In dinilu/TraCE21kaDSR: Functions to downscale TraCE21ka paleoclimate simulations
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
Trace-21ka paleoclimate data
Trace-21ka paleoclimate data can be downloaded from https://www.earthsystemgrid.org/project/trace.html. This website, however, does not provide documentation with detailed information (file names, variable names, units, etcetera) used in the data repository. You have to look for such information in the following link (http://www.cgd.ucar.edu/ccr/TraCE/fields.html). Complementary information can be found in documentation website for the model and the developing laboratory: http://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html and http://www.cesm.ucar.edu/models/atm-cam/docs/cam2.0/UsersGuide/UG-45.html.
We opted to download files for all time periods (since 22.000 years BP to 1990 CE) for 'surface temperature (radiative)' (TS), 'maximum surface temperature over output period' (TSMX), 'minimum surface temperature over output period' (TSMN), 'solar insolation' (SOLIN), 'surface pressure' (PS), 'convective precipitation rate (liq + ice)' (PRECC), 'surface latent heat flux' (LHFLX), and 'vertically-integrated total cloud' (CLDTOT).
Inspect files and variables {.tabset}
Downloaded files can be inspected with the following code:
TSMX
di.trace <- loadeR::dataInventory("../../Data/TraCE21ka/TSMX/trace.36.400BP-1990CE.cam2.h0.TSMX.2160101-2204012.nc")
str(di.trace)
TSMN
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/TSMN/trace.36.400BP-1990CE.cam2.h0.TSMN.2160101-2204012.nc")
str(di.trace)
TS
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/TS/trace.36.400BP-1990CE.cam2.h0.TS.2160101-2204012.nc")
str(di.trace)
PRECC
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/PRECC/trace.36.400BP-1990CE.cam2.h0.PRECC.2160101-2204012.nc")
str(di.trace)
RELHUM
The file for relative humidity in TraCE21ka have information for different atmospheric levels. In this case, we first selected surface preasure level and then create a new file with the information to simplify the rest of computation.
```{bash extract_relhum_surface_level, eval=FALSE}
BASH script
cdo sellevel,992.5561 ../../Data/Trace21ka/RELHUM/trace.36.400BP-1990CE.cam2.h0.RELHUM.2160101-2204012.nc ../../Data/Trace21ka/RELHUM/surface_level/trace.36.400BP-1990CE.cam2.h0.RELHUM.2160101-2204012.nc
Once, the surface pressure level was selected the new file was inspected.
```r
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/RELHUM/surface_level/trace.36.400BP-1990CE.cam2.h0.RELHUM.2160101-2204012.nc")
str(di.trace)
CLDTOT
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/CLDTOT/trace.36.400BP-1990CE.cam2.h0.CLDTOT.2160101-2204012.nc")
str(di.trace)
PS
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/PS/trace.36.400BP-1990CE.cam2.h0.PS.2160101-2204012.nc")
str(di.trace)
U
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/U/trace.36.400BP-1990CE.cam2.h0.U.2160101-2204012.nc")
str(di.trace)
V
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/V/trace.36.400BP-1990CE.cam2.h0.V.2160101-2204012.nc")
str(di.trace)
Create dictionary
In order to load those files in R, the climate4R framework allow to create a dictionary that is used to standardize variables.
file.create("../../Data/Trace21ka/dictionary.dic")
writeLines(c("identifier,short_name,time_step,lower_time_bound,upper_time_bound,cell_method,offset,scale,deaccum,derived,interface",
"tasmax,TSMX,1mo,0,12,max,-273.15,1,0,0,",
"tasmin,TSMN,1mo,0,12,min,-273.15,1,0,0,",
"tas,TS,1mo,0,12,mean,-273.15,1,0,0,",
"pr,PRECC,1mo,0,12,mean,0,2592000000,0,0,",
"hurs,RELHUM,1mo,0,12,mean,0,1,0,0,",
"cld,CLDTOT,1mo,0,12,mean,0,1,0,0,",
"ps,PS,1mo,0,12,mean,0,1,0,0,",
"u,U,1mo,0,12,mean,0,1,0,0,",
"v,V,1mo,0,12,mean,0,1,0,0,"),
"../../Data/Trace21ka/dictionary.dic")
UERRA historical reanalisis data
Reanalysis data for the historical period (1961-1990) comes from the UERRA project (https://www.uerra.eu/). The data can be downloaded from the Climate Data Store at the Copernicus website (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-single-levels?tab=overview). Technical details can be found in the same Climate Data Store or at the links provided in the website of the European Centre for Medium-Range Weather Forecasts (ECMWF) for the UERRA project (https://www.ecmwf.int/en/research/projects/uerra).
