Fetching the paper…
Reading the bibliography…
Sub-seasonal weather forecasts are becoming increasingly important for a range of socio-economic activities.
A scoring system for probability forecasts of ranked categories
Epstein, E. S. (1969) · 1969
Earlier work this paper cites.
A note on the ranked probability score
Murphy, A. H. (1971) · 1971
Earlier work this paper cites.
Improving subseasonal-to-seasonal forecasts in predicting the occurrence of extreme precipitation events over the contiguous U.S. using machine learning models
Zhang, L., Yang, T., Gao, S., Hong, Y., Zhang, Q., Wen, X. and Cheng, C. (2023) · 1971
Earlier work this paper cites.
Reliability of subjective probability forecasts of precipitation and temperature
Murphy, A. H. and Winkler, R. L. (1977) · 1977
Earlier work this paper cites.
Combining artificial intelligence with physics-based methods for probabilistic renewable energy forecasting
Haupt, S. E., McCandless, T. C., Dettling, S., Alessandrini, S., Lee, J. A., Linden, S., Petzke, W., Brummet, T., Nguyen, N. and Kosović, B. (2020) · 1979
Earlier work this paper cites.
Why do forecasts for “Near Normal” often fail?
Dool, H. M. V. D. and Toth, Z. (1991) · 1991
Earlier work this paper cites.
The rationale behind the success of multi-model ensembles in seasonal forecasting – II. Calibration and combination
Doblas-Reyes, F. J., Hagedorn, R. and Palmer, T. N. (2005) · 2005
Earlier work this paper cites.
Probabilistic forecasts, calibration and sharpness
Gneiting, T., Balabdaoui, F. and Raftery, A. E. (2007) · 2007
Earlier work this paper cites.
Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction
Brunet, G., Shapiro, M., Hoskins, B., Moncrieff, M., Dole, R., Kiladis, G. N., Kirtman, B., Lorenc, A., Mills, B., Morss, R., Polavarapu, S., Rogers, D., Schaake, J. and Shukla, J. (2010) · 2010
Earlier work this paper cites.
Deep neural networks segment neuronal membranes in electron microscopy images
Cireşan, D. C., Giusti, A., Gambardella, L. M. and Schmidhuber, J. (2012) · 2012
Earlier work this paper cites.
The Calibration Simplex: A generalization of the reliability diagram for three-category probability forecasts
Wilks, D. S. (2013) · 2013
Earlier work this paper cites.
U-Net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P. and Brox, T. (2015) · 2015
Earlier work this paper cites.
Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M. et al. (2016) · 2016
Earlier work this paper cites.
Probabilistic seasonal forecasts in the North American multimodel ensemble: A baseline skill assessment
Becker, E. and Dool, H. v. d. (2016) · 2016
Earlier work this paper cites.
Calibrated ensemble forecasts using quantile regression forests and ensemble model output statistics
Taillardat, M., Mestre, O., Zamo, M. and Naveau, P. (2016) · 2016
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J. (2017) · 2017
Earlier work this paper cites.
A survey on deep learning in medical image analysis
Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A., van Ginneken, B. and Sánchez, C. I. (2017) · 2017
Earlier work this paper cites.
Nonhomogeneous boosting for predictor selection in ensemble postprocessing
Messner, J. W., Mayr, G. J. and Zeileis, A. (2017) · 2017
Earlier work this paper cites.
Multimodel ensembling of subseasonal precipitation forecasts over North America
Vigaud, N., Robertson, A. W. and Tippett, M. K. (2017) · 2017
Cited alongside, same era.
The subseasonal to seasonal (S2S) prediction project database
Vitart, F., Ardilouze, C., Bonet, A., Brookshaw, A., Chen, M., Codorean, C., Déqué, M., Ferranti, L., Fucile, E., Fuentes, M., Hendon, H., Hodgson, J., Kang, H.-S., Kumar, A., Lin, H., Liu, G., Liu, X., Malguzzi, P., Mallas, I., Manoussakis, M., Mastrangelo, D., MacLachlan, C., McLean, P., Minami, A., Mladek, R., Nakazawa, T., Najm, S., Nie, Y., Rixen, M., Robertson, A. W., Ruti, P., Sun, C., Takaya, Y., Tolstykh, M., Venuti, F., Waliser, D., Woolnough, S., Wu, T., Won, D.-J., Xiao, H., Zaripov, R. and Zhang, L. (2017) · 2017
Cited alongside, same era.
