Observations Of Clouds Research Articles

A Time-Gated, Time-Correlated Single-Photon-Counting Lidar to Observe Atmospheric Clouds at Submeter Resolution

Most lidars used for cloud observations have the range resolution of about 10 m, so they are incapable of resolving submeter-scale processes that are crucial to cloud evolution. This article describes a prototype of a ground-based, vertically pointing, time-gated, time-correlated single-photon-counting lidar (referred to as the T2 lidar) developed to explore atmospheric clouds at range resolution two orders of magnitude finer than traditional atmospheric lidars. The T2 lidar emits green-light pulses (532 nm) at a repetition rate of 20.6 kHz and a pulse width of ∼650 ps, which enables the observation of aerosol and cloud layers at heights from a few hundred meters to 7.28 km above the ground level at range resolution down to 10 cm. In addition, a digital delay pulse generator controls the detector to only receive photons for a short period after each laser pulse. This time-gated technique blocks photons arriving from regions outside the target zone, thus significantly reducing the noise level and allowing observation even inside clouds. Initial observations show that the T2 lidar can detect sharp cloud boundaries and fine structures near the cloud base. Such refined measurements of cloud structure could lead to an improved understanding of microphysical processes such as droplet activation, entrainment and mixing, and precipitation.

Simulations of satellites mock-up fragmentation

High energy in-space collisions may lead to the catastrophic fragmentation of entire spacecraft. Current empirical models employed to predict spacecraft breakup are based on ground experiments and observation of debris cloud generated by collision events. Due to the continuous growth of the number of resident objects orbiting the Earth and the risk they pose to operational satellites, in the last years the interest in collecting new test data on spacecraft collisions has increased, as well as the request to improve current breakup models and develop new ones.In this context the University of Padova performed a set of impact simulations, with a custom fragmentation algorithm, on satellites mock-ups consisting of cubic, cylindrical, and parallelepiped shapes with internal boxes representing on-board components. The considered scenarios include several targets and impactors masses and sizes and different impact geometries (in terms of velocity, impact angle and location). Simulations results consist in the generated fragments characteristic length cumulative distributions. It was observed that all distributions show different sections that can be attributed to different damage modes: the smaller fragments are generated by the spacecraft components fragmentation, the intermediate ones by the detached internal boxes, and the largest ones consisting in intact pieces of the spacecraft separated from the main structure. The limits, extent and slope of these sections depend on the impact conditions, the satellite structure and the impact point; a piecewise analytical model is derived for the simulation data, showing a good accordance with the fragments distribution trends.

High-spatial-resolution retrieval of cloud droplet size distribution from polarized observations of the cloudbow

Abstract. The cloud droplet size distribution is often described by a gamma distribution defined by the effective radius and the effective variance. The effective radius is directly related to the cloud's optical thickness, which influences the radiative properties of a cloud. The effective variance affects, among other things, the evolution of precipitation. Both parameters can be retrieved from measurements of the cloudbow. The cloudbow (or rainbow) is an optical phenomenon that forms due to the single scattering of radiation by liquid cloud droplets at the cloud edge. The polarized radiance of the cloudbow crucially depends on the cloud droplet size distribution. The effective radius and the effective variance can be retrieved by fitting model simulations (stored in a lookup table) to polarized cloudbow observations. This study uses measurements from the wide-field polarization-sensitive camera of the spectrometer of the Munich Aerosol Cloud Scanner (specMACS) aboard the German “High Altitude and LOng range research aircraft” (HALO). Along with precise cloud geometry data derived by a stereographic method, a geolocalization of the observed clouds is possible. Observations of the same cloud from consecutive images are combined into one radiance measurement from multiple angles. Two case studies of trade-wind cumulus clouds measured during the EUREC4A (ElUcidating the RolE of Cloud-Circulation Coupling in ClimAte) field campaign are presented, and the cloudbow technique is demonstrated. The results are combined into maps of the effective radius and the effective variance with a 100 m × 100 m spatial resolution and large coverage (across-track swath width of 8 km). The first case study shows a stratiform cloud deck with distinct patches of large effective radii up to 40 µm and a median effective variance of 0.11. specMACS measures at a very high angular resolution (binned to 0.3∘) which is necessary when large droplets are present. The second case study consists of small cumulus clouds (diameters of approximately 2 km). The retrieved effective radius is 7.0 µm, and the effective variance is 0.08 (both median values). This study demonstrates that specMACS is able to determine the droplet size distribution of liquid water clouds even for small cumulus clouds, which are a problem for traditional droplet size retrievals based on total reflectances.

