Articles | Volume 15, issue 6
Atmos. Meas. Tech., 15, 1829–1848, 2022
https://doi.org/10.5194/amt-15-1829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Atmos. Meas. Tech., 15, 1829–1848, 2022
https://doi.org/10.5194/amt-15-1829-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
25 Mar 2022
Research article
| 25 Mar 2022
Identification of tropical cyclones via deep convolutional neural network based on satellite cloud images
Biao Tong et al.
Related authors
No articles found.
Lei Li, Chao Lu, Pak-Wai Chan, Zi-Juan Lan, Wen-Hai Zhang, Hong-Long Yang, and Hai-Chao Wang
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-579, https://doi.org/10.5194/acp-2021-579, 2021
Revised manuscript not accepted
Short summary
Short summary
The COVID-19 induced lockdown provided a time-window to study the impact of emission decrease on atmospheric environment. A 350 m meteorological tower in the Pearl River Delta recorded the vertical distribution of pollutants during the lockdown period. The observation confirmed that an extreme emission reduction, can reduce the concentrations of fine particles and the peak concentration of ozone at the same time, which had been taken as difficult to realize in the past in many regions.
Mattia Stagnaro, Matteo Colli, Luca Giovanni Lanza, and Pak Wai Chan
Atmos. Meas. Tech., 9, 5699–5706, https://doi.org/10.5194/amt-9-5699-2016, https://doi.org/10.5194/amt-9-5699-2016, 2016
Short summary
Short summary
The research presented in this work involves field data analysis, numerical modelling techniques and approaches to a long-standing problem of liquid precipitation measurements: the sampling and the interpretation of the tipping-bucket sensor signal. The present study shows relevant implications of the adopted data processing methods for the accuracy of the rainfall intensity measurements provided by traditional tipping-bucket gauges.
F. Peng, M. S. Wong, J. E. Nichol, and P. W. Chan
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLI-B2, 55–62, https://doi.org/10.5194/isprs-archives-XLI-B2-55-2016, https://doi.org/10.5194/isprs-archives-XLI-B2-55-2016, 2016
G. Kuhlmann, Y. F. Lam, H. M. Cheung, A. Hartl, J. C. H. Fung, P. W. Chan, and M. O. Wenig
Atmos. Chem. Phys., 15, 5627–5644, https://doi.org/10.5194/acp-15-5627-2015, https://doi.org/10.5194/acp-15-5627-2015, 2015
Short summary
Short summary
Regional NO2 distributions can be simulated by models or retrieved from satellite observations. We developed a custom OMI NO2 data product for the Pearl River delta region which reduces biases compared to the standard product. The product is used for the evaluation of a regional air quality model for which it is a useful addition to ground measurements. The unbiased NO2 data product can be very helpful for air pollution studies in urban areas.
Related subject area
Subject: Others (Wind, Precipitation, Temperature, etc.) | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Time evolution of temperature profiles retrieved from 13 years of infrared atmospheric sounding interferometer (IASI) data using an artificial neural network
Emissivity retrievals with FORUM's end-to-end simulator: challenges and recommendations
Detecting wave features in Doppler radial velocity radar observations
Remote sensing of solar surface radiation – a reflection of concepts, applications and input data based on experience with the effective cloud albedo
Snow microphysical retrieval from the NASA D3R radar during ICE-POP 2018
Retrieval improvements for the ALADIN Airborne Demonstrator in support of the Aeolus wind product validation
Cloud-probability-based estimation of black-sky surface albedo from AVHRR data
A high-resolution monitoring approach of canopy urban heat island using a random forest model and multi-platform observations
Differential absorption lidar measurements of water vapor by the High Altitude Lidar Observatory (HALO): retrieval framework and first results
Improving thermodynamic profile retrievals from microwave radiometers by including radio acoustic sounding system (RASS) observations
Calibration of radar differential reflectivity using quasi-vertical profiles
Improvement in algorithms for quality control of weather radar data (RADVOL-QC system)
Continuous temperature soundings at the stratosphere and lower mesosphere with a ground-based radiometer considering the Zeeman effect
Support vector machine tropical wind speed retrieval in the presence of rain for Ku-band wind scatterometry
Evaluation of convective boundary layer height estimates using radars operating at different frequency bands
Four-dimensional mesospheric and lower thermospheric wind fields using Gaussian process regression on multistatic specular meteor radar observations
Correction of wind bias for the lidar on board Aeolus using telescope temperatures
Leveraging machine learning for quantitative precipitation estimation from Fengyun-4 geostationary observations and ground meteorological measurements
Deriving column-integrated thermospheric temperature with the N2 Lyman–Birge–Hopfield (2,0) band
Atmospheric tomography using the Nordic Meteor Radar Cluster and Chilean Observation Network De Meteor Radars: network details and 3D-Var retrieval
Retrieval of Solar-induced Chlorophyll Fluorescence from TanSat Measurements: Comparison of SIF between TanSat and OCO-2
Using vertical phase differences to better resolve 3D gravity wave structure
High-temporal-resolution wet delay gradients estimated from multi-GNSS and microwave radiometer observations
Boundary layer water vapour statistics from high-spatial-resolution spaceborne imaging spectroscopy
GNSS-based water vapor estimation and validation during the MOSAiC expedition
Meteor radar observations of polar mesospheric summer echoes over Svalbard
Analysis of the microphysical properties of snowfall using scanning polarimetric and vertically pointing multi-frequency Doppler radars
Evaluation of micro rain radar-based precipitation classification algorithms to discriminate between stratiform and convective precipitation
On the estimation of boundary layer heights: a machine learning approach
IMK/IAA MIPAS temperature retrieval version 8: nominal measurements
Resolving the ambiguous direction of arrival of weak meteor radar trail echoes
Comparison of single-Doppler and multiple-Doppler wind retrievals in Hurricane Matthew (2016)
Insights into wind turbine reflectivity and radar cross-section (RCS) and their variability using X-band weather radar observations
Eddies in motion: visualizing boundary-layer turbulence above an open boreal peatland using UAS thermal videos
Assimilation of DAWN Doppler wind lidar data during the 2017 Convective Processes Experiment (CPEX): impact on precipitation and flow structure
Consistency of total column ozone measurements between the Brewer and Dobson spectroradiometers of the LKO Arosa and PMOD/WRC Davos
RainForest: a random forest algorithm for quantitative precipitation estimation over Switzerland
Ground-based temperature and humidity profiling: combining active and passive remote sensors
Statistically analyzing the effect of ionospheric irregularity on GNSS radio occultation atmospheric measurement
Detection of the melting level with polarimetric weather radar
Integrated water vapor and liquid water path retrieval using a single-channel radiometer
Improving atmospheric path attenuation estimates for radio propagation applications by microwave radiometric profiling
A new global grid-based weighted mean temperature model considering vertical nonlinear variation
Monitoring sudden stratospheric warmings using radio occultation: a new approach demonstrated based on the 2009 event
LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 1: Theoretical framework
LiSBOA (LiDAR Statistical Barnes Objective Analysis) for optimal design of lidar scans and retrieval of wind statistics – Part 2: Applications to lidar measurements of wind turbine wakes
Estimation of the height of the turbulent mixing layer from data of Doppler lidar measurements using conical scanning by a probe beam
Spectral correction of turbulent energy damping on wind lidar measurements due to spatial averaging
Improvement in tropospheric moisture retrievals from VIIRS through the use of infrared absorption bands constructed from VIIRS and CrIS data fusion
Hydrometeor classification of quasi-vertical profiles of polarimetric radar measurements using a top-down iterative hierarchical clustering method
Marie Bouillon, Sarah Safieddine, Simon Whitburn, Lieven Clarisse, Filipe Aires, Victor Pellet, Olivier Lezeaux, Noëlle A. Scott, Marie Doutriaux-Boucher, and Cathy Clerbaux
Atmos. Meas. Tech., 15, 1779–1793, https://doi.org/10.5194/amt-15-1779-2022, https://doi.org/10.5194/amt-15-1779-2022, 2022
Short summary
Short summary
The IASI instruments have been observing Earth since 2007. We use a neural network to retrieve atmospheric temperatures. This new temperature data record is validated against other datasets and shows good agreement. We use this new dataset to compute trends over the 2008–2020 period. We found a warming of the troposphere, more important at the poles. In the stratosphere, we found that temperatures decrease everywhere except at the South Pole. The cooling is more pronounced at the South pole.
