Cloud Index Research Articles

Retrieval of the water ice column and physical properties of water-ice clouds in the martian atmosphere using the OMEGA imaging spectrometer

Using spectral images recorded by the OMEGA instrument on Mars Express (Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité), we are able to derive physical properties of aerosols in water-ice clouds on Mars for a distribution of pixels over an observed cloud formation. These properties, mean effective radius, reff, and optical depth (at 0.67 μm), τi, were used to estimate the water ice-column (WIC), and we found an empirical relationship between the WIC and an ice cloud index (ICI). The overall mean of retrieved reff is ∼2.2 μm, with a standard deviation of 0.8 μm, and cloud formations with reff between 4.4 and 5.4 μm are observed. The optical depth varies between 0.2 and 2.0. The OMEGA spectra are primarily sensitive to water ice mass due to absorption, and we find that the ICI, very easy to compute, is a good proxy for the mass of the water-ice column (WIC) along the optical line of sight. Our retrieval of physical properties is limited in time (to before 2010) by the exhaustion of coolant for one of the OMEGA channels, and in space (to equatorial observations between 140∘W and 90∘E) by the availability of surface albedo measurements. However, we used the ICI to compute WIC values for the entire OMEGA data set, which has near-global coverage for Mars years 26–32, and we present a climatology of the WIC derived from the OMEGA data, which features enhancements on the order of 1.2–1.6 pr. μm over the aphelion cloud belt, and 1.5–2.5 pr. μm over the polar hoods. The data set analyzed is for observations between 140°W and 90°E, and between 35∘S and 35∘N. No restriction is placed on season, but the majority of cloudy observations were during the aphelion period from Ls 35∘ to 135∘. This work was motivated by the ability of the OMEGA instrument to observe the distribution of water-ice cloud physical properties, and by the availability of new a priori data sets, especially multi-spectral, aerosol-free surface albedo retrieved from a subset of the OMEGA data featuring a cloud-free sky. The main limitations of the retrieval algorithm are linked to the uncertainties on surface albedo, the dust opacity, and the quantity of water-ice suspended in the atmosphere, which can lead to spectral fits with lower accuracy or unrealistic results. We present distributions of each retrieved parameter, goodness of fit, ICI, and cloud mass, and our investigation of relationships between each parameter. Our approach was to maximize the amount of data analyzed, apply stringent data quality cuts and take a statistical approach to interpretation.

Machine learning regressors for solar radiation estimation from satellite data

In this paper we evaluate the performance of several Machine Learning regression techniques in a problem of global solar radiation estimation from geostationary satellite data. Different types of neural networks, Support Vector Regression and Gaussian Processes have been selected as regression techniques to be evaluated, due to their good performance in similar problems in the past. The study area is located in the surroundings of the radiometric station of Toledo, Spain. In order to train the regression techniques considered, one complete year of hourly global solar radiation data is used as the target of the experiments, and different input variables are considered: a cloud index, a clear-sky solar radiation model and several reflectivity values from Meteosat visible images. To assess the results obtained by the Machine Learning algorithms, we have selected as a reference three different physical-based methods, a model based on the Heliosat-2 method (Heliosat-2), the Copernicus Atmosphere Monitoring Service (CAMS) and the SolarGIS model (Soevaluate the performance of Machine Learning regressors when the physical models are included as input variables, in a class of post-processing of these physical approaches. The results obtained show the capacity of Machine Learning regressors to obtain reliable global solar radiation estimation by using satellite measurements.

A novel algorithm for differentiating cloud from snow sheets using Landsat 8 OLI imagery

The separation of clouds from snow is fundamentally very challenging because of their similar spectral signature. A new algorithm was proposed to detect clouds from snow in Landsat 8 imagery. Taking the Hetian District region, where there is frequent cloud and snow cover, in northwestern China as one of the typical case areas. The typical case is presented in detail to illustrate the approach produces and results. A band math method for cloud and snow discrimination index (CSDI) was firstly conducted in this paper, fractal digital number-frequency (DN-N) algorithm and hotspot analyses were applied to determine the threshold of the CSDI and eliminate false anomalies. The results showed that an overall accuracy exceeding 95% in areas with very bright land surfaces, which indicate that this algorithm is effective for detecting clouds in specific situations where the ground objects have some reflectance characteristics similar to cloud.

