CloudSat Measurements Research Articles

Rainfall Area Identification Algorithm Based on Himawari-8 Satellite Data and Analysis of its Spatiotemporal Characteristics

Real-time monitoring of rainfall areas based on satellite remote sensing is of vital importance for extreme rainfall research and disaster prediction. In this study, a new rainfall area identification algorithm was developed for the new generation of geostationary satellites with high spatial and temporal resolution and rich bands. As the main drivers of the rainfall process, the macro and micro physical properties of clouds play an important role in the formation and development of rainfall. We considered differences in the absorption capacity of the water vapor absorption channels in the infrared band and introduced a sensitivity difference of rainfall area in water vapor channels to construct a sensitive detection of the water vapor region. The results of this algorithm were evaluated using Global Precipitation Measurement (GPM) satellite products and CloudSat measurements in various scenarios, with hit rates of 70.03% and 81.39% and false alarm rates of 2.05% and 21.34%, respectively. Spatiotemporal analysis revealed that the types of upper clouds in the rainfall areas mainly consisted of deep convection, cirrostratus, and nimbostratus clouds. Our study provides supporting data for weather research and disaster prediction, as well as an efficient and reliable method for capturing temporal and spatial features.

CloudSat inferred contrasting monsoon intra-seasonal variation in the cloud vertical structure over Indian regions

Earlier studies reported the contrasting cloud properties over central India during intraseasonal oscillation of the Indian summer monsoon (ISM). This intraseasonal ISM variation in the cloud vertical structure (VSC) over different parts of India is yet to be explored. To fill this gap area, VSC derived from nine years of CloudSat measurements over geographically different but having homogenous ISM rainfall Indian regions are selected for the study. We investigated VSC, types of clouds, and microphysical properties during the intra-seasonal variation of ISM. Significant contrast was found in the mid-tropospheric cloud region during the ISM spells. Analysis of cloud types shows significant intra-seasonal variability with the presence or absence of mixed-phase clouds (As, Ns, and Ac). In ISM (active and break) spells, the core monsoon regions (CI, CE, WI) share similar VSC features (complete or void mid-levels). Hence, microphysical properties follow similar characteristics except for ice water content which differs profusely among them. Over these regions, ten times higher ice effective radius during active days indicates dominating ice process compared to break days. Larger-sized ice hydrometeors (> 25 μm) are present at 5–6 km, which shows good interrelation with the ISM active and break spells above freezing level, and its size decreases with altitude. Whereas no difference in VSC is found during different ISM spells over the eastern part of India (WB and EI) except in the ice microphysical parameters, which are more dominant during break days. Further, irregular and discontinuous VSC over North and South India (NI and SI) could be due to their pertinent geographical conditions. Suppression of cloud processes at mid-tropospheric altitude is one of the most important reasons behind reduced precipitation over the core ISM regions. Mid-tropospheric drying is a remarkable sign of ISM break, which is the main source of suppressed convection and cloud activity over the core ISM region. This CloudSat study brought robust composite cloud vertical structure for the ISM intra-seasonal variability of cloud over different regions of India to improve the understanding of cloud-related processes and then increase the predictive capabilities of large-scale models.

The Global Nature of Early‐Afternoon and Late‐Night Convection Through the Eyes of the A‐Train

AbstractCharacterizing macrophysical properties of deep convective systems on a global scale is a precursor to understanding their influence on Earth's Energy Budget and Water Cycle. This study documents the properties of global convective objects (COs) in the early afternoon (1:30 p.m. local time; LT) and overnight (1:30 a.m. LT) measurements from the A‐Train satellite constellation. CloudSat measurements are used to identify convective cores and establish their intensity, while other A‐Train data sets define cloud structure, storm spatial extent, rainfall yield, and radiative effects of each CO. Global distributions of storm characteristics are consistent with previous studies in which the most intense convection is located over tropical land, particularly over the Amazon and Congo Basin, while the largest COs occur over the Maritime Continent. Despite their limited twice‐daily sampling, A‐Train measurements capture that early afternoon convection over tropical land is both more intense and produces heavier rainfall than nighttime land‐based convection, while the day‐night differences are minimal over the tropical ocean. High‐resolution estimates of updraft cores reveal that CO size increases as the number of distinct cores in a CO increases owing to an increase in nonconvective rain and anvil cloud area. Convective objects generally cool the environment, which is strongest over the Northern Hemisphere midlatitudes, but cooling weakens as the nonconvective cloud fraction increases such that 30% of COs actually exert a net warming effect. These results suggest that A‐Train measurements may capture bulk connections between deep convective cloud features, precipitation, and radiative effects despite a lack of complete diurnal sampling.

