Wiki source code of Research to Operations (R2O) and S2S forecast and verification products development

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  3: 
  4: **Membership of Research to Operations (R2O) and S2S forecast and verification products development sub-project**
  5: 
  6: Caio Coelho (CPTEC/INPE, Brazil)
  7: Andrew Robertson (IRI, USA)
  8: Arun Kumar (NOAA, USA)
  9: Yuhei Takaya (JMA, Japan)
 10: Anca Brookshaw (ECMWF)
 11: Debra Hudson (BoM, Australia)
 12: Angel Muñoz (IRI, USA)
 13: Joanne Robbins (UKMO, UK)
 14: 
 15: **1) Scientific and Operational Objectives**
 16: 
 17: * Pursue research for testing and developing methodologies for calibration, multi-model combination, verification and generation of forecast products.
 18: * (((
 19: Coordinate with the relevant WMO technical commissions to define the standards and protocols for operational implementation and exchange of S2S forecasts such that by the end of the Phase II of the S2S, the infrastructure related to the data exchange to support research can be transitioned into the operational domain.
 20: )))
 21: 
 22: (% style="font-weight: normal;" %)**The **(%%)**[[//R2O and S2S forecast and verification products activities plan and report//>>url:http://s2sprediction.net/file/wiki/R2O_Sub_Projects_R2O_Verification_and_Products_15Apr2022.pdf||style="font-weight: normal;"]](% style="font-weight: normal;" %) provides additional information about this sub-project, which builds on the work of the previous [[(Phase I) S2S Verification and Products sub-project>>url:http://s2sprediction.net/xwiki/bin/view/Main/Verification||style="font-weight: normal;"]].(%%)**
 23: 
 24: **2) Linkages with WMO activities**
 25: 
 26: On the research side this sub-project has linkages with the [[Joint Working Group on Forecast Verification Research (JWGFVR)>>url:https://community.wmo.int/activity-areas/wwrp/wwrp-working-groups/wwrp-forecast-verification-research]], a WMO joint working group of the Working Group on Numerical Experimentation (WGNE) and the World Weather Research Programme (WWRP). This sub-project also has synergies with the World Climate Research Programme (WCRP) [[Working Group on Subseasonal to Interdecadal Prediction (WGSIP)>>url:https://www.wcrp-climate.org/wgsip-overview]] through the [[Climate forecast information for decision making (I4D) project>>url:https://www.wcrp-climate.org/images/modelling/WGSIP/documents/WGSIP_I4D_ConceptNote_updated.pdf]].
 27: 
 28: On the operational side this sub-project has linkages with the [[Expert Team on Operational Climate Prediction System (ET-OCPS) >>url:https://community.wmo.int/governance/commission-membership/commission-observation-infrastructure-and-information-systems-infcom/commission-infrastructure-officers/infcom-management-group/standing-committee-data-processing-applied-earth-system-modelling-and-prediction-sc-esmp-1]], a WMO team of the Infrastructure Commission (INFCOM). This sub-project also has links with the  [[Expert Team on Climate Services Information System Operations (ET-CSISO) >>url:https://community.wmo.int/governance/commission-membership/commission-weather-climate-water-and-related-environmental-service-applications-sercom/commission-services-officers/sercom-management-group/standing-committee-climate-services/expert-team-climate-services-information]], a WMO team of the Services Commission (SERCOM).
 29: 
 30: 
 31: **3) Proposed questions to be addressed**
 32: 
 33: (% style="font-weight: normal;" %)**The **(%%)**[[World Weather Research Programme (WWRP)>>url:https://public.wmo.int/en/programmes/world-weather-research-programme||style="font-weight: normal;"]](% style="font-weight: normal;" %) has flagged improving forecasts of precipitation over land as an important area for S2S to focus research and services development efforts. In order to help advance scientific knowledge and the development of forecast and verification products in this priority area this sub-project invites the S2S research and operational communities to address the following questions:(%%)**
 34: 
 35: * (((
 36: What is the current performance level of sub-seasonal precipitation forecasts over land? Over which continental regions can these forecasts be best trusted? How performance levels vary through the seasons of the year?
 37: )))
 38: * (((
 39: What is the current capability of S2S models in anticipating the occurrence of extreme precipitation events over land (periods of deficit or excess precipitation)?
 40: )))
 41: * (((
 42: How well the main patterns of precipitation variability on the sub-seasonal time scale over various continental regions are represented in S2S prediction models?
 43: )))
 44: * (((
 45: How best to combine and calibrate sub-seasonal precipitation forecasts over land in order to produce improved, combined and well-calibrated products and services?
 46: )))
 47: * (((
 48: Are there identifiable opportunities for producing sub-seasonal precipitation forecasts over land with improved quality? For example, are forecasts produced during Madden and Julian Oscillation (MJO) and/or El Niño Southern Oscillation (ENSO) events more skilful than when neutral conditions are present? Are forecasts for active and break rainfall phases and dry/wet spells (or other quantities of interest) of adequate quality for developing forecast products for use in application sectors?\\
 49: )))
 50: 
 51: (% style="font-weight: normal;" %)**In order to address these questions the research and operational communities are encouraged to explore existing and develop novel methodologies for forecast calibration, combination and verification. Following the **(%%)**[[S2S verification chapter produced by the JWGFVR for the recent S2S book>>url:http://s2sprediction.net/resources/documents/sub-projects/Ch17-S2S-forecast-verification-chapter-S2S-wiki.pdf||style="font-weight: normal;"]](% style="font-weight: normal;" %), it is particularly encouraged the identification of the most relevant forecast quality attributes for the target audiences (e.g. model and forecast developers, and various application sectors) in order to choose appropriate scores and metrics to be able to adequately address clearly and previously defined verification questions of interest. This practice helps performing a thorough assessment of sub-seasonal forecasts from both the probabilistic and deterministic points of view.(%%)**
 52: 
 53: **4) Current work of S2S operational and research communities on calibration, multi-model combination, verification and forecast products generation**
 54: 
 55: **4.1) Under development sub-seasonal multi-model ensemble (MME) forecasts and verification products at the WMO LC-SSFMME**
 56: 
 57: ECMWF has been designated in 2023 by WMO as the first Global Producing Centre for Sub-Seasonal Forecasts (GPC-SSF) and the Lead Centre for Sub-Seasonal Forecast Multi-Model Ensemble (LC-SSFMME). [[This website>>url:https://charts-dev.ecmwf.int/wmo/charts]] is being developed to provide sub-seasonal multi-model ensemble forecasts and verification products. 