Potential applications of subseasonal-to-seasonal (S2S) predictions
White, C. J., Carlsen, H., Robertson, A. W., Klein, R. J., Lazo, J. K., Kumar, A., Vitart, F., Coughlan de Perez, E., Ray, A. J., Murray, V., Bharwani, S., MacLeod, D., James, R., Fleming, L., Morse, A. P., Eggen, B., Graham, R., Kjellström, E., Becker, E., Pegion, K. V., Holbrook, N. J., McEvoy, D., Depledge, M., Perkins-Kirkpatrick, S., Brown, T. J., Street, R., Jones, L., Remenyi, T. A., Hodgson-Johnston, I., Buontempo, C., Lamb, R., Meinke, H., Arheimer, B. and Zebiak, S. E. (2017) · 2017
Cited alongside, same era.
Efficient patch-wise semantic segmentation for large-scale remote sensing images
Liu, Y., Ren, Q., Geng, J., Ding, M. and Li, J. (2018) · 2018
Cited alongside, same era.
Multimodel subseasonal precipitation forecasts over the contiguous United States: Skill assessment and statistical postprocessing
Li, Y., Tian, D. and Medina, H. (2021) · 2021
Later among the works it cites.
Subseasonal forecasts of opportunity identified by an explainable neural network
Mayer, K. J. and Barnes, E. A. (2021) · 2021
Later among the works it cites.
Learned benchmarks for subseasonal forecasting
Mouatadid, S., Orenstein, P., Flaspohler, G., Oprescu, M., Cohen, J., Wang, F., Knight, S., Geogdzhayeva, M., Levang, S., Fraenkel, E. and Mackey, L. (2021) · 2021
Later among the works it cites.
CalSim: The Calibration Simplex
Resin, J. (2021) · 2021
Later among the works it cites.
Statistical postprocessing for weather forecasts: Review, challenges, and avenues in a big data world
Vannitsem, S., Bremnes, J. B., Demaeyer, J., Evans, G. R., Flowerdew, J., Hemri, S., Lerch, S., Roberts, N., Theis, S., Atencia, A., Ben Bouallègue, Z., Bhend, J., Dabernig, M., De Cruz, L., Hieta, L., Mestre, O., Moret, L., Plenković, I. O., Schmeits, M., Taillardat, M., Van den Bergh, J., Van Schaeybroeck, B., Whan, K. and Ylhaisi, J. (2021) · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Neural networks for postprocessing ensemble weather forecasts
Rasp, S. and Lerch, S. (2018) · 2018
Cited alongside, same era.
Towards implementing artificial intelligence post-processing in weather and climate: Proposed actions from the Oxford 2019 workshop
Haupt, S. E., Chapman, W., Adams, S. V., Kirkwood, C., Hosking, J. S., Robinson, N. H., Lerch, S. and Subramanian, A. C. (2021) · 2019
Cited alongside, same era.
Probabilistic skill of subseasonal surface temperature forecasts over North America
Vigaud, N., Tippett, M. K., Yuan, J., Robertson, A. W. and Acharya, N. (2019) · 2019
Cited alongside, same era.
RainNet v1.0: A convolutional neural network for radar-based precipitation nowcasting
Ayzel, G., Scheffer, T. and Heistermann, M. (2020) · 2020
Cited alongside, same era.
Introduction to special collection: “Bridging weather and climate: Subseasonal-to-seasonal (S2S) prediction”
Lang, A. L., Pegion, K. and Barnes, E. A. (2020) · 2020
Cited alongside, same era.
Windows of opportunity for skillful forecasts subseasonal to seasonal and beyond
Mariotti, A., Baggett, C., Barnes, E. A., Becker, E., Butler, A., Collins, D. C., Dirmeyer, P. A., Ferranti, L., Johnson, N. C., Jones, J., Kirtman, B. P., Lang, A. L., Molod, A., Newman, M., Robertson, A. W., Schubert, S., Waliser, D. E. and Albers, J. (2020) · 2020
Cited alongside, same era.