Evaluating seasonal and regional distribution of snowfall in regional climate model simulations in the Arctic

Abstract. In this study, we investigate how the regional climate model HIRHAM5 reproduces the spatial and temporal distribution of Arctic snowfall when compared to CloudSat satellite observations during the examined period of 2007–2010. For this purpose, both approaches, i.e., the assessments of the surface snowfall rate (observation-to-model) and the radar reflectivity factor profiles (model-to-observation), are carried out considering spatial and temporal sampling differences. The HIRHAM5 model, which is constrained in its synoptic representation by nudging to ERA-Interim, represents the snowfall in the Arctic region well in comparison to CloudSat products. The spatial distribution of the snowfall patterns is similar in both identifying the southeastern coast of Greenland and the North Atlantic corridor as regions gaining more than twice as much snowfall as the Arctic average, defined here for latitudes between 66 and 81∘ N. Excellent agreement (difference less than 1 %) in the Arctic-averaged annual snowfall rate between HIRHAM5 and CloudSat is found, whereas ERA-Interim reanalysis shows an underestimation of 45 % and significant deficits in the representation of the snowfall rate distribution. From the spatial analysis, it can be seen that the largest differences in the mean annual snowfall rates are an overestimation near the coastlines of Greenland and other regions with large orographic variations as well as an underestimation in the northern North Atlantic Ocean. To a large extent, the differences can be explained by clutter contamination, blind zone or higher resolution of CloudSat measurements, but clearly HIRHAM5 overestimates the orographic-driven precipitation. The underestimation of HIRHAM5 within the North Atlantic corridor south of Svalbard is likely connected to a poor description of the marine cold air outbreaks which could be identified by separating snowfall into different circulation weather type regimes. By simulating the radar reflectivity factor profiles from HIRHAM5 utilizing the Passive and Active Microwave TRAnsfer (PAMTRA) forward-modeling operator, the contribution of individual hydrometeor types can be assessed. Looking at a latitude band at 72–73∘ N, snow can be identified as the hydrometeor type dominating radar reflectivity factor values across all seasons. The largest differences between the observed and simulated reflectivity factor values are related to the contribution of cloud ice particles, which is underestimated in the model, most likely due to the small sizes of the particles. The model-to-observation approach offers a promising diagnostic when improving cloud schemes, as illustrated by comparison of different schemes available for HIRHAM5.

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.

Ice Water Content as a Precursor to Tropical Cyclone Rapid Intensification

AbstractThis study examines how the structure and amount of cloud ice water content are related to rates of tropical cyclone (TC) intensification using CloudSat profiling radar measurements and simulations from the Hurricane Weather Research and Forecasting (HWRF) model. Observational studies have demonstrated the signal of TC intensification in the passive satellite measurements of frozen water concentration. However, the vertical and horizontal resolution of passive satellite observations are limited. CloudSat measurements and HWRF simulations provide high‐resolution data sets of ice water content to better understand its relationship with the rate of TC intensification. It is found that rapidly intensifying TCs have larger ice water content compared to TCs with slower intensification rates. Similar results are obtained even after accounting for the effect of initial TC intensity. Such precursors of rapidly intensifying TCs may be used to better understand and improve the prediction of TC intensification.

