ORCID Profile
0000-0002-3064-7986
Current Organisation
Australian Bureau of Meteorology
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Elsevier BV
Date: 02-2018
Publisher: American Meteorological Society
Date: 06-2018
Abstract: The process of pyroconvection occurs when fire-released heat, moisture, and/or aerosols induce or augment convection in the atmosphere. Prediction of pyroconvection presents a set of complex problems for meteorologists and wildfire managers. In particular, the turbulent characteristics of a pyroconvective plume exert bidirectional feedback on fire behavior, often with resulting severe impacts on life and property. Here, we present the motivation, field strategy, and initial results from the Bushfire Convective Plume Experiment, which through the use of mobile radar aims to quantify the kinematics of pyroconvection and its role in fire behavior. The case studies presented include world-first observations from two wildfires and one prescribed burn using the University of Queensland’s portable, dual-polarized X-band Doppler radar (UQ-XPOL). The initial analyses of reflectivity, Doppler winds, polarimetric variables, and spectrum width data provide insights into these relatively unexplored datasets within the context of pyroconvection. Weather radar data are supported by mesonet observations, time-lapse photography, airborne multispectral imaging, and spot-fire mapping. The ability to combine ground-validated fire intensity and progression at an hourly scale with quantitative data documenting the evolution of the convective plume kinematics at the scale of hundreds of meters represents a new capability for advancing our understanding of wildfires. The results demonstrate the suitability of portable, dual-polarized X-band Doppler radar to investigate pyroconvection and associated plume dynamics.
Publisher: Elsevier BV
Date: 04-2015
Publisher: Elsevier BV
Date: 07-2014
Publisher: Oxford University Press (OUP)
Date: 16-02-2014
DOI: 10.1093/AOB/MCT311
Publisher: Springer Science and Business Media LLC
Date: 21-12-2014
Publisher: Copernicus GmbH
Date: 05-09-2019
Publisher: American Geophysical Union (AGU)
Date: 02-08-2019
DOI: 10.1029/2018JD029986
Publisher: Copernicus GmbH
Date: 05-09-2019
Publisher: Copernicus GmbH
Date: 05-09-2019
Publisher: American Geophysical Union (AGU)
Date: 09-01-2019
DOI: 10.1029/2018JD029285
Publisher: Copernicus GmbH
Date: 20-06-2022
Abstract: Abstract. Cloud and aerosol lidars measuring backscatter and depolarization ratio are the most suitable lidars to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water containing clouds (SLCC) based on ceilometers measuring only co-polarization backscatter. We utilize observations collected at Davis, Antarctica, where low-level, mixed-phase clouds, including supercooled liquid water (SLW) droplets and ice crystals, remain poorly understood due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018 to February 2019, with a variety of instruments including a depolarization lidar and a W-band cloud radar which were used to build a two-dimensional cloud phase mask distinguishing SLW and mixed-phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarization ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar–lidar approach with an accuracy as high as 0.91. This study provides a new SLCC retrieval approach based on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations. Finally, the two algorithms were applied to a full year of ceilometer observations to retrieve cloud phase and frequency of occurrences of SLCC: SLCC was present 29 ± 6 % of the time for T19 and 24 ± 5 % of the time for G22-Davis over that annual cycle.
