Random preamble stuff
Contents
Table of Contents
import glob
import os
from pathlib import Path
import act
import imageio as imageio
import matplotlib
import matplotlib.pyplot as plt
import pyart
from copy import deepcopy
import numpy as np
import cartopy.crs as ccrs
import shapely
import pyart
from tint.data_utils import get_nexrad_keys, read_nexrad_key
from tint import Cell_tracks, animate
from tint.visualization import embed_mp4_as_gif
import pandas as pd
from csu_radartools import (csu_fhc, csu_liquid_ice_mass, csu_blended_rain,csu_dsd, csu_kdp, csu_misc)
import warnings
import matplotlib.cm as cm
import pyart.graph.cm as pcm
from lmatools.io.LMA_h5_file import LMAh5File
from lmatools.coordinateSystems import RadarCoordinateSystem, GeographicSystem, TangentPlaneCartesianSystem, MapProjection
from dualpol import DualPolRetrieval # https://github.com/nasa/DualPol
from netCDF4 import Dataset
from pyproj import Geod
## You are using the Python ARM Radar Toolkit (Py-ART), an open source
## library for working with weather radar data. Py-ART is partly
## supported by the U.S. Department of Energy as part of the Atmospheric
## Radiation Measurement (ARM) Climate Research Facility, an Office of
## Science user facility.
##
## If you use this software to prepare a publication, please cite:
##
## JJ Helmus and SM Collis, JORS 2016, doi: 10.5334/jors.119
Random preamble stuff#
plt.rcParams['font.size'] = 14
import matplotlib.gridspec as gridspec
#define walkspace function
def walklevel(some_dir, level=1):
some_dir = some_dir.rstrip(os.path.sep)
assert os.path.isdir(some_dir)
num_sep = some_dir.count(os.path.sep)
for root, dirs, files in os.walk(some_dir):
yield root, dirs, files
num_sep_this = root.count(os.path.sep)
if num_sep + level <= num_sep_this:
#print('hello!')
del dirs[:]
C-SAPR data download#
act.discovery.download_data?
arm_username = os.getenv("ARM_USERNAME")
arm_password = os.getenv("ARM_PASSWORD")
datastream = "houcsapr2cfrS2.a1"
#start_date = "2022-06-04T21:00:00"
#end_date = "2022-06-04T21:20:00"
startdate = "2022-06-04"
enddate = "2022-06-04"
#..Download a single time
#startdate = "2022-06-02T21:07:00"
#enddate = "2022-06-02T21:08:00"
startdate = "2022-06-04"
enddate = "2022-06-04"
june4_csapr_files = act.discovery.download_data(username,token,datastream,
startdate,enddate,time='210735')
[DOWNLOADING] houcsapr2cfrS2.a1.20220604.210735.nc
#..approximate metling level
melt_0604 = 4400/1000.
#..Downlaod CSAPR data or just load in pre-existing filenames
brand_new = False
if brand_new:
june4_csapr_files = act.discovery.download_data(username,token,datastream,
startdate,enddate)
else:
basepath = './houcsapr2cfrS2.a1/'
numfiles = 0
filepre = 'houcsapr'
june4_csapr_files = []
for root,dirs,files in walklevel(basepath,level=1):
for file in files:
if file.startswith(filepre): #change start with as string
june4_csapr_files.append(file)
numfiles += 1
june4_csapr_files = sorted(june4_csapr_files)
print(numfiles)
644
!mkdir quicklooks/rhi
!mkdir quicklooks/ppi
!mkdir quicklooks/vpt
#..Plot quicklooks of CSAPR
for file in june4_csapr_files:
radar = pyart.io.read(file)
print(radar.scan_type)
if (radar.scan_type != 'other'):
fig = plt.figure(figsize=(6, 4))
ax = fig.add_subplot(111)
display = pyart.graph.RadarDisplay(radar)
display.plot("reflectivity", 0)
fig.savefig(f"quicklooks/{radar.scan_type}/{Path(file).stem}.png", dpi=200)
plt.close(fig)
fig.clf()
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for i in range(numfiles):
print(i,june4_csapr_files[i])
0 houcsapr2cfrS2.a1.20220604.125221.nc
1 houcsapr2cfrS2.a1.20220604.125451.nc
2 houcsapr2cfrS2.a1.20220604.130438.nc
3 houcsapr2cfrS2.a1.20220604.130522.nc
4 houcsapr2cfrS2.a1.20220604.130638.nc
5 houcsapr2cfrS2.a1.20220604.130739.nc
6 houcsapr2cfrS2.a1.20220604.130949.nc
7 houcsapr2cfrS2.a1.20220604.131018.nc
8 houcsapr2cfrS2.a1.20220604.131035.nc
9 houcsapr2cfrS2.a1.20220604.131103.nc
10 houcsapr2cfrS2.a1.20220604.131118.nc
11 houcsapr2cfrS2.a1.20220604.131315.nc
12 houcsapr2cfrS2.a1.20220604.131518.nc
13 houcsapr2cfrS2.a1.20220604.131619.nc
14 houcsapr2cfrS2.a1.20220604.200008.nc
15 houcsapr2cfrS2.a1.20220604.200037.nc
16 houcsapr2cfrS2.a1.20220604.200053.nc
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336 houcsapr2cfrS2.a1.20220604.215047.nc
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350 houcsapr2cfrS2.a1.20220604.215653.nc
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355 houcsapr2cfrS2.a1.20220604.215901.nc
356 houcsapr2cfrS2.a1.20220604.215923.nc
357 houcsapr2cfrS2.a1.20220604.215951.nc
358 houcsapr2cfrS2.a1.20220604.220018.nc
359 houcsapr2cfrS2.a1.20220604.220046.nc
360 houcsapr2cfrS2.a1.20220604.220110.nc
361 houcsapr2cfrS2.a1.20220604.220138.nc
362 houcsapr2cfrS2.a1.20220604.220215.nc
363 houcsapr2cfrS2.a1.20220604.220243.nc
364 houcsapr2cfrS2.a1.20220604.220308.nc
365 houcsapr2cfrS2.a1.20220604.220336.nc
366 houcsapr2cfrS2.a1.20220604.220403.nc
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368 houcsapr2cfrS2.a1.20220604.220456.nc
369 houcsapr2cfrS2.a1.20220604.220524.nc
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371 houcsapr2cfrS2.a1.20220604.220616.nc
372 houcsapr2cfrS2.a1.20220604.220645.nc
373 houcsapr2cfrS2.a1.20220604.220713.nc
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375 houcsapr2cfrS2.a1.20220604.220809.nc
376 houcsapr2cfrS2.a1.20220604.220840.nc
377 houcsapr2cfrS2.a1.20220604.220905.nc
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379 houcsapr2cfrS2.a1.20220604.221006.nc
380 houcsapr2cfrS2.a1.20220604.221047.nc
381 houcsapr2cfrS2.a1.20220604.221116.nc
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384 houcsapr2cfrS2.a1.20220604.221309.nc
385 houcsapr2cfrS2.a1.20220604.221335.nc
386 houcsapr2cfrS2.a1.20220604.221422.nc
387 houcsapr2cfrS2.a1.20220604.221447.nc
388 houcsapr2cfrS2.a1.20220604.221526.nc
389 houcsapr2cfrS2.a1.20220604.221551.nc
390 houcsapr2cfrS2.a1.20220604.221631.nc
391 houcsapr2cfrS2.a1.20220604.221728.nc
392 houcsapr2cfrS2.a1.20220604.221806.nc
393 houcsapr2cfrS2.a1.20220604.221847.nc
394 houcsapr2cfrS2.a1.20220604.221959.nc
395 houcsapr2cfrS2.a1.20220604.222031.nc
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398 houcsapr2cfrS2.a1.20220604.222151.nc
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403 houcsapr2cfrS2.a1.20220604.222454.nc
404 houcsapr2cfrS2.a1.20220604.222518.nc
405 houcsapr2cfrS2.a1.20220604.222600.nc
406 houcsapr2cfrS2.a1.20220604.222638.nc
407 houcsapr2cfrS2.a1.20220604.222720.nc
408 houcsapr2cfrS2.a1.20220604.222744.nc
409 houcsapr2cfrS2.a1.20220604.222824.nc
410 houcsapr2cfrS2.a1.20220604.222850.nc
411 houcsapr2cfrS2.a1.20220604.222932.nc
412 houcsapr2cfrS2.a1.20220604.222958.nc
413 houcsapr2cfrS2.a1.20220604.223039.nc
414 houcsapr2cfrS2.a1.20220604.223101.nc
415 houcsapr2cfrS2.a1.20220604.223143.nc
416 houcsapr2cfrS2.a1.20220604.223211.nc
417 houcsapr2cfrS2.a1.20220604.223259.nc
418 houcsapr2cfrS2.a1.20220604.223350.nc
419 houcsapr2cfrS2.a1.20220604.223430.nc
420 houcsapr2cfrS2.a1.20220604.223502.nc
421 houcsapr2cfrS2.a1.20220604.223551.nc
422 houcsapr2cfrS2.a1.20220604.223623.nc
423 houcsapr2cfrS2.a1.20220604.223711.nc
424 houcsapr2cfrS2.a1.20220604.223740.nc
425 houcsapr2cfrS2.a1.20220604.223822.nc
426 houcsapr2cfrS2.a1.20220604.223854.nc
427 houcsapr2cfrS2.a1.20220604.223942.nc
428 houcsapr2cfrS2.a1.20220604.224104.nc
429 houcsapr2cfrS2.a1.20220604.224152.nc
430 houcsapr2cfrS2.a1.20220604.224225.nc
431 houcsapr2cfrS2.a1.20220604.224313.nc
432 houcsapr2cfrS2.a1.20220604.224340.nc
433 houcsapr2cfrS2.a1.20220604.224428.nc
434 houcsapr2cfrS2.a1.20220604.224502.nc
435 houcsapr2cfrS2.a1.20220604.224544.nc
436 houcsapr2cfrS2.a1.20220604.224618.nc
437 houcsapr2cfrS2.a1.20220604.224700.nc
438 houcsapr2cfrS2.a1.20220604.224751.nc
439 houcsapr2cfrS2.a1.20220604.224819.nc
440 houcsapr2cfrS2.a1.20220604.224843.nc
441 houcsapr2cfrS2.a1.20220604.224918.nc
442 houcsapr2cfrS2.a1.20220604.224943.nc
443 houcsapr2cfrS2.a1.20220604.225011.nc
444 houcsapr2cfrS2.a1.20220604.225030.nc
445 houcsapr2cfrS2.a1.20220604.225058.nc
446 houcsapr2cfrS2.a1.20220604.225118.nc
447 houcsapr2cfrS2.a1.20220604.225146.nc
448 houcsapr2cfrS2.a1.20220604.225210.nc
