scholarly journals Representation of microphysical processes in cloud-resolving models: Spectral (bin) microphysics versus bulk parameterization

2015 ◽  
Vol 53 (2) ◽  
pp. 247-322 ◽  
Author(s):  
A. P. Khain ◽  
K. D. Beheng ◽  
A. Heymsfield ◽  
A. Korolev ◽  
S. O. Krichak ◽  
...  
Keyword(s):  
Microphysical Processes ◽  
Bin Microphysics ◽  
Bulk Parameterization

Convective and turbulent motions in non-precipitating Cu. Part 1: Method of separation of convective and turbulent motions

2021 ◽  
Author(s):  
Mark Pinsky ◽  
Eshkol Eytan ◽  
Ilan Koren ◽  
Orit Altaratz ◽  
Alexander Khain
Keyword(s):  
Velocity Field ◽  
Vertical Velocity ◽  
Isotropic Turbulence ◽  
Convective Velocity ◽  
Cloud Dynamics ◽  
Wide Range ◽  
Homogeneous And Isotropic Turbulence ◽  
Microphysical Processes ◽  
Bin Microphysics ◽  
Parameter Values

AbstractAtmospheric motions in clouds and cloud surrounding have a wide range of scales, from several kilometers to centimeters. These motions have different impacts on cloud dynamics and microphysics. Larger-scale motions (hereafter referred to as convective motions) are responsible for mass transport over distances comparable with cloud scale, while motions of smaller scales (hereafter referred to as turbulent motions) are stochastic and responsible for mixing and cloud dilution. This distinction substantially simplifies the analysis of dynamic and microphysical processes in clouds. The present research is Part 1 of the study aimed at describing the method for separating the motion scale into a convective component and a turbulent component. An idealized flow is constructed, which is a sum of an initially prescribed field of the convective velocity with updrafts in the cloud core and downdrafts outside the core, and a stochastic turbulent velocity field obeying the turbulent properties, including the -5/3 law and the 2/3 structure function law. A wavelet method is developed allowing separation of the velocity field into the convective and turbulent components, with parameter values being in a good agreement with those prescribed initially. The efficiency of the method is demonstrated by an example of a vertical velocity field of a cumulus cloud simulated using SAM with bin-microphysics and resolution of 10 m. It is shown that vertical velocity in clouds indeed can be represented as a sum of convective velocity (forming zone of cloud updrafts and subsiding shell) and a stochastic velocity obeying laws of homogeneous and isotropic turbulence.

The adiabaticity of warm cumulus clouds simulated in high-resolution 

2021 ◽  
Author(s):  
Eshkol Eytan ◽  
Ilan Koren ◽  
Alexander Khain ◽  
Orit Altaratz ◽  
Mark Pinsky ◽  
...  
Keyword(s):  
High Resolution ◽  
Environmental Parameters ◽  
Liquid Water Content ◽  
Cumulus Clouds ◽  
Local Boundary ◽  
Aerosol Loading ◽  
Hebrew University ◽  
Key Variables ◽  
Microphysical Processes ◽  
Bin Microphysics

<p>The strong coupling between dynamic, thermodynamic, and microphysical processes and the numerous environmental parameters on which they depend makes clouds a highly complex system. Adiabatic regions (i.e., undiluted core) in the cloud allow to approximate in a simple way thermodynamic and microphysical profiles and provide local boundary conditions (i.e. core is a source of adiabatic values in each level). Mixing of the cloud with its environment affects both the cloud and the environmental properties. While environmental humidity, temperature and aerosol loading affect the clouds’ buoyancy and droplets size distribution (DSD), clouds simultaneously affect their surrounding via detrainment of droplets, humid air, and processed aerosols. Mixing occurs within a large spectrum of scales and leads to deviation of parts of the cloud from adiabaticity. The level of adiabaticity can be represented continuously by the adiabatic fraction (AF; defined as the ratio of the liquid water content to the theoretical adiabatic value). In this work we used the System of Atmosphere Modeling (SAM) with the Hebrew University Spectral Bin Microphysics to simulate a few isolated non-precipitating trade cumulus clouds (in different sizes and aerosol loading) in high resolution (10m). Passive tracer was added to all the simulations. We found cloudy volumes that contain both high tracer concentration and high AF (up to the clouds’ top), compared these two measures of mixing, and discuss their differences. The accuracy of AF calculations, based on different known methods is tested. For example, we show that the saturation adjustment assumption that is often used in AF calculations can lead to an underestimation of AF in pristine environments. This will mask microphysical effects and cause biases when comparing the adiabaticity of clouds under different aerosols loading. We show that the space spanned by the AF versus height in the cloud is a good measure for describing changes in cloud’s key variables in space and time (like temperature, updraft, and DSD properties). This space of AF vs height demonstrates how certain processes (e.g. in-cloud nucleation, mixing, evaporation, etc.) dominate different regions in the cloud (core, edge), and cause different dependence of the DSD on AF under different aerosols loading.</p>

scholarly journals Sensitivity of warm clouds to large particles in measured marine aerosol size distributions – a theoretical study

