scholarly journals Small-scale patchiness of the chlorophyll-fluorescence in the sea: aspects of instrumentation, data processing, and interpretation

1984 ◽  
Vol 15 ◽  
pp. 233-245 ◽  
Author(s):  
H Astheimer ◽  
H Haardt
Keyword(s):  
Chlorophyll Fluorescence ◽  
Data Processing ◽  
Small Scale

Quantification of small-scale heterogeneity in aquatic aminopeptidase activity

2020 ◽  
Vol 84 ◽  
pp. 127-140
Author(s):  
BM Gaas ◽  
JW Ammerman
Keyword(s):  
Chlorophyll Fluorescence ◽  
Leucine Aminopeptidase ◽  
Hudson River ◽  
Aminopeptidase Activity ◽  
Small Scale ◽  
Analysis Instrument ◽  
Sequential Samples ◽  
Degree Of Confidence ◽  
The Mean ◽  
Hydrolysis Of

Leucine aminopeptidase (LAP) is one of the enzymes involved in the hydrolysis of peptides, and is sometimes used to indicate potential nitrogen limitation in microbes. Small-scale variability has the potential to confound interpretation of underlying patterns in LAP activity in time or space. An automated flow-injection analysis instrument was used to address the small-scale variability of LAP activity within contiguous regions of the Hudson River plume (New Jersey, USA). LAP activity had a coefficient of variation (CV) of ca. 0.5 with occasional values above 1.0. The mean CVs for other biological parameters—chlorophyll fluorescence and nitrate concentration—were similar, and were much lower for salinity. LAP activity changed by an average of 35 nmol l-1 h-1 at different salinities, and variations in LAP activity were higher crossing region boundaries than within a region. Differences in LAP activity were ±100 nmol l-1 h-1 between sequential samples spaced <10 m apart. Variogram analysis indicated an inherent spatial variability of 52 nmol l-1 h-1 throughout the study area. Large changes in LAP activity were often associated with small changes in salinity and chlorophyll fluorescence, and were sensitive to the sampling frequency. This study concludes that LAP measurements in a sample could realistically be expected to range from zero to twice the average, and changes between areas or times should be at least 2-fold to have some degree of confidence that apparent patterns (or lack thereof) in activity are real.

scholarly journals COSMIC Radio Occultation Processing: Cross-Center Comparison and Validation

2011 ◽  
Vol 28 (6) ◽  
pp. 737-751 ◽  
Author(s):  
Michael E. Gorbunov ◽  
A. V. Shmakov ◽  
Stephen S. Leroy ◽  
Kent B. Lauritsen
Keyword(s):  
Data Processing ◽  
Radio Occultation ◽  
Weather Prediction ◽  
Processing System ◽  
Fourier Integral ◽  
Small Scale ◽  
Cosmic Radio ◽  
Structural Uncertainty ◽  
Height Range ◽  
The Tropics

Abstract A radio occultation data processing system (OCC) was developed for numerical weather prediction and climate benchmarking. The data processing algorithms use the well-established Fourier integral operator–based methods, which ensure a high accuracy of retrievals. The system as a whole, or in its parts, is currently used at the Global Navigation Satellite System Receiver for Atmospheric Sounding (GRAS) Satellite Application Facility at the Danish Meteorological Institute, German Weather Service, and Wegener Center for Climate and Global Change. A statistical comparison of the inversions of the Constellation Observing System for Meteorology, Ionosphere and Climate (COSMIC) data by the system herein, University Corporation for Atmospheric Research (UCAR) data products, and ECMWF analyses is presented. Forty days of 2007 and 2008 were processed (from 5 days in the middle of each season) for the comparison of OCC and ECMWF, and 20 days of April 2009 were processed for the comparison of OCC, UCAR, and ECMWF. The OCC and UCAR inversions are consistent. For the tropics, the systematic difference between OCC and UCAR in the retrieved refractivity in the 2–30-km height interval does not exceed 0.1%; in particular, in the 9–25-km interval it does not exceed 0.03%. Below 1 km in the tropics the OCC – UCAR bias reaches 0.2%, which is explained by different cutoff and filtering schemes implemented in the two systems. The structure of the systematic OCC – ECMWF difference below 4 km changes in 2007, 2008, and 2009, which is explained by changes in the ECMWF analyses and assimilation schemes. It is estimated that in the 4–30-km height range the OCC occultation processing system obtains refractivities with a bias not exceeding 0.2%. The random error ranges from 0.3%–0.5% in the upper troposphere–lower stratosphere to about 2% below 4 km. The estimate of the bias below 4 km can currently be done with an accuracy of 0.5%–1% resulting from the structural uncertainty of the radio occultation (RO) data reflecting the insufficient knowledge of the atmospheric small-scale structures and instrumental errors. The OCC – UCAR bias is below the level of the structural uncertainty.

scholarly journals Microalgae Monitoring in Microscale Photobioreactors via Multivariate Image Analysis

2021 ◽  
Vol 5 (3) ◽  
pp. 49
Author(s):  
Christopher Castaldello ◽  
Alessio Gubert ◽  
Eleonora Sforza ◽  
Pierantonio Facco ◽  
Fabrizio Bezzo
Keyword(s):  
Chlorophyll Fluorescence ◽  
Biomass Concentration ◽  
Small Scale ◽  
Least Squares Regression ◽  
Non Invasive ◽  
Microalgae Culture ◽  
Fluorescence Measurements ◽  
Biomass Characterization ◽  
Invasive Techniques ◽  
Multivariate Image

