CloudSat Cloud Profiling Radar Research Articles

Multi-year evaluation of CloudSat, ERA5, and IMERG global snowfall products

Snowfall products derived from satellite-based radar observations, reanalysis products and phase-classified precipitation products are the three primary sources of information on surface snowfall. This study compares snowfall rate products from CloudSat, ERA5 and IMERG using the Triple Collocation (TC) method on a global scale covering almost the entire period of CloudSat cloud-profiling radar (CPR) 2C products from 2006 to 2019, especially considering the precipitation phase classification field of IMERG (probability of Liquid Precipitation, pLP). The main conclusions include but are not limited to the following: (1) The pLP threshold significantly impacts the ability of IMERG to identify snowfall from precipitation, with an optimal pLP threshold of 25 percent determined by matching IMERG with CloudSat and ERA5 snowfall events globally. (2) CloudSat presents the highest performance and stability on a global scale, with average TC-estimated CC and RMSE of 0.472 and 1.465, respectively. (3) The TC-estimated CC of IMERG exhibit significant spatiotemporal heterogeneities, but IMERG product with the optimal pLP threshold show comparable performance with CloudSat, with an average TC-estimated RMSE of ∼ 1.533. (4) ERA5 performed worse than the other two products, except in some high-latitude and high-altitude areas. This study provides novel insights into the performances of snowfall rate products from different sources at the global scales and provides a foundation for improving satellite- and model-based snowfall rate retrievals.

Comparison and Synthesis of Precipitation Data from CloudSat CPR and GPM KaPR

Employing different bands of radar to detect precipitation information in identical regions enables the acquisition of a more comprehensive precipitation cloud structure, thereby refining the continuity and completeness of precipitation measurements. This study first compared the coincident data from CloudSat W-band cloud profiling radar (CPR) and Global Precipitation Measurement Mission (GPM) Ka-band precipitation radar (KaPR) from 2014 to 2017, and then a synthesis of the radar reflectivity from CPR and KaPR was attempted to obtain a complete cloud and precipitation structure. The findings of the reflectivity comparisons indicated that the echo-top height identified by CPR is on average 3.6 to 4.2 km higher than that from KaPR, due to the higher sensitivity. Because of strong attenuation of CPR by liquid-phase particles, the reflectivity below the height of the melting layer usually shows an opposite gradient to KaPR with decreasing altitude. The difference in the near-surface rain rates of CPR and KaPR was found to be related to reflectivity gradients in the vertical direction, and the larger the reflectivity gradients, the greater the differences in near-surface rain rates. For better representing the complete vertical structure of precipitation clouds and improving the consistency of the reflectivity and precipitation rate, the radar reflectivity was weighted, synthesized from CPR and KaPR based on the gradient difference of the reflectivity from the two radars. We presented the synthesis results for a stratiform cloud and a deep convective case, and Spearman’s rank correlation coefficient (rs) between the GPM combined radiometer precipitation rate and the radar reflectivity was utilized to analyze the performance of the synthesis. The consistency between synthesized reflectivity and precipitation rate in the non-liquid phase was improved compared with KaPR, and the rs of the ice and mixed phases was increased by about 12% and 10%, respectively.

Evaluation of Snowfall Retrieval Performance of GPM Constellation Radiometers Relative to Spaceborne Radars

Abstract This study assesses the level-2 snowfall retrieval results from 11 passive microwave radiometers generated by the version 5 Goddard profiling algorithm (GPROF) relative to two spaceborne radars: CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Measurement (GPM) Ku-band Precipitation Radar (KuPR). These 11 radiometers include six conical scanning radiometers [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), its successor sensor AMSR2, GPM Microwave Imager (GMI), and three Special Sensor Microwave Imager/Sounders (SSMIS)] and five cross-track scanning radiometers [Advanced Technology Microwave Sounder (ATMS) and four Microwave Humidity Sounders (MHS)]. Results show that over ocean conical scanning radiometers have better detection and intensity estimation skills than cross-track sensors, likely due to the availability and usage of the low-frequency channels (e.g., 19 and 37 GHz). Over land, AMSR-E and AMSR2 have noticeably worse performance than other sensors, primarily due to the lack of higher than 89-GHz channels (e.g., 150, 166, and 183 GHz). Over both land and ocean, all 11 sensors severely underestimate the snowfall intensity, which propagates to the widely used level 3 precipitation product [i.e., Integrated Multi-satelliteE Retrievals for GPM (IMERG)]. These conclusions hold regardless of using either KuPR or CPR as the reference, though the statistical metrics vary quantitatively. The conclusions drawn from these comparisons apply solely to the GPROF version 5 algorithm.

