scholarly journals Evaluation of a Flexible Single Ice Microphysics and a Gaussian Probability-Density-Function Macrophysics Scheme in a Single Column Model

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 638
Author(s):  
Jiabo Li ◽  
Xindong Peng ◽  
Xiaohan Li ◽  
Yanluan Lin ◽  
Wenchao Chu
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Water Content ◽  
Density Function ◽  
Liquid Water ◽  
Cloud Fraction ◽  
Gaussian Probability Density Function ◽  
Single Column ◽  
Ice Water Content ◽  
Ice Water

Scale-aware parameterizations of subgrid scale physics are essentials for multiscale atmospheric modeling. A single-ice (SI) microphysics scheme and Gaussian probability-density-function (Gauss-PDF) macrophysics scheme were implemented in the single-column Global-to-Regional Integrated forecast System model (SGRIST) and they were tested using the Tropical Warm Pool-International Cloud Experiment (TWP-ICE) and the Atmospheric Radiation Measurement Southern Great Plains Experiment in 1997 (ARM97). Their performance was evaluated against observations and other reference schemes. The new schemes simulated reasonable precipitation with proper fluctuations and peaks, ice, and liquid water contents, especially in lower levels below 650 hPa during the wet period in the TWP-ICE. The root mean square error (RMSE) of the simulated cloud fraction was below 200 hPa was 0.10/0.08 in the wet/dry period, which showed an obvious improvement when compared to that, i.e., 0.11/0.11 of original scheme. Accumulated ice water content below the melting level decreased by 21.57% in the SI. The well-matched average liquid water content displayed between the new scheme and observations, which was two times larger than those with the referencing scheme. In the ARM97 simulations, the SI scheme produced considerable ice water content, especially when convection was active. Low-level cloud fraction and precipitation extremes were improved using the Gauss-PDF scheme, which displayed the RMSE of cloud fraction of 0.02, being only half of the original schemes. The study indicates that the SI and Gauss-PDF schemes are promising approaches to simplify the microphysics process and improve the low-level cloud modeling.

scholarly journals Measuring ice- and liquid-water properties in mixed-phase cloud layers at the Leipzig Cloudnet station

2016 ◽  
Vol 16 (16) ◽  
pp. 10609-10620 ◽  
Author(s):  
Johannes Bühl ◽  
Patric Seifert ◽  
Alexander Myagkov ◽  
Albert Ansmann
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Mass Flux ◽  
Mixed Phase ◽  
Cloud Base ◽  
Doppler Velocity ◽  
Special Focus ◽  
Data Set ◽  
Ice Water Content ◽  
Ice Water

Abstract. An analysis of the Cloudnet data set collected at Leipzig, Germany, with special focus on mixed-phase layered clouds is presented. We derive liquid- and ice-water content together with vertical motions of ice particles falling through cloud base. The ice mass flux is calculated by combining measurements of ice-water content and particle Doppler velocity. The efficiency of heterogeneous ice formation and its impact on cloud lifetime is estimated for different cloud-top temperatures by relating the ice mass flux and the liquid-water content at cloud top. Cloud radar measurements of polarization and Doppler velocity indicate that ice crystals formed in mixed-phase cloud layers with a geometrical thickness of less than 350 m are mostly pristine when they fall out of the cloud.

scholarly journals A Phenomenological Epidemic Model Based On the Spatio-Temporal Evolution of a Gaussian Probability Density Function

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2000
Author(s):  
Domingo Benítez ◽  
Gustavo Montero ◽  
Eduardo Rodríguez ◽  
David Greiner ◽  
Albert Oliver ◽  
...  
Keyword(s):  
Standard Deviation ◽  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Epidemic Model ◽  
Temporal Evolution ◽  
Growth Models ◽  
Model Fit ◽  
Gaussian Probability Density Function ◽  
Spatio Temporal

A novel phenomenological epidemic model is proposed to characterize the state of infectious diseases and predict their behaviors. This model is given by a new stochastic partial differential equation that is derived from foundations of statistical physics. The analytical solution of this equation describes the spatio-temporal evolution of a Gaussian probability density function. Our proposal can be applied to several epidemic variables such as infected, deaths, or admitted-to-the-Intensive Care Unit (ICU). To measure model performance, we quantify the error of the model fit to real time-series datasets and generate forecasts for all the phases of the COVID-19, Ebola, and Zika epidemics. All parameters and model uncertainties are numerically quantified. The new model is compared with other phenomenological models such as Logistic Grow, Original, and Generalized Richards Growth models. When the models are used to describe epidemic trajectories that register infected individuals, this comparison shows that the median RMSE error and standard deviation of the residuals of the new model fit to the data are lower than the best of these growing models by, on average, 19.6% and 35.7%, respectively. Using three forecasting experiments for the COVID-19 outbreak, the median RMSE error and standard deviation of residuals are improved by the performance of our model, on average by 31.0% and 27.9%, respectively, concerning the best performance of the growth models.

