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Remote Sensing Methods and Applications for Traffic Meteorology

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Atmospheric Remote Sensing".

Deadline for manuscript submissions: closed (30 June 2019) | Viewed by 25458

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Special Issue Editor

Dr. Matthias Jerg

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Guest Editor

German Meteorological Service, Aeronautical Meteorology Department, Frankfurter Straße 135, 63067 Offenbach, Germany

Special Issue Information

Dear Colleagues,

In recent years, remote sensing in the context of traffic meteorology has enjoyed increased attention by users in the context of air, road, and ship traffic for a number of reasons.

Especially for the airline industry and air navigation service providers, as well as aeronautical meteorological service providers, the need to integrate remote-sensing data more tightly in order to optimize their operations has increased. Similarly, for road and ship traffic, requirements to use remote-sensing data and integrate those with surface measurements have increased, along with the technical capabilities and vulnerability of those means of transportation.

Aeronautical meteorological service providers have implemented projects in order to automate the generation of meteorological reports, based on observations at airports. Among other things, these automatic METARs require a variety of remote-sensing techniques in order to achieve the same quality and reliability as human observers.

At the same time, in order to increase existing capacities, methods for optimized joint-operations of airlines, airports, and air-navigation service providers demand the direct integration of meteorological data, not least based on remote sensing, into the respective systems to support collaborative decision making.

In addition, recently, the bandwidth of aircraft communication systems has reached a level that allows for the in-flight transmission of meteorological data from the ground to the cockpit. As a result, pilots are enabled to base their decisions on an enhanced database of near real-time remote-sensing information.

In road traffic, the availability of a multitude of on-board sensors of vehicles has created new challenges and possibilities for integrating this data with other surface measurements, as well as with remote sensing techniques and modelling approaches, in order to provide improved products to road users, service providers, and traffic management agencies, in order to support decision making.

Finally, the dawn of a new generation of geostationary satellites promises unprecedented capabilities. Not only will the spatial and temporal resolution increase notably but also new instruments like lightning sensors will advance global 24/7 weather surveillance and nowcasting of both common phenomena like thunderstorms, snowfall, land-sea, and aircraft icing conditions and turbulence, as well as less frequent events like volcanic ash eruptions. Moreover, the new generation of radar systems will, by combination of Doppler and dual polarization measurements, significantly extend the level of detail of observations in this area as well.

Submission of papers on the above-mentioned topics is invited; a special focus on automated synergetic systems that provide customized interpretation of the collected data is particularly welcome.

Dr. Matthias Jerg
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

Remote Sensing
Nowcasting
Aeronautical Meteorology
Road and Sea Traffic Meteorology
Satellite
Radar
Lightning
Decision Making

Published Papers (6 papers)

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Editorial

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2 pages, 134 KiB

Open AccessEditorial

Editorial for: Remote Sensing Methods and Applications for Traffic Meteorology

by Matthias Jerg

Remote Sens. 2019, 11(19), 2197; https://doi.org/10.3390/rs11192197 - 20 Sep 2019

Viewed by 1670

Abstract

Recently, remote sensing for traffic and especially aviation meteorology has become a focus of attention by the aviation industry and air navigation services [...] Full article

(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)

Research

Jump to: Editorial

12 pages, 3200 KiB

Open AccessArticle

A Novel Approach for Satellite-Based Turbulence Nowcasting for Aviation

by Axel Barleben, Stéphane Haussler, Richard Müller and Matthias Jerg

Remote Sens. 2020, 12(14), 2255; https://doi.org/10.3390/rs12142255 - 14 Jul 2020

Cited by 4 | Viewed by 3182

Abstract

The predictability of aviation turbulence is influenced by energy-intensive flow patterns that are significantly smaller than the horizontal grid scale of current numerical weather prediction (NWP) models. The parameterization of these subgrid scale (SGS) processes is possible by means of an additional prognostic equation for the temporal change of turbulence kinetic energy (TKE), whereby scale transfer terms are used. This turbulence scheme has been applied operationally for 5 years in the NWP model ICON (Icosahedral Nonhydrostatic). The most important of the source terms parameterizes the Kelvin–Helmholtz instability, better known as clear air turbulence. This shear term was subjected to a nowcasting technique, is calculated with satellite data, and shifted forward in time using motion based on optical flow estimates and atmospheric motion vector (AMV). The nowcasts include turbulence altitude as determined by an adapted height assignment scheme presented here. The case studies illustrate that the novel approach for satellite-based turbulence nowcasting is a supplement to the NWP models. Full article