 58: 
 59: 
 60: **4.2) Pilot real-time sub-seasonal multi-model ensemble (MME) forecasts and verification products at the WMO LC-LRFMME**
 61: 
 62: The WMO Lead Centre for Long Range Forecast Multi-Model Ensemble ([[LC-LRFMME>>url:https://www.wmolc.org/]]) has been developing a pilot system for real-time multi-model subseasonal forecasts using real-time forecasts (and hindcasts) from a subset of models contributing to the WWRP/WCRP S2S research project accessible via ECMWF data archive. //**Following [[this link>>url:http://s2sprediction.net/resources/documents/sub-projects/WMO_LRFMME_products_information.pdf]] the S2S research and operational communities have the opportunity to see the characteristics of the pilot real-time sub-seasonal MME prediction system developed by the LC-LRFMME, which includes forecast and verification products. [[These additional slides>>url:http://s2sprediction.net/resources/documents/sub-projects/Pilot-real-time-sub-seasonal-MME_LC_LRFMME_Status_KMA(2020).pdf]] provide examples of products developed in support of future global, regional and national operational activities performed by WMO members**//. Subseasonal models from eight Global Producing Centers (GPCs) are currently used: Beijing, ECMWF, Exeter, Melbourne, Montreal, Seuol, Tokyo and Washington. A range of forecast products has been developed including probabilities for tercile categories of weekly/fortnightly averages of 2m temperature and precipitation as well as the MJO and BSISO indices. Hindcast verification has also been generated using ROC curves and scores, reliability diagrams, root mean square error and correlation between hindcast and observed anomalies.
 63: 
 64: **4.3) IRI initiative on investigating the seasonality of subseasonal rainfall and temperature global prediction skill**
 65: 
 66: [[This document>>url:http://s2sprediction.net/file/documents_reports/On-the-seasonality-of-subseasonal-rainfall-and-temperature-skill.pdf]] summarizes the methods followed to conduct a global predictive skill assessment for uncalibrated rainfall and 2-m temperature forecasts, produced using the ECMWF’s IFS model, available through the [[WWRP/WCRP S2S Prediction Project>>url:http://s2sprediction.net/]] Database via the International Research Institute for Climate and Society (IRI) [[Data Library>>url:https://iridl.ldeo.columbia.edu/SOURCES/.ECMWF/.S2S/index.html?Set-Language=en]].
 67: 
 68: **4.4) The Australian Bureau of Meteorology adaptable framework for development and real time production of experimental sub-seasonal to seasonal forecast products**
 69: 
 70: [[This document>>url:http://s2sprediction.net/file/documents_reports/BRR-042.pdf||style="background-color: rgb(255, 255, 255);"]] describes the new post-processing pipeline developed to add value to sub-seasonal and seasonal forecasts produced by the Australian Bureau of Meteorology.
 71: 
 72: **4.5) Projects and networks dealing with S2S predictions**
 73: 
 74: (% style="font-weight: normal;" %)**//[[ACToday:>>url:https://iri.columbia.edu/actoday/]] The Adapting Agriculture to Climate Today, for Tomorrow project.//**
 75: 
 76: **[[African SWIFT:>>url:https://africanswift.org/||style="font-weight: normal;"]](% style="font-weight: normal;" %) Science for Weather Information and Forecasting Techniques.(%%)**
 77: 
 78: ===== [[CLIMAX:>>url:http://www.climax-sa.org/]] Climate Services Through Knowledge Co-Production: A Euro-South American Initiative for Strengthening Societal Adaptation Response to Extreme Events. =====
 79: 
 80: (((
 81: **[[CSSP Brazil:>>url:https://www.metoffice.gov.uk/research/approach/collaboration/newton/cssp-brazil/index||style="font-weight: normal;"]](% style="font-weight: normal;" %) Climate Science for Service Partnership Brazil(%%)**
 82: )))
 83: 
 84: **[[CSSP China:>>url:https://www.metoffice.gov.uk/research/approach/collaboration/newton/cssp-china/index||style="font-weight: normal;"]](% style="font-weight: normal;" %) Climate Science for Service Partnership China(%%)**
 85: 
 86: ===== [[SNAP:>>url:https://www.sparc-climate.org/activities/assessing-predictability/]] Stratospheric Network for the Assessment of Predictability. See verification plans on page 6 of [[this presentation>>url:http://s2sprediction.net/resources/documents/sub-projects/SNAP_to_S2S.pdf]]. =====
 87: 
 88: **[[WCSSP India:>>url:https://www.metoffice.gov.uk/research/approach/collaboration/newton/cssp-india/weather-and-climate-science-for-service-partnership-india-wcssp-india||style="font-weight: normal;"]](% style="font-weight: normal;" %) Weather and Climate Science for Service Partnership India(%%)**
 89: 
 90: **4.6) Software tools**
 91: 
 92: (((