Forecasting photovoltaics/concentrated photovoltaics at national level – Portugal experience

The solar power installed capacity all over the world is growing and this renewable energy technology is playing an important role to build a clean energy future. The solar forecast is a necessary tool for the transmission system operator in order to maintain the electricity network safety and reliability. This study aims to report the experience of R&D Nester in the solar forecast at national level in Portugal, forecasting for all solar plants connected to the very high voltage (400, 220 and 150 kV) and high voltage (60 kV) networks, providing this information to the Portuguese transmission system operator, REN. A novel approach to calculate cloud index from images from a sky camera is presented.

A model for calculating daily near infrared solar radiation

In this work, a semi-empirical model for calculating daily global near infrared solar radiation (NIR) was developed. The model is written in the form of the ratio of daily global NIR (HNIR) to daily global broadband solar radiation (H) as an empirical function of atmospheric parameters influencing HNIR/H namely, precipitable water (w), total ozone column (O3), aerosol optical depth (AOD) and cloud index (n). A 5-year period (2009-2013) data of HNIR, H, w, O3, AOD and n obtained from 4 meteorological stations situated in the main regions of Thailand were used to calculate the empirical coefficients of the model. This model was tested against an independent measured data set. It is found that HNIR calculated from the model and that from the measurement are in good agreement, with the difference in terms of root mean square error of 7.7% and mean bias error of 5.3%.

Long-range transport of volcanic aerosol from the 2010 Merapi tropical eruption to Antarctica

Abstract. Volcanic sulfate aerosol is an important source of sulfur for Antarctica, where other local sources of sulfur are rare. Midlatitude and high-latitude volcanic eruptions can directly influence the aerosol budget of the polar stratosphere. However, tropical eruptions can also enhance polar aerosol load following long-range transport. In the present work, we analyze the volcanic plume of a tropical eruption, Mount Merapi in 2010, and investigate the transport pathway of the volcanic aerosol from the tropical tropopause layer (TTL) to the lower stratosphere over Antarctica. We use the Lagrangian particle dispersion model Massive-Parallel Trajectory Calculations (MPTRAC) and Atmospheric Infrared Sounder (AIRS) SO2 measurements to reconstruct the altitude-resolved SO2 injection time series during the explosive eruption period and simulate the transport of the volcanic plume using the MPTRAC model. AIRS SO2 and aerosol measurements, the aerosol cloud index values provided by Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), are used to verify and complement the simulations. The Lagrangian transport simulation of the volcanic plume is compared with MIPAS aerosol measurements and shows good agreement. Both the simulations and the observations presented in this study suggest that volcanic plumes from the Merapi eruption were transported to the south of 60∘ S 1 month after the eruption and even further to Antarctica in the following months. This relatively fast meridional transport of volcanic aerosol was mainly driven by quasi-horizontal mixing from the TTL to the extratropical lower stratosphere, and most of the quasi-horizontal mixing occurred between the isentropic surfaces of 360 to 430 K. When the plume went to Southern Hemisphere high latitudes, the polar vortex was displaced from the South Pole, so that the volcanic plume was carried to the South Pole without penetrating the polar vortex. Although only 4 % of the sulfur injected by the Merapi eruption was transported into the lower stratosphere south of 60∘ S, the Merapi eruption contributed up to 8800 t of sulfur to the Antarctic lower stratosphere. This indicates that the long-range transport under favorable meteorological conditions enables a moderate tropical volcanic eruption to be an important remote source of sulfur for the Antarctic stratosphere.

