Thick Clouds Research Articles

Impact of aerosols on warm clouds over the Sichuan Basin, China in winter based on the MERRA-2 reanalysis dataset

The impacts of aerosols on low warm clouds over the Sichuan Basin are still not well understood. We used the long-term Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) dataset to examine the relationships between aerosols and warm clouds in winter and explored the influence of meteorological conditions and different types of aerosols. There was a positive relationship between the aerosol optical depth and the cloud effective radius, cloud fraction and cloud optical thickness. However, the cloud top pressure tended to decrease with increasing aerosol optical depth. The influence of aerosols on warm clouds varied with the meteorological conditions. An analysis of the effects of thermal conditions, the vertical velocity and the relative humidity showed that stable atmospheric conditions, strong updrafts and a higher relative humidity increased the cloud fraction. Unstable atmospheric conditions and a high relative humidity also facilitated the formation of higher and thicker clouds. High sulfate aerosols were associated with moist air, a greater cloud fraction, a greater cloud optical thickness and a smaller cloud top pressure. Conversely, there was a smaller cloud fraction and cloud optical thickness and a greater cloud top pressure with drier air when there was a higher load of dust and black carbon aerosols.

Evaluating cloud liquid detection against Cloudnet using cloud radar Doppler spectra in a pre-trained artificial neural network

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 underestimations of liquid-containing cloud layers. Here we evaluate the Cloudnet liquid detection against the approach of Luke et al. (2010) which extracts morphological features in cloud-penetrating cloud radar Doppler spectra measurements in an artificial neural network (ANN) approach to classify liquid beyond full lidar signal attenuation based on the simulation of the two lidar parameters particle backscatter coefficient and particle depolarization ratio. We show that the ANN of Luke et al. (2010) which was trained under Arctic conditions can successfully be applied to observations at the mid-latitudes obtained during the 7-week-long ACCEPT field experiment in Cabauw, the Netherlands, in 2014. In a sensitivity study covering the whole duration of the ACCEPT campaign, different liquid-detection thresholds for ANN-predicted lidar variables are applied and evaluated against the Cloudnet target classification. Independent validation of the liquid mask from the standard Cloudnet target classification against the ANN-based technique is realized by comparisons to observations of microwave radiometer liquid-water path, ceilometer liquid-layer base altitude, and radiosonde relative humidity. In addition, a case-study comparison against the cloud feature mask detected by the space-borne lidar aboard the CALIPSO satellite is presented. Three conclusions were drawn from the investigation. First, it was found that the threshold selection criteria of liquid-related lidar backscatter and depolarization alone control the liquid detection considerably. Second, all threshold values used in the ANN framework were found to outperform the Cloudnet target classification for deep or multi-layer cloud situations where the lidar signal is fully attenuated within low liquid layers and the cloud radar is able to detect the microphysical fingerprint of liquid in higher cloud layers. Third, if lidar data are available, Cloudnet is at least as good as the ANN. The times when Cloudnet outperforms the ANN in liquid detections are often associated with situations where cloud dynamics smear the imprint of cloud microphysics on the radar Doppler spectra.

Lagrangian formation pathways of moist anomalies in the trade-wind region during the dry season: two case studies from EUREC<sup>4</sup>A

