Spatial Distribution Of Clouds Research Articles

The Cloudspotting on Mars citizen science project: Seasonal and spatial cloud distributions observed by the Mars Climate Sounder

As tracers of the major volatile cycles of MarsCO2, H2O, and dustclouds are important for understanding the circulation of the martian atmosphere and hence martian climate. We present the spatial and seasonal distribution of laterally-confined clouds in the middle atmosphere of Mars during one Mars Year as identified in limb radiance measurements by the Mars Climate Sounder. Cloud identifications were made by citizen scientists through the “Cloudspotting on Mars” citizen science project, hosted on the citizen science platform Zooniverse. A method to aggregate the crowdsourced data using a novel clustering algorithm is developed. The derived cloud catalog is presented and the seasonal and spatial distribution of clouds is discussed in terms of key populations.

Development of a compact space-borne Lidar for atmospheric aerosol and cloud detection

Space-borne lidar is preferred for observing different characteristic aerosols that should be distributed vertically in every corner of the world. Therefore, a compact space-borne lidar (CSBL) with a high repetition rate laser and photon counting mode is developed to meet the detection requirements in this paper. The detection principle is described in detail, and the design parameters of the CSBL are simulated. The CSBL is assembled and calibrated in its entirety after the development of a long-life, high-beam quality laser and a highly sensitive, quantum-efficient single-photon counting core component. Moreover, after ground and flight tests, the CSBL completed the integrated inversion of atmospheric aerosols and clouds, and it can remotely detect global atmospheric aerosols and cloud spatial distribution structures.

Obstacle Detection by Autonomous Vehicles: An Adaptive Neighborhood Search Radius Clustering Approach

For autonomous vehicles, obstacle detection results using 3D lidar are in the form of point clouds, and are unevenly distributed in space. Clustering is a common means for point cloud processing; however, improper selection of clustering thresholds can lead to under-segmentation or over-segmentation of point clouds, resulting in false detection or missed detection of obstacles. In order to solve these problems, a new obstacle detection method was required. Firstly, we applied a distance-based filter and a ground segmentation algorithm, to pre-process the original 3D point cloud. Secondly, we proposed an adaptive neighborhood search radius clustering algorithm, based on the analysis of the relationship between the clustering radius and point cloud spatial distribution, adopting the point cloud pitch angle and the horizontal angle resolution of the lidar, to determine the clustering threshold. Finally, an autonomous vehicle platform and the offline autonomous driving KITTI dataset were used to conduct multi-scene comparative experiments between the proposed method and a Euclidean clustering method. The multi-scene real vehicle experimental results showed that our method improved clustering accuracy by 6.94%, and the KITTI dataset experimental results showed that the F1 score increased by 0.0629.

An All-Sky Scattering Index Derived from Microwave Sounding Data at Dual Oxygen Absorption Bands

Combining the temperature sounding channels near 118 GHz onboard Fengyun-3D (FY-3D) with channels near 50 GHz makes it possible to obtain the spatial and vertical distributions of the clouds through a cloud emission and scattering index (CESI). Previous research has shown its advantages in cloud detection over oceans. In this study, the CESI method is expanded and validated under different surface conditions, and angular corrections are conducted to remove the effect of viewing angles. The landfall process of Typhoon MANGKHUT and a case over special terrain are chosen to investigate its sensitivities to different surface types. It is found that the cloud spatial distribution is well demonstrated in both of the cases. Moreover, the CESI vertical distributions are compared with the Global Precipitation Measurement (GPM) hydrometeor profiles. The results show that CESIs are highly related to the GPM hydrometeor profiles in all of the conditions, and the correlations with the sea surface vary with the weighting functions of the matched channels, while the phenomenon is not obvious for the land surface. In addition, the validation results show that the new threshold combination for different surface types at different heights can be more effective for cloud identification. The probability density distribution for most of the channels of the screened-out clear sky data approximates a Gaussian distribution well, and these radiances can be well assimilated into the numerical weather prediction models.

