Hydrometeor Species Research Articles

Optimization of Cloud-Radiation Databases for Passive Microwave Precipitation Retrievals over Ocean

AbstractUsing Tropical Rainfall Measuring Mission (TRMM) observations from storms collected over the oceans surrounding East Asia, during summer, a method of creating physically consistent cloud-radiation databases to support satellite radiometer retrievals is introduced. In this method, vertical profiles of numerical model-simulated cloud and precipitation fields are optimized against TRMM radar and radiometer observations using a hybrid empirical orthogonal function (EOF)–one-dimensional variational (1DVAR) approach.The optimization is based on comparing simulated to observed radar reflectivity profiles and the corresponding passive microwave observations at the frequencies of the TRMM Microwave Imager (TMI) instrument. To minimize the discrepancies between the actual and the synthetic observations, the simulated cloud and precipitation profiles are optimized by adjusting the contents of the hydrometeors. To reduce the dimension of the hydrometeor content profiles in the optimization, multivariate relations among hydrometeor species are used.After applying the optimization method to modify the simulated clouds, the optimized cloud-radiation database has a joint distribution of reflectivity and associated brightness temperatures that is considerably closer to that observed by TRMM PR and TMI, especially at 85 GHz. This implies that the EOF–1DVAR approach can generate profiles with realistic distributions of frozen hydrometeors, such as snow and graupel. This approach may be similarly adapted to operate with the variety and capabilities of the passive microwave radiometers that compose the Global Precipitation Measurement (GPM) constellation. Furthermore, it can be extended to other oceanic regions and seasons.

Signatures of Hydrometeor Species from Airborne Passive Microwave Data for Frequencies 10–183 GHz

AbstractPassive microwave brightness temperatures (BTs) collected above severe thunderstorms using the Advanced Microwave Precipitation Radiometer and Conical Scanning Millimeter-Wave Imaging Radiometer during the Midlatitude Continental Convective Clouds Experiment are compared with a hydrometeor identification applied to dual-polarimetric Weather Surveillance Radar-1988 Doppler radar data collected at Vance Air Force Base, Oklahoma (KVNX). The goal of this work is to determine the signatures of various hydrometeor species in terms of BTs measured at frequencies used by the Global Precipitation Measurement mission Microwave Imager. Results indicate that hail is associated with an ice-scattering signature at all frequencies examined, including 10.7 GHz. However, it appears that frequencies ≤ 37.1 GHz are most useful for identifying hail. Low-level (below 2.5 km) hail becomes probable for a BT below 240 K at 19.4 GHz, 170 K at 37.1 GHz, 90 K at 85.5 GHz, 80 K at 89.0 GHz, 100 K at 165.5 GHz, and 100 K at 183.3 ± 7 GHz. Graupel may be distinguished from hail and profiles without any hydrometeor species by its strong scattering signature at higher frequencies (e.g., 165.5 GHz) and its relative lack of scattering at frequencies ≤ 19.4 GHz. There is a clearer distinction between profiles that contain liquid precipitation and profiles without any hydrometeors when the liquid is associated above with hail and/or graupel (i.e., a hydrometeor category with a strong scattering signature) than when the liquid is associated with smaller ice. Near-surface precipitation is much more likely for a 19.4-GHz BT < 250 K, 37.1-GHz BT < 240 K, 89.0-GHz BT < 220 K, and 165.5-GHz BT < 140 K.

The Characterization of Ice Hydrometeor Gamma Size Distributions as Volumes in N0–λ–μ Phase Space: Implications for Microphysical Process Modeling

Abstract Gamma distributions represent particle size distributions (SDs) in mesoscale and cloud-resolving models that predict one, two, or three moments of hydrometeor species. They are characterized by intercept (N0), slope (λ), and shape (μ) parameters prognosed by such schemes or diagnosed based on fits to SDs measured in situ in clouds. Here, ice crystal SDs acquired in arctic cirrus during the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and in hurricanes during the National Aeronautic and Space Administration (NASA) African Monsoon Multidisciplinary Analyses (NAMMA) are fit to gamma distributions using multiple algorithms. It is shown that N0, λ, and μ are not independent parameters but rather exhibit mutual dependence. Although N0, λ, and μ are not highly dependent on choice of fitting routine, they are sensitive to the tolerance permitted by fitting algorithms, meaning a three-dimensional volume in N0–λ–μ phase space is required to represent a single SD. Depending on the uncertainty in the measured SD and on how well a gamma distribution matches the SD, parameters within this volume of equally realizable solutions can vary substantially, with N0, in particular, spanning several orders of magnitude. A method to characterize a family of SDs as an ellipsoid in N0–λ–μ phase space is described, with the associated scatter in N0–λ–μ for such families comparable to scatter in N0, λ, and μ observed in prior field campaigns conducted in different conditions. Ramifications for the development of cloud parameterization schemes and associated calculations of microphysical process rates are discussed.

