Ground-based Cloud Observations Research Articles

CloudRaednet: residual attention-based encoder–decoder network for ground-based cloud images segmentation in nychthemeron

ABSTRACT Obtaining accurate cloudage information through ground-based cloud observation is of great significance to astronomical telescope observatory site selection. This paper proposes a residual attention-based encoder–decoder network (CloudRAEDNet) for ground-based cloud image segmentation in nychthemeron. CloudRAEDNet uses ImageNet pre-trained ResNet50 as the encoder backbone network, which reduces the number of network training. The network decoder introduces residual modules to solve the problem of network degradation caused by the increase in the number of network layers. CloudRAEDNet connects encoder and decoder through attention gates to suppress the features of irrelevant areas and automatically focus on areas with prominent features. In addition, the segmentation performance of the network is further improved by the ranger optimizer. The comparative experimental results show that CloudRAEDNet can segment the local details of the ground-based cloud images more finely without increasing the time complexity. Compared with CloudSegNet, EFCN, CloudU-Net and CloudU-Netv2, CloudRAEDNet has the best segmentation performance. The results of ablation experiments show that the attention module contributes the most to CloudRAEDNet and the residual module contributes the least to CloudRAEDNet. In addition, the pre-training and Ranger optimizer also contribute to improving the segmentation performance of CloudRAEDNet.

TransCloudSeg: Ground-Based Cloud Image Segmentation With Transformer

Cloud image segmentation plays an important role in ground-based cloud observation. Recently, most existing methods for ground-based cloud image segmentation learn feature representations using the convolutional neural network (CNN), which results in the loss of global information because of the limited receptive field size of the filters in the CNN. In this article, we propose a novel deep model named TransCloudSeg, which makes full use of the advantages of the CNN and transformer to extract detailed information and global contextual information for ground-based cloud image segmentation. Specifically, TransCloudSeg hybridizes the CNN and transformer as the encoders to obtain different features. To recover and fuse the feature maps from the encoders, we design the CNN decoder and the transformer decoder for TransCloudSeg. After obtaining two sets of feature maps from two different decoders, we propose the heterogeneous fusion module to effectively fuse the heterogeneous feature maps by applying the self-attention mechanism. We conduct a series of experiments on Tianjin Normal University large-scale cloud detection database and Tianjin Normal University cloud detection database, and the results show that our method achieves a better performance than other state-of-the-art methods, thus proving the effectiveness of the proposed TransCloudSeg.

Global Land High-Resolution Cloud Climatology Based on an Improved MOD09 Cloud Mask

Clouds play an important role in the energy and moisture cycle of the earth–atmosphere system, which affects many important processes in nature and human societies. However, there are very few fine-grained and high-precision global cloud climatology data available for high-resolution models. In this paper, we produced a fine-grained (1 km resolution) global land cloud climatology (GLHCC) report based on MOD09 cloud masks from 2001 to 2016, with a temporal resolution of 10 days. The two improvements (short-wave infrared and Band 2/6 ratio threshold method) on the original MOD09 cloud mask have reduced the snow, ice, and bright areas mistakenly classified as clouds. The preliminary cloud products undergo the removal of orbital artifacts by Variational Stationary Noise Remover (VSNR) and the removal of abnormal albedo areas to generate the final cloud climatology data. The new product was directly validated by ground-based cloud observations collected from 3777 global weather stations. PATMOS-X from the Advanced Very High Resolution Radiometer (AVHRR) and MOD/MYD35 served as comparison products for consistency check of GLHCC. The assessment results show that GLHCC demonstrated a strong correlation with ground station observations, MOD/MYD35, and PATMOS-X. When the ground observations were taken as the truth value, GLHCC and MOD/MYD35 displayed higher accuracy than PATMOS-X. In most selected interested areas where the three behave differently, GLHCC matched the facts better than MOD/MYD35 and PATMOS-X. The GLHCC can well represent the cloud distribution over the past 16 years and will play an important role in the fine-grained demands of many aspects of nature and human society.