Abstract. Shallow clouds in the trade-wind region over the North Atlantic contribute substantially to the global radiative budget. In the vicinity of the Caribbean island of Barbados, they appear in different mesoscale organization patterns with distinct net cloud radiative effects (CREs). Cloud formation processes in this region are typically controlled by the prevailing large-scale subsidence. However, occasionally weather systems from remote origin cause significant disturbances. This study investigates the complex cloud–circulation interactions during the field campaign EUREC4A (Elucidate the Couplings Between Clouds, Convection and Circulation) from 16 January to 20 February 2020, using a combination of Eulerian and Lagrangian diagnostics. Based on observations and ERA5 reanalyses, we identify the relevant processes and characterize the formation pathways of two moist anomalies above the Barbados Cloud Observatory (BCO), one in the lower troposphere (∼ 1000–650 hPa) and one in the middle troposphere (∼ 650–300 hPa). These moist anomalies are associated with strongly negative CRE values and with contrasting long-range transport processes from the extratropics and the tropics, respectively. The first case study about the low-level moist anomaly is characterized by an unusually thick cloud layer, high precipitation totals, and a strongly negative CRE. The formation of the low-level moist anomaly is connected to an extratropical dry intrusion (EDI) that interacts with a trailing cold front. A quasi-climatological (2010-2020) analysis reveals that EDIs lead to different conditions at the BCO depending on how they interact with the associated trailing cold front. Based on this climatology, we discuss the relevance of the strong large-scale forcing by EDIs for the low-cloud patterns near the BCO and the related CRE. The second case study about the mid-tropospheric moist anomaly is associated with an extended and persistent mixed-phase shelf cloud and the lowest daily CRE value observed during the campaign. The formation of the mid-level moist anomaly is linked to “tropical mid-level detrainment” (TMD), which refers to detrainment from tropical deep convection near the melting layer. The quasi-climatological analysis shows that TMDs consistently lead to mid-tropospheric moist anomalies over the BCO and that the detrainment height controls the magnitude of the anomaly. However, no systematic relationship was found between the amplitude of this mid-tropospheric moist anomaly and the CRE at the BCO. This is most likely due to the modulation of the CRE by above and below lying clouds and the fact that we used daily mean CREs, thereby ignoring the impact of the timing of the synoptic anomaly with respect to the daily cycle. Overall, this study reveals the important impact of the long-range moisture transport, driven by dynamical processes either in the extratropics or the tropics, on the variability of the vertical structure of moisture and clouds, and on the resulting CRE in the North Atlantic winter trades.

Vertical Distributions of Aerosol and Cloud Microphysical Properties and the Aerosol Impact on a Continental Cumulus Cloud Based on Aircraft Measurements From the Loess Plateau of China

Based on aircraft measurements of aerosols and continental cumulus clouds made over the Loess Plateau of China (Xinzhou, Shanxi Province) on 30 July 2020, this study focuses on the vertical profiles of microphysical properties of aerosols and cumulus clouds, and use them to study aerosol-cloud interactions. During the study period, the boundary layer was stable with a height ∼1,500 m above sea level. Aerosols in the boundary layer mainly came from local emissions, while aerosols above this layer were mostly dust aerosols transported over long distances. Vertical profiles of aerosols and cloud condensation nuclei were obtained, and aerosol activation ratios at different supersaturation (SS) levels ranged from 0.16 to 0.32 at 0.2% SS and 0.70 to 0.85 at 0.8% SS. A thick cumulus cloud in the development stage was observed from the bottom to the top with the horizontal dimension of 10 km by 7 km, the cloud-base height of 2,450 m (15.8°C), and the cloud-top height of 5,400 m (−3°C). The maximum updraft velocity near the cloud top was 13.45 m s−1, and the maximum downdraft velocity occuring in the upper-middle part of the cloud was 4.44 ms−1. The temperature inside the cloud was higher than the outside, with their difference being positively correlated with the cloud water content. The temperature lapse rate inside the cloud was about −6.5°C km−1. The liquid water content and droplet effective radius (Re) increased with increasing height. The cloud droplet number concentration (Nc) increased first then decreased, peaking in the middle and lower part of the cloud, the average values of Nc and Re were 767.9 cm−3 and 5.17 μm, respectively. The cloud droplet spectrum had a multi-peak distribution, with the first appearing at ∼4.5 μm. SS in the cloud first increased then decreased with height. The maximum SS is ∼0.7% appearing at ∼3,800 m. The conversion rate of intra-cloud aerosols to cloud droplets was between 0.2 and 0.54, with the ratio increasing gradually with increasing height. The cloud droplet spectral dispersion and Nc were positively correlated. The aerosol indirect effect (AIE) was estimated to be 0.245 and 0.16, based on Nc and Re, respectively. The cloud droplet dispersion mainly attenuated the AIE, up to ∼34.7%.

