Log in

Relevant bibliographies by topics / CLOUDLET CLASSIFICATION / Journal articles

To see the other types of publications on this topic, follow the link: CLOUDLET CLASSIFICATION.

Journal articles on the topic 'CLOUDLET CLASSIFICATION'

Author: Grafiati

Published: 11 September 2023

Last updated: 31 July 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'CLOUDLET CLASSIFICATION.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Ashima, Ashima, and Vikramjit Singh. "A NOVEL APPROACH OF JOB ALLOCATION USING MULTIPLE PARAMETERS IN IN CLOUD ENVIRONMENT." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 17, no. 1 (2018): 7103–10. http://dx.doi.org/10.24297/ijct.v17i1.7004.

Full text

Abstract:

Cloud computing is Internet ("cloud") based development and use of computer technology ("computing"). It is a style of computing in which dynamically scalable and often virtualized resources are provided as a service over the Internet. This research deals with the balancing of work load in cloud environment. Load balancing is one of the essential factors to enhance the working performance of the cloud service provider. Grid computing utilizes the distributed heterogeneous resources in order to support complicated computing problems. Grid can be classified into two types: computing grid and dat

APA, Harvard, Vancouver, ISO, and other styles

2

Deng, Minjie, Yong Han, Yan Liu, et al. "Development of a Novel One-Dimensional Nested U-Net Cloud-Classification Model (1D-CloudNet)." Remote Sensing 17, no. 3 (2025): 519. https://doi.org/10.3390/rs17030519.

Full text

Abstract:

Cloud classification is fundamental to advancing climate research and improving weather forecasting. However, existing cloud classification models are constrained by several limitations. For instance, simple statistical methods depend heavily on prior knowledge, leading to frequent misclassifications in regions with high latitudes or complex terrains. Machine learning approaches based on two-dimensional images face challenges such as data scarcity and high annotation costs, which hinder accurate pixel-level cloud identification. Additionally, single-pixel classification methods fail to effecti

APA, Harvard, Vancouver, ISO, and other styles

3

Kalesse-Los, Heike, Willi Schimmel, Edward Luke, and Patric Seifert. "Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network." Atmospheric Measurement Techniques 15, no. 2 (2022): 279–95. http://dx.doi.org/10.5194/amt-15-279-2022.

Full text

Abstract:

Abstract. Detection of liquid-containing cloud layers in thick mixed-phase clouds or multi-layer cloud situations from ground-based remote-sensing instruments still poses observational challenges, yet improvements are crucial since the existence of multi-layer liquid layers in mixed-phase cloud situations influences cloud radiative effects, cloud lifetime, and precipitation formation processes. Hydrometeor target classifications such as from Cloudnet that require a lidar signal for the classification of liquid are limited to the maximum height of lidar signal penetration and thus often lead to

APA, Harvard, Vancouver, ISO, and other styles

4

Kalesse-Los, Heike, Anton Kötsche, Andreas Foth, Johannes Röttenbacher, Teresa Vogl, and Jonas Witthuhn. "The Virga-Sniffer – a new tool to identify precipitation evaporation using ground-based remote-sensing observations." Atmospheric Measurement Techniques 16, no. 6 (2023): 1683–704. http://dx.doi.org/10.5194/amt-16-1683-2023.

Full text

Abstract:

Abstract. Continuous long-term ground-based remote-sensing observations combined with vertically pointing cloud radar and ceilometer measurements are well suited for identifying precipitation evaporation fall streaks (so-called virga). Here we introduce the functionality and workflow of a new open-source tool, the Virga-Sniffer, which was developed within the framework of RV Meteor observations during the ElUcidating the RolE of Cloud–Circulation Coupling in ClimAte (EUREC4A) field experiment in January–February 2020 in the tropical western Atlantic. The Virga-Sniffer Python package is highly

APA, Harvard, Vancouver, ISO, and other styles

5

Roschke, Johanna, Jonas Witthuhn, Marcus Klingebiel, et al. "Discriminating between “drizzle or rain” and sea salt aerosols in Cloudnet for measurements over the Barbados Cloud Observatory." Atmospheric Measurement Techniques 18, no. 2 (2025): 487–508. https://doi.org/10.5194/amt-18-487-2025.

Full text

Abstract:

Abstract. The highly sensitive Ka-band cloud radar at the Barbados Cloud Observatory (BCO) frequently reveals radar reflectivity signals below −50 dBZ within the convective sub-cloud layer. These so-called haze echoes are signals from hygroscopically grown sea salt aerosols. Within the Cloudnet target classification scheme, haze echoes are generally misclassified as precipitation (target class: “drizzle or rain”). We present a technique to discriminate between “drizzle or rain” and sea salt aerosols in Cloudnet that is applicable to marine Cloudnet sites. The method is based on deriving heuris

APA, Harvard, Vancouver, ISO, and other styles

6

Pîrloagă, Răzvan, Dragoş Ene, Mihai Boldeanu, Bogdan Antonescu, Ewan J. O’Connor, and Sabina Ştefan. "Ground-Based Measurements of Cloud Properties at the Bucharest–Măgurele Cloudnet Station: First Results." Atmosphere 13, no. 9 (2022): 1445. http://dx.doi.org/10.3390/atmos13091445.

