Opaque Clouds Research Articles

Satellite Observed Sensitivity of Tropical Clouds and Moisture to Sea Surface Temperature on Various Time and Space Scales: 1. Focus on High Level Cloud Situations Over Ocean

AbstractThis study examines variations in high cloud properties and relative humidity (RH) with sea surface temperature (SST) over tropical oceans (30°N–30°S) using spaceborne lidar and microwave radiometer observations. The mean values over the tropics indicate that middle‐tropospheric RH increases, high cloud covers decrease and cloud altitudes rise with SST. These signatures are consistent with the hypotheses proposed in the literature. The analysis of this same data set but at the scale of local processes shows different behaviors for SSTs <302 K and SSTs >302 K. Between 299 and 302 K, middle‐tropospheric RH, opaque cloud cover, and cloud top altitude increase together with SST, while optically thin cloud cover decreases. Over SSTs >302 K, middle‐tropospheric RH, opaque cloud cover, and opaque cloud top altitude decrease with SST, while the cover of optically thin clouds increases. Interestingly, the altitude of high clouds (not the cloud top) increases monotonically with SST from 299 to 305 K on a range of space and time scales, and the altitude of optically thin clouds remains higher than that of opaque clouds. The observed relationships on different time and space scales are compared to simulations of a global atmospheric model. Despite systematic biases, the model reproduces the sensitivity of the middle‐tropospheric RH and cloud altitude to SST rather well but fails to reproduce the variations of the balance between high opaque cloud cover and high optically thin cloud cover.

Estimate of daytime single-layer cloud base height from advanced baseline imager measurements

Cloud base height (CBH) is an important parameter to describe cloud state and is highly related to the vertical motions in the atmosphere. CBH information is critical for both aviation safety and synoptic analysis. In this study, daytime CBH is estimated directly from Geostationary Operational Environmental Satellite-R Series (GOES-16) Advanced Baseline Imager (ABI) level 1b data and the European Centre for Medium-Range Weather Forecasts' (ECMWF) fifth generation reanalysis (ERA5) data using the Gradient Boosted Regression Trees (GBRT) machine learning technique. The CBH estimate algorithm, which is named as GETCBH, covers the same areal extent as the full disk of the ABI/GOES-16 and only for single-layer clouds. The 2-years of CBH measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite is used as the label (which is the true value/class of the model output for regression/classification problem in machine learning terminology). A quality flag algorithm using another machine learning technique, the Gradient Boosted Decision Trees machine learning technique is developed to provide a confidence level for the CBH estimate. The evaluations show an overall root mean square error (RMSE) of 1.87 km and Pearson's correlation coefficient (Pearson's r) of 0.92 before any quality control. After excluding CBH estimates with low confidence (19.2% of all samples), the RMSE is reduced to 1.14 km, Pearson's r increases to 0.97, and 96% of the estimates are within 2 km of the CALIOP results. By analyzing model bias and feature importance, cloud phase information has the biggest impact on the CBH estimate, although all input features have positive impact on the estimate accuracy. Limited by the penetrability of CALIOP, GETCBH is valid for clouds with COD < 8.5. The CBH estimates have reduced accuracy (Pearson's r of 0.88) for optically thin clouds (clouds with cloud optical depth [COD] < 0.1) where little cloud information is contained in the ABI measurements, as well as for optically thick clouds (clouds with COD ≥ 3) where a larger proportion of opaque clouds is excluded. Furthermore, for the GBTCBH model using 9 months of CloudSat measurements as label, the CBH estimates are improved with an RMSE of 1.41 km and Pearson's r of 0.92. In a case study of Hurricane Dorian, CBHs for most of the single-layer clouds are successfully estimated with small errors and flagged with high confidence, for both high and low clouds. Deep convective clouds and multi-layer clouds, both of which are not included in the training, are reasonably flagged as low confidence with large CBH estimate errors. In this particular case, 65% of cloudy pixels have CBH estimate with high confidence in the scene. Daytime CBH with high spatial (2 km) and temporal (10 min) resolution can be derived from ABI measurements using this methodology.

