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Short-lead seasonal precipitation forecast in northeastern Brazil using an ensemble of artificial neural networks
This study assesses the deterministic and probabilistic forecasting skill of a 1-month-lead ensemble of Artificial Neural Networks (EANN) based on low-frequency climate oscillation indices. The predictand is the February-April (FMA) rainfall in the Brazilian state of Ceará, which is a prominent subject in climate forecasting studies due to its high seasonal predictability. Additionally, the study proposes combining the EANN with dynamical models into a hybrid multi-model ensemble (MME). The forecast verification is carried out through a leave-one-out cross-validation based on 40 years of data. The EANN forecasting skill is compared with traditional statistical models and the dynamical models that compose Ceará’s operational seasonal forecasting system. A spatial comparison showed that the EANN was among the models with the smallest Root Mean Squared Error (RMSE) and Ranked Probability Score (RPS) in most regions. Moreover, the analysis of the area-aggregated reliability showed that the EANN is better calibrated than the individual dynamical models and has better resolution than Multinomial Logistic Regression for above-normal (AN) and below-normal (BN) categories. It is also shown that combining the EANN and dynamical models into a hybrid MME reduces the overconfidence of the extreme categories observed in a dynamically-based MME, improving the reliability of the forecasting system.
Raw/Translated data and metadata from sensor measurements at Manaus, Brazil, 15 Feb 2015-14 July 2016
Leaf Surface Temperature, Direct/Diffuse solar radiation, sap velocity, leaf gas exchange, and soil moisture content at or near the K14, K34, and B34 towers. These are mostly raw data from the data loggers, except in the case of the ICT sensors where the datalogger output was translated using ICT software.
Sap Velocity, Leaf-level Measurements, and Meteorological Data in Manaus and Santarem, Plant Science, Brazil: 2015-2017
These datasets were used in the paper Gimenez et al., 2019 (Species-Specific Shifts in Diurnal Sap Velocity Dynamics and Hysteretic Behavior of Ecophysiological Variables During the 2015–2016 El Niño Event in the Amazon Forest) published in Frontiers in Plant Science, special issue: Water-Use Efficiency: Advances and Challenges in a Changing Climate. This data package contains data from ICT sap flow sensors, leaf temperature (Infrared Sensors), leaf gas exchange, leaf water potential and leaf thermocouple data used for some experiments of the NGEE-Tropics project in the Amazon Forest (sites: BR-Ma2 and BR-Sa1). The data package also includes environmental data (Relative humidity, air temperature, and VPD) collected using thermo-hygrometers installed in K-34 tower vertical profile (Manaus - BR-Ma2).
Isoprene, Chlorophyll fluorescence, and leaf temperature data from Manaus, Brazil, 2017 - 2018
The data included in this data package are a demonstration of an inverse relationship in the early successional species Vismia guianensis in the central Amazon between stomatal conductance (gs) and leaf temperature, while net photosynthesis (Pn) showed an optimum value of 32.6 ± 0.4°C. In contrast to Pn, photosynthetic electron transport rates (ETR) and the QA oxidation state (qL) increased linearly with leaf temperature. Leaf isoprene emissions, a primary product of photosynthesis and strongly linked to plant high temperature stress tolerance, showed strong linear correlations with ETR (ƿ = 0.98) and qL (ƿ = 0.99). Furthermore, inhibition of isoprenoid biosynthesis repressed Pn at high temperatures through a mechanism that was independent of stomatal closure. Data is used in the publication, "Stimulation of isoprene emissions and electron transport rates as a key mechanism of thermal tolerance in the tropical species Vismia guianensis." See the below Dataset References field for the full citation.
