Atmospheric Radiation Measurement Program Research Articles

A Novel Machine Learning Algorithm for Planetary Boundary Layer Height Estimation Using AERI Measurement Data

Accurately determining the height of the planetary boundary layer (PBL) is important since it can affect the climate, weather, and air quality. Ground-based infrared hyperspectral remote sensing is an effective way to obtain this parameter. Compared with radiosonde measurements, its temporal resolution is much higher. In this study, a method to retrieve the PBL height (PBLH) from the ground-based infrared hyperspectral radiance data is proposed based on machine learning. In this method, the channels that are sensitive to temperature and humidity profiles are selected as the feature vectors, and the PBLHs derived from radiosonde are taken as the true values. The support vector machine (SVM) is applied to train and test the data set, and the parameters are optimized in the process. The data set collected at the Atmospheric Radiation Measurement (ARM) program Southern Great Plains (SGP) from 2012 to 2015 is analyzed. The instruments used in this letter include Atmospheric Emitted Radiance Interferometer (AERI), Vaisala CL31 ceilometer, and radiosonde. It shows that the root mean square error (RMSE) between the PBLHs calculated by the proposed method using AERI data and those from radiosonde data can be within 370 m, and the square correlation coefficient (SCC) is greater than 0.7. Compared with the PBLHs derived from the ceilometer, it can be found that the new method is more stable and less affected by clouds.

Improving Millimeter Radar Attenuation Corrections in High-Latitude Mixed-Phase Clouds via Radio Soundings and a Suite of Active and Passive Instruments

Supercooled liquid clouds are very frequent in high-latitude regions. In addition to their substantial effect on visible and infrared radiation, they affect the signal of millimeter radars by producing nonnegligible attenuation. Such attenuation must be properly corrected if the information of millimeter radars is used in quantitative retrievals for inferring ice microphysical properties. This study proposes a multisensor scheme for refining the vertical distribution of supercooled liquid water content (SLWC) compared to state-of-the-art methods that equipartition the liquid water path measured by microwave radiometer to all pixels identified as cloudy by the radars and warmer than −40 °C. Our methodology is applicable in high-latitude, mixed-phase environments based on the synergy between radar and lidar binary cloud phase masking, microwave radiometer, and radio sounding observations. The technique is demonstrated via data collected by the U.S. Department of Energy (DoE) Atmospheric Radiation Measurement (ARM) Program climate research facility at the North Slope of Alaska (NSA) and compared with the state-of-the-art methods. Path integrated attenuation (PIA) at W- and G-band frequencies (>95 GHz) is then assessed. Results indicate that the different in-cloud distributions of the liquid condensate lead to round-trip PIA discrepancies of cloudy volumes that range in [2, 5] dB at W- and G-band frequencies. These differences far exceed those encountered when changing some of the algorithm’s arbitrary assumptions and weighting functions.

Evaluation of CloudSat Radiative Kernels Using ARM and CERES Observations and ERA5 Reanalysis

AbstractDespite the widespread use of the radiative kernel technique for studying radiative feedbacks and radiative forcings, there has not been any systematic, observation‐based validation of the radiative kernel method. Here, we utilize observed and reanalyzed radiative fluxes and atmospheric profiles from the Atmospheric Radiation Measurement (ARM) program and ERA5 reanalysis to assess a set of observation‐based radiative kernels from CloudSat for six ARM sites. The CloudSat radiative kernels, convoluted with the ERA5 state variables, can almost perfectly reconstruct the monthly anomalies of shortwave (SW) and longwave (LW) radiative fluxes in ERA5 at the surface (SFC) and top‐of‐atmosphere (TOA) with correlations significantly being greater than 0.95. The biases of kernel‐estimated flux anomalies calculated using the ARM‐observed state variables can be more than twice as large when compared with the ARM‐observed surface flux anomalies and Clouds and Earth's Radiant Energy System observed anomalies at the TOA. Generally, clouds contribute to most (>60%) of the variance of flux anomalies at Southern Great Plain (SGP), Tropical Western Pacific (TWP), and Eastern North Atlantic (ENA), and surface albedo dominates (>69%) the variance of SW flux anomalies at North Slope of Alaska. The radiative kernels exhibit the lowest correlation (r∼[0.55,0.85]) when reconstructing SFC LW flux anomalies at SGP, TWP, and ENA, whose biases are related to the possibility that the kernels may not fully capture the characteristics associated with Madden‐Julian oscillation and El Niño‐Southern Oscillation at TWP and the presence of clouds at SGP and ENA.

