scholarly journals Impact of Phased-Array Radar Observations over a Short Assimilation Period: Observing System Simulation Experiments Using an Ensemble Kalman Filter

2010 ◽  
Vol 138 (2) ◽  
pp. 517-538 ◽  
Author(s):  
Nusrat Yussouf ◽  
David J. Stensrud
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Phased Array ◽  
System Simulation ◽  
Scale Model ◽  
Simulation Experiments ◽  
Phased Array Radar ◽  
Weather Phenomenon ◽  
Data Assimilation Technique

Abstract The conventional Weather Surveillance Radar-1988 Doppler (WSR-88D) scans a given weather phenomenon in approximately 5 min, and past results suggest that it takes 30–60 min to establish a storm into a model assimilating these data using an ensemble Kalman filter (EnKF) data assimilation technique. Severe-weather events, however, can develop and evolve very rapidly. Therefore, assimilating observations for a 30–60-min period prior to the availability of accurate analyses may not be feasible in an operational setting. A shorter assimilation period also is desired if forecasts are produced to increase the warning lead time. With the advent of the emerging phased-array radar (PAR) technology, it is now possible to scan the same weather phenomenon in less than 1 min. Therefore, it is of interest to see if the faster scanning rate of PAR can yield improvements in storm-scale analyses and forecasts from assimilating over a shorter period of time. Observing system simulation experiments are conducted to evaluate the ability to quickly initialize a storm into a numerical model using PAR data in place of WSR-88D data. Synthetic PAR and WSR-88D observations of a splitting supercell storm are created from a storm-scale model run using a realistic volume-averaging technique in native radar coordinates. These synthetic reflectivity and radial velocity observations are assimilated into the same storm-scale model over a 15-min period using an EnKF data assimilation technique followed by a 50-min ensemble forecast. Results indicate that assimilating PAR observations at 1-min intervals over a short 15-min period yields significantly better analyses and ensemble forecasts than those produced using WSR-88D observations. Additional experiments are conducted in which the adaptive scanning capability of PAR is utilized for thunderstorms that are either very close to or far away from the radar location. Results show that the adaptive scanning capability improves the analyses and forecasts when compared with the nonadaptive PAR data. These results highlight the potential for flexible rapid-scanning PAR observations to help to quickly and accurately initialize storms into numerical models yielding improved storm-scale analyses and very short range forecasts.

An ensemble Kalman filter data assimilation system for the martian atmosphere: Implementation and simulation experiments

Icarus ◽  
2010 ◽  
Vol 209 (2) ◽  
pp. 470-481 ◽  
Author(s):  
Matthew J. Hoffman ◽  
Steven J. Greybush ◽  
R. John Wilson ◽  
Gyorgyi Gyarmati ◽  
Ross N. Hoffman ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Martian Atmosphere ◽  
Simulation Experiments ◽  
Data Assimilation System ◽  
Assimilation System

Meteorological Studies with the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter

2008 ◽  
Author(s):  
Tian-You Yu ◽  
Ming Xue ◽  
Mark Yeary ◽  
Robert Palmer ◽  
Sebastian Torres ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Phased Array ◽  
Weather Radar

Using ensemble Kalman filter to determine parameters for computational crowd dynamics simulations

2018 ◽  
Vol 35 (7) ◽  
pp. 2612-2628 ◽  
Author(s):  
Fumiya Togashi ◽  
Takashi Misaka ◽  
Rainald Löhner ◽  
Shigeru Obayashi
Keyword(s):  
Numerical Simulation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Numerical Simulations ◽  
Ensemble Kalman Filter ◽  
Input Parameter ◽  
Observation Data ◽  
Pedestrian Flow ◽  
Content Type ◽  
Data Assimilation Technique