SLC-index: A scalable skip list-based index for cloud data processing

Due to the increasing number of cloud applications, the amount of data in the cloud shows signs of growing faster than ever before. The nature of cloud computing requires cloud data processing systems that can handle huge volumes of data and have high performance. However, most cloud storage systems currently adopt a hash-like approach to retrieving data that only supports simple keyword-based enquiries, but lacks various forms of information search. Therefore, a scalable and efficient indexing scheme is clearly required. In this paper, we present a skip list-based cloud index, called SLC-index, which is a novel, scalable skip list-based indexing for cloud data processing. The SLC-index offers a two-layered architecture for extending indexing scope and facilitating better throughput. Dynamic load-balancing for the SLC-index is achieved by online migration of index nodes between servers. Furthermore, it is a flexible system due to its dynamic addition and removal of servers. The SLC-index is efficient for both point and range queries. Experimental results show the efficiency of the SLC-index and its usefulness as an alternative approach for cloud-suitable data structures.

Effect of thermosonication on the quality attributes of star fruit juice

AbstractThe effect of pasteurization and thermosonication on the quality of star fruit (Averrhoa carambola) juice was reported in the present study. Freshly extracted star fruit juice was pasteurized and thermosonicated to compare the effect on physiochemical properties, antioxidant activity, total phenolic content (TPC), total flavonoid content (TFC), ascorbic acid levels, microbial populations, and sensorial properties. The results were compared with the raw star fruit juice. The treatments did not show any significant (p < .05) variations in pH, total soluble solids, and titratable acidity. A significant (p < .05) increase in antioxidant activity, TPC, TFC, ascorbic acid content, cloud index, and browning index of thermosonicated juices was observed. Pasteurization and thermosonication treatments also displayed significant effect in the reduction of microbial populations. Thus, findings in this study revealed that thermosonication may be a viable option for fruit juice industry, especially, for star fruit processing.Practical applicationsUltrasound processing is an emerging nonthermal technique which can be utilized in the processing of liquid food products. In general, fruit juices are processed through thermal treatments to keep the safe microbial levels. However, thermal processing adversely affects the quality of food products. The present study demonstrated the application of thermosonication that effectively maintained the quality of star fruit juices. Furthermore, it can be added that, thermosonication may be employed as a suitable technique to improve the quality parameters of star fruit juice.

Cloud/shadow detection based on spectral indices for multi/hyperspectral optical remote sensing imagery

Cloud and cloud shadow detection is a necessary preprocessing step for optical remote sensing applications because of the huge negative influence cloud and cloud shadow can have on data analysis. However, most of the existing cloud/shadow detection methods are designed based on specific band configurations of certain sensors, and their working mechanisms are relatively complex and computationally complicated, which limits their application. In view of this, in this paper, a unified cloud/shadow detection algorithm based on spectral indices (CSD-SI) is proposed for most of the widely used multi/hyperspectral optical remote sensing sensors with both visible and infrared spectral channels. On the one hand, the cloud index (CI) and cloud shadow index (CSI) are proposed to indicate the potential clouds and cloud shadows based on their physical reflective characteristics. In addition, considering the spatial coexistence of cloud and cloud shadow, a spatial matching strategy is utilized to remove the pseudo cloud shadows. The effectiveness of the proposed CSD-SI algorithm is demonstrated on eight different types of widely used multi/hyperspectral optical sensors, with different spectral and spatial resolution levels. Overall, in the experiments undertaken in this study, CSD-SI achieved a mean overall accuracy of 98.52% for cloud, with a mean producer’s accuracy of 93.13% and a mean user’s accuracy of 98.13%. For cloud shadow, CSD-SI achieved a means producer’s accuracy of 84.33% and a mean user’s accuracy of 89.12%. The experimental results show that the proposed CSD-SI method based on spectral indices can obtain a comparable cloud/shadow detection performance to that of the other state-of-the-art methods.

A view to the cloud: What really happens when your data is stored on far-off servers in distant data centers

We live in a world that's awash in information. Way back in 2011, an IBM study estimated that nearly 3 quintillion-that's a 3 with 18 zeros after it-bytes of data were being generated every single day. We're well past that mark now, given the doubling in the number of Internet users since 2011, the powerful rise of social media and machine learning, and the explosive growth in mobile computing, streaming services, and Internet of Things devices. Indeed, according to the latest Cisco Global Cloud Index, some 220,000 quintillion bytes-or if you prefer, 220 zettabytes-were generated "by all people, machines, and things" in 2016, on track to reach nearly 850 ZB in 2021.

