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A series of model simulations were conducted to investigate the effects of cloud condensation nuclei (CCN) loading and convective available potential energy (CAPE) on tropical maritime and midlatitude continental deep convection. Dynamical downscaling from global aerosol reanalysis was used to represent aerosol fields for the two convective regimes. We describe a control run and multiple sensitivity experiments using a limited‐area model, employing spectral‐bin cloud microphysics. The CCN loading is perturbed between the target maritime and continental conditions, roughly 40–2,000 cm−3 at 850 hPa and 1% supersaturation. Surface precipitation rates monotonically increase with increasing CCN loading for both the maritime and continental situations, while these monotonic increases are disrupted in the simulations with reduced CAPE. The increase in precipitation is in the form of convective precipitation, at the expense of stratiform precipitation. CCN increases promote increases in supercooled cloud water, in agreement with previous modeling studies. However, in the simulations investigated herein, the changes in supercooled water have different impacts on the cloud microphysics in the maritime and continental simulations. Increased supercooled water contents lead to more hail and less graupel in the continental simulation. For the maritime simulation, enhanced supercooled cloud water contents promote an increase in graupel since little or no hail is produced. This distinction is due to the difference in relative magnitudes and peak altitudes of supercooled water and snow amounts, which is further attributable to moisture and dynamical differences in the two cases. Key Points: Impacts of aerosol and thermodynamics conditions on deep convective systems are investigated using a bin microphysical modelIncreasing aerosol concentrations promote stronger convective precipitation in both the maritime and continental caseIncreasing aerosol concentrations increases graupel (hail) in maritime (continental) case owing to height of snow‐dominant layer [ABSTRACT FROM AUTHOR]

This study contrasts midlatitude continental and tropical maritime deep convective cores using polarimetric radar observables and retrievals from selected deep convection episodes during the MC3E and TWPICE field campaigns. The continental convective cores produce stronger radar reflectivities throughout the profiles, while maritime convective cores produce more positive differential reflectivity Zdr and larger specific differential phase Kdp above the melting level. Hydrometeor identification retrievals revealed the presence of large fractions of rimed ice particles (snow aggregates) in the continental (maritime) convective cores, consistent with the Zdr and Kdp observations. The regional cloud-resolving model simulations with bulk and size-resolved bin microphysics are conducted for the selected cases, and the simulation outputs are converted into polarimetric radar signals and retrievals identical to the observational composites. Both the bulk and the bin microphysics reproduce realistic land and ocean (L-O) contrasts in reflectivity, polarimetric variables of rain drops, and hydrometeor profiles, but there are still large uncertainties in describing Zdr and Kdp of ice crystals associated with the ice particle shapes/orientation assumptions. Sensitivity experiments are conducted by swapping background aerosols between the continental and maritime environments, revealing that background aerosols play a role in shaping the distinct L-O contrasts in radar reflectivity associated with raindrop sizes, in addition to the dominant role of background thermodynamics. [ABSTRACT FROM AUTHOR]

This paper introduces a synthetic polarimetric radar simulator and retrieval package, POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS), for evaluating cloud‐resolving models (CRMs). POLARRIS is composed of forward (POLARRIS‐f) and inverse (retrieval and diagnostic) components (iPOLARRIS) to generate not only polarimetric radar observables (Zh, Zdr, Kdp, ρhv) but also radar‐consistent geophysical parameters such as hydrometeor identification, vertical velocity, and rainfall rates retrieved from CRM data. To demonstrate its application and uncertainties, POLARRIS is applied to simulations of a mesoscale convective system over the Southern Great Plains on 23 May 2011, using the Weather Research and Forecasting model with both spectral bin microphysics (SBM) and the Goddard single‐moment bulk 4ICE microphysics. Statistical composites reveal a significant dependence of simulated polarimetric observables (Zdr, Kdp) on the assumptions of the particle axis ratio (oblateness) and orientation angle distributions. The simulated polarimetric variables differ considerably between the SBM and 4ICE microphysics in part due to the differences in their ice particle size distributions as revealed by comparisons with aircraft measurements. Regardless of these uncertainties, simulated hydrometeor identification distributions overestimate graupel and hail fractions, especially from the simulation with SBM. To minimize uncertainties in forward model, the particle shape and orientation angle distributions of frozen particles should be predicted in a microphysics scheme in addition to the size distributions and particle densities. Key Points: New polarimetric radar simulator was developed for CRM evaluationThere are uncertainties remained in the assumptions of axis ratio and canting angles of ice particlesHydrometeor identification allows robust evaluation for bulk and bin microphysics [ABSTRACT FROM AUTHOR]

