scholarly journals Detection of Particulate Matter Changes Caused by 2020 California Wildfires Based on GNSS and Radiosonde Station

2021 ◽  
Vol 13 (22) ◽  
pp. 4557
Author(s):  
Jinyun Guo ◽  
Rui Hou ◽  
Maosheng Zhou ◽  
Xin Jin ◽  
Guowei Li
Keyword(s):  
Machine Learning ◽  
Particulate Matter ◽  
Water Vapor ◽  
Principal Components ◽  
Radiosonde Data ◽  
Economic Losses ◽  
Fifth Generation ◽  
Experimental Area ◽  
Radiosonde Station ◽  
Medium Range

From August to October 2020, a serious wildfire occurred in California, USA, which produced a large number of particulate matter and harmful gases, resulting in huge economic losses and environmental pollution. Particulate matter delays the GNSS signal, which affects the like precipitable water vapor (LPWV) derived by the GNSS non-hydrostatic delay. Most of the information of GNSS-derived LPWV is caused by water vapor, and a small part of the information is caused by particulate matter. A new method based on the difference (ΔPWV) between the PWV of virtual radiosonde stations network and GNSS-derived LPWV is proposed to detect the changes of particulate matter in the atmosphere during the 2020 California wildfires. There are few radiosonde stations in the experimental area and they are far away from the GNSS station. In order to solve this problem, we propose to use the multilayer perceptron (MLP) neural network method to establish the virtual radiosonde network in the experimental area. The PWV derived by the fifth-generation European center for medium-range weather forecasts reanalysis model (PWVERA5) is used as the input data of machine learning. The PWV derived by radiosonde data (PWVRAD) is used as the training target data of machine learning. The ΔPWV is obtained based on PWV derived by the virtual radiosonde station network and GNSS in the experimental area. In order to further reduce the influence of noise and other factors on ΔPWV, this paper attempts to decompose ΔPWV time series by using the singular spectrum analysis method, and obtain its principal components, subsequently, analyzing the relationship between the principal components of ΔPWV with particulate matter. The results indicate that the accuracy of PWV predicted by the virtual radiosonde network is significantly better than the fifth-generation European center for the medium-range weather forecast reanalysis model, and the change trend of ΔPWV is basically consistent with the change law of particulate matter in which the value of ΔPWV in the case of fire is significantly higher than that before and after the fire. The mean of correlation coefficients between ΔPWV and PM10 at each GNSS station before, during and after wildfires are 0.068, 0.397 and 0.065, respectively, which show the evident enhancement of the correlation between ΔPWV and particulate matter during wildfires. It is concluded that because of the high sensitiveness of ΔPWV to the change of particulate matter, the GNSS technique can be used as an effective new approach to detect the change of particulate matter and, then, to detect wildfires effectively.

scholarly journals Application of GNSS Methodologies to Obtain Precipitable Water Vapor (PWV) and Its Comparison with Radiosonde Data

Proceedings ◽  
2019 ◽  
Vol 19 (1) ◽  
pp. 24 ◽  
Author(s):  
Raquel Perdiguer-López ◽  
José Luis Berné-Valero ◽  
Natalia Garrido-Villén
Keyword(s):  
Water Vapor ◽  
Precipitable Water ◽  
Weather Conditions ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Tropospheric Delay ◽  
The North ◽  
Radiosonde Station ◽  
Observation Method ◽  
North Of Spain

A processing methodology with GNSS observations to obtain Zenith Tropospheric Delay using Bernese GNSS Software version 5.2 is revised in order to obtain Precipitable Water Vapor (PWV). The most traditional PWV observation method is the radiosonde and it is often used as a standard to validate those derived from GNSS. For this reason, a location in the north of Spain, in A Coruña, which has a GNSS station with available data and also a radiosonde station, was chosen. Two GPS weeks, in different weather conditions were calculated. The result of the comparison between the GNSS- retrieved PWV and Radiosonde-PWV is explained in the last section of this paper.

scholarly journals Completeness of radiosonde humidity observations based on the IGRA

2018 ◽  
Author(s):  
António P. Ferreira ◽  
Raquel Nieto ◽  
Luis Gimeno
Keyword(s):  
Water Vapor ◽  
Climatic Variability ◽  
Precipitable Water ◽  
Radiosonde Data ◽  
Vertical Resolution ◽  
Radiosonde Station ◽  
Humidity Measurements ◽  
The Mean ◽  
One Year ◽  
Selection Of

