An Application of Deep Learning Technique to Improve Subseasonal to Seasonal Rainfall Forecast over Java Island, Indonesia
Abstract
Subseasonal to seasonal (S2S) rainfall forecast can benefit several sectors, such as water resources, hazard management, and agriculture. However, the forecast remains challenging due to its lack of skill. This study applies Convolutional AutoEncoders (ConvAE), a deep learning technique, to improve the quality of the S2S rainfall forecast. Seven S2S model output incorporated with Subseasonal Experiments Projects (SubX), including CCSM4, CFSv2, FIMr1p1, GEFS, GEOS_v2p1, GEPS6, and NESM, are corrected using the ConvAE approach. We combine 407 ground observations and the CHIRPS dataset using regression kriging methods producing gridded daily precipitation data with 0.05° spatial resolution. We utilize this dataset as a label to train ConvAE models and to perform bias corrections to all members of the SubX forecasts data. The results show that ConvAE is able to increase the quality of weekly S2S rainfall forecasts over Java, Indonesia. The Correlation Coefficient for 1 – 4 weeks lead time are improved from: 0.76, 0.715, 0.692 and 0.722 towards 0.809, 0.751, 0.719 and 0.74, respectively. Furthermore, the average CRPSS improves between 20 – 30% for all lead times.
References
Babaousmail H, Hou R, Gnitou GT, Ayugi B. 2021. Novel statistical downscaling emulator for precipitation projections using deep Convolutional Autoencoder over Northern Africa. J Atmos Solar-Terrestrial Phys. 218(March):105614.
Baker SA, Wood AW, Rajagopalan B. 2020. Application of postprocessing to watershed-scale subseasonal climate forecasts over the contiguous united states. J Hydrometeorol. 21(5):971–987.
Baño-Medina J, Manzanas R, Gutierrez JM. 2020. Configuration and intercomparison of deep learning neural models for statistical downscaling. Geosci Model Dev. 13(4):2109–2124.
Barker TW. 1991. The Relationship between Spread and Forecast Error In Extended-range Forecasts. J Clim [Internet]. 4(7):733–742. https://www.jstor.org/stable/26196422
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J. 2015. The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Sci Data. 2:1–21.
Han L, Chen M, Chen K, Chen H, Zhang Y, Lu B, Song L, Qin R. 2021. A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts. Adv Atmos Sci. 38(9):1444–1459.
Kirtman BP, Pegion K, DelSole T, Tippett M, Robertson AW, Bell M, Burgman R, Lin H, Gottschalck J, Collins DC, et al. 2017. The subseasonal experiment (SUBX). IRI Data Libr [Internet]. http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/
Kolachian R, Saghafian B. 2019. Deterministic and probabilistic evaluation of raw and post-processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes. Theor Appl Climatol. 137(1–2):1479–1493.
Krasnopolsky VM, Lin Y. 2012. A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental US. Adv Meteorol. 2012(1).
Le XH, Lee G, Jung K, An HU, Lee S, Jung Y. 2020. Application of convolutional neural network for spatiotemporal bias correction of daily satellite-based precipitation. Remote Sens. 12(17).
Li W, Chen J, Li L, Chen H, Liu B, Xu C-Y, Li X. 2019. Evaluation and bias correction of S2S precipitation for hydrological extremes. J Hydrometeorol. 20(9):1887–1906.
Mao X-J, Shen C, Yang Y-B. 2016. Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections [Internet]. :1–17. http://arxiv.org/abs/1606.08921
Misnawati, Boer R, June T, Faqih A. 2018. Perbandingan metodologi koreksi bias data curah hujan CHIRPS. LIMNOTEK - Perair Darat Trop di Indones [Internet]. 25(1):18–29. https://limnotek.limnologi.lipi.go.id/index.php/limnotek/article/view/224
Monier E, Gao X, Scott JR, Sokolov AP, Schlosser CA. 2015. A framework for modelling uncertainty in regional climate change. Clim Change. 131(1):51–66.
Muharsyah R, Hadi TW, Indratno SW. 2020. Penerapan metode Bayesian Model Averaging untuk kalibrasi prediksi ensemble curah hujan bulanan di Pulau Jawa. Agromet. 34(1):20–33.
