Urban Effect on Sea-Breeze-Initiated Rainfall: A Case Study for Seoul Metropolitan Area

We examined the sea-breeze-initiated rainfall in the Seoul Metropolitan area (SMA) on 6 July 2017 using the weather research and forecasting (WRF) model. The model captures the arrival of the sea breeze front (SBF), the development of afternoon rainfall in the SMA, and the location of the sea-breeze-initiated maximum rainfall in the northeastern SMA reasonably well but overestimates the subsequent rainfall. We conducted sensitivity tests to better understand the urban effect on the sea-breeze-initiated rainfall event. Through factor separation analysis, we first examined the explicit role of sea and urban effect on sea-breeze-initiated rainfall. The results show that the interaction of sea and urban effects cause rainfall in the northwest and northeast of the SMA, indicating that both urban heat island circulation (UHIC) and sea breeze play an important role in the study case’s rainfall. We further examined the relative role of urban roughness and anthropogenic heat on the sea-breeze-initiated rainfall through factor separation analysis. Both anthropogenic heat and urban roughness play a role in increasing precipitation in the northeastern area of the SMA, with a larger contribution of anthropogenic heat than urban roughness. The relationship between low-level convergence at the SBF and urban factors is discussed.

Multi-variate factorisation of numerical simulations

Factorisation (also known as “factor separation”) is widely used in the analysis of numerical simulations. It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system. There are many possible factorisation methods; here we discuss three previously proposed factorisations that have been applied in the field of climate modelling: the linear factorisation, the Stein and Alpert (1993) factorisation, and the Lunt et al. (2012) factorisation. We show that, when more than two variables are being considered, none of these three methods possess all four properties of “uniqueness”, “symmetry”, “completeness”, and “purity”. Here, we extend each of these factorisations so that they do possess these properties for any number of variables, resulting in three factorisations – the “linear-sum” factorisation, the “shared-interaction” factorisation, and the “scaled-residual” factorisation. We show that the linear-sum factorisation and the shared-interaction factorisation reduce to be identical in the case of four or fewer variables, and we conjecture that this holds for any number of variables. We present the results of the factorisations in the context of three past studies that used the previously proposed factorisations.

Impacts of Non-Local versus Local Moisture Sources on a Heavy (and Deadly) Rain Event in Israel

Motivated by poor forecasting of a deadly convective event within the Levant, the factor separation technique was used to investigate the impact of non-local versus local moisture sources on simulated precipitation and lightning rates in central and southern Israel on 25 and 26 April 2018. Both days saw unusually heavy rains, and it was hypothesized that antecedent precipitation on 25 April contributed to the development of deadly flooding late morning on the 26th, as well as strong lightning and heavy rains later the same day. Antecedent precipitation led to an increase in the precipitable water content and an overall increase in instability as measured by the Convective Available Potential Energy (CAPE). The deadly flood occurred in the area of the Tzafit river gorge (hereafter, Tzafit river), about 25 km southeast of the city of Dimona, a semi-arid region in the northeastern Negev desert. The heavy rains and strong lightning occurred throughout the Levant with local peaks in the vicinity of Jerusalem. Factor separation conducted in model simulations showed that local ground moisture sources had a large impact on the CAPE and subsequent precipitation and lightning rates in the area of Jerusalem, while non-local moisture sources enabled weak convection to occur over broad areas, with particularly strong convection in the area of the Tzafit river. The coupled impact of both moisture sources also led to localized enhanced areas of convective activity. The results suggest that forecast models for the Levant should endeavor to incorporate an accurate depiction of soil moisture to predict convective rain, especially during the typically drier spring-time season.

Reversible Pebble Games and the Relation Between Tree-Like and General Resolution Space

We show a new connection between the clause space measure in tree-like resolution and the reversible pebble game on graphs. Using this connection, we provide several formula classes for which there is a logarithmic factor separation between the clause space complexity measure in tree-like and general resolution. We also provide upper bounds for tree-like resolution clause space in terms of general resolution clause and variable space. In particular, we show that for any formula F, its tree-like resolution clause space is upper bounded by space$$(\pi)$$ ( π ) $$(\log({\rm time}(\pi))$$ ( log ( time ( π ) ) , where $$\pi$$ π is any general resolution refutation of F. This holds considering as space$$(\pi)$$ ( π ) the clause space of the refutation as well as considering its variable space. For the concrete case of Tseitin formulas, we are able to improve this bound to the optimal bound space$$(\pi)\log n$$ ( π ) log n , where n is the number of vertices of the corresponding graph

