scholarly journals Macroscopic impacts of cloud and precipitation processes on maritime shallow convection as simulated by a large eddy simulation model with bin microphysics

2015 ◽  
Vol 15 (2) ◽  
pp. 913-926 ◽  
Author(s):  
W. W. Grabowski ◽  
L.-P. Wang ◽  
T. V. Prabha
Keyword(s):  
Remote Sensing ◽  
Cloud Condensation Nuclei ◽  
Collision Kernel ◽  
Small Scale ◽  
Shallow Convection ◽  
Large Eddy Simulation Model ◽  
Large Eddy ◽  
Precipitation Processes ◽  
Bin Microphysics ◽  
The Impact

Abstract. This paper discusses impacts of cloud and precipitation processes on macrophysical properties of shallow convective clouds as simulated by a large eddy model applying warm-rain bin microphysics. Simulations with and without collision–coalescence are considered with cloud condensation nuclei (CCN) concentrations of 30, 60, 120, and 240 mg−1. Simulations with collision–coalescence include either the standard gravitational collision kernel or a novel kernel that includes enhancements due to the small-scale cloud turbulence. Simulations with droplet collisions were discussed in Wyszogrodzki et al. (2013) focusing on the impact of the turbulent collision kernel. The current paper expands that analysis and puts model results in the context of previous studies. Despite a significant increase of the drizzle/rain with the decrease of CCN concentration, enhanced by the effects of the small-scale turbulence, impacts on the macroscopic cloud field characteristics are relatively minor. Model results show a systematic shift in the cloud-top height distributions, with an increasing contribution of deeper clouds for stronger precipitating cases. We show that this is consistent with the explanation suggested in Wyszogrodzki et al. (2013); namely, the increase of drizzle/rain leads to a more efficient condensate offloading in the upper parts of the cloud field. A second effect involves suppression of the cloud droplet evaporation near cloud edges in low-CCN simulations, as documented in previous studies (e.g., Xue and Feingold, 2006). We pose the question whether the effects of cloud turbulence on drizzle/rain formation in shallow cumuli can be corroborated by remote sensing observations, for instance, from space. Although a clear signal is extracted from model results, we argue that the answer is negative due to uncertainties caused by the temporal variability of the shallow convective cloud field, sampling and spatial resolution of the satellite data, and overall accuracy of remote sensing retrievals.

scholarly journals Impacts of cloud and precipitation processes on maritime shallow convection as simulated by an LES model with bin microphysics

2014 ◽  
Vol 14 (13) ◽  
pp. 19837-19873 ◽  
Author(s):  
W. W. Grabowski ◽  
L.-P. Wang ◽  
T. V. Prabha
Keyword(s):  
Remote Sensing ◽  
Cloud Droplet ◽  
Convective Cloud ◽  
Collision Kernel ◽  
Small Scale ◽  
Shallow Convection ◽  
Precipitation Processes ◽  
Macrophysical Properties ◽  
Bin Microphysics ◽  
The Impact

Abstract. This paper discusses impacts of cloud and precipitation processes on macrophysical properties of shallow convective clouds as simulated by a large-eddy model applying warm-rain bin microphysics. Simulations with and without collision-coalescence are considered with CCN concentrations of 30, 60, 120, and 240 mg−1. Simulations with collision-coalescence include either the traditional gravitational collision kernel or a novel kernel that includes enhancements due to the small-scale cloud turbulence. Simulations with droplet collisions were discussed in Wyszogrodzki et al. (2013) focusing on the impact of the turbulent collision kernel. The current paper expands that analysis and puts model results in the context of previous studies. Despite a significant increase of the drizzle/rain with the decrease of CCN concentration, enhanced by the impact of the small-scale turbulence, impacts on the macroscopic cloud field characteristics are relatively minor. We document a clear feedback between cloud-scale processes and the mean environmental profiles that increases with the amount of drizzle/rain. Model results show a systematic shift in the cloud top height distributions, with an increasing contributions of deeper clouds and an overall increase of the number of cloudy columns for stronger precipitating cases. We argue that this is consistent with the explanation suggested in Wyszogrodzki et al. (2013) namely, the increase of drizzle/rain leading to a more efficient condensate off-loading in the upper parts of the cloud field. An additional effect involves suppressing cloud droplet evaporation near cloud edges in low-CCN simulations as documented in previous studies. We pose a question whether the effects of cloud turbulence on drizzle/rain formation can be corroborated by remote sensing observations, for instance, from space. Although a clear signal is extracted from model results, we argue that the answer is negative due to uncertainties caused by the temporal variability of the shallow convective cloud field, sampling and spatial resolution of the satellite data, and overall accuracy of remote sensing retrievals.