Modeled and observed properties related to the direct aerosol radiative effect of biomass burning aerosol over the southeastern Atlantic

Abstract. Biomass burning smoke is advected over the southeastern Atlantic Ocean between July and October of each year. This smoke plume overlies and mixes into a region of persistent low marine clouds. Model calculations of climate forcing by this plume vary significantly in both magnitude and sign. NASA EVS-2 (Earth Venture Suborbital-2) ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) had deployments for field campaigns off the west coast of Africa in 3 consecutive years (September 2016, August 2017, and October 2018) with the goal of better characterizing this plume as a function of the monthly evolution by measuring the parameters necessary to calculate the direct aerosol radiative effect. Here, this dataset and satellite retrievals of cloud properties are used to test the representation of the smoke plume and the underlying cloud layer in two regional models (WRF-CAM5 and CNRM-ALADIN) and two global models (GEOS and UM-UKCA). The focus is on the comparisons of those aerosol and cloud properties that are the primary determinants of the direct aerosol radiative effect and on the vertical distribution of the plume and its properties. The representativeness of the observations to monthly averages are tested for each field campaign, with the sampled mean aerosol light extinction generally found to be within 20 % of the monthly mean at plume altitudes. When compared to the observations, in all models, the simulated plume is too vertically diffuse and has smaller vertical gradients, and in two of the models (GEOS and UM-UKCA), the plume core is displaced lower than in the observations. Plume carbon monoxide, black carbon, and organic aerosol masses indicate underestimates in modeled plume concentrations, leading, in general, to underestimates in mid-visible aerosol extinction and optical depth. Biases in mid-visible single scatter albedo are both positive and negative across the models. Observed vertical gradients in single scatter albedo are not captured by the models, but the models do capture the coarse temporal evolution, correctly simulating higher values in October (2018) than in August (2017) and September (2016). Uncertainties in the measured absorption Ångstrom exponent were large but propagate into a negligible (<4 %) uncertainty in integrated solar absorption by the aerosol and, therefore, in the aerosol direct radiative effect. Model biases in cloud fraction, and, therefore, the scene albedo below the plume, vary significantly across the four models. The optical thickness of clouds is, on average, well simulated in the WRF-CAM5 and ALADIN models in the stratocumulus region and is underestimated in the GEOS model; UM-UKCA simulates cloud optical thickness that is significantly too high. Overall, the study demonstrates the utility of repeated, semi-random sampling across multiple years that can give insights into model biases and how these biases affect modeled climate forcing. The combined impact of these aerosol and cloud biases on the direct aerosol radiative effect (DARE) is estimated using a first-order approximation for a subset of five comparison grid boxes. A significant finding is that the observed grid box average aerosol and cloud properties yield a positive (warming) aerosol direct radiative effect for all five grid boxes, whereas DARE using the grid-box-averaged modeled properties ranges from much larger positive values to small, negative values. It is shown quantitatively how model biases can offset each other, so that model improvements that reduce biases in only one property (e.g., single scatter albedo but not cloud fraction) would lead to even greater biases in DARE. Across the models, biases in aerosol extinction and in cloud fraction and optical depth contribute the largest biases in DARE, with aerosol single scatter albedo also making a significant contribution.

Thick Cloud Removal in Optical Remote Sensing Images Using a Texture Complexity Guided Self-Paced Learning Method

