Profiles Of Liquid Water Content Research Articles

Seasonal Variation in Vertical Structure for Stratiform Rain at Mêdog Site in Southeastern Tibetan Plateau

Mêdog is located at the entrance of the water vapor channel of the Yarlung Tsangpo Great Canyon on the southeastern Tibetan Plateau (TP). In this study, the seasonal variation in the microphysical vertical structure of stratiform precipitation at the Mêdog site in 2022 was investigated using micro rain radar (MRR) observations, as there is a lack of similar studies in this region. The average melting layer height is the lowest in February, after which it gradually increases, reaches its peak in August, and then gradually decreases. For lower rain categories, the vertical distribution of small drops remains uniform in winter below the melting layer. The medium-sized drops show slight increases, leading to negative gradients in the microphysical profiles. Slight or evident decreases in concentrations of small drops are observed with decreasing height in the premonsoon, monsoon, and postmonsoon seasons, likely due to significant evaporation. The radar reflectivity, rain rate, and liquid water content profiles decrease with decreasing height according to the decrease in concentrations of small drops. With increasing rain rate, the drop size distribution (DSD) displays significant variations in winter, and the fall velocity decreases rapidly with decreasing height. In the premonsoon, monsoon, and postmonsoon seasons, the concentrations of large drops significantly decrease below the melting layer because of the breakup mechanism, leading to the decreases in the fall velocity profiles with decreasing height during these seasons. Raindrops with sizes ranging from 0.3–0.5 mm are predominant in terms of the total drop number concentration in all seasons. Precipitation in winter and postmonsoon seasons is mainly characterized by small raindrops, while that in premonsoon and monsoon seasons mainly comprises medium-sized raindrops. Understanding the seasonal variation in the vertical structure of precipitation in Mêdog will improve the radar quantitative estimation and the use of microphysical parameterization schemes in numerical weather forecast models over the TP.

What CloudSat cannot see: liquid water content profiles inferred from MODIS and CALIOP observations

Abstract. Single-layer nonprecipitating warm clouds are integral to Earth's climate, and accurate estimates of cloud liquid water content for these clouds are critical for constraining cloud models and understanding climate feedbacks. As the only cloud-sensitive radar currently in space, CloudSat provides very important cloud-profiling capabilities. However, a significant fraction of clouds is missed by CloudSat because they are either too thin or too close to the Earth's surface. We find that the CloudSat Radar-Visible Optical Depth Cloud Water Content Product, 2B-CWC-RVOD, misses about 73 % of nonprecipitating liquid cloudy pixels and about 63 % of total nonprecipitating liquid cloud water content compared to coincident Moderate Resolution Imaging Spectroradiometer (MODIS) observations. Those percentages increase to 84 % and 69 %, respectively, if MODIS “partly cloudy” pixels are included. We develop a method, based on adiabatic parcel theory but modified to account for the fact that observed clouds are often subadiabatic, to estimate profiles of cloud liquid water content based on MODIS observations of cloud-top effective radius and cloud optical depth combined with lidar observations of cloud-top height. We find that, for cloudy pixels that are detected by CloudSat, the resulting subadiabatic profiles of cloud water are similar to what is retrieved from CloudSat. For cloudy pixels that are not detected by CloudSat, the subadiabatic profiles can be used to supplement the CloudSat profiles, recovering much of the missing cloud water and generating realistic-looking merged profiles of cloud water. Adding this missing cloud water to the CWC-RVOD product increases the mean cloud liquid water path by 228 % for single-layer nonprecipitating warm clouds. This method will be included in a subsequent reprocessing of the 2B-CWC-RVOD algorithm.