A Constrained Data Assimilation Algorithm Based on GSI Hybrid 3D-EnVar and Its Application

AbstractData assimilation (DA) at mesoscales is important for severe weather forecasts, yet the techniques of data assimilation at this scale remain a challenge. This study introduces dynamical constraints in the Gridpoint Statistical Interpolation (GSI) three-dimensional ensemble variational (3D-EnVar) data assimilation algorithm to enable the use of high-resolution surface observations of precipitation to improve atmospheric analysis at mesoscales. The constraints use the conservations of mass and moisture. Mass constraint suppresses the unphysical high-frequency oscillation, while moisture conservation constrains the atmospheric states to conform with the observed high-resolution precipitation. We show that the constrained data assimilation (CDA) algorithm significantly reduced the spurious residuals of the mass and moisture budgets compared to the original data assimilation (ODA). A case study is presented for a squall line over the Southern Great Plains on 20 May 2011 during Midlatitude Continental Convective Clouds Experiment (MC3E) of the Atmospheric Radiation Measurement (ARM) program by using ODA or CDA analysis as initial condition of forecasts. The state variables, and the location and intensity of the squall line are better simulated in the CDA experiment. Results show how surface observation of precipitation can be used to improve atmospheric analysis through data assimilation by using the dynamical constraints of mass and moisture conservations.

The Diurnal Variation of the Aerosol Optical Depth at the ARM SGP Site

AbstractThis study examines the diurnal variation of the aerosol optical depth (AOD) at 355 nm observed by Raman lidar (RL) at the Atmospheric Radiation Measurement Program Southern Great Plains (SGP) site under both clear and cloudy‐sky conditions. Here only cloudy‐skies when the lidar signal is not fully attenuated are considered. The daytime AOD and its variation from the RL showed an excellent agreement with the Aerosol Robotic Network, demonstrating that the RL‐retrieved AOD is not affected by solar background contamination. The climatological annual‐mean daytime‐mean AOD is only slightly larger than the nighttime‐mean AOD (by 1%–3%). However, day‐to‐day variations are observed such that the daytime‐ and nighttime‐mean AOD difference for a given day can be large (about 95% of days have differences within 0.2). The seasonal AOD diurnal range (i.e., the difference between the maximum and minimum values) relative to the mean was10%–15% except in the winter when it was44%. The seasonal‐mean cloudy‐sky AOD diurnal variation is similar to that for clear‐sky, except that the AODs are larger (the annual‐mean cloudy‐sky AOD is larger than the clear‐sky by24%). The aerosol lidar ratio diurnal variations are also examined, which are10%–20% for all seasons with a minimum near 9 a.m. to 15 p.m. for all seasons except winter. Also presented is the annual‐mean AOD from the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite at SGP site: its daytime AOD is about 0.1 smaller than nighttime AOD because of daytime solar background contamination.

A New Approach for Simultaneous Estimation of Entrainment and Detrainment Rates in Non‐Precipitating Shallow Cumulus

AbstractA new approach is developed for estimating entrainment and detrainment rates in cumulus clouds based on aircraft observations. Equations relating entrainment and detrainment rates to gross entrainment and detrainment are derived. This approach is applied to the Holistic Interactions of Shallow Clouds, Aerosols, and Land‐Ecosystems field campaign, supported by the U.S. Department of Energy's Atmospheric Radiation Measurement program. The results show that both entrainment and detrainment rates decrease with increasing height. Sensitivity tests with different detrained air assumptions yield similar results. The entrainment and detrainment rates can reproduce the cloud thermodynamic variables. Partial correlation analysis indicates that entrainment rate is positively correlated with environmental relative humidity (RH), and detrainment rate is negatively correlated with environmental RH and positively correlated with entrainment rate. This new approach can be applied to other cloud observations to obtain a data set of entrainment and detrainment rates in cumulus clouds.

