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GEUS-Glaciology-and-Climate / GC-Net-level-1-data-processing

This repository contains scripts used to flag, adjust and interpolate GC-Net data
GNU General Public License v3.0
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NAU snow height adjustments #102

Closed jasonebox closed 1 year ago

jasonebox commented 1 year ago

adjustment parameters below graphics, still lots of filterable noise at ~10 cm scale

before image

after image

    if nicknames[0]=='NAU':
        t=datetime(1997, 1, 11)
        df['HS1'][t:]+=0.87
        t=datetime(1999, 5, 14)
        df['HS1'][t:]-=1.85
        df['HS2'][t:]-=0.3
        t0=datetime(1999, 5, 1) # filter downward spikes
        t1=datetime(2000, 8, 1) 
        df['HS1'][t0:t1][df['HS1'][t0:t1]<2.5]=np.nan
        df['HS2'][t0:t1][df['HS2'][t0:t1]<2.5]=np.nan
        t=datetime(2003, 5, 14)
        df['HS1'][t:]+=0.05
        t0=datetime(2002, 5, 1) # filter downward spikes
        t1=datetime(2004, 8, 1) 
        df['HS1'][t0:t1][df['HS1'][t0:t1]<6]=np.nan
        df['HS2'][t0:t1][df['HS2'][t0:t1]<6]=np.nan
        t=datetime(2005, 5, 25)
        df['HS1'][t:]+=0.3
        t=datetime(2005, 8, 22)
        df['HS1'][t:]+=0.25
        t0=datetime(2005, 4, 28)
        t1=datetime(2007, 9, 1) # filter upward spikes
        df['HS1'][t0:t1][df['HS1'][t0:t1]>11.5]=np.nan
        df['HS2'][t0:t1][df['HS2'][t0:t1]>11.5]=np.nan
        t0=datetime(2007, 9, 1)
        t1=datetime(2008, 9, 1) # filter upward spikes
        df['HS1'][t0:t1][df['HS1'][t0:t1]>12]=np.nan
        df['HS2'][t0:t1][df['HS2'][t0:t1]>12]=np.nan
        t0=datetime(2008, 9, 1)
        t1=datetime(2009, 9, 1) # filter upward spikes
        df['HS1'][t0:t1][df['HS1'][t0:t1]>13]=np.nan
        df['HS2'][t0:t1][df['HS2'][t0:t1]>13]=np.nan
        t=datetime(2009, 1, 1)
        df['HS1'][t:]+=0.14
        t=datetime(2011, 10, 1)
        df['HS1'][t:]+=0.05
        t=datetime(2013, 6, 1)
        df['HS1'][t:]+=0.05
        t=datetime(2013, 1, 1)
        df['HS2'][t:]+=0.05
        t=datetime(2013, 6, 2)
        df['HS1'][t:]+=0.1
        t0=datetime(2013, 5, 1) # filter downward spikes
        t1=datetime(2013, 8, 1) 
        df['HS1'][t0:t1][df['HS1'][t0:t1]<14.9]=np.nan
        df['HS2'][t0:t1][df['HS2'][t0:t1]<14.9]=np.nan
        t1=datetime(2014, 8, 1) # filter upward spikes
        df['HS1'][t0:t1][df['HS1'][t0:t1]>16]=np.nan
        df['HS2'][t0:t1][df['HS2'][t0:t1]>16]=np.nan
        t=datetime(2018, 1, 1)
        df['HS1'][t:]+=0.5
        df['HS2'][t:]+=1.
BaptisteVandecrux commented 1 year ago

Fixed in the latest commit: image