Using Artificial Neural Networks to Improve CFS Week-3–4 Precipitation and 2-m Air Temperature Forecasts

Yun Fan aClimate Prediction Center, NOAA/Center for Weather and Climate Prediction, College Park, Maryland

Search for other papers by Yun Fan in
Current site
Google Scholar
PubMed
Close
,
Vladimir Krasnopolsky bEnvironmental Modeling Center, NOAA/Center for Weather and Climate Prediction, College Park, Maryland

Search for other papers by Vladimir Krasnopolsky in
Current site
Google Scholar
PubMed
Close
,
Huug van den Dool aClimate Prediction Center, NOAA/Center for Weather and Climate Prediction, College Park, Maryland

Search for other papers by Huug van den Dool in
Current site
Google Scholar
PubMed
Close
,
Chung-Yu Wu aClimate Prediction Center, NOAA/Center for Weather and Climate Prediction, College Park, Maryland

Search for other papers by Chung-Yu Wu in
Current site
Google Scholar
PubMed
Close
, and
Jon Gottschalck aClimate Prediction Center, NOAA/Center for Weather and Climate Prediction, College Park, Maryland

Search for other papers by Jon Gottschalck in
Current site
Google Scholar
PubMed
Close
Restricted access

Abstract

Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.

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

This article is included in the The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction Special Collection.

Corresponding author: Yun Fan, Yun.Fan@noaa.gov

Abstract

Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, postprocessing methods, from simple bias correction methods to more complicated multiple linear regression–based model output statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as week-3–4 forecasts. In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve week-3–4 precipitation and 2-m temperature forecasts made by the NOAA/NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and covariabilities hidden within the multidimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model week-3–4 precipitation and 2-m temperature forecasts. The results show that to some extent neural network techniques can significantly improve the week-3–4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.

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

This article is included in the The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction Special Collection.

Corresponding author: Yun Fan, Yun.Fan@noaa.gov
Save
  • Baggett, C., K. Nardi, S. Childs, S. Zito, E. Barnes, and E. Maloney, 2018: Skillful subseasonal forecasts of weekly tornado and hail activity using the Madden–Julian Oscillation. J. Geophys. Res. Atmos., 123, 12 66112 675, https://doi.org/10.1029/2018JD029059.

    • Search Google Scholar
    • Export Citation
  • Bauer, P., A. Thorpe, and G. Brunet, 2015: The quiet revolution of numerical weather prediction. Nature, 525, 4755, https://doi.org/10.1038/nature14956.

    • Search Google Scholar
    • Export Citation
  • Chen, M., W. Shi, P. Xie, V. B. S. Silva, V. E. Kousky, R. Wayne Higgins, and J. E. Janowiak, 2008: Assessing objective techniques for gauge-based analyses of global daily precipitation. J. Geophys. Res., 113, D04110, https://doi.org/10.1029/2007JD009132.

    • Search Google Scholar
    • Export Citation
  • Cohen, J., D. Coumou, J. Hwang, L. Mackey, P. Orenstein, S. Totz, and E. Tziperman, 2019: S2S reboot: An argument for greater inclusion of machine learning in subseasonal to seasonal forecasts. Wiley Interdiscip. Rev.: Climate Change, 10, e00567, https://doi.org/10.1002/wcc.567.

    • Search Google Scholar
    • Export Citation
  • DelSole, T., L. Trenary, M. K. Tippett, and K. Pegion, 2017: Predictability of week-3–4 average temperature and precipitation over the contiguous United States. J. Climate, 30, 34993512, https://doi.org/10.1175/JCLI-D-16-0567.1.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., and H. van den Dool, 2008: A global monthly land surface air temperature analysis for 1948–present. J. Geophys. Res., 113, D01103, https://doi.org/10.1029/2007JD008470.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., and H. van den Dool, 2011: Bias correction and forecast skill of NCEP GFS ensemble week-1 and week-2 precipitation, 2-m surface air temperature and soil moisture forecasts. Wea. Forecasting, 26, 355370, https://doi.org/10.1175/WAF-D-10-05028.1.

    • Search Google Scholar
    • Export Citation
  • Fan, Y., K. Gilbert, D. Rudack, W. Yan, S. Scallion, and P. Shafer, 2015: The characteristics of GFS MOS temperature forecast guidance errors for the past decade. Special Symp. on Model Postprocessing and Downscaling, Phoenix, AZ, Amer. Meteor. Soc., 4.2, https://ams.confex.com/ams/95Annual/webprogram/Paper265326.html.

