Distribution Of Clouds Research Articles

Ampycloud: an open-source algorithm to determine cloud base heights and sky coverage fractions from ceilometer data

Abstract. Ceilometers are used routinely at aerodromes worldwide to derive the height and sky coverage fraction of cloud layers. This information, possibly combined with direct observations by human observers, contributes to the production of meteorological aerodrome reports (METARs). Here, we present ampycloud, a new algorithm, and its associated Python package for automatic processing of ceilometer data with the aim of determining the sky coverage fraction and base height of cloud layers above aerodromes. The ampycloud algorithm was developed at the Swiss Federal Office of Meteorology and Climatology (MeteoSwiss) as part of the AMAROC (AutoMETAR/AutoReport rOund the Clock) program to help in the fully automatic production of METARs at Swiss civil aerodromes. ampycloud is designed to work with no direct human supervision. The algorithm consists of three distinct, sequential steps that rely on agglomerative clustering methods and Gaussian mixture models to identify distinct cloud layers from individual cloud base hits reported by ceilometers. The robustness of the ampycloud algorithm stems from the first processing step, which is simple and reliable. It constrains the two subsequent processing steps that are more sensitive but also better suited to handling complex cloud distributions. The software implementation of the ampycloud algorithm takes the form of an eponymous, pip-installable Python package developed on GitHub and made publicly accessible.

Novel Insights on the Dust Distribution in the Zodiacal Dust Cloud from PSP/WISPR Observations at Large Elongations

The Wide-Field Imager for Solar Probe (WISPR) on the Parker Solar Probe (PSP) mission maps the brightness produced by the zodiacal dust cloud (ZDC) from an historically unprecedented viewpoint. The brightness results from the scattering of photospheric light by dust particles in the ZDC, and is called zodiacal light (ZL). We exploit the PSP nominal science encounters in orbits 10 through 16 for an in-depth study of the location and brightness evolution of the symmetry axis of the ZL in images taken with the WISPR outer telescope (WISPR-O). During these 11 day encounters, PSP covered heliocentric distances between 0.25 and 0.0617 au (∼53.78−13.28 R ☉) and ∼255° in helioecliptic longitude from within the orbital plane of Venus. The unique WISPR-O viewpoint, which comprises line-of-sight elongations of 80° ± 27°, has led to further insights about the ZDC. Namely, we find that the gravitational pull of the planets warps the ZDC symmetry plane and shifts the ZDC towards the solar system barycenter, creating an east–west asymmetry in the ZL brightness. Additionally, our analysis provides the first consistent observational evidence of a circumsolar dust enhancement resulting from the sublimation of dust grains at ∼25 R ☉. Overall, the WISPR observations from the PSP platform are opening a new window in the remote sensing of the ZDC.

Collaborative Obstacle Detection for Dual USVs Using MGNN-DANet with Movable Virtual Nodes and Double Attention

To reduce missed detections in LiDAR-based obstacle detection, this paper proposes a dual unmanned surface vessels (USVs) obstacle detection method using the MGNN-DANet template matching framework. Firstly, point cloud templates for each USV are created, and a clustering algorithm extracts suspected targets from the point clouds captured by a single USV. Secondly, a graph neural network model based on the movable virtual nodes is designed, introducing a neighborhood distribution uniformity metric. This model enhances the local point cloud distribution features of the templates and suspected targets through a local sampling strategy. Furthermore, a feature matching model based on double attention is developed, employing self-attention to aggregate the features of the templates and cross-attention to evaluate the similarity between suspected targets and aggregated templates, thereby identifying and locating another USV within the targets detected by each USV. Finally, the deviation between the measured and true positions of one USV is used to correct the point clouds obtained by the other USV, and obstacle positions are annotated through dual-view point cloud clustering. Experimental results show that, compared to single USV detection methods, the proposed method reduces the missed detection rate of maritime obstacles by 7.88% to 14.69%.