(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)

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Graphical abstract

17 pages, 3949 KiB

Open AccessArticle

A Novel Framework of Detecting Convective Initiation Combining Automated Sampling, Machine Learning, and Repeated Model Tuning from Geostationary Satellite Data

by Daehyeon Han, Juhyun Lee, Jungho Im, Seongmun Sim, Sanggyun Lee and Hyangsun Han

Remote Sens. 2019, 11(12), 1454; https://doi.org/10.3390/rs11121454 - 19 Jun 2019

Cited by 31 | Viewed by 3874

Abstract

This paper proposes a complete framework of a machine learning-based model that detects convective initiation (CI) from geostationary meteorological satellite data. The suggested framework consists of three main processes: (1) An automated sampling tool; (2) machine learning-based CI detection modelling; (3) repeated model tuning through validation. In this study, the automated sampling tool was able to track the CI objects iteratively, even without ancillary data such as an atmospheric motion vector (AMV). The collected samples were used to train the machine learning model for CI detection. Random forest (RF) was used to classify the CI and non-CI. To enhance the advantages of the machine learning approach, we adopted model tuning to iteratively update the training dataset from each validation result by adding hits and misses to the CI samples, and false alarms and correct negatives to the non-CI samples. Using 12 interest fields from the Himawari-8 Advanced Himawari Imager (AHI) over the Korean Peninsula, this simple and intuitive tuning process increased the overall probability of detection (POD) from 0.79 to 0.82 and decreased the overall false alarm rate (FAR) from 0.46 to 0.37 with around 40 min of the lead-time. Amongst the 12 interest fields,

T_{b}

(11.2) µm was identified as the most significant predictor in the RF model, followed by

T_{b}

(8.6—11.2) µm, and

T_{b}

(6.2–7.3) µm. The effect of model tuning on the CI detection performance was also analyzed using spatiotemporal validation maps. By automatically collecting and updating the machine learning training dataset, the suggested framework is expected to help the maintenance of the CI detection model from an operational perspective. Full article

(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)

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Graphical abstract

15 pages, 2314 KiB

Open AccessArticle

A Novel Approach for the Detection of Developing Thunderstorm Cells

by Richard Müller, Stéphane Haussler, Matthias Jerg and Dirk Heizenreder

Remote Sens. 2019, 11(4), 443; https://doi.org/10.3390/rs11040443 - 21 Feb 2019

Cited by 10 | Viewed by 4617

Abstract

This study presents a novel approach for the early detection of developing thunderstorms. To date, methods for the detection of developing thunderstorms have usually relied on accurate Atmospheric Motion Vectors (AMVs) for the estimation of the cooling rates of convective clouds, which correspond to the updraft strengths of the cloud objects. In this study, we present a method for the estimation of the updraft strength that does not rely on AMVs. The updraft strength is derived directly from the satellite observations in the SEVIRI water vapor channels. For this purpose, the absolute value of the vector product of spatio-temporal gradients of the SEVIRI water vapor channels is calculated for each satellite pixel, referred to as Normalized Updraft Strength (NUS). The main idea of the concept is that vertical updraft leads to NUS values significantly above zero, whereas horizontal cloud movement leads to NUS values close to zero. Thus, NUS is a measure of the strength of the vertical updraft and can be applied to distinguish between advection and convection. The performance of the method has been investigated for two summer periods in 2016 and 2017 by validation with lightning data. Values of the Critical Success Index (CSI) of about 66% for 2016 and 60% for 2017 demonstrate the good performance of the method. The Probability of Detection (POD) values for the base case are 81.8% for 2016 and 89.2% for 2017, respectively. The corresponding False Alarm Ratio (FAR) values are 22.6% (2016) and 36.4% (2017), respectively. In summary, the method has the potential to reduce forecast lead time significantly and can be quite useful in regions without a well-maintained radar network. Full article