Modeling and experiment on 3D position and size measurement of opaque droplet cloud with astigmatic dual-beam interferometric particle imaging (ADIPI)

This work investigates the astigmatic dual-beam interferometric particle imaging (ADIPI) to enable the measurement of the three-dimensional (3D) spatial position and particle size of spherical opaque particles. A general theoretical model based on the generalized Huygens-Fresnel integral and the ray transfer matrix model is established to characterize ADIPI signal, revealing the orientation and spacing of typical ADIPI fringes relating to the depth position and size. The model is applied to study the effects of several parameters, such as the focal length of cylindrical lens, the focal length of spherical lens and the distances between the elements in the optical system, on the relationship between the inclination angle of fringes and depth position by theoretical simulation. Experiments of ADIPI, incorporating dual-view digital inline holography (DIH), is performed to demonstrate the utility of ADIPI in the measurement of opaque spherical particles. Through the analysis of the general ADIPI signal based on two-dimensional Fourier transforms, 3D position and the particle size are obtained. The results of ADIPI, in both of the depth position and size measuring, exhibit a high degree of consistency with DIH's, given the average relative deviations of 5% in size measuring and 1% in depth position, respectively. As a new measurement approach, the ADIPI signal exists at arbitrary angle except the two laser beam axes, determining the flexibility of ADIPI configurations which facilitates its great prospect in opaque particles measurement, e.g., metal combustion, chip manufacturing, 3D metal printing.

Link Between Opaque Cloud Properties and Atmospheric Dynamics in Observations and Simulations of Current Climate in the Tropics, and Impact on Future Predictions

AbstractUsing spaceborne lidar observations and reanalyzes (2008–2014), we relate the vertical wind speed at 500 hPa (ω500), indicator of atmospheric circulation, to properties of opaque clouds (altitude and cover) and to the Cloud Radiative Effect (CRE) in the Tropics. We confront those observations with simulations by IPSL‐CM6 and CESM1 climate models using early 21st century emissions. Both models overestimate the average opaque cloud cover. IPSL‐CM6 puts high opaque clouds too high (+2 km), especially in ascendance. CESM1 overestimates the intermediate opaque cloud cover and underestimates small and large opaque cloud covers. Both models agree that cloud properties behave differently at wind speed above (strong subsidence) or below (weak subsidence and ascendance) 20 hPa/day. In future climate (2089–2095), variables affected by biases in current climate are affected by notable changes: IPSL‐CM6 puts high opaque clouds even higher (+2 km) while opaque cloud cover above 30% decreases and below 30% increases in CESM1. Both models predict very little change in the average net CRE in the future. We find that predicted changes of cloud properties can be regionally driven by dynamic or thermodynamic changes, depending on the relationship between opaque cloud altitude and ω500 in the model. Overall, most changes are due to thermodynamic changes in the relationship between cloud property and atmospheric dynamics.

Comparison of TOA and BOA LW Radiation Fluxes Inferred From Ground‐Based Sensors, A‐Train Satellite Observations and ERA Reanalyzes at the High Arctic Station Eureka Over the 2002–2020 Period

AbstractThis study focuses on the accuracy of longwave radiation flux retrievals at the top and bottom of the atmosphere at Eureka station, Canada, in the high Arctic. We report comparisons between seven products derived from (a) calculations based on a combination of ground‐based and space‐based lidar and radar observations, (b) standard radiometric observations from the CERES sensor, (c) direct observations at the surface from a broadband radiation station, and (d) the ERA‐Interim and ERA5 reanalyzes. Statistical, independent analyses are first performed to look at recurring bias and trends in fluxes at Top and Bottom of the Atmosphere (TOA, BOA). The analysis is further refined by comparing fluxes derived from coincident observations decomposed by scene types. Results show that radiative transfer calculations using ground‐based lidar‐radar profiles derived at Eureka agree well with TOA LW fluxes observed by CERES and with BOA LW fluxes reference. CloudSat‐CALIPSO also shows good agreement with calculations from ground‐based sensor observations, with a relatively small bias. This bias is shown to be largely due to low and thick cloud occurrences that the satellites are insensitive to owing to attenuation from clouds above and surface clutter. These conditions of opaque low clouds, cause an even more pronounced bias for CERES BOA flux calculation in winter, due to the deficit of low clouds identified by MODIS. ERA‐I and ERA5 fluxes behave differently, the large positive bias observed with ERA‐I is much reduced in ERA5. ERA5 is closer to reference observations due to better behavior of low and mid‐level clouds and surface temperature.

