Comparison of prediction performance using statistical postprocessing methods

As the 2018 Winter Olympics are to be held in Pyeongchang, both general weather information on Pyeongchang and specific weather information on this region, which can affect game operation and athletic performance, are required. An ensemble prediction system has been applied to provide more accurate weather information, but it has bias and dispersion due to the limitations and uncertainty of its model. In this study, homogeneous and nonhomogeneous regression models as well as Bayesian model averaging (BMA) were used to reduce the bias and dispersion existing in ensemble prediction and to provide probabilistic forecast. Prior to applying the prediction methods, reliability of the ensemble forecasts was tested by using a rank histogram and a residualquantile-quantile plot to identify the ensemble forecasts and the corresponding verifications. The ensemble forecasts had a consistent positive bias, indicating over-forecasting, and were under-dispersed. To correct such biases, statistical post-processing methods were applied using fixed and sliding windows. The prediction skills of methods were compared by using the mean absolute error, root mean square error, continuous ranked probability score, and continuous ranked probability skill score. Under the fixed window, BMA exhibited better prediction skill than the other methods in most observation station. Under the sliding window, on the other hand, homogeneous and non-homogeneous regression models with positive regression coefficients exhibited better prediction skill than BMA. In particular, the homogeneous regression model with positive regression coefficients exhibited the best prediction skill.     

AMSR-E sea surface temperature (SST) charts enhancement

Microwave satellite images used for retrieving sea surface temperatures often have such distortions as noise and blurring of the thermal fronts. An image processing approach based on the Mumford-Shah model of optimal image approximation is considered for the solution to this problem. We divide images into flat areas and frontal zones, and then process these areas separately. Image fragmentation is based on automatic detection of the thermal front lines. SST enhancement in frontal zones is achieved by using image deconvolution methods. It has been shown that SST errors in high gradient areas reach 1–3 °C. The proposed approach can decrease this discrepancy.     

The Pedagogical Benefits of SimCity in Urban Geography Education

This article investigated the pedagogical potential of the SimCity simulation game in an urban geography course. University students used SimCity to build their own cities and applied a wide range of theories to support their urban structures. Moreover, the students critically evaluated the logic and functioning of the SimCity simulation compared to real-world contexts and urban geography principles. The students believed the SimCity activity provided them with opportunities to promote their geographic creativity, resulting in diverse, unique, and interesting cities. The findings demonstrate that the use of SimCity can be an effective tool for geography education.