Evaluating and understanding the role of convective processes in general circulation model simulations of the Madden-Julian Oscillation

Weak temporal variability in tropical climate such as the Madden-Julian Oscillation (MJO) is one of the outstanding deficiencies in numerical weather prediction (NWP) models and coupled general circulation models (GCMs), which have beleaguered the model developer and user communities for years. The representation of convection, cloud and radiation processes has long been recognized as one of the major problems responsible for these deficiencies in climate models. Recently, with the improvement made to the convection scheme, the Iowa State University (ISU) GCM, which is based on the NCAR Community Climate Model version 3 (CCM3), is able to simulate many features of the MJO as revealed by the observational studies. It provides a unique opportunity to investigating the mechanisms and physical processes through which convection affects the MJO. In this study, four 10-year (1979-88) simulations by the ISUGCM with observed sea surface temperatures (SSTs) are used for the analysis. The control simulation (CTL) is conducted with the standard NCAR CCM3. The ISUGCM simulation with the revised closure, convection trigger condition and convective momentum transport (CMT) is labeled by ISUCCM3. Two sensitivity simulations, NOCMT and NOTRI, are performed to investigate the impact of convection trigger condition and CMT on the MJO. The modifications made in convection schemes improve the simulations of MJO in the amplitude, spatial distribution, eastward propagation, and horizontal and vertical structures, especially for the coherent feature of the MJO-related eastward propagating convection and the precursor sign of the convective center. The revised convection closure plays a key role in the improvement of eastward propagation of MJO. The convection trigger helps produce less frequent but more vigorous moist convection and enhance the amplitude of the MJO signal. The inclusion of CMT results in more coherent structure for the MJO-related deep convective center and its corresponding atmospheric variances. The kinetic energy budget is conducted to analyze the physical processes responsible for the improved MJO simulated by ISUGCM with the modified convection scheme. The increased equatorial (10oS-10oN) MJO-related perturbation kinetic energy (PKE) is presented in the upper troposphere due to the three modifications, and leads to more robust and coherent eastward propagating MJO signal. In the MJO source region-the Indian Ocean (45oE-120oE), the upper-tropospheric MJO PKE is maintained by the convergence of vertical wave energy flux and the barotropic conversion through the horizontal shear of mean flow. In the convectively active region-the western Pacific (120oE-180o), the upper-tropospheric MJO PKE is supported by the convergence of horizontal and vertical wave energy fluxes. Over the central-eastern Pacific (180o-120oW), where convection is suppressed, the upper-tropospheric MJO PKE is mainly due to the convergence of horizontal wave energy flux. The deep convection trigger condition produces stronger convective heating which enhances the upward wave energy fluxes, and leads to the increased MJO PKE over the Indian Ocean and western Pacific. The revised convection closure affects the response of mean zonal wind shear to the convective heating over the Indian Ocean and leads to the enhanced upper-tropospheric MJO PKE through the barotropic conversion. The stronger eastward wave energy flux due to the increase of convective heating over the Indian Ocean and western Pacific by the revised closure is favorable to the eastward propagation of MJO and the convergence of horizontal wave energy flux over the central-eastern pacific. The convection-induced momentum tendency tends to decelerate the upper-tropospheric wind which results in a negative work to the PKE budget in the upper troposphere. However, the convection momentum tendency accelerates the westerly wind below 850 hPa over the western Pacific, which is partially responsible for the improved MJO simulation. With the composite analyses of MJO events, the different phase relationships between MJO 850-hPa zonal wind, precipitation and surface latent heat flux are simulated over the Indian Ocean and western Pacific in ISUGCM, which is greatly influenced by the convection closure, trigger and CMT. With these modifications in the convection scheme, ISUGCM produces better MJO recharge-discharge process of moist static energy than the original GCM. The convection trigger condition for deep convection contributes to the striking difference between ISUCCM3 and CTL and plays the major role for this improved MJO simulation through the horizontal and vertical advections of moist static energy. The inclusion of the revised closure helps build up the precondition of the MJO convection with the redistribution of moisture through the positive contributions of the horizontal and vertical advection of moist static energy before the onset of MJO convection. The impact of CMT through the interaction between horizontal and vertical advection of moist static energy helps the more coherent atmospheric structure over the Indian Ocean and western Pacific. The budget analysis for ISUGCM with the modifications shows the increase of moist static energy is in phase with the horizontal advection of moist static energy over the western Pacific, but in phase with the vertical advection of moist static energy over the Indian Ocean