ALS Point Cloud Classification With Small Training Data Set Based on Transfer Learning

Point cloud classification of airborne light detection and ranging (LiDAR) data is essential to extract geoinformation. Although deep learning provides a new approach for classification, the time-consuming training process and data dependence prevent its widespread application to point clouds. To solve these problems and leverage the potential of high-performing neural networks, we propose an airborne LiDAR point cloud classification method based on transfer learning. A strategy to generate feature images considering the point cloud spatial distribution is first introduced for applying traditional convolutional neural networks to point clouds. Then, transfer learning is used to extract multiscale and multiview deep features. A simple neural network classifier is designed to reduce dimensionality, fuse and learn high-level features, and postprocessing considering contextual information further improves the classification accuracy. We verified the performance of the proposed method through experiments on two airborne LiDAR data sets with different characteristics and containing eight classes. The results demonstrate that the proposed method can achieve a satisfactory classification accuracy with relatively short training time and less training samples than if using conventional methods.

Satellite-based Cloudiness and Solar Energy Potential in Texas and Surrounding Regions

Global horizontal irradiance (i.e., shortwave downward solar radiation received by a horizontal surface on the ground) is an important geophysical variable for climate and energy research. Since solar radiation is attenuated by clouds, its variability is intimately associated with the variability of cloud properties. The spatial distribution of clouds and the daily, monthly, seasonal, and annual solar energy potential (i.e., the solar energy available to be converted into electricity) derived from satellite estimates of global horizontal irradiance are explored over the state of Texas, USA and surrounding regions, including northern Mexico and the western Gulf of Mexico. The maximum (minimum) monthly solar energy potential in the study area is 151–247 kWhm−2 (43–145 kWhm−2) in July (December). The maximum (minimum) seasonal solar energy potential is 457–706 kWhm−2 (167–481 kWhm−2) in summer (winter). The available annual solar energy in 2015 was 1295–2324 kWhm−2. The solar energy potential is significantly higher over the Gulf of Mexico than over land despite the ocean waters having typically more cloudy skies. Cirrus is the dominant cloud type over the Gulf which attenuates less solar irradiance compared to other cloud types. As expected from our previous work, there is good agreement between satellite and ground estimates of solar energy potential in San Antonio, Texas, and we assume this agreement applies to the surrounding larger region discussed in this paper. The study underscores the relevance of geostationary satellites for cloud/solar energy mapping and provides useful estimates on solar energy in Texas and surrounding regions that could potentially be harnessed and incorporated into the electrical grid.

A Multi-Feature Search Window Method for Road Boundary Detection Based on LIDAR Data

In order to improve the accuracy of structured road boundary detection and solve the problem of the poor robustness of single feature boundary extraction, this paper proposes a multi-feature road boundary detection algorithm based on HDL-32E LIDAR. According to the road environment and sensor information, the former scenic cloud data is extracted, and the primary and secondary search windows are set according to the road geometric features and the point cloud spatial distribution features. In the search process, we propose the concept of the largest and smallest cluster points set and a two-way search method. Finally, the quadratic curve model is used to fit the road boundary. In the actual road test in the campus road, the accuracy of the linear boundary detection is 97.54%, the accuracy of the curve boundary detection is 92.56%, and the average detection period is 41.8 ms. In addition, the algorithm is still robust in a typical complex road environment.

Terran World Spectral Simulator

The Terran world Spectral Simulator (TSS) is a flexible software package for modeling direct detection of reflected stellar radiation from Earth-similar terrestrial exoplanets. Exoplanets similar to Earth require more sophisticated simulation tools than planets with optically thick atmospheres, because both the atmospheric composition and spatial distribution of clouds and surface materials will impact the integrated reflected radiation. Thus, accurate simulations need to employ a three-dimensional approach where the exoplanet surface and cloud field are explicitly modeled. Our modeling framework is designed using a modular approach which splits the explicit radiative transfer calculations from the geometric calculations to produce a disk-integrated reflectance. The modular layout allows different radiative transfer models to be used, and their outputs can be efficiently re-used in larger simulations of orbital phase or rotational light curves. The model is designed to compute unpolarized disk-integrated reflectance spectra. These simulated spectra can help inform preparatory science activities for future direct-imaging missions, such as the WFIRST CGI, HabEx, and LUVOIR. This work highlights several case studies using the TSS: simulation of rotational light curves for a modern Earth twin, simulation of the EPOXI Earth observations, and simulation of a past Earth from a paleoclimate simulation. These case studies illustrate that the TSS simulations agree well with the EPOXI Earth observations, and illustrates how the TSS can be used to support exoplanet research.

