Particulate Backscattering Coefficient Research Articles

On the Seasonal Cycle of Phytoplankton Bio‐Optical Properties Inside a Warm Core Ring in the Gulf of Mexico

AbstractFour underwater glider missions were carried out to sample the physical and bio‐optical properties inside a Loop Current Eddy (LCE) in the Gulf of Mexico, to investigate whether the winter deepening of the mixed‐layer and erosion of the nitracline stimulates phytoplankton growth. Recent coupled physical‐biogeochemical numerical models support this mechanism, but observations using profiling floats suggest that there is no seasonal cycle on integrated phytoplankton biomass. Here, data collected by underwater gliders during a full seasonal cycle and inside the LCE Poseidon support the idea of an increase in phytoplankton biomass during winter, consistent with nutrient entrainment into the euphotic zone. The changes in fluorescence emission per chlorophyll‐a unit and their implications for interpreting bio‐optical variability were also assessed. Linear regressions between in vivo chlorophyll‐a fluorescence and satellite chlorophyll‐a concentration show the largest (smallest) slopes during winter (summer), suggesting a shift in the phytoplankton community along the year or photoacclimation. Although the glider data set is convolved by temporal and spatial variability, and chlorophyll‐a fluorescence is affected by multiple factors, the concomitant enhancement of particle backscattering coefficient and chlorophyll‐a observed during winter supports the occurrence of a seasonal cycle in phytoplankton biomass. Deep vertical mixing in winter inside the core of the LCE, can promote fertilization through vertical diffusion of nutrients. Poseidon was an extraordinary, large, and strong, LCE that prompted phytoplankton blooms in winter highlighting their relevance for primary production and in general for biogeochemical processes.

Retrieval and analysis of the composition of an aerosol mixture through Mie–Raman–fluorescence lidar observations

Abstract. In the atmosphere, aerosols can originate from numerous sources, leading to the mixing of different particle types. This paper introduces an approach to the partitioning of aerosol mixtures in terms of backscattering coefficients. The method utilizes data collected from the Mie–Raman–fluorescence lidar, with the primary input information being the aerosol backscattering coefficient (β), particle depolarization ratio (δ), and fluorescence capacity (GF). The fluorescence capacity is defined as the ratio of the fluorescence backscattering coefficient to the particle backscattering coefficient at the laser wavelength. By solving a system of equations that model these three properties (β, δ and GF), it is possible to characterize a three-component aerosol mixture. Specifically, the paper assesses the contributions of smoke, urban, and dust aerosols to the overall backscattering coefficient at 532 nm. It is important to note that aerosol properties (δ and GF) may exhibit variations even within a specified aerosol type. To estimate the associated uncertainty, we employ the Monte Carlo technique, which assumes that GF and δ are random values uniformly distributed within predefined intervals. In each Monte Carlo run, a solution is obtained. Rather than relying on a singular solution, an average is computed across the whole set of solutions, and their dispersion serves as a metric for method uncertainty. This methodology was tested using observations conducted at the ATOLL (ATmospheric Observation at liLLe) observatory, Laboratoire d'Optique Atmosphérique, University of Lille, France.

Sensitivity of a carbon-based primary production model on satellite ocean color products

Satellite remote sensing plays a crucial role in estimating global primary production. One well-known model is the carbon-based production model (CbPM), which focuses on the estimation of carbon biomass (Cph) via particulate backscattering coefficient at 443 nm (bbp(443)) and emphasizes the physiological characteristics of phytoplankton. However, as there are various remote sensing algorithms for the estimation of bbp(443) and chlorophyll-a concentration (Chl), the impacts introduced by different bbp(443) and Chl products on the estimated water-column integrated primary production (PPeu) by CbPM are unknown, especially with the new CbPM version (CbPM08) (Westberry et al., 2008). In this study, we conducted analyses to examine the impact of Chl, bbp(443), and Kd(490) (the diffuse attenuation coefficient at 490 nm) that were derived from ocean color remote sensing on PPeu estimated by CbPM08. Our results revealed that Chl constitutes the primary source of model uncertainty, followed by Kd(490), while variations of bbp(443) exhibit negligible impact on the estimation of PPeu. Especially, between Chl and Cph, sensitivity studies indicate that the PPeu values estimated by CbPM08 are fundamentally driven by Chl, rather than by Cph (or bbp(443)). These results provide important insights on understanding the uncertainties associated with CbPM08-estimated PPeu and future directions for further improvement for the estimation of PPeu via this model, as well as its applications on estimating water-column primary production.