A 14-yr climatology of Tropical Rainfall Measuring Mission (TRMM) collocated multisensor signal statistics reveals a distinct land-ocean contrast as well as geographical variability of precipitation type, intensity, and microphysics. Microphysics information inferred from the TRMM Precipitation Radar and Microwave Imager show a large land-ocean contrast for the deep category, suggesting continental convective vigor. Over land, TRMM shows higher echo-top heights and larger maximum echoes, suggesting taller storms and more intense precipitation, as well as larger microwave scattering, suggesting the presence of more/larger frozen convective hydrometeors. This strong land-ocean contrast in deep convection is invariant over seasonal and multiyear time scales. Consequently, relatively short-term simulations from two global storm-resolving models can be evaluated in terms of their land-ocean statistics using the TRMM Triple-Sensor Three-Step Evaluation Framework via a satellite simulator. The models evaluated are the NASA Multiscale Modeling Framework (MMF) and the Nonhydrostatic Icosahedral Cloud Atmospheric Model (NICAM). While both simulations can represent convective land-ocean contrasts in warm precipitation to some extent, near-surface conditions over land are relatively moister in NICAM than MMF, which appears to be the key driver in the divergent warm precipitation results between the two models. Both the MMF and NICAM produced similar frequencies of large CAPE between land and ocean. The dry MMF boundary layer enhanced microwave scattering signals over land, but only NICAM had an enhanced deep convection frequency over land. Neither model could reproduce a realistic land-ocean contrast in deep convective precipitation microphysics. A realistic contrast between land and ocean remains an issue in global storm-resolving modeling. [ABSTRACT FROM AUTHOR]

The multiscale modeling framework (MMF), which replaces traditional cloud parameterizations with cloud-resolving models (CRMs) within a host atmospheric general circulation model (GCM), has become a new approach for climate modeling. The embedded CRMs make it possible to apply CRM-based cloud microphysics directly within a GCM. However, most such schemes have never been tested in a global environment for long-term climate simulation. The benefits of using an MMF to evaluate rigorously and improve microphysics schemes are here demonstrated. Four one-moment microphysical schemes are implemented into the Goddard MMF and their results validated against three CloudSat/CALIPSO cloud ice products and other satellite data. The new four-class (cloud ice, snow, graupel, and frozen drops/hail) ice scheme produces a better overall spatial distribution of cloud ice amount, total cloud fractions, net radiation, and total cloud radiative forcing than earlier three-class ice schemes, with biases within the observational uncertainties. Sensitivity experiments are conducted to examine the impact of recently upgraded microphysical processes on global hydrometeor distributions. Five processes dominate the global distributions of cloud ice and snow amount in long-term simulations: (1) allowing for ice supersaturation in the saturation adjustment, (2) three additional correction terms in the depositional growth of cloud ice to snow, (3) accounting for cloud ice fall speeds, (4) limiting cloud ice particle size, and (5) new size-mapping schemes for snow and graupel. Despite the cloud microphysics improvements, systematic errors associated with subgrid processes, cyclic lateral boundaries in the embedded CRMs, and momentum transport remain and will require future improvement. [ABSTRACT FROM AUTHOR]

The present study aims to develop a general circulation model (GCM) with improved simulation of heavy precipitation frequency by improving the representations of cloud and rain processes. GCMs with conventional convective parameterizations produce common bias in precipitation frequency: they overestimate light precipitation and underestimate heavy precipitation with respect to observed values. This frequency shift toward light precipitation is attributed here to a lack of consideration of cloud microphysical processes related to heavy precipitation. The budget study of cloud microphysical processes using a cloud-resolving model shows that the melting of graupel and accretion of cloud water by graupel and rain water are important processes in the generation of heavy precipitation. However, those processes are not expressed explicitly in conventional GCMs with convective parameterizations. In the present study, the cloud microphysics is modified to allow its implementation into a GCM with a horizontal resolution of 50 km. The newly developed GCM, which includes explicit cloud microphysics, produces more heavy precipitation and less light precipitation than conventional GCMs, thus simulating a precipitation frequency that is closer to the observed. This study demonstrates that the GCM requires a full representation of cloud microphysics to simulate the extreme precipitation frequency realistically. It is also shown that a coarse-resolution GCM with cloud microphysics requires an additional mixing process in the lower troposphere. [ABSTRACT FROM AUTHOR]

