Confronting the Challenge of Modeling Cloud and Precipitation Microphysics.

In the atmosphere, microphysics refers to the microscale processes that affect cloud and precipitation particles and is a key linkage among the various components of Earth's atmospheric water and energy cycles. The representation of microphysical processes in models continues to pose a major challenge leading to uncertainty in numerical weather forecasts and climate simulations. In this paper, the problem of treating microphysics in models is divided into two parts: (i) how to represent the population of cloud and precipitation particles, given the impossibility of simulating all particles individually within a cloud, and (ii) uncertainties in the microphysical process rates owing to fundamental gaps in knowledge of cloud physics. The recently developed Lagrangian particle-based method is advocated as a way to address several conceptual and practical challenges of representing particle populations using traditional bulk and bin microphysics parameterization schemes. For addressing critical gaps in cloud physics knowledge, sustained investment for observational advances from laboratory experiments, new probe development, and next-generation instruments in space is needed. Greater emphasis on laboratory work, which has apparently declined over the past several decades relative to other areas of cloud physics research, is argued to be an essential ingredient for improving process-level understanding. More systematic use of natural cloud and precipitation observations to constrain microphysics schemes is also advocated. Because it is generally difficult to quantify individual microphysical process rates from these observations directly, this presents an inverse problem that can be viewed from the standpoint of Bayesian statistics. Following this idea, a probabilistic framework is proposed that combines elements from statistical and physical modeling. Besides providing rigorous constraint of schemes, there is an added benefit of quantifying uncertainty systematically. Finally, a broader hierarchical approach is proposed to accelerate improvements in microphysics schemes, leveraging the advances described in this paper related to process modeling (using Lagrangian particle-based schemes), laboratory experimentation, cloud and precipitation observations, and statistical methods.

Vertical Profiles of Droplet Size Distributions Derived from Cloud-Side Observations by the Research Scanning Polarimeter: Tests on Simulated Data.

The Research Scanning Polarimeter (RSP) is an airborne along-track scanner measuring the polarized and total reflectances with high angular resolution. It allows for accurate characterization of liquid water cloud droplet sizes using the rainbow structure in the polarized reflectance. RSP's observations also provide constraints on the cumulus cloud's 2D cross section, yielding estimates of its geometric shape. In this study for the first time we evaluate the possibility to retrieve vertical profiles of microphysical characteristics along the cloud side by combining these micro- and macrophysical retrieval methods. First we constrain cloud's geometric shape, then for each point on the bright side of its surface we collect data from different scans to obtain the multi-angle polarized reflectance at that point. The rainbow structures of the reflectances from multiple points yield the corresponding droplet size distributions (DSDs), which are then combined into vertical profiles. We present the results of testing the proposed profiling algorithm on simulated data obtained using large eddy simulations and 3D radiative transfer computations. The virtual RSP measurements were used for retrieval of DSD profiles, which then were compared to the actual data from the LES-model output. A cumulus congestus cloud was selected for these tests in preparation for analysis of real measurements made during the Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex). We demonstrate that the use of the non-parametric Rainbow Fourier Transform (RFT) allows for adequate retrieval of the complex altitude-dependent bimodal structure of cloud DSDs.

Persistent Supercooled Drizzle at Temperatures below -25°C Observed at McMurdo Station, Antarctica.

The rarity of reports in the literature of brief and spatially limited observations of drizzle at temperatures below -20°C suggest that riming and other temperature-dependent cloud microphysical processes such as heterogeneous ice nucleation and ice crystal depositional growth prevent drizzle persistence in cold environments. In this study, we report on a persistent drizzle event observed by ground-based remote-sensing measurements at McMurdo Station, Antarctica. The temperatures in the drizzle-producing cloud were below -25°C and the drizzle persisted for a period exceeding 7.5 hours. Using ground-based, satellite, and reanalysis data we conclude that drizzle was likely present in parts of a widespread cloud field, which stretched more than ~1000 km along the Ross Ice Shelf coast. Parameter space sensitivity tests using two-moment bulk microphysics in large-eddy simulations constrained by the observations suggest that activated ice freezing nuclei (IFN) and accumulation-mode aerosol number concentrations aloft during this persistent drizzle period were likely on the order of 0.2 L-1 and 20 cm-3, respectively. In such constrained simulations, the drizzle moisture flux through cloud base exceeds that of ice. The simulations also indicate that drizzle can lead to the formation of multiple peaks in cloud water content profiles. This study suggests that persistent drizzle at these low temperatures may be common at the low aerosol concentrations typical of the Antarctic and Southern Ocean atmospheres.

