---

Related links and dataset

New S2S database at ECMWF Dat Store

S2S database at ECMWF

S2S database at CMA

Graphical products of S2S forecasting systems by the S2S Museum

SNAPSI simulations: Mastrangelo, D. (2024). Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): Data produced by the GLOBO model at CNR-ISAC [Dataset]. NERC EDS Centre for Environmental Data Analysis. http://catalogue.ceda.ac.uk/uuid/181b2e501be0452984371d1c77fdab2a

WWRP/WCRP S2S Project Subseasonal-to-Seasonal Prediction (S2S) Project
S2S database at IRI/Columbia University

---

Related Publications

Ayarzaguena, B., Butler, A. H., Hitchcock, P., Garfinkel, C. I., Lawrence, Z. D., Ning, W., Rupp, P., Wu, Z., Afargan-Gerstman, H., Calvo, N., de la Camara, A., Jucker, M., Koren, G., De Maeseneire, D., Manney, G. L., Osman, M., Taguchi, M., Barton, C., Hong, D.-C., Hyun, Y.-K., Kim, H., Knight, J., Malguzzi, P., Mastrangelo, D., Oh, J., Polichtchouk, I., Richter, J. H., Simpson, I. R., Son, S.-W., Specq, D., and Stockdale, T.: The role of the stratospheric state in upward wave flux prior to Sudden Stratospheric Warmings: a SNAPSI analysis, Weather Clim. Dynam., 7, 411-437, https://doi.org/10.5194/wcd-7-411-2026, 2026.

Hitchcock, P., Butler, A., Charlton-Perez, A., Garfinkel, C., Stockdale, T., Anstey, J., Mitchell, D., Domeisen, D. I. V., Wu, T., Lu, Y., Mastrangelo, D., Malguzzi, P., Lin, H., Muncaster, R., Merryfield, B., Sigmond, M., Xiang, B., Jia, L., Hyun, Y.-K., Oh, J., Specq, D., Simpson, I. R., Richter, J. H., Barton, C., Knight, J., Lim, E.-P., and Hendon, H.: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts, Geosci. Model Dev., 2022, https://doi.org/10.5194/gmd-2021-394

Davolio S., P. Malguzzi, O. Drofa, D. Mastrangelo and A. Buzzi, 2020: The Piedmont flood of November 1994: a test-bed of forecasting capabilities of the CNR-ISAC meteorological model suite. Bull. of Atmos. Sci.& Technol. (2020). https://doi.org/10.1007/s42865-020-00015-4

Mastrangelo, D. and P. Malguzzi, 2019: Verification of two years of CNR-ISAC subseasonal forecasts. Wea. Forecasting, 34, 331–344, https://doi.org/10.1175/WAF-D-18-0091.1.

Ferrone, A., Mastrangelo, D., and Malguzzi, P.: Multimodel probabilistic prediction of 2-m temperature anomalies on the monthly timescale, Adv. Sci. Res., 14, 123-129, 2017, https://doi.org/10.5194/asr-14-123-2017

Mastrangelo, D. and Malguzzi, P.: CNR-ISAC 2-m temperature monthly forecasts: a first probabilistic evaluation, Adv. Sci. Res., 14, 85-88, 2017, https://doi.org/10.5194/asr-14-85-2017

Vitart, F., C. Ardilouze, A. Bonet, A. Brookshaw, M. Chen, C. Codorean, M. Deque, L. Ferranti, E. Fucile, M. Fuentes, H. Hendon, J. Hodgson, H. Kang, A. Kumar, H. Lin, G. Liu, X. Liu, P. Malguzzi, I. Mallas, M. Manoussakis, D. Mastrangelo, C. MacLachlan, P. McLean, A. Minami, R. Mladek, T. Nakazawa, S. Najm, Y. Nie, M. Rixen, A.W. Robertson, P. Ruti, C. Sun, Y. Takaya, M. Tolstykh, F. Venuti, D. Waliser, S. Woolnough, T. Wu, D. Won, H. Xiao, R. Zaripov, and L. Zhang, 2017: The Subseasonal to Seasonal (S2S) Prediction Project Database. Bull. Amer. Meteor. Soc., 98, 163-173, https://doi.org/10.1175/BAMS-D-16-0017.1