Biological template hydrothermal synthesis of hollow La-doped one-dimensional CaMnO3 fibers and their enhanced microwave absorption performance

The advancement of military technology, 5G communication, and electronic equipment has increased the demand for efficient electromagnetic wave absorption materials. Calcium manganate (CaMnO3) is a commonly used dielectric absorber due to its excellent dielectric polarization effect. However, traditional ceramic materials like CaMnO3 have low conductivity, making it difficult to achieve impedance matching and electromagnetic loss simultaneously. In this work, to address this issue and create ideal wave-absorbing materials, one-dimensional La-doped CaMnO3 materials were synthesized using a simple biological template hydrothermal method. By studying the relationship between doping content and electromagnetic parameters, it was found that the La-doped CaMnO3 material exhibited excellent electromagnetic wave absorption properties, with a minimum reflection loss (RLmin) of −73.62 dB and a maximum effective absorption bandwidth of 3.3 GHz in the X-band. The introduction of La elements, which have different electronegativities from Ca ions, altered the electron cloud distribution in the sample, leading to the formation of electric dipoles that disrupt charge distribution and dissipate electromagnetic waves. The dielectric loss in fibrous La-doped CaMnO3 is mainly due to interfacial polarization. The fibrous structure allows for increased contact area with the air medium, while the porous structure introduces air bubbles that alter the dielectric properties between the sample and the air medium. This not only enhances impedance characteristics to some extent but also triggers interfacial polarization. Both of these factors help improve the material's ability to absorb electromagnetic waves. The successful creation of this one-dimensional absorbing material opens up possibilities for developing new one-dimensional absorbing materials using a straightforward method.

Singular-Value-Decomposition-Based Matrix Surgery.

This paper is motivated by the need to stabilise the impact of deep learning (DL) training for medical image analysis on the conditioning of convolution filters in relation to model overfitting and robustness. We present a simple strategy to reduce square matrix condition numbers and investigate its effect on the spatial distributions of point clouds of well- and ill-conditioned matrices. For a square matrix, the SVD surgery strategy works by: (1) computing its singular value decomposition (SVD), (2) changing a few of the smaller singular values relative to the largest one, and (3) reconstructing the matrix by reverse SVD. Applying SVD surgery on CNN convolution filters during training acts as spectral regularisation of the DL model without requiring the learning of extra parameters. The fact that the further away a matrix is from the non-invertible matrices, the higher its condition number is suggests that the spatial distributions of square matrices and those of their inverses are correlated to their condition number distributions. We shall examine this assertion empirically by showing that applying various versions of SVD surgery on point clouds of matrices leads to bringing their persistent diagrams (PDs) closer to the matrices of the point clouds of their inverses.

An Enhanced Predictive Method for Large Droplet Breakage Based on the Discrete Particle Model

Abstract In high-speed flows, a single droplet undergoes fragmentation, yielding numerous child droplets. Conventional breakup models usually group these droplets into a single parcel, neglecting the post-breakup spatial distribution. To address this limitation, detailed numerical simulations have been conducted focusing on the bag and bag-stamen breakup modes of 200 µm- diameter droplet at Weber numbers of 15 and 30. The result shows that the largest length of the child droplets cloud can reach up to 29 relative to the original diameter. Importantly, if the grid size is less than 29 times the droplet diameter, traditional models that use a single parcel for child droplets could produce inaccurate results. To overcome this limitation, a disk-like breakup (DLB) model was introduced. With this model, multiple parcels replace the original droplet and are initialized on a virtual disk. Additionally, partitioned breakup modeling was employed for both bag and bag-stamen breakups. Calculations using the DLB model were compared with the TAB, KH-RT and ETAB models. The result indicates that the DLB model is capable of simulating the spatial distribution of the droplet cloud post-breakup, achieving better agreement with the detailed numerical simulations using the VOF method in this paper and experiments in the literature.

