CloudSat Data Research Articles

Validation of Satellite‐Based Cloud Phase Distributions Using Global‐Scale In Situ Airborne Observations

AbstractUnderstanding distributions of cloud thermodynamic phases is important for accurately representing cloud radiative effects and cloud feedback in a changing climate. Satellite‐based cloud phase data have been frequently used to compare with climate models, yet few studies validated them against in situ observations at a near‐global scale. This study aims to validate three satellite‐based cloud phase products using a compositive in situ airborne data set developed from 11 flight campaigns. Latitudinal‐altitudinal cross sections of cloud phase occurrence frequencies are examined. The Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) show the most similar vertical profiles of ice phase frequencies compared with in situ observations. The CloudSat data overestimate mixed‐phase frequencies up to 15 km but provide better sampling through cloud layers than lidar data. The DARDAR (raDAR/liDAR) data show a sharp transition between ice and liquid phase and overestimate ice phase frequency at most altitudes and latitudes. The satellite data are further evaluated for various latitudes, longitudes, and seasons, which show higher ice phase frequency in the extratropics in their respective wintertime and smaller impacts from longitudinal variations. The Southern Ocean shows a thicker mixing region where liquid and ice phases have similar frequencies compared with tropics and Northern Hemisphere (NH) extratropics. Two comparison methods with different spatiotemporal windows show similar results, which demonstrates the statistical robustness of these comparisons. Overall, this study develops a near global‐scale in situ observational data set to assess the accuracy of satellite‐based cloud phase products and investigates the key factors affecting the distributions of cloud phases.

Impact of the tilted cloud vertical structure on a northward-progress episode of the East Asian summer monsoonal precipitation belt

Impact of cloud vertical structure (CVS) on a northward-progressing rainfall episode of the East Asian summer monsoon (EASM) is explored using the Weather Research and Forecasting model, in which CloudSat observation-based vertical structure of cloud liquid water content (LWC) can be imposed. Composite LWC anomaly from CloudSat data shows a northward tilted structure from the upper to the lower troposphere. Compared to the control simulation (without modification of LWC), the one with LWC imposed, but without tilted structure, doesn’t show significant changes. When LWC is introduced and northward tilted, the geopotential height (HGT) decreases in the north of the convective center, which increases the meridional wind and provides favorable conditions for the northward shift of the precipitation belt. When LWC is southward tilted, HGT decreases in the middle and lower troposphere in the south of the convective center and increases in the north, which slows down the northward shift of the precipitation belt. Adding cloud water leads to increase in humidity and decrease in temperature, causing significant increase in stratiform clouds and related precipitation. In the configuration of northward tilted LWC, low-temperature and high-humidity area is located on the north side of the convective center, favorable for the occurrence and northward shift of the precipitation belt. Deep convection is weakened with convective precipitation reduced, while shallow convection enhances the latent heat release in the lower troposphere. Therefore, more water vapor and energy are transported from boundary layer to free atmosphere, promoting the northward shift of the precipitation belt.

A global gridded dataset for cloud vertical structure from combined CloudSat and CALIPSO observations

Abstract. The vertical structure of clouds has a profound effect on the global energy budget, the global circulation, and the atmospheric hydrological cycle. The CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) missions have taken complementary, colocated observations of cloud vertical structure for over a decade. However, no globally gridded dataset is available to the public for the full length of this unique combined data record. Here we present the 3S-GEOPROF-COMB product (Bertrand et al. 2023, https://doi.org/10.5281/zenodo.8057791), a globally gridded (level 3S) community data product summarizing geometrical profiles (GEOPROF) of hydrometeor occurrence from combined (COMB) CloudSat and CALIPSO data. Our product is calculated from the latest release (R05) of per-orbit (level-2) combined cloud mask profiles. We process a set of cloud cover, vertical cloud fraction, and sampling variables at 2.5, 5, and 10° spatial resolutions and monthly and seasonal temporal resolutions. We address the 2011 reduction in CloudSat data collection with Daylight-Only Operations (DO-Op) mode by subsampling pre-2011 data to mimic DO-Op collection patterns, thereby allowing users to evaluate the impact of the reduced sampling on their analyses. We evaluate our data product against CloudSat-only and CALIPSO-only global-gridded data products as well as four comparable surface-based sites, underscoring the added value of the combined product. Interest in the product is anticipated for the study of cloud processes, cloud–climate interactions, and as a candidate baseline climate data record for comparison to follow-up satellite missions, among other uses.

