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For ERA-Interim (1st January 1979 to 31st August 2019) access through the ECMWF Web API stopped on 01 June 2023 Its successor ERA5 is available from the Climate Data Store (CDS) (What are the changes from ERA-Interim to ERA5?) and users are strongly advised to migrate to ERA5 (How to download ERA5). For those users who still need access to ERA-Interim after 01 June 2023 (subject to further notice), they can do so via the Climate Data Store (CDS) API. |
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Introduction
Some users are interested on geopotential (z) of the different model levels (ml). ECMWF provides two tools for this, a MetView macro and a Python script, which are the recommended methods, but only work on Linux, and output geopotential as an area, not for a specific location.
One of our customers, Mark Jackson from Cambridge Environmental Research Consultants (CERC), wanted to calculate geopotential and height above the surface for model levels, and this for one particular point location.
So Mark wrote his own script and kindly provided it to us. The script calculates the geopotential in m^2/s^2 on each model level for a single point location. It then also calculates the
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Introduction
Some users are interested on geopotential (z) of the different model levels (ml). ECMWF provide a script for this calculation, which is the recommended method, but that script requires the ECMWF GRIB API. The script below is intended as a workaround for users who can not work with the ECMWF GRIB API.
The script below uses as inputs temperature and specific humidity on model levels, along with geopotential and the pressure (z and lnsp) on the surface, and it creates as output the geopotential in m^2/s^2 for each model level. You can then also calculate the height in meters by dividing the geopotential by the gravity of Earth (9.80665 m/s^2).
Notes:
This is a two step process: first you have to obtain the required input data, then you perform the actual geopotential and height calculation.
Notes:
- All data is in NetCDF All data is in NetCDF format
- The computation script requires Python and the ; the input data script requires Python and the ECMWF WebAPI to access ECMWF public datasets
The script only works correctly for ECMWF ERA-Interim data, do not use it with other datasets
Input data has to be gridded, not spectral
- In the computation script, paths and other arguments are hard-coded, so you will need to adapt the script to your system
Step1: Get
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data
The first script downloads ERA-Interim data from ECMWF through the ECMWF Web API:
- Temperature (t) and specific humidity (q)
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- , both on each model level
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- , as file 'tq_ml.nc'.
- The log of surface pressure (lnsp) and geopotential (z), both on model level 1, as file 'zlnsp_ml.nc'.
The Python script:
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The scripts below downloads all this data from ECMWF, generating two output files. You can change date, type, step, time, grid and area in the script, but make sure you use the same values in both blocks so that the two output files are synchronized. Later the calculation of geopotential will iterate through the date/time/step parameters, calculating values for multiple times.
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#!/usr/bin/env python from ecmwfapi import ECMWFDataServer server = ECMWFDataServer() server.retrieve({ "class": "ei", "dataset": "interim", "expver": "1", "levelist": "all", "levtype": "ml", "param": "t/q", "stream": "oper", "date": "2015-0801-01", #date: Specify a single date as "2015-08-01" or a period as "2015-08-01/to/2015-08-31". "type": "an", #type: Use an (analysis) unless you have a particular reason to use fc (forecast). "time": "00:00:00", #time: With type=an, time can be any of "00:00:00/06:00:00/12:00:00/18:00:00". With type=fc, time can be any of "00:00:00/12:00:00", "step": "0", #step: With type=an, step is always "0". With type=fc, step can be any of "3/6/9/12". "grid": "0.75/0.75", #grid: Only regular lat/lon grids are supported. "area": "75/-20/10/60", #area: N/W/S/E, here we have Europe. "format": "netcdf", "target": "tq_ml.nc", #target: the name of the output file. }) server.retrieve({ "class": "ei", "dataset": "interim", "expver": "1", "levelist": "1", "levtype": "ml", "param": "z/lnsp", "stream": "oper", "date": "2015-0801-01", "type": "an", "time": "00:00:00", "step": "0", "grid": "0.75/0.75", "area": "75/-20/10/60", "format": "netcdf", "target": "zlnsp_ml.nc", }) |
Compute geopotential on model levels
The following script was written by Mark Jackson from Cambridge Environmental Research Consultants Ltd., based on a similar script for data in GRIB format.