Assessment of global total column water vapor sounding using a spaceborne differential absorption radar

Abstract. The feasibility of using a differential absorption radar (DAR) to retrieve total column water vapor from space is investigated. DAR combines at least two radar tones near an absorption line, in this case a water vapor line, to measure humidity information from the differential absorption “on” and “off” the line. From a spaceborne platform, DAR can be used to retrieve total column water vapor by measuring the differential reflection from the Earth's surface. We assess the expected precision, yield, and potential biases of retrieved total column water vapor values by applying an end-to-end radar instrument simulator to near-global weather analysis fields collocated with CloudSat measurements. The approach allows us to characterize the DAR performance across a globally representative dataset of atmospheric conditions including clouds and precipitation as well as different surface types. We assume a hypothetical spaceborne G-band radar with pulse compression orbiting the Earth at 405 km with a 1 m antenna, equivalent to a footprint diameter of 850, and 500 m horizontal integration. The simulations include the scattering effects of rain, snow, as well as liquid and ice clouds, spectroscopic uncertainties, and uncertainties due to the initial assumed water vapor profile. Results indicate that using two radar tones at 167 and 174.8 GHz with a transmit power of 20 W ensures that both pulses will be detected with a signal-to-noise ratio greater than 1 at least 70 % of the time in the tropics and more than 90 % of the time outside the tropics and that total column water vapor can be retrieved with a precision better than 1.3 mm.

The Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD)

Abstract. Low clouds continue to contribute greatly to the uncertainty in cloud feedback estimates. Depending on whether a region is dominated by cumulus (Cu) or stratocumulus (Sc) clouds, the interannual low-cloud feedback is somewhat different in both spaceborne and large-eddy simulation studies. Therefore, simulating the correct amount and variation of the Cu and Sc cloud distributions could be crucial to predict future cloud feedbacks. Here we document spatial distributions and profiles of Sc and Cu clouds derived from Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) and CloudSat measurements. For this purpose, we create a new dataset called the Cumulus And Stratocumulus CloudSat-CALIPSO Dataset (CASCCAD), which identifies Sc, broken Sc, Cu under Sc, Cu with stratiform outflow and Cu. To separate the Cu from Sc, we design an original method based on the cloud height, horizontal extent, vertical variability and horizontal continuity, which is separately applied to both CALIPSO and combined CloudSat–CALIPSO observations. First, the choice of parameters used in the discrimination algorithm is investigated and validated in selected Cu, Sc and Sc–Cu transition case studies. Then, the global statistics are compared against those from existing passive- and active-sensor satellite observations. Our results indicate that the cloud optical thickness – as used in passive-sensor observations – is not a sufficient parameter to discriminate Cu from Sc clouds, in agreement with previous literature. Using clustering-derived datasets shows better results although one cannot completely separate cloud types with such an approach. On the contrary, classifying Cu and Sc clouds and the transition between them based on their geometrical shape and spatial heterogeneity leads to spatial distributions consistent with prior knowledge of these clouds, from ground-based, ship-based and field campaigns. Furthermore, we show that our method improves existing Sc–Cu classifications by using additional information on cloud height and vertical cloud fraction variation. Finally, the CASCCAD datasets provide a basis to evaluate shallow convection and stratocumulus clouds on a global scale in climate models and potentially improve our understanding of low-level cloud feedbacks. The CASCCAD dataset (Cesana, 2019, https://doi.org/10.5281/zenodo.2667637) is available on the Goddard Institute for Space Studies (GISS) website at https://data.giss.nasa.gov/clouds/casccad/ (last access: 5 November 2019) and on the zenodo website at https://zenodo.org/record/2667637 (last access: 5 November 2019).

Spatial and seasonal variability of clouds over the southwest Indian Ocean based on the DARDAR mask product

AbstractWe present a characterization of the variability of clouds over the southwest Indian Ocean between 2007 and 2010. Cloud occurrence is derived from the DARDAR (raDAR/liDAR) mask, a synergistic product based on Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) and CloudSat measurements. It provides a target classification for hydrometeors. We demonstrate that this product is suitable for studying the vertical, spatial and seasonal cloud distribution in the southwest Indian Ocean. The variability of cloud occurrence increases approaching the tropics: the average maximum amplitude of monthly occurrence is ∼7% between 30°S and 60°S and ∼14% between 10°S and 30°S. The 10–30°S latitudinal band exhibits the largest contrasts: summer (winter) total cloud occurrence is mainly driven by high‐ (low‐) level clouds. The vertical distribution of clouds differs on either side of 55°E and this is related to a land–ocean contrast and to large‐scale influence. In winter, east of 55°E, the maximum of cloud occurrence corresponds to warm and mixed‐phased precipitating clouds associated with the Mascarene High. In summer, west of 55°E, the vertical distribution of cloud occurrence is driven by deep convection associated with the InterTropical Convergence Zone and the Near Equatorial Trough. The vertical distribution of cloud occurrence shows an interannual variability that is related to El Niño events. The influence of other oscillation modes should be further investigated.

