Advanced functionality

interpolation2pressure(A::ClimArray, P::ClimArray, plevels::Vector; kwargs...) → B

Interpolate A, which has some arbitrary height dimension (height, model levels, etc.), into a new B::ClimArray where the vertical dimension is pressure. P is another ClimArray, that has the pressure values in Pascal, and otherwise same spatiotemporal structure as A. The plevels variable denotes which pressure levels B should have.

The keword vertical_dim = Hei() denotes which dimension of A denotes "height". The keyword extrapolation_bc = Line() configures extrapolation. It is linear by default, but can be any value such as extrapolation_bc = NaN. For other extrapolation methods use the Interpolations.jl package.

Keyword descending = true specifies whether pressure is ordered descendingly or ascendingly along the vertical dimension.

interpolate_height2pressure(A::ClimArray, pressure_levels::Vector; extrapolation_bc=NaN )

Return a ClimArray where the vertical coordinate is pressure. Pressure levels are calculated with the height2pressure function, based on hydrostatic equilibrium, and then interpolated to the given levels.

A: ClimArray with height above ground [m] as height coordinate. pressure_levels: Vector which contains the pressure levels in Pascal that A should be interpolated on. extrapolation_bc: Extrapolation is set to NaN by default, but can be any value or linear extrapolation with extrapolation_bc = Line(). For other extrapolation methods use the Interpolations package.

interpolate_pressure2height(A::ClimArray,heights::Vector; extrapolation_bc=NaN)

Return a ClimArray where the vertical coordinate is height above ground. Height above ground is calculated with the pressure2height function, based on hydrostatic equilibrium, and then interpolated to the given heights.

A: ClimArray with pressure [Pa] as height coordinate. heights: Vector which contains the heights that A should be interpolated on in meters above ground. extrapolation_bc: Extrapolation is set to NaN by default, but can be any value or linear extrapolation with extrapolation_bc = Line(). For other extrapolation methods use the Interpolations package.

quantile_space(A, B; n = 50) → Aq, Bq, q

Express the array B into the quantile space of A. B, A must have the same indices.

The array B is binned according to the quantile values of the elements of A. n bins are created in total and the bin edges p are returned. The i-th bin contains data whose A-quantile values are ∈ [q[i], q[i+1]). The indices of these A values are used to group B.

In each bin, the binned values of A, B are averaged, resulting in Aq, Bq.

The amount of datapoints per quantile is by definition length(A)/n.

value_space(A, B; Arange) → Bmeans, bin_indices

Express the array B into the value space of A. B, A must have the same indices. This means that A is binned according to the given Arange, and the same indices that binned A are also used to bin B. The i-th bin contains data whose A values ∈ [Arange[i], Arange[i+1]). In each bin, the binned values of B are averaged, resulting in Bmeans.

Elements of A that are not ∈ Arange are skipped. The returned bin_indices are the indices in each bin (hence, the weights for the means are just length.(bin_indices))

By default Arange = range(minimum(A), nextfloat(maximum(A)); length = 100).

value_space(A, B, C; Arange, Brange) → Cmeans, bin_indices

Express the array C into the joint value space of A, B. A, B, C must have the same indices.

A 2D histogram is created based on the given ranges, the and elements of C are binned according to the values of A, B. Then, the elements are averaged, which returns a matrix Cmeans, defined over the joint space S = Arange × Brange. bin_indices is also a matrix (with vector elements). Cmeans is NaN for bins without any elements.

insolation(t, ϕ; kwargs...)

Calculate daily averaged insolation in W/m² at given time and latitude t, φ. φ is given in degrees, and t in days (real number or date).

Keywords:

Ya = DAYS_IN_ORBIT # = 365.26 # days
t_VE = 76.0 # days of vernal equinox
S_0 = 1362.0 # W/m^2
γ=23.44
ϖ=282.95
e=0.017 # eccentricity

sinusoidal_continuation(T::ClimArray, fs = [1, 2]; Tmin = -Inf, Tmax = Inf)

Fill-in the missing values of spatiotemporal field T, by fitting sinusoidals to the non-missing values, and then using the fitted sinusoidals for the missing values.

Frequencies are given per year (frequency 2 means 1/2 of a year).

Tmin, Tmax limits are used to clamp the result into this range (no clamping in the default case).

seasonal_decomposition(A::ClimArray, fs = [1, 2]) → seasonal, residual

Decompose A into a seasonal and residual components, where the seasonal contains the periodic parts of A, with frequencies given in fs, and residual contains what's left.

fs is measured in 1/year. This function works even for non-equispaced time axis (e.g. monthly averages) and uses LPVSpectral.jl and SignalDecomposition.jl.