Cloud Optical Thickness Research Articles

Analysis of Diurnal Evolution of Cloud Properties and Convection Tracking over the South China Coastal Area

Different diurnal rainfall cycles occur over the offshore and inland regions of the South China coastal area (SCCA). Inspired by these findings, in this study, we investigated the diurnal evolution features of cloud systems and cloud properties inside such systems for both the SCCA offshore and inland regions, using cloud data retrieved from a recently developed deep neural network model. Rainy day data for June 2017 revealed that the ice cloud optical thickness and top height reached their peak intensities at noon (~12 local standard time (LST)) over the offshore region, approximately 2 h later than the rainfall peak. Over the inland region, cloud and rainfall peaks simultaneously appeared from ~18 to 20 LST. When further examining the cloud-amount variation of different ice-cloud types, we found a clear diurnal oscillation in the medium-thick cloud amount over the offshore region, while for the inland region, this cloud type had no obvious diurnal peak, showing a low cloud amount throughout the day. This phenomenon suggests different inner structures and intensities between offshore and inland convections. To better elucidate the convection features over different regions, a tracking algorithm was applied to obtain various parameters, such as size, number, and duration of mesoscale convective systems. The strongest convections, which lasted over 12 h, tended to be abundant over the offshore region from ~03 to 12 LST, and an inland to offshore migration at ~03 LST was facilitated by the beneficial meteorological conditions observed at 113–116˚E, 20.5–22.5˚N.

In situ and satellite-based estimates of cloud properties and aerosol–cloud interactions over the southeast Atlantic Ocean

Abstract. In situ cloud probe data from the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) field campaign were used to estimate the effective radius (Re), cloud optical thickness (τ), and cloud droplet concentration (Nc) for marine stratocumulus over the southeast Atlantic Ocean. The in situ Re, τ, and Nc were compared with co-located Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals of Re and τ and MODIS-derived Nc. For 145 cloud profiles, a MODIS retrieval was co-located with in situ data with a time gap of less than 1 h. On average, the MODIS Re and τ (11.3 µm and 11.7) were 1.6 µm and 2.3 higher than the in situ Re and τ with Pearson's correlation coefficients (R) of 0.77 and 0.73, respectively. The average MODIS Nc (151.5 cm−3) was within 1 cm−3 of the average in situ Nc with an R of 0.90. The 145 cloud profiles were classified into 67 contact profiles where an aerosol concentration (Na) greater than 500 cm−3 was sampled within 100 m above cloud tops and 78 separated profiles where Na less than 500 cm−3 was sampled up to 100 m above cloud tops. Contact profiles had a higher in situ Nc (by 88 cm−3), higher τ (by 2.5), and lower in situ Re (by 2.2 µm) compared to separated profiles. These differences were associated with aerosol–cloud interactions (ACI), and MODIS estimates of the differences were within 5 cm−3, 0.5, and 0.2 µm of the in situ estimates when profiles with MODIS Re>15 µm or MODIS τ>25 were removed. The agreement between MODIS and in situ estimates of changes in Re, τ, and Nc associated with ACI was driven by small biases in MODIS retrievals of cloud properties relative to in situ measurements across different aerosol regimes. Thus, when combined with estimates of aerosol location and concentration, MODIS retrievals of marine stratocumulus cloud properties over the southeast Atlantic can be used to study ACI over larger domains and longer timescales than possible using in situ data.

Quantification of Effects of Errors in the Cloud Properties on the Representation of the Surface Downward Shortwave Flux Based on MERRA-2 in Japan

