This document will show you how the pollynet processing program works and how to add a new processing routine for new polly systems. Before your reading, we highly suggest you to turn to the paper "The automated multiwavelength Raman polarization and water-vapor lidar PollyXT: the neXT generation" to have a general idea about PollyXT.
- MATLAB
Before you download and install the pollynet_processing_program, make sure you've installed MATLAB 2014a and python3 in your development machine. Because nearly all the code was based on MATLAB and some part of the data visualization is based on python3 and python packages of matplotlib, numpy and scipy.
To install MATLAB, you may need to contact with your IT department or find detailed information in the MATLAB official webpage (https://de.mathworks.com/help/install/index.html?s_tid=srctitle).
- python and python packages
An convenient way to install all of these is to download the Anaconda. Anaconda is a standard platform for Python data science. Therefore, it will get everything ready for python once you installed it.
After you've installed the dependences, you can check it with using linux command which
check matlab and python dependence
The whole program was mangaged with GitHub. You can download it through (make sure git was installed in your local environment)
git clone https://github.com/ZPYin/Pollynet_Processing_Chain
download Pollynet_Processing_Chain
After you downloaded the code, you can find the folder structure of the code base as below,
folder structure of Pollynet_Processing_Chain
The repository you've downloaded has not been configured yet. You need to tell it which kind of polly data you want to process. This is achieved through setting up entries in the link file. Since polly has been upgraded after many years, the system configuration could also be different. Therefore, you need to specify the settings for the polly. This can be done by setting up new polly configuration file, which is names as polly_config.json.
- Finish the global settings with following the example of template_pollynet_processing_chain_config.json. The better way is just changing this file with your own preferences and requirements.
- Configure the polly processing settings with creating a json file, naming {polly}config{date}.json. Write in the polly settings with the help from the template_polly_config.json.
- Add the entry to the processing program with adding a new line in template_pollynet_processing_chain_link.txt. For each polly data, it will search the processing settings in this history file. If no history was found, the polly data will be neglected.
- Add the polly campaign history in the /todo_filelist/pollynet_history_of_places_new.txt.
flowchart of Pollynet_Processing_Chain
Top is the flowchart of the processing program. It includes three main processing parts, create processing task, find processing function and activate processing.
The fileinfo_new.txt is generated by top-level function which is controlled by our pollynet website maintainers. In this file, there contains the folder and file of the stored polly raw data, instrument label, data size and so on. For processing program, the data file is necessary to start a processing task and the instrument label is used to search for the proper running configurations. (The configurations of different polly can be found in pollynet.md). In the fileinfo_new.txt, each row content stands for a new task. All the task related info will be saved in taskInfo
Every polly lidar system is not stationary system. It is either container based or box-based which can be transferred to remote locations. PollyNET is aimed at measuring the aerosol distribution globally and the same lidar instrument can join different campaigns. The history campaign info can be loaded by searching the information stored in pollynet_history_of_places_new.txt. After that, we can got the polly data file, polly version and location info. These info will be saved in campaignInfo. With these info, we can search the predefined polly configuration, defaults and precessing function file by looking through pollynet_processing_config_history.txt. Detailed information about polly configuration and defaults can be found in polly_config.md and polly_defaults.md.
Then it will active the processing chain for the stored polly data. As different polly system can provide different products and in the future the newly built polly system can even provide some extradinary products which we could never imagine, therefore it's better not to keep the processing flowchart fixed. In the figure above, only the typical processing flow of pollyxt is displayed. But we should keep in mind, it can be super easy to be extended as the way you like. Regarding to how to make it extended, please go to Howto.
For a normal processing function, it will start with reading the polly raw data. At present, all the data is stored in netcdf format. MATLAB has provided with a lot of netcdf functions which can help you have a quick look inside the data file. Also you can follow the read function in this repository. Remember all the data sharing in the program is via data struct. This can simplify the definition part of inputs and outputs for each processing function. But it will make the struct very complicated and swelling as the processing runs. You can find the detailed information about this struct in here.
For the following parts, I will assume you have a lidar background.
PMT in the lidar receiving part will convert the light signal into electric signal. And signal recorder will sample the electric current into digit data and save them in our storage disk. Basically, there are two ways to sample the signal, photon counting and AD conversion. They both have their limits and expertise. For our decades experiences, we found the photon counting is better for our polly systems. But for photon counting system, we need to dealing with deadtime effects.
