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met_data_st.py
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met_data_st.py
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#!/usr/bin/env python
# coding: utf-8
# In[1]:
#https://github.com/giswqs/geemap/blob/master/examples/notebooks/11_export_image.ipynb
import ee
import geemap
import numpy as np
import matplotlib.pyplot as plt
import os
#from paths import *
import requests
import pandas as pd
import xarray as xr
from os import listdir
from datetime import datetime, timedelta, date
import contextlib
# Initialize the Earth Engine module
ee.Initialize()
# In[9]:
#########################################################################
############################ USER INPUTS ################################
#########################################################################
# PATHS
# path to temporary folder to store tif files from gee
TIFpath = '/nfs/depot/cce_u1/hill/dfh/op_snowmodel/get_met_data/GEE_Downloads_st/'
# path to where you want your output met .dat fime
OUTpath = '/nfs/depot/cce_u1/hill/dfh/op_snowmodel/st_snowmodel/met/mm_st.dat'
# DOMAIN
# choose the modeling domain
domain = 'ST'
# TIME
# choose if want to set 'manual' or 'auto' date
date_flag = 'auto'
# If you choose 'manual' set your dates below
# This will start on the 'begin' date at 0:00 and the last iteration will
# be on the day before the 'end' date below.
st_dt = '2020-10-01'
ed_dt = '2021-09-30'
#########################################################################
# In[12]:
# Date setup function
def set_dates(st_dt,ed_dt,date_flag):
if date_flag == 'auto':
# ###automatically select date based on today's date
hoy = date.today()
antes = timedelta(days = 1)
#end date 3 days before today's date
fecha = hoy - antes
eddt = fecha.strftime("%Y-%m-%d")
#whole water year
if (hoy.month == 10) & (hoy.day == 2):
eddt = fecha.strftime("%Y-%m-%d")
stdt = str(hoy.year - 1)+'-10-01'
#start dates
elif fecha.month <10:
stdt = str(fecha.year - 1)+'-10-01'
else:
stdt = str(fecha.year)+'-10-01'
elif date_flag == 'manual':
stdt = st_dt
# add one day to end date because GEE ends on date before last date
eddt = (datetime.strptime(ed_dt, "%Y-%m-%d")+timedelta(days = 1)).strftime("%Y-%m-%d")
return stdt, eddt
# Download CFSv2 met data function
def get_cfsv2(domain, TIFpath, stdt, eddt):
# in GEE the last iteration is on the day before the 'end' date below
# we adjust this here since it is not intuative
#eddt = (datetime.strptime(eddt, '%Y-%m-%d')+timedelta(days = 1)).strftime('%Y-%m-%d')
#create directory with initiation date for ensemble if it doesn't exist
get_ipython().system('mkdir -p $TIFpath')
#path to CSO domains
domains_resp = requests.get("https://raw.githubusercontent.com/snowmodel-tools/preprocess_python/master/CSO_domains.json")
domains = domains_resp.json()
'''
// These are the min and max corners of your domain in Lat, Long
// Western Wyoming
// Input the minimum lat, lower left corner
'''
minLat = domains[domain]['Bbox']['latmin']
#// Input the minimum long, lower left corner
minLong = domains[domain]['Bbox']['lonmin']
#// Input the max lat, upper right corner
maxLat = domains[domain]['Bbox']['latmax']
#// Input the max Long, upper right corner
maxLong = domains[domain]['Bbox']['lonmax']
#/ These are the min and max corners of your reanalysis in Lat, Long (create a slightly larger box)
#// Input the minimum lat, lower left corner
minLatMET = (minLat - 0.25);
#// print(minLat2);
#// Input the minimum long, lower left corner
minLongMET = (minLong - 0.5);
#// Input the max lat, upper right corner
maxLatMET = (maxLat + 0.25);
#// Input the max Long, upper right corner
maxLongMET = (maxLong + 0.5);
# This resolution for the NLCD and DEM outputs for the SnowModel domain in meters
sm_resolution = 50
'''// Resolution for the PRISM output. This shoud change by Latitude of the domain
// because the PRISM product spatial resolution is 2.5 minutes, which equals 150 arc seconds.
