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We have compiled list of scientific papers, conference presentations and other reports of research using TIGGE data. We survey the literature every year or so to update the list, but we encourage all researchers using TIGGE data to inform us, so that we can publicise your work more quickly via this website.

How to refer to TIGGE in a paper

TIGGE DOI for scientific papers: https://doi.org/10.1175/2010BAMS2853.1

Please use the following acknowledgement to refer to TIGGE:
"This work is based on TIGGE data. TIGGE (The Interactive Grand Global Ensemble) is an initiative of the World Weather Research Programme (WWRP).”

It is important to mention the data source of your research to be able to keep the TIGGE project alive for longer.

Regarding dataset source, please cite:


2019 2018 2017 2016 2015 2014  2013  2012  2011  2010  2009  2008  2007  2006  2005

(* below means number of articles weakly related to TIGGE)

2019 (2) 

  • Tang N J et al. (2019). Characteristic of adiabatic and diabatic water vapor transport from the troposphere to the stratosphere over the Tibetan Plateau and its comparison with the Rocky Mountains in the Summer. Chinese Journal of Atmospheric Sciences (in Chinese, in press), doi:10.3878/j.issn.1006-9895.1804.17255.
  • Weihong Qian, Jing Huang (2019) Applying the anomaly-based weather analysis on Beijing severe haze episodes. Science of the Total Environment, 647, 878–887, https://doi.org/10.1016/j.scitotenv.2018.07.408

2018 (26) 

  • Agogbuo CN, Nwagbara MO, Bekele E, Olusegun A (2017) Evaluation of Selected Numerical Weather Prediction Models for a Case of Widespread Rainfall over Central and Southern Nigeria. J Environ Anal Toxicol 7: 491. doi: 10.4172/2161- 0525.1000491
  • Cai et al. (2018) Improving TIGGE precipitation forecasts using an SVR ensemble approach in the Huaihe River Basin. Advances in Meteorology,vol. 2018. https://doi.org/10.1155/2018/7809302
  • Cafaro, C., Frame, T. H., Methven, J., Roberts, N. and Broecker, J. (2018). The added value of convection-permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble. Q. J. Roy. Met. Soc.

