Abstract Measurements by the submersible ultraviolet nitrate analyzer (SUNA) can be used to derive high‐resolution in situ nitrate concentration with reliable accuracy and precision. Here we report our operational practices for SUNA deployment (including pre‐cruise instrument preparation and in‐cruise instrument maintenance) and detailed post‐cruise nitrate quality control procedures for SUNA integrated onto the CTD rosette. This work is based on experiences and findings from over 500 individual SUNA casts collected from 24 cruises (of which 14 cruises have been quality controlled so far) over the past 5 yr. After applying previously published spectral corrections for temperature, salinity, and pressure effects, we found residual biases in SUNA nitrate estimates compared to independently measured discrete samples. We further develop and assess a new two‐step procedure to remove remaining biases: (1) a general temperature‐dependent adjustment at low‐nitrate concentrations; and (2) a cruise‐specific full‐range bias correction. Our final quality‐controlled SUNA nitrate data achieve an accuracy of 0.34–0.78 μM, with a precision of 0.08–0.21 μM, at a vertical resolution of 1 m. Additional comparisons between the nitrate and density data confirm the high quality of the quality‐controlled SUNA data. Although applying spectral correction algorithms increases the accuracy and precision of the instrument‐output nitrate concentration, we emphasize that additional constraints of SUNA measurements against other independent sources (e.g., bottle data, temperature, and density) are irreplaceable to ensure the accuracy of final nitrate data.
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Updated temperature correction for computing seawater nitrate with in situ ultraviolet spectrophotometer and submersible ultraviolet nitrate analyzer nitrate sensors
Abstract Sensors that use ultraviolet (UV) light absorption to measure nitrate in seawater at in situ temperatures require a correction to the calibration coefficients if the calibration and sample temperatures are not identical. This is mostly due to the bromide molecule, which absorbs more UV light as temperature increases. The current correction applied to in situ ultraviolet spectrophotometer (ISUS) and submersible ultraviolet nitrate analyzer (SUNA) nitrate sensors generally follows Sakamoto et al. (2009, Limnol. Oceanogr. Methods 7, 132–143). For waters warmer than the calibration temperature, this correction model can lead to a 1–2 μmol kg−1positive bias in nitrate concentration. Here we present an updated correction model, which reduces this small but noticeable bias by at least 50%. This improved model is based on additional laboratory data and describes the temperature correction as an exponential function of wavelength and temperature difference from the calibration temperature. It is a better fit to the experimental data than the current model and the improvement is validated using two populations of nitrate profiles from Biogeochemical Argo floats navigating through tropical waters. One population is from floats equipped with ISUS sensors while the other arises from floats with SUNA sensors on board. Although this model can be applied to both ISUS and SUNA nitrate sensors, it should not be used for OPUS UV nitrate sensors at this time. This new approach is similar to that used for OPUS sensors (Nehir et al., 2021, Front. Mar. Sci. 8, 663800) with differing model coefficients. This difference suggests that there is an instrumental component to the temperature correction or that there are slight differences in experimental methodologies.
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- PAR ID:
- 10469412
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Limnology and Oceanography: Methods
- Volume:
- 21
- Issue:
- 10
- ISSN:
- 1541-5856
- Format(s):
- Medium: X Size: p. 581-593
- Size(s):
- p. 581-593
- Sponsoring Org:
- National Science Foundation
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The Ocean Observatories Initiative (OOI) deployed both the In-Situ Ultraviolet Spectrophotometer (ISUS) and Submersible Underwater Nitrate Sensor (SUNA) for continuous, in-situ measurement of nitrate. At the Pioneer-New England Shelf Array (Pioneer-NES), ISUS/SUNA sensors were deployed at 7-meters depth at the Inshore (ISSM), Central (CNSM), and Offshore (OSSM) Surface Mooring locations. The SUNA sensor replaced the ISUS sensors spring 2018. The SUNA was a major improvement in technology, with significant improvements in accuracy and precision. However, it still suffers from calibration drift due to lamp fatigue and biofouling as well as spectral interference due to bromide and fluorometric CDOM. Drift is corrected by application of post-cruise calibrations to recalculate the temperature-and-salinity corrected nitrate concentration following Sakamoto (2009a) and estimating a linear drift between pre-and-post cruise deployments. Validation is performed by comparison with discrete water samples collected during deployment/recovery of the sensors. These datasets include the nitrate data from the Pioneer-NES ISSM (CP03ISSM-RID26-07-NUTNRB000.nc), CNSM (CP01CNSM-RID26-07-NUTNRB000.nc), and OSSM (CP04OSSM-RID26-07-NUTNRB000.nc) SUNA instruments spanning Spring 2018 through Fall 2022. Each dataset contains the measured nitrate, the temperature-salinity corrected nitrate, the drift-corrected nitrate, and the nitrate following validation with bottle samples.more » « less
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This dataset contains high-quality high-resolution nitrate data collected from a submersible ultraviolet nitrate analyzer (SUNA) mounted on the CTD rosette. Both raw and bias-corrected, quality-controlled data are included. The raw data include the instrument's estimate of nitrate concentration with dark values and light intensity at 256 wavelengths. Our quality-controlled SUNA nitrate data have an accuracy of 0.34-0.78 micromoles per liter, with a precision of 0.08-0.21 micromoles per liter, at a vertical resolution of 1 m. These data are collected from about 21 routinely occupied stations along a cross-shelf transect spanning southward from Martha's Vineyard, MA to just beyond the shelf break onboard the seasonal Northeast U.S. Shelf Long-Term Ecological Research (NES-LTER) survey cruises since February 2019. Data in CSV format.more » « less
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Using standard calibration schemes commercial oxygen optode sensors typically yield oxygen concentrations in the range of 2-4 umol/kg under anoxic conditions. They are thus unable to detect the roughly 0.1 umol/kg levels of oceanic functional anoxia. Here, a modified Stern-Volmer equation is used to characterize and calibrate 26 optodes deployed on 16 autonomous floats in the Eastern Tropical Pacific (ETNP) oxygen deficient zone (ODZ) using a combination of manufacturers', laboratory, and in-situ data. Laboratory calibrations lasting several months and conducted over 2 years show that optodes kept under anoxic conditions drift at rates of order 0.2 umol/kg/yr, with much higher drifts in the first month. The initial transient is plausibly due to the degassing of plastic components of the optodes and might be reduced by replacing these with metal. Oxygen concentrations measured by these calibrated optodes in the nearly anoxic ODZ core of the ETNP deviated from both the laboratory calibrations and ship-based STOX measurements by similar amounts. Thus with current sensors, an in-situ anoxic oxygen calibration only once or twice a year is needed to maintain an accuracy close to 0.2 umol/kg. An algorithm to find the anoxic cores of the ETNP ODZ is developed and used to remove the drift in the float optodes to this accuracy. This is an order-of-magnitude improvement in the low oxygen performance of the optodes and could be implemented on the existing database of Argo oxygen floats to map the geography of functional anoxia. This dataset contains the raw float data, the float data calibrated using the manufacturers’ schemes and our new scheme. The calibration points and our final calibration constants, as well as the STOX data used to validate our new calibrations, are included. Data was collected on 10 custom-built profiling 'ODZ' floats equipped with oxygen optodes and gas tension devices and on 6 standard Argo floats with oxygen sensors. Argo data was processed by Argo and recalibrated at APL/UW. ODZ float data was processed at APL/UW as described in the associated manuscript. # Oxygen data from Eastern Tropical North Pacific cruises and floats 2021-2022 [https://doi.org/10.5061/dryad.8kprr4xwk](https://doi.org/10.5061/dryad.8kprr4xwk) ## Description of the data and file structure ## **ODZ Level2.zip** contains scientific data for the ODZ floats converted from raw data using nominal calibrations. Level_2 in NASAspeak. A README, Diagnostic plots, and a Matlab conversion program are included. The script ***MRVFloatDecode_2023.m*** reads the raw files for the ODZ floats and puts them in a single Matlab file **xo110-.mat** where the first is the float number and the second is the boot number. It makes lots of plots, which I also include. Matlab substructures and variables are: ***CTD*** – Structure containing Seabird 41CT data * P, T, S – pressure [dbar], temperature [deg C], practica salinity as computed by Seabird [psu] * time, mtime – time in Matlab datetime and datenum formats * SA, CT, Sig0 – Absolute salinity [g/kg], conservative temperature [deg C], potential density [kg/m^3] * CC, W, Drag–estimated oil volume [cc], vertical velocity [m/s], Drag force (for ballasting) [N] ***GPS*** – position * time, mtime - time as Matlab datetime, Matlab datnum * lat,lon- location degrees latitude, degrees longitude * nsat, hdop – number of satellites, horizontal dilution of precision ***GTD*** – Gas Tension Sensor * time, mtime - time as Matlab datetime, Matlab datnum, * P, T, S, Sig0 – Pressure [dbar], temperature [deg C], practical salinity [psu], potential density [kg/m^3] * GT – gas tension [mbar] * Tgtd – temperature of GTD [ deg C] * Ref- time [matlab datenum], temperature [deg C], pressure [mbar] for reference sensor * Other variables are calibration constants and check values. ***SBE5M1,SBE5M2*** - status of pumps. 1 is for optode(1) and GTD. 2 is for reference optod ***oldGTD*** - One float had an old-style GTD for reference. ***optode*** - SBE63 optodes (1) is water optode, (2) is reference optode * time, time -time as Matlab datum and date time * SN – optode serial number * red_amp, blue_amp- amplitudes of red and blue LEDs [counts] * red-phase, blue-phase- phases [microvolts] of fluorescence phase. * O2phase- their difference [microvolts] used to compute oxygen * T – optode temperature [deg C] * O2uM – optode’s computed oxygen concentration converted to uMol/kg. * Tctd, S, P, Sig0 – CTD interpolated to optode time - temperature [deg C], practical salinity [psu], pressure [dbar], potential density [kg/m^3] ***ADC, AirPump, AirValve, OilPump,*** ***OilValve*** - structures diagnosing the buoyancy system operations. Scientfically uninteresting. ## ***STOX Oxygen Profiles.zip*** Contains high precision oxygen profiles taken on the two Sally Ride cruises using STOX oxygen sensors. The data is provided as .txt and .mat formats along with miscellaneous data from the CTD. Oxygen measurements from the floats were referenced to STOX oxygen profiles taken from the ship on the two cruises because these provide much more stable and high precision measurements. STOX sensors are described in detail in Revsbech, N. P.; Larsen, L. H.; Gundersen, J.; Dalsgaard, T.; Ulloa, O. and Thamdrup, B. ( 2009) Determination of ultra‐low oxygen concentrations in oxygen minimum zones by the STOX sensor. Limnology and Oceanography: Methods, 7, pp.371-381. DOI:10.4319/lom.2009.7.371. And from their manufacturer [https://unisense.com/products/stox-microsensor/](https://unisense.com/products/stox-microsensor/) STOX data was collected on two cruises of the research vessel, Sally Ride, SR 2114 and SR2011. Data from each CTD cast with a STOX profile is in a separate folder in this archive. In each, the raw data is in a ****.txt*** file and the converted Matlab data is in a ****.mat*** file. MATLAB scripts to read the ****.mat*** file are included in each folder. Data names and units are: Ship Cruise Station Cast Year Month Day Hour Minute * Depth [m] * Latitude [deg] * Longitude [deg] * Density [sigma-theta,kg/m^3] * Temperature [ºC] * Salinity * Beam Attenuation [1/m] * Fluorescence [mg Chla/m3] * PAR [umol/m2/s] * Oxygen_SBE [µmol/kg]) * Oxygen_STOX [µmol/kg] * STOX_SD [µmol/kg] * STOX_n [µmol/kg] * NO3-Suna [uM] ## **Optode Calibration.zip** Contains all of the calibration data used to calibrate the optodes including the anoxic laboratory points, the manufacturers' calibration points, and the coefficients of the calibration model for each optode. **Seabird 63 Optodes** Anoxic calibration data and model fit are in ***AnoxicCalibration/SBE63/2020/*** and ***/2021/***. The 2020 data was used in the final calibration. * Files are *******Tau0model.mat*** where **** is the optode serial number * Variable ***meta*** explains each variable, repeated here. Calibration model is '1./Taup.*exp(-(Etau+Etau2.*(K-283.15).^2)/R/K )*(1+Drift *(days since start) )' Variables are * Taup: 'Phase [uS]' * Etau: 'Energy is Etau+Etau2*(T-10C) [J/mol] * Drift: 'Drift coefficient in the model [1/days] * Ttau: 'Time scale of drift [days] * Drift_uSday: 'Model Drift uS/day' * Dcal: 'Robust Drift. The drift line is Dcal(2)+ Dcal(1)*(Yearday of 2021) in uMol. Drift is Dcal(1) [uMol/day] * Drms: 'RMS drift fit error [uS] * Derr: 'Uncertainty in Dcal; Drift uncertainty is Derr(1) [uMol/day]' Calibration points from the anoxic tank are in structure ***RawS.*** Variable ***meta*** explains each variable, repeated here. * K: 'Temperature [Kelvin]' * O2phase: 'O2 phase tau [uS]' * R: 'Gas constant [J/K/mol] * dyd: 'Time since start of record [days]' * TIME: 'Time [matlab datetime] * Omodel: 'Tau computed from model with drift [uS] * OmodelND: 'Tau computed from model with drift removed [uS] **Full Calibration/** contains the oxic calibration points and calibration coefficients Calibration points from Seabird supplied with optode are in **SBE63/*FactoryCalibration/ ****_dd_mmm_yyyy.mat ***where **** is the optode serial number. The calibration date follows. Variables are * Caltime - Calibration time [matlab datum] * ID - Serial number * O2in_mll - Oxygen in tank from winklers [ml/L] * O2out_mll - Oxygen computed from Seabird calibration [ml/L] * S - Salinity [psu] * T - Temperature [deg C] * resid_mll - model residual [ml/L] * tau_us - optode phase lag [microseconds] The oxic part of the optode model calibration coefficients are in ***SBE63/Calfiles/*** Calibration model, coefficients, and check values are in ***Calfiles/_oxic_model.mat*** where **** is the optode SN Data is in structure ***Kfile*** ***Kfile.meta*** explains the variables, repeated here. Model is pO2=eta/K(T) * (1 + a(T)*eta^2.3)^q(T) ; eta= tau0(T)/tau-1. Note that tau0(T) is computed from *******_Tau0model.mat*** coefficients above. Variables are * Check: 'Test values of T, Tau, and pO2 from SBE cal' * Lk: 'K(T)=polyval(Lk, T) - Matlab call to compute K from Lk polynomial coefficients and T [deg C] * La: 'a(T)=polyval(La,T)' * Lq: 'q(T)=polyval(Lq,T)' **Aanderaa 4330 Optodes** **Anoxic calibration** data and model fit is in ***AnoxicCalibration/AA/*** \** **File names and formats are the same as for SBE63 optodes **Full Calibration/AA** **/Factory Calibrations** contains the calibration information supplied with the optodes Files are *******_dd-mmm-yyyy.mat*** with the same format as for the SBE63 The relevant variables are: * Caltime - Calibration time [matlab datenum] * ID - Serial number of optode * O2in_uMol - Calibration bath oxygen [uMol/L] * S - Salinity [psu] * T - Temperature from optode [deg C] * tau_deg - optode output phase [degrees] * meta - Misc information **/Calfiles/********_M0_oxic_model.mat** contain oxic part of the optode model calibration coefficients The format is the same as for SBE63, but there is an extra variable * eta_off: Add this to eta to account for drift since calibration [uS] ## **Calibrated Oxygen.zip** contains both uncalibrated and calibrated optode data for both the ODZ and Argo floats. A README file and Matlab processing programs are included. /***SBE63/xo110**-***.mat*** contain the calibrated data for **ODZ float xo110** Format and data is identical to that in the ***optode*** structure in ***ODZ_Level2_Mat,*** but with 2 extra variables * pO2 – partial pressure of oxygen [mbar] in uncalibrated data * Cal – a structure containing calibrated data -- FINAL DATA IS HERE * pO2: partial pressure of oxygen [mbar] in calibrated data * Tau0m: Calibration model of anoxic phase [microsecond]. Includes offset. * Tau: Measured phase [microsecond] * Tauoff: offset in Tau from in situ calibration [uS] * eta: (Tau0m+Tauoff)/Tau-1 * O2uM: oxygen concentration [micromoles/kg] * O2umol: same Note that optode(1) is the water oxygen. Optode(2) is a reference optode, which is not of scientific interest. **/SBE63/Reprocess_SBE63.m** is a MATLAB script showing how to combine calibration data and float data to make calibrated data for SBE63 optodes **/AA/Mat/*FloatID*/*FloatID_profilenum*.mat** contains Argo float data from float FloatID, profile number profilenum. Variables are Data from Argos float archive * mtime, time - time in datetime and datenum formats * lat, lon - GPS position latitude degrees and longitude degrees * P, T, S - CTD pressure [dbar], temperature [deg C], salinity [psu] * Optode - Optode serial number * O2T - Optode temperature [deg C] * O2phase - Optode phase [degrees] * O2umol - Optode oxygen [micromole/kg] Added variables * Kfile - Structure as in Optode Calibration files. Kfile.meta also has metadata * Cal - Structure containing calibrated optode data on the same timebase * Tau - measured phase [degrees] * Tau0m - Model anoxic phase [degrees] * Tauoff - Offset from laboratory calibration [degrees]. Includes offset & drift. * Drift - Drift [degrees/year] * mtime0 - base time for drift [matlab datenum format] * eta - Tau0m/Tau-1 * pO2 - Calibrated Oxygen partial pressure [mbar] * O2umol - Calibrated Oxygen concentration [micromole/kg] * meta - similar list to this one. * SN - same as Optode * Float - FloatID **/AA/Mat/*FloatID*/*FloatID_profilenum*.xls** contains the calibrated data in Excel format ***/AA/Reprocess_3_AA.m*** is a MATLAB script showing how to combine calibration data and float data to make calibrated data for AA optodes ## ***ODZ Raw\.zip*** contains the raw data from 9 custom-built ODZ floats. Level_1 in NASAspeak. They can be read by ***MRVFloatDecode_2023.m*** included in ***ODZ Level 2 files*** ## Code/Software Processing and reading scripts in Matlab (24.1.0.2628055 (R2024a) Update 4) are provided.more » « less
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