Simulations of marine stratocumulus using a new microphysical parameterization scheme

1998 ◽  
Vol 47-48 ◽  
pp. 505-528 ◽  
Author(s):  
Graham Feingold ◽  
R.L. Walko ◽  
Bjorn Stevens ◽  
W.R. Cotton
Keyword(s):  
Parameterization Scheme ◽  
Marine Stratocumulus ◽  
Microphysical Parameterization ◽  
Microphysical Parameterization Scheme

scholarly journals More on the Scale Dependence of the Predictability of Precipitation Patterns: Extension to the 2009–13 CAPS Spring Experiment Ensemble Forecasts

2017 ◽  
Vol 145 (9) ◽  
pp. 3625-3646 ◽  
Author(s):  
Madalina Surcel ◽  
Isztar Zawadzki ◽  
M. K. Yau ◽  
Ming Xue ◽  
Fanyou Kong
Keyword(s):  
Large Scale ◽  
Forecast Error ◽  
Quasi Equilibrium ◽  
Relative Role ◽  
Ensemble Forecasts ◽  
Adjustment Time ◽  
Convective Adjustment ◽  
Microphysical Parameterization ◽  
Microphysical Parameterization Scheme

This paper analyzes the scale and case dependence of the predictability of precipitation in the Storm-Scale Ensemble Forecast (SSEF) system run by the Center for Analysis and Prediction of Storms (CAPS) during the NOAA Hazardous Weather Testbed Spring Experiments of 2008–13. The effect of different types of ensemble perturbation methodologies is quantified as a function of spatial scale. It is found that uncertainties in the large-scale initial and boundary conditions and in the model microphysical parameterization scheme can result in the loss of predictability at scales smaller than 200 km after 24 h. Also, these uncertainties account for most of the forecast error. Other types of ensemble perturbation methodologies were not found to be as important for the quantitative precipitation forecasts (QPFs). The case dependences of predictability and of the sensitivity to the ensemble perturbation methodology were also analyzed. Events were characterized in terms of the extent of the precipitation coverage and of the convective-adjustment time scale [Formula: see text], an indicator of whether convection is in equilibrium with the large-scale forcing. It was found that events characterized by widespread precipitation and small [Formula: see text] values (representative of quasi-equilibrium convection) were usually more predictable than nonequilibrium cases. No significant statistical relationship was found between the relative role of different perturbation methodologies and precipitation coverage or [Formula: see text].

scholarly journals Discrimination between Winter Precipitation Types Based on Spectral-Bin Microphysical Modeling

2016 ◽  
Vol 55 (8) ◽  
pp. 1747-1761 ◽  
Author(s):  
Heather Dawn Reeves ◽  
Alexander V. Ryzhkov ◽  
J. Krause
Keyword(s):  
Drop Size ◽  
Winter Precipitation ◽  
Horizontal Distribution ◽  
Ensemble Model ◽  
Freezing Rain ◽  
New Approach ◽  
Water Fraction ◽  
Precipitation Type ◽  
Microphysical Parameterization ◽  
Microphysical Parameterization Scheme

AbstractA new approach for distinguishing precipitation types at the surface, the spectral bin classifier (SBC), is presented. This algorithm diagnoses six categories of precipitation: rain (RA), snow (SN), a rain–snow mix (RASN), freezing rain (FZRA), ice pellets (PL), and a freezing rain–ice pellet mix (FZRAPL). It works by calculating the liquid-water fraction fw for a spectrum of falling hydrometeors given a prescribed temperature T and relative humidity profile. Demonstrations of the SBC output show that it provides reasonable estimates of fw of various-sized hydrometeors for the different categories of precipitation. The SBC also faithfully represents the horizontal distribution of precipitation type inasmuch as the model analyses and surface observations are consistent with each other. When applied to a collection of observed soundings associated with RA, SN, FZRA, and PL, the classifier has probabilities of detection (PODs) that range from 62.4% to 98.3%. The PODs do decrease when the effects of model uncertainty are accounted for. This decrease is modest for RA, SN, and PL but is large for FZRA as a result of the fact that this form of precipitation is very sensitive to small changes in the thermal profile. The effects of the choice of the degree of riming above the melting layer, the drop size distribution, and the assumed temperature at which ice nucleates are also examined. Recommendations on how to mitigate all forms of uncertainty are discussed. These include the use of dual-polarized radar observations, incorporating output from the microphysical parameterization scheme, and the use of ensemble model forecasts.