Three different correction schemes are embedded in the preprocessing function. You can find the detailed information in polly_config.md. Although some of the deadtime effects can be corrected in some degree, we still want to warn the users not to apply the signal with Photon Count Rate above 70 MHz.
Then, we need to substract the background from the raw signal. The background can be calculated by using the pretriggering signal (1-250 bin for pollyxt system).
Also we will make a depol-calibration mask and fog mask by inspecting the angle of the polarizer and signal strength at certain height.
For pollyxt systems, the depol-calibration is implemented by using the
Depol-Calibration-Results
There are different ways to realize cloud-screen, gradient method, wavelet method, variance method and so on. No matter which kind of method you use, you have to set a threshold. In the program, I only used the gradient method with comparing the slope of range-corrected signal to the pre-defined threshold. We can control the filtering strength by using different thresholds. In the overlap estimation part, the results is highly sensible with cloud contamination. Therefore, we can decrease the threshold to further limit the filtering.
There are two basic ways to estimate the overlap function for polly system. Firstly, using the Raman signal and tuning the overlap to achieve the convergence between Klett retrieving results and Raman retrieving results. But this method is controlled by the assumed lidar ratio and not easy to be implemented in an automatic way. Secondly, we can take advantage of the Near-Range and Far-Range channel, as the full overlap height for the Near-Range channel is only 120 m, we can easily estimate the overlap of Far-Range channel down to 120m by comparing the signal ratio between Near-Range and Far-Range signal.
Overlap
This part is used to split the continuous cloud-free profiles into several certain-length subgroups. The default length is 60 min.
This part is used to read the meteorological data. We have GDAS1 profiles in our server, which we can access with one-day delay. Therefore for realtime analysis, we need standard-atmosphere model data. Besides, an interface of reading radiosonde is also added to support the data analysis from Polarstern cruise.
Rayleigh fitting part is used to search the reference height for aerosol retrieving. The philosophy is based on the similarity between the detected signal and pure molecular signal. Detailed information can be found in Pollynet_Processing_Chain.pptx
Normally, the transmission ratio of parallel and vertical polarized component is not 1. Therefore, the signal in the total channel does not strictly equal to elastic signal. You can find the correction formula in dissertation_baars.pdf.
You can find thousands of documents about the analysis of klett and raman method. Read one or two of them, then you can easily understand this part in the program.
The key about water vapor calibration is to find a standard instrument. For most of PollyNET site, you can find a collocated AERONET site. You can find the site by looking at AERONET-station-list or go to AERONET website. Detailed information about the calibration method can be found in Guangyao's paper.
For LACROS campaign, there will be a good option by using the IWV from MWR. Because, MWR can provide nighttime measurement results which is ideal for water vapor calibration. You need to specify the folder of the MWR products to allow the program to read the IWV. Go to pollyxt_lacros to have a look.
Lidar calibration is based on the Raman or Klett retrieving results. At present, overlap correction is not implemented, which means the klett method could suffer a strong overlap effect at the lower altitude. In the calibration part, we treat the blind area to be constant. This is a good assumption at most conditions, but we should keep in mind if there is low level aerosol layers, it will create very large deviations. So our priority is using the Raman results when it is available.
O'Connor method has been tested. But for the photon counting system, it's not so pratical due to deadtime effects. But we need to keep an eye on this, because this can complement the scope of the calibrated conditions.
The program can generate
You can follow the below steps to create a new process procedure for a new polly systems.
- add a new configuration file in config folder to save all the related configurations. You can copy the content from other similar polly systems to speed up this process
- add a default file in
/lib/pollyDefaults. Don't forget to add the default overlap files. - create a new folder, named with
{polly Version}_func_lib. Write all the related processing functions inside or copy all the functions from the folder of other polly systems. If you copy all the functions from other folder, don't forget to change the function definitions inside. - create a new main processing function in the root folder and name it with
pollynet_processing_chain_{polly version}.m. Write all the processing part here. - add a new entry in
/config/pollynet_processing_config_history.txt.
data: struct
rawSignal: matrix [channel×bin×time]
raw signal from netcdf files. [Photon Count]
mShots: matrix [channel×time]
number of accumulated laser shots.
mTime: array [time]
date and time for each profile. [datenum]
depCalAng: array [time]
angle of the polarizer. [°]
hRes: float
spatial resolution. [m]
zenithAng: float
zenith angle of the whole system. [°]
mSite: char
location of the system.
