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C-OPS Processing

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README.md

README.md
In belasi01/Cops: C-OPS Processing

The Cops Package

Simon Bélanger April 23, 2020

The Cops package was initially developped by Bernard Gentili at the Laboratoire d’Océanologie de Villefranche (LOV). It gathers several routines to process raw data collected using a C-OPS instrument. The C-OPS is a Compact Optical Profilling System commercialized by Biospherical instruments.

We have updated the package and implemented new functions over time. This is an ongoing work that requires collaborations. For that purpose, the code was uploaded to GitHub by Simon Bélanger in November 2016.

Here I present some recommendations about data collection and preparation at first, and then I will further explain how to use theCops package.

Preliminaries

Data collection and log book

In the field, we always document the deployement operations in a log book. Make sure you have this log book in hand before starting. This will save you a lot a time. In fact, it is common that a bad profile was recorded in the field for any reason. For example:

Normally this kind of problem should be logged and the data can be discarded before trying to process them. Usually, we don’t have time on the boat to delete the data.

The log file should also provide insight about the profile quality. This can really help when it is the time to quality control the data.

Data preparation

We, at UQAR, have adopted a systematic way to store our raw data collected in the field. Most of the time, field data are store in different folders, often one folder per instrument or one folder for a given date, etc. We store the raw data in a folder named ./L1/. This folder will be write-protected to preserve raw data. Processed data will then be stored in a folder named ./L2/, where data are organized in a more systematic way.

We strongly recommend to create one folder per station. The recommended folder name contains the date and the Station ID such as:

./L2/YYYYMMDD_StationID/

In this folder, you can then create one subfolder for each type of measure (COPS, IOPs, ASD, SVC, PSR, etc.). Then you should copy your raw data in their respective sub-folder. For example, supposed you have visited the station P1 on the 6th of June 2015. You deployed the COPS (three profiles) and one IOP package. So you will create one folder for the station:

./L2/20150606_StationP1/

Next you will create two subfolders for the COPS and IOPs, respectively:

./L2/20150606_StationP1/COPS/ and ./L2/20150606_StationP1/IOPs/

It takes some time to organize at first, but it will make it easier to retrieve your data in the future (even many years later!!).

The C-OPS data processing

Installation of the Cops package

As any other package available on GitHub, the installation is straitforward using the devtools package utilities, i.e.:

devtools::install_github("belasi01/Cops")

This will install the binaries and all the dependencies, which can take some time.

To install the full code sources, you can also “clone” the package in a local folder. You have to create a “New project…” from the file menu and choose the “Version Control” project type, and then choose “Git” option. Next you have to indicate the full path of the R package repository on GitHub, as illustrated below.

Step 0 : Get started with Cops processing and configuration of the INIT file

Unfortunately, most functions of the Cops package do not have a help page. This is because the user only need to know one single function to launch the processing, i.e. the cops.go(). So let’s get started.

library(Cops)

## Loading required package: tcltk

## Loading required package: e1071

## Loading required package: stringr

## Loading required package: ncdf4

## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
## TO BEGIN A PROCESSING AND PRODUCE WINDOWS PLOTS,
##     TYPE : cops.go(interactive = TRUE)
## TO BEGIN A PROCESSING AND PRODUCE   PDF   PLOTS,
##     TYPE : cops.go()
## TO BEGIN A PROCESSING AND PRODUCE   PDF   PLOTS, AND OUTPUT DATA IN ASCII FORMAT
##     TYPE : cops.go(ASCII=TRUE)
## TO BEGIN A PROCESSING AND PRODUCE   PDF   PLOTS, AND CLEAN THE PROFILE INTERACTIVELY
##     TYPE : cops.go(CLEAN.FILES=TRUE)
## TO SHIFT Rrs TO NEW BANDS- MELIN-SCLEP algorithm using QAA, TYPE : cops.shift()
## N.B. : if you want to *** use *** cops.shift() *** and ***
##          you have allready processed data - cops.go() function -
##          with an older version of this package :
##          (older means whithout cops.shift() function) :
##        1) add a parameter called bandwidth in file init.cops.dat
##        2) use cops.go() again
##        3) use cops.shift()
## @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

As you can see, when you load the package with the library() function, you got a message explaining how to launch the processing with the different options proposed by cops.go(). By default, the options interactive, ASCII and CLEAN.FILES are set to FALSE. I strongly recommand to first set the working directory (i.e., a folder where you put the COPS data for a given station) using setwd() and than type cops.go(). See what happens.

setwd("/data/ProjetX/L2/20500619_StationY1/cops")
cops.go()

You will get the following message:

CREATE a file named directories.for.cops.dat in current directory (where R is launched) and put in it the names of the directories where data files can be found (one by line)

In the present example, I will create a very simple ASCII file named directories.for.cops.dat in my working directory in which I will put the full path of the folder I want to process,

/data/ProjetX/L2/20500619_StationY1/cops

One can process as many folders as wanted, but I don’t recommand that when you process the COPS data for a given station for the first time. In fact you need to quality control each vertical profile (one by one). That being said, the batch processing is very useful when the code change, which is expected as we constantly improve the Cops package. So, after the QC all C-OPS profiles at the end of the processing, we generally create a directories.for.cops.dat file, in the ./L2/ folder, which contains all the station folder paths.

You can launch again the code.

cops.go()

This time you get the following message:

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ PROCESSING DIRECTORY C:/data/ProjetX/L2/20500619_StationY1/cops @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

EDIT file C:/data/ProjetX/L2/20500619_StationY1/cops/init.cops.dat and CUSTOMIZE IT

As you can see, the program has created a file named init.cops.dat in your working directory. This file contains several informations/parameters that are required in the data processing, but also for reading the data properly. In general, the parameters (or global variable) found in the init.cops.dat file remain almost the same for all stations.

the init.cops.data file set up

You have to edit the following lines:

The next parameters are important for reading the data correctly. You need to look into one CSV or TSV file to see how the data have been recorded.

As mentioned above, the init.cops.dat file should not change much from one station to another and can be copy/paste to every folder you want to process.