Version 4 CALIPSO Imaging Infrared Radiometer ice and liquid water cloud microphysical properties – Part II: Results over oceans

Abstract. Following the release of the version 4 Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) data products from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) mission, a new version 4 (V4) of the CALIPSO Imaging Infrared Radiometer (IIR) Level 2 data products has been developed. The IIR Level 2 data products include cloud effective emissivities and cloud microphysical properties such as effective diameter (De) and water path estimates for ice and liquid clouds. This paper (Part II) shows retrievals over ocean and describes the improvements made with respect to version 3 (V3) as a result of the significant changes implemented in the V4 algorithms, which are presented in a companion paper (Part I). The analysis of the three-channel IIR observations (08.65, 10.6, and 12.05 µm) is informed by the scene classification provided in the V4 CALIOP 5 km cloud layer and aerosol layer products. Thanks to the reduction of inter-channel effective emissivity biases in semi-transparent (ST) clouds when the oceanic background radiance is derived from model computations, the number of unbiased emissivity retrievals is increased by a factor of 3 in V4. In V3, these biases caused inconsistencies between the effective diameters retrieved from the 12/10 (βeff12/10 = τa,12/τa,10) and 12/08 (βeff12/08 = τa,12/τa,08) pairs of channels at emissivities smaller than 0.5. In V4, microphysical retrievals in ST ice clouds are possible in more than 80 % of the pixels down to effective emissivities of 0.05 (or visible optical depth ∼0.1). For the month of January 2008, which was chosen to illustrate the results, median ice De and ice water path (IWP) are, respectively, 38 µm and 3 g m−2 in ST clouds, with random uncertainty estimates of 50 %. The relationship between the V4 IIR 12/10 and 12/08 microphysical indices is in better agreement with the “severely roughened single column” ice habit model than with the “severely roughened eight-element aggregate” model for 80 % of the pixels in the coldest clouds (&lt;210 K) and 60 % in the warmest clouds (&gt;230 K). Retrievals in opaque ice clouds are improved in V4, especially at night and for 12/10 pair of channels, due to corrections of the V3 radiative temperature estimates derived from CALIOP geometric altitudes. Median ice De and IWP are 58 µm and 97 g m−2 at night in opaque clouds, with again random uncertainty estimates of 50 %. Comparisons of ice retrievals with Moderate Resolution Imaging Spectroradiometer (MODIS)/Aqua in the tropics show a better agreement of IIR De with MODIS visible–3.7 µm than with MODIS visible–2.1 µm in the coldest ST clouds and the opposite for opaque clouds. In prevailingly supercooled liquid water clouds with centroid altitudes above 4 km, retrieved median De and liquid water path are 13 µm and 3.4 g m−2 in ST clouds, with estimated random uncertainties of 45 % and 35 %, respectively. In opaque liquid clouds, these values are 18 µm and 31 g m−2 at night, with estimated uncertainties of 50 %. IIR De in opaque liquid clouds is smaller than MODIS visible–2.1 µm and visible–3.7 µm by 8 and 3 µm, respectively.

Toward Cloud Tomography from Space Using MISR and MODIS: Locating the “Veiled Core” in Opaque Convective Clouds