Full text

Abstract:

Data collected over a period of 18 months (December 2019–May 2021) at the Bucharest–Măgurele Cloudnet station were analysed for the first time to determine the macrophysical and microphysical cloud properties over this site. A total number of 1,327,680 vertical profiles containing the target classification based on the Cloudnet algorithm were analysed, of which 1,077,858 profiles contained hydrometeors. The highest number of profiles with hydrometeors (>60%) was recorded in December 2020, with hydrometeors being observed mainly below 5 km. Above 5 km, the frequency of occurrence of hydromet

APA, Harvard, Vancouver, ISO, and other styles

7

Kulie, Mark S., Lisa Milani, Norman B. Wood, Samantha A. Tushaus, Ralf Bennartz, and Tristan S. L’Ecuyer. "A Shallow Cumuliform Snowfall Census Using Spaceborne Radar." Journal of Hydrometeorology 17, no. 4 (2016): 1261–79. http://dx.doi.org/10.1175/jhm-d-15-0123.1.

Full text

Abstract:

Abstract The first observationally based near-global shallow cumuliform snowfall census is undertaken using multiyear CloudSat Cloud Profiling Radar observations. CloudSat snowfall observations and snowfall rate estimates from the CloudSat 2C-Snow Water Content and Snowfall Rate (2C-SNOW-PROFILE) product are partitioned between shallow cumuliform and nimbostratus cloud structures by utilizing coincident cloud category classifications from the CloudSat 2B-Cloud Scenario Classification (2B-CLDCLASS) product. Shallow cumuliform (nimbostratus) snowfall events comprise about 36% (59%) of snowfall e

APA, Harvard, Vancouver, ISO, and other styles

8

Liu, Cheng-Chien, Yu-Cheng Zhang, Pei-Yin Chen, et al. "Clouds Classification from Sentinel-2 Imagery with Deep Residual Learning and Semantic Image Segmentation." Remote Sensing 11, no. 2 (2019): 119. http://dx.doi.org/10.3390/rs11020119.

Full text

Abstract:

Detecting changes in land use and land cover (LULC) from space has long been the main goal of satellite remote sensing (RS), yet the existing and available algorithms for cloud classification are not reliable enough to attain this goal in an automated fashion. Clouds are very strong optical signals that dominate the results of change detection if they are not removed completely from imagery. As various architectures of deep learning (DL) have been proposed and advanced quickly, their potential in perceptual tasks has been widely accepted and successfully applied to many fields. A comprehensive

APA, Harvard, Vancouver, ISO, and other styles

9

Zhang, Jinglin, Pu Liu, Feng Zhang, and Qianqian Song. "CloudNet: Ground‐Based Cloud Classification With Deep Convolutional Neural Network." Geophysical Research Letters 45, no. 16 (2018): 8665–72. http://dx.doi.org/10.1029/2018gl077787.

Full text

APA, Harvard, Vancouver, ISO, and other styles

10

Behrangi, Ali, Terry Kubar, and Bjorn Lambrigtsen. "Phenomenological Description of Tropical Clouds Using CloudSat Cloud Classification." Monthly Weather Review 140, no. 10 (2012): 3235–49. http://dx.doi.org/10.1175/mwr-d-11-00247.1.

Full text

Abstract:

Abstract Two years of tropical oceanic cloud observations are analyzed using the operational CloudSat cloud classification product and Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar. Relationships are examined between cloud types, sea surface temperature (SST), and location during the CloudSat early morning and afternoon overpasses. Based on CloudSat and combined lidar–radar products, the maximum and minimum cloud fractions occur at SSTs near 303 and 299 K, respectively, corresponding to deep convective/detrained cloud populations and the transition from sha

APA, Harvard, Vancouver, ISO, and other styles

11

Schimmel, Willi, Heike Kalesse-Los, Maximilian Maahn, et al. "Identifying cloud droplets beyond lidar attenuation from vertically pointing cloud radar observations using artificial neural networks." Atmospheric Measurement Techniques 15, no. 18 (2022): 5343–66. http://dx.doi.org/10.5194/amt-15-5343-2022.

Full text

Abstract:

Abstract. In mixed-phase clouds, the variable mass ratio between liquid water and ice as well as the spatial distribution within the cloud plays an important role in cloud lifetime, precipitation processes, and the radiation budget. Data sets of vertically pointing Doppler cloud radars and lidars provide insights into cloud properties at high temporal and spatial resolution. Cloud radars are able to penetrate multiple liquid layers and can potentially be used to expand the identification of cloud phase to the entire vertical column beyond the lidar signal attenuation height, by exploiting morp

APA, Harvard, Vancouver, ISO, and other styles

12

Casey, S. P. F., E. J. Fetzer, and B. H. Kahn. "Revised identification of tropical oceanic cumulus congestus as viewed by CloudSat." Atmospheric Chemistry and Physics Discussions 11, no. 5 (2011): 14883–902. http://dx.doi.org/10.5194/acpd-11-14883-2011.

