• Ahijevych, D., J. O. Pinto, J. K. Williams, and M. Steiner, 2016: Probabilistic forecasts of mesoscale convective system initiation using the random forest data mining technique. Wea. Forecasting, 31, 581599, https://doi.org/10.1175/WAF-D-15-0113.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Audebert, N., B. Le Saux, and S. Lefèvre, 2016: Semantic segmentation of Earth observation data using multimodal and multi-scale deep networks. 13th Asian Conf. on Computer Vision, Taipei, Taiwan, Asian Federation of Computer Vision, 180–196.

    • Crossref
    • Export Citation
  • Badrinarayanan, V., A. Kendall, and R. Cipolla, 2017: SegNet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 39, 24812495, https://doi.org/10.1109/TPAMI.2016.2644615.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bechini, R., and V. Chandrasekar, 2017: An enhanced optical flow technique for radar nowcasting of precipitation and winds. J. Atmos. Oceanic Technol., 34, 26372658, https://doi.org/10.1175/JTECH-D-17-0110.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bengio, Y., 2009: Learning deep architectures for AI. Found. Trends Mach. Learn., 2, 1127, https://doi.org/10.1561/2200000006.

  • Bessho, K., and Coauthors, 2016: An introduction to Himawari-8/9—Japan’s new-generation geostationary meteorological satellites. J. Meteor. Soc. Japan, 94, 151183, https://doi.org/10.2151/jmsj.2016-009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Betz, H. D., K. Schmidt, W. P. Oettinger, and B. Montag, 2008: Cell-tracking with lightning data from LINET. Adv. Geosci., 17, 5561, https://doi.org/10.5194/adgeo-17-55-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bonelli, P., and P. Marcacci, 2008: Thunderstorm nowcasting by means of lightning and radar data: Algorithms and applications in northern Italy. Nat. Hazards Earth Syst. Sci., 8, 11871198, https://doi.org/10.5194/nhess-8-1187-2008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Buechler, D. E., and S. J. Goodman, 1990: Echo size and asymmetry: Impact on NEXRAD storm identification. J. Appl. Meteor., 29, 962969, https://doi.org/10.1175/1520-0450(1990)029<0962:ESAAIO>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, L. C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, 2018: DeepLab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell., 40, 834848, https://doi.org/10.1109/TPAMI.2017.2699184.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chen, M., Y. Wang, F. Gao, and X. Xiao, 2012: Diurnal variations in convective storm activity over contiguous North China during the warm season based on radar mosaic climatology. J. Geophys. Res., 117, D20115, https://doi.org/10.1029/2012JD018158.

    • Search Google Scholar
    • Export Citation
  • Dabberdt, W. F., and Coauthors, 2005: Multifunctional mesoscale observing networks. Bull. Amer. Meteor. Soc., 86, 961982, https://doi.org/10.1175/BAMS-86-7-961.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Dixon, M., and G. Wiener, 1993: TITAN: Thunderstorm identification, tracking, analysis, and nowcasting—A radar-based methodology. J. Atmos. Oceanic Technol., 10, 785797, https://doi.org/10.1175/1520-0426(1993)010<0785:TTITAA>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Farnell, C., T. Rigo, and N. Pineda, 2017: Lightning jump as a nowcast predictor: Application to severe weather events in Catalonia. Atmos. Res., 183, 130141, https://doi.org/10.1016/j.atmosres.2016.08.021.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Garcia-Garcia, A., S. Orts-Escolano, S. Oprea, V. Villena-Martinez, and J. Garcia-Rodriguez, 2017: A review on deep learning techniques applied to semantic segmentation. arXiv, http://arxiv.org/abs/1704.06857.

  • Gatlin, P. N., and S. J. Goodman, 2010: A total lightning trending algorithm to identify severe thunderstorms. J. Atmos. Oceanic Technol., 27, 322, https://doi.org/10.1175/2009JTECHA1286.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Golik, P., P. Doetsch, and H. Ney, 2013: Cross-entropy vs. squared error training: A theoretical and experimental comparison. Proc. 14th Annual Conf., Lyon, France, International Speech Communication Association, 1756–1760.

