pydda.cost_functions.calculate_point_gradient

pydda.cost_functions.calculate_point_gradient(u, v, x, y, z, point_list, Cp=0.001, roi=500.0)[source]

Calculates the gradient of the cost function related to point observations. A mean square error cost function term is applied to points that are within the sphere of influence whose radius is determined by roi.

Parameters
u: Float array

Float array with u component of wind field

v: Float array

Float array with v component of wind field

x: Float array

X coordinates of grid centers

y: Float array

Y coordinates of grid centers

z: Float array

Z coordinated of grid centers

point_list: list of dicts

List of point constraints. Each member is a dict with keys of “u”, “v”, to correspond to each component of the wind field and “x”, “y”, “z” to correspond to the location of the point observation.

In addition, “site_id” gives the METAR code (or name) to the station.

Cp: float

The weighting coefficient of the point cost function.

roi: float

Radius of influence of observations

Returns
gradJ: float array

The gradient of the cost function related to the difference between wind field and points.