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The investigator and DOE-supported student [1] retrieved vertical air velocity and microphysical fall velocity retrieval for VOCALS and CAP-MBL homogeneous clouds. [2] Calculated in-cloud and cloud top dissipation calculation and diurnal cycle computed for VOCALS. [3] Compared CAP-MBL Doppler cloud radar scenes with (Remillard et al. 2012) automated classification.
Based on long-term observations by the Atmospheric Radiation Measurement program at its Southern Great Plains site, a new composite case of continental shallow cumulus (ShCu) convection is constructed for large-eddy simulations (LES) and single-column models. The case represents a typical daytime nonprecipitating ShCu whose formation and dissipation are driven by the local atmospheric conditions and land surface forcing and are not influenced by synoptic weather events. The case includes early morning initial profiles of temperature and moisture with a residual layer; diurnally varying sensible and latent heat fluxes, which represent a domain average over different land surface types; simplified large-scale horizontal advective tendencies and subsidence; and horizontal winds with prevailing direction and average speed. Observed composite cloud statistics are provided for model evaluation. The observed diurnal cycle is well reproduced by LES; however, the cloud amount, liquid water path, and shortwave radiative effect are generally underestimated. LES are compared between simulations with an all-or-nothing bulk microphysics and a spectral bin microphysics. The latter shows improved agreement with observations in the total cloud cover and the amount of clouds with depths greater than 300 m. When compared with radar retrievals of in-cloud air motion, LES produce comparable downdraft vertical velocities, but a larger updraft area, velocity, and updraft mass flux. Both observations and LES show a significantly larger in-cloud downdraft fraction and downdraft mass flux than marine ShCu.
Recently launched cloud observing satellites provide information about the vertical structure of deep convection and its microphysical characteristics. In this study, CloudSat reflectivity data is stratified by cloud type, and the contoured frequency by altitude diagrams reveal a doublearc structure in deep convective cores (DCCs) above 8 km. This suggests two distinct hydrometeor modes (snow versus hail/graupel) controlling variability in reflectivity profiles. The day–night contrast in the double arcs is about four times larger than the wet–dry season contrast. Using QuickBeam, the vertical reflectivity structure of DCCs is analyzed in two versions of the Superparameterized Community Atmospheric Model (SP-CAM) with single-moment (no graupel) and double-moment (with graupel) microphysics. Doublemoment microphysics shows better agreement with observed reflectivity profiles; however, neither model variant captures the double-arc structure. Ultimately, the results show that simulating realistic DCC vertical structure and its variability requires accurate representation of ice microphysics, in particular the hail/graupel modes, though this alone is insufficient.
Ice nucleation in mixed-phase clouds has been identified as a critical factor in projections of future climate. Here we explore how this process influences climate sensitivity using the Community Earth System Model 2 (CESM2). We find that ice nucleation affects simulated cloud feedbacks over most regions and levels of the troposphere, not just extratropical low clouds. However, with present-day global mean cloud phase adjusted to replicate satellite retrievals, similar total cloud feedback is attained whether ice nucleation is simulated as aerosol-sensitive, insensitive, or absent. These model experiments all result in a strongly positive total cloud feedback, as in the default CESM2. A microphysics update from CESM1 to CESM2 had substantially weakened ice nucleation, due partly to a model issue. Our findings indicate that this update reduced global cloud phase bias, with CESM2's high climate sensitivity reflecting improved mixed-phase cloud representation.
Abstract. Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments. It aims to optimize the radar observation strategy for deep convection through the use of high-spatiotemporal cloud-resolving model simulations, which resolve the evolution of individual convective cells every 1 min, coupled with a radar simulator and a cell tracking algorithm. The radar simulation settings are based on the Tracking Aerosol Convection Interactions ExpeRiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation and Environment (ESCAPE) field campaigns held in the Houston, TX, area but are generalizable to other field campaigns focusing on isolated deep convection. Our analysis produces the following four outcomes. First, a 5–7 m s−1 median difference in maximum updrafts of tracked cells is shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute capture the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves multi-Doppler radar updraft retrievals above 5 km above ground level for regions with updraft velocities greater than 10 m s−1. Third, we propose an optimized strategy composed of cell tracking by quick (1–2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single-RHI (range height indicator) updraft retrieval technique for cells close to the radars, for which multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s−1.