Thick clouds seriously impact the quality of optical remote sensing images (RSIs) and limit their application. For removing the cloud, some learning-based methods have been proposed and attracted considerable attention. However, these methods need to train paired multitemporal images with/without cloud, which are difficult and costly to collect. To solve this problem, we propose a novel texture complexity-guided self-paced learning (SPL) framework to remove the thick cloud from single RSIs. The framework does not need paired images and it exploits a texture complexity-guided mechanism to rank the self-generated cloud-corrupted training samples by texture complexity from low to high and then trains the generative adversarial cloud removal network using the SPL technique. In this way, the cloud removal network learns to restore the cloud-corrupted areas from easy to hard and thus to realize the image reconstruction for different difficulty levels. In addition, we introduce a structural similarity (SSIM) loss function to optimize the training network and improve the coherence of the image structure. Simulated and real experiments are performed on the single images acquired by Gao Fen-1 (GF-1) and Sentinel-2 satellites to validate the effectiveness of the proposed method. The results show that the proposed method has a better performance in cloud removal than other state-of-the-art methods, especially for the images of the areas with complex textures. The source codes are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/GeoX-Lab/TPL</uri> .

Reconstructing Missing Information of Remote Sensing Data Contaminated by Large and Thick Clouds Based on an Improved Multitemporal Dictionary Learning Method

The presence of clouds and cloud shadows has limited the applications of optical remote sensing data. Currently, most cloud removal methods are focused on reconstructing remote sensing data contaminated by small or thin clouds. This study proposes an improved method based on multitemporal dictionary learning to reconstruct missing information of remote sensing data contaminated by large and thick clouds. First, the contaminated target image is initialized using all available adjacent cloud-free reference images. Second, reconstructed images from each of the reference images are produced using dictionary learning and sparse representation methods. Then, weights are determined for the abovementioned reconstructed images based on their reconstruction errors over uncontaminated regions and are used to generate the preliminary reconstruction result. Finally, an error correction step for the contaminated regions is applied to the preliminary result, which is then combined with the original uncontaminated pixels to produce the final reconstruction result. The proposed method was evaluated on simulated clouds/cloud shadows based on remote sensing data with various sizes and land cover types. Visual and quantitative analyses of the reconstruction results show that the proposed method outperformed the generally used geostatistical neighborhood similar pixel interpolator (GNSPI) and nonnegative matrix factorization and error correction (S-NMF-EC) methods. Therefore, the results indicated that the proposed method was capable of accurately and effectively reconstructing data contaminated by large and thick clouds.

PLFM: Pixel-Level Merging of Intermediate Feature Maps by Disentangling and Fusing Spatial and Temporal Data for Cloud Removal

Cloud removal is a relevant topic in Remote Sensing, fostering medium- and high-resolution optical image usability for Earth monitoring and study. Recent applications of deep generative models and sequence-to-sequence-based models have proved their capability to advance the field significantly. Nevertheless, there are still some gaps: the amount of cloud coverage, the landscape temporal changes, and the density and thickness of clouds need further investigation. We fill some of these gaps in this work by introducing an innovative deep model. The proposed model is multi-modal, relying on both spatial and temporal sources of information to restore the whole optical scene of interest. We use the outcomes of both temporal-sequence blending and direct translation from Synthetic Aperture Radar (SAR) to optical images to obtain a pixel-wise restoration of the whole scene. The reconstructed images preserve scene details without resorting to a considerable portion of a clean image. Our approach’s advantage is demonstrated across various atmospheric conditions tested on different datasets. Quantitative and qualitative results prove that the proposed method obtains cloud-free images coping with landscape changes.

An Improved Algorithm for Estimating Surface Shortwave Radiation: Preliminary Evaluation With MODIS Products

Cloud parameters, as key inputs in radiative transfer algorithms, have a critical impact on surface shortwave radiation (SSR) computation. By introducing a parameterization of cloud transmittance and reflectance, based on radiative transfer simulations, this study improves the accuracy of an existing physically based model which severely underestimates SSR under thick cloud conditions. The cloud parameterization adopts the single-layer cloud model and simulates cloud transmittances and reflectances by varying cloud optical thickness, cloud particle size, and solar zenith angle. The revised model is applied to estimate instantaneous SSR using Moderate-resolution Imaging Spectroradiometer (MODIS) atmospheric and land products. The retrieved SSR is evaluated against observation data from 41 Baseline Surface Radiation Network (BSRN) stations and is also compared with the MODIS official SSR product. The root mean square error (RMSE) of the estimated instantaneous radiation is approximately 52 and 98 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−2</sup> under clear-sky and all-sky conditions, respectively. The accuracy of the improved parameterization is higher than that of the original model, and there is no obvious underestimation of SSR in the case of high cloud optical thickness. Therefore, the new algorithm improves the accuracy of SSR estimates in the presence of thick clouds. Retrievals with the improved model also achieve higher accuracy than the MODIS official SSR product (MCD18A1). Finally, the reliable performance of the scheme at most BSRN stations illustrates that the improved model can be used to map SSR on a global scale.