An automatic method for screening clouds and cloud shadows in optical satellite image time series in cloudy regions

Clouds and cloud shadows block land surface information in optical satellite images. Accurate detection of clouds and cloud shadows can help exclude these contaminated pixels in further applications. Existing cloud screening methods are challenged by cloudy regions where most of satellite images are contaminated by clouds. To solve this problem for landscapes where the typical frequency of cloud-free observations of a pixel is too small to use existing methods to mask clouds and shadows, this study presents a new Automatic Time-Series Analysis (ATSA) method to screen clouds and cloud shadows in multi-temporal optical images. ATSA has five main steps: (1) calculate cloud and shadow indices to highlight cloud and cloud shadow information; (2) obtain initial cloud mask by unsupervised classifiers; (3) refine initial cloud mask by analyzing time series of a cloud index; (4) predict the potential shadow mask using geometric relationships; and (5) refine the potential shadow mask by analyzing time series of a shadow index. Compared with existing methods, ATSA needs fewer predefined parameters, does not require a thermal infrared band, and is more suitable for areas with persistent clouds. The performance of ATSA was tested with Landsat-8 OLI images, Landsat-4 MSS images, and Sentinel-2 images in three sites. The results were compared with a popular method, Function of Mask (Fmask), which has been adopted by USGS to produce Landsat cloud masks. These tests show that ATSA and Fmask can get comparable cloud and shadow masks in some of the tested images. However, ATSA can consistently obtain high accuracy in all images, while Fmask has large omission or commission errors in some images. The quantitative accuracy was assessed using manual cloud masks of 15 images. The average cloud producer's accuracy of these 15 images is as high as 0.959 and the average shadow producer's accuracy reaches 0.901. Given that it can be applied to old satellite sensors and it is capable for cloudy regions, ATSA is a valuable supplement to the existing cloud screening methods.

A Fast Cloud Detection Algorithm Applicable to Monitoring and Nowcasting of Daytime Cloud Systems

The Advanced Himawari Imager (AHI) onboard Japanese geostationary satellite Himawari-8 provides two more visible, three more near-infrared, and six more infrared channels than the only one visible and four infrared channels available from the previous geostationary imager instruments. By taking advantage of AHI’s newly added channels 1, 3, and 4 with wavelengths centered at 0.46, 0.64, and $0.86~\mu \text{m}$ , respectively, a fast cloud detection algorithm is developed. Since the spectral differences of the reflectance between any two of AHI’s channels 1, 3, and 4 over clouds are smaller than those over land and ocean, a visible-based cloud index (VCI) for daytime cloud detection can thus be defined by the root mean square of the three differences between any two of these three channels. An AHI pixel is identified as cloudy if the VCI is smaller than a threshold, which has different values over ocean and land. Cloud detection is further adjusted by a bias correction using AHI channels 7 and 13. The average accuracy of the proposed simple cloud detection is comparable with those obtained from a more complicated cloud mask algorithm involving not only more channels but also model simulations. It is also found that the bias correction is needed mostly over cirrus clouds and Gobi.

Short-term solar radiation forecasting by advecting and diffusing MSG cloud index

Abstract A new method for short-term solar radiation forecasting (referred to as Cloud Index Advection and Diffusion, CIADCast) is proposed and validated. The method is based on the advection and diffusion of Meteosat Second Generation (MSG) cloud index estimates using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model. The forecasted cloud index is transformed in global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts by means of the Heliosat-2 method. The cloud index maps are inserted in the WRF vertical layer which corresponds to the cloud height provided by a ceilometer. GHI and DNI are forecasted up to 6 h ahead with 15 min of time resolution. The method was tested using 25 days of radiometric data collected at three stations located in southern Spain. Benchmarking models such as smart persistence, a cloud motion vector (CMV) based approach and the WRF-Solar suite of the WRF model are also evaluated. Results were analyzed in the light of the different topographic characteristics of the evaluation stations areas. Results proved that CIADCast is able to provide enhanced forecasts in areas with low topographic complexity, where cloud advection by the atmospheric mesoscale dynamics is not perturbed by mountain features. In these areas, CIADCast generally outperforms the other models, especially for DNI and partially cloudy conditions. On the other hand, in regions with complex topography, where the mesoscale cloud pattern is influenced by the mountains, the performance of the CIADCast model is poor and the use of persistence or the WRF-Solar model proved to be more appropriate.

