Multilayer Cloud Research Articles

A Hybrid Cloud Testing System Based on Virtual Machines and Networks

Traditional software testing typically uses many physical resources to manually build various test environments, resulting in high resource costs and long test time due to limited resources, especially for small enterprises. Cloud computing can provide sufficient low-cost virtual resources to alleviate these problems through the virtualization of physical resources. However, the provision of various test environments and services for implementing software testing rapidly and conveniently based on cloud computing is challenging. This paper proposes a multilayer cloud testing model based on cloud computing and implements a hybrid cloud testing system based on virtual machines (VMs) and networks. This system realizes the automatic and rapid creation of test environments and the remote use of test tools and test services. We conduct experiments on this system and evaluate its applicability in terms of the VM provision time, VM performance and virtual network performance. The experimental results demonstrate that the performance of the VMs and virtual networks is satisfactory and that this system can improve the test efficiency and reduce test costs through rapid virtual resource provision and convenient test services.

Autonomic resource provisioning for multilayer cloud applications with K‐nearest neighbor resource scaling and priority‐based resource allocation

SummaryProviding a pool of various resources and services to customers on the Internet in exchanging money has made cloud computing as one of the most popular technologies. Management of the provided resources and services at the lowest cost and maximum profit is a crucial issue for cloud providers. Thus, cloud providers proceed to auto‐scale the computing resources according to the users' requests in order to minimize the operational costs. Therefore, the required time and costs to scale‐up and down computing resources are considered as one of the major limits of scaling which has made this issue an important challenge in cloud computing. In this paper, a new approach is proposed based on MAPE‐K loop to auto‐scale the resources for multilayered cloud applications. K‐nearest neighbor (K‐NN) algorithm is used to analyze and label virtual machines and statistical methods are used to make scaling decision. In addition, a resource allocation algorithm is proposed to allocate requests on the resources. Results of the simulation revealed that the proposed approach results in operational costs reduction, as well as improving the resource utilization, response time, and profit.

An autonomic risk‐ and penalty‐aware resource allocation with probabilistic resource scaling mechanism for multilayer cloud resource provisioning

SummaryCloud computing environment allows presenting different services on the Internet in exchange for cost payment. Cloud providers can minimize their operational costs by auto‐scaling of the computational resources based on demand received from users. However, the time and cost required to increase and decrease the number of active computational resources are among the biggest limitations of scalability. Thus, auto‐scaling is considered as one of the most important challenges in the field of cloud computing. The present study aimed to present a new solution to automatic scalability of resources for multilayered cloud applications under the Monitor‐Analysis‐Plan‐Execute‐Knowledge loop. In addition, the Google penalty payment model was used to model the penalty costs in the problem and to accurately evaluate the earned profit. A hybrid resource load prediction algorithm was proposed to evaluate the future of resources in each cloud layer. Further, we used statistical solution to determine the statuses of VMs in addition to presenting a risk‐aware algorithm to allocate the user requests to active resources. The experimental results by Cloudsim indicated the improvement of the proposed approach in terms of operational costs, the number of used resources, and the amount of profit.

Aerosol layers sensing by an eye-safe lidar near the Elbrus summit

A new generation lidar (LIght Detection And Ranging) based on a diode laser with eye-safe energy density (~10 nJ cm−2) and a single photon avalanche photodiode was used for the first time, to our knowledge, to detect clouds along the Garabashi glacier near the Elbrus mountain summit. We have observed single- and multilayered clouds near the Elbrus volcano summit from the Garbashi camp located at a 4 km distance. A high signal-to-noise ratio demonstrated the potential of the remote sensing of aerosols from distances of up to 10 km.