  • Cheng-Chien Liu *, Ming-Chang Shieh, Ming-Hsun Ko, Kung-Hwa Wang (2018) Flood Prevention and Emergency Response System Powered by Google Earth Engine, Remote Sensing, 10, 1283
  • M. David et P. Lauret (2018) Solar Radiation Probabilistic Forecasting », in Wind Field and Solar Radiation Characterization and Forecasting, R. Perez, Éd. Cham: Springer International Publishing, p. 201‑227.
  • Deng H., et al.(2018) Changes of snowfall under warmer and wetter in the Tianshan Mountains. Scientia Geographica Sinica, 38(11):1932-1941, http:/10.13249/j.cnki.sgs.2018.11.021[Chinese Journal with english abstract]
  • Duriez O, Péron G, Gremillet, D, Sforzi A, Monti F. (2018) Migrating ospreys use thermal uplift over the open sea. Biol. Lett. 20180687. http://dx.doi.org/10.1098/rsbl.2018.0687
  • Elless, T.J. and R.D. Torn, 2018: African Easterly Wave Forecast Verification and Its Relation to Convective Errors within the ECMWF Ensemble Prediction System. Wea. Forecasting, 33, 461–477, https://doi.org/10.1175/WAF-D-17-0130.1
  • Falck et al (2018) Improving the use of ground-based radar rainfall data for monitoring and predicting floods in the iguacu river basin. Journal of Hydrology, 567:626–636, 2018. https://doi.org/10.1016/j.jhydrol.2018.10.046
  • González-Arribas, D., Sanjurjo-Rivo, M., Soler, M. (2018) Multiobjective Optimisation of Aircraft Trajectories Under Wind Uncertainty Using GPU Parallelism and Genetic Algorithms. Computational Methods in Applied Sciences, vol 49. Springer, Cham https://link.springer.com/chapter/10.1007%2F978-3-319-89890-2_29
  • González-Alemán et al. (2018) Use of Ensemble Forecasts to Investigate Synoptic Influences on the Structural Evolution and Predictability of Hurricane Alex (2016) in the Midlatitudes. Mon. Wea. Rev., 146, 3143-3162, https://doi.org/10.1175/MWR-D-18-0015.1
  • Hamill, T. M., and Scheuerer, M., 2018: Probabilistic precipitation forecast postprocessing using quantile mapping and rank-weighted best-member dressing. Mon. Wea. Rev., 146, 4079-4098. Also: Online appendix 1.
  • Herman, G.R., and R.S. Schumacher (2018) Money doesn't grow on trees, but forecasts do: Forecasting extreme precipitation with random forests. Monthly Weather Review, 146, 1571-1600.
  • Jaiswal, Neeru, C. M. Kishtawal, and Swati Bhomia. "Similarity-based multi-model ensemble approach for 1–15-day advance prediction of monsoon rainfall over India." Theoretical and Applied Climatology 132.1-2 (2018): 639-645.
  • Kikuchi, Ryota, et al. (2018) Nowcasting algorithm for wind fields using ensemble forecasting and aircraft flight data. Meteorological Applications 25.3, 365-375, https://doi.org/10.1002/met.1704
  • Luitel, B., G. Villarini, and G.A. Vecchi (2018) Verification of the skill of numerical weather prediction models in forecasting rainfall from U.S. landfalling tropical cyclones, Journal of Hydrology, 556, 1026-1037
  • Nakanowatari, T., Inoue, J., Sato, K., Bertino, L., Xie, J., Matsueda, M., Yamagami, A., Sugimura, T., Yabuki, H., and Otsuka, N.: Medium-range predictability of early summer sea ice thickness distribution in the East Siberian Sea based on the TOPAZ4 ice–ocean data assimilation system, The Cryosphere, 12, 2005-2020, https://doi.org/10.5194/tc-12-2005-2018, 2018.
  • Martinez-Alvarado, O., Maddison, J., Gray, S. and Williams, K. (2018) Atmospheric blocking and upper-level Rossby wave forecast skill dependence on model configuration. Quarterly Journal of the Royal Meteorological Society, 144, 2165-2181, https://doi.org/10.1002/qj.3326
  • MA Hongyuan,HUANG Jianxi,HUANG Hai,ZHANG Xiaodong,ZHU Deha. (2018) Ensemble Forecasting of Regional Yield of Winter Wheat Based on WOFOST Model Using Historical Metrological Dataset[J].Transactions of the Chinese Society for Agricultural Machinery, 49(9):257-266.
  • Parker, T., Woollings, T., and Weisheimer, A. (2018) Ensemble sensitivity analysis of Greenland blocking in medium-range forecasts, Quart. J. Roy. Meteor. Soc, 144, 2358-2379, https://doi.org/:10.1002/qj.3391
  • Tomasella et al (2018) Probabilistic flood forecasting in the Doce basin in Brazil: Effects of the basin scale and orientation and the spatial distribution of rainfall. Journal of Flood Risk Management, 0(0):e12452, 2018/11/26 2018. https://doi.org/10.1111/jfr3.12452
  • Torn, R. D., T. J. Elless, P. Papin, C. A. Davis, 2018: The sensitivity of TC track forecasts within deformation steering flows. Mon. Wea. Rev., 146, 3183–3201, https://doi.org/10.1175/MWR-D18-0153.1 
  • Uno, F., et al., (2018) A diagnostic for advance detection of forecast busts of regional surface solar radiation using multi-center grand ensemble forecasts , Solar Energy, 162, 196- 204.https://doi.org/10.1016/j.solener.2017.12.060
  • Yamagami et al. (2018) Predictability of the 2012 great Arctic cyclone on medium-range timescales. Polar Science, 15, 13-23, https://doi.org/10.1016/j.polar.2018.01.002 
  • Yamagami et al. (2018) Medium-range forecast skill for extraordinary Arctic cyclones in summer of 2008- 2016. Geophys. Res. Lett., 45, https://doi.org/10.1029/2018GL077278
  • Wang Haibo et al.(2018). Effects of different cloud overlapping patameters on simulated total cloud fraction over the globe and East Asian region.Acta Meteorologica Sinica, 76(5):767- 778,doi:10.11676/qxxb2018.027

2017 (27) 