Abstract. Radiosonde measurements from the 1930s to present give unique information on the distribution and variability of water vapor in the troposphere. The sounding data compiled in the Integrated Global Radiosonde Archive (IGRA) Version 2 (released by the NOAA's National Centers for Environmental Information) are examined here until the end of 2016, aiming to describe the completeness of humidity observations from radiosondes in different times and locations. The IGRA stations reporting radiosonde data in at least 5 % of the annual soundings for at least one year are evaluated according to specified completeness parameters for every year in their period of record. The selection of source data essentially removes pilot-balloon sites, retaining a set of 1723 stations (designated IGRA-RS), including 1300 WMO upper-air stations, of which 178 belong to the current GUAN network. Completeness of humidity observations (either relative humidity or dewpoint-depression) for a radiosonde station and a full year is defined by: the number of humidity soundings; the fraction of days having humidity data; the mean vertical resolution of humidity data; the mean atmospheric pressure and altitude at the highest measuring level; and the maximum number of consecutive days without humidity data. The completeness of the observations qualified for calculating precipitable water vapor – i.e., having adequate vertical sampling between the surface and 500 hPa – is particularly studied. Individual soundings are described by the (vertically averaged) vertical resolution and the pressure level and altitude of the top of humidity measurements. For illustration, the study presents a global picture of the completeness of radiosonde humidity observations over the years, including their latitudinal coverage. This overview shows that the number of radiosonde stations having a long enough record length for studies on the climatic variability and trends of humidity-related quantities depends critically on the temporal continuity, regularity and vertical sampling of the humidity time-series. It is hoped that the derived metadata will help climate and environmental scientists to find the most appropriate radiosonde data for humidity studies by selecting upper-air stations, observing years or individual soundings according to various completeness criteria – even if differences in instrumentation and observing practices require extra attention. A dataset is presented for that purpose, consisting of two main sub-sets: 1) humidity metadata for each of the IGRA-RS stations and year within the period of record (yearly metadata); and 2) humidity metadata for individual observations from the same stations (ascent metadata). These are complemented by 3) a list of the stations represented in the dataset, along with the observing periods for humidity and the corresponding counts of observations. The dataset is to be updated on a two-year basis, starting in 2019, and is available at https://doi.org/10.5281/zenodo.1332686.

scholarly journals Monitoring 2019 Forest Fires in Southeastern Australia with GNSS Technique

2021 ◽  
Vol 13 (3) ◽  
pp. 386
Author(s):  
Jinyun Guo ◽  
Rui Hou ◽  
Maosheng Zhou ◽  
Xin Jin ◽  
Chengming Li ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Water Vapor ◽  
Forest Fire ◽  
Forest Fires ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Pollution Monitoring ◽  
New Method ◽  
Radiosonde Data ◽  
Southeastern Australia

From late 2019 to early 2020, forest fires in southeastern Australia caused huge economic losses and huge environmental pollution. Monitoring forest fires has become increasingly important. A new method of fire detection using the difference between global navigation satellite system (GNSS)-derived precipitable water vapor and radiosonde-derived precipitable water vapor (ΔPWV) is proposed. To study the feasibility of the new method, the relationship is studied between particulate matter 10 (PM10) (2.5 to 10 microns particulate matter) and ΔPWV based on Global Positioning System (GPS) data, radiosonde data, and PM10 data from 1 June 2019 to 1 June 2020 in southeastern Australia. The results show that before the forest fire, ΔPWV and PM10 were smaller and less fluctuating. When the forest fire happened, ΔPWV and PM10 were increasing. Then after the forest fire, PM10 became small with relatively smooth fluctuations, but ΔPWV was larger and more fluctuating. Correlation between the 15-day moving standard deviation (STD) time series of ΔPWV and PM10 after the fire was significantly higher than that before the fire. This study shows that ΔPWV is effective in monitoring forest fires based on GNSS technique before and during forest fires in climates with more uniform precipitation, and using ΔPWV to detect forest fires based on GNSS needs to be further investigated in climates with more precipitation and severe climate change.

scholarly journals Completeness of radiosonde humidity observations based on the Integrated Global Radiosonde Archive

2019 ◽  
Vol 11 (2) ◽  
pp. 603-627 ◽  
Author(s):  
António P. Ferreira ◽  
Raquel Nieto ◽  
Luis Gimeno
Keyword(s):  
Water Vapor ◽  
Atmospheric Pressure ◽  
Precipitable Water ◽  
Dew Point ◽  
Radiosonde Data ◽  
Pilot Balloon ◽  
Radiosonde Station ◽  
Basic Parameters ◽  
Global Coverage ◽  
Climate Studies