Step 1 : Configure the info.cops.dat file and run the code for the first time to generate results

Once you are done with the init.cops.dat file, you can launch again the code.

cops.go()

This time you get the following message:

@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ PROCESSING DIRECTORY C:/data/ProjetX/L2/20500619_StationY1/cops @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@

Read 17 items

EDIT file C:/data/ProjetX/L2/20500619_StationY1/cops/info.cops.dat and CUSTOMIZE IT this file must contain as much lines as cops-experiments you want to process you will find a header with instructions to fill this file

Now if you look into the working directory, you will find a file named info.cops.dat. This is another ASCII file you need to edit. As mentioned above, the header of that file provides instruction on how to arrange the informations to process each C-OPS profile you have in your working directory. The header lines start with a “#”. After the header, you have to provide a line for each profile you want to process. Each line will need to have 8 mandatory fields separated by “;”. The created file already contains one line per file found in the working disrectory. The first field is the file name. IMPORTANT: you have to remove the lines that are not corresponding to calibrated light profile file (e.g., the init.cops.dat or the GPS file). Then you have to set the processing parameters for each line.

When you process the data for the first time, the fields 4 to 8 can be left as is. The processing will take the default values found in init.cops.dat for fields 6, 7 and 8. The later fields were described above and they stand for sub.surface.removed.layer, tiltmax.optics, and depth.interval.for.smoothing. All of them contains 3 (or 4) values separated with “,” for each sensors.

You can launch again the code.

cops.go()

Normally the code will run without errors, except if the data is not good (a very bad profile that was recorded by error on the field) or if you have made a mistake in the init.cops.dat file (e.g., often you did not change Master to NA for field instruments.others, or you made a mistake in the date/time format, etc.) or if the data file was recorded specifically for the Bioshade measurements.

Step 2: Preliminary analysis of the results output and processing parameters adjustment

First of all, when the code is run without error, it creates two (or three) new directories (BIN/, PDF/, and optionally ASC/) in the working directory as well as two ASCII files named absorption.cops.dat and select.cops.dat (version prior 4.0 generated a file named remove.cops.dat). The former is the file you have to edit if you want to correct for instrument self-shadow effect (see above) using measured absorption coefficients (one line per profile).

The select.cops.dat file

The select.cops.dat file lists the same file names found in info.cops.dat followed by three parameters separated by semi-colons. The second column is an integer (0,1,2 or 3) indicating the type of C-OPS cast. By default, all files are set to 1, which consider a normal light profile. To remove profile, we change the integer to 0. Other C-OPS casts are Bioshade (2) or under-ice profile (3). In case of an under-ice profile, no extrapolation to the air-sea interface is performed and sub-surface AOP (nLw, Lw, Rrs, etc) are not calculated. The column 3 indicates the best extrapolation methods for the remote sensing reflectance (Rrs). Column 4 indicates whether or not the COPS profile was made in shallow waters (1 = shallow). In that case, the bottom reflectnace is extracted from the light profile. Therefore the third column will accept Rrs.0p.linear or Rrs.0p for linear and loess extrapolation, respectively.

Let’s focus now on the PDF/ directory in which one PDF per profile was generated. You will have to open each PDF and analyse the results to adjust the processing parameters.

Step 2.1 : Clean the files

The second page of the PDF document shows the depth of the profiler versus time, in seconds, since the begining of the recording (Figure 2). The title of the plot provides the date/time, the duration of the cast, the position and the sun zenith angle. Check this information if it is correct and in particular the sun zenith angle, which has been calculated from the position and the UTC time. In that example the cast duration was 26 seconds. The profiler was at 1 m from the surface right at the begining, then reached the surface after 3 seconds, and dropped tp the bottom at 18:18:58 UTC, i.e. about 22 seconds from the begining. Such a profile MUST be cleaned. In fact, the profiler should be in free-fall to pass the linear fit conditions. Figure 2. Example of pressure or depth of the profiler versus time in second since the begining of the recording

With the version 4.0, you can clean the file by runing the code again, but with the CLEAN.FILES option set to TRUE. Note that this step can be avoided if the data were already cleaned using the Shiny App developped by Guislain Bécu. This application can be downloaded from https://github.com/GuislainBecu/01.COPS.CLEAN.INPUT.FILES

cops.go(CLEAN.FILES = TRUE)

The user will be prompted to click on the plot of depth versus index to determine the begining of the cast and the end of the cast (Figure 3).

Figure 3. Example of depth of the profiler versus the number of records (index) available in the file

Here the user clicked on the index 46 for the starting point and the index 352 for the ending point. In this example, the profiler hit the bottom, as the depth became constant to 5.5 m from the index 352 to the end of the cast. IMPORTANT NOTE: To avoid bad data near the bottom, the profile will be cut 15 cm above the ending point set by the user. This processing greatly improves the results in shallow waters, allowing easier extraction of the bottom reflectance property.

Figures 4 and 5 show the depth vs duration and the instrument tilt during the cast, respectively. Here the tilt of both Ed0 and EdZ instruments were below the thresholds, except for a few points near the surface. In difficult conditions, i.e., due to strong current or wind, too much tension in the cable, the C-OPS tilt may be high. The Figure 6 shows an extreme case we encountered in the Labrador Sea in 2014. Keeping the tilt threshold at 5 degrees for the in-water sensors would have removed nearly all the data! In such case we need to increased the threshold to 10 degrees to get AOPs, which is acceptable in the open ocean when the profiler is far from the ship.

Figure 4. Example of pressure or depth of the profiler versus time in second since the begining of the recording after cleaning the file.

Figure 5. Example of instrument tilt for Ed0 and EdZ (i.e., the profiler) during the cast. Red points have been discarded for the rest of the analysis using the tiltmax.optics parameter.

Figure 6. Example of instrument tilt for Ed0 and EdZ (i.e., the profiler) during the VITALS cruise in 2014 onboard the Hudson. Note that the figure was generated by the version 3 of the Cops package.

Step 2.2 : Check the dowelling irradiance conditions during the cast.