scholarly journals Comparison between Dual-Doppler and EnKF Storm-Scale Wind Analyses: The 29–30 May 2004 Geary, Oklahoma, Supercell Thunderstorm

2013 ◽  
Vol 141 (5) ◽  
pp. 1612-1628 ◽  
Author(s):  
Corey K. Potvin ◽  
Louis J. Wicker ◽  
Michael I. Biggerstaff ◽  
Daniel Betten ◽  
Alan Shapiro
Keyword(s):  
Doppler Radar ◽  
Three Dimensional ◽  
Radar Observations ◽  
Field Gradients ◽  
Single Moment ◽  
Supercell Thunderstorm ◽  
Model Initialization ◽  
Wind Retrievals ◽  
Microphysical Parameterization ◽  
Microphysical Parameterization Scheme

Abstract Kinematical analyses of storm-scale mobile radar observations are critical to advancing our understanding of supercell thunderstorms. Maximizing the accuracy of these analyses, and characterizing the uncertainty in ensuing conclusions about storm structure and processes, requires knowledge of the error characteristics of different retrieval techniques under different observational scenarios. Using storm-scale mobile radar observations of a tornadic supercell, this study examines the impacts on ensemble Kalman filter (EnKF) wind analyses of the number of available radars (one versus two), uncertainty in the model-initialization sounding, the sophistication of the microphysical parameterization scheme (double versus single moment), and assimilating reflectivity observations. The relative accuracy of three-dimensional variational data assimilation (3DVAR) dual-Doppler wind retrievals and single- and dual-radar EnKF wind analyses of the supercell is also explored. The results generally reinforce the findings of a previous study that used observing system simulation experiments to explore the same issues. Both studies suggest that single-radar EnKF wind analyses can be very useful once enough data have been assimilated, but that subsequent analyses that operate on the retrieved wind field gradients should be interpreted with caution. In the present study, severe errors appear to occur in computed Lagrangian circulation time series, imperiling interpretation of the underlying dynamics. This result strongly suggests that dual- and multiple-Doppler radar deployment strategies continue to be used in mobile field campaigns.

scholarly journals A Simple Parameterization Scheme for Subtropical Marine Stratocumulus

SOLA ◽  
2006 ◽  
Vol 2 ◽  
pp. 17-20 ◽  
Author(s):  
Hideaki Kawai ◽  
Toshiro Inoue
Keyword(s):  
Parameterization Scheme ◽  
Marine Stratocumulus

scholarly journals Factors Controlling Rapid Stratocumulus Cloud Thinning

2014 ◽  
Vol 71 (2) ◽  
pp. 655-664 ◽  
Author(s):  
J. J. van der Dussen ◽  
S. R. de Roode ◽  
A. P. Siebesma
Keyword(s):  
Moisture Flux ◽  
Steady State Solution ◽  
Cloud Layer ◽  
Cloud Base ◽  
Liquid Water Path ◽  
Marine Stratocumulus ◽  
Buoyancy Reversal ◽  
Stratocumulus Clouds ◽  
Budget Equation ◽  
Heat And Moisture

Abstract The relationship between the inversion stability and the liquid water path (LWP) tendency of a vertically well-mixed, adiabatic stratocumulus cloud layer is investigated in this study through the analysis of the budget equation for the LWP. The LWP budget is mainly determined by the turbulent fluxes of heat and moisture at the top and the base of the cloud layer, as well as by the source terms due to radiation and precipitation. Through substitution of the inversion stability parameter κ into the budget equation, it immediately follows that the LWP tendency will become negative for increasing values of κ due to the entrainment of increasingly dry air. Large κ values are therefore associated with strong cloud thinning. Using the steady-state solution for the LWP, an equilibrium value κeq is formulated, beyond which the stratocumulus cloud will thin. The Second Dynamics and Chemistry of Marine Stratocumulus field study (DYCOMS-II) is used to illustrate that, depending mainly on the magnitude of the moisture flux at cloud base, stratocumulus clouds can persist well within the buoyancy reversal regime.