Abstract The evaluation of the representation of the surface downward shortwave flux (DSF) from atmospheric reanalysis data products is required to obtain reliable information for the resource assessment of surface solar energy. The representation of the DSF from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), reanalysis data product was evaluated using surface solar radiation from ground-based observations in Japan. The cloud fraction (CFR) and cloud optical thickness (COT) from Moderate Resolution Imaging Spectroradiometer (MODIS) were also used as references. The CFR from MERRA-2 tends to be smaller than that from MODIS, and the correlation between the difference in the CFR and that in the DSF is negative. The correlation between the difference in the COT and that in the DSF is weakly negative. To quantify the effects of the difference in the CFR and COT to that of the DSF, a regression model based on an artificial neural network architecture that emulates the process of the DSF in MERRA-2 was constructed. Numerical experiments using the emulator quantify contributions of each of the differences in the CFR and COT and joint contributions of the two variables. In addition, a cluster analysis was performed to clarify the differences in the seasonal changes in the monthly mean bias error (MBE) in the DSF among ground observation stations, and three clusters were identified. Contributions of the differences in the CFR and COT to the seasonal change in the monthly MBE were also clarified using the results of the numerical experiments.

DVORAK MODEL FOR TYPHOON CYCLONE INTENSITY ESTIMATION

The Apache log4j implementation provides a logging framework that is extremely scalable, reliable, and configurable. This API makes it easier to write logging This study fosters a goal profound learningbased model for Typhoon Cyclone (TC) power assessment. The model's essential design is a convolutional brain organization (CNN), which is a broadly involved innovation in PC vision errands. To streamline the model's design and to further develop the element extraction capacity, both remaining learning and consideration systems are installed into the model. Five cloud items, including cloud optical thickness, cloud top temperature, cloud top level, cloud successful span, and cloud type, which are level-2 items from the geostationary satellite Himawari-8, are utilized as the model preparation inputs. We tested the cloud items under the 13 rotational points of every TC to expand the preparation dataset. For the free test information, the model shows improvement, with a generally low RMSE of 4.06 m/s and a mean outright blunder (MAE) of 3.23 m/s, which are practically identical to the outcomes seen in past examinations. Different cloud association designs, storm spinning examples, and TC structures from the component maps are introduced to decipher the model preparation process. An investigation of the misjudged predisposition and underrated inclination shows that the model's presentation is profoundly impacted by the underlying cloud items. Also, a few controlled tests utilizing other profound learning structures show that our planned model is helpful for assessing TC power, in this manner giving knowledge into the determining of other TC measurements

Segmentation-based multi-pixel cloud optical thickness retrieval using a convolutional neural network

Abstract. We introduce a new machine learning approach to retrieve cloud optical thickness (COT) fields from visible passive imagery. In contrast to the heritage independent pixel approximation (IPA), our convolutional neural network (CNN) retrieval takes the spatial context of a pixel into account and thereby reduces artifacts arising from net horizontal photon transfer, which is commonly known as independent pixel (IP) bias. The CNN maps radiance fields acquired by imaging radiometers at a single wavelength channel to COT fields. It is trained with a low-complexity and therefore fast U-Net architecture with which the mapping is implemented as a segmentation problem with 36 COT classes. As a training data set, we use a single radiance channel (600 nm) generated from a 3D radiative transfer model using large eddy simulations (LESs) from the Sulu Sea. We study the CNN model under various conditions based on different permutations of cloud aspect ratio and morphology, and we use appropriate cloud morphology metrics to measure the performance of the retrievals. Additionally, we test the general applicability of the CNN on a new geographic location with LES data from the equatorial Atlantic. Results indicate that the CNN is broadly successful in overcoming the IP bias and outperforms IPA retrievals across all morphologies. Over the Atlantic, the CNN tends to overestimate the COT but shows promise in regions with high cloud fractions and high optical thicknesses, despite being outside the general training envelope. This work is intended to be used as a baseline for future implementations of the CNN that can enable generalization to different regions, scales, wavelengths, and sun-sensor geometries with limited training.