Maya Ben-Yami, Hilke Oetjen, Helen Brindley, William Cossich, Dulce Lajas, Tiziano Maestri, Davide Magurno, Piera Raspollini, Luca Sgheri, and Laura Warwick
Atmos. Meas. Tech., 15, 1755–1777, https://doi.org/10.5194/amt-15-1755-2022, https://doi.org/10.5194/amt-15-1755-2022, 2022
Short summary
Short summary
Spectral emissivity is a key property of the Earth's surface. Few measurements exist in the far-infrared, despite recent work showing that its contribution is important for accurate modelling of global climate. In preparation for ESA’s EE9 FORUM mission (launch in 2026), this study takes the first steps towards the development of an operational emissivity retrieval for FORUM by investigating the sensitivity of the emissivity product to different physical and operational parameters.
Matthew A. Miller, Sandra E. Yuter, Nicole P. Hoban, Laura M. Tomkins, and Brian A. Colle
Atmos. Meas. Tech., 15, 1689–1702, https://doi.org/10.5194/amt-15-1689-2022, https://doi.org/10.5194/amt-15-1689-2022, 2022
Short summary
Short summary
Apparent waves in the atmosphere and similar features in storm winds can be detected by taking the difference between successive Doppler weather radar scans measuring radar-relative storm air motions. Applying image filtering to the difference data better isolates the detected signal. This technique is a useful tool in weather research and forecasting since such waves can trigger or enhance precipitation.
Richard Müller and Uwe Pfeifroth
Atmos. Meas. Tech., 15, 1537–1561, https://doi.org/10.5194/amt-15-1537-2022, https://doi.org/10.5194/amt-15-1537-2022, 2022
Short summary
Short summary
The great works of physics teach us that a central paradigm of science should be to make methods and theories as easy as possible and as complex as needed. This paper provides a brief review of remote sensing of solar surface irradiance based on this paradigm.
S. Joseph Munchak, Robert S. Schrom, Charles N. Helms, and Ali Tokay
Atmos. Meas. Tech., 15, 1439–1464, https://doi.org/10.5194/amt-15-1439-2022, https://doi.org/10.5194/amt-15-1439-2022, 2022
Short summary
Short summary
The ability to measure snowfall with weather radar has greatly advanced with the development of techniques that utilize dual-polarization measurements, which provide information about the snow particle shape and orientation, and multi-frequency measurements, which provide information about size and density. This study combines these techniques with the NASA D3R radar, which provides dual-frequency polarimetric measurements, with data that were observed during the 2018 Winter Olympics.
Oliver Lux, Christian Lemmerz, Fabian Weiler, Uwe Marksteiner, Benjamin Witschas, Stephan Rahm, Alexander Geiß, Andreas Schäfler, and Oliver Reitebuch
Atmos. Meas. Tech., 15, 1303–1331, https://doi.org/10.5194/amt-15-1303-2022, https://doi.org/10.5194/amt-15-1303-2022, 2022
Short summary
Short summary
The article discusses modifications in the wind retrieval of the ALADIN Airborne Demonstrator (A2D) – one of the key instruments for the validation of Aeolus. Thanks to the retrieval refinements, which are demonstrated in the context of two airborne campaigns in 2019, the systematic and random wind errors of the A2D were significantly reduced, thereby enhancing its validation capabilities. Finally, wind comparisons between A2D and Aeolus for the validation of the satellite data are presented.
Terhikki Manninen, Emmihenna Jääskeläinen, Niilo Siljamo, Aku Riihelä, and Karl-Göran Karlsson
Atmos. Meas. Tech., 15, 879–893, https://doi.org/10.5194/amt-15-879-2022, https://doi.org/10.5194/amt-15-879-2022, 2022
Short summary
Short summary
A new method for cloud-correcting observations of surface albedo is presented for AVHRR data. Instead of a binary cloud mask, it applies cloud probability values smaller than 20% of the A3 edition of the CLARA (CM SAF cLoud, Albedo and surface Radiation dataset from AVHRR data) record provided by the Satellite Application Facility on Climate Monitoring (CM SAF) project of EUMETSAT. According to simulations, the 90% quantile was 1.1% for the absolute albedo error and 2.2% for the relative error.
Shihan Chen, Yuanjian Yang, Fei Deng, Yanhao Zhang, Duanyang Liu, Chao Liu, and Zhiqiu Gao
Atmos. Meas. Tech., 15, 735–756, https://doi.org/10.5194/amt-15-735-2022, https://doi.org/10.5194/amt-15-735-2022, 2022
Short summary
Short summary
This paper proposes a method for evaluating canopy UHI intensity (CUHII) at high resolution by using remote sensing data and machine learning with a random forest (RF) model. The spatial distribution of CUHII was evaluated at 30 m resolution based on the output of the RF model. The present RF model framework for real-time monitoring and assessment of high-resolution CUHII provides scientific support for studying the changes and causes of CUHII.
