diff --git a/dev/biascorrection/index.html b/dev/biascorrection/index.html index 2d93a6c..9404429 100644 --- a/dev/biascorrection/index.html +++ b/dev/biascorrection/index.html @@ -1,2 +1,2 @@ -Bias correction · ClimateTools.jl
Edit on GitHub

Bias correction

Quantile-quantile mapping (Themeßl et al. 2012, Grenier et al. 2015) is provided with ClimateTools.jl through the function qqmap.

qqmap(obs::ClimGrid, ref::ClimGrid, fut::ClimGrid; method::String="Additive", detrend::Bool=true, window::Int=15, rankn::Int=50, thresnan::Float64=0.1, keep_original::Bool=false, interp = Linear(), extrap = Flat())

More information can be found in these references.

Themeßl, Matthias Jakob, Andreas Gobiet, and Georg Heinrich. 2012. “Empirical-Statistical Downscaling and Error Correction of Regional Climate Models and Its Impact on the Climate Change Signal.” Climatic Change 112 (2). Springer: 449–68.

Grenier, Patrick, Ramón de Elía, and Diane Chaumont. 2015. “Chances of Short-Term Cooling Estimated from a Selection of CMIP5-Based Climate Scenarios during 2006-2035 over Canada.” Journal of Climate, January 2015. American Meteorological Society. doi:10.1175/JCLI-D-14-00224.1.

+Bias correction · ClimateTools.jl
Edit on GitHub

Bias correction

Quantile-quantile mapping (Themeßl et al. 2012, Grenier et al. 2015) is provided with ClimateTools.jl through the function qqmap.

qqmap(obs::ClimGrid, ref::ClimGrid, fut::ClimGrid; method::String="Additive", detrend::Bool=true, window::Int=15, rankn::Int=50, thresnan::Float64=0.1, keep_original::Bool=false, interp = Linear(), extrap = Flat())

More information can be found in these references.

Themeßl, Matthias Jakob, Andreas Gobiet, and Georg Heinrich. 2012. “Empirical-Statistical Downscaling and Error Correction of Regional Climate Models and Its Impact on the Climate Change Signal.” Climatic Change 112 (2). Springer: 449–68.

Grenier, Patrick, Ramón de Elía, and Diane Chaumont. 2015. “Chances of Short-Term Cooling Estimated from a Selection of CMIP5-Based Climate Scenarios during 2006-2035 over Canada.” Journal of Climate, January 2015. American Meteorological Society. doi:10.1175/JCLI-D-14-00224.1.

diff --git a/dev/datasets/index.html b/dev/datasets/index.html index 5d73798..a3768c8 100644 --- a/dev/datasets/index.html +++ b/dev/datasets/index.html @@ -24,4 +24,4 @@ globalattribs::Dict # Global attributes dictionary end

Manipulations

Once the data is loaded in a ClimGrid struct, options to further subset the data are available.

Spatial

spatialsubset function acts on ClimGrid type and subset the data through a spatial subset using a provided polygon. The function returns a ClimGrid. Polygons needs to be on a -180, +180 longitude coordinates, as data coordinates defaults to such grid. For instance, global models are often on a 0-360 degrees grid but the load function shift the data onto a -180,+180 coordinates.

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

Temporal

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

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

Discontinuous temporal (e.g. resampling)

It is also possible to only keep a given non-continuous period for a given timeframe. For example, we might be interested in keeping only northern summer months (June-July-August) from a continuous ClimGrid covering 1961-2100. resample returns such a subsetted ClimGrid.

Csub = resample(C, "JJA") # hardcoded ClimateTools's season
 Csub = resample(C, 6, 8) # custom subset example for June-July-August
-Csub = resample(C, 1, 2) # custom subset example for January-February
+Csub = resample(C, 1, 2) # custom subset example for January-February diff --git a/dev/examples/index.html b/dev/examples/index.html index 3ae5fb9..ee2e018 100644 --- a/dev/examples/index.html +++ b/dev/examples/index.html @@ -191,4 +191,4 @@ # Plots plot(max_obs, label="OBS") plot(max_modelinterp, label="MIROC5 - interpolated") -plot(max_modelqqmap, label="MIROC5 - bias corrected", titlefig = "Effect of bias correction on annual maximum values")

timeseries

Exporting

Once calculations are done, results can be written to disk to a netCDF file with the write command. Here, we export the annual maximum values of the bias corrected model to the current folder.

write(max_modelqqmap, "annualmax_model_qqmap.nc")
+plot(max_modelqqmap, label="MIROC5 - bias corrected", titlefig = "Effect of bias correction on annual maximum values")

timeseries

Exporting

Once calculations are done, results can be written to disk to a netCDF file with the write command. Here, we export the annual maximum values of the bias corrected model to the current folder.

write(max_modelqqmap, "annualmax_model_qqmap.nc")
diff --git a/dev/functions/index.html b/dev/functions/index.html index ec60fbf..4bf4799 100644 --- a/dev/functions/index.html +++ b/dev/functions/index.html @@ -1,4 +1,4 @@ -Index · ClimateTools.jl
Edit on GitHub

Index

Base.findmax — Method
findmax(C::ClimGrid; skipnan::Bool=false)

Return the maximum element of ClimGrid C and its index. If there are multiple maximal elements, then the first one will be returned. If any data element is NaN, this element is returned. The result is in line with max. As climate data can often have NaN values (irregular polygons, missing weather data), the option to skip NaNs is provided as a keyword argument.

source
Base.findmin — Method
findmin(C::ClimGrid; skipnan::Bool=false)

Return the minimum element of ClimGrid C and its index. If there are multiple minimal elements, then the first one will be returned. If any data element is NaN, this element is returned. The result is in line with min. As climate data can often have NaN values (irregular polygons, missing weather data), the option to skip NaNs is provided as a keyword argument.

source
Base.merge — Method
merge(A::ClimGrid, B::ClimGrid)

Combines two ClimGrid. Based on the AxisArrays method.

source
Base.write — Method
write(C::ClimGrid, filename::String)

Write to disk ClimGrid C to netCDF file. Still experimental, some attributes are hardcoded.

source
ClimateTools.RXday — Method
RXday(C::ClimGrid; days=5)

Annual maximum consecutive x days precipitation amount (defaults to days=5).

Let RRkj be the precipitation amount for the 5-day interval ending k, period j. Then maximum 5-day values for period j are:

Rx5dayj = max (RRkj)

source
ClimateTools.annualmax — Method
annualmax(C::ClimGrid)

Annual maximum of array data.

Let data[i,j] be daily time serie on day i in year j. Extract the highest value for year j.

source
ClimateTools.annualmean — Method
annualmean(C::ClimGrid)

Annual mean of array data.

Let data[i,j] be daily time serie on day i in year j. Calculate the mean value for year j.

source
ClimateTools.annualmin — Method
annualmin(C::ClimGrid)

Annual minimum of array data.

Let data[i,j] be daily time serie on day i in year j. Extract the lowest value for year j.

source
ClimateTools.annualsum — Method
annualsum(C::ClimGrid)

Annual sum of array data.

Let data[i,j] be daily time serie on day i in year j. Sums daily values for year j.

source
ClimateTools.applymask — Method
applymask(A::AbstractArray{N, n}, mask::AbstractArray{N, n})

Applies a mask on the array A. Return an AbstractArray{N, n}.

source
ClimateTools.approx_surfacepressure — Method

approxsurfacepressure(sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)

Returns the approximated surface pressure (sp) (Pa) using sea level pressure (psl) (Pa), orography (orog) (m), and daily mean temperature (tas) (K).

$sp = psl * 10^{x}$

where $x = \frac{-orog}{18400 * tas / 273.15}$

source
ClimateTools.biascorrect_extremes — Method
biascorrect_extremes(obs::ClimGrid, ref::ClimGrid, fut::ClimGrid; detrend=false, window::Int=15, rankn::Int=50, thresnan::Float64=0.1, keep_original::Bool=false, interp=Linear(), extrap=Flat(), gev_params::DataFrame, frac=0.25, power=1.0)

Combine the empirial Quantile-Quantile mapping (see qqmap) and Generalized Pareto Distribution bias-correction methods.

