carra2py Modules and Methods

Description of the carra2py modules and its methods.

class carra2py.AVHRR(date):

Attributes: date: str

the processed date, only in ‘yyyymmdd’ format

Methods:

AVHRR.get_data(polar=None)

Import the raw AVHRR data of the northern hempishere in native resolution of 5000 meters.

Parameter: polar: bool, optional:

if None or False the output will be in a EPSG:4326 projection, if True the output will be in a EPSG:3413 projection.

Returns:  raw_lon or raw_x: (m,n) array of floats

Longitude coordinates (EPSG:4326) of raw data if polar == None or False, x cooordinates (EPSG:3413) of raw data if polar == True.

raw_lat or raw_y: (m,n) array of floats

Latitiude coordinates (EPSG:4326) of raw data if polar == None or False, y cooordinate (EPSG:3413) of raw data if polar == True.

raw_alb: (m,n) array of floats

Raw albedo data in native 5000 meter resolution

AVHRR.proc(raw_data=None, area=None, res=2500)

Reproject and Intepolate raw AVHRR data to ESPG:3413 and a better resolution. The data is also masked to 9 possible search areas in the arctic region

Parameter: raw_data: tuple

if None, the data for the processing wil come from get_data(), else, the user has tp input the data wants processed. the tuple needs to include (x,y,albedo) in that order, x and y is the data coordinates in EPSG:3413 with shape (m,n). Albedo is the raw albedo data from AVHRR in shape (m,n)

area: str, or list

if None, all areas in the arctic with glaciers are processed. Else, indicate which area or areas you want to process. Areas included: [AlaskaYukon, Greenland, Iceland, NorthernArcticCanada, SoutherArcticCanada, Norway, SevernayaZemlya, NovayaZemlya, Svalbard]

res: int

The resoultion of the output, there are three options, 1000,2500 or 5000 all in meters, the default is set to 2500 meters.

Returns:  output: dict of floats

outputs a dictionary of the albedo at each area in the specifiec resolution in EPSG:3413 projection. The dictionary strutcture: output[area] = {“x” : x_coordinates, “y” : y_coordinates, “albedo” : albedo}

AVHRR.export_to_tif(output=None, path=’default’)

Export the processed data to tif files in ESPG:3413

Parameter: output: dict

If None, the exported processed data will come from AVHRR.proc(raw_data=None, area=None, res=2500) with the default inputs. Else, input the processed data you want to export, the input needs to be a dictionary with this format: output[area] = {“x” : x_coordinates, “y” : y_coordinates, “albedo” : albedo}

path: str

if “default”, the data will be exported to /output/”yyyymmdd”, Else, specify the output folder.

Returns:  None

The method will not return anything

AVHRR.export_to_csv(output=None, path=’default’)

Export the processed data to csv files in ESPG:3413, the files will include three columns “x”, “y” and “albedo” with length m*n

Parameter: output: dict

If None, the exported processed data will come from AVHRR.proc(raw_data=None, area=None, res=2500) with the default inputs. Else, input the processed data you want to export, the input needs to be a dictionary with this format: output[area] = {“x” : x_coordinates, “y” : y_coordinates, “albedo” : albedo}

path: str

if “default”, the data will be exported to /output/”yyyymmdd”, Else, specify the output folder.

Returns:  None

The method will not return anything

AVHRR.export_to_nc(output=None, path=’default’)

Export the processed data to netcdf4 files in ESPG:3413, the files will include three variables “x”, “y” and “albedo” with shape (m,n)

Parameter: output: dict

If None, the exported processed data will come from AVHRR.proc(raw_data=None, area=None, res=2500) with the default inputs. Else, input the processed data you want to export, the input needs to be a dictionary with this format: output[area] = {“x” : x_coordinates, “y” : y_coordinates, “albedo” : albedo}

path: str

if “default”, the data will be exported to /output/”yyyymmdd”, Else, specify the output folder.

Returns:  None

The method will not return anything