D. Mastrangelo, P. Malguzzi, C. Rendina, O. Drofa, and A. Buzzi: First outcomes from the CNR-ISAC monthly forecasting system, Adv. Sci. Res., 8, 77-82, 2012 doi:10.5194/asr-8-77-2012

Malguzzi, P., Buzzi, A., & Drofa, O. (2011). The Meteorological Global Model GLOBO at the ISAC-CNR of Italy Assessment of 1.5 Yr of Experimental Use for Medium-Range Weather Forecasts, Weather and Forecasting, 26(6), 1045-1055. https://journals.ametsoc.org/view/journals/wefo/26/6/waf-d-11-00027_1.xml

---

Other Publications

Federico, S., Coscarelli, R., Mastrangelo D., Panegrossi, G., Piervitali, E., Torcasio, R.C., Vulpiani, G. 2024. 'Il regime meteo-climatico', in Siccita`, scarsita` e crisi idriche, Emanuele Romano, Ivan Portoghese (a cura di), Habitat signa 1, 103-143. Roma: Cnr Edizioni. https://www.cnr.it/sites/default/files/public/media/attivita/editoria/collana_habitat_signa/Cap-4_SiccitaInterattivo_ver2.pdf

Mastrangelo D., Delli Passeri L., Campione E., Malguzzi P., 2021: The contribution of S2S forecasts to the activities of the Italian Civil Protection Department. S2S Newsletter, No. 17 (Aug. 2021) S2S Newsletter, No. 17 (Aug. 2021)

---

Publications using S2S Globo data

Liu, X., Su, J., Peng, Y. et al. Prolonged High-skill Windows for Subseasonal Forecasting of Surface Air Temperature over East Asia in Summer 1993 and 1994. Adv. Atmos. Sci. (2026). https://doi.org/10.1007/s00376-025-5187-x

Huang, J., Hitchcock, P., Tian, W., Li, Q., & He, L. (2026). Why do extended-range forecasts underpredict the extreme negative Pacific/North American pattern in February 2018? Journal of Geophysical Research: Atmospheres, 131, e2025JD045964. https://doi.org/10.1029/2025JD045964

Millin, O.T. & Furtado, J.C. (2026) Subseasonal predictability of cold spells in the central United States. Quarterly Journal of the Royal Meteorological Society, e70081. https://doi.org/10.1002/qj.70081

Liu, H., Liu, Y., Bao, Q. et al. Heatwave forecast skill in the mid-high latitudes of the Northern Hemisphere at subseasonal lead times: Regional and duration disparity. Sci. China Earth Sci. (2025). https://doi.org/10.1007/s11430-025-1739-8

Vitart, F., and Coauthors, 2025: The WWRP/WCRP S2S Project and Its Achievements. Bull. Amer. Meteor. Soc., 106, E791–E808, https://doi.org/10.1175/BAMS-D-24-0047.1.

Feng, K., Rao, J., Garfinkel, C.I. et al. Can stratospheric nudging improve surface predictability? Insights from the 2019 Southern Hemisphere sudden stratospheric warming. npj Clim Atmos Sci 8, 353 (2025). https://doi.org/10.1038/s41612-025-01234-2

Huang, Y., Wei, Y., Ren, H-L., Modulation of ENSO on the MJO Forecast, Atmospheric Research, 2025, 108510, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2025.108510

Garcia-Franco, J. L., C. Lee, M. K. Tippett, S. J. Camargo, D. Kim, A. Molod, and Y. Lim, 2025: Subseasonal Prediction of Tropical Cyclone Precipitation. Wea. Forecasting, 40, 1429-1444, https://doi.org/10.1175/WAF-D-24-0185.1

Dai, Y., Hitchcock, P., Butler, A. H., Garfinkel, C. I., and Seviour, W. J. M.: Assessing stratospheric contributions to subseasonal predictions of precipitation after the 2018 SSW from SNAPSI, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-484, 2025.

