• Journal of Korea Water Resources Association
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• v.44 no.10
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• pp.843-851
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• 2011

In hydrology, it is appropriate to use probabilistic method for forecasting mid/long term streamflow due to the uncertainty of input data. Through this study, it is expanded mid/long term forecasting system more effectively adding priory process function based on PDF-ratio method to the RRFS-ESP system for Guem River Basin. For implementing this purpose, weight is estimated using probabilistic weather forecasting information from KMA. Based on these results, ESP probability is updated per scenario. Through the estimated result per method, the average forecast score using ESP method is higher than that of naive forecasting and it confirmed that ESP method results in appropriate score for RRFS-ESP system. It is also shown that the score of ESP method applying revised inflow scenario using probabilistic weather forecasting is higher than that of ESP method. As a results, it will be improved the accuracy of forecasting using probabilistic weather forecasting.

• Journal of Korea Water Resources Association
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• v.50 no.8
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• pp.563-577
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• 2017

As the uncertainty of precipitation increases due to climate change, seasonal forecasting and the use of weather forecasts become essential for efficient water resources management. In this study, the categorical probabilistic long-term forecasts implemented by KMA (Korea Meteorological Administration) since June 2014 was evaluated using assessment indicators of Hit Rate, Reliability Diagram, and Relative Operating Curve (ROC) and a technique for obtaining quantitative precipitation estimates based on probabilistic forecasts was proposed. The probabilistic long-term forecasts showed its maximum predictability of 48% and the quantified precipitation estimates were closely matched with actual observations; maximum correlation coefficient (R) in predictability evaluation for 100% accurate and actual weather forecasts were 0.98 and 0.71, respectively. A precipitation quantification approach utilizing probabilistic forecasts proposed in this study is expected to enable water management considering the uncertainty of precipitation. This method is also expected to be a useful tool for supporting decision-making in the long-term planning for water resources management and reservoir operations.

• The Korean Journal of Quaternary Research
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• v.19 no.2
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• pp.74-79
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• 2005

Recently, several attempts have been made to provide reasonable information on unusual severe weather phenomena such as tolerant heavy rains and very wild typhoons. Quantitative precipitation forecasts and probabilistic quantitative precipitation forecasts (QPFs and PQPFs, respectively) might be one of the most promising methodologies for early warning on the flesh floods because those diagnostic precipitation models require less computational resources than fine-mesh full-dynamics non-hydrostatic mesoscale model. The diagnostic rainfall model used in this study is the named QPM(Quantitative Precipitation Model), which calculates the rainfall by considering the effect of small-scale topography which is not treated in the mesoscale model. We examine the capability of probabilistic diagnostic rainfall model in terms of how well represented the observed several rainfall events and what is the most optimistic resolution of the mesoscale model in which diagnostic rainfall model is nested. Also, we examine the integration time to provide reasonable fine-mesh rainfall information. When we apply this QPM directly to 27 km mesh meso-scale model (called as M27-Q3), it takes about 15 min. while it takes about 87 min. to get the same resolution precipitation information with full dynamic downscaling method (called M27-9-3). The quality of precipitation forecast by M27-Q3 is quite comparable with the results of M27-9-3 with reasonable threshold value for precipitation. Based on a series of examination we may conclude that the proosed QPM has a capability to provide fine-mesh rainfall information in terms of time and accuracy compared to full dynamical fine-mesh meso-scale model.

• International Journal of Aeronautical and Space Sciences
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• v.11 no.4
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• pp.326-337
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• 2010

This work addresses problems regarding trajectory planning for unmanned aerial vehicle sensors. Such sensors are used for taking measurements of large nonlinear systems. The sensor investigations presented here entails methods for improving estimations and predictions of large nonlinear systems. Thoroughly understanding the global system state typically requires probabilistic state estimation. Thus, in order to meet this requirement, the goal is to find trajectories such that the measurements along each trajectory minimize the expected error of the predicted state of the system. The considerable nonlinearity of the dynamics governing these systems necessitates the use of computationally costly Monte-Carlo estimation techniques, which are needed to update the state distribution over time. This computational burden renders planning to be infeasible since the search process must calculate the covariance of the posterior state estimate for each candidate path. To resolve this challenge, this work proposes to replace the computationally intensive numerical prediction process with an approximate covariance dynamics model learned using a nonlinear time-series regression. The use of autoregressive time-series featuring a regularized least squares algorithm facilitates the learning of accurate and efficient parametric models. The learned covariance dynamics are demonstrated to outperform other approximation strategies, such as linearization and partial ensemble propagation, when used for trajectory optimization, in terms of accuracy and speed, with examples of simplified weather forecasting.

