Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Haoyu (Richard) Zhuang aDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York

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Flavio Lehner aDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York
bClimate and Global Dynamics Laboratory, National Center for Atmospheric Research, Boulder, Colorado
cPolar Bears International, Bozeman, Montana

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Arthur T. DeGaetano aDepartment of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York
dNortheast Regional Climate Center, Cornell University, Ithaca, New York

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Abstract

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs.

Significance Statement

Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haoyu (Richard) Zhuang, hz542@cornell.edu

Abstract

Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs.

Significance Statement

Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

© 2024 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Haoyu (Richard) Zhuang, hz542@cornell.edu
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  • Benjamin, S. G., J. M. Brown, and T. G. Smirnova, 2016: Explicit precipitation-type diagnosis from a model using a mixed-phase bulk cloud–precipitation microphysics parameterization. Wea. Forecasting, 31, 609619, https://doi.org/10.1175/WAF-D-15-0136.1.

    • Search Google Scholar
    • Export Citation
  • Bernstein, B. C., 2000: Regional and local influences on freezing drizzle, freezing rain, and ice pellet events. Wea. Forecasting, 15, 485508, https://doi.org/10.1175/1520-0434(2000)015<0485:RALIOF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bi, K., L. Xie, H. Zhang, X. Chen, X. Gu, and Q. Tian, 2023: Accurate medium-range global weather forecasting with 3D neural networks. Nature, 619, 533538, https://doi.org/10.1038/s41586-023-06185-3.

    • Search Google Scholar
    • Export Citation
  • Birk, K., E. Lenning, K. Donofrio, and M. T. Friedlein, 2021: A revised Bourgouin precipitation-type algorithm. Wea. Forecasting, 36, 425438, https://doi.org/10.1175/WAF-D-20-0118.1.

    • Search Google Scholar
    • Export Citation
  • Bourgouin, P., 2000: A method to determine precipitation types. Wea. Forecasting, 15, 583592, https://doi.org/10.1175/1520-0434(2000)015<0583:AMTDPT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Bresson, E., R. Laprise, D. Paquin, J. M. Thériault, and R. de Elía, 2017: Evaluating the ability of CRCM5 to simulate mixed precipitation. Atmos.–Ocean, 55, 7993, https://doi.org/10.1080/07055900.2017.1310084.

    • Search Google Scholar
    • Export Citation
  • Chantry, M., C. Hannah, D. Peter, and P. Tim, 2021: Opportunities and challenges for machine learning in weather and climate modelling: Hard, medium and soft AI. Philos. Trans. Roy. Soc., A379, 20200083, https://doi.org/10.1098/rsta.2020.0083.

    • Search Google Scholar
    • Export Citation
  • Chen, T., and C. Guestrin, 2016: XGBoost: A scalable tree boosting system. KDD’16: Proc. 22nd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Francisco, CA, Association for Computing Machinery, 785–794, https://doi.org/10.1145/2939672.2939785.

  • Cheng, C. S., H. Auld, G. Li, J. Klaassen, and Q. Li, 2007: Possible impacts of climate change on freezing rain in south-central Canada using downscaled future climate scenarios. Nat. Hazards Earth Syst. Sci., 7, 7187, https://doi.org/10.5194/nhess-7-71-2007.

    • Search Google Scholar
    • Export Citation
  • Cortinas, J. V., Jr., B. C. Bernstein, C. C. Robbins, and J. Walter Strapp, 2004: An analysis of freezing rain, freezing drizzle, and ice pellets across the United States and Canada: 1976–90. Wea. Forecasting, 19, 377390, https://doi.org/10.1175/1520-0434(2004)019<0377:AAOFRF>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Das, S., and Coauthors, 2022: A comprehensive machine learning study to classify precipitation type over land from Global Precipitation Measurement Microwave Imager (GPM-GMI) measurements. Remote Sens., 14, 3631, https://doi.org/10.3390/rs14153631.

    • Search Google Scholar
    • Export Citation
  • DeGaetano, A. T., 2000: Climatic perspective and impacts of the 1998 northern New York and New England ice storm. Bull. Amer. Meteor. Soc., 81, 237254, https://doi.org/10.1175/1520-0477(2000)081<0237:CPAIOT>2.3.CO;2.

