scholarly journals Comparison of different machine learning approaches for tropospheric profiling based on COSMIC-2 data

2021 ◽  
Vol 73 (1) ◽  
Author(s):  
Elżbieta Lasota
Keyword(s):  
Machine Learning ◽  
Water Vapor ◽  
Meteorological Data ◽  
Satellite System ◽  
Tropospheric Temperature ◽  
Learning Approaches ◽  
Vapor Pressures ◽  
Data Set ◽  
Artificial Neural Network Ann ◽  
Global Navigation Satellite

Abstract Precise and reliable information on the tropospheric temperature and water vapor profiles play a key role in weather and climate studies. Among the sensors supporting the observations of the troposphere, one can distinguish the Global Navigation Satellite System Radio Occultation (RO) technique, which provides accurate and high-quality meteorological profiles. However, external knowledge about temperature is essential to estimate other physical atmospheric parameters. To overcome this constraint, I trained and evaluated four different machine learning models comprising Artificial Neural Network (ANN) and Random Forest regression algorithms, where no auxiliary meteorological data is needed. To develop the models, I employed 150,000 globally distributed (45°S–45°N) RO profiles between October 2019 and December 2020. Input vectors consisted of bending angle or refractivity profiles from the Formosa Satellite-7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 mission together with the month, hour, and latitude of the RO event. While temperature, pressure, and water vapor profiles derived from the modern ERA5 reanalysis and interpolated to the RO location served as the models’ targets. Evaluation on the testing data set revealed a good agreement between all model outputs and ERA5 targets, where slightly better statistics were noted for ANN and refractivity inputs. Vertically averaged root mean square error (RMSE) did not exceed 1.7 K for the temperature and reached around 1.4 hPa and 0.45 hPa for the total and water vapor pressures. Additional validation with 477 co-located radiosonde observations and the operational one-dimensional variational product showed slightly larger discrepancies with the mean RMSE of around 1.9 K, 1.9 hPa, and 0.5 hPa for the temperature, pressure, and water vapor, respectively. Graphical Abstract

New machine learning approaches for tropospheric profiling based on COSMIC-2 data over Taiwan

2021 ◽  
Author(s):  
Elżbieta Lasota
Keyword(s):  
Machine Learning ◽  
Water Vapour ◽  
Meteorological Data ◽  
A Priori ◽  
Satellite System ◽  
Tropospheric Temperature ◽  
Learning Approaches ◽  
Data Set ◽  
Artificial Neural Network Ann ◽  
Global Navigation Satellite

<p>Precise and reliable information on the tropospheric temperature and water vapour profiles plays a key role in weather and climate studies. Among the sensors supporting the atmosphere's observation, one can distinguish the Global Navigation Satellite System Radio Occultation (RO) technique, which provides accurate and high-quality meteorological profiles of temperature, pressure and water vapour. However, external knowledge about temperature is essential to estimate other physical atmospheric parameters. Hence, to overcome the constraint of the need of a priori temperature profile for each RO event, I trained and evaluated 4 different machine learning models comprising Artificial Neural Network (ANN) and Random Forest regression algorithms, where no auxiliary meteorological data is needed. To develop the models, I employed almost 7000 RO profiles between October 2019 and June 2020 over the part of the western North Pacific in Taiwan's vicinity (110-130° E; 10-30° N). Input vectors consisted of bending angle or refractivity profiles from the Formosa Satellite‐7/Constellation Observing System for Meteorology, Ionosphere, and Climate-2 mission together with the month, hour, and latitude of the RO event. Whilst temperature, pressure and water vapour profiles derived from the modern ERA5 reanalysis and interpolated to the RO location served as the models' targets. Evaluation on the testing data set revealed a good agreement between all model outputs and ERA5 targets. Slightly better statistics were noted for ANN and refractivity inputs, however, these differences can be considered as negligible. Root mean square error (RMSE) did not exceed 2 K for the temperature, 1.5 hPa for pressure, and reached slightly more than 2.5 hPa for water vapour below 2 km altitude. Additional validation with 56 colocated radiosonde observations and operational one-dimensional variational product confirms these findings with vertically averaged RMSE of around 1.3 K, 1.0 hPa and 0.5 hPa for the temperature, pressure and water vapour, respectively.</p>

scholarly journals Evaluation of Modeling Water-Vapor-Weighted Mean Tropospheric Temperature for GNSS-Integrated Water Vapor Estimates in Brazil