Aerosol Effect on the Cloud Phase of Low‐Level Clouds Over the Arctic

AbstractThree years of nighttime Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation data was used in synergy with CloudSat measurements to quantify how strongly aerosol type and aerosol load affect the cloud phase in low‐level clouds over the Arctic. Supercooled liquid layers were present in the majority of observed low‐level clouds (0.75 ≤ z ≤ 3.5 km) between −10 and −25 °C. Furthermore, based on the subset (6%) of data with high quality assurance for aerosol typing, ice formation is more common in the presence of dust or continental aerosols as opposed to marine or elevated smoke aerosols. With the first aerosol group, glaciated clouds were found at cloud top temperatures of 2 to 4 °C warmer than with the latter aerosol types. Further association of the aerosol concentration with the cloud phase showed that the aerosol concentration outweighs the aerosol type effect. Depending on the aerosol load, the temperature at which a cloud completely glaciates can vary by up to 6–10 °C. However, this behavior was most pronounced in stable atmospheric conditions and absent over open ocean with lower tropospheric stability values and probably less stratified clouds. Finally, more mixed‐phase clouds were associated with high aerosol load, suggesting that mixed‐phase clouds have an extended lifetime in the Arctic under high cloud condensation nuclei concentrations. This implies that in a pristine environment, with few or no local aerosol sources, and under the investigated conditions the amount of aerosol particles affects the cloud phase more than the aerosol type does.

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, &gt;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 &lt; 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.

A Study of the Characteristics of Vertical Cloud Base Height Distribution over Eastern China

Cloud is an important factor that affects weather and climate, and the vertical distribution of cloud determines its role in the atmospheric radiation transfer process. In this paper, the characteristics of different cloud types and their vertical cloud base height distributions over Eastern China are investigated with a four-year 2B-CLDCLASS-LIDAR product. The intercomparison of cloud base height distribution from ground-based lidar, CloudSat and CALIPSO measurements was studied with observations over the Hefei and Jinhua areas. The 2B-CLDCLASS-LIDAR product has the potential to uncover geographical and seasonal changes in cloud base height distribution over the Hefei area and Jinhua area, which may be beneficial for local climate models, although the CPR on CloudSat suffers from surface clutter or blind-zones. The results show that for non-precipitation cloud over the defined region (Eastern China), the occurrence frequencies of altocumulus, stratocumulus, and cirrus clouds are 29.4%, 21.0%, and 18.9%, respectively. The vertical occurrence frequencies of their cloud base heights are 0.5–8.5 km, below 3.5 km, and 5.5–17.0 km. The precipitation clouds are dominated by nimbostratus (48.4%), cumulus (17.9%), and deep convective clouds (24.2%), and their cloud base heights are all below 3.0 km. The cloud base height distributions have large differences below 3 km between the satellite measurement and ground-based measurement over Hefei site. Between the Hefei site and Jinhua site, the difference in cloud base height distribution measured by ground-based lidar is in good agreement with that measured by satellite over their matched grid boxes. Over the Hefei site, the vertical occurrence frequencies of cloud base height measured by ground-based lidar are higher than the satellite measurement within 0–0.5 km during all the seasons. It is suggested that more cloudy days may result from the sufficient water vapor environment in Hefei. In summer, the occurrence frequency of the cloud base height distribution at a height of 0–2.0 km is lower than other seasons over Jinhua city, which may be associated with the local weather system. Over the Jinhua site, the difference in seasonal cloud base height distribution based on satellite is in good agreement with that based on ground-based lidar. However, it does not appear over Hefei site. Thus, a multi-platform observation of cloud base height seems to be one of the essential ways for improvement in the observation of cloud macroscopic properties.

