PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2015 | 1 | 1 |
Tytuł artykułu

Predicting the Cloud Patterns for the Boreal Summer Intraseasonal Oscillation Through a Low-Order Stochastic Model

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
We assess the predictability limits of the large-scale cloud patterns in the boreal summer intraseasonal variability (BSISO), which are measured by the infrared brightness temperature, a proxy for convective activity. A recent developed nonlinear data analysis technique, nonlinear Laplacian spectrum analysis (NLSA), is applied to the brightness temperature data, defining two spatial modes with high intermittency associated with the BSISO time series. Then a recent developed data-driven physics-constrained low-ordermodeling strategy is applied to these time series. The result is a four dimensional system with two observed BSISO variables and two hidden variables involving correlated multiplicative noise through the nonlinear energyconserving interaction. With the optimal parameters calibrated by information theory, the non-Gaussian fat tailed probability distribution functions (PDFs), the autocorrelations and the power spectrum of the model signals almost perfectly match those of the observed data. An ensemble prediction scheme incorporating an effective on-line data assimilation algorithm for determining the initial ensemble of the hidden variables shows the useful prediction skill in the non-El Niño years is at least 30 days and even reaches 55 days in those years with regular oscillations and the skillful prediction lasts for 18 days in the strong El Niño year (year 1998). Furthermore, the ensemble spread succeeds in indicating the forecast uncertainty. Although the reduced linear model with time-periodic stable-unstable damping is able to capture the non-Gaussian fat tailed PDFs, it is less skillful in forecasting the BSISO in the years with irregular oscillations. The failure of the ensemble spread to include the truth also indicates failure in quantification of the uncertainty. In addition, without the energy-conserving nonlinear interactions, the linear model is sensitive with parameter variations. mcwfnally, the twin experiment with nonlinear stochastic model has comparable skill as the observed data, suggesting the nonlinear stochastic model has significant skill for determining the predictability limits of the large-scale cloud patterns of the BSISO.
Wydawca

Rocznik
Tom
1
Numer
1
Opis fizyczny
Daty
otrzymano
2015-03-30
zaakceptowano
2015-07-23
online
2015-09-01
Twórcy
autor
  • Department of Mathematics and Center for Atmosphere Ocean Science, Courant Institute
    of Mathematical Sciences, New York University, New York, USA
  • Department of Mathematics and Center for Atmosphere Ocean Science, Courant Institute
    of Mathematical Sciences, New York University, New York, USA
  • Center for Prototype Climate Modeling, NYU Abu Dhabi, Saadiyat Island,
    Abu Dhabi, UAE
Bibliografia
  • ---
  • [1] William KM Lau and Duane E Waliser. Intraseasonal variability in the atmosphere-ocean climate system. Springer, 2012.
  • [2] Peter J Webster, Vo Oo Magana, TN Palmer, J Shukla, RA Tomas, M u Yanai, and T Yasunari. Monsoons: Processes, predictability,and the prospects for prediction. Journal of Geophysical Research: Oceans (1978–2012), 103(C7):14451–14510,1998.
  • [3] Tiruvalam Natarajan Krishnamurti and D Subrahmanyam. The 30-50 day mode at 850 mb during MONEX. Journal of theAtmospheric Sciences, 39(9):2088–2095, 1982.
  • [4] In-Sik Kang, Chang-Hoi Ho, Young-Kwon Lim, and KM Lau. Principal modes of climatological seasonal and intraseasonalvariations of the Asian summer monsoon. Monthly weather review, 127(3):322–340, 1999.[Crossref]
  • [5] Qinghua Ding and BinWang. Predicting extreme phases of the Indian summer monsoon. Journal of Climate, 22(2):346–363,2009.[Crossref]
  • [6] V Krishnamurthy and J Shukla. Intraseasonal and seasonally persisting patterns of Indian monsoon rainfall. Journal ofclimate, 20(1):3–20, 2007.[Crossref]
  • [7] V Krishnamurthy and J Shukla. Seasonal persistence and propagation of intraseasonal patterns over the Indian monsoonregion. Climate Dynamics, 30(4):353–369, 2008.[Crossref]
  • [8] Bin Wang, June-Yi Lee, In-Sik Kang, J Shukla, C-K Park, A Kumar, J Schemm, S Cocke, J-S Kug, J-J Luo, et al. Advance andprospectus of seasonal prediction: assessment of the APCC/CliPAS 14-model ensemble retrospective seasonal prediction(1980–2004). Climate Dynamics, 33(1):93–117, 2009.
  • [9] June-Yi Lee, Bin Wang, I-S Kang, J Shukla, A Kumar, J-S Kug, JKE Schemm, J-J Luo, T Yamagata, X Fu, et al. How are seasonalprediction skills related to models’ performance on mean state and annual cycle? Climate Dynamics, 35(2-3):267–283,2010.[Crossref]
  • [10] In-Sik Kang, June-Yi Lee, and Chung-Kyu Park. Potential predictability of summer mean precipitation in a dynamical seasonalprediction system with systematic error correction. Journal of climate, 17(4):834–844, 2004.[Crossref]
  • [11] Bin Wang, Qinghua Ding, Xiouhua Fu, In-Sik Kang, Kyung Jin, J Shukla, and Francisco Doblas-Reyes. Fundamental challengein simulation and prediction of summer monsoon rainfall. Geophysical Research Letters, 32(15), 2005.
