NGEE
Site Visits
- 6/24/2017 (Bob and Lily Site Install)
- 8/19/2017 (Lily and ABOVE Team)
- 10/5/2017 (Bob and Lily Fall Visit)
- 4/3/2018 (Snow Team)
- 6/14/2018 (Bob and Nathan)
- 9/22/2018 (Bobs and CAS)
Processed through
- AT/RH/Precip / WS / WD / BP through 4/3/2018
- Radiation through 4/3/2018
- VATP through 4/3/2018
Met Notes
- Fall 2017: Muskox knock over precip bucket.
- 10/16/2017 12:00 end of precip season for kougarok.
- 6/14/2018 Fixed precip bucket, noticed Musk ox twisted tower center mast at some point so Wind Direction and Radiation have been off for a TBD amount of time.
- NAN through 6/15/2018 00:00 time step (the test tips)
- 9/22/2018 musk ox twisted center mast a bit again. WD off, Radiation still pretty closer to still on.
Radiation Notes
- I have open questions about PRI index value but the data from the sensors themselves looks ok.
- Snippet for trying to pin down when the muskox move the CNR4...
- Need to compare albedo here to K2 Met...
- ... albedo didn't show anything but incoming shortwave radiation shows a shift on 5/14 so I'm going to 6999 from there till June station visit.
- second bump was gentle enough there is no obvious error in SW data from June till September visit even though the Musk Ox twisted 10 degrees or something the station.
- Declaring data bad 5/14/18 13:00 till station visit on 6/14/18 22:00
import numpy
import pandas
stryear="2018.csv"
sensor_k2 = "/var/site/uaf_sp/k-met/outputs/k2_met_albedo_" + stryear
df_k2 = pandas.read_csv(sensor_k2, skiprows=3, index_col=0)
df_k2.index = pandas.to_datetime(df_k2.index)
df_k2 [df_k2>1]= numpy.NaN
df_k2 = df_k2.resample('24H').mean()
path="/var/site/uaf_sp/ngee_koug_met/outputs/years/albedo_Avg/albedo_Avg_" + stryear
df4 = pandas.read_csv(sensor4, skiprows=3, index_col=0)
df4.index = pandas.to_datetime(df4.index)
df4 [df4>100]= numpy.NaN
df4 = df4.resample('24H').mean()
df4.rename(columns = {'Avg' : 'Albedo'}, inplace=True)
outfile = "/var/site/uaf_sp/ngee_koug_met/outputs/koug_summary_albedo.csv"
dataset=pandas.concat([ df4, df_k2] ,axis=1)
dataset.to_csv(outfile)
Soil Notes
