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Theoretical and Applied Climatology

, Volume 135, Issue 3–4, pp 1349–1359 | Cite as

A combined stochastic analysis of mean daily temperature and diurnal temperature range

  • B. Sirangelo
  • T. Caloiero
  • R. Coscarelli
  • E. FerrariEmail author
Original Paper
  • 79 Downloads

Abstract

In this paper, a stochastic model, previously proposed for the maximum daily temperature, has been improved for the combined analysis of mean daily temperature and diurnal temperature range. In particular, the procedure applied to each variable sequentially performs the deseasonalization, by means of truncated Fourier series expansions, and the normalization of the temperature data, with the use of proper transformation functions. Then, a joint stochastic analysis of both the climatic variables has been performed by means of a FARIMA model, taking into account the stochastic dependency between the variables, namely introducing a cross-correlation between the standardized noises. The model has been applied to five daily temperature series of southern Italy. After the application of a Monte Carlo simulation procedure, the return periods of the joint behavior of the mean daily temperature and the diurnal temperature range have been evaluated. Moreover, the annual maxima of the temperature excursions in consecutive days have been analyzed for the synthetic series. The results obtained showed different behaviors probably linked to the distance from the sea and to the latitude of the station.

References

  1. Akaike H (1974) Maximum likelihood identification of Gaussian autoregressive moving average models. Biometrika 60:255–265CrossRefGoogle Scholar
  2. Anderson TW, Darling DA (1952) Asymptotic theory of certain “goodness-of-fit” criteria based on stochastic processes. Ann Math Stat 23:193–212CrossRefGoogle Scholar
  3. Baillie RT, Chung S (2002) Modeling and forecasting from trend stationary long memory models with applications to climatology. Int J Forecasting 18:215–226CrossRefGoogle Scholar
  4. Bechini L, Bocchi S, Maggiore T, Confalonieri R (2006) Parameterization of a crop growth and development simulation model at sub-model components level. An example for winter wheat (Triticum aestivum L.) Environ Model Softw 21:1042–1054CrossRefGoogle Scholar
  5. Box GEP, Jenkins GM (1976) Time series analysis: forecasting and control. Holden-Day, SanFranciscoGoogle Scholar
  6. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  7. Buttafuoco G, Caloiero T, Coscarelli R (2015) Analyses of drought events in Calabria (southern Italy) using standardized precipitation index. Water Resour Manag 29:557–573CrossRefGoogle Scholar
  8. Caballero R, Jewson S, Brix A (2002) Long memory in surface air temperature: detection, modeling, and application to weather derivative valuation. Clim Res 21:127–140CrossRefGoogle Scholar
  9. Caldiz DO, Gaspari FJ, Haverkort AJ, Struik PC (2001) Agro-ecological zoning and potential yield of single or double cropping of potato in Argentina. Agric For Meteorol 109:311–320CrossRefGoogle Scholar
  10. Caloiero T, Coscarelli R, Ferrari E, Sirangelo B (2015) Analysis of dry spells in southern Italy (Calabria). Water 7:3009–3023CrossRefGoogle Scholar
  11. Cardon AH, Fukuda H, Reifsnider KL, Verchery G (eds) (2000) Recent development in durability analysis of composite systems. A.A. Balkeema, Rotterdam, Brookfield ISBN 90 5809 103 1Google Scholar
  12. Coscarelli R, Caloiero T (2012) Analysis of daily and monthly rainfall concentration in southern Italy (Calabria region). J Hydrol 416–417:145–156CrossRefGoogle Scholar
  13. Curriero FC, Heiner KS, Samet JM, Zeger SL, Strug L, Patz JA (2002) Temperature and mortality in 11 cities of the eastern United States. Am J Epidemiol 155:80–87CrossRefGoogle Scholar
  14. Ehsanzadeh E, Adamowski K (2010) Trends in timing of low stream flows in Canada: impact of autocorrelation and long-term persistence. Hydrol Process 24:970–980CrossRefGoogle Scholar
  15. Ferrari E, Caloiero T, Coscarelli R (2013) Influence of the North Atlantic oscillation on winter rainfall in Calabria (southern Italy). Theor Appl Climatol 114:479–494CrossRefGoogle Scholar
  16. Franco T (1991) Effects of stressful and unstressful low temperature on vegetable crops: morphological and physiological aspects. Acta Hortic 287:67–76.  https://doi.org/10.17660/ActaHortic.1991.287.6 CrossRefGoogle Scholar
  17. Granger CWJ, Joyeux R (1980) An introduction to long-range time series models and fractional differencing. J Time Ser Anal 1:15–30CrossRefGoogle Scholar
  18. Grimaldi S (2004) Linear parametric models applied on daily hydrological series. J Hydrol Eng 9:383–391CrossRefGoogle Scholar