Particle composition-specific approach to estimate the particulate organic carbon concentration off the coastal areas of Guangdong and eastern Hainan Island

High optical complexity caused by the variability of marine particles poses a major challenge to the development of bio-optical algorithms for particulate organic carbon (POC) concentration retrievals from optical measurements in coastal waters. Here, we developed a particle composition-specific approach to estimate POC off the coastal areas of Guangdong and eastern Hainan Island, China. The ratio of phytoplankton absorption to detritus absorption coefficient aph(443)/ad(443) was used to optically discriminate water types. The samples with aph(443)/ad(443) ≤ 4.9 showed a significant correlation between POC and absorption line height at 676 nm aLH(676) (R2 = 0.75, n = 70, p < 0.01). In contrast, aph-dominant samples with aph(443)/ad(443) > 4.9 had a high covariance between POC and particle scattering coefficient at 675 nm bp(675) (R2 = 0.85, n = 37, p < 0.01). Validation with an independent dataset yielded a small positive bias (R2 = 0.81, APD = 23.10%, RMSE = 29.01 mg m–3, RPD = 16.31%). The approach provided a better estimation of POC concentration in coastal waters compared with univariate algorithms. A depth-resolved index aLH(676)/bbp(442) was defined as the ratio of absorption line height to particle backscattering coefficient. Using the depth-resolved index instead of aph(443)/ad(443) for optical water type classification can be utilized to represent the vertical variations of POC in 1 m bins, and can complement remote sensing observations to accurately characterize the three-dimensional structure of POC distribution in the oceans.

Comparison of ocean-colour algorithms for particulate organic carbon in global ocean

In the oceanic surface layer, particulate organic carbon (POC) constitutes the biggest pool of particulate material of biological origin, encompassing phytoplankton, zooplankton, bacteria, and organic detritus. POC is of general interest in studies of biologically-mediated fluxes of carbon in the ocean, and over the years, several empirical algorithms have been proposed to retrieve POC concentrations from satellite products. These algorithms can be categorised into those that make use of remote-sensing-reflectance data directly, and those that are dependent on chlorophyll concentration and particle backscattering coefficient derived from reflectance values. In this study, a global database of in situ measurements of POC is assembled, against which these different types of algorithms are tested using daily matchup data extracted from the Ocean Colour Climate Change Initiative (OC-CCI; version 5). Through analyses of residuals, pixel-by-pixel uncertainties, and validation based on optical water types, areas for POC algorithm improvement are identified, particularly in regions underrepresented in the in situ POC data sets, such as coastal and high-latitude waters. We conclude that POC algorithms have reached a state of maturity and further improvements can be sought in blending algorithms for different optical water types when the required in situ data becomes available. The best performing band ratio algorithm was tuned to the OC-CCI version 5 product and used to produce a global time series of POC between 1997–2020 that is freely available.

A semi-analytical approach for estimating inland water inherent optical properties and chlorophyll a using airborne hyperspectral imagery