scholarly journals Turbulent collision-coalescence in maritime shallow convection

2013 ◽  
Vol 13 (16) ◽  
pp. 8471-8487 ◽  
Author(s):  
A. A. Wyszogrodzki ◽  
W. W. Grabowski ◽  
L.-P. Wang ◽  
O. Ayala
Keyword(s):  
Spatial Scales ◽  
Cloud Condensation Nuclei ◽  
Shallow Convection ◽  
Convective Clouds ◽  
Numerical Studies ◽  
Turbulence Intermittency ◽  
Cloud Dynamics ◽  
Large Eddy ◽  
Condensed Water ◽  
Bin Microphysics

Abstract. This paper discusses cloud simulations aiming at quantitative assessment of the effects of cloud turbulence on rain development in shallow ice-free convective clouds. Cloud fields from large-eddy simulations (LES) applying bin microphysics with the collection kernel enhanced by cloud turbulence are compared to those with the standard gravitational collection kernel. Simulations for a range of cloud condensation nuclei (CCN) concentrations are contrasted. Details on how the parameterized turbulent collection kernel is used in LES simulations are presented. Because of the disparity in spatial scales between the bottom-up numerical studies guiding the turbulent kernel development and the top-down LES simulations of cloud dynamics, we address the consequence of the turbulence intermittency in the unresolved range of scales on the mean collection kernel applied in LES. We show that intermittency effects are unlikely to play an important role in the current simulations. Highly-idealized single-cloud simulations are used to illustrate two mechanisms that operate in cloud field simulations. First, the microphysical enhancement leads to earlier formation of drizzle through faster autoconversion of cloud water into drizzle, as suggested by previous studies. Second, more efficient removal of condensed water from cloudy volumes when a turbulent collection kernel is used leads to an increased cloud buoyancy and enables clouds to reach higher levels. This is the dynamical enhancement. Both mechanisms operate in the cloud field simulations. The microphysical enhancement leads to the increased drizzle and rain inside clouds in simulations with high CCN. In low-CCN simulations with significant surface rainfall, dynamical enhancement leads to a larger contribution of deeper clouds to the entire cloud population, and results in a dramatically increased mean surface rain accumulation. These results call for future modeling and observational studies to corroborate the findings.

scholarly journals Turbulent collision-coalescence in maritime shallow convection

2013 ◽  
Vol 13 (4) ◽  
pp. 9217-9265 ◽  
Author(s):  
A. A. Wyszogrodzki ◽  
W. W. Grabowski ◽  
L.-P. Wang ◽  
O. Ayala
Keyword(s):  
Spatial Scales ◽  
Cloud Condensation Nuclei ◽  
Cloud Water ◽  
Collision Kernel ◽  
Shallow Convection ◽  
Water Path ◽  
Convective Clouds ◽  
Turbulence Intermittency ◽  
Cloud Dynamics ◽  
Bin Microphysics

Abstract. This paper discusses cloud simulations aiming at quantitative assessment of the effects of cloud turbulence on rain development in shallow ice-free convective clouds. Cloud fields from large-eddy simulations (LES) applying the bin microphysics with the collision kernel enhanced by cloud turbulence are compared to those with the standard gravitational collision kernel. Simulations for a range of cloud condensation nuclei (CCN) concentrations are contrasted. Details of the turbulent kernel and how it is used in LES simulations are presented. Because of the disparity in spatial scales between the bottom-up numerical studies guiding the turbulent kernel development and the top-down LES simulations of cloud dynamics, we address the consequence of the turbulence intermittency in the unresolved range of scales on the mean collision kernel applied in LES. We show that intermittency effects are unlikely to play an important role in the current simulations. Highly-idealized single-cloud simulations are used to illustrate two mechanisms that operate in cloud field simulations. First, the microphysical enhancement leads to earlier formation of drizzle through faster autoconversion of cloud water into drizzle, as suggested by previous studies. Second, more efficient removal of condensed water from cloudy volumes when a turbulent collection kernel is used leads to an increased cloud buoyancy and enables clouds to reach higher levels. This is the dynamical enhancement. Both mechanisms seem to operate in the cloud field simulations. The microphysical enhancement leads to the increased drizzle and rain inside clouds in simulations with high CCN. In low-CCN simulations with significant surface rainfall, dynamical enhancement allows maintenance of the cloud water path despite significant increase of the precipitation water path and dramatically increased mean surface rain accumulation. These results call for future modeling and observational studies to corroborate the findings.

