scholarly journals Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

2021 ◽  
Author(s):  
Magfira Syarifuddin ◽  
Susanna F. Jenkins ◽  
Ratih Indri Hapsari ◽  
Qingyuan Yang ◽  
Benoit Taisne ◽  
...  
Keyword(s):  
Grain Size ◽  
Real Time ◽  
Ensemble Prediction ◽  
Merapi Volcano ◽  
Ensemble Prediction System ◽  
Multiple Parameters ◽  
In Situ Data ◽  
Detection And Tracking ◽  
X Band

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band Multi-Parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was done by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of Ensemble Prediction System (EPS). Cross-validation was done using field-survey data, radar observations, and Himawari-8 imagery. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results with ground-based data, where the radar-based estimated grain size distribution fell within the range of in-situ data. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth-and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

scholarly journals Real-Time Tephra Detection and Dispersal Forecasting by a Ground-Based Weather Radar

2021 ◽  
Vol 13 (24) ◽  
pp. 5174
Author(s):  
Magfira Syarifuddin ◽  
Susanna F. Jenkins ◽  
Ratih Indri Hapsari ◽  
Qingyuan Yang ◽  
Benoit Taisne ◽  
...  
Keyword(s):  
Grain Size ◽  
Real Time ◽  
Ensemble Prediction ◽  
Merapi Volcano ◽  
Ensemble Prediction System ◽  
Tephra Fall ◽  
Multiple Parameters ◽  
Detection And Tracking ◽  
X Band

Tephra plumes can cause a significant hazard for surrounding towns, infrastructure, and air traffic. The current work presents the use of a small and compact X-band multi-parameter (X-MP) radar for the remote tephra detection and tracking of two eruptive events at Merapi Volcano, Indonesia, in May and June 2018. Tephra detection was performed by analysing the multiple parameters of radar: copolar correlation and reflectivity intensity factor. These parameters were used to cancel unwanted clutter and retrieve tephra properties, which are grain size and concentration. Real-time spatial and temporal forecasting of tephra dispersal was performed by applying an advection scheme (nowcasting) in the manner of an ensemble prediction system (EPS). Cross-validation was performed using field-survey data, radar observations, and Himawari-8 imageries. The nowcasting model computed both the displacement and growth and decaying rate of the plume based on the temporal changes in two-dimensional movement and tephra concentration, respectively. Our results are in agreement with ground-based data, where the radar-based estimated grain size distribution falls within the range of in situ grain size. The uncertainty of real-time forecasted tephra plume depends on the initial condition, which affects the growth and decaying rate estimation. The EPS improves the predictability rate by reducing the number of missed and false forecasted events. Our findings and the method presented here are suitable for early warning of tephra fall hazard at the local scale.

scholarly journals A Probabilistic Multimodel Ensemble Approach to Seasonal Prediction

2009 ◽  
Vol 24 (3) ◽  
pp. 812-828 ◽  
Author(s):  
Young-Mi Min ◽  
Vladimir N. Kryjov ◽  
Chung-Kyu Park
Keyword(s):  
Real Time ◽  
Gaussian Approximation ◽  
Seasonal Prediction ◽  
Asia Pacific ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Sampling Errors ◽  
Seasonal Forecasts ◽  
Multimodel Ensemble ◽  
Ensemble Prediction System

Abstract A probabilistic multimodel ensemble prediction system (PMME) has been developed to provide operational seasonal forecasts at the Asia–Pacific Economic Cooperation (APEC) Climate Center (APCC). This system is based on an uncalibrated multimodel ensemble, with model weights inversely proportional to the errors in forecast probability associated with the model sampling errors, and a parametric Gaussian fitting method for the estimate of tercile-based categorical probabilities. It is shown that the suggested method is the most appropriate for use in an operational global prediction system that combines a large number of models, with individual model ensembles essentially differing in size and model weights in the forecast and hindcast datasets being inconsistent. Justification for the use of a Gaussian approximation of the precipitation probability distribution function for global forecasts is also provided. PMME retrospective and real-time forecasts are assessed. For above normal and below normal categories, temperature forecasts outperform climatology for a large part of the globe. Precipitation forecasts are definitely more skillful than random guessing for the extratropics and climatological forecasts for the tropics. The skill of real-time forecasts lies within the range of the interannual variability of the historical forecasts.