Assessing Snow Water Retrievals over Ocean from Coincident Spaceborne Radar Measurements

Spaceborne snow water retrievals over oceans are assessed using a multiyear coincident dataset of CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Various factors contributing to differences in snow water retrievals between CPR and DPR are carefully considered. A set of relationships between radar reflectivity (Ze) and snow water content (SWC) at Ku- and W-bands is developed using the same microphysical assumptions. It is found that surface snow water contents from CPR are much larger than those from DPR at latitudes above 60°, while surface snow water contents from DPR slightly exceed those from CPR at latitudes below 50°. Coincident snow water content profiles between CPR and DPR are further divided into two conditions. One is that only CPR detects the falling snow. Another is that both CPR and DPR detect the falling snow. The results indicate that about 88% of all snow water content profiles are under the first condition and usually associated with light snowfall events. The remaining snow water content profiles are generally associated with moderate and heavy snowfall events. Moreover, CPR surface snow water contents are larger than DPR ones at high latitudes because most light snowfall events are misdetected by DPR due to its low sensitivity. DPR surface snow water contents exceed CPR ones at low latitudes because CPR may experience a significant reduction in backscattering efficiency of large particles and attenuation in heavy snowfall events. The low sensitivity of DPR also causes a noticeable decrease in detected snow layer depth. The results presented here can help in developing global snowfall retrieval algorithms using multi-radars.

A Microphysics-Scheme-Consistent Snow Optical Parameterization for the Community Radiative Transfer Model

Abstract The satellite observational data assimilation community requires consistent hydrometer descriptions—including mass–size relation and particle size distribution—to be used in both the forecast model and observation operator. We develop a microphysics-scheme-consistent snow and graupel single-scattering property database to meet this requirement. In this database, snowflakes are modeled as a mixture of small column and large aggregated ice particles, the mixing ratios of which may be adjusted to satisfy a given mass–size relation. Snow single-scattering properties are computed for four different mass–size relations. Subsequently, the snow description in the Thompson microphysics scheme is used as an example to demonstrate how microphysics-scheme-consistent snow bulk optical properties are derived. The Thompson-scheme-consistent snow bulk optical properties are added to the Community Radiative Transfer Model (CRTM), version 2.4.0. With CloudSat Cloud Profiling Radar (CPR) snow and liquid precipitation retrievals as the inputs, CRTM simulations are performed over global oceans and compared with four collocated Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channel observations. The CRTM simulated brightness temperatures show agreement with the GMI observed brightness temperatures in cases of light-to-moderate precipitation over extratropical and polar ice-free oceans, with root-mean-square errors of 4.3, 13.0, 1.8, and 3.3 K in the 166-GHz (vertical polarization), 166-GHz (horizontal polarization), 183 ± 3-GHz (vertical polarization), and 183 ± 7-GHz (vertical polarization) channels, respectively. The result demonstrates the potential of using the newly developed microphysics-scheme-consistent snow optical parameterization in data assimilation applications.

Regional Dependency of the Cloud Droplet Growth Process in Combined Analysis of Aqua MODIS and CloudSat CPR

This paper shows the CFODD of the regional dependence of cloud growth processes in low-level clouds obtained by the combined use of the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite and the Cloud Profiling Radar (CPR) on the CloudSat satellite. This enabled the evaluation of the cloud growth process based on the cloud optical thickness (τ) and the effective radius of cloud particle (Re), similar to previous studies that performed statical analysis on low-level clouds over the globe. Our targets were regions in East Asian, Californian, and Peruvian. In all analysis areas, our results showed that the internal structure of clouds changed as Re increased, indicating cloud growth. In the East Asian region, the maximum τ remained relatively constant even when cloud droplet size grew. In contrast, in the regions of Californian and Peruvian, the maximum τ increased with Re during the condensation growth process and then decreased as drizzle particles transformed into rain. It was also found that Re was smaller in the East Asian region unlike in Californian and Peruvian. This indicates that there are more aerosols in the East Asian region, which is consistent with its geographical characteristics.