ОПРЕДЕЛЕНИЕ ПАРАМЕТРОВ ОБЛАЧНОГО ПОКРОВА СИСТЕМАМИ АВТОМАТИЧЕСКОЙ ОБРАБОТКИ СПУТНИКОВЫХ ДАННЫХ

The paper discusses the results of comparing cloud cover properties determined by using polar orbiting satellite data (AVHRR/NOAA and MSU-MR/Meteor-M No. 2) for the European territory of Russia and Western Siberia. The cloud characteristics were computed by two threshold techniques: Complex Threshold Technique (CTT) (developed at the European Centre of the State Research Center ‗Planeta‘) and Cloud Cover Detection Technique (CCDT) (developed at the Siberian Centre of ‗Planeta‘). Pixel-by-pixel comparison was performed for very close in time satellite observations, and it showed that in spite of technical similarity of the two radiometers and little difference between both techniques used for the classifications, the results were not the same. The quality of the MSU-MR classification is significantly worse than that of the two AVHRR classifications. In fact, the MSU-MR derivation of cloud parameters fails in optically thin cirrus and altocumulus clouds, thus underestimating the cloud top height for multilayered clouds. As a result, the cloud top is found to be lower, warmer and less iced in comparison with both AVHRR estimates, regardless of the region and other conditions; on the contrary, the cloud top of low and middle clouds appears to be colder, higher and more iced according to MSU-MR data. The MSU-MR cloud mask is strongly underestimated at night during the cold period of the year. The CTT and CCDT‘s cloud top height, temperature and water phase retrieved by AVHRR data are quite close for both regions.

Application of Statistical Models of Image Texture and Physical Parameters of Clouds for Their Classification on MODIS Satellite Images

Modifications of algorithms for the classification of single-layer, vertical development clouds, and multilayer clouds based on a probabilistic neural network and a neuro-fuzzy classifier are proposed. Clouds are classified into 16 types according to the meteorological standard, including the combined subtypes of stratus, altocumulus, cirrus, and cirrostratus clouds. The article uses a description of clouds based on information about the texture of their images on satellite images from MODIS and its products with data on the physical parameters of clouds. The structure of classification algorithms is described. The results of the use of statistical models of image texture and physical parameters of clouds for initializing membership functions for a neural-fuzzy classifier are presented. Systems of effective classification characteristics for different classification algorithms are formed based on the GRAD modified truncated search method. The recognition results of single-layer, vertical development clouds, and multilayer clouds based on the corresponding test samples and full-size sets of MODIS satellite data with different spatial resolution are discussed.

Differences in Cloud Vertical Structures between the Tibetan Plateau and Eastern China Plains during Rainy Season as Measured by CloudSat/CALIPSO

Cloud vertical structures over the Tibetan Plateau (TP) and Eastern China Plains (ECP) were analyzed by using data in rainy seasons from 2006 to 2009, in order to clarify the cloud development over adjacent regions but with distinct topographies. Results indicate that the largest occurrences of cloud top height over the TP are at 7-8 km above mean sea level, which is about 4 km lower than that over the ECP. Mixed-phase clouds dominated more than 30% over the TP, while it is lower than 10% over the ECP. The infrequent mixed-phase clouds over the ECP are attributed to the unique dynamic and moisture situations over the downstream areas of the TP. Ice clouds have similar occurrences over the two regions. The prominent distinctions are manifested by the probability density of cloud thickness. The probability density of cloud thickness around 4–8 km is about 2% higher over the TP than the ECP. However, there is almost no ice cloud thicker than 10 km over the TP, while it is about 1% over the ECP. Compared with those over the ECP, every cloud layer within multilayered clouds is generally higher and thinner over the TP, which is closely related to the elevated surface and the resulting thinner troposphere. The significant differences in cloud vertical structures between the TP and the ECP present in this study emphasize that topographical characteristics and the resulting moisture and circulation conditions have strong impacts on the cloud vertical structures.

