Cloud Types Research Articles

Understanding heavy precipitation events in southern Israel through atmospheric electric field observations

Characterizing the interaction between meteorological variables such as humidity, wind speed, cloud cover, and precipitation with the atmospheric electric field is vital for improving the nowcast of extreme weather events such as heavy precipitation. With this aim, we provide minute-scale electric field observations in southern Israel. These were taken during low-pressure weather systems in winter, often termed ‘Cyprus Lows.’ We focus only on precipitating (‘wet’) events, where rain was measured at the surface during and after the cold front's passage. The mean |PG| values for ‘wet’ Cyprus Lows are higher (Hundreds to thousands V m−1) compared with the mean fair-weather values (~100–200 V m−1, and exhibit a sharp and rapid increase of the |PG| of up to tens of V m−1 min−1 during the arrival of the cold front and hundreds of V m−1 min−1 during precipitation. Then, we analyzed selected case studies in detail. The response of the |PG| to thunderstorm clouds, i.e., Cumulonimbus, is an increase to values of thousands of V m−1. The temporal evolution of the |PG| allowed us to identify the type of cloud and its life cycle stage. We suggest that using state-of-the-art 1 Hz measurements of the |PG| and deducing cloud patterns at strategic locations, such as in arid regions like southern Israel, may improve the nowcasting capabilities of localized heavy precipitation events.

Testing Cloud Adjustment Hypotheses for the Maintenance of Earth's Hemispheric Albedo Symmetry With Natural Experiments

AbstractEarth's Northern and Southern Hemispheres reflect essentially equal amounts of sunlight. How—and whether—this hemispheric albedo symmetry is maintained remains a mystery. We decompose Earth's hemispheric albedo symmetry into components associated with the surface, clear‐sky atmosphere, and different cloud types as defined by cloud effective pressure and optical thickness. Climatologically, greater reflection by the surface, aerosols, and high clouds in the Northern Hemisphere is balanced by greater low and mid‐level cloudiness in the Southern Hemisphere. Both hemispheres have darkened at similar rates over the past two decades; whether the darkening from more rapidly declining aerosol in the Northern Hemisphere is causing a departure from all‐sky symmetry remains uncertain. Natural experiments including long‐term trends, sea ice loss, and volcanic eruptions provide strong evidence against the hypothesis that extratropical low clouds compensate changing clear‐sky asymmetries on annual‐to‐decadal timescales but some evidence that tropical high cloud shifts may do so.

Benchmarking of Individual Tree Segmentation Methods in Mediterranean Forest Based on Point Clouds from Unmanned Aerial Vehicle Imagery and Low-Density Airborne Laser Scanning

Three raster-based (RB) and one point cloud-based (PCB) algorithms were tested to segment individual Aleppo pine trees and extract their tree height (H) and crown diameter (CD) using two types of point clouds generated from two different techniques: (1) Low-Density (≈1.5 points/m2) Airborne Laser Scanning (LD-ALS) and (2) photogrammetry based on high-resolution unmanned aerial vehicle (UAV) images. Through intensive experiments, it was concluded that the tested RB algorithms performed best in the case of UAV point clouds (F1-score > 80.57%, H Pearson’s r > 0.97, and CD Pearson´s r > 0.73), while the PCB algorithm yielded the best results when working with LD-ALS point clouds (F1-score = 89.51%, H Pearson´s r = 0.94, and CD Pearson´s r = 0.57). The best set of algorithm parameters was applied to all plots, i.e., it was not optimized for each plot, in order to develop an automatic pipeline for mapping large areas of Mediterranean forests. In this case, tree detection and height estimation showed good results for both UAV and LD-ALS (F1-score > 85% and >76%, and H Pearson´s r > 0.96 and >0.93, respectively). However, very poor results were found when estimating crown diameter (CD Pearson´s r around 0.20 for both approaches).

Cloud classification through machine learning and global horizontal irradiance data analysis

AbstractCloud observations and characterization are crucial owing to their influence on energy balance, climate, and weather. Their particular effects on radiation vary depending on different cloud parameters, such as cloud base or top height, water content, and cloud optical thickness, all of them closely related to the specific cloud type. Cloud classification therefore becomes a crucial task in meteorology, although it remains challenging for weather services worldwide owing to the intensive associated labor and cost. In this study we introduce a new low‐cost method for automating cloud classification based on a combination of ground‐based global horizontal irradiance (GHI) measurements, a clear‐sky model, and machine learning. Based on the hypothesis that different cloud types have their own GHI signatures, we trained different supervised learning algorithms using GHI data manually labeled by meteorological observers from time‐synchronized all‐sky images. Multiple time windows were extracted from each GHI series, with eight features defined in each case to characterize the sequence. The best outcome was achieved using an XGBoost model on features extracted on time windows of 33 min, obtaining an accuracy of 0.88 and a Cohen's kappa of 0.84 in a held‐out test set. The development presented in this study has the ability to provide low‐cost cloud classification from ground‐based observations, which is a challenge for weather services worldwide owing to intensive labor and cost.