    • Search Google Scholar
    • Export Citation
  • Fernández-González, S., F. Valero, J. L. Sanchez, E. Gascón, L. López, E. García-Ortega, and A. Merino, 2014: Observation of a freezing drizzle episode: A case study. Atmos. Res., 149, 244254, https://doi.org/10.1016/j.atmosres.2014.06.014.

    • Search Google Scholar
    • Export Citation
  • Herman, G. R., and R. S. Schumacher, 2018: Money doesn’t grow on trees, but forecasts do: Forecasting extreme precipitation with random forests. Mon. Wea. Rev., 146, 15711600, https://doi.org/10.1175/MWR-D-17-0250.1.

    • Search Google Scholar
    • Export Citation
  • Hersbach, H., and Coauthors, 2020: The ERA5 global reanalysis. Quart. J. Roy. Meteor. Soc., 146, 19992049, https://doi.org/10.1002/qj.3803.

    • Search Google Scholar
    • Export Citation
  • Ikeda, K., M. Steiner, J. Pinto, and C. Alexander, 2013: Evaluation of cold-season precipitation forecasts generated by the hourly updating high-resolution rapid refresh model. Wea. Forecasting, 28, 921939, https://doi.org/10.1175/WAF-D-12-00085.1.

    • Search Google Scholar
    • Export Citation
  • IPCC, 2022: Climate Change 2022: Impacts, Adaptation and Vulnerability. H.-O. Pörtner et al. Eds., Cambridge University Press, 3056 pp., https://doi.org/10.1017/9781009325844.

  • Jensen, A. A., C. Weeks, M. Xu, S. Landolt, A. Korolev, M. Wolde, and S. DiVito, 2023: The prediction of supercooled large drops by a microphysics and a machine learning model for the ICICLE field campaign. Wea. Forecasting, 38, 11071124, https://doi.org/10.1175/WAF-D-22-0105.1.

    • Search Google Scholar
    • Export Citation
  • Jeong, D. I., A. J. Cannon, and X. Zhang, 2019: Projected changes to extreme freezing precipitation and design ice loads over North America based on a large ensemble of Canadian regional climate model simulations. Nat. Hazards Earth Syst. Sci., 19, 857872, https://doi.org/10.5194/nhess-19-857-2019.

    • Search Google Scholar
    • Export Citation
  • Ke, G., Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T. Y. Liu, 2017: LightGBM: A highly efficient gradient boosting decision tree. NIPS’17: Proc. 31st Int. Conf. on Neural Information Processing Systems, Long Beach, CA, Curran Associates Inc., 3149–3157, https://dl.acm.org/doi/10.5555/3294996.3295074.

  • Lambert, S. J., and B. K. Hansen, 2011: Simulated changes in the freezing rain climatology of North America under global warming using a coupled climate model. Atmos.–Ocean, 49, 289295, https://doi.org/10.1080/07055900.2011.607492.

    • Search Google Scholar
    • Export Citation
  • Lang, Z., Q. H. Wen, B. Yu, L. Sang, and Y. Wang, 2023: Forecast of winter precipitation type based on machine learning method. Entropy, 25, 138, https://doi.org/10.3390/e25010138.

    • Search Google Scholar
    • Export Citation
  • Matte, D., J. M. Thériault, and R. Laprise, 2019: Mixed precipitation occurrences over southern Québec, Canada, under warmer climate conditions using a regional climate model. Climate Dyn., 53, 11251141, https://doi.org/10.1007/s00382-018-4231-2.

    • Search Google Scholar
    • Export Citation
  • McCray, C. D., J. R. Gyakum, and E. H. Atallah, 2020: Regional thermodynamic characteristics distinguishing long- and short-duration freezing rain events over North America. Wea. Forecasting, 35, 657671, https://doi.org/10.1175/WAF-D-19-0179.1.

    • Search Google Scholar
    • Export Citation
  • McCray, C. D., J. M. Thériault, D. Paquin, and É. Bresson, 2022: Quantifying the impact of precipitation-type algorithm selection on the representation of freezing rain in an ensemble of regional climate model simulations. J. Appl. Meteor. Climatol., 61, 11071122, https://doi.org/10.1175/JAMC-D-21-0202.1.