Facts about the gaussian probability density function

1995 ◽  
Vol 59 (1-4) ◽  
pp. 289-306 ◽  
Author(s):  
B. Aldershof ◽  
J.S. Marron ◽  
B.U. Park ◽  
M.P. Wand
Keyword(s):  
Probability Density Function ◽  
Probability Density ◽  
Density Function ◽  
Gaussian Probability Density Function

Evaluation of Microphysics Schemes in Tropical Cyclones using Polarimetric Radar Observations: Convective Precipitation in an Outer Rainband

2021 ◽  
Author(s):  
Dan Wu ◽  
Fuqing Zhang ◽  
Xiaomin Chen ◽  
Alexander Ryzhkov ◽  
Kun Zhao ◽  
...  
Keyword(s):  
Water Content ◽  
Liquid Water ◽  
Liquid Water Content ◽  
Cloud Microphysics ◽  
Convective Precipitation ◽  
Polarimetric Radar ◽  
Error Source ◽  
Ice Water Content ◽  
Ice Water ◽  
Ice Phase

AbstractCloud microphysics significantly impact tropical cyclone precipitation. A prior polarimetric radar observational study by Wu et al. (2018) revealed the ice-phase microphysical processes as the dominant microphysics mechanisms responsible for the heavy precipitation in the outer rainband of Typhoon Nida (2016). To assess the model performance regarding microphysics, three double-moment microphysics schemes (i.e., Thompson, Morrison, and WDM6) are evaluated by performing a set of simulations of the same case. While these simulations capture the outer rainband’s general structure, microphysics in the outer rainbands are strikingly different from the observations. This discrepancy is primarily attributed to different microphysics parameterizations in these schemes, rather than the differences in large-scale environments due to cloud-environment interactions. An interesting finding in this study is that the surface rain rate or liquid water content is inversely proportional to the simulated mean raindrop sizes. The mass-weighted raindrop diameters are overestimated in the Morrison and Thompson schemes and underestimated in the WDM6 scheme, while the former two schemes produce lower liquid water content than WDM6. Compared with the observed ice water content based on a new polarimetric radar retrieval method, the ice water content above the environmental 0 °C level in all simulations is highly underestimated, especially at heights above 12 km MSL where large concentrations of small ice particles are typically prevalent. This finding suggests that the improper treatment of ice-phase processes is potentially an important error source in these microphysics schemes. Another error source identified in the WDM6 scheme is overactive warm-rain processes that produce excessive concentrations of smaller raindrops.

scholarly journals Diagnosing the average spatio-temporal impact of convective systems – Part 1: A methodology for evaluating climate models

2013 ◽  
Vol 13 (23) ◽  
pp. 12043-12058 ◽  
Author(s):  
M. S. Johnston ◽  
S. Eliasson ◽  
P. Eriksson ◽  
R. M. Forbes ◽  
K. Wyser ◽  
...  
Keyword(s):  
Water Content ◽  
Outgoing Longwave Radiation ◽  
Longwave Radiation ◽  
Cloud Fraction ◽  
Convective Systems ◽  
Ice Water Content ◽  
The Mean ◽  
Spatio Temporal ◽  
Cloud Ice ◽  
Ice Water

Abstract. An earlier method to determine the mean response of upper-tropospheric water to localised deep convective systems (DC systems) is improved and applied to the EC-Earth climate model. Following Zelinka and Hartmann (2009), several fields related to moist processes and radiation from various satellites are composited with respect to the local maxima in rain rate to determine their spatio-temporal evolution with deep convection in the central Pacific Ocean. Major improvements to the earlier study are the isolation of DC systems in time so as to prevent multiple sampling of the same event, and a revised definition of the mean background state that allows for better characterisation of the DC-system-induced anomalies. The observed DC systems in this study propagate westward at ~4 m s−1. Both the upper-tropospheric relative humidity and the outgoing longwave radiation are substantially perturbed over a broad horizontal extent and for periods >30 h. The cloud fraction anomaly is fairly constant with height but small maximum can be seen around 200 hPa. The cloud ice water content anomaly is mostly confined to pressures greater than 150 hPa and reaches its maximum around 450 hPa, a few hours after the peak convection. Consistent with the large increase in upper-tropospheric cloud ice water content, albedo increases dramatically and persists about 30 h after peak convection. Applying the compositing technique to EC-Earth allows an assessment of the model representation of DC systems. The model captures the large-scale responses, most notably for outgoing longwave radiation, but there are a number of important differences. DC systems appear to propagate eastward in the model, suggesting a strong link to Kelvin waves instead of equatorial Rossby waves. The diurnal cycle in the model is more pronounced and appears to trigger new convection further to the west each time. Finally, the modelled ice water content anomaly peaks at pressures greater than 500 hPa and in the upper troposphere between 250 hPa and 500 hPa, there is less ice than the observations and it does not persist as long after peak convection. The modelled upper-tropospheric cloud fraction anomaly, however, is of a comparable magnitude and exhibits a similar longevity as the observations.

