Dynamically and Statistically Downscaled Seasonal Simulations of Maximum Surface Air Temperature Over the Southeastern United States

Coarsely resolved surface air temperature (2 m height) seasonal integrations from the Florida State University/Center for Ocean-Atmospheric Prediction Studies Global Spectral Model (FSU/COAPS GSM) (~1.8º lon.-lat. (T63)) for the period of 1994 to 2002 (March through September each year) are downscaled to a fine spatial scale of ~20 km. Dynamical and statistical downscaling methods are applied for the southeastern United States region, covering Florida, Georgia, and Alabama. Dynamical downscaling is conducted by running the FSU/COAPS Nested Regional Spectral Model (NRSM), which is nested into the domain of the FSU/COAPS GSM. We additionally present a new statistical downscaling method. The rationale for the statistical approach is that clearer separation of prominent climate signals (e.g., seasonal cycle, intraseasonal, or interannual oscillations) in observation and GSM, respectively, over the training period can facilitate the identification of the statistical relationship in climate variability between two data sets. Cyclostationary Empirical Orthogonal Function (CSEOF) analysis and multiple regressions are trained with those data sets to extract their statistical relationship, which eventually leads to better prediction of regional climate from the large-scale simulations. Downscaled temperatures are compared with the FSU/COAPS GSM fields and observations. Downscaled seasonal anomalies exhibit strong agreement with observations and a reduction in bias relative to the direct GSM simulations. Interannual temperature change is also reasonably simulated at local grid points. A series of evaluations including mean absolute errors, anomaly correlations, frequency of extreme events, and categorical predictability reveal that both downscaling techniques can be reliably used for numerous seasonal climate applications

Machine learning-based investigation of the association between CMEs and filaments

YesIn this work we study the association between eruptive filaments/prominences and coronal mass ejections (CMEs) using machine learning-based algorithms that analyse the solar data available between January 1996 and December 2001. The Support Vector Machine (SVM) learning algorithm is used for the purpose of knowledge extraction from the association results. The aim is to identify patterns of associations that can be represented using SVM learning rules for the subsequent use in near real-time and reliable CME prediction systems. Timing and location data in the NGDC filament catalogue and the SOHO/LASCO CME catalogue are processed to associate filaments with CMEs. In the previous studies which classified CMEs into gradual and impulsive CMEs, the associations were refined based on CME speed and acceleration. Then the associated pairs were refined manually to increase the accuracy of the training dataset. In the current study, a data- mining system has been created to process and associate filament and CME data, which are arranged in numerical training vectors. Then the data are fed to SVMs to extract the embedded knowledge and provide the learning rules that could have the potential, in the future, to provide automated predictions of CMEs. The features representing the event time (average of the start and end times), duration, type and extent of the filaments are extracted from all the associated and not-associated filaments and converted to a numerical format that is suitable for SVM use. Several validation and verification methods are used on the extracted dataset to determine if CMEs can be predicted solely and efficiently based on the associated filaments. More than 14000 experiments are carried out to optimise the SVM and determine the input features that provide the best performance

Use of healthcare consumer voices to increase empathy in nursing students

Nurses need to be well prepared to address the needs of a diverse population and facilitate positive experiences in an equitable and inclusive approach to care. The aim of the study was to determine whether the integration of consumer lived experience interviews into the content of a first-year course influenced empathy in nursing students. A one group pre-test, post-test design was used. A convenience sample of first-year undergraduate nursing students (N = 32) from a regional Australian university was recruited for the study. The pre and post tests were conducted using the Kiersma Chen Empathy Scale and t-tests performed to analyse the data. Results showed overall that nursing students demonstrated moderate levels of empathy; pre-test score of (M = 75.53; SD = 5.76). After the intervention the post-test results showed that there was a statistically significant increase in students' empathy towards vulnerable, disadvantaged and stigmatised population groups. The healthcare consumer voice has the potential to strengthen current teaching practices that promote caring behaviours in nursing students.Associated Grant:CQUniversity Learning and Teaching GrantAssociated Grant Code:RSH/345

Using blogging to engage nursing students in reflective practice

Blogging is a practice that educators in creative and professional writing, journalism, communication and a variety of other writing-based programs utilise to enhance their students’ reflective writing, alongside other skills. This article suggests that this practice may be useful in other non-writing based disciplines, in this case the discipline of nursing, where reflective practice has been identified as a framework to assist in developing personal growth, problem solving and the identification of innate strengths, allowing professional maturation in difficult environments. It describes the use of blogging in an assessment item in a final year undergraduate nursing unit. Analysis of students’ blogs revealed blogging was cathartic and triggered reflection and transformation