Apply contour-shifting to bias correct a predicted contour using existing mappings.
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object obtained from the
array of predicted values of dimension year x month x longitude x latitude
year to be bias-corrected
year prediction array starts in
region information list (see details)
concentration level for which to build contour
string indicating the format of the prediction: either "gfdl" or "simple" (see details)
binary matrix specifying land locations
maps is obtained from running the
createMapping function. It is a list of five objects where
endYear give the month, first year, and last year that were mapped. The variables
are lists of arrays with one 3-dimensional array for each region. The first dimension is for the year. The other two dimensions
are for the fixed points' y-coordinates, the mapped points' x-coordinates, the mapped points' y-coordinates, the length of the mapping vectors in the
x-direction, the length of the vectors in the y-direction, and the angles of the mapping vectors.
A region information list is a list six objects:
centFrom. The first three objects are ordered lists giving information about each of the regions that will be mapped outside the
Central Arctic region. The variable
SpatialPolygons objects for the corresponding regions and the variable
SpatialLines objects for the corresponding fixed lines. The variable
SpatialPolygons objects that are outside the corresponding regions,
but that border the fixed lines. These are used when building new polygons to determine if points are being mapped outside the region of interest.
centRegions is the
SpatialPolygons object corresponding to the central Arctic region,
centLines is the
SpatialLines object for the fixed line,
centFrom is an n x 2 matrix with each row repeatedly giving the coordinates of the center point from which mapping vectors will emanate.
The package contains
regionInfo in the
regionInfo.rda file, which can be used unless you want to define your own regions.
The predicted data array,
predicted, should be single array of dimension: years x longitude (304) x latitude (448).
datTypePred = "simple", the values in the array should indicate whether each grid box is
categorized to contain ice (1: ice-covered, 0: no ice, NA: land). If
datTypePred = "gfdl".
datTypePred = "gfdl" , the values in the
predicted array correspond
to the raw ice concentrations values predicted (including indicators for missing data, land etc.)
formatted as in the CM2.5 Forecast-oriented Low-Ocean Resolution (FLOR) model produced by the National Oceanic and Atmospheric Administration<e2><80><99>s
Geophysical Fluid Dynamics Laboratory converted to a Polar Stereographic grid (Vecchi et al. 2014; Msadek et al. 2014). Weights for
converting to a polar stereograhic grid were obtained from the spherical coordinate remapping and interpolation package (SCRIP) (Jones 1997).
SpatialPolygons object of the adjusted region
CM2.5 Forecast-oriented Low-Ocean Resolution (FLOR) model:
Jones, P.W. "A user<e2><80><99>s guide for SCRIP: A spherical coordinate remapping and interpolation package." Los Alamos National Laboratory, Los Alamos, NM (1997).
Msadek, R., et al. "Importance of initial conditions in seasonal predictions of Arctic sea ice extent." Geophysical Research Letters 41.14 (2014): 5208-5215.
National Center for Atmospheric Research, 2017: Earth system grid at NCAR. https://www.earthsystemgrid.org/home.html.
Vecchi, Gabriel A., et al. "On the seasonal forecasting of regional tropical cyclone activity." Journal of Climate 27.21 (2014): 7994-8016.
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