Exploring the Environmental Conditions of Snow Particles Using Spaceborne Triple-Frequency Radar Measurements over Ocean

The environmental conditions of snow particles with different particle sizes and bulk effective densities over the ocean are explored using a coincidence dataset of National Aeronautics and Space Administration (NASA) CloudSat Cloud Profiling Radar (CPR) and Global Precipitation Mission (GPM) Dual-frequency Precipitation Radar (DPR). Observed triple-frequency radar signatures for snow particles over the ocean are firstly derived. Based on modeled triple-frequency signatures for various snow particles, DFR Ku/Ka and the ratio of DFR Ku/Ka to DFR Ku/W from observations are selected to indicate the snow particle size and bulk effective density, respectively. The dependences of two indicators on temperature, relative humidity and cloud liquid water content are presented. The snow particle size range becomes wider at warmer temperatures, higher relative humidities or lower cloud liquid water contents. At cold temperatures, low relative humidities or high cloud liquid water contents, large snow particles are prevalent. At high cloud liquid water contents, the riming process mainly contributes to the increase in snow particle bulk effective density. When supersaturation occurs, a large portion of snow particles have large sizes and low bulk effective densities at cold temperatures. This study can improve the understanding of snow microphysics and demonstrate the potential of spaceborne radar measurements in global snowfall retrievals.

A Snowfall Detection Algorithm for ATMS Over Ocean, Sea Ice, and Coast

This study developed a snowfall detection algorithm over ocean, sea ice, and coast for the advanced technology microwave sounder (ATMS) onboard both NPP and NOAA-20 satellites. The detection algorithm was trained from collocated observations between ATMS-NPP and CloudSat cloud profiling radar (CPR) snowfall product. Both brightness temperature (TB) variables and global forecast system (GFS) output variables are evaluated for snowfall detection in this algorithm. Results show that combining TB variables and GFS variables provide the optimal snowfall detection performance. The Heidke skill score (HSS) values are about 0.56 over all three surface types, and the probability of detection (POD) values are 0.76, 0.70, and 0.72 over ocean, sea ice, and coast, respectively. The importance of the GFS variables differs greatly among these three surface types. The detection algorithm primarily depends on TB variables over ocean and HSS only increased by 0.05 by adding GFS variables. In contrast, GFS variables are critically important to snowfall detection over sea ice and coastal regions. Without GFS variables, the HSS values over both sea ice and coastal regions decrease sharply from about 0.56 to about 0.40. Over ocean, we also developed a regional snowfall detection model in each 10° grid box, which greatly outperform the global detection model over certain regions (e.g., sea of Okhotsk and Labrador Sea). Case studies and validation against NOAA-20 observations showed that the snowfall detection algorithm performs well, which will benefit coastal communities by providing information on snowstorms offshore before they transition to land.

Using CloudSat‐Derived Snow Accumulation Estimates to Constrain Gridded Snow Water Equivalent Products

AbstractChanges in the quantity of terrestrial Arctic snow have far‐reaching implications for the global water‐energy budget, ecosystem development, and cold region flooding. Snow water equivalent (SWE) is a useful metric for monitoring these changes, however only a sparse observing network is available in the Arctic. Space‐based remote sensing offers the potential to fill these measurement gaps, and the CloudSat cloud profiling radar (CPR) has been shown to provide high‐quality estimates of surface snowfall rates across the Arctic. We propose a novel method to leverage monthly information from CloudSat to identify data quality issues in the Blended‐4 (B4) gridded SWE product. A regression of estimated monthly mean snow accumulation between CloudSat and B4 provides a confidence interval to objectively flag individual months in B4 with data quality issues. Applying this method to a case study in the Canadian high Arctic identifies an unphysical January melt event in B4 which is traced back to a measurement error in the assimilated station observations. We generalize this technique to a one‐degree grid and find a total of 4,885 cases in B4 with low data quality. We find that the low‐quality SWE product values are not random errors, and that they introduce a systematic bias of −3.1 mm on B4's estimates of mean Arctic SWE. CloudSat is uniquely positioned as one of the only observational data sets for observing snowfall at high latitudes, and when combined with the methodology developed here, its estimates can be used to further enhance the accuracy of current gridded SWE products.