Improving Convection Trigger Functions in Deep Convective Parameterization Schemes Using Machine Learning

AbstractDeficiencies in convection trigger functions, used in deep convection parameterizations in General Circulation Models (GCMs), have critical impacts on climate simulations. A novel convection trigger function is developed using the machine learning (ML) classification model XGBoost. The large‐scale environmental information associated with convective events is obtained from the long‐term constrained variational analysis forcing data from the Atmospheric Radiation Measurement (ARM) program at its Southern Great Plains (SGP) and Manaus (MAO) sites representing, respectively, continental mid‐latitude and tropical convection. The ML trigger is separately trained and evaluated per site, and jointly trained and evaluated at both sites as a unified trigger. The performance of the ML trigger is compared with four convective trigger functions commonly used in GCMs: dilute convective available potential energy (CAPE), undilute CAPE, dilute dynamic CAPE (dCAPE), and undilute dCAPE. The ML trigger substantially outperforms the four CAPE‐based triggers in terms of the F1 score metric, widely used to estimate the performance of ML methods. The site‐specific ML trigger functions can achieve, respectively, 91% and 93% F1 scores at SGP and MAO. The unified trigger also has a 91% F1 score, with virtually no degradation from the site‐specific training, suggesting the potential of a global ML trigger function. The ML trigger alleviates a GCM deficiency regarding the overprediction of convection occurrence, offering a promising improvement to the simulation of the diurnal cycle of precipitation. Furthermore, to overcome the black box issue of the ML methods, insights derived from the ML model are discussed, which may be leveraged to improve traditional CAPE‐based triggers.

Observational Constraints on Warm Cloud Microphysical Processes Using Machine Learning and Optimization Techniques.

We introduce new parameterizations for autoconversion and accretion rates that greatly improve representation of the growth processes of warm rain. The new parameterizations capitalize on machine‐learning and optimization techniques and are constrained by in situ cloud probe measurements from the recent Atmospheric Radiation Measurement Program field campaign at Azores. The uncertainty in the new estimates of autoconversion and accretion rates is about 15% and 5%, respectively, outperforming existing parameterizations. Our results confirm that cloud and drizzle water content are the most important factors for determining accretion rates. However, for autoconversion, in addition to cloud water content and droplet number concentration, we discovered a key role of drizzle number concentration that is missing in current parameterizations. The robust relation between autoconversion rate and drizzle number concentration is surprising but real, and furthermore supported by theory. Thus, drizzle number concentration should be considered in parameterizations for improved representation of the autoconversion process.

The development of rainfall retrievals from radar at Darwin

Abstract. The U.S. Department of Energy Atmospheric Radiation Measurement program Tropical Western Pacific site hosted a C-band polarization (CPOL) radar in Darwin, Australia. It provides 2 decades of tropical rainfall characteristics useful for validating global circulation models. Rainfall retrievals from radar assume characteristics about the droplet size distribution (DSD) that vary significantly. To minimize the uncertainty associated with DSD variability, new radar rainfall techniques use dual polarization and specific attenuation estimates. This study challenges the applicability of several specific attenuation and dual-polarization-based rainfall estimators in tropical settings using a 4-year archive of Darwin disdrometer datasets in conjunction with CPOL observations. This assessment is based on three metrics: statistical uncertainty estimates, principal component analysis (PCA), and comparisons of various retrievals from CPOL data. The PCA shows that the variability in R can be consistently attributed to reflectivity, but dependence on dual-polarization quantities was wavelength dependent for 1<R<10mmh-1. These rates primarily originate from stratiform clouds and weak convection (median drop diameters less than 1.5 mm). The dual-polarization specific differential phase and differential reflectivity increase in usefulness for rainfall estimators in times with R>10mmh-1. Rainfall estimates during these conditions primarily originate from deep convective clouds with median drop diameters greater than 1.5 mm. An uncertainty analysis and intercomparison with CPOL show that a Colorado State University blended technique for tropical oceans, with modified estimators developed from video disdrometer observations, is most appropriate for use in all cases, such as when 1<R<10mmh-1 (stratiform rain) and when R>10mmh-1 (deeper convective rain).

An Algorithm to Retrieve Total Precipitable Water Vapor in the Atmosphere from FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) Data

The atmosphere has substantial effects on optical remote sensing imagery of the Earth’s surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth’s surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy’s Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales.

