• Arbogast, P., A. Hally, J. Cheung, J. Heijstek, A. Marsman, and J.-L. Brenguier, 2015: Recommendations on trajectory selection in flight planning based on weather uncertainty. Proc. Fifth SESAR Innovation Days (SID2015), Bologna, Italy, Meteo-France, 17 pp., https://library.wmo.int/doc_num.php?explnum_id=4390.

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

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellier, J., G. Bontron, and I. Zin, 2017: Using meteorological analogues for reordering postprocessed precipitation ensembles in hydrological forecasting. Water Resour. Res., 53, 10 08510 107, https://doi.org/10.1002/2017WR021245.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bellier, J., I. Zin, and G. Bontron, 2018: Generating coherent ensemble forecasts after hydrological postprocessing: Adaptations of ECC-based methods. Water Resour. Res., 54, 57415762, https://doi.org/10.1029/2018WR022601.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Berndt, D. J., and J. Clifford, 1994: Using dynamic time warping to find patterns in time series. KDD Workshop, Seattle, WA, Vol. 10, 359–370.

  • Bouttier, F., and L. Raynaud, 2018a: Clustering and selection of boundary conditions for limited-area ensemble prediction. Quart. J. Roy. Meteor. Soc., 144, 23812391, https://doi.org/10.1002/qj.3304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bouttier, F., and L. Raynaud, 2018b: Clustering and selection of boundary conditions for limited area ensemble prediction. Quart. J. Roy. Meteor. Soc., 144, 23812391, https://doi.org/10.1002/qj.3304.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Calanca, P., D. Bolius, A. Weigel, and M. Liniger, 2011: Application of long-range weather forecasts to agricultural decision problems in Europe. J. Agric. Sci., 149, 1522, https://doi.org/10.1017/S0021859610000729.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Candille, G., and O. Talagrand, 2005: Evaluation of probabilistic prediction systems for a scalar variable. Quart. J. Roy. Meteor. Soc., 131, 21312150, https://doi.org/10.1256/qj.04.71.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Candille, G., C. Côté, P. Houtekamer, and G. Pellerin, 2007: Verification of an ensemble prediction system against observations. Mon. Wea. Rev., 135, 26882699, https://doi.org/10.1175/MWR3414.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Chavent, F., 1983: Etude de la dynamique des populations d’Eudémis de la vigne (Lobesia botrana Den. et Schiff.). Adaptation d’un modèle d’évolution aux conditions climatiques de Provence, mémoire de fin d’études.

  • Christ, E. H., P. J. Webster, G. D. Collins, V. E. Toma, and S. A. Byrd, 2015: Using precipitation forecasts to irrigate cotton. J. Cotton Sci., 19, 351358, https://www.cotton.org/journal/2015-19/3/upload/JCS19-351.pdf.

    • Search Google Scholar
    • Export Citation
  • Clark, M., S. Gangopadhyay, L. Hay, B. Rajagopalan, and R. Wilby, 2004: The Schaake shuffle: A method for reconstructing space–time variability in forecasted precipitation and temperature fields. J. Hydrometeor., 5, 243262, https://doi.org/10.1175/1525-7541(2004)005<0243:TSSAMF>2.0.CO;2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cooke, B. M., and et al. , 2006: The Epidemiology of Plant Diseases. Vol. 2. Springer, 568 pp.

    • Crossref
    • Export Citation
  • Descamps, L., C. Labadie, A. Joly, E. Bazile, P. Arbogast, and P. Cébron, 2015: PEARP, the Météo-France short-range ensemble prediction system. Quart. J. Roy. Meteor. Soc., 141, 16711685, https://doi.org/10.1002/qj.2469.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Fundel, V. J., N. Fleischhut, S. M. Herzog, M. Göber, and R. Hagedorn, 2019: Promoting the use of probabilistic weather forecasts through a dialogue between scientists, developers and end-users. Quart. J. Roy. Meteor. Soc., 145, 210231, https://doi.org/10.1002/qj.3482.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ghaffary, M. T., 2011: Efficacy and mapping of resistance to Mycosphaerella graminicola in wheat. Ph.D. thesis, Wageningen University, https://library.wur.nl/WebQuery/wda/abstract/1964546.

  • Giorgino, T., and et al. , 2009: Computing and visualizing dynamic time warping alignments in R: The DTW package. J. Stat. Software, 31, 124, https://doi.org/10.18637/jss.v031.i07.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., 2014: Calibration of medium-range weather forecasts. ECMWF Tech. Memo. 719, ECMWF, 30 pp., https://doi.org/10.21957/8xna7glta.