The Impact of Ice Phase Cloud Parameterizations on Tropical Cyclone Prediction

Abstract The impact of ice phase cloud microphysical processes on prediction of tropical cyclone environment is examined for two microphysical parameterizations using the Coupled Ocean–Atmosphere Mesoscale Prediction System–Tropical Cyclone (COAMPS-TC) model. An older version of microphysical parameterization is a relatively typical single-moment scheme with five hydrometeor species: cloud water and ice, rain, snow, and graupel. An alternative newer method uses a hybrid approach of double moment in cloud ice and rain and single moment in the other three species. Basin-scale synoptic flow simulations point to important differences between these two schemes. The upper-level cloud ice concentrations produced by the older scheme are up to two orders of magnitude greater than the newer scheme, primarily due to differing assumptions concerning the ice nucleation parameterization. Significant (1°–2°C) warm biases near the 300-hPa level in the control experiments are not present using the newer scheme. The warm bias in the control simulations is associated with the longwave radiative heating near the base of the cloud ice layer. The two schemes produced different track and intensity forecasts for 15 Atlantic storms. Rightward cross-track bias and positive intensity bias in the control forecasts are significantly reduced using the newer scheme. Synthetic satellite imagery of Hurricane Igor (2010) shows more realistic brightness temperatures from the simulations using the newer scheme, in which the inner core structure is clearly discernible. Applying the synthetic satellite imagery in both quantitative and qualitative analyses helped to pinpoint the issue of excessive upper-level cloud ice in the older scheme.

Low-Level ZDR Signatures in Supercell Forward Flanks: The Role of Size Sorting and Melting of Hail

Abstract The low levels of supercell forward flanks commonly exhibit distinct differential reflectivity (ZDR) signatures, including the low-ZDR hail signature and the high-ZDR “arc.” The ZDR arc has been previously associated with size sorting of raindrops in the presence of vertical wind shear; here this model is extended to include size sorting of hail. Idealized simulations of a supercell storm observed by the Norman, Oklahoma (KOUN), polarimetric radar on 1 June 2008 are performed using a multimoment bulk microphysics scheme, in which size sorting is allowed or disallowed for hydrometeor species. Several velocity–diameter relationships for the hail fall speed are considered, as well as fixed or variable bulk densities that span the graupel-to-hail spectrum. A T-matrix-based emulator is used to derive polarimetric fields from the hydrometeor state variables. Size sorting of hail is found to have a dominant impact on ZDR and can result in a ZDR arc from melting hail even when size sorting is disallowed in the rain field. The low-ZDR hail core only appears when size sorting is allowed for hail. The mean storm-relative wind in a deep layer is found to align closely with the gradient in mean mass diameter of both rain and hail, with a slight shift toward the storm-relative mean wind below the melting level in the case of rain. The best comparison with the observed 1 June 2008 supercell is obtained when both rain and hail are allowed to sort, and the bulk density and associated fall-speed curve for hail are predicted by the model microphysics.

The Subgrid Importance Latin Hypercube Sampler (SILHS): a multivariate subcolumn generator

Abstract. Coarse-resolution climate and weather forecast models cannot accurately parameterize small-scale, nonlinear processes without accounting for subgrid-scale variability. To do so, some models integrate over the subgrid variability analytically. Although analytic integration methods are attractive, they can be used only with physical parameterizations that have a sufficiently simple functional form. Instead, this paper introduces a method to integrate subgrid variability using a type of Monte Carlo integration. The method generates subcolumns with suitable vertical correlations and feeds them into a microphysics parameterization. The subcolumn methodology requires little change to the parameterization source code and can be used with a wide variety of physical parameterizations. Our subcolumn generator is multivariate, which is important for physical processes that involve two or more hydrometeor species, such as accretion of cloud droplets by rain drops. In order to reduce sampling noise in the integrations, our subcolumn generator employs two variance-reduction methods, namely importance and stratified (Latin hypercube) sampling. For this reason, we name the subcolumn generator the Subgrid Importance Latin Hypercube Sampler (SILHS). This paper tests SILHS in interactive, single-column simulations of a marine stratocumulus case and a shallow cumulus case. The paper then compares simulations that use SILHS with those that use analytic integration. Although the SILHS solutions exhibit considerable noise from time step to time step, the noise is greatly damped in most of the time-averaged profiles.