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

Abstract. Satellite instruments provide high-temporal-resolution data on a global scale, but extracting 3D information from current instruments remains a challenge. Most observational data are two-dimensional (2D), offering either cloud top information or vertical profiles. We trained a neural network (Res-UNet) to merge high-resolution satellite images from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) with 2D CloudSat radar reflectivities to generate 3D cloud structures. The Res-UNet extrapolates the 2D reflectivities across the full disk of MSG SEVIRI, enabling a reconstruction of the cloud intensity, height, and shape in three dimensions. The imbalance between cloudy and clear-sky CloudSat profiles results in an overestimation of cloud-free pixels. Our root mean square error (RMSE) accounts for 2.99 dBZ. This corresponds to 6.6 % error on a reflectivity scale between −25 and 20 dBZ. While the model aligns well with CloudSat data, it simplifies multi-level and mesoscale clouds in particular. Despite these limitations, the results can bridge data gaps and support research in climate science such as the analysis of deep convection over time and space.

Radiative heating and upwelling by different types of TTL clouds over the tropical convective regions

Tropical tropopause layer (TTL) clouds, defined as clouds whose tops are located above the tropopause level, play important roles in Earth's radiation balance and troposphere-to-stratosphere transport. Examining how different TTL cloud type gives influence on the radiative heating, we classified TTL clouds into three types (i.e., deep convective cloud, optically thick convective anvil cloud, and optically thin cirrus cloud) over the convectively active four tropical regions (i.e., Central Africa and South America continental regions and the Central and Western Pacific oceanic regions), using the combined Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat data. Using the classified cloud data, we first examined characteristics of the macrophysical and microphysical properties of the classified cloud types. Amongst three types of TTL clouds over selected four regions, thin cirrus clouds are most abundant (almost four-fifths), and approximately 7% and 17% of TTL clouds are deep convective cloud and thick convective anvil clouds, respectively. It is noted that the land–ocean contrast in TTL clouds appears weak in spite of well-known land–ocean contrast in the convection intensity, suggesting that TTL cirrus clouds may be dominated by in situ cirrus clouds. We further examined radiative heating over the TTL altitudes by each cloud type and the associated radiatively-driven upwelling motion. It is revealed that the dominant contribution to the overall radiative heating is from thin cirrus clouds (∼48.4%) because of their abundance. Nevertheless, convective anvil clouds and deep convective clouds are also found to give significant contribution to the radiative heating. Such cloud radiative heating tends to lower the level of zero net radiative heating rate found in the clear sky by ∼400–900 m. The TTL clouds play an important role in the secondary circulation in the TTL by inducing the upwelling motion throughout the TTL, supporting the mechanism that the radiative effect of TTL clouds can enhance the mass flux from the troposphere to the stratosphere.