Publisher: American Geophysical Union (AGU)
Date: 15-04-2020
DOI: 10.1029/2019GL084305
Abstract: Pyrometeors are the large ( mm) debris lofted above wildfires that are composed of the by‐products of combustion of the fuels. One speciation of pyrometeor is firebrands, which are burning debris that lead to ignitions ahead of the surface fire and can be the dominant mechanism of fire spread and structure loss. Pyrometeors are observed by meteorological radar. To date, there have been no investigations into identification of pyrometeor speciation with radar. Here we present an unsupervised machine learning method (Gaussian mixture model) to classify pyrometeor modes using X‐band radar data. The coherent features of the mode of pyrometeor identified most likely to transport firebrands were tracked in time and space. The radar classification and tracking method shows that wildfires do produce signatures in radar returns that could be used for spot fire risk prediction. In wildfires, different types of debris (known as pyrometeors) are lofted in the smoke plumes and transported downwind. Some types of pyrometeors may, when in the air, still be burning and capable of starting new wildfires. Here we investigate the potential for meteorological radar to classify different types of pyrometeors and to track them to determine their potential for starting new fires downwind of the main fire front.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2021
Publisher: Elsevier BV
Date: 11-2014
Publisher: Elsevier BV
Date: 2017
Publisher: Copernicus GmbH
Date: 12-06-2019
Abstract: Abstract. Knowledge of the full rainfall Drop Size Distribution (DSD) is critical for characterising liquid water precipitation for applications such as rainfall retrievals using electromagnetic signals and atmospheric model parameterisation. Southern Hemisphere temperate latitudes have a lack of DSD observations and their integrated variables. Laser-based disdrometers rely on the attenuation of a beam by falling particles and is currently the most commonly used type of instrument to observe the DSD. However, there remain questions on the accuracy and variability in the DSDs measured by co-located instruments wether identical models, different models or from different manufacturers. In this study, raw and processed DSD observations obtained from two of the most commonly deployed laser disdrometers, namely the Parsivel1 from OTT and the Laser Precipitation Monitor (LPM) from Thies Clima, are analysed and compared. Four co-located instruments of each type were deployed over 3 years from 2014 to 2017 in the proximity of Melbourne, a region prone to coastal rainfall in Southeast Australia. This dataset includes a total of approximately 1.5 million recorded minutes, including over 40,000 minutes of quality rainfall data common to all instruments, equivalent to a cumulative amount of rainfall ranging from 1093 to 1244 mm (depending on the instrument records) for a total of 318 rainfall events. Most of the events lasted between 20 and 40 min for rainfall amounts of 0.12 mm to 26.0 mm. The co-located LPM sensors show very similar observations while the co-located Parsivel1 systems show significantly different results. The LPM recorded one to two orders of magnitude more smaller droplets for drop diameters below 0.6 mm compared to the Parsivel1, with differences increasing at higher rainfall rates. The LPM integrated variables showed systematically lower values compared to the Parsivel1. Radar reflectivity-rainfall rate (ZH-R) relationships and resulting potential errors are also presented. Specific ZH-R relations for drizzle and convective rainfall are also derived based on DSD collected for each instrument type. Variability of the DSD as observed by co-located instruments of the same manufacturer had little impact on the estimated ZH-R relationships for stratiform rainfall, but differs when considering convective rainfall relations or ZH-R relations fitted to all available data. Conversely, disdrometer-derived ZH-R relations as compared to the Marshall-Palmer relation ZH =200R1.6 led to a bias in rainfall rates for reflectivities of 50 dBZ of up to 21.6 mm h−1. This study provides an open-source high-resolution dataset of co-located DSD to further explore s ling effects at micro-scale, along with rainfall microphysics.
Publisher: Elsevier BV
Date: 08-2009
Publisher: American Geophysical Union (AGU)
Date: 03-2021
DOI: 10.1029/2020AV000258
Abstract: The monitoring of wildfire smoke is important to help mitigate impacts on people such as by sending early warnings to affected areas. Received signal levels (RSLs) from radio links have been used as an opportunistic way to accurately measure rainfall and humidity. Radio links provide integrated measurements along their paths and are an exceptional untapped resource to complement air quality stations in areas affected by smoke events, or in developing countries without air quality monitoring capability. This study analyzed radio link signal fluctuations during smoke events associated with the 2019–2020 Australian wildfires. Concurrently, the atmospheric boundary layer was characterized using atmospheric soundings and surface observations, as well as air quality proxies such as particulate matter concentrations less than 2.5 μm (10 μm), or PM 2.5 (PM 10 ). Observations showed that dry air containing large amounts of smoke within a surface layer above the ground acted as a lid, reducing dispersion, trapping and maintaining high ground‐level concentrations of smoke. These conditions also created anomalous propagation conditions for radio links and operational weather radars. Power‐law relations between signal fluctuations and PM 10 and PM 2.5 were derived based on the link data collected and the closest air quality station observations. While there was variability in retrieval performance across smoke events, the best performance determination coefficients exceeded 0.5, with an RMSE on the order of less than 50 μg m −3 for concentrations of more than 400 μg m −3 . Mid‐range link lengths (5–20 km) provided the best results.