449 houcsapr2cfrS2.a1.20220604.225238.nc
450 houcsapr2cfrS2.a1.20220604.225257.nc
451 houcsapr2cfrS2.a1.20220604.225326.nc
452 houcsapr2cfrS2.a1.20220604.225402.nc
453 houcsapr2cfrS2.a1.20220604.225430.nc
454 houcsapr2cfrS2.a1.20220604.225452.nc
455 houcsapr2cfrS2.a1.20220604.225520.nc
456 houcsapr2cfrS2.a1.20220604.225546.nc
457 houcsapr2cfrS2.a1.20220604.225614.nc
458 houcsapr2cfrS2.a1.20220604.225634.nc
459 houcsapr2cfrS2.a1.20220604.225702.nc
460 houcsapr2cfrS2.a1.20220604.225726.nc
461 houcsapr2cfrS2.a1.20220604.225754.nc
462 houcsapr2cfrS2.a1.20220604.225818.nc
463 houcsapr2cfrS2.a1.20220604.225847.nc
464 houcsapr2cfrS2.a1.20220604.225911.nc
465 houcsapr2cfrS2.a1.20220604.225939.nc
466 houcsapr2cfrS2.a1.20220604.225957.nc
467 houcsapr2cfrS2.a1.20220604.230025.nc
468 houcsapr2cfrS2.a1.20220604.230100.nc
469 houcsapr2cfrS2.a1.20220604.230128.nc
470 houcsapr2cfrS2.a1.20220604.230149.nc
471 houcsapr2cfrS2.a1.20220604.230217.nc
472 houcsapr2cfrS2.a1.20220604.230318.nc
473 houcsapr2cfrS2.a1.20220604.230346.nc
474 houcsapr2cfrS2.a1.20220604.230537.nc
475 houcsapr2cfrS2.a1.20220604.230605.nc
476 houcsapr2cfrS2.a1.20220604.230951.nc
477 houcsapr2cfrS2.a1.20220604.231019.nc
478 houcsapr2cfrS2.a1.20220604.231034.nc
479 houcsapr2cfrS2.a1.20220604.231051.nc
480 houcsapr2cfrS2.a1.20220604.231109.nc
481 houcsapr2cfrS2.a1.20220604.231123.nc
482 houcsapr2cfrS2.a1.20220604.231152.nc
483 houcsapr2cfrS2.a1.20220604.231209.nc
484 houcsapr2cfrS2.a1.20220604.231224.nc
485 houcsapr2cfrS2.a1.20220604.231239.nc
486 houcsapr2cfrS2.a1.20220604.231253.nc
487 houcsapr2cfrS2.a1.20220604.231322.nc
488 houcsapr2cfrS2.a1.20220604.231339.nc
489 houcsapr2cfrS2.a1.20220604.231354.nc
490 houcsapr2cfrS2.a1.20220604.231408.nc
491 houcsapr2cfrS2.a1.20220604.231421.nc
492 houcsapr2cfrS2.a1.20220604.231450.nc
493 houcsapr2cfrS2.a1.20220604.231503.nc
494 houcsapr2cfrS2.a1.20220604.231519.nc
495 houcsapr2cfrS2.a1.20220604.231533.nc
496 houcsapr2cfrS2.a1.20220604.231549.nc
497 houcsapr2cfrS2.a1.20220604.231618.nc
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500 houcsapr2cfrS2.a1.20220604.231704.nc
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502 houcsapr2cfrS2.a1.20220604.231747.nc
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507 houcsapr2cfrS2.a1.20220604.231916.nc
508 houcsapr2cfrS2.a1.20220604.231934.nc
509 houcsapr2cfrS2.a1.20220604.231949.nc
510 houcsapr2cfrS2.a1.20220604.232003.nc
511 houcsapr2cfrS2.a1.20220604.232018.nc
512 houcsapr2cfrS2.a1.20220604.232047.nc
513 houcsapr2cfrS2.a1.20220604.232104.nc
514 houcsapr2cfrS2.a1.20220604.232119.nc
515 houcsapr2cfrS2.a1.20220604.232134.nc
516 houcsapr2cfrS2.a1.20220604.232147.nc
517 houcsapr2cfrS2.a1.20220604.232216.nc
518 houcsapr2cfrS2.a1.20220604.232233.nc
519 houcsapr2cfrS2.a1.20220604.232250.nc
520 houcsapr2cfrS2.a1.20220604.232304.nc
521 houcsapr2cfrS2.a1.20220604.232317.nc
522 houcsapr2cfrS2.a1.20220604.232346.nc
523 houcsapr2cfrS2.a1.20220604.232405.nc
524 houcsapr2cfrS2.a1.20220604.232418.nc
525 houcsapr2cfrS2.a1.20220604.232433.nc
526 houcsapr2cfrS2.a1.20220604.232448.nc
527 houcsapr2cfrS2.a1.20220604.232516.nc
528 houcsapr2cfrS2.a1.20220604.232534.nc
529 houcsapr2cfrS2.a1.20220604.232548.nc
530 houcsapr2cfrS2.a1.20220604.232606.nc
531 houcsapr2cfrS2.a1.20220604.232623.nc
532 houcsapr2cfrS2.a1.20220604.232652.nc
533 houcsapr2cfrS2.a1.20220604.232708.nc
534 houcsapr2cfrS2.a1.20220604.232723.nc
535 houcsapr2cfrS2.a1.20220604.232739.nc
536 houcsapr2cfrS2.a1.20220604.232752.nc
537 houcsapr2cfrS2.a1.20220604.232820.nc
538 houcsapr2cfrS2.a1.20220604.232834.nc
539 houcsapr2cfrS2.a1.20220604.232848.nc
540 houcsapr2cfrS2.a1.20220604.232905.nc
541 houcsapr2cfrS2.a1.20220604.232918.nc
542 houcsapr2cfrS2.a1.20220604.232946.nc
543 houcsapr2cfrS2.a1.20220604.233004.nc
544 houcsapr2cfrS2.a1.20220604.233019.nc
545 houcsapr2cfrS2.a1.20220604.233033.nc
546 houcsapr2cfrS2.a1.20220604.233046.nc
547 houcsapr2cfrS2.a1.20220604.233115.nc
548 houcsapr2cfrS2.a1.20220604.233128.nc
549 houcsapr2cfrS2.a1.20220604.233143.nc
550 houcsapr2cfrS2.a1.20220604.233158.nc
551 houcsapr2cfrS2.a1.20220604.233214.nc
552 houcsapr2cfrS2.a1.20220604.233242.nc
553 houcsapr2cfrS2.a1.20220604.233259.nc
554 houcsapr2cfrS2.a1.20220604.233314.nc
555 houcsapr2cfrS2.a1.20220604.233328.nc
556 houcsapr2cfrS2.a1.20220604.233343.nc
557 houcsapr2cfrS2.a1.20220604.233412.nc
558 houcsapr2cfrS2.a1.20220604.233428.nc
559 houcsapr2cfrS2.a1.20220604.233445.nc
560 houcsapr2cfrS2.a1.20220604.233458.nc
561 houcsapr2cfrS2.a1.20220604.233512.nc
562 houcsapr2cfrS2.a1.20220604.233541.nc
563 houcsapr2cfrS2.a1.20220604.233554.nc
564 houcsapr2cfrS2.a1.20220604.233609.nc
565 houcsapr2cfrS2.a1.20220604.233626.nc
566 houcsapr2cfrS2.a1.20220604.233638.nc
567 houcsapr2cfrS2.a1.20220604.233706.nc
568 houcsapr2cfrS2.a1.20220604.233724.nc
569 houcsapr2cfrS2.a1.20220604.233739.nc
570 houcsapr2cfrS2.a1.20220604.233753.nc
571 houcsapr2cfrS2.a1.20220604.233807.nc
572 houcsapr2cfrS2.a1.20220604.233836.nc
573 houcsapr2cfrS2.a1.20220604.233854.nc
574 houcsapr2cfrS2.a1.20220604.233909.nc
575 houcsapr2cfrS2.a1.20220604.233923.nc
576 houcsapr2cfrS2.a1.20220604.233937.nc
577 houcsapr2cfrS2.a1.20220604.234006.nc
578 houcsapr2cfrS2.a1.20220604.234022.nc
579 houcsapr2cfrS2.a1.20220604.234039.nc
580 houcsapr2cfrS2.a1.20220604.234055.nc
581 houcsapr2cfrS2.a1.20220604.234107.nc
582 houcsapr2cfrS2.a1.20220604.234136.nc
583 houcsapr2cfrS2.a1.20220604.234153.nc
584 houcsapr2cfrS2.a1.20220604.234209.nc
585 houcsapr2cfrS2.a1.20220604.234222.nc
586 houcsapr2cfrS2.a1.20220604.234239.nc
587 houcsapr2cfrS2.a1.20220604.234308.nc
588 houcsapr2cfrS2.a1.20220604.234324.nc
589 houcsapr2cfrS2.a1.20220604.234338.nc
590 houcsapr2cfrS2.a1.20220604.234353.nc
591 houcsapr2cfrS2.a1.20220604.234407.nc
592 houcsapr2cfrS2.a1.20220604.234436.nc
593 houcsapr2cfrS2.a1.20220604.234453.nc
594 houcsapr2cfrS2.a1.20220604.234510.nc
595 houcsapr2cfrS2.a1.20220604.234525.nc
596 houcsapr2cfrS2.a1.20220604.234537.nc
597 houcsapr2cfrS2.a1.20220604.234607.nc
598 houcsapr2cfrS2.a1.20220604.234623.nc
599 houcsapr2cfrS2.a1.20220604.234639.nc
600 houcsapr2cfrS2.a1.20220604.234653.nc
601 houcsapr2cfrS2.a1.20220604.234708.nc
602 houcsapr2cfrS2.a1.20220604.234737.nc
603 houcsapr2cfrS2.a1.20220604.234755.nc
604 houcsapr2cfrS2.a1.20220604.234809.nc
605 houcsapr2cfrS2.a1.20220604.234824.nc
606 houcsapr2cfrS2.a1.20220604.234840.nc
607 houcsapr2cfrS2.a1.20220604.234911.nc
608 houcsapr2cfrS2.a1.20220604.234924.nc
609 houcsapr2cfrS2.a1.20220604.234939.nc
610 houcsapr2cfrS2.a1.20220604.234955.nc
611 houcsapr2cfrS2.a1.20220604.235007.nc
612 houcsapr2cfrS2.a1.20220604.235037.nc
613 houcsapr2cfrS2.a1.20220604.235054.nc
614 houcsapr2cfrS2.a1.20220604.235110.nc
615 houcsapr2cfrS2.a1.20220604.235125.nc
616 houcsapr2cfrS2.a1.20220604.235137.nc
617 houcsapr2cfrS2.a1.20220604.235207.nc
618 houcsapr2cfrS2.a1.20220604.235225.nc
619 houcsapr2cfrS2.a1.20220604.235237.nc
620 houcsapr2cfrS2.a1.20220604.235254.nc
621 houcsapr2cfrS2.a1.20220604.235309.nc
622 houcsapr2cfrS2.a1.20220604.235340.nc
623 houcsapr2cfrS2.a1.20220604.235354.nc
624 houcsapr2cfrS2.a1.20220604.235411.nc
625 houcsapr2cfrS2.a1.20220604.235425.nc
626 houcsapr2cfrS2.a1.20220604.235439.nc
627 houcsapr2cfrS2.a1.20220604.235510.nc
628 houcsapr2cfrS2.a1.20220604.235526.nc
629 houcsapr2cfrS2.a1.20220604.235539.nc
630 houcsapr2cfrS2.a1.20220604.235556.nc
631 houcsapr2cfrS2.a1.20220604.235608.nc
632 houcsapr2cfrS2.a1.20220604.235640.nc
633 houcsapr2cfrS2.a1.20220604.235655.nc
634 houcsapr2cfrS2.a1.20220604.235711.nc
635 houcsapr2cfrS2.a1.20220604.235724.nc
636 houcsapr2cfrS2.a1.20220604.235737.nc
637 houcsapr2cfrS2.a1.20220604.235807.nc
638 houcsapr2cfrS2.a1.20220604.235824.nc
639 houcsapr2cfrS2.a1.20220604.235839.nc
640 houcsapr2cfrS2.a1.20220604.235855.nc
641 houcsapr2cfrS2.a1.20220604.235909.nc
642 houcsapr2cfrS2.a1.20220604.235940.nc
643 houcsapr2cfrS2.a1.20220604.235954.nc
#..Hand selected "sets" of RHIs named by (arbitrary) index in my directory. Original
#..integers refer to listing if you follow above directions for download of CSAPR
offset = (1193-235)
csapr_idx1 = [1194-offset,1196-offset,1199-offset,1200-offset,1201-offset,1204-offset,
