VHDL Based Maximum Power Point Tracking of Photovoltaic Using Fuzzy Logic Control

It is important to have an efficient maximum power point tracking (MPPT) technique to increase the photovoltaic (PV) generation system output efficiency. This paper presents a design of MPPT techniques for PV module to increase its efficiency. Perturb and Observe method (P&O), incremental conductance method (IC), and Fuzzy logic controller (FLC) techniques are designed to be used for MPPT. Also FLC is built using MATLAB/ SIMULINK and compared with the FLC toolbox existed in the MATLAB library. FLC does not need knowledge of the exact model of the system so it is easy to implement. A comparison between different techniques shows the effectiveness of the fuzzy logic controller techniques.  Finally, the proposed FLC is built in very high speed integrated circuit description language (VHDL). The simulation results obtained with ISE Design Suite 14.6 software show a satisfactory performance with a good agreement compared to obtained values from MATLAB/SIMULINK. The good tracking efficiency and rapid response to environmental parameters changes are adopted by the simulation results

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

Sensitivity of estimated total canopy SIF emission to remotely sensed LAI and BRDF products

Remote sensing of solar-induced chlorophyll fluorescence (SIF) provides new possibilities to estimate terrestrial gross primary production (GPP). To mitigate the angular and canopy structural effects on original SIF observed by sensors (SIFobs), it is recommended to derive total canopy SIF emission (SIFtotal) of leaves within a canopy using canopy interception (i0) and reflectance of vegetation (RV). However, the effects of the uncertainties in i0 and RV on the estimation of SIFtotal have not been well understood. Here, we evaluated such effects on the estimation of GPP using the Soil-Canopy-Observation of Photosynthesis and the Energy balance (SCOPE) model. The SCOPE simulations showed that the R2 between GPP and SIFtotal was clearly higher than that between GPP and SIFobs and the differences in R2 (ΔR2) tend to decrease with the increasing levels of uncertainties in i0 and RV. The resultant ΔR2 decreased to zero when the uncertainty level in i0 and RV was ~30% for red band SIF (RSIF, 683 nm) and ~20% for far-red band SIF (FRSIF, 740 nm). In addition, as compared to the TROPOspheric Monitoring Instrument (TROPOMI) SIFobs at both red and far-red bands, SIFtotal derived using any combination of i0 (from MCD15, VNP15, and CGLS LAI products) and RV (from MCD34, MCD19, and VNP43 BRDF products) showed comparable improvements in estimating GPP. With this study, we suggest a way to advance our understanding in the estimation of a more physiological relevant SIF datasets (SIFtotal) using current satellite products

One-year measurement of size-resolved particle fluxes in an urban area

Size-resolved particle flux measurements were carried out in an urban area from April 2012 to April 2013. Together with a standard eddy covariance system, two fast optical particle counters have been employed on a 65-meter-high tower in Munster, Germany. Particle number fluxes were directly calculated for particles with diameters from 0.06 to 10 mm within 16 individual size-bins. Whereas particle number concentrations show a distinct yearly pattern with maxima in winter and minima in summer, the flux time series is more multifaceted. Average daily maxima of 3.0e + 07 particles m(-2) s(-1) occurred during winter while minima of 2.0e + 06 particles m(-2) s(-1) were observed in fall. The size-resolved measurements revealed that during spring and summer a considerable number of accumulation mode particles deposits while a simultaneous net particle emission occurred, which is mostly driven by particles smaller than 0.12 mm. These bi-directional fluxes lead to a net mass deposition of up to 13.5 mg m(-2) d(-1). The tipping-point between the emission and deposition lay between 0.16 and 0.19 mm. In a comprehensive analysis of the flux and concentration time series, the degree of atmospheric stability, the seasons, and the type of source region have been identified as key influences for particle fluxes. Different responses between particle fluxes and concentrations have been found along these drivers

Seasonal adaptation of the thermal‐based two‐source energy balance model for estimating evapotranspiration in a semiarid tree‐grass ecosystem

