Earth System Modeling 2.0: A Blueprint for Models That Learn From Observations and Targeted High-Resolution Simulations

Climate projections continue to be marred by large uncertainties, which originate in processes that need to be parameterized, such as clouds, convection, and ecosystems. But rapid progress is now within reach. New computational tools and methods from data assimilation and machine learning make it possible to integrate global observations and local high-resolution simulations in an Earth system model (ESM) that systematically learns from both. Here we propose a blueprint for such an ESM. We outline how parameterization schemes can learn from global observations and targeted high-resolution simulations, for example, of clouds and convection, through matching low-order statistics between ESMs, observations, and high-resolution simulations. We illustrate learning algorithms for ESMs with a simple dynamical system that shares characteristics of the climate system; and we discuss the opportunities the proposed framework presents and the challenges that remain to realize it.Comment: 32 pages, 3 figure

Contemporary perspectives of the child in action: An investigation into children’s connectedness with, and contribution to, the world around them

Childcare within Australia has undergone significant reform as a result of the implementation of the nationally mandated Belonging, Being and Becoming: The Early Years Learning Framework [EYLF] (Department for Education, Employment and Workplace Relations [DEEWR]. 2009. Belonging, Being and Becoming. The Early Years Learning Framework for Australia. Canberra: Australian Government Department of Education, Employment and Workplace Relations). The EYLF articulates contemporary perspectives of the child through its principles, practices and learning outcomes. Educators are required to promote these principles, practices and learning outcomes with children aged from birth to 5 years. This paper reports the findings from a research project that sought to investigate how educators applied their understanding of learning outcome two of the EYLF (children are connected with and contribute to their world). The focus of this research was educators working with children aged two to three years within childcare centres operating on school sites, in metropolitan Western Australian. The research design was qualitative and situated within the interpretivist paradigm. Observations were used as the method for gathering data and these were analysed through a process of coding. This paper presents the observational findings of educators’ practices within learning outcome two. Composite vignettes from the voice of the child are included to present the observational findings. In centralising the voice of the child, contemporary perspectives are made explicit

Bayesian Reconstruction of Approximately Periodic Potentials at Finite Temperature

The paper discusses the reconstruction of potentials for quantum systems at finite temperatures from observational data. A nonparametric approach is developed, based on the framework of Bayesian statistics, to solve such inverse problems. Besides the specific model of quantum statistics giving the probability of observational data, a Bayesian approach is essentially based on "a priori" information available for the potential. Different possibilities to implement "a priori" information are discussed in detail, including hyperparameters, hyperfields, and non--Gaussian auxiliary fields. Special emphasis is put on the reconstruction of potentials with approximate periodicity. The feasibility of the approach is demonstrated for a numerical model.Comment: 18 pages, 17 figures, LaTe

Teamwork, interdependence, and learning in a handbell ensemble

According to Sundstrom (1999), performing teams conduct “complex, time-limited engagements with audiences in performance events for which teams maintain specialized, collective skill” (p. 20). Musical ensembles have been included in team research on orchestral leadership, yet as a performing team, the internal connections between musicians have not been studied. The handbell ensemble operates as a performing team while sustaining a prominent degree of interdependence. It is generally unknown how musical performing teams such as the handbell ensemble function and learn interdependently. Using Salas et al.’s (2005) Big Five theory of teamwork as a theoretical lens, I conducted a case study of a community handbell ensemble to understand: (a) how interdependent team interactions of team leadership, mutual performance monitoring, backup behavior, adaptability, and/or team orientation contribute to the function of and learning within this handbell ensemble and (b) how interdependent team interactions of shared mental models, closed-loop communication, and/or mutual trust contribute to the function of and learning within this handbell ensemble. The case was limited to one handbell ensemble known as the Campana Ringers, a group who performed for a community church. Members included their director and 13 ringers, one of whom was myself. In individual and group sessions, I interviewed the ensemble director and all team members. Observational and rehearsal notes were coded and primary themes were presented through the core components and coordinating mechanisms of the Big Five theory of teamwork (Salas et al., 2005). Secondary themes emerged connected to the uniqueness of handbell playing and co-mentoring occurring in the ensemble. In data from my findings, I recognized all elements of the Big Five theory were present in interactions between handbell ensemble members. Implications from this case study are connected to co-mentoring, a type of collaborative learning utilizing reciprocal teaching and learning (Mullen, 2005). Findings from this study may inform music educators in community and school settings who wish to develop or incorporate components of teamwork and co-mentoring practices into their ensembles

Data Assimilation by Artificial Neural Networks for an Atmospheric General Circulation Model: Conventional Observation

This paper presents an approach for employing artificial neural networks (NN) to emulate an ensemble Kalman filter (EnKF) as a method of data assimilation. The assimilation methods are tested in the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localization of balloon soundings. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLP-NN), is applied. The MLP-NN are able to emulate the analysis from the local ensemble transform Kalman filter (LETKF). After the training process, the method using the MLP-NN is seen as a function of data assimilation. The NN were trained with data from first three months of 1982, 1983, and 1984. A hind-casting experiment for the 1985 data assimilation cycle using MLP-NN were performed with synthetic observations for January 1985. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses is of order 0.02. The simulations show that the major advantage of using the MLP-NN is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-NN is 90 times faster than cycle assimilation with LETKF for the numerical experiment.Comment: 17 pages, 16 figures, monthly weather revie

Distributed Kernel Regression: An Algorithm for Training Collaboratively

This paper addresses the problem of distributed learning under communication constraints, motivated by distributed signal processing in wireless sensor networks and data mining with distributed databases. After formalizing a general model for distributed learning, an algorithm for collaboratively training regularized kernel least-squares regression estimators is derived. Noting that the algorithm can be viewed as an application of successive orthogonal projection algorithms, its convergence properties are investigated and the statistical behavior of the estimator is discussed in a simplified theoretical setting.Comment: To be presented at the 2006 IEEE Information Theory Workshop, Punta del Este, Uruguay, March 13-17, 200