Regional Simulation of the October and November MJO Events Observed during the CINDY/DYNAMO Field Campaign at Gray Zone Resolution

This study investigates the October and November MJO events observed during the Cooperative Indian Ocean Experiment on Intraseasonal Variability in the Year 2011 (CINDY)/Dynamics of the MJO (DYNAMO) field campaign through cloud-permitting numerical simulations. The simulations are compared to multiple observational datasets. The control simulation at 9-km horizontal grid spacing captures the slow eastward progression of both the October and November MJO events in surface precipitation, outgoing longwave radiation, zonal wind, humidity, and large-scale vertical motion. The vertical motion shows weak ascent in the leading edge of the MJO envelope, followed by deep ascent during the peak precipitation stage and trailed by a broad second baroclinic mode structure with ascent in the upper troposphere and descent in the lower troposphere. Both the simulation and the observations also show slow northward propagation components and tropical cyclone–like vortices after the passage of the MJO active phase. Comparison with synthesized observations from the northern sounding array shows that the model simulates the passage of the two MJO events over the sounding array region well. Sensitivity experiments to SST indicate that daily SST plays an important role for the November MJO event, but much less so for the October event. Analysis of the moist static energy (MSE) budget shows that both advection and diabatic processes (i.e., surface fluxes and radiation) contribute to the development of the positive MSE anomaly in the active phase, but their contributions differ by how much they lead the precipitation peak. In comparison to the observational datasets used here, the model simulation may have a stronger surface flux feedback and a weaker radiative feedback. The normalized gross moist stability in the simulations shows an increase from near-zero values to ~0.8 during the active phase, similar to what is found in the observational datasets

Small RNAs Targeting Transcription Start Site Induce Heparanase Silencing through Interference with Transcription Initiation in Human Cancer Cells

Heparanase (HPA), an endo-h-D-glucuronidase that cleaves the heparan sulfate chain of heparan sulfate proteoglycans, is overexpressed in majority of human cancers. Recent evidence suggests that small interfering RNA (siRNA) induces transcriptional gene silencing (TGS) in human cells. In this study, transfection of siRNA against −9/+10 bp (siH3), but not −174/−155 bp (siH1) or −134/−115 bp (siH2) region relative to transcription start site (TSS) locating at 101 bp upstream of the translation start site, resulted in TGS of heparanase in human prostate cancer, bladder cancer, and gastric cancer cells in a sequence-specific manner. Methylation-specific PCR and bisulfite sequencing revealed no DNA methylation of CpG islands within heparanase promoter in siH3-transfected cells. The TGS of heparanase did not involve changes of epigenetic markers histone H3 lysine 9 dimethylation (H3K9me2), histone H3 lysine 27 trimethylation (H3K27me3) or active chromatin marker acetylated histone H3 (AcH3). The regulation of alternative splicing was not involved in siH3-mediated TGS. Instead, siH3 interfered with transcription initiation via decreasing the binding of both RNA polymerase II and transcription factor II B (TFIIB), but not the binding of transcription factors Sp1 or early growth response 1, on the heparanase promoter. Moreover, Argonaute 1 and Argonaute 2 facilitated the decreased binding of RNA polymerase II and TFIIB on heparanase promoter, and were necessary in siH3-induced TGS of heparanase. Stable transfection of the short hairpin RNA construct targeting heparanase TSS (−9/+10 bp) into cancer cells, resulted in decreased proliferation, invasion, metastasis and angiogenesis of cancer cells in vitro and in athymic mice models. These results suggest that small RNAs targeting TSS can induce TGS of heparanase via interference with transcription initiation, and significantly suppress the tumor growth, invasion, metastasis and angiogenesis of cancer cells

Intrinsic versus Practical Limits of Atmospheric Predictability and the Significance of the Butterfly Effect

Abstract Limits of intrinsic versus practical predictability are studied through examining multiscale error growth dynamics in idealized baroclinic waves with varying degrees of convective instabilities. In the dry experiment free of moist convection, error growth is controlled primarily by baroclinic instability under which forecast accuracy is inversely proportional to the amplitude of the baroclinically unstable initial-condition error (thus the prediction can be continuously improved without limit through reducing the initial error). Under the moist environment with strong convective instability, rapid upscale growth from moist convection leads to the forecast error being increasingly less sensitive to the scale and amplitude of the initial perturbations when the initial-error amplitude is getting smaller; these diminishing returns may ultimately impose a finite-time barrier to the forecast accuracy (limit of intrinsic predictability and the so-called “butterfly effect”). However, if the initial perturbation is sufficiently large in scale and amplitude (as for most current-day operational models), the baroclinic growth of large-scale finite-amplitude initial error will control the forecast accuracy for both dry and moist baroclinic waves; forecast accuracy can be improved (thus the limit of practical predictability can be extended) through the reduction of initial-condition errors, especially those at larger scales. Regardless of the initial-perturbation scales and amplitude, the error spectrum will adjust toward the slope of the background flow. Inclusion of strong moist convection changes the mesoscale kinetic energy spectrum slope from −3 to ~−5/3. This change further highlights the importance of convection and the relevance of the butterfly effect to both the intrinsic and practical limits of atmospheric predictability, especially at meso- and convective scales.</jats:p