Physical and Optical Properties of Clouds in the Southwest Vortex from FY-4A Cloud Retrievals

Abstract The southwest vortex (SWV) is a critical weather system in China, but our knowledge of this system remains incomplete. Here, we investigate the cloud properties in the SWV. First, we search for the SWVs with time steps and center locations that are consistent between the SWV yearbook and ERA-Interim reanalysis data. Second, we supplement these SWVs’ life spans and movement paths. Third, we relocate the Fengyun (FY) satellite FY-4A cloud retrievals in the 10° × 10° region centered on each SWV and analyze the cloud occurrence frequency (COF), cloud-top height (CTH), and cloud optical thickness (COT). A distribution mode of cloud types is summarized from the COFs, with water clouds, supercooled clouds, mixed clouds, ice clouds, cirrus clouds, and overlap clouds occurring sequentially from west to east. The CTH probability density (PD) distribution features a significant north–south difference. In addition, the COT PD distributions exhibit a common trend: with increasing COT, the PD increases rapidly and then slowly before peaking, whereupon the PD decreases abruptly. From spring to summer, the region with the highest convective COF shifts from the northeast to the northwest, and an east–west gradient of the convective COF appears in autumn and winter. Furthermore, we investigate the cloud properties during SWV-related heavy rainfall. Heavy rain occurs mainly in the west of the SWV, and convective clouds are mainly in the northwest, partly in the southwest and near the SWV center. The average CTH in heavy rainfall is generally higher than 6 km, and the average COT is greater than 20. Significance Statement The southwest vortex (SWV) is an important weather system in China. However, we do not yet comprehensively know this weather system. The cloud properties can indicate the structures of weather systems and are key parameters in numerical weather prediction (NWP) models. Thus, investigating cloud properties is necessary and meaningful to understand the SWV and accurately predict SWV-related precipitation in NWP models. In this paper, a typical distribution mode of six cloud types in the SWV is summarized from the cloud occurrence frequency, and the distribution features of convective clouds, cloud-top height, and cloud optical thickness in the SWV are analyzed. Furthermore, the cloud properties in SWV-related heavy rain are also studied.

Improving the Treatment of Subgrid Cloud Variability in Warm Rain Simulation in CESM2

Representing subgrid variability of cloud properties has always been a challenge in global climate models (GCMs). In many cloud microphysics schemes, the warm rain non-linear process rates calculated based on grid-mean cloud properties are usually scaled by an enhancement factor (EF) to account for the effects of subgrid cloud variability. In our study, we find that the EF derived from Cloud Layers Unified by Binormals in Community Atmosphere Model version 6 (CAM6) is severely overestimated in most of the cloudy oceanic areas, which leads to strong overestimation of the autoconversion rate. We improve the EF in warm rain simulation by developing a new formula for in-cloud subgrid cloud water variance. With the updated subgrid cloud water variance and EF treatment, the liquid cloud fraction (LCF) and cloud optical thickness (COT) increases noticeably for marine stratocumulus, and the shortwave cloud forcing (SWCF) matches better with observations. The updated formula improves the relationship between autoconversion rate and cloud droplet number concentration, and it decreases the sensitivity of autoconversion rate to aerosols. The sensitivity of liquid water path to aerosols decreases noticeably and is in better agreement with that in MODIS. Although the sensitivity of COT is similar to that in MODIS, CAM6 underestimates the sensitivity of grid-mean SWCF to aerosols because of the underestimation in the sensitivities of LCF and in-cloud SWCF. Our results indicate the importance of representing reasonable subgrid cloud variability in the simulation of cloud properties and aerosol-cloud interaction in GCMs.

Exploring relations between cloud morphology, cloud phase, and cloud radiative properties in Southern Ocean's stratocumulus clouds