1205-offset,1206-offset,1213-offset,1223-offset]
csapr_idx2 = [1209-offset,1210-offset,1214-offset,1215-offset,1216-offset,1219-offset]
csapr_idx3 = [393,]
print(csapr_idx1)
[236, 238, 241, 242, 243, 246, 247, 248, 255, 265]
print(june4_csapr_files[393])
houcsapr2cfrS2.a1.20220604.221847.nc
Process C-SAPR data (phase)#
#..Process all data specified by index for downloaded files
ii = 0
if brand_new:
for idx in csapr_idx3:
fname = june4_csapr_files[idx]
radar = pyart.io.read(basepath+fname)
retrieve = DualPolRetrieval(radar, gs=100.0, dz='reflectivity',
dr='differential_reflectivity',
#dr='RD',
dp='differential_phase',
rh='copol_correlation_coeff',
#fhc_T_factor=2, ice_flag=True,
#fhc_flag=False, ice_flag=False,dsd_flag=False,precip_flag=False,liquid_ice_flag=False,
fhc_flag=True, ice_flag=False,dsd_flag=True,precip_flag=True,liquid_ice_flag=False,
kdp_window=1.8, thresh_sdp=20.0, speckle=3,
#kdp_window=5, thresh_sdp=20.0, speckle=3, #..prev
#kdp_window=5, thresh_sdp=20.0, speckle=10,
kdp_method='CSU',
rain_method='hidro',
qc_flag=True, verbose=False)
print(testpath+fname[:-3] + '_proc.nc')
pyart.io.write_cfradial(testpath+fname[:-3]+'_proc.nc', radar)
ii += 1
else:
pass
print(june4_csapr_files)
['houcsapr2cfrS2.a1.20220604.125221.nc', 'houcsapr2cfrS2.a1.20220604.125451.nc', 'houcsapr2cfrS2.a1.20220604.130438.nc', 'houcsapr2cfrS2.a1.20220604.130522.nc', 'houcsapr2cfrS2.a1.20220604.130638.nc', 'houcsapr2cfrS2.a1.20220604.130739.nc', 'houcsapr2cfrS2.a1.20220604.130949.nc', 'houcsapr2cfrS2.a1.20220604.131018.nc', 'houcsapr2cfrS2.a1.20220604.131035.nc', 'houcsapr2cfrS2.a1.20220604.131103.nc', 'houcsapr2cfrS2.a1.20220604.131118.nc', 'houcsapr2cfrS2.a1.20220604.131315.nc', 'houcsapr2cfrS2.a1.20220604.131518.nc', 'houcsapr2cfrS2.a1.20220604.131619.nc', 'houcsapr2cfrS2.a1.20220604.200008.nc', 'houcsapr2cfrS2.a1.20220604.200037.nc', 'houcsapr2cfrS2.a1.20220604.200053.nc', 'houcsapr2cfrS2.a1.20220604.200109.nc', 'houcsapr2cfrS2.a1.20220604.200124.nc', 'houcsapr2cfrS2.a1.20220604.200138.nc', 'houcsapr2cfrS2.a1.20220604.200208.nc', 'houcsapr2cfrS2.a1.20220604.200225.nc', 'houcsapr2cfrS2.a1.20220604.200238.nc', 'houcsapr2cfrS2.a1.20220604.200254.nc', 'houcsapr2cfrS2.a1.20220604.200307.nc', 'houcsapr2cfrS2.a1.20220604.200337.nc', 'houcsapr2cfrS2.a1.20220604.200353.nc', 'houcsapr2cfrS2.a1.20220604.200409.nc', 'houcsapr2cfrS2.a1.20220604.200425.nc', 'houcsapr2cfrS2.a1.20220604.200438.nc', 'houcsapr2cfrS2.a1.20220604.200507.nc', 'houcsapr2cfrS2.a1.20220604.200524.nc', 'houcsapr2cfrS2.a1.20220604.200540.nc', 'houcsapr2cfrS2.a1.20220604.200554.nc', 'houcsapr2cfrS2.a1.20220604.200607.nc', 'houcsapr2cfrS2.a1.20220604.200637.nc', 'houcsapr2cfrS2.a1.20220604.200654.nc', 'houcsapr2cfrS2.a1.20220604.200711.nc', 'houcsapr2cfrS2.a1.20220604.200724.nc', 'houcsapr2cfrS2.a1.20220604.200740.nc', 'houcsapr2cfrS2.a1.20220604.200809.nc', 'houcsapr2cfrS2.a1.20220604.200824.nc', 'houcsapr2cfrS2.a1.20220604.200839.nc', 'houcsapr2cfrS2.a1.20220604.200853.nc', 'houcsapr2cfrS2.a1.20220604.200909.nc', 'houcsapr2cfrS2.a1.20220604.200938.nc', 'houcsapr2cfrS2.a1.20220604.200954.nc', 'houcsapr2cfrS2.a1.20220604.201009.nc', 'houcsapr2cfrS2.a1.20220604.201024.nc', 'houcsapr2cfrS2.a1.20220604.201037.nc', 'houcsapr2cfrS2.a1.20220604.201107.nc', 'houcsapr2cfrS2.a1.20220604.201125.nc', 'houcsapr2cfrS2.a1.20220604.201141.nc', 'houcsapr2cfrS2.a1.20220604.201154.nc', 'houcsapr2cfrS2.a1.20220604.201208.nc', 'houcsapr2cfrS2.a1.20220604.201237.nc', 'houcsapr2cfrS2.a1.20220604.201255.nc', 'houcsapr2cfrS2.a1.20220604.201310.nc', 'houcsapr2cfrS2.a1.20220604.201323.nc', 'houcsapr2cfrS2.a1.20220604.201338.nc', 'houcsapr2cfrS2.a1.20220604.201407.nc', 'houcsapr2cfrS2.a1.20220604.201423.nc', 'houcsapr2cfrS2.a1.20220604.201439.nc', 'houcsapr2cfrS2.a1.20220604.201454.nc', 'houcsapr2cfrS2.a1.20220604.201509.nc', 'houcsapr2cfrS2.a1.20220604.201538.nc', 'houcsapr2cfrS2.a1.20220604.201555.nc', 'houcsapr2cfrS2.a1.20220604.201608.nc', 'houcsapr2cfrS2.a1.20220604.201624.nc', 'houcsapr2cfrS2.a1.20220604.201637.nc', 'houcsapr2cfrS2.a1.20220604.201707.nc', 'houcsapr2cfrS2.a1.20220604.201723.nc', 'houcsapr2cfrS2.a1.20220604.201738.nc', 'houcsapr2cfrS2.a1.20220604.201754.nc', 'houcsapr2cfrS2.a1.20220604.201808.nc', 'houcsapr2cfrS2.a1.20220604.201837.nc', 'houcsapr2cfrS2.a1.20220604.201855.nc', 'houcsapr2cfrS2.a1.20220604.201909.nc', 'houcsapr2cfrS2.a1.20220604.201924.nc', 'houcsapr2cfrS2.a1.20220604.201937.nc', 'houcsapr2cfrS2.a1.20220604.202007.nc', 'houcsapr2cfrS2.a1.20220604.202025.nc', 'houcsapr2cfrS2.a1.20220604.202038.nc', 'houcsapr2cfrS2.a1.20220604.202054.nc', 'houcsapr2cfrS2.a1.20220604.202106.nc', 'houcsapr2cfrS2.a1.20220604.202136.nc', 'houcsapr2cfrS2.a1.20220604.202150.nc', 'houcsapr2cfrS2.a1.20220604.202205.nc', 'houcsapr2cfrS2.a1.20220604.202219.nc', 'houcsapr2cfrS2.a1.20220604.202233.nc', 'houcsapr2cfrS2.a1.20220604.202303.nc', 'houcsapr2cfrS2.a1.20220604.202319.nc', 'houcsapr2cfrS2.a1.20220604.202335.nc', 'houcsapr2cfrS2.a1.20220604.202349.nc', 'houcsapr2cfrS2.a1.20220604.202403.nc', 'houcsapr2cfrS2.a1.20220604.202433.nc', 'houcsapr2cfrS2.a1.20220604.202452.nc', 'houcsapr2cfrS2.a1.20220604.202511.nc', 'houcsapr2cfrS2.a1.20220604.202526.nc', 'houcsapr2cfrS2.a1.20220604.202537.nc', 'houcsapr2cfrS2.a1.20220604.202609.nc', 'houcsapr2cfrS2.a1.20220604.202626.nc', 'houcsapr2cfrS2.a1.20220604.202639.nc', 'houcsapr2cfrS2.a1.20220604.202656.nc', 'houcsapr2cfrS2.a1.20220604.202708.nc', 'houcsapr2cfrS2.a1.20220604.202738.nc', 'houcsapr2cfrS2.a1.20220604.202755.nc', 'houcsapr2cfrS2.a1.20220604.202809.nc', 'houcsapr2cfrS2.a1.20220604.202824.nc', 'houcsapr2cfrS2.a1.20220604.202838.nc', 'houcsapr2cfrS2.a1.20220604.202907.nc', 'houcsapr2cfrS2.a1.20220604.202925.nc', 'houcsapr2cfrS2.a1.20220604.202939.nc', 'houcsapr2cfrS2.a1.20220604.202953.nc', 'houcsapr2cfrS2.a1.20220604.203008.nc', 'houcsapr2cfrS2.a1.20220604.203038.nc', 'houcsapr2cfrS2.a1.20220604.203054.nc', 'houcsapr2cfrS2.a1.20220604.203110.nc', 'houcsapr2cfrS2.a1.20220604.203123.nc', 'houcsapr2cfrS2.a1.20220604.203138.nc', 'houcsapr2cfrS2.a1.20220604.203209.nc', 'houcsapr2cfrS2.a1.20220604.203222.nc', 'houcsapr2cfrS2.a1.20220604.203238.nc', 'houcsapr2cfrS2.a1.20220604.203254.nc', 'houcsapr2cfrS2.a1.20220604.203308.nc', 'houcsapr2cfrS2.a1.20220604.203340.nc', 'houcsapr2cfrS2.a1.20220604.203355.nc', 'houcsapr2cfrS2.a1.20220604.203411.nc', 'houcsapr2cfrS2.a1.20220604.203427.nc', 'houcsapr2cfrS2.a1.20220604.203443.nc', 'houcsapr2cfrS2.a1.20220604.203514.nc', 'houcsapr2cfrS2.a1.20220604.203531.nc', 'houcsapr2cfrS2.a1.20220604.203544.nc', 'houcsapr2cfrS2.a1.20220604.203601.nc', 'houcsapr2cfrS2.a1.20220604.203613.nc', 'houcsapr2cfrS2.a1.20220604.203644.nc', 'houcsapr2cfrS2.a1.20220604.203658.nc', 'houcsapr2cfrS2.a1.20220604.203716.nc', 'houcsapr2cfrS2.a1.20220604.203729.nc', 'houcsapr2cfrS2.a1.20220604.203744.nc', 'houcsapr2cfrS2.a1.20220604.203816.nc', 'houcsapr2cfrS2.a1.20220604.203830.nc', 'houcsapr2cfrS2.a1.20220604.203846.nc', 'houcsapr2cfrS2.a1.20220604.203900.nc', 'houcsapr2cfrS2.a1.20220604.203914.nc', 'houcsapr2cfrS2.a1.20220604.203946.nc', 'houcsapr2cfrS2.a1.20220604.204001.nc', 'houcsapr2cfrS2.a1.20220604.204015.nc', 'houcsapr2cfrS2.a1.20220604.204030.nc', 'houcsapr2cfrS2.a1.20220604.204043.nc', 'houcsapr2cfrS2.a1.20220604.204114.nc', 'houcsapr2cfrS2.a1.20220604.204130.nc', 'houcsapr2cfrS2.a1.20220604.204146.nc', 'houcsapr2cfrS2.a1.20220604.204200.nc', 'houcsapr2cfrS2.a1.20220604.204213.nc', 'houcsapr2cfrS2.a1.20220604.204244.nc', 'houcsapr2cfrS2.a1.20220604.204300.nc', 'houcsapr2cfrS2.a1.20220604.204315.nc', 'houcsapr2cfrS2.a1.20220604.204330.nc', 'houcsapr2cfrS2.a1.20220604.204343.nc', 'houcsapr2cfrS2.a1.20220604.204414.nc', 'houcsapr2cfrS2.a1.20220604.204430.nc', 'houcsapr2cfrS2.a1.20220604.204445.nc', 'houcsapr2cfrS2.a1.20220604.204500.nc', 'houcsapr2cfrS2.a1.20220604.204516.nc', 'houcsapr2cfrS2.a1.20220604.204547.nc', 'houcsapr2cfrS2.a1.20220604.204605.nc', 'houcsapr2cfrS2.a1.20220604.204621.nc', 'houcsapr2cfrS2.a1.20220604.204635.nc', 'houcsapr2cfrS2.a1.20220604.204647.nc', 'houcsapr2cfrS2.a1.20220604.204719.nc', 'houcsapr2cfrS2.a1.20220604.204736.nc', 'houcsapr2cfrS2.a1.20220604.204750.nc', 'houcsapr2cfrS2.a1.20220604.204805.nc', 