  • Haberlie, A. M., and W. S. Ashley, 2018: A method for identifying midlatitude mesoscale convective systems in radar mosaics. Part I: Segmentation and classification. J. Appl. Meteor. Climatol., 57, 15751598, https://doi.org/10.1175/JAMC-D-17-0293.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Han, L., J. Sun, W. Zhang, Y. Xiu, H. Feng, and Y. Lin, 2017: A machine learning nowcasting method based on real-time reanalysis data. J. Geophys. Res. Atmos., 122, 40384051, https://doi.org/10.1002/2016JD025783.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Harris, R. J., J. R. Mecikalski, W. M. Mackenzie, P. A. Durkee, and K. E. Nielsen, 2010: The definition of GOES infrared lightning initiation interest fields. J. Appl. Meteor. Climatol., 49, 25272543, https://doi.org/10.1175/2010JAMC2575.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hou, A. Y., and Coauthors, 2014: The Global Precipitation Measurement Mission. Bull. Amer. Meteor. Soc., 95, 701722, https://doi.org/10.1175/BAMS-D-13-00164.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ioffe, S., and C. Szegedy, 2015: Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv, https://arxiv.org/abs/1502.03167.

  • Ji, S., M. Yang, and K. Yu, 2013: 3D convolutional neural networks for human action recognition. IEEE Trans. Pattern Anal. Mach. Intell., 35, 221231, https://doi.org/10.1109/TPAMI.2012.59.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Johnson, J. T., P. L. MacKeen, A. Witt, E. D. W. Mitchell, G. J. Stumpf, M. D. Eilts, and K. W. Thomas, 1998: The storm cell identification and tracking algorithm: An enhanced WSR-88D algorithm. Wea. Forecasting, 13, 263276, https://doi.org/10.1175/1520-0434(1998)013<0263:TSCIAT>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karagiannidis, A., K. Lagouvardos, and V. Kotroni, 2016: The use of lightning data and Meteosat infrared imagery for the nowcasting of lightning activity. Atmos. Res., 168, 5769, https://doi.org/10.1016/j.atmosres.2015.08.011.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kingma, D., and J. Ba, 2015: ADAM: A method for stochastic optimization. Third Int. Conf. Learning Representations, San Diego, CA, International Machine Learning Society.

  • Kohn, M., E. Galanti, C. Price, K. Lagouvardos, and V. Kotroni, 2011: Nowcasting thunderstorms in the Mediterranean region using lightning data. Atmos. Res., 100, 489502, https://doi.org/10.1016/j.atmosres.2010.08.010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • LeCun, Y., Y. Bengio, and G. Hinton, 2015: Deep learning. Nature, 521, 436444, https://doi.org/10.1038/nature14539.