Abstract For passive satellite imagers, current retrievals of cloud optical thickness and effective particle size fail for convective clouds with 3D morphology. Indeed, being based on 1D radiative transfer (RT) theory, they work well only for horizontally homogeneous clouds. A promising approach for treating clouds as fully 3D objects is cloud tomography, which has been demonstrated for airborne observations. However, more efficient forward 3D RT solvers are required for cloud tomography from space. Here, we present a path forward by acknowledging that optically thick clouds have “veiled cores” (VCs). Sunlight scattered into and out of this deep region does not contribute significant information about the inner structure of the cloud to the spatially detailed imagery. We investigate the VC location for the MISR and MODIS imagers. While MISR provides multiangle imagery in the visible and near-infrared (IR), MODIS includes channels in the shortwave IR, albeit at a single view angle. This combination will enable future 3D retrievals to disentangle the cloud’s effective particle size and extinction fields. We find that, in practice, the VC is located at an optical distance of ~5, starting from the cloud boundary along the line of sight. For MODIS’s absorbing wavelengths the VC covers a larger volume, starting at smaller optical distances. This concept will not only lead to a reduction in the number of unknowns for the tomographic reconstruction but also significantly increase the speed and efficiency of the 3D RT solver at the heart of the algorithm by applying, say, the photon diffusion approximation inside the VC.

Investigating ESA Sentinel-2 products' systematic cloud cover overestimation in very high altitude areas

Cloud detection in optical remote sensing imagery is crucial because undetected clouds can produce misleading results in analyses. Almost all optical remote sensing data access portals rely to some degree on a cloud cover filter. Here we show that cirrus as well as opaque cloud cover in Sentinel-2 Level-1C (L1C) and Level-2A (L2A) imagery is systematically and significantly overestimated in very high altitude areas (e.g. Himalayas, Andes). We argue that this systematic bias is created by applying simple thresholds to single bands instead of using a multi-band spectral signature in the cloud detection process. This results in a lot of “hidden” data for very high altitude areas when each image's estimated cloud cover is used as an automated selection criterion for analysis (e.g. global analyses, cloud-free mosaic production). We show geographic locations exemplifying this overestimation, and compare the L1C and L2A cloud masks produced by ESA to cloud masks generated by an expert system that uses comprehensive spectral signatures, showing that reliable cloud estimations are possible in very high altitudes. Based on this comparison, we argue for changes to L1C and L2A cloud detection algorithms in order to improve initial querying and selection of big EO data, where reliable yet automated quality indicators are necessary to handle an overwhelming data volume and velocity. Our contribution raises awareness of potential bias when pre-selecting images based on reported cloud cover in very high altitude areas for researchers and users of Sentinel-2 imagery in the environmental domain.

WASP-117 b: An Eccentric Hot Saturn as a Future Complex Chemistry Laboratory

Abstract We present spectral analysis of the transiting Saturn-mass planet WASP-117 b, observed with the G141 grism of the Hubble Space Telescope's (HST) Wide Field Camera 3. We reduce and fit the extracted spectrum from the raw transmission data using the open-source software Iraclis before performing a fully Bayesian retrieval using the publicly available analysis suite TauREx 3.0. We detect water vapor alongside a layer of fully opaque cloud, retrieving a terminator temperature of K. In order to quantify the statistical significance of this detection, we employ the atmospheric detectability index (ADI), deriving a value of ADI = 2.30, which provides positive but not strong evidence against the flat-line model. Due to the eccentric orbit of WASP-117 b, it is likely that chemical and mixing timescales oscillate throughout orbit due to the changing temperature, possibly allowing warmer chemistry to remain visible as the planet begins transit, despite the proximity of its point of ingress to apastron. We present simulated spectra of the planet as would be observed by the future space missions such as the Atmospheric Remote-sensing Infrared Exoplanet Large-survey and the James Webb Space Telescope and show that, despite not being able to probe such chemistry with current HST data, these observatories should make it possible in the not too distant future.

Quality Estimates of INSAT-3D Derived Cloud Top Temperature for Climate Data Record