The ARM Data-Oriented Metrics and Diagnostics Package for Climate Models: A New Tool for Evaluating Climate Models with Field Data

AbstractThe U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program User Facility produces ground-based long-term continuous unique measurements for atmospheric state, precipitation, turbulent fluxes, radiation, aerosol, cloud, and the land surface, which are collected at multiple sites. These comprehensive datasets have been widely used to calibrate climate models and are proven to be invaluable for climate model development and improvement. This article introduces an evaluation package to facilitate the use of ground-based ARM measurements in climate model evaluation. The ARM data-oriented metrics and diagnostics package (ARM-DIAGS) includes both ARM observational datasets and a Python-based analysis toolkit for computation and visualization. The observational datasets are compiled from multiple ARM data products and specifically tailored for use in climate model evaluation. In addition, ARM-DIAGS also includes simulation data from models participating the Coupled Model Intercomparison Project (CMIP), which will allow climate-modeling groups to compare a new, candidate version of their model to existing CMIP models. The analysis toolkit is designed to make the metrics and diagnostics quickly available to the model developers.

The diurnally-averaged aerosol direct radiative effect and the use of the daytime-mean and insolation-weighted-mean solar zenith angles

This study examines the application of the daytime-mean and insolation-weighted-mean solar zenith angles to calculate the diurnally-averaged aerosol direct radiative effect (DRE), DREDA, for observed clear-sky conditions at the Atmospheric Radiation Measurement Program (ARM) Southern Great Plains (SGP) and Tropical Western Pacific (TWP) sites. As compared with the reference calculation, the DREDA are too strong (i.e., more negative) using daytime-mean solar zenith angles (DTMSZA) and are too weak (i.e., less negative) using insolation-weighted-mean solar zenith angles (IWMSZA). The biases in the DREDA for DTMSZA (IWMSZA) are about −0.8 (0.7) W m−2 at the TOA and about −1.5 (0.9) W m−2 at the surface. Noting the mean TOA DREDA of −2.85 W m−2 and the surface DREDA of −7.09 W m−2 at SGP, the relative biases for DTMSZA (IWMSZA) are 26% (−24%) for the TOA DREDA, and 17% (−11%) for the surface DREDA. At TWP, the mean TOA DREDA is −2.81 W m−2 and the surface DREDA is −11.84 W m−2, which corresponds to relative biases for DTMSZA (IWMSZA) of 29% (−27%) for the TOA DREDA and 15% (−9%) for the surface DREDA. By introducing an adjusted insolation-weighted-mean solar zenith angle, the mean biases in the DREDA at the TOA becomes −0.09 W m−2, and the biases in the DREDA at the surface are about −0.41 W m−2. Also presented are the effects of using the mean solar zenith angles on the shortwave (SW) upward fluxes at the TOA and SW downward fluxes at the surface in both clear-sky and pristine-clear-sky (i.e., gases only) conditions.

Separating Cloud and Drizzle Signals in Radar Doppler Spectra Using a Parametric Time Domain Method

AbstractThe ability to separate cloud and drizzle returns in active remote sensing observations is important for understanding the microphysics of clouds and precipitation. Yet, robust separations remain challenging in radar remote sensing. Prior methods for cloud and drizzle separation for radar observations use the properties of the Doppler spectra such as skewness. However, these methods have challenges when the drizzle becomes dominant in the observation volume. This paper presents a parametric time domain method (PTDM) that separates cloud and drizzle using the Doppler spectra measurements without assuming any prior properties of cloud and drizzle. The advantage of PTDM is that it can estimate the signal properties in the time domain and can obtain the cloud and drizzle estimates simultaneously. Based on our radar signal simulations, the uncertainty in estimated power and velocity from PTDM are within 2 dB and 0.02 m s−1, respectively. We have also evaluated the PTDM algorithm using observations from the Atmospheric Radiation Measurement (ARM) Program W-band cloud radar in the Clouds, Aerosols, and Precipitation in the Marine Boundary Layer (CAP-MBL) campaign at the Azores in 2009–10. Two cases corresponding to light and moderate drizzling conditions are considered for the study. The statistics of the estimates obtained show that the PTDM method performs well in separating the cloud and drizzle returns. Finally, the estimated cloud and drizzle reflectivity from PTDM were used to retrieve their corresponding microphysical properties, showing that the retrieved liquid water path agrees to 25 g m−2 with the benchmark microwave method.