© 2020 by the authors.The thermal-based two-source energy balance (TSEB) model has accurately simulated energy fluxes in a wide range of landscapes with both remote and proximal sensing data. However, tree-grass ecosystems (TGE) have notably complex heterogeneous vegetation mixtures and dynamic phenological characteristics presenting clear challenges to earth observation and modeling methods. Particularly, the TSEB modeling structure assumes a single vegetation source, making it difficult to represent the multiple vegetation layers present in TGEs (i.e., trees and grasses) which have different phenological and structural characteristics. This study evaluates the implementation of TSEB in a TGE located in central Spain and proposes a new strategy to consider the spatial and temporal complexities observed. This was based on sensitivity analyses (SA) conducted on both primary remote sensing inputs (local SA) and model parameters (global SA). The model was subsequently modified considering phenological dynamics in semi-arid TGEs and assuming a dominant vegetation structure and cover (i.e., either grassland or broadleaved trees) for different seasons (TSEB-2S). The adaptation was compared against the default model and evaluated against eddy covariance (EC) flux measurements and lysimeters over the experimental site. TSEB-2S vastly improved over the default TSEB performance decreasing the mean bias and root-mean-square-deviation (RMSD) of latent heat (LE) from 40 and 82 W m−2 to −4 and 59 W m−2, respectively during 2015. TSEB-2S was further validated for two other EC towers and for different years (2015, 2016 and 2017) obtaining similar error statistics with RMSD of LE ranging between 57 and 63 W m−2. The results presented here demonstrate a relatively simple strategy to improve water and energy flux monitoring over a complex and vulnerable landscape, which are often poorly represented through remote sensing models.The research received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 721995. It was also funded by Ministerio de Economía y Competitividad through FLUXPEC CGL2012-34383 and SynerTGE CGL2015-G9095-R (MINECO/FEDER, UE) projects. The research infrastructure at the measurement site in Majadas de Tiétar was partly funded through the Alexander von Humboldt Foundation, ELEMENTAL (CGL 2017-83538-C3-3-R, MINECO-FEDER) and IMAGINA (PROMETEU 2019; Generalitat Valenciana).Peer reviewe

Technical note: A view from space on global flux towers by MODIS and Landsat: The FluxnetEO dataset

Funding Information: Acknowledgements. We thank the team at the ICOS Carbon Portal for their support in publishing the FluxnetEO data sets, with great thanks in particular to Ute Karstens and Zois Zogopoulos. SW acknowledges funding from an ESA Living Planet Fellowship in the project Vad3e mecum. Alexey Vasilevich Panov acknowledges funding from the Max Planck Society (Germany), Russian Foundation for Basic Re- search, Krasnoyarsk Territory and Krasnoyarsk Regional Fund of Science, project no. 20-45-242908. Frederik Schrader and Christian Brümmer acknowledge funds from the German Federal Ministry of Food and Agriculture (BMEL) received through Thünen Institute of Climate-Smart Agriculture. Simon Besnard acknowledges funding from the European Union through the BIOMAS-CAT (project code: 4000115192/18/I/NB) (https://eo4society.esa. int/projects/biomascat/, last access: 3 May 2022) and VERIFY (project code: BO-55-101-006) (https://cordis.europa.eu/project/id/ 776810, last access: 3 May 2022) projects. Funding Information: Financial support. This research has been supported by the Euro- Publisher Copyright: © 2022 Sophia Walther et al.The eddy-covariance technique measures carbon, water, and energy fluxes between the land surface and the atmosphere at hundreds of sites globally. Collections of standardised and homogenised flux estimates such as the LaThuile, Fluxnet2015, National Ecological Observatory Network (NEON), Integrated Carbon Observation System (ICOS), AsiaFlux, AmeriFlux, and Terrestrial Ecosystem Research Network (TERN)/OzFlux data sets are invaluable to study land surface processes and vegetation functioning at the ecosystem scale. Space-borne measurements give complementary information on the state of the land surface in the surroundings of the towers. They aid the interpretation of the fluxes and support the benchmarking of terrestrial biosphere models. However, insufficient quality and frequent and/or long gaps are recurrent problems in applying the remotely sensed data and may considerably affect the scientific conclusions. Here, we describe a standardised procedure to extract, quality filter, and gap-fill Earth observation data from the MODIS instruments and the Landsat satellites. The methods consistently process surface reflectance in individual spectral bands, derived vegetation indices, and land surface temperature. A geometrical correction estimates the magnitude of land surface temperature as if seen from nadir or 40g off-nadir. Finally, we offer the community living data sets of pre-processed Earth observation data, where version 1.0 features the MCD43A4/A2 and MxD11A1 MODIS products and Landsat Collection 1 Tier 1 and Tier 2 products in a radius of 2 km around 338 flux sites. The data sets we provide can widely facilitate the integration of activities in the eddy-covariance, remote sensing, and modelling fields.publishersversionpublishe