Limited-Area Ensemble-Based Data Assimilation

2011 ◽  
Vol 139 (7) ◽  
pp. 2025-2045 ◽  
Author(s):  
Zhiyong Meng ◽  
Fuqing Zhang
Keyword(s):  
Data Assimilation ◽  
Variational Methods ◽  
Prediction Models ◽  
Weather Prediction ◽  
Limited Area ◽  
Ensemble Kalman Filters ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Error Covariance ◽  
Numerical Weather Prediction Models

Abstract Ensemble-based data assimilation is a state estimation technique that uses short-term ensemble forecasts to estimate flow-dependent background error covariance and is best known by varying forms of ensemble Kalman filters (EnKFs). The EnKF has recently emerged as one of the primary alternatives to the variational data assimilation methods widely used in both global and limited-area numerical weather prediction models. In addition to comparing the EnKF with variational methods, this article reviews recent advances and challenges in the development and applications of the EnKF, including its hybrid with variational methods, in limited-area models that resolve weather systems from convective to meso- and regional scales.

Practical Ensemble-Based Approaches to Estimate Atmospheric Background Error Covariances for Limited-Area Deterministic Data Assimilation

2018 ◽  
Vol 146 (11) ◽  
pp. 3717-3733 ◽  
Author(s):  
Joël Bédard ◽  
Mark Buehner ◽  
Jean-François Caron ◽  
Seung-Jong Baek ◽  
Luc Fillion
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Low Cost ◽  
Time Windows ◽  
Weather Prediction ◽  
Limited Area ◽  
Ensemble Kalman Filters ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Northern United States

Abstract High-resolution flow-dependent background error covariances can allow for a better usage of dense observation networks in applications of data assimilation for numerical weather prediction. The generation of high-resolution ensembles, however, can be computationally cost prohibitive. In this study, practical and low-cost ensemble generation methods are presented and compared against both global and regional ensemble Kalman filters (G-EnKF and R-EnKF, respectively). The goal is to provide limited-area deterministic assimilation schemes with higher-resolution flow-dependent background error covariances that perform at least as well as those from the G-EnKF when assimilating the same observations. The low-cost methods are based on short-range regional ensemble forecasts initialized from 1) deterministic analysis plus balanced perturbations (filter free approach) and 2) a simplified ensemble square root filter (S-EnSRF), centered on deterministic analyses. The resulting ensembles from the different approaches are used within a 4D ensemble–variational (4D-EnVar) assimilation system covering most of Canada and the northern United States. Diagnostic results show that the mean is an important component of the ensembles. Results also show that the persistence of the homogeneous characteristics of the perturbations in the filter free approach makes this method unsuited for short assimilation time windows since some error structures take longer to develop. The S-EnSRF approach overcomes this limitation by recycling part of the prior perturbations. Results from 1-month assimilation experiments show that the S-EnSRF and R-EnKF experiments provide forecasts of similar quality to those from G-EnKF. Furthermore, results from precipitation verification indicate that the R-EnKF experiment provides the best precipitation accumulation predictions over 24-h periods.

E4DVar: Coupling an Ensemble Kalman Filter with Four-Dimensional Variational Data Assimilation in a Limited-Area Weather Prediction Model

2012 ◽  
Vol 140 (2) ◽  
pp. 587-600 ◽  
Author(s):  
Meng Zhang ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Prediction Model ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Coupled System ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Hybrid Data Assimilation

A hybrid data assimilation approach that couples the ensemble Kalman filter (EnKF) and four-dimensional variational (4DVar) methods is implemented for the first time in a limited-area weather prediction model. In this coupled system, denoted E4DVar, the EnKF and 4DVar systems run in parallel while feeding into each other. The multivariate, flow-dependent background error covariance estimated from the EnKF ensemble is used in the 4DVar minimization and the ensemble mean in the EnKF analysis is replaced by the 4DVar analysis, while updating the analysis perturbations for the next cycle of ensemble forecasts with the EnKF. Therefore, the E4DVar can obtain flow-dependent information from both the explicit covariance matrix derived from ensemble forecasts, as well as implicitly from the 4DVar trajectory. The performance of an E4DVar system is compared with the uncoupled 4DVar and EnKF for a limited-area model by assimilating various conventional observations over the contiguous United States for June 2003. After verifying the forecasts from each analysis against standard sounding observations, it is found that the E4DVar substantially outperforms both the EnKF and 4DVar during this active summer month, which featured several episodes of severe convective weather. On average, the forecasts produced from E4DVar analyses have considerably smaller errors than both of the stand-alone EnKF and 4DVar systems for forecast lead times up to 60 h.

