Variations In Cloud Cover Research Articles

Modeling and Simulation of High-Frequency Solar Irradiance

As the use of solar power as a source of electricity is increasing, so is the interest in modeling radiation at high temporal resolutions. High-dimensional remote sensing data products depend on cloud cover variability, atmosphere aerosol levels, and other atmospheric parameters. Because of weather fronts and aerosols, it is difficult to quantify solar power variability based on distributed solar networks. The global horizontal irradiance (GHI) component in the National Solar Radiation Database (NSRDB) is available at a 30-min time resolution. The algorithm proposed in this paper produces 1-min-resolution GHI samples at locations where NSRDB data are available. The synthetic irradiance datasets are produced using log-additive non-Gaussian mixture models and resampling techniques. The model is trained over historical data, and predicted values are compared with in situ data. This approach allows for estimating the solar irradiance at subhourly temporal resolutions, while featuring variability for locations where measurements are otherwise not available.

春季北极地区云量年际变化特征及其对北极放大反馈的作用

春季北极地区云量年际变化特征及其对北极放大反馈的作用

Exoplanet spectroscopy and photometry with the Twinkle space telescope

The Twinkle space telescope has been designed for the characterisation of exoplanets and Solar System objects. Operating in a low Earth, Sun-synchronous orbit, Twinkle is equipped with a 45 cm telescope and visible (0.4 – 1 μm) and infrared (1.3 – 4.5 μm) spectrometers which can be operated simultaneously. Twinkle is a general observatory which will provide on-demand observations of a wide variety of targets within wavelength ranges that are currently not accessible using other space telescopes or accessible only to oversubscribed observatories in the short-term future. Here we explore the ability of Twinkle’s spectrometers to characterise the currently-known exoplanets. We study the spectral resolution achievable by combining multiple observations for various planetary and stellar types. We also simulate spectral retrievals for some well-known planets (HD 209458 b, GJ 3470 b and 55 Cnc e). From the exoplanets known today, we find that with a single transit or eclipse, Twinkle could probe 89 planets at low spectral resolution (R < 20) as well as 12 planets at higher resolution (R > 20) in channel 1 (1.3 – 4.5 μm). With 10 observations, the atmospheres of 144 planets could be characterised with R <20 and 81 at higher resolutions. Upcoming surveys will reveal thousands of new exoplanets, many of which will be located within Twinkle’s field of regard. TESS in particular is predicted to discover many targets around bright stars which will be suitable for follow-up observations. We include these anticipated planets and find that the number of planets Twinkle could observe in the near infrared in a single transit or eclipse increases R > 20. By stacking 10 transits, there are 1185 potential targets for study at R < 20 as well as 388 planets at higher resolutions. The majority of targets are found to be large gaseous planets although by stacking multiple observations smaller planets around bright stars (e.g. 55 Cnc e) could be observed with Twinkle. Photometry and low resolution spectroscopy with Twinkle will be useful to refine planetary, stellar and orbital parameters, monitor stellar activity through time and search for transit time and duration variations (TTVs and TDVs). Refinement of these parameters could be used to in the planning of observations with larger space-based observatories such as JWST and ARIEL. For planets orbiting very bright stars, Twinkle observations at higher spectral resolution will enable us to probe the chemical and thermal properties of an atmosphere. Simultaneous coverage across a wide wavelength range will reduce the degeneracies seen with Hubble and provide access to detections of a wide range molecules. There is the potential to revisit them many times over the mission lifetime to detect variations in cloud cover.