Abstract. Radiosonde measurements from the 1930s to present give unique information on the distribution and variability of water vapor in the troposphere. The sounding data from the Integrated Global Radiosonde Archive (IGRA) Version 2 are examined here until the end of 2016, aiming to describe the completeness of humidity observations (simultaneous measurements of pressure, temperature, and humidity) in different times and locations. Upon finding the stations with a non-negligible number of radiosonde observations in their period of record, thus removing pilot-balloon stations from IGRA, the selected set (designated IGRA-RS) comprises 1723 stations, including 1300 WMO stations, of which 178 belong to the current GCOS Upper-Air Network (GUAN) and 16 to the GCOS Reference Upper-Air Network (GRUAN). Completeness of humidity observations for a radiosonde station and a full year is herein defined by five basic parameters: number of humidity soundings, fraction of days with humidity data, average vertical resolution, average atmospheric pressure and altitude at the highest measuring level, and maximum number of consecutive days without data. The observations eligible for calculating precipitable water vapor – i.e., having adequate vertical sampling between the surface and 500 hPa – are particularly studied. The present study presents the global coverage of humidity data and an overall picture of the temporal and vertical completeness parameters over time. This overview indicates that the number of radiosonde stations potentially useful for climate studies involving humidity depends not only on their record length, but also on the continuity, regularity, and vertical sampling of the humidity time series. Additionally, a dataset based on IGRA is described with the purpose of helping climate and environmental scientists to select radiosonde data according to various completeness criteria – even if differences in instrumentation and observing practices require extra attention. This dataset consists of two main subsets: (1)  statistical metadata for each IGRA-RS station and year within the period of record; and (2) metadata for individual observations from each station. These are complemented by (3) a list of the stations represented in the whole dataset, along with the observing periods for humidity (relative humidity or dew-point depression) and the corresponding counts of observations. The dataset is to be updated on a 2-year basis, starting in 2019, and is available at https://doi.org/10.5281/zenodo.1332686.

scholarly journals Spatiotemporal characteristics of GNSS-derived precipitable water vapor during heavy rainfall events in Guilin, China

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Liangke Huang ◽  
Zhixiang Mo ◽  
Shaofeng Xie ◽  
Lilong Liu ◽  
Jun Chen ◽  
...  
Keyword(s):  
Water Vapor ◽  
Heavy Rainfall ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Radiosonde Data ◽  
Spatiotemporal Characteristics ◽  
Weighted Mean Temperature ◽  
Radiosonde Station ◽  
The Mean ◽  
Heavy Rainfalls

AbstractPrecipitable Water Vapor (PWV), as an important indicator of atmospheric water vapor, can be derived from Global Navigation Satellite System (GNSS) observations with the advantages of high precision and all-weather capacity. GNSS-derived PWV with a high spatiotemporal resolution has become an important source of observations in meteorology, particularly for severe weather conditions, for water vapor is not well sampled in the current meteorological observing systems. In this study, an empirical atmospheric weighted mean temperature (Tm) model for Guilin is established using the radiosonde data from 2012 to 2017. Then, the observations at 11 GNSS stations in Guilin are used to investigate the spatiotemporal features of GNSS-derived PWV under the heavy rainfalls from June to July 2017. The results show that the new Tm model in Guilin has better performance with the mean bias and Root Mean Square (RMS) of − 0.51 and 2.12 K, respectively, compared with other widely used models. Moreover, the GNSS PWV estimates are validated with the data at Guilin radiosonde station. Good agreements are found between GNSS-derived PWV and radiosonde-derived PWV with the mean bias and RMS of − 0.9 and 3.53 mm, respectively. Finally, an investigation on the spatiotemporal characteristics of GNSS PWV during heavy rainfalls in Guilin is performed. It is shown that variations of PWV retrieved from GNSS have a direct relationship with the in situ rainfall measurements, and the PWV increases sharply before the arrival of a heavy rainfall and decreases to a stable state after the cease of the rainfall. It also reveals the moisture variation in several regions of Guilin during a heavy rainfall, which is significant for the monitoring of rainfalls and weather forecast.

scholarly journals AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3830
Author(s):  
Ahmad Almadhor ◽  
Hafiz Tayyab Rauf ◽  
Muhammad Ikram Ullah Lali ◽  
Robertas Damaševičius ◽  
Bader Alouffi ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Color Difference ◽  
Plant Diseases ◽  
Economic Losses ◽  
Production Loss ◽  
Postharvest Diseases ◽  
Tropical Regions ◽  
Guava Fruit ◽  
Tree Classifier

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics’ apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers’ improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants’ leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

scholarly journals Leveraging Machine-Learning for D2D Communications in 5G/Beyond 5G Networks

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 169
Author(s):  
Sherief Hashima ◽  
Basem M. ElHalawany ◽  
Kohei Hatano ◽  
Kaishun Wu ◽  
Ehab Mahmoud Mohamed
Keyword(s):  
Machine Learning ◽  
Network Architecture ◽  
D2d Communication ◽  
Neighbor Discovery ◽  
Research Directions ◽  
Fifth Generation ◽  
Research Activities ◽  
Comprehensive Survey ◽  
Network Overhead

