scholarly journals Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions

2019 ◽  
Author(s):  
Guy Dagan ◽  
Philip Stier
Keyword(s):  
Initial Conditions ◽  
Cloud Droplet ◽  
Large Data ◽  
Data Set ◽  
Cloud Properties ◽  
Large Spread ◽  
The Difference ◽  
Ice Content ◽  
Aerosol Effects ◽  
Ice Water

Abstract. Aerosol effects on cloud properties and the atmospheric energy and radiation budgets are studied through ensemble simulations over two month-long periods during the NARVAL campaigns (December 2013 and August 2016). For each day, two simulations are conducted with low and high cloud droplet number concentrations (CDNC), representing low and high aerosol concentrations, respectively. This large data-set, which is based on a large spread of co-varying realistic initial conditions, enables robust identification of the effect of CDNC changes on cloud properties. We show that increases in CDNC drive a reduction in the top of atmosphere (TOA) net shortwave flux (more reflection) and a decrease in the lower tropospheric stability for all cases examined, while the TOA longwave flux and the liquid and ice water path changes are generally positive. However, changes in cloud fraction or precipitation, that could appear significant for a given day, are not as robustly affected, and, at least for the summer month, are not statistically distinguishable from zero. These results highlight the need for using large statistics of initial conditions for cloud–aerosol studies for identifying the significance of the response. In addition, we demonstrate the dependence of the aerosol effects on the season, as it is shown that the TOA net radiative effect is doubled during the winter month as compared to the summer month. By separating the simulations into different dominant cloud regimes, we show that the difference between the different months emerge due to the compensation of the longwave effect induced by an increase in ice content as compared to the shortwave effect of the liquid clouds. The CDNC effect on the longwave is stronger in the summer as the clouds are deeper and the atmosphere is more unstable.

scholarly journals Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions

2020 ◽  
Vol 20 (11) ◽  
pp. 6291-6303
Author(s):  
Guy Dagan ◽  
Philip Stier
Keyword(s):  
Initial Conditions ◽  
Cloud Droplet ◽  
Large Data ◽  
Data Set ◽  
Cloud Properties ◽  
Large Spread ◽  
The Difference ◽  
Ice Content ◽  
Aerosol Effects ◽  
Longwave Flux

Abstract. Aerosol effects on cloud properties and the atmospheric energy and radiation budgets are studied through ensemble simulations over two month-long periods during the NARVAL campaigns (Next-generation Aircraft Remote-Sensing for Validation Studies, December 2013 and August 2016). For each day, two simulations are conducted with low and high cloud droplet number concentrations (CDNCs), representing low and high aerosol concentrations, respectively. This large data set, which is based on a large spread of co-varying realistic initial conditions, enables robust identification of the effect of CDNC changes on cloud properties. We show that increases in CDNC drive a reduction in the top-of-atmosphere (TOA) net shortwave flux (more reflection) and a decrease in the lower-tropospheric stability for all cases examined, while the TOA longwave flux and the liquid and ice water path changes are generally positive. However, changes in cloud fraction or precipitation, that could appear significant for a given day, are not as robustly affected, and, at least for the summer month, are not statistically distinguishable from zero. These results highlight the need for using a large sample of initial conditions for cloud–aerosol studies for identifying the significance of the response. In addition, we demonstrate the dependence of the aerosol effects on the season, as it is shown that the TOA net radiative effect is doubled during the winter month as compared to the summer month. By separating the simulations into different dominant cloud regimes, we show that the difference between the different months emerges due to the compensation of the longwave effect induced by an increase in ice content as compared to the shortwave effect of the liquid clouds. The CDNC effect on the longwave flux is stronger in the summer as the clouds are deeper and the atmosphere is more unstable.

scholarly journals Do Women Get Fewer Votes? No.