Brian J. Carroll, Amin R. Nehrir, Susan A. Kooi, James E. Collins, Rory A. Barton-Grimley, Anthony Notari, David B. Harper, and Joseph Lee
Atmos. Meas. Tech., 15, 605–626, https://doi.org/10.5194/amt-15-605-2022, https://doi.org/10.5194/amt-15-605-2022, 2022
Short summary
Short summary
HALO is a recently developed lidar system that demonstrates new technologies and advanced algorithms for profiling water vapor as well as aerosol and cloud properties. The high-resolution, high-accuracy measurements have unique advantages within the suite of atmospheric instrumentation, such as directly trading water vapor measurement resolution for precision. This paper provides the methodology and first water vapor results, showing agreement with in situ and spaceborne sounder measurements.
Irina V. Djalalova, David D. Turner, Laura Bianco, James M. Wilczak, James Duncan, Bianca Adler, and Daniel Gottas
Atmos. Meas. Tech., 15, 521–537, https://doi.org/10.5194/amt-15-521-2022, https://doi.org/10.5194/amt-15-521-2022, 2022
Short summary
Short summary
In this paper we investigate the synergy obtained by combining active (radio acoustic sounding system – RASS) and passive (microwave radiometer) remote sensing observations to obtain temperature vertical profiles through a radiative transfer model. Inclusion of the RASS observations leads to more accurate temperature profiles from the surface to 5 km above ground, well above the maximum height of the RASS observations themselves (2000 m), when compared to the microwave radiometer used alone.
Daniel Sanchez-Rivas and Miguel A. Rico-Ramirez
Atmos. Meas. Tech., 15, 503–520, https://doi.org/10.5194/amt-15-503-2022, https://doi.org/10.5194/amt-15-503-2022, 2022
Short summary
Short summary
In this work, we review the use of quasi-vertical profiles for monitoring the calibration of the radar differential reflectivity ZDR. We validate the proposed method by comparing its results against the traditional approach based on measurements taken at 90°; we observed good agreement as the errors are within 0.2 dB. Additionally, we compare the results of the proposed method with ZDR derived from disdrometers; the errors are reasonable considering factors discussed in the paper.
Katarzyna Ośródka and Jan Szturc
Atmos. Meas. Tech., 15, 261–277, https://doi.org/10.5194/amt-15-261-2022, https://doi.org/10.5194/amt-15-261-2022, 2022
Short summary
Short summary
Weather radar data are used in weather monitoring and forecasting, but they are affected by numerous errors and require advanced corrections. Different systems are designed and implemented to suit specific local conditions, like the RADVOL-QC system. The radar errors are divided into several groups: disturbance by non-meteorological echoes (from the mountains, RLAN signals, wind turbines, etc.), beam blockage, attenuation, etc. Each of them has different properties and is corrected differently.
Witali Krochin, Francisco Navas-Guzmán, David Kuhl, Axel Murk, and Gunter Stober
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-344, https://doi.org/10.5194/amt-2021-344, 2021
Revised manuscript accepted for AMT
Short summary
Short summary
This study leverages atmospheric temperature measurements performed with a ground-based radiometer making use of data that was collected during a four year observational campaign applying a new retrieval algorithm which improves the maximal altitude range from 45 km to 55 km. The measurements are validated against two independent data sets, MERRA2 reanalysis data and the meteorological analysis of NAVGEM-HA.
Xingou Xu and Ad Stoffelen
Atmos. Meas. Tech., 14, 7435–7451, https://doi.org/10.5194/amt-14-7435-2021, https://doi.org/10.5194/amt-14-7435-2021, 2021
Short summary
Short summary
The support vector machine can effectively represent the increasing effect of rain affecting wind speeds. This research provides a correction of deviations that are skew- to Gaussian-like features caused by rain in Ku-band scatterometer wind. It demonstrates the effectiveness of a machine learning method when used based on elaborate analysis of the model establishment and result validation procedures. The corrected winds provide information previously lacking, which is vital for nowcasting.
Anna Franck, Dmitri Moisseev, Ville Vakkari, Matti Leskinen, Janne Lampilahti, Veli-Matti Kerminen, and Ewan O'Connor
Atmos. Meas. Tech., 14, 7341–7353, https://doi.org/10.5194/amt-14-7341-2021, https://doi.org/10.5194/amt-14-7341-2021, 2021
Short summary
Short summary
We proposed a method to derive a convective boundary layer height, using insects in radar observations, and we investigated the consistency of these retrievals among different radar frequencies (5, 35 and 94 GHz). This method can be applied to radars at other measurement stations and serve as additional way to estimate the boundary layer height during summer. The entrainment zone was also observed by the 5 GHz radar above the boundary layer in the form of a Bragg scatter layer.
Ryan Volz, Jorge L. Chau, Philip J. Erickson, Juha P. Vierinen, J. Miguel Urco, and Matthias Clahsen
Atmos. Meas. Tech., 14, 7199–7219, https://doi.org/10.5194/amt-14-7199-2021, https://doi.org/10.5194/amt-14-7199-2021, 2021
Short summary
Short summary
We introduce a new way of estimating winds in the upper atmosphere (about 80 to 100 km in altitude) from the observed Doppler shift of meteor trails using a statistical method called Gaussian process regression. Wind estimates and, critically, the uncertainty of those estimates can be evaluated smoothly (i.e., not gridded) in space and time. The effective resolution is set by provided parameters, which are limited in practice by the number density of the observed meteors.
Fabian Weiler, Michael Rennie, Thomas Kanitz, Lars Isaksen, Elena Checa, Jos de Kloe, Ngozi Okunde, and Oliver Reitebuch
Atmos. Meas. Tech., 14, 7167–7185, https://doi.org/10.5194/amt-14-7167-2021, https://doi.org/10.5194/amt-14-7167-2021, 2021
Short summary
Short summary
This paper summarizes the identification and correction of one of the most important systematic error sources for the wind measurements of the ESA satellite Aeolus. It depicts the effects of small temperature variations in the primary telescope mirror on the quality of the wind products and describes the approach to correct for it in the near-real-time processing. Moreover, the performance of the correction approach is assessed, and alternative approaches are discussed.