The tail of the distribution is corrected with a paramatric GPD, using provided parameters μ, σ and ξ contained in gevparams DataFrame. The DataFrame gevparams has column :lat, :lon, :mu, :sigma and :xi, representing the GEV parameters and the spatial location of the parameters. For each grid point, the function extracts the closest GEV parameters.

Options specific to GPD

gevparams::DataFrame represents the (external) GEV parameters to correct each grid-points.

frac=0.25 is the fraction where the cutoff happens between QQM and GPD, as defined by (maximum(x) - minimum(x))*frac (e.g. For a maximum value of 150mm and a minimum value of 30mm, the linear transition will be between 30mm and 60mm).

power=1.0 is the shape of the transition.

Options specific to QQM

detrend::Bool = false (default). A 4th order polynomial is adjusted to the time series and the residuals are corrected.

window::Int = 15 (default). The size of the window used to extract the statistical characteristics around a given julian day.

rankn::Int = 50 (default). The number of bins used for the quantile estimations. The quantiles uses by default 50 bins between 0.01 and 0.99. The bahavior between the bins is controlled by the interp keyword argument. The behaviour of the quantile-quantile estimation outside the 0.01 and 0.99 range is controlled by the extrap keyword argument.

thresnan::Float64 = 0.1 (default). The fraction is missing values authorized for the estimation of the quantile-quantile mapping for a given julian days. If there is more than treshnan missing values, the output for this given julian days returns NaNs.

keep_original::Bool = false (default). If keep_original is set to true, the values are set to the original values in presence of too many NaNs.

interp = Interpolations.Linear() (default). When the data to be corrected lies between 2 quantile bins, the value of the transfer function is linearly interpolated between the 2 closest quantile estimation. The argument is from Interpolations.jl package.

extrap = Interpolations.Flat() (default). The bahavior of the quantile-quantile transfer function outside the 0.01-0.99 range. Setting it to Flat() ensures that there is no "inflation problem" with the bias correction. The argument is from Interpolation.jl package.

See also: qqmap

source
ClimateTools.customthresover — Method
customthresover(C::ClimGrid, thres)

customthresover, annual number of days over a specified threshold.

Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:

TS[i,j] > thres.

source
ClimateTools.customthresunder — Method
customthresunder(C::ClimGrid, thres)

customthresunder, annual number of days under a specified threshold.

Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:

TS[i,j] < thres.

source
ClimateTools.diurnaltemperature — Method

diurnaltemperature(temperatureminimum::ClimGrid, temperaturemaximum::ClimGrid, α::Float64)

Returns an estimation of the diurnal temperature (temperature between 7:00 (7am) and 17:00 (5pm)). The estimation is a linear combination of the daily minimum temperature (temperatureminimum) and daily maximum temperature (temperaturemaximum). The value of α has to be estimated seperatly from observations and depends on the location. The daily max and min must be in the same unit and in Celsius or Kelvin The diurnal temperature returned is in the same units as the daily minimum temperature and daily maximum temperature.

$Tdiu = α * Tmin + (1 - α) * Tmax$

source
ClimateTools.drought_dc — Method

drought_dc(pr::ClimGrid, tas::Climgrid)

Returns the drought index. The index is defined as a function of daily temperature and daily precipitation. This indice correspond to the DC indice in Wang et al. 2015.

Reference: Wang et al. 2015. Updated source code for calculating fire danger indices in the Canadian Forest Fire Weather Index System. Information Report NOR-X-424. Canadian Forst Service. 36pp.

source
ClimateTools.drought_dc — Method

drought_dc(prvec::Array{N,1}, tasvec::Array{N,1}, timevec)

Returns the drought index. The index is defined as a function of daily temperature and daily precipitation. This indice correspond to the DC indice in Wang et al. 2015.

Reference: Wang et al. 2015. Updated source code for calculating fire danger indices in the Canadian Forest Fire Weather Index System. Information Report NOR-X-424. Canadian Forst Service. 36pp.

source
ClimateTools.frostdays — Method
frostdays(C::ClimGrid)

FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0 Celsius.

Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:

TN[i,j] < 0 Celsius.

source
ClimateTools.get_dimname — Method
get_dimname(::NCDatasets.Dataset, dim::String)

Scans dataset ds and extract the dimensions X, Y, Z and T. The dataset needs to be CF-compliant. See http://cfconventions.org/.

source
ClimateTools.getdim_lat — Method
getdim_lat(ds::NCDatasets.Dataset)

Returns the name of the "latitude" dimension and the status related to a regular grid. The latitude dimension is usually "latitude", "lat", "y", "yc", "rlat".

source
ClimateTools.getdim_lon — Method
getdim_lon(ds::NCDatasets.Dataset)

Returns the name of the "longitude" dimension and the status related to a regular grid. The longitude dimension is usually "longitue", "lon", "x", "xc", "rlon".

source
ClimateTools.griddata — Method
C = griddata(A::ClimGrid, londest::AbstractArray{N, 1} where N, latdest::AbstractArray{N, 1} where N)

Interpolate ClimGrid A onto lat-lon grid defined by londest and latdest vector or array. If an array is provided, it is assumed that the grid is curvilinear (not a regular lon-lat grid) and the user needs to provide the dimension vector ("x" and "y") for such a grid.

source
ClimateTools.griddata — Method
C = griddata(A::ClimGrid, B::ClimGrid; min=[], max=[])

Interpolate ClimGrid A onto the lon-lat grid of ClimGrid B, where A and B are ClimGrid.

Min and max optional keyword are used to constraint the results of the interpolation. For example, interpolating bounded fields can lead to unrealilstic values, such as negative precipitation. In that case, one would use min=0.0 to convert negative precipitation to 0.0.

source
ClimateTools.icingdays — Method
icingdays(C::ClimGrid)

ID, Number of summer days: Annual count of days when TX (daily maximum temperature) < 0 degree Celsius.

Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:

TX[i,j] < 0 Celsius.

source
ClimateTools.inpoly — Method
inpoly(p, poly::Matrix)

Determines if a point is inside a polygon.

  • p – point (x,y) or [x,y]
  • poly – polygon vertices [x1 x2 ... xn x1 y1 y2 ... yn y1]

Returns true if point has an odd winding number. This should label points as exterior which are inside outcrops. See test for a test.

Author: Github "Mauro3" / "Mauro"

source
ClimateTools.inpolygrid — Method
inpolygrid(lon, lat, poly::AbstractArray{N,2} where N)

Used to test a grid of points. Returns a mask of ones and NaNs of the same size as lon and lat.

source
ClimateTools.load — Method
load(file::String, vari::String; poly = Array{Float64}([]), start_date::Tuple, end_date::Tuple, data_units::String = "")

Returns a ClimGrid type with the data in file of variable vari inside the polygon poly. Metadata is built-in the ClimGrid type, from the netCDF attributes.

Inside the ClimgGrid type, the data is stored into an AxisArray data type, with time, longitude/x and latitude/y dimensions.

The polygon provided should be in the -180, +180 longitude format. If the polygon crosses the International Date Line, the polygon should be splitted in multiple parts (i.e. multi-polygons).

Options for data_units are for precipitation : "mm", which converts the usual "kg m-2 s-1" unit found in netCDF files. For temperature : "Celsius", which converts the usual "Kelvin" unit.

Temporal subsetting can be done by providing start_date and end-date Tuples of length 1 (year), length 3 (year, month, day) or 6 (hour, minute, second).

Note: load uses CF conventions. If you are unable to read the netCDF file with load, the user will need to read it with low-level functions available in NetCDF.jl package or NCDatasets.jl or re-create standartized netCDF files.

source
ClimateTools.load — Method
load(files::Array{String,1}, vari::String; poly = ([]), start_date::Date = Date(-4000), end_date::Date = Date(-4000), data_units::String = "")

Loads and merge the files contained in the arrar files.

source
ClimateTools.load2D — Method
load2D(file::String, vari::String; poly=[], data_units::String="")

Returns a 2D array. Should be used for fixed data, such as orography.

source
ClimateTools.meantemperature — Method

meantemperature(temperatureminimum::ClimGrid, temperaturemaximum::ClimGrid)

Returns the daily mean temperature calculated from the maximum and minimum temperature. Daily maximum and minimum temperature must be in the same units. The mean temperature returned is in the same units as the daily minimum temperature and daily maximum temperature.