'houcsapr2cfrS2.a1.20220604.204818.nc', 'houcsapr2cfrS2.a1.20220604.204849.nc', 'houcsapr2cfrS2.a1.20220604.204905.nc', 'houcsapr2cfrS2.a1.20220604.204921.nc', 'houcsapr2cfrS2.a1.20220604.204935.nc', 'houcsapr2cfrS2.a1.20220604.204950.nc', 'houcsapr2cfrS2.a1.20220604.205021.nc', 'houcsapr2cfrS2.a1.20220604.205036.nc', 'houcsapr2cfrS2.a1.20220604.205050.nc', 'houcsapr2cfrS2.a1.20220604.205105.nc', 'houcsapr2cfrS2.a1.20220604.205117.nc', 'houcsapr2cfrS2.a1.20220604.205148.nc', 'houcsapr2cfrS2.a1.20220604.205205.nc', 'houcsapr2cfrS2.a1.20220604.205221.nc', 'houcsapr2cfrS2.a1.20220604.205235.nc', 'houcsapr2cfrS2.a1.20220604.205247.nc', 'houcsapr2cfrS2.a1.20220604.205318.nc', 'houcsapr2cfrS2.a1.20220604.205335.nc', 'houcsapr2cfrS2.a1.20220604.205352.nc', 'houcsapr2cfrS2.a1.20220604.205410.nc', 'houcsapr2cfrS2.a1.20220604.205423.nc', 'houcsapr2cfrS2.a1.20220604.205455.nc', 'houcsapr2cfrS2.a1.20220604.205510.nc', 'houcsapr2cfrS2.a1.20220604.205526.nc', 'houcsapr2cfrS2.a1.20220604.205540.nc', 'houcsapr2cfrS2.a1.20220604.205552.nc', 'houcsapr2cfrS2.a1.20220604.205624.nc', 'houcsapr2cfrS2.a1.20220604.205640.nc', 'houcsapr2cfrS2.a1.20220604.205656.nc', 'houcsapr2cfrS2.a1.20220604.205709.nc', 'houcsapr2cfrS2.a1.20220604.205722.nc', 'houcsapr2cfrS2.a1.20220604.205753.nc', 'houcsapr2cfrS2.a1.20220604.205811.nc', 'houcsapr2cfrS2.a1.20220604.205825.nc', 'houcsapr2cfrS2.a1.20220604.205841.nc', 'houcsapr2cfrS2.a1.20220604.205852.nc', 'houcsapr2cfrS2.a1.20220604.205924.nc', 'houcsapr2cfrS2.a1.20220604.205941.nc', 'houcsapr2cfrS2.a1.20220604.205955.nc', 'houcsapr2cfrS2.a1.20220604.210010.nc', 'houcsapr2cfrS2.a1.20220604.210024.nc', 'houcsapr2cfrS2.a1.20220604.210055.nc', 'houcsapr2cfrS2.a1.20220604.210109.nc', 'houcsapr2cfrS2.a1.20220604.210125.nc', 'houcsapr2cfrS2.a1.20220604.210140.nc', 'houcsapr2cfrS2.a1.20220604.210154.nc', 'houcsapr2cfrS2.a1.20220604.210226.nc', 'houcsapr2cfrS2.a1.20220604.210242.nc', 'houcsapr2cfrS2.a1.20220604.210303.nc', 'houcsapr2cfrS2.a1.20220604.210320.nc', 'houcsapr2cfrS2.a1.20220604.210334.nc', 'houcsapr2cfrS2.a1.20220604.210405.nc', 'houcsapr2cfrS2.a1.20220604.210419.nc', 'houcsapr2cfrS2.a1.20220604.210436.nc', 'houcsapr2cfrS2.a1.20220604.210450.nc', 'houcsapr2cfrS2.a1.20220604.210504.nc', 'houcsapr2cfrS2.a1.20220604.210535.nc', 'houcsapr2cfrS2.a1.20220604.210550.nc', 'houcsapr2cfrS2.a1.20220604.210605.nc', 'houcsapr2cfrS2.a1.20220604.210619.nc', 'houcsapr2cfrS2.a1.20220604.210647.nc', 'houcsapr2cfrS2.a1.20220604.210718.nc', 'houcsapr2cfrS2.a1.20220604.210735.nc', 'houcsapr2cfrS2.a1.20220604.210755.nc', 'houcsapr2cfrS2.a1.20220604.210814.nc', 'houcsapr2cfrS2.a1.20220604.210827.nc', 'houcsapr2cfrS2.a1.20220604.210858.nc', 'houcsapr2cfrS2.a1.20220604.210914.nc', 'houcsapr2cfrS2.a1.20220604.210931.nc', 'houcsapr2cfrS2.a1.20220604.210950.nc', 'houcsapr2cfrS2.a1.20220604.211008.nc', 'houcsapr2cfrS2.a1.20220604.211039.nc', 'houcsapr2cfrS2.a1.20220604.211054.nc', 'houcsapr2cfrS2.a1.20220604.211110.nc', 'houcsapr2cfrS2.a1.20220604.211130.nc', 'houcsapr2cfrS2.a1.20220604.211148.nc', 'houcsapr2cfrS2.a1.20220604.211228.nc', 'houcsapr2cfrS2.a1.20220604.211250.nc', 'houcsapr2cfrS2.a1.20220604.211312.nc', 'houcsapr2cfrS2.a1.20220604.211331.nc', 'houcsapr2cfrS2.a1.20220604.211348.nc', 'houcsapr2cfrS2.a1.20220604.211429.nc', 'houcsapr2cfrS2.a1.20220604.211452.nc', 'houcsapr2cfrS2.a1.20220604.211512.nc', 'houcsapr2cfrS2.a1.20220604.211531.nc', 'houcsapr2cfrS2.a1.20220604.211547.nc', 'houcsapr2cfrS2.a1.20220604.211628.nc', 'houcsapr2cfrS2.a1.20220604.211650.nc', 'houcsapr2cfrS2.a1.20220604.211711.nc', 'houcsapr2cfrS2.a1.20220604.211730.nc', 'houcsapr2cfrS2.a1.20220604.211749.nc', 'houcsapr2cfrS2.a1.20220604.211829.nc', 'houcsapr2cfrS2.a1.20220604.211851.nc', 'houcsapr2cfrS2.a1.20220604.211910.nc', 'houcsapr2cfrS2.a1.20220604.211931.nc', 'houcsapr2cfrS2.a1.20220604.211948.nc', 'houcsapr2cfrS2.a1.20220604.212030.nc', 'houcsapr2cfrS2.a1.20220604.212052.nc', 'houcsapr2cfrS2.a1.20220604.212111.nc', 'houcsapr2cfrS2.a1.20220604.212130.nc', 'houcsapr2cfrS2.a1.20220604.212201.nc', 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'houcsapr2cfrS2.a1.20220604.234653.nc', 'houcsapr2cfrS2.a1.20220604.234708.nc', 'houcsapr2cfrS2.a1.20220604.234737.nc', 'houcsapr2cfrS2.a1.20220604.234755.nc', 'houcsapr2cfrS2.a1.20220604.234809.nc', 'houcsapr2cfrS2.a1.20220604.234824.nc', 'houcsapr2cfrS2.a1.20220604.234840.nc', 'houcsapr2cfrS2.a1.20220604.234911.nc', 'houcsapr2cfrS2.a1.20220604.234924.nc', 'houcsapr2cfrS2.a1.20220604.234939.nc', 'houcsapr2cfrS2.a1.20220604.234955.nc', 'houcsapr2cfrS2.a1.20220604.235007.nc', 'houcsapr2cfrS2.a1.20220604.235037.nc', 'houcsapr2cfrS2.a1.20220604.235054.nc', 'houcsapr2cfrS2.a1.20220604.235110.nc', 'houcsapr2cfrS2.a1.20220604.235125.nc', 'houcsapr2cfrS2.a1.20220604.235137.nc', 'houcsapr2cfrS2.a1.20220604.235207.nc', 'houcsapr2cfrS2.a1.20220604.235225.nc', 'houcsapr2cfrS2.a1.20220604.235237.nc', 'houcsapr2cfrS2.a1.20220604.235254.nc', 'houcsapr2cfrS2.a1.20220604.235309.nc', 'houcsapr2cfrS2.a1.20220604.235340.nc', 'houcsapr2cfrS2.a1.20220604.235354.nc', 'houcsapr2cfrS2.a1.20220604.235411.nc', 'houcsapr2cfrS2.a1.20220604.235425.nc', 'houcsapr2cfrS2.a1.20220604.235439.nc', 'houcsapr2cfrS2.a1.20220604.235510.nc', 'houcsapr2cfrS2.a1.20220604.235526.nc', 'houcsapr2cfrS2.a1.20220604.235539.nc', 'houcsapr2cfrS2.a1.20220604.235556.nc', 'houcsapr2cfrS2.a1.20220604.235608.nc', 'houcsapr2cfrS2.a1.20220604.235640.nc', 'houcsapr2cfrS2.a1.20220604.235655.nc', 'houcsapr2cfrS2.a1.20220604.235711.nc', 'houcsapr2cfrS2.a1.20220604.235724.nc', 'houcsapr2cfrS2.a1.20220604.235737.nc', 'houcsapr2cfrS2.a1.20220604.235807.nc', 'houcsapr2cfrS2.a1.20220604.235824.nc', 'houcsapr2cfrS2.a1.20220604.235839.nc', 'houcsapr2cfrS2.a1.20220604.235855.nc', 'houcsapr2cfrS2.a1.20220604.235909.nc', 'houcsapr2cfrS2.a1.20220604.235940.nc', 'houcsapr2cfrS2.a1.20220604.235954.nc']
testpath = './csapr_test/'
if False: #...This is just a test with a single filename, can be ignored
fname = june4_csapr_files[243]
#nc = Dataset(basepath+fname[:-3]+'_proc.nc','r+')
#nc = Dataset(testpath+fname[:-3]+'_proc.nc','r+')
# nc = Dataset(testpath+fname,'r+')
# nc.renameVariable(u'specific_differential_phase',u'old_kdp')
# nc.close()
#radar = pyart.io.read(testpath+fname[:-3]+'_proc.nc')
radar = pyart.io.read(testpath+fname)
#zc = deepcopy(radar.fields['reflectivity']['data'])
#radar.add_field_like('reflectivity', 'ZC', zc, replace_existing=True)
# retrieve = DualPolRetrieval(radar, dz='reflectivity',
# dr='differential_reflectivity',dp='differential_phase',
# rh='cross_correlation_ratio',
# fhc_T_factor=2, kdp_window=2,ice_flag=False,
# rain_method='hidro', qc_flag=True, gs=100.0,
# verbose=True, thresh_sdp=20.0, speckle=3)
retrieve = DualPolRetrieval(radar, gs=100.0, dz='reflectivity',
dr='differential_reflectivity',
#dr='RD',
dp='differential_phase',
rh='copol_correlation_coeff',
#fhc_T_factor=2, ice_flag=True,
#fhc_flag=False, ice_flag=False,dsd_flag=False,precip_flag=False,liquid_ice_flag=False,
fhc_flag=True, ice_flag=False,dsd_flag=True,precip_flag=True,liquid_ice_flag=False,
kdp_window=1.8, thresh_sdp=20.0, speckle=3,
#kdp_window=5, thresh_sdp=20.0, speckle=3, #..prev
#kdp_window=5, thresh_sdp=20.0, speckle=10,
kdp_method='CSU',
rain_method='hidro',
qc_flag=True, verbose=False)
pyart.io.write_cfradial(testpath+fname[:-3] + '_proc.nc', radar)
Plot figures, including NEXRAD thing#
Do main plots (run this after 4.2)#
#...This is an example to test plotting
fname = june4_csapr_files[393]
radar = pyart.io.read(testpath+fname[:-3]+'_proc.nc')
#..
maxx = 25
maxz = 12
fig = plt.figure(figsize=(6, 15))
ax = fig.add_subplot(311)
display = pyart.graph.RadarDisplay(radar)
display.plot("reflectivity", 0,vmin=-10,vmax=64,cmap=pcm.LangRainbow12)
ax.plot([0,maxx],[melt_0604,melt_0604],'k:',lw=2)
ax.set_xlim(0,maxx)
ax.set_ylim(0,maxz)
#..
ax = fig.add_subplot(312)
display = pyart.graph.RadarDisplay(radar)
#display.plot("differential_reflectivity", 0)
#display.plot("differential_reflectivity",0,vmin=-1,vmax=5,
display.plot("attenuation_corrected_differential_reflectivity",0,vmin=-1,vmax=5,
cmap=pcm.BuOrR14, mask_tuple=['reflectivity', -5.])
