#!/usr/bin/env python from os.path import expanduser, join from datetime import timedelta # data area # latN = 35 # latS = -35 # lonW = -100 # lonE = 45 data_lat = (-35, 35) data_lon = (-100, 45) aew_clim_dir = join(expanduser("~") + '/phd/data/aew/clim/') cv_data_dir = join(expanduser("~") + '/phd/data/aew/cv/') # reference where ALL cv data is (for clim calc.) # construct clim file, regenerate for each window? def get_clim_file(): fn = (aew_clim_dir + "cv_clim_" + str(abs(data_lat[1])) + ('N' if data_lat[1] > 0 else 'S') + "_" + str(abs(data_lat[0])) + ('N' if data_lat[0] > 0 else 'S') + "_" + str(abs(data_lon[0])) + ('E' if data_lon[0] > 0 else 'W') + "_" + str(abs(data_lon[1])) + ('E' if data_lon[1] > 0 else 'W')) + ".nc" return fn # wave area to be extracted: at least one point of trough needs to be in this range wave_filter_lat = (0, 30) wave_filter_lon = (-110, 55) levels = [70000] # 700 hPa # time of interest start_date = '2008-08-01T00:00' end_date = '2008-08-08T00:00' # Algorithm parameters # max u wind (m/s) (0 = only keep west-propagating). Belanger: 2.5; Berry: 0.0 max_u_thresh = 0.0 # m/s # CV anomaly percentile # NOTE: this is precomputed from the climatology, so delete climatology file and change this after to be re-executed. cv_percentile = 66 # 66% percentile of PV anomalies as reference on what areas to consider # need positive 2nd time derivative second_advection_min_thr = 0.0 ### FILTERING # spatial filtering: if wave to small, discard # threshold in degrees of wave length (sum of wave segments) degree_len_thr = 5 ### TRACKING duration_threshold = timedelta(days=2) # speed range of AEWs # at 10°N we have in longitude direction 0.00914 degrees/km (360/(40,000*cos(10deg))) speed_range_m_per_s = [0.0, 15.0] # [5,10], but be more gentle with polygons. speed_deg_per_h = [-m_per_s * 3.6 * 0.00914 for m_per_s in speed_range_m_per_s] # negative -> westward [-5, -10]