Prospects for Water Vapor Detection in the Atmospheres of Temperate and Arid Rocky Exoplanets around M-dwarf Stars

Abstract Detection of water vapor in the atmospheres of temperate, rocky exoplanets would be a major milestone on the path toward characterization of exoplanet habitability. Past modeling work has shown that cloud formation may prevent the detection of water vapor on Earth-like planets with surface oceans using the James Webb Space Telescope (JWST). Here we analyze the potential for atmospheric detection of H2O on a different class of targets: arid planets. Using transit spectrum simulations, we show that atmospheric H2O may be easier to detect on arid planets with cold-trapped ice deposits on the surface because such planets will not possess thick H2O cloud decks that limit the transit depth of spectral features. However, additional factors such as band overlap with CO2 and other gases, extinction by mineral dust, overlap of stellar and planetary H2O lines, and the ultimate noise floor obtainable by JWST still pose important challenges. For this reason, a combination of space- and ground-based spectroscopic observations will be essential for reliable detection of H2O on rocky exoplanets in the future.

Experimental determination of the lower explosion limit for two gasoline samples

The explosive atmosphere may be caused by flammable gases / vapours or combustible dust. If the amount of the substance, mixed with air, is sufficient, then a source of ignition is needed to cause an explosion. Liquids (for example petrol and other fuels) and solvents from industrial products emit flammable vapours which, when mixed with air, can ignite or explode. At normal temperatures, flammable liquids can emit enough vapours to form combustible mixtures with air, heat, and often thick, black, and toxic clouds of smoke. The behaviour of a fuel-oxidant mixture is characterized by certain explosions parameters, including explosion limits, which characterize the range of concentrations in which combustion propagates at very high speeds. For this work were performed experimental determination of the lower explosion limit for two commercial gasoline samples.

Transient obscuration event captured in NGC 3227

The relationship between warm absorber (WA) outflows of active galactic nuclei and nuclear obscuration activities caused by optically thick clouds (obscurers) crossing the line of sight is still unclear. NGC 3227 is a suitable target for studying the properties of both WAs and obscurers because it matches the following selection criteria: WAs in both ultraviolet (UV) and X-rays, suitably variable, bright in UV and X-rays, and adequate archival spectra for making comparisons with the obscured spectra. In the aim of investigating WAs and obscurers of NGC 3227 in detail, we used a broadband spectral-energy-distribution model that is built in findings of the first paper in our series together with the photoionization code of SPEX software to fit the archival observational data taken by XMM-Newton and NuSTAR in 2006 and 2016. Using unobscured observations, we find four WA components with different ionization states (log ξ [erg cm s−1] ∼ −1.0, 2.0, 2.5, 3.0). The highest-ionization WA component has a much higher hydrogen column density (∼1022 cm−2) than the other three components (∼1021 cm−2). The outflow velocities of these WAs range from 100 to 1300 km s−1, and show a positive correlation with the ionization parameter. These WA components are estimated to be distributed from the outer region of the broad line region (BLR) to the narrow line region. It is worth noting that we find an X-ray obscuration event in the beginning of the 2006 observation, which was missed by previous studies. We find that it can be explained by a single obscurer component. We also study the previously published obscuration event captured in one observation in 2016, which needs two obscurer components to fit the spectrum. A high-ionization obscurer component (log ξ ∼ 2.80; covering factor Cf ∼ 30%) only appears in the 2016 observation, which has a high column density (∼1023 cm−2). A low-ionization obscurer component (log ξ ∼ 1.0 − 1.9; Cf ∼ 20%−50%) exists in both 2006 and 2016 observations, which has a lower column density (∼1022 cm−2). These obscurer components are estimated to reside within the BLR by their crossing time of transverse motions. The obscurers of NGC 3227 are closer to the center and have larger number densities than the WAs, which indicate that the WAs and obscurers might have different origins.