CLOWN: The PASO Cloud Detection for Optimization of Automatic Optical Surveys

Orbiting space objects have become in the last decade a major nuisance impacting ground astronomy and orbiting space assets, from observatories to satellites and space stations. In particular with the rise of the satellite population in Low Earth Orbits, space objects are becoming an even bigger threat and a strong problem to astronomical observations. To tackle these threats, several coordinated surveillance networks composed of dedicated sensors (telescopes, radars, and laser ranging facilities) track and survey space objects, from debris to active satellites. As part of the European Space Surveillance & Tracking network, Portugal is developing the Pampilhosa da Serra Space Observatory, with both radio and optical telescopes dedicated to the Space Situational Awareness domain, deployed at a Dark Sky destination. To optimize telescope survey time, we developed CLOud Watcher at Night (CLOWN), an application interface that automatically monitors clouds in real time. This software can correctly trace cloud positions in the sky and provide accurate pointing information to the observation planning of the optical telescope to avoid cloudy areas. CLOWN only requires the use of an all-sky camera, which is already a norm in observatories with optical telescopes and can be used with any camera, including those for which no information about its model specification do exist. CLOWN does not require great computing power, and it does not require the installation of additional equipment. CLOWN results are very promising and confirm that the app can correctly identify clouds in a variety of different conditions and cloud types.

Vertical Profile Analysis of Cloud Feedbacks

AbstractCloud feedback is the largest source of uncertainty in climate sensitivity. This feedback is generally discussed in terms of radiative flux at the top of the atmosphere, but the vertical distribution of the contribution of cloud changes to the top‐of‐atmosphere cloud feedback should be discussed to understand the feedback in greater detail. We have developed a simple analysis method called “vertical profile analysis of cloud feedback,” which simply uses radiative flux data from each level of the model. The analysis is applied for typical cloud regimes and the results are discussed together with cloud fraction change profiles. The advantages and disadvantages of the vertical profile analysis, compared with the commonly used International Satellite Cloud Climatology Project (ISCCP) histogram kernel method, are discussed. Our analysis has the advantage of resolving the detailed vertical profiles of the contribution to the top‐of‐atmosphere cloud feedback from the changes in cloud regimes associated with warming, such as reduced cloud cover and upward shifts of the cloud layer in subtropical low‐cloud regions as well as the increased height of the melting layer in tropical deep convection. The main disadvantage is that the vertical profile analysis cannot represent the feedback components sorted by cloud optical thickness as in the ISCCP histogram kernel method.

Role of midtropospheric heating and surface forcing on monsoon intraseasonal transitions

AbstractMonsoon intraseasonal transitions are intrinsically chaotic; hence, their predictability is limited. In the past the majority of studies were conducted solely keeping in account of dynamic prospective like boreal summer intraseasonal oscillation or Madden–Julian oscillation; however its thermodynamic aspect got limited attention. The current study looks at the internal thermodynamic mechanisms pertaining to monsoon intraseasonal transition using two independent sets of reanalysis data (European Centre for Medium‐range Weather Forecasts Reanalysis version 5 and Indian Monsoon Data Assimilation and Analysis), along with the observed cloud type from Met Office Integrated Data Archive System Land and Marine surface station data. A lead–lag composite analysis shows that the midtropospheric warming by diabatic heating is a thermal control to restrict the deepening of convective cloud (cumulonimbus) as the wet spell progresses. This midtroposhperic heating cuts off the formation of deep convective clouds and results in the formation of relatively shallow clouds, such as stratocumulus and stratus. On the other hand, surface forcing during dry spells humidifies the troposphere by detrainment from the shallow convection with the formation of cumulus and cumulus congestus clouds. This facilitates the preconditioning of the atmosphere for deep convection and results in the onset of wet spells by the formation of cumulonimbus clouds. The comprehensive approach used in this work may further help in identifying the limitation of the predictability in monsoon transition period from the numerical weather prediction models.

