Rain Liquid Water Research Articles

Insights of warm-cloud biases in Community Atmospheric Model 5 and 6 from the single-column modeling framework and Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) observations

Abstract. There has been a growing concern that most climate models predict precipitation that is too frequent, likely due to lack of reliable subgrid variability and vertical variations in microphysical processes in low-level warm clouds. In this study, the warm-cloud physics parameterizations in the singe-column configurations of NCAR Community Atmospheric Model version 6 and 5 (SCAM6 and SCAM5, respectively) are evaluated using ground-based and airborne observations from the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Aerosol and Cloud Experiments in the Eastern North Atlantic (ACE-ENA) field campaign near the Azores islands during 2017–2018. The 8-month single-column model (SCM) simulations show that both SCAM6 and SCAM5 can generally reproduce marine boundary layer cloud structure, major macrophysical properties, and their transition. The improvement in warm-cloud properties from the Community Atmospheric Model 5 and 6 (CAM5 to CAM6) physics can be found through comparison with the observations. Meanwhile, both physical schemes underestimate cloud liquid water content, cloud droplet size, and rain liquid water content but overestimate surface rainfall. Modeled cloud condensation nuclei (CCN) concentrations are comparable with aircraft-observed ones in the summer but are overestimated by a factor of 2 in winter, largely due to the biases in the long-range transport of anthropogenic aerosols like sulfate. We also test the newly recalibrated autoconversion and accretion parameterizations that account for vertical variations in droplet size. Compared to the observations, more significant improvement is found in SCAM5 than in SCAM6. This result is likely explained by the introduction of subgrid variations in cloud properties in CAM6 cloud microphysics, which further suppresses the scheme's sensitivity to individual warm-rain microphysical parameters. The predicted cloud susceptibilities to CCN perturbations in CAM6 are within a reasonable range, indicating significant progress since CAM5 which produces an aerosol indirect effect that is too strong. The present study emphasizes the importance of understanding biases in cloud physics parameterizations by combining SCM with in situ observations.

Comparative Study of Cloud Liquid Water and Rain Liquid Water Obtained From Microwave Radiometer and Micro Rain Radar Observations Over Central China During the Monsoon

AbstractWe investigated the cloud liquid water (CLW) and rain liquid water (RLW) during weak precipitations (rain rate below 12 mm/h) using microwave radiometer and microrain radar measurements collected by the Integrative Monsoon Frontal Rainfall Experiment over central China in 2018. The CLW path increased sharply from 0.6 to 4.1 mm for precipitation clouds. RLW path presented a similar trend, although it had a larger correlation coefficient with rain rate. Precipitation efficiency reached up to ~50% and then clearly decreased as precipitation weakened. Because weak precipitation is mostly formed in stable nimbostratus, CLW content (CLWC) during precipitation tends to has a quasi‐normal distribution with mode at 0.38 g/m3, whereas RLW content (RLWC) shows a positively skewed distribution with mode at 0.06 g/m3. Normalized CLWC initially increases then decreases with height in nonprecipitation clouds but varies slightly in precipitation clouds due to relatively monodispersed droplets in the weaker convective motion. CLWC derived from millimeter‐wave cloud radar (MMCR) shows similar vertical distribution but with larger values. The mean normalized CLWCs are 0.06 and 0.38 g/m3 for nonprecipitation and precipitation clouds, respectively. RLWC varies slightly with height with a mean of 0.22 g/m3 because both the collision and breakup of raindrops are weak. A case study showed different distributions and vertical structures of CLWC and RLWC in various stages of precipitation. Thicker clouds result in larger CLWC and RLWC, which will cause greater rain rate. This qualitatively explains relationships among cloud thickness, CLW, RLW, and rain rate in precipitation during the monsoon.

Mind the gap – Part 2: Improving quantitative estimates of cloud and rain water path in oceanic warm rain using spaceborne radars

Abstract. The intrinsic small spatial scales and low-reflectivity structure of oceanic warm precipitating clouds suggest that millimeter spaceborne radars are best suited to providing quantitative estimates of cloud and rain liquid water paths (LWPs). This assertion is based on their smaller horizontal footprint; high sensitivities; and a wide dynamic range of path-integrated attenuations associated with warm-rain cells across the millimeter wavelength spectrum, with diverse spectral responses to rain and cloud partitioning. State-of-the-art single-frequency radar profiling algorithms of warm rain seem to be inadequate because of their dependence on uncertain assumptions about the rain–cloud partitioning and because of the rain microphysics. Here, high-resolution cloud-resolving model simulations for the Rain in Cumulus over the Ocean field study and a spaceborne forward radar simulator are exploited to assess the potential of existing and future spaceborne radar systems for quantitative warm-rain microphysical retrievals. Specifically, the detrimental effects of nonuniform beam filling on estimates of path-integrated attenuation (PIA), the added value of brightness temperature (TB) derived adopting radiometric radar modes, and the performances of multifrequency PIA and/or TB combinations when retrieving liquid water paths partitioned into cloud (c-LWPs) and rain (r-LWPs) are assessed. Results show that (1) Ka- and W-band TB values add useful constraints and are effective at lower LWPs than the same-frequency PIAs; (2) matched-beam combined TB values and PIAs from single-frequency or multifrequency radars can significantly narrow down uncertainties in retrieved cloud and rain liquid water paths; and (3) the configuration including PIAs, TB values and near-surface reflectivities for the Ka-band–W-band pairs in our synthetic retrieval can achieve an RMSE of better than 30 % for c-LWPs and r-LWPs exceeding 100 g m−2.