The project provide reanalysis from two different systems (UERRA-HARMONIE and MESCAN-SURFEX) that produce data in Lambert Conformal Conic Grid at two spatial resolutions: 11km x 11km for the UERRA-HARMONIE system and 5.5km x 5.5km for the MESCAN-SURFEX system. Because we were mainly insterested in downscaling temperature and precipitation variables and reanalysis data would be our predictand variables, we downloaded temperature and precipitation data using the following python scripts.
Note that CDS requires to register a free account to access data. Downloading scripts could request user and password in each iteration or can be configured to store account data in the system.
Data format: Data were downloaded in GRIB format because the Climate Data Operator library (cdo) seems to behave better with GRIB than with NetCDF files.
Downloading files from dataserver {.tabset}
Temperature
```{python Download_UERRA-HARMONIE_2m_temperature, eval=FALSE}
Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990):
print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': '2m_temperature',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/'+ str(y) +'_2m_temperature.grib')
### Precipitation
```{python Download_MESCAN-SURFEX_total_precipitation, eval=FALSE}
# Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990):
print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'origin': 'mescan_surfex',
'variable': 'total_precipitation',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
'format': 'grib',
},
'MESCAN-SURFEX/'+ str(y) +'_total_precipitation.grib')
Relative humidity
```{python Download_UERRA-HARMONIE_2m_relative_humidity, eval=FALSE}
Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990):
print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': '2m_relative_humidity',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/2m_relative_humidity/'+ str(y) +'_2m_relative_humidity.grib')
### Wind speed
```{python Download_UERRA-HARMONIE_10m_wind_speed, eval=FALSE}
# Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990):
print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': '10m_wind_speed',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/10m_wind_speed/'+ str(y) +'_10m_wind_speed.grib')
Total cloud cover
```{python Download_UERRA-HARMONIE_total_cloud_cover, eval=FALSE}
Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990):
print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': 'total_cloud_cover',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/total_cloud_cover/'+ str(y) +'_total_cloud_cover.grib')
### Surface pressure
```{python Download_UERRA-HARMONIE_surface_pressure, eval=FALSE}
# Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990):
print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': 'surface_pressure',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/surface_pressure/'+ str(y) +'_surface_pressure.grib')
While we were interested in downscaling at the spatial resolution of the UERRA-HARMONIE, precipitation data were downloaded from the MESCAN-SURFEX system. The main reason is that precipitation data from UERRA-HARMONIE were not available at the mean time. Because MESCAN-SURFEX system produce reanalysis data at a finer resolution they could be afterwards resampled at the desired resolution.
Data aggregation
Subdaily data from the original files were aggregated to daily and then to monthly data using the Climate Data Operator library (cdo). All variables were kept in their original metric and units, but temperature that was used to calculate maximum, minimum and average daily temperatures. In the case of precipitations, the aggregation was carried out by suming daily data, for the rest of variables aggregation was carried out by averaging subdaily and daily data.