Garfinkel, C. I., Lawrence, Z. D., Butler, A. H., Dunn-Sigouin, E., Statnaia, I., Karpechko, A. Y., Koren, G., Abalos, M., Ayarzaguena, B., Barriopedro, D., Calvo, N., de la Camara, A., Charlton-Perez, A., Cohen, J., Domeisen, D. I. V., Garcia-Serrano, J., Hindley, N. P., Jucker, M., Kim, H., Lee, R. W., Lee, S. H., Osman, M., Palmeiro, F. M., Polichtchouk, I., Rao, J., Richter, J. H., Schwartz, C., Son, S.-W., Taguchi, M., Tyrrell, N. L., Wright, C. J., and Wu, R. W.-Y.: A process-based evaluation of biases in extratropical stratosphere-troposphere coupling in subseasonal forecast systems, Weather Clim. Dynam., 6, 171-195, https://doi.org/10.5194/wcd-6-171-2025, 2025.

Rao, J., Garfinkel, C.I., Zhang, X. et al. Subseasonal to seasonal (s2s) prediction of continental cold following the sudden stratospheric warming in the 2022/23 winter. Clim Dyn 63, 75 (2025). https://doi.org/10.1007/s00382-024-07582-8

Jian Rao, Xiaoqi Zhang, Qian Lu, Siming Liu, Prediction of near-surface conditions following the 2023/24 sudden stratospheric warming by the S2S project models, Atmospheric Research, 2024,107882, ISSN 0169-8095, https://doi.org/10.1016/j.atmosres.2024.107882

Zhu, T., Yang, J., Bao, Q., & Vitart, F. (2024). Boreal summer extratropical intraseasonal oscillation prediction in current subseasonal-to-seasonal operational models over Eurasia. Journal of Geophysical Research: Atmospheres, 129, e2024JD042015. https://doi.org/10.1029/2024JD042015

Xiaolei Liu et al. A statistic of the subseasonal forecast skill windows of 2-meter air temperature, Environ. Res. Commun. 6 085002 https://doi.org/10.1088/2515-7620/ad6667

Yang, J., Zhu, T., Vitart, F. et al. Synchronous Eurasian heat extremes tied to boreal summer combined extratropical intraseasonal waves. npj Clim Atmos Sci 7, 169 (2024). https://doi.org/10.1038/s41612-024-00714-1

Ryu, J., Wang, SY., Jeong, JH. et al. Sub-seasonal prediction skill: is the mean state a good model evaluation metric?. Clim Dyn (2024). https://doi.org/10.1007/s00382-024-07315-x

Xiao, H., Xu, P., & Wang, L. (2024). The Unprecedented 2023 North China heatwaves and their S2S predictability. Geophysical Research Letters, 51, e2023GL107642. https://doi.org/10.1029/2023GL107642

Iqura Malik, Vimal Mishra, Sub-seasonal to seasonal (S2S) prediction of dry and wet extremes for climate adaptation in India, Climate Services, Volume 34, 2024, 100457, ISSN 2405-8807, https://doi.org/10.1016/j.cliser.2024.100457.

Wie, J., Kang, J. & Moon, BK. Role of Madden-Julian Oscillation in predicting the 2020 East Asian summer precipitation in subseasonal-to-seasonal models. Sci Rep 14, 865 (2024). https://doi.org/10.1038/s41598-024-51506-9

Liang, X.; Vitart, F.; Wu, T. Evaluation of Probabilistic Forecasts of Extreme Cold Events in S2S Models. Water, 2023, 15, 2795. https://doi.org/10.3390/w15152795

Lu, M. M., Tsai, W. Y. H., Huang, S. F., Cho, Y. M., Sui, C. H., Solis, A. L., & Chen, M. S. (2023).The Philippine springtime (February-April) sub-seasonal rainfall extremes and extended-range forecast skill assessment using the S2S database. Weather and Climate Extremes, 100582.https://doi.org/10.1016/j.wace.2023.100582

Garcia-Franco, J. L., C. Lee, S. J. Camargo, M. K. Tippett, D. Kim, A. Molod, and Y. Lim, 2023: Climatology of Tropical Cyclone Precipitation in the S2S Models. Wea. Forecasting, 38, 1759-1776, https://doi.org/10.1175/WAF-D-23-0029.1.

