Skip to main content

Advertisement

Log in

Modelling collocation uncertainty of 3D atmospheric profiles

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

Atmospheric thermodynamic data are gathered by high technology remote instruments such as radiosondes, giving rise to profiles that are usually modelled as functions depending only on height. The radiosonde balloons, however, drift away in the atmosphere resulting in not necessarily vertical but three-dimensional trajectories. To model this kind of functional data, we introduce a “point based” formulation of an heteroskedastic functional regression model that includes a trivariate smooth function and results to be an extension of a previously introduced unidimensional model. Functional coefficients of both the conditional mean and variance are estimated by reformulating the model as a standard generalized additive model and subsequently as a mixed model. This reformulation leads to a double mixed model whose parameters are fitted by using an iterative algorithm that allows to adjust for heteroskedasticity. The proposed modelling approach is applied to describe collocation mismatch when we deal with couples of balloons launched at two different locations. In particular, we model collocation error of atmospheric pressure in terms of meteorological covariates and space and time mismatch. Results show that model fitting is improved once heteroskedasticity is taken into account.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  • Berberan-Santos MN, Bodunov EN, Pogliani L (2010) On the barometric formula inside the earth. J Math Chem 47(3):990–1004

    Article  CAS  Google Scholar 

  • Caballero W, Giraldo R, Mateu J (2013) A universal kriging approach for spatial functional data. Stoch Environ Res Risk Assess 27(7):1553–1563

    Article  Google Scholar 

  • Cuevas A (2014) A partial overview of the theory of statistics with functional data. J Stat Plan Inference 147:1–23

    Article  Google Scholar 

  • Eilers PHC, Marx BD (2003) Multivariate calibration with temperature interaction using two-dimensional penalized signal regression. Chemom Intell Lab Syst 66:159–174

    Article  CAS  Google Scholar 

  • Fassò A, Ignaccolo R, Madonna F, Demoz B (2013) Statistical modelling of atmospheric vertical profiles and the collocation problem. Atmos Meas Tech Discuss 6:7505–7533. doi:10.5194/amtd-6-7505-2013

    Article  Google Scholar 

  • Escabias M, Valderrama J, Aguilera AM, Santofimia ME, Aguilera-Morillo MC (2013) Stepwise selection of functional covariates in forecasting peak levels of olive pollen. Stoch Environ Res Risk Assess 27(2):367–376

    Article  Google Scholar 

  • Ferraty F, Vieu P (2006) Nonparametric functional data analysis: theory and practice. Springer, New York

    Google Scholar 

  • Gijbels I, Prosdocimi I, Claeskens G (2010) Nonparametric estimation of mean and dispersion functions in extended generalized linear models. Test 19:580–608

    Article  Google Scholar 

  • Guo W (2004) Functional data analysis in longitudinal settings using smoothing splines. Stat Methods Med Res 13:49–62

    Article  Google Scholar 

  • Harezlak J, Coull BA, Laird NM, Magari SR, Christiani DC (2007) Penalized solutions to functional regression problems. Comput Stat Data Anal 99:4911–4925

    Article  Google Scholar 

  • Hastie T, Tibshirani R (1993) Varying-coefficient models. J R Stat Soc B 55:757–796

    Google Scholar 

  • Horvàth L, Kokoszka P (2012) Inference for functional data with applications. Springer, New York

    Book  Google Scholar 

  • Ignaccolo R, Mateu J, Giraldo R (2013) Kriging with external drift for functional data for air quality monitoring. Stoch Environ Res Risk Assess. doi:10.1007/s00477-013-0806-y

  • Immler FJ, Dykema J, Gardiner T, Whiteman DN, Thorne PW, Vomel H (2010) Reference quality upper-air measurements: guidance for developing GRUAN data products. Atmos Meas Tech 3:1217–1231

    Article  Google Scholar 

  • Ivanescu AE, Staicu AM, Greven S, Scheipl F, Crainiceanu CM (2012) Penalized function-on-function regression (April 2012). Johns Hopkins University, Dept. of Biostatistics Working Papers. Working Paper 240