deadtime: matrix [channel×orders]
parameters for deadtime correction which are stored in the netcdf file.
lat: float
latitude of measurement site. [degree]
lon: float
longtitude of measurement site. [degree]
alt0: float
altitude of measurement site. [m]
monitorStatus: struct
time: array
date and time for each monitor data. [datenum]
ExtPyro: array
gauge data from ExtPyro. [mJ]
Temp1064: array
temperature of the PMT at 1064 channel. [°C]
Temp1: array
temperature of the emitting cabin. [°C]
Temp2: array
temperature of the receiving cabin. [°C]
OutsideT: array
ambient temperature. [°C]
OutsideRH: array
ambient relative humidity. [%]
roof: array
status of the roof.
rain: array
status of rain.
shutter: array
stauts of shutter.
signal: matrix [channel×bin×time]
background-substracted and deadtime corrected signal. [Photon Count]
bg: matrix[channel×bin×time]
background of each bin. [Photon Count]
height: array
height above ground. [m]
alt: array
altitude above mean sea level. [m]
distance0: array
tilted distance of each bin. [m]
lowSNRMask: logical matrix [channel×bin×time]
mask for low signal-noise ratio.
depCalMask: array
mask for depolarization calibration.
fogMask: array
mask for fog.
flagSaturation: matrix [channel×bin×time]
flag to show whether the bin is saturated by clouds.
depol_cal_fac_532: float
depolarization calibration factor at 532 nm.
depol_cal_fac_std_532: float
uncertainty of depolarization calibration factor at 532 nm.
depol_cal_fac_355: float
depolarization calibration factor at 355 nm.
depol_cal_fac_std_355: float
uncertainty of depolarization calibration factor at 355 nm.
depCaliAttri: struct
depCalAttri532 or depCalAttri355: struct
sig_t_p: cell
mean signal at total channel at positive calibration angle in each calibration period.
sig_t_m: cell
mean signal at total channel at negative calibration angle in each calibration period.
sig_x_p: cell
mean signal at cross channel at positive calibration angle in each calibration period.
sig_x_m: cell
mean signal at cross channel at negative calibration angle in each calibration period.
caliHIndxRange: cell
index of calibration range along the height axisinr each calibration period.
indx_45m: cell
index of the profiles at negative calibration angle in each calibration period.
indx_45p: cell
index of the profiles at positive calibration angle in each calibration period.
dplus: cell
signal ratio at positive calibration angle in each calibration period.
dminus: cell
signal ratio at negative calibration angle in each calibration period.
segmentLen: cell
segment length to calculate the mean value of signal ratioinr each calibration period.
indx: cell
index of the base index of the most stable spatial segment in each calibration period.
mean_dplus_tmp: cell
mean signal ratio at positive calibration angle at each calibration segment in each calibration period.
std_dplus_tmp: cell
uncertainty of signal ratio at positive calibration angle for all the calibration segments in each calibration period.
mean_dminus_tmp: cell
mean signal ratio at negative calibration angle at each calibration segment in each calibration period.
std_dminus_tmp: cell
uncertainty of signal ratio at negative calibration angle at each calibration segment in each calibration period.
TR_t: cell
Transmission ratio at total channel in each calibration period.
TR_x: cell
Transmission ratio at cross channel in each calibration period.
segIndx: cell
index of the most stable segment in each calibration period.
thisCaliTime: cell
date and time for each calibration period. [datenum]
depol_cal_fac_532: array
depolarization calibration factor at 532 nm for each calibration period.
depol_cal_fac_std_532: array
uncertainty of depolarization calibration factor at 532 nm for each calibration period.
depol_cal_time_532: array
date and time for each calibration period. [datenum]
depol_cal_fac_355: array
depolarization calibration factor at 355 nm for each calibration period.
depol_cal_fac_std_355: array
uncertainty of depolarization calibration factor at 355 nm for each calibration period.
depol_cal_time_355: array
date and time for each calibration period. [datenum]
flagCloudFree2km: logical array
flag to show whether it's cloud-free below 2 km.
flagCloudFree8km: logical array
flag to show whether it's cloud-free below 8 km.
overlap532: array
the estimated overlap function for 532 Far-Range channel.
flagOverlapUsedDefault532: logical
flag to show whether the overlap at 532 nm is the default overlap.
overlap355: array
the estimated overlap function for 355 Far-Range channel.
flagOverlapUsedDefault355: logical
flag to show whether the overlap at 355 nm is the default overlap.
cloudFreeGroups: matrix [group×2]
start and end index of each cloud-free group.
temperature: matrix [group×bin]
temperature at each range bin for each cloud-free group. [°C]
pressure: matrix [group×bin]
pressure at each range bin for each cloud-free group. [hPa]
relh: matrix [group×bin]
relative humidity at each range bin for each cloud-free group. [%]
meteorAttri: struct
dataSource: cell
data source of meteorological data for each cloud-free group.