Copy the script and save it to your computer.
You can change date, type, step, time, grid and area in the script, but make sure you use the same values in both 'execute' blocks so that the two output files are synchronized. Later the calculation of geopotential will iterate through the date/time/step parameters, calculating values for multiple times.
Run the script.
Outputs: A file 'tq_ml.nc' and a file 'zlnsp_ml.nc', both in the current working directory.
Step2: Compute geopotential on model levels
This script was kindly provided by Mark Jackson from Cambridge Environmental Research Consultants Ltd. The script is provided 'as is' and is not supported by ECMWF/Copernicus or by CERC.
The Python script:
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# Copyright 2016 Cambridge Environmental Research Consultants Ltd.
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# This software is licensed under | ||||||||
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# Copyright 2016 Cambridge Environmental Research Consultants Ltd. # # This software is licensed under the terms of the Apache Licence Version 2.0 # which can be obtained at http://www.apache.org/licenses/LICENSE-2.0 # # ************************************************************************** # Function : compute_geopotential_on_ml_netcdf # # Author (date) : Mark Jackson (1/12/2016) # # Category : COMPUTATION # # OneLineDesc : Computes geopotential and height on model levels using netCDF files # # Description : Computes geopotential on model levels using netCDF files. # Based on the Python script by Cristian Simarro which uses GRIB files: # https://softwareconfluence.ecmwf.int/wiki/display/GRIB/Compute+geopotential+on+model+levels # Which was based on the code of the Metview function mvl_geopotential_on_ml: # https://softwareconfluence.ecmwf.int/metview/mvl_geopotential_on_ml # This in turn was based on code from Nils Wedi, the IFS documentation: # https://softwareconfluence.ecmwf.int/wiki/display/IFS/CY41R1+Official+IFS+Documentation # part III. Dynamics and numerical procedures # and an optimised implementation by Dominique Lucas. # Ported to Python by Cristian Simarro # # Parameters : FileA.nc - netCDF file with the levelist of t and q. Does not require all levels # but does require a contiguous set of levels all the way to the bottom. # FileB.nc - netCDF file with levelist 1 for params z and lnsp # # Return Value : outputs CSV files # with geopotential and height (relative to terrain) on each model level. # # Dependencies : netCDF4, numpy, scipy # from __future__ import print_function # make sure print behaves the same in Python 2.7 and 3.x import netCDF4 from netCDF4 import num2date import numpy as np from scipy import interpolate import datetime import sys import io import math #Variable names in the netCDF #(File A - model levels) # level = model level numbers, posssiblepossible values 1-60 # t = temperature K # q = specific humidity kg/kg #(File B) # lnsp = log surface pressure # z = surface geopotential #arguments #ONEDAY - read these from the command line FILE_A_PATH="G:\\MiscProjects\\ERA-Interim-Python\\20160922-InvestigateProfiles-netCDF\\FileA.nc" FILE_B_PATH="G:\\MiscProjects\\ERA-Interim-Python\\20160922-InvestigateProfiles-netCDF\\FileB.nc" GRID_LAT=5554.87575 #(degrees N) GRID_LONG=-4.5 #(degrees E) OUT_DIR_PATH="G:\\MiscProjects\\ERA-Interim-Python\\20161101-Check MarkJ geopotentials against ECMWF geopotentials\\output\\" #Routine to Read File A data file for t, q values at a particular grid point def readfa(fileapath): #Connect to data file for reading print() print("==================================================") print("File A