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 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 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.

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.

scholarly journals A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

2015 ◽  
Vol 30 (5) ◽  
pp. 1158-1181 ◽  
Author(s):  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Morris L. Weisman ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Wrf Model ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

scholarly journals Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

2016 ◽  
Vol 20 (1) ◽  
pp. 505-527 ◽  
Author(s):  
L. Foresti ◽  
M. Reyniers ◽  
A. Seed ◽  
L. Delobbe
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Dynamic Scaling ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Hydraulic Models ◽  
Radar Images ◽  
Probabilistic Forecasts

Abstract. The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e., the observation that large-scale rainfall structures are more persistent and predictable than small-scale convective cells. This paper presents the development, adaptation and verification of the STEPS system for Belgium (STEPS-BE). STEPS-BE provides in real-time 20-member ensemble precipitation nowcasts at 1 km and 5 min resolutions up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h−1 are reliable up to 60–90  min lead time, while the ones of exceeding 5.0 mm h−1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 75–90 % of the forecast errors.

scholarly journals Development and verification of a real-time stochastic precipitation nowcasting system for urban hydrology in Belgium

2015 ◽  
Vol 12 (7) ◽  
pp. 6831-6879 ◽  
Author(s):  
L. Foresti ◽  
M. Reyniers ◽  
A. Seed ◽  
L. Delobbe
Keyword(s):  
Real Time ◽  
Lead Time ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Dynamic Scaling ◽  
Ensemble Prediction System ◽  
Stochastic Perturbations ◽  
Hydraulic Models ◽  
Radar Images ◽  
Probabilistic Forecasts

Abstract. The Short-Term Ensemble Prediction System (STEPS) is implemented in real-time at the Royal Meteorological Institute (RMI) of Belgium. The main idea behind STEPS is to quantify the forecast uncertainty by adding stochastic perturbations to the deterministic Lagrangian extrapolation of radar images. The stochastic perturbations are designed to account for the unpredictable precipitation growth and decay processes and to reproduce the dynamic scaling of precipitation fields, i.e. the observation that large scale rainfall structures are more persistent and predictable than small scale convective cells. This paper presents the development, adaptation and verification of the system STEPS for Belgium (STEPS-BE). STEPS-BE provides in real-time 20 member ensemble precipitation nowcasts at 1 km and 5 min resolution up to 2 h lead time using a 4 C-band radar composite as input. In the context of the PLURISK project, STEPS forecasts were generated to be used as input in sewer system hydraulic models for nowcasting urban inundations in the cities of Ghent and Leuven. Comprehensive forecast verification was performed in order to detect systematic biases over the given urban areas and to analyze the reliability of probabilistic forecasts for a set of case studies in 2013 and 2014. The forecast biases over the cities of Leuven and Ghent were found to be small, which is encouraging for future integration of STEPS nowcasts into the hydraulic models. Probabilistic forecasts of exceeding 0.5 mm h-1 are reliable up to 60–90 min lead time, while the ones of exceeding 5.0 mm h-1 are only reliable up to 30 min. The STEPS ensembles are slightly under-dispersive and represent only 80–90 % of the forecast errors.

scholarly journals Development and Evaluation of an Objective Criterion for the Real-Time Prediction of Indian Summer Monsoon Onset in a Coupled Model Framework

2015 ◽  
Vol 28 (15) ◽  
pp. 6234-6248 ◽  
Author(s):  
Susmitha Joseph ◽  
A. K. Sahai ◽  
S. Abhilash ◽  
R. Chattopadhyay ◽  
N. Borah ◽  
...  
Keyword(s):  
Real Time ◽  
Large Scale ◽  
Coupled Model ◽  
Monsoon Onset ◽  
Ensemble Prediction ◽  
Model Framework ◽  
Objective Criterion ◽  
Ensemble Prediction System ◽  
The Real ◽  
Low Level

Abstract This study reports an objective criterion for the real-time extended-range prediction of monsoon onset over Kerala (MOK), using circulation as well as rainfall information from the 16 May initial conditions of the Grand Ensemble Prediction System based on the coupled model CFSv2. Three indices are defined, one from rainfall measured over Kerala and the others based on the strength and depth of the low-level westerly jet over the Arabian Sea. While formulating the criterion, the persistence of both rainfall and low-level wind after the MOK date has been considered to avoid the occurrence of “bogus onsets” that are unrelated to the large-scale monsoon system. It is found that the predicted MOK date matches well with the MOK date declared by the India Meteorological Department, the authorized principal weather forecasting agency under the government of India, for the period 2001–14. The proposed criterion successfully avoids predicting bogus onsets, which is a major challenge in the prediction of MOK. Furthermore, the evolution of various model-predicted large-scale and local meteorological parameters corresponding to the predicted MOK date is in good agreement with that of the observation, suggesting the robustness of the devised criterion and the suitability of CFSv2 model for MOK prediction. However, it should be noted that the criterion proposed in the present study can be used only in the dynamical prediction framework, as it necessitates input data on the future evolution of rainfall and low-level wind.

scholarly journals NCAR’s Experimental Real-Time Convection-Allowing Ensemble Prediction System

2015 ◽  
Vol 30 (6) ◽  
pp. 1645-1654 ◽  
Author(s):  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Ryan A. Sobash ◽  
Kathryn R. Fossell ◽  
Morris L. Weisman
Keyword(s):  
Real Time ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Experimental System ◽  
Severe Weather ◽  
Ensemble Prediction ◽  
Limited Area ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Expository Paper

Abstract This expository paper documents an experimental, real-time, 10-member, 3-km, convection-allowing ensemble prediction system (EPS) developed at the National Center for Atmospheric Research (NCAR) in spring 2015. The EPS is particularly unique in that continuously cycling, limited-area, mesoscale ensemble Kalman filter analyses provide diverse initial conditions. In addition to describing the EPS configurations, initial forecast assessments are presented that suggest the EPS can provide valuable severe weather guidance and skillful predictions of precipitation. The EPS output is available to operational forecasters, many of whom have incorporated the products into their toolboxes. Given such rapid embrace of an experimental system by the operational community, acceleration of convection-allowing EPS development is encouraged.

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