Evaluation of the Ice Particle Simulation of Microphysics Schemes with Aircraft Measurements of a Stratiform Cloud in North China

Abstract Airborne microphysical measurements of a frontal precipitation event in North China were used to evaluate five microphysics schemes for predicting the bulk properties of ice particles. They are the Morrison and Thompson schemes, which use predetermined categories, the 1-ice- and 2-ice-category configurations of the Predicted Particle Properties (P3) scheme and the Ice-Spheroids Habit Model with Aspect-Ratio Evolution (ISHMAEL) scheme, which model the evolution of particle properties, and the spectral bin fast version (SBM_fast) microphysics scheme within the Weather Research and Forecasting (WRF) Model. WRF simulations with these schemes successfully reproduced the observed temperature and the liquid and total water content profiles at corresponding times and locations, allowing for a credible comparison of the predictions of particle properties with the aircraft measurements. The simulated results with the 1-ice-category P3 scheme are in good agreement with the observations for all the particle properties we examined. The 2-ice-category P3 scheme overestimates the spectrum width and underestimates the number concentration, which can be alleviated by reducing the ice collection efficiency. The simulation with the SBM_fast scheme deviates from the observed ice particle size distributions since the mass–diameter relationship of snow-sized particles adopted in this scheme may not be applicable to this stratiform cloud case.

Large-Eddy Simulations of Marine Boundary Layer Clouds Associated with Cold-Air Outbreaks during the ACTIVATE Campaign. Part II: Aerosol–Meteorology–Cloud Interaction

Abstract Aerosol effects on micro/macrophysical properties of marine stratocumulus clouds over the western North Atlantic Ocean (WNAO) are investigated using in situ measurements and large-eddy simulations (LES) for two cold-air outbreak (CAO) cases (28 February and 1 March 2020) during the Aerosol Cloud Meteorology Interactions over the Western Atlantic Experiment (ACTIVATE). The LES is able to reproduce the vertical profiles of liquid water content (LWC), effective radius reff and cloud droplet number concentration Nc from fast cloud droplet probe (FCDP) in situ measurements for both cases. Furthermore, we show that aerosols affect cloud properties (Nc, reff, and LWC) via the prescribed bulk hygroscopicity of aerosols () and aerosol size distribution characteristics. Nc, reff, and liquid water path (LWP) are positively correlated to and aerosol number concentration (Na) while cloud fractional cover (CFC) is insensitive to and aerosol size distributions for the two cases. The realistic changes to aerosol size distribution (number concentration, width, and the geometrical diameter) with the same meteorology state allow us to investigate aerosol effects on cloud properties without meteorological feedback. We also use the LES results to evaluate cloud properties from two reanalysis products, ERA5 and MERRA-2. Compared to LES, the ERA5 is able to capture the time evolution of LWP and total cloud coverage within the study domain during both CAO cases while MERRA-2 underestimates them.

Automated observation of physical snowpack properties in Ny-Ålesund

The snow season in the Svalbard archipelago generally lasts 6–10 months a year and significantly impacts the regional climate, glaciers mass balance, permafrost thermal regime and ecology. Due to the lack of long-term continuous snowpack physical data, it is still challenging for the numerical snow physics models to simulate multi-layer snowpack evolution, especially for remote Arctic areas. To fill this gap, in November 2020, an automated nivometric station (ANS) was installed ∼1 km Southwest from the settlement of Ny-Ålesund (Spitzbergen, Svalbard), in a flat area over the lowland tundra. It automatically provides continuous snow data, including NIR images of the fractional snow-cover area (fSCA), snow depth (SD), internal snow temperature and liquid water content (LWC) profiles at different depths with a 10 min time resolution. Here we present the first-year record of automatic snow preliminary measurements collected between November 2020 and July 2021 together with weekly manual observations for comparison. The snow season at the ANS site lasted for 225 days with an annual net accumulation of 117 cm (392 mm of water equivalent). The LWC in the snowpack was generally low (<4%) during wintertime, nevertheless, we observed three snow-melting events between November and February 2021 and one in June 2021, connected with positive temperature and rain on snow events (ROS). In view of the foreseen future developments, the ANS is the first automated, comprehensive snowpack monitoring system in Ny-Ålesund measuring key essential climate variables needed to understand the seasonal evolution of the snow cover on land.

