API#
Core#
These functions make up the core of the TAMS algorithm:
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Identify clouds in 2-D (lat/lon) or 3-D (lat/lon + time) cloud-top temperature data ctt. |
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Assign group IDs to the CEs identified at each time, returning a single CE frame. |
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Classify the CE groups into MCS classes, adding a categorical |
The helper function tams.run()
combines the above plus additional processing,
including computing stats on gridded data within the identified cloud element regions.
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Run all TAMS steps, including precip assignment. |
Lower level functions used in the above include:
Compute the (first) eccentricity of the least-squares best-fit ellipse to the coordinates of the polygon's exterior. |
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Find contour definitions for data x at value value. |
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Compute statistics on data within the shapes of contours. |
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For each contour in a, determine those in b that overlap and by how much. |
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Project the coordinates by u * dt meters in the x direction. |
Data#
Load the example derived satellite brightness temperature data. |
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Load the example MPAS dataset. |
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Load the example MPAS unstructured grid dataset. |
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Derive a TAMS input dataset from post-processed MPAS runs for the PRECIP field campaign. |
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Download the example datasets. |
Load the example satellite infrared radiance data. |
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Compute brightness temperature from IR satellite radiances (r) in channel ch of the EUMETSAT MSG SEVIRI instrument. |
Utilities#
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Plot CEs at a range of times (colors) with CE group ID (MCS ID) identified. |