Operational Analysis and Medium-Term Forecasting of the Greenhouse Gas Generation Intensity in the Cryolithozone (original) (raw)
Related papers
Predicting and Analysing Global Warming using Artificial Intelligence
International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2021
Global Warming refers to an increase in average global temperature. Natural Events and human activities are believed to be contributing to increase in average global temperatures. Long Term effects of climate change are frequent wildfires, longer periods of drought in some regions and an increase in the number, duration and intensity of tropical storms. Prediction of Global Warming can be of major importance in agricultural, energy and medical domain. This paper evaluates performance of several algorithms in annual global warming prediction, from previous measured values over the Globe. The first challenge is creating a reliable, efficient and accurate data model on large dataset and capture relationship between the average annual temperatures and potential factors that contributes to global warming such as concentration of Greenhouse gases. The data is predicted and forecasted using linear regression for obtaining the highest accuracy for greenhouse gases and temperature compares to other methods. After observing the analyzed and predicted data, global warming can be reduced comparatively within few years. The reduction of global temperature can help us prevent harmful long-term effects of Global warming and Climate change.
Acta Horticulturae, 2015
This study evaluated if an Artificial Intelligence climate forecasting model can be considered as a useful tool for saving energy in semi-closed greenhouses. Preliminary results are presented on the 5-Minutes prediction of the internal air temperature and humidity modeled with Artificial Neural Networks (ANN). Since the final goal of the simulation is to integrate the predictions in a control system, the inputs were selected according to the standard signals in control theory: Set Points, Perturbations and Current State Vector. These inputs were: energy for heating, energy taken from cooling, ventilation opening, thermal screen opening, outside conditions (temperature, relative humidity, solar radiation, wind velocity) and current internal conditions (temperature and relative humidity). Data for the models were recorded in 2011, taken of 30-seconds-intervals. The ANN was created, trained and validated using different data sets. The prediction showed a very good fit to measured data and suggests that the ANN methods can be used to make short-term climate predictions, which are useful to take control actions before the trigger setpoints are reached.
Applied Sciences, 2020
The presence of road ice has always been a key issue during winter months. A reliable forecast system capable of predicting the Land Surface Temperature (LST) and, consequently, its formation is one of the best strategies to operate towards reducing both vehicles accidents and waste of chemical solvents used for prevention which have a significant economic and environmental impact. Hence, the Meteo Expert Centre (MEC) has developed an algorithm for LST forecasts able to issue ice risk warnings as well. This algorithm operationally works every day in real-time and it is here tested, first, on a paved area of the Pedemontana Lombarda motorway and the Milano Linate airport airstrip, and, afterwards, since the LST plays a crucial role in understanding phenomena of energy exchange between soil, vegetation, and atmosphere, its knowledge and prediction becomes relevant also for other purposes such as agricultural management and irrigation system control, further experiments are carried out...
Greenhouse air temperature modelling
2004
This paper describes two implementation approaches for modelling the air temperature of an automated greenhouse located in the campus of the University of Trás-os-Montes e Alto Douro. Linear models, based in the discretization of the heat transfer physicallaws, and non-linear neural networks models are used. These models are described as functions of the outside climate and the control actions performed for heating and cooling. Results are presented to illustrate the performance of each model in the simulation and prediction of the greenhouse air temperature. The data used to compute the simulation models was collected with a PC-based acquisition and control system using a sampling time interval of I minute.
GREENHOUSE CLIMATE MODELS: AN OVERVIEW
2003
Greenhouse climate and crop models are essential for improving environmental management and control efficiencies. In this paper, are described several types of models that could be used to simulate and predict the greenhouse environment, as well as the tuning methods to compute their parameters. This study focuses on the dynamical behaviours of the inside air temperature, humidity and carbon dioxide concentration models and their domains of application. Linear and nonlinear models will be covered, focusing on issues such as: physical models, black-box models, and neural networks models. Several experiments will be presented to illustrate the performance of each model in the simulation and prediction of the greenhouse climate. The models are described as functions of the outside climate, the control actions performed and the transpiration and photosynthesis responses of the plants. The data used to compute the simulation models were acquired in an experimental greenhouse using a sampling time interval of 1 minute. The greenhouse is automated with several actuators and sensors that are connected to an acquisition and control system based on a personal computer.
Comparison of Time Series Approaches applied to Greenhouse Gas Analysis: ANFIS, RNN, and LSTM
2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2019
Forecasting is the process of predicting the future using past and current data. Uncertainty in real-world data makes this process challenging. The two major forecasting techniques usually applied are causal forecasting and time series forecasting. In causal forecasting the independent variables are used to predict the dependent variable. Time series forecasting on the other hand is a technique used to predict the future values based on historical observations of the same variable and patterns that exist in the data. This paper analyzes time series data of greenhouse gas concentrations at different grid cells in California. The forecasting methods used are Adaptive Neuro-Fuzzy Inference System (ANFIS), Recurrent Neural Network (RNN) and Long Short-term Memory (LSTM) NN. The experimental results reveal that LSTM and ANFIS perform equally well with ANFIS having the shortest execution time.