#cmap=pcm.RefDiff)
#cmap=pcm.RefDiff)
ax.plot([0,maxx],[melt_0604,melt_0604],'k:',lw=2)
ax.set_xlim(0,maxx)
ax.set_ylim(0,maxz)
#..
ax = fig.add_subplot(313)
display = pyart.graph.RadarDisplay(radar)
#display.plot("KDP_CSU", 0,vmin=-1.5,vmax=4.,cmap=cm.gist_stern)
display.plot("specific_differential_phase", 0,vmin=-1.5,vmax=4.,cmap=cm.gist_stern)
ax.plot([0,maxx],[melt_0604,melt_0604],'k:',lw=2)
ax.set_xlim(0,maxx)
ax.set_ylim(0,maxz)
fig.patch.set_facecolor('white')
fig.tight_layout()
fig.savefig("example_defkdp.png", dpi=200)
#plt.close(fig)
#fig.clf()
#..This is the main plotting routine
#..
maxx = 25
maxz = 12
#for idx in [236,]:
for idx in csapr_idx3:
fname = june4_csapr_files[idx]
fig = plt.figure(figsize=(6, 18))
gs = gridspec.GridSpec(4,1)
#..
radar = pyart.io.read(testpath+fname[:-3]+'_proc.nc')
ax1 = fig.add_subplot(gs[0])
display = pyart.graph.RadarDisplay(radar)
display.plot("reflectivity", 0,vmin=-10,vmax=64,cmap=pcm.LangRainbow12,ax=ax1)
ax1.plot([0,maxx],[melt_0604,melt_0604],'k:',lw=2)
ax1.set_xlim(0,maxx)
ax1.set_ylim(0,maxz)
#..
ax2 = fig.add_subplot(gs[1])
display = pyart.graph.RadarDisplay(radar)
#display.plot("differential_reflectivity", 0)
#display.plot("differential_reflectivity",0,vmin=-1,vmax=5,
display.plot("attenuation_corrected_differential_reflectivity",0,vmin=-1,vmax=5,
cmap=pcm.BuOrR14, mask_tuple=['reflectivity', -5.],ax=ax2)
#cmap=pcm.RefDiff)
#cmap=pcm.RefDiff)
ax2.plot([0,maxx],[melt_0604,melt_0604],'k:',lw=2)
ax2.set_xlim(0,maxx)
ax2.set_ylim(0,maxz)
#..
ax3 = fig.add_subplot(gs[2])
display = pyart.graph.RadarDisplay(radar)
display.plot("KDP_CSU", 0,vmin=-1.5,vmax=4.,cmap=cm.gist_stern,ax=ax3)
#display.plot("specific_differential_phase", 0,vmin=-1.5,vmax=4.,cmap=cm.gist_stern)
ax3.plot([0,maxx],[melt_0604,melt_0604],'k:',lw=2)
ax3.set_xlim(0,maxx)
ax3.set_ylim(0,maxz)
#..
#..
#...
# Setting projection and ploting the second tilt
projection = ccrs.LambertConformal(central_latitude=csapr_lat,
central_longitude=csapr_lon)
ax4 = fig.add_subplot(gs[3],projection=projection)
numba = int(fname[-9:-3])
thing = min(enumerate(khgx_times), key=lambda x: abs(x[1]-numba))
if (thing[1]>numba):
nexidx = thing[0]-1
else:
nexidx = thing[0]
radar = pyart.io.read(testpath+fname[:-3]+'_proc.nc')
az = radar.azimuth['data'][0]
csapr_lat = radar.latitude['data'][0]
csapr_lon = radar.longitude['data'][0]
del radar
radar = read_nexrad_key(keys[nexidx])
display = pyart.graph.RadarMapDisplay(radar)
lon_off = .4
lat_off = .3
display.plot_ppi_map('reflectivity', 2, ax=ax4, vmin=-4, vmax=64,
min_lon=csapr_lon-lon_off, max_lon=csapr_lon+lon_off,
min_lat=csapr_lat-lat_off, max_lat=csapr_lat+lat_off,
lon_lines=np.arange(-158, -154, .2), resolution='10m',
lat_lines=np.arange(69, 72, .1), projection=projection,
fig=fig, lat_0=csapr_lat,
lon_0=csapr_lon)
g = Geod(ellps='sphere')
lon = csapr_lon
lat = csapr_lat
rng = 1000 *25
lon2, lat2, _ = g.fwd(lon, lat, az, rng)
plt.plot([csapr_lon, lon2], [csapr_lat, lat2], transform=ccrs.Geodetic(),c='r',lw=2)
display.plot_point(csapr_lon, csapr_lat,c='r')
#...
#...
fig.patch.set_facecolor('white')
fig.tight_layout()
fig.savefig('./csapr_figs/rhi_'+fname[:-3]+'.png', dpi=200)
plt.close(fig)
fig.clf()
print(radar.fields)
{'attenuation_corrected_differential_reflectivity': {'_FillValue': -32767.0, 'long_name': 'Rainfall attenuation-corrected differential reflectivity', 'units': 'dB', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_reflectivity_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 7.9947205 , -1.9256239 , 5.012208 , ..., 10.270708 ,
1.2583165 , 30. ],
[ 5.3105354 , 5.161753 , 20.91131 , ..., 30. ,
-20. , -0.13336992],
[ 0.54110956, 7.1684065 , 23.850332 , ..., -20. ,
-0.13336992, -20. ],
...,
[ 7.5926266 , 5.104529 , 4.34841 , ..., -2.1674905 ,
1.1194532 , 0.05050993],
[ 8.233535 , 5.0877433 , 4.461332 , ..., -20. ,
-0.13336992, 1.6489654 ],
[ 13.138772 , 3.2893853 , 5.3433437 , ..., 0.89971304,
-0.13336992, -0.13336992]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'attenuation_corrected_differential_reflectivity_lag_1': {'_FillValue': -32767.0, 'long_name': 'Differential reflectivity estimated at lag 1 corrected for rainfall attenuation.', 'units': 'dB', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_reflectivity_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 7.4219737e+00, -1.1981931e+00, 7.3689952e+00, ...,
7.3682981e+00, 2.7405286e-01, 6.2835932e-02],
[ 4.8580914e+00, 6.2696905e+00, 2.2189045e+01, ...,
7.0190582e+00, -1.6708174e+00, -6.6771030e-01],
[-1.5237808e-02, 8.9911146e+00, 2.5356606e+01, ...,
1.2813424e+00, 6.9354076e+00, -6.0915518e-01],
...,
[-1.7412229e+00, 5.6973829e+00, 5.3983326e+00, ...,
-5.1032691e+00, 6.9235573e+00, 7.6366763e+00],
[ 1.0806327e+01, 4.6113224e+00, 5.5028958e+00, ...,
-1.1919193e+00, 4.7361012e+00, 1.5218371e+00],
[ 1.7585489e+01, -7.5681658e+00, 6.3679800e+00, ...,
-5.9154272e-02, 1.0715195e+00, 5.8388910e+00]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'attenuation_corrected_reflectivity_h': {'_FillValue': -32767.0, 'long_name': 'Rainfall attenuation-corrected reflectivity, horizontal channel', 'units': 'dBZ', 'coordinates': 'elevation azimuth range', 'standard_name': 'equivalent_reflectivity_factor', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[16.42726707458496, 27.604021072387695, --, ...,
-6.458559036254883, --, -3.880202054977417],
[14.141201972961426, 28.207962036132812, --, ...,
1.148756980895996, --, --],
[14.694812774658203, 20.881322860717773, --, ..., --, --, --],
...,
[9.826518058776855, 15.658793449401855, 28.891263961791992, ...,
-12.470855712890625, --, --],
[3.4948301315307617, 13.643726348876953, 26.291614532470703, ...,
--, --, --],
[11.787385940551758, 7.9275922775268555, 20.76324462890625, ...,
--, --, --]],
mask=[[False, False, True, ..., False, True, False],
[False, False, True, ..., False, True, True],
[False, False, True, ..., True, True, True],
...,
[False, False, False, ..., False, True, True],
[False, False, False, ..., True, True, True],
[False, False, False, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'censor_mask': {'long_name': 'Censor Mask', 'units': '1', 'flag_masks': array([ 1, 2, 4, 8, 16, 32, 64, 128, 256, 512, 1024,
2048], dtype=int32), 'flag_meanings': 'horizontal_snr_below_noise_threshold vertical_snr_below_noise_threshold horizontal_ccor_below_ccor_threshold vertical_ccor_below_ccor_threshold horizontal_sqi_below_sqi1_threshold vertical_sqi_below_sqi1_threshold horizontal_sqi_below_sqi2_threshold vertical_sqi_below_sqi2_threshold horizontal_sigpow_below_sigpow_threshold vertical_sigpow_below_sigpow_threshold urhohv_below_rhohv_threshold censored_by_clutter_micro_suppression', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_quality_mask', 'data': masked_array(
data=[[120, 0, 120, ..., 120, 120, 120],
[120, 0, 0, ..., 120, 120, 120],
[120, 0, 0, ..., 120, 120, 120],
...,
[ 48, 0, 0, ..., 120, 120, 120],
[ 48, 0, 0, ..., 120, 120, 120],
[120, 16, 0, ..., 120, 120, 120]],
mask=False,
fill_value=999999,
dtype=int32)}, 'classification_mask': {'long_name': 'Classification Mask', 'units': '1', 'flag_masks': array([ 1, 2, 4, 8, 16], dtype=int32), 'flag_meanings': 'second_trip third_trip interference clutter sunspoke', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_quality_mask', 'data': masked_array(
data=[[ 8, 8, 8, ..., 8, 8, 8],
[ 8, 8, 8, ..., 8, 8, 8],
[ 8, 8, 8, ..., 8, 8, 8],
...,
[ 8, 8, 8, ..., 8, 8, 8],
[ 8, 8, 8, ..., 8, 8, 8],
[12, 8, 8, ..., 8, 8, 8]],
mask=False,
fill_value=999999,
dtype=int32)}, 'copol_correlation_coeff': {'_FillValue': -32767.0, 'long_name': 'Copolar correlation coefficient (also known as rhohv)', 'units': '1', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_correlation_coefficient_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[0.77641165, 0.90934736, 0.701928 , ..., 0.09605241, 0.05771995,
0.08711377],
[0.782025 , 0.8492937 , 0.9505475 , ..., 0.17283937, 0.11367038,
0.11100098],
[0.5983099 , 0.49237314, 0.8781231 , ..., 0.19483516, 0.16237533,
0.07082283],
...,
[0.2189512 , 0.8261844 , 0.9990084 , ..., 0.27095094, 0.02463475,
0.18701002],
[0.12369204, 0.6376338 , 0.9973153 , ..., 0.17656127, 0.08993569,
0.1598585 ],
[0.18225086, 0.45544404, 0.9883156 , ..., 0.11005527, 0.08432233,
0.02492455]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'differential_phase': {'_FillValue': -32767.0, 'long_name': 'Differential propagation phase shift', 'units': 'degree', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_phase_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ -19.6472 , 113.79336 , -178.72552 , ..., -68.802284,
107.04744 , -125.15923 ],
[ -4.25465 , 111.40921 , -173.87485 , ..., 50.67401 ,
-14.038424, -128.46077 ],
[ 60.974167, 17.241085, -170.50737 , ..., -96.642914,
-118.54516 , 99.906 ],
...,
[ 112.64523 , 124.934006, 132.75662 , ..., -65.31946 ,
96.41768 , 14.137305],
[ -69.34613 , 135.75603 , 126.14805 , ..., 24.942854,
-144.75974 , 137.14037 ],
[ 78.24546 , 99.906 , 106.67389 , ..., 79.47599 ,
-63.2814 , -27.101765]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'differential_reflectivity': {'_FillValue': -32767.0, 'long_name': 'Differential reflectivity', 'units': 'dB', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_reflectivity_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 7.9948726 , -3.4053593 , 5.012513 , ..., 10.270402 ,
-0.13367516, 20. ],
[ 5.310383 , 3.7136056 , 20. , ..., 20. ,
-20. , -0.13367516],
[ -0.2508698 , 6.9443936 , 20. , ..., -20. ,
-0.13367516, -20. ],
...,
[ 6.127693 , 3.480437 , 2.62162 , ..., -2.1674907 ,
-0.13367516, -0.13367516],
[ 8.233535 , 3.3229568 , 2.821217 , ..., -20. ,
-0.13367516, -0.13367516],
[ 12.122322 , 1.9898676 , 3.9571507 , ..., -0.13367516,
-0.13367516, -0.13367516]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'differential_reflectivity_lag_1': {'_FillValue': -32767.0, 'long_name': 'Differential reflectivity estimated at lag 1', 'units': 'dB', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_reflectivity_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 7.421835 , -2.677158 , 7.369232 , ..., 7.3678484 ,
-1.1177702 , 0.06301808],
[ 4.858154 , 4.8207784 , 22.18861 , ..., 6.360094 ,
-1.670789 , -0.66788006],
[-0.8083842 , 8.767352 , 25.35722 , ..., 1.2818741 ,
6.9352617 , -1.9081924 ],
...,
[-3.2045677 , 4.073269 , 3.6725206 , ..., -5.1031036 ,
5.6707244 , 7.452981 ],
[10.806393 , 2.846107 , 3.8628588 , ..., -1.5164421 ,
4.7363377 , -0.26159477],
[16.568445 , -8.866953 , 4.9813547 , ..., -1.0921609 ,
1.071464 , 5.838914 ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'mean_doppler_velocity': {'_FillValue': -32767.0, 'long_name': 'Radial mean Doppler velocity, positive for motion away from the instrument', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radial_velocity_of_scatterers_away_from_instrument', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[-1.07711904e-01, 1.22308373e-01, -3.72461714e-02, ...,
1.30547829e+01, 1.36904850e+01, -3.79306960e+00],
[ 6.03991980e-03, 1.60057873e-01, -3.47295403e-02, ...,
-1.21201057e+01, -9.88785172e+00, -9.44593143e+00],