$Tmean = \frac{Tmax + Tmin}{2}$

source
ClimateTools.meshgrid — Method
X, Y = meshgrid{T}(vx::AbstractVector{T}, vy::AbstractVector{T})

This function creates a 2-D mesh-grid in a format consistent with Matlab's function meshgrid(). XV and YV are vectors.

source
ClimateTools.ndgrid — Method
X, Y = ndgrid(XV, YV)

This function creates a 2-D mesh-grid in a format consistent with Matlab's function ndgrid(). XV and YV are vectors.

source
ClimateTools.polyfit — Method
polyfit(C::ClimGrid)

Returns an array of the polynomials functions of each grid points contained in ClimGrid C.

source
ClimateTools.polyval — Method
polyval(C::ClimGrid, polynomial::Array{Poly{Float64}})
+Index · ClimateTools.jl
Edit on GitHub
Index
Base.findmax — Method
findmax(C::ClimGrid; skipnan::Bool=false)
Return the maximum element of ClimGrid C and its index. If there are multiple maximal elements, then the first one will be returned. If any data element is NaN, this element is returned. The result is in line with max. As climate data can often have NaN values (irregular polygons, missing weather data), the option to skip NaNs is provided as a keyword argument.
source
Base.findmin — Method
findmin(C::ClimGrid; skipnan::Bool=false)
Return the minimum element of ClimGrid C and its index. If there are multiple minimal elements, then the first one will be returned. If any data element is NaN, this element is returned. The result is in line with min. As climate data can often have NaN values (irregular polygons, missing weather data), the option to skip NaNs is provided as a keyword argument.
source
Base.merge — Method
merge(A::ClimGrid, B::ClimGrid)
Combines two ClimGrid. Based on the AxisArrays method.
source
Base.write — Method
write(C::ClimGrid, filename::String)
Write to disk ClimGrid C to netCDF file. Still experimental, some attributes are hardcoded.
source
ClimateTools.RXday — Method
RXday(C::ClimGrid; days=5)
Annual maximum consecutive x days precipitation amount (defaults to days=5).
Let RRkj be the precipitation amount for the 5-day interval ending k, period j. Then maximum 5-day values for period j are:
Rx5dayj = max (RRkj)
source
ClimateTools.annualmax — Method
annualmax(C::ClimGrid)
Annual maximum of array data.
Let data[i,j] be daily time serie on day i in year j. Extract the highest value for year j.
source
ClimateTools.annualmean — Method
annualmean(C::ClimGrid)
Annual mean of array data.
Let data[i,j] be daily time serie on day i in year j. Calculate the mean value for year j.
source
ClimateTools.annualmin — Method
annualmin(C::ClimGrid)
Annual minimum of array data.
Let data[i,j] be daily time serie on day i in year j. Extract the lowest value for year j.
source
ClimateTools.annualsum — Method
annualsum(C::ClimGrid)
Annual sum of array data.
Let data[i,j] be daily time serie on day i in year j. Sums daily values for year j.
source
ClimateTools.applymask — Method
applymask(A::AbstractArray{N, n}, mask::AbstractArray{N, n})
Applies a mask on the array A. Return an AbstractArray{N, n}.
source
ClimateTools.approx_surfacepressure — Method
approxsurfacepressure(sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)
Returns the approximated surface pressure (sp) (Pa) using sea level pressure (psl) (Pa), orography (orog) (m), and daily mean temperature (tas) (K).
$sp = psl * 10^{x}$
where $x = \frac{-orog}{18400 * tas / 273.15}$
source
ClimateTools.biascorrect_extremes — Method
biascorrect_extremes(obs::ClimGrid, ref::ClimGrid, fut::ClimGrid; detrend=false, window::Int=15, rankn::Int=50, thresnan::Float64=0.1, keep_original::Bool=false, interp=Linear(), extrap=Flat(), gev_params::DataFrame, frac=0.25, power=1.0)
Combine the empirial Quantile-Quantile mapping (see qqmap) and Generalized Pareto Distribution bias-correction methods.
The tail of the distribution is corrected with a paramatric GPD, using provided parameters μ, σ and ξ contained in gevparams DataFrame. The DataFrame gevparams has column :lat, :lon, :mu, :sigma and :xi, representing the GEV parameters and the spatial location of the parameters. For each grid point, the function extracts the closest GEV parameters.
Options specific to GPD
gevparams::DataFrame represents the (external) GEV parameters to correct each grid-points.
frac=0.25 is the fraction where the cutoff happens between QQM and GPD, as defined by (maximum(x) - minimum(x))*frac (e.g. For a maximum value of 150mm and a minimum value of 30mm, the linear transition will be between 30mm and 60mm).
power=1.0 is the shape of the transition.
Options specific to QQM
detrend::Bool = false (default). A 4th order polynomial is adjusted to the time series and the residuals are corrected.
window::Int = 15 (default). The size of the window used to extract the statistical characteristics around a given julian day.
rankn::Int = 50 (default). The number of bins used for the quantile estimations. The quantiles uses by default 50 bins between 0.01 and 0.99. The bahavior between the bins is controlled by the interp keyword argument. The behaviour of the quantile-quantile estimation outside the 0.01 and 0.99 range is controlled by the extrap keyword argument.
thresnan::Float64 = 0.1 (default). The fraction is missing values authorized for the estimation of the quantile-quantile mapping for a given julian days. If there is more than treshnan missing values, the output for this given julian days returns NaNs.
keep_original::Bool = false (default). If keep_original is set to true, the values are set to the original values in presence of too many NaNs.
interp = Interpolations.Linear() (default). When the data to be corrected lies between 2 quantile bins, the value of the transfer function is linearly interpolated between the 2 closest quantile estimation. The argument is from Interpolations.jl package.
extrap = Interpolations.Flat() (default). The bahavior of the quantile-quantile transfer function outside the 0.01-0.99 range. Setting it to Flat() ensures that there is no "inflation problem" with the bias correction. The argument is from Interpolation.jl package.
See also: qqmap
source
ClimateTools.customthresover — Method
customthresover(C::ClimGrid, thres)
customthresover, annual number of days over a specified threshold.
Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:
TS[i,j] > thres.
source
ClimateTools.customthresunder — Method
customthresunder(C::ClimGrid, thres)
customthresunder, annual number of days under a specified threshold.
Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:
TS[i,j] < thres.
source
ClimateTools.diurnaltemperature — Method
diurnaltemperature(temperatureminimum::ClimGrid, temperaturemaximum::ClimGrid, α::Float64)