 93: [[PyCPT>>url:https://bitbucket.org/py-iri/iri-pycpt/src/master/]] and [[PyWR>>url:https://github.com/agmunozs/Weather-typing]] of the International Research Institute for Climate and Society ([[IRI>>url:https://iri.columbia.edu/]])
 94: 
 95: [[WRtool>>url:https://github.com/fedef17/WRtool/]] of the Institute of Atmospheric Sciences and Climate  ([[ISAC—CNR, Bologna, Italy>>url:https://link.springer.com/article/10.1007/s00382-020-05271-w/]])
 96: 
 97: [[MJO Diagnostics tools>>url:https://github.com/S2S-ICO/MJO-Diagnostics]] of the [[climpred development team>>url:https://climpred.readthedocs.io/en/stable/examples.html#subseasonal]]
 98: )))
 99: 
100: 
101: **4.7) Web portals**
102: 
103: **[[IRI Sub-seasonal forecasts map room>>url:https://iridl.ldeo.columbia.edu/maproom/Global/ForecastsS2S/index.html||style="font-weight: normal;"]]**
104: 
105: (% style="font-weight: normal;" %)**CLIMAX project forecast maps: **(%%)**[[Precipitation>>url:http://climar.cima.fcen.uba.ar/CFS/CFS_pre.php||style="font-weight: normal;"]](% style="font-weight: normal;" %), [[2 meter temperature>>url:http://climar.cima.fcen.uba.ar/CFS/CFS_tmp2m.php||style="font-weight: normal;"]], [[200 hPa Geopotential height>>url:http://climar.cima.fcen.uba.ar/CFS/CFS_z200.php||style="font-weight: normal;"]] and [[Outgoing Longwave Radiation>>url:http://climar.cima.fcen.uba.ar/CFS/CFS_olr.php||style="font-weight: normal;"]].(%%)**
106: 
107: **[[BoM Sub-seasonal forecasts>>url:http://www.bom.gov.au/climate/outlooks/#/rainfall/median/weekly/0||style="font-weight: normal;"]]**
108: 
109: **[[CPTEC Sub-seasonal forecasts>>url:http://subsazonal.cptec.inpe.br||style="font-weight: normal;"]]**
110: 
111: **[[JMA Sub-seasonal forecasts>>url:https://www.data.jma.go.jp/tcc/tcc/products/model/index.html||style="font-weight: normal;"]]**
112: 
113: **[[HMC Sub-seasonal forecasts>>url:http://seakc.meteoinfo.ru/en/forecasts/subseasonal-forecast||style="font-weight: normal;"]]**
114: 
115: **[[ECMWF Extended-range forecasts>>url:https://charts.ecmwf.int/?facets=%7B%22Product%20type%22%3A%5B%5D%2C%22Parameters%22%3A%5B%5D%2C%22Range%22%3A%5B%22Extended%20%2830%20days%29%22%2C%22Extended%20%2842%20days%29%22%5D%2C%22Type%22%3A%5B%5D%7D||style="font-weight: normal;"]]**
116: 
117: 
118: 
119: **5) Publications**
120: 
121: (((
122: Below is a list of publications recently produced by the international S2S research community on forecast verification, calibration and multi-model combination, including prediction quality assessment and methodological studies. Additional references including books and technical reports on methods relevant for S2S verification are available in [[section 4 of the phase I S2S verification and products sub-project wiki page>>url:http://s2sprediction.net/xwiki/bin/view/Main/Verification]]. A more comprehensive list of publications produced by the international community including various other S2S research aspects is available in the [[main S2S project website>>url:http://s2sprediction.net/static/documents#publications]].
123: 
124: 
125: 2023
126: 
127: ·         Zhang, Z., DeFlorio, M. J., Delle Monache, L., Subramanian, A. C., Ralph, F. M., Waliser, D. E., et al. (2023) 'Multi-model subseasonal prediction skill assessment of water vapor transport associated with atmospheric rivers over the western U.S.', //Journal of Geophysical Research: Atmospheres//. 128, e2022JD037608. https://doi.org/10.1029/2022JD037608 
128: 
129: ·         Li, X., Tang, Y., Shen, Z., & Li, Y. (2023) 'Spatial variations in seamless predictability of subseasonal precipitation over Asian summer monsoon region in S2S models.', //Journal of Geophysical Research: Atmospheres,//. 128, e2023JD038480. https://doi.org/10.1029/2023JD038480 
130: 
131: ·         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
132: 
133: ·         Chen, D., Pan, C., Qiao, S., Zhi, R., Tang, S., Yang, J., Feng, G., & Dong, W. (2023) 'Evolution and prediction of the extreme rainstorm event in July 2021 in Henan province, China.', //Atmospheric Science Letters//. e1156. https://doi.org/10.1002/asl.1156 
134: 
135: ·         Ma, R., and Yuan, X. (2023) 'Sub-seasonal ensemble prediction of flash droughts over China.', //J. Hydrometeor.//. https://doi.org/10.1175/JHM-D-22-0150.1. 
136: 
137: ·         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 
138: 
139: ·         Xie, J., Hsu, P., Hu, Y., Ye, M., & Yu, J. (2023) 'Skilful Extended-Range Forecast of Rainfall and Extreme Events in East China Based on Deep Learning,.', //Weather and Forecasting//. 
140: 
141: 2022
142: 
143: ·         Stan, C. (2022) 'The forecast skill of the Northern Hemisphere middle latitudes seasonal oscillation and its impact on the surface air temperature.', //Geophysical Research Letters//. 49, e2021GL095543. https://doi.org/10.1029/2021GL. 