Remote Sensing of Droplet Number Concentration in Warm Clouds: A Review of the Current State of Knowledge and Perspectives.

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol-cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel-level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high-quality ground-based observations are examined.

Simulation of mesoscale cellular convection in marine stratocumulus.: Part I: Drizzling conditions.

This study uses eddy-permitting simulations to investigate the mechanisms that promote mesoscale variability of moisture in drizzling stratocumulus-topped marine boundary layers. Simulations show that precipitation tends to increase horizontal scales. Analysis of terms in the prognostic equation for total water mixing ratio variance indicates that moisture stratification plays a leading role in setting horizontal scales. This result is supported by simulations in which horizontal mean thermodynamic profiles are strongly nudged to their initial well-mixed state, which limits cloud scales. It is found that the spatial variability of sub-cloud moist cold pools surprisingly tends to respond to, rather than determine, the mesoscale variability, which may distinguish them from dry cold pools associated with deeper convection. Simulations also indicate that moisture stratification increases cloud scales specifically by increasing latent heating within updrafts, which increases updraft buoyancy and favors greater horizontal scales.

On the role of ice-nucleating aerosol in the formation of ice particles in tropical mesoscale convective systems.

Over decades, the cloud physics community has debated the nature and role of aerosol particles in ice initiation. The present study shows that the measured concentration of ice crystals in tropical mesoscale convective systems exceeds the concentration of ice nucleating particles (INPs) by several orders of magnitude. The concentration of INPs was assessed from the measured aerosol particles concentration in the size range of 0.5 to 1 µm. The observations from this study suggest that primary ice crystals formed on INPs make only a minor contribution to the total concentration of ice crystals in tropical mesoscale convective systems. This is found by comparing the predicted INP number concentrations with in-situ ice particle number concentrations. The obtained measurements suggest that ice multiplication is the likely explanation for the observed high concentrations of ice crystals in this type of convective system.

On averaging aspect ratios and distortion parameters over ice crystal population ensembles for estimating effective scattering asymmetry parameters.

The use of ensemble-average values of aspect ratio and distortion parameter of hexagonal ice prisms for the estimation of ensemble-average scattering asymmetry parameters is evaluated. Using crystal aspect ratios greater than unity generally leads to ensemble-average values of aspect ratio that are inconsistent with the ensemble-average asymmetry parameters. When a definition of aspect ratio is used that limits the aspect ratio to below unity (α≤1) for both hexagonal plates and columns, the effective asymmetry parameters calculated using ensemble-average aspect ratios are generally consistent with ensemble-average asymmetry parameters, especially if aspect ratios are geometrically averaged. Ensemble-average distortion parameters generally also yield effective asymmetry parameters that are largely consistent with ensemble-average asymmetry parameters. In the case of mixtures of plates and columns, it is recommended to geometrically average the α≤1 aspect ratios and to subsequently calculate the effective asymmetry parameter using a column or plate geometry when the contribution by columns to a given mixture's total projected area is greater or lower than 50%, respectively. In addition, we show that ensemble-average aspect ratios, distortion parameters and asymmetry parameters can generally be retrieved accurately from simulated multi-directional polarization measurements based on mixtures of varying columns and plates. However, such retrievals tend to be somewhat biased toward yielding column-like aspect ratios. Furthermore, generally large retrieval errors can occur for mixtures with approximately equal contributions of columns and plates and for ensembles with strong contributions of thin plates.

On polarimetric radar signatures of deep convection for model evaluation: columns of specific differential phase observed during MC3E.