  • [12] Hye-Mi Kim and In-Sik Kang. The impact of ocean–atmosphere coupling on the predictability of boreal summer intraseasonaloscillation. Climate Dynamics, 31(7-8):859–870, 2008.[Crossref]
  • [13] DR Pattanaik and Arun Kumar. Prediction of summer monsoon rainfall over India using the NCEP climate forecast system.Climate Dynamics, 34(4):557–572, 2010.[Crossref]
  • [14] Nachiketa Acharya, Sarat C Kar, UC Mohanty, Makarand A Kulkarni, and SK Dash. Performance of GCMs for seasonal predictionover india – a case study for 2009 monsoon. Theoretical and applied climatology, 105(3-4):505–520, 2011.[Crossref]
  • [15] Archana Nair, UC Mohanty, Andrew W Robertson, TC Panda, Jing-Jia Luo, and Toshio Yamagata. An analytical study of hindcastsfrom general circulation models for Indian summer monsoon rainfall. Meteorological Applications, 21(3):695–707,2014.[Crossref]
  • [16] Sun-Seon Lee, Bin Wang, Duane E Waliser, Joseph Mani Neena, and June-Yi Lee. Predictability and prediction skill of theboreal summer intraseasonal oscillation in the intraseasonal variability hindcast experiment. Climate Dynamics, pages1–13, 2015.
  • [17] Timothy DelSole and J Shukla. Linear prediction of indian monsoon rainfall. Journal of Climate, 15(24):3645–3658, 2002.[Crossref]
  • [18] Charles Jones, Leila MV Carvalho, R Wayne Higgins, Duane E Waliser, and JK E Schemm. A statistical forecast model oftropical intraseasonal convective anomalies. Journal of climate, 17(11):2078–2095, 2004.[Crossref]
  • [19] MRajeevan, DS Pai, R Anil Kumar, and B Lal. New statistical models for long-range forecasting of southwest monsoon rainfallover india. Climate Dynamics, 28(7-8):813–828, 2007.[Crossref]
  • [20] Sun-Seon Lee and Bin Wang. Regional boreal summer intraseasonal oscillation over indian ocean and western pacific:comparison and predictability study. Climate Dynamics, pages 1–17, 2015.
  • [21] Matthew C Wheeler and Harry H Hendon. An all-season real-time multivariate MJO index: Development of an index formonitoring and prediction. Monthly Weather Review, 132(8):1917–1932, 2004.[Crossref]
  • [22] June-Yi Lee, BinWang,Matthew C Wheeler, Xiouhua Fu, Duane EWaliser, and In-Sik Kang. Real-time multivariate indices forthe boreal summer intraseasonal oscillation over the Asian summer monsoon region. Climate Dynamics, 40(1-2):493–509,2013.[Crossref]
  • [23] E Suhas, JM Neena, and BN Goswami. An indian monsoon intraseasonal oscillations (miso) index for real time monitoringand forecast verification. Climate Dynamics, 40(11-12):2605–2616, 2013.[Crossref]
  • [24] Eniko Székely, Dimitrios Giannakis, and Andrew J Majda. Extraction and predictability of coherent intraseasonal signals ininfrared brightness temperature data. Climate Dynamics, accepted 2015.
  • [25] Dimitrios Giannakis, Wen-wen Tung, and Andrew J Majda. Hierarchical structure of the Madden-Julian oscillation in infraredbrightness temperature revealed through nonlinear Laplacian spectral analysis. In Intelligent Data Understanding (CIDU),2012 Conference on, pages 55–62. IEEE, 2012.
  • [26] Dimitrios Giannakis and Andrew J Majda. Comparing low-frequency and intermittent variability in comprehensiveclimate models through nonlinear Laplacian spectral analysis. Geophysical Research Letters, 39(10), 2012. doi: 10.1029/2012GL051575.[Crossref]
  • [27] Dimitrios Giannakis and Andrew J Majda. Nonlinear Laplacian spectral analysis for time series with intermittency and lowfrequencyvariability. Proceedings of the National Academy of Sciences, 109(7):2222–2227, 2012.
  • [28] Dimitrios Giannakis and Andrew J Majda. Nonlinear Laplacian spectral analysis: capturing intermittent and low-frequencyspatiotemporal patterns in high-dimensional data. Statistical Analysis and Data Mining, 6(3):180–194, 2013.
  • [29] Wen-wen Tung, Dimitrios Giannakis, and Andrew J Majda. Symmetric and antisymmetric convection signals in the Madden-Julian oscillation. Part I: basic modes in infrared brightness temperature. Journal of the Atmospheric Sciences, 71(9):3302–3326, 2014.
