I'm dealing with NO2 column density of troposphere, and my data source is TEMIS.
The NO2 level3 data can be derived from the original information of OMI instrument.
I have downloaded two types of data, .grd and .kml for the same month.
The data I have downloaded are uploaded here1, 2
KML data
I open it in Google Earth, the figure shows like this:
We can see the data range of NO2 trop. column is 0~20 which are idential to the templete figure on the website.
GRD data
I have not found any detailed information about this data.
In the contents of it, -999
is deemed as no_data place.
I use python to read and plot the spatial distribution.
filename = './CH2O-NO2/no2_201306.grd'
def read_grd(filename):
ncols = np.array(linecache.getline(filename, 1)[6:10]).astype(float)
nrows = np.array(linecache.getline(filename, 2)[6:10]).astype(float)
xllcorner = np.array(linecache.getline(filename, 3)[10:14]).astype(float)
yllcorner = np.array(linecache.getline(filename, 4)[10:14]).astype(float)
cellsize = np.array(linecache.getline(filename, 5)[9:14]).astype(float)
nan_value = np.array(linecache.getline(filename, 6)[13:17]).astype(float)
longitude = xllcorner + cellsize * np.arange(ncols)
latitude = yllcorner + cellsize * np.arange(nrows)
value = np.loadtxt(filename, skiprows=7)
value = value[::-1]
return value, longitude, latitude, nan_value
no2,lon_no2, lat_no2, nan_value = read_grd(filename)
no2[no2 == nan_value] = np.nan
def isnt_NaN(num):
return num == num
no2[isnt_NaN(no2)].max()
> 9999.0
It seems that the high value in grd format data are out of the regular condition which can be learned from previous resarch (Hot spots of NO2 column are about 15~20 10^15 molec/cm2).
Does anyone familiar with the OMI-NO2 data? I don't know how to deal with the irregular value which are way too higher than realistic.