Publisher: International Society for Horticultural Science (ISHS)
Date: 05-2013
Publisher: Elsevier BV
Date: 07-2014
Publisher: Oxford University Press (OUP)
Date: 17-03-2018
Abstract: Mangrove forests depend on a dense structure of sufficiently large trees to fulfil their essential functions as providers of food and wood for animals and people, CO2 sinks and protection from storms. Growth of these forests is known to be dependent on the salinity of soil water, but the influence of foliar uptake of rainwater as a freshwater source, additional to soil water, has hardly been investigated. Under field conditions in Australia, stem diameter variation, sap flow and stem water potential of the grey mangrove (Avicennia marina (Forssk.) Vierh.) were simultaneously measured during alternating dry and rainy periods. We found that sap flow in A. marina was reversed, from canopy to roots, during and shortly after rainfall events. Simultaneously, stem diameters rapidly increased with growth rates up to 70 μm h-1, which is about 25-75 times the normal growth rate reported in temperate trees. A mechanistic tree model was applied to provide evidence that A. marina trees take up water through their leaves, and that this water contributes to turgor-driven stem growth. Our results indicate that direct uptake of freshwater by the canopy during rainfall supports mangrove tree growth and serve as a call to consider this water uptake pathway if we aspire to correctly assess influences of changing rainfall patterns on mangrove tree growth.
Publisher: Elsevier BV
Date: 02-2018
Publisher: Copernicus GmbH
Date: 07-02-2020
Publisher: Elsevier BV
Date: 03-2012
Publisher: American Geophysical Union (AGU)
Date: 29-06-2020
DOI: 10.1029/2019WR026255
Abstract: Commercial microwave links (CMLs) have proven useful for providing rainfall information close to the ground surface. However, large uncertainties are associated with these retrievals, partly due to challenges in the type of data collection and processing. In particular, the most common case is when only minimum and maximum received signal levels (RSLs) over a given time interval (hereafter 15 min) are stored by mobile network operators. The average attenuation and the corresponding rainfall rate are then calculated based on a weighted average method using the minimum and maximum attenuation. In this study, an alternative to using a constant weighted average method is explored, based on a machine learning model trained to produce actual attenuation from minimum/maximum values. A rainfall retrieval deep learning model was designed based on a long short‐term memory (LSTM) model architecture and trained with disdrometer data in a form that is comparable to the data provided by mobile network operators. A first evaluation used only disdrometer data to mimic both attenuation from a CML and corresponding rainfall rates. For the test data set, the relative bias was reduced from 5.99% to 2.84% and the coefficient of determination ( R 2 ) increased from 0.86 to 0.97. The second evaluation used this disdrometer‐trained LSTM to retrieve rainfall rates from an actual CML located nearby the disdrometer. A significant improvement in the overall rainfall estimation compared to existing microwave link attenuation models was observed. The relative bias reduced from 7.39% to −1.14% and the R 2 improved from 0.71 to 0.82.