URL: cell
URL for meteorological data for each cloud-free group. [URL or directory]
datetime: array
date and time for the applied meteorological data at each cloud-free group.
AERONET: struct
datetime: array
date and time for each AERONET results.
AOD_{wavelength}: array
AOD at certain wavelength.
wavelength: array
wavelength of each channel. [nm]
IWV: array
Integral water vapor. [kg*m^{-2}]
angstrexp440_870: array
Å 440-870nm.
AERONETAttri: struct
URL: char
URL for accessing the AERONET data.
level: char
product level.
status: int
the retrieving stauts for the data. 0 means no successful access.
IWVUnit: char
unit for IWV.
location: char
location for the current measurement.
PI: char
PI for the current AERONET site.
contact: char
contact of PI.
refHIndx355: matrix [group×bin]
index of reference height at 355 nm for each cloud-free group.
refHIndx532: matrix [group×bin]
index of reference height at 532 nm for each cloud-free group.
refHIndx1064: matrix [group×bin]
index of reference height at 1064 nm for each cloud-free group.
dpIndx{wavelength}: cell
index of douglas-peucker decomposed points for each cloud-free signal profile.
el{wavelength}: matrix [bin×time]
elastic signal. [Phtoton Count]
bgEl{wavelength}: matrix [bin×time]
background of elastic signal. [Photon Count]
aerBsc{wavelength}_{method}: matrix [group×bin]
aerosol backscatter coefficients. [m^{-1}*Sr^{-1}]
aerExt{wavelength}_{method}: matrix [group×bin]
aerosol extinction coefficients. [m^{-1}]
LR{wavelength}_raman: matrix [group×bin]
aerosol lidar ratio retrieved by raman method. [Sr]
LR{wavelength}_aeronet: array [group]
aerosol ldiar ratio retrieved by constrained-AOD klett method. [Sr]
voldepol{wavelength}_{method}: matrix [group×bin]
volume depolarization ratio at each cloud-free group with the same smoothing for the corresponding backscatter.
pardepol{wavelength}_{method}: matrix [group×bin]
particle depolarization ratio.
pardepolStd{wavelength}_{method}: matrix [group×bin]
uncertainty of particle depolarization ratio.
flagDefaultMoldepol{wavelength}: array
flag to show whether the used molecule depolarization ratio is from default settings.
moldepol{wavelength}: array [group]
molecule depolarization ratio.
moldepolStd{wavelength}: array [group]
uncertainty of molecule depolarization ratio.
ang_{bsc,ext}_{wavelength}_{method}: matrix [group×bin]
Ångstroem exponent.
IWV: array
Integrated water vapor from lidar measurements for each cloud-free groups. [kg*m^{-2}]
IWVAttri: struct
source: char
data source of IWV.
site: char
site of the external instrument.
datetime: array
date and time for the used IWV from external instrument for each water vapor calibration period.
PI: char
PI for the external instrument.
contact: char
contact of PI.
wvconstUsed: float
applied water vapor calibration constant in the processing. [g*kg^{-1}]
wvconstUsedStd: float
Uncertainty of the applied water vapor calibration constant in the processing. [g*kg^{-1}]
wvconstUsedInfo: struct
flagCalibrated: int
flag to show whether there is a successful calibration.
IWVInstrument: char
data source for the external instrument.
nIWVCali: int
number of successful water vapor calibration periods.
wvmr: matrix [group×bin]
water vapor mixing ratio profile for each cloud-free group. [g*kg^{-1}]
rh: matrix [group×bin]
relative humidity for each cloud-free group. [%]
wvmr: matrix [bin×time]
high resolved water vapor mixing ratio profiles. [g*kg^{-1}]
rh: matrix [bin×time]
high resolved relative humidity profiles. [%]
LC: struct
LC_{method}_{wavelength}: array
lidar calibration constants. This constant is calibrated for each 30-s resolved profile.
LCUsed: struct
LCUsed{wavelength}: float
appiled lidar constant.