Reliable measurements of cloud top temperature (CTT) are crucial for several applications including climate research and evaluation of cloud-resolving models. Being a relevant geophysical property of climate system, CTT is identified as an essential climate variable by global climate observing system (GCOS). This article evaluates quality of INSAT-3D retrieved CTT products at 4 km (CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4 km</sub> ) and 50 km (CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50 km</sub> ) resolutions in terms of GCOS requirements by validating them with concurrent measurements from ground-based hyper-spectral microwave radiometer (MWR). CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4 km</sub> and CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50 km</sub> agree with MWR retrieved CTTs at respective spatial resolutions, within 4 K and 5 K respectively, which are close to the GCOS required accuracy of CTT (1-5 K) for climate studies. The study also examines adequacy of GCOS standard spatial resolution for CTT (50 km) and dependence of spatial resolution on CTT, by comparing CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50 km</sub> and CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4 km</sub> . Analysis shows deviation of 7 K between CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">50 km</sub> and CTT <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">4 km</sub> . It is observed that the effect of spatial resolution on CTT is relatively less for opaque clouds (with uncertainty within 3 K for 50% cloud fraction) compared to that for semitransparent cirrus (STC) clouds (within 9 K for 50% cloud fraction). These uncertainties are observed to be decreasing with increasing cloud fraction. Thus, our study shows that INSAT-3D CTT at 50-km resolution agrees with GCOS recommended spatial resolution for opaque clouds. However, caution should be taken in case of STCs. The results presented here are particularly important for scale-dependent approaches of cloud characterization and have significant implications for the parameterization of clouds in climate models.

Evaluation and comparison of a machine learning cloud identification algorithm for the SLSTR in polar regions

A Feed Forward Neural Net (NN) approach to distinguish between clouds and the surface has been applied to the Sea and Land Surface Temperature Radiometer in polar regions. The masking algorithm covers the Arctic, Antarctic and regions typically classified as the cryosphere such as northern hemisphere permafrost. The mask has been trained using collocations with the CALIOP active lidar, which in narrow strips provide more accurate detection of cloud, and was subsequently evaluated as a function of cloud type and surface type. The mask was compared with the existing operational Bayesian and Empirical cloud masks by eye and also statistically using CALIOP data. It was found to perform exceptionally well in the polar regions. The Kuiper skill score improved from 0.28, for the operational Bayesian and 0.17 for the Empirical masks to 0.77 for the NN. The NN algorithm also has a much more homogeneous performance over all surface types. The key improvement came from better identification of clear scenes; for the NN mask, the same performance in terms of contamination of cloudy pixels in the sample of identified clear pixels can be achieved while retaining 40% of the clear pixels compared with 10% for the operational cloud identification. The algorithm performed with almost the same skill over sea and land. The best performance was achieved for opaque clouds while transparent and broken clouds showed slightly reduced accuracy.

Impact of Planetary Mass Uncertainties on Exoplanet Atmospheric Retrievals

Abstract In current models used to interpret exoplanet atmospheric observations, the planetary mass is treated as a prior and is measured/estimated independently with external methods, such as radial velocity or transit timing variation techniques. This approach is necessary as available spectroscopic data do not have sufficient wavelength coverage and/or signal-to-noise to infer the planetary mass. We examine here whether the planetary mass can be directly retrieved from transit spectra as observed by future space observatories, which will provide higher quality spectra. More in general, we quantify the impact of mass uncertainties on spectral retrieval analyses for a host of atmospheric scenarios. Our approach is both analytical and numerical: we first use simple approximations to extract analytically the influence of each atmospheric/planetary parameter to the wavelength-dependent transit depth. We then adopt a fully Bayesian retrieval model to quantify the propagation of the mass uncertainty onto other atmospheric parameters. We found that for clear-sky, gaseous atmospheres the posterior distributions are the same when the mass is known or retrieved. The retrieved mass is very accurate, with a precision of more than 10%, provided the wavelength coverage and signal-to-noise ratio are adequate. When opaque clouds are included in the simulations, the uncertainties in the retrieved mass increase, especially for high altitude clouds. However, atmospheric parameters such as the temperature and trace-gas abundances are unaffected by the knowledge of the mass. Secondary atmospheres, expected to be present in many super-Earths, are more challenging due to the higher degree of freedom for the atmospheric main component, which is unknown. For broad wavelength range and adequate signal-to-noise observations, the mass can still be retrieved accurately and precisely if clouds are not present, and so are all the other atmospheric/planetary parameters. When clouds are added, we find that the mass uncertainties may impact substantially the retrieval of the mean molecular weight: an independent characterization of the mass would therefore be helpful to capture/confirm the main atmospheric constituent.