information") print("Filename{}".format(fileapath)) fa = netCDF4.Dataset(fileapath, 'r') #Variables as netCDF variable objects print() print("--------------------------------------------------") print("Variables") print(fa.variables.keys()) # get all variable names fanclongs = fa.variables['longitude'] print(fanclongs) fanclats = fa.variables['latitude'] print(fanclats) fanclevels = fa.variables['level'] print(fanclevels) fanctimes = fa.variables['time'] print(fanctimes) fancts = fa.variables['t'] print(fancts) fancqs = fa.variables['q'] print(fancqs) #Get level values (either model levels or pressure levels) and number of levels falevels=fanclevels[:] print() print("--------------------------------------------------") print("Model levels") fanlevels=falevels.shape[0] print("There are {} levels: {} - {}".format(fanlevels, falevels[0], falevels[fanlevels-1])) #Get time values and number of times fatimes=fanctimes[:] print() print("--------------------------------------------------") print("File A Times") print(fatimes) fantimes=fatimes.shape[0] #Get python datetime for each time fapydts=num2date(fatimes, fanctimes.units) #Output first 10 datetimes print() print("First 10 times as date-time") print([pydt.strftime('%Y-%m-%d %H:%M:%S') for pydt in fapydts[:10]]) #Get lat and long values falats = fanclats[:] print() print("--------------------------------------------------") print("File A Latitudes") print(falats) falongs = fanclongs[:] print() print("--------------------------------------------------") print("File A Longitudes") print(falongs) #Get index of grid point of interest failat=np.where(falats==GRID_LAT)[0] failong=np.where(falongs==GRID_LONG)[0] print() print("==================================================") print("Grid point location: latitude and longitude indexes for lat {} and long {}".format(GRID_LAT, GRID_LONG)) print(failat) print(failong) #Get t, q values for specified grid point for all levels (slicing) #The result is still a 4D array with 1 latitude and 1 longitude print() print("==================================================") print("Get t, q values for all levels") fats=fancts[range(fantimes),range(fanlevels),failat,failong] faqs=fancqs[range(fantimes),range(fanlevels),failat,failong] return (fats, faqs, falevels, fapydts) #Routine to Read File B data file for z, lnsp values at a particular grid point def readfb(fbfilepath): #Connect to file for reading print() print("==================================================") print("fb File information") print("Filename{}".format(fbfilepath)) fbf = netCDF4.Dataset(fbfilepath, 'r') print(fbf) print() #Variables as netCDF variable objects print() print("--------------------------------------------------") print("Variables") print(fbf.variables.keys()) # get all variable names fbnclongs = fbf.variables['longitude'] print(fbnclongs) fbnclats = fbf.variables['latitude'] print(fbnclats) fbnctimes = fbf.variables['time'] print(fbnctimes) fbnczs = fbf.variables['z'] print(fbnczs) fbnclnsps = fbf.variables['lnsp'] print(fbnclnsps) #Get time values and number of times fbtimes=fbnctimes[:] print() print("--------------------------------------------------") print("fb Times") print(fbtimes) fbntimes=fbtimes.shape[0] #Get python datetime for each time fbpydts=num2date(fbtimes, fbnctimes.units) #Output first 10 datetimes print() print("fb First 10 times as date-time") print([pydt.strftime('%Y-%m-%d %H:%M:%S') for pydt in fbpydts[:10]]) #Get lat and long values fblats = fbnclats[:] print() print("--------------------------------------------------") print("fb Latitudes") print(fblats) fblongs = fbnclongs[:] print() print("--------------------------------------------------") print("fb