We examined cloud radar data in monsoon climates, using cloud radars at Darwin in the Australian monsoon, on a ship in the Bay of Bengal in the South Asian monsoon, and at Niamey in the West African monsoon. We followed on with a more in-depth study of the continental MCSs over West Africa. We investigated whether the West African anvil clouds connected with squall line MCSs passing over the Niamey ARM site could be simulated in a numerical model by comparing the observed anvil clouds to anvil structures generated by the Weather Research and Forecasting (WRF) mesoscale model at high resolution using six different ice-phase microphysical schemes. We carried out further simulations with a cloud-resolving model forced by sounding network budgets over the Niamey region and over the northern Australian region. We have devoted some of the effort of this project to examining how well satellite data can determine the global breadth of the anvil cloud measurements obtained at the ARM ground sites. We next considered whether satellite data could be objectively analyzed to so that their large global measurement sets can be systematically related to the ARM measurements. Further differences were detailed between the land and ocean MCS anvil clouds by examining the interior structure of the anvils with the satellite-detected the CloudSat Cloud Profiling Radar (CPR). The satellite survey of anvil clouds in the Indo-Pacific region was continued to determine the role of MCSs in producing the cloud pattern associated with the MJO.
Ice crystals play an important role in radiation and precipitation formation. However, the prediction of ice number concentration has proven to be problematic, because the predicted concentration of ice nucleating particles is often smaller than the observed by several orders of magnitude. This difference between the observed and the predicted number concentration has been explained by so-called secondary ice production (SIP), which includes hypothesized mechanisms like rime splintering, shattering of large frozen drops, and ice-ice collision breakup. To better observe SIP and understand its frequency, production rate, and impact on cloud evolution, we developed a new method for retrieving microphysical properties of concurrent pristine ice and snow aggregates from X-band polarimetric radar observations. Compared to aircraft cloud probe measurements, our retrieved total number concentration and ice water content are overestimated by 98% and 44%, respectively, which outperforms other empirical relationships. The capability of quantifying pristine ice number concentration, which is the most essential observable in SIP studies, opens a new opportunity to build a long-term record of SIP from surface-based radar measurements. We have also conducted simulations using a cloud-resolving model to evaluate the representativeness of the parameterizations of three SIP mechanisms and for studying the impact of SIP on a frontal system. We found a surprisingly good agreement between the observed and retrieved pristine ice number concentrations. The good performance is attributed to the rime splintering process in the temperature zone between–3° and –8°C, and to the ice-ice collision breakup process at higher altitudes. The impact of the shattering of large frozen drops appears minimal. Apart from the enhancement of ice number concentration and increased horizontal coverage of pristine ice, the addition of three SIP processes does not change cloud evolutions dramatically in our simulations.
On July 13, 2002 a widespread, subvisible tropopause cirrus layer occurred over the Florida region. This cloud was observed in great detail with the NASA Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment (CRYSTAL-FACE) instrumentation, including in situ measurements with the WB-57 aircraft. In this paper, we use the 13 July cloud as a case study to evaluate the physical processes controlling the formation and evolution of tropopause cirrus layers. Microphysics measurements indicate that ice crystal diameters in the cloud layer ranged from about 7 to 50 microns, and the peak number mode was about 10-25 microns. In situ water vapor and temperature measurements in the cloud indicated supersaturation with respect to ice throughout, with ice saturation ratios as large as 1.8. Even when the ice surface area density was as high as about 500 sq microns/cu cm, ice supersaturations of 20-30% were observed. Trajectory analysis shows that the air sampled near the tropopause on this day generally came from the north and cooled considerably during the previous few days. Examination of infrared satellite imagery along air parcel back trajectories from the WB-57 flight track indicates that the tropopause cloud layer formation was, in general, not simply left over ice from recently generated anvil cirrus. Simulations of cloud formation using time-height curtains of temperature along the trajectory paths show that the cloud could have formed in situ near the tropopause as the air was advected into the south Florida region and cooled to unusually low temperatures. If we assume a high threshold for ice nucleation via homogeneous freezing of aqueous sulfate aerosols, the model reproduces the observed cloud structure, ice crystal size distributions, and ice supersaturation statistics. Inclusion of observed gravity wave temperature perturbations in the simulations is essential to reproduce the observed cloud properties. Without waves, crystal number densities are too low, crystal sizes are too large, and the crystals fall out too fast, leaving very little cloud persisting at the end of the simulations. In the cloud simulations, coincidence of high supersaturations and high surface areas can be produced by either recent nucleation or sedimentation of crystals into supersaturated layers. The agreement between model results and observed supersaturations is improved somewhat if we assume that the steady state relative humidity within cirrus at T<200 K is enhanced by about 30%. The WB-57 measurements and the model results suggest that the cloud layer irreversibly dehydrated air near the tropopause.
