Skip to main content
SpringerLink
Log in

A new Kalman filter based on Information Geometry techniques for optimizing numerical environmental simulations

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

Abstract

A new Kalman filter is presented in this work, enhanced with abilities and tools obtained from the area of Information Geometry (IG), for the optimization of the results of numerical weather prediction models. The new developments allow the better estimation of the discrepancies between modeled and corresponding observed data sets by categorizing them in the appropriate geometric framework and utilizing the associated geodesics/minimum-length-curves in order to estimate and minimize the necessary cost functions, instead of adopting least square based approximations. Numerical analysis techniques are developed and used for solving the associated boundary value problems. The proposed new IG Kalman filter is evaluated over idealized and real world test cases with very promising results. The systematic error of the simulations is almost eliminated and the forecast uncertainty is critically reduced.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

  • Amari S-I (1985) Differential geometrical methods in statistics. Springer, Berlin

    Book  Google Scholar 

  • Amari S-I, Nagaoka H (2000) Methods of information geometry. American Mathematical Society, Oxford University Press, Oxford

    Google Scholar 

  • Arwini K, Dodson CTJ (2007) Alpha-geometry of the Weibull manifold. In: Second basic science conference, Tripoli

  • Arwini K, Dodson CTJ (2008) Information geometry: near randomness and near independence. Springer, Berlin

    Book  Google Scholar 

  • Astitha M, Kallos G, Mihalopoulos N (2005) Analysis of air quality observations with the aid of the source-receptor relationship approach. J Air Waste Manag Assoc 55:523–535

    Article  CAS  Google Scholar 

  • Balis D, Amiridis V, Kazadzis S, Papayannis A, Tsaknakis G, Tzortzakis S, Kalivitis N (2006) Optical characteristics of desert dust over the East Mediterranean during summer: a case study. Ann Geophys 24:807–821

    Article  Google Scholar 

  • Correia P, Lozano S, Chavez R, Loureiro Y, Cantero E, Benito P, Sanz Rodrigo J (2013) Wind characterization at the Alaiz-Las Balsas experimental wind farm using high-resolution simulations with mesoscale models. Development of a “low cost” methodology that address promoters needs. In: EWEA-13 proceedings, Vienna

  • Crochet P (2004) Adaptive Kalman filtering of 2-metre temperature and 10-metre wind-speed forecasts in Iceland. Meteorol Appl 11:173–187

    Article  Google Scholar 

  • D’Agostino RB, Stephens MA (1986) Goodness-of-fit techniques. Marcel Dekker, New York

    Google Scholar 

  • Dykes JD, Wang DW, Book JW (2009) An evaluation of a high-resolution operational wave forecasting system in the adriatic sea. J Mar Syst 78(SUPPL. 1):S255–S271

    Article  Google Scholar 

  • Eskridge RE, Ku JY, Rao ST, Porter PS, Zurbenko IG (1997) Separating different scales of motion in time series of meteorological variables. Bull Am Meteorol Soc 78(7):1473–1483

    Article  Google Scholar 

  • Famelis I (2014) Runge–Kutta solutions for an environmental parameter prediction boundary value problem. System 2(2):62–69

    Google Scholar 

  • Famelis I, Tsitouras Ch (2015) Quadratic shooting solution for an environmental parameter prediction boundary value problem. Far East J Appl Math 91(2):81–98

    Article  Google Scholar 

  • Famelis I, Galanis G, Ehrhardt M, Triantafyllou D (2014) Classical and quasi-Newton methods for a meteorological parameters prediction boundary value problem. Appl Math Inf Sci 8(6):2683–2693

    Article  Google Scholar 

  • Galanis G, Anadranistakis M (2002) A one dimensional Kalman filter for the correction of near surface temperature forecasts. Meteorol Appl 9:437–441

    Article  Google Scholar 

  • Galanis G, Louka P, Katsafados P, Kallos G, Pytharoulis I (2006) Applications of Kalman filters based on non-linear functions to numerical weather predictions. Ann Geophys 24:2451–2460

    Article  Google Scholar 

  • Galanis G, Emmanouil G, Kallos G, Chu PC (2009) A new methodology for the extension of the impact in sea wave assimilation systems. Ocean Dyn 59(3):523–535