```{bash aggregate_climate_data, eval=FALSE}
BASH script
years=($(seq 1961 1990))
for y in "${years[@]}"
do
Temperature data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg -daymin ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/${y}_2m_temperature.grib ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/${y}_2m_tmin.nc
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg -daymax ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/${y}_2m_temperature.grib ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/${y}_2m_tmax.nc
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg -dayavg ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/${y}_2m_temperature.grib ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/${y}_2m_temperature.nc
Precipitation data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_SURFEX.nc -monsum ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/${y}_total_precipitation.grib ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/latlon/${y}_total_precipitation.nc
Relative Humidity data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/${y}_2m_relative_humidity.grib ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/latlon/${y}_2m_relative_humidity.nc
Total Cloud Cover data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/${y}_total_cloud_cover.grib ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/latlon/${y}_total_cloud_cover.nc
Wind Speed data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/${y}_10m_wind_speed.grib ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/latlon/${y}_10m_wind_speed.nc
Surface pressure data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/${y}_surface_pressure.grib ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/latlon/${y}_surface_pressure.nc
done
Temporal merging
tmin_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/*_2m_tmin.nc )
cdo mergetime ${tmin_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/1961-90_2m_tmin.nc
tmax_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/*_2m_tmax.nc )
cdo mergetime ${tmax_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/1961-90_2m_tmax.nc
tmean_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/*_2m_temperature.nc )
cdo mergetime ${tmean_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/1961-90_2m_temperature.nc
prec_files=( ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/latlon/*_total_precipitation.nc )
cdo mergetime ${prec_files[@]} ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/latlon/1961-90_total_precipitation.nc
cloud_files=( ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/latlon/*_total_cloud_cover.nc )
cdo mergetime ${cloud_files[@]} ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/latlon/1961-90_total_cloud_cover.nc
rhum_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/latlon/*_2m_relative_humidity.nc )
cdo mergetime ${rhum_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/latlon/1961-90_2m_relative_humidity.nc
wspeed_files=( ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/latlon/*_10m_wind_speed.nc )
cdo mergetime ${wspeed_files[@]} ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/latlon/1961-90_10m_wind_speed.nc
ps_files=( ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/latlon/*_surface_pressure.nc )
cdo mergetime ${ps_files[@]} ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/latlon/1961-90_surface_pressure.nc
Remove temporary files
rm ${tmin_files[@]} ${tmax_files[@]} ${tmean_files[@]} ${prec_files[@]} ${cloud_files[@]} ${rhum_files[@]} ${wspeed_files[@]} ${ps_files}
## Create dictionary
In order to load those files in R, the `climate4R` framework allow to create a dictionary that is used to standardize variables to the `climate4R` vocabulary (check `climate4R.UDG::C4R.vocabulary()`).
```r
file.create("../../Data/UERRA/UERRA_dictionary.dic")
writeLines(c("identifier,short_name,time_step,lower_time_bound,upper_time_bound,cell_method,offset,scale,deaccum,derived,interface",
"tasmin,2t,1mo,1,12,min,-273.15,1,0,0,",
"tasmax,2t,1mo,1,12,max,-273.15,1,0,0,",
"tas,2t,1mo,1,12,mean,-273.15,1,0,0,",
"pr,tp,1mo,1,12,sum,0,1,0,0,",
"hurs,2r,1mo,1,12,mean,0,1,0,0,",
"cld,tcc,1mo,1,12,mean,0,1,0,0,",
"wss,10si,1mo,1,12,mean,0,1,0,0,",
"ps,sp,1mo,1,12,mean,0,1,0,0,"),
"../../Data/UERRA/UERRA_dictionary.dic")
dinilu/TraCE21kaDSR documentation built on July 17, 2022, 12:42 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )
Trace-21ka paleoclimate data
Trace-21ka paleoclimate data can be downloaded from https://www.earthsystemgrid.org/project/trace.html. This website, however, does not provide documentation with detailed information (file names, variable names, units, etcetera) used in the data repository. You have to look for such information in the following link (http://www.cgd.ucar.edu/ccr/TraCE/fields.html). Complementary information can be found in documentation website for the model and the developing laboratory: http://www.cesm.ucar.edu/projects/community-projects/LENS/data-sets.html and http://www.cesm.ucar.edu/models/atm-cam/docs/cam2.0/UsersGuide/UG-45.html.
We opted to download files for all time periods (since 22.000 years BP to 1990 CE) for 'surface temperature (radiative)' (TS), 'maximum surface temperature over output period' (TSMX), 'minimum surface temperature over output period' (TSMN), 'solar insolation' (SOLIN), 'surface pressure' (PS), 'convective precipitation rate (liq + ice)' (PRECC), 'surface latent heat flux' (LHFLX), and 'vertically-integrated total cloud' (CLDTOT).