The inversion of inherent optical properties (IOPs) and chlorophyll a (Chla) is one of the key objectives in water color remote sensing, and hyperspectral remote sensing with rich spectral information makes precise inversion possible. In this study, we developed a semi-analytical estimation method for inland water IOPs based on the quasi-analytical algorithm (QAA). Considering the complex optical characteristics of inland waters, empirical parameter regional optimization was conducted. Furthermore, a dual-band joint inversion strategy and Gaussian function fitting method were utilized to optimize the solution processes for the backscattering coefficient of particles (bbp) and the absorption coefficient of phytoplankton pigments (aph), respectively. This approach overcomes the limitations of single-band bbp inversion in inland waters. In addition, it directly decomposes the absorption coefficient to obtain aph using a Gaussian function, which can reduce the intermediate steps and errors caused by indirect inversion. For Chla, we constructed a binary inversion model using aph(677) and remote sensing reflectance (Rrs), where the coefficient of determination (R2) exceeded 0.8. We also constructed an airborne hyperspectral image correction process, including vicarious calibration, atmospheric correction, and bidirectional reflectance distribution function (BRDF) correction, obtaining high-precision Rrs images. The ground models were successfully applied to the airborne hyperspectral images, mapping the spatial distribution of IOPs and Chla concentration in the study area. The experiments demonstrated that the proposed semi-analytical method using airborne hyperspectral imagery exhibits a good performance in terms of modeling accuracy and mapping analysis, and successfully applied to long-term monitoring using satellite hyperspectral images, highlighting the significant potential of hyperspectral remote sensing for high-precision monitoring of regional water bodies.

Variability in the relationship between light scattering and chlorophyll a concentration in oligotrophic tropical regions of the Western Pacific Ocean.

It is important to determine the relationship between the concentration of chlorophyll a (Chla) and the inherent optical properties (IOPs) of ocean water to develop optical models and algorithms that characterize the biogeochemical properties and estimate biological pumping and carbon flux in this environment. However, previous studies reported relatively large variations in the particulate backscattering coefficient (bbp(λ)) and Chla from more eutrophic high-latitude waters to clear oligotrophic waters, especially in oligotrophic oceanic areas where these two variables have little covariation. In this study, we examined the variability of bbp(λ) and Chla in the euphotic layer in oligotrophic areas of the tropical Western Pacific Ocean and determined the sources of these variations by reassessment of in-situ measurements and the biogeochemical-argo (BGC-Argo) database. Our findings identified covariation of bbp(λ) and Chla in the water column below the deep Chla maximum (DCM) layer, and indicated that there was no significant correlation relationship between bbp(λ) and Chla in the upper layer of the DCM. Particles smaller than 3.2 µm that were in the water column above the DCM layer had a large effect on the bbp(λ) in the vertical profile, but particles larger than 3.2 µm and smaller than 10 µm had the largest effect on the bbp(λ) in the water column below the DCM layer. The contribution of non-algal particles (NAPs) to backscattering is up to 50%, which occurs in the water depth of 50 m and not consistent with the distribution of Chla. Phytoplankton and NAPs were modeled as coated spheres and homogeneous spherical particles to simulate the bbp(λ) of the vertical profile by Aden-Kerker method and Mie theory, and the results also indicated that the backscattering caused by particles less than 20 µm were closer to the measured data when they were below and above the DCM layer, respectively. This relationship also reflects the bbp(λ) of particles in the upper water was significantly affected particle size, but bbp(λ) in the lower water was significantly affected by Chla concentration. This effect may have relationship with phytoplankton photoacclimation and the relationship of a phytoplankton biomass maximum with particle size distribution in the water column according to the previous relevant studies. These characteristics also had spatial and seasonal variations due to changes of Chla concentration at the surface and at different depths. There was mostly a linear relationship between Chla and bbp(700) during winter. During other seasons, the relationship between these two variables was better characterized by a power function (or a logarithmic function) in the lower layer of the DCM. The spatial and vertical relationships between the bbp(λ) and Chla and the corresponding variations in the types of particles described in this study provide parameters that can be used for accurate estimation of regional geochemical processes.