2018 ◽  
Vol 52 (1) ◽  
pp. 201-210 ◽  
Author(s):  
Semra Sevi ◽  
Vincent Arel-Bundock ◽  
André Blais
Keyword(s):  
Political Parties ◽  
Gender Gap ◽  
Time Trends ◽  
Large Data ◽  
Study Data ◽  
Data Set ◽  
Men And Women ◽  
Women Candidates ◽  
Percentage Points ◽  
The Difference

AbstractWe study data on the gender of more than 21,000 unique candidates in all Canadian federal elections since 1921, when the first women ran for seats in Parliament. This large data set allows us to compute precise estimates of the difference in the electoral fortunes of men and women candidates. When accounting for party effects and time trends, we find that the difference between the vote shares of men and women is substantively negligible (±0.5 percentage point). This gender gap was larger in the 1920s (±2.5 percentage points), but it is now statistically indistinguishable from zero. Our results have important normative implications: political parties should recruit and promote more women candidates because they remain underrepresented in Canadian politics and because they do not suffer from a substantial electoral penalty.

Elevation gaps in fluvial sandbar deposition and their implications for paleodepth estimation

Geology ◽  
2020 ◽  
Vol 48 (7) ◽  
pp. 718-722
Author(s):  
Jason S. Alexander ◽  
Brandon J. McElroy ◽  
Snehalata Huzurbazar ◽  
Marissa L. Murr
Keyword(s):  
Large Data ◽  
Accurate Estimation ◽  
Systematic Bias ◽  
Flow Depth ◽  
Mean Flow ◽  
Data Set ◽  
Bed Elevation ◽  
The Difference ◽  
Base Elevation ◽  
Thickness Measurements

Abstract Accurate estimation of paleo–streamflow depth from outcrop is important for estimation of channel slopes, water discharges, sediment fluxes, and basin sizes of ancient river systems. Bar-scale inclined strata deposited from slipface avalanching on fluvial bar margins are assumed to be indicators of paleodepth insofar as their thickness approaches but does not exceed formative flow depths. We employed a unique, large data set from a prolonged bank-filling flood in the sandy, braided Missouri River (USA) to examine scaling between slipface height and measures of river depth during the flood. The analyses demonstrated that the most frequent slipface height observations underestimate study-reach mean flow depth at peak stage by a factor of 3, but maximum values are approximately equal to mean flow depth. At least 70% of the error is accounted for by the difference between slipface base elevation and mean bed elevation, while the difference between crest elevation and water surface accounts for ∼30%. Our analysis provides a scaling for bar-scale inclined strata formed by avalanching and suggests risk of systematic bias in paleodepth estimation if mean thickness measurements of these deposits are equated to mean bankfull depth.

Linguistic Proficiency: A Quantitative Approach to Immigrant and Heritage Speakers of Danish

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Jan Heegård Petersen ◽  
Gert Foget Hansen ◽  
Jacob Thøgersen ◽  
Karoline Kühl
Keyword(s):  
Speech Rate ◽  
Large Data ◽  
Foreign Born ◽  
Heritage Speakers ◽  
Statistical Tool ◽  
Data Set ◽  
Language Competence ◽  
The Difference ◽  
Linguistic Proficiency ◽  
Rare Opportunity

AbstractThis paper presents a corpus-based quantitative study on linguistic proficiency of approx. 300 immigrant and heritage speakers of Danish in North America and Argentina, aiming at the question whether linguistic proficiency is connected to ‘immigrant generation’ (i.e. the difference between speakers who migrated as adults with a fully acquired language competence and foreign-born heritage speakers) or the sociocultural setting, or both. The large data base at hand provides a rare opportunity to compare developments within the same minority language in different places, representing different sociocultural settings for the immigrant or heritage speakers and, accordingly, different language ecologies. The study relies on the Corpus of American Danish (1.6 million tokens, including both words and non-word utterances). Based on this data set, the paper explores the distribution of 13 linguistic and non-linguistic variables representing linguistic proficiency (i.e. Danish words, L2 words, word-internal codeswitching, type-token ratio, empty and filled pauses, self-interruption, lengthening, speech rate, word length, runlength and the ratio of main and subclauses) by applying Factor Analysis as a statistical tool. On an empirically solid basis, the paper concludes that (a) the sociolinguistic setting is the crucial factor in the development of linguistic proficiency and (b) linguistic proficiency is a non-universal cognitive phenomenon.