Deep-Learning-Based Spatio-Temporal-Spectral Integrated Fusion of Heterogeneous Remote Sensing Images

It is a challenging task to integrate the spatial, temporal, and spectral information of multi-source remote sensing images, especially in the case of heterogeneous images. To this end, for the first time, this paper proposes a heterogeneous integrated framework based on a novel deep residual cycle generative adversarial network (GAN). The proposed network consists of a forward fusion part and a backward degeneration feedback part. The forward part generates the desired fusion result from the various observations; the backward degeneration feedback part considers the imaging degradation process and regenerates the observations inversely from the fusion result. The heterogeneous integrated fusion framework supported by the proposed network can simultaneously merge the complementary spatial, temporal, and spectral information of multi-source heterogeneous observations to achieve heterogeneous spatio-spectral fusion, spatio-temporal fusion, and heterogeneous spatio-temporal-spectral fusion. Furthermore, the proposed heterogeneous integrated fusion framework can be leveraged to relieve the two bottlenecks of land-cover change and thick cloud cover. Thus, the inapparent and unobserved variation trends of surface features, which are caused by the low-resolution imaging and cloud contamination, can be detected and reconstructed well. Images from many different remote sensing satellites, i.e., Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat 8, Sentinel-1, and Sentinel-2, were utilized in the experiments conducted in this study, and both the qualitative and quantitative evaluations confirmed the effectiveness of the proposed image fusion method.

SI-SA GAN: A Generative Adversarial Network Combined With Spatial Information and Self-Attention for Removing Thin Cloud in Optical Remote Sensing Images

In agricultural remote sensing monitoring, the quality of optical remote sensing image data acquisition is often affected by climate, and the acquired satellite imagery results usually contain cloud information, which can lead to a lack of ground data information. Unlike thick clouds, the semi-transparent nature of thin clouds prevents thin clouds from completely obscuring the ground scene. In order to remove thin clouds in the cultivated land and restore the real ground information as much as possible, we proposed a cloud removal method of spatial information fusion self-attention generative adversarial network (SI-SA GAN) based on multi-directional perceptual attention and self-attention mechanism. The proposed method identifies and focuses on cloud regions by using the spatial attention, channel attention, and self-attention mechanism, which can enhance image information. The modules of the discriminator utilize residual networks and self-attention non-local neural networks to guide the output of image information. The generative adversarial network (GAN) is applied to remove clouds and restore the corresponding irregular occlusion area according to the depth characteristics of the input information, and a gradient penalty is applied to improve the robustness of the generative network. In this paper, we compared the evaluation indexes of other advanced models, the qualitative and quantitative results of Sentinel-2A datasets and public RICE datasets confirmed that the proposed method could enhance the image quality effectively after cloud removal, and the model has excellent thin cloud removal performance with small-scale training data.

Thin Cloud Removal for Remote Sensing Images Using a Physical-Model-Based CycleGAN With Unpaired Data

Thin cloud removal from remote sensing (RS) images is challenging. Recently, deep-learning-based methods have achieved excellent results using supervised training on paired image data. However, in practice, real paired image data are unavailable. Therefore, in this letter, we propose a novel thin cloud removal method, a physical-model-based CycleGAN (PM-CycleGAN), which can be trained using only unpaired data. The PM-CycleGAN training process comprises forward and backward loops. The forward loop first decomposes a cloudy image into a cloud-free image, thin cloud thickness map, and thickness coefficient using three generators. Then, it combines these three components using a physical model to reconstruct the original cloudy image to obtain the cycle consistency constraint. The backward loop first uses the physical model to synthesize a cloud-free image, thin cloud thickness map, and thickness coefficient into a cloudy image, which are then decomposed into the original three components using the three generators. Visual and quantitative comparisons against several state-of-the-art (SOTA) methods on a cloudy image dataset demonstrated the superiority of PM-CycleGAN.

