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Annales Geophysicae An interactive open-access journal of the European Geosciences Union
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Volume 24, issue 10
Ann. Geophys., 24, 2451–2460, 2006
https://doi.org/10.5194/angeo-24-2451-2006
© Author(s) 2006. This work is distributed under
the Creative Commons Attribution 3.0 License.
Ann. Geophys., 24, 2451–2460, 2006
https://doi.org/10.5194/angeo-24-2451-2006
© Author(s) 2006. This work is distributed under
the Creative Commons Attribution 3.0 License.

  20 Oct 2006

20 Oct 2006

Applications of Kalman filters based on non-linear functions to numerical weather predictions

G. Galanis1,2, P. Louka1,3, P. Katsafados1, I. Pytharoulis1,4, and G. Kallos1 G. Galanis et al.
  • 1University of Athens, School of Physics, Division of Applied Physics, Atmospheric Modelling and Weather Forecasting Group, University Campus, Bldg. PHYS-V, 15784 Athens, Greece
  • 2Naval Academy of Greece, Section of Mathematics, Xatzikyriakion, Piraeus 185 39, Greece
  • 3Hellenic National Meteorological Service, El. Venizelou 14, Hellinikon 167 77, Athens, Greece
  • 4Section of Meteorology and Climatology, Department of Geology, Aristotle University of Thessaloniki, University Campus, 54124 Thessaloniki, Greece.

Abstract. This paper investigates the use of non-linear functions in classical Kalman filter algorithms on the improvement of regional weather forecasts. The main aim is the implementation of non linear polynomial mappings in a usual linear Kalman filter in order to simulate better non linear problems in numerical weather prediction. In addition, the optimal order of the polynomials applied for such a filter is identified. This work is based on observations and corresponding numerical weather predictions of two meteorological parameters characterized by essential differences in their evolution in time, namely, air temperature and wind speed. It is shown that in both cases, a polynomial of low order is adequate for eliminating any systematic error, while higher order functions lead to instabilities in the filtered results having, at the same time, trivial contribution to the sensitivity of the filter. It is further demonstrated that the filter is independent of the time period and the geographic location of application.

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