Nover DM, Witt JW, Butcher JB, Johnson TE, Weaver CP. 2016. EPA Public Access. Earth Interact. 20(11):1–27.
Pegion K, Kirtman BP, Becker E, Collins DC, Lajoie E, Burgman R, Bell R, Delsole T, Min D, Zhu Y, et al. 2019. The subseasonal experiment (SUBX). Bull Am Meteorol Soc. 100(10):2043–2060.
Ratri DN, Whan K, Schmeits M. 2019. A comparative verification of raw and bias-corrected ECMWF seasonal ensemble precipitation reforecasts in Java (Indonesia). J Appl Meteorol Climatol. 58(8):1709–1723.
Robertson AW, Kumar A, Peña M, Vitart F. 2015. Improving and promoting subseasonal to seasonal prediction. Bull Am Meteorol Soc. 96(3):ES49–ES53.
Robertson AW, Vitart F. 2019. Sub-seasonal to seasonal prediction: The gap between weather and climate forecasting. 1st ed. Oxford, UK: Elsevier Inc.
Sandu I, van Niekerk A, Shepherd TG, Vosper SB, Zadra A, Bacmeister J, Beljaars A, Brown AR, Dörnbrack A, McFarlane N, et al. 2019. Impacts of orography on large-scale atmospheric circulation. npj Clim Atmos Sci [Internet]. 2(1):1–8. http://dx.doi.org/10.1038/s41612-019-0065-9
Vitart F, Ardilouze C, Bonet A, Brookshaw A, Chen M, Codorean C, Déqué M, Ferranti L, Fucile E, Fuentes M, et al. 2017. The subseasonal to seasonal (S2S) prediction project database. Bull Am Meteorol Soc. 98(1):163–173.
Vuillaume JF, 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. Nat Hazards [Internet]. 94(1):385–404. https://doi.org/10.1007/s11069-018-3394-9
Wang C, Jia Z, Yin Z, Liu F, Lu G, Zheng J. 2021. Improving the Accuracy of Subseasonal Forecasting of China Precipitation With a Machine Learning Approach. Front Earth Sci. 9(May):1–9.
White CJ, Carlsen H, Robertson AW, Klein RJT, Lazo JK, Kumar A, Vitart F, Coughlan de Perez E, Ray AJ, Murray V, et al. 2017. Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteorol Appl. 24(3):315–325.
Baker SA, Wood AW, Rajagopalan B. 2020. Application of postprocessing to watershed-scale subseasonal climate forecasts over the contiguous united states. J Hydrometeorol. 21(5):971–987.
Baño-Medina J, Manzanas R, Gutierrez JM. 2020. Configuration and intercomparison of deep learning neural models for statistical downscaling. Geosci Model Dev. 13(4):2109–2124.
Barker TW. 1991. The Relationship between Spread and Forecast Error In Extended-range Forecasts. J Clim [Internet]. 4(7):733–742. https://www.jstor.org/stable/26196422
Funk C, Peterson P, Landsfeld M, Pedreros D, Verdin J, Shukla S, Husak G, Rowland J, Harrison L, Hoell A, Michaelsen J. 2015. The climate hazards infrared precipitation with stations - A new environmental record for monitoring extremes. Sci Data. 2:1–21.
Han L, Chen M, Chen K, Chen H, Zhang Y, Lu B, Song L, Qin R. 2021. A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts. Adv Atmos Sci. 38(9):1444–1459.
Kirtman BP, Pegion K, DelSole T, Tippett M, Robertson AW, Bell M, Burgman R, Lin H, Gottschalck J, Collins DC, et al. 2017. The subseasonal experiment (SUBX). IRI Data Libr [Internet]. http://iridl.ldeo.columbia.edu/SOURCES/.Models/.SubX/
Kolachian R, Saghafian B. 2019. Deterministic and probabilistic evaluation of raw and post-processed sub-seasonal to seasonal precipitation forecasts in different precipitation regimes. Theor Appl Climatol. 137(1–2):1479–1493.