Full text

Abstract:

Abstract. Congestus cloud convective features are examined in one year of tropical oceanic cloud observations from the CloudSat/CALIPSO instruments. Two types of convective clouds (cumulus and deep convective, based on classification profiles from radar), and associated differences in radar reflectivity and radar/lidar cloud-top height are considered. Congestus convective features are defined as contiguous convective clouds with heights between 3 and 9 km. A majority of congestus convective features satisfy one of three criteria used in previous studies: (1) CloudSat and CALIPSO cloud-top heig

APA, Harvard, Vancouver, ISO, and other styles

13

Wang, Yuanmou, Chunmei Hu, Zhi Ding, Zhiyi Wang, and Xuguang Tang. "All-Day Cloud Classification via a Random Forest Algorithm Based on Satellite Data from CloudSat and Himawari-8." Atmosphere 14, no. 9 (2023): 1410. http://dx.doi.org/10.3390/atmos14091410.

Full text

Abstract:

It remains challenging to accurately classify complicated clouds owing to the various types of clouds and their distribution on multiple layers. In this paper, multi-band radiation information from the geostationary satellite Himawari-8 and the cloud classification product of the polar orbit satellite CloudSat from June to September 2018 are investigated. Based on sample sets matched by two types of satellite data, a random forest (RF) algorithm was applied to train a model, and a retrieval method was developed for cloud classification. With the use of this method, the sample sets were inverte

APA, Harvard, Vancouver, ISO, and other styles

14

Xie, Qinghua, Jinfei Wang, Chunhua Liao, et al. "On the Use of Neumann Decomposition for Crop Classification Using Multi-Temporal RADARSAT-2 Polarimetric SAR Data." Remote Sensing 11, no. 7 (2019): 776. http://dx.doi.org/10.3390/rs11070776.

Full text

Abstract:

In previous studies, parameters derived from polarimetric target decompositions have proven as very effective features for crop classification with single/multi-temporal polarimetric synthetic aperture radar (PolSAR) data. In particular, a classical eigenvalue-eigenvector-based decomposition approach named after Cloude–Pottier decomposition (or “H/A/α”) has been frequently used to construct classification approaches. A model-based decomposition approach proposed by Neumann some years ago provides two parameters with very similar physical meanings to polarimetric scattering entropy H and the al

APA, Harvard, Vancouver, ISO, and other styles

15

Casey, S. P. F., E. J. Fetzer, and B. H. Kahn. "Revised identification of tropical oceanic cumulus congestus as viewed by CloudSat." Atmospheric Chemistry and Physics 12, no. 3 (2012): 1587–95. http://dx.doi.org/10.5194/acp-12-1587-2012.

Full text

Abstract:

Abstract. Congestus cloud convective features are examined in one year of tropical oceanic cloud observations from the CloudSat/CALIPSO instruments. Two types of convective clouds (cumulus and deep convective, based on classification profiles from radar), and associated differences in radar reflectivity and radar/lidar cloud-top height are considered. Congestus convective features are defined as contiguous convective clouds with heights between 3 and 9 km. Three criteria were used in previous studies to identify congestus: (1) CloudSat and CALIPSO cloud-top heights less than 1 km apart; (2) Cl

APA, Harvard, Vancouver, ISO, and other styles

16

Wu, Zhenjiang, Jiahua Zhang, Fan Deng, et al. "Fusion of GF and MODIS Data for Regional-Scale Grassland Community Classification with EVI2 Time-Series and Phenological Features." Remote Sensing 13, no. 5 (2021): 835. http://dx.doi.org/10.3390/rs13050835.

Full text

Abstract:

Satellite-borne multispectral data are suitable for regional-scale grassland community classification owing to comprehensive coverage. However, the spectral similarity of different communities makes it challenging to distinguish them based on a single multispectral data. To address this issue, we proposed a support vector machine (SVM)–based method integrating multispectral data, two-band enhanced vegetation index (EVI2) time-series, and phenological features extracted from Chinese GaoFen (GF)-1/6 satellite with (16 m) spatial and (2 d) temporal resolution. To obtain cloud-free images, the Enh

APA, Harvard, Vancouver, ISO, and other styles

17

Unglaub, Claudia, Karoline Block, Johannes Mülmenstädt, Odran Sourdeval, and Johannes Quaas. "A new classification of satellite-derived liquid water cloud regimes at cloud scale." Atmospheric Chemistry and Physics 20, no. 4 (2020): 2407–18. http://dx.doi.org/10.5194/acp-20-2407-2020.

Full text

Abstract:

Abstract. Clouds are highly variable in time and space, affecting climate sensitivity and climate change. To study and distinguish the different influences of clouds on the climate system, it is useful to separate clouds into individual cloud regimes. In this work we present a new cloud classification for liquid water clouds at cloud scale defined using cloud parameters retrieved from combined satellite measurements from CloudSat and CALIPSO. The idea is that cloud heterogeneity is a measure that allows us to distinguish cumuliform and stratiform clouds, and cloud-base height is a measure to d

APA, Harvard, Vancouver, ISO, and other styles

18

Yue, Qing, Eric J. Fetzer, Brian H. Kahn, et al. "Cloud-State-Dependent Sampling in AIRS Observations Based on CloudSat Cloud Classification." Journal of Climate 26, no. 21 (2013): 8357–77. http://dx.doi.org/10.1175/jcli-d-13-00065.1.