Xinyan Li, Yuanjian Yang, Jiaqin Mi, Xueyan Bi, You Zhao, Zehao Huang, Chao Liu, Lian Zong, and Wanju Li
Atmos. Meas. Tech., 14, 7007–7023, https://doi.org/10.5194/amt-14-7007-2021, https://doi.org/10.5194/amt-14-7007-2021, 2021
Short summary
Short summary
A random forest (RF) model framework for Fengyun-4A (FY-4A) daytime and nighttime quantitative precipitation estimation (QPE) is established using FY-4A multi-band spectral information, cloud parameters, high-density precipitation observations and physical quantities from reanalysis data. The RF model of FY-4A QPE has a high accuracy in estimating precipitation at the heavy-rain level or below, which has advantages for quantitative estimation of summer precipitation over East Asia in future.
Clayton Cantrall and Tomoko Matsuo
Atmos. Meas. Tech., 14, 6917–6928, https://doi.org/10.5194/amt-14-6917-2021, https://doi.org/10.5194/amt-14-6917-2021, 2021
Short summary
Short summary
This paper presents a new technique to determine temperature in the thermosphere from observations of far ultraviolet radiation emitted by molecular nitrogen. The technique utilizes a ratio of two far ultraviolet spectral channels to capture the thermosphere temperature signal. Applying the technique to NASA GOLD observations results in temperatures that agree well with other thermosphere observations during a geomagnetic disturbance.
Gunter Stober, Alexander Kozlovsky, Alan Liu, Zishun Qiao, Masaki Tsutsumi, Chris Hall, Satonori Nozawa, Mark Lester, Evgenia Belova, Johan Kero, Patrick J. Espy, Robert E. Hibbins, and Nicholas Mitchell
Atmos. Meas. Tech., 14, 6509–6532, https://doi.org/10.5194/amt-14-6509-2021, https://doi.org/10.5194/amt-14-6509-2021, 2021
Short summary
Short summary
Wind observations at the edge to space, 70–110 km altitude, are challenging. Meteor radars have become a widely used instrument to obtain mean wind profiles above an instrument for these heights. We describe an advanced mathematical concept and present a tomographic analysis using several meteor radars located in Finland, Sweden and Norway, as well as Chile, to derive the three-dimensional flow field. We show an example of a gravity wave decelerating the mean flow.
Lu Yao, Yi Liu, Dongxu Yang, Zhaonan Cai, Chao Lin, Naimeng Lu, Daren Lyu, Longfei Tian, Jing Wang, Maohua Wang, Zengshan Yin, Yuquan Zheng, and Sisi Wang
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2021-66, https://doi.org/10.5194/amt-2021-66, 2021
Revised manuscript accepted for AMT
Short summary
Short summary
A physical-based SIF retrieval algorithm is introduced and it was applied to OCO-2 and TanSat measurements. The retrieved OCO-2 SIF data and its official product have a strong linear relationship, indicating the reliability of the algorithm. The good consistency in spatial-temporal patterns and magnitude of OCO-2 and TanSat SIF product suggests that the combinative usage of multi-satellite products is possible and such work would contribute to further researches.
Corwin J. Wright, Neil P. Hindley, M. Joan Alexander, Laura A. Holt, and Lars Hoffmann
Atmos. Meas. Tech., 14, 5873–5886, https://doi.org/10.5194/amt-14-5873-2021, https://doi.org/10.5194/amt-14-5873-2021, 2021
Short summary
Short summary
Measuring atmospheric gravity waves in low vertical-resolution data is technically challenging, especially when the waves are significantly longer in the vertical than in the length of the measurement domain. We introduce and demonstrate a modification to the existing Stockwell transform methods of characterising these waves that address these problems, with no apparent reduction in the other capabilities of the technique.
Tong Ning and Gunnar Elgered
Atmos. Meas. Tech., 14, 5593–5605, https://doi.org/10.5194/amt-14-5593-2021, https://doi.org/10.5194/amt-14-5593-2021, 2021
Short summary
Short summary
We have estimated horizontal gradients of the propagation delay caused by water vapour in the atmosphere using two independent techniques, namely global navigation satellite systems (GNSS) and microwave radiometry. The highest resolution was 5 min. We found that the sampling of the atmosphere in different directions is an important factor for high correlations between the two techniques and that GNSS data can be used to detect large short-lived gradients, however, with increased formal errors.
Mark T. Richardson, David R. Thompson, Marcin J. Kurowski, and Matthew D. Lebsock
Atmos. Meas. Tech., 14, 5555–5576, https://doi.org/10.5194/amt-14-5555-2021, https://doi.org/10.5194/amt-14-5555-2021, 2021
Short summary
Short summary
Modern and upcoming hyperspectral imagers will take images with spatial resolutions as fine as 20 m. They can retrieve column water vapour, and we show evidence that from these column measurements you can get statistics of planetary boundary layer (PBL) water vapour. This is important information for climate models that need to account for sub-grid mixing of water vapour near the surface in their PBL schemes.
Benjamin Männel, Florian Zus, Galina Dick, Susanne Glaser, Maximilian Semmling, Kyriakos Balidakis, Jens Wickert, Marion Maturilli, Sandro Dahlke, and Harald Schuh
Atmos. Meas. Tech., 14, 5127–5138, https://doi.org/10.5194/amt-14-5127-2021, https://doi.org/10.5194/amt-14-5127-2021, 2021
Short summary
Short summary
Within the MOSAiC expedition, GNSS was used to monitor variations in atmospheric water vapor. Based on 15 months of continuously tracked data, coordinates and hourly zenith total delays (ZTDs) were determined using kinematic precise point positioning. The derived ZTD values agree within few millimeters with ERA5 and terrestrial GNSS and VLBI stations. The derived integrated water vapor corresponds to the frequently launched radiosondes (0.08 ± 0.04 kg m−2, rms of the differences of 1.47 kg m−2).
Joel P. Younger, Iain M. Reid, Chris L. Adami, Chris M. Hall, and Masaki Tsutsumi
Atmos. Meas. Tech., 14, 5015–5027, https://doi.org/10.5194/amt-14-5015-2021, https://doi.org/10.5194/amt-14-5015-2021, 2021
Short summary
Short summary
A radar in Svalbard usually used to study meteor trails was used to observe a thin icy layer in the upper atmosphere. New methods used the layer to measure wind speed over short periods of time and found that the layer is most reflective within 6.8 ± 3.3° of vertical. Analysis of meteor trail radar echo durations found that the layer may shorten meteor trail echoes, but more data are needed. This study shows new uses for data collected by meteor radars for other purposes.