Improved multivariable algorithms for estimating oceanic particulate organic carbon concentration from optical backscattering and chlorophyll-a measurements

The capability to estimate the oceanic particulate organic carbon concentration (POC) from optical measurements is crucial for assessing the dynamics of this carbon reservoir and the capacity of the biological pump to sequester atmospheric carbon dioxide in the deep ocean. Optical approaches are routinely used to estimate oceanic POC from the spectral particulate backscattering coefficient bbp, either directly (e.g., with backscattering sensors on underwater platforms like BGC-Argo floats) or indirectly (e.g., with satellite remote sensing). However, the reliability of algorithms which relate POC to bbp is typically limited due to the complexity of interactions between light and natural assemblages of marine particles, which depend on variations in particle concentration, composition, and size distribution. This study expands on our previous work by analysis of an extended field dataset created with judicious data inclusion criteria with the aim to provide POC algorithms for multiple light wavelengths of measured bbp, which can be useful for applications with in situ optical sensors as well as above-water active or passive measurement systems. We describe an improved empirical multivariable approach to estimate POC from simultaneous measurements of bbp and chlorophyll-a concentration (Chla) to better account for the effects of variable particle composition on the relationship between POC and bbp. The multivariable regression models are formulated using a relatively large dataset of coincident measurements of POC, bbp, and Chla, including surface and subsurface data from the Atlantic, Pacific, Arctic, and Southern Oceans. We show that the multivariable algorithm provides reduced uncertainty of estimated POC across diverse marine environments when compared with a traditional univariate algorithm based on only bbp. We also propose an improved formulation of univariate algorithm based on bbp alone. Finally, we examine performance of several algorithms to estimate POC using our dataset as well as a dataset consisting of optical measurements from BGC-Argo floats and traditional POC measurements collected during a coincident research cruise in the Atlantic Ocean.

Uncertainty Analysis of Particle Backscattering Coefficient Measurement for Multiple Highly Turbid Water Areas in Ocean Color Remote Sensing

Uncertainty Analysis of Particle Backscattering Coefficient Measurement for Multiple Highly Turbid Water Areas in Ocean Color Remote Sensing

Informing ocean color inversion products by seeding with ancillary observations.

Ocean reflectance inversion algorithms provide many products used in ecological and biogeochemical models. While a number of different inversion approaches exist, they all use only spectral remote-sensing reflectances (Rrs(λ)) as input to derive inherent optical properties (IOPs) in optically deep oceanic waters. However, information content in Rrs(λ) is limited, so spectral inversion algorithms may benefit from additional inputs. Here, we test the simplest possible case of ingesting optical data ('seeding') within an inversion scheme (the Generalized Inherent Optical Property algorithm framework default configuration (GIOP-DC)) with both simulated and satellite datasets of an independently known or estimated IOP, the particulate backscattering coefficient at 532 nm (bbp(532)). We find that the seeded-inversion absorption products are substantially different and more accurate than those generated by the standard implementation. On global scales, seasonal patterns in seeded-inversion absorption products vary by more than 50% compared to absorption from the GIOP-DC. This study proposes one framework in which to consider the next generation of ocean color inversion schemes by highlighting the possibility of adding information collected with an independent sensor.

A comparative analysis of in situ measurements of high-altitude cirrus in the tropics

Abstract. We analyze cirrus cloud measurements from two dual-instrument cloud spectrometers, two hygrometers and a backscattersonde with the goal of connecting cirrus optical parameters usually accessible by remote sensing with microphysical size-resolved and bulk properties accessible in situ. Specifically, we compare the particle backscattering coefficient and depolarization ratio to the particle size distribution, effective and mean radius, surface area density, particle aspherical fraction, and ice water content. Data were acquired by instruments on board the M55 Geophysica research aircraft in July and August 2017 during the Asian Monsoon campaign based in Kathmandu, Nepal, in the framework of the StratoClim (Stratospheric and upper tropospheric processes for better climate predictions) project. Cirrus clouds have been observed over the Himalayan region between 10 km and the tropopause, situated at 17–18 km. The observed particle number densities varied between 10 and 10−4 cm−3 in the dimensional range from 1.5 to 468.5 µm in radius. Correspondingly, backscatter ratios from 1.1 up to 50 have been observed. Optical-scattering theory has been used to compare the backscattering coefficients computed from the measured particle size distribution with those directly observed by the backscattersonde. The aspect ratio of the particles, modeled as spheroids for the T-matrix approach, was left as a free parameter to match the calculations to the optical measures. The computed backscattering coefficient can be brought into good agreement with the observed one, but the match between simulated and measured depolarization ratios is insufficient. Relationships between ice particle concentration, mean and effective radius, surface area density, and ice water content with the measured backscattering coefficient are investigated for an estimate of the bulk microphysical parameters of cirrus clouds from remote sensing lidar data. The comparison between particle depolarization and aspherical fraction as measured by one of the cloud spectrometers equipped with a detector for polarization represents a novelty since it was the first time the two instruments were operated simultaneously on an aircraft. The analysis shows the difficulty of establishing an univocal link between depolarization values and the presence and amount of aspherical scatterers. This suggests the need for further investigation that could take into consideration not only the fraction of aspheric particles but also their predominant morphology.