Abstract. Marine stratocumuli are the most dominant cloud type by area coverage in the Southern Ocean (SO). They can be divided into different self-organized cellular morphological regimes known as open and closed mesoscale-cellular convective (MCC) clouds. Open and closed cells are the two most frequent types of organizational regimes in the SO. Using the liDAR-raDAR (DARDAR) version 2 retrievals, we quantify 59 % of all MCC clouds in this region as mixed-phase clouds (MPCs) during a 4-year time period from 2007 to 2010. The net radiative effect of SO MCC clouds is governed by changes in cloud albedo. Both cloud morphology and phase have previously been shown to impact cloud albedo individually, but their interactions and their combined impact on cloud albedo remain unclear. Here, we investigate the relationships between cloud phase, organizational patterns, and their differences regarding their cloud radiative properties in the SO. The mixed-phase fraction, which is defined as the number of MPCs divided by the sum of MPC and supercooled liquid cloud (SLC) pixels, of all MCC clouds at a given cloud-top temperature (CTT) varies considerably between austral summer and winter. We further find that seasonal changes in cloud phase at a given CTT across all latitudes are largely independent of cloud morphology and are thus seemingly constrained by other external factors. Overall, our results show a stronger dependence of cloud phase on cloud-top height (CTH) than CTT for clouds below 2.5 km in altitude. Preconditioning through ice-phase processes in MPCs has been observed to accelerate individual closed-to-open cell transitions in extratropical stratocumuli. The hypothesis of preconditioning has been further substantiated in large-eddy simulations of open and closed MPCs. In this study, we do not find preconditioning to primarily impact climatological cloud morphology statistics in the SO. Meanwhile, in-cloud albedo analysis reveals stronger changes in open and closed cell albedo in SLCs than in MPCs. In particular, few optically thick (cloud optical thickness >10) open cell stratocumuli are characterized as ice-free SLCs. These differences in in-cloud albedo are found to alter the cloud radiative effect in the SO by 21 to 39 W m−2 depending on season and cloud phase.

Errors in global cloud climatology due to transect sampling with the CALIPSO satellite lidar mission

Although cloud profiling lidars provide the most accurate data on cloud amount (CA), cloud top height (CTH) and cloud optical thickness (COT), they only sample cloud fields along a one-dimensional transect. This approach introduces uncertainty into studies that interpret cloud layers as two- or three-dimensional. The present study verified the accuracy of mean annual CA, COT, and CTH when calculated based on transect sampling, and when spatiotemporally averaged over grid cells measuring 1°, 2.5°, and 5° in latitude and longitude. The study evaluates the sampling scheme used by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar mission. In this study for the very first time, transect observations are investigated globally, at the level of climatological means, and for actual cloud regimes. Lidar data were simulated based on MODIS observations for 2005, resulting in 3,762,678 individual laser transects, and 58,647 annual means for each parameter under study. The results of this study demonstrate that, for most locations on Earth, CALIPSO sampling does report ‘true’ mean annual CA, COT, and CTH. At 1° resolution, statistically significant (α ≤ 0.05) differences in CA were only noted for 11% of cells, and for 2–3% of cells in the case of COT and CTH. Polar regions were identified as the most affected by transect sampling. Mean global errors (RMSE) observed at the annual timescale were within or close to the accuracy expected for satellite data used in climate change studies: 1.9% (CA), 2.4% (COT), and 187 m (CTH). This study also found that the spatial resolution of the grid impacts the magnitude of errors: an increase in cell size increased the underestimation of cloud parameters, increased the extent of areas where statistically significant errors/ differences occurred, and increased uncertainty in the climatological mean estimation (wider confidence intervals). However, at the cell level, errors were up to ±5% (CA), ±25% (COT) and ± 400 m (CTH); moreover, the magnitude of error was difficult to estimate a priori due to the noisy spatial distribution. These findings suggest that a dedicated uncertainty analysis should be made a requirement in CALIPSO lidar-based studies at fine spatial resolution.