Full text

Abstract:

Abstract The precision, accuracy, and potential sampling biases of temperature T and water vapor q vertical profiles obtained by satellite infrared sounding instruments are highly cloud-state dependent and poorly quantified. The authors describe progress toward a comprehensive T and q climatology derived from the Atmospheric Infrared Sounder (AIRS) suite that is a function of cloud state based on collocated CloudSat observations. The AIRS sampling rates, biases, and center root-mean-square differences (CRMSD) are determined through comparisons of pixel-scale collocated ECMWF model analysis dat

APA, Harvard, Vancouver, ISO, and other styles

19

Ustuner, Mustafa, and Fusun Balik Sanli. "Polarimetric Target Decompositions and Light Gradient Boosting Machine for Crop Classification: A Comparative Evaluation." ISPRS International Journal of Geo-Information 8, no. 2 (2019): 97. http://dx.doi.org/10.3390/ijgi8020097.

Full text

Abstract:

In terms of providing various scattering mechanisms, polarimetric target decompositions provide certain benefits for the interpretation of PolSAR images. This paper tested the capabilities of different polarimetric target decompositions in crop classification, while using a recently launched ensemble learning algorithm—namely Light Gradient Boosting Machine (LightGBM). For the classification of different crops (maize, potato, wheat, sunflower, and alfalfa) in the test site, multi-temporal polarimetric C-band RADARSAT-2 images were acquired over an agricultural area near Konya, Turkey. Four dif

APA, Harvard, Vancouver, ISO, and other styles

20

Peterson, Colten A., Qing Yue, Brian H. Kahn, Eric Fetzer, and Xianglei Huang. "Evaluation of AIRS Cloud Phase Classification over the Arctic Ocean against Combined CloudSat–CALIPSO Observations." Journal of Applied Meteorology and Climatology 59, no. 8 (2020): 1277–94. http://dx.doi.org/10.1175/jamc-d-20-0016.1.

Full text

Abstract:

AbstractCloud phase retrievals from the Atmospheric Infrared Sounder (AIRS) are evaluated against combined CloudSat–CALIPSO (CCL) observations using four years of data (2007–10) over the Arctic Ocean. AIRS cloud phase is evaluated over sea ice and open ocean separately using collocated CCL and AIRS fields of view (FOVs). In addition, AIRS and CCL cloud phase occurrences are evaluated seasonally, zonally, and with respect to total column water vapor (TCWV) and the temperature difference between 1000 and 300 hPa (ΔT1000−300). Last, collocated MODIS cloud information is implemented in a 1-month c

APA, Harvard, Vancouver, ISO, and other styles

21

Guillaume, A., B. H. Kahn, Q. Yue, et al. "Horizontal and Vertical Scaling of Cloud Geometry Inferred from CloudSat Data." Journal of the Atmospheric Sciences 75, no. 7 (2018): 2187–97. http://dx.doi.org/10.1175/jas-d-17-0111.1.

Full text

Abstract:

AbstractA method is described to characterize the scale dependence of cloud chord length using cloud-type classification reported with the 94-GHz CloudSat radar. The cloud length along the CloudSat track is quantified using horizontal and vertical structures of cloud classification separately for each cloud type and for all clouds independent of cloud type. While the individual cloud types do not follow a clear power-law behavior as a function of horizontal or vertical scale, a robust power-law scaling of cloud chord length is observed when cloud type is not considered. The exponent of horizon

APA, Harvard, Vancouver, ISO, and other styles

22

Wang, Di, Chang-An Liu, Yan Zeng, Tian Tian, and Zheng Sun. "Dryland Crop Classification Combining Multitype Features and Multitemporal Quad-Polarimetric RADARSAT-2 Imagery in Hebei Plain, China." Sensors 21, no. 2 (2021): 332. http://dx.doi.org/10.3390/s21020332.

Full text

Abstract:

The accuracy of dryland crop classification using satellite-based synthetic aperture radar (SAR) data is often unsatisfactory owing to the similar dielectric properties that exist between the crops and their surroundings. The main objective of this study was to improve the accuracy of dryland crop (maize and cotton) classification by combining multitype features and multitemporal polarimetric SAR (PolSAR) images in Hebei plain, China. Three quad-polarimetric RADARSAT-2 scenes were acquired between July and September 2018, from which 117 features were extracted using the Cloude–Pottier, Freeman

APA, Harvard, Vancouver, ISO, and other styles

23

Li, J., J. Huang, K. Stamnes, et al. "Distributions and radiative forcings of various cloud types based on active and passive satellite datasets – Part 1: Geographical distributions and overlap of cloud types." Atmospheric Chemistry and Physics Discussions 14, no. 7 (2014): 10463–514. http://dx.doi.org/10.5194/acpd-14-10463-2014.

Full text

Abstract:

Abstract. Based on four year' 2B-CLDCLASS-Lidar (Radar-Lidar) cloud classification product from CloudSat, we analyze the geographical distributions of different cloud types and their co-occurrence frequency across different seasons, moreover, utilize the vertical distributions of cloud type to further evaluate the cloud overlap assumptions. The statistical results show that more high clouds, altocumulus, stratocumulus or stratus and cumulus are identified in the Radar-Lidar cloud classification product compared to previous results from Radar-only cloud classification (2B-CLDCLASS product from

APA, Harvard, Vancouver, ISO, and other styles

24

Irbah, Abdanour, Julien Delanoë, Gerd-Jan van Zadelhoff, et al. "The classification of atmospheric hydrometeors and aerosols from the EarthCARE radar and lidar: the A-TC, C-TC and AC-TC products." Atmospheric Measurement Techniques 16, no. 11 (2023): 2795–820. http://dx.doi.org/10.5194/amt-16-2795-2023.

