 4 (2011), 443 -- 466.
[27]
M. A. Shapiro and Daniel Keyser. 1990. Fronts, Jet Streams and the Tropopause. In Extratropical Cyclones: The Erik Palmén Memorial Volume, Chester W. Newton and Eero O. Holopainen (Eds.). American Meteorological Society, Boston, MA, 167--191.
[28]
B. Teufel, F. Carmo, L. Sushama, L. Sun, M. N. Khaliq, S. Bélair, A. Shamseldin, D. Nagesh Kumar, and J. Vaze. 2023. Physics-informed deep learning framework to model intense precipitation events at super resolution. Geoscience Letters 10, 1 (18 Apr 2023), 19.
[29]
Carl M. Thomas and David M. Schultz. 2019. What are the Best Thermodynamic Quantity and Function to Define a Front in Gridded Model Output? Bulletin of the American Meteorological Society 100, 5 (2019), 873 -- 895.
[30]
Fang Wang, Di Tian, Lisa Lowe, Latif Kalin, and John Lehrter. 2021. Deep Learning for Daily Precipitation and Temperature Downscaling. Water Resources Research 57, 4 (2021), e2020WR029308.

Recommendations

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences
PASC '24: Proceedings of the Platform for Advanced Scientific Computing Conference
June 2024
296 pages
ISBN:9798400706394
DOI:10.1145/3659914
This work is licensed under a Creative Commons Attribution International 4.0 License.

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2024

Check for updates

Author Tags

  1. GPUs
  2. atmospheric science
  3. weather fronts
  4. parallelization

Qualifiers

  • Research-article

Funding Sources

Conference

PASC '24
Sponsor:

Acceptance Rates

PASC '24 Paper Acceptance Rate 26 of 36 submissions, 72%;
Overall Acceptance Rate 109 of 221 submissions, 49%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • 0
    Total Citations
  • 95
    Total Downloads
  • Downloads (Last 12 months)95
  • Downloads (Last 6 weeks)32
Reflects downloads up to 25 Nov 2024

Other Metrics

Citations

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media