// You can use this arc-second calculator to estimate the correct value for the PRISM resolution by latitude
// https://opendem.info/arc2meters.html
// This is one arc-second in meters for 43 degrees N Latitude'''
one_arcsecond = 22.57
PRISM_resolution = one_arcsecond * 150
'''// Define the final output projection using EPSG codes'''
epsg_code = domains[domain]['mod_proj']
#// Name the DEM output
dem_name = 'DEM'
#// Name the Land Cover output
lc_name = 'NLCD2016'
my_domain = ee.Geometry.Rectangle(**{'coords':[minLong,minLat,maxLong,maxLat],'proj': 'EPSG:4326','geodesic':True,});
my_domain_met = ee.Geometry.Rectangle([minLongMET,minLatMET,maxLongMET,maxLatMET])
# download reanalysis data
cfsv2 = ee.ImageCollection('NOAA/CFSV2/FOR6H') .filterBounds(my_domain_met) .filter(ee.Filter.date(stdt,eddt))
data = cfsv2.select('Temperature_height_above_ground', 'Geopotential_height_surface', 'u-component_of_wind_height_above_ground', 'v-component_of_wind_height_above_ground', 'Pressure_surface', 'Specific_humidity_height_above_ground', 'Precipitation_rate_surface_6_Hour_Average')
with contextlib.redirect_stdout(None):
geemap.ee_export_image_collection(data, out_dir=TIFpath,region=my_domain_met,scale=22200,crs=epsg_code)
# In[21]:
# function to check for missing dates
def missing_slice_check(stdt, eddt, TIFpath):
# create a 6-hourly timeseries with no missing values from the start to end date
timesin = pd.date_range(start=stdt, end=eddt, freq='6H')[:-1]
for time in timesin:
nam = time.strftime('%Y%m%d%H')
# compile list of all tif files downloaded from gee
gee_times =[]
for file in listdir(TIFpath):
if file.endswith("tif"):
datetmp = datetime.strptime(file[:-4], '%Y%m%d%H')
gee_times.append(datetmp)
gee_times = sorted(gee_times)
# check for to see if all time slices downloaded from GEE
if len(timesin) != len(gee_times):
#### on 4/16 Nina edited code to print all missing timeslices
print('gee is missing timeslice(s):\n',timesin[~timesin.isin(gee_times)].values)
# if 4 or more consecutive timeslices are missing - quit the function
duration = []
for i in range(len(gee_times)-1):
time_delta = gee_times[i+1] - gee_times[i]
duration.append(time_delta.total_seconds()/60/60)
if max(duration) >= 48:
print('at least two full days of met data are missing - quitting function')
# if there are less than 4 missing consecutive time slices
# repeat the met data from the last valid time slice
else:
missing_idx = np.where(~timesin.isin(gee_times))
missing_dt = timesin[missing_idx]
if len(missing_dt)==1:
if missing_idx == 0:
pre_dt=TIFpath+timesin[np.squeeze(missing_idx)+1].strftime('%Y%m%d%H')+'.tif'
mis_dt = TIFpath+timesin[np.squeeze(missing_idx)].strftime('%Y%m%d%H')+'.tif'
get_ipython().system('cp $pre_dt $mis_dt')
print('replaced', timesin[np.squeeze(missing_idx)].strftime('%Y%m%d%H'),' with ', timesin[np.squeeze(missing_idx)-1].strftime('%Y%m%d%H'))
else:
pre_dt=TIFpath+timesin[np.squeeze(missing_idx)-1].strftime('%Y%m%d%H')+'.tif'
mis_dt = TIFpath+timesin[np.squeeze(missing_idx)].strftime('%Y%m%d%H')+'.tif'
get_ipython().system('cp $pre_dt $mis_dt')
print('replaced', timesin[np.squeeze(missing_idx)].strftime('%Y%m%d%H'),' with ', timesin[np.squeeze(missing_idx)-1].strftime('%Y%m%d%H'))
else:
for j in range(len(missing_dt)):
if np.squeeze(missing_idx)[j] == 0:
print('choose earlier start date so missing time slices can be filled in')
else:
pre_dt=TIFpath+timesin[np.squeeze(missing_idx)[j]-1].strftime('%Y%m%d%H')+'.tif'
mis_dt = TIFpath+timesin[np.squeeze(missing_idx)[j]].strftime('%Y%m%d%H')+'.tif'
get_ipython().system('cp $pre_dt $mis_dt')
print('replaced', timesin[np.squeeze(missing_idx)[j]].strftime('%Y%m%d%H'),' with ', timesin[np.squeeze(missing_idx)[j]-1].strftime('%Y%m%d%H'))
# Format gee files for SnowModel function
def MET2SM(TIFpath, OUTpath, stdt, eddt):