The representation of deep convection in general circulation models is in part informed by cloud-resolving models (CRMs) that function at higher spatial and temporal resolution; however, recent studies have shown that CRMs often fail at capturing the details of deep convection updrafts. With the goal of providing constraint on CRM simulation of deep convection updrafts, ground-based remote-sensing observations are analyzed and statistically correlated for four deep convection events observed during the Midlatitude Continental Convective Clouds Experiment (MC3E). Since positive values of specific differential phase (KDP) observed above the melting level are associated with deep convection updraft cells, so-called "KDP columns" are analyzed using two scanning polarimetric radars in Oklahoma: the National Weather Service Vance WSR-88D (KVNX) and the Department of Energy C-band Scanning Atmospheric Radiation Measurement (ARM) Precipitation Radar (C-SAPR). KVNX and C-SAPR KDP volumes and columns are then statistically correlated with vertical winds retrieved via multi-Doppler wind analysis, lightning flash activity derived from the Oklahoma Lightning Mapping Array, and KVNX differential reflectivity (ZDR). Results indicate strong correlations of KDP volume above the melting level with updraft mass flux, lightning flash activity, and intense rainfall. Analysis of KDP columns reveals signatures of changing updraft properties from one storm event to another as well as during event evolution. Comparison of ZDR to KDP shows commonalities in information content of each, as well as potential problems with ZDR associated with observational artifacts.

Vertical variation of ice particle size in convective cloud tops.

A novel technique is used to estimate derivatives of ice effective radius with respect to height near convective cloud tops (dre /dz) from airborne shortwave reflectance measurements and lidar. Values of dre /dz are about -6 µm/km for cloud tops below the homogeneous freezing level, increasing to near 0 µm/km above the estimated level of neutral buoyancy. Retrieved dre /dz compares well with previously documented remote sensing and in situ estimates. Effective radii decrease with increasing cloud top height, while cloud top extinction increases. This is consistent with weaker size sorting in high, dense cloud tops above the level of neutral buoyancy where fewer large particles are present, and with stronger size sorting in lower cloud tops that are less dense. The results also confirm that cloud-top trends of effective radius can generally be used as surrogates for trends with height within convective cloud tops. These results provide valuable observational targets for model evaluation.

High-resolution NU-WRF simulations of a deep convective-precipitation system during MC3E: Further improvements and comparisons between Goddard microphysics schemes and observations.

The Goddard microphysics was recently improved by adding a fourth ice class (frozen drops/hail). This new 4ICE scheme was developed and tested in the Goddard Cumulus Ensemble (GCE) model for an intense continental squall line and a moderate, less organized continental case. Simulated peak radar reflectivity profiles were improved in intensity and shape for both cases, as were the overall reflectivity probability distributions versus observations. In this study, the new Goddard 4ICE scheme is implemented into the regional-scale NASA Unified-Weather Research and Forecasting (NU-WRF) model, modified and evaluated for the same intense squall line, which occurred during the Midlatitude Continental Convective Clouds Experiment (MC3E). NU-WRF simulated radar reflectivities, total rainfall, propagation, and convective system structures using the 4ICE scheme modified herein agree as well as or significantly better with observations than the original 4ICE and two previous 3ICE (graupel or hail) versions of the Goddard microphysics. With the modified 4ICE, the bin microphysics-based rain evaporation correction improves propagation and in conjunction with eliminating the unrealistic dry collection of ice/snow by hail can replicate the erect, narrow, and intense convective cores. Revisions to the ice supersaturation, ice number concentration formula, and snow size mapping, including a new snow breakup effect, allow the modified 4ICE to produce a stronger, better organized system, more snow, and mimic the strong aggregation signature in the radar distributions. NU-WRF original 4ICE simulated radar reflectivity distributions are consistent with and generally superior to those using the GCE due to the less restrictive domain and lateral boundaries.

Evidence for the predominance of mid-tropospheric aerosols as subtropical anvil cloud nuclei.

NASA's recent Cirrus Regional Study of Tropical Anvils and Cirrus Layers-Florida Area Cirrus Experiment focused on anvil cirrus clouds, an important but poorly understood element of our climate system. The data obtained included the first comprehensive measurements of aerosols and cloud particles throughout the atmospheric column during the evolution of multiple deep convective storm systems. Coupling these new measurements with detailed cloud simulations that resolve the size distributions of aerosols and cloud particles, we found several lines of evidence indicating that most anvil crystals form on mid-tropospheric rather than boundary-layer aerosols. This result defies conventional wisdom and suggests that distant pollution sources may have a greater effect on anvil clouds than do local sources.