Returns an estimation of the diurnal temperature (temperature between 7:00 (7am) and 17:00 (5pm)). The estimation is a linear combination of the daily minimum temperature (temperatureminimum) and daily maximum temperature (temperaturemaximum). The value of α has to be estimated seperatly from observations and depends on the location. The daily max and min must be in the same unit and in Celsius or Kelvin The diurnal temperature returned is in the same units as the daily minimum temperature and daily maximum temperature.
$Tdiu = α * Tmin + (1 - α) * Tmax$
source
ClimateTools.drought_dc — Method
drought_dc(pr::ClimGrid, tas::Climgrid)
Returns the drought index. The index is defined as a function of daily temperature and daily precipitation. This indice correspond to the DC indice in Wang et al. 2015.
Reference: Wang et al. 2015. Updated source code for calculating fire danger indices in the Canadian Forest Fire Weather Index System. Information Report NOR-X-424. Canadian Forst Service. 36pp.
source
ClimateTools.drought_dc — Method
drought_dc(prvec::Array{N,1}, tasvec::Array{N,1}, timevec)
Returns the drought index. The index is defined as a function of daily temperature and daily precipitation. This indice correspond to the DC indice in Wang et al. 2015.
Reference: Wang et al. 2015. Updated source code for calculating fire danger indices in the Canadian Forest Fire Weather Index System. Information Report NOR-X-424. Canadian Forst Service. 36pp.
source
ClimateTools.frostdays — Method
frostdays(C::ClimGrid)
FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0 Celsius.
Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:
TN[i,j] < 0 Celsius.
source
ClimateTools.get_dimname — Method
get_dimname(::NCDatasets.Dataset, dim::String)
Scans dataset ds and extract the dimensions X, Y, Z and T. The dataset needs to be CF-compliant. See http://cfconventions.org/.
source
ClimateTools.getdim_lat — Method
getdim_lat(ds::NCDatasets.Dataset)
Returns the name of the "latitude" dimension and the status related to a regular grid. The latitude dimension is usually "latitude", "lat", "y", "yc", "rlat".
source
ClimateTools.getdim_lon — Method
getdim_lon(ds::NCDatasets.Dataset)
Returns the name of the "longitude" dimension and the status related to a regular grid. The longitude dimension is usually "longitue", "lon", "x", "xc", "rlon".
source
ClimateTools.griddata — Method
C = griddata(A::ClimGrid, londest::AbstractArray{N, 1} where N, latdest::AbstractArray{N, 1} where N)
Interpolate ClimGrid A onto lat-lon grid defined by londest and latdest vector or array. If an array is provided, it is assumed that the grid is curvilinear (not a regular lon-lat grid) and the user needs to provide the dimension vector ("x" and "y") for such a grid.
source
ClimateTools.griddata — Method
C = griddata(A::ClimGrid, B::ClimGrid; min=[], max=[])
Interpolate ClimGrid A onto the lon-lat grid of ClimGrid B, where A and B are ClimGrid.
Min and max optional keyword are used to constraint the results of the interpolation. For example, interpolating bounded fields can lead to unrealilstic values, such as negative precipitation. In that case, one would use min=0.0 to convert negative precipitation to 0.0.
source
ClimateTools.icingdays — Method
icingdays(C::ClimGrid)
ID, Number of summer days: Annual count of days when TX (daily maximum temperature) < 0 degree Celsius.
Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:
TX[i,j] < 0 Celsius.
source
ClimateTools.inpoly — Method
inpoly(p, poly::Matrix)
Determines if a point is inside a polygon.
  • p – point (x,y) or [x,y]
  • poly – polygon vertices [x1 x2 ... xn x1                           y1 y2 ... yn y1]
Returns true if point has an odd winding number.  This should label points as exterior which are inside outcrops.  See test for a test.
Author: Github "Mauro3" / "Mauro"
source
ClimateTools.inpolygrid — Method
inpolygrid(lon, lat, poly::AbstractArray{N,2} where N)
Used to test a grid of points. Returns a mask of ones and NaNs of the same size as lon and lat.
source
ClimateTools.load — Method
load(file::String, vari::String; poly = Array{Float64}([]), start_date::Tuple, end_date::Tuple, data_units::String = "")
Returns a ClimGrid type with the data in file of variable vari inside the polygon poly. Metadata is built-in the ClimGrid type, from the netCDF attributes.
Inside the ClimgGrid type, the data is stored into an AxisArray data type, with time, longitude/x and latitude/y dimensions.
The polygon provided should be in the -180, +180 longitude format. If the polygon crosses the International Date Line, the polygon should be splitted in multiple parts (i.e. multi-polygons).
Options for data_units are for precipitation : "mm", which converts the usual "kg m-2 s-1" unit found in netCDF files. For temperature : "Celsius", which converts the usual "Kelvin" unit.
Temporal subsetting can be done by providing start_date and end-date Tuples of length 1 (year), length 3 (year, month, day) or 6 (hour, minute, second).
Note: load uses CF conventions. If you are unable to read the netCDF file with load, the user will need to read it with low-level functions available in NetCDF.jl package or NCDatasets.jl or re-create standartized netCDF files.
source
ClimateTools.load — Method
load(files::Array{String,1}, vari::String; poly = ([]), start_date::Date = Date(-4000), end_date::Date = Date(-4000), data_units::String = "")
Loads and merge the files contained in the arrar files.
source
ClimateTools.load2D — Method
load2D(file::String, vari::String; poly=[], data_units::String="")
Returns a 2D array. Should be used for fixed data, such as orography.
source
ClimateTools.meantemperature — Method
meantemperature(temperatureminimum::ClimGrid, temperaturemaximum::ClimGrid)
Returns the daily mean temperature calculated from the maximum and minimum temperature. Daily maximum and minimum temperature must be in the same units. The mean temperature returned is in the same units as the daily minimum temperature and daily maximum temperature.
$Tmean = \frac{Tmax + Tmin}{2}$
source
ClimateTools.meshgrid — Method
X, Y = meshgrid{T}(vx::AbstractVector{T}, vy::AbstractVector{T})
This function creates a 2-D mesh-grid in a format consistent with Matlab's function meshgrid(). XV and YV are vectors.
source
ClimateTools.ndgrid — Method
X, Y = ndgrid(XV, YV)
This function creates a 2-D mesh-grid in a format consistent with Matlab's function ndgrid(). XV and YV are vectors.
source
ClimateTools.polyfit — Method
polyfit(C::ClimGrid)
Returns an array of the polynomials functions of each grid points contained in ClimGrid C.
source
ClimateTools.polyval — Method
polyval(C::ClimGrid, polynomial::Array{Poly{Float64}})
 