scholarly journals Large-Eddy Simulations of Real-World Episodes in Complex Terrain Based on ERA-Reanalysis and Validated by Ground-Based Remote Sensing Data

2019 ◽  
Vol 147 (12) ◽  
pp. 4325-4343 ◽  
Author(s):  
Cornelius Hald ◽  
Matthias Zeeman ◽  
Patrick Laux ◽  
Matthias Mauder ◽  
Harald Kunstmann
Keyword(s):  
Remote Sensing ◽  
Boundary Conditions ◽  
Complex Terrain ◽  
Computing Time ◽  
Remote Sensing Data ◽  
Large Eddy Simulations ◽  
Small Scale ◽  
Sensing Data ◽  
Large Eddy ◽  
Local Weather

Abstract A computationally efficient and inexpensive approach for using the capabilities of large-eddy simulations (LES) to model small-scale local weather phenomena is presented. The setup uses the LES capabilities of the Weather Research and Forecasting Model (WRF-LES) on a single domain that is directly driven by reanalysis data as boundary conditions. The simulated area is an example for complex terrain, and the employed parameterizations are chosen in a way to represent realistic conditions during two 48-h periods while still keeping the required computing time around 105 CPU hours. We show by evaluating turbulence characteristics that the model results conform to results from typical LES. A comparison with ground-based remote sensing data from a triple Doppler-lidar setup, employed during the “ScaleX” campaigns, shows the grade of adherence of the results to the measured local weather conditions. The representation of mesoscale phenomena, including nocturnal low-level jets, strongly depends on the temporal and spatial resolution of the meteorological boundary conditions used to drive the model. Small-scale meteorological features that are induced by the terrain, such as katabatic flows, are present in the simulated output as well as in the measured data. This result shows that the four-dimensional output of WRF-LES simulations for a real area and real episode can be technically realized, allowing a more comprehensive and detailed view of the micrometeorological conditions than can be achieved with measurements alone.

The Strong Impact of Weak Horizontal Convergence on Continental Shallow Convection

2020 ◽  
Vol 77 (9) ◽  
pp. 3119-3137
Author(s):  
Marcin J. Kurowski ◽  
Wojciech W. Grabowski ◽  
Kay Suselj ◽  
João Teixeira
Keyword(s):  
Large Scale ◽  
Cloud Microphysics ◽  
Cloud Base ◽  
Small Scale ◽  
Strong Impact ◽  
Shallow Convection ◽  
Liquid Water Path ◽  
Strong Response ◽  
The Impact ◽  
Benchmark Solutions

Abstract Idealized large-eddy simulation (LES) is a basic tool for studying three-dimensional turbulence in the planetary boundary layer. LES is capable of providing benchmark solutions for parameterization development efforts. However, real small-scale atmospheric flows develop in heterogeneous and transient environments with locally varying vertical motions inherent to open multiscale interactive dynamical systems. These variations are often too subtle to detect them by state-of-the-art remote and in situ measurements, and are typically excluded from idealized simulations. The present study addresses the impact of weak [i.e., O(10−6) s−1] short-lived low-level large-scale convergence/divergence perturbations on continental shallow convection. The results show a strong response of shallow nonprecipitating convection to the applied weak large-scale dynamical forcing. Evolutions of CAPE, mean liquid water path, and cloud-top heights are significantly affected by the imposed convergence/divergence. In contrast, evolving cloud-base properties, such as the area coverage and mass flux, are only weakly affected. To contrast those impacts with microphysical sensitivity, the baseline simulations are perturbed assuming different observationally based cloud droplet number concentrations and thus different rainfall. For the tested range of microphysical perturbations, the imposed convergence/divergence provides significantly larger impact than changes in the cloud microphysics. Simulation results presented here provide a stringent test for convection parameterizations, especially important for large-scale models progressing toward resolving some nonhydrostatic effects.