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

scholarly journals A Transformed Lagged Ensemble Forecasting Technique for Increasing Ensemble Size

2007 ◽  
Vol 135 (4) ◽  
pp. 1424-1438 ◽  
Author(s):  
Andrew R. Lawrence ◽  
James A. Hansen
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Ensemble Size ◽  
Probabilistic Forecasting ◽  
Ensemble Forecasts ◽  
Chaotic Model ◽  
The Impact

Abstract An ensemble-based data assimilation approach is used to transform old ensemble forecast perturbations with more recent observations for the purpose of inexpensively increasing ensemble size. The impact of the transformations are propagated forward in time over the ensemble’s forecast period without rerunning any models, and these transformed ensemble forecast perturbations can be combined with the most recent ensemble forecast to sensibly increase forecast ensemble sizes. Because the transform takes place in perturbation space, the transformed perturbations must be centered on the ensemble mean from the most recent forecasts. Thus, the benefit of the approach is in terms of improved ensemble statistics rather than improvements in the mean. Larger ensemble forecasts can be used for numerous purposes, including probabilistic forecasting, targeted observations, and to provide boundary conditions to limited-area models. This transformed lagged ensemble forecasting approach is explored and is shown to give positive results in the context of a simple chaotic model. By incorporating a suitable perturbation inflation factor, the technique was found to generate forecast ensembles whose skill were statistically comparable to those produced by adding nonlinear model integrations. Implications for ensemble forecasts generated by numerical weather prediction models are briefly discussed, including multimodel ensemble forecasting.

scholarly journals Ensemble Hail Prediction for the Storms of 10 May 2010 in South-Central Oklahoma Using Single- and Double-Moment Microphysical Schemes

2017 ◽  
Vol 145 (12) ◽  
pp. 4911-4936 ◽  
Author(s):  
Jonathan Labriola ◽  
Nathan Snook ◽  
Youngsun Jung ◽  
Bryan Putnam ◽  
Ming Xue
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Radar Data ◽  
Ensemble Forecasts ◽  
South Central ◽  
Single Moment ◽  
Double Moment ◽  
Horizontal Grid ◽  
Severe Weather Event ◽  
Numerical Weather Prediction Models

Explicit prediction of hail using numerical weather prediction models remains a significant challenge; microphysical uncertainties and errors are a significant contributor to this challenge. This study assesses the ability of storm-scale ensemble forecasts using single-moment Lin or double-moment Milbrandt and Yau microphysical schemes in predicting hail during a severe weather event over south-central Oklahoma on 10 May 2010. Radar and surface observations are assimilated using an ensemble Kalman filter (EnKF) at 5-min intervals. Three sets of ensemble forecasts, launched at 15-min intervals, are then produced from EnKF analyses at times ranging from 30 min prior to the first observed hail to the time of the first observed hail. Forty ensemble members are run at 500-m horizontal grid spacing in both EnKF assimilation cycles and subsequent forecasts. Hail forecasts are verified using radar-derived products including information from single- and dual-polarization radar data: maximum estimated size of hail (MESH), hydrometeor classification algorithm (HCA) output, and hail size discrimination algorithm (HSDA) output. Resulting hail forecasts show at most marginal skill, with the level of skill dependent on the forecast initialization time and microphysical scheme used. Forecasts using the double-moment scheme predict many small hailstones aloft, while the single-moment members predict larger hailstones. Near the surface, double-moment members predict larger hailstone sizes than their single-member counterparts. Hail in the forecasts is found to melt too quickly near the surface for members using either of the microphysics schemes examined. Analysis of microphysical budgets in both schemes indicates that both schemes suboptimally represent hail processes, adversely impacting the skill of surface hail forecasts.

scholarly journals Ensemble–Variational Integrated Localized Data Assimilation

2016 ◽  
Vol 144 (10) ◽  
pp. 3677-3696 ◽  
Author(s):  
Thomas Auligné ◽  
Benjamin Ménétrier ◽  
Andrew C. Lorenc ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Deterministic System ◽  
Current State ◽  
Filter Methods ◽  
Variational Framework ◽  
Operational Systems ◽  
Numerical Weather Prediction Models ◽  
New Framework

Hybrid variational–ensemble data assimilation (hybrid DA) is widely used in research and operational systems, and it is considered the current state of the art for the initialization of numerical weather prediction models. However, hybrid DA requires a separate ensemble DA to estimate the uncertainty in the deterministic variational DA, which can be suboptimal both technically and scientifically. A new framework called the ensemble–variational integrated localized (EVIL) data assimilation addresses this inconvenience by updating the ensemble analyses using information from the variational deterministic system. The goal of EVIL is to encompass and generalize existing ensemble Kalman filter methods in a variational framework. Particular attention is devoted to the affordability and efficiency of the algorithm in preparation for operational applications.

scholarly journals Forecast Sensitivity Observation Impact in the 4DVAR and Hybrid-4DVAR Data Assimilation Systems

2019 ◽  
Vol 36 (8) ◽  
pp. 1563-1575 ◽  
Author(s):  
Sung-Min Kim ◽  
Hyun Mee Kim
Keyword(s):  
Data Assimilation ◽  
East Asia ◽  
Forecast Error ◽  
Integration Time ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Error Covariance ◽  
Atmospheric Motion ◽  
Observation Impact ◽  
Atmospheric Motion Vectors