ICE GENESIS: Synergetic Aircraft and Ground-Based Remote Sensing and In Situ Measurements of Snowfall Microphysical Properties

Abstract An international field experiment took place in the Swiss Jura in January 2021 as a milestone of the European ICE GENESIS project (www.ice-genesis.eu/), which aims to better measure, understand, and model the ice/snow particle properties and mechanisms responsible for icing of rotor-craft and aircraft. The field campaign was designed to collect observations of clouds and snowfall at a prescribed range of temperatures (−10° to +2°C). The suite of in situ and remote sensing instruments included airborne probes and imagers on board a SAFIRE ATR-42 aircraft, able to sample liquid and ice particles from the micron to the millimeter size range, as well as icing sensors and cameras. Two 95 GHz Doppler cloud radars were installed on the SAFIRE ATR-42, while six Doppler weather radars operating at frequencies ranging from 10 to 95 GHz (and one lidar) were ground based. An operational polarimetric weather radar in nearby France (Montancy) complements the coverage. Finally, observations of standard meteorological variables as well as high-resolution pictures of falling snowflakes from a multiangle snowflake camera were collected at the ground level. The campaign showed its full potential during five (multihourly) flights where precipitation was monitored from cloud to ground. The originality of this campaign resides in the targeted specific temperature range for snowfall and in the synchronization between the ground-based remote sensing and the aircraft trajectories designed to maximize the collection of in situ observations within the column above the radar systems.

Isotopic measurements in water vapor, precipitation, and seawater during EUREC4A

Abstract. In early 2020, an international team set out to investigate trade-wind cumulus clouds and their coupling to the large-scale circulation through the field campaign EUREC4A: ElUcidating the RolE of Clouds-Circulation Coupling in ClimAte. Focused on the western tropical Atlantic near Barbados, EUREC4A deployed a number of innovative observational strategies, including a large network of water isotopic measurements collectively known as EUREC4A-iso, to study the tropical shallow convective environment. The goal of the isotopic measurements was to elucidate processes that regulate the hydroclimate state – for example, by identifying moisture sources, quantifying mixing between atmospheric layers, characterizing the microphysics that influence the formation and persistence of clouds and precipitation, and providing an extra constraint in the evaluation of numerical simulations. During the field experiment, researchers deployed seven water vapor isotopic analyzers on two aircraft, on three ships, and at the Barbados Cloud Observatory (BCO). Precipitation was collected for isotopic analysis at the BCO and from aboard four ships. In addition, three ships collected seawater for isotopic analysis. All told, the in situ data span the period 5 January–22 February 2020 and cover the approximate area 6 to 16∘ N and 50 to 60∘ W, with water vapor isotope ratios measured from a few meters above sea level to the mid-free troposphere and seawater samples spanning the ocean surface to several kilometers depth. This paper describes the full EUREC4A isotopic in situ data collection – providing extensive information about sampling strategies and data uncertainties – and also guides readers to complementary remotely sensed water vapor isotope ratios. All field data have been made publicly available even if they are affected by known biases, as is the case for high-altitude aircraft measurements, one of the two BCO ground-based water vapor time series, and select rain and seawater samples from the ships. Publication of these data reflects a desire to promote dialogue around improving water isotope measurement strategies for the future. The remaining, high-quality data create unprecedented opportunities to close water isotopic budgets and evaluate water fluxes and their influence on cloudiness in the trade-wind environment. The full list of dataset DOIs and notes on data quality flags are provided in Table 3 of Sect. 5 (“Data availability”).