Climatology of estimated liquid water content and scaling factor for warm clouds using radar–microwave radiometer synergy

Abstract. Cloud radars are capable of providing continuous high-resolution observations of clouds and now offer new capabilities within fog layers thanks to the development of frequency-modulated continuous-wave 95 GHz cloud radars. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z=a⋅LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z–LWC relation for a continuous cloud system is complicated. Additional information such as liquid water path (LWP) from a co-located microwave radiometer (MWR) is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of fog and warm clouds using 95 GHz cloud radar–microwave radiometer synergy in a variational framework is presented. This paper also aims to propose an algorithm configuration that retrieves the LWC of clouds and fog using radar reflectivity and a climatology of the power law parameters. To do so, variations in the scaling factor ln a (the logarithm of pre-factor a from power law relation) when MWR observations are available are allowed in each cloud profile to build a climatology of the scaling factor ln a that can be used when MWR observations are not available. The algorithm also accounts for attenuation due to cloud droplets. In this algorithm formulation, the measure of uncertainty in the observations, the forward model, and the a priori information of desired variables acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information because LWP is point information for the whole cloud column. By further investigating the sensitivity analysis of various biases in LWP information, it was found that it is beneficial to incorporate LWP, even if it is biased, rather than not assimilate any LWP. The algorithm is then implemented in various cloud and fog cases at the SIRTA observatory to estimate LWC and the scaling factor. The scaling factor (ln a) changes for each cloud profile, and the range of ln a is consistent with suggested values in the literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (southwest of France, October 2019 to March 2020), in situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud–fog heterogeneity played a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for the scaling factor are proposed for fog and clouds. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of the scaling factor as a priori information to estimate the LWC of warm clouds. This method allows the LWC estimation using only radar reflectivity and climatology of the scaling factor.

Establishment of an analytical model for remote sensing of typical stratocumulus cloud profiles under various precipitation and entrainment conditions

Abstract. Structural patterns of cloud effective radius (ER) and liquid water content (LWC) profiles are essential variables of cloud lifecycle and precipitation processes, while observing cloud profiles from passive remote-sensing sensors remains highly challenging. Understanding whether there are typical structural patterns of ER and LWC profiles in liquid clouds and how they are linked to cloud entrainment or precipitating status is critical in developing algorithms to derive cloud profiles from passive satellite sensors. This study aims to address these questions and provide a preliminary foundation for the development of liquid cloud profile retrievals for the Multi-viewing, Multi-channel and Multi-polarization Imaging (3MI) sensor aboard the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System-Second Generation (EPS-SG) satellite, which is scheduled to be launched in 2025. Firstly, we simulate a large ensemble of stratocumulus cloud profiles using the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS). The empirical orthogonal function (EOF) analysis is adopted to describe the shape of simulated profiles with a limited number of elemental profile variations. Our results indicate that the first three EOFs of LWC and ER profiles can explain >90 % of LWC and ER profiles. The profiles are divided into four prominent patterns and all of these patterns can be simplified as triangle-shaped polylines. The frequency of these four patterns is found to relate to intensities of the cloud-top entrainment and precipitation. Based on these analyses, we propose a simplified triangle-shaped cloud profile parameterization scheme allowing us to represent these main patterns of LWC and ER. This simple yet physically realistic analytical model of cloud profiles is expected to facilitate the representation of cloud properties in advanced retrieval algorithms such as those developed for the 3MI/EPS-SG.

An optimal estimation algorithm for the retrieval of fog and low cloud thermodynamic and micro-physical properties