Observations on the distribution of clouds over northern India using joint CloudSat and CALIPSO measurements

ABSTRACTA detailed study on the distribution of basic cloud types over the northern states of India (NSI) is performed using the decadal (2007–2016) raDAR-liDAR (DARDAR) data, which facilitates merged CloudSat and Cloud aerosol liDAR infrared pathfinder satellite observations (CALIPSO) observations onto the high resolution matched geographical grid points. The study demonstrates the seasonal variations, with south-west monsoon (June–September) exhibiting the maximum (>50%) cloud appearances while during the active period of western disturbances (December–March), ~25% of the total annual clouds, over the NSI region. Altostratus is found to be the most frequent cloud type (~20%) among others in the last decade over this region, while during south-west monsoon and western disturbances, the most dominant cloud types are deep convective (~25%) and nimbostratus (~33%), respectively.

An Efficient Method for Microphysical Property Retrievals in Vertically Inhomogeneous Marine Water Clouds Using MODIS‐CloudSat Measurements

AbstractAn efficient method is developed to infer cloud optical thickness (COT) and cloud droplet effective radius (CDER) of marine water clouds from Moderate Resolution Imaging Spectroradiometer (MODIS) and CloudSat measurements, incorporating droplet size vertical inhomogeneity. Empirical orthogonal function (EOF) analysis is employed to reduce the degrees of freedom of the droplet size profile. The first two EOFs can explain 94% of the variability in the droplet size profile. Compared to the existing bispectral CDER retrieval from MODIS assuming plane parallel vertically homogeneous clouds, the new retrieval produces smaller CDER values in clouds in which the adiabatic growth process is dominant and larger CDER values in clouds in which the collision‐coalescence process is dominant. To evaluate the performance of the retrieval algorithm, we compare retrieved COT and CDER in this study with their MODIS and CloudSat counterparts on a pixel‐by‐pixel basis. CDER retrieval based on the vertically homogeneous assumption may be underestimated by 30% due to droplet size vertical inhomogeneity when COT is large and the collision‐coalescence process is dominant in the cloud. Retrieved CDER in conjunction with the two scores for EOFs can reconstruct the vertical profile of CDER, which is useful for cloud microphysical process studies. Furthermore, potential expansion of this algorithm to MODIS pixels without CloudSat collocations is discussed.

Real-Time Observations of Dust–Cloud Interactions Based on Polarization and Raman Lidar Measurements

Dust aerosols have significant impact on the environment and climate through long-range transport. We report, in this paper, a case of dust–cloud interaction process using combined measurements of a ground-based polarization and Raman (PR) lidar systems, and implemented by the spaceborne Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) lidar observations. The dust event occurred on 14 March 2009 over East Asia. During the two hours of observing time, the ground-based lidar observed an ongoing process of decreasing of the depolarization ratio (DR) accompanied by the increase of the water vapor simultaneously, indicating a dust–cloud interaction and particle transformation. CALIPSO measurements also found similar layers of dusts and clouds over lands and oceans with properties similar to the ground based lidar measurements. Our observation was a real-time dust–cloud process with the observation of occurrence of particle transformation. The depolarization reduced from 0.2 to 0.1 corresponding to a change of aspect ratio from 1.2 to 1.1. A discussion of a dust–cloud interaction in terms of three-stage cloud processes is made based on back-trajectory analyses and lidar observations. The result shows that dust aerosols decrease the cloud extinction coefficient by 41% but increase the cloud optical depth (COD) of water cloud by 12.79%, compared with that of pure water clouds. Furthermore, if dust aerosols participate as cloud condensation nuclei (CCN) in cloud physical processes, then they significantly reduce the size of the cloud droplet by 44–79%. Finally, based on three-year collocated CALIPSO and CloudSat measurements from 2007 to 2010, we found approximately one-third of clouds are originally dusty in the spring over the Pacific Coast areas.