  • Lee, S., H. Han, J. Im, E. Jang, and M. Lee, 2017: Detection of deterministic and probabilistic convection initiation using Himawari-8 Advanced Himawari Imager data. Atmos. Meas. Tech., 10, 18591874, https://doi.org/10.5194/amt-10-1859-2017.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Lynn, B. H., Y. Yair, C. Price, G. Kelman, and A. J. Clark, 2012: Predicting cloud-to-ground and intracloud lightning in weather forecast models. Wea. Forecasting, 27, 14701488, https://doi.org/10.1175/WAF-D-11-00144.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., and K. M. Bedka, 2006: Forecasting convective initiation by monitoring the evolution of moving cumulus in daytime GOES imagery. Mon. Wea. Rev., 134, 4978, https://doi.org/10.1175/MWR3062.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., W. M. Mackenzie, M. König, and S. Muller, 2010: Cloud-top properties of growing cumulus prior to convective initiation as measured by Meteosat Second Generation. Part II: Use of visible reflectance. J. Appl. Meteor. Climatol., 49, 25442558, https://doi.org/10.1175/2010JAMC2480.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., X. Li, L. D. Carey, E. W. McCaul, and T. A. Coleman, 2013: Regional comparison of GOES cloud-top properties and radar characteristics in advance of first-flash lightning initiation. Mon. Wea. Rev., 141, 5574, https://doi.org/10.1175/MWR-D-12-00120.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mecikalski, J. R., J. K. Williams, C. P. Jewett, D. Ahijevych, A. LeRoy, and J. R. Walker, 2015: Probabilistic 0–1-h convective initiation nowcasts that combine geostationary satellite observations and numerical weather prediction model data. J. Appl. Meteor. Climatol., 54, 10391059, https://doi.org/10.1175/JAMC-D-14-0129.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Medina, B. L., L. D. Carey, C. G. Amiot, R. M. Mecikalski, W. P. Roeder, T. M. McNamara, and R. J. Blakeslee, 2019: A random forest method to forecast downbursts based on dual-polarization radar signatures. Remote Sens., 11, 826, https://doi.org/10.3390/rs11070826.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Metzger, E., and W. A. Nuss, 2013: The relationship between total cloud lightning behavior and radar-derived thunderstorm structure. Wea. Forecasting, 28, 237253, https://doi.org/10.1175/WAF-D-11-00157.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Mosier, R. M., C. Schumacher, R. E. Orville, and L. D. Carey, 2011: Radar nowcasting of cloud-to-ground lightning over Houston, Texas. Wea. Forecasting, 26, 199212, https://doi.org/10.1175/2010WAF2222431.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Orlanski, I., 1975: A rational subdivision of scales for atmospheric processes. Bull. Amer. Meteor. Soc., 56, 527530, https://doi.org/10.1175/1520-0477-56.5.527.

    • Search Google Scholar
    • Export Citation
  • Perol, T., M. Gharbi, and M. Denolle, 2018: Convolutional neural network for earthquake detection and location. Sci. Adv., 4, e1700578, https://doi.org/10.1126/sciadv.1700578.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ray, P. S., 1986: Mesoscale Meteorology and Forecasting. Amer. Meteor. Soc., 793 pp.

    • Crossref
    • Export Citation
  • Rigo, T., N. Pineda, and J. Bech, 2010: Analysis of warm season thunderstorms using an object-oriented tracking method based on radar and total lightning data. Nat. Hazards Earth Syst. Sci., 10, 18811893, https://doi.org/10.5194/nhess-10-1881-2010.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ronneberger, O., P. Fischer, and T. Brox, 2015: U-Net: Convolutional networks for biomedical image segmentation. Medical Image Computing and Computer-Assisted Intervention, Vol. 9351, Springer, 234–241.

  • Salamon, J., and J. P. Bello, 2017: Deep convolutional neural networks and data augmentation for environmental sound classification. IEEE Signal Process. Lett., 24, 279283, https://doi.org/10.1109/LSP.2017.2657381.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sánchez, J. L., J. L. Marcos, M. T. De La Fuente, and A. Castro, 1998: A logistic regression model applied to short term forecast of hail risk. Phys. Chem. Earth, 23, 645648, https://doi.org/10.1016/S0079-1946(98)00102-5.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sánchez, J. L., E. García Ortega, and J. L. Marcos, 2001: Construction and assessment of a logistic regression model applied to short-term forecasting of thunderstorms in León (Spain). Atmos. Res., 56, 5771, https://doi.org/10.1016/S0169-8095(00)00089-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schmidhuber, J., 2015: Deep learning in neural networks: An overview. Neural Networks, 61, 85117, https://doi.org/10.1016/j.neunet.2014.09.003.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Schultz, C. J., L. D. Carey, E. V. Schultz, and R. J. Blakeslee, 2017: Kinematic and microphysical significance of lightning jumps versus nonjump increases in total flash rate. Wea. Forecasting, 32, 275288, https://doi.org/10.1175/WAF-D-15-0175.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shelhamer, E., J. Long, and T. Darrell, 2017: Fully convolutional networks for semantic segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 39, 640651, https://doi.org/10.1109/TPAMI.2016.2572683.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shi, X., Z. Chen, H. Wang, D.-Y. Yeung, W. Wong, and W. Woo, 2015: Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proc. Advances in Neural Information Processing Systems, Montreal, QC, Canada, Curran Associates, 802–810, http://papers.nips.cc/paper/5955-convolutional-lstm-network-a-machine-learning-approach-for-precipitation-nowcasting.pdf.