The Ice Cloud Imager: retrieval of frozen water column properties

Abstract. The Ice Cloud Imager (ICI) aboard the second generation of the EUMETSAT Polar System (EPS-SG) will provide novel measurements of ice hydrometeors. ICI is a passive conically scanning radiometer that will operate within a frequency range of 183 to 664 GHz, helping to cover the present wavelength gap between microwave and infrared observations. Reliable global data will be produced on a daily basis. This paper presents the retrieval database to be used operationally and performs a final pre-launch assessment of ICI retrievals. Simulations are performed within atmospheric states that are consistent with radar reflectivities and represent the three-dimensional (3D) variability of clouds. The radiative transfer calculations use empirically based hydrometeor models. Azimuthal orientation of particles is mimicked, allowing for the consideration of polarisation. The degrees of freedom (DoFs) of the ICI retrieval database are shown to vary according to cloud type. The simulations are considered to be the most detailed performed to this date. Simulated radiances are shown to be statistically consistent with real observations. Machine learning is applied to perform inversions of the simulated ICI observations. The method used allows for the estimation of non-Gaussian uncertainties for each retrieved case. Retrievals of ice water path (IWP), mean mass height (Zm), and mean mass diameter (Dm) are presented. Distributions and zonal means of both database and retrieved IWP show agreement with DARDAR. Retrieval tests indicate that ICI will be sensitive to IWP between 10−2 and 101 kg m−2. Retrieval performance is shown to vary with climatic region and surface type, with the best performance achieved over tropical regions and over ocean. As a consequence of this study, retrievals from real observations will be possible from day one of the ICI operational phase.

Designing an optimal task scheduling and VM placement in the cloud environment with multi-objective constraints using Hybrid Lemurs and Gannet Optimization Algorithm

ABSTRACT An efficient resource utilization method can greatly reduce expenses and unwanted resources. Typical cloud resource planning approaches lack support for the emerging paradigm regarding asset management speed and optimization. The use of cloud computing relies heavily on task planning and allocation of resources. The task scheduling issue is more crucial in arranging and allotting application jobs supplied by customers on Virtual Machines (VM) in a specific manner. The task planning issue needs to be specifically stated to increase scheduling efficiency. The task scheduling in the cloud environment model is developed using optimization techniques. This model intends to optimize both the task scheduling and VM placement over the cloud environment. In this model, a new hybrid-meta-heuristic optimization algorithm is developed named the Hybrid Lemurs-based Gannet Optimization Algorithm (HL-GOA). The multi-objective function is considered with constraints like cost, time, resource utilization, makespan, and throughput. The proposed model is further validated and compared against existing methodologies. The total time required for scheduling and VM placement is 30.23%, 6.25%, 11.76%, and 10.44% reduced than ESO, RSO, LO, and GOA with 2 VMs. The simulation outcomes revealed that the developed model effectively resolved the scheduling and VL placement issues.

Cloud detection for HY-1C/COCTS over the ocean based on spectral-and-textural-information-guided deep neural network

AbstractGiven that clouds can absorb and scatter radiation signals in the visible and infrared bands, cloud detection is a key preprocessing step for ocean color and sea surface temperature retrievals. In this research, a Spectral-and-Textural-Information-Guided deep neural Network (STIGNet) is designed for cloud detection in global ocean data from the Haiyang-1C (HY-1C)/Chinese Ocean Color and Temperature Scanner (COCTS). Considering the spectral and textural properties of clouds, the model incorporates HY-1C/COCTS spectral data, differences in brightness temperature (BT), local statistical characteristics of BT, and geographical location information–all of which are closely related to cloud features. Notably, an edge learning module is implemented to emphasize edge features during the training process. We construct a HY-1C/COCTS cloud detection dataset to train and test the cloud detection model. In the dataset, labels are generated by combining the Bayesian cloud detection method with a manual mask. Analysis of the resulting cloud detection images indicates that STIGNet exhibits accurate performance across various types of clouds while showing minimal overestimated errors in areas such as ocean fronts or sun glints, where they tend to occur frequently. The ablation experiments performed on physical-based input features and edge learning modules show enhancements in cloud detection accuracy. Evaluation results demonstrate an overall accuracy of 96.64%, with a cloud overestimated error of 1.61% and a cloud missed error of 1.76%. These findings highlight the effectiveness of STIGNet in generating precise cloud masks for HY-1C/COCTS data.