Multi-variate factor separation of numerical simulations

<p>Factor separation is widely used in the analysis of numerical simulations.  It allows changes in properties of a system to be attributed to changes in multiple variables associated with that system.  There are many possible factor separation methods; here we discuss three previously-proposed methods that have been applied in the field of climate modelling: the linear factor separation, the Stein and Alpert (1993) factor separation, and the Lunt et al (2012) factor separation.  We show that, when more than two variables are being considered, none of these three methods possess all four properties of 'uniqueness', 'symmetry', 'completeness', and 'purity'.  Here, we extend each of these methods so that they do possess these properties for any number of variables, resulting in three factor separation methods -- the 'linear-sum' , the 'shared-interaction', and the 'scaled-total'.  We show that the linear-sum method and the shared-interaction method reduce to be identical in the case of four or fewer variables, and we conjecture that this holds for any number of variables.  We present the results of the factor separations in the context of studies that used the previously-proposed methods.  This reveals that only the linear-sum/shared-interaction factor separation method possesses a fifth property -- `boundedness', and as such we recommend the use of this method in applications for which these properties are desirable.   The work described here is in review in Geoscientific Model Development - see https://gmd.copernicus.org/preprints/gmd-2020-69 .</p>

Factor Effects in Numerical Simulations

Numerical simulations allow users to adjust factor settings in experimental runs to understand how changes in those factors affect the output. However, it is not straightforward to analyze these outputs when multiple input factors are changed, especially simultaneously. For the atmospheric sciences, Stein and Alpert introduced a method they termed “factor separation” in order to separate the “pure contribution” of a factor from “pure interactions” of combinations of factors. Although factor separation appears to be used exclusively within the atmospheric sciences, other communities achieve a similar result by computing “main effects” via design of experiments methods. While both methods yield different estimates for the factor effects or contributions, we show that factor separation effects are identical to “simple effects” in the design of experiments literature. We demonstrate how both factor separation effects and design of experiments main effects correspond to multiple linear regression coefficients with different coding methods; thus, effect estimates produced by each method are equivalent through a variable transformation. We illustrate the application of both methods using a shallow-water simulation. This connection between factor separation and the design of experiments discipline extends factor separation to more applications by making available design of experiments methods for decreasing the computational cost and calculating effects for factors with more than two settings, both of which are limitations of factor separation.

Sequential Factor Separation for the Analysis of Numerical Model Simulations

Models are attractive tools to deepen the understanding of atmospheric and climate processes. In practice, such investigations often involve numerical experiments that switch on or off individual factors (such as latent heating, nonlinear coupling, or some climate forcing). However, as in general many factors can be considered, the analysis of these experiments is far from straightforward. In particular, as pointed out in an influential study on factor separation by Stein and Alpert, the analysis will often require the consideration of nonlinear interaction terms. In the current paper an alternative factor separation methodology is proposed and analyzed. Unlike the classical method, sequential factor separation (SFS) does not involve the derivation of the interaction terms but, rather, provides some uncertainty measure that addresses the quality of the separation. The main advantage of the proposed methodology is that in the case of n factors it merely requires 2n simulations (rather than 2n for the classical analysis). The paper provides an outline of the methodology, a detailed mathematical analysis, and a theoretical intercomparison against the classical methodology. In addition, an example and an intercomparison using regional climate model experiments with n = 3 factors are presented. The results relate to the Mediterranean amplification and demonstrate that—at least in the particular example considered—the two methodologies yield almost identical results and that the SFS is rather insensitive with respect to design choices.

Contributions of trans-boundary transport to summertime air quality in Beijing, China

In the present study, the WRF-CHEM model is used to evaluate the contributions of trans-boundary transport to the air quality in Beijing during a persistent air pollution episode from 5 to 14 July 2015 in Beijing–Tianjin–Hebei (BTH), China. Generally, the predicted temporal variations and spatial distributions of PM2.5 (fine particulate matter), O3 (ozone), and NO2 are in good agreement with observations in BTH. The WRF-CHEM model also reproduces reasonably well the temporal variations of aerosol species compared to measurements in Beijing. The factor separation approach is employed to evaluate the contributions of trans-boundary transport of non-Beijing emissions to the PM2.5 and O3 levels in Beijing. On average, in the afternoon during the simulation episode, the local emissions contribute 22.4 % to the O3 level in Beijing, less than 36.6 % from non-Beijing emissions. The O3 concentrations in Beijing are decreased by 5.1 % in the afternoon due to interactions between local and non-Beijing emissions. The non-Beijing emissions play a dominant role in the PM2.5 level in Beijing, with a contribution of 61.5 %, much higher than 13.7 %, from Beijing local emissions. The emission interactions between local and non-Beijing emissions enhance the PM2.5 concentrations in Beijing, with a contribution of 5.9 %. Therefore, the air quality in Beijing is generally determined by the trans-boundary transport of non-Beijing emissions during summertime, showing that the cooperation with neighboring provinces to mitigate pollutant emissions is key for Beijing to improve air quality.