    Article  Google Scholar 

  • Galanis G, Chu PC, Kallos G (2011) Statistical post processes for the improvement of the results of numerical wave prediction models. A combination of Kolmogorov–Zurbenko and Kalman filters. J Op Oceanogr 4(1):23–31

    Google Scholar 

  • Galanis G, Chu PC, Kallos G, Kuo Y-H, Dodson CTJ (2012) Wave height characteristics in the North Atlantic Ocean: a new approach based on statistical and geometrical techniques. Stoch Environ Res Risk Assess 26:83–103

    Article  Google Scholar 

  • Giebel G (2001) On the benefits of distributed generation of wind energy in Europe. Ph.D thesis from the Carl von Ossietzky Universität Oldenburg, Düsseldorf

  • Ide K, Courtier P, Ghil M, Lorenc AC (1997) Unified notation for data assimilation: operational, sequential and variational. J Meteorol Soc 75:181–189

    Article  Google Scholar 

  • Irigoyen U, Cantero E, Correia P, Frías L, Loureiro Y, Lozano S, Pascal E, Sanz Rodrigo J (2011) Navarre virtual wind series: physical mesoscale downscaling wind WAsP. Methodology and validation. In: EWEC-11 European wind energy conference, Brussels

  • Janjic ZI (1994) The step-mountain eta coordinate model: further developments of the convection, viscous sublayer, and turbulence closure schemes. Mon Weather Rev 122:927–945

    Article  Google Scholar 

  • Joensen A, Giebel G, Landberg L, Madsen H, Nielsen H (1999) Model output statistics applied to wind power prediction. In: Proceedings of the European wind energy conference, 1177–1180, Nice

  • Kallos G (1997) The regional weather forecasting system skiron. In: Proceedings, symposium on regional weather prediction on parallel computer environments, 15–17 Oct, Athens, p 9

  • Kallos G, Papadopoulos A, Katsafados P, Nickovic S (2005) Trans-Atlantic Saharan dust transport: model simulation and results. J Geophys Res. doi:10.1029/2005JD006207

    Google Scholar 

  • Kalman RE (1960) A new approach to linear filtering and prediction problems. J Basic Eng 82:35–45

    Article  Google Scholar 

  • Kalman RE, Bucy RS (1961) New results in linear filtering and prediction problems. Trans ASME Ser D 83:95–108

    Article  Google Scholar 

  • Kalnay E (2002) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge, p 341

    Book  Google Scholar 

  • Kariniotakis G, Marti I et al (2004) What performance can be expected by short-term wind power prediction models depending on site characteristics? In: CD-proceedings of the European wind energy conference EWEC, London

  • Kariniotakis G, Pinson P (2003) Evaluation of the MORE-CARE wind power prediction platform. Performance of the fuzzy logic based models. In: CD-proceedings of the European wind energy conference, Madrid

  • Korres G, Lascaratos A, Hatziapostolou E, Katsafados P (2002) Towards an ocean forecasting system for the Aegean sea. Glob Atmos Ocean Syst 8(2–3):191–218

    Article  Google Scholar 

  • Landberg L (1994) Short-term predictions of local wind conditions. Ph.D thesis, Riso National Laboratory, Denmark

  • Louka P, Galanis G, Siebert N, Kariniotakis G, Katsafados P, Pytharoulis I, Kallos G (2008) Improvements in wind speed forecasts for wind power prediction purposes using Kalman filtering. J Wind Eng Ind Aerodyn 96:2348–2362

    Article  Google Scholar 

  • Makarynskyy O (2004) Improving wave predictions with artificial neural networks. Ocean Eng 31(5–6):709–724

    Article  Google Scholar 

  • Makarynskyy O (2005) Neural pattern recognition and prediction for wind wave data assimilation. Pac Oceanogr 3(2):76–85

    Google Scholar 

  • Mass CF, Ovens D, Westrick K, Colle BA (2002) Does increasing horizontal resolution produce more skillful forecasts? Bull Am Meteorol Soc 83(3):407–430

    Article  Google Scholar 

  • Nickovic S, Kallos G, Papadopoulos A, Kakaliagou O (2001) A model for prediction of desert dust cycle in the atmosphere. J Geophys Res 106:18113–18129