Krasnopolsky VM, Lin Y. 2012. A neural network nonlinear multimodel ensemble to improve precipitation forecasts over continental US. Adv Meteorol. 2012(1).
Le XH, Lee G, Jung K, An HU, Lee S, Jung Y. 2020. Application of convolutional neural network for spatiotemporal bias correction of daily satellite-based precipitation. Remote Sens. 12(17).
Li W, Chen J, Li L, Chen H, Liu B, Xu C-Y, Li X. 2019. Evaluation and bias correction of S2S precipitation for hydrological extremes. J Hydrometeorol. 20(9):1887–1906.
Mao X-J, Shen C, Yang Y-B. 2016. Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections [Internet]. :1–17. http://arxiv.org/abs/1606.08921
Misnawati, Boer R, June T, Faqih A. 2018. Perbandingan metodologi koreksi bias data curah hujan CHIRPS. LIMNOTEK - Perair Darat Trop di Indones [Internet]. 25(1):18–29. https://limnotek.limnologi.lipi.go.id/index.php/limnotek/article/view/224
Monier E, Gao X, Scott JR, Sokolov AP, Schlosser CA. 2015. A framework for modelling uncertainty in regional climate change. Clim Change. 131(1):51–66.
Muharsyah R, Hadi TW, Indratno SW. 2020. Penerapan metode Bayesian Model Averaging untuk kalibrasi prediksi ensemble curah hujan bulanan di Pulau Jawa. Agromet. 34(1):20–33.
Nover DM, Witt JW, Butcher JB, Johnson TE, Weaver CP. 2016. EPA Public Access. Earth Interact. 20(11):1–27.
Pegion K, Kirtman BP, Becker E, Collins DC, Lajoie E, Burgman R, Bell R, Delsole T, Min D, Zhu Y, et al. 2019. The subseasonal experiment (SUBX). Bull Am Meteorol Soc. 100(10):2043–2060.
Ratri DN, Whan K, Schmeits M. 2019. A comparative verification of raw and bias-corrected ECMWF seasonal ensemble precipitation reforecasts in Java (Indonesia). J Appl Meteorol Climatol. 58(8):1709–1723.
Robertson AW, Kumar A, Peña M, Vitart F. 2015. Improving and promoting subseasonal to seasonal prediction. Bull Am Meteorol Soc. 96(3):ES49–ES53.
Robertson AW, Vitart F. 2019. Sub-seasonal to seasonal prediction: The gap between weather and climate forecasting. 1st ed. Oxford, UK: Elsevier Inc.
Sandu I, van Niekerk A, Shepherd TG, Vosper SB, Zadra A, Bacmeister J, Beljaars A, Brown AR, Dörnbrack A, McFarlane N, et al. 2019. Impacts of orography on large-scale atmospheric circulation. npj Clim Atmos Sci [Internet]. 2(1):1–8. http://dx.doi.org/10.1038/s41612-019-0065-9
Vitart F, Ardilouze C, Bonet A, Brookshaw A, Chen M, Codorean C, Déqué M, Ferranti L, Fucile E, Fuentes M, et al. 2017. The subseasonal to seasonal (S2S) prediction project database. Bull Am Meteorol Soc. 98(1):163–173.
Vuillaume JF, 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. Nat Hazards [Internet]. 94(1):385–404. https://doi.org/10.1007/s11069-018-3394-9
Wang C, Jia Z, Yin Z, Liu F, Lu G, Zheng J. 2021. Improving the Accuracy of Subseasonal Forecasting of China Precipitation With a Machine Learning Approach. Front Earth Sci. 9(May):1–9.
White CJ, Carlsen H, Robertson AW, Klein RJT, Lazo JK, Kumar A, Vitart F, Coughlan de Perez E, Ray AJ, Murray V, et al. 2017. Potential applications of subseasonal-to-seasonal (S2S) predictions. Meteorol Appl. 24(3):315–325.
Authors
RaharjaA. B., FaqihA. and SetiawanA. M. (2022) “An Application of Deep Learning Technique to Improve Subseasonal to Seasonal Rainfall Forecast over Java Island, Indonesia”, Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management). Bogor, ID, 12(4), pp. 587-598. doi: 10.29244/jpsl.12.4.587-598.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