# create a 6-hourly timeseries with no missing values from the start to end date
timesin = pd.date_range(start=stdt, end=eddt, freq='6H')[:-1]
#load first tif to get dimensions
ar = xr.open_rasterio(TIFpath+timesin[0].strftime('%Y%m%d%H')+'.tif')
# empty arrays for each met variable
T = np.empty((len(timesin),ar.shape[1],ar.shape[2]))
Z = np.empty((len(timesin),ar.shape[1],ar.shape[2]))
U = np.empty((len(timesin),ar.shape[1],ar.shape[2]))
V = np.empty((len(timesin),ar.shape[1],ar.shape[2]))
P = np.empty((len(timesin),ar.shape[1],ar.shape[2]))
H = np.empty((len(timesin),ar.shape[1],ar.shape[2]))
PR = np.empty((len(timesin),ar.shape[1],ar.shape[2]))
# extract met data from tifs
for i in range(len(timesin)):
#load tif file
nam = TIFpath+timesin[i].strftime('%Y%m%d%H')+'.tif'
ar = xr.open_rasterio(nam)
T[i,:,:] = ar[0,:,:]
Z[i,:,:] = ar[1,:,:]
U[i,:,:] = ar[2,:,:]
V[i,:,:] = ar[3,:,:]
P[i,:,:] = ar[4,:,:]
H[i,:,:] = ar[5,:,:]
PR[i,:,:] = ar[6,:,:]
#number of timesteps per dat
pointsperday = 4
#compute number of grid points and time steps from size of 3d matrix
t,y,x=PR.shape
gridpts=x*y
tsteps=t
#create y m d h vectors
year = timesin.year
month = timesin.month
day = timesin.day
hour = timesin.hour
#create ID numbers for the grid points
ID=1000000+np.linspace(1,gridpts,gridpts)
#create matrices of x and y values
X, Y = np.meshgrid(ar.x.values, ar.y.values)
X=X.flatten(order='F')
Y=Y.flatten(order='F')
#elevation is static (doesn't change with time)
elev=Z[1,:,:].flatten(order='F')
#find number of grid points with <0 elevation. Note: this is related to the
#subroutine met_data_check in the preprocess_code.f. that subroutine seems
#to suggest that negative elevations are ok (say, death valley). But, the
#code itself checks for negative elevations and stops execution is any
#negatives are found.
I = np.where(elev>=0)
validgridpts=np.shape(I)[1]
#remove data at points with neg elevations
ID=ID[I]
X=X[I]
Y=Y[I]
elev=elev[I]
#we are now ready to begin our main loop over the time steps.
fid= open(OUTpath,"w+")
for j in range(tsteps):
#first we write the number of grid points
fid.write('{0:6d}\n'.format(validgridpts))
#prep data matrix for this time step. First, grab the jth time slice
Prtmp=PR[j,:,:].flatten(order='F')
Htmp=H[j,:,:].flatten(order='F')
Ptmp=P[j,:,:].flatten(order='F')
Ttmp=T[j,:,:].flatten(order='F')
Utmp=U[j,:,:].flatten(order='F')
Vtmp=V[j,:,:].flatten(order='F')
#remove data at points with neg elevations
Prtmp=Prtmp[I]
Htmp=Htmp[I]
Ptmp=Ptmp[I]
Ttmp=Ttmp[I]
Utmp=Utmp[I]
Vtmp=Vtmp[I]
#convert precip rate to precip DEPTH (mm) during time interval
Prtmp=Prtmp*24*3600/pointsperday
#convert specific hum. to RH from Clausius-Clapeyron. T is still in K
RHtmp=0.263*Ptmp*Htmp*(np.exp(17.67*(Ttmp-273.16)/(Ttmp-29.65)))**(-1)
#compute wind speed
SPDtmp=np.sqrt(Utmp**2+Vtmp**2)
#compute wind direction. 0-360, with 0 being true north! 90 east, etc.
DIRtmp=np.degrees(np.arctan2(Utmp,Vtmp))
K=np.where(DIRtmp>=180)
J=np.where(DIRtmp<180)
DIRtmp[K]=DIRtmp[K]-180
DIRtmp[J]=DIRtmp[J]+180
#put T in C
Ttmp=Ttmp-273.16
for z in range(len(Prtmp)):
fid.write('{:5.0f}\t'.format(int(year[j]))+'{:5.0f}\t'.format(int(month[j]))+
'{:3.0f}\t'.format(int(day[j]))+'{:6.3f}\t'.format(hour[j])+
'{:9.0f}\t'.format(int(ID[z]))+'{:12.1f}\t'.format(X[z])+
'{:12.1f}\t'.format(Y[z])+'{:8.1f}\t'.format(elev[z])+
'{:9.2f}\t'.format(Ttmp[z])+'{:9.2f}\t'.format(RHtmp[z])+
'{:9.2f}\t'.format(SPDtmp[z])+'{:9.2f}\t'.format(DIRtmp[z])+
'{:9.2f}\n'.format(Prtmp[z]))
fid.close()
# # RUN THE THANG
# In[13]:
# set time parameters
stdt, eddt = set_dates(st_dt,ed_dt,date_flag)
# In[17]:
# download GEE data
get_cfsv2(domain, TIFpath, stdt, eddt)
# In[22]:
# fill in missing time slices or throw error if missing >4 slices
missing_slice_check(stdt, eddt, TIFpath)
# In[26]:
# build SnowModel met file
MET2SM(TIFpath, OUTpath, stdt, eddt)
# In[30]:
# delete directory with tif files
get_ipython().system('rm -rf $TIFpath')