Downwelling irradiance above water (Ed0) must be stable during the C-OPS vertical profile. Cloudy sky can make it highly variable. Some shadow on the instrument from the ship structure a person near by (on small boat sometimes) can be a problem as well. Big change in Ed0 will likely result in a bad light profile because LuZ and EdZ are normalized by the Ed0 variability (see NASA protocols). In the example shown in Figure 7, Ed0 was very stable. The variability was due to tilt of the instrument probably resulting from a moving boat by waves. The LOESS smoothing completely remove these artefacts. In this example, the conditions were perfect.

Figure 7. Example of stable Ed0 conditions during a vertical profile

The next example (Fig. 8) shows a drastic drop in Ed0 during a profile. This kind of unstable conditions is bad for the rest of the data processing. This profile should be discarded by changing the value of 1 to 0 in the file select.cops.dat.

Figure 8. Example of an unstable Ed0 conditions during a vertical profile

Step 2.3 : Check the quality of the extrapolation of the radiometric quantity to the air-water interface

One of the most important thing to check when processing C-OPS data is the quality of the extrapolation of the upwelling radiance (LuZ) or irradiance (EuZ) at the air-water interface. This will determine the quality of the remote sensing reflectance, Rrs, which is one of the most important apparent optical property (AOP) we want to extract from the C-OPS data.

In the current Cops package version (4.0), there is two methods implemented to extrapolate to the air-water interface: a non-linear method call LOESS and a linear fit on the log-transformed radiance or irradiance profiles. The LOESS is applied to the whole profile, while the linear fit is limited to a thin layer near the surface. Several plots are available in the PDF to diagnose the quality of the fit. Note that all data points below the instrument detection limit are eliminated in the fitting process. This feature has been implemented in the version 4.0 of the Copspackage. One can visualize the default values implemented in the package by accessing the variable detection.limit, which is a Global Environment Variable (shown below). To obtain more information about the instrument detection limit or update these threshold, the user can refer to the help page of the function cops.detection.limit().

detection.limit

##    waves   EdZ   EuZ   LuZ
## 1    305 4e-03 4e-03 5e-05
## 2    320 4e-03 4e-03 5e-05
## 3    330 4e-03 4e-03 5e-05
## 4    340 4e-03 4e-03 5e-05
## 5    380 7e-03 7e-03 2e-04
## 6    395 8e-04 8e-04 5e-05
## 7    412 8e-04 8e-04 5e-05
## 8    443 8e-04 8e-04 4e-05
## 9    465 8e-04 8e-04 1e-04
## 10   490 5e-04 5e-04 2e-05
## 11   510 5e-04 5e-04 3e-05
## 12   532 5e-04 5e-04 4e-05
## 13   555 5e-04 5e-04 4e-05
## 14   560 5e-04 5e-04 4e-05
## 15   589 5e-04 5e-04 2e-05
## 16   625 5e-04 5e-04 1e-05
## 17   665 5e-04 5e-04 1e-05
## 18   683 5e-04 5e-04 1e-05
## 19   694 1e-04 1e-04 1e-05
## 20   710 1e-04 1e-04 1e-05
## 21   780 2e-04 2e-04 1e-05
## 22   875 2e-04 2e-04 2e-05

The following plots allow you to evaluate the quality of the LuZ extrapolation to the surface (x = 0-). The figure 9 is a zoom on the surface layer of the water column. It presents all 19 wavelenghts and the 2 fitting methods on the same plot. Note that no fit was performed for the 305 nm and 320 nm channels due to the lack of measurements above the instrument detection limit (here set at 5e-5), which is wavelength-specific and sensor-specific.

Figure 9. Example of LuZ extrapolation to the surface (z=0-) using non-linear fitting with LOESS method (solid line) and the linear method (dashed line) described in Bélanger et al (2017). C-OPS measurements are the small dots while the big solid circles indicate the maximum depth used to make the linear extrapolation of LuZ to 0- depth.

The next plot (Fig. 10) is good to appreciate the overall quality of the LOESS fit for each 19 wavelenghts. In this optically shallow water example, the increase in LuZ near the bottom is NOT an artefact. In these type of environnement, LuZ and EuZ field are highly non-linear. It is due to the bottom reflectance which reflect a large fraction of the EdZ that reached the bottom. So the LuZ or EuZ at the bottom is then being attenuated upward above th bottom. For an insightful discussion, please refer to Maritorena, Morel, and Gentili (1994).

Overall the fit is pretty good but not perfect. It can be improved by using a slightly smaller depth windows for the LuZ smoothing. This is again given by the parameter depth.interval.for.smoothing.optics, which set by default at 4 meters. Figure 11 is the same as Fig. 10 but for a depth interval set at 3 meters for LuZ. This reduction improve the fit for some wavelenghts near the bottom (i.e., 465 to 560 nm). Note that two additional lines appear on these plots. The vertical orange lines are the detection limits (e.g. for 340 and 380 nm). So every measurements at the left of them are not considered in the fit. The green horizontal lines are the threshold specified by the parameter sub.surface.removed.layer.optics, which was set to 0 in this example.

Figure 10. Example of LuZ fitted with the LOESS method for each individual channels.

Figure 11. Same as 10, but for a depth interval for smoothing reduced at 3 meters

To compare the linear versus the LOESS fit near the air-water interface, two additional plots focusing on the surface are available in the PDF. Figure 12 is similar to Figures 10 and 11, but for the surface. The red curves are the LOESS fit while the blue lines are the linear fits. Note the absence of linear fit at 532 nm. This is due to a low R-squared value of the fit, as shown at the top of each plot. In the Cops package version 4.0, a threshold of 0.6 is hard-coded for LuZ and EuZ for the R-squared value. In other words, the linear fit is considered to fail if the R-squared value is below 0.6. So here the R-squared value at 532 was set to NA because it was below 0.6. This profile looks particularly noisy near the surface due to clear sky condition and wave focussing effect (Zaneveld, Boss, and Barnard 2001).

Figure 13 is similar to Fig 12, but for four selected wavelengths to better appreciate the fitting methods comparison.

Overall, for this profile, we would consider the LOESS fit more appropriate because of the strong non-linearity of the light profile. For further data exploitation, we will edit the select.cops.dat to indicate the use of the LOESS for the remote sensing refletance as:

WISE_MAN_F5_CAST_003_190818_181835_URC.tsv;1;Rrs.0p;NA

Figure 12. Same as 11, but for the surface layer.