Device-to-device (D2D) communication is a promising paradigm for the fifth generation (5G) and beyond 5G (B5G) networks. Although D2D communication provides several benefits, including limited interference, energy efficiency, reduced delay, and network overhead, it faces a lot of technical challenges such as network architecture, and neighbor discovery, etc. The complexity of configuring D2D links and managing their interference, especially when using millimeter-wave (mmWave), inspire researchers to leverage different machine-learning (ML) techniques to address these problems towards boosting the performance of D2D networks. In this paper, a comprehensive survey about recent research activities on D2D networks will be explored with putting more emphasis on utilizing mmWave and ML methods. After exploring existing D2D research directions accompanied with their existing conventional solutions, we will show how different ML techniques can be applied to enhance the D2D networks performance over using conventional ways. Then, still open research directions in ML applications on D2D networks will be investigated including their essential needs. A case study of applying multi-armed bandit (MAB) as an efficient online ML tool to enhance the performance of neighbor discovery and selection (NDS) in mmWave D2D networks will be presented. This case study will put emphasis on the high potency of using ML solutions over using the conventional non-ML based methods for highly improving the average throughput performance of mmWave NDS.

scholarly journals Tracking an Atmospheric River in a Warmer Climate: from Water Vapor to Economic Impacts

2017 ◽  
Author(s):  
Francina Dominguez ◽  
Sandy Dall'erba ◽  
Shuyi Huang ◽  
Andre Avelino ◽  
Ali Mehran ◽  
...  
Keyword(s):  
Water Vapor ◽  
Economic Impacts ◽  
Heavy Precipitation ◽  
Integrated Modeling ◽  
Vapor Transport ◽  
Water Vapor Transport ◽  
Economic Losses ◽  
Radiative Forcings ◽  
Hypothetical Scenario ◽  
Western Washington

Abstract. Atmospheric rivers (ARs) account for more than 75 % of heavy precipitation events and nearly all of the extreme flooding events along the Olympic Mountains and western Cascade mountains of western Washington state. In a warmer climate, ARs in this region are projected to become more frequent and intense, primarily due to increases in atmospheric water vapor. However, it is unclear how the changes in water vapor transport will affect regional flooding and associated economic impacts. In this work, we present an integrated modeling system to quantify the atmospheric-hydrologic-hydraulic and economic impacts of the December 2007 AR event that impacted the Chehalis river basin in western Washington. We use the modeling system to project impacts under a hypothetical scenario where the same December 2007 event occurs in a warmer climate. This method allows us to incorporate different types of uncertainty including: a) alternative future radiative forcings, b) different responses of the climate system to future radiative forcings and c) different responses of the surface hydrologic system. In the warming scenario, AR integrated vapor transport increases, however, these changes do not translate into generalized increases in precipitation throughout the basin. The changes in precipitation translate into spatially heterogeneous changes in sub-basin runoff and increased streamflow along the entire Chehalis main stem. Economic losses due to stock damages increased moderately, but losses in terms of business interruption were significant. Our integrated modeling tool provides communities in the Chehalis region with a range of possible future physical and economic impacts associated with AR flooding.

scholarly journals Machine Learning Approaches to Traffic Accident Analysis and Hotspot Prediction

Computers ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 157
Author(s):  
Daniel Santos ◽  
José Saias ◽  
Paulo Quaresma ◽  
Vítor Beires Nogueira
Keyword(s):  
Machine Learning ◽  
Predictive Model ◽  
Road Accident ◽  
Influential Factors ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Economic Losses ◽  
Learning Approaches ◽  
Road Accidents ◽  
Accident Data

Traffic accidents are one of the most important concerns of the world, since they result in numerous casualties, injuries, and fatalities each year, as well as significant economic losses. There are many factors that are responsible for causing road accidents. If these factors can be better understood and predicted, it might be possible to take measures to mitigate the damages and its severity. The purpose of this work is to identify these factors using accident data from 2016 to 2019 from the district of Setúbal, Portugal. This work aims at developing models that can select a set of influential factors that may be used to classify the severity of an accident, supporting an analysis on the accident data. In addition, this study also proposes a predictive model for future road accidents based on past data. Various machine learning approaches are used to create these models. Supervised machine learning methods such as decision trees (DT), random forests (RF), logistic regression (LR), and naive Bayes (NB) are used, as well as unsupervised machine learning techniques including DBSCAN and hierarchical clustering. Results show that a rule-based model using the C5.0 algorithm is capable of accurately detecting the most relevant factors describing a road accident severity. Further, the results of the predictive model suggests the RF model could be a useful tool for forecasting accident hotspots.

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