    Article  CAS  Google Scholar 

  • Papadopoulos A, Katsafados P (2009) Verification of operational weather forecasts from the POSEIDON system across the Eastern Mediterranean. Nat Hazards Earth Syst Sci 9:1299–1306

    Article  Google Scholar 

  • Papadopoulos A, Katsafados P, Kallos G (2001) Regional weather forecasting for marine application. Glob Atmos Ocean Syst 8(2-3):219–237

    Article  Google Scholar 

  • Pelland S, Galanis G, Kallos G (2011) Solar and photovoltaic forecasting through post-processing of the global environmental multiscale numerical weather prediction model. Prog Photovolt Res Appl. doi:10.1002/pip.1180

    Google Scholar 

  • Rao ST, Zurbenko IG, Neagu R, Porter PS, Ku JY, Henry RF (1997) Space and time scales in ambient ozone data. Bull Am Meteorol Soc 78(10):2153–2166

    Article  Google Scholar 

  • Resconi G (2009) Geometry of risk analysis (morphogenetic system). Stoch Environ Res Risk Assess 23:425–432

    Article  Google Scholar 

  • Silva AP, Moulin LS (2000) Confidence intervals for neural network based short-term load forecasting. IEEE Trans Power Syst 15(4):1191–1196

    Article  Google Scholar 

  • Spyrou C, Mitsakou C, Kallos G, Louka P, Vlastou G (2010) An improved limited area model for describing the dust cycle in the atmosphere. J Geophys Res 115(D17):1–19

    Article  Google Scholar 

  • Stathopoulos C, Kaperoni A, Galanis G, Kallos G (2013) Wind power prediction based on numerical and statistical Models. J Wind Eng Ind Aerodyn 112:25–38

    Article  Google Scholar 

  • Vanem E (2011) Long-term time-dependent stochastic modelling of extreme waves. Stoch Environ Res Risk Assess 25:185–209

    Article  Google Scholar 

  • Varinou M, Kallos G, Kotroni V, Lagouvardos K (2000) The influence of the lateral boundaries and background concentrations on limited area photochemical model simulations. Int J Environ Pollut 14:354–363

    Article  CAS  Google Scholar 

  • Wilks D (1995) Statistical methods in the atmospheric sciences. Academic Press, Lake City

    Google Scholar 

  • Zodiatis G, Lardner R, Georgiou G, Demirov E, Manzella G, Pinardi N (2003) An operational European global ocean observing system for the eastern Mediterranean Levantine basin: the Cyprus coastal ocean forecasting and observing system. Mar Technol Soc J 37(3):115–123

    Article  Google Scholar 

  • Zodiatis G, Lardner R, Hayes D, Georgiou G, Sofianos S, Skliris N, Lascaratos A (2008) Operational ocean forecasting in the Eastern Mediterranean: implementation and evaluation. Ocean Sci 4(1):31–47

    Article  Google Scholar 

Download references

Acknowledgements

This research has been co-funded by the European Union (European Social Fund) and Greek national resources under the framework of the “Archimedes III: Funding of Research Groups in TEI of Athens” project of the “Education & Lifelong Learning” Operational Programme. Moreover, the authors would like to thank the Atmospheric Modeling and Weather Forecasting Group of the Physics Department of the University of Athens (www.mg.uoa.gr) for providing environmental data valuable for the proposed analysis as well as further material and scientific support.

Author information

Authors and Affiliations

  1. Section of Mathematics, Mathematical Modeling and Applications Laboratory, Hellenic Naval Academy, Hatzikiriakion, 18539, Piraeus, Greece

    George Galanis & Aristotelis Liakatas

  2. microSENSES Laboratory, Department of Electronic Engineering, Technical Educational Institute of Athens, 11210, Egaleo, Greece

    Ioannis Famelis

Authors
  1. George Galanis
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ioannis Famelis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aristotelis Liakatas
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to George Galanis.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Galanis, G., Famelis, I. & Liakatas, A. A new Kalman filter based on Information Geometry techniques for optimizing numerical environmental simulations. Stoch Environ Res Risk Assess 31, 1423–1435 (2017). https://doi.org/10.1007/s00477-016-1332-5

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-016-1332-5

Keywords

Search

Navigation