Cloud computing ecosystem model: evaluation and role clusters

Several significantly different attempts have been undertaken to visualize the complex and opaque cloud computing ecosystem by a descriptive model. Recently, scholars have developed a new role-based cloud ecosystem model (Passau Cloud Computing Ecosystem Model (PaCE model)) based on a systematic analysis and synthesis of the previous models. However, its scientific evaluation is still outstanding. The main goal is therefore to conduct an evaluation regarding its structural equivalence and completeness compared to the real cloud business according to the design science research paradigm. The second goal is to demonstrate the model’s usefulness by using it as an instrument for the identification of role clusters, which are frequently covered by organizations. To this end, a cross-sectional analysis is conducted, in which 758 organizations are manually analysed and matched with the model’s current roles. As a result, roles have been newly added, removed, broken down into sub-roles, abstracted to a meta-role and renamed. The cluster analysis unveiled five role clusters. Whereas some roles were predominantly found as single role, further roles were exclusively or very frequently found in combination with others. Overall, the revised model creates, compared to the common rather technically orientated market overviews by market research institutes, a detailed categorization schema helping to understand and investigate the organizations belonging to the cloud ecosystem.

Warm Temperature Extremes Across Greenland Connected to Clouds

AbstractGreenland ice sheet melt is a large contributor to rising global sea level and melt is dependent on surface air temperature. Arctic temperatures are strongly coupled to clouds but spatial connections between clouds and temperature have yet to be established across Greenland. By mapping spaceborne lidar measurements and surface observations, it is shown that radiatively opaque clouds generally coincide with anomalously warm near‐surface temperatures at Greenland sites. These results indicate that both temperatures over 0 °C as well as positive daily temperature anomalies relate to spatially extensive opaque cloud cover. While prior studies indicate that clouds enhance extreme melt events, this research shows that opaque cloud cover and surface warming are closely related across the Greenland ice sheet, particularly in the ablation region. These findings establish broadly the spatial relationships between opaque clouds and temperatures and demonstrate the importance of direct observations across Greenland.

Coupling of Clouds and Tropospheric Relative Humidity in the Tropical Western Atlantic: Insights From Multisatellite Observations

AbstractWe investigated the interactions between clouds and moisture at the diurnal scale in the Western Atlantic trade winds region. Profiles of tropospheric relative humidity from the SAPHIR/Megha‐Tropiques sounder are combined with cloud categories obtained from geostationary satellites. In winter, the midtroposphere undergoes strong daytime drying due to air masses coming from the colder upper troposphere. The moistening near the surface triggered by solar radiation precludes the development of low‐level clouds. At night rising moist air in the upper troposphere triggers the formation of high‐altitude clouds and favors their presence. In summer, daytime high‐altitude clouds shield the solar forcing on the atmosphere and reduce drying from large‐scale subsidence. After sunset, the development of upper tropospheric opaque clouds constitutes a local source of moisture. We argue that modulations of the diurnal cycle of clouds and relative humidity by season may be related to diurnal pulses of the Intertropical Convergence Zone.

Cloud Identification from All-sky Camera Data with Machine Learning

Abstract Most ground-based observatories are equipped with wide-angle all-sky cameras to monitor the night sky conditions. Such camera systems can be used to provide an early warning of incoming clouds that can pose a danger to the telescope equipment through precipitation, as well as for sky quality monitoring. We investigate the use of different machine-learning approaches for automating the identification of mostly opaque clouds in all-sky camera data as a cloud warning system. In a deep-learning approach, we train a residual neural network (ResNet) on pre-labeled camera images. Our second approach extracts relevant and localized image features from camera images and uses these data to train a gradient-boosted tree-based model (lightGBM). We train both model approaches on a set of roughly 2000 images taken by the all-sky camera located at Lowell Observatory’s Discovery Channel Telescope, in which the presence of clouds has been labeled manually. The ResNet approach reaches an accuracy of 85% in detecting clouds in a given region of an image, but requires a significant amount of computing resources. Our lightGBM approach achieves an accuracy of 95% with a training sample of ∼1000 images and rather modest computing resources. Based on different performance metrics, we recommend the latter feature-based approach for automated cloud detection. Code that was built for this work is available online.