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-17073
Abstract: The 2020 worldwide bushfire activity was the most intense and widespread since the existence of satellite-based observational capabilities. The economic, societal, and ecological consequences have been immense: in Australia alone, the 2019-2020 Black Summer bushfires resulted in an economic cost of more than $100 billion, a burnt area of more than 18 M ha, 10,000 destroyed buildings, 34 direct deaths and more than 400 deaths due to smoke exposure. On the Australian East Coast, these intense wildfires lasting for almost two months produced very large smoke plumes and often fire-triggered thunderstorms - pyrocumulonimbus. These plumes and storms were predominantly within the range of operational weather radars, enabling observations of the plume thermodynamics, kinetics, and their composition. Here, we present two months of observations from a dual pol weather radar located near Sydney: a newly developed texture- and machine learning-based method enables us to extract smoke plumes and associated clouds from complex weather radar scenes including clear air and sea clutter. The characteristics of these smoke plumes are quantified including cloud top heights, volumes, projected areas, horizontal extends and daily dynamics. Using dual polarisation data, in-depth insights can be gained on the plumes& #8217 microphysics and the transition zone from smoke to pyrocumulus and pyrocumulonimbus. These high-resolution observations contribute to a better understanding of smoke plume dynamics and provide the foundations to develop nowcasting tools to predict associated hazards such as fire-triggered storms such as downbursts, plume collapse, and ember transport.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-13664
Abstract: & & A& novel proposal to create& robabilistic attenuation nowcasting& as& a& by-product from ensembles of rainfall forecasts& is presented in this study.& These attenuation nowcasts& may& eventually be used by& mobile network& operators& to& dynamically& adjust& their& wireless network& operations in advance and& during heavy and extreme rainfall events.& It& may also facilitate& mobile& network operators to see a direct benefit of& widely& sharing& its received power level data& of their backhaul towers& for& 'opportunistic'& rainfall estimation in real-time& in urban areas& becoming a clear& win-win situation for telecom operators and hydrologists.& It is proposed here that probabilistic attenuation forecasts& can& be& derived& from& the& ensembles of& high-resolution& forecast rainfall& fields& with lead times of 15 to 90 minutes& generated from weather radar& using the Short-Term Ensemble Prediction System (STEPS). The& ensembles of& rainfall predictions& can be& easily& converted to attenuation for specific& operating& frequencies.& This study used 109 microwave links ranging from 15 to 40 GHz to verify the results of this probabilistic attenuation forecast. Results suggest that the STEPS-based attenuation forecast was within the narrow span of the 90 percent& confidence region for all microwave links tested, with up to 30-minute lead time, and& was found to be skilful for lead times of up to 30-45 minutes.& & &
Publisher: Copernicus GmbH
Date: 09-02-2023
DOI: 10.5194/EGUSPHERE-2023-181
Abstract: Abstract. Weather radars are increasingly being used to study the interaction between wildfires and the atmosphere, owing to the enhanced spatio-temporal resolution of radar data compared to conventional measurements, such as satellite imagery and in-situ sensing. An important requirement for the continued proliferation of radar data for this application is the automatic identification of fire-generated particle returns (pyrometeors) from a scene containing a erse range of echo sources, including clear air, ground and sea clutter, and precipitation. The classification of such particles is a challenging problem for common image segmentation approaches (e.g. fuzzy logic or unsupervised machine learning) due to the strong overlap in radar variable distributions between each echo type. Here, we propose the following two-step method to address these challenges: 1) the introduction of secondary, texture-based fields, calculated using statistical properties of Gray Level Co-occurrence Matrices (GLCM), and 2) a Gaussian Mixture Model (GMM), used to classify echo sources by combining radar variables with texture-based fields from 1). Importantly, we retain all information from the original measurements by performing calculations in the radar's native spherical coordinate system and introduce a range-varying window methodology for our GLCM calculations to avoid range-dependent biases. We show that our method can accurately classify pyrometeors’ plumes, clear air, sea clutter, and precipitation using radar data from recent wildfire events in Australia and find that the contrast of the radar correlation coefficient, is the most skilful variable for the classification. The technique we propose enables the automated detection of pyrometeors’ plumes from operational weather radar networks, which may be used by fire agencies for emergency management purposes, or by scientists for case study analyses or historical event identification.
Publisher: Copernicus GmbH
Date: 20-02-2020
Publisher: Elsevier BV
Date: 11-2016
Publisher: Wiley
Date: 18-02-2016
DOI: 10.1002/ECO.1612
Publisher: Copernicus GmbH
Date: 14-02-2022
DOI: 10.5194/AMT-2022-10
Abstract: Abstract. Cloud and aerosol lidars measuring backscatter and depolarization ratio are most suitable instruments to detect cloud phase (liquid, ice, or mixed phase). However, such instruments are not widely deployed as part of operational networks. In this study, we propose a new algorithm to detect supercooled liquid water clouds based solely on ceilometers measuring only co-polarisation backscatter. We utilise observations collected at Davis, Antarctica, where low-level, mixed phase clouds, including supercooled liquid water (SLW) droplets and ice crystals remain poorly understood, due to the paucity of ground-based observations. A 3-month set of observations were collected during the austral summer of November 2018–February 2019, with a variety of instruments including a depolarization lidar and a W-Band cloud radar which were used to build a 2-dimensional cloud phase mask distinguishing SLW and mixed phase clouds. This cloud phase mask is used as the reference to develop a new algorithm based on the observations of a single polarisation ceilometer operating in the vicinity for the same period. Deterministic and data-driven retrieval approaches were evaluated: an extreme gradient boosting (XGBoost) framework ingesting backscatter average characteristics was the most effective method at reproducing the classification obtained with the combined radar-lidar approach with an accuracy as high as 0.91. This study provides a new SLW retrieval approach based solely on ceilometer data and highlights the considerable benefits of these instruments to provide intelligence on cloud phase in polar regions that usually suffer from a paucity of observations.