[ 6.59357905e-02, -5.53659312e-02, -4.58027236e-02, ...,
6.92476797e+00, 1.44515152e+01, -1.76214659e+00],
...,
[ 9.88785172e+00, 4.43934113e-01, 5.48626035e-02, ...,
1.71181393e+00, 5.78120995e+00, -9.25416374e+00],
[-2.41999459e+00, -1.63832819e+00, 4.37894203e-02, ...,
2.10138869e+00, -1.56720819e+01, -1.62901669e+01],
[ 1.39169824e+00, 1.36326027e+01, 7.29823634e-02, ...,
4.79368305e+00, -1.19555178e+01, -1.38550730e+01]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'mean_doppler_velocity_v': {'_FillValue': -32767.0, 'long_name': 'Doppler velocity, vertical channel', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radial_velocity_of_scatterers_away_from_instrument', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 2.91920546e-02, -4.52980166e-03, -1.19284771e-01, ...,
6.92556334e+00, 2.95846367e+00, -1.14603984e+00],
[ 1.10728480e-02, 2.51655638e-01, 1.56026501e-02, ...,
1.13823843e+01, 4.46437120e-01, -1.34998150e+01],
[ 4.98278178e-02, 9.35655653e-01, -1.52503312e-01, ...,
3.64800024e+00, 3.30927157e+00, 1.47017221e+01],
...,
[-2.66855645e+00, 1.17271528e-01, 6.64370880e-02, ...,
-5.17051697e+00, 1.11302261e+01, 1.37655640e+00],
[ 9.33894062e+00, -9.04450357e-01, 7.19735101e-02, ...,
-1.97197366e+00, 7.63321877e+00, 6.42325878e+00],
[ 8.94686127e+00, -2.65245050e-01, 1.24317884e-01, ...,
5.70956326e+00, -1.41279478e+01, -9.92781544e+00]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'normalized_coherent_power': {'_FillValue': -32767.0, 'long_name': 'Normalized coherent power, also known as SQI.', 'units': '1', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_normalized_coherent_power', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[0.66270024, 0.9091381 , 0.99786425, ..., 0.05473688, 0.09749815,
0.05292147],
[0.67841345, 0.9000305 , 0.9979253 , ..., 0.18918386, 0.14019838,
0.11167052],
[0.54491234, 0.8916552 , 0.99896264, ..., 0.1160031 , 0.25136542,
0.08921438],
...,
[0.01704049, 0.736476 , 0.99438596, ..., 0.07334864, 0.20057976,
0.19491994],
[0.18099165, 0.48834482, 0.98308164, ..., 0.16393596, 0.2995271 ,
0.13531661],
[0.24010682, 0.035698 , 0.9503585 , ..., 0.16660568, 0.10051873,
0.12968731]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'normalized_coherent_power_v': {'_FillValue': -32767.0, 'long_name': 'Normalized coherent power, also known as SQI, Vertical Channel', 'units': '1', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_normalized_coherent_power', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[0.9653431 , 0.981558 , 0.74043965, ..., 0.01337782, 0.17099634,
0.07457647],
[0.96119404, 0.89037025, 0.949296 , ..., 0.06759018, 0.19311449,
0.16974553],
[0.7908995 , 0.74809706, 0.9014293 , ..., 0.09515396, 0.07018334,
0.13380733],
...,
[0.18650955, 0.820248 , 0.9967509 , ..., 0.2903886 , 0.06472245,
0.03124014],
[0.1277363 , 0.6957152 , 0.9872782 , ..., 0.24375746, 0.14353934,
0.17432171],
[0.11010277, 0.555059 , 0.9584179 , ..., 0.1641626 , 0.12088728,
0.03496209]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'reflectivity': {'_FillValue': -32767.0, 'long_name': 'Equivalent reflectivity factor', 'units': 'dBZ', 'coordinates': 'elevation azimuth range', 'standard_name': 'equivalent_reflectivity_factor', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'reflectivity_v': {'_FillValue': -32767.0, 'long_name': 'Equivalent reflectivity factor, vertical channel', 'units': 'dBZ', 'coordinates': 'elevation azimuth range', 'standard_name': 'equivalent_reflectivity_factor', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[9.025615692138672, 23.297189712524414, 33.68834686279297, ...,
-16.13416862487793, -80.0, -80.0],
[9.424535751342773, 16.954978942871094, 18.087745666503906, ...,
-80.0, -10.451138496398926, -80.0],
[11.090620040893555, 13.270822525024414, 14.951837539672852, ...,
-13.670242309570312, -80.0, -4.0662617683410645],
...,
[-3.931865692138672, 3.6519079208374023, 17.168306350708008, ...,
-9.708760261535645, -80.0, -80.0],
[-4.145192623138428, 1.0045204162597656, 14.857975006103516, ...,
-42.93444061279297, -80.0, -80.0],
[-5.452887058258057, -0.7596940994262695, 9.612264633178711, ...,
-80.0, -80.0, -80.0]],
mask=[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
fill_value=-32767.0,
dtype=float32)}, 'signal_to_noise_ratio_copolar_h': {'_FillValue': -32767.0, 'long_name': 'Signal-to-noise ratio, horizontal channel', 'units': 'dB', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_signal_to_noise_ratio_copolar_h', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 80.765976 , 74.09652 , 88.46573 , ..., -10.699484 ,
-31.999998 , -8.143652 ],
[ 78.48147 , 74.87067 , 88.763626 , ..., -6.7926846,
-31.999998 , -31.999998 ],
[ 74.58605 , 74.41908 , 88.56629 , ..., -31.999998 ,
-31.999998 , -31.999998 ],
...,
[ 65.943275 , 61.336323 , 69.55408 , ..., -16.712431 ,
-31.999998 , -31.999998 ],
[ 67.83691 , 58.531925 , 67.44224 , ..., -31.999998 ,
-31.999998 , -31.999998 ],
[ 70.41361 , 55.433426 , 63.334312 , ..., -31.999998 ,
-31.999998 , -31.999998 ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'signal_to_noise_ratio_copolar_v': {'_FillValue': -32767.0, 'long_name': 'Signal-to-noise ratio, vertical channel', 'units': 'dB', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_signal_to_noise_ratio_copolar_v', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 72.63805 , 77.36559 , 83.320854, ..., -21.105257, -32. ,
-32. ],
[ 73.0371 , 71.022736, 67.71956 , ..., -32. , -15.434492,
-32. ],
[ 74.703964, 67.33769 , 64.582504, ..., -18.642199, -32. ,
-9.053446],
...,
[ 59.681206, 57.720303, 66.799255, ..., -14.678389, -32. ,
-32. ],
[ 59.467354, 55.073944, 64.48704 , ..., -32. , -32. ,
-32. ],
[ 58.157543, 53.309708, 59.242054, ..., -32. , -32. ,
-32. ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'specific_differential_phase': {'_FillValue': -32767.0, 'long_name': 'Specific differential phase (KDP)', 'units': 'degree/km', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_specific_differential_phase_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ -5.2270646, -5.2838306, -5.3131294, ..., 0. ,
0. , 0. ],
[ -1.9767442, -2.203351 , -2.4020326, ..., 0. ,
0. , 0. ],
[ 15. , 15. , 15. , ..., 0. ,
0. , 0. ],
...,
[ 2.1713057, 2.1323934, 2.0916498, ..., 0. ,
0. , 0. ],
[-15. , -15. , -15. , ..., 0. ,
0. , 0. ],
[ 2.812214 , 2.8552463, 2.9097235, ..., 0. ,
0. , 0. ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'spectral_width': {'_FillValue': -32767.0, 'long_name': 'Spectral width', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_doppler_spectrum_width', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 4.7619495, 2.2914329, 16.492 , ..., 4.4503055, 7.9458027,
7.12607 ],
[ 4.6243634, 2.4093995, 16.492 , ..., 2.2172318, 6.2288666,
5.601553 ],
[ 5.784666 , 2.5142875, 16.492 , ..., 1.9264641, 4.1872063,
4.947075 ],
...,
[14.982073 , 4.106214 , 0.556386 , ..., 8.3228445, 4.6107807,
8.422954 ],
[ 9.706002 , 6.2852087, 0.9693966, ..., 2.2881627, 14.1215925,
5.1840153],
[ 8.867405 , 13.553137 , 1.6751866, ..., 9.45548 , 5.9164677,
8.558527 ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'spectral_width_v': {'_FillValue': -32767.0, 'long_name': 'Spectral Width, Vertical Channel', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_doppler_spectrum_width', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 1.3943133 , 1.0127211 , 4.0699897 , ..., 5.522155 ,
3.921327 , 14.714336 ],
[ 1.4770718 , 2.5297842 , 1.6933994 , ..., 6.3474717 ,
10.3336935 , 4.8641148 ],
[ 3.595829 , 3.9998088 , 2.3916864 , ..., 10.377462 ,
9.12276 , 3.2441702 ],
...,
[ 9.620565 , 3.3045406 , 0.4225979 , ..., 10.953751 ,
4.486044 , 10.47808 ],
[10.649633 , 4.471706 , 0.83965826, ..., 16.491749 ,
2.5411038 , 2.5390916 ],
[11.027452 , 5.6962233 , 1.5301476 , ..., 2.8754063 ,
8.714503 , 10.357339 ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_copol_correlation_coeff': {'_FillValue': -32767.0, 'long_name': 'Uncorrected copolar correlation coefficient', 'units': '1', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_correlation_coefficient_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[0.77641165, 0.90934736, 0.701928 , ..., 0.09605241, 0.05771995,
0.08711377],
[0.782025 , 0.8492937 , 0.9505475 , ..., 0.17283937, 0.11367038,
0.11100098],
[0.5983099 , 0.49237314, 0.8781231 , ..., 0.19483516, 0.16237533,
0.07082283],
...,
[0.2189512 , 0.8261844 , 0.9990084 , ..., 0.27095094, 0.02463475,
0.18701002],
[0.12369204, 0.6376338 , 0.9973153 , ..., 0.17656127, 0.08993569,
0.1598585 ],
[0.18225086, 0.45544404, 0.9883156 , ..., 0.11005527, 0.08432233,
0.02492455]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_differential_phase': {'_FillValue': -32767.0, 'long_name': 'Uncorrected Differential Phase', 'units': 'degree', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_phase_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ -19.6472 , 113.79336 , -178.72552 , ..., -68.802284,
107.04744 , -125.15923 ],
[ -4.25465 , 111.40921 , -173.87485 , ..., 50.67401 ,
-14.038424, -128.46077 ],
[ 60.974167, 17.241085, -170.50737 , ..., -96.642914,
-118.54516 , 99.906 ],
...,
[ 112.64523 , 124.934006, 132.75662 , ..., -65.31946 ,
96.41768 , 14.137305],
[ -69.34613 , 135.75603 , 126.14805 , ..., 24.942854,
-144.75974 , 137.14037 ],
[ 78.24546 , 99.906 , 106.67389 , ..., 79.47599 ,
-63.2814 , -27.101765]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_differential_reflectivity': {'_FillValue': -32767.0, 'long_name': 'Uncorrected Differential Reflectivity, Vertical Channel', 'units': 'dBZ', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_reflectivity_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 7.9948726 , -3.405054 , 5.0128183 , ..., 10.270097 ,
-0.13367516, 30. ],
[ 5.310383 , 3.7136056 , 20.911005 , ..., 30. ,
-30. , -0.13367516],
[ -0.2508698 , 6.9437833 , 23.850027 , ..., -30. ,
-0.13367516, -30. ],
...,
[ 6.1279984 , 3.4801319 , 2.6213148 , ..., -2.1671855 ,
-0.13367516, -0.13367516],
[ 8.232924 , 3.3226516 , 2.820912 , ..., -30. ,
-0.13367516, -0.13367516],
[ 12.122322 , 1.9895624 , 3.9571507 , ..., -0.13367516,
-0.13367516, -0.13367516]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_differential_reflectivity_lag_1': {'_FillValue': -32767.0, 'long_name': 'Uncorrected differential reflectivity estimated at lag 1', 'units': 'dB', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_differential_reflectivity_hv', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 7.421835 , -2.677158 , 7.369232 , ..., 7.3678484 ,
-1.1177702 , 0.06301808],
[ 4.858154 , 4.8207784 , 22.18861 , ..., 6.360094 ,
-1.670789 , -0.66788006],
[-0.8083842 , 8.767352 , 25.35722 , ..., 1.2818741 ,
6.9352617 , -1.9081924 ],
...,
[-3.2045677 , 4.073269 , 3.6725206 , ..., -5.1031036 ,
5.6707244 , 7.452981 ],
[10.806393 , 2.846107 , 3.8628588 , ..., -1.5164421 ,
4.7363377 , -0.26159477],
[16.568445 , -8.866953 , 4.9813547 , ..., -1.0921609 ,