Abstract. A new generation of cloud radars, with the ability to make observations close to the surface, presents the possibility of observing fog properties with better insight than was previously possible. The use of these instruments as part of an operational observation network could improve the prediction of fog events, something which is still a problem for even high-resolution numerical weather prediction models. However, the retrieval of liquid water content (LWC) profiles from radar reflectivity alone is an under-determined problem, something which ground-based microwave radiometer observations can help to constrain. In fact, microwave radiometers are not only sensitive to temperature and humidity profiles but are also known to be instruments of reference for the liquid water path. By providing the thermodynamic state of the atmosphere, to which the formation and evolution of fog events are highly sensitive, in addition to accurate liquid water path, which can be used to constrain the LWC retrieval from the cloud radar alone, combining microwave radiometers with cloud radars seems a natural next step to better understand and forecast fog events. To that end, a newly developed one-dimensional variational (1D-Var) algorithm designed for the retrieval of temperature, specific humidity and liquid water content profiles with both cloud radar and microwave radiometer (MWR) observations is presented in this study. The algorithm was developed to evaluate the capability of cloud radar and MWR to provide accurate LWC profiles in addition to temperature and humidity in view of assimilating the retrieved profiles into a 3D- and 4D-Var operational assimilation system. The algorithm is firstly tested on a synthetic dataset, which allows the evaluation of the developed algorithm in idealised conditions. This dataset was constructed by perturbing a high-resolution forecast dataset of fog and low-cloud cases by its expected errors. The algorithm is then tested with real data from the recent field campaign SOFOG-3D, carried out with the use of LWC measurements made from a tethered balloon platform. As expected, results from the synthetic dataset study were found to contain lower errors than those found from the retrievals on the dataset of real observations. It was found that LWC can be retrieved in idealised conditions with an uncertainty of less than 0.04 g m−3. With real data, as expected, retrievals with a good correlation (0.7) to in situ measurements were found but with a higher uncertainty than the synthetic dataset of around 0.06 g m−3 (41 %). This was reduced to 0.05 g m−3 (35 %) when an accurate droplet number concentration could be prescribed to the algorithm. A sensitivity study was conducted to discuss the impact of different settings used in the 1D-Var algorithm and the forward operator. Additionally, retrievals of LWC from a real fog event observed during the SOFOG-3D field campaign were found to significantly improve the operational background profiles of the AROME (Application of Research to Operations at MEsoscale) model, showing encouraging results for future improvement of the AROME model initial state during fog conditions.

Formation of fog due to stratus lowering: An observational and modelling case study

AbstractWe numerically investigate the processes responsible for a fog event formed by stratus cloud lowering, observed on December 1–2, 2016, during the experimental campaign in the northeast of France. The observations revealed a complex temporal evolution with stratus followed by a relatively drier period and then its reformation leading to fog formation by stratus lowering. Microphysical observations below a tethered balloon exhibit different vertical profiles of liquid water content and droplet concentration between the stratus and the fog formed below. A simulation at 100‐m grid spacing reproduced the main observed characteristics of the cloud life cycle despite a time lag in stratus formation due to large‐scale conditions. The advection of cloud water in the stratus and at its top appears crucial to feed the stratus lowering, resulting in radiative cooling, vertical transport, droplet sedimentation, evaporation, and cooling of the sub‐cloud layer. The advection of cold or warm air in the lowest 250 m, mainly driven by fine‐scale orographic circulations, impacts the fog formation due to stratus cloud lowering. When non‐local conditions are favourable, the most important microphysical process to favour fog formation is the droplet sedimentation, leading to the cooling and moistening in the sub‐cloud layer by evaporation. Droplet sedimentation appears more efficient when the droplet concentration is low, and a two‐moment microphysical scheme more appropriate than a one‐moment scheme to reproduce the observed variability of the droplet concentration. Given the predominance of non‐local processes on this case study, a three‐dimensional high‐resolution model appears crucial to perform accurate forecasts of fog by stratus lowering.

LiDAR-Based Remote Sensing of the Vertical Profile of Aerosol Liquid Water Content Using a Machine-Learning Model