A Neural Network‐Based Scale‐Adaptive Cloud‐Fraction Scheme for GCMs

AbstractCloud fraction (CF) significantly affects the short‐ and long‐wave radiation. Its realistic representation in general circulation models (GCMs) still poses great challenges in modeling the atmosphere. Here, we present a neural network‐based (NN‐based) diagnostic scheme that uses the grid‐mean temperature, pressure, liquid and ice water mixing ratios, and relative humidity to simulate the sub‐grid CF. The scheme, trained using CloudSat data with explicit consideration of grid sizes, realistically simulates the observed CF with a correlation coefficient >0.9 for liquid‐, mixed‐, and ice‐phase clouds. The scheme also captures the observed non‐monotonic relationship between CF and relative humidity and is computationally efficient, and robust for GCMs with a variety of horizontal and vertical resolutions. For illustrative purposes, we conducted comparative analyses of the 2006–2019 climatological‐mean cloud fractions among CloudSat, and simulations from the NN‐based scheme and the Xu‐Randall scheme (optimized the same way as the NN‐based scheme). The NN‐based scheme improves not only the spatial distribution of the total CF but also the cloud vertical structure. For example, the biases of too‐many high‐level clouds over the tropics and too‐many low‐level clouds over regions around 60°S and 60°N in the Xu‐Randall scheme are significantly reduced. These improvements are also found to be insensitive to the spatio‐temporal variability of large‐scale meteorology conditions, implying that the scheme can be used in different climate regimes.

Revisiting the Land‐Ocean Contrasts in Deep Convective Cloud Intensity Using Global Satellite Observations

AbstractTropical convection tends to be more intense over land than ocean, but why? Numerous previous studies have investigated the causes of this difference. This paper revisits this question using CloudSat data and focuses on interconnecting various environmental parameters and cloud properties, which have often been examined in a piecemeal way in the past. Our analysis shows that if convection is treated as a process by which potential energy (convective available potential energy) is converted to kinetic energy (vertical velocity), then the conversion is more efficient over land than ocean. A key factor that affects this conversion efficiency is the lifting condensation level (LCL). Higher LCLs over land give rise to broader dry boundary layer thermals that transition to wider deep convective cores. Wider cores, in turn, are better protected from the dilution by entrainment, thus leading to stronger updrafts. This study highlights the importance of the dry stage of convection.

A kriging-based analysis of cloud liquid water content using CloudSat data

Abstract. Spatiotemporal statistical learning has received increased attention in the past decade, due to spatially and temporally indexed data proliferation, especially data collected from satellite remote sensing. In the meantime, observational studies of clouds are recognized as an important step toward improving cloud representation in weather and climate models. Since 2006, the satellite CloudSat of NASA is carrying a 94 GHz cloud-profiling radar and is able to retrieve, from radar reflectivity, microphysical parameter distribution such as water or ice content. The collected data are piled up with the successive satellite orbits of nearly 2 h, leading to a large compressed database of 2 Tb (http://cloudsat.atmos.colostate.edu/, last access: 8 June 2022). These observations offer the opportunity to extend the cloud microphysical properties beyond the actual measurement locations using an interpolation and prediction algorithm. To do so, we introduce a statistical estimator based on the spatiotemporal covariance and mean of the observations known as kriging. An adequate parametric model for the covariance and the mean is chosen from an exploratory data analysis. Beforehand, it is necessary to estimate the parameters of this spatiotemporal model; this is performed in a Bayesian setting. The approach is then applied to a subset of the CloudSat dataset.

Cloud radar perspective on tropical warm clouds associated with summer monsoon rainfall and warm rain onset process

The role of warm clouds in monsoon rainfall and the warm rain onset process are investigated using high-resolution cloud-sensitive radar measurements. Quality controlled data from Ka-band radar consisting of 1.8 million profiles during July–August 2015, have been explored. A method is devised using the Ze profiles, for identifying warm clouds whose cloud tops are confined below 5.3 km. Warm rain microphysical parameters are examined utilizing the complemented measurements from Disdrometer, rain gauge, and CloudSat data. The strong temperature inversion of 2 to 4 K km−1 at 3 km explained the generation of shallow warm clouds. Strong low-level updraft and subsidence above the warm clouds are also evident. Contoured frequency by altitude diagram infers that 75% of Ze (velocity) dominates at −20 dBZe (−1 m s−1). Simultaneous Disdrometer measurements revealed 78% of cases with zero-drop count and zero rain rates. The dominating non-precipitating characteristics of warm clouds are also confirmed from the CloudSat inferred cloud droplet effective radius below 10 μm. During the rainy episodes, peak at 0.91 mm in the drop size distribution suggests prominence of drizzle-sized drops. The precipitation caused by warm clouds accounts for a small fraction (28%) of the total rainfall. The warm rain onset process involving the transformation of smaller cloud droplets into big raindrops is investigated using the radar's three lower-order moments. Before cloud droplet to raindrop transition an intense spike in the spectral width (SW) is observed for a brief period in 98% of the cases. Therefore, SW, which infers turbulence, triggers the collision-coalescence of cloud droplets to form raindrops. This study provides a multi-observation-based perspective on the macro- and micro-physical characteristics of monsoon warm clouds and the onset of warm rain for a better representation in the global climate models.