A Research on Automatic Hyperparameter Recommendation via Meta-Learning

The performance of classification algorithms is mainly governed by the hyperparameter configurations deployed. Traditional search-based algorithms tend to require extensive hyperparameter evaluations to select the desirable configurations during the process, and they are often very inefficient for implementations on large-scale tasks. In this dissertation, we resort to solving the problem of hyperparameter selection via meta-learning which provides a mechanism that automatically recommends the promising ones without any inefficient evaluations. In its approach, a meta-learner is constructed on the metadata extracted from historical classification problems which directly determines the success of recommendations. Designing fine meta-learners to recommend effective hyperparameter configurations efficiently is of practical importance. This dissertation divides into six chapters: the first chapter presents the research background and related work, the second to the fifth chapters detail our main work and contributions, and the sixth chapter concludes the dissertation and pictures our possible future work. In the second and third chapters, we propose two (kernel) multivariate sparse-group Lasso (SGLasso) approaches for automatic meta-feature selection. Previously, meta-features were usually picked by researchers manually based on their preferences and experience or by wrapper method, which is either less effective or time-consuming. SGLasso, as an embedded feature selection model, can select the most effective meta-features during the meta-learner training and thus guarantee the optimality of both meta-features and meta-learner which are essential for successful recommendations. In the fourth chapter, we formulate the problem of hyperparameter recommendation as a problem of low-rank tensor completion. The hyperparameter search space was often stretched to a one-dimensional vector, which removes the spatial structure of the search space and ignores the correlations that existed between the adjacent hyperparameters and these characteristics are crucial in meta-learning. Our contributions are to instantiate the search space of hyperparameters as a multi-dimensional tensor and develop a novel kernel tensor completion algorithm that is applied to estimate the performance of hyperparameter configurations. In the fifth chapter, we propose to learn the latent features of performance space via denoising autoencoders. Although the search space is usually high-dimensional, the performance of hyperparameter configurations is usually correlated to each other to a certain degree and its main structure lies in a much lower-dimensional manifold that describes the performance distribution of the search space. Denoising autoencoders are applied to extract the latent features on which two effective recommendation strategies are built. Extensive experiments are conducted to verify the effectiveness of our proposed approaches, and various empirical outcomes have shown that our approaches can recommend promising hyperparameters for real problems and significantly outperform the state-of-the-art meta-learning-based methods as well as search algorithms such as random search, Bayesian optimization, and Hyperband

Using nonlinear electret effects to design piezolectricity and magnetoelectricity in soft materials

Piezoelectricity and magnetoelectricity are contradictory properties with a rather limited set of natural (often hard) materials that exhibit both. Composite materials—almost always restricted to hard ones—provide a limited recourse with the attendant limitations of small strains, fabrication challenges among others. In this article, using the concept of electrets, we propose a simple scheme to design soft, highly deformable materials that simultaneously exhibit piezoelectricity and magnetoelectricity. We demonstrate that merely by embedding charges and ensuring elastic heterogeneity, the geometrically nonlinear behavior of soft materials leads to an emergent piezoelectric and magnetoelectric behavior. We find that an electret configuration made of sufficiently soft (nonpiezoelectric and nonmagnetic) polymer foams can exhibit simultaneous magnetoelectricity and piezoelectricity with large coupling constants that exceed the best-known ceramic composites

Comparisons of the Generalized Potential Temperature in Moist Atmosphere with the Equivalent Potential Temperature in Saturated Moist Atmosphere

The real tropospheric atmosphere is neither absolutely dry nor completely saturated. It is in general moist but not saturated. Here the generalized potential temperature (GPT) was introduced to describe this humid feature of real moist atmosphere. GPT's conservation property in moist adiabatic process was discussed and proved. Comparisons of GPT in moist atmosphere with the equivalent potential temperature (EPT) in saturated moist atmosphere were made by analyzing three torrential rain cases occurring over Jianghuai Valleys in 2003, the north China in 2004, and with the typhoon Fung-Wong in 2008, respectively. Results showed that the relative humidity is not up to 100% even in torrential rain systems, the saturated condition for EPT is not always held, and thus GPT can describe the moisture concentration and moisture gradient better than EPT. The GPT's definition includes the process that the air changes from dry to moist, then up to saturated. Therefore, potential temperature (PT) and EPT can be considered as its two special status. Similar as PT and EPT, GPT can be used to study atmospheric dynamic and thermodynamic processes more generally because of its conservation property in moist adiabatic process

Building an Online Learning and Research Environment to Enhance Use of Geospatial Data

Geospatial data availability, interoperability, and integration remain a problem today. Current spatial data infrastructures (SDIs) are of limited use particularly to non-expert user communities. GeoBrain, a NASA funded project, has aimed to address those challenges, facilitate easy use of geospatial data and overcome some limitations of current SDIs through building a data-intensive, Service-oriented Architecture (SOA) based online learning and research environment. By adopting the latest developing Web services and knowledge management technologies, this online environment enables easy, open, seamless, and on-demand discovery, access, retrieval, visualization and analysis of distributed geospatial data, information, services, and models from any computer connected to the Internet. Such an online environment is able to serve the different needs of global Earth sciences research and higher education communities, bridge gaps between data user needs and provider capabilities, and greatly enhance use of geospatial data