Empirical conversion of the vertical profile of reflectivity from Ku‐band to S‐band frequency

ABSTRACTThis paper presents an empirical method for converting reflectivity from Ku‐band (13.8 GHz) to S‐band (2.8 GHz) for several hydrometeor species, which facilitates the incorporation of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) measurements into quantitative precipitation estimation (QPE) products from the U.S. Next‐Generation Radar (NEXRAD). The development of empirical dual‐frequency relations is based on theoretical simulations, which have assumed appropriate scattering and microphysical models for liquid and solid hydrometeors (raindrops, snow, and ice/hail). Particle phase, shape, orientation, and density (especially for snow particles) have been considered in applying the T‐matrix method to compute the scattering amplitudes. Gamma particle size distribution (PSD) is utilized to model the microphysical properties in the ice region, melting layer, and raining region of precipitating clouds. The variability of PSD parameters is considered to study the characteristics of dual‐frequency reflectivity, especially the variations in radar dual‐frequency ratio (DFR). The empirical relations between DFR and Ku‐band reflectivity have been derived for particles in different regions within the vertical structure of precipitating clouds. The reflectivity conversion using the proposed empirical relations has been tested using real data collected by TRMM‐PR and a prototype polarimetric WSR‐88D (Weather Surveillance Radar 88 Doppler) radar, KOUN. The processing and analysis of collocated data demonstrate the validity of the proposed empirical relations and substantiate their practical significance for reflectivity conversion, which is essential to the TRMM‐based vertical profile of reflectivity correction approach in improving NEXRAD‐based QPE.

Parameterizing correlations between hydrometeor species in mixed-phase Arctic clouds

Mixed-phase Arctic clouds, like other clouds, contain small-scale variability in hydrometeor fields, such as cloud water or snow mixing ratio. This variability may be worth parameterizing in coarse-resolution numerical models. In particular, for modeling processes such as accretion and aggregation, it would be useful to parameterize subgrid correlations among hydrometeor species. However, one difficulty is that there exist many hydrometeor species and many microphysical processes, leading to complexity and computational expense.Existing lower and upper bounds (inequalities) on linear correlation coefficients provide useful guidance, but these bounds are too loose to serve directly as a method to predict subgrid correlations. Therefore, this paper proposes an alternative method that is based on a blend of theory and empiricism. The method begins with the spherical parameterization framework of Pinheiro and Bates (1996), which expresses the correlation matrix in terms of its Cholesky factorization. The values of the elements of the Cholesky matrix are parameterized here using a cosine row-wise formula that is inspired by the aforementioned bounds on correlations. The method has three advantages: 1) the computational expense is tolerable; 2) the correlations are, by construction, guaranteed to be consistent with each other; and 3) the methodology is fairly general and hence maymore » be applicable to other problems. The method is tested non-interactively using simulations of three Arctic mixed-phase cloud cases from two different field experiments: the Indirect and Semi-Direct Aerosol Campaign (ISDAC) and the Mixed-Phase Arctic Cloud Experiment (M-PACE). Benchmark simulations are performed using a large-eddy simulation (LES) model that includes a bin microphysical scheme. The correlations estimated by the new method satisfactorily approximate the correlations produced by the LES.« less

Microphysics parameterization for convective clouds in a global climate model: Description and single-column model tests