Spatiotemporal distributions of cloud parameters and their response to meteorological factors over the Tibetan Plateau during 2003–2015 based on MODIS data

The Tibetan Plateau (TP) has important influences on regional and global climate change. Here, we perform an in‐depth study of the relationship between cloud parameters and meteorological factors over the TP. The spatiotemporal variations in cloud cover and cloud optical thickness over the TP during the daytime from 2003 to 2015 are analysed using the Aqua‐MODIS level 2 atmospheric product data MYD06. Results show that the annual average cloud cover over the TP decreases from the southeast to the northwest. The cloud cover of the western TP is highest in spring and lowest in autumn, while the cloud cover of the eastern TP is higher in spring and summer. The cloud covers in most areas of the TP exceed 30% in spring and summer, and the cloud optical thickness in the southeastern TP exceeds 10 in summer, with substantial cloud cover and cloud optical thickness changes. Compared to other seasons, the cooling effect of the near surface cloud net radiative forcing is greater during daytime in summer, which is likely associated with thick cloud optical thickness or large cloud cover. The results of the analysis of relationships among cloud cover, water vapour and air temperature show that positive correlations exist between cloud cover and water vapour, and that significantly negative correlations exist between cloud cover and air temperature over the TP. Combined with the analysis of variation features in the cloud parameters and meteorological factors, we find that water vapour variations during the daytime over the TP lead to cloud cover changes, which affect the air temperature variations over the TP by the cooling effects of clouds, especially in summer.

Analyzing of Cloud Macroscopic Characteristics in the Shigatse Area of the Tibetan Plateau Using the Total-Sky Images

AbstractThe macroscopic characteristics of clouds in the Tibetan Plateau are crucial to understanding the local climatic conditions and their impact on the global climate and water vapor cycle. In this study, the variations of cloud cover and cloud types are analyzed by using total-sky images of two consecutive years in Shigatse, Tibetan Plateau. The results show that the cloud cover in Shigatse presents a distinct seasonal difference that is characterized by low cloud cover in autumn and winter and high cloud cover in summer and spring. July is the month with the largest cloud coverage, and its average cloud cover exceeds 75%. The probability of clouds in the sky is the lowest in November, with an average cloud cover of less than 20%. The diurnal variations of cloud cover in different months also have considerable differences. Specifically, cloud cover is higher in the afternoon than that in the morning in most months, whereas the cloud cover throughout the day varies little from July to September. The dominant cloud types in different months are also not the same. The proportion of clear sky is large in autumn and winter. Stratiform cloud occupies the highest percentage in March, April, July, and August. The probability of emergence of cirrus is highest in May and June. The Shigatse region has clear rainy and dry seasons, and correlation analysis between precipitation and clouds shows that the largest cumulative precipitation, the highest cloud cover, and the highest proportion of stratiform clouds occur simultaneously in July.

A 7.5 ka chronology of stable carbon isotopes from tree rings with implications for their use in palaeo-cloud reconstruction

Abstract Tree-ring stable isotope chronologies provide very high-resolution palaeoclimatic data, and the number of records is increasing rapidly worldwide. To extend the chronologies back in time, before the period covered by the old living trees, the use of subfossil wood samples is required. Typically, the longest continuous subfossil chronologies consist of regionally collected tree-ring materials, rather than tree rings from a single site, and are likely more sensitive to data heterogeneity. Yet, the characteristics of such datasets remain hitherto unexplored. Here we produce a continuous, decadally resolved chronology of 13C/12C ratio (δ13C) from Finnish Lapland over the past 7.5 ka (5500 BCE to 2010 CE) for which there is replication of at least five Pinus sylvestris trees. Less negative δ13C values were observed as trees age and for western sites (higher in elevation and further from the cold oceanic air flow). The age-related trends in living tree δ13C data were expressed mainly over the first fifty years mimicking the “juvenile effect” whereas the subfossil data showed trend over the trees' lifespan. These findings demonstrated the need to detrend the individual δ13C series before averaging them into the mean chronology. The corresponding biases were removed from the isotopic data using the methods frequently applied for tree-ring width and density proxies, the Regional Curve Standardization (RCS) combined with signal-free approach. While the RCS procedures commonly preserve the long-term variations in the resulting chronology, not all types of them did so as demonstrated for chronologies produced using separate RCS models for the δ13C series with relatively high and low isotopic level (offset from the grand mean). It was shown that these δ13C levels (i.e. the relative isotopic enrichment) result both from the low-frequency climate signal and biogeographical aspects (the site longitude/altitudes). The non-climatic biases were removed from the δ13C series by using separate RCS models for the subsets (western and eastern) of isotopic series. Similar to previous investigations using annually resolved δ13C data from Lapland, our chronology had strong negative correlations to variations in cloud cover. Here, a bootstrapping experiment was used to verify this dendroclimatic association. The resulting palaeo-cloud reconstruction portrayed decadal to multi-millennial variations with centennial anomalies coinciding with the mid and late Holocene events of climate transitions, highlighting the value of subfossil isotope chronologies from tree rings in synthesising climate dynamics from several proxy sources over the present interglacial.