  • Karlis D, Vasdekis VGS, Banti M (2009) Heteroscedastic semiparametric models for domestic water consumption aggregated data. Environ Ecol Stat 16:355–367

    Article  Google Scholar 

  • Nash J, Oakley T, Vömel H, LI Wei (2010) WMO Intercomparison of high Quality Radiosonde Systems Yangjiang, China, 12 July–3 August 2010; WMO report reference number IOM 107 (TD 1580). available at: http://www.wmo.int/pages/prog/www/IMOP/publications-IOM-series.html

  • Ngo L, Wand MP (2004) Smoothing with mixed model software. J Stat Softw 71(9):1–54

    Google Scholar 

  • Nott DJ (2006) Semiparametric estimation of mean and variance functions for non-Gaussian data. Comput Stat 21:603–620

    Article  Google Scholar 

  • Ramsay JO, Silverman BW (2005) Functional data analysis. Springer, New York

    Google Scholar 

  • R Core Team (2013) R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/

  • Reiss PT, Ogden RT (2009) Smoothing parameter selection for a class of semiparametric linear models. J R Stati Soc B 71:50517523

    Google Scholar 

  • Robinson GK (1991) That BLUP is a good thing: the estimation of random effects. Stati Sci 6:15–32

    Article  Google Scholar 

  • Ruiz-Medina MD, Espejo RM (2012) Spatial autoregressive functional plug-in prediction of ocean surface temperature. Stoch Environ Res Risk Assess 26:335–344

    Article  Google Scholar 

  • Ruppert D, Wand MP, Carroll RJ (2003) Semiparametric regression. Cambridge University Press, New York

    Book  Google Scholar 

  • Seidel DJ, Sun B, Pettey M, Reale A (2011), Global radiosonde balloon drift statistics. J Geophys Res 116:D7

  • Speed T (1991) Comment on paper by Robinson. Stati Sci 6:421744

    Google Scholar 

  • Thorne PW, V\(\ddot{\rm o}\)mel H, Bodeker G et al (2013) GCOS reference upper air network (GRUAN): Steps towards assuring future climate records. AIP Conference Proceedings 1552:1042–1047. doi:http://dx.doi.org/10.1063/1.4821421

  • Wahba G (1990) Spline models for observational data. SIAM, Philadelphia

    Book  Google Scholar 

  • Wand MP (2003) Smoothing and mixed models. Comput Stat 18:223–249

    Google Scholar 

  • Wang H, Akritas MG (2010) Inference from heteroscedastic functional data. J Nonparametric Stat 22(2):149–168

    Article  CAS  Google Scholar 

  • Wood SN (2004) Stable and efficient multiple smoothing parameter estimation for generalized additive models. J Am Stat Assoc 99:673–686

    Article  Google Scholar 

  • Wood AN (2006) Generalized additive models: an introduction with R. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

  • Wood SN (2011) Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J R Stat Soc B 73(1):3–36

    Article  Google Scholar 

  • Wood SN (2012) mgcv: Mixed GAM computation vehicle with GCV/AIC/REML smoothness estimation, R package version 1.7–22

  • Wood SN (2013) On p values for smooth components of an extended generalized additive model. Biometrika 100(1):221–228

    Article  Google Scholar 

  • Zhang JT (2013) Analysis of variance for functional data. Chapman & Hall/CRC, Boca Raton

    Google Scholar 

Download references

Acknowledgments

The authors would like to thank Belay Demoz and Fabio Madonna for useful discussions and comments and two anonymous referees whose comments and suggestions improved the reading and quality of the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rosaria Ignaccolo.

Additional information

Work partially supported by FIRB 2012 grant (project no. RBFR12URQJ) provided by the Italian Ministry of Education, Universities and Research.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ignaccolo, R., Franco-Villoria, M. & Fassò, A. Modelling collocation uncertainty of 3D atmospheric profiles. Stoch Environ Res Risk Assess 29, 417–429 (2015). https://doi.org/10.1007/s00477-014-0890-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-014-0890-7

Keywords

Navigation