Validation of Ice Cloud Microphysical Properties Retrieval Using a Markov Chain Monte Carlo Algorithm

AbstractThe Markov chain Monte Carlo (MCMC) algorithm has been used for retrieving ice cloud microphysical properties in this paper. The retrieval data include effective radius (re), ice water content (IWC), number density (N0), and distribution width parameter (w).First we examine the algorithm feasibility using simulated W‐band radar reflectivity. The retrievals locate in a solution space around the exact results. Two methods are applied to produce a unique solution with maximum likelihood function, that is, the mean value of all acceptable sets of parameters in the chain and the first convergence sample. The results of the second method have a better performance compared with that of the first method. The algorithm has better performance when one particle size distribution (PSD) parameter is known than that when three PSD parameters are unknown.Then we apply the algorithm for CloudSat Cloud Profiling Radar (CPR) observations. Four habits, including column, plate, bullet rosette and sphere, are considered to analyze the influence on the retrieval accuracy. The averaged deviation ofres andIWCs obtained from MCMC algorithm among different habit assumptions has a maximum of about 10% and 5%, respectively. It appears clearly that the MCMC algorithm retrieval for short hexagonal column (HEXS) are the closest to that of the official products (2B‐CWC‐RO), while thick hexagonal plate (HEXF) has the largest bias. Retrievedre,IWC,N0, andwfrom radar reflectivity are in reasonable agreement with 2B‐CWC‐RO product, which indicate that the MCMC algorithm can produce reliable cloud properties from radar observations.

Satellite observations of snowfall regimes over the Greenland Ice Sheet

Abstract. The mass of the Greenland Ice Sheet (GrIS) is decreasing due to increasing surface melt and ice dynamics. Snowfall both adds mass to the GrIS and has the capacity to reduce surface melt by increasing surface brightness, reflecting additional solar radiation back to space. Modeling the GrIS’s current and future mass balance and potential contribution to future sea level rise requires reliable observational benchmarks for current snowfall accumulation as well as robust connections between individual snowfall events and the large-scale atmospheric circulation patterns that produce them. Previous work using ground-based observations showed that, for one research station on the GrIS, two distinct snowfall regimes exist: those associated with exclusively ice-phase cloud processes (IC) and those involving mixed-phase processes indicated by the presence of supercooled liquid water (CLW). The two regimes have markedly different accumulation characteristics and dynamical drivers. This study leverages the synergy between two satellite instruments, CloudSat's Cloud Profiling Radar (CPR) and CALIPSO's Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP), to identify snowfall cases over the full GrIS and partition them into the IC and CLW regimes. We find that, overall, most CPR observations of snowfall over the GrIS come from IC events (70 %); however, during the summer months, close to half of the snow observed is produced in CLW events (45 %). IC snowfall plays a dominant role in adding mass to the GrIS, producing ∼ 80 % of the total estimated 399 Gt yr−1 accumulation. Beyond the cloud phase that defines the snowfall regimes, the macrophysical cloud characteristics are distinct as well; the mean IC geometric cloud depth (∼ 4 km) is deeper than the CLW geometric cloud depth (∼ 2 km), consistent with previous studies based on surface observations. Two-dimensional histograms of the vertical distribution of CPR reflectivities show that IC events demonstrate consistently increasing reflectivity toward the surface while CLW events do not. Analysis of ERA5 reanalyses shows that IC events are associated with cyclone activity and CLW events generally occur under large-scale anomalously high geopotential heights over the GrIS. When combined with future climate predictions, this snapshot of GrIS snowfall characteristics may shed light on how this source of ice sheet mass might respond to changing synoptic patterns in a warming climate.

Comparative Assessment of Snowfall Retrieval From Microwave Humidity Sounders Using Machine Learning Methods