Figure 13. Same as 12, but for selected wavelenghts across the spectrum.

Step 2.4 : Quality check of the EdZ fit

As for LuZ or EuZ, we check whether the LOESS parameters need adjustment to fit the measurements of EdZ. As shown in figures 14 and 15, the default depth.interval.for.smoothing.optics of 4 meters works well for our profile, except at 305 and 320 nm where a strange behavior of the LOESS fit is observed. This is often observed when a small number of valid observations are available near the surface. In addition, we can see that the EdZ are even more noisy compare to LuZ due to wave focusing effect, as expected under clear sky and wavy surface (Zaneveld, Boss, and Barnard 2001). One way to remove this noise is to use the parameter sub.surface.removed.layer.optics, which was set to 0.1 meter in this example. Befor adjusting this parameters, we need to make sure the surface waters are optically homogenous, which is not the case in our example. We can observed a change in the slope at around 1.5 meters, which particulary evidentin the UV wavelenghts (340, 380, 395 nm). This suggest the presence of a CDOM-rich surface layer of a 1.5 meters depth. So to keep this feature, we could have increased the sub.surface.removed.layer.optics to 0.5 meter and decreased the depth.interval.for.smoothing.optics to 3 meters, but here these changes did not improved the results significantly (not shown). As for LuZ, other plots focusing on the surface layer can be used to compare the LOESS with the linear extrapolation are available (Fig. 17).

Figure 14. Example of EdZ fit with the LOESS using a depth interval of 4 meters.

Figure 15. Example of EdZ fit with the LOESS using a depth interval of 4 meters for each wavelength.

Figure 16. Example of EdZ fit with the LOESS and linear at the surface for 4 selected wavelenghts.

Figure 17 shows two extra plots to assess the quality of the EdZ extrapolation to the air-water interface. Here the extrapolated values of Ed at 0- are compared to the irradiance spectrum measured in the air by the reference sensor, multiply by a factor of 0.957 to account for the specular reflexion at the interface. In this example, the LOESS extrapolation tend to overestimate Ed0- (by about 20% in the visible), while the linear extrapolation underestimate Ed0- in the UV/blue and overestimate in the NIR. These results can be explained when examining in more details Figure 16 that compares the extrapolation methods at selected wavelenghts. This example demonstrate the difficulty to extrapolate EdZ to 0- under clear sky and vertically inhomogenous water column. This is the reason why multiple C-OPS cast are required in such conditions to end up with good AOPs.

Figure 17. Top: relationship between extrapolated irradiance at 0- and the estimated irradiance based on the reference, Ed0*0.957. Bottom: the ratio of extrapolated to estimated Ed at 0- from EdZ and Ed0 respectively.

Step 3 : Compare replicates

Once each profile was processed with appropriate values for the tiltmax.optics, depth.interval.for.smoothing.optics and sub.surface.removed.layer.optics fields, then we compare all the C-OPS profiles of a given station (remember that we have one folder per station!!!).

Two plots are generated for Rrs and Kd (from the surface layer) with every file set to 1 in the file select.cops.dat. These plots are generated by the function plot.Rrs.Kd.for.station() after the processing of all cast to process found in each directory. The solid lines are for the LOESS method, while the dashed lines is for the linear extrapolation method.

For both Kd and Rrs, the difference between the casts are quite significant (Figs. 18 and 19). The variability in Rrs observed at station MAN-F05 presented here is mainly due to the water depth. In fact, the actual depth of the cast 1, 2, 3 and 4 was 6.37, 5.48, 5.08 and 7.5 m. The differenc in depth is consistent with the Rrs values, with higher green reflectance for shallower profiles. The cast number 4, however, is clearly an outlier in terms of Kd (higher values across the spectrum). It is also lower in term of Rrs (deeper waters). This cast should be discarded for the data base.

Figure 18. Example of 4 replicates for Kd for the WISE-Man station MAN-F05 visited on 18th Aug. 2019.

Figure 19. Same as 18, but for the remote sensing reflectance.

In general, there are always more differences in Rrs due to the extrapolation method. In this example, the linear extrapolation yield slightly lower Rrs values at all wavelengths. This is often the case in very absorbing waters as the one observed here.

For this station we should eliminate the cast number 4 and use the LOESS exprapolation for Rrs to avoid missing values.

Step 4: Remove outlier and document the data processing

In the above example, the cast number 4 must be remove for further data processing. Again, we do that by editing the file select.cops.dat such as:

WISE_MAN_F5_CAST_001_190818_181648_URC.tsv;1;Rrs.0p;NA

WISE_MAN_F5_CAST_002_190818_181748_URC.tsv;1;Rrs.0p;NA

WISE_MAN_F5_CAST_003_190818_181835_URC.tsv;1;Rrs.0p;NA

WISE_MAN_F5_CAST_004_190818_182147_URC.tsv;0;Rrs.0p;NA

You can also check again the comments logged by the operators on the field. It usually helps to identify the casts that may be better than the others.

It is also important to document the data processing in a log file. I usualy fill an EXCEL file with the following columns: COPS File name; Station; kept; Processing comments. In the later column I would explain why I did not kept the profile (here the Kd near the surface was very different than the other cast). A note should be taken about the Rrs variability, which can be explain by significant variability in terms of water depth.

Step 5 : Instrument self-shading correction

To complete the data processing, you should consider to correct for the instrument self-shading. This is done by choosing one of the shadow.correction method (field number 4 in the info.cops.dat file) to use for calculating the water-leaving radiance and the reflectance. As most of our work are in coastal or in-land or Arctic waters, I strongly recommand to set the shadow.correction to 0 and provide the total absorption coefficients for all wavelengths in the absorption.cops.dat file. The default value is 999, which uses estimations of sub-surface irradiance reflectacce (R) and diffuse attenuation (Kd) to estimate the total absorption (Morel and Maritorena 2001). Exceptionnaly, if you don’t have absorption measurement and you work in oceanic water, you can provided the chlorophyll-a concentration and the Case-1 water bio-optical model of Morel and Maritorena (2001) will be employed.