Purpose It is of paramount importance to ensure safe and fast evacuation routes in cities in case of natural disasters, environmental accidents or acts of terrorism. The same applies to large-scale events such as concerts, sport events and religious pilgrimages as airports and to traffic hubs such as airports and train stations. The prediction of pedestrian is notoriously difficult because it varies depending on circumstances (age group, cultural characteristics, etc.). In this study, the Ensemble Kalman Filter (EnKF) data assimilation technique, which uses the updated observation data to improve the accuracy of the simulation, was applied to improve the accuracy of numerical simulations of pedestrian flow. Design/methodology/approach The EnKF, one of the data assimilation techniques, was applied to the in-house numerical simulation code for pedestrian flow. Two cases were studied in this study. One was the simplified one-directional experimental pedestrian flow. The other was the real pedestrian flow at the Kaaba in Mecca. First, numerical simulations were conducted using the empirical input parameter sets. Then, using the observation data, the EnKF estimated the appropriate input parameter sets. Finally, the numerical simulations using the estimated parameter sets were conducted. Findings The EnKF worked on the numerical simulations of pedestrian flow very effectively. In both cases: simplified experiment and real pedestrian flow, the EnKF estimated the proper input parameter sets which greatly improved the accuracy of the numerical simulation. The authors believe that the technique such as EnKF could also be used effectively in other fields of computational engineering where simulations and data have to be merged. Practical implications This technique can be used to improve both design and operational implementations of pedestrian and crowd dynamics predictions. It should be of high interest to command and control centers for large crowd events such as concerts, airports, train stations and pilgrimage centers. Originality/value To the authors’ knowledge, the data assimilation technique has not been applied to a numerical simulation of pedestrian flow, especially to the real pedestrian flow handling millions pedestrian such as the Mataf at the Kaaba. This study validated the capability and the usefulness of the data assimilation technique to numerical simulations for pedestrian flow.

Meteorological Studies with the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter

2007 ◽  
Author(s):  
Tian-You Yu ◽  
Ming Xue ◽  
Mark Yeary ◽  
Robert Palmer ◽  
Sebestian Torres ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Phased Array ◽  
Weather Radar

Meteorological Studies With the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter

2006 ◽  
Author(s):  
Tian-You Yu ◽  
Ming Xue ◽  
Mark Yeary ◽  
Robert Palmer ◽  
Sebastian Torres ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Phased Array ◽  
Weather Radar

scholarly journals Assimilation of High-Resolution Tropical Cyclone Observations with an Ensemble Kalman Filter Using NOAA/AOML/HRD’s HEDAS: Evaluation of the 2008–11 Vortex-Scale Analyses

2013 ◽  
Vol 141 (6) ◽  
pp. 1842-1865 ◽  
Author(s):  
Altuğ Aksoy ◽  
Sim D. Aberson ◽  
Tomislava Vukicevic ◽  
Kathryn J. Sellwood ◽  
Sylvie Lorsolo ◽  
...  
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Inner Core ◽  
Weather Research And Forecasting ◽  
Planetary Boundary Layer Parameterization ◽  
Data Assimilation Technique ◽  
Horizontal Grid

Abstract The Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) is developed to assimilate tropical cyclone inner-core observations for high-resolution vortex initialization. It is based on a serial implementation of the square root ensemble Kalman filter (EnKF). In this study, HWRF is used in an experimental configuration with horizontal grid spacing of 9 (3) km on the outer (inner) domain. HEDAS is applied to 83 cases from years 2008 to 2011. With the exception of two Hurricane Hilary (2011) cases in the eastern North Pacific basin, all cases are observed in the Atlantic basin. Observed storm intensity for these cases ranges from tropical depression to category-4 hurricane. Overall, it is found that high-resolution tropical cyclone observations, when assimilated with an advanced data assimilation technique such as the EnKF, result in analyses of the primary circulation that are realistic in terms of intensity, wavenumber-0 radial structure, as well as wavenumber-1 azimuthal structure. Representing the secondary circulation in the analyses is found to be more challenging with systematic errors in the magnitude and depth of the low-level radial inflow. This is believed to result from a model bias in the experimental HWRF caused by the overdiffusive nature of the planetary boundary layer parameterization utilized. Thermodynamic deviations from the observed structure are believed to be caused by both an imbalance between the number of the kinematic and thermodynamic observations in general and the suboptimal ensemble covariances between kinematic and thermodynamic fields. Future plans are discussed to address these challenges.

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