AbstractAccurate quantification of snowfall rate from space is important but has remained difficult. Four years (2007–2010) of NOAA‐18 Microwave Humidity Sounder (MHS) data are trained and tested with snowfall estimates from coincident CloudSat Cloud Profiling Radar (CPR) observations using several machine learning methods. Among the studied methods, random forest using MHS (RF‐MHS) is found to be the best for both detection and estimation of global snowfall. The RF‐MHS estimates are tested using independent years of coincident CPR snowfall estimates and compared with snowfall rates from Modern‐Era Retrospective analysis for Research and Applications Version 2 (MERRA‐2), Atmospheric Infrared Sounder (AIRS), and MHS Goddard Profiling Algorithm (GPROF). It was found that RF‐MHS algorithm can detect global snowfall with approximately 90% accuracy and a Heidke skill score of 0.48 compared to independent CloudSat samples. The surface wet bulb temperatures, brightness temperatures at 190 GHz, and 157 GHz channels are found to be the most important features to delineate snowfall areas. The RF‐MHS retrieved global snowfall rates are well compared with CPR estimates and show generally better statistics than MERRA‐2, AIRS, and GPROF products. A case study over the United States verifies that the RF‐MHS estimated snowfall agrees well with the ground‐based National Center for Environmental Prediction (NCEP) Stage‐IV and MERRA‐2 product, whereas a relatively large underestimation is observed with the current GPROF product (V05). MHS snowfall estimated based on RF algorithm, however, shows some underestimation over cold and snow‐covered surfaces (e.g., Greenland, Alaska, and northern Russia), where improvements through new sensors or retrieval techniques are needed.

Evaluation of the MODIS Collection 6 multilayer cloud detection algorithm through comparisons with CloudSat Cloud Profiling Radar and CALIPSO CALIOP products

Abstract. Since multilayer cloud scenes are common in the atmosphere and can be an important source of uncertainty in passive satellite sensor cloud retrievals, the MODIS MOD06 and MYD06 standard cloud optical property products include a multilayer cloud detection algorithm to assist with data quality assessment. This paper presents an evaluation of the Aqua MODIS MYD06 Collection 6 multilayer cloud detection algorithm through comparisons with active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products that have the ability to provide cloud vertical distributions and directly classify multilayer cloud scenes and layer properties. To compare active sensor products with an imager such as MODIS, it is first necessary to define multilayer clouds in the context of their radiative impact on cloud retrievals. Three main parameters have thus been considered in this evaluation: (1) the maximum separation distance between two cloud layers, (2) the thermodynamic phase of those layers and (3) the upper-layer cloud optical thickness. The impact of including the Pavolonis–Heidinger multilayer cloud detection algorithm, introduced in Collection 6, to assist with multilayer cloud detection has also been assessed. For the year 2008, the MYD06 C6 multilayer cloud detection algorithm identifies roughly 20 % of all cloudy pixels as multilayer (decreasing to about 13 % if the Pavolonis–Heidinger algorithm output is not used). Evaluation against the merged CPR and CALIOP 2B-CLDCLASS-lidar product shows that the MODIS multilayer detection results are quite sensitive to how multilayer clouds are defined in the radar and lidar product and that the algorithm performs better when the optical thickness of the upper cloud layer is greater than about 1.2 with a minimum layer separation distance of 1 km. Finally, we find that filtering the MYD06 cloud optical properties retrievals using the multilayer cloud flag improves aggregated statistics, particularly for ice cloud effective radius.

Mind the gap – Part 1: Accurately locating warm marine boundary layer clouds and precipitation using spaceborne radars

Abstract. Ground-based radar observations show that, over the eastern North Atlantic, 50 % of warm marine boundary layer (WMBL) hydrometeors occur below 1.2 km and have reflectivities of < −17 dBZ, thus making their detection from space susceptible to the extent of surface clutter and radar sensitivity. Surface clutter limits the ability of the CloudSat cloud profiling radar (CPR) to observe the true cloud base in ∼52 % of the cloudy columns it detects and true virga base in ∼80 %, meaning the CloudSat CPR often provides an incomplete view of even the clouds it does detect. Using forward simulations, we determine that a 250 m resolution radar would most accurately capture the boundaries of WMBL clouds and precipitation; that being said, because of sensitivity limitations, such a radar would suffer from cloud cover biases similar to those of the CloudSat CPR. Observations and forward simulations indicate that the CloudSat CPR fails to detect 29 %–43 % of the cloudy columns detected by ground-based sensors. Out of all configurations tested, the 7 dB more sensitive EarthCARE CPR performs best (only missing 9.0 % of cloudy columns) indicating that improving radar sensitivity is more important than decreasing the vertical extent of surface clutter for measuring cloud cover. However, because 50 % of WMBL systems are thinner than 400 m, they tend to be artificially stretched by long sensitive radar pulses, hence the EarthCARE CPR overestimation of cloud top height and hydrometeor fraction. Thus, it is recommended that the next generation of space-borne radars targeting WMBL science should operate interlaced pulse modes including both a highly sensitive long-pulse mode and a less sensitive but clutter-limiting short-pulse mode.