Use of spectral cloud emissivities and their related uncertainties to infer ice cloud boundaries: methodology and assessment using CALIPSO cloud products

Abstract. Satellite-imager-based operational cloud property retrievals generally assume that a cloudy pixel can be treated as being plane-parallel with horizontally homogeneous properties. This assumption can lead to high uncertainties in cloud heights, particularly for the case of optically thin, but geometrically thick, clouds composed of ice particles. This study demonstrates that ice cloud emissivity uncertainties can be used to provide a reasonable range of ice cloud layer boundaries, i.e., the minimum to maximum heights. Here ice cloud emissivity uncertainties are obtained for three IR channels centered at 11, 12, and 13.3 µm. The range of cloud emissivities is used to infer a range of ice cloud temperature and heights, rather than a single value per pixel as provided by operational cloud retrievals. Our methodology is tested using MODIS observations over the western North Pacific Ocean during August 2015. We estimate minimum–maximum heights for three cloud regimes, i.e., single-layered optically thin ice clouds, single-layered optically thick ice clouds, and multilayered clouds. Our results are assessed through comparison with CALIOP version 4 cloud products for a total of 11873 pixels. The cloud boundary heights for single-layered optically thin clouds show good agreement with those from CALIOP; biases for maximum (minimum) heights versus the cloud-top (base) heights of CALIOP are 0.13 km (−1.01 km). For optically thick and multilayered clouds, the biases of the estimated cloud heights from the cloud top or cloud base become larger (0.30/−1.71 km, 1.41/−4.64 km). The vertically resolved boundaries for ice clouds can contribute new information for data assimilation efforts for weather prediction and radiation budget studies. Our method is applicable to measurements provided by most geostationary weather satellites including the GK-2A advanced multichannel infrared imager.

Reassessing the Effect of Cloud Type on Earth’s Energy Balance in the Age of Active Spaceborne Observations. Part I: Top of Atmosphere and Surface

AbstractThis study revisits the classical problem of quantifying the radiative effects of unique cloud types in the era of spaceborne active observations. The radiative effects of nine cloud types, distinguished based on their vertical structure defined by CloudSat and CALIPSO observations, are assessed at both the top of the atmosphere and the surface. The contributions from single- and multilayered clouds are explicitly diagnosed. The global, annual mean net cloud radiative effect at the top of the atmosphere is found to be −17.1 ± 4.2 W m−2 owing to −44.2 ± 2 W m−2 of shortwave cooling and 27.1 ± 3.7 W m−2 of longwave heating. Leveraging explicit cloud base and vertical structure information, we further estimate the annual mean net cloud radiative effect at the surface to be −24.8 ± 8.7 W m−2 (−51.1 ± 7.8 W m−2 in the shortwave and 26.3 ± 3.8 W m−2 in the longwave). Multilayered clouds are found to exert the strongest influence on the top-of-atmosphere energy balance. However, a strong asymmetry in net cloud radiative cooling between the hemispheres (8.6 W m−2) is dominated by enhanced cooling from stratocumulus over the southern oceans. It is found that there is no corresponding asymmetry at the surface owing to enhanced longwave emission by southern ocean clouds in winter, which offsets a substantial fraction of their impact on solar absorption in summer. Thus the asymmetry in cloud radiative effects is entirely realized as an atmosphere heating imbalance between the hemispheres.

Reassessing the Effect of Cloud Type on Earth’s Energy Balance in the Age of Active Spaceborne Observations. Part II: Atmospheric Heating

AbstractThe role of clouds in modulating vertically integrated atmospheric heating is investigated using CloudSat’s multisensor radiative flux dataset. On the global mean, clouds are found to induce a net atmospheric heating of 0.07 ± 0.08 K day−1 that derives largely from 0.06 ± 0.07 K day−1 of enhanced shortwave absorption and a small, 0.01 ± 0.04 K day−1 reduction of longwave cooling. However, this small global average longwave effect results from the near cancellation of much larger regional warming by multilayered cloud systems in the tropics and cooling from stratocumulus clouds in subtropical oceans. Clouds are observed to warm the tropical atmosphere by 0.23 K day−1 and cool the polar atmosphere by −0.13 K day−1 enhancing required zonal heat redistribution by the meridional overturning circulation. Zonal asymmetries in the occurrence of multilayered clouds that are more frequent in the Northern Hemisphere and stratocumulus that occur more frequently over the southern oceans also leads to 3 times as much cloud heating in the Northern Hemisphere (0.1 K day−1) than the Southern Hemisphere (0.04 K day−1). These findings suggest that clouds very likely make the strongest contribution to the annual mean atmospheric energy imbalance between the hemispheres (2.0 ± 3.5 PW).