Cloud macrophysical characteristics in China mainland and east coast from 2006 to 2017 using satellite active remote sensing observations

AbstractUsing the CloudSat and CALIPSO active remote sensing dataset from 2006 to 2017, this study investigates the spatial and temporal distribution along with vertical distribution of cloud macrophysical characteristics in six study areas over China mainland and east coast (15°–55°N, 70°–140°E). The results show that the cloud top height and cloud base height have significant regional, seasonal and zonal variations. The regional differences in the seasonal average vertical distribution of cloud cover are especially large, which are particularly reflected by the differences of heights with peak value of cloud cover, the curve characteristics of cloud cover with height, and the standard deviation of cloud cover. The cloud spacing, which is the distance between two cloud layers for cloud system with layers no less than 2, is found greater in the ocean area than in the land area and is greater in the low‐latitude area than in the high‐latitude area. Moreover, with the increase of the number of cloud layers in the cloud system, the cloud spacing decreases gradually. It is worth noting that the frequency distribution of cloud layers is the same as that of cloud spacing. By dividing clouds into eight types according to CloudSat data, the occurrence frequency of different cloud types shows great differences, ranking from high to low as high cloud > altostratus > altocumulus > stratus > stratocumulus > cumulus > nimbostratus > deep convective cloud. Altostratus mainly occurs in northern regions, and high cloud mostly occurs in Tibetan Plateau, southern China mainland and eastern ocean. The vertical distributions of cloud macrophysical characteristics over the six typical study areas have been further demonstrated by constructing Pc‐τ diagram.

Regional characteristics and exploitation potential of atmospheric water resources in China

AbstractThe atmospheric water resources (AWR) in China have evident regional characteristics. In order to scientifically and rationally exploit the AWR in different regions of China, we divided China into seven regions based on the ERA‐5, the CloudSat data, and so on. Such characteristics as water vapour, cloud characteristics, cloud water resources (CWR), precipitation efficiency of hydrometeors (PEh), and the exploitation potential of CWR were studied in this article. Some results in summer are shown. (a) There were relatively more precipitation, richer water vapour, stronger water vapour transport and convergence, more cloud covers, and relatively larger zones of rich total column cloud water content in China. Therefore, it is of important significance to study AWR in summer in China. (b) In respect of single‐layer clouds, low clouds were in the majority over the Tibetan Plateau (TP) and the southwest China (SW), medium clouds were predominant over the northern regions, and high clouds existed more over the central region (CR) and the southeast China (SE). The liquid water content (LWC) at each height of cloud was less than 800 mg·m−3 and decreased gradually with the increase in height in China. The convective development heights, the LWC at each altitude, and the frequencies of less LWCs over the northern regions were lower than those over the southern regions. The ice water content (IWC) at each height of cloud was less than 250 mg·m−3, and more than 96% of them were less than 100 mg·m−3. (c) CWR over the northeast China (NE), north China (NC), and CR were relatively rich, but with relatively low PEh, so there was relatively higher exploitation potential of CWR in these regions, followed by the northwest China (NW) and TP, and there was still some exploitation potential of CWR in SE and SW.