1.071464 , 5.838914 ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_mean_doppler_velocity_h': {'_FillValue': -32767.0, 'long_name': 'Radial mean Doppler velocity from horizontal channel, positive for motion away from the instrument, uncorrected', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radial_velocity_of_scatterers_away_from_instrument', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[-1.07711904e-01, 1.22308373e-01, -3.72461714e-02, ...,
1.30547829e+01, 1.36904850e+01, -3.79306960e+00],
[ 6.03991980e-03, 1.60057873e-01, -3.47295403e-02, ...,
-1.21201057e+01, -9.88785172e+00, -9.44593143e+00],
[ 6.59357905e-02, -5.53659312e-02, -4.58027236e-02, ...,
6.92476797e+00, 1.44515152e+01, -1.76214659e+00],
...,
[ 9.88785172e+00, 4.43934113e-01, 5.48626035e-02, ...,
1.71181393e+00, 5.78120995e+00, -9.25416374e+00],
[-2.41999459e+00, -1.63832819e+00, 4.37894203e-02, ...,
2.10138869e+00, -1.56720819e+01, -1.62901669e+01],
[ 1.39169824e+00, 1.36326027e+01, 7.29823634e-02, ...,
4.79368305e+00, -1.19555178e+01, -1.38550730e+01]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_mean_doppler_velocity_v': {'_FillValue': -32767.0, 'long_name': 'Radial mean Doppler velocity from vertical channel, positive for motion away from the instrument, uncorrected', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radial_velocity_of_scatterers_away_from_instrument', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 2.91920546e-02, -4.52980166e-03, -1.19284771e-01, ...,
6.92556334e+00, 2.95846367e+00, -1.14603984e+00],
[ 1.10728480e-02, 2.51655638e-01, 1.56026501e-02, ...,
1.13823843e+01, 4.46437120e-01, -1.34998150e+01],
[ 4.98278178e-02, 9.35655653e-01, -1.52503312e-01, ...,
3.64800024e+00, 3.30927157e+00, 1.47017221e+01],
...,
[-2.66855645e+00, 1.17271528e-01, 6.64370880e-02, ...,
-5.17051697e+00, 1.11302261e+01, 1.37655640e+00],
[ 9.33894062e+00, -9.04450357e-01, 7.19735101e-02, ...,
-1.97197366e+00, 7.63321877e+00, 6.42325878e+00],
[ 8.94686127e+00, -2.65245050e-01, 1.24317884e-01, ...,
5.70956326e+00, -1.41279478e+01, -9.92781544e+00]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_reflectivity_h': {'_FillValue': -32767.0, 'long_name': 'Uncorrected Reflectivity, Horizontal Channel', 'units': 'dBZ', 'coordinates': 'elevation azimuth range', 'standard_name': 'equivalent_reflectivity_factor', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[16.42726707458496, 19.297916412353516, 38.10715103149414, ...,
-6.458559036254883, --, -3.880202054977417],
[14.141201972961426, 20.074132919311523, 38.407188415527344, ...,
-2.5503733158111572, --, --],
[10.244629859924316, 19.621179580688477, 38.20780944824219, ...,
--, --, --],
...,
[1.601711630821228, 6.539691925048828, 19.199195861816406, ...,
-12.470855712890625, --, --],
[3.4948301315307617, 3.7348573207855225, 17.083471298217773, ...,
--, --, --],
[6.073187828063965, 0.6357954144477844, 12.975908279418945, ...,
--, --, --]],
mask=[[False, False, False, ..., False, True, False],
[False, False, False, ..., False, True, True],
[False, False, False, ..., True, True, True],
...,
[False, False, False, ..., False, True, True],
[False, False, False, ..., True, True, True],
[False, False, False, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'uncorrected_reflectivity_v': {'_FillValue': -32767.0, 'long_name': 'Uncorrected Reflectivity, Vertical Channel', 'units': 'dBZ', 'coordinates': 'elevation azimuth range', 'standard_name': 'equivalent_reflectivity_factor', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[9.023921966552734, 23.298072814941406, 33.68947982788086, ...,
-16.134798049926758, --, --],
[9.425077438354492, 16.955028533935547, 18.08856964111328, ...,
--, -10.452371597290039, --],
[11.092266082763672, 13.269180297851562, 14.951089859008789, ...,
-13.670818328857422, --, -4.0651631355285645],
...,
[-3.9289910793304443, 3.6543240547180176, 17.168487548828125,
..., -9.70710563659668, --, --],
[-4.144289970397949, 1.004488229751587, 14.8572416305542, ...,
-42.93310546875, --, --],
[-5.450806617736816, -0.7565485835075378, 9.612774848937988, ...,
--, --, --]],
mask=[[False, False, False, ..., False, True, True],
[False, False, False, ..., True, False, True],
[False, False, False, ..., False, True, False],
...,
[False, False, False, ..., False, True, True],
[False, False, False, ..., False, True, True],
[False, False, False, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'uncorrected_spectral_width_h': {'_FillValue': -32767.0, 'long_name': 'Uncorrected Spectral Width, Horizontal Channel', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_doppler_spectrum_width', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 4.7619495, 2.2914329, 16.492 , ..., 4.4503055, 7.9458027,
7.12607 ],
[ 4.6243634, 2.4093995, 16.492 , ..., 2.2172318, 6.2288666,
5.601553 ],
[ 5.784666 , 2.5142875, 16.492 , ..., 1.9264641, 4.1872063,
4.947075 ],
...,
[14.982073 , 4.106214 , 0.556386 , ..., 8.3228445, 4.6107807,
8.422954 ],
[ 9.706002 , 6.2852087, 0.9693966, ..., 2.2881627, 14.1215925,
5.1840153],
[ 8.867405 , 13.553137 , 1.6751866, ..., 9.45548 , 5.9164677,
8.558527 ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'uncorrected_spectral_width_v': {'_FillValue': -32767.0, 'long_name': 'Uncorrected Spectral Width, Vertical Channel', 'units': 'm/s', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_doppler_spectrum_width', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[ 1.3943133 , 1.0127211 , 4.0699897 , ..., 5.522155 ,
3.921327 , 14.714336 ],
[ 1.4770718 , 2.5297842 , 1.6933994 , ..., 6.3474717 ,
10.3336935 , 4.8641148 ],
[ 3.595829 , 3.9998088 , 2.3916864 , ..., 10.377462 ,
9.12276 , 3.2441702 ],
...,
[ 9.620565 , 3.3045406 , 0.4225979 , ..., 10.953751 ,
4.486044 , 10.47808 ],
[10.649633 , 4.471706 , 0.83965826, ..., 16.491749 ,
2.5411038 , 2.5390916 ],
[11.027452 , 5.6962233 , 1.5301476 , ..., 2.8754063 ,
8.714503 , 10.357339 ]],
mask=False,
fill_value=1e+20,
dtype=float32)}, 'unthresholded_power_copolar_h': {'_FillValue': -32767.0, 'long_name': 'Received copolar power, h channel, without thresholding.', 'units': 'dBm', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_received_signal_power_copolar_h', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[92.1434326171875, 85.47286987304688, --, ...,
0.6775398254394531, 6.109123229980469, 3.234241485595703],
[89.85831451416016, 86.2474365234375, --, ..., 5.358661651611328,
4.818717956542969, 3.549205780029297],
[85.96461486816406, 85.79588317871094, --, ...,
1.8667030334472656, 6.031986236572266, 2.244342803955078],
...,
[77.3206787109375, 72.715087890625, 80.93156433105469, ...,
5.580425262451172, 5.520969390869141, 7.547367095947266],
[79.21369934082031, 69.90930938720703, 78.81999206542969, ...,
3.216564178466797, 21.820531845092773, 3.9445228576660156],
[81.79129028320312, 66.8110580444336, 74.71095275878906, ...,
7.982856750488281, 3.4045791625976562, 6.5462188720703125]],
mask=[[False, False, True, ..., False, False, False],
[False, False, True, ..., False, False, False],
[False, False, True, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
fill_value=-32767.0,
dtype=float32)}, 'unthresholded_power_copolar_v': {'_FillValue': -32767.0, 'long_name': 'Received copolar power, v channel, without thresholding', 'units': 'dBm', 'coordinates': 'elevation azimuth range', 'standard_name': 'radar_received_signal_power_copolar_v', 'unpacking_details': 'Can be unpacked as follows: multiply packed values by scale_factor field attribute, then add the add_offset field attribute.', 'data': masked_array(
data=[[84.04740905761719, 88.77720642089844, 94.72994232177734, ...,
-4.888935089111328, 4.424873352050781, 17.192081451416016],
[84.44749450683594, 82.43409729003906, 79.12971496582031, ...,
2.745147705078125, 12.807302474975586, 4.567417144775391],
[86.11588287353516, 78.74906921386719, 75.9937973022461, ...,
9.741033554077148, 5.233150482177734, 4.015068054199219],
...,
[71.09392547607422, 69.13235473632812, 78.20967102050781, ...,
15.641939163208008, 0.7187881469726562, 4.1154937744140625],
[70.87849426269531, 66.48399353027344, 75.89823150634766, ...,
47.05458068847656, 2.840717315673828, 3.5566673278808594],
[69.5697021484375, 64.72166442871094, 70.65171813964844, ...,
3.6408958435058594, 6.517646789550781, 4.350364685058594]],
mask=[[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
fill_value=-32767.0,
dtype=float32)}, 'FDP_CSU': {'_FillValue': -32767.0, 'long_name': 'Filtered Differential Phase', 'units': 'deg', 'standard_name': 'Filtered Differential Phase', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'SDP_CSU': {'_FillValue': -32767.0, 'long_name': 'Standard Deviation of Differential Phase', 'units': 'deg', 'standard_name': 'Std Dev Differential Phase', 'data': masked_array(
data=[[100.0, 100.0, --, ..., 100.0, 100.0, 100.0],
[100.0, 100.0, --, ..., 100.0, 100.0, 100.0],
[100.0, 100.0, --, ..., 100.0, 100.0, 100.0],
...,
[100.0, 100.0, 100.0, ..., 100.0, 100.0, 100.0],
[100.0, 100.0, 100.0, ..., 100.0, 100.0, 100.0],
[100.0, 100.0, 100.0, ..., 100.0, 100.0, 100.0]],
mask=[[False, False, True, ..., False, False, False],
[False, False, True, ..., False, False, False],
[False, False, True, ..., False, False, False],
...,
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False],
[False, False, False, ..., False, False, False]],
fill_value=-32767.0,
dtype=float32)}, 'KDP_CSU': {'_FillValue': -32767.0, 'long_name': 'Specific Differential Phase', 'units': 'deg km-1', 'standard_name': 'KDP', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'FH': {'_FillValue': -32767.0, 'long_name': 'Hydrometeor ID', 'units': 'unitless', 'standard_name': 'Hydrometeor ID', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'rain': {'_FillValue': -32767.0, 'long_name': 'Rainfall Rate', 'units': 'mm h-1', 'standard_name': 'Rainfall Rate', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'method': {'_FillValue': -32767, 'long_name': 'Rainfall Method', 'units': '', 'standard_name': 'Rainfall Method', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767,
dtype=int16)}, 'D0': {'_FillValue': -32767.0, 'long_name': 'Median Volume Diameter', 'units': 'mm', 'standard_name': 'Median Volume Diameter', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'NW': {'_FillValue': -32767.0, 'long_name': 'Normalized Intercept Parameter', 'units': 'mm-1 m-3', 'standard_name': 'Normalized Intercept Parameter', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}, 'MU': {'_FillValue': -32767.0, 'long_name': 'Mu', 'units': ' ', 'standard_name': 'Mu', 'data': masked_array(
data=[[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
...,
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --],
[--, --, --, ..., --, --, --]],
mask=[[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
...,
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True],
[ True, True, True, ..., True, True, True]],
fill_value=-32767.0,
dtype=float32)}}
print(radar.azimuth['data'][0])
29.152222
Download NEXRAD (run this before 4.1)#
#del keys
keys = get_nexrad_keys('KHGX',start='20220604_210000',end='20220604_235000')
for key in keys:
fname = os.path.split(str(key))[1][:-1]
radar = read_nexrad_key(key)
fnams = fname[0:4]+'_'+fname[-3:]+'_'+fname[4:8]+'_'+fname[8:12]+'_'+fname[13:19]+'.nc'
pyart.io.write_cfradial(fnams,radar)
Found 35 keys.