  • Anitha Gera, et al. (2017) Assessment of marine weather forecasts over the Indian sector of Southern Ocean. Polar Science., 13, 1-12, ISSN 1873- 9652,https://doi.org/10.1016/j.polar.2017.04.003
  • Bhomia, Swati, Neeru Jaiswal, and C. M. Kishtawal. "Accuracy assessment of rainfall prediction by global models during the landfall of tropical cyclones in the North Indian Ocean." Meteorological Applications 24.3 (2017): 503-511. 
  • Campbell, W.F., E.A. Satterfield, B. Ruston, and N.L. Baker (2017), Accounting for Correlated Observation Error in a Dual-Formulation 4D Variational Data Assimilation System, Monthly Weather Review
  • Cannon, D. J., Brayshaw, D. J., Methven, J. and Drew, D. (2017). Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation. Meteorol. Zeitschrift, 26 (3), 239-252, doi:10.1127/metz/2016/0751

  • Gerossier, A., Girard, R., Kariniotakis, G., & Michiorri, A. (2017). Probabilistic day-ahead forecasting of household electricity demand. CIRED-Open Access Proceedings Journal, 2017(1), 2500-2504
  • González-Arribas, D., Soler, M., Sanjurjo-Rivo, M. (2017) Robust Aircraft Trajectory Planning Under Wind Uncertainty Using Optimal Control, Journal of Guidance, Control and Dynamics, 41 (3), 673-688, https://doi.org/10.2514/1.G002928

  • Hamill, T.M., E. Engle, D. Myrick, M. Peroutka, C. Finan, and M. Scheuerer, 2017: The U.S. National Blend of Models for Statistical Postprocessing of Probability of Precipitation and Deterministic Precipitation Amount. Mon. Wea. Rev., 145, 3441-3463, https://journals.ametsoc.org/doi/10.1175/MWR-D-16-0331.1