2020 ◽  
Author(s):  
Tom Dror ◽  
J. Michel Flores ◽  
Orit Altaratz ◽  
Guy Dagan ◽  
Zev Levin ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Aerosol Size Distribution ◽  
Cloud Base ◽  
Size Distributions ◽  
Marine Aerosol ◽  
Surface Precipitation ◽  
Monotonic Trend ◽  
Early Fall ◽  
Microphysical Processes ◽  
Bin Microphysics

Abstract. Aerosol size distribution has major effects on warm cloud processes. Here, we use newly acquired marine aerosol size distributions (MSD), measured in-situ over the open ocean during the Tara Pacific expedition (2016–2018), to examine how the total aerosol concentration (Ntot) and the shape of the MSD change warm cloud's properties. For this, we used a toy-model with detailed bin-microphysics. The changes in the MSDs affected the clouds' total mass and surface precipitation. In general, the clouds showed higher sensitivity to changes in Ntot than to changes in the MSD's shape, except for the case where the MSD contained giant and ultragiant cloud condensation nuclei (GCCN, UGCCN). For increased Ntot, most of the MSDs drove an expected non-monotonic trend of mass and precipitation. However, the addition of GCCN and UGCCN drastically changed this trend, such that surface rain saturated and the mass monotonically increased with Ntot. GCCN and UGCCN changed the interplay between the microphysical processes by triggering early initiation of collision-coalescence. The early fall-out of drizzle in those cases enhanced the evaporation below the cloud base. Testing the sensitivity of rain yield to GCCN and UGCCN revealed an enhancement of surface rain upon the addition of larger particles to the MSD, up to a certain particle size, when the addition of larger particles resulted in rain suppression. This finding suggests a physical lower bound can be defined for the size ranges of GCCN and UGCCN.

The Impact of Evaporation on Polarimetric Characteristics of Rain: Theoretical Model and Practical Implications

2010 ◽  
Vol 49 (6) ◽  
pp. 1247-1267 ◽  
Author(s):  
Matthew R. Kumjian ◽  
Alexander V. Ryzhkov
Keyword(s):  
Polarimetric Radar ◽  
Simple Method ◽  
Initial Shape ◽  
Mountainous Regions ◽  
Precipitation Estimates ◽  
Microphysical Processes ◽  
Bin Microphysics ◽  
Quantitative Precipitation Estimates ◽  
Differential Reflectivity ◽  
The Impact

Abstract Soon, the National Weather Service’s Weather Surveillance Radar-1988 Doppler (WSR-88D) network will be upgraded to allow dual-polarization capabilities. Therefore, it is imperative to understand and identify microphysical processes using the polarimetric variables. Though melting and size sorting of hydrometeors have been investigated, there has been relatively little focus devoted to the impacts of evaporation on the polarimetric characteristics of rainfall. In this study, a simple explicit bin microphysics one-dimensional rainshaft model is constructed to quantify the impacts of evaporation (neglecting the collisional processes) on vertical profiles of polarimetric radar variables in rain. The results of this model are applicable for light to moderate rain (<10 mm h−1). The modeling results indicate that the amount of evaporation that occurs in the subcloud layer is strongly dependent on the initial shape of the drop size distribution aloft, which can be assessed with polarimetric measurements. Understanding how radar-estimated rainfall rates may change in height due to evaporation is important for quantitative precipitation estimates, especially in regions far from the radar or in regions of complex terrain where low levels may not be adequately sampled. In addition to quantifying the effects of evaporation, a simple method of estimating the amount of evaporation that occurs in a given environment based on polarimetric radar measurements of the reflectivity factor ZH and differential reflectivity ZDR aloft is offered. Such a technique may be useful to operational meteorologists and hydrologists in estimating the amount of precipitation reaching the surface, especially in regions of poor low-level radar coverage such as mountainous regions or locations at large distances from the radar.

scholarly journals A ubiquitous ice size bias in simulations of tropical deep convection