Publisher: Copernicus GmbH
Date: 10-07-2023
DOI: 10.5194/EGUSPHERE-2023-1085
Abstract: Abstract. The use of depolarization lidar to measure atmospheric volume depolarization ratio (VDR) is a common technique to classify cloud phase (liquid or ice). Previous work using a machine learning framework, applied to peak properties derived from co-polarised attenuated backscatter data, has been demonstrated to effectively detect supercooled liquid water containing clouds (SLCC). However, the training data from Davis Station, Antarctica, includes no warm liquid water clouds (WLCC), potentially limiting the model’s accuracy in regions where WLCC are present. In this work, we apply the Davis model to a 9-month Micro Pulse Lidar dataset collected in Christchurch, New Zealand, a location which includes WLCC. We then evaluate the results relative to a reference VDR cloud phase mask. We found that the Davis model performed relatively poorly at detecting SLCC with an accuracy of 0.62, often misclassifying WLCC as SLCC. We then trained a new model, using data from Christchurch, to perform SLCC detection on the same set of co-polarized attenuated backscatter peak properties. Our new model performed well, with accuracy scores as high as 0.89, highlighting the effectiveness of the machine learning technique when appropriate training data relevant to the location is used.
Publisher: Springer Science and Business Media LLC
Date: 04-12-2018
Publisher: Copernicus GmbH
Date: 12-10-2023
Publisher: Wiley
Date: 03-04-2014
DOI: 10.1002/HYP.10193
Publisher: Copernicus GmbH
Date: 19-11-2019
DOI: 10.5194/HESS-23-4737-2019
Abstract: Abstract. Knowledge of the full rainfall drop size distribution (DSD) is critical for characterising liquid water precipitation for applications such as rainfall retrievals using electromagnetic signals and atmospheric model parameterisation. Southern Hemisphere temperate latitudes have a lack of DSD observations and their integrated variables. Laser-based disdrometers rely on the attenuation of a beam by falling particles and are currently the most commonly used type of instrument to observe the DSD. However, there remain questions on the accuracy and variability in the DSDs measured by co-located instruments, whether identical models, different models or from different manufacturers. In this study, raw and processed DSD observations obtained from two of the most commonly deployed laser disdrometers, namely the Parsivel1 from OTT and the Laser Precipitation Monitor (LPM) from Thies Clima, are analysed and compared. Four co-located instruments of each type were deployed over 3 years from 2014 to 2017 in the proximity of Melbourne, a region prone to coastal rainfall in south-eastern Australia. This dataset includes a total of approximately 1.5 million recorded minutes, including over 40 000 min of quality rainfall data common to all instruments, equivalent to a cumulative amount of rainfall ranging from 1093 to 1244 mm (depending on the instrument records) for a total of 318 rainfall events. Most of the events lasted between 20 and 40 min for rainfall amounts of 0.12 to 26.0 mm. The co-located LPM sensors show very similar observations, while the co-located Parsivel1 systems show significantly different results. The LPM recorded 1 to 2 orders of magnitude more smaller droplets for drop diameters below 0.6 mm compared to the Parsivel1, with differences increasing at higher rainfall rates. The LPM integrated variables showed systematically lower values compared to the Parsivel1. Radar reflectivity–rainfall rate (ZH–R) relationships and resulting potential errors are also presented. Specific ZH–R relations for drizzle and convective rainfall are also derived based on DSD collected for each instrument type. Variability of the DSD as observed by co-located instruments of the same manufacturer had little impact on the estimated ZH–R relationships for stratiform rainfall, but differs when considering convective rainfall relations or ZH–R relations fitted to all available data. Conversely, disdrometer-derived ZH–R relations as compared to the Marshall–Palmer relation ZH=200R1.6 led to a bias in rainfall rates for reflectivities of 50 dBZ of up to 21.6 mm h−1. This study provides an open-source high-resolution dataset of co-located DSD to further explore s ling effects at the micro scale, along with rainfall microstructure.