Observational Constraints on Southern Ocean Cloud-Phase Feedback

Abstract Shortwave radiative feedbacks from Southern Ocean clouds are a major source of uncertainty in climate projections. Much of this uncertainty arises from changes in cloud scattering properties and lifetimes that are caused by changes in cloud thermodynamic phase. Here we use satellite observations to infer the scattering component of the cloud-phase feedback mechanism and determine its relative importance by comparing it with an estimate of the overall temperature-driven cloud feedback. The overall feedback is dominated by an optical thinning of low-level clouds. In contrast, the scattering component of cloud-phase feedback is an order of magnitude smaller and is primarily confined to free-tropospheric clouds. The small magnitude of this feedback component is a consequence of counteracting changes in albedo from cloud optical thickening and enhanced forward scattering by cloud particles. These results indicate that shortwave cloud feedback is likely positive over the Southern Ocean and that changes in cloud scattering properties arising from phase changes make a small contribution to the overall feedback. The feedback constraints shift the projected 66% confidence range for the global equilibrium temperature response to doubling atmospheric CO2 by about +0.1 K relative to a recent consensus estimate of cloud feedback. Significance Statement Understanding how clouds respond to global warming is a key challenge of climate science. One particularly uncertain aspect of the cloud response involves a conversion of ice particles to liquid droplets in extratropical clouds. Here we use satellite data to infer how cloud-phase conversions affect climate by changing cloud albedo. We find that ice-to-liquid conversions increase cloud optical thickness and shift the scattering angles of cloud particles toward the forward direction. These changes in optical properties have offsetting effects on cloud albedo. This finding provides new insight about how changes in cloud phase affect climate change.

Influence of smoke aerosols on low-level clouds over the Indian region during winter

The effect of smoke aerosols on the low-level clouds over the Indian landmass during the winter season is examined using 15 years (2005–2019) of long-term multi-satellite observations and reanalyses (MERRA-2 and ERA-5) datasets. Climatologically higher values of aerosol optical depth (AOD) (> 0.6), Angstrom exponent (> 1.5), UV-aerosol index (> 0.7), and black carbon and organic carbon (BC + OC) extinction aerosol optical thickness (EXTAOT) (> 0.18) are found over the Indo-Gangetic Plain (IGP) compared to the rest of India indicating the dominance of smoke aerosols over the region. However, considerable enhancements in AOD (~ 60%) and (BC + OC) EXTAOT (20–40%) are observed over some parts of eastern India (particularly Odisha and Chhattisgarh) and central-south India compared to IGP (< 10% and < 5% respectively) in the recent years. Also, fire activities are increasing over almost the same region where smoke aerosols increased considerably, suggesting a significant role of local biomass burning (BB) in the observed rise of smoke aerosols. However, prevailing wind patterns and HYSPLIT backward air mass trajectories indicate that long-range transport from the upper IGP regions and northwestern sides of India and beyond could also increase smoke loading over this region. Climatologically, distinct spatial patterns with moderate cloud fraction (CF) (0.5–0.6) and comparatively larger liquid cloud effective radius (CER) (16–17 μm) are found over the IGP region. This study suggests that mainly low-level clouds contribute to this distinct signature of cloud distributions. However, a significant increase in CF (50–60%) is observed over almost the same region where smoke aerosols showed a considerable rise, implying the possible influence of smoke aerosols on the low-level clouds. While CF and CER showed a noticeable increase in the polluted condition, cloud optical thickness and liquid cloud water path showed a consequent decrease with increased aerosol loading. CALIPSO images suggest that mostly a mixture of dust, polluted dust, and polluted continental/smoke aerosols showed clear dominance over the inland areas, mostly confined within 2 km altitude over almost the entire study domain. However, an elevated layer of absorbing aerosols (smoke and polluted dust) over the low-level cloud increased the cloud fraction through the ‘aerosol-cloud-boundary layer’ feedback mechanism.

Impacts of active satellite sensors' low-level cloud detection limitations on cloud radiative forcing in the Arctic