Assessment of Precipitating Marine Stratocumulus Clouds in the E3SMv1 Atmosphere Model: A Case Study from the ARM MAGIC Field Campaign

AbstractThis paper presents a process-oriented evaluation of precipitating stratocumulus and its transition to cumulus in version 1 of the Energy Exascale Earth System Model (E3SMv1) using comprehensive case-study observations from a field campaign of the Atmospheric Radiation Measurement program (ARM). The E3SMv1 single-column model (SCM) of the marine boundary layer and its low clouds and precipitation are compared to observations including subcloud drizzle retrievals from a combination of Doppler radar and lidar backscatter measurements. The SCM is also compared to a large-eddy simulation (LES) of the same case. The combination of advanced remote sensing observations and LES is a powerful framework to evaluate the physical parameterizations of large-scale models. Given the observed large-scale environment, the E3SMv1 SCM realistically represents the evolution of clouds and boundary layer structure during the stratocumulus-to-cumulus transition. The model well simulates the liquid water path and its diurnal cycle in the stratocumulus period as well as the two-layer vertical thermodynamic structure and lower cloud fraction in the transition period. E3SMv1’s success in simulating the cloud in the stratocumulus period permitted examination of its precipitation processes. Here problems were identified with E3SMv1 producing an unrealistically small subcloud precipitation fraction, an unrealistic double peak in the vertical profiles of precipitation mass, and drizzle that evaporates too close to the surface. Further model diagnostics determined that these unrealistic characteristics resulted from an overly long microphysics time step and an unrealistic parameterization of the precipitation fraction. These results imply that careful consideration of these issues is needed in order to better simulate precipitation processes in marine stratocumulus.

Improvement of Atmospheric Objective Analysis Over Sloping Terrain and Its Impact on Shallow‐Cumulus Clouds in Large‐Eddy Simulations

AbstractSurface topography strongly impacts the regional atmospheric circulation dynamically and thermodynamically. Over the Great Plains in the United States, the gently tilted slope is an important factor that impacts clouds, convection, and regional circulations. This study enhances an atmospheric constrained variational analysis by using a terrain‐following sigma vertical coordinate and applies to the data collected at the Southern Great Plain (SGP) site of the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) program. Sensitivity studies are performed to examine the impact of the terrain effects on the derived large‐scale atmospheric forcing fields and the simulated shallow‐cumulus clouds in large‐eddy simulation (LES) driven by the forcing. We found that the terrain impacts on the large‐scale forcing fields are mainly at lower levels and are strongly controlled by up/downslope winds. The response of the derived forcing to the slope of the terrain is monotonic, but the response of the simulated shallow‐cumulus clouds is more complex and depends on several factors. Overall, the terrain impact is small over SGP due to the small slope angle. However, the flat‐surface assumption may cause larger biases in the large‐scale forcing fields at locations with steeper terrain. The new sigma coordinate algorithm, with its consideration of surface slope, should be more suitable to derive large‐scale objective analysis over regions with steep terrain for application to force single‐column models, cloud resolving models, and LES models.

AWARE: The Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment

AbstractThe U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE) performed comprehensive meteorological and aerosol measurements and ground-based atmospheric remote sensing at two Antarctic stations using the most advanced instrumentation available. A suite of cloud research radars, lidars, spectral and broadband radiometers, aerosol chemical and microphysical sampling equipment, and meteorological instrumentation was deployed at McMurdo Station on Ross Island from December 2015 through December 2016. A smaller suite of radiometers and meteorological equipment, including radiosondes optimized for surface energy budget measurement, was deployed on the West Antarctic Ice Sheet between 4 December 2015 and 17 January 2016. AWARE provided Antarctic atmospheric data comparable to several well-instrumented high Arctic sites that have operated for many years and that reveal numerous contrasts with the Arctic in aerosol and cloud microphysical properties. These include persistent differences in liquid cloud occurrence, cloud height, and cloud thickness. Antarctic aerosol properties are also quite different from the Arctic in both seasonal cycle and composition, due to the continent’s isolation from lower latitudes by Southern Ocean storm tracks. Antarctic aerosol number and mass concentrations are not only non-negligible but perhaps play a more important role than previously recognized because of the higher sensitivities of clouds at the very low concentrations caused by the large-scale dynamical isolation. Antarctic aerosol chemical composition, particularly organic components, has implications for local cloud microphysics. The AWARE dataset, fully available online in the ARM Program data archive, offers numerous case studies for unique and rigorous evaluation of mixed-phase cloud parameterization in climate models.