scholarly journals Using Continuous Ground-Based Radar and Lidar Measurements for Evaluating the Representation of Clouds in Four Operational Models

2010 ◽  
Vol 49 (9) ◽  
pp. 1971-1991 ◽  
Author(s):  
Dominique Bouniol ◽  
Alain Protat ◽  
Julien Delanoë ◽  
Jacques Pelon ◽  
Jean-Marcel Piriou ◽  
...  
Keyword(s):  
Water Content ◽  
Large Scale ◽  
Climate Model ◽  
Weather Forecast ◽  
Cloud Fraction ◽  
Forecast Models ◽  
Ice Water Content ◽  
Model Cloud ◽  
Intermittent Sampling ◽  
Ice Water

Abstract The ability of four operational weather forecast models [ECMWF, Action de Recherche Petite Echelle Grande Echelle model (ARPEGE), Regional Atmospheric Climate Model (RACMO), and Met Office] to generate a cloud at the right location and time (the cloud frequency of occurrence) is assessed in the present paper using a two-year time series of observations collected by profiling ground-based active remote sensors (cloud radar and lidar) located at three different sites in western Europe (Cabauw, Netherlands; Chilbolton, United Kingdom; and Palaiseau, France). Particular attention is given to potential biases that may arise from instrumentation differences (especially sensitivity) from one site to another and intermittent sampling. In a second step the statistical properties of the cloud variables involved in most advanced cloud schemes of numerical weather forecast models (ice water content and cloud fraction) are characterized and compared with their counterparts in the models. The two years of observations are first considered as a whole in order to evaluate the accuracy of the statistical representation of the cloud variables in each model. It is shown that all models tend to produce too many high-level clouds, with too-high cloud fraction and ice water content. The midlevel and low-level cloud occurrence is also generally overestimated, with too-low cloud fraction but a correct ice water content. The dataset is then divided into seasons to evaluate the potential of the models to generate different cloud situations in response to different large-scale forcings. Strong variations in cloud occurrence are found in the observations from one season to the same season the following year as well as in the seasonal cycle. Overall, the model biases observed using the whole dataset are still found at seasonal scale, but the models generally manage to well reproduce the observed seasonal variations in cloud occurrence. Overall, models do not generate the same cloud fraction distributions and these distributions do not agree with the observations. Another general conclusion is that the use of continuous ground-based radar and lidar observations is definitely a powerful tool for evaluating model cloud schemes and for a responsive assessment of the benefit achieved by changing or tuning a model cloud parameterization.

scholarly journals Signatures of Tropical Cyclone Intensification in Satellite Measurements of Ice and Liquid Water Content

2017 ◽  
Vol 145 (10) ◽  
pp. 4081-4091 ◽  
Author(s):  
Shun-Nan Wu ◽  
Brian J. Soden
Keyword(s):  
Tropical Cyclone ◽  
Water Content ◽  
Liquid Water ◽  
Vertical Profile ◽  
Liquid Water Content ◽  
Intensity Change ◽  
Latent Heating ◽  
Cloud Water Content ◽  
Ice Water Content ◽  
Ice Water

This study examines how the structure and amount of cloud water content are associated with tropical cyclone (TC) intensity change using the CloudSat Tropical Cyclone (CSTC) dataset. Theoretical and modeling studies have demonstrated the importance of both the magnitude and vertical structure of latent heating in regulating TC intensity. However, the direct observations of the latent heat release and its vertical profile are scarce. The CSTC dataset provides the opportunity to infer the vertical profile of the latent heating from CloudSat retrievals of cloud ice water content (IWC) and liquid water content (LWC). It is found that strengthening storms have ~20% higher IWC than weakening storms, especially in the midtroposphere near the eyewall. These differences in IWC exist up to 24 h prior to an intensity change and are observed for all storm categories except major TCs. A similar analysis of satellite-observed rainfall rates indicates that strengthening storms have slightly higher rainfall rates 6 h prior to intensification. However, the rainfall signal is less robust than what is observed for IWC, and disappears for lead times greater than 6 h. Such precursors of TC intensity change provide observationally based metrics that may be useful in constraining model simulations of TC genesis and intensification.

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