Inspect files and variables {.tabset}
Downloaded files can be inspected with the following code:
TSMX
di.trace <- loadeR::dataInventory("../../Data/TraCE21ka/TSMX/trace.36.400BP-1990CE.cam2.h0.TSMX.2160101-2204012.nc") str(di.trace)
TSMN
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/TSMN/trace.36.400BP-1990CE.cam2.h0.TSMN.2160101-2204012.nc") str(di.trace)
TS
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/TS/trace.36.400BP-1990CE.cam2.h0.TS.2160101-2204012.nc") str(di.trace)
PRECC
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/PRECC/trace.36.400BP-1990CE.cam2.h0.PRECC.2160101-2204012.nc") str(di.trace)
RELHUM
The file for relative humidity in TraCE21ka have information for different atmospheric levels. In this case, we first selected surface preasure level and then create a new file with the information to simplify the rest of computation.
```{bash extract_relhum_surface_level, eval=FALSE}
BASH script
cdo sellevel,992.5561 ../../Data/Trace21ka/RELHUM/trace.36.400BP-1990CE.cam2.h0.RELHUM.2160101-2204012.nc ../../Data/Trace21ka/RELHUM/surface_level/trace.36.400BP-1990CE.cam2.h0.RELHUM.2160101-2204012.nc
Once, the surface pressure level was selected the new file was inspected. ```r di.trace <- loadeR::dataInventory("../../Data/Trace21ka/RELHUM/surface_level/trace.36.400BP-1990CE.cam2.h0.RELHUM.2160101-2204012.nc") str(di.trace)
CLDTOT
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/CLDTOT/trace.36.400BP-1990CE.cam2.h0.CLDTOT.2160101-2204012.nc") str(di.trace)
PS
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/PS/trace.36.400BP-1990CE.cam2.h0.PS.2160101-2204012.nc") str(di.trace)
U
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/U/trace.36.400BP-1990CE.cam2.h0.U.2160101-2204012.nc") str(di.trace)
V
di.trace <- loadeR::dataInventory("../../Data/Trace21ka/V/trace.36.400BP-1990CE.cam2.h0.V.2160101-2204012.nc") str(di.trace)
Create dictionary
In order to load those files in R, the climate4R framework allow to create a dictionary that is used to standardize variables.
file.create("../../Data/Trace21ka/dictionary.dic") writeLines(c("identifier,short_name,time_step,lower_time_bound,upper_time_bound,cell_method,offset,scale,deaccum,derived,interface", "tasmax,TSMX,1mo,0,12,max,-273.15,1,0,0,", "tasmin,TSMN,1mo,0,12,min,-273.15,1,0,0,", "tas,TS,1mo,0,12,mean,-273.15,1,0,0,", "pr,PRECC,1mo,0,12,mean,0,2592000000,0,0,", "hurs,RELHUM,1mo,0,12,mean,0,1,0,0,", "cld,CLDTOT,1mo,0,12,mean,0,1,0,0,", "ps,PS,1mo,0,12,mean,0,1,0,0,", "u,U,1mo,0,12,mean,0,1,0,0,", "v,V,1mo,0,12,mean,0,1,0,0,"), "../../Data/Trace21ka/dictionary.dic")
UERRA historical reanalisis data
Reanalysis data for the historical period (1961-1990) comes from the UERRA project (https://www.uerra.eu/). The data can be downloaded from the Climate Data Store at the Copernicus website (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-uerra-europe-single-levels?tab=overview). Technical details can be found in the same Climate Data Store or at the links provided in the website of the European Centre for Medium-Range Weather Forecasts (ECMWF) for the UERRA project (https://www.ecmwf.int/en/research/projects/uerra).
The project provide reanalysis from two different systems (UERRA-HARMONIE and MESCAN-SURFEX) that produce data in Lambert Conformal Conic Grid at two spatial resolutions: 11km x 11km for the UERRA-HARMONIE system and 5.5km x 5.5km for the MESCAN-SURFEX system. Because we were mainly insterested in downscaling temperature and precipitation variables and reanalysis data would be our predictand variables, we downloaded temperature and precipitation data using the following python scripts.
Note that CDS requires to register a free account to access data. Downloading scripts could request user and password in each iteration or can be configured to store account data in the system.
