Artificial Intelligence Revolutionises Weather Forecast, Climate Monitoring and Decadal Prediction

被引:48
作者
Dewitte, Steven [1 ]
Cornelis, Jan P. [2 ]
Muller, Richard [3 ]
Munteanu, Adrian [2 ]
机构
[1] Royal Meteorol Inst Belgium, B-1180 Brussels, Belgium
[2] Vrije Univ Brussel, Fac Sci Appl, B-1050 Brussels, Belgium
[3] Deutsch Wetterdienst, D-63067 Offenbach, Germany
关键词
Artificial Intelligence; weather forecast; climate monitoring and prediction; observations; nowcasting; warnings; DATA ASSIMILATION; GLOBAL WEATHER; WRF MODEL; RESOLUTION; IMPACT; EVENT;
D O I
10.3390/rs13163209
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Artificial Intelligence (AI) is an explosively growing field of computer technology, which is expected to transform many aspects of our society in a profound way. AI techniques are used to analyse large amounts of unstructured and heterogeneous data and discover and exploit complex and intricate relations among these data, without recourse to an explicit analytical treatment of those relations. These AI techniques are unavoidable to make sense of the rapidly increasing data deluge and to respond to the challenging new demands in Weather Forecast (WF), Climate Monitoring (CM) and Decadal Prediction (DP). The use of AI techniques can lead simultaneously to: (1) a reduction of human development effort, (2) a more efficient use of computing resources and (3) an increased forecast quality. To realise this potential, a new generation of scientists combining atmospheric science domain knowledge and state-of-the-art AI skills needs to be trained. AI should become a cornerstone of future weather and climate observation and modelling systems.
引用
收藏
页数:12
相关论文
共 83 条
  • [1] Agreement P., 2015, Report of the Conference of the Parties to the United Nations Framework Convention on Climate Change (21st Session, 2015: Paris), V4, DOI DOI 10.1017/S0020782900004253
  • [2] On the Impact of WRF Model Vertical Grid Resolution on Midwest Summer Rainfall Forecasts
    Aligo, Eric A.
    Gallus, William A., Jr.
    Segal, Moti
    [J]. WEATHER AND FORECASTING, 2009, 24 (02) : 575 - 594
  • [3] Long short-term memory
    Hochreiter, S
    Schmidhuber, J
    [J]. NEURAL COMPUTATION, 1997, 9 (08) : 1735 - 1780
  • [4] [Anonymous], 1997, Atmosphere-Ocean, DOI [DOI 10.1080/07055900.1997.9687359, 10.1080/07055900.1997.9687359]
  • [5] Atmospheric Lifetime of Fossil Fuel Carbon Dioxide
    Archer, David
    Eby, Michael
    Brovkin, Victor
    Ridgwell, Andy
    Cao, Long
    Mikolajewicz, Uwe
    Caldeira, Ken
    Matsumoto, Katsumi
    Munhoven, Guy
    Montenegro, Alvaro
    Tokos, Kathy
    [J]. ANNUAL REVIEW OF EARTH AND PLANETARY SCIENCES, 2009, 37 : 117 - 134
  • [6] Deep Data Assimilation: Integrating Deep Learning with Data Assimilation
    Arcucci, Rossella
    Zhu, Jiangcheng
    Hu, Shuang
    Guo, Yi-Ke
    [J]. APPLIED SCIENCES-BASEL, 2021, 11 (03): : 1 - 21
  • [7] The digital revolution of Earth-system science
    Bauer, Peter
    Dueben, Peter D.
    Hoefler, Torsten
    Quintino, Tiago
    Schulthess, Thomas C.
    Wedi, Nils P.
    [J]. NATURE COMPUTATIONAL SCIENCE, 2021, 1 (02): : 104 - 113
  • [8] A digital twin of Earth for the green transition
    Bauer, Peter
    Stevens, Bjorn
    Hazeleger, Wilco
    [J]. NATURE CLIMATE CHANGE, 2021, 11 (02) : 80 - 83
  • [9] The quiet revolution of numerical weather prediction
    Bauer, Peter
    Thorpe, Alan
    Brunet, Gilbert
    [J]. NATURE, 2015, 525 (7567) : 47 - 55
  • [10] Impact of singular-vector-based satellite data thinning on NWP
    Bauer, Peter
    Buizza, Roberto
    Cardinali, Carla
    Thepaut, Jean-Noel
    [J]. QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY, 2011, 137 (655) : 286 - 302