  • Shi, X., Z. Gao, L. Lausen, H. Wang, D. Y. Yeung, W. K. Wong, and W. C. Woo, 2017: Deep learning for precipitation nowcasting: A benchmark and a new model. 31st Conf. on Neural Information Processing Systems, Long Beach, CA, Neural Information Processing Systems, 5617–5627.

  • Simonyan, K., and A. Zisserman, 2014: Very deep convolutional networks for large-scale image recognition. Third Int. Conf. on Learning Representations, San Diego, CA, International Machine Learning Society, https://arxiv.org/abs/1409.1556.

  • Stensrud, D. J., and Coauthors, 2013: Progress and challenges with warn-on-forecast. Atmos. Res., 123, 216, https://doi.org/10.1016/j.atmosres.2012.04.004.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sun, J., and Coauthors, 2014: Use of NWP for nowcasting convective precipitation: Recent progress and challenges. Bull. Amer. Meteor. Soc., 95, 409426, https://doi.org/10.1175/BAMS-D-11-00263.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vincent, B. R., L. D. Carey, D. Schneider, K. Keeter, and R. Gonski, 2003: Using WSR-88D reflectivity for the prediction of cloud-to-ground lightning: A central North Carolina study. Natl. Wea. Dig., 27, 3544.

    • Search Google Scholar
    • Export Citation
  • Walker, J. R., W. M. Mackenzie, J. R. Mecikalski, and C. P. Jewett, 2012: An enhanced geostationary satellite-based convective initiation algorithm for 0–2-h nowcasting with object tracking. J. Appl. Meteor. Climatol., 51, 19311949, https://doi.org/10.1175/JAMC-D-11-0246.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wang, Y., M. Long, J. Wang, Z. Gao, and P. S. Yu, 2017: PredRNN: Recurrent neural networks for predictive learning using spatiotemporal LSTMs. 31st Conf. on Neural Information Processing Systems, Long Beach, CA, Neural Information Processing Systems, https://papers.nips.cc/paper/6689-predrnn-recurrent-neural-networks-for-predictive-learning-using-spatiotemporal-lstms.pdf.

  • Wilson, J. W., N. A. Crook, C. K. Mueller, J. Sun, and M. Dixon, 1998: Nowcasting thunderstorms: A status report. Bull. Amer. Meteor. Soc., 79, 20792099, https://doi.org/10.1175/1520-0477(1998)079<2079:NTASR>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilson, J. W., Y. Feng, M. Chen, and R. D. Roberts, 2010: Nowcasting challenges during the Beijing Olympics: Successes, failures, and implications for future nowcasting systems. Wea. Forecasting, 25, 16911714, https://doi.org/10.1175/2010WAF2222417.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Woo, W. C., and W. K. Wong, 2017: Operational application of optical flow techniques to radar-based rainfall nowcasting. Atmosphere, 8, 48, https://doi.org/10.3390/atmos8030048.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xia, R., D. L. Zhang, and B. Wang, 2015: A 6-yr cloud-to-ground lightning climatology and its relationship to rainfall over central and eastern China. J. Appl. Meteor. Climatol., 54, 24432460, https://doi.org/10.1175/JAMC-D-15-0029.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, J., Z. Zhang, C. Wei, F. Lu, and Q. Guo, 2017: Introducing the new generation of Chinese geostationary weather satellites, Fengyun-4. Bull. Amer. Meteor. Soc., 98, 16371658, https://doi.org/10.1175/BAMS-D-16-0065.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yang, X., J. Sun, and W. Li, 2015: An analysis of cloud-to-ground lightning in China during 2010–13. Wea. Forecasting, 30, 15371550, https://doi.org/10.1175/WAF-D-14-00132.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yangli-Ao, G., and Coauthors, 2019: LightNet: A dual spatiotemporal encoder network model for lightning prediction. 25th Conf. on Knowledge Discovery and Data Mining, Anchorage, AK, Special Interest Group on Knowledge Discovery in Data, 2439–2447, https://doi.org/10.1145/3292500.3330717.