Mariko Oue, Pavlos Kollias, Sergey Y. Matrosov, Alessandro Battaglia, and Alexander V. Ryzhkov
Atmos. Meas. Tech., 14, 4893–4913, https://doi.org/10.5194/amt-14-4893-2021, https://doi.org/10.5194/amt-14-4893-2021, 2021
Short summary
Short summary
Multi-wavelength radar measurements provide capabilities to identify ice particle types and growth processes in clouds beyond the capabilities of single-frequency radar measurements. This study introduces Doppler velocity and polarimetric radar observables into the multi-wavelength radar reflectivity measurement to improve identification analysis. The analysis clearly discerns snowflake aggregation and riming processes and even early stages of riming.
Andreas Foth, Janek Zimmer, Felix Lauermann, and Heike Kalesse-Los
Atmos. Meas. Tech., 14, 4565–4574, https://doi.org/10.5194/amt-14-4565-2021, https://doi.org/10.5194/amt-14-4565-2021, 2021
Short summary
Short summary
In this paper, we present two micro rain radar-based approaches to discriminate between stratiform and convective precipitation. One is based on probability density functions and the other one is an artificial neural network classification. Both methods agree well, giving similar results. However, the results of the artificial neural network are more reasonable since it is also able to distinguish an inconclusive class, in turn making the stratiform and convective classes more reliable.
Raghavendra Krishnamurthy, Rob K. Newsom, Larry K. Berg, Heng Xiao, Po-Lun Ma, and David D. Turner
Atmos. Meas. Tech., 14, 4403–4424, https://doi.org/10.5194/amt-14-4403-2021, https://doi.org/10.5194/amt-14-4403-2021, 2021
Short summary
Short summary
Planetary boundary layer (PBL) height is a critical parameter in atmospheric models. Continuous PBL height measurements from remote sensing measurements are important to understand various boundary layer mechanisms, especially during daytime and evening transition periods. Due to several limitations in existing methodologies to detect PBL height from a Doppler lidar, in this study, a machine learning (ML) approach is tested. The ML model is observed to improve the accuracy by over 50 %.
Michael Kiefer, Thomas von Clarmann, Bernd Funke, Maya García-Comas, Norbert Glatthor, Udo Grabowski, Sylvia Kellmann, Anne Kleinert, Alexandra Laeng, Andrea Linden, Manuel López-Puertas, Daniel R. Marsh, and Gabriele P. Stiller
Atmos. Meas. Tech., 14, 4111–4138, https://doi.org/10.5194/amt-14-4111-2021, https://doi.org/10.5194/amt-14-4111-2021, 2021
Short summary
Short summary
An improved dataset of vertical temperature profiles of the Earth's atmosphere in the altitude range 5–70 km is presented. These profiles are derived from measurements of the MIPAS instrument onboard ESA's Envisat satellite. The overall improvements are based on upgrades in the input data and several improvements in the data processing approach. Both of these are discussed, and an extensive error discussion is included. Enhancements of the new dataset are demonstrated by means of examples.
Daniel Kastinen, Johan Kero, Alexander Kozlovsky, and Mark Lester
Atmos. Meas. Tech., 14, 3583–3596, https://doi.org/10.5194/amt-14-3583-2021, https://doi.org/10.5194/amt-14-3583-2021, 2021
Short summary
Short summary
When a meteor enters the atmosphere, it causes a trail of diffusing plasma that moves with the neutral wind. An interferometric radar system can measure such trails and determine its location. However, there is a chance of determining the wrong position due to noise. We simulate this behaviour and use the simulations to successfully determine the true location of ambiguous events. We also successfully test two simple temporal integration methods for avoiding such erroneous determinations.
Ting-Yu Cha and Michael M. Bell
Atmos. Meas. Tech., 14, 3523–3539, https://doi.org/10.5194/amt-14-3523-2021, https://doi.org/10.5194/amt-14-3523-2021, 2021
Short summary
Short summary
Doppler radar provides high-resolution wind measurements within tropical cyclones (TCs) for real-time monitoring and weather forecasting. Hurricane Matthew (2016) was observed by the ground-based single-Doppler and NOAA P-3 Hurricane Hunter airborne radar simultaneously, providing a novel opportunity to compare single- and multiple-Doppler wind retrieval techniques. Here, we improve the single-Doppler wind retrieval algorithm and show the pros and cons of each method for studying TC structure.
Martin Lainer, Jordi Figueras i Ventura, Zaira Schauwecker, Marco Gabella, Montserrat F.-Bolaños, Reto Pauli, and Jacopo Grazioli
Atmos. Meas. Tech., 14, 3541–3560, https://doi.org/10.5194/amt-14-3541-2021, https://doi.org/10.5194/amt-14-3541-2021, 2021
Short summary
Short summary
We show results from two unique measurement campaigns aimed at better understanding effects of large wind turbines on radar returns by deploying a mobile X-band weather radar system in the proximity of a small wind park. Measurements were taken in 24/7 operation with dedicated scan strategies to retrieve the variability and most extreme values of reflectivity and radar cross-section of the wind turbines. The findings are useful for wind turbine interference mitigation measures in radar systems.
Pavel Alekseychik, Gabriel Katul, Ilkka Korpela, and Samuli Launiainen
Atmos. Meas. Tech., 14, 3501–3521, https://doi.org/10.5194/amt-14-3501-2021, https://doi.org/10.5194/amt-14-3501-2021, 2021
Short summary
Short summary
Drones with thermal cameras are powerful new tools with the potential to provide new insights into atmospheric turbulence and heat fluxes. In a pioneering experiment, a Matrice 210 drone with a Zenmuse XT2 thermal camera was used to record 10–20 min thermal videos at 500 m a.g.l. over the Siikaneva peatland in southern Finland. A method to visualize the turbulent structures and derive their parameters from thermal videos is developed. The study provides a novel approach for turbulence analysis.
Svetla Hristova-Veleva, Sara Q. Zhang, F. Joseph Turk, Ziad S. Haddad, and Randy C. Sawaya
Atmos. Meas. Tech., 14, 3333–3350, https://doi.org/10.5194/amt-14-3333-2021, https://doi.org/10.5194/amt-14-3333-2021, 2021
Short summary
Short summary
The assimilation of airborne-based three-dimensional winds into a mesoscale weather forecast model resulted in better agreement with airborne radar-derived precipitation 3-D structure at later model time steps. More importantly, there was also a discernible impact on the resultant wind and moisture structure, in accord with independent analysis of the wind structure and external satellite observations.