scholarly journals Analysis of aerosol effects on warm clouds over the Yangtze River Delta from multi-sensor satellite observations

2017 ◽  
Vol 17 (9) ◽  
pp. 5623-5641 ◽  
Author(s):  
Yuqin Liu ◽  
Gerrit de Leeuw ◽  
Veli-Matti Kerminen ◽  
Jiahua Zhang ◽  
Putian Zhou ◽  
...  
Keyword(s):  
Yangtze River ◽  
Aerosol Particles ◽  
Cloud Droplet ◽  
Yangtze River Delta ◽  
Atmospheric Conditions ◽  
Cloud Properties ◽  
Aerosol Effects ◽  
The Impact ◽  
Aerosol Types ◽  
The Yangtze River Delta

Abstract. Aerosol effects on low warm clouds over the Yangtze River Delta (YRD, eastern China) are examined using co-located MODIS, CALIOP and CloudSat observations. By taking the vertical locations of aerosol and cloud layers into account, we use simultaneously observed aerosol and cloud data to investigate relationships between cloud properties and the amount of aerosol particles (using aerosol optical depth, AOD, as a proxy). Also, we investigate the impact of aerosol types on the variation of cloud properties with AOD. Finally, we explore how meteorological conditions affect these relationships using ERA-Interim reanalysis data. This study shows that the relation between cloud properties and AOD depends on the aerosol abundance, with a different behaviour for low and high AOD (i.e. AOD < 0.35 and AOD > 0.35). This applies to cloud droplet effective radius (CDR) and cloud fraction (CF), but not to cloud optical thickness (COT) and cloud top pressure (CTP). COT is found to decrease when AOD increases, which may be due to radiative effects and retrieval artefacts caused by absorbing aerosol. Conversely, CTP tends to increase with elevated AOD, indicating that the aerosol is not always prone to expand the vertical extension. It also shows that the COT–CDR and CWP (cloud liquid water path)–CDR relationships are not unique, but affected by atmospheric aerosol loading. Furthermore, separation of cases with either polluted dust or smoke aerosol shows that aerosol–cloud interaction (ACI) is stronger for clouds mixed with smoke aerosol than for clouds mixed with dust, which is ascribed to the higher absorption efficiency of smoke than dust. The variation of cloud properties with AOD is analysed for various relative humidity and boundary layer thermodynamic and dynamic conditions, showing that high relative humidity favours larger cloud droplet particles and increases cloud formation, irrespective of vertical or horizontal level. Stable atmospheric conditions enhance cloud cover horizontally. However, unstable atmospheric conditions favour thicker and higher clouds. Dynamically, upward motion of air parcels can also facilitate the formation of thicker and higher clouds. Overall, the present study provides an understanding of the impact of aerosols on cloud properties over the YRD. In addition to the amount of aerosol particles (or AOD), evidence is provided that aerosol types and ambient environmental conditions need to be considered to understand the observed relationships between cloud properties and AOD.

scholarly journals Aerosol midlatitude cyclone indirect effects in observations and high-resolution simulations

2018 ◽  
Vol 18 (8) ◽  
pp. 5821-5846 ◽  
Author(s):  
Daniel T. McCoy ◽  
Paul R. Field ◽  
Anja Schmidt ◽  
Daniel P. Grosvenor ◽  
Frida A.-M. Bender ◽  
...  
Keyword(s):  
High Resolution ◽  
Rain Rate ◽  
Cloud Droplet ◽  
Moisture Flux ◽  
Conveyor Belt ◽  
Liquid Water Path ◽  
Aerosol Cloud ◽  
Cloud Properties ◽  
The Difference ◽  
Aerosol Cloud Interactions