Idealized Large-Eddy and Convection-Resolving Simulations of Moist Convection over Mountainous Terrain

Abstract On summertime fair-weather days, thermally driven wind systems play an important role in determining the initiation of convection and the occurrence of localized precipitation episodes over mountainous terrain. This study compares the mechanisms of convection initiation and precipitation development within a thermally driven flow over an idealized double-ridge system in large-eddy (LESs) and convection-resolving (CRM) simulations. First, LES at a horizontal grid spacing of 200 m is employed to analyze the developing circulations and associated clouds and precipitation. Second, CRM simulations at horizontal grid length of 1 km are conducted to evaluate the performance of a kilometer-scale model in reproducing the discussed mechanisms. Mass convergence and a weaker inhibition over the two ridges flanking the valley combine with water vapor advection by upslope winds to initiate deep convection. In the CRM simulations, the spatial distribution of clouds and precipitation is generally well captured. However, if the mountains are high enough to force the thermally driven flow into an elevated mixed layer, the transition to deep convection occurs faster, precipitation is generated earlier, and surface rainfall rates are higher compared to the LES. Vertical turbulent fluxes remain largely unresolved in the CRM simulations and are underestimated by the model, leading to stronger upslope winds and increased horizontal moisture advection toward the mountain summits. The choice of the turbulence scheme and the employment of a shallow convection parameterization in the CRM simulations change the strength of the upslope winds, thereby influencing the simulated timing and intensity of convective precipitation.

Galaxy‐scale AGN feedback – theory

AbstractPowerful relativistic jets in radio galaxies are capable of driving strong outflows but also inducing star‐formation by pressure‐triggering collapse of dense clouds. We review theoretical work on negative and positive active galactic nuclei feedback, discussing insights gained from recent hydrodynamical simulations of jet‐driven feedback on galaxy scales that are applicable to compact radio sources. The simulations show that the efficiency of feedback and the relative importance of negative and positive feedback depend strongly on interstellar medium properties, especially the column depth and spatial distribution of clouds. Negative feedback is most effective if clouds are distributed spherically and individual clouds have small column depths, while positive feedback is most effective if clouds are predominantly in a disc‐like configuration. (© 2016 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

An investigation into the three-dimensional cloud structure over East Asia from the CALIPSO-GOCCP Data

The spatial distribution of clouds and their seasonal variations, and the three-dimensional (3D) cloud structures over East Asia have been analyzed with the CALIPSO-GOCCP data during the period from 2007 to 2012. The results show that there is a large cloud fraction greater than 0.7 over southern China, and the largest cloud fraction appears in southwest China. Besides, a large cloud fraction occurs over the southeast of the Tibetan Plateau. The total and high cloud fractions show notable variations with seasons, while the middle and low cloud fractions vary a little. As for cloud vertical structure, significant differences of the cloud vertical distributions are observed between over land and ocean. Cloud fractions and the height of the maximum cloud fractions decline gradually with the increasing latitude, except for the vertical-latitude profiles over the Tibetan Plateau regions. The longitude-vertical cross sections show similar patterns from the longitude 70° E to 140° E, except the profiles with large cloud fractions over the Tibetan Plateau. From the horizontal distribution patterns and vertical structures of the clouds over East Asia, it is concluded that the huge terrain of the Tibetan Plateau has significant impacts on the cloud formation over the Tibetan Plateau region and the areas to the east. At last, the clouds from the CALIPSO-GOCCP observations are compared to those from the ERA-Interim reanalysis data. The results indicate that the ERA-Interim reanalysis data provide reasonable spatial distribution patterns and the vertical structures in terms of the total cloud fraction over East Asia. However, the total cloud fraction was underestimated about 20% by the ERA-Interim reanalysis data over most parts of East Asia, especially over the neighboring areas east of the Tibetan Plateau. Additionally, the ERA-interim reanalysis data overestimate the cloud fractions at each level in the vertical direction.