-Returns a ClimGrid containing the values, as estimated from polynomial function polyn.
source
ClimateTools.prcp1 — Method
prcp1(C::ClimGrid)
Annual number with preciptation >= 1 mm. This function returns a ClimGrid. Input data should be in mm.
source
ClimateTools.qqmap — Method
qqmap(obs::ClimGrid, ref::ClimGrid, fut::ClimGrid; method="Additive", detrend=true, window::Int=15, rankn::Int=50, thresnan::Float64=0.1, keep_original::Bool=false, interp::Function = Linear(), extrap::Function = Flat())
Quantile-Quantile mapping bias correction.
For each julian day of the year (+/- window size), a transfer function is estimated through an empirical quantile-quantile mapping. The quantile-quantile transfer function between ref and obs is etimated on a julian day (and grid-point) basis with a moving window around the julian day. Hence, for a given julian day, the transfer function is then applied to the fut dataset for a given julian day.
Options
method::String = "Additive" (default) or "Multiplicative". Additive is used for most climate variables. Multiplicative is usually bounded variables such as precipitation and humidity.
detrend::Bool = true (default). A 4th order polynomial is adjusted to the time series and the residuals are corrected with the quantile-quantile mapping.
window::Int = 15 (default). The size of the window used to extract the statistical characteristics around a given julian day.
rankn::Int = 50 (default). The number of bins used for the quantile estimations. The quantiles uses by default 50 bins between 0.01 and 0.99. The bahavior between the bins is controlled by the interp keyword argument. The behaviour of the quantile-quantile estimation outside the 0.01 and 0.99 range is controlled by the extrap keyword argument.
thresnan::Float64 = 0.1 (default). The fraction is missing values authorized for the estimation of the quantile-quantile mapping for a given julian days. If there is more than treshnan missing values, the output for this given julian days returns NaNs.
keep_original::Bool = false (default). If keep_original is set to true, the values are set to the original values in presence of too many NaNs.
interp = Interpolations.Linear() (default). When the data to be corrected lies between 2 quantile bins, the value of the transfer function is linearly interpolated between the 2 closest quantile estimation. The argument is from Interpolations.jl package.
extrap = Interpolations.Flat() (default). The bahavior of the quantile-quantile transfer function outside the 0.01-0.99 range. Setting it to Flat() ensures that there is no "inflation problem" with the bias correction. The argument is from Interpolation.jl package.
source
ClimateTools.qqmap — Method
qqmap(obsvec::Array{N,1}, refvec::Array{N,1}, futvec::Array{N,1}, days, obsjul, refjul, futjul; method::String="Additive", detrend::Bool=true, window::Int64=15, rankn::Int64=50, thresnan::Float64=0.1, keeporiginal::Bool=false, interp=Linear(), extrap=Flat())
Quantile-Quantile mapping bias correction for single vector. This is a low level function used by qqmap(A::ClimGrid ..), but can work independently.
source
ClimateTools.resample — Method
resample(C::ClimGrid, startmonth::Int64, endmonth::Int64)
Return a resampled subset of ClimGrid C based on months.
source
ClimateTools.resample — Method
resample(C::ClimGrid, season::String)
Return a resampled subset of ClimGrid C for a given season. Season options are: "DJF" (December-February), "MAM" (March-May), "JJA" (June-August), "SON" (September-November)
source
ClimateTools.shapefile_coords_poly — Method
shapefile_coords_poly(poly::Shapefile.Polygon)
Return the polygons contained in shp.shapes[i]. It returns an array containing the polygons.
See also shapefile_coords, which returns vectors as opposed to array. Returned polygon is consistent with the data extraction of the load function.
source
ClimateTools.spatialsubset — Method
spatialsubset(C::ClimGrid, poly::Array{N, 2})
Returns the spatial subset of ClimGrid C. The spatial subset is defined by the polygon poly, defined on a -180, +180 longitude reference.
source
ClimateTools.summerdays — Method
summerdays(C::ClimGrid)
SD, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25 degree Celsius.
Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:
TX[i,j] > 25 Celsius.
source
ClimateTools.temporalsubset — Method
function temporalsubset(C::ClimGrid, startdate::Date, enddate::Date)
Returns the temporal subset of ClimGrid C. The temporal subset is defined by a start and end date.
source
ClimateTools.timeindex — Method
timeindex(timeVec, start_date, end_date, T)
Return the index of time vector specified by startdate and enddate. T is the DateTime type (see NCDatasets.jl documentation).
source
ClimateTools.tropicalnights — Method
tropicalnights(C::ClimGrid)
TropicalNights, Number of tropical nights: Annual count of days when TN (daily maximum temperature) > 20 degree Celsius.
Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:
TN[i,j] > 20 Celsius.
source
ClimateTools.vaporpressure — Method
vaporpressure(specifichumidity::ClimGrid, sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)
Returns the vapor pressure (vp) (Pa) estimated with the specific humidity (q), the sea level pressure (psl) (Pa), the orography (orog) (m) and the daily mean temperature (tas) (K). An approximation of the surface pressure is first computed by using the sea level pressure, orography and the daily mean temperature (see approx_surfacepressure). Then, vapor pressure is calculated by:
$vp = \frac{q * sp}{q+0.622}$
source
ClimateTools.vaporpressure — Method
vaporpressure(surfacepressure::ClimGrid, specifichumidity::ClimGrid)
Returns the vapor pressure (vp) (Pa) based on the surface pressure (sp) (Pa) and the specific humidity (q).
$vp = \frac{q * sp}{q+0.622}$
source
ClimateTools.wbgt — Method
wbgt(diurnaltemperature::ClimGrid, vaporpressure::ClimGrid)
Returns the simplified wet-bulb global temperature (wbgt) (Celsius) calculated using the vapor pressure (Pa) of the day and the estimated mean diurnal temperature (Celsius; temperature between 7:00 (7am) and 17:00 (5pm)).
$wbgt = 0.567 * Tday + 0.00393 * vp + 3.94$
source
ClimateTools.yearmonthdayhour — Method
yearmonthdayhour(dt::AbstractCFDateTime) -> (Int64, Int64, Int64, Int64)
Simultaneously return the year, month, day and hour parts of dt. Author: Alexander-Barth (Github)
source
ClimateTools.ClimGrid — Type
ClimGrid{A <: AxisArray}
In-memory representation of Climate Forecast netCDF files. struct ClimGrid
data::AxisArray # Data
longrid::AbstractArray{N,2} where N # the longitude grid
latgrid::AbstractArray{N,2} where N # the latitude grid
msk::Array{N, 2} where N # Data mask (NaNs and 1.0)
grid_mapping::Dict#{String, Any} # bindings for native grid
dimension_dict::Dict
model::String
frequency::String # Day, month, years
experiment::String # Historical, RCP4.5, RCP8.5, etc.
run::String
project::String # CORDEX, CMIP5, etc.
institute::String # UQAM, DMI, etc.
filename::String # Path of the original file
dataunits::String # Celsius, kelvin, etc.
latunits::String # latitude coordinate unit
lonunits::String # longitude coordinate unit
variable::String # Type of variable (i.e. can be the same as "typeofvar", but it is changed when calculating indices)
typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)
typeofcal::String # Calendar type
timeattrib::Dict # Time attributes (e.g. days since ... )
varattribs::Dict # Variable attributes dictionary
globalattribs::Dict # Global attributes dictionary
end
source
ClimateTools.ClimGrid — Method
ClimGrid(data; longrid=[], latgrid=[], msk=[], grid_mapping=Dict(), dimension_dict=Dict(), model="NA", frequency="NA", experiment="NA", run="NA", project="NA", institute="NA", filename="NA", dataunits="NA", latunits="NA", lonunits="NA", variable="NA", typeofvar="NA", typeofcal="NA", varattribs=Dict(), globalattribs=Dict())
Constructor of the ClimGrid function. Data is an AxisArray. Everything else is optional, but usually needed for further processing (mapping, interpolation, etc...). struct ClimGrid
data::AxisArray # Data 
longrid::AbstractArray{N,2} where N # the longitude grid 
latgrid::AbstractArray{N,2} where N # the latitude grid 
msk::Array{N, 2} where N # Data mask (NaNs and 1.0) 
grid_mapping::Dict#{String, Any} # bindings for native grid 
dimension_dict::Dict
model::String
frequency::String # Day, month, years
experiment::String # Historical, RCP4.5, RCP8.5, etc.
run::String
project::String # CORDEX, CMIP5, etc.
institute::String # UQAM, DMI, etc.
filename::String # Path of the original file
dataunits::String # Celsius, kelvin, etc.
latunits::String # latitude coordinate unit
lonunits::String # longitude coordinate unit
variable::String # Type of variable (i.e. can be the same as "typeofvar", but it is changed when calculating indices)
typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)
typeofcal::String # Calendar type
timeattrib::Dict # Time attributes (e.g. days since ... )
varattribs::Dict # Variable attributes dictionary
globalattribs::Dict # Global attributes dictionary
end
source