[1] An efficient two-moment microphysics parameterization scheme for convective clouds is developed to improve the representation of convective clouds and its interactions with stratiform clouds and aerosol in global climate models (GCMs). The scheme explicitly treats mass mixing ratio and number concentration of four hydrometeor species (cloud water, cloud ice, rain, and snow) and describes several microphysical processes, including autoconversion, self-collection, collection between hydrometeor species, freezing, cloud ice nucleation, droplet activation, and sedimentation. Thus this physically based scheme is suitable for investigating the interaction between convection and aerosol and the indirect aerosol effect on climate. An evaluation of the scheme in the single-column version of NCAR Community Atmospheric Model version 3.5 (CAM3.5) with the Tropical Warm Pool–International Cloud Experiment (TWP-ICE) data shows that the simulation of cloud microphysical properties in convective core is significantly improved, indicating that the new parameterization describes the microphysical processes in convection reasonably well. The contribution from convective detrainment to large-scale cloud ice and liquid water budgets is enhanced greatly. With more realistic convective cloud microphysical properties and their detrainment, the surface stratiform precipitation, which is seriously underestimated in the model, is increased by a factor of roughly 2.5, and therefore is much closer to the observations. In addition, the simulations of net surface shortwave radiation flux, OLR, specific humidity, and temperature are also improved to some extent. Sensitivity experiments show that the microphysics scheme is moderately sensitive to model vertical resolution, updraft vertical velocity, and numerics, but less so to the lower boundary conditions of hydrometeor budget equations. The experiments with climatological aerosol distribution show that convective precipitation is suppressed with increasing aerosol amount, consistent with some available observations.

Comparison of Evaporation and Cold Pool Development between Single-Moment and Multimoment Bulk Microphysics Schemes in Idealized Simulations of Tornadic Thunderstorms

Abstract Idealized simulations of the 3 May 1999 Oklahoma tornadic supercell storms are conducted at various horizontal grid spacings ranging from 1 km to 250 m, using a sounding extracted from a prior 3-km grid spacing real-data simulation. A sophisticated multimoment bulk microphysics parameterization scheme capable of predicting up to three moments of the particle or drop size distribution (DSD) for several liquid and ice hydrometeor species is evaluated and compared with traditional single-moment schemes. The emphasis is placed on the impact of microphysics, specifically rain evaporation and size sorting, on cold pool strength and structure, and on the overall reflectivity structure of the simulated storms. It is shown through microphysics budget analyses and examination of specific processes within the low-level downdraft regions that the multimoment scheme has important advantages, which lead to a weaker and smaller cold pool and better reflectivity structure, particularly in the forward-flank region of the simulated supercells. Specifically, the improved treatment of evaporation and size sorting, and their effects on the predicted rain DSDs by the multimoment scheme helps to control the cold bias often found in the simulations using typical single-moment schemes. The multimoment results are more consistent with observed (from both fixed and mobile mesonet platforms) thermodynamic conditions within the cold pools of the discrete supercells of the 3 May 1999 outbreak.

State estimation of convective storms with a two‐moment microphysics scheme and an ensemble Kalman filter: Experiments with simulated radar data

AbstractThe ability of the ensemble Kalman filter method to estimate an increased number of state variables associated with a double‐moment (DM) microphysics scheme is examined for the first time through observing system simulation experiments, assuming either a perfect or imperfect prediction model and/or observation operators. With the DM scheme, mixing ratios and total number concentrations of hydrometeor species are predicted.It is found that the increased number of state variables can be reasonably well estimated when both radial velocity (Vr) and reflectivity (ZH) observations are used and when the prediction model is assumed to be perfect. However, the errors increase significantly when ZH is used alone. In this case, the filter has difficulty in estimating independently‐varying mixing ratios and number concentrations, which are both directly involved in the calculation of ZH. The addition of Vr data helps alleviate a problem associated with the solution not being sufficiently constrained by observations.With the DM scheme, the correlations between ZH and model state variables exhibit complex spatial structures that depend on the location of the ZH observation. Collocated ZH and vertical velocity show negative correlation when the observation is taken where ice phase hydrometeors are dominant, but positive correlation when it is taken where large quantities of liquid hydrometeors exist. Further study is needed to fully understand the complex correlation structures.Imperfect model experiments were performed, with two types of model errors: (1) microphysical parametrization error due to incorrectly assumed shape parameter of the gamma particle size distribution (PSD), and (2) different ways of calculating hydrometeor scattering. The results show that the model error degrades the state estimation in general. Nevertheless, the estimated states are still reasonably good when both Vr and ZH are assimilated. Perturbing the shape parameter of gamma PSDs within the forecast ensemble improves the overall state estimation. Copyright © 2010 Royal Meteorological Society