    • Crossref
    • Export Citation
  • Gneiting, T., and A. E. Raftery, 2005: Weather forecasting with ensemble methods. Science, 310, 248249, https://doi.org/10.1126/science.1115255.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Gneiting, T., A. E. Raftery, A. H. Westveld III, and T. Goldman, 2005: Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation. Mon. Wea. Rev., 133, 10981118, https://doi.org/10.1175/MWR2904.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hagelin, S., J. Son, R. Swinbank, A. McCabe, N. Roberts, and W. Tennant, 2017: The Met Office convective-scale ensemble MOGREPS-UK. Quart. J. Roy. Meteor. Soc., 143, 28462861, https://doi.org/10.1002/qj.3135.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hanley, J. A., and B. J. McNeil, 1982: The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143, 2936, https://doi.org/10.1148/radiology.143.1.7063747.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hemri, S., M. Scheuerer, F. Pappenberger, K. Bogner, and T. Haiden, 2014: Trends in the predictive performance of raw ensemble weather forecasts. Geophys. Res. Lett., 41, 91979205, https://doi.org/10.1002/2014GL062472.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kollers, S., and et al. , 2013: Genetic architecture of resistance to Septoria tritici blotch (Mycosphaerella graminicola) in European winter wheat. Mol. Breed., 32, 411423, https://doi.org/10.1007/s11032-013-9880-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuhn, H. W., 1955: The Hungarian method for the assignment problem. Nav. Res. Logist. Q., 2, 8397, https://doi.org/10.1002/nav.3800020109.

  • Leutbecher, M., and T. N. Palmer, 2008: Ensemble forecasting. J. Comput. Phys., 227, 35153539, https://doi.org/10.1016/j.jcp.2007.02.014.

  • Miedaner, T., Y. Zhao, M. Gowda, C. F. H. Longin, V. Korzun, E. Ebmeyer, E. Kazman, and J. C. Reif, 2013: Genetic architecture of resistance to Septoria tritici blotch in European wheat. BMC Genomics, 14, 858, https://doi.org/10.1186/1471-2164-14-858.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Molteni, F., R. Buizza, T. N. Palmer, and T. Petroliagis, 1996: The ECMWF ensemble prediction system: Methodology and validation. Quart. J. Roy. Meteor. Soc., 122, 73119, https://doi.org/10.1002/qj.49712252905.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Moyer, M. M., D. M. Gadoury, W. F. Wilcox, and R. C. Seem, 2016: Weather during critical epidemiological periods and subsequent severity of powdery mildew on grape berries. Plant Dis., 100, 116124, https://doi.org/10.1094/PDIS-12-14-1278-RE.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olatinwo, R., T. Prabha, J. Paz, D. Riley, and G. Hoogenboom, 2011: The Weather Research and Forecasting (WRF) Model: Application in prediction of TSWV-vectors populations. J. Appl. Entomol., 135, 8190, https://doi.org/10.1111/j.1439-0418.2010.01539.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Olatinwo, R., T. Prabha, J. Paz, and G. Hoogenboom, 2012: Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) Model. Int. J. Biometeor., 56, 259268, https://doi.org/10.1007/s00484-011-0425-6.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Palmer, T., 2019: The ECMWF ensemble prediction system: Looking back (more than) 25 years and projecting forward 25 years. Quart. J. Roy. Meteor. Soc., 145, 1224, https://doi.org/10.1002/qj.3383.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pappenberger, F., F. Wetterhall, E. Dutra, F. Di Giuseppe, K. Bogner, L. Alfieri, and H. L. Cloke, 2013: Seamless forecasting of extreme events on a global scale. Climate and Land Surface Changes in Hydrology, E. Boegh et al., Eds., IAHS Publication, 3–10.

  • Pertot, I., and et al. , 2017: A critical review of plant protection tools for reducing pesticide use on grapevine and new perspectives for the implementation of IPM in viticulture. Crop Prot., 97, 7084, https://doi.org/10.1016/j.cropro.2016.11.025.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pinson, P., H. A. Nielsen, H. Madsen, and G. Kariniotakis, 2009: Skill forecasting from ensemble predictions of wind power. Appl. Energy, 86, 13261334, https://doi.org/10.1016/j.apenergy.2008.10.009.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raftery, A. E., T. Gneiting, F. Balabdaoui, and M. Polakowski, 2005: Using Bayesian model averaging to calibrate forecast ensembles. Mon. Wea. Rev., 133, 11551174, https://doi.org/10.1175/MWR2906.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Raynaud, L., and F. Bouttier, 2016: Comparison of initial perturbation methods for ensemble prediction at convective scale. Quart. J. Roy. Meteor. Soc., 142, 854866, https://doi.org/10.1002/qj.2686.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Sakoe, H., S. Chiba, A. Waibel, and K. Lee, 1990: Dynamic programming algorithm optimization for spoken word recognition. Readings in Speech Recognition, A. Waibel and K.-F. Lee, Eds., Elsevier Science, 159–165.