Latex Oxidation Defenses in Muiratinga (Maquira sclerophylla) in Manaus, Brazil
This data package contains images of stem latex oxidation (blocked under nitrogen) under air and nitrogen in the central Amazon. This data was referenced in the publication: Piva LR, Jardine KJ, Cobello LO, Gimenez BO, Durgante FM, Higuchi N, Chambers JQ. Demonstration of a strict molecular oxygen requirement of yellow latex oxidation in the Central Amazon canopy tree Muiratinga (Maquira sclerophylla (Ducke) CC Berg). Videos of stem latex oxidation are available at: https://www.dropbox.com/sh/dtp978r0nt4iwbq/AAA-J7c4Ra8_-xhalsYtz8cCa?dl=0
Monoterpene emission data during 2015/2016 El Nino in Manaus, Brazil
This dataset contains leaf and ecosystem monoterpene emissions and normalized ratios as a function of leaf and canopy temperature during the 2015/6 El Nino in the central Amazon. Also included are 13 C-labeled monoterpene data during leaf photosynthesis under 13 CO 2 . Data was used in the publication, "Monoterpene ‘thermometer’of tropical forest-atmosphere response to climate warming." See the below Dataset Reference field for the complete publication citation.
Calibrated soil moisture data from Manaus, Brazil, August 2018 - December 2019
This dataset contains calibrated soil moisture and rainfall data for ecohydrological studies in the Central Amazon (2.6099 ºS, 60.2093 ºW) taken at 30-min intervals. Soil moisture data was collected at depths of 0.025-, 0.05-, 0.15-, 0.30-, 0.50-,0.8-, 1.6-, 2.4-, 3.2-, 4.8-, 6.4-, 8.8-, and 14.20 m. Inconsistent data was removed during the QA/QC process. Further information including data collection methods are available in the associated publication, Negron-Juarez et al. 2020. Measurement and characterization of soil moisture dynamics in a Central Amazonian tropical forest (to be submitted).
Raw leaf isoprene and monoterpene emission GC-MS chromatograms/calibrations for MassHunter software, Brazil, 2014-2016
This data package contains raw gas chromatography–mass spectrometry (GC-MS) data for isoprene/monoterpene emission rates used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. The attached data files require use of the Agilent MassHunter software. Alternatively, data has been exported into Excel files and can be accessed at [DOI]. For further information on how to Raw isoprene and monoterpene emission rates data
Raw leaf temperature data from May 2017- Aug 2017 at the B34 and K34 towers in Manaus, Brazil
This data package contains raw data from leaf temperature measurements. Additional metadata, such as site information and equipment manuals, are included in the data package listed under Dataset Reference(s) along with all related data packages. Leaf temperature measurements were made using Teflon insulated type T thermocouples (OM-CP-OCTTEMP-A Nomad®, Omega Engineering) directly attached to the abaxial side of the leaf using a breathable white tape and configured to register measurements every 15 s. No data processing or QA/QC was done on the raw datasets. Processed datasets will be uploaded separately. Contact israelmdt@gmail.com if you need to use this dataset for additional information.
Stem CO2 Efflux measurements from Manaus, Brazil, 2017
This data was collected in order to test the effects of temperatures on the autotrophic respiratory process in the tropics. This data package contains raw real-time stem CO2 efflux files during the night and day from 3 different species near the K34 in Manaus during the day and the night. Raw and derived data files are included in the format of .csv and .xlsx and information about the canopy trees and temperatures recorded can be found in the field event log using microsoft excel. The data was collected from three canopy dominant trees in the central Amazon near the K34 tower. Real-time stem CO2 efflux was determined using a Li7000 gas analyzer configured in differential mode and connected to a dynamic stem chamber secured to the stem at breast height using straps to generate a reasonable seal. Ambient air was continuously pumped into the stem chamber and CO2 concentrations of the air entering and exiting the chamber were continuously monitored. CO2 efflux was calculated based on the CO2 concentration difference between ambient and stem chambers, the flow rate of air through the chamber, and the area of the enclosed stem. Additional auxiliary data, including sap flow and crown temperature were also measured. No data processing or QA/QC was done on the raw data packages.
Harmonized wood density data for Central Amazon species in the BIONTE experimental area in Manaus, Brazil
BIONTE (BIOmass and NuTrient Experiment) is a selective logging experiment established at the Experimental Station of Tropical Forestry (EEST, aka “ZF2”) field research station in the mid 1980s in the central Amazon (Higuchi et al. 1997, Amaral et al. 2019). The main data related to BIONTE are available as a separate dataset (Lima et al. 2022). Only species identified within the BIONTE plots were included in this dataset. Wood density, expressed in g cm-3, was collated from multiple sources that were integrated to compose the wood density for BIONTE. The starting point used was wood density data available from Chave et al. (2006), which was then adapted by Marra et al. (2016) and Marra et al. (2018). We also included data collected and synthesized by Ramírez-Méndez (2018) and Gimenez et al.(2021), who combined local estimates of wood density at the site with other sources collected in the Central Amazon. Data are included in .csv files, while BIONTE_WD_headers.txt provides descriptions of data file headers.