2017 ◽  
Author(s):  
McKenna W. Stanford ◽  
Adam Varble ◽  
Ed Zipser ◽  
J. Walter Strapp ◽  
Delphine Leroy ◽  
...  
Keyword(s):  
Water Content ◽  
Deep Convection ◽  
Particle Sizes ◽  
Size Distributions ◽  
Particle Properties ◽  
Ice Water Content ◽  
High Bias ◽  
Microphysical Processes ◽  
Bin Microphysics ◽  
Ice Water

Abstract. The High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) joint field campaign produced aircraft retrievals of total condensed water content (TWC), hydrometeor particle size distributions (PSDs), and vertical velocity (w) in high ice water content regions of mature and decaying tropical mesoscale convective systems (MCSs). The resulting dataset is used here to explore causes of the commonly documented high bias in radar reflectivity within cloud-resolving simulations of deep convection. This bias has been linked to overly strong simulated convective updrafts lofting excessive condensate mass but is also modulated by parameterizations of hydrometeor size distributions, single particle properties, species separation, and microphysical processes. Observations are compared with three Weather Research and Forecasting model simulations of an observed MCS using differing microphysics while controlling for w, TWC, and temperature. Two bulk microphysics schemes (Thompson and Morrison) and one bin microphysics scheme (Fast Spectral Bin Microphysics) are compared. For temperatures between −10 °C and −40 °C and TWC > 1 g m−3 inside updrafts, all microphysics schemes produce median mass diameters (MMDs) that are generally larger than observed, and the precipitating ice species that controls this size bias varies by scheme, temperature, and w. Despite a much greater number of samples, all simulations fail to reproduce observed high TWC conditions (> 2 g m−3) between −20 °C and −40 °C in which only a small fraction of condensate mass is found in relatively large particle sizes greater than 1 mm in diameter. Although more mass is distributed to relatively large particle sizes relative to observed across all schemes when controlling for temperature, w, and TWC, differences with observations for a given particle size vary greatly between schemes. As a result, this bias is hypothesized to partly result from errors in parameterized hydrometeor PSD and single particle properties, but because it is present in all schemes, it may also partly result from errors in parameterized microphysical processes present in all schemes. Because of these ubiquitous ice size biases, microphysical parameterizations inherently produce a high bias in convective reflectivity for a wide range of temperatures, vertical velocities, and TWCs.

scholarly journals Sensitivity of warm clouds to large particles in measured marine aerosol size distributions – a theoretical study

2020 ◽  
Vol 20 (23) ◽  
pp. 15297-15306
Author(s):  
Tom Dror ◽  
J. Michel Flores ◽  
Orit Altaratz ◽  
Guy Dagan ◽  
Zev Levin ◽  
...  
Keyword(s):  
Cloud Condensation Nuclei ◽  
Aerosol Size Distribution ◽  
Cloud Base ◽  
Size Distributions ◽  
Marine Aerosol ◽  
Surface Precipitation ◽  
Monotonic Trend ◽  
Shallow Clouds ◽  
Microphysical Processes ◽  
Bin Microphysics

Abstract. Aerosol size distribution has major effects on warm cloud processes. Here, we use newly acquired marine aerosol size distributions (MSDs), measured in situ over the open ocean during the Tara Pacific expedition (2016–2018), to examine how the total aerosol concentration (Ntot) and the shape of the MSDs change warm clouds' properties. For this, we used a toy model with detailed bin microphysics initialized using three different atmospheric profiles, supporting the formation of shallow to intermediate and deeper warm clouds. The changes in the MSDs affected the clouds' total mass and surface precipitation. In general, the clouds showed higher sensitivity to changes in Ntot than to changes in the MSD's shape, except for the case where the MSD contained giant and ultragiant cloud condensation nuclei (GCCN, UGCCN). For increased Ntot (for the deep and intermediate profiles), most of the MSDs drove an expected non-monotonic trend of mass and precipitation (the shallow clouds showed only the decreasing part of the curves with mass and precipitation monotonically decreasing). The addition of GCCN and UGCCN drastically changed the non-monotonic trend, such that surface rain saturated and the mass monotonically increased with Ntot. GCCN and UGCCN changed the interplay between the microphysical processes by triggering an early initiation of collision–coalescence. The early fallout of drizzle in those cases enhanced the evaporation below the cloud base. Testing the sensitivity of rain yield to GCCN and UGCCN revealed an enhancement of surface rain upon the addition of larger particles to the MSD, up to a certain particle size, when the addition of larger particles resulted in rain suppression. This finding suggests a physical lower bound can be defined for the size ranges of GCCN and UGCCN.