Characteristics of Oceanic Warm Cloud Layers within Multilevel Cloud Systems Derived by Satellite Measurements

Low-level warm clouds are a major component in multilayered cloud systems and they are generally hidden from the top-down view of satellites with passive measurements. This study conducts an investigation on oceanic warm clouds embedded in multilayered structures by using spaceborne radar data with fine vertical resolution. The occurrences of warm cloud overlapping and the geometric features of several kinds of warm cloud layers are examined. It is found that there are three main types of cloud systems that involve warm cloud layers, including warm single layer clouds, cold-warm double layer clouds, and warm-warm double layer clouds. The two types of double layer clouds account for 23% and in the double layer occurrences warm-warm double layer subsets contribute about 13%. The global distribution patterns of these three types differ from each other. Single-layer warm clouds and the lower warm clouds in the cold-warm double layer system they have nearly identical geometric parameters, while the upper and lower layer warm clouds in the warm-warm double layer system are distinct from the previous two forms of warm cloud layers. In contrast to the independence of the two cloud layers in cold-warm double layer system, the two kinds of warm cloud layers in the warm-warm double layer system may be coupled. The distance between the two layers in the warm-warm double layer system is weakly dependent on cloud thickness. Given the upper and lower cloud layer with moderate thickness of around 1 km, the cloudless gap reaches its maximum when exceeding 600 m. The cloudless gap decreases in thickness as the two cloud layers become even thinner or thicker.

Entrapment: An Important Mechanism to Explain the Shortwave 3D Radiative Effect of Clouds

AbstractSeveral mechanisms have previously been proposed to explain differences between the shortwave reflectance of realistic cloud scenes computed using the 1D independent column approximation (ICA) and 3D solutions of the radiative transfer equation. When the sun is low in the sky, interception of sunlight by cloud sides tends to increase reflectance relative to ICA estimates that neglect this effect. When the sun is high, 3D radiative transfer tends to make clouds less reflective, which we argue is explained by the mechanism of “entrapment” whereby horizontal transport of radiation beneath a cloud layer increases the chances, relative to the ICA, of light being absorbed by cloud or the surface. It is especially important for multilayered cloud scenes. We describe modifications to the previously described Speedy Algorithm for Radiative Transfer through Cloud Sides (SPARTACUS) to represent different entrapment assumptions, and test their impact on 65 contrasting scenes from a cloud-resolving model. When entrapment is represented explicitly via a calculation of the mean horizontal distance traveled by reflected light, SPARTACUS predicts a mean “3D radiative effect” (the difference in top-of-atmosphere irradiances between 3D and ICA calculations) of 8.1 W m−2 for overhead sun. This is within 2% of broadband Monte Carlo calculations on the same scenes. The importance of entrapment is highlighted by the finding that the extreme assumptions in SPARTACUS of “zero entrapment” and “maximum entrapment” lead to corresponding mean 3D radiative effects of 1.7 and 19.6 W m−2, respectively.

Reconstruction of Cloud Vertical Structure With a Generative Adversarial Network

AbstractWe demonstrate the feasibility of solving atmospheric remote sensing problems with machine learning using conditional generative adversarial networks (CGANs), implemented using convolutional neural networks. We apply the CGAN to generating two‐dimensional cloud vertical structures that would be observed by the CloudSat satellite‐based radar, using only the collocated Moderate‐Resolution Imaging Spectrometer measurements as input. The CGAN is usually able to generate reasonable guesses of the cloud structure and can infer complex structures such as multilayer clouds from the Moderate‐Resolution Imaging Spectrometer data. This network, which is formulated probabilistically, also estimates the uncertainty of its own predictions. We examine the statistics of the generated data and analyze the response of the network to each input parameter. The success of the CGAN in solving this problem suggests that generative adversarial networks are applicable to a wide range of problems in atmospheric science, a field characterized by complex spatial structures and observational uncertainties.