Estimating the concentration of total suspended solids in inland and coastal waters from Sentinel-2 MSI: A semi-analytical approach

Inland and coastal waters provide key ecosystem services and are closely linked to human well-being. In this study, we propose a semi-analytical method, which can be applied to Sentinel-2 MultiSpectral Instrument (MSI) images to retrieve high spatial-resolution total suspended solids (TSS) concentration in a broad spectrum of aquatic ecosystems ranging from clear to extremely turbid waters. The presented approach has four main steps. First, the remote sensing reflectance (Rrs) at a band lacking in MSI (620 nm) is estimated through an empirical relationship from Rrs at 665 nm. Second, waters are classified into four types (clear, moderately turbid, highly turbid, and extremely turbid). Third, semi-analytical algorithms are used to estimate the particulate backscattering coefficient (bbp) at a reference band depending on the water types. Last, TSS is estimated from bbp at the reference band. Validation and comparison of the proposed method with three existing methods are performed using a simulated dataset (N = 1000), an in situ dataset collected from global inland and coastal waters (N = 1265) and satellite matchups (N = 40). Results indicate that the proposed method can improve TSS estimation and provide accurate retrievals of TSS from all three datasets, with a median absolute percentage error (MAPE) of 14.88 %, 31.50 % and 41.69 % respectively. We also present comparisons of TSS mapping between the Sentinel-3 Ocean and Land Colour Instrument (OLCI) and MSI in Lake Kasumigaura, Japan and the Tagus Estuary, Portugal. Results clearly demonstrate the advantages of using MSI for TSS monitoring in small water bodies such as rivers, river mouths and other nearshore waters. MSI can provide more detailed and realistic TSS estimates than OLCI in these water bodies. The proposed TSS estimation method was applied to MSI images to produce TSS time-series in Lake Kasumigaura, which showed good agreements with in situ and OLCI-derived TSS time-series.

Regional Algorithm of Quantitative Assessment of Cyanobacteria Blooms in the Eastern Part of the Gulf of Finland Using Satellite Ocean Color Data

Summer blooms of potentially harmful cyanobacteria are common in the Baltic Sea. Under clear sky conditions, the cyanobacterial blooms are easily detectable from space. We propose a new regional algorithm for cyanobacteria biomass estimation from satellite data in the eastern part of the Gulf of Finland, developed on the basis of field measurements in July–August 2012–2014. The multi-regression equation defines the cyanobacteria biomass as a function of the particle backscattering coefficient and chlorophyll concentration. The use of this equation provides the best performance in comparison to the linear one, which is reflected in both R2 and RMSE values (0.61 and 272 mg m−3 respectively). Unlike other algorithms, which determine only the cyanobacteria bloom area in the Baltic Sea, our algorithm allows the determination of both a bloom area and its intensity. Considering the algorithm errors, the bloom detection threshold has been shifted from the 200 mg m−3 determined by biologists to 300 mg m−3. Based on data from the MODIS-Aqua satellite ocean color scanner, the spatial and temporal variability of cyanobacterial blooms in this region from 2003 to 2022 was analyzed. Significant interannual variability of cyanobacteria biomass was revealed in the central part of the studied region, with minimum values in 2014 and maximum in 2004. The record bloom during the studied period occurred in July 2004 (the average cyanobacteria biomass was 780 mg m−3). The weakest blooms were observed in 2009, 2010, and 2014, when both in July and August, the bloom areas did not exceed 30% of the study region.