Cloud-MobiNet: An Abridged Mobile-Net Convolutional Neural Network Model for Ground-Based Cloud Classification

More than 60 percent of the global surface is covered by clouds, and they play a vital role in the hydrological circle, climate change, and radiation budgets by modifying shortwaves and longwave. Weather forecast reports are critical to areas such as air and sea transport, energy, agriculture, and the environment. The time has come for artificial intelligence-powered devices to take the place of the current method by which decision-making experts determine cloud types. Convolutional neural network models (CNNs) are starting to be utilized for identifying the types of clouds that are caused by meteorological occurrences. This study uses the publicly available Cirrus Cumulus Stratus Nimbus (CCSN) dataset, which consists of 2543 ground-based cloud images altogether. We propose a model called Cloud-MobiNet for the classification of ground-based clouds. The model is an abridged convolutional neural network based on MobileNet. The architecture of Cloud-MobiNet is divided into two blocks, namely the MobileNet building block and the support MobileNet block (SM block). The MobileNet building block consists of the weights of the depthwise separable convolutions and pointwise separable convolutions of the MobileNet model. The SM block is made up of three dense network layers for feature extraction. This makes the Cloud-MobiNet model very lightweight to be implemented on a smartphone. An overall accuracy success of 97.45% was obtained for the CCSN dataset used for cloud-type classification. Cloud-MobiNet promises to be a significant model in the short term, since automated ground-based cloud classification is anticipated to be a preferred means of cloud observation, not only in meteorological analysis and forecasting but also in the aeronautical and aviation industries.

Deep Clouds on Jupiter

Jupiter’s atmospheric water abundance is a highly important cosmochemical parameter that is linked to processes of planetary formation, weather, and circulation. Remote sensing and in situ measurement attempts still leave room for substantial improvements to our knowledge of Jupiter’s atmospheric water abundance. With the motivation to advance our understanding of water in Jupiter’s atmosphere, we investigate observations and models of deep clouds. We discuss deep clouds in isolated convective storms (including a unique storm site in the North Equatorial Belt that episodically erupted in 2021–2022), cyclonic vortices, and northern high-latitude regions, as seen in Hubble Space Telescope visible/near-infrared imaging data. We evaluate the imaging data in continuum and weak methane band (727 nm) filters by comparison with radiative transfer simulations, 5 micron imaging (Gemini), and 5 micron spectroscopy (Keck), and conclude that the weak methane band imaging approach mostly detects variation in the upper cloud and haze opacity, although sensitivity to deeper cloud layers can be exploited if upper cloud/haze opacity can be separately constrained. The cloud-base water abundance is a function of cloud-base temperature, which must be estimated by extrapolating 0.5-bar observed temperatures downward to the condensation region near 5 bar. For a given cloud base pressure, the largest source of uncertainty on the local water abundance comes from the temperature gradient used for the extrapolation. We conclude that spatially resolved spectra to determine cloud heights—collected simultaneously with spatially-resolved mid-infrared spectra to determine 500-mbar temperatures and with improved lapse rate estimates—would be needed to answer the following very challenging question: Can observations of deep water clouds on Jupiter be used to constrain the atmospheric water abundance?

Added Value of Aerosol Observations of a Future AOS High Spectral Resolution Lidar with Respect to Classic Backscatter Spaceborne Lidar Measurements

In the context of the Atmosphere Observing System (AOS) international program, a new-generation spaceborne lidar is expected to be in polar orbit for deriving new observations of aerosol and clouds. In this work, we analyze the added values of these new observations for characterizing aerosol vertical distribution. For this, synthetic observations are simulated using the BLISS lidar simulator in terms of the backscatter coefficient at 532 nm. We consider two types of lidar instruments, an elastic backscatter lidar instrument and a high spectral resolution lidar (HSRL). These simulations are performed with atmospheric profiles from a nature run (NR) modeled by the MOCAGE chemical transport model. In three case studies involving large events of different aerosol species, the added value of the HSRL channel (for measuring aerosol backscatter profiles with respect to simple backscatter measurements) is shown. Observations independent of an a priori lidar ratio assumption, as done typically for simple backscattering instruments, allow probing the vertical structures of aerosol layers without divergence, even in cases of intense episodes. A 5-day study in the case of desert dust completes the study of the added value of the HSRL channel with relative mean bias from the NR of the order of 1.5%. For low abundances, relative errors in the backscatter coefficient profiles may lay between +40% and −40%, with mean biases between +5% and −5%.

Do simulated molecular clouds look like real ones?