144: 
145: ·         Amaya, D. J., Jacox, M. G., Dias, J., Alexander, M. A., Karnauskas, K. B., Scott, J. D., & Gehne, M. (2022) 'Subseasonal-to-seasonal forecast skill in the California Current System and its connection to coastal Kelvin waves', //Journal of Geophysical Research: Oceans//. 127, e2021JC017892. https://doi.org/10.1029/2021JC017892. 
146: 
147: ·         Vitart, F., Robertson, A.W., Spring, A., Pinault, F., Roškar, R., Cao, W., Bech, S., Bienkowski, A., Caltabiano, N., De Coning, E., Denis, B., Dirkson, A., Dramsch, J., Dueben, P., Gierschendorf, J., Kim, H. S., Nowak, K., Landry, D., Lledó, L., Palma, L., Rasp, S., & Zhou, S. (2022) 'Outcomes of the WMO Prize Challenge to Improve Sub-Seasonal to Seasonal Predictions Using Artificial Intelligence', //Bulletin of the American Meteorological Society//. 
148: 
149: ·         Gonzalez, P.L., Howard, E., Ferrett, S., Frame, T.H., Martínez-Alvarado, O., Methven, J. and Woolnough, S.J. (2022) 'Weather Patterns in Southeast Asia: Enhancing high-impact weather sub-seasonal forecast skill', // Q J R Meteorol Soc.//. 
150: 
151: ·         Li, Y., Wu, Z., He, H., and Yin, H. (2022) 'Probabilistic subseasonal precipitation forecasts using preceding atmospheric intraseasonal signals in a Bayesian perspective', //Hydrol. Earth Syst. Sci.//. 26, 4975–4994. 
152: 
153: ·         Garfinkel, C. I., Chen, W., Li, Y., Schwartz, C., Yadav, P., & Domeisen, D. (2022) 'The winter North Pacific teleconnection in response to ENSO and the MJO in operational subseasonal forecasting models is too weak', //Journal of Climate//. 
154: 
155: ·         Wang, X.; Li, S.; Liu, L.; Bai, H.; Feng, G. (2022) 'The Performance of S2S Models on Predicting the 21.7 Extreme Rainfall Event in Henan China', //Atmosphere//. 13, 1516.
156: 
157: ·         Jia, Z., Zheng, Z., Zhu, Y. //et al.// (2022) 'Predictable patterns of midsummer surface air temperature over Eastern China and their corresponding signal sources in ECMWF subseasonal forecasts', //Clim Dyn//. 
158: 
159: ·         Sun, L., Hoerling, M. P., Richter, J. H., Hoell, A., Kumar, A., & Hurrell, J. W. (2022) 'Attribution of North American Subseasonal Precipitation Prediction Skill', //Weather and Forecasting//.
160: 
161: ·         Wu, J., Li, J., Zhu, Z. //et al.// (2022) 'Factors determining the subseasonal prediction skill of summer extreme rainfall over southern China', //Clim Dyn//.
162: 
163: ·         Qin J, Zhou L, Li B and Meng Z (2022) 'Prediction of the Central Indian Ocean Mode in S2S Models', //Front. Mar. Sci.// 9:880469. doi: 10.3389/fmars.2022.880469
164: 
165: ·         Graham, R. M., Browell, J., Bertram, D., & White, C. J. (2022) 'The application of sub-seasonal to seasonal (S2S) predictions for hydropower forecasting', //Meteorological Applications//. 29( 1), e2047.
166: 
167: ·         Chevuturi, A., Klingaman, N.P., Guo, L., Holloway, C.E., Guimarães, B.S., Coelho, C.A.S., Kubota, P.Y., Young, M., Black, E., Baker, J.C.A., Vidale, P.L. (2022) 'Subseasonal prediction performance for South American land–atmosphere coupling in extended austral summer', //Climate Resilience and Sustainability//, 1, e28.
168: 
169: ·         Fischer, C., Fink, A. H., Schömer, E., van der Linden, R., Maier-Gerber, M., Rautenhaus, M., and Riemer, M. (2022) 'A novel method for objective identification of 3-D potential vorticity anomalies', //Geosci. Model Dev.//, 15, 4447–4468.
170: 
171: ·         Zeqing Huang, Tongtiegang Zhao, Weixin Xu, Huayang Cai, Jiabiao Wang, Yongyong Zhang, Zhiyong Liu, Yu Tian, Denghua Yan, Xiaohong Chen (2022) 'A seven-parameter Bernoulli-Gamma-Gaussian model to calibrate subseasonal to seasonal precipitation forecasts', //Journal of Hydrology//. ISSN 0022-1694.
172: 
173: ·         Goutham, N., Plougonven, R., Omrani, H., Parey, S., Tankov, P., Tantet, A., Hitchcock, P., & Drobinski, P. (2022) 'How skillful are the European sub-seasonal forecasts of wind speed and surface temperature?', //Monthly Weather//.
174: 
175: ·         Domeisen, D. I. V. //et al.// (2022) 'Advances in the subseasonal prediction of extreme events: Relevant case studies across the globe', //Bulletin of the American Meteorological Society//. American Meteorological Society, 1(aop). doi: 10.1175/BAMS-D-20-0221.1.
176: 
177: ·         Becker, E. J. //et al.// (2022) 'A Decade of the North American Multimodel Ensemble (NMME): Research, Application, and Future Directions', //Bulletin of the American Meteorological Society//. American Meteorological Society, 103(3), pp. E973–E995. doi: 10.1175/BAMS-D-20-0327.1.