Multi-Layered Cloud Distribution Over Tropical Station Using Radiosonde Humidity Observations and CloudSat Measurements

Owing to its importance of role played by multi-layered clouds in climate of earth’s atmosphere, a decadal observation (January, 2000 to December, 2009) from India Meteorological Department, Trivandrum regular radiosonde (00 & 12 UTC) ascents and CloudSat observations were used to study the distribution of multi-layered cloud formation at this location. Both the ground and space based observations at Trivandrum locations shows the more or less same percentage of occurrence of single-, double-, three-, four- and five-layered clouds. The important findings are: Radiosonde derived cloud ‐free cases and one to five cloud layers account for 30.63%, 42.51%, 19.76%, 5.85%, 1.08%, and 0.16% all cases, respectively, whereas CloudSat shows 47.17%, 24.74%, 6.41%, 1.81%, 0.13% of the total samples, respectively. In general, the thickness of cloud layers does not change much from summer to winter. However, the occurrences of multi-layered clouds are more frequent in the summer. Further, keeping the CloudSat limitations in view, an attempt is made to evaluate the CloudSat observations using radiosonde measurements and which has the great potential for studying the multi-layered cloud structures over the globe and important in climate point of view.

Improving quantitative precipitation estimates in mountainous regions by modelling low-level seeder-feeder interactions constrained by Global Precipitation Measurement Dual-frequency Precipitation Radar measurements

A physically-based framework to address the underestimation and missed detection errors in Quantitative Precipitation Estimates (QPE) from Global Precipitation Measurement (GPM) Precipitation Radar (PR) in regions of complex terrain is presented. The framework is demonstrated using GPM Ku-PR because of its wider swath. GPM Ku-PR precipitation estimates are evaluated against ground validation (GV) observations from the long-term ground-based rain-gauge network in the Southern Appalachian Mountains. The detection and estimation errors exhibit a diurnal cycle consistent with the diurnal cycle of low-level clouds and fog (LLCF), thus suggesting the importance of low-level orographic microphysical processes. Contamination of near-surface reflectivity profiles due to ground-clutter is the major source of error in the Ku-PR QPE with spatial features that mirror landform. In particular, GPM Ku-PR drop size distribution (DSD) retrieval algorithms systematically overestimate Dm (mass-weighted mean diameter), and underestimate Nw (normalized DSD intercept) and the precipitation-rate when low-level multilayer clouds and fog are present. Second, column simulations of rainfall dynamics constrained by reflectivity measurements show an emergent relationship in Dm-Nw phase-space that explains an increase in the frequency of Dm < 1 mm in disdrometer observations due to seeder-feeder interactions (SFI) not captured by current retrieval microphysical products. To resolve ambiguity in the detection and characterization of SFI regimes, we demonstrate a physically-based framework to improve GPM Ku-PR orographic QPE that relies on a coupled microphysics-radar rainfall forward model to estimate DSD parameters using initial and boundary conditions from Ku-PR DSD estimates (Method-1), Ku-PR corrected reflectivity measurements (Method-2), and LLCF microphysics from GV observations. Model simulations using Method-2 produce realistic surface DSDs confirming that representation of SFI processes is critical. Application of the framework to GPM overpasses shows potential for robust improvement in QPE and elucidates the physical basis for improved retrievals against ground observations corresponding to which Nw increases by 3–5 dBNw, Dm decreases by approximately 0.03 mm, and rain-rate increases up to ten-fold in the presence of SFI.

