scholarly journals Convection-Permitting Forecasts Initialized with Continuously Cycling Limited-Area 3DVAR, Ensemble Kalman Filter, and “Hybrid” Variational–Ensemble Data Assimilation Systems

2014 ◽  
Vol 142 (2) ◽  
pp. 716-738 ◽  
Author(s):  
Craig S. Schwartz ◽  
Zhiquan Liu
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Three Dimensional ◽  
Skill Score ◽  
Limited Area ◽  
Computational Domain ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Precipitation Characteristics ◽  
High Precipitation

Abstract Analyses with 20-km horizontal grid spacing were produced from parallel continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems between 0000 UTC 6 May and 0000 UTC 21 June 2011 over a domain spanning the contiguous United States. Beginning 9 May, the 0000 UTC analyses initialized 36-h Weather Research and Forecasting Model (WRF) forecasts containing a large convection-permitting 4-km nest. These 4-km 3DVAR-, EnSRF-, and hybrid-initialized forecasts were compared to benchmark WRF forecasts initialized by interpolating 0000 UTC Global Forecast System (GFS) analyses onto the computational domain. While important differences regarding mean state characteristics of the 20-km DA systems were noted, verification efforts focused on the 4-km precipitation forecasts. The 3DVAR-, hybrid-, and EnSRF-initialized 4-km precipitation forecasts performed similarly regarding general precipitation characteristics, such as timing of the diurnal cycle, and all three forecast sets had high precipitation biases at heavier rainfall rates. However, meaningful differences emerged regarding precipitation placement as quantified by the fractions skill score. For most forecast hours, the hybrid-initialized 4-km precipitation forecasts were better than the EnSRF-, 3DVAR-, and GFS-initialized forecasts, and the improvement was often statistically significant at the 95th percentile. These results demonstrate the potential of limited-area continuously cycling hybrid DA configurations and suggest additional hybrid development is warranted.

Comparing Limited-Area 3DVAR and Hybrid Variational-Ensemble Data Assimilation Methods for Typhoon Track Forecasts: Sensitivity to Outer Loops and Vortex Relocation

2013 ◽  
Vol 141 (12) ◽  
pp. 4350-4372 ◽  
Author(s):  
Craig S. Schwartz ◽  
Zhiquan Liu ◽  
Xiang-Yu Huang ◽  
Ying-Hwa Kuo ◽  
Chin-Tzu Fong
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Limited Area ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Typhoon Track ◽  
The Mean ◽  
The Impact

Abstract The Weather Research and Forecasting Model (WRF) “hybrid” variational-ensemble data assimilation (DA) algorithm was used to initialize WRF model forecasts of three tropical cyclones (TCs). The hybrid-initialized forecasts were compared to forecasts initialized by WRF's three-dimensional variational (3DVAR) DA system. An ensemble adjustment Kalman filter (EAKF) updated a 32-member WRF-based ensemble system that provided flow-dependent background error covariances for the hybrid. The 3DVAR, hybrid, and EAKF configurations cycled continuously for ~3.5 weeks and produced new analyses every 6 h that initialized 72-h WRF forecasts with 45-km horizontal grid spacing. Additionally, the impact of employing a TC relocation technique and using multiple outer loops (OLs) in the 3DVAR and hybrid minimizations were explored. Model output was compared to conventional, dropwindsonde, and TC “best track” observations. On average, the hybrid produced superior forecasts compared to 3DVAR when only one OL was used during minimization. However, when three OLs were employed, 3DVAR forecasts were dramatically improved but the mean hybrid performance changed little. Additionally, incorporation of TC relocation within the cycling systems further improved the mean 3DVAR-initialized forecasts but the average hybrid-initialized forecasts were nearly unchanged.