Angular Calibration of Visible and Infrared Binocular All-Sky-View Cameras Using Sun Positions

Visible and infrared binocular all-sky-view cameras can provide continuous and complementary ground-based cloud observations. Accurate angular calibration for every pixel is an essential premise to further cloud analysis and georeferencing. However, most current calibration methods mainly rely on calibration plates, which still remains difficult for simultaneously calibrating visible and infrared binocular cameras, especially with different imaging resolutions. Thus, in this study, we present a simple and convenient angular calibration method for wide field-of-view visible and infrared binocular cameras. Without any extra instruments, the proposed method only utilizes the relation between the angular information of direct sun lights and the projected sun pixel coordinates to compute the geometric imaging parameters of the two cameras. According to the obtained parameters, the pixel-view-angle for the visible and infrared all-sky images is efficiently computed via back projection. Meanwhile, the projected pixel coordinates for the incident lights at any angle can also be computed via reprojection. Experimental results show the effectiveness and accuracy of the proposed angular calibration through the error estimation of reprojection and back projection. As a novel application, we successfully achieve visible and infrared binocular image registration at the pixel level after finishing angular calibration, which not only verifies the accuracy of calibration results, but also contributes to further cloud parameter analysis under these two different imaging features. The registration results, to our knowledge, also provide a reference for the current blank in visible and infrared binocular cloud image registration.

Day and Night Clouds Detection Using a Thermal-Infrared All-Sky-View Camera

The formation and evolution of clouds are associated with their thermodynamical and microphysical progress. Previous studies have been conducted to collect images using ground-based cloud observation equipment to provide important cloud characteristics information. However, most of this equipment cannot perform continuous observations during the day and night, and their field of view (FOV) is also limited. To address these issues, this work proposes a day and night clouds detection approach integrated into a self-made thermal-infrared (TIR) all-sky-view camera. The TIR camera consists of a high-resolution thermal microbolometer array and a fish-eye lens with a FOV larger than 160°. In addition, a detection scheme was designed to directly subtract the contamination of the atmospheric TIR emission from the entire infrared image of such a large FOV, which was used for cloud recognition. The performance of this scheme was validated by comparing the cloud fractions retrieved from the infrared channel with those from the visible channel and manual observation. The results indicated that the current instrument could obtain accurate cloud fraction from the observed infrared image, and the TIR all-sky-view camera developed in this work exhibits good feasibility for long-term and continuous cloud observation.

The influence of water vapor anomalies on clouds and their radiative effect at Ny-Ålesund

Abstract. The occurrence of events with increased and decreased integrated water vapor (IWV) at the Arctic site Ny-Ålesund, their relation to cloud properties, and the surface cloud radiative effect (CRE) is investigated. For this study, we used almost 2.5 years (from June 2016 to October 2018) of ground-based cloud observations processed with the Cloudnet algorithm, IWV from a microwave radiometer (MWR), long-term radiosonde observations, and backward trajectories FLEXTRA. Moist and dry anomalies were found to be associated with North Atlantic flows and air transport within the Arctic region, respectively. The amount of water vapor is often correlated to cloud occurrence, presence of cloud liquid water, and liquid water path (LWP) and ice water path (IWP). In turn, changes in the cloud properties cause differences in surface CRE. During dry anomalies, in autumn, winter, and spring, the mean net surface CRE was lower by 2–37 W m−2 with respect to normal conditions, while in summer the cloud-related surface cooling was reduced by 49 W m−2. In contrast, under moist conditions in summer the mean net surface CRE becomes more negative by 25 W m−2, while in other seasons the mean net surface CRE was increased by 5–37 W m−2. Trends in the occurrence of dry and moist anomalies were analyzed based on a 25-year radiosonde database. Dry anomalies have become less frequent, with rates for different seasons ranging from −12.8 % per decade to −4 % per decade, while the occurrence of moist events has increased at rates from 2.8 % per decade to 6.4 % per decade.

Development of an all-sky imaging system for cloud cover assessment.

This study develops a novel automatic all-sky imaging system, namely, an all-sky camera (ASC) system, for cloud cover assessment. The proposed system does not require conventional solar occulting devices and can capture complete hemispheric sky images. Cloud detection is performed innovatively using a convolutional neural network model (i.e., the optimized U-Net model). Experiments demonstrate that the optimized U-Net model can effectively detect clouds from sky images. In terms of cloud cover, the estimation results of the ASC system exhibit a high correlation with those obtained via manual observation, thereby indicating the applicability of the ASC system in ground-based cloud observation and analysis.

Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations

The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation (COMET, https://doi.org/10.5676/EUM_SAF_CM/CFC_METEOSAT/V001) covering 1991–2015 has been recently released by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF). COMET is derived from the MVIRI and SEVIRI imagers aboard geostationary Meteosat satellites and features a Cloud Fractional Cover (CFC) climatology in high temporal (1 h) and spatial (0.05° × 0.05°) resolution. The CM SAF long-term cloud fraction climatology is a unique long-term dataset that resolves the diurnal cycle of cloudiness. The cloud detection algorithm optimally exploits the limited information from only two channels (broad band visible and thermal infrared) acquired by older geostationary sensors. The underlying algorithm employs a cyclic generation of clear sky background fields, uses continuous cloud scores and runs a naïve Bayesian cloud fraction estimation using concurrent information on cloud state and variability. The algorithm depends on well-characterized infrared radiances (IR) and visible reflectances (VIS) from the Meteosat Fundamental Climate Data Record (FCDR) provided by EUMETSAT. The evaluation of both Level-2 (instantaneous) and Level-3 (daily and monthly means) cloud fractional cover (CFC) has been performed using two reference datasets: ground-based cloud observations (SYNOP) and retrievals from an active satellite instrument (CALIPSO/CALIOP). Intercomparisons have employed concurrent state-of-the-art satellite-based datasets derived from geostationary and polar orbiting passive visible and infrared imaging sensors (MODIS, CLARA-A2, CLAAS-2, PATMOS-x and CC4CL-AVHRR). Averaged over all reference SYNOP sites on the monthly time scale, COMET CFC reveals (for 0–100% CFC) a mean bias of −0.14%, a root mean square error of 7.04% and a trend in bias of −0.94% per decade. The COMET shortcomings include larger negative bias during the Northern Hemispheric winter, lower precision for high sun zenith angles and high viewing angles, as well as an inhomogeneity around 1995/1996. Yet, we conclude that the COMET CFC corresponds well to the corresponding SYNOP measurements, and it is thus useful to extend in both space and time century-long ground-based climate observations.

Cloud Type Classification of Total-Sky Images Using Duplex Norm-Bounded Sparse Coding

Cloud type classification plays an essential role in ground-based cloud observation. However, it is a challenge to accurately identify the cloud categories that involve a large variety of structural patterns and visual appearances. Image representation and classifier are the crucial factors for cloud classification, though they are individually investigated in the literature. This paper proposes a new cloud type classification method using duplex norm-bounded sparse coding (DNSC), which designs image representation and classifier under the same framework, i.e., norm-bounded sparse coding (NSC). NSC is not only used to encode local descriptors, but also well explored to develop an effective classifier. NSC takes both locality and sparseness into account, and it can be benefit to capture discriminative patterns for image representation and have discriminative power for classifier. Furthermore, NSC has closed-form solution and can be computed efficiently. More specifically, DNSC first extracts local descriptor from an input cloud image, and then DNSC forms a holistic representation leveraging NSC and max-pooling strategy. Finally, a classifier is built on the holistic representation using NSC. The proposed DNSC is evaluated on the total-sky cloud image set, and the experimental results demonstrate that DNSC outperforms the state-of-the-art methods and its accuracy increases by about $7\%$ compared with baselines. In addition, the categorywise performance improvement is particularly pronounced over the complex categories, such as Cirriform and Mixed cloudiness.

8-Year ground-based observational analysis about the seasonal variation of the aerosol-cloud droplet effective radius relationship at SGP site