AbstractIn this study, the observation impacts on 24-h forecast error reduction (FER), based on the adjoint method in the four-dimensional variational (4DVAR) data assimilation (DA) and hybrid-4DVAR DA systems coupled with the Unified Model, were evaluated from 0000 UTC 5 August to 1800 UTC 26 August 2014. The nonlinear FER in hybrid-4DVAR was 12.2% greater than that in 4DVAR due to the use of flow-dependent background error covariance (BEC), which was a weighted combination of the static BEC and the ensemble BEC based on ensemble forecasts. In hybrid-4DVAR, the observation impacts (i.e., the approximated nonlinear FER) for most observation types increase compared to those in 4DVAR. The increased observation impact from using hybrid-4DVAR instead of 4DVAR changes depending on the analysis time and regions. To calculate the ensemble BEC in hybrid-4DVAR, analyses at 0600 and 1800 UTC (0000 and 1200 UTC) used 3-h (9-h) ensemble forecasts. Greater observation impact was obtained when 3-h ensemble forecasts were used for the ensemble BEC at 0600 and 1800 UTC, than with 9-h ensemble forecasts at 0000 and 1200 UTC. Different from other observations, the atmospheric motion vectors (AMVs) deduced from geostationary satellite are more frequently observed in the same area. When the ensemble forecasts with longer integration times were used for the ensemble BEC in hybrid-4DVAR, the observation impact of the AMVs decreased the most in East Asia. This implies that the observation impact of AMVs in East Asia shows the highest sensitivity to the integration time of the ensemble members used for deducing the flow-dependent BEC in hybrid-4DVAR.

An Ensemble-Based Four-Dimensional Variational Data Assimilation Scheme. Part I: Technical Formulation and Preliminary Test

2008 ◽  
Vol 136 (9) ◽  
pp. 3363-3373 ◽  
Author(s):  
Chengsi Liu ◽  
Qingnong Xiao ◽  
Bin Wang
Keyword(s):  
Data Assimilation ◽  
Preliminary Test ◽  
Background Field ◽  
Variational Data Assimilation ◽  
Water Model ◽  
Observation Error ◽  
Ensemble Forecasts ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance

Abstract Applying a flow-dependent background error covariance (𝗕 matrix) in variational data assimilation has been a topic of interest among researchers in recent years. In this paper, an ensemble-based four-dimensional variational (En4DVAR) algorithm, designed by the authors, is presented that uses a flow-dependent background error covariance matrix constructed by ensemble forecasts and performs 4DVAR optimization to produce a balanced analysis. A great advantage of this En4DVAR design over standard 4DVAR methods is that the tangent linear and adjoint models can be avoided in its formulation and implementation. In addition, it can be easily incorporated into variational data assimilation systems that are already in use at operational centers and among the research community. A one-dimensional shallow water model was used for preliminary tests of the En4DVAR scheme. Compared with standard 4DVAR, the En4DVAR converges well and can produce results that are as good as those with 4DVAR but with far less computation cost in its minimization. In addition, a comparison of the results from En4DVAR with those from other data assimilation schemes [e.g., 3DVAR and ensemble Kalman filter (EnKF)] is made. The results show that the En4DVAR yields an analysis that is comparable to the widely used variational or ensemble data assimilation schemes and can be a promising approach for real-time applications. In addition, experiments were carried out to test the sensitivities of EnKF and En4DVAR, whose background error covariance is estimated from the same ensemble forecasts. The experiments indicated that En4DVAR obtained reasonably sound analysis even with larger observation error, higher observation frequency, and more unbalanced background field.

On Space Weather Data Assimilation

2020 ◽  
Author(s):  
Mihail Codrescu ◽  
Stefan Codrescu ◽  
Mariangel Fedrizzi ◽  
Claudia Borries
Keyword(s):  
Data Assimilation ◽  
Space Weather ◽  
Prediction Models ◽  
Mass Density ◽  
Weather Prediction ◽  
Weather Data ◽  
Space Systems ◽  
Use Of Data ◽  
Data Assimilation System ◽  
Numerical Weather Prediction Models

<p>Most if not all terrestrial weather prediction services today are based on data assimilation and numerical weather prediction models. Space Weather services are expected to follow a similar path towards data assimilation. However, the application of data assimilation in Space Weather requires a different implementation compared to terrestrial weather because space systems tend to be strongly forced and because the amount of data available for assimilation is critically small. In this paper we review the implementation of an ensemble Kalman filter data assimilation system based on the Space Weather Prediction Center operational Coupled Thermosphere Ionosphere Plasmasphere Electrodynamics (CTIPe) model. We present assimilation results for neutral mass density during geomagnetically quiet and disturbed conditions and discuss the future use of data assimilation for the thermosphere ionosphere system.<span> </span></p>

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