178: 
179: ·         Specq, D., & Batté, L. (2022). Do subseasonal forecasts take advantage of Madden–Julian oscillation windows of opportunity? Atmospheric Science Letters.
180: 
181: ·         Lin, H., Mo, R., & Vitart, F. (2022). The 2021 western North American heatwave and its subseasonal predictions. Geophysical Research Letters, 49.
182: 
183: ·         Parker, D. J., Blyth, A. M., Woolnough, S. J., Dougill, A. J., Bain, C. L., de Coning, E., Diop-Kane, M., Kamga Foamouhoue, A., Lamptey, B., Ndiaye, O., Ruti, P., Adefisan, E. A., Amekudzi, L. K., Antwi-Agyei, P., Birch, C. E., Cafaro, C., Carr, H., Chanzu, B., Clarke, S. J., Coskeran, H., Danuor, S. K., de Andrade, F. M., Diakaria, K., Dione, C., Diop, C. A., Fletcher, J. K., Gaye, A. T., Groves, J. L., Gudoshava, M., Hartley, A. J., Hirons, L. C., Ibrahim, I., James, T. D., Lawal, K. A., Marsham, J. H., Mutemi, J. N., Okogbue, E. C., Olaniyan, E., Omotosho, J. B., Portuphy, J., Roberts, A. J., Schwendike, J., Segele, Z. T., Stein, T. H. M., Taylor, A. L., Taylor, C. M., Warnaars, T. A., Webster, S., Woodhams, B. J., & Youds, L. (2022). The African SWIFT Project: Growing Science Capability to Bring about a Revolution in Weather Prediction, Bulletin of the American Meteorological Society, 103(2), E349-E369.
184: 
185: ·         Wulff, C.O., Vitart, F. and Domeisen, D.I.V. (2022), Influence of Trends on Subseasonal Temperature Prediction Skill. Q J R Meteorol Soc. Accepted Author Manuscript.
186: 
187: ·         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 (published online ahead of print 2022).
188: 
189: ·         Stan, C. (2022). The forecast skill of the Northern Hemisphere middle latitudes seasonal oscillation and its impact on the surface air temperature. Geophysical Research Letters, 49.
190: 
191: ·         Amaya, D. J., Jacox, M. G., Dias, J., Alexander, M. A., Karnauskas, K. B., Scott, J. D., & Gehne, M. (2022). Subseasonal-to-seasonal forecast skill in the California Current System and its connection to coastal Kelvin waves. Journal of Geophysical Research: Oceans, 127, e2021JC017892. https:~/~/doi.org/10.1029/2021JC017892 Pham-Thanh, H., Phan-Van, T., van der Linden, R., & Fink, A. H. (2022). The Performance of ECMWF Subseasonal Forecasts to Predict the Rainy Season Onset Dates in Vietnam, Weather and Forecasting, 37(1), 113-124.
192: 
193: ·         Zhang, M., Yang, X.-Y., & Huang, Y. (2022). Impacts of sudden stratospheric warming on extreme cold events in early 2021: An ensemble-based sensitivity analysis. Geophysical Research Letters, 49, e2021GL096840.
194: 
195: ·         Pham-Thanh, H., Phan-Van, T., van der Linden, R., & Fink, A. H. (2022). The Performance of ECMWF Subseasonal Forecasts to Predict the Rainy Season Onset Dates in Vietnam, Weather and Forecasting, 37(1), 113-124.
196: 
197: 2021
198: 
199: ·         White, C. J., Domeisen, D. I. V., Acharya, N., Adefisan, E. A., Anderson, M. L., Aura, S., Balogun, A. A., Bertram, D., Bluhm, S., Brayshaw, D. J., Browell, J., Büeler, D., Charlton-Perez, A., Chourio, X., Christel, I., Coelho, C. A. S., DeFlorio, M. J., Delle Monache, L., Di Giuseppe, F., García-Solórzano, A. M., Gibson, P. B., Goddard, L., González Romero, C., Graham, R. J., Graham, R. M., Grams, C. M., Halford, A., Katty Huang, W. T., Jensen, K., Kilavi, M., Lawal, K. A., Lee, R. W., MacLeod, D., Manrique-Suñén, A., Martins, E. S. P. R., Maxwell, C. J., Merryfield, W. J., Muñoz, Á. G., Olaniyan, E., Otieno, G., Oyedepo, J. A., Palma, L., Pechlivanidis, I. G., Pons, D., Ralph, F. M., Reis, D. S., Jr., Remenyi, T. A., Risbey, J. S., Robertson, D. J. C., Robertson, A. W., Smith, S., Soret, A., Sun, T., Todd, M. C., Tozer, C. R., Vasconcelos, F. C., Jr., Vigo, I., Waliser, D. E., Wetterhall, F., & Wilson, R. G. (2021). Advances in the application and utility of subseasonal-to-seasonal predictions, BAMS.