+Returns a ClimGrid containing the values, as estimated from polynomial function polyn.
source
ClimateTools.prcp1 — Method
prcp1(C::ClimGrid)

Annual number with preciptation >= 1 mm. This function returns a ClimGrid. Input data should be in mm.

source
ClimateTools.qqmap — Method

qqmap(obs::ClimGrid, ref::ClimGrid, fut::ClimGrid; method="Additive", detrend=true, window::Int=15, rankn::Int=50, thresnan::Float64=0.1, keep_original::Bool=false, interp::Function = Linear(), extrap::Function = Flat())

Quantile-Quantile mapping bias correction.

For each julian day of the year (+/- window size), a transfer function is estimated through an empirical quantile-quantile mapping. The quantile-quantile transfer function between ref and obs is etimated on a julian day (and grid-point) basis with a moving window around the julian day. Hence, for a given julian day, the transfer function is then applied to the fut dataset for a given julian day.

Options

method::String = "Additive" (default) or "Multiplicative". Additive is used for most climate variables. Multiplicative is usually bounded variables such as precipitation and humidity.

detrend::Bool = true (default). A 4th order polynomial is adjusted to the time series and the residuals are corrected with the quantile-quantile mapping.

window::Int = 15 (default). The size of the window used to extract the statistical characteristics around a given julian day.

rankn::Int = 50 (default). The number of bins used for the quantile estimations. The quantiles uses by default 50 bins between 0.01 and 0.99. The bahavior between the bins is controlled by the interp keyword argument. The behaviour of the quantile-quantile estimation outside the 0.01 and 0.99 range is controlled by the extrap keyword argument.

thresnan::Float64 = 0.1 (default). The fraction is missing values authorized for the estimation of the quantile-quantile mapping for a given julian days. If there is more than treshnan missing values, the output for this given julian days returns NaNs.

keep_original::Bool = false (default). If keep_original is set to true, the values are set to the original values in presence of too many NaNs.

interp = Interpolations.Linear() (default). When the data to be corrected lies between 2 quantile bins, the value of the transfer function is linearly interpolated between the 2 closest quantile estimation. The argument is from Interpolations.jl package.

extrap = Interpolations.Flat() (default). The bahavior of the quantile-quantile transfer function outside the 0.01-0.99 range. Setting it to Flat() ensures that there is no "inflation problem" with the bias correction. The argument is from Interpolation.jl package.

source
ClimateTools.qqmap — Method

qqmap(obsvec::Array{N,1}, refvec::Array{N,1}, futvec::Array{N,1}, days, obsjul, refjul, futjul; method::String="Additive", detrend::Bool=true, window::Int64=15, rankn::Int64=50, thresnan::Float64=0.1, keeporiginal::Bool=false, interp=Linear(), extrap=Flat())

Quantile-Quantile mapping bias correction for single vector. This is a low level function used by qqmap(A::ClimGrid ..), but can work independently.

source
ClimateTools.resample — Method
resample(C::ClimGrid, startmonth::Int64, endmonth::Int64)

Return a resampled subset of ClimGrid C based on months.

source
ClimateTools.resample — Method
resample(C::ClimGrid, season::String)

Return a resampled subset of ClimGrid C for a given season. Season options are: "DJF" (December-February), "MAM" (March-May), "JJA" (June-August), "SON" (September-November)

source
ClimateTools.shapefile_coords_poly — Method
shapefile_coords_poly(poly::Shapefile.Polygon)

Return the polygons contained in shp.shapes[i]. It returns an array containing the polygons.

See also shapefile_coords, which returns vectors as opposed to array. Returned polygon is consistent with the data extraction of the load function.

source
ClimateTools.spatialsubset — Method
spatialsubset(C::ClimGrid, poly::Array{N, 2})

Returns the spatial subset of ClimGrid C. The spatial subset is defined by the polygon poly, defined on a -180, +180 longitude reference.

source
ClimateTools.summerdays — Method
summerdays(C::ClimGrid)

SD, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25 degree Celsius.

Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:

TX[i,j] > 25 Celsius.

source
ClimateTools.temporalsubset — Method
function temporalsubset(C::ClimGrid, startdate::Date, enddate::Date)

Returns the temporal subset of ClimGrid C. The temporal subset is defined by a start and end date.

source
ClimateTools.timeindex — Method
timeindex(timeVec, start_date, end_date, T)

Return the index of time vector specified by startdate and enddate. T is the DateTime type (see NCDatasets.jl documentation).

source
ClimateTools.tropicalnights — Method
tropicalnights(C::ClimGrid)

TropicalNights, Number of tropical nights: Annual count of days when TN (daily maximum temperature) > 20 degree Celsius.

Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:

TN[i,j] > 20 Celsius.

source
ClimateTools.vaporpressure — Method

vaporpressure(specifichumidity::ClimGrid, sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)

Returns the vapor pressure (vp) (Pa) estimated with the specific humidity (q), the sea level pressure (psl) (Pa), the orography (orog) (m) and the daily mean temperature (tas) (K). An approximation of the surface pressure is first computed by using the sea level pressure, orography and the daily mean temperature (see approx_surfacepressure). Then, vapor pressure is calculated by:

$vp = \frac{q * sp}{q+0.622}$

source
ClimateTools.vaporpressure — Method

vaporpressure(surfacepressure::ClimGrid, specifichumidity::ClimGrid)

Returns the vapor pressure (vp) (Pa) based on the surface pressure (sp) (Pa) and the specific humidity (q).

$vp = \frac{q * sp}{q+0.622}$

source
ClimateTools.wbgt — Method

wbgt(diurnaltemperature::ClimGrid, vaporpressure::ClimGrid)

Returns the simplified wet-bulb global temperature (wbgt) (Celsius) calculated using the vapor pressure (Pa) of the day and the estimated mean diurnal temperature (Celsius; temperature between 7:00 (7am) and 17:00 (5pm)).

$wbgt = 0.567 * Tday + 0.00393 * vp + 3.94$

source
ClimateTools.yearmonthdayhour — Method
yearmonthdayhour(dt::AbstractCFDateTime) -> (Int64, Int64, Int64, Int64)

Simultaneously return the year, month, day and hour parts of dt. Author: Alexander-Barth (Github)

source
ClimateTools.ClimGrid — Type
ClimGrid{A <: AxisArray}

In-memory representation of Climate Forecast netCDF files. struct ClimGrid

data::AxisArray # Data

longrid::AbstractArray{N,2} where N # the longitude grid

latgrid::AbstractArray{N,2} where N # the latitude grid

msk::Array{N, 2} where N # Data mask (NaNs and 1.0)

grid_mapping::Dict#{String, Any} # bindings for native grid

dimension_dict::Dict

model::String

frequency::String # Day, month, years

experiment::String # Historical, RCP4.5, RCP8.5, etc.

run::String

project::String # CORDEX, CMIP5, etc.

institute::String # UQAM, DMI, etc.

filename::String # Path of the original file

dataunits::String # Celsius, kelvin, etc.

latunits::String # latitude coordinate unit

lonunits::String # longitude coordinate unit

variable::String # Type of variable (i.e. can be the same as "typeofvar", but it is changed when calculating indices)

typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)

typeofcal::String # Calendar type

timeattrib::Dict # Time attributes (e.g. days since ... )

varattribs::Dict # Variable attributes dictionary

globalattribs::Dict # Global attributes dictionary

end

source
ClimateTools.ClimGrid — Method
ClimGrid(data; longrid=[], latgrid=[], msk=[], grid_mapping=Dict(), dimension_dict=Dict(), model="NA", frequency="NA", experiment="NA", run="NA", project="NA", institute="NA", filename="NA", dataunits="NA", latunits="NA", lonunits="NA", variable="NA", typeofvar="NA", typeofcal="NA", varattribs=Dict(), globalattribs=Dict())

Constructor of the ClimGrid function. Data is an AxisArray. Everything else is optional, but usually needed for further processing (mapping, interpolation, etc...). struct ClimGrid

data::AxisArray # Data

longrid::AbstractArray{N,2} where N # the longitude grid

latgrid::AbstractArray{N,2} where N # the latitude grid

msk::Array{N, 2} where N # Data mask (NaNs and 1.0)

grid_mapping::Dict#{String, Any} # bindings for native grid

dimension_dict::Dict

model::String

frequency::String # Day, month, years

experiment::String # Historical, RCP4.5, RCP8.5, etc.

run::String

project::String # CORDEX, CMIP5, etc.

institute::String # UQAM, DMI, etc.

filename::String # Path of the original file

dataunits::String # Celsius, kelvin, etc.

latunits::String # latitude coordinate unit

lonunits::String # longitude coordinate unit

variable::String # Type of variable (i.e. can be the same as "typeofvar", but it is changed when calculating indices)

typeofvar::String # Variable type (e.g. tasmax, tasmin, pr)

typeofcal::String # Calendar type

timeattrib::Dict # Time attributes (e.g. days since ... )

varattribs::Dict # Variable attributes dictionary

globalattribs::Dict # Global attributes dictionary

end

source
diff --git a/dev/index.html b/dev/index.html index 4dd5177..5a46197 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,2 +1,2 @@ -Home · ClimateTools.jl
Edit on GitHub

Home

Overview

ClimateTools.jl 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 from netCDF files that follows Climate Forecast conventions and the creation of climate scenarios.

Philosophy

The idea behind ClimateTools is that most, if not all, climate fields can be represented by a 2D (e.g. topography), 3D (e.g. air temperature) or 4D (e.g. winds at multiple levels) grids that are georeferenced. Those grids are named ClimGrid in ClimateTools. Every functions acts on such structure and returns a similar structure. The ClimGrid structure contains all elements needed to be manipulated: latitude, longitude, calendars, variable attributes, etc. that was either available in the original netCDF file or that was inferred by the metadata. Note that a ClimGrid is defined for a single variable.

The metadata follows the various transformations and is modified when necessary. For example, calculating the annual number of days with precipitation higher than 1mm will modify the variable name from pr (for precipitation) to prcp1, the name of the indicator. It will not, however, modify the base variable type (it will remain pr).

