This study conducted a broad review of the pre-and post-processor methods for ensemble streamflow prediction using a Korean case study. Categorical forecasts offered by the Korea Meteorogical Administration and deterministic forecasts of a regional climate model called Seoul National University Regional Climate Model(SNURCM) were selected as climate forecast information for the pre-processors and incorporated into Ensemble Streamflow Prediction(ESP) runs with the TANK hydrologic model. The post-processors were then used to minimize a possible error propagated through the streamflow generation. The application results show that use of the post-processor more effectively reduced the uncertainty of the no-processor ESP than use of the pre-processor, especially in dry season.

Solid desiccant, open cooling cycles use low temperature heat efficiently making them attractive for solar air conditioning. Advanced cycles using nearly reversible evaporative coolers have previously been proposed and shown to have high ideal performance. This parametric study shows that, with real components comparable to those used in studies of classical cycles, these open cycles can give more than twice the thermal coefficient of performance of a ventilation cycle.

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The verification of probabilistic forecasts in hydro-climatology is integral to their development, use, and adoption. We propose here a means of utilizing goodness of fit measures for verifying the reliability of probabilistic forecasts. The difficulty in measuring the goodness of fit for a probabilistic prediction or forecast is that predicted probability distributions for a target variable are not stationary in time, meaning one observation alone exists to quantify goodness of fit for each prediction issued. Therefore, we suggest an additional dissociation that can dissociate target information from the other time variant part—the target to be verified in this study is the alignment of observations to the predicted probability distribution. For this dissociation, the probability integral transformation is used. To measure the goodness of fit for the predicted probability distributions, this study uses the root mean squared deviation metric. If the observations after the dissociation can be assumed to be independent, the mean square deviation metric becomes a chi-square test statistic, which enables statistically testing the hypothesis regarding whether the observations are from the same population as the predicted probability distributions. An illustration of our proposed rationale is provided using the multi-model ensemble prediction for El Niño–Southern Oscillation.