Optimal Control of an Endoreversible Solar Power Plant

AbstractWhile in the classic Curzon–Ahlborn and Novikov engines the temperatures of the heat baths are kept fixed or follow a deterministic time function, it is the aim of this work to study the impact of fluctuating heat bath temperatures. As an example serves a solar power plant, where the stochastically varying cloud cover leads to fluctuations in the temperature of the hot heat bath. This solar thermal power plant is modeled as a stochastic endoreversible system. On the basis of this model the maximum expected work output of the power plant and the corresponding optimal control policy is derived. For the considered system it is found that the maximum expected work output changes with the reversion speed of the hot temperature depending on the relation of the starting hot temperature and the temperature of the power plant’s receiver. Additionally, it is found that the maximum expected work output increases with the hot temperature’s fluctuation strength.

Trends of night-time hourly cloud-cover values over Manila Observatory: ground-based remote-sensing observations using a digital camera for 13 months

ABSTRACTNight-time cloud detection provides data sets of cloud-cover percentage. Although night-time cloud-cover data sets from satellite-based instruments are common, these data sets do not have relatively high temporal resolution. To quantify local temporal cloud-cover variability and to attain long-term cloud-cover measurements, ground-based instruments would be the appropriate apparatus. In this study, a digital camera is used to continuously gather images of the night sky at 5-min intervals over Manila Observatory (14.64° N, 121.07° E). For the first time in Manila, ground-based remote-sensing data gathered from October 2015 to October 2016 are analysed for hourly cloud cover. The results indicate that wet season has relatively higher cloud-cover values (median >40%) as compared to the dry season (median <40%). Moreover, cloud-cover values are observed to decrease during the night. For the wet season, August having the highest cloud-cover values has the highest value of change of hourly cloud-cover percentage (−0.82% h−1). For the dry season, February having the lowest cloud-cover values has the highest value of change of hourly cloud-cover percentage (−1.04% h−1).

Seasonal and interannual assessment of cloud cover and atmospheric constituents across the Amazon (2000–2015): Insights for remote sensing and climate analysis

The quantitative assessment of cloud cover and atmospheric constituents improves our ability to exploit the climate feedback into the Amazon basin. In the 21st century, three droughts have already occurred in the Amazonia (e.g. 2005, 2010, 2015), inducing regional changes in the seasonal patterns of atmospheric constituents. In addition to climate, the atmospheric dynamic and attenuation properties are long-term challenges for satellite-based remote sensing of this ecosystem: high cloudiness, abundant water vapor content and biomass burning season. Therefore, while climatology analysis supports the understanding of atmospheric variability and trends, it also offers valuable insights for remote sensing applications. In this study, we evaluate the seasonal and interannual variability of cloud cover and atmospheric constituents (aerosol loading, water vapor and ozone content) over the Amazon basin, with focus on both climate analysis and remote sensing implications. We take the advantage of new atmosphere daily products at 1 km resolution derived from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm developed for Moderate Resolution Imaging Spectroradiometer (MODIS) data. An intercomparison of Aerosol Robotic Network (AERONET) and MAIAC aerosol optical depth (AOD) and columnar water vapor (CWV) showed quantitative information with a correlation coefficient higher than 0.81. Our results show distinct regional patterns of cloud cover across the Amazon basin: northwestern region presets a persistent cloud cover (>80%) throughout the year, while low cloud cover (0–20%) occurs in the southern Amazon during the dry season. The cloud-free period in the southern Amazon is followed by an increase in the atmospheric burden due to fire emissions. Our results reveal that AOD records are changing in terms of area and intensity. During the 2005 and 2010 droughts, the positive AOD anomalies (δ > 0.1) occurred over 39.03% (240.3 million ha) and 27.14% (165.99 million ha) of total basin in the SON season, respectively. In contrast, the recent 2015 drought occurred towards the end of year (October through December) and these anomalies were observed over 23.72% (145 million ha) affecting areas in the central and eastern Amazon – unlike previous droughts. The water vapor presents high concentration values (4.0–5.0 g cm−2) in the wet season (DJF), while we observed a strong spatial gradient from northwestern to southeastern of the basin during the dry season. In addition, we also found a positive trend of water vapor content (∼0.3 g/cm2) between 2000 and 2015. The total ozone typically varies between 220 and 270 DU, and it has a seasonal change of ∼25–35 DU from wet season to dry season caused by large emissions of ozone precursors and long-range transport. Finally, while this study contributes to climatological analysis of atmospheric constituents, the remote sensing users can also understand the regional constraints caused by atmospheric attenuation, such as high aerosol loading and cloud obstacles for surface observations.