Development of a method for mapping monthly average hourly diffuse erythemal ultraviolet radiation

In this study, a model for calculating monthly average hourly diffuse erythemal solar ultraviolet (EUV) radiation under all sky conditions in Thailand was developed. The model is a function of air mass, aerosol optical depth and satellite-derived cloud index, as these parameters are sensitive to diffuse EUV radiation. Diffuse EUV irradiance were measured using UV-biometers equipped with shade ball during April 2011–March 2014 at four stations in Thailand. Data from these stations were used to formulate the model. The model was validated using an independent dataset (April 2014–March 2016) at the same sites. The modeled diffuse EUV irradiance showed good agreement with that from the ground-based measurements, with root mean square difference (RMSD) of 12.7% and mean bias difference (MBD) of −2.8%. The use of satellite data provided a simple and practical method to calculate monthly average hourly diffuse EUV irradiance over Thailand. Spatial monthly distributions are shown as maps.

MMB$^{cloud}$ -Tree: Authenticated Index for Verifiable Cloud Service Selection

Cloud brokers have been recently introduced as an additional computational layer to facilitate cloud selection and service management tasks for cloud consumers. However, existing brokerage schemes on cloud service selection typically assume that brokers are completely trusted, and do not provide any guarantee over the correctness of the service recommendations. It is then possible for a compromised or dishonest broker to easily take advantage of the limited capabilities of the clients and provide incorrect or incomplete responses. To address this problem, we propose an innovative cloud service selection verification (CSSV) scheme and index structures (MMB $^{cloud}$ -tree) to enable cloud clients to detect misbehavior of the cloud brokers during the service selection process. We demonstrate correctness and efficiency of our approaches both theoretically and empirically.

Uncertainty in Fengyun-3C Microwave Humidity Sounder Measurements at 118 GHz With Respect to Simulations From GPS RO Data

Microwave Humidity Sounder (MWHS) onboard the Chinese FengYun-3C satellite has a total of 12 channels. Eight of these channels are located near the 118-GHz oxygen absorption band for probing atmospheric temperature and humidity fields from space. While the water vapor sounding channels near 183 GHz have been extensively studied in the past, we report the first satellite observations at 118 GHz. In this paper, the MWHS calibration accuracy is assessed by comparing the satellite observations with simulations. Using the collocated Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) radio occultation (RO) data in clear-sky conditions as inputs to Community Radiative Transfer Model, MWHS brightness temperatures are simulated for the five upper level sounding channels 2–9 located near the 118-GHz oxygen absorption band. For quality control of clear-sky radiance, a new cloud index is first developed based on the two MWHS window channels 1 and 10. Monthly mean biases of the antenna brightness temperature observations for the 118-GHz sounding channels are quantified by using more than 2000–5000 collocated COSMIC and MWHS data from August 2014 to February 2015. It is found that the bias of MWHS data relative to COSMIC RO simulation is dependent on channel and ranges within ±1.5 K. The standard deviation from the bias is the largest at MWHS channel 2, indicating a larger variability of the measurements of channel 2 than the other channels.

The influence of urban heat island phenomenon on PM concentration: an observation study during the summer half-year in metropolitan Taipei, Taiwan