The aerosol liquid water content (ALWC) dictates the hygroscopicity of aerosol particles. To date, measurements of ALWC have been confined primarily to ground-based observations although vertical profiles of ALWC are crucial for understanding its interactions with meteorology. This study proposes a novel method for deriving profiles of ALWC using data acquired by a Light Detection and Ranging (LiDAR), a microwave radiometer, and a suite of aerosol instruments measuring aerosol physical and chemical properties, deployed during a five-month field experiment in Guangzhou, China. The retrieval approach is based on a machine-learning model named the gradient-boosted decision tree model. The inversion accuracy and stability are assessed through comparisons with ALWC data acquired on the ground and at the top of the Guangzhou tower of 532 m above ground. The agreements are encouraging: with the coefficient of determination <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$({R} ^{2}) = 0.870$ </tex-math></inline-formula> and root-mean-square error (RMSE) = <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$3.28 ~\mu \text{g} ~\cdot $ </tex-math></inline-formula> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> for all data; <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{2} = 0.776$ </tex-math></inline-formula> and RMSE = <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$2.18 ~\mu \text{g}~ \cdot $ </tex-math></inline-formula> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> for tower data; and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{2} = 0.872$ </tex-math></inline-formula> and RMSE = <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$4.1~ \mu \text{g} ~\cdot $ </tex-math></inline-formula> m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−3</sup> for ground data. From the vertical distribution of the retrieved ALWC in Guangzhou, ALWC is higher in the lower boundary layer, especially when air pollution is severe. The proportion of liquid water in aerosol particles is closely related to the relative humidity in the environment, which will affect the morphology of aerosol particles (with about every 10% increase in liquid water, the depolarization ratio decreases by 0.02). The model may be of general use for studying air pollution and secondary aerosol generation.

Analysis of precipitation microstructure characteristic during Madden Julian Oscillation (MJO) using micro rain radar (MRR) and disdrometer in South Tangerang

Rain microstructure is a critical aspect to understand the dynamics and microphysics character of the clouds. It is characterized by the distribution of size, fall velocity and shape of raindrop. Raindrop size distribution (DSD) explains the detail of the microphysical process because it represents a process of rain to the surface. One of the phenomena that influence the rain patterns in Indonesia is Madden Julian Oscillation (MJO). Therefore, observing rain microstructure with its relation to MJO can determine the differences in rainfall characteristic and microphysical processes during active and inactive MJO period. The data used in this study are Micro Rain Radar (MRR), disdrometer, and real-time multivariate (RMM) index data. The period/date selection of active MJO event performed using RMM index method is more than 1 in phases 4 and 5 and otherwise for inactive MJO. Types of rain are divided into stratiform and convective rain based on disdrometer data. From that, there are 46 active and 52 inactive MJO events. Rain microstructure in this study focuses on DSD from disdrometer and micro rain radar data analyzed with liquid water content profile, fall velocity, reflectivity, and rain rate from MMR. Besides, there are parameters of DSD, which are the mass-weighted diameter (Dm) and total concentration (Nw), calculated using the moment and gamma distribution method. The result shows that DSD and other parameters are greater during inactive MJO period. It means that process of collision-coalescence, evaporation, and updraft is dominant during inactive MJO period.

Comparative analysis of precipitable liquid water content from micro rain radar and era-interim data: implication on radio wave propagation in a tropical location

The characteristics of the vertical profile of precipitable liquid water content (PLWC) obtained from Micro Rain Radar (MRR) and ERA-Interim data at a tropical site (Akure, Nigeria) is reported in this work. One (1) year (2014) data of PLWC was obtained from a vertically-looking MRR sited at the Department of Physics, FUTA (7°15’N, 5°15’E) and data set of PLWC obtained from the European Centre for Medium-Range Weather Forecast (ECMWF) re-analysis ERA-Interim with a resolution of 0.25 × 0.25. Comparative studies based on the vertical profile of the PLWC diurnally, seasonally, and monthly were carried out to deduce the significant trend. The profile assessment over the two sources of data shows low PLWC which is less than 0.1 gm−3 with a maximum value of about 1.5 gm−3. The specific attenuation due to cloud obtained using the two data sets at a frequency range of 15 - 75 GHz (Ku – V band) shows that attenuation values are more pronounced using data obtained from the ground-based radar. The ERA-Interim data tends to underestimate the cloud-based attenuation in this region.