2014 ◽  
Vol 53 (3) ◽  
pp. 715-730 ◽  
Author(s):  
Luiz F. Sapucci
Keyword(s):  
Water Vapor ◽  
Weather Prediction ◽  
Satellite System ◽  
The Other ◽  
Tropospheric Temperature ◽  
Regional Models ◽  
Weighted Mean ◽  
Multivariate Statistical ◽  
Global Models ◽  
Global Navigation Satellite

AbstractMeteorological application of Global Navigation Satellite System (GNSS) data over Brazil has increased significantly in recent years, motivated by the significant amount of investment from research agencies. Several projects have, among their principal objectives, the monitoring of humidity over Brazilian territory. These research projects require integrated water vapor (IWV) values with maximum quality, and, accordingly, appropriate data from the installed meteorological stations, together with the GNSS antennas, have been used. The model that is applied to estimate the water-vapor-weighted mean tropospheric temperature (Tm) is a source of uncertainty in the estimate of IWV values using the ground-based GNSS receivers in Brazil. Two global models and one algorithm for Tm, developed through the use of radiosondes, numerical weather prediction products, and 40-yr ECMWF Re-Analysis (ERA-40), as well as two regional models, were evaluated using a dataset of ~78 000 radiosonde profiles collected at 22 stations in Brazil during a 12-yr period (1999–2010). The regional models (denoted the Brazilian and regional models) were developed with the use of multivariate statistical analysis using ~90 000 radiosonde profiles launched at 12 stations over a 32-yr period (1961–93). The main conclusion is that the Brazilian model and two global models exhibit similar performance if the complete dataset and the entire period are taken into consideration. However, for seasonal and local variations of the Tm values, the Brazilian model was better than the other two models for most stations. The Tm values from ERA-40 present no bias, but their scatter is larger than that in the other models.

Evaluation of Machine Learning Approaches for Automated Diagnosis of COVID-19 using X-Ray images (Preprint)

2020 ◽  
Author(s):  
Mazin Mohammed ◽  
Karrar Hameed Abdulkareem ◽  
Mashael S. Maashi ◽  
Salama A. Mostafa A. Mostafa ◽  
Abdullah Baz ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computational Method ◽  
Learning Performance ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
X Ray ◽  
Wide Range ◽  
Artificial Neural Network Ann

BACKGROUND In most recent times, global concern has been caused by a coronavirus (COVID19), which is considered a global health threat due to its rapid spread across the globe. Machine learning (ML) is a computational method that can be used to automatically learn from experience and improve the accuracy of predictions. OBJECTIVE In this study, the use of machine learning has been applied to Coronavirus dataset of 50 X-ray images to enable the development of directions and detection modalities with risk causes.The dataset contains a wide range of samples of COVID-19 cases alongside SARS, MERS, and ARDS. The experiment was carried out using a total of 50 X-ray images, out of which 25 images were that of positive COVIDE-19 cases, while the other 25 were normal cases. METHODS An orange tool has been used for data manipulation. To be able to classify patients as carriers of Coronavirus and non-Coronavirus carriers, this tool has been employed in developing and analysing seven types of predictive models. Models such as , artificial neural network (ANN), support vector machine (SVM), linear kernel and radial basis function (RBF), k-nearest neighbour (k-NN), Decision Tree (DT), and CN2 rule inducer were used in this study.Furthermore, the standard InceptionV3 model has been used for feature extraction target. RESULTS The various machine learning techniques that have been trained on coronavirus disease 2019 (COVID-19) dataset with improved ML techniques parameters. The data set was divided into two parts, which are training and testing. The model was trained using 70% of the dataset, while the remaining 30% was used to test the model. The results show that the improved SVM achieved a F1 of 97% and an accuracy of 98%. CONCLUSIONS :. In this study, seven models have been developed to aid the detection of coronavirus. In such cases, the learning performance can be improved through knowledge transfer, whereby time-consuming data labelling efforts are not required.the evaluations of all the models are done in terms of different parameters. it can be concluded that all the models performed well, but the SVM demonstrated the best result for accuracy metric. Future work will compare classical approaches with deep learning ones and try to obtain better results. CLINICALTRIAL None