Previous studies have shown the negative or positive relationship between cloud droplet effective radius (re) and aerosol amount based on limited observations, indicative of the uncertainties of this relationship caused by many factors. Using 8-year ground-based cloud and aerosol observations at Southern Great Plain (SGP) site in Oklahoma, US, we here analyze the seasonal variation of aerosol effect on low liquid cloud re. It shows positive instead of negative AOD- re relationship in all seasons except summer. Potential contribution to AOD- re relationship from the precipitable water vapor (PWV) has been analyzed. Results show that the AOD- re relationship is indeed negative in low PWV condition regardless of seasonality, but it turns positive in high PWV condition for all seasons other than summer. The most likely explanation for the positive AOD-re relationship in high PWV condition for spring, fall and winter is that high PWV could promote the growth of cloud droplets by providing sufficient water vapor. The different performance of AOD- re relationship in summer could be related to the much heavier aerosol loading, which makes the PWV not sufficient any more so that the droplets compete water with each other. By limiting the variation of other meteorological conditions such as low tropospheric stability and wind speed near cloud bases, further analysis shows that higher PWVs not only help change AOD- re relationship from negative to positive, but also make cloud depth and cloud top height higher.

From pixels to patches: a cloud classification method based on a bag of micro-structures

Abstract. Automatic cloud classification has attracted more and more attention with the increasing development of whole sky imagers, but it is still in progress for ground-based cloud observation. This paper proposes a new cloud classification method, named bag of micro-structures (BoMS). This method treats an all-sky image as a collection of micro-structures mapped from image patches, rather than a collection of pixels. It represents the image with a weighted histogram of micro-structures. Based on this representation, BoMS recognizes the cloud class of the image by a support vector machine (SVM) classifier. Five classes of sky condition are identified: cirriform, cumuliform, stratiform, clear sky, and mixed cloudiness. BoMS is evaluated on a large data set, which contains 5000 all-sky images captured by a total-sky cloud imager located in Tibet (29.25° N, 88.88° E). BoMS achieves an accuracy of 90.9 % for 10-fold cross-validation, and it outperforms state-of-the-art methods with an increase of 19 %. Furthermore, influence of key parameters in BoMS is investigated to verify their robustness.

Quantitative evaluation of seven optical sensors for cloud microphysical measurements at the Puy-de-Dôme Observatory, France

Abstract. Clouds have an important role in Earth's radiative budget. Since the late 1970s, considerable instrumental developments have been made in order to quantify cloud microphysical and optical properties, for both airborne and ground-based applications. Intercomparison studies have been carried out in the past to assess the reliability of cloud microphysical properties inferred from various measurement techniques. However, observational uncertainties still exist, especially for droplet size distribution measurements and need to be reduced. In this work, we discuss results from an intercomparison campaign, performed at the Puy de Dôme in May 2013. During this campaign, a unique set of cloud instruments was operating simultaneously in ambient air conditions and in a wind tunnel. A Particle Volume Monitor (PVM-100), a Forward Scattering Spectrometer Probe (FSSP), a Fog Monitor (FM-100), and a Present Weather Detector (PWD) were sampling on the roof of the station. Within a wind tunnel located underneath the roof, two Cloud Droplet Probes (CDPs) and a modified FSSP (SPP-100) were operating. The main objectives of this paper are (1) to study the effects of wind direction and speed on ground-based cloud observations, (2) to quantify the cloud parameters discrepancies observed by the different instruments, and (3) to develop methods to improve the quantification of the measurements. The results revealed that all instruments showed a good agreement in their sizing abilities, both in terms of amplitude and variability. However, some of them, especially the FM-100, the FSSP and the SPP, displayed large discrepancies in their capability to assess the magnitude of the total number concentration of the cloud droplets. As a result, the total liquid water content can differ by up to a factor of 5 between the probes. The use of a standardization procedure, based on data of integrating probes (PVM-100 or visibilimeter) and extinction coefficient comparison substantially enhanced the instrumental agreement. During this experiment, the total concentration agreed in variations with the visibilimeter, except for the FSSP, so a corrective factor can be applied and it ranges from 0.44 to 2.2. This intercomparison study highlights the necessity to have an instrument which provides a bulk measurement of cloud microphysical or optical properties during cloud ground-based campaigns. Moreover, the FM and FSSP orientation was modified with an angle ranging from 30 to 90° angle with wind speeds from 3 to 7 m s−1. The results show that the induced number concentration loss is between 29 and 98 % for the FSSP and between 15 and 68 % for the FM-100. In particular, FSSP experiments showed strong discrepancies when the wind speed was lower than 3 m s−1 and/or when the angle between the wind direction and the orientation of the instruments is greater than 30°. An inadequate orientation of the FSSP towards the wind direction leads to an underestimation of the measured effective diameter.