  • JUN, Sanghee, et al.  (2017) An Alternative Multi-Model Ensemble Forecast for Tropical Cyclone Tracks in the Western North Pacific. Atmosphere, 8.9: 174. 
  • Leonardo, N.M. and B.A. Colle (2017), Verification of Multimodel Ensemble Forecasts of North Atlantic Tropical Cyclones, Weather and Forecasting
  • Loeser, C.F., M.A. Herrera, and I. Szunyogh (2017), An Assessment of the Performance of the Operational Global Ensemble Forecast Systems in Predicting the Forecast Uncertainty, Weather and Forecasting
  • Lu, X., X. Wang, Y. Li, M. Tong and X. Ma. (2017) GSI-based ensemble-variational hybrid data assimilation for HWRF for hurricane initialization and prediction: impact of various error covariances for airborne radar observation assimilation. Q. J. R. Meteorol. Soc., 143: 223-239. doi: 10.1002/qj.2914.
  • McDaniel, R.L., C. Munster, C., Nielsen-Gammon, J. (2017) Crop and location specific agricultural drought quantification: Part III – Forecasting water stress and yield trends. Trans. Amer. Soc. Agric. Biol. Eng., 60(3), 741-752, https://doi.org/10.13031/trans.11651
  • Parsons, D.B., M. Beland, D. Burridge, P. Bougeault, G. Brunet, J. Caughey, S.M. Cavallo, M. Charron, H.C. Davies, A.D. Niang, V. Ducrocq, P. Gauthier, T.M. Hamill, P.A. Harr, S.C. Jones, R.H. Langland, S.J. Majumdar, B.N. Mills, M. Moncrieff, T. Nakazawa, T. Paccagnella, F. Rabier, J. Redelsperger, C. Riedel, R.W. Saunders, M.A. Shapiro, R. Swinbank, I. Szunyogh, C. Thorncroft, A.J. Thorpe, X. Wang, D. Waliser, H. Wernli, and Z. Toth  (2017): THORPEX Research and the Science of Prediction. Bull. Amer. Meteor. Soc., 98, 807–830, https://doi.org/10.1175/BAMS-D-14-00025.1
  • Quandt, L., J.H. Keller, O. Martius, and S.C. Jones (2017), Forecast Variability of the Blocking System over Russia in Summer 2010 and Its Impact on Surface Conditions, Weather and Forecasting
  • Qian WH, Leung JCH, Luo WM, Du J, Gao JD. (2017) An index of anomalous convective instability to detect tornadic and hail storms. Meteorol Atmos Phys. DOI 10.1007/s00703-017-0576-z.
  • Qu B, Zhang X, Pappenberger F, Zhang T, Fang Y. (2017) Multi-Model Grand Ensemble Hydrologic Forecasting in the Fu River Basin Using Bayesian Model Averaging. Water, 9, 74, https://doi.org/10.3390/w9020074
  • Sasaki, W. (2017) Predictability of global offshore wind and wave power, International Journal of Marine Energy, 17, 98-109, DOI: 10.1016/j.ijome.2017.01.003
  • Satterfield, E., D. Hodyss, D.D. Kuhl, and C.H. Bishop (2017), Investigating the Use of Ensemble Variance to Predict Observation Error of Representation, Monthly Weather Review
  • S. Karuna sagar et.al., (2017), Prediction skill of Rainstorm events over India in the TIGGE weather prediction models, Atmospheric Research, 198, 194-204.
  • Vikram Khade, Jaison Kurian, Ping Chang, Istvan Szunyogh, Kristen Thyng, Raffaele Montuoro, 2017. Oceanic ensemble forecasting in the Gulf of Mexico: An application to the case of the Deep Water Horizon oil spill. Ocean Modelling, Volume 113, 171–184. http://www.sciencedirect.com/science/article/pii/S1463500317300525
  • Ying, Y. and F. Zhang (2017), Practical and Intrinsic Predictability of Multiscale Weather and Convectively Coupled Equatorial Waves during the Active Phase of an MJO, Journal of the Atmospheric Sciences
  • Yamaguchi, M., J. Ishida, H. Sato, and M. Nakagawa (2017), WGNE Intercomparison of Tropical Cyclone Forecasts by Operational NWP Models: A Quarter Century and Beyond, Bulletin of the American Meteorological Society
  • Yamaguchi, M. and N. Koide (2017), Tropical Cyclone Genesis Guidance Using the Early Stage Dvorak Analysis and Global Ensembles, Weather and Forecasting
  • Tirkey, S., & Mukhopadhyay, P. (2017). Evaluation of NCEP TIGGE short-range forecast for Indian summer monsoon intraseasonal oscillation. Theoretical and Applied Climatology, 129(3- 4), 745-782. DOI:10.1007/s00704-016-1811-0
  • A. Thiboult, F. Anctil, M.H. Ramos (2017) How does the quantification of uncertainties affect the quality and value of flood early warning systems?, Journal of Hydrology, Volume 551, 2017, Pages 365-373, ISSN 0022-1694, https://doi.org/10.1016/j.jhydrol.2017.05.014.
  • Zheng, M., E.K. Chang, B.A. Colle, Y. Luo, and Y. Zhu (2017), Applying Fuzzy Clustering to a Multimodel Ensemble for U.S. East Coast Winter Storms: Scenario Identification and Forecast Verification, Weather and Forecasting
  • Xiping Zhang and Hui Yu (2017), A Probabilistic Tropical Cyclone Track Forecast Scheme Based on the Selective Consensus of Ensemble Prediction Systems, Weather and Forecasting

2016 (21) 

  • Bauer, P., Magnusson, L., Thépaut, J.-N. and Hamill, T. M. (2016), Aspects of ECMWF model performance in polar areas. Q.J.R. Meteorol. Soc., 142: 583–596. doi:10.1002/qj.2449
  • Bhomia, Swati, et al. "Multimodel Prediction of Monsoon Rain Using Dynamical Model Selection." IEEE Transactions on Geoscience and Remote Sensing 54.5 (2016): 2911-2917. Jaiswal, Neeru, et al. "Multi-model ensemble-based probabilistic prediction of tropical cyclogenesis using TIGGE model forecasts." Meteorology and Atmospheric Physics 128.5 (2016): 601-611. 
  • Colby, Frank P. Jr., 2016: Tropical Cyclone Track and Intensity Errors in the 2015 NCEP Global Ensemble Model. Proceedings of the 32nd Conference on Hurricanes and Tropical Meteorology, San Juan, PR, 18-22 April, 2016