Longitudes") print(fblongs) #Get index of grid point of interest fbilat=np.where(fblats==GRID_LAT)[0] fbilong=np.where(fblongs==GRID_LONG)[0] print() print("==================================================") print("Grid point location: fb latitude and longitude indexes for lat {} and long {}".format(GRID_LAT, GRID_LONG)) print(fbilat) print(fbilong) #Get z, lnsp values for specified grid point (slicing) #The result is still a 3D array with 1 latitude and 1 longitude print() print("==================================================") print("Get z, lnsp values ") fbzs=fbnczs[range(fbntimes),fbilat,fbilong] print('shape of z slice: %s' % repr(fbzs.shape)) fblnsps=fbnclnsps[range(fbntimes),fbilat,fbilong] return (fbzs, fblnsps, fbpydts) #Read File A file fats, faqs, falevels, fapydts = readfa(FILE_A_PATH) fanlevels=falevels.shape[0] fantimes=fapydts.shape[0] #Read File B file fbzs, fblnsps, fbpydts = readfb(FILE_B_PATH) print() print("==================================================") print("Running...") #Calculation of geopotential and height def calculategeoh(z, lnsp, ts, qs, levels): heighttoreturn=np.full(ts.shape[0], -999, np.double) geotoreturn=np.full(ts.shape[0], -999, np.double) Rd = 287.06 z_h = 0 #surface pressure sp = math.exp(lnsp) # A and B parameters to calculate pressures for model levels, # extracted from an ECMWF ERA-Interim GRIB file and then hardcoded here pv = [ 0.0000000000e+000, 2.0000000000e+001, 3.8425338745e+001, 6.3647796631e+001, 9.5636962891e+001, 1.3448330688e+002, 1.8058435059e+002, 2.3477905273e+002, 2.9849584961e+002, 3.7397192383e+002, 4.6461816406e+002, 5.7565112305e+002, 7.1321801758e+002, 8.8366040039e+002, 1.0948347168e+003, 1.3564746094e+003, 1.6806403809e+003, 2.0822739258e+003, 2.5798886719e+003, 3.1964216309e+003, 3.9602915039e+003, 4.9067070313e+003, 6.0180195313e+003, 7.3066328125e+003, 8.7650546875e+003, 1.0376125000e+004, 1.2077445313e+004, 1.3775324219e+004, 1.5379804688e+004, 1.6819472656e+004, 1.8045183594e+004, 1.9027695313e+004, 1.9755109375e+004, 2.0222203125e+004, 2.0429863281e+004, 2.0384480469e+004, 2.0097402344e+004, 1.9584328125e+004, 1.8864750000e+004, 1.7961359375e+004, 1.6899468750e+004, 1.5706449219e+004, 1.4411125000e+004, 1.3043218750e+004, 1.1632757813e+004, 1.0209500000e+004, 8.8023554688e+003, 7.4388046875e+003, 6.1443164063e+003, 4.9417773438e+003, 3.8509133301e+003, 2.8876965332e+003, 2.0637797852e+003, 1.3859125977e+003, 8.5536181641e+002, 4.6733349609e+002, 2.1039389038e+002, 6.5889236450e+001, 7.3677425385e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 0.0000000000e+000, 7.5823496445e-005, 4.6139489859e-004, 1.8151560798e-003, 5.0811171532e-003, 1.1142909527e-002, 2.0677875727e-002, 3.4121163189e-002, 5.1690407097e-002, 7.3533833027e-002, 9.9674701691e-002, 1.3002252579e-001, 1.6438430548e-001, 2.0247590542e-001, 2.4393314123e-001, 2.8832298517e-001, 3.3515489101e-001, 3.8389211893e-001, 4.3396294117e-001, 4.8477154970e-001, 5.3570991755e-001, 5.8616840839e-001, 6.3554745913e-001, 6.8326860666e-001, 7.2878581285e-001, 7.7159661055e-001, 8.1125342846e-001, 8.4737491608e-001, 8.7965691090e-001, 9.0788388252e-001, 9.3194031715e-001, 9.5182150602e-001, 9.6764522791e-001, 9.7966271639e-001, 9.8827010393e-001, 9.9401944876e-001, 9.9763011932e-001, 1.0000000000e+000 ] levelSize=60 A = pv[0:levelSize+1] B = pv[levelSize+1:] Ph_levplusone = A[levelSize] + (B[levelSize]*sp) #Get a list of level numbers in reverse order reversedlevels=np.full(levels.shape[0], -999, np.int32) for iLev in list(reversed(range(levels.shape[0]))): reversedlevels[levels.shape[0] - 1 - iLev] = levels[iLev] #Integrate up into the atmosphere from lowest level for lev in reversedlevels: #lev is the level number 1-60, we need a corresponding index into ts and qs ilevel=np.where(levels==lev)[0] t_level=ts[ilevel] q_level=qs[ilevel] #compute moist temperature t_level = t_level * (1.