Publisher: Springer Science and Business Media LLC
Date: 30-10-2014
Publisher: Copernicus GmbH
Date: 23-03-2020
DOI: 10.5194/EGUSPHERE-EGU2020-13974
Abstract: & & Coastal wetlands play a pivotal role in regulating both carbon (CO& sub& & /sub& ) and methane (CH& sub& & /sub& ) concentrations across the globe. The amount of CO& sub& & /sub& and CH& sub& & /sub& stored and released by these ecosystems is becoming more understood, in particular, within each aspect of the ecosystem. However, how the dynamics of the ecosystem affect CO& sub& & /sub& and CH& sub& & /sub& fluxes on a microclimate level is poorly understood, as well as the overall flux of these Greenhouse Gases (GHGs) within temperate, coastal wetlands. Current research primarily focuses on inland wetlands and coastal wetlands in sub-tropical and tropical regions. Thus, this research aims to investigate CO& sub& & /sub& and CH& sub& & /sub& fluxes within coastal, temperate wetlands, and improve the understanding of how environmental dynamics impact the flux of these critically important Greenhouse Gases (GHGs).& & & & & & & & & To satisfy this aim, the use of the Eddy-Covariance (EC) method was employed. An EC station was installed on the South-West tip of French Island, Victoria, Australia in late February 2018. The collected data demonstrates the challenges with collecting micro-climate data in an ecosystem with ever-changing environmental conditions. The preliminary results indicate how sensitive flux dynamics are within coastal, temperate wetlands, in particular, to factors such as: tidal and seasonal inundation, seasonal vegetation dynamics, and shifting ecological gradients. The data obtained by the EC station provides a preliminary indication of the complexities of accounting for, and understanding, carbon and methane movement through coastal wetlands in general. The full dataset will aid in improving this understanding, specifically for rare, temperate wetland environments, increasing the knowledge base on how flux dynamics of carbon and methane are affected when collected via open-source methods in dynamic environments.& &
Publisher: Oxford University Press (OUP)
Date: 17-01-2013
Abstract: Estimating sapwood area is one of the main sources of error when upscaling point scale sap flow measurements to whole-tree water use. In this study, the potential use of electrical resistivity tomography (ERT) to determine the sapwood-heartwood (SW-HW) boundary is investigated for Pinus elliottii Engelm var. elliottii × Pinus caribaea Morelet var. hondurensis growing in a subtropical climate. Specifically, this study investigates: (i) how electrical resistivity is correlated to either wood moisture content, or electrolyte concentration, or both, and (ii) how the SW-HW boundary is defined in terms of electrical resistivity. Tree cross-sections at breast height are analysed using ERT before being felled and the cross-section surface s led for analysis of major electrolyte concentrations, wood moisture content and density. Electrical resistivity tomography results show patterns with high resistivities occurring in the inner part of the cross-section, with much lower values towards the outside. The high-resistivity areas were generally smaller than the low-resistivity areas. A comparison between ERT and actual SW area measured after felling shows a slope of the linear regression close to unity (=0.96) with a large spread of values (R(2) = 0.56) mostly due to uncertainties in ERT. Electrolyte concentrations along s led radial transects (cardinal directions) generally showed no trend from the centre of the tree to the bark. Wood moisture content and density show comparable trends that could explain the resistivity patterns. While this study indicates the potential for application of ERT for estimating SW area, it shows that there remains a need for refinement in locating the SW-HW boundary (e.g., by improvement of the inversion method, or perhaps electrode density) in order to increase the robustness of the method.
Publisher: Copernicus GmbH
Date: 22-01-2020
Location: France
No related grants have been discovered for Adrien Guyot.