Comparison of TRMM Microwave Imager Rainfall Datasets from NASA and JAXA

AbstractThis study compares three TMI rainfall datasets generated by two versions of NASA’s Goddard Profiling algorithm (GPROF2010 and GPROF2017) and JAXA’s Global Satellite Mapping of Precipitation algorithm (GSMaP) over land, coast, and ocean. We use TRMM precipitation radar observations as the reference, and also include CloudSat cloud profiling radar (CPR) observations as the reference over ocean. First, the dynamic thresholds for rainfall detection used by GSMaP and GPROF2017 have better detection capability, indicating by larger Heidke skill score (HSS) values, compared with GPROF2010 over both land and coast. Over ocean, all three datasets have very similar HSS regardless of including CPR observations. Next, intensity analysis shows that no single dataset performs the best according to all three statistical metrics (correlation, root-mean-square error, and relative bias), except that GSMaP performs the best for stratiform precipitation over coast, and GPROF2017 performs the best for convective precipitation over ocean, based on all three metrics. Finally, an error decomposition analysis shows that the total error and its three components have very different characteristics over several regions among these three datasets. For example, the positive total error in GPROF2010 and GSMaP is primarily caused by the positive hit bias over central Africa, while the false bias in GPROF2017 is largely responsible for this positive total error. For future algorithm development, results from this study imply that a convective–stratiform separation technique may be necessary to reduce the large underestimation for convective rain intensity.

Snowfall distribution and its response to the Arctic Oscillation: an evaluation of HighResMIP models in the Arctic using CPR/CloudSat observations

Abstract. A realistic representation of snowfall in general circulation models (GCMs) of global climate is important to accurately simulate snow cover, surface albedo, high-latitude precipitation and thus the surface radiation budget. Hence, in this study, we evaluate snowfall in a range of climate models run at two different resolutions by comparing to the latest estimates of snowfall from the CloudSat Cloud Profiling Radar over the northern latitudes. We also evaluate whether the finer-resolution versions of the GCMs simulate the accumulated snowfall better than their coarse-resolution counterparts. As the Arctic Oscillation (AO) is the prominent mode of natural variability in the polar latitudes, the snowfall variability associated with the different phases of the AO is examined in both models and in our observational reference. We report that the statistical distributions of snowfall differ considerably between the models and CloudSat observations. While CloudSat shows an exponential distribution of snowfall, the models show a Gaussian distribution that is heavily positively skewed. As a result, the 10th and 50th percentiles, representing the light and median snowfall, are overestimated by up to factors of 3 and 1.5, respectively, in the models investigated here. The overestimations are strongest during the winter months compared to autumn and spring. The extreme snowfall represented by the 90th percentiles, on the other hand, is positively skewed, underestimating the snowfall estimates by up to a factor of 2 in the models in winter compared to the CloudSat estimates. Though some regional improvements can be seen with increased spatial resolution within a particular model, it is not easy to identify a specific pattern that holds across all models. The characteristic snowfall variability associated with the positive phase of AO over Greenland Sea and central Eurasian Arctic is well captured by the models.

Main Detrainment Height of Deep Convection Systems over the Tibetan Plateau and Its Southern Slope

Deep convection systems (DCSs) can rapidly lift water vapor and other pollutants from the lower troposphere to the upper troposphere and lower stratosphere. The main detrainment height determines the level to which the air parcel is lifted. We analyzed the main detrainment height over the Tibetan Plateau and its southern slope based on the CloudSat Cloud Profiling Radar 2B_GEOPROF dataset and the Aura Microwave Limb Sounder Level 2 cloud ice product onboard the A-train constellation of Earth-observing satellites. It was found that the DCSs over the Tibetan Plateau and its southern slope have a higher main detrainment height (about 10–16 km) than other regions in the same latitude. The mean main detrainment heights are 12.9 and 13.3 km over the Tibetan Plateau and its southern slope, respectively. The cloud ice water path decreases by 16.8% after excluding the influences of DCSs, and the height with the maximum increase in cloud ice water content is located at 178 hPa (about 13 km). The main detrainment height and outflow horizontal range are higher and larger over the central and eastern Tibetan Plateau, the west of the southern slope, and the southeastern edge of the Tibetan Plateau than that over the northwestern Tibetan Plateau. The main detrainment height and outflow horizontal range are lower and broader at nighttime than during daytime.