Absorption coefficients can be measured using in-water instruments, such as AC-s or a-sphere, or from discrete samples for CDOM and particulate matter using filter pad thecnique. If in-water coefficients are available, it will be relatively strait forward to edit the absorption.cops.dat file using compute.aTOT.for.COPS() function from the Riops package.

If only discrete samples is available, the absorption.cops.dat file may be edited using compute.discrete.aTOT.for.COPS() function from the RspectroAbs package.

The importance of this correction can be visualised in the PDF document in the page showing the various water-leaving radiances and reflectances spectra. The Figure 20 shows a typical Case-2 water case. The correction is relatively important in the NIR and UV bands.

Figure 20. Example of Rrs and shadow correction coefficient

Step 6 : Generate the data base

Once you have processed all stations individually and discarded the casts you consider of lower quality, then you can easily generate a database. You have to create an ASCII file named directories.for.cops.dat in the parent folder of the stations folder, i.e. for example

/data/ProjetX/L2/

and put all the station paths you want to include in the data base. Next you can run the generate.cops.DB(). This function compute mean and standard deviation of selected parameters using the profiles that passed the QC (as found in select.cops.dat file). The folowing parameters are computed :

The object COPS.DB is saved in RData format. The data are also saved in ASCII (.dat with comma separator) and a figure showing the measured Rrs spectra of the data base is produced.

COPS.DB <- generate.cops.DB(path="/data/ProjetX/L2/", 
                            waves.DB = c(412,443,490,555), 
                            mission = "MISSION_IMPOSSIBLE")

Note here that the user can select the actual wavelengths to include in the database. In the example above only 4 channels were selected. The default wavelenths are: 320, 330, 340, 380, 412, 443, 465, 490, 510, 532, 555, 589, 625, 665, 683, 694, 710, 780, 865 nm.

Data format

The /BIN folders contain the binary data stored in the RData format. These files contain list of variables named cops. All the information for a given cast, including the raw data, is stored in this data structure (i.e. an R list). This is very easy in R to deal with this type of data. The example below contains as much as 112 variables, including processing parameters, raw data, fitted data and so on.

load("~/OneDrive - UQAR/data/WISEMan/L2/20190818_StationMAN-F05/COPS_BU/BIN/WISE_MAN_F5_CAST_003_190818_181835_URC.tsv.RData")
str(cops)