Using remote sensing to monitor peatland fire occurrence and recovery

2020 ◽  
Author(s):  
Kirsten Lees ◽  
Josh Buxton ◽  
Chris Boulton ◽  
Tim Lenton
Keyword(s):  
Remote Sensing ◽  
Great Britain ◽  
Management Practice ◽  
Google Earth ◽  
Small Scale ◽  
Fire Occurrence ◽  
Wildfire Occurrence ◽  
Visual Record ◽  
Remote Sensing Methods ◽  
The Impact

<p>Many peatland areas in Great Britain are managed as grouse moors, with regular burns as part of management practice to encourage heather growth. Remote sensing has the potential to monitor the size, location, and impact of these burns using new fine resolution satellites such as Sentinel-2. Google Earth Engine allows large areas to be analysed at small scale over several years, building up a visual record of fire occurrence. This study uses satellite data to map managed burns on several areas of moorland around Great Britain, and uses remote sensing methods to assess the impact of this management strategy on vegetation cover. The project also considers how areas subject to managed burns react to wildfire occurrence, with the 2018 Saddleworth wildfire as a case study.</p>

scholarly journals Comparison of Observed and Simulated Drop Size Distributions from Large-Eddy Simulations with Bin Microphysics

2019 ◽  
Vol 147 (2) ◽  
pp. 477-493
Author(s):  
Mikael K. Witte ◽  
Patrick Y. Chuang ◽  
Orlando Ayala ◽  
Lian-Ping Wang ◽  
Graham Feingold
Keyword(s):  
Liquid Water ◽  
Drop Size ◽  
Potential Temperature ◽  
Liquid Water Content ◽  
Liquid Water Path ◽  
Cloud Drop ◽  
Large Eddy Simulation Model ◽  
Surface Precipitation ◽  
Large Eddy ◽  
Bin Microphysics

Abstract Two case studies of marine stratocumulus (one nocturnal and drizzling, the other daytime and nonprecipitating) are simulated by the UCLA large-eddy simulation model with bin microphysics for comparison with aircraft in situ observations. A high-bin-resolution variant of the microphysics is implemented for closer comparison with cloud drop size distribution (DSD) observations and a turbulent collision–coalescence kernel to evaluate the role of turbulence on drizzle formation. Simulations agree well with observational constraints, reproducing observed thermodynamic profiles (i.e., liquid water potential temperature and total moisture mixing ratio) as well as liquid water path. Cloud drop number concentration and liquid water content profiles also agree well insofar as the thermodynamic profiles match observations, but there are significant differences in DSD shape among simulations that cause discrepancies in higher-order moments such as sedimentation flux, especially as a function of bin resolution. Counterintuitively, high-bin-resolution simulations produce broader DSDs than standard resolution for both cases. Examination of several metrics of DSD width and percentile drop sizes shows that various discrepancies of model output with respect to the observations can be attributed to specific microphysical processes: condensation spuriously creates DSDs that are too wide as measured by standard deviation, which leads to collisional production of too many large drops. The turbulent kernel has the greatest impact on the low-bin-resolution simulation of the drizzling case, which exhibits greater surface precipitation accumulation and broader DSDs than the control (quiescent kernel) simulations. Turbulence effects on precipitation formation cannot be definitively evaluated using bin microphysics until the artificial condensation broadening issue has been addressed.

scholarly journals The Effect of Ice Nuclei Efficiency on Arctic Mixed-Phase Clouds from Large-Eddy Simulations

2017 ◽  
Vol 74 (12) ◽  
pp. 3901-3913 ◽  
Author(s):  
Shizuo Fu ◽  
Huiwen Xue
Keyword(s):  
Simulation Analysis ◽  
Mixed Phase ◽  
Ice Formation ◽  
Microphysics Scheme ◽  
Large Eddy Simulation Model ◽  
Eddy Simulation ◽  
Ice Nuclei ◽  
Large Eddy ◽  
Bin Microphysics ◽  
Mixed Phase Clouds

Abstract The effects of ice nuclei (IN) efficiency on the persistent ice formation in Arctic mixed-phase clouds (AMCs) are investigated using a large-eddy simulation model, coupled to a bin microphysics scheme with a prognostic IN formulation. In the three cases where the IN efficiency is high, ice formation and IN depletion are fast. When the IN concentration is 1 and 10 g−1, IN are completely depleted and the cloud becomes purely liquid phase before the end of the 24-h simulation. When the IN concentration is 100 g−1, the IN supply is sufficient but the liquid water is completely consumed so that the cloud dissipates quickly. In the three cases when the IN efficiency is low, ice formation is negligible in the first several hours but becomes significant as the temperature is decreased through longwave cooling. Before the end of the simulation, the cloud is in mixed phase when the IN concentration is 1 and 10 g−1 but dissipates when the IN concentration is 100 g−1. In the case where two types of IN are considered, ice formation persists throughout the simulation. Analysis shows that as the more efficient IN are continuously removed through ice formation, the less efficient IN gradually nucleate more ice crystals because the longwave cooling decreases the cloud temperature. This mechanism is further illustrated with a simple model. These results indicate that a spectrum of IN efficiency is necessary to maintain the persistent ice formation in AMCs.