Julian Gröbner, Herbert Schill, Luca Egli, and René Stübi
Atmos. Meas. Tech., 14, 3319–3331, https://doi.org/10.5194/amt-14-3319-2021, https://doi.org/10.5194/amt-14-3319-2021, 2021
Short summary
Short summary
The world's longest continuous total column ozone time series was initiated in 1926 at the Lichtklimatisches Observatorium (LKO), at Arosa, in the Swiss Alps. The measurements between Dobson and Brewer spectroradiometers have shown seasonal variations of the order of 2 %. The results of the study show that the consistency between the two instrument types can be significantly improved when the ozone cross-sections from Serdyuchenko et al. (2013) and the measured slit functions are used.
Daniel Wolfensberger, Marco Gabella, Marco Boscacci, Urs Germann, and Alexis Berne
Atmos. Meas. Tech., 14, 3169–3193, https://doi.org/10.5194/amt-14-3169-2021, https://doi.org/10.5194/amt-14-3169-2021, 2021
Short summary
Short summary
In this work, we present a novel quantitative precipitation estimation method for Switzerland that uses random forests, an ensemble-based machine learning technique. The estimator has been trained with a database of 4 years of ground and radar observations. The results of an in-depth evaluation indicate that, compared with the more classical method in use at MeteoSwiss, this novel estimator is able to reduce both the average error and bias of the predictions.
David D. Turner and Ulrich Löhnert
Atmos. Meas. Tech., 14, 3033–3048, https://doi.org/10.5194/amt-14-3033-2021, https://doi.org/10.5194/amt-14-3033-2021, 2021
Short summary
Short summary
Temperature and humidity profiles in the lowest couple of kilometers near the surface are very important for many applications. Passive spectral radiometers are commercially available, and observations from these instruments have been used to get these profiles. However, new active lidar systems are able to measure partial profiles of water vapor. This paper investigates how the derived profiles of water vapor and temperature are improved when the active and passive observations are combined.
Mingzhe Li and Xinan Yue
Atmos. Meas. Tech., 14, 3003–3013, https://doi.org/10.5194/amt-14-3003-2021, https://doi.org/10.5194/amt-14-3003-2021, 2021
Short summary
Short summary
In this study, we statistically analyzed the correlation between the ionospheric irregularity and the quality of the GNSS atmospheric radio occultation (RO) products. The results show that the ionospheric irregularity could affect the GNSS atmospheric RO in terms of causing failed inverted RO events and the bending angle oscillation. Awareness of the ionospheric irregularity effect on RO could be beneficial to improve the RO data quality for weather and climate research.
Daniel Sanchez-Rivas and Miguel A. Rico-Ramirez
Atmos. Meas. Tech., 14, 2873–2890, https://doi.org/10.5194/amt-14-2873-2021, https://doi.org/10.5194/amt-14-2873-2021, 2021
Short summary
Short summary
In our paper, we propose a robust and operational algorithm to determine the height of the melting level that can be applied to either quasi-vertical profiles (QVPs) or vertical profiles (VPs) of polarimetric radar variables. The algorithm is applied to 1 year of rainfall events that occurred over southeast England and validated using radiosonde data. The algorithm proves to be accurate as the errors (mean absolute error and root mean square error) are close to 200 m.
Anne-Claire Billault-Roux and Alexis Berne
Atmos. Meas. Tech., 14, 2749–2769, https://doi.org/10.5194/amt-14-2749-2021, https://doi.org/10.5194/amt-14-2749-2021, 2021
Short summary
Short summary
In the context of climate studies, understanding the role of clouds on a global and local scale is of paramount importance. One aspect is the quantification of cloud liquid water, which impacts the Earth’s radiative balance. This is routinely achieved with radiometers operating at different frequencies. In this study, we propose an approach that uses a single-frequency radiometer and that can be applied at any location to retrieve vertically integrated quantities of liquid water and water vapor.
Ayham Alyosef, Domenico Cimini, Lorenzo Luini, Carlo Riva, Frank S. Marzano, Marianna Biscarini, Luca Milani, Antonio Martellucci, Sabrina Gentile, Saverio T. Nilo, Francesco Di Paola, Ayman Alkhateeb, and Filomena Romano
Atmos. Meas. Tech., 14, 2737–2748, https://doi.org/10.5194/amt-14-2737-2021, https://doi.org/10.5194/amt-14-2737-2021, 2021
Short summary
Short summary
Telecommunication is based on the propagation of radio signals through the atmosphere. The signal power diminishes along the path due to atmospheric attenuation, which needs to be estimated to be accounted for. In a study funded by the European Space Agency, we demonstrate an innovative method improving atmospheric attenuation estimates from ground-based radiometric measurements by 10–30 %. More accurate atmospheric attenuation estimates imply better telecommunication services in the future.
Peng Sun, Suqin Wu, Kefei Zhang, Moufeng Wan, and Ren Wang
Atmos. Meas. Tech., 14, 2529–2542, https://doi.org/10.5194/amt-14-2529-2021, https://doi.org/10.5194/amt-14-2529-2021, 2021
Short summary
Short summary
In GPS or Global navigation satellite systems (GNSS) meteorology, precipitable water vapor (PWV) at a station is obtained from a conversion of the GNSS signal zenith wet delay (ZWD) using a conversion factor which is a function of weighted mean temperature (Tm) over the site. We developed a new global grid-based empirical Tm model using ERA5 reanalysis data. The model-predicted Tm value has significance for applications needing real-time or near real-time PWV converted from GNSS signals.
Ying Li, Gottfried Kirchengast, Marc Schwärz, Florian Ladstädter, and Yunbin Yuan
Atmos. Meas. Tech., 14, 2327–2343, https://doi.org/10.5194/amt-14-2327-2021, https://doi.org/10.5194/amt-14-2327-2021, 2021
Short summary
Short summary
We introduce a new method to detect and monitor sudden stratospheric warming (SSW) events using Global Navigation Satellite System (GNSS) radio occultation (RO) data at high northern latitudes and demonstrate it for the well-known Jan.–Feb. 2009 event. We found that RO data are capable of SSW monitoring. Based on our method, a SSW event can be detected and tracked, and the duration and the strength of the event can be recorded. The results are consistent with other research on the 2009 event.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2065–2093, https://doi.org/10.5194/amt-14-2065-2021, https://doi.org/10.5194/amt-14-2065-2021, 2021
Short summary
Short summary
A LiDAR Statistical Barnes Objective Analysis (LiSBOA) for the optimal design of lidar scans and retrieval of velocity statistics is proposed. The LiSBOA is validated and characterized via a Monte Carlo approach applied to a synthetic velocity field. The optimal design of lidar scans is formulated as a two-cost-function optimization problem, including the minimization of the volume not sampled with adequate spatial resolution and the minimization of the error on the mean of the velocity field.