• Journal of Astronomy and Space Sciences
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• v.31 no.2
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• pp.149-157
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• 2014

As the prediction of geomagnetic storms is becoming an important and practical problem, conditions in the Earth's magnetosphere have been studied rigorously in terms of those in the interplanetary space. Another approach to space weather forecast is to deal with it as a probabilistic geomagnetic storm forecasting problem. In this study, we carry out detailed statistical analysis of solar wind parameters and geomagnetic indices examining the dependence of the distribution on the solar cycle and annual variations. Our main findings are as follows: (1) The distribution of parameters obtained via the superimposed epoch method follows the Gaussian distribution. (2) When solar activity is at its maximum the mean value of the distribution is shifted to the direction indicating the intense environment. Furthermore, the width of the distribution becomes wider at its maximum than at its minimum so that more extreme case can be expected. (3) The distribution of some certain heliospheric parameters is less sensitive to the phase of the solar cycle and annual variations. (4) The distribution of the eastward component of the interplanetary electric field BV and the solar wind driving function BV2, however, appears to be all dependent on the solar maximum/minimum, the descending/ascending phases of the solar cycle and the equinoxes/solstices. (5) The distribution of the AE index and the Dst index shares statistical features closely with BV and $BV^2$ compared with other heliospheric parameters. In this sense, BV and $BV^2$ are more robust proxies of the geomagnetic storm. We conclude by pointing out that our results allow us to step forward in providing the occurrence probability of geomagnetic storms for space weather and physical modeling.

• Journal of Integrative Natural Science
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• v.4 no.2
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• pp.149-157
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• 2011

Urbanization of the world's population has given rise to more than 450 cities around the world with populations in excess of 1 million (megacity) and more than 25 so-called metacities with populations over 10 million (Brinkhoff, 2010). The United States today has a total resident population of more than 308,500,000 people, with 81 percent residing in cities and suburbs as of mid - 2005 (UN, 2008). Urban meteorology is the study of the physics, dynamics, and chemistry of the interactions of Earth's atmosphere and the urban built environment, and the provision of meteorological services to the populations and institutions of metropolitan areas. While the details of such services are dependent on the location and the synoptic climatology of each city, there are common themes, such as enhancing quality of life and responding to emergencies. Experience elsewhere (e.g., Shanghai, Helsinki, Tokyo, Seoul, etc.) shows urban meteorological support is a key part of an integrated or multi-hazard warning system that considers the full range of environmental challenges and provides a unified response from municipal leaders. Urban meteorology has come to require much more than observing and forecasting the weather of our cities and metropolitan areas. Forecast improvement as a function of more and better observations of various kinds and as a function of model resolution, larger ensembles, predicted probability distributions; Responses of emergency managers, government officials, and users to improved and probabilistic forecasts; Benefits of improved forecasts in reduction of loss of life, property damage, and other adverse effects. A national initiative to enhance urban meteorological services is a high-priority need for a wide variety of stakeholders, including the general, commerce and industry, and all levels of government. Some of the activities of such an initiative include: conducting basic research and development; prototyping and other activities to enable very--short and short range predictions; supporting and improving productivity and efficiency in commercial and industrial sectors; and urban planning for long term sustainability. In addition urban test-beds are an effective means for developing, testing, and fostering the necessary basic and applied meteorological and socioeconomic research, and transitioning research findings to operations. An extended, multi-year period of continuous effort, punctuated with intensive observing and forecasting periods, is envisioned.