  19. Grimaldi S, Serinaldi F, Tallerini C (2005) Multivariate linear parametric models applied to daily rainfall time series. Adv Geosci 2:87–92CrossRefGoogle Scholar
  20. Hajat S, Kovats RS, Atkinson RW, Haines A (2002) Impact of hot temperatures on death in London: a time series approach. J Epidemiol Community Health 56:367–372CrossRefGoogle Scholar
  21. Hosking JRM (1981) Fractional differencing. Biometrika 68:165–176CrossRefGoogle Scholar
  22. Hosking JRM (1984) Modeling persistence in hydrological time series using fractional differencing. Water Resour Res 20:1898–1908CrossRefGoogle Scholar
  23. Hurst HE (1951) Long-term storage capacity of reservoirs. Trans Am Soc Civil Eng 116:770–799Google Scholar
  24. Johnson NL (1949) Systems of frequency curves generated by methods of translation. Biometrika 36:149–176CrossRefGoogle Scholar
  25. Keellings D, Waylen P (2012) The stochastic properties of high daily maximum temperatures applying crossing theory to modeling high temperature event variables. Theor Appl Climatol 108:579–590CrossRefGoogle Scholar
  26. Koscielny-Bunde E, Kantelhardt JW, Braun P, Bunde A, Havlin S (2006) Long-term persistence and multifractality of river runoff records: detrended fluctuation studies. J Hydrol 322:120–137CrossRefGoogle Scholar
  27. Koutsoyiannis D (2002) The Hurst phenomenon and fractional Gaussian noise made easy. Hydrolog Sci J 47:573–595CrossRefGoogle Scholar
  28. Kunst AE, Looman CWN, Mackenbach JP (1993) Outdoor air temperature and mortality in the Netherlands: a time-series analysis. Am J Epidemiol 137:331–341CrossRefGoogle Scholar
  29. Lohre M, Sibbertsen P, Könning T (2003) Modeling water flow of the Rhine River using seasonal long memory. Water Resour Res 39:1132CrossRefGoogle Scholar
  30. Lye LM, Lin Y (1994) Long-term dependence in annual peak flows of Canadian rivers. J Hydrol 160:89–103CrossRefGoogle Scholar
  31. Montanari A, Rosso R, Taqqu MS (1997) Fractionally differenced ARIMA models applied to hydrologic time series: identification, estimation, and simulation. Water Resour Res 33:1035–1044CrossRefGoogle Scholar
  32. Montanari A, Rosso R, Taqqu MS (2000) A seasonal fractional ARIMA model applied to the Nile River monthly flows at Aswan. Water Resour Res 36:1249–1259CrossRefGoogle Scholar
  33. Pelletier JD, Turcotte DL (1997) Long-range persistence in climatological and hydrological time series: analysis, modeling and application to drought hazard assessment. J Hydrol 203:198–208CrossRefGoogle Scholar
  34. Prass TS, Bravo JM, Clarke RT, Collischonn W, Lopes SRC (2012) Comparison of forecasts of mean monthly water level in the Paraguay River, Brazil, from two fractionally differenced models. Water Resour Res 48:W05502CrossRefGoogle Scholar
  35. Sirangelo B, Caloiero T, Coscarelli R, Ferrari E (2017) A stochastic model for the analysis of maximum daily temperature. Theor Appl Climatol 130:275–289CrossRefGoogle Scholar
  36. Smith RL (1993) Long-range dependence and global warming. In: Barnett V, Turkerman KF (eds) Statistics for the environment. Wiley, New York, pp 141–146Google Scholar
  37. Sugiura N (1978) Further analysis of the data by Akaike’s information criterion and the finite corrections. Commun Stat A-Theor 7:13–26CrossRefGoogle Scholar
  38. Verdoodt A, Van Ranst E, Ye L (2004) Daily simulation of potential dry matter production of annual field crops in tropical environments. Agron J 96:1739–1753CrossRefGoogle Scholar
  39. Ye L, Tang H, Zhu J, Verdoodt A, Van Ranst E (2008) Spatial patterns and effects of soil organic carbon on grain productivity assessment in China. Soil Use Manag 24:80–91CrossRefGoogle Scholar
  40. Ye L, Van Ranst E (2002) Population carrying capacity and sustainable agricultural use of land resources in Caoxian County (North China). J Sustain Agr 19:75–94CrossRefGoogle Scholar
  41. Ye L, Van Ranst E (2009) Production scenarios and the effect of soil degradation on long-term food security in China. Global Environ Chang 19:464–481CrossRefGoogle Scholar
  42. Ye L, Xiong W, Li Z, Yang P, Wu W, Yang G, Fu Y, Zou J, Chen Z, Van Ranst E, Tang H (2013) Climate change impact on China food security in 2050. Agron Sustain Dev 33:363–374CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Environmental and Chemical Engineering (DIATIC)University of CalabriaRendeItaly
  2. 2.National Research Council of Italy, Institute for Agriculture and Forest Systems in the Mediterranean (CNR-ISAFOM)RendeItaly
  3. 3.National Research Council of Italy, Research Institute for Geo-hydrological Protection (CNR-IRPI)RendeItaly
  4. 4.Department of Computer Engineering, Modeling, Electronics, and Systems Science (DIMES)University of CalabriaRendeItaly

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