scholarly journals A ubiquitous ice size bias in simulations of tropical deep convection

2017 ◽  
Vol 17 (15) ◽  
pp. 9599-9621 ◽  
Author(s):  
McKenna W. Stanford ◽  
Adam Varble ◽  
Ed Zipser ◽  
J. Walter Strapp ◽  
Delphine Leroy ◽  
...  
Keyword(s):  
Water Content ◽  
Deep Convection ◽  
Particle Sizes ◽  
Size Distributions ◽  
Particle Properties ◽  
Ice Water Content ◽  
High Bias ◽  
Microphysical Processes ◽  
Bin Microphysics ◽  
Ice Water

Abstract. The High Altitude Ice Crystals – High Ice Water Content (HAIC-HIWC) joint field campaign produced aircraft retrievals of total condensed water content (TWC), hydrometeor particle size distributions (PSDs), and vertical velocity (w) in high ice water content regions of mature and decaying tropical mesoscale convective systems (MCSs). The resulting dataset is used here to explore causes of the commonly documented high bias in radar reflectivity within cloud-resolving simulations of deep convection. This bias has been linked to overly strong simulated convective updrafts lofting excessive condensate mass but is also modulated by parameterizations of hydrometeor size distributions, single particle properties, species separation, and microphysical processes. Observations are compared with three Weather Research and Forecasting model simulations of an observed MCS using different microphysics parameterizations while controlling for w, TWC, and temperature. Two popular bulk microphysics schemes (Thompson and Morrison) and one bin microphysics scheme (fast spectral bin microphysics) are compared. For temperatures between −10 and −40 °C and TWC  >  1 g m−3, all microphysics schemes produce median mass diameters (MMDs) that are generally larger than observed, and the precipitating ice species that controls this size bias varies by scheme, temperature, and w. Despite a much greater number of samples, all simulations fail to reproduce observed high-TWC conditions ( >  2 g m−3) between −20 and −40 °C in which only a small fraction of condensate mass is found in relatively large particle sizes greater than 1 mm in diameter. Although more mass is distributed to large particle sizes relative to those observed across all schemes when controlling for temperature, w, and TWC, differences with observations are significantly variable between the schemes tested. As a result, this bias is hypothesized to partly result from errors in parameterized hydrometeor PSD and single particle properties, but because it is present in all schemes, it may also partly result from errors in parameterized microphysical processes present in all schemes. Because of these ubiquitous ice size biases, the frequently used microphysical parameterizations evaluated in this study inherently produce a high bias in convective reflectivity for a wide range of temperatures, vertical velocities, and TWCs.

scholarly journals What Polarimetric Weather Radars Offer to Cloud Modelers: Forward Radar Operators and Microphysical/Thermodynamic Retrievals

Atmosphere ◽  
2020 ◽  
Vol 11 (4) ◽  
pp. 362 ◽  
Author(s):  
Alexander V. Ryzhkov ◽  
Jeffrey Snyder ◽  
Jacob T. Carlin ◽  
Alexander Khain ◽  
Mark Pinsky
Keyword(s):  
Cloud Model ◽  
Weather Prediction ◽  
Primary Source ◽  
Polarimetric Radar ◽  
Microphysical Process ◽  
Weather Radars ◽  
Cloud Models ◽  
Cloud Modeling ◽  
Bin Microphysics ◽  
The Impact

The utilization of polarimetric weather radars for optimizing cloud models is a next frontier of research. It is widely understood that inadequacies in microphysical parameterization schemes in numerical weather prediction (NWP) models is a primary cause of forecast uncertainties. Due to its ability to distinguish between hydrometeors with different microphysical habits and to identify “polarimetric fingerprints” of various microphysical processes, polarimetric radar emerges as a primary source of needed information. There are two approaches to leverage this information for NWP models: (1) radar microphysical and thermodynamic retrievals and (2) forward radar operators for converting the model outputs into the fields of polarimetric radar variables. In this paper, we will provide an overview of both. Polarimetric measurements can be combined with cloud models of varying complexity, including ones with bulk and spectral bin microphysics, as well as simplified Lagrangian models focused on a particular microphysical process. Combining polarimetric measurements with cloud modeling can reveal the impact of important microphysical agents such as aerosols or supercooled cloud water invisible to the radar on cloud and precipitation formation. Some pertinent results obtained from models with spectral bin microphysics, including the Hebrew University cloud model (HUCM) and 1D models of melting hail and snow coupled with the NSSL forward radar operator, are illustrated in the paper.

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