NSEMBLE FINE TUNED MULTI LAYER PERCEPTRON FOR PREDICTIVE ANALYSIS OF WEATHER PATTERNS AND RAINFALL FORECASTING FROM SATELLITE DATA

The accurate prediction of weather patterns and rainfall forecasting is critical for various sectors, including agriculture, disaster management, and water resource planning. Traditional models often struggle to capture the complex interactions between atmospheric variables, particularly when integrating diverse types of satellite data (binary, categorical, and numerical). To address this challenge, an ensemble fine-tuned multi-layer perceptron (MLP) model is developed, combining the strengths of multiple machine learning techniques for more robust predictions. The primary problem is the difficulty in handling mixed data types while maintaining high prediction accuracy. Satellite data, including binary indicators (e.g., cloud presence), categorical features (e.g., cloud types), and numerical variables (e.g., temperature, humidity, and wind speed), provide rich information but require specialized processing for effective forecasting. The proposed method involves fine-tuning an ensemble of MLP models with backpropagation, dropout regularization, and batch normalization to reduce overfitting and enhance generalization. The ensemble integrates predictions from individual MLP models, each trained on different subsets of features (binary, categorical, numerical). This technique allows the model to leverage complementary strengths and produce more accurate rainfall forecasts. Satellite data is preprocessed and normalized before training, and categorical variables are one-hot encoded to ensure compatibility with the MLP architecture. Results from testing on historical satellite weather datasets demonstrate significant improvements in forecast accuracy. The ensemble MLP achieved an accuracy of 91.3%, with a precision of 90.7%, recall of 89.5%, and an F1-score of 90.1%. The model performed exceptionally well in identifying critical rainfall events, reducing false positives by 12% compared to traditional models.

Design and performance of the Climate Change Initiative Biomass global retrieval algorithm

The increase in Earth observations from space in recent years supports improved quantification of carbon storage by terrestrial vegetation and fosters studies that relate satellite measurements to biomass retrieval algorithms. However, satellite observations are only indirectly related to the carbon stored by vegetation. While ground surveys provide biomass stock measurements to act as reference for training the models, they are sparsely distributed. Here, we addressed this problem by designing an algorithm that harnesses the interplay of satellite observations, modeling frameworks and field measurements, and generated global estimates of above-ground biomass (AGB) density that meet the requirements of the scientific community in terms of accuracy, spatial and temporal resolution. The design was adapted to the amount, type and spatial distribution of satellite data available around the year 2020. The retrieval algorithm estimated AGB annually by merging estimates derived from C- and L-band Synthetic Aperture Radar (SAR) backscatter observations with a Water Cloud type of model and does not rely on AGB reference data at the same spatial scale as the SAR data. This model is integrated with functions relating to forest structural variables that were trained on spaceborne LiDAR observations and sub-national AGB statistics. The yearly estimates of AGB were successively harmonized using a cost function that minimizes spurious fluctuations arising from the moderate-to-weak sensitivity of the SAR backscatter to AGB. The spatial distribution of the AGB estimates was correctly reproduced when the retrieval model was correctly set. Over-predictions occasionally occurred in the low AGB range (<50 Mg ha−1) and under-predictions in the high AGB range (>300 Mg ha−1). These errors were a consequence of sometimes too strong generalizations made within the modeling framework to allow reliable retrieval worldwide at the expense of accuracy. The precision of the estimates was mostly between 30% and 80% relative to the estimated value. While the framework is well founded, it could be improved by incorporating additional satellite observations that capture structural properties of vegetation (e.g., from SAR interferometry, low-frequency SAR, or high-resolution observations), a dense network of regularly monitored high-quality forest biomass reference sites, and spatially more detailed characterization of all model parameters estimates to better reflect regional differences.

Removing cloud shadows from ground-based solar imagery

The study and prediction of space weather entails the analysis of solar images showing structures of the Sun’s atmosphere. When imaged from the Earth’s ground, images may be polluted by terrestrial clouds which hinder the detection of solar structures. We propose a new method to remove cloud shadows, based on a U-Net architecture, and compare classical supervision with conditional GAN. We evaluate our method on two different imaging modalities, using both real images and a new dataset of synthetic clouds. Quantitative assessments are obtained through image quality indices (RMSE, PSNR, SSIM, and FID). We demonstrate improved results with regards to the traditional cloud removal technique and a sparse coding baseline, on different cloud types and textures.

