scholarly journals Barometric tides from ECMWF operational analyses

2003 ◽  
Vol 21 (8) ◽  
pp. 1897-1910 ◽  
Author(s):  
R. D. Ray ◽  
R. M. Ponte
Keyword(s):  
Weather Forecasting ◽  
Ground Truth ◽  
Data Set ◽  
Operational Analysis ◽  
Meteorology And Atmospheric Dynamics ◽  
Temporal Interpolation ◽  
Medium Range ◽  
Long Time ◽  
Waves And Tides ◽  
Simple Empirical Model

Abstract. The solar diurnal and semidiurnal tidal oscillations in surface pressure are extracted from the operational analysis product of the European Centre for Medium Range Weather Forecasting (ECMWF). For the semidiurnal tide this involves a special temporal interpolation, following Van den Dool et al. (1997). The resulting tides are compared with a "ground truth" tide data set, a compilation of well-determined tide estimates deduced from many long time series of station barometer measurements. These comparisons show that the ECMWF (analysis) tides are significantly more accurate than the tides deduced from two other widely available reanalysis products. Spectral analysis of ECMWF pressure series shows that the tides consist of sharp central peaks with modulating sidelines at integer multiples of 1 cycle/year, superimposed on a broad cusp of stochastic energy. The integrated energy in the cusp dominates that of the side-lines. This complicates the development of a simple empirical model that can characterize the full temporal variability of the tides.Key words. Meteorology and atmospheric dynamics (waves and tides)

scholarly journals Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

2019 ◽  
Vol 11 (10) ◽  
pp. 1157 ◽  
Author(s):  
Jorge Fuentes-Pacheco ◽  
Juan Torres-Olivares ◽  
Edgar Roman-Rangel ◽  
Salvador Cervantes ◽  
Porfirio Juarez-Lopez ◽  
...  
Keyword(s):  
Precision Agriculture ◽  
Image Data ◽  
Ground Truth ◽  
Aerial Images ◽  
Aerial Image ◽  
Data Set ◽  
Visual Appearance ◽  
Aerial Robots ◽  
Lighting Conditions ◽  
Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

scholarly journals Tropical cyclone simulations over Bangladesh at convection permitting 4.4 km & 1.5 km resolution

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hamish Steptoe ◽  
Nicholas Henry Savage ◽  
Saeed Sadri ◽  
Kate Salmon ◽  
Zubair Maalick ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tropical Cyclone ◽  
Sea Level ◽  
Tropical Cyclones ◽  
Weather Forecasting ◽  
Sea Level Pressure ◽  
Mean Sea Level ◽  
Level Pressure ◽  
Medium Range ◽  
Gust Speed

AbstractHigh resolution simulations at 4.4 km and 1.5 km resolution have been performed for 12 historical tropical cyclones impacting Bangladesh. We use the European Centre for Medium-Range Weather Forecasting 5th generation Re-Analysis (ERA5) to provide a 9-member ensemble of initial and boundary conditions for the regional configuration of the Met Office Unified Model. The simulations are compared to the original ERA5 data and the International Best Track Archive for Climate Stewardship (IBTrACS) tropical cyclone database for wind speed, gust speed and mean sea-level pressure. The 4.4 km simulations show a typical increase in peak gust speed of 41 to 118 knots relative to ERA5, and a deepening of minimum mean sea-level pressure of up to −27 hPa, relative to ERA5 and IBTrACS data. The downscaled simulations compare more favourably with IBTrACS data than the ERA5 data suggesting tropical cyclone hazards in the ERA5 deterministic output may be underestimated. The dataset is freely available from 10.5281/zenodo.3600201.

scholarly journals Quantifying the structure of strong gravitational lens potentials with uncertainty-aware deep neural networks

2020 ◽  
Vol 499 (4) ◽  
pp. 5641-5652
Author(s):  
Georgios Vernardos ◽  
Grigorios Tsagkatakis ◽  
Yannis Pantazis
Keyword(s):  
Confidence Intervals ◽  
Galaxy Evolution ◽  
Gravitational Lensing ◽  
Probability Distributions ◽  
Mass Density ◽  
Ground Truth ◽  
Gaussian Random Fields ◽  
Training Data ◽  
Gravitational Lens ◽  
Data Set

ABSTRACT Gravitational lensing is a powerful tool for constraining substructure in the mass distribution of galaxies, be it from the presence of dark matter sub-haloes or due to physical mechanisms affecting the baryons throughout galaxy evolution. Such substructure is hard to model and is either ignored by traditional, smooth modelling, approaches, or treated as well-localized massive perturbers. In this work, we propose a deep learning approach to quantify the statistical properties of such perturbations directly from images, where only the extended lensed source features within a mask are considered, without the need of any lens modelling. Our training data consist of mock lensed images assuming perturbing Gaussian Random Fields permeating the smooth overall lens potential, and, for the first time, using images of real galaxies as the lensed source. We employ a novel deep neural network that can handle arbitrary uncertainty intervals associated with the training data set labels as input, provides probability distributions as output, and adopts a composite loss function. The method succeeds not only in accurately estimating the actual parameter values, but also reduces the predicted confidence intervals by 10 per cent in an unsupervised manner, i.e. without having access to the actual ground truth values. Our results are invariant to the inherent degeneracy between mass perturbations in the lens and complex brightness profiles for the source. Hence, we can quantitatively and robustly quantify the smoothness of the mass density of thousands of lenses, including confidence intervals, and provide a consistent ranking for follow-up science.