200: 
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380: ·         [[Materia, S., Muñoz, Á. G., Álvarez-Castro, M. C., Mason, S. J., Vitart, F., & Gualdi, S. (2020). Multimodel Subseasonal Forecasts of Spring Cold Spells: Potential Value for the Hazelnut Agribusiness. Weather and Forecasting, 35(1), 237-254.>>url:http://s2sprediction.net/file/documents_publications/waf-d-19-0086.1.pdf]]
381: 
382: 2019
383: 
384: 
385: ·         Muñoz, Á.G. //et al.// (2019) 'NextGen: A Next-Generation System for Calibrating, Ensembling and Verifying Regional Seasonal and Subseasonal Forecasts', //AGUFM//, 2019, pp. A23U-3024. Available at: [[https:~~/~~/ui.adsabs.harvard.edu/abs/2019AGUFM.A23U3024M/abstract>>url:https://ui.adsabs.harvard.edu/abs/2019AGUFM.A23U3024M/abstract]]
386: 
387: ·         Muñoz, Á. G. //et al.// (2019) 'Can We Predict "Climate Migrations"? The 2018 Guatemalan Case', in //American Geophysical Union, Fall Meeting 2019, abstract #GC13E-1213//. Available at: [[https:~~/~~/ui.adsabs.harvard.edu/abs/2019AGUFMGC13E1213M/abstract>>url:https://ui.adsabs.harvard.edu/abs/2019AGUFMGC13E1213M/abstract]] 
388: 
389: ·         [[Vigaud, N., Tippett, M. K., & Robertson, A. W. (2019). Deterministic Skill of Subseasonal Precipitation Forecasts for the East Africa‐West Asia Sector from September to May. Journal of Geophysical Research: Atmospheres, 124(22), 11887-11896.>>url:http://s2sprediction.net/file/documents_publications/2019JD030747.pdf]]
390: 
391: ·         [[Vigaud, N., Tippett, M. K., Yuan, J., Robertson, A. W., & Acharya, N. (2019). Probabilistic skill of subseasonal surface temperature forecasts over North America. Weather and Forecasting, 34(6), 1789-1806.>>url:http://s2sprediction.net/file/documents_publications/waf-d-19-0117.1.pdf]]
392: 
393: ·         [[Lin, H. (2019). Subseasonal Forecast Skill over the Northern Polar Region in Boreal Winter. Journal of Climate, (Accepted)>>url:http://s2sprediction.net/file/documents_publications/jcli-d-19-0408.1.pdf]]
394: 
395: ·         [[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>>url:http://s2sprediction.net/file/documents_publications/jcli-d-18-0355.1_2.pdf]]
396: 
397: ·         [[Albers, J. R., & Newman, M. (2019). A Priori Identification of Skillful Extratropical Subseasonal Forecasts. Geophysical Research Letters.>>url:http://s2sprediction.net/file/documents_publications/2019GL085270.pdf]]
398: 
399: ·         [[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>>url:http://s2sprediction.net/file/documents_publications/jhm-d-19-0042.1.pdf]]
400: 
401: ·         [[Richardson, D., H. Fowler, C. Kilsby, R. neal and R. Dankers, (2019). Improving sub-seasonal forecast skill of meteorological drought: a weather pattern approach. Nat. Hazards Earth Syst. Sci., https:~~/~~/doi.org/10.5194/nhess-2019-221>>url:http://s2sprediction.net/file/documents_publications/nhess-2019-221-RC2-supplement.pdf]]
402: 
403: ·         [[Lee, Seungsoo;Kim, Gayoung;Yoon, Soonjo;An, Hyunuk, (2019). Improvement of precipitation forecasting skill of ECMWF data using multi-layer perceptron technique. Journal of Korea Water Resources Association, Volume 52, Issue 7, pp 475-482. https:~~/~~/doi.org/10.3741/JKWRA.2019.52.7.475>>url:http://s2sprediction.net/file/documents_publications/Improvement_of_precipitation_forecasting_skill_of_ECMWF_data_using_multi-layer_perceptron_technique.pdf]]
404: 
405: ·         [[ZHU, Hanchen and Haishan CHEN and Yang ZHOU and Xuan DONG, (2019). Evaluation of the subseasonal forecast skill of surface soil moisture in the S2S database. Atmospheric and Oceanic Science Letters, 12, 6, 467-474, https:~~/~~/doi.org/10.1080/16742834.2019.1663123>>url:http://s2sprediction.net/file/documents_publications/Evaluation%20of%20the%20subseasonal%20forecast%20skill%20of%20surface%20soil%20moisture%20in%20the%20S2S%20database.pdf]]
406: 
407: ·         [[Kim, H., Janiga, M. A., & Pegion, K. ( 2019). MJO propagation processes and mean biases in the SubX and S2S reforecasts. Journal of Geophysical Research: Atmospheres, 124, 9314– 9331. https:~~/~~/doi.org/10.1029/2019JD031139>>url:http://s2sprediction.net/file/documents_publications/2019JD031139.pdf]]
408: 
409: ·         [[Rao, J., Ren, R., Chen, H., Liu, X., Yu, Y., Hu, J., & Zhou, Y. ( 2019). Predictability of stratospheric sudden warmings in the Beijing Climate Center Forecast System with statistical error corrections. Journal of Geophysical Research: Atmospheres, 124, 8385– 8400. https:~~/~~/doi.org/10.1029/2019JD030900>>url:http://s2sprediction.net/file/documents_publications/2019JD030900.pdf]]
410: 
411: ·         [[Olaniyan, E., Adefisan, E. A., Balogun, A. A., & Lawal, K. A. (2019). The influence of global climate drivers on monsoon onset variability in Nigeria using S2S models. Modeling Earth Systems and Environment, 1-24.>>url:http://s2sprediction.net/file/documents_publications/Olaniyan2019_Article_TheInfluenceOfGlobalClimateDri.pdf]]
412: 