Microscale photobioreactors for microalgae growth represent an interesting technology for fast data production and biomass characterization; however, the small scale poses severe monitoring challenges, as traditional methods cannot be used. Non-invasive techniques are therefore needed to quantify biomass concentration and other culture properties, for example, pigment composition. To this purpose, a soft sensing approach based on multivariate image regression is proposed to exploit RGB images and/or PAM-imaging chlorophyll fluorescence. Different PLS (Partial Least Squares) regression models are used to estimate: (a) biomass concentration from the features extracted by RGB indices and/or PAM-imaging chlorophyll fluorescence measurements; and (b) Chlorophyll a content per cell from the features extracted by RGB indices and biomass concentration measurements. Every single model is aimed at characterizing the microalgae culture at different light intensities during batch growth. Results show that the proposed monitoring approach is as accurate as traditional measurement approaches and may represent a promising methodology for fast and inexpensive monitoring of microscale photobioreactors.

scholarly journals METHODS OF BUILDING A MODEL OF USER BEHAVIOR

2020 ◽  
Vol 2 (1) ◽  
pp. 43-51
Author(s):  
N. B. Shakhovskaya Shakhovskaya ◽  
◽  
N. I. Melnykova ◽  
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Large Scale ◽  
Clustering Algorithms ◽  
Free Form ◽  
Small Scale ◽  
Clustering Methods ◽  
User Behaviour ◽  
Input Parameters ◽  
Scale Data

The number of clustering methods and algorithms were analysed and the peculiarities of their application were singled out. The main advantages of density based clustering methods are the ability to detect free-form clusters of different sizes and resistance to noise and emissions, and the disadvantages include high sensitivity to input parameters, poor class description and unsuitability for large data. The analysis showed that the main problem of all clustering algorithms is their scalability with increasing amount of processed data. The main problems of most of them are the difficulty of setting the optimal input parameters (for density, grid or model algorithms), identification of clusters of different shapes and densities (distribution algorithms, grid-based algorithms), fuzzy completion criteria (hierarchical, partition and model-based). Since the clustering procedure is only one of the stages of data processing of the system as a whole, the chosen algorithm should be easy to use and easy to configure the input parameters. Results of researches show that hierarchical clustering methods include a number of algorithms suitable for both small-scale data processing and large-scale data analysis, which is relevant in the field of social networks. Based on the data analysis, information was collected within fill a smart user profile. Much attention is paid to the study of associative rules, based on which an algorithm for extracting associative rules is proposed, which allows to find statistically significant rules and to look only for dependencies defined by a common set of input data, and has high computational complexity if there are many classification rules. An approach has been developed that focuses on creating and understanding models of user behaviour, predicting future behaviour using the created template. Methods of modelling pre-processing of data (clustering) are investigated and regularities of planning of meetings of friends on the basis of the analysis of daily movement of people and their friends are revealed. Methods of creating and understanding models of user behaviour were presented. The k-means algorithm was used to group users to determine how well each object lay in its own cluster. The concept of association rules was introduced; the method of search of dependences is developed. The accuracy of the model was evaluated.

scholarly journals Satellite-based Sun-Induced Chlorophyll Fluorescence in the Greater Alpine Space: Spatial Patterns and Relationship to Gross Primary Productivity

2020 ◽  
Author(s):  
Ulisse Gomarasca ◽  
Gregory Duveiller ◽  
Alessandro Cescatti ◽  
Georg Wohlfahrt
Keyword(s):  
Remote Sensing ◽  
Chlorophyll Fluorescence ◽  
Land Cover ◽  
Spatial Patterns ◽  
Primary Productivity ◽  
Climate Models ◽  
Gross Primary Productivity ◽  
Accurate Estimation ◽  
Small Scale ◽  
Alpine Space

&lt;p&gt;Accurate estimation of terrestrial gross primary productivity is essential for the development of credible future carbon cycle and climate simulations. Current remote sensing techniques allow retrieval of sun-induced chlorophyll fluorescence (SIF) as a valid proxy for GPP, but low resolution, sparse coverage, or resolution mismatches between the different satellite sensors hinder our ability to effectively link SIF to many environmental variables at fine scales. In order to better characterize heterogeneous landscapes, several attempts to downscale SIF products to higher resolutions have been made. We investigate the ability of the downscaled GOME-2 product developed by Duveiller et al. (2019), to capture the differences in spatiotemporal dynamics over the Greater Alpine Space. We analyse SIF in connection to land cover and elevation, and calculate land phenology metrics based on seasonal SIF time series. Ground-based GPP validation suggests biome-specific SIF-GPP relationships, but the comparison was hindered by the resolution mismatch of the data. Moreover, missing data are present at high elevations, diminishing the suitability of current SIF products to analyse cloud-prone mountainous areas. Important insights into spatial patterns and seasonal trends could be inferred at forest and other large-area land cover types, typical of mid elevations in the Alps, but many anthropogenic habitats at low elevations, as well as high elevation grasslands and other small-scale heterogeneous environments could not be thoroughly investigated and are likely to be underrepresented or prone to biases. Similar downscaling procedures might be applied at finer scales to e.g. TROPOMI products, or alternative advanced remote sensing SIF techniques and instruments might be needed in order to enable detailed and systematic evaluations of the Alpine region or similar highly heterogenous landscapes, before a process-oriented monitoring and unbiased implementation into climate models may be performed.&lt;/p&gt;

scholarly journals Data processing for a small-scale long-term coastal ocean observing system near Mobile Bay, Alabama

2016 ◽  
Vol 3 (12) ◽  
pp. 510-522 ◽  
Author(s):  
Mimi W. Tzeng ◽  
Brian Dzwonkowski ◽  
Kyeong Park
Keyword(s):  
Data Processing ◽  
Coastal Ocean ◽  
Small Scale ◽  
Mobile Bay ◽  
Ocean Observing
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