Full text

Abstract:

Abstract. The EarthCARE mission aims to probe the Earth's atmosphere by measuring cloud and aerosol profiles using its active instruments, the Cloud Profiling Radar (CPR) and ATmospheric LIDar (ATLID). The correct identification of hydrometeors and aerosols from atmospheric profiles is an important step in retrieving the properties of clouds, aerosols and precipitation. Ambiguities in the nature of atmospheric targets can be removed using the synergy of collocated radar and lidar measurements, which is based on the complementary spectral response of radar and lidar relative to atmospheric targ

APA, Harvard, Vancouver, ISO, and other styles

25

Zhang, Chengwei, Xiaoyong Zhuge, and Fan Yu. "Development of a high spatiotemporal resolution cloud-type classification approach using Himawari-8 and CloudSat." International Journal of Remote Sensing 40, no. 16 (2019): 6464–81. http://dx.doi.org/10.1080/01431161.2019.1594438.

Full text

APA, Harvard, Vancouver, ISO, and other styles

26

Xiang, Hongmao, Shanwei Liu, Ziqi Zhuang, and Naixin Zhang. "A classification algorithm based on Cloude decomposition model for fully polarimetric SAR image." IOP Conference Series: Earth and Environmental Science 46 (November 2016): 012060. http://dx.doi.org/10.1088/1755-1315/46/1/012060.

Full text

APA, Harvard, Vancouver, ISO, and other styles

27

Skofronick-Jackson, Gail, Mark Kulie, Lisa Milani, Stephen J. Munchak, Norman B. Wood, and Vincenzo Levizzani. "Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective." Journal of Applied Meteorology and Climatology 58, no. 7 (2019): 1429–48. http://dx.doi.org/10.1175/jamc-d-18-0124.1.

Full text

Abstract:

AbstractRetrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification

APA, Harvard, Vancouver, ISO, and other styles

28

Szyga-Pluta, Katarzyna. "Circulation Influence On Cloudiness In Poznań." Quaestiones Geographicae 34, no. 3 (2015): 141–49. http://dx.doi.org/10.1515/quageo-2015-0021.

Full text

Abstract:

Abstract The frequency of occurrence of cloud cover was analysed taking into consideration its circulation-related conditioning. The atmospheric circulation types according to Osuchowska-Klein (1978) classification were used. The study was made based on diurnal climatological observations carried out in Poznań-Ławica in years 1966–1998. It was found that the cloudless skies and small cloudiness were associated with anticyclonic types of atmospheric circulation and the east macrotype. Moderate cloudiness occurred equally at cyclonic and anticyclonic circulation types. Larger cloud coverage of t

APA, Harvard, Vancouver, ISO, and other styles

29

Marinou, Eleni, Kalliopi Artemis Voudouri, Ioanna Tsikoudi, et al. "Geometrical and Microphysical Properties of Clouds Formed in the Presence of Dust above the Eastern Mediterranean." Remote Sensing 13, no. 24 (2021): 5001. http://dx.doi.org/10.3390/rs13245001.

Full text

Abstract:

In this work, collocated lidar–radar observations are used to retrieve the vertical profiles of cloud properties above the Eastern Mediterranean. Measurements were performed in the framework of the PRE-TECT experiment during April 2017 at the Greek atmospheric observatory of Finokalia, Crete. Cloud geometrical and microphysical properties at different altitudes were derived using the Cloudnet target classification algorithm. We found that the variable atmospheric conditions that prevailed above the region during April 2017 resulted in complex cloud structures. Mid-level clouds were observed in

APA, Harvard, Vancouver, ISO, and other styles

30

Afzali Gorooh, Vesta, Subodh Kalia, Phu Nguyen, et al. "Deep Neural Network Cloud-Type Classification (DeepCTC) Model and Its Application in Evaluating PERSIANN-CCS." Remote Sensing 12, no. 2 (2020): 316. http://dx.doi.org/10.3390/rs12020316.

Full text

Abstract:

Satellite remote sensing plays a pivotal role in characterizing hydrometeorological components including cloud types and their associated precipitation. The Cloud Profiling Radar (CPR) on the Polar Orbiting CloudSat satellite has provided a unique dataset to characterize cloud types. However, data from this nadir-looking radar offers limited capability for estimating precipitation because of the narrow satellite swath coverage and low temporal frequency. We use these high-quality observations to build a Deep Neural Network Cloud-Type Classification (DeepCTC) model to estimate cloud types from

APA, Harvard, Vancouver, ISO, and other styles

31

Jiang, Yuhang, Wei Cheng, Feng Gao, et al. "A Cloud Classification Method Based on a Convolutional Neural Network for FY-4A Satellites." Remote Sensing 14, no. 10 (2022): 2314. http://dx.doi.org/10.3390/rs14102314.

Full text

Abstract:

The study of cloud types is critical for understanding atmospheric motions and climate predictions; for example, accurately classified cloud products help improve meteorological predicting accuracies. However, the current satellite cloud classification methods generally analyze the threshold change in a single pixel and do not consider the relationship between the surrounding pixels. The classification development relies heavily on human recourses and does not fully utilize the data-driven advantages of computer models. Here, a new intelligent cloud classification method based on the U-Net net

APA, Harvard, Vancouver, ISO, and other styles

32

Shen, Jing, Chao Tao, Ji Qi, and Hao Wang. "Semi-Supervised Convolutional Long Short-Term Memory Neural Networks for Time Series Land Cover Classification." Remote Sensing 13, no. 17 (2021): 3504. http://dx.doi.org/10.3390/rs13173504.