Over the past twenty years, rainfall retrieval algorithms have been developed to retrieve rainfall and vertical hydrometeor structures from passive microwave observations by making use of the fact that weighting functions for various frequencies peak at different levels within a rainy atmosphere. GPROF is one of two TMI rainfall algorithms. It is physically based retrieval that finds the vertical hydrometeor profile that best fits the brightness temperatures in the available passive radiometer channels. Matching is achieved using a library of hydrometeor profiles generated by cloud-resolving models (CRMs). The hydrometeor profiles have a corresponding surface precipitation rate. The algorithm retrieves the hydrometeor profiles and associated surface rainfall using a Bayesian approach that gives the estimated expected values. The ability of CRMs to produce cloud structures that are reliable and representative of observed storms is crucial for the success of GPROF. The cloud mycrophysics are one of the keys to achieving this. In addition, CRMs have been a very useful tool for GPM-algorithm developers through Cloud-Radiation Simulations (CRS), one of the nine GPM disciplinary research themes. This paper will discuss how to generate consistent and comprehensive 4D cloud datasets from an improved (i.e., in regard to bulk and multi-moment microphysics) CRM for TRMM and GPM rainfall retrieval algorithm developers. These cloud datasets include CRM-simulated clouds and cloud systems from different geographic locations in the tropics and midlatitudes. By linking the CRM with a passive microwave radiative-transfer model and using satellite and airborne data, the performance of the "cloud physics" can be assessed and in turn modified and improved. This paper will also address how to assess and improve the performance of various latent and diabatic heating algorithms and develop an algorithm to retrieve the vertical structure of apparent moistening (Q2). Considering that the GPM will produce high (temporal and spatial) resolution heating and rainfall data, these algorithms will be used to obtain the temporal and spatial distributions of surface rainfall and the associated vertical heating and moistening profiles throughout the subtropical and midlatitudes.
This proposal, a collaboration between two universities, firstly aims to describe the high-latitude CAO cloud regime using the array of instruments at the NSA (Barrow, Alaska) and at two mobile ARM sites (MARCUS 2017-18, and COMBLE 2020). These ARM data, and the multi-sensor variables we propose to derive, allow detailed descriptions of mesoscale organization of precipitation, thermodynamic profiles, vertical velocity, cloud depth, and cloud and precipitation properties. These observations are key for our second goal, which is to use output from validated cloud- and eddy-resolving simulations to gain insights into the linkages between microphysical processes, the MBL vertical structure, and the MBL dynamics that control cloud macrostructure.
Satellite observations of denitrification and ice clouds in the Arctic lower stratosphere in February 1997 are used with Lagrangian microphysical box model calculations to evaluate nucleation mechanisms of solid polar stratospheric cloud (PSC) particles. The occurrences of ice clouds are not correlated in time and space with the locations of back trajectories of denitrified air masses, indicating that ice particle surfaces are not always a prerequisite for the formation of solid PSCs that lead to denitrification. In contrast, the model calculations incorporating a pseudoheterogeneous freezing process occurring at the vapor-liquid interface can quantitatively explain most of the observed denitrification when the nucleation activation free energy for nitric acid dihydrate formation is raised by only approx.10% relative to the current published values. Once nucleated, the conversion of nitric acid dihydrate to the stable trihydrate phase brings the computed levels of denitrification closer to the measurements. INDEX TERMS: 0305 Atmospheric Composition and Structure: Aerosols and particles (0345, 4801); 0320 Atmospheric Composition and Sb~lctureC: loud physics and chemistry; 0340 Atmospheric Composition and Structure: Middle atmosphere-composition and chemistry
Abstract Mixed-phase clouds contain both supercooled cloud liquid and ice crystals. In principle, precipitation may be initiated either by the liquid phase or by the ice phase. Ice crystals may grow by vapor diffusion to become snow (“ice crystal process”), forming “cold” precipitation. Equally, cloud droplets, when large enough, coalesce to form “warm” precipitation by the “warm rain process.” Warm rain could be supercooled and freeze as “warm” graupel. In the present paper, a new simplified theoretical analysis is provided to examine the microphysical system consisting of three species of hydrometeor, namely, cloud liquid, “cold ice” (crystals, snow), and “warm rain” (frozen or supercooled). This is obtained by nondimensionalizing and simplifying the evolution equations for the mass of each species. Analytical formulas are given for equilibria. Feedback analysis shows that the sign of the feedback is linked to the abundance of precipitation, with a neutral surface in the 3D phase space. The system’s precipitation amount explodes while in the initial unstable regime, crossing the neutral surface and approaching the equilibrium point that is a stable attractor. Positive and negative feedbacks are elucidated. In a standard case, the cold ice mass is about 1000 times larger than the warm rain mass. To illustrate the physical behavior of the theory, sensitivity tests are performed with respect to environmental conditions (e.g., aerosol, updraft speed) and microphysical parameters (e.g., riming and sedimentation rates for cold ice). Cold ice prevails, especially in fast ascent, due to its low bulk density, favoring slow sedimentation and a wide cross-sectional area for riming. Significance Statement The theory elucidates how the ice phase can prevail in the precipitation from any mixed-phase clouds with supercooled cloud liquid and crystals. The ice phase radically suppresses cloud liquid by riming when active and “wins” the competition against coalescence. This prevalence of ice is shown to arise from the low bulk density of snow. The cloud is viewed as a system of negative and positive feedbacks that prevail in realms of stability and instability in a 3D phase space.