Stefano Letizia, Lu Zhan, and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 2095–2113, https://doi.org/10.5194/amt-14-2095-2021, https://doi.org/10.5194/amt-14-2095-2021, 2021
Short summary
Short summary
The LiDAR Statistical Barnes Objective Analysis (LiSBOA) is applied to lidar data collected in the wake of wind turbines to reconstruct mean wind speed and turbulence intensity. Various lidar scans performed during a field campaign for a wind farm in complex terrain are analyzed. The results endorse the application of the LiSBOA for lidar-based wind resource assessment and farm diagnosis.
Viktor A. Banakh, Igor N. Smalikho, and Andrey V. Falits
Atmos. Meas. Tech., 14, 1511–1524, https://doi.org/10.5194/amt-14-1511-2021, https://doi.org/10.5194/amt-14-1511-2021, 2021
Matteo Puccioni and Giacomo Valerio Iungo
Atmos. Meas. Tech., 14, 1457–1474, https://doi.org/10.5194/amt-14-1457-2021, https://doi.org/10.5194/amt-14-1457-2021, 2021
Short summary
Short summary
A procedure for correcting the turbulent-energy damping connected with spatial averaging of wind lidars is proposed. This effect of the lidar measuring process is modeled through a low-pass filter, whose order and cut-off frequency are estimated directly from the lidar data. The proposed procedure is first assessed through simultaneous and colocated lidar and sonic-anemometer measurements. Then it is applied to several datasets collected at sites with different terrain roughness.
E. Eva Borbas, Elisabeth Weisz, Chris Moeller, W. Paul Menzel, and Bryan A. Baum
Atmos. Meas. Tech., 14, 1191–1203, https://doi.org/10.5194/amt-14-1191-2021, https://doi.org/10.5194/amt-14-1191-2021, 2021
Short summary
Short summary
As the VIIRS satellite sensor has no infrared (IR) H2O absorption bands, we construct the missing bands through the fusion of imager (VIIRS) and sounder (CrIS) data in an attempt to improve derivation of moisture products. This study clearly demonstrates the positive impact by adding fusion IR absorption spectral bands and the potential for continuing the moisture record from MODIS and the previous generations of polar-orbiting satellite sensors.
Maryna Lukach, David Dufton, Jonathan Crosier, Joshua M. Hampton, Lindsay Bennett, and Ryan R. Neely III
Atmos. Meas. Tech., 14, 1075–1098, https://doi.org/10.5194/amt-14-1075-2021, https://doi.org/10.5194/amt-14-1075-2021, 2021
Short summary
Short summary
This paper presents a novel technique of data-driven hydrometeor classification (HC) from quasi-vertical profiles, where the hydrometeor types are identified from an optimal number of hierarchical clusters, obtained recursively. This data-driven HC approach is capable of providing an optimal number of classes from dual-polarimetric weather radar observations. The embedded flexibility in the extent of granularity is the main advantage of this technique.
Cited articles
Chattopadhay, A., Sarkar, A., Howlader, P., and Balasubramanian, V. N.: Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks, in: 2018 IEEE Winter Conference on Applications of Computer Vision, Lake Tahoe, NV, USA, 12–15 March 2018, IEEE WACV, 839–847, https://doi.org/10.1109/WACV.2018.00097, 2018.
Chen, R., Zhang, W., and Wang, X.: Machine learning in tropical cyclone forecast modeling: A review, Atmosphere-Basel, 11, 676, https://doi.org/10.3390/atmos11070676, 2020.
Dvorak, V. F.:
Tropical cyclone intensity analysis using satellite data, in: NOAA Technical Report NESDIS, 11, US Department of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, Washington, DC, USA, pp. 1–47, 1984.
Goerss, J. S.: Tropical cyclone track forecasts using an ensemble of dynamical models, Mon. Weather Rev., 128, 1187–1193, https://doi.org/10.1175/1520-0493(2000)128<1187:TCTFUA>2.0.CO;2, 2000.
Hanley, J. A. and McNeil, B. J.: The meaning and use of the area under a receiver operating characteristic (ROC) curve, Radiology,
143, 29–36, https://doi.org/10.1148/radiology.143.1.7063747, 2006.
Hayatbini, N., Hsu, K., Sorooshian, S., Zhang, Y., and Zhang, F.:
Effective cloud detection and segmentation using a gradient-based algorithm for satellite imagery: Application to improve PERSIANN-CCS, J. Hydrometeorol., 20, 901–913, https://doi.org/10.1175/JHM-D-18-0197.1, 2019.
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y.: The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, HI, USA, 21–26 July 2017, IEEE CVPRW, 1175–1183, https://doi.org/10.1109/CVPRW.2017.156, 2017.
Krizhevsky, A., Sutskever, I., and Hinton, G. E.: Imagenet classification with deep convolutional neural networks, Adv Neur. Inf., 60, 84–90, https://doi.org/10.1145/3065386, 2019.
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, https://doi.org/10.1038/nature14539, 2015.
Liao, X. L., Tian, Y. G., and Liu, J.: Research on the TC Segmentation Method with Priori Features, Remote Sensing for Land and Resources, 23, 37–42, http://en.cgsjournals.com/zgdzdcqkw-data/gtzyyg/2011/3/PDF/gtzyyg201103007.pdf (last access: 15 March 2022), 2011.
Lin, T., Dollár, P., Girshick, R., He, K., Hariharan, B., and Belongie, S.: Feature pyramid networks for object detection, in: 2017 IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA, 21–26 July 2017, IEEE CVPR, 936–944, https://doi.org/10.1109/CVPR.2017.106, 2017.
Lu, J., Zhang, C. J., Zhang, X., Yang, B., and Duanmu, C.J.: Auto-recognition of typhoon cloud based on boundary features, J. Remote Sens., 14, 990–1003, http://www.gissky.net/paper/UploadFiles_4495/201208/2012080521453763.pdf (last access: 15 March 2022), 2010.