+0.609133*q_level) #compute the pressures (on half-levels) Ph_lev = A[lev-1] + (B[lev-1] * sp) if lev == 1: dlogP = math.log(Ph_levplusone/0.1) alpha = math.log(2) else: dlogP = math.log(Ph_levplusone/Ph_lev) dP = Ph_levplusone-Ph_lev alpha = 1. - ((Ph_lev/dP)*dlogP) TRd = t_level*Rd # z_f is the geopotential of this full level # integrate from previous (lower) half-level z_h to the full level z_f = z_h + (TRd*alpha) #Convert geopotential to height heighttoreturn[ilevel] = z_f / 9.80665 #Geopotential (add in surface geopotential) geotoreturn[ilevel] = z_f + z # z_h is the geopotential of 'half-levels' # integrate z_h to next half level z_h=z_h+(TRd*dlogP) Ph_levplusone = Ph_lev return geotoreturn, heighttoreturn #Output all the values for the specified grid point #Iterate over all the times and write out the data for itime in range(fantimes): if fapydts[itime]!=fbpydts[itime]: print("ERROR! Mismatching times in the files. Time number {}: File A = {} and File B = {}".format( itime, fapydts[itime].strftime('%Y-%m-%d %H:%M:%S'), fbpydts[itime].strftime('%Y-%m-%d %H:%M:%S'))) sys.exit() pydt=fapydts[itime] print(" Processing {}/{} : {} ...".format(itime, fantimes, pydt.strftime('%Y-%m-%d %H:%M:%S'))) z, lnsp = fbzs[itime,0,0], fblnsps[itime,0,0] sline="{}, {}, {}".format(pydt.strftime('%Y-%m-%d %H:%M:%S'), z, lnsp) #Calculate geopotentials and heights for the model levels geo, h = calculategeoh(z, lnsp, fats[itime,range(fanlevels),0,0], faqs[itime,range(fanlevels),0,0], falevels) with io.open(OUT_DIR_PATH + pydt.strftime('%Y%m%d-%H') + ".csv", 'w', newline='\r\n') as writer: sheader1 = "lat, long, geopotential, h\n" writer.write(unicode(sheader1)) #Iterate over levels for this time for ilevel in range(fanlevels): sline="{}, {}, {}, {}".format( GRID_LAT, GRID_LONG, geo[ilevel], h[ilevel]) sline+="\n" writer.write(unicode(sline)) print("Finished") |
Examples
- This example will compute the geopotential on the 2015-10-08 time 00 operational analysis model levels (137). Below you can see the MARS user documentation request used to retrieve both files. You can set a different class/stream/type for the input data. The gribType and resolution can also be changed.
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python compute_geopotential_on_ml.py tq_ml_20151008_00.grib zlnsp_ml_20151008_00.grib
python compute_geopotential_on_ml.py tq_ml_20151008_00.grib zlnsp_ml_20151008_00.grib -o my_grib.grib
Copy the script and save it to your computer.
You have to adapt:
- line 58: specify your file containing t and q ('tq_ml.nc')
- line 59: specify your file containing z and lnsp ('tzlnsp.nc')
- line 60/61: specify lat/long of your point of interest. These must be multiples of the grid resolution of your input files.
- line 62: specify an output directory.
Run the script.
Outputs: CSV files (one per timestamp) with geopotential and height (relative to terrain) on each model level.
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This document has been produced in the context of the Copernicus Climate Change Service (C3S). The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement signed on 11/11/2014). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose. The users thereof use the information at their sole risk and liability. For the avoidance of all doubt, the European Commission and the European Centre for Medium-Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view. |
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