Regional high‐resolution cloud climatology based on MODIS cloud detection data

ABSTRACTMost satellite cloud climatologies come in the form of global, low‐resolution datasets: so‐ called ‘gridded’ Level 3 products, resulting from the reprojection and spatio‐temporal aggregation of swath (Level 2) data. Their coarse resolution means that global datasets are of limited usefulness in regional studies. In this paper we develop and evaluate a new, regional cloud climatology over Poland and its neighbouring countries (∼10% of the area covered by Europe), based on observations performed with the state‐of‐the‐art cloud imager, the moderate resolution imaging spectroradiometer (MODIS). In contrast to the operational, global MODIS cloud climatology, which is delivered as a Level 3 product at a spatial resolution of 1° × 1°, this regional climatology maintains the MODIS nadir spatial resolution of 1 km/pixel. The resulting high‐spatial‐resolution climatology is compared with AVHRR and SEVIRI datasets, and surface‐based (SYNOP) observations at the level of monthly and annual means. The results shows that the standard MODIS Level 2 cloud mask product MOD35/MYD35 can be successfully used to develop a regional, high‐resolution cloud climatology. MODIS provides reliable estimates of cloud amount at the national scale (annual mean: 64.0% or 70.8%, depending on the MODIS data interpretation scheme), and correctly reproduces the annual cloud amount cycle (correlation between monthly means with SEVIRI/AVHRR >0.98). A comparison with monthly mean surface observations reveals a bias ranging from −1.1% up to 5.9%, and a root mean square error of 4.2% − 6.6%. MODIS data also correctly indicates the spatial distribution of clouds. However, local anomalies were detected that were identified as artifacts of the MODIS cloud detection algorithm. Those artifacts covered 9% of the study area, but had no impact on spatially‐averaged metrics.

The impact of surface properties on downward surface shortwave radiation over the Tibetan Plateau

The complexity of inhomogeneous surface-atmosphere radiation transfer is one of the foremost problems in the field of atmospheric physics and atmospheric radiation. To date, the influence of surface properties on shortwave radiation has not been well studied. The daily downward surface shortwave radiation of the latest FLASHFlux/CERES (Fast Longwave And Shortwave Fluxes_Time Interpolated and Spatially Averaged/Clouds and the Earth’s Radiant Energy System) satellite data was evaluated against in situ data. The comparison indicated that the differences between the two data sets are unstable and large over rugged terrain compared with relatively flat terrain, and the mean absolute error of the satellite products reaches 31.4 W m−2 (12.3%) over rugged terrain. Based on the SSF (single satellite footprint)/CERES product, the influence of surface properties on the distribution of downward surface shortwave radiation (DSSR) was analyzed. The influence of surface properties on DSSR over the Tibetan Plateau is about twice as large as that in two other regions located at the same latitude (eastern China-western Pacific and subtropical North Pacific). A simulation was carried out with the help of the I3RC (International Intercomparision of Three-Dimensional Radiation Code) Monte Carlo 3D radiative transfer community model. The results showed that DSSR increases as surface albedo increases. Moreover, the impact of surface albedo on DSSR is larger if the spatial distribution of clouds is more non-uniform. It is hoped that these results will contribute to the development of 3D radiative transfer models and the improvement of satellite inversion algorithms.

Measurement of the low-energyNa+−Natotal collision rate in an ion-neutral hybrid trap

We present measurements of the total elastic and resonant charge-exchange ion-atom collision rate coefficient $k_\mathrm{ia}$ of cold sodium (\ce{Na}) with optically-dark low energy \ce{Na+} ions in a hybrid ion-neutral trap. To determine $k_\mathrm{ia}$, we measured the trap loading and loss from both a \ce{Na} magneto-optical trap (MOT) and a linear radio frequency quadrupole Paul trap. We found the total rate coefficient to be $7.4 \pm 1.9 \times 10^{-8}$ cm$^3$/s for the type I \ce{Na} MOT immersed within an $\approx 140$ K ion cloud and $1.10 \pm 0.25 \times 10^{-7}$ cm$^3$/s for the type II \ce{Na} MOT within an $\approx 1070$ K ion cloud. Our measurements show excellent agreement with previously reported theoretical fully quantal \textit{ab initio} calculations. In the process of determining the total rate coefficient, we demonstrate that a MOT can be used to probe an optically dark ion cloud's spatial distribution within a hybrid trap.