Abstract. Previous studies revealed that satellites sensors with the best detection capability identify 25 %–40 % and 0 %–25 % fewer clouds below 0.5 and between 0.5–1.0 km, respectively, over the Arctic. Quantifying the impacts of cloud detection limitations on the radiation flux are critical especially over the Arctic Ocean considering the dramatic changes in Arctic sea ice. In this study, the proxies of the space-based radar, CloudSat, and lidar, CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations), cloud masks are derived based on simulated radar reflectivity with QuickBeam and cloud optical thickness using retrieved cloud properties from surface-based radar and lidar during the Surface Heat Budget of the Arctic Ocean (SHEBA) experiment. Limitations in low-level cloud detection by the space-based active sensors, and the impact of these limitations on the radiation fluxes at the surface and the top of the atmosphere (TOA), are estimated with radiative transfer model Streamer. The results show that the combined CloudSat and CALIPSO product generally detects all clouds above 1 km, while detecting 25 % (9 %) fewer in absolute values below 600 m (600 m to 1 km) than surface observations. These detection limitations lead to uncertainties in the monthly mean cloud radiative forcing (CRF), with maximum absolute monthly mean values of 2.5 and 3.4 Wm−2 at the surface and TOA, respectively. Cloud information from only CALIPSO or CloudSat lead to larger cloud detection differences compared to the surface observations and larger CRF uncertainties with absolute monthly means larger than 10.0 Wm−2 at the surface and TOA. The uncertainties for individual cases are larger – up to 30 Wm−2. These uncertainties need to be considered when radiation flux products from CloudSat and CALIPSO are used in climate and weather studies.

Comparison of microphysics parameterization schemes on cloud macrophysics forecasts for mixed convective-stratiform cloud events

In this study, the performances and uncertainties of cloud microphysics parameterization (MP) schemes for simulating cloud macrophysics associated with mixed convective-stratiform cloud (MCSC) events are investigated. MCSC system is the primary object of cloud seeding for the purpose of rain enhancement in North China. Understanding the forecast uncertainties of cloud macrophysics, such as cloud top temperature (CTT), cloud base height (CBH), and cloud optical thickness (COT), is of great significance for cloud seeding operations. Simulations of cloud macrophysics are verified using cloud data retrieved from the Feng-Yun-2F geostationary meteorological satellite, radiosondes and Moderate Resolution Imaging Spectroradiometer (MODIS) products. The performances of several MP schemes to accurately simulate cloud macrophysics within ensemble forecasts is explored using both grid-wise and object-based metrics. Large differences are found among schemes in simulated cloud macrophysics compared with similar performances in simulated precipitation. In most cases, the ensemble mean simulates more realistically than each single scheme. The ensemble spread varies somewhat according to the simulated variables. For CBH, the ensemble shows adequate ensemble spread, while relative underdispersion occurs for CTT and COT. Detailed COT comparisons of reveal that COT bias mainly comes from the contribution of cloud droplets and snow. The causes of COT bias are further investigated by a detailed comparison of the cloud water path (CWP) and cloud effective radius (EFFR). The study establishes a baseline for ensemble forecasting of cloud macrophysics.

INDIRECT EFFECT OF LAND COVER TOWARD ON CLOUD OPTICAL THICKNESS OVER INDONESIA

The inter-relation between land surface changes (land cover) and local climate affect other atmospheric phenomenon such as clouds and their formation and properties. The Earth’s hydrological cycle is complex system describing the mutual relationship between Earth's surface and the atmospheric component, as a consequence, small changes to one part of the system can accrue to have larger effects on the other system as a whole. NDVI and cloud optical thickness obviously allocated in wet season than dry season, with fluctuated in uphill and downhill polynomial. According to wet season, downhill line of cloud optical thickness ware detected as mean value on every November during 14 years. At 1 percent of NDVI fluctuation declined two times of optical depth otherwise. Absolute result in wet season may be due to more stable and homogeneous data variability. Least sunlight for vegetation growth and the least amount of evapotranspiration energy, less cloud forms.