scholarly journals Improvements in medium range weather forecasting system of India

2014 ◽  
Vol 123 (2) ◽  
pp. 247-258 ◽  
Author(s):  
V S PRASAD ◽  
SAJI MOHANDAS ◽  
SURYA KANTI DUTTA ◽  
M DAS GUPTA ◽  
G R IYENGAR ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Medium Range ◽  
Forecasting System

Spatio-temporal interpolation of soil water, temperature, and electrical conductivity in 3D + T: The Cook Agronomy Farm data set

2015 ◽  
Vol 14 ◽  
pp. 70-90 ◽  
Author(s):  
Caley K. Gasch ◽  
Tomislav Hengl ◽  
Benedikt Gräler ◽  
Hanna Meyer ◽  
Troy S. Magney ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Water Temperature ◽  
Soil Water ◽  
Data Set ◽  
Temporal Interpolation ◽  
Spatio Temporal

Comparativa de modelos multivariados basados en aprendizaje automático para la predicción del riesgo de diabetes en etapas tempranas

2021 ◽  
Author(s):  
◽  
Zayra Ramírez Gaytán
Keyword(s):  
World Health ◽  
Degenerative Diseases ◽  
Decision Tree Algorithm ◽  
Data Set ◽  
The World ◽  
Long Time ◽  
Life Threatening ◽  
Health Organization ◽  
Logistic Regression Algorithm ◽  
Asymptomatic Phase

Diabetes is one of the fastest-growing, life-threatening, chronic degenerative diseases. According to the World Health Organization (WHO), it has affected 422 million people worldwide in 2018. Approximately 50% of all people who suffer diabetes are not diagnosed due to the asymptomatic phase which usually lasts a long time. In this work, a data set of 520 instances has been used. The data set has been analyzed with the next three algorithms: logistic regression algorithm, decision trees and random forest. The results show that the decision tree algorithm had better performance with an AUC of 98%. Also, it was found the most common symptoms that a person with a risk of diabetes presents are polyuria, polydipsia and sudden weight loss.

scholarly journals Autonomous feature type selection based on environment using expectation maximization in self-localization

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881470
Author(s):  
Nezih Ergin Özkucur ◽  
H Levent Akın
Keyword(s):  
Expectation Maximization ◽  
Autonomous Robots ◽  
Expectation Maximization Algorithm ◽  
Sensory Information ◽  
Ground Truth ◽  
Local Environment ◽  
Localization Algorithm ◽  
Data Set ◽  
Public Data ◽  
The Individual

Self-localization in autonomous robots is one of the fundamental issues in the development of intelligent robots, and processing of raw sensory information into useful features is an integral part of this problem. In a typical scenario, there are several choices for the feature extraction algorithm, and each has its weaknesses and strengths depending on the characteristics of the environment. In this work, we introduce a localization algorithm that is capable of capturing the quality of a feature type based on the local environment and makes soft selection of feature types throughout different regions. A batch expectation–maximization algorithm is developed for both discrete and Monte Carlo localization models, exploiting the probabilistic pose estimations of the robot without requiring ground truth poses and also considering different observation types as blackbox algorithms. We tested our method in simulations, data collected from an indoor environment with a custom robot platform and a public data set. The results are compared with the individual feature types as well as naive fusion strategy.

scholarly journals Estimation of the parameters of fish stock dynamics from catch-at-age data and indices of abundance: can natural and fishing mortality be separated?

2007 ◽  
Vol 64 (8) ◽  
pp. 1130-1142 ◽  
Author(s):  
Sondre Aanes ◽  
Steinar Engen ◽  
Bernt-Erik Sæther ◽  
Ronny Aanes
Keyword(s):  
Gadus Morhua ◽  
State Space Model ◽  
Population Projections ◽  
Quality Data ◽  
Fish Stock ◽  
Natural Mortality ◽  
Fishing Mortality ◽  
Absolute Size ◽  
Data Set ◽  
Long Time

Models for fluctuations in size of fish stocks must include parameters that describe expected dynamics, as well as stochastic influences. In addition, reliable population projections also require assessments about the uncertainties in estimates of vital parameters. Here we develop an age-structured model of population dynamics based on catch-at-age data and indices of abundance in which the natural and fishing mortality are separated in a Bayesian state–space model. Markov chain Monte Carlo methods are used to fit the model to the data. The model is fitted to a data set of 19 years for Northeast Arctic cod (Gadus morhua). By simulations of the fitted model we show that the model captures the dynamical pattern of natural mortality adequately, whereas the absolute size of natural mortality is difficult to estimate. Access to long time series of high-quality data are necessary for obtaining precise estimates of all the parameters in the model, but some parameters cannot be estimated without including some prior information. Nevertheless, our model demonstrates that temporal variability in natural mortality strongly affects perceived variability in stock sizes. Thus, using estimation procedures that neglect temporal fluctuations in natural mortality may therefore give biased estimates of fluctuations in fish stock sizes.

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