Notes

The climate indices and bias correction functions are coded to leverage multiple threads. To gain maximum performance, use (bash shell Linux/MacOSX) 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.THREADS. This is especially useful for bias correction.

Features

  • Climate scenarios creation
  • Extraction and visualization of CF-compliant netCDF datasets
  • Custom user-provided polygons and start and end date for localized studies
  • Climate indices from The joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) as well as custom climate indices
  • Regridding of a datasets onto another grid
  • Post-processing of climate timeseries using Quantile-Quantile mapping method (cf. Themeßl et al. 2012, Piani et al. 2010)
  • Exportation of results to a CF-compliant netCDF file
  • Support for typical climate models calendars: 360day, 365day, Standard, Prolectip Gregorian through NCDatasets.jl.
  • Support for physical units through the Unitful.jl package.

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

  • 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...
  • Extreme value theory analysis

Documentation

This documentation was built using Documenter.jl.

Documentation built 2023-06-19T19:03:42.109 with Julia 1.6.7
+Home · ClimateTools.jl
Edit on GitHub

Home

Overview

ClimateTools.jl 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 from netCDF files that follows Climate Forecast conventions and the creation of climate scenarios.

Philosophy

The idea behind ClimateTools is that most, if not all, climate fields can be represented by a 2D (e.g. topography), 3D (e.g. air temperature) or 4D (e.g. winds at multiple levels) grids that are georeferenced. Those grids are named ClimGrid in ClimateTools. Every functions acts on such structure and returns a similar structure. The ClimGrid structure contains all elements needed to be manipulated: latitude, longitude, calendars, variable attributes, etc. that was either available in the original netCDF file or that was inferred by the metadata. Note that a ClimGrid is defined for a single variable.

The metadata follows the various transformations and is modified when necessary. For example, calculating the annual number of days with precipitation higher than 1mm will modify the variable name from pr (for precipitation) to prcp1, the name of the indicator. It will not, however, modify the base variable type (it will remain pr).

Notes

The climate indices and bias correction functions are coded to leverage multiple threads. To gain maximum performance, use (bash shell Linux/MacOSX) 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.THREADS. This is especially useful for bias correction.

Features

  • Climate scenarios creation
  • Extraction and visualization of CF-compliant netCDF datasets
  • Custom user-provided polygons and start and end date for localized studies
  • Climate indices from The joint CCl/CLIVAR/JCOMM Expert Team (ET) on Climate Change Detection and Indices (ETCCDI) as well as custom climate indices
  • Regridding of a datasets onto another grid
  • Post-processing of climate timeseries using Quantile-Quantile mapping method (cf. Themeßl et al. 2012, Piani et al. 2010)
  • Exportation of results to a CF-compliant netCDF file
  • Support for typical climate models calendars: 360day, 365day, Standard, Prolectip Gregorian through NCDatasets.jl.
  • Support for physical units through the Unitful.jl package.

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

  • 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...
  • Extreme value theory analysis

Documentation

This documentation was built using Documenter.jl.

Documentation built 2023-06-20T18:32:41.795 with Julia 1.6.7
diff --git a/dev/indices/index.html b/dev/indices/index.html index 57a6cf7..d78fa53 100644 --- a/dev/indices/index.html +++ b/dev/indices/index.html @@ -2,4 +2,4 @@ Climate Indices · ClimateTools.jl
Edit on GitHub

Climate Indices

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.

mapclimgrid(ind) # mapping the indice previously calculated

BNU-ESM

Ensemble mean

You can calculate the ensemble mean with ensemble_mean function, where the input argument is an array of ClimGrids.

Abstract example:

C_model1 = ClimGrid(...) # model #1
 C_model2 = ClimGrid(...) # model #2
 ens = [C_model1, C_model2] # Create an Array of ClimGrids
-E = ensemble_mean(ens) # Returns the mean of all models climatologies

Climate Indices

Here's a list of climate indices currently provided by ClimateTools. This list may not be always up-to-date. See here for all exported functions.

ClimateTools.annualmax — Function
annualmax(C::ClimGrid)

Annual maximum of array data.

Let data[i,j] be daily time serie on day i in year j. Extract the highest value for year j.

source
ClimateTools.annualmean — Function
annualmean(C::ClimGrid)

Annual mean of array data.

Let data[i,j] be daily time serie on day i in year j. Calculate the mean value for year j.

source
ClimateTools.annualmin — Function
annualmin(C::ClimGrid)

Annual minimum of array data.

Let data[i,j] be daily time serie on day i in year j. Extract the lowest value for year j.

source
ClimateTools.annualsum — Function
annualsum(C::ClimGrid)

Annual sum of array data.

Let data[i,j] be daily time serie on day i in year j. Sums daily values for year j.

source
ClimateTools.approx_surfacepressure — Function

approxsurfacepressure(sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)

Returns the approximated surface pressure (sp) (Pa) using sea level pressure (psl) (Pa), orography (orog) (m), and daily mean temperature (tas) (K).

$sp = psl * 10^{x}$

where $x = \frac{-orog}{18400 * tas / 273.15}$

source
ClimateTools.customthresover — Function
customthresover(C::ClimGrid, thres)

customthresover, annual number of days over a specified threshold.

Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:

TS[i,j] > thres.

source
ClimateTools.customthresunder — Function
customthresunder(C::ClimGrid, thres)

customthresunder, annual number of days under a specified threshold.

Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:

TS[i,j] < thres.

source
ClimateTools.diurnaltemperature — Function

diurnaltemperature(temperatureminimum::ClimGrid, temperaturemaximum::ClimGrid, α::Float64)

Returns an estimation of the diurnal temperature (temperature between 7:00 (7am) and 17:00 (5pm)). The estimation is a linear combination of the daily minimum temperature (temperatureminimum) and daily maximum temperature (temperaturemaximum). The value of α has to be estimated seperatly from observations and depends on the location. The daily max and min must be in the same unit and in Celsius or Kelvin The diurnal temperature returned is in the same units as the daily minimum temperature and daily maximum temperature.

$Tdiu = α * Tmin + (1 - α) * Tmax$

source
ClimateTools.icingdays — Function
icingdays(C::ClimGrid)

ID, Number of summer days: Annual count of days when TX (daily maximum temperature) < 0 degree Celsius.

Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:

TX[i,j] < 0 Celsius.

source
ClimateTools.frostdays — Function
frostdays(C::ClimGrid)

FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0 Celsius.

Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:

TN[i,j] < 0 Celsius.

source
ClimateTools.prcp1 — Function
prcp1(C::ClimGrid)

Annual number with preciptation >= 1 mm. This function returns a ClimGrid. Input data should be in mm.

source
ClimateTools.summerdays — Function
summerdays(C::ClimGrid)

SD, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25 degree Celsius.

Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:

TX[i,j] > 25 Celsius.

source
ClimateTools.tropicalnights — Function
tropicalnights(C::ClimGrid)

TropicalNights, Number of tropical nights: Annual count of days when TN (daily maximum temperature) > 20 degree Celsius.

Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:

TN[i,j] > 20 Celsius.

source
ClimateTools.vaporpressure — Function

vaporpressure(surfacepressure::ClimGrid, specifichumidity::ClimGrid)

Returns the vapor pressure (vp) (Pa) based on the surface pressure (sp) (Pa) and the specific humidity (q).

$vp = \frac{q * sp}{q+0.622}$

source

vaporpressure(specifichumidity::ClimGrid, sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)

Returns the vapor pressure (vp) (Pa) estimated with the specific humidity (q), the sea level pressure (psl) (Pa), the orography (orog) (m) and the daily mean temperature (tas) (K). An approximation of the surface pressure is first computed by using the sea level pressure, orography and the daily mean temperature (see approx_surfacepressure). Then, vapor pressure is calculated by:

$vp = \frac{q * sp}{q+0.622}$

source
ClimateTools.wbgt — Function

wbgt(diurnaltemperature::ClimGrid, vaporpressure::ClimGrid)

Returns the simplified wet-bulb global temperature (wbgt) (Celsius) calculated using the vapor pressure (Pa) of the day and the estimated mean diurnal temperature (Celsius; temperature between 7:00 (7am) and 17:00 (5pm)).