Full text

Abstract:

Time series images with temporal features are beneficial to improve the classification accuracy. For abstract temporal and spatial contextual information, deep neural networks have become an effective method. However, there is usually a lack of sufficient samples in network training: one is the loss of images or the discontinuous distribution of time series data because of the inevitable cloud cover, and the other is the lack of known labeled data. In this paper, we proposed a Semi-supervised convolutional Long Short-Term Memory neural network (SemiLSTM) for time series remote sensing images,

APA, Harvard, Vancouver, ISO, and other styles

33

Hung, Meng-Pai, Wei-Ting Chen, Chien-Ming Wu, Peng-Jen Chen, and Pei-Ning Feng. "Intraseasonal Vertical Cloud Regimes Based on CloudSat Observations over the Tropics." Remote Sensing 12, no. 14 (2020): 2273. http://dx.doi.org/10.3390/rs12142273.

Full text

Abstract:

This study identifies the evolution of tropical vertical cloud regimes (CRs) and their associated heating structures on the intraseasonal time scales. Using the cloud classification retrievals of CloudSat during boreal winter between 2006 and 2017, the CR index is defined as the leading pair of the combined multivariate empirical orthogonal functions of the daily mean frequency of deep, high, and low clouds over the tropical Indian Ocean, Maritime Continents, and the Western Pacific. The principal components of the CR index exhibit robust temporal variance in the 30 to 80 day intraseasonal ban

APA, Harvard, Vancouver, ISO, and other styles

34

Cesana, Grégory, Anthony D. Del Genio, and Hélène Chepfer. "The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)." Earth System Science Data 11, no. 4 (2019): 1745–64. http://dx.doi.org/10.5194/essd-11-1745-2019.

Full text

Abstract:

Abstract. Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CAL

APA, Harvard, Vancouver, ISO, and other styles

35

Mackovjak, Š., C. Martinis, J. Wroten, J. Baumgardner, and M. Mendillo. "Synergy of Traditional Techniques and Convolutional Neural Networks for Classification of Cloudless Conditions by All-Sky Imagers." Publications of the Astronomical Society of the Pacific 137, no. 4 (2025): 045004. https://doi.org/10.1088/1538-3873/adca59.

Full text

Abstract:

Abstract This work explores the capabilities of our two methods for the determination of cloudless conditions from All-Sky Imager (ASI) data. For the first time, it was demonstrated that the combination of a well-established traditional computer vision technique based on the calculation of a Clearness index and a new data-driven method, that utilizes Convolutional Neural Networks, leverages the benefits of both methods and suppresses their individual disadvantages. The developed tool is reliable and efficient, allowing us to study the occurrence of clear sky conditions over an excellent astron

APA, Harvard, Vancouver, ISO, and other styles

36

Jing, S., and T. Chao. "TIME SERIES LAND COVER CLASSIFICATION BASED ON SEMI-SUPERVISED CONVOLUTIONAL LONG SHORT-TERM MEMORY NEURAL NETWORKS." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 14, 2020): 1521–28. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-1521-2020.

Full text

Abstract:

Abstract. Time series imagery containing high-dimensional temporal features are conducive to improving classification accuracy. With the plenty accumulation of historical images, the inclusion of time series data becomes available to utilize, but it is difficult to avoid missing values caused by cloud cover. Meanwhile, seeking a large amount of training labels for long time series also makes data collection troublesome. In this study, we proposed a semi-supervised convolutional long short-term memory neural network (Semi-LSTM) in long time series which achieves an accurate and automated land c

APA, Harvard, Vancouver, ISO, and other styles

37

Chen, Sijie, Chonghui Cheng, Xingying Zhang, et al. "Construction of Nighttime Cloud Layer Height and Classification of Cloud Types." Remote Sensing 12, no. 4 (2020): 668. http://dx.doi.org/10.3390/rs12040668.

Full text

Abstract:

A cloud structure construction algorithm adapted for the nighttime condition is proposed and evaluated. The algorithm expands the vertical information inferred from spaceborne radar and lidar via matching of infrared (IR) radiances and other properties at off-nadir locations with their counterparts that are collocated with active footprints. This nighttime spectral radiance matching (NSRM) method is tested using measurements from CloudSat/Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and Moderate Resolution Imaging Spectroradiometer (MODIS). Cloud layer heights a

APA, Harvard, Vancouver, ISO, and other styles

38

Ghods, S., V. Shojaeddini, and Y. Maghsoudi. "A MODIFIED H-α PLANE FOR THE EXTRACTION OF SCATTERING MECHANISMS FROM DUAL CIRCULAR POLARIZATION SAR DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-1-W5 (December 11, 2015): 237–40. http://dx.doi.org/10.5194/isprsarchives-xl-1-w5-237-2015.