  • Chen, P., Yu, H., Brown, B., Chen, G. and Wan, R. (2016), A probabilistic climatology-based analogue intensity forecast scheme for tropical cyclones. Q.J.R. Meteorol. Soc., 142: 2386–2397. doi:10.1002/qj.2831
  • Don, P.K., J.L. Evans, F. Chiaromonte, and A.M. Kowaleski (2016), Mixture-Based Path Clustering for Synthesis of ECMWF Ensemble Forecasts of Tropical Cyclone Evolution, Monthly Weather Review
  • Dong, L. and F. Zhang (2016), OBEST: An Observation-Based Ensemble Subsetting Technique for Tropical Cyclone Track Prediction, Weather and Forecasting
  • Du, Y., Qi, L. and Cao, X. (2016), Selective ensemble-mean technique for tropical cyclone track forecast by using time-lagged ensemble and multi-centre ensemble in the western North Pacific. Q.J.R. Meteorol. Soc., 142: 2452–2462. doi:10.1002/qj.2838
  • FAN, F. M.; SCHWANENBERG, D. ; ALVARADO, R. ; REIS, A. A. ; COLLISCHONN, W. ; NAUMANN, S. (2016) . Performance of Deterministic and Probabilistic Hydrological Forecasts for the Short-Term Optimization of a Tropical Hydropower Reservoir. Water Resources Management, p. 1-17.
  • Halperin, D.J., H.E. Fuelberg, R.E. Hart, and J.H. Cossuth (2016), Verification of Tropical Cyclone Genesis Forecasts from Global Numerical Models: Comparisons between the North Atlantic and Eastern North Pacific Basins, Weather and Forecasting
  • Herrera, M.A., I. Szunyogh, and J. Tribbia (2016), Forecast Uncertainty Dynamics in the THORPEX Interactive Grand Global Ensemble (TIGGE), Monthly Weather Review
  • Jung, T., N.D. Gordon, P. Bauer, D.H. Bromwich, M. Chevallier, J.J. Day, J. Dawson, F. Doblas-Reyes, C. Fairall, H.F. Goessling, M. Holland, J. Inoue, T. Iversen, S. Klebe, P. Lemke, M. Losch, A. Makshtas, B. Mills, P. Nurmi, D. Perovich, P. Reid, I.A. Renfrew,  G. Smith, G. Svensson, M. Tolstykh, and Q. Yang2016),  Advancing Polar Prediction Capabilities on Daily to Seasonal Time Scales, Bulletin of the American Meteorological Society
  • Kowaleski, A.M. and J.L. Evans (2016), Regression Mixture Model Clustering of Multimodel Ensemble Forecasts of Hurricane Sandy: Partition Characteristics, Monthly Weather Review
  • Lamberson, W.S., R.D. Torn, L.F. Bosart, and L. Magnusson, 2016: Diagnosis of the Source and Evolution of Medium-Range Forecast Errors for Extratropical Cyclone Joachim. Wea. Forecasting, 31, 1197–1214, https://doi.org/10.1175/WAF-D-16-0026.1
  • Lee, H.-J., Lee, W.-S. and Yoo, J. H. (2016), Assessment of medium-range ensemble forecasts of heat waves. Atmos. Sci. Lett., 17: 19–25. doi:10.1002/asl.593
  • Martinez-Alvarado, O., Madonna, E., Gray, S. L. and Joos, H. (2016) A route to systematic error in forecasts of Rossby waves. Quarterly Journal of the Royal Meteorological Society, 142, 196- 210, https://doi.org/10.1002/qj.2645
  • Ushiyama, Sayama, Iwami (2016), Ensemble flood forecasting caused by typhoons Talas and Roke at Hiyoshi dam basin. Journal of Diaster Research, Vol.11(6),1032-1039.
  • Thiboult, A. and Anctil, F. and Boucher, M.-A. (2016) Accounting for three sources of uncertainty in ensemble hydrological forecasting, Hydrology and Earth System Sciences, 20, 5, 1809--1825, 10.5194/hess-20- 1809-2016
  • Tsing-Chang Chen, Jenq-Dar Tsay, Eugene S. Takle (2016), A Forecast Advisory for Afternoon Thunderstorm Occurrence in the Taipei Basin during Summer Developed from Diagnostic Analysis, Weather and Forecasting
  • Yang Bo, Sun Jisong, Mao Xu,Lin yinjing. 2016. Multi-scale characteristics of atmospheric circulation related to short-time strong rainfall events in Beijing. Acta Meteorologica Sinica, 74(6):919-934
  • Zhou, B. and P. Zhai (2016), A New Forecast Model Based on the Analog Method for Persistent Extreme Precipitation, Weather and Forecasting
  • Zsótér, E., F. Pappenberger, P. Smith, R.E. Emerton, E. Dutra, F. Wetterhall, D. Richardson, K. Bogner, and G. Balsamo (2016), Building a Multimodel Flood Prediction System with the TIGGE Archive, Journal of Hydrometeor.

