GriddedData2PotentialCoefficients
This program estimate potential coefficients from inputfileGriddedData gravity field functionals. It used a simple quadrature formular \[ c_{nm} = \frac{1}{4\pi}\frac{R}{GM} \sum_i f_i \left(\frac{r_i}{R}\right)^{n+1} k_n C_{nm}(\lambda_i,\vartheta_i)\,\Delta\Phi_i \]or a leastSquares adjustment with block diagonal normal matrix (order by order). For the latter one the data must be regular distributed.
The values $f_i$ and the weights $\Delta\Phi_i$ are expressions using the common data variables for grids, see dataVariables. Multiple outputfilePotentialCoefficients can be estimated in one step. For each an indivdual value must be specified. The type of the gridded data (e.g gravity anomalies or geoid heights) must be set with kernel $k_n$.
The expansion is limited in the range between minDegree and maxDegree inclusively. The coefficients are related to the reference radius R and the Earth gravitational constant GM.
For irregular distributed data and using the full variance covariance matrix use NormalsSolverVCE together with oberservation:terrestrial and parametrizationGravity:sphericalHarmonics.
See also GriddedDataTimeSeries2PotentialCoefficients.
| Name | Type | Annotation |
|---|---|---|
outputfilePotentialCoefficients | filename | one file for each value expression |
inputfileGriddedData | filename | |
value | expression | expression to compute values (input columns are named data0, data1, ...) |
weight | expression | expression to compute values (input columns are named data0, data1, ...) |
kernel | kernel | data type of input values |
minDegree | uint | |
maxDegree | uint | |
GM | double | Geocentric gravitational constant |
R | double | reference radius |
leastSquares | boolean | false: quadrature formular, true: least squares adjustment order by order |