Two mixed-phase precipitation events were observed on 21 September and 20 October 2010 over the southern part of Finland during the Light Precipitation Validation Experiment (LPVEx). These events have been simulated using the Weather Research and Forecasting Model coupled with spectral bin microphysics (WRF-SBM). The detailed ice-melting scheme with prognosis of the liquid water fraction during melting enables explicit simulation of microphysical properties in the melting layer. First, the simulations have been compared with C-band 3D radar measurements for the purpose of evaluating the overall profiles of cloud and precipitation. The simulation has some artificial convective patterns and errors in the forecast displacement of the precipitation system. The overall overestimation of reflectivity is consistent with a bias toward the range characterized by large-diameter droplets in the surface drop size distribution. Second, the structure of the melting bands has been evaluated against vertically pointing K-band radar measurements. A peak in reflectivity and a gradual change in Doppler velocity are observed and similarly simulated in the common temperature range from approximately 0° to 3°C. The effectiveness of the time-dependent melting scheme has been justified by intercomparison with a corresponding simulation using an instantaneous melting scheme. A weakness of the new melting scheme is that melting particles having high liquid water fractions on the order of 80%-90% cannot be simulated. This situation may cause underestimation of radar reflectivity in the melting layer because of the assumptions of melting-particle structure used to calculate the scattering properties. [ABSTRACT FROM AUTHOR]

Current cloud microphysical schemes used in cloud and mesoscale models range from simple one-moment to multimoment, multiclass to explicit bin schemes. This study details the benefits of adding a fourth ice class (frozen drops/hail) to an already improved single-moment three-class ice (cloud ice, snow, graupel) bulk microphysics scheme developed for the Goddard Cumulus Ensemble model. Besides the addition and modification of several hail processes from a bulk three-class hail scheme, further modifications were made to the three-ice processes, including allowing greater ice supersaturation and mitigating spurious evaporation/sublimation in the saturation adjustment scheme, allowing graupel/hail to transition to snow via vapor growth and hail to transition to graupel via riming, wet graupel to become hail, and the inclusion of a rain evaporation correction and vapor diffusivity factor. The improved three-ice snow/graupel size-mapping schemes were adjusted to be more stable at higher mixing ratios and to increase the aggregation effect for snow. A snow density mapping was also added. The new scheme was applied to an intense continental squall line and a moderate, loosely organized continental case using three different hail intercepts. Peak simulated reflectivities agree well with radar for both the intense and moderate cases and were superior to earlier three-ice versions when using a moderate and large intercept for hail, respectively. Simulated reflectivity distributions versus height were also improved versus radar in both cases compared to earlier three-ice versions. The bin-based rain evaporation correction affected the squall line more but overall the agreement among the reflectivity distributions was unchanged. The new scheme also improved the simulated surface rain-rate histograms. [ABSTRACT FROM AUTHOR]

Abstract: Convection is the primary transport process in the Earth's atmosphere. About two-thirds of the Earth's rainfall and severe floods derive from convection. In addition, two-thirds of the global rain falls in the tropics, while the associated latent heat release accounts for three-fourths of the total heat energy for the Earth's atmosphere. Cloud-resolving models (CRMs) have been used to improve our understanding of cloud and precipitation processes and phenomena from micro-scale to cloud-scale and mesoscale as well as their interactions with radiation and surface processes. CRMs use sophisticated and realistic representations of cloud microphysical processes and can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems. CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results. The Goddard Cumulus Ensemble model (GCE) has been developed and improved at NASA/Goddard Space Flight Center over the past three decades. It is a multi-dimensional non-hydrostatic CRM that can simulate clouds and cloud systems in different environments. Early improvements and testing were presented in Tao and Simpson (1993) and Tao et al. (2003a). A review on the application of the GCE to the understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003). In this paper, recent model improvements (microphysics, radiation and land surface processes) are described along with their impact and performance on cloud and precipitation events in different geographic locations via comparisons with observations. In addition, recent advanced applications of the GCE are presented that include understanding the physical processes responsible for diurnal variation, examining the impact of aerosols (cloud condensation nuclei or CCN and ice nuclei or IN) on precipitation processes, utilizing a satellite simulator to improve the microphysics, providing better simulations for satellite-derived latent heating retrieval, and coupling with a general circulation model to improve the representation of precipitation processes. Future research is also discussed. [Copyright &y& Elsevier]