413: ·         [[Kolachian, R., & Saghafian, B. (2019). Deterministic and probabilistic evaluation of raw and post processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes. Theoretical and Applied Climatology, 137(1-2), 1479-1493.>>url:http://s2sprediction.net/file/documents_publications/Kolachian-Saghafian2019_Article_DeterministicAndProbabilisticE.pdf]]
414: 
415: ·         [[Pan, B., Hsu, K., AghaKouchak, A., Sorooshian, S., & Higgins, W. (2019). Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range. Journal of Climate, 32(1), 161-182.>>url:http://s2sprediction.net/file/documents_publications/jcli-d-18-0355.1_1.pdf]]
416: 
417: ·         [[DeFlorio, M. J., Waliser, D. E., Guan, B., Ralph, F. M., & Vitart, F. (2019). Global evaluation of atmospheric river subseasonal prediction skill. Climate Dynamics, 52(5-6), 3039-3060.>>url:http://s2sprediction.net/file/documents_publications/DeFlorio2019_Article_GlobalEvaluationOfAtmosphericR.pdf]]
418: 
419: ·         [[Miao, Q., Pan, B., Wang, H., Hsu, K., & Sorooshian, S. (2019). Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network. Water, 11(5), 977.>>url:http://s2sprediction.net/file/documents_publications/water-11-00977.pdf]]
420: 
421: ·         [[Pan, B., Hsu, K., AghaKouchak, A., Sorooshian, S., & Higgins, W. (2019). Precipitation Prediction Skill for the West Coast United States: From Short to Extended Range. Journal of Climate, 32(1), 161-182.>>url:http://s2sprediction.net/file/documents_publications/jcli-d-18-0355.1.pdf]]
422: 
423: ·         [[de Andrade, F. M., Coelho, C. A., & Cavalcanti, I. F. (2019). Global precipitation hindcast quality assessment of the Subseasonal to Seasonal (S2S) prediction project models. Climate Dynamics, 52(9-10), 5451-5475.>>url:http://s2sprediction.net/file/documents_publications/Andrade2019_Article_GlobalPrecipitationHindcastQua.pdf]]
424: 
425: 2018
426: 
427: ·         [[Olaniyan E, Adefisan EA, Oni F, Afiesimama E, Balogun AA and Lawal KA (2018). Evaluation of the ECMWF Sub-seasonal to Seasonal Precipitation Forecasts during the Peak of West Africa Monsoon in Nigeria, Front. Environ. Sci. 6:4. doi: 10.3389/fenvs.2018.00004>>url:http://s2sprediction.net/file/documents_publications/fenvs-06-00004.pdf]]
428: 
429: ·         [[Doss-Gollin, J., Muñoz, Á. G., Mason, S. J., & Pastén, M. (2018). Heavy Rainfall in Paraguay during the 2015/16 Austral Summer: Causes and Subseasonal-to-Seasonal Predictive Skill. Journal of Climate, 31(17), 6669-6685.>>url:http://s2sprediction.net/file/documents_publications/jcli-d-17-0805.1_2.pdf]]
430: 
431: ·         [[Goessling, H. F., & Jung, T. (2018). A probabilistic verification score for contours: Methodology and application to Arctic ice‐edge forecasts. Quarterly Journal of the Royal Meteorological Society, 144(712), 735-743.>>url:http://s2sprediction.net/file/documents_publications/qj.3242.pdf]]
432: 
433: ·         [[Coelho, C. A., Firpo, M. A., & de Andrade, F. M. (2018). A verification framework for South American sub-seasonal precipitation predictions. Meteorologische Zeitschrift, 503-520.>>url:http://s2sprediction.net/file/documents_publications/Coelho_et_al_2018.pdf]]
434: 
435: ·         [[DeFlorio, M. J., Waliser, D. E., Guan, B., Lavers, D. A., Ralph, F. M., & Vitart, F. (2018). Global assessment of atmospheric river prediction skill. Journal of Hydrometeorology, 19(2), 409-426.>>url:http://s2sprediction.net/file/documents_publications/jhm-d-17-0135.1.pdf]]
436: 
437: ·         [[Ferranti, L., Magnusson, L., Vitart, F., & Richardson, D. S. (2018). How far in advance can we predict changes in large‐scale flow leading to severe cold conditions over Europe?. Quarterly Journal of the Royal Meteorological Society, 144(715), 1788-1802.>>url:http://s2sprediction.net/file/documents_publications/qj.3341.pdf]]
438: 
439: ·         [[Ford, T. W., Dirmeyer, P. A., & Benson, D. O. (2018). Evaluation of heat wave forecasts seamlessly across subseasonal timescales. npj Clim. Atmos. Sci., 1, 20.>>url:http://s2sprediction.net/file/documents_publications/s41612-018-0027-7.pdf]]
440: 
441: ·         [[Gao, M., Wang, B., Yang, J., & Dong, W. (2018). Are peak summer sultry heat wave days over the Yangtze–Huaihe River basin predictable?. Journal of Climate, 31(6), 2185-2196.>>url:http://s2sprediction.net/file/documents_publications/jcli-d-17-0342.1.pdf]]
442: 
443: ·         [[Janiga, M. A., J. Schreck III, C., Ridout, J. A., Flatau, M., Barton, N. P., Metzger, E. J., & Reynolds, C. A. (2018). Subseasonal forecasts of convectively coupled equatorial waves and the MJO: Activity and predictive skill. Monthly Weather Review, 146(8), 2337-2360.>>url:http://s2sprediction.net/file/documents_publications/mwr-d-17-0261.1.pdf]]
444: 
445: ·         [[Karpechko, A. Y., Charlton‐Perez, A., Balmaseda, M., Tyrrell, N., & Vitart, F. (2018). Predicting Sudden Stratospheric Warming 2018 and Its Climate Impacts With a Multimodel Ensemble. Geophysical Research Letters, 45(24), 13-538.>>url:http://s2sprediction.net/file/documents_publications/2018GL081091.pdf]]
446: 