scholarly journals IWV retrieval from ground GNSS receivers during NAWDEX

2021 ◽  
Vol 55 ◽  
pp. 13-22
Author(s):  
Pierre Bosser ◽  
Olivier Bock
Keyword(s):  
Water Vapor ◽  
North Atlantic ◽  
Regional Scale ◽  
Satellite System ◽  
Tropospheric Delay ◽  
Spatial And Temporal Variability ◽  
Data Set ◽  
The North ◽  
The North Atlantic ◽  
Global Navigation Satellite

Abstract. A ground-based network of more than 1200 Global Navigation Satellite System (GNSS) Continuously Operating Reference Stations (CORS) was analysed using GIPSY-OASIS II software package for the documentation of time and space variations of water vapor in atmosphere during the North Atlantic Waveguide and Downstream impact EXperiment (NAWDEX) during fall 2016. The network extends throughout the North Atlantic, from the Caribbeans to Morocco through Greenland. This paper presents the methodology used for GNSS data processing, screening, and conversion of Zenith Tropospheric Delay (ZTD) estimates to Integrated Water Vapor content (IWV) using surface parameters from reanalysis. The retrieved IWV are used to evaluate the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalyses ERAI and ERA5. ERA5 shows an overall improvement over ERAI in representing the spatial and temporal variability of IWV over the study area. The mean bias is decreased from 0.31±0.63 to 0.19±0.56 kg m−2 (mean ±1σ over all stations) and the standard deviation reduced from 2.17±0.67 to 1.64±0.53 kg m−2 combined with a slight improvement in correlation coefficient from 0.95 to 0.97. At regional scale, both reanalyses show a general wet bias at mid and northern latitudes but a dry bias in the Caribbeans. We hypothesize this results from the different nature of data being assimilated over the tropical oceans. This GNSS IWV data set is intended to be used for a better description of the high impact weather events that occurred during the NAWDEX experiment.

scholarly journals A numerical method to improve the spatial interpolation of water vapor from numerical weather models: a case study in South and Central America

2019 ◽  
Vol 37 (6) ◽  
pp. 1181-1195
Author(s):  
Laura I. Fernández ◽  
Amalia M. Meza ◽  
M. Paula Natali ◽  
Clara E. Bianchi
Keyword(s):  
Water Vapor ◽  
Grid Point ◽  
Satellite System ◽  
Specific Humidity ◽  
Data Set ◽  
Numerical Weather ◽  
Weather Model ◽  
Additive Correction ◽  
Grid Points ◽  
Global Navigation Satellite

Abstract. Commonly, numerical weather model (NWM) users can get the vertically integrated water vapor (IWV) value at a given location from the values at nearby grid points. In this study we used a validated and freely available global navigation satellite system (GNSS) IWV data set to analyze the very well-known effect of height differences. To this end, we studied the behavior of 67 GNSS stations in Central and South America with the prerequisite that they have a minimum of 5 years of data during the period from 2007 to 2013. The values of IWV from GNSS were compared with the respective values from ERA-Interim and MERRA-2 from the same period. Firstly, the total set of stations was compared in order to detect cases in which the geopotential difference between GNSS and NWM required correction. An additive integral correction to the IWV values from ERA-Interim was then proposed. For the calculation of this correction, the multilevel values of specific humidity and temperature given at 37 pressure levels by ERA-Interim were used. The performance of the numerical integration method was tested by accurately reproducing the IWV values at every individual grid point surrounding each of the GNSS sites under study. Finally, considering the IWVGNSS values as a reference, the improvement introduced to the IWVERA-Interim values after correction was analyzed. In general, the corrections were always recommended, but they are not advisable in marine coastal areas or on islands as at least two grid points of the model are usually in the water. In such cases, the additive correction could overestimate the IWV.

scholarly journals Jammer Classification in GNSS Bands Via Machine Learning Algorithms