The interdependence of continental warm cloud properties derived from unexploited solar background signals in ground-based lidar measurements

Abstract. We have extensively analysed the interdependence between cloud optical depth, droplet effective radius, liquid water path (LWP) and geometric thickness for stratiform warm clouds using ground-based observations. In particular, this analysis uses cloud optical depths retrieved from untapped solar background signals that are previously unwanted and need to be removed in most lidar applications. Combining these new optical depth retrievals with radar and microwave observations at the Atmospheric Radiation Measurement (ARM) Climate Research Facility in Oklahoma during 2005–2007, we have found that LWP and geometric thickness increase and follow a power-law relationship with cloud optical depth regardless of the presence of drizzle; LWP and geometric thickness in drizzling clouds can be generally 20–40% and at least 10% higher than those in non-drizzling clouds, respectively. In contrast, droplet effective radius shows a negative correlation with optical depth in drizzling clouds and a positive correlation in non-drizzling clouds, where, for large optical depths, it asymptotes to 10 μm. This asymptotic behaviour in non-drizzling clouds is found in both the droplet effective radius and optical depth, making it possible to use simple thresholds of optical depth, droplet size, or a combination of these two variables for drizzle delineation. This paper demonstrates a new way to enhance ground-based cloud observations and drizzle delineations using existing lidar networks.

GROUND-BASED CLOUD OBSERVATION FOR SATELLITE-BASED CLOUD DISCRIMINATION AND ITS VALIDATION

Abstract. Solar radiation is a source of energy for ground surfaces. Clouds control energy budget on both local and global scales. We propose the simple ground-based observation method for various sky conditions instead of the existing visual observation. In the paper, we mainly describe how to discriminate clouds area on whole sky images, how to overlay the cloud areas to the satellite-based images and the test experiment. As for the discrimination of sky conditions, we use Sky Index (SI) and Brightness Index (BI) calculated from whole sky imageries. SI shows the extent of the blueness and grayscale and BI indicates the extent of the brightness. Sun, cloud and blue sky areas are divided by SI and BI threshold. As an extension of ground-based observation to satellite-based observation, clouds area discriminated by ground-based observation overlay to satellite images. Through this experiment, the utilities of ground-based cloud observation are discussed. The proposed ground-based cloud observation method and its satellite-based extension should be connected to satellite products with high quality by implementation of sky and clouds observation covering a wide area.

Variations in Low Cloud Cover over the United States during the Second Half of the Twentieth Century

Abstract Several changes in U.S. observational practice [in particular, the introduction of the Automated Surface Observing System (ASOS) in the early 1990s] have led to a challenging heterogeneity of time series of most ground-based cloud observations. In this article, an attempt is made to preserve/restore the time series of average low cloud cover (LCC) over the country up to the year 2001 using cloud sky condition and cloud-base height information collected in the national archive data and to describe its spatial and temporal variability. The switch from human observations to ASOS can be bridged through the use of frequency of overcast/broken cloudiness. During the past 52 yr, the nationwide LCC appears to exhibit a significant increase but all of this increase occurred prior to the early 1980s and thereafter tends to decrease. This finding is consistent with similar changes in the frequency of days with precipitation. When the cloud-type information was still available (i.e., during the pre-ASOS peri...

A Bispectral Method for Cloud Parameter Determination

Abstract A technique is presented for determining cloud heights and amounts through the use of simultaneous. infrared and visible satellite radiance data. A set of simultaneous equations are developed which solve for cloud-top temperature (Tcld) and cloud amount (Acld) within the geometric field of view of the sensor. The cloud height is determined by comparing Tcld to upper air soundings, An error analysis is also presented showing the accuracy that can he obtained in Tcld and Acld when uncertainties exist in the measured visible and infrared radiances and in the assumptions required. Actual satellite measurements taken from the NOAA Scanning Radiometer (SR) are input into the technique and run for three specific geographical locations during several seasons where ground-based cloud observations are available. Results show an rms error in cloud amount of 0.2 and in cloud height of 0.5 km for a 75 km×75 km area for all cloud types except cirrus. For this case we have developed alternate solutions which ac...