447: ·         [[Kolachian, R., & Saghafian, B. (2018). Deterministic and probabilistic evaluation of raw and post processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes. Theoretical and Applied Climatology, 1-15.>>url:http://s2sprediction.net/file/documents_publications/Kolachian-Saghafian2018_Article_DeterministicAndProbabilisticE.pdf]]
448: 
449: ·         [[Lim, Y., Son, S. W., & Kim, D. (2018). MJO prediction skill of the subseasonal-to-seasonal prediction models. Journal of Climate, 31(10), 4075-4094.>>url:http://s2sprediction.net/file/documents_publications/jcli-d-17-0545.1.pdf]]
450: 
451: ·         [[Nardi, K. M., Barnes, E. A., & Ralph, F. M. (2018). Assessment of numerical weather prediction model reforecasts of the occurrence, intensity, and location of atmospheric rivers along the West Coast of North America. Monthly Weather Review, 146(10), 3343-3362.>>url:http://s2sprediction.net/file/documents_publications/mwr-d-18-0060.1.pdf]]
452: 
453: ·         [[Vigaud, N., Tippett, M. K., & Robertson, A. W. (2018). Probabilistic Skill of Subseasonal Precipitation Forecasts for the East Africa–West Asia Sector during September–May. Weather and Forecasting, 33(6), 1513-1532.>>url:http://s2sprediction.net/file/documents_publications/waf-d-18-0074.1.pdf]]
454: 
455: ·         [[Vuillaume, J. F., Dorji, S., Komolafe, A., & Herath, S. (2018). Sub-seasonal extreme rainfall prediction in the Kelani River basin of Sri Lanka by using self-organizing map classification. Natural Hazards, 94(1), 385-404.>>url:http://s2sprediction.net/file/documents_publications/Vuillaume2018_Article_Sub-seasonalExtremeRainfallPre.pdf]]
456: 
457: ·         [[Zhou, Y., Yang, B., Chen, H., Zhang, Y., Huang, A., & La, M. (2018). Effects of the Madden–Julian Oscillation on 2-m air temperature prediction over China during boreal winter in the S2S database. Climate Dynamics, 1-19.>>url:http://s2sprediction.net/file/documents_publications/Zhou2019_Article_EffectsOfTheMaddenJulianOscill.pdf]]
458: 
459: 2017
460: 
461: ·         [[Vitart, F. (2017). Madden—Julian Oscillation prediction and teleconnections in the S2S database. Quarterly Journal of the Royal Meteorological Society, 143(706), 2210-2220.>>url:http://s2sprediction.net/file/documents_publications/qj.3079.pdf]]
462: 
463: ·         [[Bombardi RJ, Pegion KV, Kinter JL, Cash BA and Adams JM (2017): Sub-seasonal Predictability of the Onset and Demise of the Rainy Season over Monsoonal Regions. Front. Earth Sci. 5:14. doi: 10.3389/feart.2017.00014>>url:http://s2sprediction.net/file/documents_publications/feart-05-00014.pdf]]
464: 
465: ·         [[Ferrone, A., D. Mastrangelo, and P. Malguzzi. (2017) Multimodel probabilistic prediction of 2 meter-temperature anomalies on the monthly timescale. Advances in Science and Research 14, 123-129. Online publication date: 8-May-2017.>>url:http://s2sprediction.net/file/documents_publications/asr-14-123-2017.pdf]]
466: 
467: ·         [[Schiraldi, N.J. and P.E. Roundy, 2017: Seasonal-to-Subseasonal Model Forecast Performance during Agricultural Drought Transition Periods in the U.S. Corn Belt, 2017. Mon. Wea. Rev, 145, 3687-3708. DOI: 10.1175/MWR-D-17-0026.1>>url:http://s2sprediction.net/file/documents_publications/mwr-d-17-0026.1.pdf]]
468: 
469: ·         [[Vigaud, N., A. W. Robertson and M. K. Tippett, 2017a: Multi-model ensembling of subseasonal precipitation forecasts over North America, Mon. Wea. Rev., 145, 3913-3928.>>url:http://s2sprediction.net/file/documents_publications/mwr-d-17-0092.1.pdf]]
470: 
471: ·         [[Ichikawa, Y. and M. Inatsu, 2017: An alternative estimate of potential predictability on the Madden–Julian oscillation phase space using S2S models. Atmosphere 2017, 8(8), 150; doi:10.3390/atmos8080150>>url:http://s2sprediction.net/file/documents_publications/atmosphere-08-00150-v3.pdf]]
472: 
473: ·         [[Liu, X., Wu, T., Yang, S., Li, T., Jie, W., Zhang, L., ... & Ren, H. (2017). MJO prediction using the sub-seasonal to seasonal forecast model of Beijing Climate Center. Climate Dynamics, 48(9-10), 3283-3307.>>url:http://s2sprediction.net/file/documents_publications/Liu2017_Article_MJOPredictionUsingTheSub-seaso.pdf]]
474: 
475: ·         [[Vigaud, N., Robertson, A. W., Tippett, M. K., & Acharya, N. (2017). Subseasonal predictability of boreal summer monsoon rainfall from ensemble forecasts. Frontiers in Environmental Science, 5, 67.>>url:http://s2sprediction.net/file/documents_publications/fenvs-05-00067.pdf]]
476: 
477:  2016
478: 
479: ·         [[Lowe, R., M. García-Díez, J. Ballester, J. Creswick, J.-M. Robine, F. R. Herrmann, and X. Rodó, 2016: Evaluation of an Early-Warning System for Heat Wave-Related Mortality in Europe: Implications for Sub-seasonal to Seasonal Forecasting and Climate Services, Int J Environ Res Public Health. 13(2): 206>>url:http://s2sprediction.net/file/documents_publications/ijerph-13-00206-v3.pdf]]
480: 
481:  
482: 
483:  
484: )))
485: 
486: ----
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