Analysis of hydrogen leakage behavior and risk mitigation measures in a hydrogen refueling station

With the wide application of hydrogen, the research related to 70 MPa pressure level hydrogen storage tank is very important. This paper takes the 70 MPa hydrogen storage tank in the hydrogen refueling station as the research object, and analyses the diffusion characteristics of hydrogen after leakage from the tank under different wind conditions by FLUENT. Studies find that after hydrogen ejects to the obstacle by the effect of high-pressure, it transforms to be dominated by the simple diffusion and the wind condition. The ambient wind helps to reduce the volume fraction of hydrogen. This paper proposes risk mitigation measures based on the distribution of the flammable clouds — installing nitrogen outlets and reducing the height of the enclosure. The volume of flammable clouds is reduced from about 2.72 m3 to less than 0.045 m3 after adding measures, which proves the feasibility of the measures.

A method to assess the cloud-aerosol transition zone from ceilometer measurements

Cloud and aerosol contribution to the Earth's radiative budget constitutes one of the most significant uncertainties in future climate projections. To distinguish between clouds and cloud-free air, atmospheric scientists have been using a wide range of instruments, techniques, and algorithms with various detection thresholds. However, since the change from a cloudy to a cloud-free atmosphere may be gradual and, in some cases, far from obvious, recent research is questioning where the threshold between both phases should be established. These considerations lead to contemplate a transition zone (TZ) between cloud and cloud-free conditions. In the present study, backscatter profiles obtained by a Vaisala CL31 ceilometer were processed to assess the transition zone. First, two widely used cloud detection algorithms were applied and compared: the method provided by the ceilometer manufacturer (Vaisala) and Cloudnetpy, the algorithm from ACTRIS Cloudnet, a project devoted to aerosol, clouds, and trace gases research. Second, a sensitivity analysis was applied to the backscatter and signal-to-noise ratio thresholds used for cloud detection in Cloudnetpy. This methodology has allowed us to assess the vertical distribution of clouds, aerosols, the TZ, and its frequency of occurrence. Results indicate a gradual transition in backscatter retrievals from cloud to cloud-free, where particles detected near cloud boundaries induced higher backscatter values than those found further away. Depending on the thresholds used, we observed a 9.3% (with an estimated range of uncertainty of 5.420%) variation in cloud occurrence which can be attributed to TZ conditions. Analysing the whole backscatter profile, we found as many TZ conditions as cloudy values, which emphasises the importance of studying the vertical distribution of the TZ. Moreover, the analysis of TZ occurrence in height and time revealed that such conditions concentrate below 800m during night periods, although annual height-hour distributions involve a remarkable variability among seasons. These findings highlight the importance of either including an additional phase between ‘pure clouds’ and ‘pure aerosols’ or treating them as a continuum of suspended particles in the atmosphere.

Vision-based relative position and attitude determination of non-cooperative spacecraft using a generative model architecture

A wide range of spacecraft operations in orbit relies on the precise and accurate estimation of the relative attitude and position of target objects. This requirement is especially critical during missions involving non-cooperative targets, where even minor errors can result in mission failure or catastrophic collisions. This paper presents an innovative approach that integrates a novel Point Cloud Reconstruction Generative Adversarial Network (PCR-GAN) with a Generalized Iterative Closest Point (GICP) algorithm to enhance pose estimation of known non-cooperative targets using stereo-camera data. Initially, a machine learning algorithm is employed to extract feature points from the stereo images of the target spacecraft. The PCR-GAN model then is used to improve the resolution and distribution of the point clouds generated, which are subsequently aligned using the GICP algorithm. Experimental results from a testbed implementation demonstrate that this architecture significantly enhances the accuracy of attitude and position estimation, thereby representing a potential tool for improving the efficiency and safety of proximity operations in space.

Bilinear Distance Feature Network for Semantic Segmentation in PowerLine Corridor Point Clouds.