Observed Evolution of the Tropical Atmospheric Water Cycle with Sea Surface Temperature

AbstractBetter understanding of how moisture, clouds, and precipitation covary under climate warming lacks a comprehensive observational view. This paper analyzes the tropical atmospheric water cycle’s evolution with sea surface temperature (SST), using for the first time, the synergistic dataset of instantaneous observations of the relative humidity profile from the Megha-Tropiques satellite, clouds from the CALIPSO satellite, and near-surface precipitation from the CloudSat satellite, and quantifies their rates of change with SST warming. The dataset is partitioned into three vertical velocity regimes, with cloudy grid boxes categorized by phase (ice or liquid), opacity (opaque or thin), and the presence of near-surface precipitation. Opaque cloud cover is always larger in the presence of near-surface precipitation (high ice clouds especially). Low liquid water clouds in the descending regime dominate for SSTs &lt; 299.25 K, where the free troposphere is dry (~20%), and opaque liquid water cloud cover decreases with SST warming (−8% K−1) and thin liquid water cloud cover stays constant (~20%). High ice clouds dominate the ascending regime in which, for 299.25 &lt; SST &lt; 301.75 K, humidity increases with SST in the lower free troposphere and peaks around 302 K. Over the warm SST range (&gt;301.75 K), in the ascending regime, opaque high ice cloud cover decreases with SST (−13% K−1), while thin ice cloud cover increases (+6% K−1). Over the warm SST range, total cloudiness decreases with warming in all regimes. This paper characterizes fundamental relationships between aspects of the tropical atmospheric water cycle and SST.

Combination of AIRS Dual CO2 Absorption Bands to Develop an Ice Clouds Detection Algorithm in Different Atmospheric Layers

The use of infrared (IR) sensors to detect clouds in different layers of the atmosphere is a big challenge, especially for ice clouds. This study aims to improve ice cloud detection using Lin’s algorithm and apply it to Atmospheric Infrared Sounder (AIRS). To achieve these objectives, the scattering and emission characteristics of clouds as perceived by AIRS longwave infrared (LWIR, ~15 μm) and shortwave infrared (SWIR, ~4.3 μm) CO2 absorption bands are applied for ice cloud detection. Hence, the weighting function peak (WFP), cut-off pressure, and correlation coefficients between the brightness temperatures (BTs) of LWIR and SWIR channels are used to pair the LWIR and SWIR channels. After that, the linear relationship between the clear-sky BTs of the paired LWIR and SWIR channels is established by the cloud scattering and emission Index (CESI). However, the linear relationship fails in the presence of ice clouds. Comparing these results with collocated Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) observations show that the probability of detection of ice clouds for Pair-8 (WFP~330hPa), Pair-19 (WFP~555hPa), and Pair-24 (WFP~866hPa) are 0.63, 0.71, and 0.73 in the daytime and 0.46, 0.62, and 0.7 in the nighttime at a false alarm rate of 0.1 when ice clouds top pressure above 330 hPa, 555 hPa, and 866 hPa, respectively. Furthermore, the thresholds of the three pairs are 2.4 K, 3 K, and 8.7 K in the daytime and 1.7 K, 1.7 K, and 4.4 K in the nighttime at the highest Heike Skill Score (HSS). The error of HSS values based on thresholds of ice clouds is between 0.01 and 0.02 which is comparable with the ice cloud detection results in both day and night conditions. It is shown that Pair-8 (WFP~330hPa) can detect opaque and thick ice clouds above its WFP altitude over the tropical areas but it is unable to observe ice clouds over the mid-latitude while Pair-19 and Pair-24 can identify ice clouds above their WFP altitude.

Cloud Aerosol Transport System (CATS) 1064 nm Calibration and Validation.