The Impact of Positive-Definite Moisture Advection and Low-Level Moisture Flux Bias over Orography

Abstract Overprediction of precipitation has been a frequently noted problem near terrain. Higher model resolution helps simulate sharp microphysical gradients more realistically but can increase spuriously generated moisture in some numerical schemes. The positive-definite moisture advection (PDA) scheme in the Weather Research and Forecasting (WRF) model reduces a significant nonphysical moisture source in the model. This study examines the 13–14 December 2001 second Improvement of Microphysical Parameterization through Observational Verification Experiment (IMPROVE-2) case, in which PDA reduces storm-total precipitation over Oregon by 3%–17%, varying with geographical region, model resolution, and the phase of the storm. The influence of PDA is then analyzed for each hydrometeor species in the Thompson microphysics scheme. Without PDA, the cloud liquid water field generates most of the spurious moisture because of the high frequency of sharp gradients near upwind cloud edges. It is shown that PDA substantially improves precipitation verification and that subtle, but significant, changes occur in the distribution of microphysical species aloft. Finally, another potential source of orographic precipitation error is examined: excessive low-level moisture flux upstream of regional orography.

Simultaneous state estimation and attenuation correction for thunderstorms with radar data using an ensemble Kalman filter: tests with simulated data

AbstractA new approach to dealing with attenuated radar reflectivity data in the data assimilation process is proposed and tested with simulated data using the ensemble square‐root Kalman filter. This approach differs from the traditional method where attenuation is corrected in observation space first before observations are assimilated into numerical models. We build attenuation correction into the data assimilation system by calculating the expected attenuation within the forward observation operators using the estimated atmospheric state. Such a procedure does not require prior assumption about the types of hydrometeor species along the radar beams, and allows us to take advantage of knowledge about the hydrometeors obtained through data assimilation and state estimation. Being based on optimal estimation theory, error and uncertainty information on the observations and prior estimate can be effectively utilized, and additional observed parameters, such as those from polarimetric radar, can potentially be incorporated into the system. Tests with simulated reflectivity data of an X‐band 3 cm wavelength radar for a supercell storm show that the attenuation correction procedure is very effective—the analyses obtained using attenuated data are almost as good as those obtained using unattenuated data. The procedure is also robust in the presence of moderate dropsize‐distribution‐related observation operator error and when systematic radar calibration error exists. The analysis errors are very large if no attenuation correction is applied. The effect of attenuation and its correction when radial velocity data are also assimilated is discussed as well. In general, attenuation correction is equally important when quality radial velocity data are also assimilated. Copyright © 2009 Royal Meteorological Society

On the Definition of Precipitation Efficiency

Abstract A modified definition of precipitation efficiency (PE) is proposed based on either cloud microphysics precipitation efficiency (CMPE) or water cycling processes including water vapor and hydrometeor species [large-scale precipitation efficiency (LSPE)]. These PEs are examined based on a two-dimensional cloud-resolving simulation. The model is integrated for 21 days with the imposed large-scale vertical velocity, zonal wind, and horizontal advections obtained from the Tropical Ocean Global Atmosphere Coupled Ocean–Atmosphere Response Experiment (TOGA COARE). It is found that the properly defined PEs include all moisture and hydrometeor sources associated with surface rainfall processes so that they range from 0% to 100%. Furthermore, the modified LSPE and CMPE are highly correlated. Their linear correlation coefficient and root-mean-squared difference are insensitive to the spatial scales of averaged data and are moderately sensitive to the time period of averaged data.

SHDOMPPDA: A Radiative Transfer Model for Cloudy Sky Data Assimilation

Abstract The spherical harmonics discrete ordinate method for plane-parallel data assimilation (SHDOMPPDA) model is an unpolarized plane-parallel radiative transfer forward model, with corresponding tangent linear and adjoint models, suitable for use in assimilating cloudy sky visible and infrared radiances. It is derived from the spherical harmonics discrete ordinate method plane-parallel (SHDOMPP, also described in this article) version of the spherical harmonics discrete ordinate method (SHDOM) model for three-dimensional atmospheric radiative transfer. The inputs to the SHDOMPPDA forward model are profiles of pressure, temperature, water vapor, and mass mixing ratio and number concentration for a number of hydrometeor species. Hydrometeor optical properties, including detailed phase functions, are determined from lookup tables as a function of mass mean radius. The SHDOMPP and SHDOMPPDA algorithms and construction of the tangent-linear and adjoint models are described. The SHDOMPPDA forward model is validated against the Discrete Ordinate Radiative Transfer Model (DISORT) by comparing upwelling radiances in multiple directions from 100 cloud model columns at visible and midinfrared wavelengths. For this test in optically thick clouds the computational time for SHDOMPPDA is comparable to DISORT for visible reflection, and roughly 5 times faster for thermal emission. The tangent linear and adjoint models are validated by comparison to finite differencing of the forward model.