Semantic segmentation of target objects in power transmission line corridor point cloud scenes is a crucial step in powerline tree barrier detection. The massive quantity, disordered distribution, and non-uniformity of point clouds in power transmission line corridor scenes pose significant challenges for feature extraction. Previous studies have often overlooked the core utilization of spatial information, limiting the network's ability to understand complex geometric shapes. To overcome this limitation, this paper focuses on enhancing the deep expression of spatial geometric information in segmentation networks and proposes a method called BDF-Net to improve RandLA-Net. For each input 3D point cloud data, BDF-Net first encodes the relative coordinates and relative distance information into spatial geometric feature representations through the Spatial Information Encoding block to capture the local spatial structure of the point cloud data. Subsequently, the Bilinear Pooling block effectively combines the feature information of the point cloud with the spatial geometric representation by leveraging its bilinear interaction capability thus learning more discriminative local feature descriptors. The Global Feature Extraction block captures the global structure information in the point cloud data by using the ratio between the point position and the relative position, so as to enhance the semantic understanding ability of the network. In order to verify the performance of BDF-Net, this paper constructs a dataset, PPCD, for the point cloud scenario of transmission line corridors and conducts detailed experiments on it. The experimental results show that BDF-Net achieves significant performance improvements in various evaluation metrics, specifically achieving an OA of 97.16%, a mIoU of 77.48%, and a mAcc of 87.6%, which are 3.03%, 16.23%, and 18.44% higher than RandLA-Net, respectively. Moreover, comparisons with other state-of-the-art methods also verify the superiority of BDF-Net in point cloud semantic segmentation tasks.

Application of Mesoporous Carbon-Based Highly Dispersed K-O2 Strong Lewis Base in the Efficient Catalysis of Methanol and Ethylene Carbonate.

As an atom-economical reaction, the direct generation of dimethyl carbonate (DMC) and ethylene glycol (EG) via the transesterification of CH3OH and ethylene carbonate (EC) has several promising applications, but the exploration of carriers with high specific surface areas and novel heterogeneous catalysts with more basic sites remains a long-standing research challenge. For this purpose, herein, a nitrogen-doped mesoporous carbon (NMC, 439 m2/g) based K-O2 Lewis base catalyst (K-O2/NMC) with well-dispersed strongly basic sites (2.23 mmol/g, 84.5%) was designed and synthesized. The compositions and structures of NMC and K-O2/NMC were comprehensively investigated via Fourier transform infrared spectroscopy, X-ray diffraction, X-ray photoelectron spectroscopy, scanning electron microscopy, transmission electron microscopy, N2 adsorption-desorption, CO2 temperature-programmed desorption, and contact angle measurements. The optimal structural configuration and electron cloud distribution of the K-O2/NMC catalyst were simulated using first-principles calculations. The electron transfer predominantly manifested as a flow from K-O to C-O/C-N, and the interatomic interactions between each atom were enhanced and exhibited a tendency for a more stable state after redistribution. Furthermore, the adsorption energies (Eads) of CH3OH at K-O-O and K-O-N sites were -1.4185 eV and -1.3377 eV, respectively, and the O atom in CH3OH exhibited a stronger adsorption tendency for the K atom at the K-O-O site. Under the optimal conditions, the EC conversion, DMC/EG selectivity, and turnover number/frequency were 80.9%, 98.6%/99.4%, and 40.5/60.8 h-1, respectively, with a reaction rate constant (k) of 0.1005 mol/(L·min). Results showed that the heterogeneous K-O2/NMC catalyst prepared herein greatly reduced the reaction cost while guaranteeing the catalytic effect, and the whole system required a lower reaction temperature (65 °C), a shorter reaction time (40 min), and a lower catalyst amount (2.0 wt % of EC). Therefore, K-O2/NMC can be used as a catalyst in different transesterification reactions.