Yang, J., Zhu, T. & Vitart, F. An extratropical window of opportunity for subseasonal prediction of East Asian summer surface air temperature. npj Clim Atmos Sci 6, 46 (2023). https://doi.org/10.1038/s41612-023-00384-5

Inatsu, M., M. Matsueda, N. Nakano, and S. Kawazoe, 2023: Prediction skill and practical predictability depending on the initial atmospheric states in S2S forecasts. J. Atmos. Sci., https://doi.org/10.1175/JAS-D-22-0262.1, 2023

Yan, Y., Zhu, C., & Liu, B. (2023). Subseasonal predictability of the July 2021 extreme rainfall event over Henan China in S2S operational models. Journal of Geophysical Research: Atmospheres, 128, e2022JD037879. https://doi.org/10.1029/2022JD037879, 2023

Brum, M. and Schwanenberg, D.: Long-term evaluation of the Sub-seasonal to Seasonal (S2S) dataset and derived hydrological forecasts at the catchment scale, EGUsphere [preprint],https://doi.org/10.5194/egusphere-2022-419, 2022

Chwat, D., Garfinkel, C. I., Chen, W., & Rao, J. (2022). Which Sudden Stratospheric Warming Events are Most Predictable? Journal of Geophysical Research: Atmospheres, 127, e2022JD037521. https://doi.org/10.1029/2022JD037521

Li, W., Song, J., Hsu, P., & Wang, Y. (2022). Evaluation of the Forecast Performance for Week-2 Winter Surface Air Temperature from the Model for Prediction Across Scales - Atmosphere (MPAS-A), Weather and Forecasting. https://doi.org/10.1175/WAF-D-22-0054.1

Deoras, A., Turner, A.G. and Hunt, K.M.R. (2022), The structure of strong Indian monsoon low-pressure systems in Subseasonal-to-Seasonal prediction models. Q J R Meteorol Soc. https://doi.org/10.1002/qj.4296

Wie, J.; Kang, J.; Moon, B.-K. Superensemble Approach to S2S Model for Predicting Surface Air Temperature in Summer in East Asia from 2016 to 2020. Atmosphere 2022, 13, 701. https://doi.org/10.3390/atmos13050701

Cowan, T., Wheeler, M.C., de Burgh-Day, C. et al. Multi-week prediction of livestock chill conditions associated with the northwest Queensland floods of February 2019. Sci Rep 12, 5907 (2022). https://doi.org/10.1038/s41598-022-09666-z

Lin, H., Mo, R., & Vitart, F. (2022). The 2021 western North American heatwave and its subseasonal predictions. Geophysical Research Letters, 49, e2021GL097036. https://doi.org/10.1029/2021GL097036

Lawrence, Z. D., Abalos, M., Ayarzagüena, B., Barriopedro, D., Butler, A. H., Calvo, N., de la Cámara, A., Charlton-Perez, A., Domeisen, D. I. V., Dunn-Sigouin, E., García-Serrano, J., Garfinkel, C. I., Hindley, N. P., Jia, L., Jucker, M., Karpechko, A. Y., Kim, H., Lang, A. L., Lee, S. H., Lin, P., Osman, M., Palmeiro, F. M., Perlwitz, J., Polichtchouk, I., Richter, J. H., Schwartz, C., Son, S.-W., Statnaia, I., Taguchi, M., Tyrrell, N. L., Wright, C. J., and Wu, R. W.-Y.: Quantifying stratospheric biases and identifying their potential sources in subseasonal forecast systems, Weather Clim. Dynam., 3, 977-1001, https://doi.org/10.5194/wcd-3-977-2022, 2022.

Stan, C., Zheng, C., Chang, E. K., Domeisen, D. I., Garfinkel, C. I., Jenney, A. M., Kim, H., Lim, Y., Lin, H., Robertson, A., Schwartz, C., Vitart, F., Wang, J., & Yadav, P. (2022). Advances in the prediction of MJO-Teleconnections in the S2S forecast systems, Bulletin of the American Meteorological Society (2022). https://journals.ametsoc.org/view/journals/bams/aop/BAMS-D-21-0130.1/BAMS-D-21-0130.1.xml

Yan, Y., Liu, B., Zhu, C. et al.: Subseasonal forecast barrier of the North Atlantic oscillation in S2S models during the extreme mei-yu rainfall event in 2020. Clim Dyn (2021). https://doi.org/10.1007/s00382-021-06076-1