Two mesoscale convective systems (MCSs) observed during the African Monsoon Multidisciplinary Analyses (AMMA) experiment are simulated using the three-dimensional (3D) Goddard Cumulus Ensemble model. This study was undertaken to determine the performance of the cloud-resolving model in representing distinct convective and microphysical differences between the two MCSs over a tropical continental location. Simulations are performed using 1-km horizontal grid spacing, a lower limit on current embedded cloud-resolving models within a global multiscale modeling framework. Simulated system convective structure and microphysics are compared to radar observations using contoured frequency-by-altitude diagrams (CFADs), calculated ice and water mass, and identified hydrometeor variables. Vertical distributions of ice hydrometeors indicate underestimation at the mid- and upper levels, partially due to the inability of the model to produce adequate system heights. The abundance of high-reflectivity values below and near the melting level in the simulation led to a broadening of the CFAD distributions. Observed vertical reflectivity profiles show that high reflectivity is present at greater heights than the simulations produced, thought to be a result of using a single-moment microphysics scheme. Relative trends in the population of simulated hydrometeors are in agreement with observations, though a secondary convective burst is not well represented. Despite these biases, the radar-observed differences between the two cases are noticeable in the simulations as well, suggesting that the model has some skill in capturing observed differences between the two MCSs. [ABSTRACT FROM AUTHOR]

Numerical weather prediction (NWP) simulations using the Japan Meteorological Agency Nonhydrostatic Model (JMA-NHM) are conducted for three precipitation events observed by shipborne or spaceborne W-band cloud radars. Spectral bin and single-moment bulk cloud microphysics schemes are employed separately for an intercomparative study. A radar product simulator that is compatible with both microphysics schemes is developed to enable a direct comparison between simulation and observation with respect to the equivalent radar reflectivity factor Ze, Doppler velocity (DV), and path-integrated attenuation (PIA). In general, the bin model simulation shows better agreement with the observed data than the bulk model simulation. The correction of the terminal fall velocities of snowflakes using those of hail further improves the result of the bin model simulation. The results indicate that there are substantial uncertainties in the mass-size and size-terminal fall velocity relations of snowflakes or in the calculation of terminal fall velocity of snow aloft. For the bulk microphysics, the overestimation of Ze is observed as a result of a significant predominance of snow over cloud ice due to substantial deposition growth directly to snow. The DV comparison shows that a correction for the fall velocity of hydrometeors considering a change of particle size should be introduced even in single-moment bulk cloud microphysics. [ABSTRACT FROM AUTHOR]

A well-known bias common to many bulk microphysics schemes currently being used in cloud-resolving models is the tendency to produce excessively large reflectivity values (e.g., 40 dB Z) in the middle and upper troposphere in simulated convective systems. The Rutledge and Hobbs-based bulk microphysics scheme in the Goddard Cumulus Ensemble model is modified to reduce this bias and improve realistic aspects. Modifications include lowering the efficiencies for snow/graupel riming and snow accreting cloud ice; converting less rimed snow to graupel; allowing snow/graupel sublimation; adding rime splintering, immersion freezing, and contact nucleation; replacing the Fletcher formulation for activated ice nuclei with that of Meyers et al.; allowing for ice supersaturation in the saturation adjustment; accounting for ambient RH in the growth of cloud ice to snow; and adding/accounting for cloud ice fall speeds. In addition, size-mapping schemes for snow/graupel were added as functions of temperature and mixing ratio, lowering particle sizes at colder temperatures but allowing larger particles near the melting level and at higher mixing ratios. The modifications were applied to a weakly organized continental case and an oceanic mesoscale convective system (MCS). Strong echoes in the middle and upper troposphere were reduced in both cases. Peak reflectivities agreed well with radar for the weaker land case but, despite improvement, remained too high for the MCS. Reflectivity distributions versus height were much improved versus radar for the less organized land case but not for the MCS despite fewer excessively strong echoes aloft due to a bias toward weaker echoes at storm top. [ABSTRACT FROM AUTHOR]

The ice crystal enhancement (IE) factor, defined as the ratio of the ice crystal to ice nuclei (IN) number concentrations for any particular cloud condition, is needed to quantify the contribution of changes in IN to global warming. However, the ensemble characteristics of IE are still unclear. In this paper, a representation of the IE factor is incorporated into a three-ice-category microphysical scheme for use in long-term cloud-resolving model (CRM) simulations. Model results are compared with remote sensing observations, which suggest that, absent a physically based consideration of how IE comes about, the IE factor in tropical clouds is about 103 times larger than that in midlatitudinal ones. This significant difference in IE between the tropics and middle latitudes is consistent with the observation of stronger entrainment and detrainment in the tropics. In addition, the difference also suggests that cloud microphysical parameterizations depend on spatial resolution (or subgrid turbulence parameterizations within CRMs). [ABSTRACT FROM AUTHOR]