scholarly journals Machine Learning Comparison and Parameter Setting Methods for the Detection of Dump Sites for Construction and Demolition Waste Using the Google Earth Engine

2021 ◽  
Vol 13 (4) ◽  
pp. 787
Author(s):  
Lei Zhou ◽  
Ting Luo ◽  
Mingyi Du ◽  
Qiang Chen ◽  
Yang Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Google Earth ◽  
Construction And Demolition Waste ◽  
Parameterization Scheme ◽  
Classification Methods ◽  
Demolition Waste ◽  
Optimal Method ◽  
Identification Method ◽  
Google Earth Engine

Machine learning has been successfully used for object recognition within images. Due to the complexity of the spectrum and texture of construction and demolition waste (C&DW), it is difficult to construct an automatic identification method for C&DW based on machine learning and remote sensing data sources. Machine learning includes many types of algorithms; however, different algorithms and parameters have different identification effects on C&DW. Exploring the optimal method for automatic remote sensing identification of C&DW is an important approach for the intelligent supervision of C&DW. This study investigates the megacity of Beijing, which is facing high risk of C&DW pollution. To improve the classification accuracy of C&DW, buildings, vegetation, water, and crops were selected as comparative training samples based on the Google Earth Engine (GEE), and Sentinel-2 was used as the data source. Three classification methods of typical machine learning algorithms (classification and regression trees (CART), random forest (RF), and support vector machine (SVM)) were selected to classify the C&DW from remote sensing images. Using empirical methods, the experimental trial method, and the grid search method, the optimal parameterization scheme of the three classification methods was studied to determine the optimal method of remote sensing identification of C&DW based on machine learning. Through accuracy evaluation and ground verification, the overall recognition accuracies of CART, RF, and SVM for C&DW were 73.12%, 98.05%, and 85.62%, respectively, under the optimal parameterization scheme determined in this study. Among these algorithms, RF was a better C&DW identification method than were CART and SVM when the number of decision trees was 50. This study explores the robust machine learning method for automatic remote sensing identification of C&DW and provides a scientific basis for intelligent supervision and resource utilization of C&DW.

scholarly journals A Modeling Case Study of Mixed-Phase Clouds over the Southern Ocean and Tasmania

2010 ◽  
Vol 138 (3) ◽  
pp. 839-862 ◽  
Author(s):  
Anthony E. Morrison ◽  
Steven T. Siems ◽  
Michael J. Manton ◽  
Alex Nazarov
Keyword(s):  
Southern Ocean ◽  
Supercooled Liquid ◽  
Mixed Phase ◽  
Weather Research And Forecasting ◽  
Marine Stratocumulus ◽  
Cloud Structure ◽  
In Situ Observations ◽  
Cloud Tops ◽  
Mixed Phase Clouds

Abstract The cloud structure associated with two frontal passages over the Southern Ocean and Tasmania is investigated. The first event, during August 2006, is characterized by large quantities of supercooled liquid water and little ice. The second case, during October 2007, is more mixed phase. The Weather Research and Forecasting model (WRFV2.2.1) is evaluated using remote sensed and in situ observations within the post frontal air mass. The Thompson microphysics module is used to describe in-cloud processes, where ice is initiated using the Cooper parameterization at temperatures lower than −8°C or at ice supersaturations greater than 8%. The evaluated cases are then used to numerically investigate the prevalence of supercooled and mixed-phase clouds over Tasmania and the ocean to the west. The simulations produce marine stratocumulus-like clouds with maximum heights of between 3 and 5 km. These are capped by weak temperature and strong moisture inversions. When the inversion is at temperatures warmer than −10°C, WRF produces widespread supercooled cloud fields with little glaciation. This is consistent with the limited in situ observations. When the inversion is at higher altitudes, allowing cooler cloud tops, glaciated (and to a lesser extent mixed phase) clouds are more common. The simulations are further explored to evaluate any orographic signature within the cloud structure over Tasmania. No consistent signature is found between the two cases.

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