  • Fan, Y., C.-Y. Wu, J. Gottschalck, and V. Krasnopolsky, 2019: Improve CFS Week 3-4 precipitation and 2 meter air temperature forecasts with neural network techniques. 43rd NOAA Annual Climate Diagnostics and Prediction Workshop, Santa Barbara, CA, NOAA/National Weather Service, 59–63, https://www.nws.noaa.gov/ost/climate/STIP/43CDPW/43cdpw-YFan.pdf.

  • Glahn, H. R., and D. A. Lowry, 1972: The use of Model Output Statistics (MOS) in objective weather forecasting. J. Appl. Meteor., 11, 12031211, https://doi.org/10.1175/1520-0450(1972)011<1203:TUOMOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Glahn, H. R., K. Gilbert, R. Cosgrove, D. P. Ruth, and K. Sheets, 2009: The gridding of MOS. Wea. Forecasting, 24, 520529, https://doi.org/10.1175/2008WAF2007080.1.

    • Search Google Scholar
    • Export Citation
  • Gneiting, T., 2014: Calibration of medium-range weather forecast. ECMWF Tech. Memo. 719, 30 pp., https://www.ecmwf.int/en/elibrary/74623-calibration-medium-range-weather-forecasts.

  • Hornik, K., 1991: Approximation capabilities of multilayer feedforward network. Neural Networks, 4, 251257, https://doi.org/10.1016/0893-6080(91)90009-T.

    • Search Google Scholar
    • Export Citation
  • Hornik, K., 1993: Some new results on neural network approximation. Neural Networks, 6, 10691072, https://doi.org/10.1016/S0893-6080(09)80018-X.

    • Search Google Scholar
    • Export Citation
  • Jenney, A., K. Nardi, E. Barnes, and D. Randall, 2019: The seasonality and regionality of MJO impacts on North American temperature. Geophys. Res. Lett., 46, 91939202, https://doi.org/10.1029/2019GL083950.

    • Search Google Scholar
    • Export Citation
  • Johnson, N. C., D. C. Collins, S. B. Feldstein, M. L. L’Heureux, and E. E. Riddle, 2014: Skillful wintertime North American temperature forecasts out to 4 weeks based on the state of ENSO and the MJO. Wea. Forecasting, 29, 2338, https://doi.org/10.1175/WAF-D-13-00102.1.

    • Search Google Scholar
    • Export Citation
  • Klein, W. H., and H. R. Glahn, 1974: Forecasting local weather by means of model output statistics. Bull. Amer. Meteor. Soc., 55, 12171227, https://doi.org/10.1175/1520-0477(1974)055<1217:FLWBMO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Krasnopolsky, V., 2007: Reducing uncertainties in neural network Jacobians and improving accuracy of neural network emulations with NN ensemble approaches. Neural Networks, 20, 454461, https://doi.org/10.1016/j.neunet.2007.04.008.

    • Search Google Scholar
    • Export Citation
  • Krasnopolsky, V., 2013: The Application of Neural Networks in the Earth System Sciences: Neural Network Emulations for Complex Multidimensional Mappings. Springer, 200 pp.

  • Krasnopolsky, V., and Y. Lin, 2012: A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental U.S. Adv. Meteor., 2012, 649450, https://doi.org/10.1155/2012/649450.

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

  • Liu, Y., E. Racah, J. Correa, A. Khosrowshahi, D. Lavers, K. Kunkel, M. Wehner, and W. Collins, 2016: Application of deep convolutional neural networks for detecting extreme weather in climate datasets. arXiv, 1605.01156v1, https://arxiv.org/abs/1605.01156.

  • McGovern, A., K. L. Elmore, D. J. Gagne, S. E. Haupt, C. D. Karstens, R. Lagerquist, T. Smith, and J. K. Williams, 2017: Using artificial intelligence to improve real-time decision-making for high-impact weather. Bull. Amer. Meteor. Soc., 98, 20732090, https://doi.org/10.1175/BAMS-D-16-0123.1.

    • Search Google Scholar
    • Export Citation
  • Mundhenk, B., E. Barnes, E. Maloney, and C. Baggett, 2018: Skillful empirical subseasonal prediction of landfalling atmospheric river activity using the Madden–Julian Oscillation and quasi-biennial oscillation. Nat. Climate Atmos. Sci., 1, 20177, https://doi.org/10.1038/s41612-017-0008-2.

    • Search Google Scholar
    • Export Citation
  • Nardi, K., E. Barnes, and F. Ralph, 2018: Assessment of numerical weather prediction model reforecasts of the occurrence, intensity, and location of atmospheric rivers along the west coast of North America. Mon. Wea. Rev., 146, 33433362, https://doi.org/10.1175/MWR-D-18-0060.1.