Inter-Zone Differences of Convective Development in a Convection Outbreak Event over Southeastern Coast of China: An Observational Analysis

Among the densely-populated coastal areas of China, the southeastern coast has received less attention in convective development despite having been suffering from significantly increasing thunderstorm activities. The convective complexity under such a region with extremely complex underlying and convective conditions deserves in-depth observational surveys. This present study examined a high-impact convection outbreak event with over 40 hail reports in the southeastern coast of China on 6 May 2020 by focusing on contrasting the convective development (from convective initiation to supercell occurrences) among three adjacent convection-active zones (north (N), middle (M), and south (S)). The areas from N to S featured overall flatter terrain, higher levels of free convection, lower relative humidity, larger convective inhibition, more convective available potential energy, and greater vertical wind shears. With these mesoscale environmental variations, distinct inter-zone differences in the convective development were observed with the region’s surveillance radar network and the Himawari-8 geostationary satellite. Convection initiated in succession from N to S and began with more warm-rain processes in N and M and more ice-phase processes in S. The subsequent convection underwent more vigorous vertical growth from N to S. The extremely deep convection in S was characterized by the considerably strong precipitation above the freezing level, echo tops of up to 18 km, and a great amount of deep (even overshooting) and thick convective clouds with significant cloud-top glaciation. Horizontal anvil expansion in convective clouds was uniquely apparent over S. From N to S, more pronounced mesocyclone and weak-echo region signatures indicated high risks of severe supercell hailstorms. These results demonstrate the strong linkage between the occurrence likelihood of severe convection and associated weather (such as supercells and hailstones) and the early-stage convective development that can be well-captured by high-resolution observations and may facilitate fine-scale convection nowcasting.

Investigation of Atmospheric Boundary Layer characteristics using Ceilometer Lidar, COSMIC GPS RO satellite, Radiosonde and ERA-5 reanalysis dataset over Western Indian Region

Atmospheric Boundary Layer (ABL) characteristics are investigated using a Ceilometer Lidar over Ahmedabad, a semi-arid region in western India. Strong diurnal variations of ABL are observed during 2019, the observation period. There is a stark winter-summer difference in ABL, with summer Boundary Layer Height (BLH) exceeding winter BLH by 1–1.5 km. ABL usually collapses during monsoon and is equivocal due to the presence of thick clouds on top of ABL. The ABL is thicker during the onset of monsoon in contrast to active monsoon, rises again during the withdrawal of monsoon. Lidar observed ABL has been compared with satellite, radiosonde, and European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA5) dataset. ERA5 shows good agreement with differences within 500 m; radiosonde observations have under-estimated ground-based measurements, especially during summer. Satellite observations highly overestimated BLH. This comparative study reveals the importance of ground-based lidars in continuous monitoring of ABL at high resolution because radiosonde, satellite, and reanalysis datasets have coarser resolutions and sparse observations. Such quantitative evaluation of ABL is formerly unavailable over this region, which can now be used to improve the representation in numerical models and thereby estimates of radiative and climate effects due to ABL.

The Interaction between African Easterly Waves and Different Types of Deep Convection and Its Influence on Atlantic Tropical Cyclones