LCUsedTage{wavelength}: int
lidar constant status.
0: 'no calibration';
1: klett method;
2: raman method;
3: defaults
flagLCWarning{wavelength}: int
whether there is a strong fluctuation of the lidar constants.
att_beta_{wavelength}: matrix [bin×time]
attenuated backscatter. [m^{-1}*Sr^{-1}]
quasi_par_beta_{wavelength}{_V2}: matrix [bin×time]
quasi particle backscatter coefficient. [m^{-1}*Sr^{-1}]
volDepol_{wavelength}: matrix [bin×time]
high resolved volume depolarization ratio profile.
quasi_parDepol_532{_V2}: matrix [binxtime]
high resolved particle depolarization ratio profile.
quasi_ang_532_1064{_V2}: matrix [bin×time]
quasi Ångstroem exponent 532-1064.
quality_mask_{wavelength}{_V2}: matrix [bin×time]
signal quality mask. This mask is calculated with the smoothed signal.
0: good signal;
1: low SNR;
2: depolarization calibration
quasiAttri{_V2}: struct
flagGDAS1: int
flag to show whetehr GDAS1 data was used.
timestamp: float
time stamp of the appled GDAS1 data.
meteorDataSource: str
meteorological data type.
e.g., 'gdas1', 'standard_atmosphere', 'websonde', 'radiosonde'
tc_mask: matrix [bin×time]
target classification mask.
0: No signal;
1: Clean atmosphere;
2: Non-typed particles/low conc.;
3: Aerosol: small;
4: Aerosol: large, spherical;
5: Aerosol: mixture, partly non-spherical;
6: Aerosol: large, non-spherical;
7: Cloud: non-typed;
8: Cloud: water droplets;
9: Cloud: likely water droplets;
10: Cloud: ice crystals;
11: Cloud: likely ice crystals
defaults: struct
depolCaliConst{wavelength}: float
default depolariazation constant.
depolCaliConstStd{wavelength}: float
default uncertainty of depolarization constant.
LC: array
lidar constant for each channel.
LCStd: array
uncertainty of lidar constant.
overlapFile{wavelength}: char
absolute directory of the default overlap file.
molDepol{wavelength}: float
molecule depolarization ratio.
molDepolStd{wavelength}: float
uncertainty of molecule depolarization ratio.
wvconst: float
water vapor calibration constant. [g*kg^{-1}]
wvconstStd: float
uncertainty of water vapor calibration constant. [g*kg^{-1}]
taskInfo: struct
todoPath: char
the todo path which saves the polly data and fileinfo_new.txt
dataPath: char
the relative folder of the polly data to the todo path.
dataFilename: char
filename of the polly data. You can construct the absolute path of the polly data by combining todoPath, dataPath and dataFilename.
zipFile: char
filename of the zipped polly data.
dataSize: single
the size of the polly data file.
pollyVersion: char
polly version. Detailed information can be found in [polly version](pollynet.md)
startTime: float
start time for processing the task. [datenum]
dataTime: float
date and time for the creation of the current polly data. [datenum]
campaignInfo: struct
name: char
polly version. Detailed information can be found in [polly version](pollynet.md)
location: char
location of the current measurement campaign. Detailed information can be found in [pollynet history info](../todo_filelist/pollynet_history_of_places_new.txt)
startTime: float
start time for the current campaign. [datenum]
endTime: float
end time for the current campaign. [datenum]
lon: float
longitude of the campaign. [°]
lat: float
latitude of the campaign. [°]
asl: float
height above average sea level. [m]
depolConst: float
depolarization constant for 532 nm.
molDepol: float
molecule depolarization ratio for 532 nm.
caption: char
some description about the campaign.
processInfo: struct
fileinfo_new: char
filename of the fileinfo_new.txt. This is the collection of all present polly data from PollyNER which is waited for processing.
doneListFile: char
done list file which contains all the information of generated figures by the program.
pollynet_history_of_places_new: char
pollynet history file which contains all the history information about different finished or ongoing campaigns.
log_folder: char
folder to save the log file.
gdas1_folder: char
the root folder of GDAS1 profiles.
defaultsFile_folder: char
folder of defaults files.
results_folder: char
folder to save the processing results and calibration results.
pic_folder: char
folder to save the figures.
pollynet_config_history_file: char
history file to log all the configuration files, defaults_reading function and processing function for all polly systems.
minDataSize: int32
Mininmum data size that was accepted for the data processing.