Accuracy of Cirrus Detection by Surface-Based Human Observers

Abstract The longest cirrus time series are ground-based, visual observations captured by human observers [synoptic observations (SYNOP)]. However, their reliability is impacted by an unfavorable viewing geometry (cloud overlap) and misclassification due to low cloud optical thickness, especially at night. For the very first time, this study assigns a quantitative value to uncertainty. We validate 15 years of SYNOP observations (2006–20) against data from the cloud lidar flown on board the Cloud–Aerosol Lidar and Infrared Pathfinder (CALIPSO) spacecraft. We develop a dedicated method to match SYNOP reports (with a hemispherical field of view) with lidar samples (along-track profiles). Our evaluation of the human eye’s sensitivity to cirrus revealed that it is moderate, at best. In perfect conditions (daytime with no mid/low-level clouds) the probability of correct detection was 44%–83% (Cohen’s kappa coefficient &lt; 0.6), and this fell to 24%–42% (kappa &lt; 0.3) at night. Lunar illumination improved detection, but only when the moon’s phase exceeded 50%. Cirrus optical depth had a clear impact on detection. When clouds at all levels were considered (i.e., real-life conditions), the reliability of the visual method was moderate to poor: it detected 47%–71% of cirrus (kappa &lt; 0.45) during the day and 28%–43% (kappa &lt; 0.2) at night and decreased with an increasing low/midlevel cloud fraction. These kappa coefficients suggest that agreement with CALIPSO data was close to random. Our findings can be directly applied to estimations of cirrus frequency/trends. Our reported probabilities of detection can serve as a benchmark for other ground-based cirrus detection methods. Significance Statement Cirrus clouds heat the atmosphere, so any increase in their frequency will contribute to climate warming. The longest cirrus time series (including the presatellite era) are surface-based detections by a human observer at a meteorological station. Our study is the first to quantitatively evaluate the reliability of these observations. Our results show that, because of the viewing geometry (cloud overlap) and human eye sensitivity, reliability ranges from moderate at best to very low. Nighttime detections are especially unreliable, as well as those in the presence of low/midlevel cloud. Cirrus frequencies and trends calculated from visual observations should, thus, be considered with caution.

Clouds independently appear to have as much or greater effect than man-made CO2 on radiative forcing

The patterns of behaviour of clouds, both for cloud area and cloud optical thickness, are studied over the period of available data, 1983 to 2017. There was a decrease in cloud cover over the study period, while global surface temperatures increased. The patterns of clouds and temperature indicate that the cloud cover decrease could not have been caused by the increased surface temperature. The clear implication is that the decrease in global cloud area must have been caused by some other unspecified factor, and was not caused directly or indirectly by CO2. Evaluation of the changes in clouds and CO2 over the study period indicate that this unspecified factor had as much positive impact as the increase in CO2, with respect to the amount of radiation reaching the surface (radiative forcing), and possibly a much larger positive impact. The climate models, which have zero or negative cloud impact on radiative forcing independently from CO2, need to take this into account in order to avoid over-estimating the influence of CO2.

Simplified estimation modeling of land surface solar irradiation: A comparative study in Australia and China

Solar irradiation maps are fundamental geospatial datasets that have been used in a variety of research fields. However, it is difficult to estimate the continuous distribution of solar irradiation over large areas accurately by using conventional interpolation or extrapolation methods based on only a few observation stations. To tackle this problem, this study proposed a method to estimate spatially continuous land surface solar irradiation based on four machine learning models, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP). Clear-sky solar irradiation data were computed based on time and location, cloud optical thickness (COT) and aerosol optical thickness (AOT) that significantly influence solar irradiation were retrieved from Himawari-8 meteorological satellite images, and land surface solar irradiation data were obtained from observation stations for training and evaluation. To explore the weather effects on land surface solar irradiation, air temperatures, humidity, wind, and atmospheric pressure were also quantified and integrated into the models. As a comparative study, this study collected six-year historical data and estimated solar distribution at a 5-km spatial resolution in Australia and China. Based on the coefficient of determination (R2), normalized Root Mean Square Error (nRMSE), normalized mean bias error (nMBE), and consumption of time (t), the results show that GBM achieved the highest accuracy with R2>0.7 at all stations, followed by RF, SVR, and MLP. It suggests that the proposed method can provide an accurate and reliable estimation of land surface solar irradiation, compared with the theoretical solar irradiation without the obstacle of the atmosphere. The annual solar distribution maps created by the built methods indicate that the proposed method is simple and effective for large geographical regions and can be used worldwide when similar datasets are obtained.