ABSTRACT Simulations of molecular clouds often begin from highly idealized initial conditions, such as a uniform-density sphere with an artificially imposed turbulent velocity field. While the resulting structures may appear qualitatively similar to those detected in continuum and line observations, it is unclear whether they are genuinely representative of real molecular clouds. Recent observational work has discovered a tight, often close-to-linear relationship between the integrated intensity of molecular lines and the total column density of the cloud material. We combine magnetohydrodynamical simulations, time-dependent chemistry, and radiative transfer to produce synthetic molecular line observations of model clouds. We find similarly tight correlations between line intensity and column density to those observed, although the linear behaviour is only seen in isolated (as opposed to colliding) model clouds. This linear relationship is not due to optically thin emission; all lines investigated have high optical depths, and the increase in integrated intensity with column density is due to higher velocity dispersion along the line of sight. Overall, the idealized models commonly used in the literature appear to be reasonably accurate representations of real molecular clouds.

Effective shielding of ≲10 GeV cosmic rays from dense molecular clumps

The density of cosmic rays inside molecular clouds determines the ionization rate in the dense cores where stars form. It is also one of the drivers of astrochemistry leading to the creation of complex molecules. Through Fermi Large Area Telescope observations of nearby giant molecular clouds, we observed deficits (holes) in the gamma-ray residual map when modelling with the expected gamma-ray diffuse emission from uniform cosmic rays interacting with the molecular content. We propose that the deficit is due to the lack of penetration of the low-energy (sub-GeV to GeV) cosmic rays into denser regions or clumps. This differs from the prevailing view of fast cosmic ray transport in giant molecular clouds where the magnetic turbulence is suppressed by neutral-ion damping, as our results require a slow diffusion inside dense molecular clumps. Through modelling we find that while the shielding is negligible on the cloud scale, it becomes important in the denser, parsec-sized regions where the gravitational collapse is already at play, changing the initial condition of star formation and astrochemistry.

Implications of Mini-EUSO measurements for a space-based observation of UHECRs

Mini-EUSO is the first mission of the JEM-EUSO program on board the International Space Station. It was launched in 2019 and it is currently located in the Russian section (Zvezda module) of the station and viewing our planet from a nadir-facing UV-transparent window. The instrument is based on the concept of the original JEM-EUSO mission and consists of an optical system employing two Fresnel lenses and a focal surface composed of 36 Multi-Anode Photomultiplier tubes, 64 channels each, for a total of 2304 channels with single photon counting sensitivity and an overall field of view of 44° × 44°. Mini-EUSO can map the night-time Earth in the near UV range (predominantly between 290 nm and 430 nm), with a spatial resolution of about 6.3 km and different temporal resolutions of 2.5 µ, 320 µs and 41 ms. Mini-EUSO observations are extremely important to better assess the potential of a space-based detector in studying Ultra-High Energy Cosmic Rays (UHECRs) such as K-EUSO and POEMMA. In this contribution we focus the attention on UV measurements, the observation of clouds and of certain categories of events that Mini-EUSO triggers with the shortest temporal resolution. We place them in the context of UHECR observations from space, namely the estimation of exposure and sensitivity to Extensive Air Showers.

Cloud Identification and Properties Retrieval of the Fengyun-4A Satellite Using a ResUnet Model

The Advanced Geostationary Radiation Imager (AGRI) onboard the Fengyun-4A (FY4A) satellite has good cloud observation ability, but it still absents all-weather and high-precision official cloud products. This study develops a deep-learning ResUnet model for all-weather retrieval of cloud phase (CLP) and cloud properties using the brightness temperature from water vapor and longwave infrared channels of AGRI. The ResUnet model is trained with the Himawari-8 satellite Level-2 (H8-L2) cloud products as true targets, and adopts image-by-image way to learn the spatial structure information of clouds, which compensates for the difficulty of retrieving thick clouds by thermal infrared radiation at night to some extent. On an independent testing dataset, the model has an overall accuracy of 90.64% for CLP identification and performs well at retrieving cloud top height (CTH). Even without using visible and near-infrared radiation, the root mean square error of cloud effective radius (CER) and cloud optical thickness (COT) estimations still reaches 7.14 μm and 9.01 in the range of 0–60. To further illustrate the reliability and applicability, CLP and cloud properties provided by the CALIPSO and MODIS are used as benchmarks to assess the quality of cloud products from FY4A satellite Level-2 (FY4A-L2), H8-L2 and ResUnet model retrieval. The ResUnet model provides a significant improvement over FY4A-L2 for the accuracy of cloud identification and in the quality of CTH products. In the range of 0–40 μm (0–60), the CER (COT) product of ResUnet model retrieval has a reliable and higher precision that is comparable with H8-L2.

