How to select a lat long box with nomads data

[stackjohn]

Hello,

I'm trying to follow the example here

But I can't seem to query over the prime meridian as the source data seems to run from lon 0 - 360.

Lets say I have the following:

lats = np.arange(25,55,0.25)
lons = np.arange(-10,10,0.25)

u8 = data['u'].metpy.sel(lev=850.0,latitude=lats,longitude=lons).squeeze()*1.94384449
v8 = data['v'].metpy.sel(lev=850.0,latitude=lats,longitude=lons).squeeze()*1.94384449
h8 = data['gph'].metpy.sel(lev=850.0,latitude=lats,longitude=lons).squeeze()
pwat = data['pwat'].metpy.sel(latitude=lats,longitude=lons).squeeze()*0.0393700787402

That gives me:

KeyError: "not all values found in index 'lon'"

If I change it so I have the following:

lat_slice = slice(45,65)
long_slice = slice(-10,10)

u8 = data['u'].metpy.sel(lev=850.0,latitude=lat_slice,longitude=long_slice).squeeze()*1.94384449
v8 = data['v'].metpy.sel(lev=850.0,latitude=lat_slice,longitude=long_slice).squeeze()*1.94384449
h8 = data['gph'].metpy.sel(lev=850.0,latitude=lat_slice,longitude=long_slice).squeeze()
pwat = data['pwat'].metpy.sel(latitude=lat_slice,longitude=long_slice).squeeze()*0.0393700787402

This completes, but, all the data west of the prime meridian is missing.

I'm sure this is pretty basic...... would someone please explain how I can display the data across the prime meridian?

[Z-Richard]

Something you can try is to first query data from lons = np.arange(350, 360, 0.25) and then concatenate it with the subset from lons = np.arange(0, 10, 0.25). It is a bit manual, but I do not know of a better option here.

[stackjohn]

Hi @Z-Richard, thanks for taking a look at this, I really appreciate it.

I managed to get it to work ( I can't quite believe it given how long it took me 😄 ), with the following:

u8 = data['u'].where((data['u'].lon>=0) & (data['u'].lon<=10) &(data['u'].lat>=45) & (data['u'].lat<=65), drop=True)
v8 = data['v'].where((data['v'].lon>=0) & (data['v'].lon<=10) &(data['v'].lat>=45) & (data['v'].lat<=65), drop=True)
h8 = data['gph'].where((data['gph'].lon>=0) & (data['gph'].lon<=10) &(data['gph'].lat>=45) & (data['gph'].lat<=65), drop=True)
pwat = data['pwat'].where((data['pwat'].lon>=0) & (data['pwat'].lon<=10) &(data['pwat'].lat>=45) & (data['pwat'].lat<=65), drop=True)


u8_t = data['u'].where((data['u'].lon>=345) & (data['u'].lon<=360) &(data['u'].lat>=45) & (data['u'].lat<=65), drop=True)
v8_t = data['v'].where((data['v'].lon>=345) & (data['v'].lon<=360) &(data['v'].lat>=45) & (data['v'].lat<=65), drop=True)
h8_t = data['gph'].where((data['gph'].lon>=345) & (data['gph'].lon<=360) &(data['gph'].lat>=45) & (data['gph'].lat<=65), drop=True)
pwat_t = data['pwat'].where((data['pwat'].lon>=345) & (data['pwat'].lon<=360) &(data['pwat'].lat>=45) & (data['pwat'].lat<=65), drop=True)

u8_t['lon'] = np.where(u8_t['lon'] > 180, u8_t['lon'] - 360, u8_t['lon'])
v8_t['lon'] = np.where(v8_t['lon'] > 180, v8_t['lon'] - 360, v8_t['lon'])
h8_t['lon'] = np.where(h8_t['lon'] > 180, h8_t['lon'] - 360, h8_t['lon'])
pwat_t['lon'] = np.where(pwat_t['lon'] > 180, pwat_t['lon'] - 360, pwat_t['lon'])

u8 = u8.combine_first(u8_t)
v8 = v8.combine_first(v8_t)
h8 = h8.combine_first(h8_t)
pwat = pwat.combine_first(pwat_t)


u8 = u8.metpy.sel(lev=850.0).squeeze()*1.94384449
v8 = v8.metpy.sel(lev=850.0).squeeze()*1.94384449
h8 = h8.metpy.sel(lev=850.0).squeeze()
pwat = pwat.metpy.sel().squeeze()*0.0393700787402

I think perhaps this can be simplified but at least it works.

I would have expected there to be a nice in built option where you can convert all the lat lons together.

Also I've had issues where sometimes it will fail to return the data, I'm not sure if the datasets are lazily loaded and when you filter the data and execute the query, it goes away and talks to the server to get the data you want. In some cases it seems to fail to execute if the lons span over the meridian.

However the above seems to work fine. I would hope there is a cleaner solution however.

This nomads.ncep.noaa.gov server with this dataset is really useful as it provides 0.25 degrees at hourly intervals.

I have been using thredds.ucar.edu/thredds/catalog/grib/NCEP/GFS/Global_0p25deg/catalog.xml but that seems to be limited to 3 hourly intervals. However the thredds data seems much easier to work with. Is there an hourly interval version of the thredds data?

[dopplershift]

It would be nice to have a simpler way to handle this, but that would need some work on a longitude-specific index in Xarray itself most likely.

Regarding the 1-hour GFS, for what's currently on THREDDS, we are getting data from NOAA's CONDUIT feed. As far as I'm aware, hourly output are not available on this feed, so unfortunately there's no easy solution at the moment.

[stackjohn]

@dopplershift thanks again for taking the time to reply.