A New Theoretical Framework for Understanding Multiscale Atmospheric Predictability

AbstractHere we present a new theoretical framework that connects the error growth behavior in numerical weather prediction (NWP) with the atmospheric kinetic energy spectrum. Building on previous studies, our newly proposed framework applies to the canonical observed atmospheric spectrum that has a −3 slope at synoptic scales and a −5/3 slope at smaller scales. Based on this realistic hybrid energy spectrum, our new experiment using hybrid numerical models provides reasonable estimations for the finite predictable ranges at different scales. We further derive an analytical equation that helps understand the error growth behavior. Despite its simplicity, this new analytical error growth equation is capable of capturing the results of previous comprehensive theoretical and observational studies of atmospheric predictability. The success of this new theoretical framework highlights the combined effects of quasi-two-dimensional dynamics at synoptic scales (−3 slope) and three-dimensional turbulence-like small-scale chaotic flows (−5/3 slope) in dictating the error growth. It is proposed that this new framework could serve as a guide for understanding and estimating the predictability limit in the real world.</jats:p

Modelowanie niezawodnościowe dwuwymiarowych danych dotyczących okresu eksploatacji z wykorzystaniem dwuwymiarowego rozkładu Weibulla z badań nad wywrotkami kopalnianymi

An engineering system can exhibit two- or multi-dimensions in its lifetime. As the classical univariate distribution cannot model this multi-dimensional characteristic, it is necessary to extend it to multivariate distribution in order to capture the multi-dimensional characteristics. This paper proposes a bivariate Weibull distribution that combines two classical Weibull models by a common exponent. The common exponent can represent the correlation between the two dimensions. A ratio likelihood test is proposed to test the significance of the correlation between the two dimensions. To solve the parameter estimation problem, this paper suggests a Bayesian method. Moreover, a goodness of fit test method is developed to visually check the fitness of the model. A case study considering mining trucks is presented to apply the bivariate Weibull distribution to model the two-dimensional life data.Systemy inżynieryjne można charakteryzować za pomocą dwóch lub więcej wymiarów dotyczących okresu ich eksploatacji (np. przebieg i czas pracy pojazdu). Ponieważ klasyczny rozkład jednowymiarowy nie wystarcza do zamodelowania tej wielowymiarowej charakterystyki, konieczne jest wykorzystanie rozkładu wielowymiarowego, który pozwala uchwycić wielowymiarowość cyklu życia systemu. W artykule zaproponowano dwuwymiarowy rozkład Weibulla, który łączy w sobie dwa klasyczne modele Weibulla za pomocą wspólnego wykładnika. Wspólny wykładnik może reprezentować korelację między dwoma wymiarami. Zaproponowano test ilorazu wiarygodności, który umożliwia badanie istotności korelacji pomiędzy dwoma wymiarami. Do rozwiązania problemu estymacji parametrów zastosowano metodę bayesowską. Ponadto opracowano metodę badania dopasowania modelu do danych empirycznych służącą do wizualizacji dopasowania modelu. Przedstawiono studium przypadku dotyczące wywrotek kopalnianych, w którym dwuwymiarowy rozkład Weibulla zastosowano do modelowania dwuwymiarowych danych dotyczących okresu eksploatacji tych pojazdów

Contributions of Moist Convection and Internal Gravity Waves to Building the Atmospheric −5/3 Kinetic Energy Spectra

Abstract With high-resolution mesoscale model simulations, the authors have confirmed a recent study demonstrating that convective systems, triggered in a horizontally homogeneous environment, are able to generate a background mesoscale kinetic energy spectrum with a slope close to −5/3, which is the observed value for the kinetic energy spectrum at mesoscales. This shallow slope can be identified at almost all height levels from the lower troposphere to the lower stratosphere in the simulations, implying a strong connection between different vertical levels. The present study also computes the spectral kinetic energy budget for these simulations to further analyze the processes associated with the creation of the spectrum. The buoyancy production generated by moist convection, while mainly injecting energy in the upper troposphere at small scales, could also contribute at larger scales, possibly as a result of the organization of convective cells into mesoscale convective systems. This latter injected energy is then transported by energy fluxes (due to gravity waves and/or convection) both upward and downward. Nonlinear interactions, associated with the velocity advection term, finally help build the approximate −5/3 slope through upscale and/or downscale propagation at all levels.</jats:p

The Governing Dynamics of the Secondary Eyewall Formation of Typhoon Sinlaku (2008)

Abstract Through successful convection-permitting simulations of Typhoon Sinlaku (2008) using a high-resolution nonhydrostatic model, this study examines the role of peripheral convection in the storm's secondary eyewall formation (SEF) and its eyewall replacement cycle (ERC). The study demonstrates that before SEF the simulated storm intensifies via an expansion of the tangential winds and an increase in the boundary layer inflow, which are accompanied by peripheral convective cells outside the primary eyewall. These convective cells, which initially formed in the outer rainbands under favorable environmental conditions and move in an inward spiral, play a crucial role in the formation of the secondary eyewall. It is hypothesized that SEF and ERC ultimately arise from the convective heating released from the inward-moving rainbands, the balanced response in the transverse circulation, and the unbalanced dynamics in the atmospheric boundary layer, along with the positive feedback between these processes.</jats:p