Estimating Greenhouse Gas Emissions using Computational Intelligence
2009
This work proposes a Neuro-Fuzzy Intelligent System -ANFIS (Adaptive Network based Fuzzy Inference System) for the annual forecast of greenhouse gases emissions (GHG) into the atmosphere. The purpose of this work is to apply a Neuro-Fuzzy System for annual GHG forecasting based on existing emissions data including the last 37 years in Brazil. Such emissions concern tCO 2 (tons of carbon dioxide) resulting from fossil fuels consumption for energetic purposes, as well as those related to changes in the use of land, obtained from deforestation indexes. Economical and population growth index have been considered too. The system modeling took into account the definition of the input parameters for the forecast of the GHG measured in terms of tons of CO 2 . Three input variables have been used to estimate the total tCO 2 one year ahead emissions. The ANFIS Neuro-Fuzzy Intelligent System is a hybrid system that enables learning capability in a Fuzzy inference system to model non-linear and complex processes in a vague information environment. The results indicate the Neural-Fuzzy System produces consistent estimates validated by actual test data.
Journal of Geophysical Research, 2005
A statistical-dynamical climate model is used for investigating the relative contribution of the changes in the radiation budget and surface air temperature due to the increase of the anthropogenic greenhouse gases predicted for 2100 on the basis of IPCC SRES A1FI (the highest greenhouse level scenario). Five experiments are performed considering the changes in concentrations of (1) CO2, (2) CH4, (3) N2O, (4) tropospheric O3, and (5) all the changes together. The results show that the mean global planetary absorbed solar radiation increases in response to the predicted conditions according to the scenario A1FI for year 2100 (A1FI-2100). This is due to the effect of O3 absorptions. This increase leads to a decrease in the mean global planetary net thermal infrared radiation emitted to space by the Earth-atmosphere system to space and to an increase in mean global planetary net radiation. These changes are controlled mainly by the increase in CO2 concentration. The changes in the radiation budget due to N2O and CH4 are small. The mean global surface air temperature response to the predicted conditions for A1FI-2100 was +0.59°C. The change in CO2 concentration is responsible for an increase of +0.49°C. The higher increases occur in the polar regions: +2.15°C (at 85°S) and +1.55°C (at 85°N) in the case of the predicted conditions for A1FI-2100. Additional experiments indicate that the changes in surface air temperature are similar in the cases of the predicted conditions for A1FI-2100 and 4 × CO2, 2 × CO2 and 4 × N2O, and in 2 × N2O and 4 × CH4.
Estimating the permafrost-carbon feedback on 2 global warming 3 4
2011
Thawing of permafrost and the associated release of carbon constitutes a positive feedback in the climate system, elevating the effect of anthropogenic GHG emissions on global-mean temperatures. Multiple factors have hindered the quantification of this feedback, which was not included in the CMIP3 and C 4 MIP generation of AOGCMs and carbon cycle models. There are considerable uncertainties in the rate and extent of permafrost thaw, the hydrological and vegetation response to permafrost thaw, the decomposition timescales of freshly thawed organic material, the proportion of soil carbon that might be emitted as carbon dioxide via aerobic decomposition or as methane via anaerobic decomposition, and in the magnitude of the high latitude amplification of global warming that will drive permafrost degradation. Additionally, there are extensive and poorly characterized regional heterogeneities in soil properties, carbon content, and hydrology. Here, we couple a new permafrost module to a reduced complexity carbon-cycle climate model, which allows us to perform a large ensemble of simulations. The ensemble is designed to span the uncertainties listed above and thereby the results provide an estimate of the potential strength of the permafrost-carbon feedback. For the high CO 2 concentration scenario (RCP8.5), 12-52 PgC, or an extra 3-11% above projected net CO 2 emissions from land carbon cycle feedbacks, are released by 2100 (68% uncertainty range). This leads to an additional warming of 0.02-0.11°C. Though projected 21 st century emissions are relatively modest, ongoing permafrost thaw and slow but steady soil carbon decomposition means that, by 2300, more than half of the potentially vulnerable permafrost carbon stock in the upper 3m of soil layer (600-1000PgC) could be released as CO 2 , with an extra 1-3% being released as methane. Our results also suggest that mitigation action in line with the lower scenario RCP3-PD could contain Arctic temperature increase sufficiently that thawing of the permafrost area is limited to 15-30% and the permafrost-carbon induced temperature increase does not exceed 0.01-0.07°C by 2300.