The use of weather scenarios from convective-allowing ensembles to convey forecast uncertainty

-Decision makers often use the mean value (and the spread) from an ensemble

-Ensembles contain far more information than simply their mean and spread

-Decision makers could use this information to determine a National Air Space (NAS) ‘plan B’ that mitigates multiple weather scenarios

-A set of unique forecast solutions can be clustered into a most likely outcome, as well as second most likely outcome

Code Structure

This repository contains the pyhton codes to extract weather scenarios from weather model ensembles using two consecutive clustering applications:

1. k-means clustering to separate weather regions
2. k-means clustering on the Principal Components to separate weather cores

The main code is Execute_Objectize.py

Below are each of these codes and their brief explanations.

ObjDetectAtt_HRRRE.py: This code does the initial work on identification of objects from the field given a threshold, and stores object information and attributes as numpy arrays. The output arrays are:

• Objects_hrrre_{threshold value}th_{time}.npy (this one stores ensemble member name, object labels, object values, the raw field itself) • Attrib_hrrre_{threshold value}th_{time}.npy (this one stores object area, object center of mass, object mean value, object max value (value is VIL in this case))

Linkage.py: This code creates the linkage matrix for clustering using Wards method. I separated it because it can take time in case of large datasets and I wanted to do it once and then play with the results later. It outputs two arrays:

• Linkage_{source}{threshold value}th{time}size{size threshold}.npy (this one stores the linkage matrix to be used for clustering) • CmassMember{source}{threshold value}th{time}_size{size threshold}.npy (this one stores the centers of mass of each object stratified my ensemble member. Used mainly for plotting later)

Plot_Members.py: Contains several plotting functions.

ScreePlot.py: This code specifically plots the scree plot showing intercluster distance with respect to the partition number. The main code also asks for a cutoff value for the number of partitions, and this code shows that cutoff as a line on the plot.

ClusterPlot_Members_Cmass.py: This code plots the centers of mass of each member (on a single plot) overlaid on the decision boundary decided by the cutoff value given for the scree plot.

cluster_eof.py: The code that computes the EOF clustering

ObjCluster_SAL_v3.py: This code computes the SAL values and make comparisons between members.