Relationship Between Short and Long Wave Radiation with Cloud Cover in Baghdad City

Short waves are waves that have a short wavelength and are characterised by high frequencies. Therefore, they propagate over short distances from the source and are used for wireless communications and radio broadcasting. As for long waves, they have a long wavelength characterised by low frequencies and usually extend far from the source easily. When the clouds prevent sunlight from reaching Earth, it reflects a large part of it and absorbs part of the radiation that passes through it, so that less radiation reaches the Earth’s surface as a result of the clouds. The daily data of the high, medium, and low cloud cover, as well as the daily and annual data of solar radiation (long and short-wave radiation) were extracted from satellite data collected by the European Center for Medium-Range Weather Forecasting (ECMWF) over the Baghdad station at 00:00 am and 12:00 pm for the period (2015-2019). The study aims to find the effect of cloud cover on long and short waves and to find the type of relationship between them. The results showed that. The High and medium clouds are more visible in winter and less in summer. The clouds of all kinds decrease at 00:00 am and can be observed at noon. The relationship between solar radiation and cloud cover is inverse. In 2015 different types of clouds appeared, including low-lying cumulus and cumulus-medium. The radiation of long waves increases in the spring and summer when the amount of solar radiation entering the atmosphere is greater, and therefore the atmosphere derives its heat from these long waves emanating from the surface of the earth, at a time when the air could not absorb the short waves that make up the sun’s rays when it penetrated it. Through the Pearson coefficient test, several results were obtained, including that the relationship between high, medium, and low cloud cover, as well as total solar radiation, showed an inverse association; the more clouds there are, the less solar radiation reaches the earth’s surface.

Climate Bistability at the Inner Edge of the Habitable Zone due to Runaway Greenhouse and Cloud Feedbacks

Understanding the climate dynamics at the inner edge of the habitable zone is crucial for predicting the habitability of rocky exoplanets. Previous studies using global climate models (GCMs) have indicated that planets receiving high stellar flux can exhibit climate bifurcations, leading to bistability between a cold (temperate) and a hot (runaway) climate. However, the mechanism causing this bistability has not been fully explained, in part due to the difficulty associated with inferring mechanisms from small numbers of expensive numerical simulations in GCMs. In this study, we employ a two-column (dayside and nightside), two-layer climate model to investigate the physical mechanisms driving this bistability. Through mechanism-denial experiments, we demonstrate that the runaway greenhouse effect, coupled with a cloud feedback on either the dayside or nightside, leads to climate bistability. We also map out the parameters that control the location of the bifurcations and size of the bistability. This work identifies which mechanisms and GCM parameters control the stellar flux at which rocky planets are likely to retain a hot, thick atmosphere if they experience a hot start. This is critical for the prioritization of targets and interpretation of observations by the James Webb Space Telescope. Furthermore, our modeling framework can be extended to planets with different condensable species and cloud types.

LESA-Net: Semantic segmentation of multi-type road point clouds in complex agroforestry environment

Point-cloud semantic segmentation is a visual task essential for agricultural robots to comprehend natural agroforestry environments. However, owing to the extremely large amount of point-cloud data in agroforestry environments, learning effective features for semantic segmentation from large-scale point clouds is challenging. Therefore, to address this issue and achieve accurate semantic segmentation of different types of road-surface point clouds in large-scale agroforestry environments, this study proposes a point-cloud semantic segmentation network framework based on double-distance self-attention. First, a point-cloud local feature enhancement module is proposed. This module primarily extends the receptive field and enhances the generalizability of multidimensional features by incorporating reflection intensity information and a spatial feature-encoding block that is enhanced with contextual semantic information. Second, we introduce a dual-distance attention pooling (DDAPS) block based on the self-attention mechanism. This block initially learns the feature representation of the local neighborhood of each point through the self-attention mechanism. Then, it uses the DDAPS block to aggregate more discriminative local neighborhood point features. Finally, extensive experimental results on large-scale point-cloud datasets, SemanticKITTI and RELLIS-3D, demonstrate that our algorithm outperforms similar algorithms in large-scale agroforestry environments.