Evaluation of an Improved Convective Triggering Function: Observational Evidence and SCM Tests

AbstractThis study provides a strong observational support for a recently developed convective triggering function that uses the large‐scale dynamic convective available potential energy (dCAPE) as a constraint combined with an unrestricted air parcel launch level (ULL) to relax the unrealistic strong coupling of convection to surface heating and capture nocturnal elevated convection. Both case study and statistical analysis are conducted using the observations collected from the Department of Energy's Atmospheric Radiation Measurement program at its Southern Great Plains and Manaus (MAO) sites. They show that dCAPE has a much stronger correlation with precipitation than convective available potential energy, and ULL is essential to detect elevated convection above boundary layer under both midlatitude and tropical conditions and for both afternoon and nighttime deep convection regimes. Sensitivity tests with the single‐column model (SCM) of the Department of Energy's Energy Exascale Earth System Model indicate that the role of dCAPE in suppressing daytime convection is more effective for tropical convection than midlatitude convection. Even though the dCAPE can suppress the overestimated convection, ULL plays a much bigger role in improving the diurnal cycle of precipitation than dCAPE. It not only helps capture nocturnal elevated convection but also significantly removes the spurious morning precipitation seen in the default model, due to the release of unstable energy at night. However, the use of ULL has led to an overestimation of light‐to‐moderate precipitation (1–10 mm/day) due to more convection being triggered above boundary layer.

Updraft and Downdraft Core Size and Intensity as Revealed by Radar Wind Profilers: MCS Observations and Idealized Model Comparisons

AbstractThis study explores the updraft and downdraft properties of mature stage mesoscale convective systems (MCSs) in terms of draft core width, shape, intensity, and mass flux characteristics. The observations use extended radar wind profiler (RWP) and surveillance radar data sets from the U.S. Department of Energy Atmospheric Radiation Measurement program for midlatitude (Oklahoma, USA) and tropical (Amazon, Brazil) sites. MCS drafts behave qualitatively similar to previous aircraft and RWP cloud summaries. The Oklahoma MCSs indicate larger and more intense convective updraft and downdraft cores, and greater mass flux than Amazon MCS counterparts. However, similar size‐intensity relationships and draft vertical profile behaviors are observed for both regions. Additional similarities include weak positive correlations between core intensity and core width (correlation coefficient r ∼ 0.5) and increases in draft intensity with altitude. A model‐observational intercomparison for draft properties (core width, intensity, and mass flux) is also performed to illustrate the potential usefulness of statistical observed draft characterizations. Idealized simulations with the Weather Research and Forecasting model aligned with midlatitude MCS conditions are performed at model grid spacings (△x) that range from 4 km to 250 m. It is shown that the simulations performed at △x = 250 m at similar mature MCS lifecycle stages are those that exhibit draft intensity, width, mass flux, and shape parameter performances best matching with observed properties.

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.

Impacts of Representing Heterogeneous Distribution of Cloud Liquid and Ice on Phase Partitioning of Arctic Mixed‐Phase Clouds with NCAR CAM5

AbstractIn this study, we conduct sensitivity experiments with the Community Atmosphere Model version 5 to understand the impact of representing heterogeneous distribution between cloud liquid and ice on the phase partitioning in mixed‐phase clouds through different perturbations on the Wegener‐Bergeron‐Findeisen (WBF) process. In two experiments, perturbation factors that are based on assumptions of pocket structure and the partial homogeneous cloud volume derived from the High‐performance Instrumented Airborne Platform for Environmental Research (HIAPER) Pole‐to‐Pole Observation (HIPPO) campaign are utilized. Alternately, a mass‐weighted assumption is used in the calculation of WBF process to mimic the appearance of unsaturated area in mixed‐phase clouds as the result of heterogeneous distribution. Model experiments are tested in both single column and weather forecast modes and evaluated against data from the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program's Mixed‐Phase Arctic Cloud Experiment (M‐PACE) field campaign and long‐term ground‐based multisensor measurements. Model results indicate that perturbations on the WBF process can significantly modify simulated microphysical properties of Arctic mixed‐phase clouds. The improvement of simulated cloud water phase partitioning tends to be linearly proportional to the perturbation magnitude that is applied in the three different sensitivity experiments. Cloud macrophysical properties such as cloud fraction and frequency of occurrence of low‐level mixed‐phase clouds are less sensitive to the perturbation magnitude than cloud microphysical properties. Moreover, this study indicates that heterogeneous distribution between cloud hydrometeors should be treated consistently for all cloud microphysical processes. The model vertical resolution is also important for liquid water maintenance in mixed‐phase clouds.