This study aims to evaluate the consistency and discrepancies in estimates of diabatic heating profiles associated with precipitation based on satellite observations and microphysics and those derived from the thermodynamics of the large-scale environment. It presents a survey of diabatic heating profile estimates from four Tropical Rainfall Measuring Mission (TRMM) products, four global reanalyses, and in situ sounding measurements from eight field campaigns at various tropical locations. Common in most of the estimates are the following: (i) bottom-heavy profiles, ubiquitous over the oceans, are associated with relatively low rain rates, while top-heavy profiles are generally associated with high rain rates; (ii) temporal variability of latent heating profiles is dominated by two modes, a deep mode with a peak in the upper troposphere and a shallow mode with a low-level peak; and (iii) the structure of the deep modes is almost the same in different estimates and different regions in the tropics. The primary uncertainty is in the amount of shallow heating over the tropical oceans, which differs substantially among the estimates. [ABSTRACT FROM AUTHOR]

This paper proposes a methodology known as the Tropical Rainfall Measuring Mission (TRMM) Triple-Sensor Three-Step Evaluation Framework (T3EF) for the systematic evaluation of precipitating cloud types and microphysics in a cloud-resolving model (CRM). T3EF utilizes multisensor satellite simulators and novel statistics of multisensor radiance and backscattering signals observed from the TRMM satellite. Specifically, T3EF compares CRM and satellite observations in the form of combined probability distributions of precipitation radar (PR) reflectivity, polarization-corrected microwave brightness temperature (Tb), and infrared Tb to evaluate the candidate CRM. T3EF is used to evaluate the Goddard Cumulus Ensemble (GCE) model for cases involving the South China Sea Monsoon Experiment (SCSMEX) and the Kwajalein Experiment (KWAJEX). This evaluation reveals that the GCE properly captures the satellite-measured frequencies of different precipitating cloud types in the SCSMEX case but overestimates the frequencies of cumulus congestus in the KWAJEX case. Moreover, the GCE tends to simulate excessively large and abundant frozen condensates in deep precipitating clouds as inferred from the overestimated GCE-simulated radar reflectivities and microwave Tb depressions. Unveiling the detailed errors in the GCE’s performance provides the better direction for model improvements. [ABSTRACT FROM AUTHOR]

A two-dimensional cloud-resolving model is used to study the sensitivities of two microphysical schemes, a bulk scheme and an explicit spectral bin scheme, in simulating a midlatitude summertime squall line [Preliminary Regional Experiment for Storm-Scale Operational and Research Meteorology (PRE-STORM), 10–11 June 1985]. In this first part of a two-part paper, the developing and mature stages of simulated storms are compared in detail. Some variables observed during the field campaign are also presented for validation. It is found that both schemes agree well with each other, and also with published observations and retrievals, in terms of storm structures and evolution, average storm flow patterns, pressure and temperature perturbations, and total heating profiles. The bin scheme is able to produce a much more extensive and homogeneous stratiform region, which compares better with observations. However, instantaneous fields and high temporal resolution analyses show distinct characteristics in the two simulations. During the mature stage, the bulk simulation produces a multicell storm with convective cells embedded in its stratiform region. Its leading convection also shows a distinct life cycle (strong evolution). In contrast, the bin simulation produces a unicell storm with little temporal variation in its leading cell regeneration (weak evolution). More detailed, high-resolution observations are needed to validate and, perhaps, generalize these model results. Interactions between the cloud microphysics and storm dynamics that produce the sensitivities described here are discussed in detail in Part II of this paper. [ABSTRACT FROM AUTHOR]

A three-dimensional cloud-resolving model (CRM) with observed large-scale forcing is used to study how ice nuclei (IN) affect the net radiative flux at the top of the atmosphere (TOA). In all the numerical experiments carried out, the cloud ice content in the upper troposphere increases with IN number concentration via the Bergeron process. As a result, the upward solar flux at the TOA increases whereas the infrared one decreases. Because of the opposite response of the two fluxes to IN concentration, the sensitivity of the net radiative flux at the TOA to IN concentration varies from one case to another. Six tropical and three midlatitudinal field campaigns provide data to model the effect of IN on radiation in different latitudes. Classifying the CRM simulations into tropical and midlatitudinal and then comparing the two types reveals that the indirect effect of IN on radiation is greater in the middle latitudes than in the tropics. Furthermore, comparisons between model results and observations suggest that observational IN data are necessary to evaluate long-term CRM simulations. [ABSTRACT FROM AUTHOR]