A Deep Learning Lidar Denoising Approach for Improving Atmospheric Feature Detection

Space-based atmospheric backscatter lidars provide critical information about the vertical distribution of clouds and aerosols, thereby improving our understanding of the climate system. They are additionally useful for detecting hazards to aviation and human health, such as volcanic plumes and man-made pollution events. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP, 2006–2023), Cloud-Aerosol Transport System (CATS, 2015–2017), and Advanced Topographic Laser Altimeter System (ATLAS 2018–present) are three such lidars that operated within the past 20 years. The signal-to-noise ratio (SNR) for these lidars is significantly lower in daytime data compared with nighttime data due to the solar background signal increasing the detector response noise. Averaging horizontally across profiles has been the standard way to increase SNR, but this comes at the expense of resolution. Modern, deep learning-based denoising algorithms can be applied to improve the SNR without coarsening resolution. This paper describes how one such model architecture, Dense Dense U-Net (DDUNet), was trained to denoise CATS 1064 nm raw signal data (photon counts) using artificially noised nighttime data. Simulated CATS daytime 1064 nm data were then created to assess the model’s performance. The denoised simulated data increased the daytime SNR by a factor of 2.5 (on average) and decreased minimum detectable backscatter (MDB) to ~7.3×10−4 km−1sr−1, which is lower than the CALIOP 1064 nm night MDB value of 8.6×10−4 km−1sr−1. Layer detection was performed on simulated 2 km horizontal resolution denoised and 60 km averaged data. Despite the finer resolution input, the denoised layers had more true positives, fewer false positives, and an overall Jaccard Index of 0.54 versus 0.44 when compared to the layers detected on averaged data. Layer detection was also performed on a full month of denoised daytime CATS data (Aug. 2015) to detect layers for comparison with CATS standard Level 2 (L2) product layers. The detection on the denoised data yielded 2.33 times more, higher-quality bins within detected layers at 2.7–33 times finer resolution than the CATS L2 products.

Significance of anthropogenic black carbon in modulating atmospheric and cloud properties through aerosol-radiation interaction during a winter-time fog-haze

The present study investigated the aerosol-radiation-cloud interaction of anthropogenic black carbon (BC) during a severe fog-haze event by utilizing WRF-Chem, multi-satellite and in-situ observations, reanalyses datasets, and HYSPLIT model. The WRF-Chem model adequately captured the regional distribution of aerosol, cloud, and meteorological variables over the study domain. The maximum BC surface concentration (≥7 μg m−3) was predominantly evident over densely populated central and lower Indo-Gangetic Plain (IGP) regions. Although smoke aerosols usually persisted within 4 km above the surface at daylight hours, they reached as high as 6 km during nighttime, specifically over the central IGP regions. The heavily influenced areas by BC aerosols expanded to almost the entire landmass and the continental outflow region upon doubling the anthropogenic emission. The geographical distribution of maximum perturbations in net shortwave radiation flux at the surface closely matched the spatial patterns of the highest BC concentration, indicating a crucial role of BC in directly preventing the incoming sunlight from reaching the surface. Consequently, the reduced solar radiation at the ground might result in substantial surface cooling (between −0.3 and −0.5 °C), eventually prohibiting surface evapotranspiration and diminishing the outgoing sensible and latent heat fluxes. Also, more amplified cooling in the polluted atmosphere can prevent further development of the planetary boundary layer (PBL), increasing particle entrapment near the surface via an ‘aerosol-radiation-PBL’ feedback loop. However, the most striking contrast between the two scenarios (typical and polluted) was the prolonged and intensified warming (0.5–0.8 °C) in the mid-troposphere, causing a remarkable drop in moisture (more than two to three times) and hence burn-off of liquid cloud droplets due to the semi-direct effect of increased BC aerosols. The intense mid-tropospheric heating could remarkably enhance the updraft velocity, supporting the vertical transport of the additional moisture to higher altitudes, forming more ice clouds at night. Therefore, the current study highlighted the urgency of mitigating anthropogenic BC emissions in highly polluted regions since increased emissions could considerably worsen severe fog-haze conditions and influence both liquid water and ice clouds, depending on the atmospheric conditions.