Retrieval of cloud properties from thermal infrared radiometry using convolutional neural network

In this study, a deep learning algorithm is developed to consistently retrieve the daytime and nighttime cloud properties from passive satellite observations without auxiliary atmospheric parameters. The algorithm involves the thermal infrared (TIR) radiances, viewing geometry, and altitude into a convolutional neural network (denoted as TIR-CNN), and retrieves the cloud mask, cloud optical thickness (COT), effective particle radius (CER), and cloud top height (CTH) simultaneously. The TIR-CNN model is trained using daytime Moderate Resolution Imaging Spectroradiometer (MODIS) products during a full year, and the results are validated and evaluated using passive and active products observed in independent years. The evaluation results show that the cloud properties retrieved by the TIR-CNN are well consistent with all available MODIS day-time products (cloud mask, COT, CER, and CTH) and night-time products (cloud mask and CTH). The retrieved COT and CTH also show good agreements with active sensors for both daytime and nighttime, indicating that the algorithm performs stably in the diurnal cycle.

Assessment of Two Stochastic Cloud Subcolumn Generators Using Observed Fields of Vertically Resolved Cloud Extinction

Abstract We evaluate two stochastic subcolumn generators used in GCMs to emulate subgrid cloud variability enabling comparisons with satellite observations and simulations of certain physical processes. Our evaluation necessitated the creation of a reference observational dataset that resolves horizontal and vertical cloud variability. The dataset combines two CloudSat cloud products that resolve two-dimensional cloud optical depth variability of liquid, ice, and mixed phase clouds when blended at ~200 m vertical and ~ 2 km horizontal scales. Upon segmenting the dataset to individual “scenes”, mean profiles of the cloud fields are passed as input to generators that produce scene-level cloud subgrid variability. The assessment of generator performance at the scale of individual scenes and in a mean sense is largely based on inferred joint histograms that partition cloud fraction within predetermined combinations of cloud top pressure – cloud optical thickness ranges. Our main finding is that both generators tend to underestimate optically thin clouds, while one of them also tends to overestimate some cloud types of moderate and high optical thickness. Associated radiative flux errors are also calculated by applying a simple transformation to the cloud fraction histogram errors, and are found to approach values almost as high as 3 W m−2 for the cloud radiative effect in the shortwave part of the spectrum.

Cloud identification and property retrieval from Himawari-8 infrared measurements via a deep neural network

Clouds constitute a key component of weather and climate systems, whereas the uniform retrieval of cloud properties, such as cloud top height (CTH) and cloud optical thickness (COT), requires accuracy and computational efficiency improvements. In this study, an image-based deep neural network (DNN) model for cloud identification and simultaneous retrieval of CTH and ice-COT is developed for Himawari-8 satellite infrared measurements. The DNN model is trained with brightness temperature data from four months in 2016 as the input, and cloud properties of an active remote sensing product from CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) as the target truth. Supplementary variables, including the vertical temperature profile, the surface elevation, and the geometrical parameters, are added as the input data. DNN model performance is first tested with an independent dataset, and then cases over a CloudSat track and a Himawari-8 granule (85°E–205°E, 60°S–60°N) are selected for further validation of the model by comparing its results with those from two physics-based models. For both the water- and ice-CTH estimates, the DNN model shows high consistency with the target values, with an overall CTH correlation coefficient of 0.90 for high ice clouds with COT ≥0.3. Notably, as an infrared method in nature, the DNN extends the predictable ice-COT to ~200, with relative biases of ~20% for high ice clouds with COT >1. The strong accuracy of the DNN model is primarily derived from its ability to learn from the spatial features imprinted on the input brightness temperature image, and its integration of information from neighboring pixels in a three-dimensional space. A single full disk estimation with the DNN model takes about 20 min using one processor; therefore, near-real-time cloud property retrieval that is uniformly available over 24 h can be obtained for severe weather monitoring and mesoscale cloud-system studies.