The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.
Quantifying the microphysical process contributions to surface precipitation in numerical simulations can be challenging. This is due to the fact that many microphysical processes contribute to the formation and depletion of rain drops and there is almost always a spatial/temporal mismatch between where/when rain is formed and where/when it strikes the surface. In this work, we develop a tracing method that tracks the sources and sinks of raindrop mass and number as they are advected by the Weather Research and Forecasting model. Applying the method to an idealized squall line confirms that convective precipitation is dominated by warm rain processes (autoconversion and accretion) while stratiform precipitation is dominated by the melting of rimed and unrimed ice crystals. Sensitivity experiments in which the prescribed cloud drop number concentration is increased confirm the conventional wisdom that weakened autoconversion increases the fraction of raindrops originating from cold rain processes. The method also reveals that when applied to deep convection the Khairoutdinov and Kogan autoconversion scheme produces an excessive number of raindrops which are subsequently clipped in P3 microphysics to keep the rain size distribution within prescribed limits. This problem can mostly be mitigated by increasing the assumed radius for raindrops created by autoconversion.
The centerpiece of the accomplishments under this (and related) grants is the increased knowledge of the capabilities of cloud-resolving models of convective systems, and their associated microphysics, especially of the biases of those models and their microphysics parameterization. Dr. Varble's leadership has been focused especially on the careful comparison of advanced model results with equally advanced observations to uncover and elucidate model successes, but clearly uncovering instances of model biases, in a way that suggests ways of overcoming those biases, toward model improvement in the future. Even a glance at the titles of the papers below will reveal the central significance of these advances; if desired, however, this report can be expanded upon request to discuss these advances in greater detail.
This data file contains retrievals of cloud-effective-radius and cloud-droplet-number-concentration for low clouds based on the Dong et al. 1998 retrieval technique for the MICRE campaign. The retrieval is based on observed downwelling broadband SW fluxes and microwave-radiometer-retrieved-liquid-water-path averaged on a 5 minute time scale (mean when low cloud is present). Details on the algorithm and analysis of results are given in Tansey et al. 2024 (submitted DOI: 10.22541/essoar.173482310.03809503/v1). The data is limited to the period 20160410 to 20160612 AND 20170103 to 20170214 during which time good quality microwave-radiometer measurements are available.
This data file contains retrievals of cloud-effective radius and cloud-droplet-number concentration for low clouds based on the Dong et al. 1998 retrieval technique for the MICRE campaign. The retrieval is based on observed downwelling broadband shortwave (SW) fluxes and microwave-radiometer-retrieved-liquid-water-path averaged on a 5-minute time scale (mean when low cloud is present). Details on the algorithm and analysis of results are given in Tansey et al. 2024 (submitted DOI: 10.22541/essoar.173482310.03809503/v1). The data are limited to the period of 20160410 to 20160612 and 20170103 to 20170214 during which time good quality microwave radiometer measurements are available.
Measurement of ice number concentration in clouds is important but still challenging. Stratiform mixed-phase clouds (SMCs) provide a simple scenario for retrieving ice number concentration from remote sensing measurements. The simple ice generation and growth pattern in SMCs offers opportunities to use cloud radar reflectivity (Ze) measurements and other cloud properties to infer ice number concentration quantitatively. To understand the strong temperature dependency of ice habit and growth rate quantitatively, we develop a 1-D ice growth model to calculate the ice diffusional growth along its falling trajectory in SMCs. The radar reflectivity and fall velocity profiles of ice crystals calculated from the 1-D ice growth model are evaluated with the Atmospheric Radiation Measurements (ARM) Climate Research Facility (ACRF) ground-based high vertical resolution radar measurements. Combining Ze measurements and 1-D ice growth model simulations, we develop a method to retrieve the ice number concentrations in SMCs at given cloud top temperature (CTT) and liquid water path (LWP). The retrieved ice concentrations in SMCs are evaluated with in situ measurements and with a three-dimensional cloud-resolving model simulation with a bin microphysical scheme. These comparisons show that the retrieved ice number concentrations are within an uncertainty of a factor of 2, statistically.