Month: August 2024

Abstract

The Weather Research & Forecasting Model (WRF, Version 4.4) was applied to simulate meteorological conditions in the city of Quito, Ecuador, located in a tropical Andean landscape. These simulations included the urban canopy into WRF, using the Building Environment Parameterization (BEP) scheme combined with Local Climate Zones (LCZ) land use classification; the innermost domain had a horizontal resolution of 2 km. The simulation results showed that using LCZ + BEP options improved the representation of wind speed and planetary boundary layer height (PBLH), in comparison with WRF counter fact simulations which did not use BEP. For temperature and relative humidity, implementation of LCZ did not improve WRF simulations with respect to those counter fact simulations. This may be ascribed to the use of the default LCZ thermophysical parameters, suggesting the need for gathering local built environment features. The best WRF configuration found for wind speed was the one that combined BEP scheme, LCZ land use and the Yonsei University (YSU) PBL model with topographic option activated; this happened for dry and wet seasons and for the unique meteorological conditions in December. Regarding PBLH modeling, the best configurations were YSU-BEP-LCZ (December), MYJ-BEP-LCZ (April, wet season) and YSU (August, dry season). The findings showed the major influence of urban canopy — described by LCZ — on wind circulation and PBLH simulated within the city at high horizontal resolution (2 km). This effect should be considered when modeling atmospheric pollutant dispersion, choosing urban development strategies, and analyzing prospective climate change scenarios, among other goals.

Abstract

Accurate forecasting of the temporal distribution pattern of sub-daily precipitation is of paramount importance for effective flood control design and early warning systems. This study focuses on improving the accuracy of such forecasts by employing post-processing techniques. The European Centre for Medium-Range Weather Forecasts (ECMWF) precipitation product over Iran was adopted along with three post-processing methods including Quantile Mapping (QM), Support Vector Machine (SVM), and Random Forest (RF). The accuracy of the forecasts for various precipitation temporal characteristics, including the start, duration, and end of precipitation events were evaluated. The RF method proved to be the most effective in improving forecast accuracy, especially in regions with higher precipitation rates. Additionally, RF corrected the first quartile of precipitation forecasts across all precipitation regions, significantly enhancing forecast accuracy in regions 3 and 5 of Iran. As for the temporal distribution pattern, post-processing methods improved the accuracy of the forecasts across all regions. The QM method performed better in terms of distributing precipitation amounts among quartiles. Moreover, all post-processing methods showed a high degree of similarity between observed and forecasted temporal distribution patterns. The deterministic evaluation showed that RF outperforms other methods in enhancing the accuracy of most precipitation quartiles, particularly that of the third quartile. The SVM and QM methods showed mixed performances, improving accuracy in some quartiles but performing adversely in others. Overall, this research highlighted the importance of data post-processing in enhancing the accuracy of precipitation forecasts and their temporal distribution patterns. The RF method proved to be the most effective post-processing technique. These findings have significant implications for flood forecasting and management in regions prone to extreme precipitation events.

Abstract

When a hydrological drought occurs due to a decrease in water storage, there is no choice but to supply limited water. Because this has a devastating impact on the community, it is necessary to identify causes and make predictions for emergency planning. The state of change in dam inflow can be used to confirm hydrological drought conditions using the Standardized Runoff Index (SRI), and meteorological drought and climate variability are used to identify causal relationships. Multiple Linear Regression (MLR) and Generalized Additive Model (GAM) models are developed to predict accumulated hydrological drought for 6, 12, and 24 months in the Boryeong Dam basin, and the Nash-Sutcliffe model efficiency coefficient (NSE) exceeded 0.4, satisfying the suitability criteria. The estimation ability is highest when predicting a 12-month annual drought, and reliability can be further increased by reflecting some climate fluctuations in a non-linear form. The droughts of 6 month and 24 month cumulative scales are significantly influenced by the Western Hemisphere Warm Pool (WHWP) extending from the eastern North Pacific to the North Atlantic and by the Nino 3.4 region in the tropical Pacific. Furthermore, it is anticipated that the drought conditions of the inflow volume to the Boryeong Dam will worsen with increasing sea surface temperatures in both regions.

Abstract

The study investigated the use of a Hardware-in-the-Loop (HiL) technique applied in model ice experiments to enable the analysis of offshore structures with low natural frequencies under dynamic ice loading. Traditional approaches were limited by facility capacities and ineffective downscaling of the geometry of the offshore structures. The goal of the present study was to overcome these challenges and to enhance the understanding and explore the applicability of a hybrid testing technique in model ice experiments. To achieve the objective, 204 Hardware-in-the-Loop simulations in model Ice (HiLI) were analyzed. Results showed robust behavior and good performance of the HiLI due to minimal variation in measured delay, normalized root mean square error, and peak tracking error and low magnitudes of such parameters despite alterations in factors such as the choice of the numerical structural model, physical prototype, measurement system, and ice type. Notably, the performance of the HiLI was affected when testing with warm model ice or scaling for harsh ice conditions, attributed to a reduced signal-to-noise ratio and instability of the system, respectively. Experimental identification of the critical delay, along with the application of an analytical stability criterion, revealed that the instability observed, was likely induced by reducing the structural stiffness of the numerical structural model to fulfil the scaling requirements when testing for harsh ice conditions. Additionally, the study showed improved HiLI performance when the physical prototype was in contact with the model ice. This observation was further analyzed and is assumed to be caused by the coupling between the ice and physical prototype, causing a coupled and thus increased eigenfrequency of the physical prototype-ice system.