Unambiguous detection of mesospheric CO2 clouds on Mars using 2.7 μm absorption band from the ACS/TGO solar occultations

Mesospheric CO2 clouds are one of two types of carbon dioxide clouds known on Mars. We present observations of mesospheric CO2 clouds made by Atmospheric Chemistry Suite (ACS) onboard the ESA-Roscosmos ExoMars Trace Gas Orbiter (TGO). We analyzed 1663 solar occultation sessions of Thermal InfraRed (TIRVIM) and Middle InfraRed (MIR) channels of ACS covering more than two Martian years that contain spectra of 2.7 μm carbon dioxide ice absorption band. That allowed us to unambiguously discriminate carbon dioxide ice aerosols from mineral dust and water ice aerosols, not relying on the information of atmospheric thermal conditions. CO2 clouds were detected in eleven solar occultation observations at altitudes from 39 km to 90 km. In five cases, there were two or three layers of CO2 clouds that were vertically separated by 5–15 km gaps. Effective radius of CO2 aerosol particles is in the range of 0.1–2.2 μm. Spectra produced by the smallest particles indicate a need for a better resolved CO2 ice refractive index. Nadir optical depth of CO2 clouds is in the range 5 × 10−4–4 × 10−2 at both 2.7 μm and 0.8 μm. Asymmetrical diurnal distribution of detections observed by ACS is potentially due to local time variations of temperature induced by thermal tides. Two out of five cases of carbon dioxide cloud detections made by the TIRVIM instrument reveal the simultaneous presence of CO2 ice and H2O ice aerosols. Temperature profiles measured by the Near InfraRed (NIR) channel of ACS are used to calculate CO2 saturation ratio S at locations of carbon dioxide clouds. Supersaturation S > 1 is detected in only 6 out of 19 cases of CO2 cloud layers; extremely low values of S < 0.1 are found in 9 out of 19 cases.

Forecasting In-Flight Icing over Greece: Insights from a Low-Pressure System Case Study

Forecasting in-flight icing conditions is crucial for aviation safety, particularly in regions with variable and complex meteorological configurations, such as Greece. Icing accretion onto the aircraft’s surfaces is influenced by the presence of supercooled water in subfreezing environments. This paper outlines a methodology of forecasting icing conditions, with the development of the Icing Potential Algorithm that takes into consideration the meteorological scenarios related to icing accretion, using state-of-the-art Numerical Weather Prediction model results, and forming a fuzzy logic tree based on different membership functions, applied for the first time over Greece. The synoptic situation of an organized low-pressure system passage, with occlusion, cold and warm fronts, over Greece that creates dynamically significant conditions for icing formation was investigated. The sensitivity of the algorithm was revealed upon the precipitation, cloud type and vertical velocity effects. It was shown that the greatest icing intensity is associated with single-layer ice and multi-layer clouds that are comprised of both ice and supercooled water, while convectivity and storm presence lead to also enhancing the icing formation. A qualitative evaluation of the results with satellite, radar and METAR observations was performed, indicating the general agreement of the method mainly with the ground-based observations.

Design and experiment of a lightweight cloud particle imager

Abstract Accurate in-situ measurement of cloud microphysical parameters such as particle diameter, number concentration and droplet spectrum distribution are of great significance in cloud physics, climate change, numerical weather forecasting and weather modification. This work describes the design and characterization of a newly developed in situ measurement instrument for cloud and fog named lightweight cloud particle imager (LCPI). The basic measurement principle of LCPI is based on scattering light imaging of particles in a dark field. A ring LED lighting source was designed to improve the image quality. Field measurements were carried out at a high-altitude research station on Lushan Mountain. The measured size distributions showed good agreement with parallel measurements of a cloud droplet spectrometer (fog monitor-120). The field data also showed that LCPI was able to measure the size distribution of cloud droplets from 5 to 300 μm with a high spatial resolution. In addition, another observation experiment carried out in the Tuli River weather station showed that LCPI also had the ability to acquire clear images of ice crystal particles larger than 10 μm. Thus, LCPI provides an opportunity to simultaneously quantify the microphysical structure of different cloud types.

Longwave Radiative Feedback Due To Stratiform and Anvil Clouds

AbstractStudies have implicated the importance of longwave (LW) cloud‐radiative forcing (CRF) in facilitating or accelerating the upscale development of tropical moist convection. While different cloud types are known to have distinct CRF, their individual roles in driving upscale development through radiative feedback is largely unexplored. Here we examine the hypothesis that CRF from stratiform regions has the greatest positive effect on upscale development of tropical convection. We do so through numerical model experiments using convection‐permitting ensemble WRF (Weather Research and Forecasting) simulations of tropical cyclone formation. Using a new column‐by‐column cloud classification scheme, we identify the contributions of five cloud types (shallow, congestus, and deep convective; and stratiform and anvil clouds). We examine their relative impacts on longwave radiation moist static energy (MSE) variance feedback and test the removal of this forcing in additional mechanism‐denial simulations. Our results indicate the importance stratiform and anvil regions in accelerating convective upscale development.