Comprehensive analysis of cloudiness over Iran with CloudSat data

Comprehensive analysis of cloudiness over Iran with CloudSat data

3D radiative heating of tropical upper tropospheric cloud systems derived from synergistic A-Train observations and machine learning

Abstract. Upper tropospheric (UT) cloud systems constructed from Atmospheric Infrared Sounder (AIRS) cloud data provide a horizontal emissivity structure, allowing the convective core to be linked to anvil properties. By using machine learning techniques, we composed a horizontally complete picture of the radiative heating rates deduced from CALIPSO lidar and CloudSat radar measurements, which are only available along narrow nadir tracks. To train the artificial neural networks, we combined the simultaneous AIRS, CALIPSO and CloudSat data with ERA-Interim meteorological reanalysis data in the tropics over a period of 4 years. The resulting non-linear regression models estimate the radiative heating rates as a function of about 40 cloud, atmospheric and surface properties, with a column-integrated mean absolute error (MAE) of 0.8 K d−1 (0.5 K d−1) for cloudy scenes and 0.4 K d−1 (0.3 K d−1) for clear sky in the longwave (shortwave) spectral domain. Developing separate models for (i) high opaque clouds, (ii) cirrus, (iii) mid- and low-level clouds and (iv) clear sky, independently over ocean and over land, leads to a small improvement, when considering the profiles. These models were applied to the whole AIRS cloud dataset, combined with ERA-Interim, to build 3D radiative heating rate fields. Over the deep tropics, UT clouds have a net radiative heating effect of about 0.3 K d−1 throughout the troposphere from 250 hPa downward. This radiative heating enhances the column-integrated latent heating by about 22±3 %. While in warmer regions the net radiative heating profile is nearly completely driven by deep convective cloud systems, it is also influenced by low-level clouds in the cooler regions. The heating rates of the convective systems in both regions also differ: in the warm regions the net radiative heating by the thicker cirrus anvils is vertically more extended, and their surrounding thin cirrus heat the entire troposphere by about 0.5 K d−1. The 15-year time series reveal a slight increase of the vertical heating in the upper and middle troposphere by convective systems with tropical surface temperature warming, which can be linked to deeper systems. In addition, the layer near the tropopause is slightly more heated by increased thin cirrus during periods of surface warming. While the relative coverage of convective systems is relatively stable with surface warming, their depth increases, measured by a decrease of their near-top temperature of -3.4±0.2 K K−1. Finally, the data reveal a connection of the mesoscale convective system (MCS) heating in the upper and middle troposphere and the (low-level) cloud cooling in the lower atmosphere in the cool regions, with a correlation coefficient equal to 0.72, which consolidates the hypothesis of an energetic connection between the convective regions and the subsidence regions.

Brief communication: Evaluating Antarctic precipitation in ERA5 and CMIP6 against CloudSat observations

Abstract. CMIP5, CMIP6, and ERA5 Antarctic precipitation is evaluated against CloudSat data. At continental and regional scales, ERA5 and the median CMIP models are biased high, with insignificant improvement from CMIP5 to CMIP6. However, there are fewer positive outliers in CMIP6. AMIP configurations perform better than the coupled ones, and, surprisingly, relative errors in areas of complex topography are higher (up to 50 %) in the five higher-resolution models. The seasonal cycle is reproduced well by the median of the CMIP models, but not by ERA5. Progress from CMIP5 to CMIP6 being limited, there is still room for improvement.

A framework for estimating cloudy sky surface downward longwave radiation from the derived active and passive cloud property parameters