    • Search Google Scholar
    • Export Citation
  • O’Gorman, P. A., and J. G. Dwyer, 2018: Using machine learning to parameterize moist convection: Potential for modeling of climate, climate change, and extreme events. J. Adv. Model. Earth Syst., 10, 25482563, https://doi.org/10.1029/2018MS001351.

    • Search Google Scholar
    • Export Citation
  • Owens, R., and T. Hewson, 2018: ECMWF forecast user guide. ECMWF, https://doi.org/10.21957/m1cs7h.

  • Półrolniczak, M., L. Kolendowicz, B. Czernecki, M. Taszarek, and G. Tóth, 2021: Determination of surface precipitation type based on the data fusion approach. Adv. Atmos. Sci., 38, 387399, https://doi.org/10.1007/s00376-020-0165-9.

    • Search Google Scholar
    • Export Citation
  • Ralph, F. M., and Coauthors, 2005: Improving short-term (0–48 h) cool-season quantitative precipitation forecasting: Recommendations from a USWRP workshop. Bull. Amer. Meteor. Soc., 86, 16191632, https://doi.org/10.1175/BAMS-86-11-1619.

    • Search Google Scholar
    • Export Citation
  • Ramer, J., 1993: An empirical technique for diagnosing precipitation type from model output. Preprints, Fifth Int. Conf. on Aviation Weather Systems, Vienna, VA, Amer. Meteor. Soc., 227–230.

  • Reeves, H. D., 2016: The uncertainty of precipitation-type observations and its effect on the validation of forecast precipitation type. Wea. Forecasting, 31, 19611971, https://doi.org/10.1175/WAF-D-16-0068.1.

    • Search Google Scholar
    • Export Citation
  • Reeves, H. D., A. V. Ryzhkov, and J. Krause, 2016: Discrimination between winter precipitation types based on spectral-bin microphysical modeling. J. Appl. Meteor. Climatol., 55, 17471761, https://doi.org/10.1175/JAMC-D-16-0044.1.

    • Search Google Scholar
    • Export Citation
  • Roebber, P. J., 2009: Visualizing multiple measures of forecast quality. Wea. Forecasting, 24, 601608, https://doi.org/10.1175/2008WAF2222159.1.

    • Search Google Scholar
    • Export Citation
  • Shin, K., K. Kim, J. J. Song, and G. Lee, 2022: Classification of precipitation types based on machine learning using dual-polarization radar measurements and thermodynamic fields. Remote Sens., 14, 3820, https://doi.org/10.3390/rs14153820.

    • Search Google Scholar
    • Export Citation
  • Stewart, R. E., J. M. Thériault, and W. Henson, 2015: On the characteristics of and processes producing winter precipitation types near 0°C. Bull. Amer. Meteor. Soc., 96, 623639, https://doi.org/10.1175/BAMS-D-14-00032.1.

    • Search Google Scholar
    • Export Citation
  • St-Pierre, M., J. M. Thériault, and D. Paquin, 2019: Influence of the model horizontal resolution on atmospheric conditions leading to freezing rain in regional climate simulations. Atmos.–Ocean, 57, 101119, https://doi.org/10.1080/07055900.2019.1583088.

    • Search Google Scholar
    • Export Citation
  • Thompson, G., P. R. Field, R. M. Rasmussen, and W. D. Hall, 2008: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part II: Implementation of a new snow parameterization. Mon. Wea. Rev., 136, 50955115, https://doi.org/10.1175/2008MWR2387.1.

    • Search Google Scholar
    • Export Citation
  • Toms, B. A., E. A. Barnes, and I. Ebert-Uphoff, 2020: Physically interpretable neural networks for the geosciences: Applications to Earth system variability. J. Adv. Model. Earth Syst., 12, e2019MS002002, https://doi.org/10.1029/2019MS002002.

    • Search Google Scholar
    • Export Citation
  • Trenberth, K. E., J. C. Berry, and L. E. Buja, 1993: Vertical interpolation and truncation of model-coordinate data. NCAR Tech. Note NCAR/TN-396+STR, 60 pp., https://doi.org/10.5065/D6HX19NH.

  • Zarzycki, C. M., 2018: Projecting changes in societally impactful northeastern U.S. snowstorms. Geophys. Res. Lett., 45, 12 06712 075, https://doi.org/10.1029/2018GL079820.

    • Search Google Scholar
    • Export Citation
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