Lu, X., Yu, H., Ying, M., Zhao, B., Zhang, S., Lin, L., Bai, L., and Wan, R.:
Western north pacific tropical cyclone database created by the China meteorological administration, Adv. Atmos. Sci., 38, 690–699, https://doi.org/10.1007/s00376-020-0211-7, 2021 (data available at: http://tcdata.typhoon.org.cn/, last access: 29 November 2021).
Meteorological Satellite Research Cooperation Institute, University of Wisconsin-Madison (CIMSS): Tropical Cyclones ... A Satellite Perspective, CIMSS, http://tropic.ssec.wisc.edu/, last access: 29 November 2021.
Miller, R. J., Schrader, A. J., Sampson, C. R., and Tsui, T. L.: The Automated Tropical Cyclone Forecasting System (ATCF), Weather Forecast.,
5, 653–660, https://doi.org/10.1175/1520-0434(1990)005<0653:TATCFS>2.0.CO;2, 1990.
Molodianovitch, K., Faraggi, D., and Reiser, B.: Comparing the areas under two correlated ROC curves: parametric and nonparametric approaches, Biometrical J., 48, 745–757, https://doi.org/10.1002/bimj.200610223, 2006.
National Institute of Informatics (NII): Digital Typhoon: Typhoon Images and Information, NII, http://agora.ex.nii.ac.jp/digital-typhoon/, last access: 29 November 2021.
Olander, T. L. and Velden, C. S.: The Advanced Dvorak Technique (ADT) for estimating tropical cyclone intensity: Update and new capabilities,
Weather Forecast., 34, 905–922, https://doi.org/10.1175/WAF-D-19-0007.1, 2019.
Pang, S., Xie, P., Xu, D., Meng, F., Tao, X., Li, B., Li, Y., and Song, T.:
NDFTC: A New Detection Framework of Tropical Cyclones from Meteorological Satellite Images with Deep Transfer Learning,
Remote Sens.-Basel,
13, 1860, https://doi.org/10.3390/rs13091860, 2021.
Powers, D.: Evaluation: From Predcision, Recall and F-Factor to ROC, Informedness, Markedness & Correlation, J. Mach. Learn. Technol.,
2, 37–63, https://doi.org/10.9735/2229-3981, 2011.
Ron, K.: A study of cross-validation and bootstrap for accuracy estimation and model selection, in: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence, Montreal, Quebec, Canada, 20–25 August 1995, IJCAI, 1137–1143, 1995.
Sampson, C. R. and Schrader, A. J.: The automated tropical cyclone forecasting system (version 3.2), B. Am. Meteorol. Soc., 81, 1231–1240, https://doi.org/10.1175/1520-0477(2000)081<1231:TATCFS>2.3.CO;2, 2000.
Schmidhuber, J.: Deep learning in neural networks: An overview, Neural Networks, 61, 85–117, https://doi.org/10.1016/j.neunet.2014.09.003, 2015.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D.: Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization, Int. J. Comput. Vision, 128, 336–359, https://doi.org/10.1007/s11263-019-01228-7, 2020.
Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image identification,
arXiv [preprint], arXiv:1409.1556, 10 April 2015.
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R.:
Dropout: A Simple Way to Prevent Neural Networks from Overfitting,
J. Mach. Learn. Res.,
15, 1929–1958, http://jmlr.org/papers/volume15/srivastava14a/srivastava14a.pdf (last access: 2 July 2020), 2014.
Sun, L., Wei, J., Wang, J., Mi, X., Guo, Y., Lv, Y., Yang, Y., Gan, P., Zhou, X., and Jia, C.: A universal dynamic threshold cloud de-tection algorithm (UDTCDA) supported by a prior surface reflectance database, J. Geophys. Res.-Atmos., 121, 7172–7196, https://doi.org/10.1002/2015JD024722, 2016.
Velden, C., Harper, B., Wells, F., Beven, J. L., Zehr, R., Olander, T., Mayfield, M., Guard, C. C., Lander, M., and Edson, R.: The Dvorak tropical cyclone intensity estimation technique: A satellite-based method that has endured for over 30 years, B. Am. Meteorol. Soc., 87, 1195–1210, https://doi.org/10.1175/BAMS-87-9-1195, 2006.
Velden, C. S., Olander, T. L., and Zehr, R. M.: Development of an objective scheme to estimate tropical cyclone intensity from digital geostationary satellite infrared imagery, Weather Forecast., 13, 172–186, https://doi.org/10.1175/1520-0434(1998)013<0172:DOAOST>2.0.CO;2, 1998.
Wang, K., Li, X., and Trinder, J. C.: Mathematical morphological analysis of typical cyclone eyes on ocean SAR, in: 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014, 4393–4396, https://doi.org/10.1109/IGARSS.2014.6947464, 2014.
Wang, Y.: Vortex Rossby waves in a numerically simulated tropical cyclone. Part II: The role in tropical cyclone structure and intensity changes,
J. Atmos. Sci., 59, 1239–1262, https://doi.org/10.1175/1520-0469(2002)059<1239:VRWIAN>2.0.CO;2, 2002.
Ying, M., Zhang, W., Yu, H., Lu, X., Feng, J., Fan, Y., Zhu, Y., and Chen, D.: An overview of the China Meteorological Administration tropical cyclone database, J. Atmos. Ocean. Tech., 31, 287–301, https://doi.org/10.1175/JTECH-D-12-00119.1, 2014 (data available at: http://tcdata.typhoon.org.cn/, last access: 29 November 2021).
Zeiler, M. D. and Fergus, R.: Visualizing and Understanding Convolutional Networks, in: Computer Vision – ECCV 2014, Lecture Notes in Computer Science, chapter 53, Springer, Cham, Switzerland, 818–833, https://doi.org/10.1007/978-3-319-10590-1_53, 2014.
Zhang, K., Zhang, Z., Li, Z., and Qiao, Y.: Joint face detection and alignment using multitask cascaded convolutional networks, IEEE Signal Proc. Let., 23, 1499–1503, https://doi.org/10.1109/LSP.2016.2603342 2016.
Zou, Z., Shi, Z., Guo, Y., and Ye, J.: Object detection in 20 years: A survey, arXiv [preprint], arXiv:1905.05055, 16 May 2019.
Short summary
In recent years, there has been numerous research on tropical cyclone (TC) observation based on satellite cloud images (SCIs), but most methods are limited by low efficiency and subjectivity. To overcome subjectivity and improve efficiency of traditional methods, this paper uses deep learning technology to do further research on fingerprint identification of TCs. Results provide an automatic and objective method to distinguish TCs from SCIs and are convenient for subsequent research.
In recent years, there has been numerous research on tropical cyclone (TC) observation based on...