    • Crossref
    • Export Citation
  • Yumimoto, K., and Coauthors, 2016: Aerosol data assimilation using data from Himawari-8, a next-generation geostationary meteorological satellite. Geophys. Res. Lett., 43, 58865894, https://doi.org/10.1002/2016GL069298.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, J., 2010: Multi-source remote sensing data fusion: Status and trends. Int. J. Image Data Fusion, 1, 524, https://doi.org/10.1080/19479830903561035.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhang, W., L. Han, J. Sun, H. Guo, and J. Dai, 2017: Application of multi-channel 3D-cube successive convolution network for convective storm nowcasting. 2019 Int. Conf. on Big Data, Los Angeles, CA, IEEE, 1705–1710, https://doi.org/10.1109/BigData47090.2019.9005568.

    • Crossref
    • Export Citation
  • Zheng, Y. G., J. Chen, and P. J. Zhu, 2008: Climatological distribution and diurnal variation of mesoscale convective systems over China and its vicinity during summer. Chin. Sci. Bull., 53, 15741586, https://doi.org/10.1007/s11434-008-0116-9.

    • Search Google Scholar
    • Export Citation
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A Deep Learning Network for Cloud-to-Ground Lightning Nowcasting with Multisource Data

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  • 1 Chinese Academy of Meteorological Science, and University of Chinese Academy of Science, and National Meteorological Center, Beijing, China
  • 2 National Meteorological Center, Beijing, China
  • 3 Chinese Academy of Meteorological Science, Beijing, China
  • 4 National Meteorological Center, Beijing, China
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Abstract

Precise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (TB) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kanghui Zhou, zhoukh@cma.gov.cn

Abstract

Precise and timely lightning nowcasting is still a great challenge for meteorologists. In this study, a new semantic segmentation deep learning network for cloud-to-ground (CG) lightning nowcasting, named LightningNet, has been developed. This network is based on multisource observation data, including data from a geostationary meteorological satellite, Doppler weather radar network, and CG lightning location system. LightningNet, with an encoder–decoder architecture, consists of 20 three-dimensional convolutional layers, pooling and upsampling layers, normalization layers, and a softmax classifier. The central–eastern and southern China was selected as the study area, with considerations given to the topography and spatial coverage of the weather radar and lightning observation networks. Brightness temperatures (TB) of six infrared bands from the Himawari-8 satellite, composite reflectivity mosaic, and CG lightning densities were used as the predictors because of their close relationships with lightning activity. The multisource data were first interpolated into a uniform spatial/temporal resolution of 0.05° × 0.05°/10 min, and then training and test datasets were constructed, respectively. LightningNet was trained to extract the features of lightning initiation, development, and dissipation. The evaluation results demonstrated that LightningNet was able to achieve good performance of 0–1-h lightning nowcasts using the multisource data. The probability of detection, the false alarm ratio, the area under relative operating characteristic curve, and the threat score (TS) of LightningNet with all three types of data reached 0.633, 0.386, 0.931, and 0.453, respectively. Because geostationary meteorological satellite and radar both possess the capability of capturing lightning initiation (LI) features, LightningNet also showed good performance in LI nowcasting. When all three types of data were used, more than 50% LI was predicted accurately and the TS exceeded 0.36. LightningNet’s nowcast performance using triple-source data was clearly superior to that using only single-source or dual-source data, and these findings indicate that LightningNet has good capability of combining multisource data effectively to produce more reliable lightning nowcasts.

© 2020 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Kanghui Zhou, zhoukh@cma.gov.cn
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