Data format: Data were downloaded in GRIB format because the Climate Data Operator library (cdo) seems to behave better with GRIB than with NetCDF files.
Downloading files from dataserver {.tabset}
Temperature
```{python Download_UERRA-HARMONIE_2m_temperature, eval=FALSE}
Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990): print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': '2m_temperature',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/'+ str(y) +'_2m_temperature.grib')
### Precipitation ```{python Download_MESCAN-SURFEX_total_precipitation, eval=FALSE} # Python script import cdsapi c = cdsapi.Client() for y in range(1961, 1990): print(y) c.retrieve( 'reanalysis-uerra-europe-single-levels', { 'origin': 'mescan_surfex', 'variable': 'total_precipitation', 'year': [ str(y), ], 'month': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', ], 'day': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', ], 'time': [ '00:00', '06:00', '12:00', '18:00', ], 'format': 'grib', }, 'MESCAN-SURFEX/'+ str(y) +'_total_precipitation.grib')
Relative humidity
```{python Download_UERRA-HARMONIE_2m_relative_humidity, eval=FALSE}
Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990): print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': '2m_relative_humidity',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/2m_relative_humidity/'+ str(y) +'_2m_relative_humidity.grib')
### Wind speed ```{python Download_UERRA-HARMONIE_10m_wind_speed, eval=FALSE} # Python script import cdsapi c = cdsapi.Client() for y in range(1961, 1990): print(y) c.retrieve( 'reanalysis-uerra-europe-single-levels', { 'format': 'grib', 'origin': 'uerra_harmonie', 'variable': '10m_wind_speed', 'year': [ str(y), ], 'month': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', ], 'day': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', ], 'time': [ '00:00', '06:00', '12:00', '18:00', ], }, 'UERRA-HARMONIE/10m_wind_speed/'+ str(y) +'_10m_wind_speed.grib')
Total cloud cover
```{python Download_UERRA-HARMONIE_total_cloud_cover, eval=FALSE}
Python script
import cdsapi
c = cdsapi.Client()
for y in range(1961, 1990): print(y)
c.retrieve(
'reanalysis-uerra-europe-single-levels',
{
'format': 'grib',
'origin': 'uerra_harmonie',
'variable': 'total_cloud_cover',
'year': [
str(y),
],
'month': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
],
'day': [
'01', '02', '03',
'04', '05', '06',
'07', '08', '09',
'10', '11', '12',
'13', '14', '15',
'16', '17', '18',
'19', '20', '21',
'22', '23', '24',
'25', '26', '27',
'28', '29', '30',
'31',
],
'time': [
'00:00', '06:00', '12:00',
'18:00',
],
},
'UERRA-HARMONIE/total_cloud_cover/'+ str(y) +'_total_cloud_cover.grib')
### Surface pressure ```{python Download_UERRA-HARMONIE_surface_pressure, eval=FALSE} # Python script import cdsapi c = cdsapi.Client() for y in range(1961, 1990): print(y) c.retrieve( 'reanalysis-uerra-europe-single-levels', { 'format': 'grib', 'origin': 'uerra_harmonie', 'variable': 'surface_pressure', 'year': [ str(y), ], 'month': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', ], 'day': [ '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24', '25', '26', '27', '28', '29', '30', '31', ], 'time': [ '00:00', '06:00', '12:00', '18:00', ], }, 'UERRA-HARMONIE/surface_pressure/'+ str(y) +'_surface_pressure.grib')
While we were interested in downscaling at the spatial resolution of the UERRA-HARMONIE, precipitation data were downloaded from the MESCAN-SURFEX system. The main reason is that precipitation data from UERRA-HARMONIE were not available at the mean time. Because MESCAN-SURFEX system produce reanalysis data at a finer resolution they could be afterwards resampled at the desired resolution.
Data aggregation
Subdaily data from the original files were aggregated to daily and then to monthly data using the Climate Data Operator library (cdo). All variables were kept in their original metric and units, but temperature that was used to calculate maximum, minimum and average daily temperatures. In the case of precipitations, the aggregation was carried out by suming daily data, for the rest of variables aggregation was carried out by averaging subdaily and daily data.