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 4841 ◽  
Author(s):  
Ruben Morales Ferre ◽  
Alberto de la Fuente ◽  
Elena Simona Lohan
Keyword(s):  
Machine Learning ◽  
Classification Problem ◽  
Satellite System ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Learning Approaches ◽  
Time Frequency ◽  
Black And White ◽  
Image Mapping ◽  
Global Navigation Satellite

This paper proposes to treat the jammer classification problem in the Global Navigation Satellite System bands as a black-and-white image classification problem, based on a time-frequency analysis and image mapping of a jammed signal. The paper also proposes to apply machine learning approaches in order to sort the received signal into six classes, namely five classes when the jammer is present with different jammer types and one class where the jammer is absent. The algorithms based on support vector machines show up to 94 . 90 % accuracy in classification, and the algorithms based on convolutional neural networks show up to 91 . 36 % accuracy in classification. The training and test databases generated for these tests are also provided in open access.

scholarly journals NLOS Multipath Classification of GNSS Signal Correlation Output Using Machine Learning

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2503
Author(s):  
Taro Suzuki ◽  
Yoshiharu Amano
Keyword(s):  
Machine Learning ◽  
Satellite System ◽  
Training Data ◽  
Support Vector ◽  
Positioning Errors ◽  
Automated Method ◽  
Global Navigation Satellite ◽  
Better Than ◽  
Signal Correlation

This paper proposes a method for detecting non-line-of-sight (NLOS) multipath, which causes large positioning errors in a global navigation satellite system (GNSS). We use GNSS signal correlation output, which is the most primitive GNSS signal processing output, to detect NLOS multipath based on machine learning. The shape of the multi-correlator outputs is distorted due to the NLOS multipath. The features of the shape of the multi-correlator are used to discriminate the NLOS multipath. We implement two supervised learning methods, a support vector machine (SVM) and a neural network (NN), and compare their performance. In addition, we also propose an automated method of collecting training data for LOS and NLOS signals of machine learning. The evaluation of the proposed NLOS detection method in an urban environment confirmed that NN was better than SVM, and 97.7% of NLOS signals were correctly discriminated.

scholarly journals Water Vapor Retrievals from Spectral Direct Irradiance Measured with an EKO MS-711 Spectroradiometer—Intercomparison with Other Techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 350
Author(s):  
Rosa Delia García ◽  
Emilio Cuevas ◽  
Victoria Eugenia Cachorro ◽  
Omaira E. García ◽  
África Barreto ◽  
...  
Keyword(s):  
Water Vapor ◽  
Power Plants ◽  
Precipitable Water ◽  
Satellite System ◽  
Infrared Spectrometry ◽  
Radiative Balance ◽  
Direct Normal Irradiance ◽  
Routine Basis ◽  
Solar Power Plants ◽  
Global Navigation Satellite

Precipitable water vapor retrievals are of major importance for assessing and understanding atmospheric radiative balance and solar radiation resources. On that basis, this study presents the first PWV values measured with a novel EKO MS-711 grating spectroradiometer from direct normal irradiance in the spectral range between 930 and 960 nm at the Izaña Observatory (IZO, Spain) between April and December 2019. The expanded uncertainty of PWV (UPWV) was theoretically evaluated using the Monte-Carlo method, obtaining an averaged value of 0.37 ± 0.11 mm. The estimated uncertainty presents a clear dependence on PWV. For PWV ≤ 5 mm (62% of the data), the mean UPWV is 0.31 ± 0.07 mm, while for PWV > 5 mm (38% of the data) is 0.47 ± 0.08 mm. In addition, the EKO PWV retrievals were comprehensively compared against the PWV measurements from several reference techniques available at IZO, including meteorological radiosondes, Global Navigation Satellite System (GNSS), CIMEL-AERONET sun photometer and Fourier Transform Infrared spectrometry (FTIR). The EKO PWV values closely align with the above mentioned different techniques, providing a mean bias and standard deviation of −0.30 ± 0.89 mm, 0.02 ± 0.68 mm, −0.57 ± 0.68 mm, and 0.33 ± 0.59 mm, with respect to the RS92, GNSS, FTIR and CIMEL-AERONET, respectively. According to the theoretical analysis, MB decreases when comparing values for PWV > 5 mm, leading to a PWV MB between −0.45 mm (EKO vs. FTIR), and 0.11 mm (EKO vs. CIMEL-AERONET). These results confirm that the EKO MS-711 spectroradiometer is precise enough to provide reliable PWV data on a routine basis and, as a result, can complement existing ground-based PWV observations. The implementation of PWV measurements in a spectroradiometer increases the capabilities of these types of instruments to simultaneously obtain key parameters used in certain applications such as monitoring solar power plants performance.