Ocean Temperatures Do Not Account for a Record-Setting Winter in the U.S. West

The record-setting winter of 2022–2023 came as an answer to both figurative and literal prayers for political leaders, policy makers, and water managers reliant on snowpacks in the Upper Colorado River Basin, a vital source of water for tens of millions of people across the Western United States. But this “drought-busting” winter was not well-predicted, in part because while interannual patterns of tropical ocean temperatures have a well-known relationship to precipitation patterns across much of the American West, the Upper Colorado is part of a liminal region where these connections tend to be comparatively weak. Using historical sea surface temperature and snowpack records, and leveraging a long-term cross-basin relationship to extend the timeline for evaluation, this analysis demonstrates that the 2022–2023 winter did not present in accordance with other high-snowpack winters in this region, and that the associative pattern of surface temperatures in the tropical Pacific, and snow water equivalent in the regions that stored and supplied most of the water to the Colorado River during the 2022–2023 winter, was not substantially different from a historically incoherent arrangement of long-term correlation. These findings suggest that stochastic variability plays an outsized role in influencing water availability in this region, even in extreme years, reinforcing the importance of other trends to inform water policy and management

Using DNA Barcoding to Identify the Genus Lolium

Seeds of the genus Lolium are difficult to identify based on morphology for morphological likeness and some physical deformation such as friction and flattening during storage and transport. DNA barcoding, a newly-established method, has been used to discriminate a variety of agricultural crops with its own advantages. In present study, DNA barcodes for the genus Lolium were investigated for the first time. DNA sequences of psbA-trnH, rbcL, atpF-atpH, and the ITS2 region were evaluated for their ability to differentiate Lolium from the related genus Festuca. As confirmed by inter-intraspecific divergence and Kimura 2 parameter analysis, the greatest divergence existed in ITS2, followed by psbA-trnH. On the contrary, rbcL and atpF-atpH possessed poor genetic variation of 0-0.0115, and was relatively difficult in discrimination of genus Lolium. For ITS2 sequence, no inter-intraspecific distance overlaps were observed and each species has a distinct barcoding gap. ITS2 could effectively discriminate all species based on a neighbor-joining tree. Thus, the ITS2 region is a candidate for DNA barcoding of Lolium

Global attracting sets and exponential stability of nonlinear uncertain differential equations

Uncertain differential equation is a type of differential equation driven by canonical Liu process. By applying some uncertain theories, the sufficient conditions of the exponential stability in mean square is obtained for nonlinear uncertain differential equations. At the same time, some new criteria ensuring the existence of the global attracting sets of considered equations are presented

Electrets in Soft Materials: Nonlinearity, Size Effects,

Development of soft electromechanical materials is critical for several tantalizing applications such as soft robots and stretchable electronics, among others. Soft nonpiezoelectric materials can be coaxed to behave like piezoelectrics by merely embedding charges and dipoles in their interior and assuring some elastic heterogeneity. Such so-called electret materials have been experimentally shown to exhibit very large electromechanical coupling. In this work, we derive rigorous nonlinear expressions that relate effective electromechanical coupling to the creation of electret materials. In contrast to the existing models, we are able to both qualitatively and quantitatively capture the known experimental results on the nonlinear response of electret materials. Furthermore, we show that the presence of another form of electromechanical coupling, flexoelectricity, leads to size effects that dramatically alter the electromechanical response at submicron feature sizes. One of our key conclusions is that nonlinear deformation (prevalent in soft materials) significantly enhances the flexoelectric response and hence the aforementioned size effects

On Tanzania’s Precipitation Climatology, Variability, and Future Projection

We investigate historical and projected precipitation in Tanzania using observational and climate model data. Precipitation in Tanzania is highly variable in both space and time due to topographical variations, coastal influences, and the presence of lakes. Annual and seasonal precipitation trend analyses from 1961 to 2016 show maximum rainfall decline in Tanzania during the long rainy season in the fall (March–May), and an increasing precipitation trend in northwestern Tanzania during the short rainy season in the spring (September–November). Empirical orthogonal function (EOF) analysis applied to Tanzania’s precipitation patterns shows a stronger correlation with warmer temperatures in the western Indian Ocean than with the eastern-central Pacific Ocean. Years with decreasing precipitation in Tanzania appear to correspond with increasing sea surface temperatures (SST) in the Indian Ocean, suggesting that the Indian Ocean Dipole (IOD) may have a greater effect on rainfall variability in Tanzania than the El Niño-Southern Oscillation (ENSO) does. Overall, the climate model ensemble projects increasing precipitation trend in Tanzania that is opposite with the historical decrease in precipitation. This observed drying trend also contradicts a slightly increasing precipitation trend from climate models for the same historical time period, reflecting challenges faced by modern climate models in representing Tanzania’s precipitation