Properties of Arctic liquid and mixed-phase clouds from shipborne Cloudnet observations during ACSE 2014

Abstract. This study presents Cloudnet retrievals of Arctic clouds from measurements conducted during a 3-month research expedition along the Siberian shelf during summer and autumn 2014. During autumn, we find a strong reduction in the occurrence of liquid clouds and an increase for both mixed-phase and ice clouds at low levels compared to summer. About 80 % of all liquid clouds observed during the research cruise show a liquid water path below the infrared black body limit of approximately 50 g m−2. The majority of mixed-phase and ice clouds had an ice water path below 20 g m−2. Cloud properties are analysed with respect to cloud-top temperature and boundary layer structure. Changes in these parameters have little effect on the geometric thickness of liquid clouds while mixed-phase clouds during warm-air advection events are generally thinner than when such events were absent. Cloud-top temperatures are very similar for all mixed-phase clouds. However, more cases of lower cloud-top temperature were observed in the absence of warm-air advection. Profiles of liquid and ice water content are normalized with respect to cloud base and height. For liquid water clouds, the liquid water content profile reveals a strong increase with height with a maximum within the upper quarter of the clouds followed by a sharp decrease towards cloud top. Liquid water content is lowest for clouds observed below an inversion during warm-air advection events. Most mixed-phase clouds show a liquid water content profile with a very similar shape to that of liquid clouds but with lower maximum values during events with warm air above the planetary boundary layer. The normalized ice water content profiles in mixed-phase clouds look different from those of liquid water content. They show a wider range in maximum values with the lowest ice water content for clouds below an inversion and the highest values for clouds above or extending through an inversion. The ice water content profile generally peaks at a height below the peak in the liquid water content profile – usually in the centre of the cloud, sometimes closer to cloud base, likely due to particle sublimation as the crystals fall through the cloud.

Assimilation of Satellite Microwave Observations over the Rainbands of Tropical Cyclones

AbstractA novel Bayesian Monte Carlo integration (BMCI) technique was developed to retrieve geophysical variables from satellite microwave radiometer data in the presence of tropical cyclones. The BMCI technique includes three steps: generating a stochastic database, simulating satellite brightness temperatures using a radiative transfer model, and retrieving geophysical variables such as profiles of temperature, relative humidity, and cloud liquid and ice water content from real observations. The technique also provides uncertainty estimates for each retrieval and can output the error covariance matrix of selected parameters. The measurements from the Advanced Technology Microwave Sounder (ATMS) on board Suomi National Polar-Orbiting Partnership (Suomi NPP) and the Global Precipitation Measurement (GPM) Microwave Imager (GMI) were used as input. A new technique was developed to correct the ATMS and GMI observations for the beam-filling effect, which is due to small-scale variability of precipitation and clouds when compared with the instrument footprint and also the nonlinear relation between the brightness temperature and precipitation. In addition, the assimilation of the BMCI retrievals into the NASA GEOS model is discussed for Hurricane Maria. The results show that assimilating the BMCI retrievals can influence the dynamical features of the cyclone, including a stronger warm core, a symmetric eye, and vertically aligned wind columns. Two possible factors that may limit the impact of the BMCI retrievals include 1) the resolution of the model (about 25 km), which was too coarse to show the potential of the BMCI data in improving the representation of tropical storms in the model forecast, and 2) the data assimilation system not being able to consider vertically correlated observation errors.

The Vertical Structure of Liquid Water Content in Shallow Clouds as Retrieved From Dual‐Wavelength Radar Observations

AbstractObservations collected over 3 months by the beam‐matched second‐generation Ka/W band Scanning Cloud Radar located at the Atmospheric Radiation Measurement Program Eastern North Atlantic observatory are used to advance existing liquid water content (LWC) retrieval techniques, quantify retrieval uncertainty, and subsequently characterize the impact of cloud dynamics and rain rates on the vertical distribution of LWC in boundary layer clouds both precipitating and broken. A threefold technique is proposed that involves (1) temporally averaging measured radar reflectivities collected at two wavelengths to 30‐s resolution, (2) smoothing via fitting a second‐degree polynomial to their dual‐wavelength ratios within 187.5‐m vertical overlapping sliding windows, and (3) averaging the multiple LWC estimates produced at each height. It is estimated that this technique reduced LWC retrieval uncertainty to 0.10–0.65 g/m3, depending on cloud thickness. Although individual retrievals remained noisy, statistics on subgroups of height‐normalized LWC profiles show that, on average, the vertical distributions of LWC in most of the observed clouds followed a linear relationship with a degree of adiabaticity ranging from 0.6 to 0.2 for 200‐ to 600‐m thick clouds. However, nonlinear LWC profiles were present in subgroups of cloud segments presenting intense (0.1–0.5 mm/hr) drizzle rates where LWC was observed to pool near cloud base and in subgroups of cloud segments within strong (0.6 m/s) downdrafts near cloud top where LWC was coincidently reduced. This nonlinearity is inconsistent with the use of adiabatic cloud assumptions for process studies and supports further development of retrievals like the one proposed.

