Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments

2006 ◽  
Vol 134 (2) ◽  
pp. 722-736 ◽  
Author(s):  
Fuqing Zhang ◽  
Zhiyong Meng ◽  
Altug Aksoy
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Mesoscale Model ◽  
Regional Scale ◽  
Model Assumption ◽  
Filter Performance ◽  
Perfect Model ◽  
Scale Data ◽  
Surface Observations

Abstract Through observing system simulation experiments, this two-part study exploits the potential of using the ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation. Part I focuses on the performance of the EnKF under the perfect model assumption in which the truth simulation is produced with the same model and same initial uncertainties as those of the ensemble, while Part II explores the impacts of model error and ensemble initiation on the filter performance. In this first part, the EnKF is implemented in a nonhydrostatic mesoscale model [the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)] to assimilate simulated sounding and surface observations derived from simulations of the “surprise” snowstorm of January 2000. This is an explosive East Coast cyclogenesis event with strong error growth at all scales as a result of interactions between convective-, meso-, and subsynoptic-scale dynamics. It is found that the EnKF is very effective in keeping the analysis close to the truth simulation under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the control experiment, in which the truth simulation was produced with the same model and same initial uncertainties as those of the ensemble, a 24-h continuous EnKF assimilation of sounding and surface observations of typical temporal and spatial resolutions is found to reduce the error by as much as 80% (compared to a 24-h forecast without data assimilation) for both observed and unobserved variables including zonal and meridional winds, temperature, and pressure. However, it is observed to be relatively less efficient in correcting errors in the vertical velocity and moisture fields, which have stronger smaller-scale components. The analysis domain-averaged root-mean-square error after 24-h assimilation is ∼1.0–1.5 m s−1 for winds and ∼1.0 K for temperature, which is comparable to or less than typical observational errors. Various sensitivity experiments demonstrated that the EnKF is quite successful in all realistic observational scenarios tested. However, as will be presented in Part II, the EnKF performance may be significantly degraded if an imperfect forecast model is used, as is likely the case when real observations are assimilated.

scholarly journals Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part II: Imperfect Model Experiments

2007 ◽  
Vol 135 (4) ◽  
pp. 1403-1423 ◽  
Author(s):  
Zhiyong Meng ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Regional Scale ◽  
Warm Season ◽  
Model Errors ◽  
Model Assumption ◽  
Event Scale ◽  
Parameterization Schemes ◽  
Scale Data

Abstract In Part I of this two-part work, the feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation through various observing system simulation experiments was demonstrated assuming a perfect forecast model for a winter snowstorm event that occurred on 24–26 January 2000. The current study seeks to explore the performance of the EnKF for the same event in the presence of significant model errors due to physical parameterizations by assimilating synthetic sounding and surface observations with typical temporal and spatial resolutions. The EnKF performance with imperfect models is also examined for a warm-season mesoscale convective vortex (MCV) event that occurred on 10–13 June 2003. The significance of model error in both warm- and cold-season events is demonstrated when the use of different cumulus parameterization schemes within different ensembles results in significantly different forecasts in terms of both ensemble mean and spread. Nevertheless, the EnKF performed reasonably well in most experiments with the imperfect model assumption (though its performance can sometimes be significantly degraded). As in Part I, where the perfect model assumption was utilized, most analysis error reduction comes from larger scales. Results show that using a combination of different physical parameterization schemes in the ensemble forecast can significantly improve filter performance. A multischeme ensemble has the potential to provide better background error covariance estimation and a smaller ensemble bias. There are noticeable differences in the performance of the EnKF for different flow regimes. In the imperfect scenarios considered, the improvement over the reference ensembles (pure ensemble forecasts without data assimilation) after 24 h of assimilation for the winter snowstorm event ranges from 36% to 67%. This is higher than the 26%–45% improvement noted after 36 h of assimilation for the warm-season MCV event. Scale- and flow-dependent error growth dynamics and predictability are possible causes for the differences in improvement. Compared to the power spectrum analyses for the snowstorm, it is found that forecast errors and ensemble spreads in the warm-season MCV event have relatively smaller power at larger scales and an overall smaller growth rate.

scholarly journals Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study

2008 ◽  
Vol 136 (2) ◽  
pp. 522-540 ◽  
Author(s):  
Zhiyong Meng ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Regional Scale ◽  
Warm Season ◽  
Real Data ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Scale Data

Abstract The feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation has been demonstrated in the authors’ recent studies via observing system simulation experiments (OSSEs) both under a perfect-model assumption and in the presence of significant model error. The current study extends the EnKF to assimilate real-data observations for a warm-season mesoscale convective vortex (MCV) event on 10–12 June 2003. Direct comparison between the EnKF and a three-dimensional variational data assimilation (3DVAR) system, both implemented in the Weather Research and Forecasting model (WRF), is carried out. It is found that the EnKF consistently performs better than the 3DVAR method by assimilating either individual or multiple data sources (i.e., sounding, surface, and wind profiler) for this MCV event. Background error covariance plays an important role in the performance of both the EnKF and the 3DVAR system. Proper covariance inflation and the use of different combinations of physical parameterization schemes in different ensemble members (the so-called multischeme ensemble) can significantly improve the EnKF performance. The 3DVAR system can benefit substantially from using short-term ensembles to improve the prior estimate (with the ensemble mean). Noticeable improvement is also achieved by including some flow dependence in the background error covariance of 3DVAR.

scholarly journals Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part IV: Comparison with 3DVAR in a Month-Long Experiment

2008 ◽  
Vol 136 (10) ◽  
pp. 3671-3682 ◽  
Author(s):  
Zhiyong Meng ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Single Case ◽  
Regional Scale ◽  
Wrf Model ◽  
Real Data ◽  
Model Errors ◽  
Scale Data

In previous works in this series study, an ensemble Kalman filter (EnKF) was demonstrated to be promising for mesoscale and regional-scale data assimilation in increasingly realistic environments. Parts I and II examined the performance of the EnKF by assimilating simulated observations under both perfect- and imperfect-model assumptions. Part III explored the application of the EnKF to a real-data case study in comparison to a three-dimensional variational data assimilation (3DVAR) method in the Weather Research and Forecasting (WRF) model. The current study extends the single-case real-data experiments over a period of 1 month to examine the long-term performance and comparison of both methods at the regional scales. It is found that the EnKF systematically outperforms 3DVAR for the 1-month period of interest in which both methods assimilate the same standard rawinsonde observations every 12 h over the central United States. Consistent with results from the real-data case study of Part III, the EnKF can benefit from using a multischeme ensemble that partially accounts for model errors in physical parameterizations. The benefit of using a multischeme ensemble (over a single-scheme ensemble) is more pronounced in the thermodynamic variables (including temperature and moisture) than in the wind fields. On average, the EnKF analyses lead to more accurate forecasts than the 3DVAR analyses when they are used to initialize 60 consecutive, deterministic 60-h forecast experiments for the month. Results also show that deterministic forecasts of up to 60 h initiated from the EnKF analyses consistently outperform the WRF forecasts initiated from the National Centers for Environmental Prediction final analysis field of the Global Forecast System.

scholarly journals Regional scale ozone data assimilation using an Ensemble Kalman Filter and the CHIMERE Chemical-Transport Model

2013 ◽  
Vol 6 (2) ◽  
pp. 3033-3083
Author(s):  
B. Gaubert ◽  
A. Coman ◽  
G. Foret ◽  
F. Meleux ◽  
A. Ung ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Transport Model ◽  
Regional Scale ◽  
Random Variable ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Background Error ◽  
Filter Performance

Abstract. The Ensemble Kalman Filter is an efficient algorithm for data assimilation; it allows for an estimation of forecast and analysis error by updating the model error covariance matrices at the analysis step. This algorithm has been coupled to the CHIMERE chemical transport model in order to assimilate ozone ground measurements at the regional scale. The analyzed ozone field is evaluated using a consistent set of observations and shows a reduction of the quadratic error by about a third and an improvement of the hourly correlation coefficient despite of a low ensemble size designed for operational purposes. A classification of the European observation network is derived from the ozone temporal variability in order to qualitatively determine the observation spatial representativeness. Then, an estimation of the temporal behavior of both model and observations error variances of the assimilated stations is checked using a posteriori Desroziers diagnostics. The amplitude of the additive noise applied to the ozone fields can be diagnosed and tuned online. The evaluation of the obtained background error variance distribution through the Reduced Centered Random Variable standard deviation shows improved statistics. The use of the diagnostics indicates a strong diurnal cycle of both the model and the representativeness errors. Another design of the ensemble is constructed by perturbing model parameter, but does not allow creating enough variability if used solely. Finally, the overall filter performance over evaluation stations is found to be relatively unaffected by different formulations of observation and simulation errors.

Influence of Surface Observations in Mesoscale Data Assimilation Using an Ensemble Kalman Filter

2014 ◽  
Vol 142 (4) ◽  
pp. 1489-1508 ◽  
Author(s):  
So-Young Ha ◽  
Chris Snyder
Keyword(s):  
Boundary Layer ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Positive Impact ◽  
Precipitation Forecast ◽  
Squall Line ◽  
Horizontal Gradient ◽  
Retrospective Case ◽  
Surface Observations

Abstract The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach was successfully performed in the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed (DART) system. The mesoscale cycling experiment for the continuous ensemble data assimilation was verified against independent surface mesonet observations and demonstrated the positive impact on short-range forecasts over the contiguous U.S. (CONUS) domain throughout the month-long period of June 2008. The EnKF assimilation of surface observations was found useful for systematically improving the simulation of the depth and the structure of the planetary boundary layer (PBL) and the reduction of surface bias errors. These benefits were extended above PBL and resulted in a better precipitation forecast for up to 12 h. With the careful specification of observation errors, not only the reliability of the ensemble system but also the quality of the following forecast was improved, especially in moisture. In this retrospective case study of a squall line, assimilation of surface observations produced analysis increments consistent with the structure and dynamics of the boundary layer. As a result, it enhanced the horizontal gradient of temperature and moisture across the frontal system to provide a favorable condition for the convective initiation and the following heavy rainfall prediction in the Oklahoma Panhandle. Even with the assimilation of upper-level observations, the analysis without the assimilation of surface observations simulated a surface cold front that was much weaker and slower than observed.

scholarly journals Influence of Assimilating Surface Observations on Numerical Prediction of Landfalls of Hurricane Katrina (2005) with an Ensemble Kalman Filter

2014 ◽  
Vol 142 (8) ◽  
pp. 2915-2934 ◽  
Author(s):  
Hailing Zhang ◽  
Zhaoxia Pu
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Hurricane Katrina ◽  
Ensemble Kalman Filter ◽  
Surface Wind ◽  
Initial Time ◽  
Filter Method ◽  
Low Level ◽  
The Impact ◽  
Surface Observations

Abstract A series of numerical experiments are conducted to examine the impact of surface observations on the prediction of landfalls of Hurricane Katrina (2005), one of the deadliest disasters in U.S. history. A specific initial time (0000 UTC 25 August 2005), which led to poor prediction of Hurricane Katrina in several previous studies, is selected to begin data assimilation experiments. Quick Scatterometer (QuikSCAT) ocean surface wind vectors and surface mesonet observations are assimilated with the minimum central sea level pressure and conventional observations from NCEP into an Advanced Research version of the Weather Research and Forecasting Model (WRF) using an ensemble Kalman filter method. Impacts of data assimilation on the analyses and forecasts of Katrina’s track, landfalling time and location, intensity, structure, and rainfall are evaluated. It is found that the assimilation of QuikSCAT and mesonet surface observations can improve prediction of the hurricane track and structure through modifying low-level thermal and dynamical fields such as wind, humidity, and temperature and enhancing low-level convergence and vorticity. However, assimilation of single-level surface observations alone does not ensure reasonable intensity forecasts because of the lack of constraint on the mid- to upper troposphere. When surface observations are assimilated with other conventional data, obvious enhancements are found in the forecasts of track and intensity, realistic convection, and surface wind structures. More importantly, surface data assimilation results in significant improvements in quantitative precipitation forecasts (QPFs) during landfalls.

scholarly journals Pseudo-Orbit Data Assimilation. Part I: The Perfect Model Scenario

2014 ◽  
Vol 71 (2) ◽  
pp. 469-482 ◽  
Author(s):  
Hailiang Du ◽  
Leonard A. Smith
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
State Estimation ◽  
Ensemble Kalman Filter ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Attractive Alternative ◽  
Perfect Model ◽  
Improved Performance ◽  
Orbit Data

Abstract State estimation lies at the heart of many meteorological tasks. Pseudo-orbit-based data assimilation provides an attractive alternative approach to data assimilation in nonlinear systems such as weather forecasting models. In the perfect model scenario, noisy observations prevent a precise estimate of the current state. In this setting, ensemble Kalman filter approaches are hampered by their foundational assumptions of dynamical linearity, while variational approaches may fail in practice owing to local minima in their cost function. The pseudo-orbit data assimilation approach improves state estimation by enhancing the balance between the information derived from the dynamic equations and that derived from the observations. The potential use of this approach for numerical weather prediction is explored in the perfect model scenario within two deterministic chaotic systems: the two-dimensional Ikeda map and 18-dimensional Lorenz96 flow. Empirical results demonstrate improved performance over that of the two most common traditional approaches of data assimilation (ensemble Kalman filter and four-dimensional variational assimilation).

Using Precipitation Observations in a Mesoscale Short-Range Ensemble Analysis and Forecasting System

2008 ◽  
Vol 23 (3) ◽  
pp. 357-372 ◽  
Author(s):  
Tadashi Fujita ◽  
David J. Stensrud ◽  
David C. Dowell
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Grid Point ◽  
Precipitation Data ◽  
Simple Method ◽  
Forecasting System ◽  
Filter Approach ◽  
Water Vapor Mixing Ratio ◽  
Surface Observations

Abstract A simple method to assimilate precipitation data from a synthesis of radar and gauge data is developed to operate alongside an ensemble Kalman filter that assimilates hourly surface observations. The mesoscale ensemble forecast system consists of 25 members with 30-km grid spacing and incorporates variability in both initial and boundary conditions and model physical process schemes. The precipitation assimilation method only incorporates information on when and where rainfall is observed. Model temperature and water vapor mixing ratio profiles at each grid point are modified if rainfall is observed but not predicted, or if rainfall is predicted but not observed. These modifications act to either increase or decrease, respectively, the likelihood that precipitation develops at that grid point. Two cases are examined in which this technique is applied to assimilate precipitation data every 15 min from 1200 to 1800 UTC, while hourly surface observations are also assimilated at the same time using the more sophisticated ensemble Kalman filter approach. Results show that the simple method for assimilating precipitation data helps the model develop precipitation where it is observed, resulting in the precipitation area being reproduced more accurately than in the run without precipitation-data assimilation, while not negatively influencing the positive results from the surface data assimilation. Improvement is also seen in the reliability of precipitation probabilities for a 1 mm h−1 threshold after the assimilation period, indicating that assimilating precipitation data may provide improved forecasts of the mesoscale environment for a few hours.

A Multicase Comparative Assessment of the Ensemble Kalman Filter for Assimilation of Radar Observations. Part I: Storm-Scale Analyses

2009 ◽  
Vol 137 (6) ◽  
pp. 1805-1824 ◽  
Author(s):  
Altuğ Aksoy ◽  
David C. Dowell ◽  
Chris Snyder
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Cloud Model ◽  
Wrf Model ◽  
Horizontal Wind ◽  
Observation Error ◽  
Ensemble Spread ◽  
Radar Observations ◽  
Filter Performance

Abstract The effectiveness of the ensemble Kalman filter (EnKF) for assimilating radar observations at convective scales is investigated for cases whose behaviors span supercellular, linear, and multicellular organization. The parallel EnKF algorithm of the Data Assimilation Research Testbed (DART) is used for data assimilation, while the Weather Research and Forecasting (WRF) Model is employed as a simplified cloud model at 2-km horizontal grid spacing. In each case, reflectivity and radial velocity measurements are utilized from a single Weather Surveillance Radar-1988 Doppler (WSR-88D) within the U.S. operational network. Observations are assimilated every 2 min for a duration of 60 min and correction of folded radial velocities occurs within the EnKF. Initial ensemble uncertainty includes random perturbations to the horizontal wind components of the initial environmental sounding. The EnKF performs effectively and with robust results across all the cases. Over the first 18–30 min of assimilation, the rms and domain-averaged prior fits to observations in each case improve significantly from their initial levels, reaching comparable values of 3–6 m s−1 and 7–10 dBZ. Representation of mesoscale uncertainty, albeit in the simplest form of initial sounding perturbations, is a critical part of the assimilation system, as it increases ensemble spread and improves filter performance. In addition, assimilation of “no precipitation” observations (i.e., reflectivity observations with values small enough to indicate the absence of precipitation) serves to suppress spurious convection in ensemble members. At the same time, it is clear that the assimilation is far from optimal, as the ensemble spread is consistently smaller than what would be expected from the innovation statistics and the assumed observation-error variance.

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