Long-Term Annual Mapping of Four Cities on Different Continents by Applying a Deep Information Learning Method to Landsat Data

Urbanization is a substantial contributor to anthropogenic environmental change, and often occurs at a rapid pace that demands frequent and accurate monitoring. Time series of satellite imagery collected at fine spatial resolution using stable spectral bands over decades are most desirable for this purpose. In practice, however, temporal spectral variance arising from variations in atmospheric conditions, sensor calibration, cloud cover, and other factors complicates extraction of consistent information on changes in urban land cover. Moreover, the construction and application of effective training samples is time-consuming, especially at continental and global scales. Here, we propose a new framework for satellite-based mapping of urban areas based on transfer learning and deep learning techniques. We apply this method to Landsat observations collected during 1984–2016 and extract annual records of urban areas in four cities in the temperate zone (Beijing, New York, Melbourne, and Munich). The method is trained using observations of Beijing collected in 1999, and then used to map urban areas in all target cities for the entire 1984–2016 period. The method addresses two central challenges in long term detection of urban change: temporal spectral variance and a scarcity of training samples. First, we use a recurrent neural network to minimize seasonal urban spectral variance. Second, we introduce an automated transfer strategy to maximize information gain from limited training samples when applied to new target cities in similar climate zones. Compared with other state-of-the-art methods, our method achieved comparable or even better accuracy: the average change detection accuracy during 1984–2016 is 89% for Beijing, 94% for New York, 93% for Melbourne, and 89% for Munich, and the overall accuracy of single-year urban maps is approximately 96 ± 3% among the four target cities. The results demonstrate the practical potential and suitability of the proposed framework. The method is a promising tool for detecting urban change in massive remote sensing data sets with limited training data.

OMI Satellite and Ground‐Based Pandora Observations and Their Application to Surface NO2 Estimations at Terrestrial and Marine Sites

AbstractThe Pandora spectrometer that uses direct‐Sun measurements to derive total column amounts of gases provides an approach for (1) validation of satellite instruments and (2) monitoring of total column (TC) ozone (O3) and nitrogen dioxide (NO2). We use for the first time Pandora and Ozone Monitoring Instrument (OMI) observations to estimate surface NO2 over marine and terrestrial sites downwind of urban pollution and compared with in situ measurements during campaigns in contrasting regions: (1) the South African Highveld (at Welgegund, 26°34′10″S, 26°56′21″E, 1,480 m asl, ~120 km southwest of the Johannesburg‐Pretoria megacity) and (2) shipboard U.S. mid‐Atlantic coast during the 2014 Deposition of Atmospheric Nitrogen to Coastal Ecosystems (DANCE) cruise. In both cases, there were no local NOx sources but intermittent regional pollution influences. For TC NO2, OMI and Pandora difference is ~20%, with Pandora higher most times. Surface NO2 values estimated from OMI and Pandora columns are compared to in situ NO2 for both locations. For Welgegund, the planetary boundary layer (PBL) height, used in converting column to surface NO2 value, has been estimated by three methods: co‐located Atmospheric Infrared Sounder (AIRS) observations; a model simulation; and radiosonde data from Irene, 150 km northeast of the site. AIRS PBL heights agree within 10% of radiosonde‐derived values. Absolute differences between Pandora‐ and OMI‐estimated surface NO2 and the in situ data are better at the terrestrial site (~0.5 ppbv and ~1 ppbv or greater, respectively) than under clean marine air conditions, with differences usually &gt;3 ppbv. Cloud cover and PBL variability influence these estimations.