Assessment of Hydrometeor Collection Rates from Exact and Approximate Equations. Part II: Numerical Bounding

Abstract Past microphysical investigations, including Part I of this study, have noted that the collection equation, when applied to the interaction between different hydrometeor species, can predict large mass transfer rates, even when an exact solution is used. The fractional depletion in a time step can even exceed unity for the collected species with plausible microphysical conditions and time steps, requiring “normalization” by a microphysical scheme. Although some of this problem can be alleviated through the use of more moment predictions and hydrometeor categories, the question as to why such “overdepletion” can be predicted in the first place remains insufficiently addressed. It is shown through both physical and conceptual arguments that the explicit time discretization of the bulk collection equation for any moment is not consistent with a quasi-stochastic view of collection. The result, under certain reasonable conditions, is a systematic overprediction of collection, which can become a serious error in the prediction of higher-order moments in a bulk scheme. The term numerical bounding is used to refer to the use of a quasi-stochastically consistent formula that prevents fractional collections exceeding unity for any moments. Through examples and analysis it is found that numerical bounding is typically important in mass moment prediction for time steps exceeding approximately 10 s. The Poisson-based numerical bounding scheme is shown to be simple in application and conceptualization; within a straightforward idealization it completely corrects overdepletion while even being immune to the rediagnosis error of the time-splitting method. The scheme’s range of applicability and utility are discussed.

Mesoscale Modeling of Springtime Arctic Mixed-Phase Stratiform Clouds Using a New Two-Moment Bulk Microphysics Scheme

Abstract A new two-moment bulk microphysics scheme is implemented into the polar version of the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) to simulate arctic mixed-phase boundary layer stratiform clouds observed during Surface Heat Budget of the Arctic (SHEBA) First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment (FIRE) Arctic Cloud Experiment (ACE). The microphysics scheme predicts the number concentrations and mixing ratios of four hydrometeor species (cloud droplets, small ice, rain, snow) and includes detailed treatments of droplet activation and ice nucleation from a prescribed distribution of aerosol obtained from observations. The model is able to reproduce many features of the observed mixed-phase cloud, including a near-adiabatic liquid water content profile located near the top of a well-mixed boundary layer, droplet number concentrations of about 200–250 cm−3 that were distributed fairly uniformly through the depth of the cloud, and continuous light snow falling from the cloud base to the surface. The impacts of droplet and ice nucleation, radiative transfer, turbulence, large-scale dynamics, and vertical resolution on the simulated mixed-phase stratiform cloud are examined. The cloud layer is largely self-maintained through strong cloud-top radiative cooling that exceeds 40 K day−1. It persists through extended periods of downward large-scale motion that tend to thin the layer and reduce water contents. Droplet activation rates are highest near cloud base, associated with subgrid vertical motion that is diagnosed from the predicted turbulence kinetic energy. A sensitivity test neglecting subgrid vertical velocity produces only weak activation and small droplet number concentrations (<90 cm−3). These results highlight the importance of parameterizing the impact of subgrid vertical velocity to generate local supersaturation for aerosol-droplet closure. The primary ice nucleation mode in the simulated mixed-phase cloud is contact freezing of droplets. Sensitivity tests indicate that the assumed number and size of contact nuclei can have a large impact on the evolution and characteristics of mixed-phase cloud, especially the partitioning of condensate between droplets and ice.