2015 (22) 

  • Buizza, R. (2015), The TIGGE global, medium-range ensembles. ECMWF Technical Memorandum.,739, http://www.ecmwf.int/en/elibrary/7529-tigge-global-medium-range-ensembles
  • Carolyn A. Reynolds, Elizabeth A. Satterfield, Craig H. Bishop (2015), Using Forecast Temporal Variability to Evaluate Model Behavior, Monthly Weather Review
  • Colby Jr, F. P.  (2015), Global Ensemble Forecast Tracks for Tropical Storm Debby. Weather and Forecasting, e-View, doi: http://dx.doi.org/10.1175/WAF-D-14-00083.1, http://journals.ametsoc.org/doi/abs/10.1175/WAF-D-14-00083.1
  • Colby, Frank P., Jr., 2015: Analysis of Ensemble Forecasts for Hurricane Isaac. Proceedings of the 16th Conference on Mesoscale Processes, Boston, MA, August 3 – 6, 201

  • Jun, S., W. Lee, K. Kang, K. Byun, J. Kim, and W. Yun. (2015), Applicability of the superensemble to the tropical cyclone track forecasts in the western North Pacific. Asia-Pacific Journal of Atmospheric Sciences, 51, 1, 39-48. http://link.springer.com/article/10.1007/s13143-014-0058-x#page-1
  • FAN, F. M.; SCHWANENBERG, D. ; COLLISCHONN, W. ; WEERTS, A. (2015). Verification of inflow into hydropower reservoirs using ensemble forecasts of the TIGGE database for large scale basins in Brazil. Journal of Hydrology: Regional Studies, v. 4, p. 196-227.
  • Frank P. Colby Jr (2015)., Global Ensemble Forecast Tracks for Tropical Storm Debby, Weather and Forecasting
  • Khan, M., A. Shamseldin, B. Melville, M. and Shoaib (2015), Impact of ensemble size on TIGGE precipitation forecasts: An end-user perspective. Journal of Hydrological Engineering, 20, 2, 04014046. http://ascelibrary.org/doi/abs/10.1061/%28ASCE%29HE.1943-5584.0001025
  • Komaromi, W. A., and S. J. Majumdar (2015), Ensemble-Based Error and Predictability Metrics Associated with Tropical Cyclogenesis. Part II: Wave-Relative Framework. Monthly Weather Review, e-View , doi: http://dx.doi.org/10.1175/MWR-D-14-00286.1 , http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-14-00286.1
  • Kotal, S. D., S.K. Bhattacharya, S.R. Bhowmik, and P. K. Kundu (2015), Development of NWP-Based Cyclone Prediction System for Improving Cyclone Forecast Service in the Country. In High-Impact Weather Events over the SAARC Region, pp. 111-128. Springer International Publishing, http://scholar.google.com/scholar?start=80&q=TIGGE+data&hl=en&as_sdt=0,5&as_ylo=2014
  • Mainardi Fan, D. Schwanenberg, W. Collischonn, A.H. Weerts (2015) Verification of Reservoirs Inflow Ensemble Forecasts Using Three TIGGE Database Models for Three Brazilian Large Scale Basins, F.  J. Hydrology: Regional Studies 4  196–227, doi: 10.1016/j.ejrh.2015.05.012
  • Munehiko Yamaguchi, Frédéric Vitart, Simon T. K. Lang, Linus Magnusson, Russell L. Elsberry, Grant Elliott, Masayuki Kyouda, Tetsuo Nakazawa (2015), Global Distribution of the Skill of Tropical Cyclone Activity Forecasts on Short- to Medium-Range Time Scales, Weather and Forecasting
  • NAUMANN, STEFFI ; SCHWANENBERG, DIRK ; KARIMANZIRA, DIVAS ; FAN, FERNANDO ; ALLEN, CHRISTOPHER. (2015). Short-term management of hydropower reservoirs under meteorological uncertainty by means of multi-stage optimization. AT-AUTOM, v. 63, p. 535-542.