Current and emerging developments in subseasonal to decadal prediction
Merryfield, W. J., Baehr, J., Batté, L., Becker, E. J., Butler, A. H., Coelho, C. A. S., Danabasoglu, G., Dirmeyer, P. A., Doblas-Reyes, F. J., Domeisen, D. I. V., Ferranti, L., Ilynia, T., Kumar, A., Müller, W. A., Rixen, M., Robertson, A. W., Smith, D. M., Takaya, Y., Tuma, M., Vitart, F., White, C. J., Alvarez, M. S., Ardilouze, C., Attard, H., Baggett, C., Balmaseda, M. A., Beraki, A. F., Bhattacharjee, P. S., Bilbao, R., de Andrade, F. M., DeFlorio, M. J., Díaz, L. B., Ehsan, M. A., Fragkoulidis, G., Grainger, S., Green, B. W., Hell, M. C., Infanti, J. M., Isensee, K., Kataoka, T., Kirtman, B. P., Klingaman, N. P., Lee, J.-Y., Mayer, K., McKay, R., Mecking, J. V., Miller, D. E., Neddermann, N., Justin Ng, C. H., Ossó, A., Pankatz, K., Peatman, S., Pegion, K., Perlwitz, J., Recalde-Coronel, G. C., Reintges, A., Renkl, C., Solaraju-Murali, B., Spring, A., Stan, C., Sun, Y. Q., Tozer, C. R., Vigaud, N., Woolnough, S. and Yeager, S. (2020) · 2020
Cited alongside, same era.
Subseasonal to seasonal prediction of weather to climate with application to tropical cyclones
Robertson, A. W., Vitart, F. and Camargo, S. J. (2020) · 2020
Cited alongside, same era.
Later among the works it cites.
Statistical postprocessing of wind speed forecasts using convolutional neural networks
Veldkamp, S., Whan, K., Dirksen, S. and Schmeits, M. (2021) · 2021
Later among the works it cites.
Probabilistic predictions from deterministic atmospheric river forecasts with deep learning
Chapman, W. E., Monache, L. D., Alessandrini, S., Subramanian, A. C., Ralph, F. M., Xie, S.-P., Lerch, S. and Hayatbini, N. (2022) · 2022
Later among the works it cites.
Convolutional autoencoders for spatially-informed ensemble post-processing
Lerch, S. and Polsterer, K. L. (2022) · 2022
Later among the works it cites.
Convolutional neural network-based statistical post-processing of ensemble precipitation forecasts
Li, W., Pan, B., Xia, J. and Duan, Q. (2022) · 2022
Later among the works it cites.
Evaluating ensemble post-processing for wind power forecasts
Phipps, K., Lerch, S., Andersson, M., Mikut, R., Hagenmeyer, V. and Ludwig, N. (2022) · 2022
Later among the works it cites.
EuLerian Identification of ascending AirStreams (ELIAS 2.0) in numerical weather prediction and climate models – part 1: Development of deep learning model
Quinting, J. F. and Grams, C. M. (2022) · 2022
Later among the works it cites.
Evaluation of machine learning techniques for forecast uncertainty quantification
Sacco, M. A., Ruiz, J. J., Pulido, M. and Tandeo, P. (2022) · 2022
Later among the works it cites.
Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
Schulz, B. and Lerch, S. (2022) · 2022
Later among the works it cites.
Outcomes of the WMO prize challenge to improve subseasonal to seasonal predictions using artificial intelligence
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. and Zhou, S. (2022) · 2022
Later among the works it cites.
Advances in the application and utility of subseasonal-to-seasonal predictions
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., Huang, W. T. K., 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, A. G., Olaniyan, E., Otieno, G., Oyedepo, J. A., Palma, L., Pechlivanidis, I. G., Pons, D., Ralph, F. M., Reis, D. S., 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., Vigo, I., Waliser, D. E., Wetterhall, F. and Wilson, R. G. (2022) · 2022
Later among the works it cites.
Improving medium-range ensemble weather forecasts with hierarchical ensemble transformers
Ben-Bouallegue, Z., Weyn, J. A., Clare, M. C., Dramsch, J., Dueben, P. and Chantry, M. (2023) · 2023
Closest in time.
The EUPPBench postprocessing benchmark dataset v1.0
Demaeyer, J., jonas Bhend, Lerch, S., Primo, C., Schaeybroeck, B. V., Atencia, A., Bouallègue, Z. B., Chen, J., Dabernig, M., Evans, G., Pucer, J. F., Hooper, B., Horat, N., Jobst, D., Merše, J., Mlakar, P., Möller, A., Mestre, O., Taillardat, M. and Vannitsem, S. (2023) · 2023
Closest in time.
Deep learning forecast uncertainty for precipitation over Western US
Hu, W., Ghazvinian, M., Chapman, W. E., Sengupta, A., Ralph, F. M. and Delle Monache, L. (2023) · 2023
Closest in time.