    • Crossref
    • Export Citation
  • Schefzik, R., 2011: Ensemble copula coupling. M.S. thesis, Faculty of Mathematics and Informatics, University of Heidelberg, Germany.

  • Schefzik, R., and et al. , 2013: Uncertainty quantification in complex simulation models using ensemble copula coupling. Stat. Sci., 28, 616640, https://doi.org/10.1214/13-STS443.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scheuerer, M., 2014: Probabilistic quantitative precipitation forecasting using ensemble model output statistics. Quart. J. Roy. Meteor. Soc., 140, 10861096, https://doi.org/10.1002/qj.2183.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Shipp, J. L., and N. D. Clarke, 1999: Decision tools for integrated pest management. Integrated Pest and Disease Management in Greenhouse Crops, R. Albajes et al., Eds., Springer, 168–182.

    • Crossref
    • Export Citation
  • Suffert, F., I. Sache, and C. Lannou, 2011: Early stages of Septoria tritici blotch epidemics of winter wheat: Build-up, overseasoning, and release of primary inoculum. Plant Pathol., 60, 166177, https://doi.org/10.1111/j.1365-3059.2010.02369.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Taillardat, M., A.-L. Fougères, P. Naveau, and O. Mestre, 2019: Forest-based and semiparametric methods for the postprocessing of rainfall ensemble forecasting. Wea. Forecasting, 34, 617634, https://doi.org/10.1175/WAF-D-18-0149.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Thiéry, D., and et al. , 2013: Histoire de l’installation de quelques ravageurs. Interactions Insectes-Plantes, N. Sauvion et al., Eds., Quae, 623–662.

    • Crossref
    • Export Citation
  • Thiéry, D., P. Louâpre, L. Muneret, A. Rusch, G. Sentenac, F. Vogelweith, C. Iltis, and J. Moreau, 2018: Biological protection against grape berry moths: A review. Agron. Sustain. Dev., 38, 15, https://doi.org/10.1007/s13593-018-0493-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wetterhall, F., and F. Di Giuseppe, 2018: The benefit of seamless forecasts for hydrological predictions over Europe. Hydrol. Earth Syst. Sci., 22, 34093420, https://doi.org/10.5194/hess-22-3409-2018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • White, C. J., and et al. , 2017: Potential applications of Subseasonal-to-Seasonal (S2S) predictions. Meteor. Appl., 24, 315325, https://doi.org/10.1002/met.1654.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Wilcoxon, F., S. Katti, and R. A. Wilcox, 1970: Critical values and probability levels for the Wilcoxon rank sum test and the Wilcoxon signed rank test. Selected Tables in Mathematical Statistics, Vol. 1, American Mathematical Society, 171–259.

  • Worsnop, R. P., M. Scheuerer, and T. M. Hamill, 2019: Extended-range probabilistic fire-weather forecasting based on ensemble model output statistics and ensemble copula coupling. Mon. Wea. Rev., 148, 499521, https://doi.org/10.1175/MWR-D-19-0217.1.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zamo, M., 2016: Statistical post-processing of deterministic and ensemble wind speed forecasts on a grid. Ph.D. thesis, University of Paris-Saclay, Gif-sur-Yvette, France, 166 pp.

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Design and Evaluation of Calibrated and Seamless Ensemble Weather Forecasts for Crop Protection Applications

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  • 1 a CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • | 2 b INRAE, Castanet-Tolosan, France
  • | 3 c ACTA-French Technical Institute, Castanet-Tolosan, France
  • | 4 d Institut Français de la Vigne et du Vin (IFV), UMT SEVEN, Bordeaux, France
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Abstract

Agriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required.

© 2021 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: Ivana Aleksovska, ivana.aleksovska@meteo.fr

Abstract

Agriculture is a highly weather-dependent activity, and climatic conditions impact both directly crop growth and indirectly diseases and pest developments causing yield losses. Weather forecasts are now a major component of various decision-support systems that assist farmers to optimize the positioning of crop protection treatments. However, properly accounting for weather uncertainty in these systems still remains a challenge. In this paper, three global and regional ensemble prediction systems (EPSs), covering different spatiotemporal scales, are coupled to a temperature-driven developmental model for grapevine moths in order to provide probabilistic forecasts of treatment dates. It is first shown that a parametric postprocessing of the EPSs significantly improves the prediction of treatment dates. Anticipating the need for phytosanitary treatments also requires seamless weather forecasts from the next hour to subseasonal time scales. An approach is presented to design seamless ensemble forecasts from the combination of the three EPSs used. The proposed method is able to leverage the increased performance of high-resolution EPS at short ranges, while ensuring a smooth transition toward larger-scale EPSs for longer ranges. The added value of this seamless integration on agronomic predictions is, however, difficult to assess with the current experimental setup. Additional simulations over a larger number of locations and years may be required.

© 2021 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: Ivana Aleksovska, ivana.aleksovska@meteo.fr
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