Tutorial
This tutorial shows how to use the ALCF to process ceilometer observations, simulate lidar from model output and compare the two. As an example, we will use 24 hours of Lufft CHM 15k ceilometer observations at the Birdlings Flat site in Canterbury, New Zeland on 4 July 2016, and the corresponding Antarctic Mesoscale Prediction System (AMPS) numerical weather prediction (NWP) model and MERRA-2 renalaysis output. To start, download the dataset archive alcf-tutorial-datasets.zip (4.3 GB).
Note: Normally, you would want to compare more than 24 hours of measurements, such as one month or one year. This tutorial uses 24 hours of data due to the large size of the datasets.
Processing with the ALCF can be done in two modes: automatic and manual. The automatic mode is easier and is convered in this tutorial. Both automatic and manual processing are equivalent, but manual processing gives you a better understanding of the processing steps and can be more useful if anything goes wrong during the processing. Please see the documentation on how to perform manual processing.
Preparation
First, make sure that you have installed the ALCF following the installation
instructions, and the you can run alcf
in the
terminal. Extract the archive alcf-tutorial-datasets.tar
in your working
directory:
unzip alcf-tutorial-datasets.zip
cd alcf-tutorial-datasets
Datasets
The contents of the dataset archive are:
raw
: raw ceilometer and model filesraw/amps
: AMPS NWP model dataraw/chm15k
: Lufft CHM 15k dataraw/merra2
: MERRA-2 reanalysis data
The data files are stored in the NetCDF format and can be previewed with Panoply, HDFView, ncdump or ds.
Automatic processing
Observations
To process the observations use the following command:
mkdir -p processed # Create directory "processed".
alcf auto lidar chm15k raw/chm15k processed/chm15k
This command will resample the lidar data, remove noise, detect clouds,
calculate statistics, and plot the lidar backscatter, backscatter histogram
and cloud occurrence as a function of height. The output will
be stored in processed/chm15k
:
lidar
: processed lidar data as daily files (NetCDF)plot
: plotsplot/backscatter
: daily backscatter profile plotsplot/backscatter_hist.png
: plot of backscatter histogramplot/cloud_occurrence.png
: plot of cloud occurrence histogram as a function of heightstats
: statisticsstats/all.nc
: statistics (NetCDF)
Figure 1 shows the Lufft CHM 15k plots. See the ALCF output for description of the NetCDF files.
Model
To process MERRA-2 model data use the following command:
mkdir -p processed # Create directory "processed".
alcf auto model merra2 chm15k raw/merra2 processed/merra2 \
point: { 172.686 -43.821 } time: { 2016-07-04T00:00 2016-07-05T00:00 }
The command will extract a “curtain” of data along the give point and time
period, run the lidar simulator on the extracted data,
simulating the Lufft CHM 15k instrument, and run the same processing steps as
alcf auto lidar
on the observational data above.
The output will be stored in processed/merra2
:
model
: extracted model data along the geographical point and time period (NetCDF)simulate
: simulated lidar backscatter (NetCDF)lidar
: processed lidar data as daily files (NetCDF)plot
: plotsplot/backscatter
: daily backscatter profile plotsplot/backscatter_hist.png
: plot of backscatter histogramplot/cloud_occurrence.png
: plot of cloud occurrence histogram as a function of heightstats
: statisticsstats/all.nc
: statistics (NetCDF)
Figure 2 shows the MERRA-2 plots. See the ALCF output for description of the NetCDF files.
Conclusion
This tutorial introduced how to use the ALCF to process 24 hours of data collected by the Lufft CHM 15k ceilometer and the corresponding data from the MERRA-2 reanalysis. The ALCF supports more advanced options described in the documentation. For support please see the support page.