Forcing data (CESM2/CMIP6) for projection of drought impacts (2015-2100) at the K34 site in Manaus, Brazil
Historical and projected output data variables extracted and derived from Community Earth System Model 2 (CESM2) runs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) archive. CESM2 is a fully coupled Earth system model used in simulations of Earth's past, present, and future climates (Danabasoglu et al., 2020). Variables in this dataset are in six-hourly resolution, and include air temperature (both in K and ℃), specific humidity (kg kg-1), air pressure (Pa), relative humidity (%), and vapor pressure deficit (kPa). CESM2 was the only CMIP6 model that provided VPD at the high temporal resolution required for this analysis. Data are included in .csv files, and the text file CESM2-CMIP6_forcing_K34-Manaus_headers.txt provides descriptions of data file headers.
Forecasting Dengue in Brazil with Time Series Modeling in Parallel
No abstract provided.
INS–Support for Formal Cyber Security Education in Brazil–After Action Report
The adoption of digital technology into Instrumentation and Control (I&C) systems in nuclear facilities fundamentally changes the nature of these systems. Greater interconnectivity of reprogrammable, and functionally interdependent control systems has given rise to the need for computer security consideration in digital I&C Systems. The cyber security of I&C systems presents a growing risk to nuclear facilities and requires the development of educational and research tools to ensure the safety of these facilities. Currently there is a major gap in formal educational offerings on cyber security for these Operational Technology (OT) systems. To provide formal cyber security education resources, DOE’s office of International Nuclear Security (INS) partnered with the University of São Paulo (USP) to develop a training course on the cyber security of nuclear facility I&C systems using the hypothetical Nuclear Power Plant, Asherah.
Data Fusion and Disease Forecasting for Dengue in Brazil [Slides]
Abstract not provided.
Estatuto e Regimento da Revista Verde [Portuguese (Brazil)]
Esse documento regulamenta o Estatuto e o Regimento Interno da Revista Verde, fundada por iniciativa do Programa Escola Verde e do Grupo de Pesquisa em Educação Ambiental Interdisciplinar, os quais estão vinculados à Universidade Federal do Vale do São Francisco (UNIVASF), Universidade de Pernambuco (UPE), Universidade do Estado da Bahia (UNEB), Faculdade de Petrolina (FACAPE) e Instituto Federal do Sertão Pernambucano (IFSertãoPE). A Revista Verde foi criada em Outubro de 2021, com o objetivo de ser uma publicação científica internacional, editada pelo Programa Escola Verde e o Grupo de Pesquisa em Educação Ambiental Interdisciplinar, para contribuir com a divulgação das novas discussões acadêmicas e científicas, resultadas de atividades de extensão e de pesquisas relacionadas a temas socioambientais e da sustentabilidade ambiental. O foco temático da revista são as problemáticas socioambientais, assim como os grandes debates acadêmicos técnicos e políticos em torno desses temas. As abordagens da revista são plurais, interdisciplinares ou específicas de uma área, desde que sobre dimensões essenciais da sustentabilidade, relativas ao meio ambiente e que estimulem o pensamento interdisciplinar. Nessa perspectiva, a revista pretende contribuir no processo de fortalecimento de um espaço de diálogo acadêmico, político e de intercâmbio de ideias e conhecimentos que permita acompanhar as pesquisas de ponta, colaborando com a sistematização e indexação de conhecimentos que podem ser fundamentais para a elaboração de políticas públicas a favor da sustentabilidade no Brasil e no mundo. A meta é constituir um periódico científico de impacto e excelência acadêmica e relevância social, reconhecida pelos pares, no Brasil e internacionalmente.
A Comparison of Light Curves Computed using GLM and MTG-LI Observations for a Flash in Central Brazil on Nov 30, 2024 [Slides]
Abstract not provided.