  • Rajul Pandya, Abraham Hodgson, Mary H. Hayden, Patricia Akweongo, Thomas Hopson, Abudulai Adams Forgor, Tom Yoksas, Maxwell Ayindenaba Dalaba, Vanja Dukic, Roberto Mera, Arnaud Dumont, Kristen McCormack, Dominic Anaseba, Timothy Awine, Jennifer Boehnert, Gertrude Nyaaba, Arlene Laing, Fredrick Semazzi (2015), Using Weather Forecasts to Help Manage Meningitis in the West African Sahel, Bulletin of the American Meteorological Society
  • Ruoyun Niu, Panmao Zhai, Baiquan Zhou (2015), Evaluation of Forecast Performance of Asian Summer Monsoon Low-Level Winds Using the TIGGE Dataset, Weather and Forecasting
  • Swinbank, R., M. Kyouda, P. Buchanan, L. Froude, T. Hamill, T. Hewson, J. Keller, M. Matsueda, J. Methven, F. Pappenberger, M. Scheuerer, H. Titley, L. Wilson, and M. Yamaguchi (2015), The TIGGE project and its achievements. Bulletin of the American Meteorological Society. doi:10.1175/BAMS-D-13-00191.1, doi: 10.1175/BAMS-D-13-00191.1, http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-13-00191.1
  • Thorey, J., Mallet, V., Chaussin, C., Descamps, L., Blanc, P. 2015. Ensemble forecast of solar radiation using TIGGE weather forecasts and HelioClim database. Solar Energy 120, 232–243. https://hal-mines-paristech.archives-ouvertes.fr/hal-01184650/document
  • SCHWANENBERG, D. ; FAN, F. M. ; NAUMANN, S. ; KUWAJIMA, J. I. ; ALVARADO, R. ; REIS, A. A. (2015). Short-Term Reservoir Optimization for Flood Mitigation under Meteorological and Hydrological Forecast Uncertainty. Water Resources Management, p. 10.1007/s11269-.
  • Shoujuan Shu, Fuqing Zhang (2015), Influence of Equatorial Waves on the Genesis of Super Typhoon Haiyan (2013), Journal of the Atmospheric Sciences
  • Yang, Q., S. N. Losa, M. Losch, T. Jung, and L. Nerger, (2015), The role of atmospheric uncertainty in Arctic summer sea ice data assimilation and prediction. Quarterly Journal of the Royal Meteorological Society. e-View, doi: 10.1002/qj.2523, http://onlinelibrary.wiley.com/doi/10.1002/qj.2523/abstract
  • William A. Komaromi, Sharanya J. Majumdar (2015), Ensemble-Based Error and Predictability Metrics Associated with Tropical Cyclogenesis. Part II: Wave-Relative Framework, Monthly Weather Review
  • Zhou, B. Q., R. Y. Niu, and P. M. Zhai (2015), An assessment of the predictability of the East Asian Subtropical Westerly Jet based on TIGGE data. Advance in Atmospheric Sciences , 32(3), 401–412, doi: 10.1007/s00376-014-4026-2. http://link.springer.com/article/10.1007/s00376-014-4026-2#page-1

2014 (34, *0)

2013 (30,*9)

2012 (24, *5)

2011 (31, *7)

  • Colle, B. A., M. E. Charles, 2011: Spatial Distribution and Evolution of Extratropical Cyclone Errors over North America and its Adjacent Oceans in the NCEP Global Forecast System Model. Weather & Forecasting. Apr2011, Vol. 26 Issue 2, p129-149. http://journals.ametsoc.org/doi/abs/10.1175/2010WAF2222422.1
  • Cuo, L., T. C. Pagano, Q. J. Wang, 2011: A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting. Journal of ydrometeorology. Volume 12, Issue 5 (October 2011) pp. 713-728 doi: http://dx.doi.org/10.1175/2011JHM1347.1
  • Du, J., and B. Zhou, 2011: A Dynamical Performance-Ranking Method for Predicting Individual Ensemble Member Performance and Its Application to Ensemble Averaging. Monthly Weather Review. Oct2011, Vol. 139 Issue 10, p3284-3303. http://journals.ametsoc.org/doi/abs/10.1175/MWR-D-10-05007.1
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