Raindrop size distribution of different cloud types over the Western Ghats using simultaneous measurements from Micro-Rain Radar and disdrometer

Investigating the raindrop size distributions (DSDs) for different cloud types is essential for the rain characterization and understanding different microphysical processes within the cloud system. In this study, the simultaneous measurements from the Micro-Rain Radar (MRR) and Joss-Waldvogel Disdrometer (JWD) are used to investigate the DSDs of different precipitation categories during the Indian Summer Monsoon (ISM) season (June–September) for the period 2012–2015. Both the instruments deployed at Mahabaleshwar (17.92°N, 73.6°E, ~1.4km AMSL), which is located in the complex mountain terrain of the Western Ghats (WGs), India. From the MRR reflectivity factor and fall-velocity profiles, the observed precipitation systems are classified into four categories: shallow-convective, convective, stratiform and mixed convective-stratiform. In terms of rain occurrence frequency, it is found that the rain over Mahabaleshwar is mostly contributed by the shallow-convective (~89%) system while the stratiform system contribution is about 9% and the convective and the mixed convective-stratiform systems contributes <2%. For different precipitation categories, the rain microphysical parameters such as median volume diameter, rain liquid water content and normalized intercept parameter are estimated using the moment method (second, third and fourth moment) of the observed DSDs. The reflectivity–rainfall (Z–R) relation of the form Z=aRb is estimated for each precipitation categories. Studies of systematic and comprehensive classification of precipitation types in terms of their rain microphysical parameters and Z–R relationships over a region is important as it would improve the understanding on rain microphysics and rainfall estimation from active and passive remote sensing devices.

Cloud and rain liquid water statistics in the CHUVA campaign

The purpose of this study is to present statistics related to the integration of cloud and rain liquid water and the profiles for different cloud types and regimes. From 2010 to 2012, the CHUVA project collected information regarding cloud and rain characteristics in different precipitation regimes in Brazil. CHUVA had four field campaigns between 2010 and 2011, located in the North, Northeast and Southeast regions of Brazil, covering the semi-arid, Amazon, coastal and mountain regions. The synergy of several instruments allowed us to classify rain events and describe the cloud processes regionally. Microwave radiometers, LiDAR, radar, and disdrometers were employed in this study. The rain type classification was made using vertical profiles of reflectivity (VPR) and polarimetric variables from dual polarization radar (XPOL). The integrated liquid water (ILWC) for non-precipitating clouds was retrieved with a microwave ground-based radiometer using a neural network. For rainy conditions, the profiles from the rain liquid water content (LWCR) and their integrated (ILWR) properties were estimated by Micro Rain Radar (MRR) and XPOL VPRs. For non-precipitating clouds, the ILWC values were larger for the sites in tropical regions, in particular near the coast, than for Southeast Brazil. For rainy cases, distinct LWCR profiles were observed for different rain classifications and regions. The differences are small for low rain rates and a distinction between different rainfall regimes is more evident for high rain rates. Vale and Belém clouds present the deepest layers and largest convective rain rates. The clouds in the Southeast region of Brazil (Vale do Paraíba) and North region (Belém) showed the largest reflectivity in the mixed and glaciated layers, respectively. In contrast, the Northeast coastal site (e.g. Fortaleza) showed larger values in the warm part of the clouds. Several analyses are presented, describing the cloud processes and the differences among the cloud types, rain rates and regions.

