ClimateTools for Julia

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Documentation

This package is a collection of commonly-used tools in Climate Science. Basics of climate field analysis will be covered, with some forays into exploratory techniques. The package is aimed to ease the typical steps of analysis climate models outputs and observed time series from weather stations.

This package is registered on METADATA.jl and can be added with Pkg.add("ClimateTools") and used with using ClimateTools.

Pkg.add("ClimateTools") # Tagged release
Pkg.checkout("ClimateTools") # For latest master branch

The climate indices are coded to use multiple threads. To gain maximum performance, use (bash shell) export JULIA_NUM_THREADS=n, where n is the number of threads. To get an idea of the number of threads you can use type (in Julia) Sys.CPU_CORES.

Objectives

• Extraction and visualization of NetCDF datasets, with user-provided polygons and start and end date.
• Climate indices from The joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI)
• Custom climate indices
• Interpolation of a datasets onto another grid
• Post-processing of climate timeseries using Quantile-Quantile mapping method (cf. Piani et al. 2010)

Getting started

using ClimateTools

Reading a NetCDF file

The entry point of ClimateTools is to load data with the nc2julia function. Optional polygon clipping feature is available. By providing such polygon, the nc2julia function returns a ClimGrid with grid points contained in the polygon.

C = nc2julia(filename::String, var::String; poly::Array, data_units::String, start_date::Date, end_date::Date)

nc2julia return a ClimGrid type. Using the optional poly argument, the user can provide a polygon and the returned ClimGrid will only contains the grid points inside the provided polygon. For some variable, the optional keyword argument data_units can be provided. For example, precipitation in climate models are usually provided as kg/m^2/s. By specifying data_units = mm, the nc2julia function returns accumulation at the data time resolution. Similarly, the user can provide Celsius as data_units and nc2julia will return Celsius instead of Kelvin.

The ClimGrid is a in-memory representation of a CF-compliant netCDF file for a single variable.

struct ClimGrid{A <: AxisArray}
# struct ClimGrid
  data::A
  longrid::AbstractArray{N,2} where N # the longitude grid
  latgrid::AbstractArray{N,2} where N # the latitude grid
  msk::Array{N, 2} where N
  grid_mapping::Dict#{String, Any} # bindings for native grid
  dimension_dict::Dict
  model::String
  frequency::String
  experiment::String
  run::String
  project::String # CORDEX, CMIP5, etc.
  institute::String
  filename::String
  dataunits::String
  latunits::String # of the coordinate variable
  lonunits::String # of the coordinate variable
  variable::String # Type of variable (i.e. can be the same as "var", but it is changed when calculating indices)
  typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)
  typeofcal::String # Calendar type
  varattribs::Dict # Variable attributes
  globalattribs::Dict # Global attributes

end

Furthermore, there is also the spatialsubset function which acts on ClimGrid type and further subset the data through a spatial subset using a user polygon. The function returns a ClimGrid.

C = spatialsubset(C::ClimGrid, poly:Array{N, 2} where N)

Temporal subset of the data is also possible with the temporalsubset function:

C = temporalsubset(C::ClimGrid, startdate::Date, enddate::Date)

Mapping the ClimGrid type

Mapping climate information can be done by using mapclimgrid:

mapclimgrid(C::ClimGrid; region = "World")

Which should return

Note that if the ClimGrid data structure has 3 dimensions (time x longitude x latitude) the mapclimgrid function makes a time-average (i.e. climatological mean). Right now, options are available for region: World, Canada, Quebec and the default auto which use the maximum and minimum of the lat-long coordinates inside the ClimGrid structure. The user can also provide a polygon(s) and the mapclimgrid function will clip the grid points outside the specified polygon. Another option is to provide a mask (with dimensions identical to the spatial dimension of the ClimGrid data) which contains NaN and 1.0 and the data inside the ClimGrid struct will be clipped with the mask. Other regions will be added in the future, as well as the option to send a custom region defined by a lat-lon box.

In a future release, the user will have the option to specify his own time period (e.g. plotting the time-average of a given month and year, as opposed to the time-average of the whole ClimGrid structure).

Indices

More than 20 climate indices are available in the package, such as the annual number of tropical nights, annual maximum and minimum, etc. You can calculate such indices simply with:

ind = annualmax(C::ClimGrid)

Which returns another ClimGrid. You can also map this ClimGrid with the mapclimgrid function and returns the climatological mean of the annual maximum (e.g. daily precipitation in the example below). A list of indices can be found in the documentation and in the functions.jl source code.

Climate Indices functions also accept other type of argument. For example, annualmax can be called with the following type:

ind = annualmax(data::Array{Float64, 3}, dates::StepRange{Date, Base.Dates.Day})

Interpolation

A typical step in climate analysis is to interpolate a given grid onto another grid. ClimateTools provides such a tool by wrapping Scipy griddata function. It is intended for visualization or as a 1st step before bias-correcting the ClimGrid dataset.

The following command will interpolate the data contained in ClimGrid A into the coordinates of ClimGrid B and returns a new ClimGrid C which contains the interpolated data of A into the grid of B.

C = interp_climgrid(A::ClimGrid, B::ClimGrid)

It is also possible to interpolate a ClimGrid onto specified longitude and latitude vectors.

C = interp_climgrid(A::ClimGrid, lon::AbstractArray{N, 1}, lat::AbstractArray{N, 1})

Bias-correction

TODO

Merging ClimGrid type

Sometimes, the timeseries are split among multiple files (e.g. climate models outputs). To obtain the complete timeseries, you can merge 2 ClimGrid. The method is based on the merging of 2 AxisArrays and is overloaded for the ClimGrid type.

C = merge(C1::ClimGrid, C2::ClimGrid)

Contributors

If you'd like to have other climate indices coded, please, submit them through a Pull Request! I'd be more than happy to include them. Alternatively, provide the equation in Issues.

TO-DO

• Add a standard quantile-quantile mapping technique
• Dashboard tool. This will return the main characteristics of a ClimGrid: maps of minimum, maximum and mean climatological values, seasonal cycle, timeseries of annual maximum, minimum and mean values, etc...
• Create a WeatherStation type.
• Export ClimGrid to netCDF file.
• Add a more complex quantile-quantile mapping technique, combining extreme value theory and quantile-quantile standard technique
• Add GRIB file support (probably through GMT.jl)

N.B. version 0.1.2 is compatible with Julia 0.5 and version >0.2.0 is for Julia 0.6. To use a specific version of the package, you can use in Julia the following command:

Pkg.pin("ClimateTools",v"0.1.2") # if using Julia 0.5