The cloud-base temperature (CBT) is one of the parameters that dominates the cloudy sky surface downward longwave radiation (SDLR). However, CBT is rarely available at regional and global scales, and its application in estimating cloud sky SDLR is limited. In this study, a framework to globally estimate cloud sky SDLR during both daytime and nighttime is proposed. This framework is composed of three parts. First, a global cloudy property database was constructed by combing the extracted cloud vertical structure (CVS) parameters from the active CloudSat data and cloud properties from passive MODIS data. Second, the empirical methods for estimating cloud thickness (CT) under ISCCP cloud classification system and MODIS cloud classification system were developed. Additionally, the coefficients of CERES CT estimate models were refitted using the constructed cloud property database. With the estimated CT and reanalysis data, calculating the CBT is straightforward. The accuracy of the estimated CT for ISCCP cloud type is compared with the existing studies that were conducted at local scales. Our CT estimate accuracy is comparable to that of the existing studies. According to the validation results at ARM NSA and SGP stations, the CT estimated by the developed CT model for MODIS cloud type is better than that estimated by the original CERES CT model. Finally, the cloudy sky SDLR values were derived by feeding the estimated CBT and other parameters to the single-layer cloud model (SLCM). When validated by the ground measured SDLR collected from the SURFRAD network, the bias and RMSE are 5.42 W∙m−2 and 30.3 W∙m−2, respectively. This accuracy is comparable to the evaluation results of the mainstream SDLR products (Gui et al. 2010), the new evaluation results of SLCMs (Yu et al. 2018), and the accuracy of a new cloudy sky SDLR estimate method (Wang et al. 2018). All the derived CBTs improve the SDLR estimate accuracy more than the SLCM that directly uses cloud-top temperature (CTT). We will collect more ground measurements and continue to validate the developed framework in the future.

On the Frequency of Occurrence of the Ice Phase in Supercooled Southern Ocean Low Clouds Derived From CALIPSO and CloudSat

AbstractSupercooled liquid clouds are an important component of the albedo of the Southern Ocean (SO). While ice phase occurrence in liquid‐dominant clouds (hereafter mixed phase) at temperatures warmer than the homogeneous freezing point is rare in the SO, the processes that create mixed‐phase clouds are not understood. Using data from the CALIPSO lidar, we reconsider the thresholds of layer‐integrated depolarization ratio and layer‐integrated attenuated backscatter that are used to diagnose the phases of fully attenuating cloud layers. We argue that liquid‐only clouds have understood physical bounds to these thresholds allowing for unique identification of layers that are not consistent with the presence of single‐phase liquid tops. Compared to the original phase algorithm the application of these physically constrained thresholds results in a ~70% increase in mixed phase during the annual cycle considered. Combining the CALIPSO data with CloudSat data, mixed‐phase clouds seem to typically cooccur with precipitation implying secondary ice forming processes.

An Active–Passive Microwave Land Surface Database From GPM

A microwave emissivity retrieval is applied to five years of global precipitation measurement (GPM) microwave imager (GMI) observations over land and sea ice. The emissivities are colocated with GPM’s dual-frequency precipitation radar (DPR) surface backscatter measurements in clear-sky conditions. The emissivity-backscatter database is used to characterize surfaces within the GPM orbit for precipitation retrieval algorithms and other applications. The full 10–166-GHz emissivity vector is retrieved using optimal estimation. Since GMI includes water vapor sounding channels, retrieval of the atmospheric and surface states are performed simultaneously. Using the MERRA2 reanalysis as the a priori atmospheric state and with proper characterization of its error, we are able to effectively screen for cloud- and precipitation-affected emissivities. Comparisons with colocated CloudSat data show that this GMI-based screen is able to detect precipitation that DPR alone does not; however, about 10% of precipitation occurrence from CloudSat is still undetected by GMI. The unsupervised Kohonen classification technique was then applied to multiyear monthly 0.25° gridded mean retrieved emissivities and backscatter distinctly for snow-free, snow-covered, and sea ice surfaces in order to classify surfaces based on both active and passive microwave characteristics. The classes correspond to vegetation coverage and type, inundation zones, soil composition, and terrain roughness. Snow and sea ice surfaces show clear seasonal cycles representing the increase in snow and ice spatial extent and reduction in the spring. Applications toward GPM precipitation retrieval algorithms and sensitivity to accumulated rain and snowfall are also explored.