This study revisited the association of African easterly waves (AEWs) to Atlantic tropical cyclone (TC) development using weather states (WSs) from the International Satellite Cloud Climatology Project, National Hurricane Center best track hurricane data (HURDAT2), and reanalysis products. The WS data are used as a proxy for two different types of deep convection. This study covers July–October 1984–2009. Statistical analysis based on HURDAT2 and objectively tracked AEWs has shown that a small fraction (~20%) of the AEWs that propagate from Africa serve as TC precursors. About 80% of the AEWs from the continent were non-developing. As in the past work, our study showed an important difference between developing and non-developing AEWs. Composites based on developing AEWs revealed well-organized large scale deep convection (one type, composed of mesoscale systems and thick anvil clouds) is tightly coupled to the AEW trough, while scattered, less well-organized deep convection (second type, isolated cumulonimbus and cumulus congestus clouds) dominated a large area downstream of the developing AEW trough. Developing AEWs propagate westwards while strengthening. In contrast, non-developing AEWs showed that the peak well-organized deep convection is located either behind (to the east of) or far ahead (to the west) of the AEW trough (peaks values are not in close proximity). Moreover, well-organized deep convections associated with non-developing AEWs were weaker than those associated with developing AEWs. The results indicated that convective activity ahead of the non-developing AEWs is weak. Positive relative humidity (RH) anomalies dominate the area around AEWs and downstream over the main TC development region. In contrast, negative RH dominated the main TC development region ahead of non-developing AEWs, suggesting an unfavorable environment downstream of the AEWs. The results also showed that developing AEWs maintained stronger features in the lower and middle troposphere, while non-developing AEWs exhibited weaker structures, in agreement with past work.

Afterpulse correction for micro-pulse lidar to improve middle and upper tropospheric aerosol measurements

Micro-pulse lidar (MPL) measurements have been widely used in atmospheric research over the past few decades. However, the MPL afterpulse noise has a large impact on the MPL aerosol measurement in the middle and upper troposphere, and an effective correction method is still lacking. Here, a new afterpulse correction approach is presented by using measurements with low-level optically thick clouds to act as the lids blocking atmospheric signals beyond the clouds completely. Examples are provided to illustrate the effectiveness of this correction method. Using one-year 2014 MPL measurements at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) North Slope of Alaska (NSA) site, the impact of the correction on the aerosol measurements is quantified. The relative error (RE, %) of attenuated backscattering ratio (ABR) without the afterpulse correction is 30% and 190% at the height of 1.00 km and 9.00 km (AGL), and the RE is larger for weaker aerosol signals (ABR&lt;2). The RE of linear depolarization ratio (LDR), which is significantly higher than that of ABR for a given aerosol layer, is highly non-linear above 3.00 km, with a value of 300%-900% for weak aerosols (ABR&lt;2) above 3.00 km. Therefore, the afterpulse correction is critical for the middle and upper tropospheric aerosol observations. We demonstrated that our afterpulse correction can provide reasonable particle depolarization ratio (PDR) to properly identify dust aerosols. The newly developed method can be applied to long-term MPL measurements to support aerosol and mixed-phase/ice cloud interaction study at the NSA site.

Cloud Detection for Pleiades and SPOT 6/7 Imageries Using Modified K-means and Deep Learning

<p class='IJASEITAbtract'>Cloud detection is one of the important stages in optical remote sensing activities as the cloud's existence interferes with the works. Many methods have been developed to detect the cloud, but it is still a few methods for high-resolution images, which mostly have limited multispectral bands. In this paper, a novel method of cloud detection for the images is proposed by integrating an unsupervised algorithm and deep learning. This method has three main steps: (1) pre-processing; (2) segmentation using modified K-means; and (3) cloud detection using CNN. In the segmentation step, an unsupervised algorithm, K-means is modified and used to divide pixels values into k clusters. Our modified K-means method can separate thin clouds from relative bright objects in gray clusters that will be grouped into potential cloud pixels. Afterward, a design of convolutional neural network (CNN) is used to extract the multi-scale features from each cluster and classify them into two classes: (1) cloud, which consists of thin cloud and thick cloud, and (2) non-cloud. The potential cloud area from the first step is used for guiding the result of CNN to provide accurate cloud areas. Several Pleiades and SPOT 6/7 images were used to test the reliability of the proposed method. As a result, our modified K-means has an improvement to increase the accuracy of the results. The results showed that the proposed method could detect cloud and non-cloud accurately and has the highest accuracy of the results compared to the other methods.