    • Search Google Scholar
    • Export Citation
  • Nayak, M., G. Villarini, and D. Lavers, 2014: On the skill of numerical weather prediction models to forecast atmospheric rivers over the central United States. Geophys. Res. Lett., 41, 43544362, https://doi.org/10.1002/2014GL060299.

    • Search Google Scholar
    • Export Citation
  • Nguyen, D., and B. Widrow, 1990: Improving the learning speed of 2-layer neural networks by choosing initial values of the adaptive weights. 1990 IJCNN Int. Joint Conf. on Neural Networks, San Diego, CA, Institute of Electrical and Electronics Engineers, 2126, https://doi.org/10.1109/IJCNN.1990.137819.

  • Pan, B., K. Hsu, A. AghaKouchak, S. Sorooshian, and W. Higgins, 2019: Precipitation prediction skill for the west coast United States: From short to extended range. J. Climate, 32, 161182, https://doi.org/10.1175/JCLI-D-18-0355.1.

    • Search Google Scholar
    • Export Citation
  • Pegion, K., and Coauthors, 2019: The Subseasonal Experiment (SubX): A multimodel subseasonal prediction experiment. Bull. Amer. Meteor. Soc., 100, 20432060, https://doi.org/10.1175/BAMS-D-18-0270.1.

    • Search Google Scholar
    • Export Citation
  • Rasp, S., and S. Lerch, 2018: Neural network for postprocessing ensemble weather forecasts. Mon. Wea. Rev., 146, 38853900, https://doi.org/10.1175/MWR-D-18-0187.1.

    • Search Google Scholar
    • Export Citation
  • Saha, S., and Coauthors, 2006: The NCEP Climate Forecast System. J. Climate, 19, 34833517, https://doi.org/10.1175/JCLI3812.1.

  • Saha, S., and Coauthors, 2014: The NCEP Climate Forecast System version 2. J. Climate, 27, 21852208, https://doi.org/10.1175/JCLI-D-12-00823.1.

    • 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.

    • Search Google Scholar
    • Export Citation
  • Sharma, S., and Coauthors, 2017: Eastern U.S. verification of ensemble precipitation forecasts. Wea. Forecasting, 32, 117139, https://doi.org/10.1175/WAF-D-16-0094.1.

    • Search Google Scholar
    • Export Citation
  • Totz, S., E. Tziperman, D. Coumou, K. Pfeiffer, and J. Cohen, 2017: Winter precipitation forecast in the European and Mediterranean regions using cluster analysis. Geophys. Res. Lett., 44, 12 41812 426, https://doi.org/10.1002/2017GL075674.

    • Search Google Scholar
    • Export Citation
  • Vigaud, N., A. Robertson, and M. Tippett, 2018: Predictability of recurrent weather regimes over North America during winter from submonthly reforecasts. Mon. Wea. Rev., 146, 25592577, https://doi.org/10.1175/MWR-D-18-0058.1.

    • Search Google Scholar
    • Export Citation
  • Wang, L., and A. Robertson, 2018: Week 3–4 predictability over the United States assessed from two operational ensemble prediction systems. Climate Dyn., 52, 58615875, https://doi.org/10.1007/s00382-018-4484-9.

    • Search Google Scholar
    • Export Citation
  • Warner, T. T., 2011: Numerical Weather and Climate Prediction. Cambridge University Press, 550 pp.

  • Wick, G., P. Neiman, F. Ralph, and T. Hamill, 2013: Evaluation of forecasts of the water vapor signature of atmospheric rivers in operational numerical weather prediction models. Wea. Forecasting, 28, 13371352, https://doi.org/10.1175/WAF-D-13-00025.1.

    • Search Google Scholar
    • Export Citation
  • Wilson, L. J., and M. Vallee, 2002: The Canadian Updateable Model Output Statistics (UMOS) system: Design and development tests. Wea. Forecasting, 17, 206222, https://doi.org/10.1175/1520-0434(2002)017<0206:TCUMOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Wilson, L. J., and M. Vallee, 2003: The Canadian Updateable Model Output Statistics (UMOS) system: Validation against perfect prog. Wea. Forecasting, 18, 288302, https://doi.org/10.1175/1520-0434(2003)018<0288:TCUMOS>2.0.CO;2.

    • Search Google Scholar
    • Export Citation
  • Zhong, Q., J. Li, L. Zhang, R. Ding, and B. Li, 2018: Predictability of tropical cyclone intensity over the western North Pacific using the IBTrACS dataset. Mon. Wea. Rev., 146, 27412755, https://doi.org/10.1175/MWR-D-17-0301.1.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 1749 604 53
Full Text Views 1689 420 9
PDF Downloads 1366 330 9