Estimation of vertical structure of latent heat generated in thunderstorms using CloudSat radar

AbstractThe Earth's atmosphere is highly coupled between the vertical layers and the surface. An understanding of circulations in the atmosphere is important for developing models and improving weather forecasting. The latent heat produced in the atmosphere is one of the key driving forces of these circulations. It is therefore very important to estimate the latent heat in the atmosphere accurately, especially in thunderstorm clouds, which have proved to be one of the major sources of gravity waves in tropical regions. The current space‐based latent heat retrievals are limited to precipitation‐based estimation which cannot define the complete structure of a thunderstorm where precipitation is not the main indicator of the severity. A novel method is proposed in this study which retrieves the latent heat profiles of thunderstorm clouds using CloudSat W‐band radar profiles. A realistic database of simulated thunderstorm events developed using the Regional Atmospheric Modelling System – Cloud Resolving Model (RAMS‐CRM) is compared with observations using the Bayesian Monte Carlo method to derive an estimate with an uncertainty analysis for each estimate. The method is validated in the southeast Asian region with European Centre for Medium‐Range Weather Forecasts driven RAMS‐CRM profiles. The algorithm performance on observation data using linear fit, regression and bias analysis is discussed. A case study retrieval is also performed to demonstrate the retrieval on real CloudSat data.

Improved Hydrometeor Detection Method: An Application to CloudSat

AbstractClouds play an important role in the climate system and are a principal source of uncertainty in climate projections. CloudSat has provided an unprecedented opportunity to study the vertical structure of clouds, and its observations are being widely used in scientific studies. However, some clouds are not detected or are only weakly detected by CloudSat. In most studies, the weakest detections, specifically those detected by the so‐called along‐track integration scheme, are typically ignored due to the high rate of false detections, namely, a significant probability that a detected cloud is actually a region of increased measurement noise, rather than a true cloud signal. False detections have been reduced in the latest version (called R05 for release 5) of the CloudSat cloud mask product but at a cost of a significant loss in the true weak signals (i.e., a higher false omission rate). In this study, the CloudSat hydrometeor detection algorithm used in R05 is modified by adding a bilateral filter scheme to improve the detection of weak signals. By comparing with the CALIPSO lidar vertical feature mask, it is shown that the new scheme largely reduces the false detection rate compared to the R04 version, while retaining a large fraction of the true weak signals that have been lost in the R05 version. Implementing this scheme in future CloudSat data processing is expected to lead to a better detection of thin clouds.

Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets

Snowfall data are vital in calculating the surface mass balance of the Antarctic Ice Sheet (AIS), where in-situ and satellite measurements are sparse at synoptic timescales. CloudSat data are used to construct Antarctic snowfall data at synoptic timescales to compensate for the sparseness of synoptic snowfall data on the AIS and to better understand its surface mass balance. Synoptic CloudSat snowfall data are evaluated by comparison with daily snow accumulation measurements from ten automatic weather stations (AWSs) and the fifth generation of the European Centre for Medium-Range Weather Forecasts climate reanalysis (ERA5) snowfall. Synoptic snowfall data were constructed based on the CloudSat measurements within a radius of 1.41°. The results show that reconstructed CloudSat snowfall at daily and two-day resolutions cover about 28% and 29% of the area of the AIS, respectively. Daily CloudSat snowfall and AWS snow accumulation have similar trends at all stations. While influenced by stronger winds, >73.3% of extreme snow accumulation events correspond to snowfall at eight stations. Even if the CloudSat snowfall data have not been assimilated into the ERA5 dataset, the synoptic CloudSat snowfall data are almost identical to the daily ERA5 snowfall with only small biases (average root mean square error and mean absolute error < 3.9 mm/day). Agreement among the three datasets suggests that the CloudSat data can provide reliable synoptic snowfall data in most areas of the AIS. The ERA5 dataset captures a large number of extreme snowfall events at all AWSs, with capture rates varying from 56% to 88%. There are still high uncertainties in ERA5. Nevertheless, the result suggests that ERA5 can be used to represent actual snowfall events on the AIS at synoptic timescale.