Homogeneity criteria from AVHRR information within IASI pixels in a numerical weather prediction context

Abstract. This article focuses on the selection of satellite infrared IASI (Infrared Atmospheric Sounding Interferometer) observations in the global numerical weather prediction (NWP) system ARPEGE (Action de Recherche Petite Echelle Grande Echelle). The observation simulation is performed with the sophisticated radiative transfer model RTTOV-CLD, which takes into account the cloud scattering and the multilayer clouds from atmospheric profiles and cloud microphysical profiles (liquid water content, ice content and cloud fraction). The aim of this work is to select homogeneous scenes by using the information of the collocated Advanced Very High Resolution Radiometer (AVHRR) pixels inside each IASI field of view and to retain the most favourable cases for the assimilation of IASI infrared radiances. Two methods to select homogeneous scenes using homogeneity criteria already proposed in the literature were adapted: the criteria derived from Martinet et al. (2013) for cloudy sky selection in the French mesoscale model AROME (Applications of Research to Operations at MEsoscale) and the criteria from Eresmaa (2014) for clear-sky selection in the global model IFS (Integrated Forecasting System). A comparison between these methods reveals considerable differences, in both the method to compute the criteria and the statistical results. From this comparison a revised method representing a kind of compromise between the different tested methods is proposed and it uses the two infrared AVHRR channels to define the homogeneity criteria in the brightness temperature space. This revised method has a positive impact on the observation minus the simulation statistics, while retaining 36 % of observations for the assimilation. It was then tested in the NWP system ARPEGE for the clear-sky assimilation. These criteria were added to the current data selection based on the McNally and Watts (2003) cloud detection scheme. It appears that the impact on analyses and forecasts is rather neutral.

Evaluation of Cloud Top Height Retrievals from China’s Next-Generation Geostationary Meteorological Satellite FY-4A

To evaluate the validity of cloud top height (CTH) retrievals from FY-4A, the first of China’s next-generation geostationary meteorological satellite series, the retrievals are compared to those from Himawari-8, CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Moderate Resolution Imaging Spectroradiometer (MODIS) operational products from August to October 2017. Regarding CTHs from CloudSat, CALIPSO, and MODIS as truth, the results show that the performance of FY-4A CTH retrievals is similar to that of Himawari-8. Both FY-4A and Himawari-8 retrieve reasonable CTH values for single-layer clouds, but perform poorly for multi-layer clouds. The mean bias error (MBE) shows that the mean value of FY-4A CTH retrievals is smaller than that of Himawari-8 for single-layer clouds but larger for multi-layer clouds. For ice crystal clouds, both FY-4A and Himawari-8 obtain the underestimated CTHs. However, there is a tendency for FY-4A and Himawari-8 to overestimate the CTH values of CloudSat and CALIPSO mainly for low level liquid water clouds. The temperature inversion near the tops of water clouds may result in an overestimation of CTHs. According to the MBE change with altitude, FY-4A and Himawari-8 overestimate the CTHs mainly for clouds below 3 km, and the overestimation is slightly more apparent in Himawari-8 data than that in FY-4A values. As the cloud optical thickness (COT) increases, the CTH bias of FY-4A CTH retrievals gradually decreases. Two typical cases are analyzed to illustrate the differences between different satellites’ CTH retrievals in detail.