## List of 119
##  $ verbose                            : logi TRUE
##  $ indice.water                       : num 1.34
##  $ rau.Fresnel                        : num 0.043
##  $ win.width                          : num 9
##  $ win.height                         : num 7
##  $ instruments.optics                 : chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ tiltmax.optics                     : Named num [1:3] 10 5 5
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ depth.interval.for.smoothing.optics: Named num [1:3] 10 3 3
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ sub.surface.removed.layer.optics   : Named num [1:3] 0 0.3 0
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ delta.capteur.optics               : Named num [1:3] 0 -0.05 0.238
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ radius.instrument.optics           : Named num [1:3] 0.035 0.035 0.035
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ format.date                        : chr "%m/%d/%Y %H:%M:%S"
##  $ instruments.others                 : chr "NA"
##  $ depth.is.on                        : chr "LuZ"
##  $ number.of.fields.before.date       : num 5
##  $ time.window                        : num [1:2] 0 10000
##  $ depth.discretization               : num [1:19] 0 0.01 1 0.02 2 0.05 5 0.1 10 0.2 ...
##  $ file                               : chr "WISE_MAN_F5_CAST_003_190818_181835_URC.tsv"
##  $ chl                                : num 999
##  $ EXTRAPOLATION.0m                   : logi TRUE
##  $ SHALLOW                            : logi TRUE
##  $ SHADOW.CORRECTION                  : logi TRUE
##  $ absorption.waves                   : logi NA
##  $ absorption.values                  : Named num [1:19] NA NA NA 2.26 1.8 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "340" "380" ...
##  $ blacks                             : chr(0) 
##  $ Ed0                                : num [1:297, 1:19] 2.44 2.44 2.44 2.44 2.44 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ EdZ                                : num [1:297, 1:19] 0.173 0.171 0.164 0.15 0.136 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ LuZ                                : num [1:297, 1:19] 1.92e-05 2.68e-05 1.09e-05 1.20e-05 1.55e-05 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ Ed0.anc                            :'data.frame': 297 obs. of  3 variables:
##   ..$ Roll : num [1:297] -0.979 -0.559 -0.28 -0.629 -0.629 ...
##   ..$ Pitch: num [1:297] 1.75 1.96 1.54 1.96 2.38 ...
##   ..$ Tilt : num [1:297] -2 -2.04 -1.56 -2.06 -2.46 ...
##  $ EdZ.anc                            :'data.frame': 297 obs. of  3 variables:
##   ..$ Roll : num [1:297] 1.19 1.61 1.96 2.31 2.52 ...
##   ..$ Pitch: num [1:297] 4.9 4.9 4.69 4.2 3.5 ...
##   ..$ Tilt : num [1:297] 5.04 5.15 5.08 4.79 4.3 ...
##  $ LuZ.anc                            :'data.frame': 297 obs. of  2 variables:
##   ..$ Depth: num [1:297] 0.385 0.387 0.39 0.392 0.397 ...
##   ..$ Temp : num [1:297] 9.42 9.37 9.3 9.25 9.2 ...
##  $ Ed0.waves                          : num [1:19] 305 320 340 380 395 412 443 465 490 510 ...
##  $ EdZ.waves                          : num [1:19] 305 320 340 380 395 412 443 465 490 510 ...
##  $ LuZ.waves                          : num [1:19] 305 320 340 380 395 412 443 465 490 510 ...
##  $ Others                             :'data.frame': 297 obs. of  7 variables:
##   ..$ GeneralExcelTime : num [1:297] 43696 43696 43696 43696 43696 ...
##   ..$ DateTime         : chr [1:297] "8/18/2019 6:18:38 PM" "8/18/2019 6:18:38 PM" "8/18/2019 6:18:38 PM" "8/18/2019 6:18:38 PM" ...
##   ..$ DateTimeUTC      : chr [1:297] "08/18/2019 06:18:38.37 PM" "08/18/2019 06:18:38.433 PM" "08/18/2019 06:18:38.495 PM" "08/18/2019 06:18:38.558 PM" ...
##   ..$ Millisecond      : int [1:297] 370 433 495 558 636 698 760 823 901 963 ...
##   ..$ BioGPSPosition   : num [1:297] -6807 4908 10 -998 -6807 ...
##   ..$ SolarZenithAngle : int [1:297] -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 ...
##   ..$ SolarAzimuthAngle: int [1:297] -999 -999 -999 -999 -999 -999 -999 -999 -999 -999 ...
##  $ file                               : chr "WISE_MAN_F5_CAST_003_190818_181835_URC.tsv"
##  $ potential.gps.file                 : chr "GPS_190818.tsv"
##  $ Ed0.tilt                           : num [1:297] 2 2.04 1.56 2.06 2.46 ...
##  $ EdZ.tilt                           : num [1:297] 5.04 5.15 5.08 4.79 4.3 ...
##  $ LuZ.tilt                           : NULL
##  $ change.position                    : logi FALSE
##  $ longitude                          : num -68.1
##  $ latitude                           : num 49.1
##  $ dates                              : POSIXct[1:297], format: "2019-08-18 18:18:38" "2019-08-18 18:18:38" ...
##  $ date.mean                          : POSIXct[1:1], format: "2019-08-18 18:18:47"
##  $ cops.duration.secs                 : num 19
##  $ day                                : num 18
##  $ month                              : num 8
##  $ year                               : num 2019
##  $ sunzen                             : num 41.8
##  $ Depth                              : num [1:297] 0.385 0.387 0.39 0.392 0.397 ...
##  $ Depth.good                         : logi [1:297] TRUE TRUE TRUE TRUE TRUE TRUE ...
##  $ depth.fitted                       : num [1:214] 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 ...
##  $ Ed0.th                             : num [1:19] NA 29.5 45.8 62 63.4 ...
##  $ Ed0.0p                             : num [1:19] 2.43 22.54 44.6 59.19 63.69 ...
##  $ Ed0.fitted                         : num [1:214, 1:19] 2.43 2.43 2.43 2.43 2.43 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:214] "0" "0.01" "0.02" "0.03" ...
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ Ed0.correction                     : num [1:297, 1:19] 0.997 0.997 0.998 0.998 0.998 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ LuZ.fitted                         : num [1:214, 1:19] NA NA NA NA NA NA NA NA NA NA ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:214] "0" "0.01" "0.02" "0.03" ...
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ KZ.LuZ.fitted                      : num [1:213, 1:19] NA NA NA NA NA NA NA NA NA NA ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:213] "0.01" "0.02" "0.03" "0.04" ...
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ K0.LuZ.fitted                      : num [1:213, 1:19] NA NA NA NA NA NA NA NA NA NA ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:213] "0.01" "0.02" "0.03" "0.04" ...
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ LuZ.0m                             : num [1:19] NA NA 0.00517 0.00487 0.00883 ...
##  $ K.LuZ.surf                         : num [1:19] NA NA 2.589 0.881 0.752 ...
##  $ LuZ.Z.interval                     : num [1:19] NA NA 0.93 3.13 3.13 ...
##  $ LuZ.ix.Z.interval                  : int [1:19] NA NA 28 168 168 168 150 143 143 137 ...
##  $ LuZ.0m.linear                      : num [1:19] NA NA 0.000747 0.003542 0.009707 ...
##  $ LuZ.linear.r2                      : num [1:19] NA NA 0.882 0.991 0.994 ...
##  $ LuZ.KolmolSmirnov.p.value          : num [1:19] NA NA 0.994 0.969 0.969 ...
##  $ LuZ.detection.limit                : num [1:19] 5e-05 5e-05 5e-05 2e-04 5e-05 5e-05 4e-05 1e-04 2e-05 3e-05 ...
##  $ LuZ.temperature.fitted             : num [1:214] 10.1 10.1 10.1 10 10 ...
##  $ EdZ.fitted                         : num [1:214, 1:19] 0.142 0.146 0.149 0.153 0.156 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:214] "0" "0.01" "0.02" "0.03" ...
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ KZ.EdZ.fitted                      : num [1:213, 1:19] -2.68 -2.51 -2.34 -2.16 -1.99 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:213] "0.01" "0.02" "0.03" "0.04" ...
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ K0.EdZ.fitted                      : num [1:213, 1:19] -2.68 -2.6 -2.51 -2.42 -2.34 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:213] "0.01" "0.02" "0.03" "0.04" ...
##   .. ..$ : chr [1:19] "305" "320" "340" "380" ...
##  $ EdZ.0m                             : num [1:19] 0.142 3.966 157.115 124.726 108.16 ...
##  $ K.EdZ.surf                         : num [1:19] 7.43 7.85 6.05 3.34 2.56 ...
##  $ EdZ.Z.interval                     : num [1:19] 0.842 1.242 1.442 1.442 1.542 ...
##  $ EdZ.ix.Z.interval                  : int [1:19] 42 66 79 79 86 198 198 198 198 198 ...
##  $ EdZ.0m.linear                      : num [1:19] 2.09 50.68 83.87 77.3 71.02 ...
##  $ EdZ.linear.r2                      : num [1:19] 0.972 0.984 0.989 0.967 0.954 ...
##  $ EdZ.KolmolSmirnov.p.value          : num [1:19] 0.988 0.925 0.916 0.916 0.935 ...
##  $ EdZ.detection.limit                : num [1:19] 4e-03 4e-03 4e-03 7e-03 8e-04 8e-04 8e-04 8e-04 5e-04 5e-04 ...
##  $ PARd.z                             : num [1:214] 1797 1776 1755 1735 1715 ...
##  $ PARd.at.z                          :'data.frame': 5 obs. of  2 variables:
##   ..$ z.fixed: num [1:5] 5 10 20 30 40
##   ..$ %PAR   : num [1:5] 10.31 1.77 -78.72 -481.57 -1492.11
##  $ Q.0                                : num [1:19] 3.14 3.14 3.14 3.14 3.14 ...
##  $ Q.sun.nadir                        : num [1:19] 3.14 3.14 3.14 3.14 3.14 ...
##  $ f.0                                : num [1:19] 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 ...
##  $ f.sun                              : num [1:19] 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 0.33 ...
##  $ PAR.0.fitted                       : num [1:214(1d)] 1528 1528 1528 1528 1528 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ : chr [1:214] "0" "0.01" "0.02" "0.03" ...
##  $ PAR.d.fitted                       : num [1:214(1d)] 1744 1725 1707 1690 1672 ...
##   ..- attr(*, "dimnames")=List of 1
##   .. ..$ : chr [1:214] "0" "0.01" "0.02" "0.03" ...
##  $ LuZ.shad.aR                        : Named num [1:19] NA NA NA 0.0791 0.0629 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "340" "380" ...
##  $ LuZ.shad.Edif                      : num [1:19] NA 0.219 0.279 0.288 0.274 ...
##  $ LuZ.shad.Edir                      : num [1:19] NA 0.11 0.199 0.326 0.35 ...
##  $ LuZ.shad.ratio.edsky.edsun         : num [1:19] NA 1.987 1.402 0.882 0.783 ...
##  $ LuZ.shad.eps.sun                   : Named num [1:19] NA NA NA 0.27 0.222 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "340" "380" ...
##  $ LuZ.shad.eps.sky                   : Named num [1:19] NA NA NA 0.306 0.252 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "340" "380" ...
##  $ LuZ.shad.eps                       : Named num [1:19] NA NA NA 0.287 0.235 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "340" "380" ...
##  $ LuZ.shad.correction                : Named num [1:19] NA NA NA 0.713 0.765 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "340" "380" ...
##  $ absorption.values                  : Named num [1:19] NA NA NA 2.26 1.8 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "340" "380" ...
##  $ absorption.waves                   : num [1:19] 305 320 340 380 395 412 443 465 490 510 ...
##  $ SHADOW.CORRECTION.FROM.KD          : chr "LOESS"
##   [list output truncated]

Bioshade processing

C-OPS system can include a so called Bioshade. Bioshade allows the estimation the fraction of diffuse skylight to the total downwelling irradiance, as explain in Bélanger et al. (2017). The BioSHADE system is fitted to the reference Ed0 radiometer. Briefly, the BioSHADE is a motor that moves a black aluminum band (shadowband) 1.5 mm thick and 2.5 cm wide back and forth above the Ed0 sensor. Under clear sky conditions, when the shadowband completely blocks direct sun at time t_shadow}, the radiometer measures the diffuse skylight (minus a part of the sky that is also blocked by the shadowband), Ed0_diff. When the shadowband is horizontal, the sensor measures the global solar irradiance. So to assess the global solar irradiance at the time t_shadow, we interpolate Ed0 just before and after the shadowband started to shade the sensor. This allows to approximate the fraction of diffuse skylight to the total downwelling irradiance as :

f = Ed0_diff/Ed0+

Because part of the sky is also blocked by the shadowband at t_shadow, f will be slightly underestimated. This underestimation will have negligible impact on the calculations of the shading error when the sun zenith angle is around 35 degrees, which is close to most conditions encountered.

To activate the Bioshade processing, we have to edit select.cops.dat file and change the integer to 2. For example:

IML4_150630_1339_C_data_005.csv;2;Rrs.0p.linear;NA

As for the other files, the time.window field must be edited in the info.cops.dat but the other parameters will be ignored during the processing. Here is an example of the PDF file produced by a bioshade processing. The figure 21 shows that the BioShade was activated during the recovering of the profiler. In fact, the profiler was at 30 meters depth when the acquisition was started.

Figure 21. Example of depth versus time for a BioShade measurements

The figure 22 shows the Bioshade position as a function of time, which a relative unit. The shadowband is horizontal, i.e. not shading the sensor, when it is \<5000 or >25000. So here the shadowband a round-trip, passing twice above the sensor. The red points will be used to interpolate Ed0 just before and after the shadowband started to shade the sensor.

Figure 22. Example of Bioshade position versus time

The figure 23 shows the Ed0 as a function of time. The solid lines are the interpolated data use to assess the global irradiance when the shadowband passed in front the sun, which occured at about 68 and 110 seconds atfer the begining of the data acquisition.

Figure 23. Example of downwelling irradiance measured during a Bioshade measurement

In this example is was a clear sky. The fraction of diffuse skylight to the total downwelling irradiance (green curve) increases exponentially from the NIR (\<10%) to the UV (\~50%) (Fig. 24).

Figure 24. Example of total (black), direct (red) and diffuse (blue) downwelling irradiance assessed using the Bioshade measurements

The RData structure saved for a Bioshade file is shown below.

load("./extdata/IML4_150630_1339_C_data_005.csv.RData")
str(cops)