scholarly journals River Flood Detection Using Passive Microwave Remote Sensing in a Data-Scarce Environment: A Case Study for Two River Basins in Malawi

2021 ◽  
Vol 9 ◽  
Author(s):  
Lone C. Mokkenstorm ◽  
Marc J. C. van den Homberg ◽  
Hessel Winsemius ◽  
Andreas Persson
Keyword(s):  
Remote Sensing ◽  
River Discharge ◽  
Spatial Scales ◽  
Time Lag ◽  
Microwave Remote Sensing ◽  
River Basins ◽  
Passive Microwave ◽  
Small Scale ◽  
Passive Microwave Remote Sensing ◽  
The Impact

Detecting and forecasting riverine floods is of paramount importance for adequate disaster risk management and humanitarian response. However, this is challenging in data-scarce and ungauged river basins in developing countries. Satellite remote sensing data offers a cost-effective, low-maintenance alternative to the limited in-situ data when training, parametrizing and operating flood models. Utilizing the signal difference between a measurement (M) and a dry calibration (C) location in Passive Microwave Remote Sensing (PMRS), the resulting rcm index simulates river discharge in the measurement pixel. Whilst this has been demonstrated for several river basins, it is as of yet unknown at what ratio of the spatial scales of the river width vs. the PMRS pixel resolution it remains effective in East-Africa. This study investigates whether PMRS imagery at 37 GHz can be effectively used for flood preparedness in two small-scale basins in Malawi, the Shire and North Rukuru river basins. Two indices were studied: The m index (rcm expressed as a magnitude relative to the average flow) and a new index that uses an additional wet calibration cell: rcmc. Furthermore, the results of both indices were benchmarked against discharge estimates from the Global Flood Awareness System (GloFAS). The results show that the indices have a similar seasonality as the observed discharge. For the Shire River, rcmc had a stronger correlation with discharge (ρ = 0.548) than m (ρ = 0.476), and the former predicts discharge more accurately (R2 = 0.369) than the latter (R2 = 0.245). In Karonga, the indices performed similarly. The indices do not perform well in detecting individual flood events when comparing the signal to a flood impact database. However, these results are sensitive to the threshold used and the impact database quality. The method presented simulated Shire River discharge and detected floods more accurately than GloFAS. It therefore shows potential for river monitoring in data-scarce areas, especially for rivers of a similar or larger spatial scale than the Shire River. Upstream pixels could not directly be used to forecast floods occurring downstream in these specific basins, as the time lag between discharge peaks did not provide sufficient warning time.

Comparison of Eulerian Bin and Lagrangian Particle-Based Microphysics in Simulations of Nonprecipitating Cumulus

2020 ◽  
Vol 77 (11) ◽  
pp. 3951-3970
Author(s):  
Wojciech W. Grabowski
Keyword(s):  
Cloud Condensation Nuclei ◽  
Cloud Droplet ◽  
Spectral Width ◽  
Diffusional Growth ◽  
Spectral Evolution ◽  
Numerical Diffusion ◽  
Fourfold Increase ◽  
Bin Microphysics ◽  
The Impact ◽  
Lagrangian Scheme

AbstractA single nonprecipitating cumulus congestus setup is applied to compare droplet spectra grown by the diffusion of water vapor in Eulerian bin and particle-based Lagrangian microphysics schemes. Bin microphysics represent droplet spectral evolution applying the spectral density function. In the Lagrangian microphysics, computational particles referred to as superdroplets are followed in time and space with each superdroplet representing a multiplicity of natural cloud droplets. The same cloud condensation nuclei (CCN) activation and identical representation of the droplet diffusional growth allow the comparison. The piggybacking method is used with the two schemes operating in a single simulation, one scheme driving the dynamics and the other one piggybacking the simulated flow. Piggybacking allows point-by-point comparison of droplet spectra predicted by the two schemes. The results show the impact of inherent limitations of the two microphysics simulation methods, numerical diffusion in the Eulerian scheme and a limited number of superdroplets in the Lagrangian scheme. Numerical diffusion in the Eulerian scheme results in a more dilution of the cloud upper half and thus smaller cloud droplet mean radius. The Lagrangian scheme typically has larger spatial fluctuations of droplet spectral properties. A significantly larger mean spectral width in the bin microphysics across the entire cloud depth is the largest difference between the two schemes. A fourfold increase of the number of superdroplets per grid volume and a twofold increase of the spectral resolution and thus the number of bins have small impact on the results and provide only minor changes to the comparison between simulated cloud properties.

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