The Cloud-Aerosol Transport System (CATS) lidar on board the International Space Station (ISS) operated from 10 February 2015 to 30 October 2017 providing range-resolved vertical backscatter profiles of Earth's atmosphere at 1064 and 532 nm. The CATS instrument design and ISS orbit lead to a higher 1064 nm signal-to-noise ratio than previous space-based lidars, allowing for direct atmospheric calibration of the 1064 nm signals. Nighttime CATS Version 3-00 data were calibrated by scaling the measured data to a model of the expected atmospheric backscatter between 22 and 26 km above mean sea level (AMSL). The CATS atmospheric model is constructed using molecular backscatter profiles derived from Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) re-analysis data and aerosol scattering ratios measured by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The nighttime normalization altitude region was chosen to simultaneously minimize aerosol loading and variability within the CATS data frame, which extends from 28 km to -2 km AMSL. Daytime CATS Version 3-00 data were calibrated through comparisons with nighttime measurements of the layer integrated attenuated total backscatter (iATB) from strongly scattering, rapidly attenuating opaque cirrus clouds. The CATS nighttime 1064 nm attenuated total backscatter (ATB) uncertainties for clouds and aerosols are primarily related to the uncertainties in the CATS nighttime calibration technique, which are estimated to be ~9%. Median CATS V3-00 1064 nm ATB relative uncertainty at night within cloud and aerosol layers is 7%, slightly lower than these calibration uncertainty estimates. CATS median daytime 1064 nm ATB relative uncertainty is 21% in cloud and aerosol layers, similar to the estimated 16-18% uncertainty in the CATS daytime cirrus cloud calibration transfer technique. Coincident daytime comparisons between CATS and the Cloud Physics Lidar (CPL) during the CATS-CALIPSO Airborne Validation Experiment (CCAVE) project show good agreement in mean ATB profiles for clear-air regions. Eight nighttime comparisons between CATS and the PollyXT ground based lidars also show good agreement in clear-air regions between 3-12 km, with CATS having a mean ATB of 19.7 % lower than PollyXT. Agreement between the two instruments (~7%) is even better within an aerosol layer. Six-month comparisons of nighttime ATB values between CATS and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) also show that iATB comparisons of opaque cirrus clouds agree to within 19%. Overall, CATS has demonstrated that direct calibration of the 1064 nm channel is possible from a space based lidar using the atmospheric normalization technique.

Diurnal variation of high-level clouds from the synergy of AIRS and IASI space-borne infrared sounders

Abstract. By covering about 30 % of the Earth and by exerting a strong greenhouse effect, high-level clouds play an important role in the energy balance of our planet. Their warming and cooling effects within the atmosphere strongly depend on their emissivity. The combination of cloud data from two space-borne infrared sounders, the Atmospheric InfraRed Sounder, AIRS, and the Infrared Atmospheric Sounding Interferometer, IASI, which observe the Earth four times per day, allows us to investigate the diurnal variation of these high-level clouds by distinguishing between high opaque, cirrus, and thin cirrus clouds. We demonstrate that the diurnal phase and amplitude of high-level clouds can be estimated from these measurements with an uncertainty of 1.5 h and 20 %, respectively. By applying the developed methodology to AIRS and IASI cloud observations for the period of 2008–2015, we obtained monthly geographical distributions of diurnal phase and amplitude at a spatial resolution of 1∘ latitude ×1∘ longitude. In agreement with other studies, the diurnal cycle of high-level clouds is the largest over land in the tropics. At higher latitudes, their diurnal cycle is the largest during the summer. For selected continental regions we found diurnal amplitudes of cloud amount of about 7 % for high opaque clouds and for thin cirrus, and 9 % for cirrus. Over ocean, these values are 2 to 3 times smaller. The diurnal cycle of tropical thin cirrus seems to be similar over land and over ocean, with a minimum in the morning (09:00 LT) and a maximum during the night (01:00 LT). Tropical high opaque clouds have a maximum in the evening (21:00 LT over land), a few hours after the peak of convective rain. This lag can be explained by the fact that this cloud type includes not only the convective cores, but also part of the thicker anvils. Tropical cirrus show maximum coverage during the night (01:00 LT over land). This lag indicates that they are part of the deep convective cloud systems. However, the peak local times also vary regionally. We are providing a global monthly database of detected diurnal cycle amplitude and phase for each of these three high-level cloud types.