  • [30] Andrew JMajda and John Harlim. Physics constrained nonlinear regression models for time series. Nonlinearity, 26(1):201–217, 2013.[Crossref]
  • [31] John Harlim, Adam Mahdi, and Andrew J Majda. An ensemble Kalman filter for statistical estimation of physics constrainednonlinear regression models. Journal of Computational Physics, 257:782–812, 2014.
  • [32] Nan Chen, Andrew JMajda, and Dimitrios Giannakis. Predicting the cloud patterns of the Madden-Julian oscillation througha low-order nonlinear stochastic model. Geophysical Research Letters, 41(15):5612–5619, 2014.[Crossref]
  • [33] Nan Chen and Andrew J Majda. Predicting the real-time multivariate Madden-Julian oscillation index through a low-ordernonlinear stochastic model. Monthly Weather Review, 2015. in press.
  • [34] KI Hodges, DW Chappell, GJ Robinson, and G Yang. An improved algorithm for generating global window brightness temperaturesfrom multiple satellite infrared imagery. Journal of Atmospheric & Oceanic Technology, 17(10):1296–1312, 2000.
  • [35] Tyrus Berry, Dimitrios Giannakis, and John Harlim. Nonparametric forecasting of low-dimensional dynamical systems. arXivpreprint arXiv:1411.5069, 2014.
  • [36] S Kravtsov, D Kondrashov, and M Ghil. Multilevel regression modeling of nonlinear processes: Derivation and applicationsto climatic variability. Journal of Climate, 18(21):4404–4424, 2005.[Crossref]
  • [37] D Kondrashov, MD Chekroun, AW Robertson, and M Ghil. Low-order stochastic model and “past-noise forecasting” of theMadden-Julian Oscillation. Geophysical Research Letters, 40(19):5305–5310, 2013.[Crossref]
  • [38] Andrew J Majda and Boris Gershgorin. Quantifying uncertainty in climate change science through empirical informationtheory. Proceedings of the National Academy of Sciences, 107(34):14958–14963, 2010.
  • [39] Andrew J Majda and Boris Gershgorin. Improving model fidelity and sensitivity for complex systems through empiricalinformation theory. Proceedings of the National Academy of Sciences, 108(25):10044–10049, 2011.
  • [40] Robert S Liptser and Albert N Shiryaev. Statistics of Random Processes II: II. Applications, volume 2. Springer, 2001.
  • [41] M Berkelhammer, A Sinha, M Mudelsee, H Cheng, K Yoshimura, and J Biswas. On the low-frequency component of theenso–indian monsoon relationship: a paired proxy perspective. Climate of the Past, 10(2):733–744, 2014.[Crossref]
  • [42] Boris Gershgorin, John Harlim, and Andrew J Majda. Improving filtering and prediction of spatially extended turbulent systemswith model errors through stochastic parameter estimation. Journal of Computational Physics, 229(1):32–57, 2010.[Crossref]
  • [43] Boris Gershgorin, John Harlim, and Andrew JMajda. Test models for improving filteringwith model errors through stochasticparameter estimation. Journal of Computational Physics, 229(1):1–31, 2010.[Crossref]
  • [44] M Branicki and AJ Majda. Quantifying bayesian filter performance for turbulent dynamical systems through informationtheory. Communications in Mathematical Sciences, 12(5), 2014.[Crossref]
  • [45] Peter J Webster and Carlos Hoyos. Prediction of monsoon rainfall and river discharge on 15-30-day time scales. Bulletin ofthe American Meteorological Society, 85(11):1745–1765, 2004.
  • [46] MJ MCPHADEN. Rama: The research moored array for african-asian-australian monsoon analysis and prediction. Bull. Amer.Meteor. Soc., 90:459–480, 2009.
  • [47] Michal Branicki and Andrew J Majda. An information-theoretic framework for improving imperfect predictions via MultiModel Ensemble forecasts. J. Nonlinear Science, 2014. in press.
  • [48] Andrew J Majda and Qi Di. Improving prediction skill of imperfect turbulent models through statistical response and informationtheory. J. Nonlinear Science, 2015. submitted.
  • [49] Nan Chen, Andrew J Majda, and Xin T Tong. Information barriers for noisy Lagrangian tracers in filtering random incompressibleflows. Nonlinearity, 27(9):2133–2163, 2014.[Crossref]
  • [50] Nan Chen, Andrew J Majda, and Xin T Tong. Noisy Lagrangian tracers for filtering random rotating compressible flows.Journal of Nonlinear Science, 2015. in press.
  • [51] Richard Kleeman. Measuring dynamical prediction utility using relative entropy. Journal of the atmospheric sciences,59(13):2057–2072, 2002.
  • [52] Andrew JMajda and Michal Branicki. Lessons in uncertainty quantification for turbulent dynamical systems. Discrete Contin.Dyn. Syst, 32(9):3133–3231, 2012.
  • [53] Michal Branicki, Nan Chen, and Andrew J Majda. Non-Gaussian test models for prediction and state estimation with modelerrors. Chinese Annals of Mathematics, Series B, 34(1):29–64, 2013. [Crossref]
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.-psjd-doi-10_1515_mcwf-2015-0001
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.