MOSAiC-ACA and AFLUX - Arctic airborne campaigns characterizing the exit area of MOSAiC

Two airborne field campaigns focusing on observations of Arctic mixed-phase clouds and boundary layer processes and their role with respect to Arctic amplification have been carried out in spring 2019 and late summer 2020 over the Fram Strait northwest of Svalbard. The latter campaign was closely connected to the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition. Comprehensive datasets of the cloudy Arctic atmosphere have been collected by operating remote sensing instruments, in-situ probes, instruments for the measurement of turbulent fluxes of energy and momentum, and dropsondes on board the AWI research aircraft Polar 5. In total, 24 flights with 111 flight hours have been performed over open ocean, the marginal sea ice zone, and sea ice. The datasets follow documented methods and quality assurance and are suited for studies on Arctic mixed-phase clouds and their transformation processes, for studies with a focus on Arctic boundary layer processes, and for satellite validation applications. All datasets are freely available via the world data center PANGAEA.

Revisiting cloud overlap with a merged dataset of liquid and ice cloud extinction from CloudSat and CALIPSO

We update the parameterization capturing the variation of parameters that describe how cloud occurrence (layer cloud fraction) and layer cloud optical depth (COD) distributions overlap vertically. Our updated analysis is motivated by the availability of a new dataset constructed by combining two products describing the two-dimensional extinction properties of liquid and ice phase clouds (and their mixtures) according to active cloud observations by the CloudSat and CALIPSO satellites. As before, cloud occurrence overlap is modeled with the decorrelation length of an inverse exponential function describing the decay with separation distance of the relative likelihood that two cloudy layers are overlapped maximally versus randomly. Similarly, cloud optical depth distribution vertical overlap is described again with a decorrelation length that describes the assumed inverse exponential decay with separation distance of the rank correlation between cloud optical depth distribution members in two cloudy layers. We derive the climatological zonal variability of these two decorrelation lengths using 4 years of observations for scenes of ∼100 km scale length, a typical grid size of numerical models used for climate simulations. As previously, we find a strong latitudinal dependence reflecting systematic differences in dominant cloud types with latitude, but substantially different magnitudes of decorrelation length compared to the previous work. The previously used parameterization form is therefore updated with new parameters to describe the latitudinal dependence of decorrelation lengths and its seasonal shift. Similar zonal patterns of decorrelation length are found when the analysis is broken down by different cloud classes. When the revised parameterization is implemented in a cloud subcolumn generator, simulated column cloud properties compare to observations quite well, and so do their associated cloud radiative effects, but improvements over the earlier version of the parameterization are marginal.

Role of Thermal Tides and Gravity Waves in Mars Equatorial Mesospheric Cloud Formation Revealed by Mars Climate Sounder Observations

AbstractMesospheric CO2‐ice clouds form in one of the coldest regions of Mars's atmosphere, but to date links between the broader thermal structure, dynamical phenomena, and the global and seasonal structure of mesospheric clouds have been tentative due to a limited sample of cloud observations. Combining a comprehensive set of cloud identifications and temperature observations from the Mars Climate Sounder onboard the Mars Reconnaissance Orbiter, we demonstrate for the first time that the spatial distribution and seasonal evolution—including the well‐known yet unexplained solstitial pause—of the aphelion mesospheric cloud population are governed by the interplay of thermal tides and gravity waves. In addition, we present the first investigation of the nighttime cloud distribution and our approach successfully explains the observed day‐to‐night differences. The significant day‐to‐night variability in observed cloud occurrence rates and likelihoods of subcondensation temperatures point toward larger nighttime particle sizes possibly due to more favorable growth conditions.