Retrieval of Water Constituents from Hyperspectral In-Situ Measurements under Variable Cloud Cover—A Case Study at Lake Stechlin (Germany)

Remote sensing and field spectroscopy of natural waters is typically performed under clear skies, low wind speeds and low solar zenith angles. Such measurements can also be made, in principle, under clouds and mixed skies using airborne or in-situ measurements; however, variable illumination conditions pose a challenge to data analysis. In the present case study, we evaluated the inversion of hyperspectral in-situ measurements for water constituent retrieval acquired under variable cloud cover. First, we studied the retrieval of Chlorophyll-a (Chl-a) concentration and colored dissolved organic matter (CDOM) absorption from in-water irradiance measurements. Then, we evaluated the errors in the retrievals of the concentration of total suspended matter (TSM), Chl-a and the absorption coefficient of CDOM from above-water reflectance measurements due to highly variable reflections at the water surface. In order to approximate cloud reflections, we extended a recent three-component surface reflectance model for cloudless atmospheres by a constant offset and compared different surface reflectance correction procedures. Our findings suggest that in-water irradiance measurements may be used for the analysis of absorbing compounds even under highly variable weather conditions. The extended surface reflectance model proved to contribute to the analysis of above-water reflectance measurements with respect to Chl-a and TSM. Results indicate the potential of this approach for all-weather monitoring.

Diurnal cycle and seasonal variation of cloud cover over the Tibetan Plateau as determined from Himawari-8 new-generation geostationary satellite data

Analysis of cloud cover and its diurnal variation over the Tibetan Plateau (TP) is highly reliant on satellite data; however, the accuracy of cloud detection from both polar-orbiting and geostationary satellites over this area remains unclear. The new-generation geostationary Himawari-8 satellites provide high-resolution spatial and temporal information about clouds over the Tibetan Plateau. In this study, the cloud detection of MODIS and AHI is investigated and validated against CALIPSO measurements. For AHI and MODIS, the false alarm rate of AHI and MODIS in cloud identification over the TP was 7.51% and 1.94%, respectively, and the cloud hit rate was 73.55% and 80.15%, respectively. Using hourly cloud-cover data from the Himawari-8 satellites, we found that at the monthly scale, the diurnal cycle in cloud cover over the TP tends to increase throughout the day, with the minimum and maximum cloud fractions occurring at 10:00 a.m. and 18:00 p.m. local time. Due to the limited time resolution of polar-orbiting satellites, the underestimation of MODIS daytime average cloud cover is approximately 4.00% at the annual scale, with larger biases during the spring (5.40%) and winter (5.90%).

КОСМІЧНІ ПРОМЕНІ Й АЕРОЗОЛІ В ЗЕМНІЙ АТМОСФЕРІ

КОСМІЧНІ ПРОМЕНІ Й АЕРОЗОЛІ В ЗЕМНІЙ АТМОСФЕРІ

Using Space Lidar Observations to Decompose Longwave Cloud Radiative Effect Variations Over the Last Decade

AbstractMeasurements of the longwave cloud radiative effect (LWCRE) at the top of the atmosphere assess the contribution of clouds to the Earth warming but do not quantify the cloud property variations that are responsible for the LWCRE variations. The CALIPSO space lidar observes directly the detailed profile of cloud, cloud opacity, and cloud cover. Here we use these observations to quantify the influence of cloud properties on the variations of the LWCRE observed between 2008 and 2015 in the tropics and at global scale. At global scale, the method proposed here gives good results except over the Southern Ocean. We find that the global LWCRE variations observed over ocean are mostly due to variations in the opaque cloud properties (82%); transparent cloud columns contributed 18%. Variation of opaque cloud cover is the first contributor to the LWCRE evolution (58%); opaque cloud temperature is the second contributor (28%).

