FPI Quality Issues¶

12 Feb 2015

Cloud Sensor Wet vs. Missing¶

In [5]:

######################################################################################
instr_name = 'minime08'
year = 2013
doy = 151
######################################################################################


r = FPIprocess.load_level0(instr_name, year, doy)
fpir  = r['FPI_Results']
site = r['site']
direction = fpir['direction']
LOSwind = fpir['LOSwind']
sigma_LOSwind = fpir['sigma_LOSwind']
T = fpir['T']
sigma_T = fpir['sigma_T']
I = fpir['skyI']/fpir['sky_intT']
sky_times = fpir['sky_times']
skyI = fpir['skyI']
skyB = fpir['skyB']
sigma_skyI = fpir['sigma_skyI']
intT = fpir['sky_intT']
cloud = None
if fpir['Clouds']:
    cloud = fpir['Clouds']['mean']
    
print sky_times[0]


plot(sky_times, cloud, 'k.-')
xlabel('UT')
ylabel('Cloud Parameter (Sky-Amb) [K]')
plt.gca().xaxis.set_major_formatter(dates.DateFormatter('%H'))
title('%s_%s_%s' % (instr_name,year,doy))

2013-05-31 21:54:03-04:00

Out[5]:

<matplotlib.text.Text at 0x7f16cfb2b590>

In [6]:

from IPython.display import Image
Image(url='http://airglow.ece.illinois.edu/Data/SummaryImages/minime08_ann_20130531_winds.png')

Out[6]:

Etalon gap fluctuations¶

Add extra uncertainty to characterize "lack of knowledge of gap"
How to calculate?

In [12]:

######################################################################################
instr_name = 'minime05'
year = 2013
doy = 7
######################################################################################




r = FPIprocess.load_level0(instr_name, year, doy)
fpir  = r['FPI_Results']
site = r['site']
direction = fpir['direction']
LOSwind = fpir['LOSwind']
sigma_LOSwind = fpir['sigma_LOSwind']
T = fpir['T']
sigma_T = fpir['sigma_T']
I = fpir['skyI']/fpir['sky_intT']
sky_times = fpir['sky_times']
skyI = fpir['skyI']
skyB = fpir['skyB']
sigma_skyI = fpir['sigma_skyI']
intT = fpir['sky_intT']
cloud = None
if fpir['Clouds']:
    cloud = fpir['Clouds']['mean']
    

from matplotlib import dates


fig = figure(figsize=(6,6))

ax = subplot(211)
dref, drefe = FPI.DopplerReference(fpir, reference='laser')
LOSwind_ref = LOSwind - dref
for direc in ['Zenith']:
    I = np.array([si for (si,d) in zip(LOSwind_ref, direction) if d == direc])
    Ie = np.array([si for (si,d) in zip(sigma_LOSwind, direction) if d == direc])
    t = np.array([si for (si,d) in zip(sky_times, direction) if d == direc])
    ax.errorbar(t, I, yerr=Ie, fmt='.-', label=direc)
ax.set_xlim([sky_times[0] - timedelta(hours=0.5), sky_times[-1] + timedelta(hours=0.5)])
ax.set_ylim((prctile(LOSwind,5)-100,prctile(LOSwind,95)+100))
ax.xaxis.set_major_formatter(dates.DateFormatter('%H'))
ax.set_ylabel('Vertical Wind, [m/s]')
ax.set_xlabel('Universal Time')
ax.legend(loc='best', prop={'size':8}, numpoints=1, ncol=5)
ax.grid(True)
ax.set_ylim((-200,200))

from matplotlib import dates
param = 't'
ax = fig.add_subplot(212)
ax.errorbar(fpir['laser_times'], fpir['laser_value'][param], yerr=fpir['laser_stderr'][param], fmt='k')
ax.set_xlim([sky_times[0] - timedelta(hours=0.5), sky_times[-1] + timedelta(hours=0.5)])
ax.xaxis.set_major_formatter(dates.DateFormatter('%H'))
ax.set_ylabel('Etalon Gap [m]')
ax.set_xlabel('Universal Time')
ax.grid(True)

Laser drift¶

this one is complicated

Quality Flags¶

Raw Images --> npz file (Brian)
- error bars
- read cloud data
- send emails
npz file --> ASCII file for Madrigal
- Doppler reference (should go above probably)
- Quality flags (start with manual flag, then increase if necessary)

In [16]:

def quality_flags():
    # Set Flags (Order is Important)
    t_flag = 0 # default
    w_flag = 0 # default
    if (reference == 'zenith'): # Zenith Processing  ### TODO Caution if cloudy ( if -999. aka no cloud sensor)
        t_flag = 1
        w_flag = 1
    if (a_dt > cloudthreshold or _np.isnan(a_dt)): # If cloudy or sensor is giving bad reading
    # TODO: Zenith wind could still be useful too?
    # TODO: AIRGLOW INTENSITY CHECK
    # TODO: w_flag = 1 when -30 to -25 ?  RETHINK
        t_flag = 1
        w_flag = 2
    if (a_e_w > winderrorlimit):
        w_flag = 2
    if (a_e_t > temperrorlimit):
        t_flag = 2
    if drift_flag:
        w_flag = 2
    if t_flag_manual:
        t_flag = max(t_flag,t_flag_manual)
    if w_flag_manual:
        w_flag = max(w_flag,w_flag_manual)
print '[[click for code]]'

[[click for code]]

Temperature Bias¶

Bad Lasers¶

Dan's students' PURE project

In [ ]: