scholarly journals Performance of convection-permitting hurricane initialization and prediction during 2008-2010 with ensemble data assimilation of inner-core airborne Doppler radar observations

2011 ◽  
Vol 38 (15) ◽  
Author(s):  
Fuqing Zhang ◽  
Yonghui Weng ◽  
John F. Gamache ◽  
Frank D. Marks
Keyword(s):  
Data Assimilation ◽  
Inner Core ◽  
Doppler Radar ◽  
Radar Observations ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Airborne Doppler Radar

The Effects of Sampling Errors on the EnKF Assimilation of Inner-Core Hurricane Observations

2014 ◽  
Vol 142 (4) ◽  
pp. 1609-1630 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Fuqing Zhang ◽  
Yonghui Weng
Keyword(s):  
Data Assimilation ◽  
Hurricane Katrina ◽  
Inner Core ◽  
Doppler Radar ◽  
Mesoscale Model ◽  
Outer Core ◽  
State Variables ◽  
Sampling Errors ◽  
Relaxation Coefficient ◽  
Airborne Doppler Radar

Abstract Atmospheric data assimilation methods that estimate flow-dependent forecast statistics from ensembles are sensitive to sampling errors. This sensitivity is investigated in the context of vortex-scale hurricane data assimilation by cycling an ensemble Kalman filter to assimilate observations with a convection-permitting mesoscale model. In a set of numerical experiments, airborne Doppler radar observations are assimilated for Hurricane Katrina (2005) using an ensemble size that ranges from 30 to 300 members, and a varying degree of covariance inflation through relaxation to the prior. The range of ensemble sizes is shown to produce variations in posterior storm structure that persist for days in deterministic forecasts, with the most substantial differences appearing in the vortex outer-core wind and pressure fields. Ensembles with 60 or more members converge toward similar axisymmetric and asymmetric inner-core solutions by the end of the cycling, while producing qualitatively similar sample correlations between the state variables. Though covariance relaxation has little impact on model variables far from the observations, the structure of the inner-core vortex can benefit from a more optimal tuning of the relaxation coefficient. Results from this study provide insight into how sampling errors may affect the performance of an ensemble hurricane data assimilation system during cycling.

Predicting Hurricane Intensity and Associated Hazards: A Five-Year Real-Time Forecast Experiment with Assimilation of Airborne Doppler Radar Observations

2015 ◽  
Vol 96 (1) ◽  
pp. 25-33 ◽  
Author(s):  
Fuqing Zhang ◽  
Yonghui Weng
Keyword(s):  
Inner Core ◽  
Doppler Radar ◽  
Surface Wind ◽  
Forecast Errors ◽  
Hurricane Intensity ◽  
Radar Observations ◽  
Intensity Forecast ◽  
Probabilistic Forecasts ◽  
Atlantic Hurricanes ◽  
Airborne Doppler Radar

Abstract Performance in the prediction of hurricane intensity and associated hazards has been evaluated for a newly developed convection-permitting forecast system that uses ensemble data assimilation techniques to ingest high-resolution airborne radar observations from the inner core. This system performed well for three of the ten costliest Atlantic hurricanes: Ike (2008), Irene (2011), and Sandy (2012). Four to five days before these storms made landfall, the system produced good deterministic and probabilistic forecasts of not only track and intensity, but also of the spatial distributions of surface wind and rainfall. Averaged over all 102 applicable cases that have inner-core airborne Doppler radar observations during 2008–2012, the system reduced the day-2-to-day-4 intensity forecast errors by 25%–28% compared to the corresponding National Hurricane Center’s official forecasts (which have seen little or no decrease in intensity forecast errors over the past two decades). Empowered by sufficient computing resources, advances in both deterministic and probabilistic hurricane prediction will enable emergency management officials, the private sector, and the general public to make more informed decisions that minimize the losses of life and property.

scholarly journals Analysis of Tropical Storm Formation Based on Ensemble Data Assimilation and High-Resolution Numerical Simulations of a Nondeveloping Disturbance

2016 ◽  
Vol 144 (10) ◽  
pp. 3631-3649 ◽  
Author(s):  
Andrew B. Penny ◽  
Joshua P. Hacker ◽  
Patrick A. Harr
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Simulations ◽  
Doppler Radar ◽  
Vertical Wind Shear ◽  
Heating Rates ◽  
Low Level ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Impact