Improvement of a Cloud Microphysics Scheme for a Global Nonhydrostatic Model Using TRMM and a Satellite Simulator

Abstract The cloud and precipitation simulated by a global nonhydrostatic model with a 3.5-km horizontal resolution, the Nonhydrostatic Icosahedral Atmospheric Model (NICAM), are evaluated using the Tropical Rainfall Measuring Mission (TRMM) and a satellite simulator. A previous study by Roh and Satoh evaluated the single-moment bulk microphysics and established the modified microphysics scheme for the specific tropical open ocean using a regional version of NICAM. In this study, the authors expanded the evaluation over the entire tropics and parts of the midlatitude areas (20°–36°S, 20°–36°N) using a joint histogram of the cloud-top temperature and precipitation echo-top heights and contoured frequency by altitude diagrams of the deep convective systems. The modified microphysics simulation improves the joint probability density functions of the cloud-top temperatures and precipitation cloud-top heights over not only the tropical ocean but also the land and midlatitude areas. Compared with the default microphysics simulation, the modified microphysics simulation shows a clearer distinction between the land and ocean in the tropics, which is related to the contrast between the shallow and the deep clouds. In addition, the two microphysics simulation methods were also compared over the tropics using joint histograms of the cloud-top and precipitation cloud-top heights on the basis of CloudSat measurements. It was found that the microphysics scheme that was modified for the tropical ocean displayed general cloud and precipitation improvements in the global domain over the tropics.

An A - train satellite based stratiform mixed - phase cloud retrieval algorithm by combining active and passive sensor measurements, British Journal of Environment and Climate Change, Vol

Aims: To develop a new satellite-based mixed-phase cloud retrieval algorithm for improving mixed-phase cloud liquid water path (LWP) retrievals by combining Moderate Resolution Imaging Spectroradiometer (MODIS), CloudSat, and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements. Study Design: Algorithm development and evaluation by using collocated NASA A-Train and the Atmospheric Radiation Measurement (ARM) Climate Research Facility (ACRF) measurements at the North Slope Alaska (NSA) site. Place and Duration of Study: Collocated MODIS and ground-based measurements at NSA site from March 2000 to October 2004, MODIS measurements and retrievals during July 2006 over Eastern Pacific, and MODIS, CloudSat and CALIPSO measurements on April 04, 2007 over the Arctic Region. Methodology: The stratiform mixed-phase cloudswere treated as two adjunct water and ice layers for radiative calculations with the Discrete Ordinate Radiative Transfer (DISORT) model. The ice-phase properties were provided with the 2C-ICE product, which is produced from CloudSat radar and CALIPSO lidar measurements, and they were used as inputs in DISORT for the calculations. Then, the calculated mixed-phase cloud reflectances at selected wavelengths were compared with MODIS reflectances to retrieve liquid-phase cloud properties. Results: A new algorithm was developed to retrieve LWP in stratiform mixed-phase Original Research Article British Journal of Environment & Climate Change, 3(4): 587-611, 2013 588 clouds by using MODIS radiances and ice cloud properties from active sensor measurements. The algorithm was validated separately by using Operational MODIS retrievals of warm marine stratiform clouds and collocated surface measurements of Arctic stratiform mixed-phase clouds. The results show that the new algorithm reduced the positive LWP biases in the Operational MODIS LWP retrievals for stratiform mixedphase clouds from 35 and 68% to 10 and 22% in the temperature ranges of -5 to -10oC and -10 to -20oC, respectively. Conclusion: The combined A-Train active and MODIS measurements can be used to improve global mixed-phase cloud property retrievals.