## List of 56
##  $ verbose                           : logi TRUE
##  $ indice.water                      : num 1.34
##  $ rau.Fresnel                       : num 0.043
##  $ win.width                         : num 9
##  $ win.height                        : num 7
##  $ instruments.optics                : chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ tiltmax.optics                    : Named num [1:3] 5 5 5
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ time.interval.for.smoothing.optics: Named num [1:3] 40 80 80
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ sub.surface.removed.layer.optics  : Named num [1:3] 0 0 0
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ delta.capteur.optics              : Named num [1:3] 0 -0.09 0.25
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ radius.instrument.optics          : Named num [1:3] 0.035 0.035 0.035
##   ..- attr(*, "names")= chr [1:3] "Ed0" "EdZ" "LuZ"
##  $ format.date                       : chr "%m/%d/%Y %H:%M:%S"
##  $ instruments.others                : chr "NA"
##  $ depth.is.on                       : chr "LuZ"
##  $ number.of.fields.before.date      : num 1
##  $ time.window                       : num [1:2] 0 10000
##  $ depth.discretization              : num [1:19] 0 0.01 1 0.02 2 0.05 5 0.1 10 0.2 ...
##  $ file                              : chr "IML4_150630_1339_C_data_005.csv"
##  $ chl                               : logi NA
##  $ SHADOW.CORRECTION                 : logi FALSE
##  $ absorption.waves                  : logi NA
##  $ absorption.values                 : logi NA
##  $ blacks                            : chr(0) 
##  $ Ed0                               : num [1:2745, 1:19] 0.729 0.731 0.732 0.734 0.736 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:19] "305" "320" "330" "340" ...
##  $ EdZ                               : num [1:2745, 1:19] -0.00054 0.00025 0.00026 0.000315 0.001807 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:19] "305" "320" "330" "340" ...
##  $ LuZ                               : num [1:2745, 1:19] -1.06e-05 -1.09e-05 -1.19e-05 1.40e-05 -7.65e-06 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : NULL
##   .. ..$ : chr [1:19] "305" "320" "330" "340" ...
##  $ Ed0.anc                           :'data.frame':  2745 obs. of  2 variables:
##   ..$ Roll : num [1:2745] 3.08 2.66 2.31 1.89 1.19 ...
##   ..$ Pitch: num [1:2745] 1.887 1.328 1.118 1.118 0.839 ...
##  $ EdZ.anc                           :'data.frame':  2745 obs. of  2 variables:
##   ..$ Roll : num [1:2745] -0.349 0.21 0.559 1.048 1.398 ...
##   ..$ Pitch: num [1:2745] -8.35 -6.51 -6.3 -5.32 -5.04 ...
##  $ LuZ.anc                           :'data.frame':  2745 obs. of  2 variables:
##   ..$ Depth: num [1:2745] 29.8 29.8 29.7 29.7 29.7 ...
##   ..$ Temp : num [1:2745] 2.96 2.97 2.97 2.96 2.97 ...
##  $ Ed0.waves                         : num [1:19] 305 320 330 340 380 412 443 465 490 510 ...
##  $ EdZ.waves                         : num [1:19] 305 320 330 340 380 412 443 465 490 510 ...
##  $ LuZ.waves                         : num [1:19] 305 320 330 340 380 412 443 465 490 510 ...
##  $ Others                            :'data.frame':  2745 obs. of  6 variables:
##   ..$ GeneralExcelTime : num [1:2745] 42186 42186 42186 42186 42186 ...
##   ..$ DateTime         : chr [1:2745] "06/30/2015 14:13:40" "06/30/2015 14:13:41" "06/30/2015 14:13:41" "06/30/2015 14:13:41" ...
##   ..$ DateTimeUTC      : chr [1:2745] "06-30-2015 02:13:40.968 " "06-30-2015 02:13:41.031 " "06-30-2015 02:13:41.109 " "06-30-2015 02:13:41.171 " ...
##   ..$ Millisecond      : int [1:2745] 968 31 109 171 234 296 359 437 500 562 ...
##   ..$ BioGPS_Position  : num [1:2745] 10 141342 -6834 4840 10 ...
##   ..$ BioShade_Position: int [1:2745] 31289 31289 31289 31289 31289 31289 31289 31289 31289 31289 ...
##  $ file                              : chr "IML4_150630_1339_C_data_005.csv"
##  $ potential.gps.file                : chr "IML4_150630_1339_gps.csv"
##  $ Ed0.tilt                          : num [1:2745] 3.61 2.97 2.56 2.19 1.45 ...
##  $ EdZ.tilt                          : num [1:2745] 8.35 6.52 6.33 5.42 5.23 ...
##  $ LuZ.tilt                          : NULL
##  $ change.position                   : logi FALSE
##  $ longitude                         : num -68.6
##  $ latitude                          : num 48.7
##  $ dates                             : POSIXct[1:2745], format: "2015-06-30 14:13:40" "2015-06-30 14:13:41" ...
##  $ date.mean                         : POSIXct[1:1], format: "2015-06-30 14:15:11"
##  $ cops.duration.secs                : num 182
##  $ day                               : num 30
##  $ month                             : num 6
##  $ year                              : num 2015
##  $ sunzen                            : num 37.9
##  $ Depth                             : num [1:2745] 29.8 29.8 29.7 29.7 29.7 ...
##  $ Depth.good                        : logi [1:2745] TRUE TRUE TRUE TRUE TRUE TRUE ...
##  $ depth.fitted                      : num [1:331] 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 ...
##  $ Ed0.th                            : num [1:19] NA 32.1 47.6 49.4 66.2 ...
##  $ Ed0.fitted                        : num [1:2438, 1:19] 0.749 0.749 0.749 0.749 0.749 ...
##   ..- attr(*, "dimnames")=List of 2
##   .. ..$ : chr [1:2438] "0.967999935150146" "1.03099989891052" "1.10899996757507" "1.1710000038147" ...
##   .. ..$ : chr [1:19] "305" "320" "330" "340" ...
##  $ Ed0.tot                           : Named num [1:19] 0.737 22.186 41.945 46.487 60.448 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "330" "340" ...
##  $ Ed0.dif                           : Named num [1:19] 0.381 11.144 19.931 20.476 19.835 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "330" "340" ...
##  $ Ed0.f                             : Named num [1:19] 0.517 0.502 0.475 0.44 0.328 ...
##   ..- attr(*, "names")= chr [1:19] "305" "320" "330" "340" ...

IcePro processing or under-ice data processing

C-OPS have been deployed under-ice in the Arctic using a special configuration name IcePro. In these conditions, the extrapolation of the radiometric quantity to the air-water interface does not make sense. We can disable the surface extrapolation in the select.cops.dat file, by putting a value of 3 in the second column, after the file name. No extrapolation to the air-sea interface is performed and sub-surface AOP (nLw, Lw, Rrs, etc) are not calculated.

Contact point

I also encourage any user to watch of the package update and report bugs or requests on github. For more information or to report any bugs, please contact Simon Bélanger at simon_belanger@uqar.ca.

References

Bélanger, Simon, Claudia Carrascal-Leal, Thomas Jaegler, Pierre Larouche, and Peter Galbraith. 2017. “Assessment of radiometric data from a buoy in the St. Lawrence estuary.” *Journal of Atmospheric and Oceanic Technology* 34 (4): 877–96. .
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belasi01/Cops documentation built on Feb. 26, 2024, 6:52 a.m.

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