Spatial and temporal patterns as well as major influencing factors of global and diffuse Horizontal Irradiance over China: 1960–2014

Global Horizontal Irradiance (GHI) on Earth is a central element of climate systems. With changes in the climate and regional development, the patterns and influencing factors of GHI, in addition to presenting global consistency, are increasingly showing regional particularities. Based on data for GHI, Diffuse Horizontal Irradiance (DHI) and potential impact factors (geographical position, elevation, cloud cover, water vapor, and ground atmospheric transparency related variables) from 1960 to 2014 in China, we analyzed the pattern and major influencing factors of GHI and DHI. The results showed that the major influencing factors of the GHI spatial pattern were the total cloud cover (TCC) and relative humidity (RH) in China. Dividing all of China into two regions, the major factors were the water vapor pressure (WVP) in the northern region and TCC in the southern region. And we divided the GHI and DHI data into two periods (1960–1987 and 1988–2014) due to global dimming and brightening observed in China in the late 1980s. The temporal GHI showed that 31 of 58 decreased significantly with an average decreasing rate of 95 MJ 10 yr−1 during the periods of 1960–2014 and 49 of 76 stations decreased significantly with an rate of 342 MJ 10 yr−1 during 1960–1987, whereas 57 of 88 stations did not change and 24 stations increased significantly with an rate of 201 MJ 10 yr−1 during the period of 1988–2014. The temporal DHI showed that 40 of 61sites did not change significantly from 1960 to 1987. The major influencing factors for temporal changes of GHI in nine typical cities from 1960 to 2013 were as follows: air quality-related variables in super cities, sandstorms and wind in desert oasis cities, clouds in cities with good air quality, and a low cloud amount (LCA) and annual fog days (FD) in Chengdu. Overall, we identified characteristics of GHI and DHI based on global climate change and regional urban development and found that the spatial characteristics of GHI results for China are consistent with global trends, whereas the spatial characteristics of DHI and temporal characteristics of GHI and DHI have changed significantly and exhibit these measurable trends due to strong regional influences of changing cloud amounts, water vapor, and air quality.

Seasonal Synchronization of a Simple Stochastic Dynamical Model Capturing El Niño Diversity

Recently, a simple stochastic dynamical model was developed that automatically captures the diversity and intermittency of El Niño–Southern Oscillation (ENSO) in nature, where state-dependent stochastic wind bursts and nonlinear advection of sea surface temperature (SST) are coupled to simple ocean–atmosphere processes that are otherwise deterministic, linear, and stable. In the present article, it is further shown that the model can reproduce qualitatively the ENSO synchronization (or phase locking) to the seasonal cycle in nature. This goal is achieved by incorporating a cloud radiative feedback that is derived naturally from the model’s atmosphere dynamics with no ad hoc assumptions and accounts in simple fashion for the marked seasonal variations of convective activity and cloud cover in the eastern Pacific. In particular, the weak convective response to SSTs in boreal fall favors the eastern Pacific warming that triggers El Niño events while the increased convective activity and cloud cover during the following spring contributes to the shutdown of those events by blocking incoming shortwave solar radiations. In addition to simulating the ENSO diversity with realistic non-Gaussian statistics in different Niño regions, the eastern Pacific moderate and super El Niño and the central Pacific El Niño and La Niña show a realistic chronology with a tendency to peak in boreal winter as well as decreased predictability in spring consistent with the persistence barrier in nature. The incorporation of other possible seasonal feedbacks in the model is also documented for completeness.