Improved algorithm for retrieving aerosol optical properties based on multi-wavelength Raman lidar.

Multi-wavelength Raman lidar has been widely used in profiling aerosol optical properties. The accuracy of measured aerosol optical properties largely depends on sophisticated lidar data retrieval algorithms. Commonly to retrieve aerosol optical properties of Raman lidar, the extinction-related Ångström exponent (EAE) is assumed (to be 1). This value usually generally differs from the true value (called EAE deviation) and adds uncertainty to the retrieved aerosol optical properties. Lidar-signal noise and EAE-deviation are two important error sources for retrieving aerosol optical properties. As the measurement accuracy of Raman lidar has been greatly improved in recent years, the influence of signal noise on retrieval results becomes relatively small, and the uncertainty of retrieved aerosol optical properties caused by an EAE-deviation becomes nonnegligible, especially in scenes that EAE deviation is large. In this study, an iteration retrieval algorithm is proposed to obtain more reliable EAE based on multi-wavelength Raman lidar. Results from this iteration are more precise values of aerosol optical properties. Three atmospheric scenarios where aerosol distribution and the values of EAE vary widely were simulated with a Monte Carlo method to analyze the characteristics and robustness of the iterative algorithm. The results show that the proposed iterative algorithm can eliminate the systematic errors of aerosol optical properties retrieved by traditional retrieval method. The EAEs after iteration does converge to the true value, and the accuracy of aerosol optical properties can be greatly improved, especially for the particle backscatter coefficient and lidar ratio, which has been improved by more than 10% in most cases, and even more than 30%. In addition, field observations data of a three-wavelength Raman lidar are analyzed to illustrate the necessity and reliability of the proposed iterative retrieval algorithm.

Deriving particulate backscattering coefficient at 400 nm from small-scale optically shallow waters using Landsat-8 data: a case study at Luhuitou Peninsula, Sanya.

The particulate backscattering coefficient (bbp) plays an important role in the growth of coral reefs by influencing the light field conditions. Small-scale optically shallow waters are commonly found in coastal fringing reefs, making it challenging to monitor the spatial and temporal patterns accurately using Aqua satellites with a low spatial resolution. In this study, six existing optimization-based algorithms for deriving bbp at 400 nm (bbp(400)) were evaluated with three simulated Landsat-8 (spatial resolution = 30 m) data sets and in situ data from the Luhuitou Peninsula, Sanya. The comparison results indicated that the HOPE (hyperspectral optimization process exemplar) (Fix-H-error or Fix-H-error-free) algorithm which sets an input value of the water depth alone outperformed other algorithms. However, the estimated bbp(400) from all the algorithms tended to be either overestimated and underestimated due to the improper the spectral shape value of the backscattering coefficient. The HOPE (Fix-H-error) algorithm estimated-bbp(400) from in situ reflectance also had a good correlation with the in situ total suspended particle concentrations data derived-bbp(400), with a correlation coefficient of 0.83. Therefore, the HOPE (Fix-H-error) algorithm was selected to estimate the bbp(400) from satellite-based Landsat-8 data of the Luhuitou Peninsula, Sanya. Time-series (2014-2021) results from these Landsat-8 images reveal the seasonal variation of bbp(400). The bbp(400) was low from May to September every year. From October to December or January, bbp(400) had an increasing trend, and then it decreased until May. Spatial analysis indicated that bbp(400) decreased with increasing water depth. The spatial and temporal patterns of bbp(400) were consistent with in situ observations reported in the literature. This study preliminarily showed the efficiency of an optimization-based algorithm in deriving bbp(400) in small-scale optically shallow water region using Landsat-8 data.