(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)

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Graphical abstract

19 pages, 3174 KiB

Open AccessArticle

Icing Detection over East Asia from Geostationary Satellite Data Using Machine Learning Approaches

by Seongmun Sim, Jungho Im, Sumin Park, Haemi Park, Myoung Hwan Ahn and Pak-wai Chan

Remote Sens. 2018, 10(4), 631; https://doi.org/10.3390/rs10040631 - 19 Apr 2018

Cited by 24 | Viewed by 6034

Abstract

Even though deicing or airframe coating technologies continue to develop, aircraft icing is still one of the critical threats to aviation. While the detection of potential icing clouds has been conducted using geostationary satellite data in the US and Europe, there is not yet a robust model that detects potential icing areas in East Asia. In this study, we proposed machine-learning-based icing detection models using data from two geostationary satellites—the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) and the Himawari-8 Advanced Himawari Imager (AHI)—over Northeast Asia. Two machine learning techniques—random forest (RF) and multinomial log-linear (MLL) models—were evaluated with quality-controlled pilot reports (PIREPs) as the reference data. The machine-learning-based models were compared to the existing models through five-fold cross-validation. The RF model for COMS MI produced the best performance, resulting in a mean probability of detection (POD) of 81.8%, a mean overall accuracy (OA) of 82.1%, and mean true skill statistics (TSS) of 64.0%. One of the existing models, flight icing threat (FIT), produced relatively poor performance, providing a mean POD of 36.4%, a mean OA of 61.0, and a mean TSS of 9.7%. The Himawari-8 based models also produced performance comparable to the COMS models. However, it should be noted that very limited PIREP reference data were available especially for the Himawari-8 models, which requires further evaluation in the future with more reference data. The spatio-temporal patterns of the icing areas detected using the developed models were also visually examined using time-series satellite data. Full article

(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)

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13 pages, 679 KiB

Open AccessFeature PaperArticle

The Role of NWP Filter for the Satellite Based Detection of Cumulonimbus Clouds

by Richard Müller, Stephane Haussler and Matthias Jerg

Remote Sens. 2018, 10(3), 386; https://doi.org/10.3390/rs10030386 - 02 Mar 2018

Cited by 8 | Viewed by 4526

Abstract

This study is motivated by the great importance of Cbs for aviation safety. The study investigates the role of Numerical Weather Prediction (NWP) filtering for the remote sensing of Cumulonimbus Clouds (Cbs) by implementation of about 30 different experiments, covering Central Europe. These experiments compile different stability filter settings as well as the use of different channels for the InfraRed (IR) brightness temperatures (BT). As stability filters, parameters from Numerical Weather Prediction (NWP) are used. The application of the stability filters restricts the detection of Cbs to regions with a labile atmosphere. Various NWP filter settings are investigated in the experiments. The brightness temperature information results from the infrared (IR) Spinning Enhanced Visible and InfraRed Image (SEVIRI) instrument on-board of the Meteosat Second Generation satellite and enables the detection of very cold and high clouds close to the tropopause. Various satellite channels and BT thresholds are applied in the different experiments. The satellite only approaches (no NWP filtering) result in the detection of Cbs with a relative high probability of detection, but unfortunately combined with a large False Alarm Rate (FAR), leading to a Critical Success Index (CSI) below 60% for the investigated summer period in 2016. The false alarms result from other types of very cold and high clouds. It is shown that the false alarms can be significantly decreased by application of an appropriate NWP stability filter, leading to the increase of CSI to about 70% for 2016. CSI is increased from about 70 to about 75% by application of NWP filtering for the other investigated summer period in 2017. A brief review and reflection of the literature clarify that the function of the NWP filter can not be replaced by MSG IR spectroscopy. Thus, NWP filtering is strongly recommended to increase the quality of satellite based Cb detection. Further, it has been shown that the well established convective available potential energy (CAPE) and the convection index (KO) work well as a stability filter. Full article

(This article belongs to the Special Issue Remote Sensing Methods and Applications for Traffic Meteorology)

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