Differential interferometry of QSO broad-line regions - I. Improving the reverberation mapping model fits and black hole mass estimates

Reverberation mapping (RM) estimates the size and kinematics of broad-line regions (BLR) in quasars and type I AGNs. It yields size–luminosity relation to make QSOs standard cosmological candles, and mass–luminosity relation to study the evolution of black holes and galaxies. The accuracy of these relations is limited by the unknown geometry of the BLR clouds distribution and velocities. We analyse the independent BLR structure constraints given by super-resolving differential interferometry. We developed a three-dimensional BLR model to compute all differential interferometry and RM signals. We extrapolate realistic noises from our successful observations of the QSO 3C 273 with AMBER on the VLTI. These signals and noises quantify the differential interferometry capacity to discriminate and measure BLR parameters including angular size, thickness, spatial distribution of clouds, local-to-global and radial-to-rotation velocity ratios, and finally central black hole mass and BLR distance. A Markov Chain Monte Carlo model-fit, of data simulated for various VLTI instruments, gives mass accuracies between 0.06 and 0.13 dex, to be compared to 0.44 dex for RM mass–luminosity fits. We evaluate the number of QSOs accessible to observe with current (AMBER), upcoming (GRAVITY) and possible (OASIS with new generation fringe trackers) VLTI instruments. With available technology, the VLTI could resolve more than 60 BLRs, with a luminosity range larger than four decades, sufficient for a good calibration of RM mass–luminosity laws, from an analysis of the variation of BLR parameters with luminosity.

Experimental 4D-Var Assimilation of SYNOP Rain Gauge Data at ECMWF

Abstract Four-dimensional variational data assimilation (4D-Var) experiments with 6-hourly rain gauge accumulations observed at synoptic stations (SYNOP) around the globe have been run over several months, both at high resolution in an ECMWF operations-like framework and at lower resolution with the reference observational coverage reduced to surface pressure data only, as would be expected in early twentieth-century periods. The key aspects of the technical implementation of rain gauge data assimilation in 4D-Var are described, which include the specification of observation errors, bias correction procedures, screening, and quality control. Results from experiments indicate that the positive impact of rain gauges on forecast scores remains limited in the operations-like context because of their competition with all other observations already available. In contrast, when only synoptic station surface pressure observations are assimilated in the data-poor control experiment, the additional assimilation of rain gauge measurements substantially improves not only surface precipitation scores, but also analysis and forecast scores of temperature, geopotential, wind, and humidity at most atmospheric levels and for forecast ranges up to 10 days. The verification against Meteosat infrared imagery also shows a slight improvement in the spatial distribution of clouds. This suggests that assimilating rain gauge data available during data-sparse periods of the past might help to improve the quality of future reanalyses and subsequent forecasts.

Evaluation of cloud and water vapor simulations in CMIP5 climate models using NASA “A‐Train” satellite observations

Using NASA's A‐Train satellite measurements, we evaluate the accuracy of cloud water content (CWC) and water vapor mixing ratio (H2O) outputs from 19 climate models submitted to the Phase 5 of Coupled Model Intercomparison Project (CMIP5), and assess improvements relative to their counterparts for the earlier CMIP3. We find more than half of the models show improvements from CMIP3 to CMIP5 in simulating column‐integrated cloud amount, while changes in water vapor simulation are insignificant. For the 19 CMIP5 models, the model spreads and their differences from the observations are larger in the upper troposphere (UT) than in the lower or middle troposphere (L/MT). The modeled mean CWCs over tropical oceans range from ∼3% to ∼15× of the observations in the UT and 40% to 2× of the observations in the L/MT. For modeled H2Os, the mean values over tropical oceans range from ∼1% to 2× of the observations in the UT and within 10% of the observations in the L/MT. The spatial distributions of clouds at 215 hPa are relatively well‐correlated with observations, noticeably better than those for the L/MT clouds. Although both water vapor and clouds are better simulated in the L/MT than in the UT, there is no apparent correlation between the model biases in clouds and water vapor. Numerical scores are used to compare different model performances in regards to spatial mean, variance and distribution of CWC and H2O over tropical oceans. Model performances at each pressure level are ranked according to the average of all the relevant scores for that level.