The January 2021 Sudden Stratospheric Warming and Its Prediction in Subseasonal to Seasonal Models. Jian Rao,Chaim I. Garfinkel,Tongwen Wu,Yixiong Lu,Qian Lu,Zhuoqi Liang, 2021,https://doi.org/10.1029/2021JD035057

Lin, H., Huang, Z., Hendon, H., & Brunet, G. (2021). NAO Influence on the MJO and its Prediction Skill in the Subseasonal-to-Seasonal Prediction Models, Journal of Climate, 34(23), 9425-9442. https://journals.ametsoc.org/view/journals/clim/34/23/JCLI-D-21-0153.1.xml

Schwartz, C., Garfinkel, C. I., Yadav, P., Chen, W., and Domeisen, D.: Stationary Waves and Upward Troposphere-Stratosphere Coupling in S2S Models, Weather Clim. Dynam., 3, 679-692, https://doi.org/10.5194/wcd-2021-58, 2022.

Endris, H. S., Hirons, L., Segele, Z. T., Gudoshava, M., Woolnough, S., & Artan, G. A. (2021). Evaluation of the Skill of Monthly Precipitation Forecasts from Global Prediction Systems over the Greater Horn of Africa, Weather and Forecasting, 36(4), 1275-1298. https://journals.ametsoc.org/view/journals/wefo/36/4/WAF-D-20-0177.1.xml

Pei-Ning Feng & Hai Lin (2021) Modulation of the MJO-Related Teleconnection by the QBO in Subseasonal-to-Seasonal Prediction Models, Atmosphere-Ocean, https://doi.org/10.1080/07055900.2021.1944045

Zheng, C., Kar-Man Chang, E., Kim, H., Zhang, M., & Wang, W. (2021). Subseasonal Prediction of Wintertime Northern Hemisphere Extratropical Cyclone Activity by SubX and S2S Models, Weather and Forecasting, 36(1), 75-89.https://doi.org/10.1175/WAF-D-20-0157.1

Feng, P., Lin, H., Derome, J., & Merlis, T. M. (2021). Forecast Skill of the NAO in the Subseasonal-to-Seasonal Prediction Models, Journal of Climate, 34(12), 4757-4769, https://doi.org/10.1175/JCLI-D-20-0430.1

Deoras, A., Hunt, K. M. R., & Turner, A. G. (2021). Comparison of the Prediction of Indian Monsoon Low Pressure Systems by Subseasonal-to-Seasonal Prediction Models, Weather and Forecasting, 36(3), 859-877. https://journals.ametsoc.org/view/journals/wefo/36/3/WAF-D-20-0081.1.xml

Kueh, MT., Lin, CY. The 2018 summer heatwaves over northwestern Europe and its extended-range prediction. Sci Rep 10, 19283 (2020). https://doi.org/10.1038/s41598-020-76181-4

Rao, J., Garfinkel, C. I., White, I. P., & Schwartz, C. (2020). The Southern Hemisphere minor sudden stratospheric warming in September 2019 and its predictions in S2S models. Journal of Geophysical Research: Atmospheres, 125, e2020JD032723. https://doi.org/10.1029/2020JD032723

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, 161-182, https://doi.org/10.1175/JCLI-D-18-0355.1

Rao, J., Garfinkel, C. I., Chen, H., & White, I. P. (2019). The 2019 New Year stratospheric sudden warming and its real-time predictions in multiple S2S models. Journal of Geophysical Research: Atmospheres, 124, 11155-11174. https://doi.org/10.1029/2019JD030826

Wang, S., Sobel, A.H., Tippett, M.K. et al. Prediction and predictability of tropical intraseasonal convection: seasonal dependence and the Maritime Continent prediction barrier. Clim Dyn 52, 6015-6031 (2019). https://doi.org/10.1007/s00382-018-4492-9

Zhou, Y., Yang, B., Chen, H. et al. Effects of the Madden-Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database. Clim Dyn 52, 6671-6689 (2019). https://doi.org/10.1007/s00382-018-4538-z