Global distribution of the spectral power coefficient of particulate backscattering coefficient obtained by a neural network scheme

The spectral power coefficient (η) of the particulate backscattering coefficient (bbp(λ)) is directly estimated from the remote sensing reflectance (Rrs(λ)) with a neural network-based scheme (NNη) in this study. Evaluations with both synthetic dataset and in-situ measurements show that NNη could significantly improve the accuracy of estimated η compared to several conventional schemes reported in the literature that are based on chlorophyll-a concentration (Chl), band ratios of Rrs(λ), or remotely sensed bbp(λ). Demonstrations with measurements from MODerate resolution Imaging Spectroradiometer (MODIS) Aqua further confirm the robustness of NNη, where reasonable spatial distribution and seasonality of η in the global oceans can be acquired by NNη. High and low η values are observed in the oligotrophic gyres and the coastal zones, respectively, which are consistent with the current understanding of η distribution concluded from theoretic analysis and repeated field measurements in the global ocean. Implementation of NNη to 19-year MODIS monthly composite measurements from 2003 to 2021 reveals strong seasonal variations of η in most of the global ocean, but the decadal changes of η are insignificant in the majority (∼82.2%) of the global ocean. Similar to any empirical algorithms, the performance of NNη is dependent on the training dataset, particularly its range, here a proper upper limit of η for natural waters is provided.

Validation protocol for the evaluation of space-borne lidar particulate back-scattering coefficient bbp

Introduction: Space-borne lidar measurements from sensors such as CALIOP were recently used to retrieve the particulate back-scattering coefficient, bbp, in the upper ocean layers at a global scale and those observations have a strong potential for the future of ocean color with depth-resolved observations thereby complementing the conventional ocean color remote sensed observations as well as overcoming for some of its limitations. It is critical to evaluate and validate the space-borne lidar measurements for ocean applications as CALIOP was not originally designed for ocean applications. Few validation exercises of CALIOP were published and each exercise designed its own validation protocol. We propose here an objective validation protocol that could be applied to any current and future space-borne lidars for ocean applications.Methods: We, first, evaluated published validation protocols for CALIOP bbp product. Two published validation schemes were evaluated in our study, by using in-situ measurements from the BGC-Argo floats. These studies were either limited to day- or nighttime, or by the years used or by the geographical extent. We extended the match-up exercise to day-and nighttime observations and for the period 2010–2017 globally. We studied the impact of the time and distance differences between the in-situ measurements and the CALIOP footprint through a sensitivities study. Twenty combinations of distance (from 9-km to 50-km) and time (from 9 h to 16 days) differences were tested.Results & Discussion: A statistical score was used to objectively selecting the best optimal timedistance windows, leading to the best compromise in term of number of matchups and low errors in the CALIOP product. We propose to use either a 24 h/9 km or 24 h/15 km window for the evaluation of space-borne lidar oceanic products.

Evaluation of the CALIPSO Lidar-observed particulate backscattering coefficient on different spatiotemporal matchup scales

The satellite lidar-derived ocean particulate backscattering coefficient (bbp) has rarely been validated globally within situobservations, and we need to understand how well the satellite CALIPSO lidar bbpapproach performs. Whether lidar bbpperforms better in terms of observation accuracy compared to passive ocean color remote sensing has yet to be evaluated for detailed validation. With the continued deployment of the BGC-Argo float array in the global open ocean in recent years, data have accumulated with a total of 42,932 particulate backscattering coefficients (bbp) from 2010 to 2017, allowing for a finer spatial and temporal scale evaluation of the performance of the CALIPSO lidar-observed bbp. We evaluated the performance of CALIPSO-retrieved bbpproducts using the data detected by the BGC-Argo floats at 12 spatiotemporal matchup scales and discussed the differences in product performance at various interannual, seasonal, and spatial scales. We compare lidar, float, and ocean color bbpat the same locations and times and find that lidar bbpoutperforms ocean color data. We also analyzed the key conversion factor β(π)/bbpat different spatial and temporal scales and found that there was a seasonal difference in the optimal conversion factor.