Radar–Radiometer-Based Liquid Water Content Retrievals of Warm Low-Level Clouds: How the Measurement Setup Affects Retrieval Uncertainties

Here, we propose a new methodology that increases the understanding of uncertainty sources of liquid water content (LWC) retrievals, which are caused by the combination of instruments having different beam widths and are horizontally displaced. Furthermore, we give first quantitative uncertainty estimates. This paper is based on a case study of a single-layer, warm, stratiform cloud observed at the Julich Observatory for Cloud Evolution. The LWC profiles of this cloud have been forward-simulated with the passive and active radiative transfer model providing radar and microwave radiometer (MWR) observables for all cloud columns. These observables have been converted back into LWC profiles, whereas, in this case, the MWR and radar observables from different columns were combined, representing horizontal displacement. We investigate the influence of horizontal distance between a radar and an MWR on a commonly used retrieval for LWC, which scales radar reflectivity profiles with the liquid water path given by the MWR. We found that a displacement of only 10 m already introduces an additional relative uncertainty of 10%. At 100 m displacement, the relative error grows up to 30%. Additionally, different beam widths decrease the retrieval accuracy by a few percent; however, at large displacements, radiometers with larger beam widths slightly decrease the error due to the displacement. Finally, we show that cloud edge studies require optimally matched beams between the radar and the radiometer, and already a displacement of 10 m leads to unreasonable results.

Sampling depth of L-band radiometer measurements of soil moisture and freeze-thaw dynamics on the Tibetan Plateau

Knowing the exact sampling depth of microwave radiometry is essential for quantifying the performance and appreciation of the applicability of satellite soil moisture products. We investigate in this study the sampling depth (δSM) of the L-band microwave emission under frozen and thawed soil conditions on the Tibetan Plateau. Two years of diurnal brightness temperature (TBp) measurements at a time interval of 30 min are collected by the ELBARA-III radiometer deployed at a Tibetan meadow site. Vertical profiles of soil temperature and volumetric liquid water content (θliq) are measured simultaneously at soil depths up to 1 m below the surface. The impact of the θliq measured at different depths on the microwave emission simulations is assessed using the τ-ω emission model, whereby the permittivity of frozen and thawed soil is estimated by the four-phase dielectric mixing model. It is found that: 1) the sampling depth for the effective temperature depends on the magnitude of θliq, and is estimated to be, on average, about 50 and 15 cm for the cold dry and wet warm period, respectively, because of the seasonality in θliq; 2) the δSM is determined at 2.5 cm for both frozen and thawed soil conditions during both cold and warm periods, which is shallower than the commonly used θliq measurement depth (i.e. 5 cm) adopted for the in-situ monitoring networks across the globe; 3) the TBp simulations performed with the θliq measurements taken at the estimated δSM of 2.5 cm result in lower unbiased root mean squared errors, about 14% (3.16 K) and 22% (3.36 K) for the horizontal and vertical polarizations respectively, in comparison to the simulations with the θliq measurements taken from 5 cm soil depth; and 4) the θliq retrieved with the single channel algorithm from the ELBARA-III measured vertically polarized TBp are in better agreement with the θliq measured at 2.5 cm than the one measured at 5 cm. These findings are crucial for developing strategies for the calibration/validation as well as the application of satellite based soil moisture products relying on the L-band radiometry.