Mapping pasture management in the Brazilian Amazon from dense Landsat time series

Intensification of cattle ranching has the potential to reduce deforestation rates in the Brazilian Amazon by decreasing the demand for new agricultural land. Explicit spatial knowledge on where, when and how pastures are managed and intensification takes place is needed to better estimate potentials of more sustainable management. Monitoring the frequency of management practices like burning of pasture land and tillage treatment with adequate spatial resolution therefore offers novel indicators for describing land use intensity. With dense time series of Landsat data, it appears possible to quantify land use intensity also in heterogeneous landscapes where fine-scale processes cannot be monitored with previously available datasets.Our overarching goal is to describe the occurrence or absence of extensive or intensive management regimes over time. For this study, we focused on detecting fire and tillage events in the region of Novo Progresso, Pará, Brazil, where deforested land is mostly used for cattle ranching by both largeholders and smallholders. We used a dense time series of Landsat-7 and Landsat-8 surface reflectance data to mitigate the problem of varying cloud cover. For each acquisition date, we extracted a temporal sequence of three subsequent clear observations at pixel level. The temporal variation in each clear observation sequence was characterized by a stack of spectral and temporal features. These feature stacks were classified with a random forest to identify the management events. We aggregated the classification results based on the random forest class probabilities and derived normalized annual class scores for land management events. The yielded class score maps show burned and tilled areas on a yearly basis and provide a measure of model confidence.We detected burned pastures with area adjusted user accuracies of 80–98%, burned secondary regrowth with 63–80% and tilled pastures with 74–78%. Our approach was able to discriminate management events even when they succeeded very fast. This way, the mapping approach with clear observation sequences allows us to contextualize management events directly from dense time series of high resolution satellite data. This opens new pathways to disentangle how management practices between smallholders and agribusinesses vary in the Brazilian Amazon. With sensor constellations from Sentinel-2 and Landsat data becoming a unified source for much denser time series soon, our method bears great potential to better understand and map pasture dynamics in the Amazon.

A study on the total cloud cover variability over the Mediterranean region during the period 1979–2014 with the use of the ERA-Interim database

The variability and the trends of total cloud cover over the Mediterranean region are studied for the period 1979–2014, using ERA-Interim monthly 1° × 1° grid point data. A data reduction process is applied, and the main modes of inter-annual variation are found for each of the four seasons. Statistically significant positive linear trends are found only for winter over northern Italy and the northwestern Balkans, while significant negative trends are found over large parts of the region for the rest of the seasons, especially for summer. The connection between cloud cover and the large-scale atmospheric oscillations is also examined, revealing statistically significant correlations between cloud cover and the indices of the North Atlantic Oscillation, the North Sea-Caspian Pattern, the Arctic Oscillation, the Southern Oscillation, and the Eastern Atlantic/Western Russia Pattern. These connections highlight the strong dependence of the Mediterranean climate on specific characteristics of the large-scale atmospheric circulation.

A Global Analysis of Sentinel-2A, Sentinel-2B and Landsat-8 Data Revisit Intervals and Implications for Terrestrial Monitoring

Combination of different satellite data will provide increased opportunities for more frequent cloud-free surface observations due to variable cloud cover at the different satellite overpass times and dates. Satellite data from the polar-orbiting Landsat-8 (launched 2013), Sentinel-2A (launched 2015) and Sentinel-2B (launched 2017) sensors offer 10 m to 30 m multi-spectral global coverage. Together, they advance the virtual constellation paradigm for mid-resolution land imaging. In this study, a global analysis of Landsat-8, Sentinel-2A and Sentinel-2B metadata obtained from the committee on Earth Observation Satellite (CEOS) Visualization Environment (COVE) tool for 2016 is presented. A global equal area projection grid defined every 0.05° is used considering each sensor and combined together. Histograms, maps and global summary statistics of the temporal revisit intervals (minimum, mean, and maximum) and the number of observations are reported. The temporal observation frequency improvements afforded by sensor combination are shown to be significant. In particular, considering Landsat-8, Sentinel-2A, and Sentinel-2B together will provide a global median average revisit interval of 2.9 days, and, over a year, a global median minimum revisit interval of 14 min (±1 min) and maximum revisit interval of 7.0 days.