Polarimetric retrievals of cloud droplet number concentrations

Cloud droplet number concentration (Nd) is an important parameter of liquid clouds and is crucial to understanding aerosol-cloud interactions. It couples boundary layer aerosol composition, size and concentration with cloud reflectivity. It affects cloud evolution, precipitation, radiative forcing, global climate and, through observation, can be used to partially monitor the first indirect effect.With its unique combination of multi-wavelength, multi-angle, total and polarized reflectance measurements, the Research Scanning Polarimeter (RSP) retrieves Nd with relatively few assumptions. The approach involves measuring cloud optical thickness, mean droplet extinction cross-section and cloud physical thickness. Polarimetric observations are capable of measuring the effective variance, or width, of the droplet size distribution. Estimating cloud geometrical thickness is also an important component of the polarimetric Nd retrieval, which is accomplished using polarimetric measurements in a water vapor absorption band to retrieve the amount of in-cloud water vapor and relating this to physical thickness. We highlight the unique abilities and quantify uncertainties of the polarimetric approach.We validate the approach using observational data from the North Atlantic and Marine Ecosystems Study (NAAMES). NAAMES targets specific phases in the seasonal phytoplankton lifecycle and ocean-atmosphere linkages. This study provides an excellent opportunity for the RSP to evaluate its approach of sensing Nd over a range of concentrations and cloud types with in situ measurements from a Cloud Droplet Probe (CDP). The RSP and CDP, along with an array of other instruments, are flown on the NASA C-130 aircraft, which flies in situ and remote sensing legs in sequence.Cloud base heights retrieved by the RSP compare well with those derived in situ (R = 0.83) and by a ceilometer aboard the R.V. Atlantis (R = 0.79). Comparing geometric mean values from 12 science flights throughout the NAAMES-1 and NAAMES-2 campaigns, we find a strong correlation between Nd retrieved by the RSP and CDP (R = 0.96). A linear least squares fit has a slope of 0.92 and an intercept of 0.3 cm−3. Uncertainty in this comparison can be attributed to cloud 3D effects, nonlinear liquid water profiles, multilayered clouds, measurement uncertainty, variation in spatial and temporal sampling, and assumptions used within the method. Radiometric uncertainties of the RSP measurements lead to biases on derived optical thickness and cloud physical thickness, but these biases largely cancel out when deriving Nd for most conditions and geometries. We find that a polarimetric approach to sensing Nd is viable and the RSP is capable of accurately retrieving Nd for a variety of cloud types and meteorological conditions.

Classification of Arctic multilayer clouds using radiosonde and radar data in Svalbard

Abstract. Multilayer clouds (MLCs) occur more often in the Arctic than globally. In this study we present the results of a detection algorithm applied to radiosonde and radar data from an 1-year time period in Ny-Ålesund, Svalbard. Multilayer cloud occurrence is found on 29 % of the investigated days. These multilayer cloud cases are further analysed regarding the possibility of ice crystal seeding, meaning that an ice crystal can survive sublimation in a subsaturated layer between two cloud layers when falling through this layer. For this we analyse profiles of relative humidity with respect to ice to identify super- and subsaturated air layers. Then the sublimation of an ice crystal of an assumed initial size of r=400 µm on its way through the subsaturated layer is calculated. If the ice crystal still exists when reaching a lower supersaturated layer, ice crystal seeding can potentially take place. Seeding cases are found often, in 23 % of the investigated days (100 % includes all days, as well as non-cloudy days). The identification of seeding cases is limited by the radar signal inside the subsaturated layer. Clearly separated multilayer clouds, defined by a clear interstice in the radar image, do not interact through seeding (9 % of the investigated days). There are various deviations between the relative humidity profiles and the radar images, e.g. due to the lack of ice-nucleating particles (INPs) and cloud condensation nuclei (CCN). Additionally, horizontal wind drift of the radiosonde and time restriction when comparing radiosonde and radar data cause further deviations. In order to account for some of these deviations, an evaluation by manual visual inspection is done for the non-seeding cases.

A multilayer cloud detection algorithm for the Suomi-NPP Visible Infrared Imager Radiometer Suite (VIIRS)

A new multilayer (ML) cloud detection algorithm based on three shortwave infrared (SWIR) and two longwave infrared (LWIR) channels is developed and applied to the Visible Infrared Imager Radiometer Suite (VIIRS) onboard the Suomi-NPP satellite. The algorithm identifies ML clouds, i.e., ice clouds overlying water clouds, based on satellite multispectral observations in the 1.38, 1.6, 2.25, 8.5, and 11 μm channels. We perform synthetic radiative transfer simulations to understand the sensitivities of the aforementioned channels on ML and single-layer (SL) clouds. Active CALIOP observations are used to evaluate the algorithm. Compared with the collocated CALIOP results, the algorithm can determine SL and ML clouds correctly with success rates of approximately 80% and 60%, respectively, and has similar performance to that of the current MODIS operational ML cloud detection algorithm. The misclassification of ML clouds as SL clouds is primarily caused by thin ice clouds that are practically undetectable using LWIR tests. Furthermore, the algorithm is extended to analyze data from radiometers onboard the geostationary Himawari-8 and FengYun-4A satellites, and results similar to those of VIIRS are obtained.