Parameterization of Vertical Profiles of Governing Microphysical Parameters of Shallow Cumulus Cloud Ensembles Using LES with Bin Microphysics

Abstract Shallow convection is a subgrid process in cloud-resolving models for which their grid box is larger than the size of small cumulus clouds (Cu). At the same time such Cu substantially affect radiation properties and thermodynamic parameters of the low atmosphere. The main microphysical parameters used for calculation of radiative properties of Cu in cloud-resolving models are liquid water content (LWC), effective droplet radius, and cloud fraction (CF). In this study, these parameters of fields of small, warm Cu are calculated using large-eddy simulations (LESs) performed using the System for Atmospheric Modeling (SAM) with spectral bin microphysics. Despite the complexity of microphysical processes, several fundamental properties of Cu were found. First, despite the high variability of LWC and droplet concentration within clouds and between different clouds, the volume mean and effective radii per specific level vary only slightly. Second, the values of effective radius are close to those forming during adiabatic ascent of air parcels from cloud base. These findings allow for characterization of a cloud field by specific vertical profiles of effective radius and of mean liquid water content, which can be calculated using the theoretical profile of adiabatic liquid water content and the droplet concentration at cloud base. Using the results of these LESs, a simple parameterization of cloud-field-averaged vertical profiles of effective radius and of liquid water content is proposed for different aerosol and thermodynamic conditions. These profiles can be used for calculation of radiation properties of Cu fields in large-scale models. The role of adiabatic processes in the formation of microstructure of Cu is discussed.

Comparison of Observed and Simulated Drop Size Distributions from Large-Eddy Simulations with Bin Microphysics

Abstract Two case studies of marine stratocumulus (one nocturnal and drizzling, the other daytime and nonprecipitating) are simulated by the UCLA large-eddy simulation model with bin microphysics for comparison with aircraft in situ observations. A high-bin-resolution variant of the microphysics is implemented for closer comparison with cloud drop size distribution (DSD) observations and a turbulent collision–coalescence kernel to evaluate the role of turbulence on drizzle formation. Simulations agree well with observational constraints, reproducing observed thermodynamic profiles (i.e., liquid water potential temperature and total moisture mixing ratio) as well as liquid water path. Cloud drop number concentration and liquid water content profiles also agree well insofar as the thermodynamic profiles match observations, but there are significant differences in DSD shape among simulations that cause discrepancies in higher-order moments such as sedimentation flux, especially as a function of bin resolution. Counterintuitively, high-bin-resolution simulations produce broader DSDs than standard resolution for both cases. Examination of several metrics of DSD width and percentile drop sizes shows that various discrepancies of model output with respect to the observations can be attributed to specific microphysical processes: condensation spuriously creates DSDs that are too wide as measured by standard deviation, which leads to collisional production of too many large drops. The turbulent kernel has the greatest impact on the low-bin-resolution simulation of the drizzling case, which exhibits greater surface precipitation accumulation and broader DSDs than the control (quiescent kernel) simulations. Turbulence effects on precipitation formation cannot be definitively evaluated using bin microphysics until the artificial condensation broadening issue has been addressed.

Analysis of Vertical Profiles of Precipitable Liquid Water Content in a Tropical Climate Using Micro Rain Radar

In this paper, some distinctive features of the vertical profile of precipitable liquid water content (LWC) with considerable respect to rain rates (R) and radar reflectivity (Z) obtained in a tropical location are presented. Assessment of LWC allows applications in the specific area of flight icing severity, aviation safety as well as signals traversing through the atmosphere. The parameters were typically measured using vertically-pointing Micro Rain Radar (MRR) over a period of 2 years (2011-2012) at Akure, a tropical location of Nigeria. The radar scanned at every 10 seconds and integrated over one minute samples to reduce event logging error associated with the instrument. The vertical profile of the LWC typically reveals a prominent seasonal variation. However, majority of the LWC profiles has low LWC, less than 0.1 gm−3 while the maximum observed LWC is about 3.18 gm−3. A strong like hood relation was observed between the melting layer height and the LWC, with the LWC reaches peak at the considerable height of about 4160 m which coincides precisely with the freezing height level (rain height of ~4520 m) of the study location. Good correlation was also observed between the LWC and R in most of the heights considered. The results obtained will assist system engineers to assess the level of absorption, reflection and attenuation of electromagnetic signals as a result of precipitable LWC along the transmitting paths. The novelty of the present work is in the area of linking LWC and Z as against usual relation between Z and R.