Global analysis of cloud field coverage and radiative properties, using morphological methods and MODIS observations

Abstract. The recently recognized continuous transition zone between detectable clouds and cloud-free atmosphere ("the twilight zone") is affected by undetectable clouds and humidified aerosol. In this study, we suggest to distinguish cloud fields (including the detectable clouds and the surrounding twilight zone) from cloud-free areas, which are not affected by clouds. For this classification, a robust and simple-to-implement cloud field masking algorithm which uses only the spatial distribution of clouds, is presented in detail. A global analysis, estimating Earth's cloud field coverage (50° S–50° N) for 28 July 2008, using the Moderate Resolution Imaging Spectroradiometer (MODIS) data, finds that while the declared cloud fraction is 51%, the global cloud field coverage reaches 88%. The results reveal the low likelihood for finding a cloud-free pixel and suggest that this likelihood may decrease as the pixel size becomes larger. A global latitudinal analysis of cloud fields finds that unlike oceans, which are more uniformly covered by cloud fields, land areas located under the subsidence zones of the Hadley cell (the desert belts), contain proper areas for investigating cloud-free atmosphere as there is 40–80% probability to detect clear sky over them. Usually these golden-pixels, with higher likelihood to be free of clouds, are over deserts. Independent global statistical analysis, using MODIS aerosol and cloud products, reveals a sharp exponential decay of the global mean aerosol optical depth (AOD) as a function of the distance from the nearest detectable cloud, both above ocean and land. Similar statistical analysis finds an exponential growth of mean aerosol fine-mode fraction (FMF) over oceans when the distance from the nearest cloud increases. A 30 km scale break clearly appears in several analyses here, suggesting this is a typical natural scale of cloud fields. This work shows different microphysical and optical properties of cloud fields, urging to separately investigate cloud fields and cloud-free atmosphere in future climate research.

Cloud fraction errors for trade wind cumuli from EOS‐Terra instruments

Errors in the standard cloud fraction products produced by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi‐angle Imaging SpectroRadiometer (MISR) on EOS‐Terra were examined using 15 m resolution data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Relative to 124 ASTER scenes containing only trade wind cumuli and having an average cloud fraction of 0.08, MODIS and MISR overestimated cloud fraction by 0.18 and 0.36, respectively. For non‐sunglint scenes, MODIS and MISR overestimated cloud fractions by 0.02 and 0.24, respectively. Systematic dependences in the MODIS and MISR cloud fractions with ASTER cloud fraction were observed. Large RMS errors in MODIS and MISR cloud fractions were observed because of variations in the spatial distribution of clouds, suggesting it may be difficult to decouple long‐term changes in cloud fraction from satellites from true changes in the spatial distribution of clouds.

L and T Dwarf Models and the L to T Transition

Using a model for refractory clouds, a novel algorithm for handling them, and the latest gas-phase molecular opacities, we have produced a new series of L and T dwarf spectral and atmosphere models as a function of gravity and metallicity, spanning the \teff range from 2200 K to 700 K. The correspondence with observed spectra and infrared colors for early- and mid-L dwarfs and for mid- to late-T dwarfs is good. We find that the width in infrared color-magnitude diagrams of both the T and L dwarf branches is naturally explained by reasonable variations in gravity and, therefore, that gravity is the second parameter of the L/T dwarf sequence. We investigate the dependence of theoretical dwarf spectra and color-magnitude diagrams upon various cloud properties, such as particle size and cloud spatial distribution. In the region of the L$\to$T transition, we find that no one cloud-particle-size and gravity combination can be made to fit all the observed data. Furthermore, we note that the new, lower solar oxygen abundances of Allende-Prieto, Lambert, & Asplund (2002) produce better fits to brown dwarf data than do the older values. Finally, we discuss various issues in cloud physics and modeling and speculate on how a better correspondence between theory and observation in the problematic L$\to$T transition region might be achieved.