Real-Time Analysis Of In-Situ Spectroscopic Ellipsometric Data During Mbe Growth Of III-V Semiconductors

1991 ◽  
Vol 222 ◽  
Author(s):  
B. Johs ◽  
J. L. Edwards ◽  
K. T. Shiralagi ◽  
R. Droopad ◽  
K. Y. Choi ◽  
...  
Keyword(s):  
Real Time ◽  
Optical Constants ◽  
Growth Parameters ◽  
Ex Situ ◽  
Time Analysis ◽  
Mbe Growth ◽  
In Situ Data ◽  
Real Time Analysis

ABSTRACTA modular spectroscopic ellipsometer, capable of both in-situ and ex-situ operation, has been used to measure important growth parameters of GaAs/AIGaAs structures. The ex-situ measurements provided layer thicknesses and compositions of the grown structures. In-situ ellipsometric measurements allowed the determination of growth rates, layer thicknesses, and high temperature optical constants. By performing a regression analysis of the in-situ data in real-time, the thickness and composition of an AIGaAs layer were extracted during the MBE growth of the structure.

scholarly journals On the Quality of Real-Time Altimeter Gridded Fields: Comparison with In Situ Data

2009 ◽  
Vol 26 (3) ◽  
pp. 556-569 ◽  
Author(s):  
Ananda Pascual ◽  
Christine Boone ◽  
Gilles Larnicol ◽  
Pierre-Yves Le Traon
Keyword(s):  
Real Time ◽  
Sea Level ◽  
Tide Gauge ◽  
Deep Ocean ◽  
Velocity Estimation ◽  
Time Data ◽  
In Situ Data ◽  
Delayed Time

Abstract The timeliness of satellite altimeter measurements has a significant effect on their value for operational oceanography. In this paper, an Observing System Experiment (OSE) approach is used to assess the quality of real-time altimeter products, a key issue for robust monitoring and forecasting of the ocean state. In addition, the effect of two improved geophysical corrections and the number of missions that are combined in the altimeter products are also analyzed. The improved tidal and atmospheric corrections have a significant effect in coastal areas (0–100 km from the shore), and a comparison with tide gauge observations shows a slightly better agreement with the gridded delayed-time sea level anomalies (SLAs) with two altimeters [Jason-1 and European Remote Sensing Satellite-2 (ERS-2)/Envisat] using the new geophysical corrections (mean square differences in percent of tide gauge variance of 35.3%) than those with four missions [Jason-1, ERS/Envisat, Ocean Topography Experiment (TOPEX)/Poseidoninterlaced, and Geosat Follow-On] but using the old corrections (36.7%). In the deep ocean, however, the correction improvements have little influence. The performance of fast delivery products versus delayed-time data is compared using independent in situ data (tide gauge and drifter data). It clearly highlights the degradation of real-time SLA maps versus the delayed-time SLA maps: four altimeters are needed in real time to get the similar quality performance as two altimeters in delayed time (sea level error misfit around 36%, and zonal and meridional velocity estimation errors of 27% and 33%, respectively). This study proves that the continuous improvement of geophysical corrections is very important, and that it is essential to stay above a minimum threshold of four available altimetric missions to capture the main space and time oceanic scales in fast delivery products.

Effect of the Wind Drag Estimation Methods on Numerical Storm Surge Modeling

2019 ◽  
Author(s):  
C. Gowri Shankar ◽  
Manasa Ranjan Behera
Keyword(s):  
Real Time ◽  
Storm Surge ◽  
Wind Stress ◽  
Evaluation Method ◽  
Tide Gauge ◽  
Wave Model ◽  
Estimation Methods ◽  
Computational Accuracy ◽  
In Situ Data