Evaluating industrial heritage value using cloud theory and Dempster–Shafer theory

This study proposes a systematic approach to evaluate the value of industrial heritage under conditions of uncertainty. This approach primarily incorporates cloud theory and Dempster–Shafer theory (D–S theory), allowing managers to predict the direction of heritage renovation. Moreover, through this method, corresponding renovation measures and management methods can be formulated based on value levels and probabilities. The normal cloud distribution in cloud theory is more associated with people's judgment of things, while the subordinate cloud is employed to describe the basic probability distribution of factors at different value levels. The D–S theory has considerable advantages in terms of integrating information from multiple sources. This study uses the Shenyang Aircraft Factory as a case study to demonstrate the applicability and effectiveness of both theories. The results demonstrate that the combination of these two models can integrate knowledge and data, and the results of the value assessment model are in good agreement with the actual situation. In addition, this study provides a useful tool for dealing with uncertainty, randomness, and fuzziness in decision-making, making it easier for managers to develop appropriate building renovation plans.

The Direct Radiative Effect of CO2 Increase on Summer Precipitation in North America

AbstractPrecipitation changes in full response to CO2 increase are widely studied but confidence in future projections remains low. Mechanistic understanding of the direct radiative effect of CO2 on precipitation changes, independent from CO2‐induced SST changes, is therefore necessary. Utilizing global atmospheric models, we identify robust summer precipitation decreases across North America in response to direct CO2 forcing. We find that spatial distribution of CO2 forcing at land surface is likely shaped by climatological distribution of water vapor and clouds. This, coupled with local feedback processes, changes in convection, and moisture supply resulting from CO2‐induced circulation changes, could determine North American hydroclimate changes. In central North America, increasing CO2 may decrease summertime precipitation by warming the surface and inducing dry advection into the region to reduce moisture supply. Meanwhile, for the southwest and the east, CO2‐induced shift of subtropical highs generates wet advection, which might mitigate the drying effect from warming.

Earth's Sea Ice Radiative Effect From 1980 to 2023

AbstractSea ice cools Earth by reducing its absorbed solar energy. We combine radiative transfer modeling with satellite‐derived surface albedo, sea ice, and cloud distributions to quantify the top‐of‐atmosphere sea ice radiative effect (SIRE). Averaged over 1980–2023, Arctic and Antarctic SIREs range from −0.64 to −0.86 W m−2 and −0.85 to −0.98 W m−2, respectively, with different cloud data sets and assumptions of climatological versus annually‐varying clouds. SIRE trends, however, are relatively insensitive to these assumptions. Arctic SIRE has weakened quasi‐linearly at a rate of 0.04–0.05 W m−2 decade−1, implying a 21%–27% reduction in the reflective power of Arctic sea ice since 1980. Antarctic sea ice exhibited a regime change in 2016, resulting in 2016–2023 Antarctic and global SIRE being 0.08–0.12 and 0.22–0.27 W m−2 weaker, respectively, relative to 1980–1988. Global sea ice has therefore lost 13%–15% of its planetary cooling effect since the early/mid 1980s, and the implied global sea ice albedo feedback is 0.24–0.38 W m−2 K−1.

Terminal conversion of polyamide membranes by homologous monomer vapors for chlorine-resistant desalination

Water scarcity is a global challenge and severely threatens human development. Membrane desalination shows great potential for producing freshwater from saltwater to alleviate this crisis. However, the dominating polyamide membranes suffer from the issue of poor chlorine resistance, which cause performance deterioration and shortens membrane lifespan. In this study, we report a concept of terminal conversion to design high-performance, antifouling, and especially chlorine-resistant polyamide desalination membranes. This amino-to-carboxyl conversion of commercial membranes can be simply performed by controllable and scalable chemical vapor deposition through exposure in homologous monomer vapor and hydrolysis of unreacted acyl chloride groups. We demonstrate that this conversion not only adjust the electronegativity and hydrophilicity of membranes to boost fouling resistance and reverse osmosis performance, but also tune the reactivity and electron cloud distribution of polyamide networks to restrain chlorine accessibility and substantially improve anti-chlorination property. After extreme chlorination for 240000 ppm h, the resultant membranes show limited rejection decline of only 7.2 %. For high-salinity (35000 ppm) and high-pressure (38 bar) desalination, the modified membranes before and after chlorination for 144000 ppm h still exhibit rejection of 94.5 % and 90.0 % and flux of 75.2 and 71.4 L m−2 h−1, respectively, which are much better than those of the pristine membranes (89.6 % and 63.5 %, and 64.2 and 57.5 L m−2 h−1). Our findings pave an alternative mechanism and strategy to reconstruct polyamide membranes for efficient water desalination.

