The present study shows the possibility of using X-band multi-parameter radar to detect volcanic tephra for estimating the amount of volcanic tephra in the air even when the amount of volcanic tephra is very low. The model proposed in this study can detect tephra with diameters of 0.5 mm to 3 mm. Through the observation experiment and the model proposal, the present study shows successful detection of volcanic tephra in the air by using X-band multi-parameter radar.
This paper reports a preliminary attempt to determine volcanic ash particle size distribution using the video drop size detector (VDSD) for estimating volcanic ash amount with X-band radar. The VDSD records an image showing the size and number of particles falling into the aperture by a charge coupled device camera. Size distribution spectra of a range of particles from fine ash to small lapilli were derived in discrete form from the VDSD observation. The parameterization of the particle size distribution following Gamma function was done using volcanic ash of eruptions at the Sakurajima Volcano between December 13–21, 2014. Three Gamma distribution parameters were determined analytically. The analytical results revealed a continuous distribution of particles characterized by shape, intercept, and slope. The distribution was used to determine volcanic mass concentration, ground deposit weight, and reflectivity. Verification of these results with X-band radar observations showed that the reflectivity obtained from analytical results is similar to that from radar observation. However, the ground deposit weight from analysis was overestimated, compared with the real weight of ash deposit on the ground. The algorithm proposed in this study is expected to provide a practical method for estimating ash distribution in the aftermath of a volcanic eruption using radar-reflectivity for cases where direct measurement at the location is not possible. An overview of the algorithm for volcanic ash retrieval from X-band radar observations is also presented.
We evaluated a method by which to fit gamma distributions with parameters of snowflake size distributions by measuring three physical quantities using an optical sensing disdrometer. The three physical quantities are the diameters of the snowflakes that have 50 and 99 percentiles of volumes (D 50 and D 99 , respectively) and the sum of the sixth powers of the diameters in a unit volume (Z). Snowflake size distribution was well fitted to a gamma distribution between D 50 and D 99 , inclusive. This method prevented the snowflake volume from being considerably underestimated. Although the mean absolute error based on snowflake volume for this method was large compared with that obtained using a moment method, good estimates of snowflake volume were obtained for some samples using this method, for which the snowflake size distribution was less influenced by snowflakes 1 mm or less in diameter. The correlation coefficient was 0.989, as determined by regression analysis based on the observed and estimated snowflake volumes using this method. The estimation of the snowflake volume using this method depends on the quality control of the optimum shape parameter and requires a continuous probability distribution of snowflakes for diameters above 1 mm.(Citation: Ogawa, M., S. Oishi, K. Yamaguchi, and E. Nakakita, 2015: Quantitative parametric approach to estimating snowflake size distributions using an optical sensing disdrometer. SOLA, 11, 134−137,
Recently, heavy rain by typhoon increases risk of disaster everywhere in Japan. There has been considerable interest to improve the flood control function of dams by prior releasing because an action plan was enacted to fully use the existing hydraulic structures to prevent flood disasters. Carrying out prior releasing has a risk in terms of water supply purpose, in other words, it may cause artificial drought. Therefore, it should be taken into consideration that releasing larger amount of water than rain gives water shortage. In the present study, we suggested the method of dam operation based on rainfall forecast including prior releasing considering the risk in terms of water supply purpose. Concretely, first, we investigated that it could estimate the accuracy of forecasted accumulated rainfall based on Global Spectral Model (GSM) by adding the information of spreads calculated by Weekly Ensemble Prediction System (WEPS) in the Yodo river basin. Second, accumulated rainfall based on GSM errors using gamma distribution was analyzed. Third, the method of dam operation based on rainfall forecast including prior releasing was applied to past examples and the effect was verified. As a result, peak discharge in Hirakata point was reduced than normal operation in case of rainfall prediction was accurate.Engineering
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.