Air circulation due to the urban heat island (UHI) effect can influence the dispersion of air pollutants in a metropolis. This study focusses on the influence of the UHI effect on particulate matter (PM; including PM2.5 and PM2.5–10) between May and September 2010–2012 in the Taipei basin. Meteorological and PM data were obtained from the sites, owned by the governmental authorities. The analysis was carried out using t test, relative indices (RIs), Pearson product–moment correlation and stepwise regression. The results show that the RI values for PM were the highest at moderate UHI intensity (MUI; 2 °C ≤ UHI < 4 °C) rather than at strong UHI intensity (SUI; 4 °C ≤ UHI) during the peak time for anthropogenic emissions (20:00 LST). Neither the accumulation of PM nor the surface convergence occurred in the hot centre, as shown by the case study. At MUI, more than 89 % of the synoptic weather patterns showed that the weather was clear and hot or that the atmosphere was stable. The variation in PM was associated with horizontal and vertical air dispersion. Poor horizontal air dispersion, with subsidence, caused an increase in PM at MUI. However, the updraft motion diluted the PM at SUI. The stepwise regression models show that the cloud index and surface air pressure determined the variation in PM2.5–10, while cloud index, wind speed and mixing height influenced the variation in PM2.5. In conclusion, a direct relationship between UHI effect and PM was not obvious.

Modeling the Global Solar Radiation Under Cloudy Sky Using Meteosat Second Generation High Resolution Visible Raw Data

This paper presents an improved model for mapping both hourly and daily values of the global radiation reaching the earth’s surface from satellite data over Algeria. An empirical model with a large spectral band using Linke atmospheric turbidity was chosen to modeling the clear-sky global radiation. The extinction of the solar radiation (absorption plus scattering) is described by an exponential decay function of the cloud albedo and cloud index. To compute these two parameter, we resolve the sun-pixel-satellite radiative model equation using the following hypothesizes. The first one supposes the total transmittance of the atmospheric in the pixel-satellite direction as the ratio of the direct radiation in real sky to the same quantity in case of a very clean sky. The second one estimates that the noise energy reflected back towards the radiometer by molecules of gases in the upper levels of the atmosphere represents about 5 % of the energy measured by the radiometer. The model performance was validated for five radiometric stations across the country for three selected months in 2013 and satisfactory results were obtained and discussed.

Infrared limb emission measurements of aerosol in the troposphere and stratosphere

Abstract. Altitude-resolved aerosol detection in the upper troposphere and lower stratosphere (UTLS) is a challenging task for remote sensing instruments. Infrared limb emission measurements provide vertically resolved global measurements at day- and nighttime in the UTLS. For high-spectral-resolution infrared limb instruments we present here a new method to detect aerosol and separate between ice and non-ice particles. The method is based on an improved aerosol–cloud index that identifies infrared limb emission spectra affected by non-ice aerosol or ice clouds. For the discrimination between non-ice aerosol and ice clouds we employed brightness temperature difference correlations. The discrimination thresholds for this method were derived from radiative transfer simulations (including scattering) and Michelson Interferometer for Passive Atmospheric Sounding (MIPAS)/Envisat measurements obtained in 2011. We demonstrate the value of this approach for observations of volcanic ash and sulfate aerosol originating from the Grímsvötn (Iceland, 64° N), Puyehue–Cordón Caulle (Chile, 40° S), and Nabro (Eritrea, 13° N) eruptions in May and June 2011 by comparing the MIPAS volcanic aerosol detections with Atmospheric Infrared Sounder (AIRS) volcanic ash and SO2 measurements.

The retrieval algorithm for a satellite-borne CO2-sounder: Preliminary results in near infrared band

This study developed an inversion algorithm for the CO2-sounder instrument to be launched on a Chinese satellite. By using the L1B spectral data of the Greenhouse gases Observing Satellite (GOSAT) as input, the simulation results of the column averaged CO2 dry air mole fraction (XCO2) retrieved from the CO2-sounder algorithm were obtained and compared with the GOSAT L2 XCO2 products for different seasons and geolocations. The simulation data showed a tendency to be less than the GOSAT L2 data, but the two data sets were generally in agreement with a global mean difference of −4.05±6.06ppm. For the matched data pairs, 51% had their relative differences less than 1% in absolute value, and 76% less than 2%. The seasonal and latitudinal variations of the XCO2 differences were also investigated, and the largest discrepancy was found in the desert areas with the relative differences of about 3%–4%. These results suggested that better consistency between the two data sets could be achieved if more realistic observational parameters instead of model values were used in the simulation. These parameters included the a priori profiles of atmospheric temperature, concentrations of CO2, O2, and H2O, surface reflectance, aerosol and cloud indices, etc.