Wang, S., Tippett, M. K., Sobel, A. H., Martin, Z., & Vitart, F. ( 2019). Impact of the QBO on prediction and predictability of the MJO convection. Journal of Geophysical Research: Atmospheres, 124, 11766-11782. https://doi.org/10.1029/2019JD030575

Li, W., J. Chen, L. Li, H. Chen, B. Liu, C. Xu, and X. Li, 2019: Evaluation and Bias Correction of S2S Precipitation for Hydrological Extremes. J. Hydrometeor., 20, 1887-1906, https://doi.org/10.1175/JHM-D-19-0042.1

D. I., Domeisen, Butler, A. H., Charlton-Perez, A. J., Ayarzaguena, B., Baldwin, M. P., Dunn-Sigouin, E. et al. (2020). The role of the stratosphere in subseasonal to seasonal prediction: 2. Predictability arising from stratosphere-troposphere coupling. Journal of Geophysical Research: Atmospheres, 125, e2019JD030923. https://doi.org/10.1029/2019JD030923

Domeisen, D. I. V., Butler, A. H., Charlton-Perez, A. J., Ayarzaguena, B., Baldwin, M. P., Dunn-Sigouin, E., et al (2020). The role of the stratosphere in subseasonal to seasonal prediction: 1. Predictability of the stratosphere. Journal of Geophysical Research: Atmospheres, 125, e2019JD030920.https://doi.org/10.1029/2019JD030920

Hai Lin, Ruping Mo, Frederic Vitart & Cristiana Stan (2019) Eastern Canada Flooding 2017 and its Subseasonal Predictions, Atmosphere-Ocean, 57:3, 195-207, https://doi.org/10.1080/07055900.2018.1547679

Minami, A., & Takaya, Y. (2020). Enhanced Northern Hemisphere correlation skill of subseasonal predictions in the strong negative phase of the Arctic Oscillation. Journal of Geophysical Research: Atmospheres, 125, e2019JD031268. https://doi.org/10.1029/2019JD031268

de Andrade, F.M., Coelho, C.A.S. & Cavalcanti, I.F.A. Global precipitation hindcast quality assessment of the Subseasonal to Seasonal (S2S) prediction project models. Clim Dyn 52, 5451-5475 (2019). https://doi.org/10.1007/s00382-018-4457-z

Quinting, J. F., & Vitart, F. (2019). Representation of synoptic-scale Rossby wave packets and blocking in the S2S prediction project database. Geophysical Research Letters, 46, 1070-1078. https://doi.org/10.1029/2018GL081381

Zheng, C., Chang, E. K.-M., Kim, H., Zhang, M., & Wang, W. (2019). Subseasonal to seasonal prediction of wintertime northern hemisphere extratropical cyclone activity by S2S and NMME models. Journal of Geophysical Research: Atmospheres, 124, 12057-12077. https://doi.org/10.1029/2019JD031252

Son, S.-W., Kim, H., Song, K., Kim, S.-W., Martineau, P., Hyun, Y.-K., & Kim, Y. (2020). Extratropical prediction skill of the Subseasonal-to-Seasonal (S2S) prediction models. Journal of Geophysical Research: Atmospheres, 125, e2019JD031273. https://doi.org/10.1029/2019JD031273

Lim, Y., Son, S., Marshall, A.G. et al. Influence of the QBO on MJO prediction skill in the subseasonal-to-seasonal prediction models. Clim Dyn 53, 1681-1695 (2019). https://doi.org/10.1007/s00382-019-04719-y

Lim, Y., S. Son, and D. Kim, 2018: MJO Prediction Skill of the Subseasonal-to-Seasonal Prediction Models. J. Climate, 31, 4075-4094, https://doi.org/10.1175/JCLI-D-17-0545.1

Jie, W., Vitart, F., Wu, T. and Liu, X. (2017), Simulations of the Asian summer monsoon in the subseasonal to seasonal prediction project (S2S) database. Q.J.R. Meteorol. Soc., 143: 2282-2295. https://doi.org/10.1002/qj.3085

Vitart, F. (2017), Madden-Julian Oscillation prediction and teleconnections in the S2S database.Q.J.R. Meteorol. Soc, 143: 2210-2220. https://doi.org/10.1002/qj.3079

---

---