Package org.orekit.models.earth.weather

Class AbstractGlobalPressureTemperature

  • All Implemented Interfaces:
    AzimuthalGradientProvider, ViennaAProvider, PressureTemperatureHumidityProvider
    Direct Known Subclasses:
    GlobalPressureTemperature2, GlobalPressureTemperature2w, GlobalPressureTemperature3
    public abstract class AbstractGlobalPressureTemperature
extends Object
implements ViennaAProvider, AzimuthalGradientProvider, PressureTemperatureHumidityProvider
    Base class for Global Pressure and Temperature 2, 2w and 3 models. These models are empirical models that provide the temperature, the pressure and the water vapor pressure of a site depending its latitude and longitude. These models also provide the ah and aw coefficients for Vienna models.

    The requisite coefficients for the computation of the weather parameters are provided by the Department of Geodesy and Geoinformation of the Vienna University. They are based on an external grid file like "gpt2_1.grd" (1° x 1°), "gpt2_5.grd" (5° x 5°), "gpt2_1w.grd" (1° x 1°), "gpt2_5w.grd" (5° x 5°), "gpt3_1.grd" (1° x 1°), or "gpt3_5.grd" (5° x 5°) available at: link

    A bilinear interpolation is performed in order to obtained the correct values of the weather parameters.

    The format is always the same, with and example shown below for the pressure and the temperature. The "GPT2w" model (w stands for wet) also provide humidity parameters and the "GPT3" model also provides horizontal gradient, so the number of columns vary depending on the model.

    Example: 

     %  lat    lon   p:a0    A1   B1   A2   B2  T:a0    A1   B1   A2   B2
   87.5    2.5 101421    21  409 -217 -122 259.2 -13.2 -6.1  2.6  0.3
   87.5    7.5 101416    21  411 -213 -120 259.3 -13.1 -6.1  2.6  0.3
   87.5   12.5 101411    22  413 -209 -118 259.3 -13.1 -6.1  2.6  0.3
   87.5   17.5 101407    23  415 -205 -116 259.4 -13.0 -6.1  2.6  0.3
   ...
    Since:
    12.1
    Author:
    Bryan Cazabonne, Luc Maisonobe
    See Also:
    "K. Lagler, M. Schindelegger, J. Böhm, H. Krasna, T. Nilsson (2013), GPT2: empirical slant delay model for radio space geodetic techniques. Geophys Res Lett 40(6):1069–1073. doi:10.1002/grl.50288"