Full text

Abstract:

Cloude–Pottier entropy and α-angle are two important parameters for the interpretation of fully polarimetric data. They indicate the randomness of the polarisation of the back scattered waves and the scattering mechanisms of the targets respectively. For fully polarimetric data the H-α plane is presented which using the borders of it the full polarimetric data can be classified into 8 different physical scattering mechanisms. In recent years new approaches have proposed <i>H</i>-α classification spaces by mapping the points which are belong to each PSMs of FP data into the space of

APA, Harvard, Vancouver, ISO, and other styles

39

Dobrowolska, Ksenia. "Weather Types at Selected Meteorological Stations in Siberia." Bulletin of Geography. Physical Geography Series 7, no. 1 (2014): 81–104. http://dx.doi.org/10.2478/bgeo-2014-0004.

Full text

Abstract:

Abstract This paper presents the structure of weather types at four Siberian synoptic stations: Ostrov Kotelnyj, Verkhoyansk, Oymyakon and Yakutsk. The analysis has been performed on the basis of data published in the Internet database of synoptic messages OGIMET for the period of December 1999 to November 2013. Types of weather were determined based on the modified classification of weather types by Ferdynus (1997, 2004, 2013). The occurrence of particular groups, classes, and types of weather, and sequences of days with predominant weather types was identified. During the research period the

APA, Harvard, Vancouver, ISO, and other styles

40

Görsdorf, Ulrich, Volker Lehmann, Matthias Bauer-Pfundstein, et al. "A 35-GHz Polarimetric Doppler Radar for Long-Term Observations of Cloud Parameters—Description of System and Data Processing." Journal of Atmospheric and Oceanic Technology 32, no. 4 (2015): 675–90. http://dx.doi.org/10.1175/jtech-d-14-00066.1.

Full text

Abstract:

AbstractA 35-GHz radar has been operating at the Meteorological Observatory Lindenberg (Germany) since 2004, measuring cloud parameters continuously. The radar is equipped with a powerful magnetron transmitter and a high-gain antenna resulting in a high sensitivity of −55 dBZ at 5-km height for a 10-s averaging time. The main purpose of the radar is to provide long-term datasets of cloud parameters for model evaluation, satellite validation, and climatological studies. Therefore, the system operates with largely unchanged parameter settings and a vertically pointing antenna. The accuracy of th

APA, Harvard, Vancouver, ISO, and other styles

41

Wiederkehr, Natalia C., Fabio F. Gama, Paulo B. N. Castro, et al. "Discriminating Forest Successional Stages, Forest Degradation, and Land Use in Central Amazon Using ALOS/PALSAR-2 Full-Polarimetric Data." Remote Sensing 12, no. 21 (2020): 3512. http://dx.doi.org/10.3390/rs12213512.

Full text

Abstract:

We discriminated different successional forest stages, forest degradation, and land use classes in the Tapajós National Forest (TNF), located in the Central Brazilian Amazon. We used full polarimetric images from ALOS/PALSAR-2 that have not yet been tested for land use and land cover (LULC) classification, neither for forest degradation classification in the TNF. Our specific objectives were: (1) to test the potential of ALOS/PALSAR-2 full polarimetric images to discriminate LULC classes and forest degradation; (2) to determine the optimum subset of attributes to be used in LULC classification

APA, Harvard, Vancouver, ISO, and other styles

42

Wang, Bo, Mingwei Zhou, Wei Cheng, et al. "An Efficient Cloud Classification Method Based on a Densely Connected Hybrid Convolutional Network for FY-4A." Remote Sensing 15, no. 10 (2023): 2673. http://dx.doi.org/10.3390/rs15102673.

Full text

Abstract:

Understanding atmospheric motions and projecting climate changes depends significantly on cloud types, i.e., different cloud types correspond to different atmospheric conditions, and accurate cloud classification can help forecasts and meteorology-related studies to be more effectively directed. However, accurate classification of clouds is challenging and often requires certain manual involvement due to the complex cloud forms and dispersion. To address this challenge, this paper proposes an improved cloud classification method based on a densely connected hybrid convolutional network. A dens

APA, Harvard, Vancouver, ISO, and other styles

43

Yerichev, V. P., A. A. Vitkov, A. M. Akimov та S. A. Ovsepyan. "Cliniсal aspects of static perimetry in the diagnosis of glaucoma". Modern technologies in ophtalmology 60, № 2 (2025): 53–54. https://doi.org/10.25276/2312-4911-2025-2-53-54.

Full text

Abstract:

Relevance Technologies and research programs are constantly evolving. The latest models of computer perimeters allow for qualitative and quantitative assessment of photosensitivity defects and calculation of perimetric indices [1, 2]. To assess changes in the visual field in computer perimetry, the Single Field Analysis (SFA) protocol for Humphrey Field Analyzer (HFA) and Seven-in-One for Octopus are most often used. The forms of these programs contain the maximum amount of information on one page. In addition to the main data, the protocol contains study quality indicators, the number of fals

APA, Harvard, Vancouver, ISO, and other styles

44

Ceccaldi, M., J. Delanoë, R. J. Hogan, N. L. Pounder, A. Protat, and J. Pelon. "From CloudSat-CALIPSO to EarthCare: Evolution of the DARDAR cloud classification and its comparison to airborne radar-lidar observations." Journal of Geophysical Research: Atmospheres 118, no. 14 (2013): 7962–81. http://dx.doi.org/10.1002/jgrd.50579.