Novel aerosol extinction coefficients and lidar ratios over the ocean from CALIPSO–CloudSat: evaluation and global statistics

Abstract. Aerosol extinction coefficients (σa) and lidar ratios (LRs) are retrieved over the ocean from CALIPSO's Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) attenuated backscatter profiles by solving the lidar equation constrained with aerosol optical depths (AODs) derived by applying the Synergized Optical Depth of Aerosols (SODA) algorithm to ocean surface returns measured by CALIOP and CloudSat's Cloud Profiling Radar. σa and LR are retrieved for two independent scenarios that require somewhat different assumptions: (a) a single homogeneous atmospheric layer (1L) for which the LR is constant with height and (b) a vertically homogeneous layer with a constant LR overlying a marine boundary layer with a homogenous LR fixed at 25 sr (two-layer method, 2L). These new retrievals differ from the standard CALIPSO version 4.1 (V4) product, as the CALIOP–SODA method does not rely on an aerosol classification scheme to select LR. CALIOP–SODA σa and LR are evaluated using airborne high-spectral-resolution lidar (HSRL) observations over the northwest Atlantic. CALIOP–SODA LR (1L and 2L) positively correlates with its HSRL counterpart (linear correlation coefficient r>0.67), with a negative bias smaller than 17.4 % and a good agreement for σa (r≥0.78) with a small negative bias (≤|-9.2%|). Furthermore, a global comparison of optical depths derived by CALIOP–SODA and CALIPSO V4 reveals substantial discrepancies over regions dominated by dust and smoke (0.24), whereas Aqua's Moderate resolution Imaging Spectroradiometer (MODIS) and SODA AOD regional differences are within 0.06. Global maps of CALIOP–SODA LR feature high values over littoral zones, consistent with expectations of continental aerosol transport offshore. In addition, seasonal transitions associated with biomass burning from June to October over the southeast Atlantic are well reproduced by CALIOP–SODA LR.

Assessment of GPM high‐frequency microwave measurements with radiative transfer simulation under snowfall conditions

The Global Precipitation Mission (GPM) Microwave Imager (GMI) has four channels at 166 and 183 GHz that provide critical information on snow precipitation. Since the applications of these high‐frequency microwave channels to snowfall prediction and retrieval are still in a very early stage, it is important to evaluate the biases between observed and simulated brightness temperatures for those channels under snowfall conditions. A radiative transfer model that supports the computation of single‐scattering properties for non‐spherical snow particles is employed to simulate GMI brightness temperatures. Snow water content profiles are first derived from the CloudSat Cloud Profiling Radar (CPR) measurements and then used as input to the radiative transfer model. Results show that the biases of observed minus simulated brightness temperatures for GMI channels averaged over all selected pixels are generally less than 1 K under snowfall conditions, except for the 166 GHz horizontal polarization (166H) channel that has a bias of about 3 K. Larger biases are observed under snowfall conditions when the scene brightness temperatures are low. Further investigation indicates that the GMI remaining biases are often associated with shallow snow cells or deep convective clouds. We hypothesize that in shallow snow cells the errors in retrieved cloud liquid water likely contribute to the large bias for the GMI 166H channel, while in deep convective clouds the attenuation in CPR radar reflectivities and possible sampling bias likely cause the biases for GMI channels under low scene brightness temperatures. This study lays out an applicable configuration in simulating microwave brightness temperatures at high frequency under snowfall conditions, and the explanation provided for the GMI remaining biases at 166 and 183 GHz are useful for future applications of these channels.

Retrieval of Sea Surface Wind Speed Using Spaceborne Millimeter-Wave Radar Measurements

Spaceborne millimeter-wave radars can acquire sea surface backscatter information under clear sky conditions. The analysis based on the classical sea surface scattering model and a matching data set of global CloudSat Cloud Profiling Radar (CPR) observations and AMSR-E sea surface parameters shows that the sea surface scattering cross section is significantly dependent on the sea surface wind speed (SSW) in the range of less than 13 m/s. Then, with CPR observations and the sea surface temperature as the input information, an SSW retrieval model under clear sky conditions is established using neural networks. Retrieval results show that the correlation coefficient and the RMS error, between the millimeter-wave cloud radar SSW and the AMSR-E SSW, are approximately 0.95 and 0.97 m/s, respectively.