Using the GEOS-5 Nature Run to Simulate 2053-nm Coherent Doppler Wind Lidar Observations

Abstract Wind observations are a critical part of the current global observation system used for numerical weather prediction (NWP). Wind lidars have been cited as precise instruments that can provide three-dimensional wind measurements. Several studies have conducted observing system experiments (OSEs) with existing lidar observations or observing system simulation experiments (OSSEs) with simulated lidar observations highlighting the benefits of wind lidar measurements to NWP. Previous studies using simulated lidar observations have typically tied aerosol optical properties to functions of relative humidity instead of to aerosol properties. A methodology is presented for simulating wind measurements from a novel 2053-nm lidar using aerosol properties derived using the GEOS-5 nature run, along with estimating winds derived from cloud information. Some assumptions regarding aerosol scattering and the distribution of clouds are explored, along with the role of observation weighting and implications for representativeness error. Results from a preliminary OSSE are presented highlighting the importance of assumptions used to derive data from cloud returns and aerosol scattering. While a longer duration study is required, results show a general reduction in analysis error when lidar measurements are ingested.

Pemanfaatan Data Satelit Himawari-8 dalam Analisis Kejadian Hujan Lebat di Jombang

On February 2, 2021, flooding struck Jombang, damaging 38 houses and public facilities due to heavy rainfall from cumulonimbus clouds. The analysis of convective cloud development using Himawari-8 Satellite with RGB Day Convective Storm, 24H-Microphysics, Airmass, and Cloud Convective Overlay methods revealed that the microphysics phase occurred in two different phases on February 1 and 2. The peak temperature of the Cb cloud during the development phase ranged from -20 to -60°C. Convective cloud clusters originated from the Java Sea and the Southern Sea. The peak cloud formation occurred at 12:00 UTC on February 1 and 2. Increased updraft and thickness intensity strengthened convective cloud formation. During the decay phase, downdraft increased with a decrease in cloud distribution, as observed in the Day Convective Storm and 24H-Microphysics images.

Interpretasi Fase Awan dengan Metode CCO Dan RGB pada Data Satelit Himawari-9 di DKI Jakarta (Studi Kasus: 1 Januari 2023)

Indonesia is the largest archipelagic country in the world so it receives large amounts of energy from solar radiation which makes it the most active convective region in the world. In December and January, DKI Jakarta tends to experience the rainy season. On January 1 2023, BMKG gave an early warning that it would rain in Jakarta. Reporting from several news reports, in the new year 2023, Jakarta was hit by heavy rain. Cloud phase analysis is needed to determine the potential for rain. This research identifies cloud phases using Himawari-9 satellite image data which is then processed using the Cloud Convective Overlays (CCO) method and the Red Green Blue (RGB) method. The CCO method can identify the distribution of cumulonimbus clouds and the RGB method can determine the flow of air masses, the type of air mass (Airmass), identify cloud phase, and the presence of high clouds containing ice (Day Convective Storm, Day Natural Color, and Cloud Phase Distinction ). The data used are bands 3 (0.6 µm), 5 (1.6 µm), 7 (3.7 µm), 8 (6.2 µm), 10 (7.3 µm), 12 (9.6 µm), 13 (10 .4 µm), and 15 (12.4 µm) at 7.00, 8.00, 9.00, 10.00, 11.00, 12.00, 13.00, and 14.00 WIB (GMT+7). The data was processed and visualized using Grid Analysis and Display System (GrADS) and GMSLPD SATAID software.