Abstract Tropical cyclones have always proved the extent of its catastrophe on several occurrences over the years. In particular, the Bay of Bengal (BoB) basin in the Northern Indian Ocean has produced such historic devastating events, thereby mandating accurate real-time predictions. Numerical modeling of storm surge has always been an arduous task, as it is integrated with various uncertain factors. Among those, the major governing component being the wind forcing or the wind stress — that signifies, the computational accuracy of simulated surge and wave parameters. The present study is aimed at analysing the most suited wind drag evaluation method for real-time predictions of storm surge along the BoB. Cyclone Phailin (2013) was considered for the numerical simulations. To evaluate the wind drag coefficient, three most extensively used linear empirical relations along with the enhanced Wave Boundary Layer Model (e_WBLM) were used. The surge was subsequently simulated (using the coupled hydrodynamic circulation and wave model: ADCIRC and SWAN, respectively), individually for each of the above wind stress methods to obtain the corresponding storm surge (residual) and the storm wave features. The modeled values were further validated with the in-situ data obtained from tide gauge station and buoys respectively. It was quite intuitively observed that, e_WBLM based results correlated well with the in-situ values than its linear counterparts since, the former pragmatically includes the effects of air-sea interaction at high wind speeds in the model. The e_WBLM-based computation of significant wave heights (Hs) in deep as well as shallow water, nevertheless enabled efficient and reasonably-reliable estimations of the peak incidents.

TEM Observation of Nanocrystalline Copper During Deformation

2000 ◽  
Vol 634 ◽  
Author(s):  
Carl J. Youngdahl ◽  
Richard C. Hugo ◽  
Harriet Kung ◽  
Julia R. Weertman
Keyword(s):  
Grain Size ◽  
Stress Concentration ◽  
Real Time ◽  
Dislocation Motion ◽  
Inert Gas ◽  
Nanocrystalline Copper ◽  
Gas Condensation ◽  
Inert Gas Condensation ◽  
Small Cracks

ABSTRACTNanocrystalline samples of copper were prepared using inert gas condensation and an optimized sequence of powder outgassing and compaction. TEM specimens were cut, electropolished, and mounted in a straining stage. In situ TEM observations including real-time video were captured during straining in the microscope. Areas of presumed increased stress concentration were identified near small cracks around the perimeter of the electropolished hole. Such locations were observed in the TEM while the specimen was pulled in tension. Several microstructural changes were captured during deformation including numerous sudden shifts in contrast of grains and parts of grains, occasional dislocation motion, opening and propagation of the crack. Relationships between grain size and deformation are described.

scholarly journals Application of Multimodel Superensemble Technique on the TIGGE Suite of Operational Models

Geomatics ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 81-91
Author(s):  
Amit Bhardwaj ◽  
Vinay Kumar ◽  
Anjali Sharma ◽  
Tushar Sinha ◽  
Surendra Pratap Singh
Keyword(s):  
Real Time ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Daily Rainfall ◽  
Ensemble Prediction ◽  
Individual Member ◽  
Ensemble Prediction System ◽  
Consensus Forecasts ◽  
European Centre ◽  
Medium Range

One widely recognized portal which provides numerical weather prediction forecasts is “The Observing System Research and Predictability Experiment” (THORPEX) Interactive Grand Global Ensemble (TIGGE), an initiative of WMO project. This data portal provides forecasts from 1 to 16 days (2 weeks in advance) for many variables such as rainfall, winds, geopotential height, temperature, and relative humidity. These weather forecasting centers have delivered near-real-time (with a delay of 48 hours) ensemble prediction system data to three TIGGE data archives since October 2006. This study is based on six years (2008–2013) of daily rainfall data by utilizing output from six centers, namely the European Centre for Medium-Range Weather Forecasts, the National Centre for Environmental Prediction, the Center for Weather Forecast and Climatic Studies, the China Meteorological Agency, the Canadian Meteorological Centre, and the United Kingdom Meteorological Office, and make consensus forecasts of up to 10 days lead time by utilizing the multimodal multilinear regression technique. The prediction is made over the Indian subcontinent, including the Indian Ocean. TRMM3B42 daily rainfall is used as the benchmark to construct the multimodel superensemble (SE) rainfall forecasts. Based on statistical ability ratings, the SE offers a better near-real-time forecast than any single model. On the one hand, the model from the European Centre for Medium-Range Weather Forecasting and the UK Met Office does this more reliably over the Indian domain. In a case of Indian monsoon onset, 05 June 2014, SE carries the lowest RMSE of 8.5 mm and highest correlation of 0.49 among six member models. Overall, the performance of SE remains better than any individual member model from day 1 to day 10.

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