Abstract. Aerosol–cloud interactions are a major source of uncertainty in inferring the climate sensitivity from the observational record of temperature. The adjustment of clouds to aerosol is a poorly constrained aspect of these aerosol–cloud interactions. Here, we examine the response of midlatitude cyclone cloud properties to a change in cloud droplet number concentration (CDNC). Idealized experiments in high-resolution, convection-permitting global aquaplanet simulations with constant CDNC are compared to 13 years of remote-sensing observations. Observations and idealized aquaplanet simulations agree that increased warm conveyor belt (WCB) moisture flux into cyclones is consistent with higher cyclone liquid water path (CLWP). When CDNC is increased a larger LWP is needed to give the same rain rate. The LWP adjusts to allow the rain rate to be equal to the moisture flux into the cyclone along the WCB. This results in an increased CLWP for higher CDNC at a fixed WCB moisture flux in both observations and simulations. If observed cyclones in the top and bottom tercile of CDNC are contrasted it is found that they have not only higher CLWP but also cloud cover and albedo. The difference in cyclone albedo between the cyclones in the top and bottom third of CDNC is observed by CERES to be between 0.018 and 0.032, which is consistent with a 4.6–8.3 Wm−2 in-cyclone enhancement in upwelling shortwave when scaled by annual-mean insolation. Based on a regression model to observed cyclone properties, roughly 60 % of the observed variability in CLWP can be explained by CDNC and WCB moisture flux.

Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

1982 ◽  
Vol 40 ◽  
pp. 74-75
Author(s):  
Jules S. Jaffe ◽  
Robert M. Glaeser
Keyword(s):  
Purple Membrane ◽  
Data Set ◽  
High Resolution Data ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fourier Techniques ◽  
Versus Protein ◽  
The Difference ◽  
Difference Fourier ◽  
Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

Some statistical and CI models to predict chaotic high-frequency financial data

2020 ◽  
Vol 39 (5) ◽  
pp. 6419-6430
Author(s):  
Dusan Marcek
Keyword(s):  
Time Series Data ◽  
Moving Average ◽  
Methodological Approach ◽  
Back Propagation ◽  
Large Data ◽  
Series Data ◽  
Data Set ◽  
Training Time ◽  
Optimal Population ◽  
Forecast Time

To forecast time series data, two methodological frameworks of statistical and computational intelligence modelling are considered. The statistical methodological approach is based on the theory of invertible ARIMA (Auto-Regressive Integrated Moving Average) models with Maximum Likelihood (ML) estimating method. As a competitive tool to statistical forecasting models, we use the popular classic neural network (NN) of perceptron type. To train NN, the Back-Propagation (BP) algorithm and heuristics like genetic and micro-genetic algorithm (GA and MGA) are implemented on the large data set. A comparative analysis of selected learning methods is performed and evaluated. From performed experiments we find that the optimal population size will likely be 20 with the lowest training time from all NN trained by the evolutionary algorithms, while the prediction accuracy level is lesser, but still acceptable by managers.

In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

2019 ◽  
Vol 21 (9) ◽  
pp. 662-669 ◽  
Author(s):  
Junnan Zhao ◽  
Lu Zhu ◽  
Weineng Zhou ◽  
Lingfeng Yin ◽  
Yuchen Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Regression Tree ◽  
Large Data ◽  
Thrombin Inhibitors ◽  
Coagulation Cascade ◽  
Gradient Boosting ◽  
Support Vector ◽  
Data Set ◽  
Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

scholarly journals Correlation between the structure and skin permeability of compounds

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ruolan Zeng ◽  
Jiyong Deng ◽  
Limin Dang ◽  
Xinliang Yu
Keyword(s):  
Large Data ◽  
Qsar Model ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Skin Permeability ◽  
Data Set ◽  
Test Set ◽  
Svm Algorithm ◽  
Svm Model ◽  
Toxicity Relationship

AbstractA three-descriptor quantitative structure–activity/toxicity relationship (QSAR/QSTR) model was developed for the skin permeability of a sufficiently large data set consisting of 274 compounds, by applying support vector machine (SVM) together with genetic algorithm. The optimal SVM model possesses the coefficient of determination R2 of 0.946 and root mean square (rms) error of 0.253 for the training set of 139 compounds; and a R2 of 0.872 and rms of 0.302 for the test set of 135 compounds. Compared with other models reported in the literature, our SVM model shows better statistical performance in a model that deals with more samples in the test set. Therefore, applying a SVM algorithm to develop a nonlinear QSAR model for skin permeability was achieved.

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