```{bash aggregate_climate_data, eval=FALSE}
BASH script
years=($(seq 1961 1990))
for y in "${years[@]}" do
Temperature data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg -daymin ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/${y}_2m_temperature.grib ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/${y}_2m_tmin.nc
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg -daymax ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/${y}_2m_temperature.grib ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/${y}_2m_tmax.nc
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg -dayavg ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/${y}_2m_temperature.grib ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/${y}_2m_temperature.nc
Precipitation data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_SURFEX.nc -monsum ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/${y}_total_precipitation.grib ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/latlon/${y}_total_precipitation.nc
Relative Humidity data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/${y}_2m_relative_humidity.grib ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/latlon/${y}_2m_relative_humidity.nc
Total Cloud Cover data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/${y}_total_cloud_cover.grib ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/latlon/${y}_total_cloud_cover.nc
Wind Speed data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/${y}_10m_wind_speed.grib ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/latlon/${y}_10m_wind_speed.nc
Surface pressure data aggregation
cdo -s -f nc remapcon,../../Data/UERRA/template_HARMONIE.nc -monavg ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/${y}_surface_pressure.grib ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/latlon/${y}_surface_pressure.nc
done
Temporal merging
tmin_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/*_2m_tmin.nc ) cdo mergetime ${tmin_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/1961-90_2m_tmin.nc
tmax_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/*_2m_tmax.nc ) cdo mergetime ${tmax_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/1961-90_2m_tmax.nc
tmean_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/*_2m_temperature.nc ) cdo mergetime ${tmean_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_temperature/latlon/1961-90_2m_temperature.nc
prec_files=( ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/latlon/*_total_precipitation.nc ) cdo mergetime ${prec_files[@]} ../../Data/UERRA/MESCAN-SURFEX/total_precipitation/latlon/1961-90_total_precipitation.nc
cloud_files=( ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/latlon/*_total_cloud_cover.nc ) cdo mergetime ${cloud_files[@]} ../../Data/UERRA/UERRA-HARMONIE/total_cloud_cover/latlon/1961-90_total_cloud_cover.nc
rhum_files=( ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/latlon/*_2m_relative_humidity.nc ) cdo mergetime ${rhum_files[@]} ../../Data/UERRA/UERRA-HARMONIE/2m_relative_humidity/latlon/1961-90_2m_relative_humidity.nc
wspeed_files=( ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/latlon/*_10m_wind_speed.nc ) cdo mergetime ${wspeed_files[@]} ../../Data/UERRA/UERRA-HARMONIE/10m_wind_speed/latlon/1961-90_10m_wind_speed.nc
ps_files=( ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/latlon/*_surface_pressure.nc ) cdo mergetime ${ps_files[@]} ../../Data/UERRA/UERRA-HARMONIE/surface_pressure/latlon/1961-90_surface_pressure.nc
Remove temporary files
rm ${tmin_files[@]} ${tmax_files[@]} ${tmean_files[@]} ${prec_files[@]} ${cloud_files[@]} ${rhum_files[@]} ${wspeed_files[@]} ${ps_files}
## Create dictionary
In order to load those files in R, the `climate4R` framework allow to create a dictionary that is used to standardize variables to the `climate4R` vocabulary (check `climate4R.UDG::C4R.vocabulary()`).
```r
file.create("../../Data/UERRA/UERRA_dictionary.dic")
writeLines(c("identifier,short_name,time_step,lower_time_bound,upper_time_bound,cell_method,offset,scale,deaccum,derived,interface",
"tasmin,2t,1mo,1,12,min,-273.15,1,0,0,",
"tasmax,2t,1mo,1,12,max,-273.15,1,0,0,",
"tas,2t,1mo,1,12,mean,-273.15,1,0,0,",
"pr,tp,1mo,1,12,sum,0,1,0,0,",
"hurs,2r,1mo,1,12,mean,0,1,0,0,",
"cld,tcc,1mo,1,12,mean,0,1,0,0,",
"wss,10si,1mo,1,12,mean,0,1,0,0,",
"ps,sp,1mo,1,12,mean,0,1,0,0,"),
"../../Data/UERRA/UERRA_dictionary.dic")
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.