Particle Microphysical Parameters and the Complex Refractive Index from 3β + 2α HSRL/Raman Lidar Measurements: Conditions of Accurate Retrieval, Retrieval Uncertainties and Constraints to Suppress the Uncertainties

We study retrieval methods in regard to their potential to accurately retrieve particle microphysical parameters (PMP) from 3β + 2α HSRL/Raman lidar measurements. PMPs estimated with these methods are number, surface-area and volume concentrations, the effective radius, and complex refractive index of the investigated particle size distribution (PSD). The 3β + 2α optical data are particle backscatter coefficients at 355, 532 and 1064 nm and extinction coefficients at 355 and 532 nm. We present results that are fundamental for our understanding of how uncertainties of the optical data convert into uncertainties of PMPs. PMPs can only be retrieved with preset accuracy if the input optical data are accurate to at least eight significant digits, i.e., 10−6%. Such measurement accuracy cannot be achieved by currently existing lidar measurement techniques and the fact that atmospheric conditions are not static during lidar observations. Our analysis of the results derived with the novel approach shows that (a) the uncertainty of the retrieved surface-area concentration increases proportionally to the measurement uncertainty of the extinction coefficient at 355 nm, (b) the uncertainty of the effective radius is inversely proportional to the measurement uncertainty of the extinction-related Ångström exponent, (c) the uncertainty of volume concentration is close to the one of the effective radius, and (d) the uncertainty of number concentration is proportional to the inverse of the square value of the uncertainty of the effective radius. The complex refractive index (CRI) cannot be estimated without introducing extra constraints, even if measurement uncertainties of the optical data are as low as 1−3%. We tested constraints and their impact on the solution space, and in how far these constraints could allow us to restrict the retrieval uncertainties. For example, we used information about relative humidity that can be measured with Raman lidar. Relative humidity is an important piece of information that allows for more accurate aerosol typing and thus plays a vital role in any kind of aerosol characterization. The measurement example we used in this study shows that such a constraint can reduce the retrieval uncertainty of single scattering albedo (SSA) to as low as ±0.01–±0.025 (at 532 nm), on the condition that the uncertainty of the input optical data stays below 15%. The results will be used for uncertainty analysis of data products provided by future versions of the Tikhonov Advanced Regularization Algorithm (TiARA). This algorithm has evolved into a standard tool for the derivation of microphysical particle properties from multiwavelength High-Spectral-Resolution Lidar (HSRL)/Raman lidar operated in Europe, East Asia, and the US.

Assessing the Potential of Backscattering as a Proxy for Phytoplankton Carbon Biomass

AbstractDespite phytoplankton contributing roughly half of the photosynthesis on earth and fueling marine food‐webs, field measurements of phytoplankton biomass remain scarce. The particulate backscattering coefficient (bbp) has often been used as an optical proxy to estimate phytoplankton carbon biomass (Cphyto). However, total observed bbp is impacted by phytoplankton size, cell composition, and non‐algal particles. The lack of phytoplankton field data has prevented the quantification of uncertainties driven by these factors. Here, we first review and discuss existing bbp algorithms by applying them to bbp data from the BGC‐Argo array in surface waters (<10 m). We find a bbp threshold where estimated Cphyto differs by more than an order of magnitude. Next, we use a global ocean circulation model (the MITgcm Biogeochemical and Optical model) that simulates plankton dynamics and associated inherent optical properties to quantify and understand uncertainties from bbp‐based algorithms in surface waters. We do so by developing and calibrating an algorithm to the model. Simulated error‐estimations show that bbp‐based algorithms overestimate/underestimate Cphyto between 5% and 100% in surface waters, depending on the location and time. This is achieved in the ideal scenario where Cphyto and bbp are known precisely. This is not the case for algorithms derived from observations, where the largest source of uncertainty is the scarcity of phytoplankton biomass data and related methodological inconsistencies. If these other uncertainties are reduced, the model shows that bbp could be a relatively good proxy for phytoplankton carbon biomass, with errors close to 20% in most regions.