print(int(fname[13:19]))
234533
#..Get times for each nexrad
khgx_times = []
for key in keys:
fname = os.path.split(str(key))[1][:-1]
khgx_times = np.r_[khgx_times,int(fname[13:19])]
#..Test out plot of nexrad with line showing closest (after) CSAPR RHI direction
idx = 243
fname = june4_csapr_files[idx]
numba = int(fname[-9:-3])
print(numba)
thing = min(enumerate(khgx_times), key=lambda x: abs(x[1]-numba))
print(thing)
if (thing[1]>numba):
nexidx = thing[0]-1
else:
nexidx = thing[0]
radar = pyart.io.read(testpath+fname[:-3]+'_proc.nc')
az = radar.azimuth['data'][0]
csapr_lat = radar.latitude['data'][0]
csapr_lon = radar.longitude['data'][0]
del radar
radar = read_nexrad_key(keys[nexidx])
display = pyart.graph.RadarMapDisplay(radar)
# Setting projection and ploting the second tilt
projection = ccrs.LambertConformal(central_latitude=csapr_lat,
central_longitude=csapr_lon)
fig = plt.figure(figsize=(6,6))
lon_off = .4
lat_off = .3
display.plot_ppi_map('reflectivity', 2, vmin=-4, vmax=64,
min_lon=csapr_lon-lon_off, max_lon=csapr_lon+lon_off,
min_lat=csapr_lat-lat_off, max_lat=csapr_lat+lat_off,
lon_lines=np.arange(-158, -154, .2), resolution='10m',
lat_lines=np.arange(69, 72, .1), projection=projection,
fig=fig, lat_0=csapr_lat,
lon_0=csapr_lon)
g = Geod(ellps='sphere')
lon = csapr_lon
lat = csapr_lat
rng = 1000 *25
lon2, lat2, _ = g.fwd(lon, lat, az, rng)
plt.plot([csapr_lon, lon2], [csapr_lat, lat2], transform=ccrs.Geodetic(),c='r',lw=2)
display.plot_point(csapr_lon, csapr_lat,c='r')
fig.patch.set_facecolor('white')
fig.tight_layout()
210950
(2, 211028.0)
print(radar.latitude['data'][0])
29.471900939941406
Load lightning to plot with RHIs (incomplete)#
import sys
sys.path.append("./LMAinterceptRHI/")
# Functions
from radarlma2local import geo_to_tps
from select_lma import one_flash
from plot_radar import plot_locs_lma_rhi, plot_RHI_EDR_panel,plot_EDR_dVR, plot_sources_interp
from ortho_proj import rot_mat_lma, closest_pt_radarloc, close_sources
from radar_processing import dealias_range_elev_derivatives, r_z_centers_edges_mesh
from interp_funcs import centers_to_edges_1d, coords_2d
from interp_funcs import oban, barnes, weighted_mean, weighted_std, interp_avg_std
# -- LIGHTNING
lma_base = './LYLOUT_220604_190000_18000_map500m.nc'
lma = LMAh5File(lma_base)
events, flashes = lma.data_for_table(lma.table_names[0])
flashes = pd.DataFrame(flashes)
events = pd.DataFrame(events)
events = events[events.alt < 16000.][events.stations > 5][events.chi2 < 5.]
potential = events
---------------------------------------------------------------------------
NoSuchNodeError Traceback (most recent call last)
<ipython-input-189-ccde3d04dd4a> in <module>
1 # -- LIGHTNING
2 lma_base = './LYLOUT_220604_190000_18000_map500m.nc'
----> 3 lma = LMAh5File(lma_base)
4 events, flashes = lma.data_for_table(lma.table_names[0])
5 flashes = pd.DataFrame(flashes)
~/miniconda3/envs/cmac_env/lib/python3.6/site-packages/lmatools-0.6a0-py3.6.egg/lmatools/io/LMA_h5_file.py in __init__(self, filename, min_points)
165 self.h5 = tables.open_file(filename)
166 self.min_points = min_points
--> 167 table_names = list(self.h5.root.events._v_children.keys())
168 start_times = [datetime(*getattr(self.h5.root.events,
169 the_table).attrs['start_time']) for
~/miniconda3/envs/cmac_env/lib/python3.6/site-packages/tables/group.py in __getattr__(self, name)
834 self._g_add_children_names()
835 return self.__dict__[name]
--> 836 return self._f_get_child(name)
837
838 def __setattr__(self, name, value):
~/miniconda3/envs/cmac_env/lib/python3.6/site-packages/tables/group.py in _f_get_child(self, childname)
706 self._g_check_open()
707
--> 708 self._g_check_has_child(childname)
709
710 childpath = join_path(self._v_pathname, childname)
~/miniconda3/envs/cmac_env/lib/python3.6/site-packages/tables/group.py in _g_check_has_child(self, name)
393 raise NoSuchNodeError(
394 "group ``%s`` does not have a child named ``%s``"
--> 395 % (self._v_pathname, name))
396 return node_type
397
NoSuchNodeError: group ``/`` does not have a child named ``events``
# -- RADAR
radar = pyart.io.read('houcsapr2cfrS2.a1.20220604.221847.nc')
# -- Creating EDR field parameter
gate_spacing = radar.range['meters_between_gates']/1000.
beamwidth = radar.instrument_parameters['radar_beam_width_h']['data'][0]
#pytda.calc_turb_rhi(radar, verbose = False, use_ntda=False, beamwidth=beamwidth, gate_spacing=gate_spacing)
# # -- Adding dealias velocity, dVRdr, dVRde fields to radar file
# import warnings
# warnings.simplefilter("ignore")
# radar = dealias_range_elev_derivatives(radar)
# arq_tur = './radar/turb_' + os.path.split(arq_ka)[1]
# # .nc export
# try:
# os.remove(arq_tur)
# except OSError:
# pass
# pyart.io.cfradial.write_cfradial(arq_tur, radar,format='NETCDF4')
# -- Get radar time
d = date(int(radar.time['units'][14:18]),int(radar.time['units'][19:21]),int(radar.time['units'][22:24]))
t = time(int(radar.time['units'][25:27]),int(radar.time['units'][28:30]),int(radar.time['units'][31:33]))
radar_time = datetime.combine(d,t)
print(radar_time)
# -- Radar scan processing
#data = xr.open_dataset(arq_tur)
RZ_centers, RZ_edges = r_z_centers_edges_mesh(radar, 0)
# -- WITH THIS I'M ONLY TAKING 1 SWEEP IF I WANT MORE OR ALL I UNCOMMENT THE BELLOW LINES BUT IT TAKES A LONG TIME
# for sweep_idx in np.arange(1, len(data.sweep_start_ray_index)):
# centers, edges = r_z_centers_edges_mesh(data, sweep_idx)
# RZ_centers = np.append(RZ_centers, centers, axis = 0)
# RZ_edges = np.append(RZ_edges, edges, axis = 0)
r_c = RZ_centers[:,:,0]
z_c = RZ_centers[:,:,1]
r_e = RZ_edges[:,:,0]
z_e = RZ_edges[:,:,1]
# -- To filter VHF sources out of the radar scan domain
# --- Create the polygon: all ranges, lowest and highest elevation
# --- ~ 23 km (IDX 221), 23.5 km (IDX 165)range all elevations
# RZ_edges[:, - 165]
from shapely.geometry import Polygon, Point, MultiPoint
scan_poly = Polygon(np.append(np.append(RZ_edges[-1, :-221],RZ_edges[0, :-221], axis = 0), RZ_edges[:, - 221], axis = 0))
# -- FLASH
# --- Select the flash #k
flash = one_flash(potential, flashes_id[k])
flash = flash.sort_values(by=['time'])
# --- Orthogonal projection/ Matrix Rotation of the LF sources
Xlma,Ylma,Zlma = geo_to_tps(flash, radar)
XYZlma = np.column_stack((Xlma, Ylma, Zlma))
lma_file_ortho = rot_mat_lma(radar, XYZlma, -1)
# -- Initialize coordinate systems
ctrlat, ctrlon, ctralt = data.latitude.values, data.longitude.values, data.altitude.values
geo = GeographicSystem()
tps = TangentPlaneCartesianSystem(ctrlat, ctrlon, ctralt)
rcs = RadarCoordinateSystem(ctrlat, ctrlon, ctralt)
# -- First source location in the cross section
alt_lma_init = flashes.init_alt[flashes.flash_id == flash.flash_id.values[0]].values[0]
lat_lma_init = flashes.init_lat[flashes.flash_id == flash.flash_id.values[0]].values[0]
lon_lma_init = flashes.init_lon[flashes.flash_id == flash.flash_id.values[0]].values[0]
m = rot_mat_lma(radar, np.column_stack(tps.toLocal(np.vstack(geo.toECEF(lon_lma_init, lat_lma_init, alt_lma_init)))), -1)[0]
X1, Y1, Z1 = m[0], m[1], m[2]
# 1st STEP
# -- Find and store sources within a certain distance
ds = 100 # threshold
r_cls, z_cls, y_min = close_sources(r_c, z_c, lma_file_ortho, ds)
# -- Selected LMA sources inside the radar scan
lma_shp = MultiPoint(tuple(np.vstack((r_cls, z_cls)).transpose()))
R_cls = []
Z_cls = []
Y_min = []
for i in np.arange(len(r_cls)):
if scan_poly.contains(lma_shp[i]) == True:
R_cls.append(np.asarray(lma_shp[i].coords[0])[0])
Z_cls.append(np.asarray(lma_shp[i].coords[0])[1])
Y_min.append(y_min[i])
R_cls = np.asarray(R_cls)
Z_cls = np.asarray(Z_cls)
Y_min = np.asarray(Y_min)