A New Double-Moment Microphysics Parameterization for Application in Cloud and Climate Models. Part I: Description

Abstract A new double-moment bulk microphysics scheme predicting the number concentrations and mixing ratios of four hydrometeor species (droplets, cloud ice, rain, snow) is described. New physically based parameterizations are developed for simulating homogeneous and heterogeneous ice nucleation, droplet activation, and the spectral index (width) of the droplet size spectra. Two versions of the scheme are described: one for application in high-resolution cloud models and the other for simulating grid-scale cloudiness in larger-scale models. The versions differ in their treatment of the supersaturation field and droplet nucleation. For the high-resolution approach, droplet nucleation is calculated from Kohler theory applied to a distribution of aerosol that activates at a given supersaturation. The resolved supersaturation field and condensation/deposition rates are predicted using a semianalytic approximation to the three-phase (vapor, ice, liquid) supersaturation equation. For the large-scale version of the scheme, it is assumed that the supersaturation field is not resolved and thus droplet activation is parameterized as a function of the vertical velocity and diabatic cooling rate. The vertical velocity includes a subgrid component that is parameterized in terms of the eddy diffusivity and mixing length. Droplet condensation is calculated using a quasi-steady, saturation adjustment approach. Evaporation/deposition onto the other water species is given by nonsteady vapor diffusion allowing excess vapor density relative to ice saturation.

Precipition Simulation Associated with Typhoon Sinlaku (2002) in Taiwan Area Using the LAPS Diabatic Initialization for MM5

A precipitation simulation associated with Typhoon Sinlaku using the fifth-generation Pennsylvania State University – National Center for Atmospheric Research Mesoscale Model (MM5) initialized diabatically with the Local Analysis and Prediction System (LAPS) is evaluated over the Taiwan area. Two purposes of this paper are to test the performance of the LAPS diabatic data assimilation technique and investigate the impact of the Doppler radar data on the short-range quantitative precipitation forecasts for the typhoon. Typhoon Sinlaku was selected because Doppler radar is one of the most important data sources for an accurate analysis of typhoons and Sinlaku was located close to the Wu-Fen-Shan (WSR-88D) Doppler radar station at north tip of Taiwan during part of its lifetime on 6-7 September 2002. The observed rainfall distribution associated with Sinlaku was closely related to the topography in northern Taiwan. Simulation results show that the MM5 initialized diabatically with LAPS has higher skill for precipitation simulation than the non-LAPS cold start experiment, especially for the higher thresholds in the early portion of model integration (?10 mm in 0-6 h). The assimilation of the Wu-Fen-Shan Doppler radar data played a key role in the improvement of precipitation simulation owing to improvement in the presentation of typhoon hydrometeorological features, such as clouds and the outer rainband, in the model initial conditions. The presence of these initial hydrometeor species had a beneficial impact on reduced precipitation spin-up time. The use of Doppler radar data also can enhance the forecast definition of typhoon structure and rainband simulations. However, these radar data only play a minor role on the typhoon track simulations. However, these radar data only play a minor role on the typhoon track simulation in this case study. Overall, the mesoscale model initialized diabatically with LAPS data assimilation shows improved capability on the typhoon short-range quantitative precipitation forecasts, especially when the Doppler radar data is included.

Sensitivity analysis of airborne microwave retrieval of stratiform precipitation to the melting layer parameterization

A sensitivity analysis for airborne microwave passive and active retrievals of hydrometeor profiles with respect to melting-layer parameterizations is carried out using synthetic data. The parameterizations of the melting layer include the effects of snow density, particle size distributions of hydrometeors as well as different permittivity models for mixed-phase particles. The hydrometeor profiles are obtained from a two-dimensional cloud ensemble model simulating a convective-stratiform rainfall event over the East Mediterranean sea. The statistical analysis reveals that the Maxwell-Garnett mixing formulas with water matrix and ice inclusions may be chosen for graupel, while a new permittivity model from Meneghini and Liao is suitable for snowflakes. A new Bayesian inversion framework is set up for both airborne microwave radiometric, radar, and combined radar-radiometer retrievals of hydrometeor profiles. Using the cloud profiles as control training data set, a numerical analysis was carried out by testing the inversion algorithms on each melting model data set. Results are discussed in terms of estimate sensitivity, defined as the statistical deviation bounds of the retrieved profiles from the control case ones. Relatively high values of estimate sensitivity to the melting-layer parameterizations are found for all hydrometeor species, especially for low snow-density and Maxwell-Garnett dielectric model test cases. The need of including various melting-layer characterizations within a comprehensive training data set and its implications for model-based Bayesian retrieval algorithms is finally argued and numerically tested.