cmaqsatproc.readers.goes package

Module contents

class cmaqsatproc.readers.goes.goes_aod

Bases: satellite

goes_aod processor * valid if DQF < dqflt (default = 1) * pixel corners are interpolated in projected space.

Note 1: Qualities are 0: high; 1: medium; 2: low; 3 not retrieved. Note 2: The default requires the highest quality only. This is based

on experience. Because we are gridding, high and medium would get spatially mixed. This makes it hard to see if a monthly average is a bunch of isolated medium quality pixels with no repeat measurements or many of the same pixel. Thus, the higher quality requirement.

classmethod cmr_links(*args, **kwds)

Use utils.getcmrlinks to get links from the NASA Common Metadata Repo

Parameters:

• method (str) – Options are opendap, download, or s3

• kwds (mappable) – Passed through to utils.getcmrlinks. See getcmrlinks for valid keywords

Returns:

links – List of links to OpenDAP, http downloadable, or s3 links

Return type:

list

classmethod open_dataset(path, bbox=None, dqflt=1, **kwargs)

Open a GOES AOD dataset for satellite processing.

Parameters:

• path (str) – Path to dataset

• bbox (iterable) – swlon, swlat, nelon, nelat in decimal degrees East and North

• dqflt (float) – Only AOD with data quality flags less than dqflt are considered valid

• kwargs (mappable) – Passed to xarray.open_dataset

Returns:

sat – Satellite processing object.

Return type:

satellite

classmethod open_datasets(paths, bbox, dqflt=1)

Similar to open_dataset, but assumes that all data is on a single grid. Therefore, it can mask pixels and average them before creating a “net” dataset.

Basically, ds = xr.concat([open_dataset(path, bbox) for path in paths], ‘scan’) ds = ds.where(ds[‘valid’]).mean(‘scan’)

Only variables valid, AOD, DQF, and goes_imager_projection are kept.

Parameters:

• paths (list) – List of paths

• bbox (iterable) – swlon, swlat, nelon, nelat in decimal degrees

Returns:

sat

Return type:

satellite

classmethod prep_dataset(ds, bbox=None, dqflt=1)

Prepare the dataset by adding a projection, applying valid spatial checks and requiring that the DQF variable have a value less than dqflt.

classmethod s3_links(date, satkey, product='ABI-L2-AODC', resolution='H')

Return s3 links for date

Parameters:

• date (str or date-like) – pandas.to_datetime will convert this into a date object.

• satkey (str) – goes16, goes17, or goes18… any goes satellite that has product

• product (str) – Usually ABI-L2-AODC, but could be ABI-L2-AODF

• resolution (str) – Choose, H, d, m or Y.

Returns:

links – All links within the folder based on resolution: - ‘H’: s3://noaa-{satkey}/ABI-L2-AODC/{date:%Y/%j/%H}/ - ‘d’: s3://noaa-{satkey}/ABI-L2-AODC/{date:%Y/%j}/ - ‘Y’: s3://noaa-{satkey}/ABI-L2-AODC/{date:%Y}/ - ‘m’: iterates on all julian days in month

Return type:

list

classmethod s3_to_level3(date, satkey, grid, griddims=None, weighting='area', bbox=None, verbose=0, varkeys=None, as_dataset=True, link_kwargs=None, **kwargs)

Thin wrapper around s3_links, open_datasets, and to_level3.

For keyword documentation, see those functions.