Full text

APA, Harvard, Vancouver, ISO, and other styles

45

Liang, Yao, Xuejin Sun, Steven D. Miller, et al. "Cloud Base Height Estimation from ISCCP Cloud-Type Classification Applied to A-Train Data." Advances in Meteorology 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/3231719.

Full text

Abstract:

Cloud base height (CBH) is an important cloud macro parameter that plays a key role in global radiation balance and aviation flight. Building on a previous algorithm, CBH is estimated by combining measurements from CloudSat/CALIPSO and MODIS based on the International Satellite Cloud Climatology Project (ISCCP) cloud-type classification and a weighted distance algorithm. Additional constraints on cloud water path (CWP) and cloud top height (CTH) are introduced. The combined algorithm takes advantage of active and passive remote sensing to effectively estimate CBH in a wide-swath imagery where

APA, Harvard, Vancouver, ISO, and other styles

46

Pagès, D., J. Calbó, and J. A. González. "Using routine meteorological data to derive sky conditions." Annales Geophysicae 21, no. 3 (2003): 649–54. http://dx.doi.org/10.5194/angeo-21-649-2003.

Full text

Abstract:

Abstract. Sky condition is a matter of interest for public and weather predictors as part of weather analyses. In this study, we apply a method that uses total solar radiation and other meteorological data recorded by an automatic station for deriving an estimation of the sky condition. The impetus of this work is the intention of the Catalan Meteorological Service (SMC) to provide the public with real-time information about the sky condition. The methodology for deriving sky conditions from meteorological records is based on a supervised classification technique called maximum likelihood meth

APA, Harvard, Vancouver, ISO, and other styles

47

Wang, W., and M. Gade. "A NEW SAR CLASSIFICATION SCHEME FOR SEDIMENTS ON INTERTIDAL FLATS BASED ON MULTI-FREQUENCY POLARIMETRIC SAR IMAGERY." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W2 (November 16, 2017): 223–28. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w2-223-2017.

Full text

Abstract:

We present a new classification scheme for muddy and sandy sediments on exposed intertidal flats, which is based on synthetic aperture radar (SAR) data, and use ALOS-2 (L-band), Radarsat-2 (C-band) and TerraSAR-X (X-band) fully polarimetric SAR imagery to demonstrate its effectiveness. Four test sites on the German North Sea coast were chosen, which represent typical surface compositions of different sediments, vegetation, and habitats, and of which a large amount of SAR is used for our analyses. Both Freeman-Durden and Cloude-Pottier polarimetric decomposition are utilized, and an additional

APA, Harvard, Vancouver, ISO, and other styles

48

V, Shashank, Priya D, Dr G. S. Mamatha, and Dr Nagaraju G. "Con-Ker: A Convolutional Neural Network Based Approach for Keratoconus Detection and Classification." Journal of University of Shanghai for Science and Technology 23, no. 07 (2021): 71–81. http://dx.doi.org/10.51201/jusst/21/06472.

Full text

Abstract:

The paper is on the detection of keratoconus a corneal progressive disorder leading to the thinning and also protrusion of the cornea associated with symptoms like astigmatism, increased sensitivity to bright light, glare, clouded vision, eye irritation, and others, In recent times there has been increasing in a number of keratoconus cases. Keratoconus is normally described as a non-inflammatory pathology. The main contribution of the paper is to facilitate detection and also classification of the keratoconus based on the progression using Convolution neural networks. The paper is about the im

APA, Harvard, Vancouver, ISO, and other styles

49

Li, J., J. Huang, K. Stamnes, T. Wang, Q. Lv, and H. Jin. "A global survey of cloud overlap based on CALIPSO and CloudSat measurements." Atmospheric Chemistry and Physics 15, no. 1 (2015): 519–36. http://dx.doi.org/10.5194/acp-15-519-2015.

Full text

Abstract:

Abstract. Using 2B-CLDCLASS-LIDAR (radar–lidar) cloud classification and 2B-FLXHR-LIDAR radiation products from CloudSat over 4 years, this study evaluates the co-occurrence frequencies of different cloud types, analyzes their along-track horizontal scales and cloud radiative effects (CREs), and utilizes the vertical distributions of cloud types to evaluate cloud-overlap assumptions. The statistical results show that high clouds, altostratus (As), altocumulus (Ac) and cumulus (Cu) tend to coexist with other cloud types. However, stratus (St) (or stratocumulus, Sc), nimbostratus (Ns) and convec

APA, Harvard, Vancouver, ISO, and other styles

50

Wang, Wensheng, Martin Gade, Kerstin Stelzer, Jörn Kohlus, Xinyu Zhao, and Kun Fu. "A Classification Scheme for Sediments and Habitats on Exposed Intertidal Flats with Multi-Frequency Polarimetric SAR." Remote Sensing 13, no. 3 (2021): 360. http://dx.doi.org/10.3390/rs13030360.

Full text

Abstract:

We developed an extension of a previously proposed classification scheme that is based upon Freeman–Durden and Cloude–Pottier decompositions of polarimetric Synthetic Aperture Radar (SAR) data, along with a Double-Bounce Eigenvalue Relative Difference (DERD) parameter, and a Random Forest (RF) classifier. The extension was done, firstly, by using dual-copolarization SAR data acquired at shorter wavelengths (C- and X-band, in addition to the previously used L-band) and, secondly, by adding indicators derived from the (polarimetric) Kennaugh elements. The performance of the newly developed class

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!