Sea Surface Temperature Impact on Diurnal Cycle and Seasonal Evolution of the Guinea Coast Rainfall in Boreal Spring and Summer

Abstract ERA5 reanalyses and observations of convective clouds and precipitation are used over the northern Gulf of Guinea between 7°W and 3°E to study the influence of ocean surface temperature and the land–sea temperature gradient on Guinea Coast rainfall (GCR) in boreal spring and summer. Seasonal composites are calculated around two dates indexing the onset (Tref) and demise (Tend) of the GCR. The Tref date corresponds to the emergence of the equatorial upwelling in boreal spring, which “pushes” the zonal precipitation belt northward against the Guinea coast. The Tend date characterizes the emergence of the coastal upwelling in July, which is known to coincide with the beginning of the “Little Dry Season” that lasts until September. Along the Guinea Coast, the diurnal cycle of the air–sea temperature gradient controls precipitation through the land–sea breeze, which explains why precipitation reaches its maximum around noon over the ocean, and in the late afternoon over the continent. The emergence of the Guinea Coast upwelling in July induces a weakening of southerlies on a seasonal scale, and a weaker land breeze on a diurnal scale. It induces a decrease in the convergence of humidity transport across the coast and in coastal oceanic precipitation. Therefore, the GCR is seasonally controlled by the latitude of the maximum tropospheric water vapor content and the annual cycle of the West African monsoon, but the ocean surface temperature is responsible for the abruptness of its onset via the intensification of the equatorial upwelling around the end of May, and possibly of its demise as well via the emergence of the coastal upwelling by early July.

The distance scale of Type II Cepheids from near-infrared observations in the Magellanic Clouds

AbstractWe employed data from the VISTA near-infrared YJKS survey of the Magellanic System to analyse the light curves of Type II Cepheids (T2Cs) in the Large and Small Magellanic Clouds (LMC and SMC, respectively). Using the T2Cs identified by the OGLE IV survey and Gaia mission, we built up a sample of about 330 pulsators belonging to both galaxies. For all these objects we obtained accurate intensity-averaged magnitudes in the YJKS bands by means of a template-fitting technique. We complemented our near-infrared data with optical photometry from the literature to calculate period-luminosity and period-Wesenheit relations for a variety of different bands and colour combinations and separately for the different T2C subclasses (BL Herculis, W Virginis, peculiar W Virginis, RV Tauri). These relations, calibrated with the LMC distance modulus, were tested using T2Cs belonging to Galactic globular clusters. We thus calculated the distances of 22 clusters and compared them with the literature values, mainly based on RR Lyrae stars, finding good agreement within 1 σ and dispersion of the order of 0.3 – 0.5 kpc, depending on the adopted period-luminosity/period-Wesenheit relation.

Markovian Statistical Model of Cloud Optical Thickness. Part I: Theory and Examples

Abstract We present a generalization of the binary-value Markovian model previously used for statistical characterization of cloud masks to a continuous-value model describing 1D fields of cloud optical thickness (COT). This model has simple functional expressions and is specified by four parameters: the cloud fraction, the autocorrelation (scale) length, and the two parameters of the normalized probability density function of (nonzero) COT values (this PDF is assumed to have gamma-distribution form). Cloud masks derived from this model by separation between the values above and below some threshold in COT appear to have the same statistical properties as in binary-value model described in our previous publications. We demonstrate the ability of our model to generate examples of various cloud-field types by using it to statistically imitate actual cloud observations made by the Research Scanning Polarimeter (RSP) during two field experiments.

Writhed Analytical Magnetic Flux Rope Model.

Observations of magnetic clouds, within interplanetary coronal mass ejections (ICMEs), are often well described by flux rope models. Most of these assume either a cylindrical or toroidal geometry. In some cases, these models are also capable of accounting for non-axisymmetric cross-sections but they generally all assume axial invariance. It can be expected that any ICME, and its flux rope, will be deformed along its axis due to influences such as the solar wind. In this work, we aim to develop a writhed analytical magnetic flux rope model which would allow us to analytically describe a flux rope structure with varying curvature and torsion so that we are no longer constrained to a cylindrical or toroidal geometry. In this first iteration of our model we will solely focus on a circular cross-section of constant size. We describe our flux rope geometry in terms of a parametrized flux rope axis and a parallel transport frame. We derive expressions for the axial and poloidal magnetic field components under the assumption that the total axial magnetic flux is conserved. We find an entire class of possible solutions, which differ by the choice of integration constants, and present the results for a specific example. In general, we find that the twist of the magnetic field locally changes when the geometry deviates from a cylinder or torus. This new approach also allows us to generate completely new types of in situ magnetic field profiles which strongly deviate from those generated by cylindrical or toroidal models.

Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed

Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation.