Ultraviolet (250–550  nm) absorption spectrum of pure water

Mason, John D.; Cone, Michael T.; Fry, Edward S.

doi:10.1364/ao.55.007163

Cited by 93 publications

(109 citation statements)

References 21 publications

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“…Absorption by seawater ( a w (λ)) in the UV and visible is known (Mason et al, ; Pope & Fry, ) and corrected for temperature and salinity (Sullivan et al, ), which are measured coincidently. The a p (λ) spectra are measured with the ac‐s meter deployed on a flow‐through system that records both total and dissolved absorption, and then a p (λ) spectra are calculated by difference (Slade et al, ).…”

Section: Methodsmentioning

confidence: 99%

Estimation of Phytoplankton Accessory Pigments From Hyperspectral Reflectance Spectra: Toward a Global Algorithm

et al. 2017

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Phytoplankton community composition in the ocean is complex and highly variable over a wide range of space and time scales. Able to cover these scales, remote‐sensing reflectance spectra can be measured both by satellite and by in situ radiometers. The spectral shape of reflectance in the open ocean is influenced by the particles in the water, mainly phytoplankton and covarying nonalgal particles. We investigate the utility of in situ hyperspectral remote‐sensing reflectance measurements to detect phytoplankton pigments by using an inversion algorithm that defines phytoplankton pigment absorption as a sum of Gaussian functions. The inverted amplitudes of the Gaussian functions representing pigment absorption are compared to coincident High Performance Liquid Chromatography measurements of pigment concentration. We determined strong predictive capability for chlorophylls a, b, c1+c2, and the photoprotective carotenoids. We also tested the estimation of pigment concentrations from reflectance‐derived chlorophyll a using global relationships of covariation between chlorophyll a and the accessory pigments. We found similar errors in pigment estimation based on the relationships of covariation versus the inversion algorithm. An investigation of spectral residuals in reflectance data after removal of chlorophyll‐based average absorption spectra showed no strong relationship between spectral residuals and pigments. Ultimately, we are able to estimate concentrations of three chlorophylls and the photoprotective carotenoid pigments, noting that further work is necessary to address the challenge of extracting information from hyperspectral reflectance beyond the information that can be determined from chlorophyll a and its covariation with other pigments.

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Section: Methodsmentioning

confidence: 99%

Estimation of Phytoplankton Accessory Pigments From Hyperspectral Reflectance Spectra: Toward a Global Algorithm

et al. 2017

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“…To evaluate version 4.0 OMI TCWV over the oceans, we compare against the microwave TCWV data from SSMIS onboard the Defense Meteorological Satellite Program (DMSP) F16 satellite. The SSMIS data are derived by Remote Sensing Systems (RSS) using their version 7 algorithm (http:// www.remss.com/, last access: 17 September 2019) and have a retrieval accuracy of better than 1 mm (Wentz, 1997;Mears et al, 2015). For clear-sky comparison, we use the daily 0.25 • × 0.25 • SSMIS data for January and July 2006 and filter out the pixels affected by rain and cloud liquid water.…”

Section: Omi and Ssmis Over Oceanmentioning

confidence: 99%

“…An interim version 3.0 OMI TCWV product was available at the AVDC. Compared with version 2.1, version 3.0 uses the reference spectrum for water vapor from the latest HITRAN database (Gordon et al, 2017) and that for liquid water from Mason et al (2016), as well as the newest cloud product (Veefkind et al, 2016). The version 3.0 retrieval window (427.0-467.0 nm) is adjusted from that for version 2 within 2 nm on each end based on fitting uncertainty for a randomly selected test orbit.…”

Section: Introductionmentioning

confidence: 99%

Ozone Monitoring Instrument (OMI) Total Column Water Vapor version 4 validation and applications

et al. 2019

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Abstract. Total column water vapor (TCWV) is important for the weather and climate. TCWV is derived from the Ozone Monitoring Instrument (OMI) visible spectra using the version 4.0 retrieval algorithm developed at the Smithsonian Astrophysical Observatory. The algorithm uses a retrieval window between 432.0 and 466.5 nm and includes updates to reference spectra and water vapor profiles. The retrieval window optimization results from the trade-offs among competing factors. The OMI product is characterized by comparing against commonly used reference datasets – global positioning system (GPS) network data over land and Special Sensor Microwave Imager/Sounder (SSMIS) data over the oceans. We examine how cloud fraction and cloud-top pressure affect the comparisons. The results lead us to recommend filtering OMI data with a cloud fraction less than f=0.05–0.25 and cloud-top pressure greater than 750 mb (or stricter), in addition to the data quality flag, fitting root mean square (RMS) and TCWV range check. Over land, for f=0.05, the overall mean of OMI–GPS is 0.32 mm with a standard deviation (σ) of 5.2 mm; the smallest bias occurs when TCWV = 10–20 mm, and the best regression line corresponds to f=0.25. Over the oceans, for f=0.05, the overall mean of OMI–SSMIS is 0.4 mm (1.1 mm) with σ=6.5 mm (6.8 mm) for January (July); the smallest bias occurs when TCWV = 20–30 mm, and the best regression line corresponds to f=0.15. For both land and the oceans, the difference between OMI and the reference datasets is relatively large when TCWV is less than 10 mm. The bias for the version 4.0 OMI TCWV is much smaller than that for version 3.0. As test applications of the version 4.0 OMI TCWV over a range of spatial and temporal scales, we find prominent signals of the patterns associated with El Niño and La Niña, the high humidity associated with a corn sweat event, and the strong moisture band of an atmospheric river (AR). A data assimilation experiment demonstrates that the OMI data can help improve the Weather Research and Forecasting (WRF) model skill at simulating the structure and intensity of the AR and the precipitation at the AR landfall.

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“…Absorption coefficients of pure ice are from Warren and Brandt [2008]. For pure water, absorption coefficients are from Mason et al [2016] and Pope and Fry [1997]. Photos of thick sections indicated larger brine pockets and higher brine volume fraction in the granular ice than in the columnar ice.…”

Section: Radiative Transfer Modelmentioning

confidence: 99%

Windows in Arctic sea ice: Light transmission and ice algae in a refrozen lead

et al. 2017

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The Arctic Ocean is rapidly changing from thicker multiyear to thinner first‐year ice cover, with significant consequences for radiative transfer through the ice pack and light availability for algal growth. A thinner, more dynamic ice cover will possibly result in more frequent leads, covered by newly formed ice with little snow cover. We studied a refrozen lead (≤0.27 m ice) in drifting pack ice north of Svalbard (80.5–81.8°N) in May–June 2015 during the Norwegian young sea ICE expedition (N‐ICE2015). We measured downwelling incident and ice‐transmitted spectral irradiance, and colored dissolved organic matter (CDOM), particle absorption, ultraviolet (UV)‐protecting mycosporine‐like amino acids (MAAs), and chlorophyll a (Chl a) in melted sea ice samples. We found occasionally very high MAA concentrations (up to 39 mg m−3, mean 4.5 ± 7.8 mg m−3) and MAA to Chl a ratios (up to 6.3, mean 1.2 ± 1.3). Disagreement in modeled and observed transmittance in the UV range let us conclude that MAA signatures in CDOM absorption spectra may be artifacts due to osmotic shock during ice melting. Although observed PAR (photosynthetically active radiation) transmittance through the thin ice was significantly higher than that of the adjacent thicker ice with deep snow cover, ice algal standing stocks were low (≤2.31 mg Chl a m−2) and similar to the adjacent ice. Ice algal accumulation in the lead was possibly delayed by the low inoculum and the time needed for photoacclimation to the high‐light environment. However, leads are important for phytoplankton growth by acting like windows into the water column.

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Ultraviolet (250–550 nm) absorption spectrum of pure water

Cited by 93 publications

References 21 publications

Estimation of Phytoplankton Accessory Pigments From Hyperspectral Reflectance Spectra: Toward a Global Algorithm

Estimation of Phytoplankton Accessory Pigments From Hyperspectral Reflectance Spectra: Toward a Global Algorithm

Ozone Monitoring Instrument (OMI) Total Column Water Vapor version 4 validation and applications

Windows in Arctic sea ice: Light transmission and ice algae in a refrozen lead

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