institue: char
institute full-name. This will be written to the nc files.
contact: char
contact. This will be written to the nc files.
homepage: char
pollynet home page. This will be written to the nc files.
programVersion: char
program version. This will be written to the nc files.
visualizationMode: char
visualization mode, 'matlab' or 'python'. Python is better supported in the server than matlab.
pyBinDir: char
path for the appled python interpreter.
flagEnableCaliResultsOutput: logical
flag bit to control whehter to output the calibration results
flagReduceMATLABToolboxDependence: logical
flag bit to control whether to use the matlab toolbox (statistical toolbox and curveture toolbox)
flagDeleteData: logical
flag bit to control whether to delete the data file after finishing the processing.
flagEnableResultsOutput: logical
flag bit to control whether to save the processing results.
flagEnableDataVisualization: logical
flag bit to control whether to visualize the results.
flagSendNotificationEmail: logical
flag bit to control whether to send email to the admin for each running task.
| ident | Name |
|---|---|
| ANGEXP_Raman | averaged angström exponent profile |
| ANGEXP | averaged angström exponent profile |
| ANGEXP_Klett | averaged angström exponent profile |
| averaged backscatter profile from AOD-Constrained method | |
| Bsc_Klett | averaged backscatter profile from Klett method |
| Bsc_Raman | averaged backscatter profile from Raman method |
| averaged depolarization ratio profile | |
| DepRatio_Raman | averaged depolarization ratio profile |
| DepRatio_Klett | averaged depolarization ratio profile |
| Ext_Klett | averaged extinction profile from Klett method |
| Ext_Raman | averaged extinction profile from Raman method |
| averaged extinction profile from AOD-Constrained method | |
| LR | averaged lidar ratio profile |
| Meteor_P | averaged pressure profile from input |
| SIG | averaged range-corrected signals |
| RH_av | averaged relative humidity profile |
| Meteor_T | averaged temperature profile from input |
| WVMR_av | averaged water vapour mixing ratio profile |
| ATT_BETA_1064 | Calibrated attenuated backscatter coefficient at 1064 nm |
| ATT_BETA_355 | Calibrated attenuated backscatter coefficient at 355 nm |
| ATT_BETA_532 | Calibrated attenuated backscatter coefficient at 532 nm |
| monitor | houskeeping data |
| LC_1064 | Lidar constant of 1064 FR channel |
| LC_1064_NF | Lidar constant of 1064 NR channel |
| LC_355 | Lidar constant of 355 FR channel |
| LC_355_NF | Lidar constant of 355 NR channel |
| LC_532 | Lidar constant of 532 FR channel |
| LC_532_NF | Lidar constant of 532 NR channel |
| TC | Lidar Target Categoriaztion |
| Quasi_ANGEXP_532_1064 | Quasi angström exponent 532/1064 nm |
| Quasi_Bsc_1064 | Quasi particle backscatter coefficient at 1064 nm |
| Quasi_Bsc_355 | Quasi particle backscatter coefficient at 355 nm |
| Quasi_Bsc_532 | Quasi particle backscatter coefficient at 532 nm |
| Quasi_PDR_532 | Quasi particle depolarization ratio at 532 nm |
| RCS_FR_1064 | Range-corrected signal far-range at 1064 nm |
| RCS_FR_355 | Range-corrected signal far-range at 355 nm |
| RCS_FR_532 | Range-corrected signal far-range at 532 nm |
| RCS_NR_355 | Range-corrected signal near-range at 355 nm |
| RCS_NR_532 | Range-corrected signal near-range at 532 nm |
| RH | relative humidity |
| overlap | Retrieved overlap function of the channels |
| SAT_NR_1064 | Signal status FR 1064 nm |
| SAT_FR_1064 | Signal status FR 1064 nm |
| SAT_NR_355 | Signal status FR 355 nm |
| SAT_FR_355 | Signal status FR 355 nm |
| SAT_FR_532 | Signal status FR 532 nm |
| SAT_NR_532 | Signal status FR 532 nm |
| VDR_1064 | Volume depolarization ratio at 1064 nm |
| VDR_355 | Volume depolarization ratio at 355 nm |
| VDR_532 | Volume depolarization ratio at 532 nm |
| WVMR | water vapour mixing ratio |
| LT_CaliRes | long term lidar calibration results |
Detailed information can be found in polly_config
Detailed information can be found in pollynet_processing_chain_config