Partitioning of cloud water and rainwater content by ground‐based observations with the Advanced Microwave Radiometer for Rain Identification (ADMIRARI) in synergy with a micro rain radar

Cloud and rain liquid water path and total water vapor are retrieved simultaneously from passive microwave observations with the multifrequency dual‐polarized Advanced Microwave Radiometer for Rain Identification (ADMIRARI). A data set of linearly polarized brightness temperatures has been collected at 30° elevation angle together with slant radar reflectivity profiles at 24.1 GHz from a micro rain radar (MRR) pointing into the same viewing direction. The slant path integrated values are retrieved via a Bayesian inversion approach, the quality of which is evaluated by a simulation‐based retrieval sensitivity study. The algorithm includes a physical constraint by taking into account the rain column structural information from the MRR observations. Measurements and derived path‐integrated water component estimates from 23 August to 12 November 2008, obtained in Cabauw, Netherlands, are analyzed. During raining cloud conditions the zenith‐normalized root‐mean‐square error for water vapor, cloud liquid water path, and rain liquid water path are, on average, estimated to 1.54 kg m−2, 144 g m−2, and 52 g m−2, respectively. On the basis of these results, long‐term estimated distributions of cloud water–rainwater partitioning for midlatitude precipitating clouds are presented for the first time as obtained by a ground‐based radiometer.

Typical Patterns of Microwave Signatures and Vertical Profiles of Precipitation in the Midlatitudes from TRMM Data

AbstractRepresentative patterns from multichannel microwave brightness temperature Tb in the midlatitude oceanic region, observed by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), are studied during precipitation events detected by the TRMM precipitation radar (PR) for three summer and winter seasons using empirical orthogonal function (EOF) analysis. The first three patterns are interpreted as rain liquid water, solid particles, and rain type based on the frequency distributions of vertical profiles of the radar reflectivity factor and the heights of the storm top, cloud top, and freezing level. The first EOF (EOF1) correlates with the near-surface rain rate. While the eigenvector for the 85.5-GHz channel is less significant for EOF1 variability in summer, those in all channels contribute equally to the variability in winter. This difference suggests that summer precipitation is caused by additional solid particles formed in developing precipitation systems. The second EOF (EOF2) represents the number of solid particles and also corresponds to the near-surface rain rate. This result suggests an increase of solid particles with the development of precipitation systems. EOF2 varies largely by echo-top height in summer and by echo-top height and freezing height in winter. The positive component score has double Tb peaks. Dividing the score into two patterns according to these peaks reveals highly developed precipitation systems, such as convective rainbands and frontal systems, and weak precipitation with shallow systems caused by cold outbreaks in the winter case. The negative component score also shows shallow and weak precipitation systems with warm rain. The third EOF (EOF3) is related to rain type. Vertical profiles show a significant bright band with a small height difference between the echo top and freezing level for negative EOF3, while positive EOF3 has no bright band with a high echo top relative to the freezing height. The results indicate that stratiform and convective precipitation systems can be characterized by EOF3.

Discrimination of cloud and rain liquid water path by groundbased polarized microwave radiometry

We propose a new approach for groundbased remote sensing of liquid water path (LWP) in the presence of precipitating clouds. Dual polarized groundbased microwave radiometers are capable of detecting the unique scattering signature of nonspherical precipitation sized particles. This polarization signal is only produced by the precipitation particles for which the brightness temperature emission has a different sensitivity to LWP than the smaller cloud drops. By using the information that is contained in the polarization difference of the downwelling brightness temperature the cloud and rain liquid water fractions can be estimated independently. Future retrieval algorithms based on our proposed approach will enable the detection of small precipitation fractions in thick clouds and also allow for estimates of cloud and rain LWP in raining conditions.

Behavior of an Inversion-Based precipitation Retrieval Algorithm with High-Resolution AMPR Measurements Including a Low-Frequency 10.7-GHz Channel

A microwave-based, profile-type precipitation retrieval algorithm has been used to analyze high-resolution passsive microwave measurements over an ocean background, obtained by the Advanced Microwave Precipitation Radiometer (AMPR) flown on a NASA ER-2 aircraft. The analysis is designed to first determine the improvements that can be gained by adding brightness temperature information from the AMPR low-frequency channel (10.7 GHz) to a multispectral retrieval algorithm nominally run with satellite information at 19, 37, and 85 GHz. The impact of spatial resolution degradation of the high-resolution brightness temperature information on the retrieved rain/cloud liquid water contents and ice water contents is then quantified in order to assess the possible biases inherent to satellite-based retrieval. Careful inspection of the high-resolution aircraft dataset reveals five distinctive brightness temperature features associated with cloud structure and scattering effects that are not generally detectable in current passive microwave satellite measurements. Results suggest that the inclusion of 10.7-GHz information overcomes two basic problems associated with three-channel retrieval. Intercomparisons of retrievals carried out at high-resolution and then averaged to a characteristic satellite scale to the corresponding retrievals in which the brightness temperatures are first convolved down to the satellite scale suggest that with the addition of the 10.7-GHz channel, the rain liquid water contents will not be negatively impacted by special resolution degradation. That is not the case with the ice water contents as they appear ti be quite sensitive to the imposed scale, the implication being that as spatial resolution is reduced, ice water contents will become increasingly underestimated.