scholarly journals Assessment of Sampling Effects on Various Satellite-Derived Integrated Water Vapor Datasets Using GPS Measurements in Germany as Reference

2020 ◽  
Vol 12 (7) ◽  
pp. 1170 ◽  
Author(s):  
Cintia Carbajal Henken ◽  
Lisa Dirks ◽  
Sandra Steinke ◽  
Hannes Diedrich ◽  
Thomas August ◽  
...  
Keyword(s):  
Water Vapor ◽  
Pairwise Comparison ◽  
Weather Conditions ◽  
Satellite System ◽  
High Temporal Resolution ◽  
Data Sets ◽  
Frequency Distributions ◽  
Temporal Sampling ◽  
Global Navigation Satellite ◽  
Satellite Instruments

Passive imagers on polar-orbiting satellites provide long-term, accurate integrated water vapor (IWV) data sets. However, these climatologies are affected by sampling biases. In Germany, a dense Global Navigation Satellite System network provides accurate IWV measurements not limited by weather conditions and with high temporal resolution. Therefore, they serve as a reference to assess the quality and sampling issues of IWV products from multiple satellite instruments that show different orbital and instrument characteristics. A direct pairwise comparison between one year of IWV data from GPS and satellite instruments reveals overall biases (in kg/m 2 ) of 1.77, 1.36, 1.11, and −0.31 for IASI, MIRS, MODIS, and MODIS-FUB, respectively. Computed monthly means show similar behaviors. No significant impact of averaging time and the low temporal sampling on aggregated satellite IWV data is found, mostly related to the noisy weather conditions in the German domain. In combination with SEVIRI cloud coverage, a change of shape of IWV frequency distributions towards a bi-modal distribution and loss of high IWV values are observed when limiting cases to daytime and clear sky. Overall, sampling affects mean IWV values only marginally, which are rather dominated by the overall retrieval bias, but can lead to significant changes in IWV frequency distributions.

Machine learning identifies an immunological pattern associated with multiple juvenile idiopathic arthritis subtypes

2019 ◽  
Vol 78 (5) ◽  
pp. 617-628 ◽  
Author(s):  
Erika Van Nieuwenhove ◽  
Vasiliki Lagou ◽  
Lien Van Eyck ◽  
James Dooley ◽  
Ulrich Bodenhofer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Juvenile Idiopathic Arthritis ◽  
Large Scale ◽  
Inflammatory Diseases ◽  
Adaptive Immune System ◽  
Healthy Children ◽  
Learning Approaches ◽  
Data Set ◽  
Immune Signature ◽  
Systemic Jia

ObjectivesJuvenile idiopathic arthritis (JIA) is the most common class of childhood rheumatic diseases, with distinct disease subsets that may have diverging pathophysiological origins. Both adaptive and innate immune processes have been proposed as primary drivers, which may account for the observed clinical heterogeneity, but few high-depth studies have been performed.MethodsHere we profiled the adaptive immune system of 85 patients with JIA and 43 age-matched controls with indepth flow cytometry and machine learning approaches.ResultsImmune profiling identified immunological changes in patients with JIA. This immune signature was shared across a broad spectrum of childhood inflammatory diseases. The immune signature was identified in clinically distinct subsets of JIA, but was accentuated in patients with systemic JIA and those patients with active disease. Despite the extensive overlap in the immunological spectrum exhibited by healthy children and patients with JIA, machine learning analysis of the data set proved capable of discriminating patients with JIA from healthy controls with ~90% accuracy.ConclusionsThese results pave the way for large-scale immune phenotyping longitudinal studies of JIA. The ability to discriminate between patients with JIA and healthy individuals provides proof of principle for the use of machine learning to identify immune signatures that are predictive to treatment response group.

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