Aerosol impacts on cloud thermodynamic phase change over East Asia observed with CALIPSO and CloudSat measurements

AbstractImpacts of aerosols on subfreezing cloud thermodynamic phase change over East Asia are studied by using 4 year combined CloudSat radar and Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The mean occurrence frequencies of supercooled‐liquid, mixed‐phase, and glaciated clouds over East Asia are 10.4%, 10.3%, and 16.9%, respectively. Over northwest of East Asia (32–44°N, 75–100°E), subfreezing clouds are dominated by glaciated clouds with a mean relative cloud fraction (RCF) of ~60% and over southeast (16–32°N, 95–120°E) are dominated by supercooled‐liquid clouds with a mean RCF of ~50%. Although cloud top temperature (CTT) differences contribute largely to the thermodynamic phase differences, northwest of East Asia has ~20% larger glaciated (smaller mixed‐phase) RCFs than those of southeast at the same CTT. Seasonal anomalies of glaciated and mixed‐phase RCFs correlate well with the seasonal variations of dust occurrence frequency. In addition, subfreezing clouds associated with mineral dust contain approximately 5%, 10%, and 20% larger glaciated (smaller mixed‐phase) RCFs than those associated with polluted dust, smoke, and background aerosols at any given CTT. It is suggested that dust impacts on subfreezing clouds over East Asia by providing abundant effective ice nuclei to glaciate mixed‐phase clouds, although the impacts of large‐scale dynamics and water supply mechanisms cannot be ruled out.

A global survey of cloud overlap based on CALIPSO and CloudSat measurements

Abstract. Using 2B-CLDCLASS-LIDAR (radar–lidar) cloud classification and 2B-FLXHR-LIDAR radiation products from CloudSat over 4 years, this study evaluates the co-occurrence frequencies of different cloud types, analyzes their along-track horizontal scales and cloud radiative effects (CREs), and utilizes the vertical distributions of cloud types to evaluate cloud-overlap assumptions. The statistical results show that high clouds, altostratus (As), altocumulus (Ac) and cumulus (Cu) tend to coexist with other cloud types. However, stratus (St) (or stratocumulus, Sc), nimbostratus (Ns) and convective clouds are much more likely to exhibit individual features than other cloud types. On average, altostratus-over-stratus/stratocumulus cloud systems have a maximum horizontal scale of 17.4 km, with a standard deviation of 23.5 km. Altocumulus-over-cumulus cloud types have a minimum scale of 2.8 km, with a standard deviation of 3.1 km. By considering the weight of each multilayered cloud type, we find that the global mean instantaneous net CREs of multilayered cloud systems during the daytime are approximately −41.3 and −50.2 W m−2, which account for 40.1 and 42.3% of the global mean total net CREs at the top of the atmosphere (TOA) and at the surface, respectively. The radiative contributions of high-over-altocumulus and high-over-stratus/stratocumulus (or cumulus) in the all multilayered cloud systems are dominant due to their frequency. Considering the overlap of cloud types, the cloud fraction based on the random overlap assumption is underestimated over vast oceans, except in the west-central Pacific Ocean warm pool. Obvious overestimations mainly occur over tropical and subtropical land masses. In view of a lower degree of overlap than that predicted by the random overlap assumption to occur over the vast ocean, particularly poleward of 40° S, the study therefore suggests that a linear combination of minimum and random overlap assumptions may further improve the predictions of actual cloud fractions for multilayered cloud types (e.g., As + St/Sc and Ac + St/Sc) over the Southern Ocean. The establishment of a statistical relationship between multilayered cloud types and the environmental conditions (e.g., atmospheric vertical motion, convective stability and wind shear) would be useful for parameterization design of cloud overlap in numerical models.

Spatial scales of altocumulus clouds observed with collocated CALIPSO and CloudSat measurements

Altocumulus (Ac) clouds are important, yet climate models have difficulties in simulating and predicting these clouds, due to their small horizontal scales and thin vertical extensions. In this research, 4years of collocated Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and CloudSat radar measurements is analyzed to study the along-track horizontal scales and vertical depths of Ac clouds. Methodology to calculate Ac along-track horizontal scale and vertical depth using collocated CALIPSO and CloudSat measurements is introduced firstly. The global mean Ac along-track horizontal scale is 40.2km, with a standard deviation of 52.3km. Approximately 93.6% of Ac cannot be resolved by climate models with a grid resolution of 1°. The global mean mixed-phase Ac vertical depth is 1.96km, with a standard deviation of 1.10km. Global distributions of the Ac along-track horizontal scales and vertical depths are presented and possible factors contributing to their geographical differences are analyzed. The result from this study can be used to improve Ac parameterizations in climate models and validate the model simulations.