$wbgt = 0.567 * Tday + 0.00393 * vp + 3.94$

source
+E = ensemble_mean(ens) # Returns the mean of all models climatologies

Climate Indices

Here's a list of climate indices currently provided by ClimateTools. This list may not be always up-to-date. See here for all exported functions.

ClimateTools.annualmax — Function
annualmax(C::ClimGrid)

Annual maximum of array data.

Let data[i,j] be daily time serie on day i in year j. Extract the highest value for year j.

source
ClimateTools.annualmean — Function
annualmean(C::ClimGrid)

Annual mean of array data.

Let data[i,j] be daily time serie on day i in year j. Calculate the mean value for year j.

source
ClimateTools.annualmin — Function
annualmin(C::ClimGrid)

Annual minimum of array data.

Let data[i,j] be daily time serie on day i in year j. Extract the lowest value for year j.

source
ClimateTools.annualsum — Function
annualsum(C::ClimGrid)

Annual sum of array data.

Let data[i,j] be daily time serie on day i in year j. Sums daily values for year j.

source
ClimateTools.approx_surfacepressure — Function

approxsurfacepressure(sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)

Returns the approximated surface pressure (sp) (Pa) using sea level pressure (psl) (Pa), orography (orog) (m), and daily mean temperature (tas) (K).

$sp = psl * 10^{x}$

where $x = \frac{-orog}{18400 * tas / 273.15}$

source
ClimateTools.customthresover — Function
customthresover(C::ClimGrid, thres)

customthresover, annual number of days over a specified threshold.

Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:

TS[i,j] > thres.

source
ClimateTools.customthresunder — Function
customthresunder(C::ClimGrid, thres)

customthresunder, annual number of days under a specified threshold.

Let TS[i,j] be a daily time serie value on day i in year j. Count the number of days where:

TS[i,j] < thres.

source
ClimateTools.diurnaltemperature — Function

diurnaltemperature(temperatureminimum::ClimGrid, temperaturemaximum::ClimGrid, α::Float64)

Returns an estimation of the diurnal temperature (temperature between 7:00 (7am) and 17:00 (5pm)). The estimation is a linear combination of the daily minimum temperature (temperatureminimum) and daily maximum temperature (temperaturemaximum). The value of α has to be estimated seperatly from observations and depends on the location. The daily max and min must be in the same unit and in Celsius or Kelvin The diurnal temperature returned is in the same units as the daily minimum temperature and daily maximum temperature.

$Tdiu = α * Tmin + (1 - α) * Tmax$

source
ClimateTools.icingdays — Function
icingdays(C::ClimGrid)

ID, Number of summer days: Annual count of days when TX (daily maximum temperature) < 0 degree Celsius.

Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:

TX[i,j] < 0 Celsius.

source
ClimateTools.frostdays — Function
frostdays(C::ClimGrid)

FD, Number of frost days: Annual count of days when TN (daily minimum temperature) < 0 Celsius.

Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:

TN[i,j] < 0 Celsius.

source
ClimateTools.prcp1 — Function
prcp1(C::ClimGrid)

Annual number with preciptation >= 1 mm. This function returns a ClimGrid. Input data should be in mm.

source
ClimateTools.summerdays — Function
summerdays(C::ClimGrid)

SD, Number of summer days: Annual count of days when TX (daily maximum temperature) > 25 degree Celsius.

Let TX[i,j] be daily maximum temperature on day i in year j. Count the number of days where:

TX[i,j] > 25 Celsius.

source
ClimateTools.tropicalnights — Function
tropicalnights(C::ClimGrid)

TropicalNights, Number of tropical nights: Annual count of days when TN (daily maximum temperature) > 20 degree Celsius.

Let TN[i,j] be daily minimum temperature on day i in year j. Count the number of days where:

TN[i,j] > 20 Celsius.

source
ClimateTools.vaporpressure — Function

vaporpressure(surfacepressure::ClimGrid, specifichumidity::ClimGrid)

Returns the vapor pressure (vp) (Pa) based on the surface pressure (sp) (Pa) and the specific humidity (q).

$vp = \frac{q * sp}{q+0.622}$

source

vaporpressure(specifichumidity::ClimGrid, sealevelpressure::ClimGrid, orography::ClimGrid, daily_temperature::ClimGrid)

Returns the vapor pressure (vp) (Pa) estimated with the specific humidity (q), the sea level pressure (psl) (Pa), the orography (orog) (m) and the daily mean temperature (tas) (K). An approximation of the surface pressure is first computed by using the sea level pressure, orography and the daily mean temperature (see approx_surfacepressure). Then, vapor pressure is calculated by:

$vp = \frac{q * sp}{q+0.622}$

source
ClimateTools.wbgt — Function

wbgt(diurnaltemperature::ClimGrid, vaporpressure::ClimGrid)

Returns the simplified wet-bulb global temperature (wbgt) (Celsius) calculated using the vapor pressure (Pa) of the day and the estimated mean diurnal temperature (Celsius; temperature between 7:00 (7am) and 17:00 (5pm)).

$wbgt = 0.567 * Tday + 0.00393 * vp + 3.94$

source
diff --git a/dev/installation/index.html b/dev/installation/index.html index a0a697a..293251d 100644 --- a/dev/installation/index.html +++ b/dev/installation/index.html @@ -12,4 +12,4 @@ $ conda install -c conda-forge cmocean --strict-channel-priority $ which python # gives you the path of Conda virtual environment to use in the next steps.

Once those packages are installed, you need to tell PyCall.jl (in Julia) to use this Conda environment in Julia.

julia> using PyCall
 julia> ENV["PYTHON"]="...PATH TO CONDA PYTHON..." # find the path with "which python" at previous step
-julia> using Pkg; Pkg.build("PyCall")
+julia> using Pkg; Pkg.build("PyCall") diff --git a/dev/interface/index.html b/dev/interface/index.html index b4a3e22..2243859 100644 --- a/dev/interface/index.html +++ b/dev/interface/index.html @@ -1,4 +1,4 @@ Interface · ClimateTools.jl
Edit on GitHub

Interface

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 AxisArrays merging method and is overloaded for the ClimGrid type.

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

To merge multiple ClimGrid form an array of files, load has a method that accepts an array of files to merge.

Operators

Basic statistical functions are overloaded on ClimGrid.

mean minimum maximum std var

Basic arithmetic operators are also loaded.

D = C + 2.0 # will add 2.0 to all elements of C
 D = C::ClimGrid - A::ClimGrid # subtract A from C (useful for climatological difference between a future and historical period
-D = C / A # Ratio of 2 ClimGrids

Exporting

Exporting a ClimGrid to disk to a netCDF format can be done with the write function.

write(C::ClimGrid, filename::String)
+D = C / A # Ratio of 2 ClimGrids

Exporting

Exporting a ClimGrid to disk to a netCDF format can be done with the write function.

write(C::ClimGrid, filename::String)
diff --git a/dev/interpolation/index.html b/dev/interpolation/index.html index 047e217..5c1adc5 100644 --- a/dev/interpolation/index.html +++ b/dev/interpolation/index.html @@ -6,4 +6,4 @@ n = 30 # max number of neighboring points solver = Kriging(target => (maxneighbors=n,)) -C = regrid(A::ClimGrid, B::ClimGrid, solver=solver) +C = regrid(A::ClimGrid, B::ClimGrid, solver=solver) diff --git a/dev/maps/index.html b/dev/maps/index.html index 240cea7..c1b49a6 100644 --- a/dev/maps/index.html +++ b/dev/maps/index.html @@ -1,2 +1,2 @@ -Visualization · ClimateTools.jl
Edit on GitHub
+Visualization · ClimateTools.jl
Edit on GitHub
diff --git a/dev/search/index.html b/dev/search/index.html index 0bc42a1..98410eb 100644 --- a/dev/search/index.html +++ b/dev/search/index.html @@ -1,2 +1,2 @@ -Search · ClimateTools.jl

Loading search...

    +Search · ClimateTools.jl

    Loading search...