A nondeveloping tropical disturbance, identified as TCS025, was observed during three intensive observing periods during The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC)/Tropical Cyclone Structure-2008 (TCS-08) field experiment. The low-level circulation of the disturbance was relatively weak, asymmetric, and displaced a considerable distance from the midlevel circulation. An ensemble of high-resolution numerical simulations initialized from global model analyses was used to further examine TCS025. These simulations tended to unrealistically overdevelop the TCS025 disturbance. This study extends that work by examining the impact of assimilating in situ observations of TCS025 and dual-Doppler radial velocities from the airborne Electra Doppler Radar (ELDORA) using the Data Assimilation Research Testbed (DART) ensemble data assimilation system. The assimilation of observations results in a more accurate vortex structure that is consistent with the observational analysis. In addition, forecasts initialized from the state of the ensemble after data assimilation exhibit less development than both the control simulation and an ensemble of forecasts without prior data assimilation. A composite analysis of developing and nondeveloping forecasts from the ensemble reveals that convection was more active in developing simulations, especially near the low-level circulation center. This led to larger diabatic heating rates, spinup of the low-level circulation from vorticity stretching, and greater alignment of the low- and midlevel vorticity centers. In contrast, nondeveloping simulations exhibited less convection, and the circulation was more heavily impacted by vertical wind shear.

scholarly journals Storm-Centered Ensemble Data Assimilation for Tropical Cyclones

2014 ◽  
Vol 142 (6) ◽  
pp. 2309-2320 ◽  
Author(s):  
Erika L. Navarro ◽  
Gregory J. Hakim
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Inner Core ◽  
Three Dimensional ◽  
Water Model ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Relative Coordinates ◽  
Order Of Magnitude ◽  
Storm Dynamics

Abstract A significant challenge for tropical cyclone ensemble data assimilation is that storm-scale observations tend to make analyses that are more asymmetric than the prior forecasts. Compromised structure and intensity, such as an increase of amplitude across the azimuthal Fourier spectrum, are a routine property of ensemble-based analyses, even with accurate position observations and frequent assimilation. Storm dynamics in subsequent forecasts evolve these states toward axisymmetry, creating difficulty in distinguishing between model-induced and actual storm asymmetries for predictability studies and forecasting. To address this issue, a novel algorithm using a storm-centered approach is proposed. The method is designed for use with existing ensemble filters with little or no modification, facilitating its adoption and maintenance. The algorithm consists of 1) an analysis of the environment using conventional coordinates, 2) a storm-centered analysis using storm-relative coordinates, and 3) a merged analysis that combines the large-scale and storm-scale fields together at an updated storm location. This algorithm is evaluated in two sets of observing system simulation experiments (OSSEs): first, no-cycling tests of the update step for idealized three-dimensional storms in radiative–convective equilibrium; second, full cycling tests of data assimilation applied to a shallow-water model for a field of interacting vortices. Results are compared against a control experiment based on a conventional ensemble Kalman filter (EnKF) scheme as well as an alternative EnKF scheme proposed by Lawson and Hansen. The storm-relative method yields vortices that are more symmetric and exhibit finer inner-core structure than either approach, with errors reduced by an order of magnitude over a control case with prior spread consistent with the National Hurricane Center (NHC)’s mean 5-yr forecast track error at 12 h. Azimuthal Fourier error spectra exhibit much-reduced noise associated with data assimilation as compared to both the control and the Lawson and Hansen approach. An assessment of free-surface height tendency of model forecasts after the merge step reveals a balanced trend between the storm-centered and conventional approaches, with storm-centered values more closely resembling the reference state.

Impacts of 4DVAR Assimilation of Airborne Doppler Radar Observations on Numerical Simulations of the Genesis of Typhoon Nuri (2008)

2014 ◽  
Vol 53 (10) ◽  
pp. 2325-2343 ◽  
Author(s):  
Zhan Li ◽  
Zhaoxia Pu ◽  
Juanzhen Sun ◽  
Wen-Chau Lee
Keyword(s):  
Data Assimilation ◽  
Radial Velocity ◽  
Numerical Simulations ◽  
Doppler Radar ◽  
Initial Conditions ◽  
Radar Data ◽  
Radar Observations ◽  
Wind Analysis ◽  
The Impact ◽  
Airborne Doppler Radar

AbstractThe Weather Research and Forecasting Model and its four-dimensional variational data assimilation (4DVAR) system are employed to examine the impact of airborne Doppler radar observations on predicting the genesis of Typhoon Nuri (2008). Electra Doppler Radar (ELDORA) airborne radar data, collected during the Office of Naval Research–sponsored Tropical Cyclone Structure 2008 field experiment, are used for data assimilation experiments. Two assimilation methods are evaluated and compared, namely, the direct assimilation of radar-measured radial velocity and the assimilation of three-dimensional wind analysis derived from the radar radial velocity. Results show that direct assimilation of radar radial velocity leads to better intensity forecasts, as this process enhances the development of convective systems and improves the inner-core structure of Nuri, whereas assimilation of the radar-retrieved wind analysis is more beneficial for tracking forecasts, as it results in improved environmental flows. The assimilation of both the radar-retrieved wind and the radial velocity can lead to better forecasts in both intensity and tracking, if the radial velocity observations are assimilated first and the retrieved winds are then assimilated in the same data assimilation window. In addition, experiments with and without radar data assimilation led to developing and nondeveloping disturbances in numerical simulations of Nuri’s genesis. The improved initial conditions and forecasts from the data assimilation imply that the enhanced midlevel vortex and moisture conditions are favorable for the development of deep convection in the center of the pouch and eventually contribute to Nuri’s genesis. The improved simulations of the convection and associated environmental conditions produce enhanced upper-level warming in the core region and lead to the drop in sea level pressure.

Inner Core Structure of Hurricane Alicia from Airborne Doppler Radar Observations

1987 ◽  
Vol 44 (9) ◽  
pp. 1296-1317 ◽  
Author(s):  
Frank D. Marks ◽  
Robert A. Houze
Keyword(s):  
Inner Core ◽  
Doppler Radar ◽  
Core Structure ◽  
Radar Observations ◽  
Airborne Doppler Radar

Initiation of ensemble data assimilation

2006 ◽  
Author(s):  
M. Zupanski ◽  
S. J. Fletcher ◽  
I. M. Navon ◽  
B. Uzunoglu ◽  
R. P. Heikes ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Using a machine learning proxy for localization in ensemble data assimilation

2021 ◽  
Vol 25 (3) ◽  
pp. 931-944
Author(s):  
Johann M. Lacerda ◽  
Alexandre A. Emerick ◽  
Adolfo P. Pires
Keyword(s):  
Machine Learning ◽  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Maximum Likelihood Ensemble Filter: Theoretical Aspects

2005 ◽  
Vol 133 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
Milija Zupanski
Keyword(s):  
Maximum Likelihood ◽  
Data Assimilation ◽  
Cost Function ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Analysis Error ◽  
Nonlinear Observation ◽  
The Cost ◽  
Maximum Likelihood Ensemble Filter

Abstract A new ensemble-based data assimilation method, named the maximum likelihood ensemble filter (MLEF), is presented. The analysis solution maximizes the likelihood of the posterior probability distribution, obtained by minimization of a cost function that depends on a general nonlinear observation operator. The MLEF belongs to the class of deterministic ensemble filters, since no perturbed observations are employed. As in variational and ensemble data assimilation methods, the cost function is derived using a Gaussian probability density function framework. Like other ensemble data assimilation algorithms, the MLEF produces an estimate of the analysis uncertainty (e.g., analysis error covariance). In addition to the common use of ensembles in calculation of the forecast error covariance, the ensembles in MLEF are exploited to efficiently calculate the Hessian preconditioning and the gradient of the cost function. A sufficient number of iterative minimization steps is 2–3, because of superior Hessian preconditioning. The MLEF method is well suited for use with highly nonlinear observation operators, for a small additional computational cost of minimization. The consistent treatment of nonlinear observation operators through optimization is an advantage of the MLEF over other ensemble data assimilation algorithms. The cost of MLEF is comparable to the cost of existing ensemble Kalman filter algorithms. The method is directly applicable to most complex forecast models and observation operators. In this paper, the MLEF method is applied to data assimilation with the one-dimensional Korteweg–de Vries–Burgers equation. The tested observation operator is quadratic, in order to make the assimilation problem more challenging. The results illustrate the stability of the MLEF performance, as well as the benefit of the cost function minimization. The improvement is noted in terms of the rms error, as well as the analysis error covariance. The statistics of innovation vectors (observation minus forecast) also indicate a stable performance of the MLEF algorithm. Additional experiments suggest the amplified benefit of targeted observations in ensemble data assimilation.

An adaptive covariance relaxation method for ensemble data assimilation

2015 ◽  
Vol 141 (692) ◽  
pp. 2898-2906 ◽  
Author(s):  
Yue Ying ◽  
Fuqing Zhang
Keyword(s):  
Data Assimilation ◽  
Relaxation Method ◽  
Ensemble Data Assimilation ◽  
Ensemble Data
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