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.

scholarly journals Initial Conditions for Convective-Scale Ensemble Forecasting Provided by Ensemble Data Assimilation

2015 ◽  
Vol 143 (5) ◽  
pp. 1583-1600 ◽  
Author(s):  
Florian Harnisch ◽  
Christian Keil
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Small Scale ◽  
Limited Area ◽  
Ensemble Forecasts ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Convective Scale ◽  
Data Assimilation System ◽  
Assimilation System

Abstract A kilometer-scale ensemble data assimilation system (KENDA) based on a local ensemble transform Kalman filter (LETKF) has been developed for the Consortium for Small-Scale Modeling (COSMO) limited-area model. The data assimilation system provides an analysis ensemble that can be used to initialize ensemble forecasts at a horizontal grid resolution of 2.8 km. Convective-scale ensemble forecasts over Germany using ensemble initial conditions derived by the KENDA system are evaluated and compared to operational forecasts with downscaled initial conditions for a short summer period during June 2012. The choice of the inflation method applied in the LETKF significantly affects the ensemble analysis and forecast. Using a multiplicative background covariance inflation does not produce enough spread in the analysis ensemble leading to a degradation of the ensemble forecasts. Inflating the analysis ensemble instead by either multiplicative analysis covariance inflation or relaxation inflation methods enhances the analysis spread and is able to provide initial conditions that produce more consistent ensemble forecasts. The forecast quality for short forecast lead times up to 3 h is improved, and 21-h forecasts also benefit from the increased spread. Doubling the ensemble size has not only a clear positive impact on the analysis but also on the short-term ensemble forecasts, while a simple representation of model error perturbing parameters of the model physics has only a small impact. Precipitation and surface wind speed ensemble forecasts using the high-resolution KENDA-derived initial conditions are competitive compared to the operationally used downscaled initial conditions.

scholarly journals Improving Large-Domain Convection-Allowing Forecasts with High-Resolution Analyses and Ensemble Data Assimilation

2016 ◽  
Vol 144 (5) ◽  
pp. 1777-1803 ◽  
Author(s):  
Craig S. Schwartz
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Weather Research And Forecasting ◽  
Analysis Method ◽  
Grid Spacing ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Horizontal Length ◽  
Horizontal Grid

Analyses with 20-km horizontal grid spacing were produced from continuously cycling three-dimensional variational (3DVAR), ensemble square root Kalman filter (EnSRF), and “hybrid” variational–ensemble data assimilation (DA) systems over a domain spanning the conterminous United States. These analyses initialized 36-h Weather Research and Forecasting Model forecasts containing a large convection-allowing 4-km nested domain, where downscaled 20-km 3DVAR, EnSRF, and hybrid analyses initialized the 4-km forecasts. Overall, hybrid analyses initialized the best 4-km precipitation forecasts. Furthermore, whether 4-km precipitation forecasts could be improved by initializing them with true 4-km analyses was assessed. As it was computationally infeasible to produce 4-km continuously cycling ensembles over the large 4-km domain, several “dual-resolution” hybrid DA configurations were adopted where 4-km backgrounds were combined with 20-km ensembles to produce 4-km hybrid analyses. Additionally, 4-km 3DVAR analyses were produced. In both hybrid and 3DVAR frameworks, initializing 4-km forecasts with true 4-km analyses, rather than downscaled 20-km analyses, yielded superior precipitation forecasts over the first 12 h. Differences between forecasts initialized from 4-km and downscaled 20-km hybrid analyses were smaller for 18–36-h forecasts, but there were occasionally meaningful differences. Continuously cycling the 4-km backgrounds and using static background error covariances with larger horizontal length scales in the hybrid led to better forecasts. All hybrid-initialized forecasts, including those initialized from downscaled 20-km analyses, were more skillful than forecasts initialized from 4-km 3DVAR analyses, suggesting the analysis method was more important than analysis resolution.

scholarly journals E3DVar: Coupling an Ensemble Kalman Filter with Three-Dimensional Variational Data Assimilation in a Limited-Area Weather Prediction Model and Comparison to E4DVar

2013 ◽  
Vol 141 (3) ◽  
pp. 900-917 ◽  
Author(s):  
Fuqing Zhang ◽  
Meng Zhang ◽  
Jonathan Poterjoy
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Variational Data Assimilation ◽  
Lead Times ◽  
Limited Area ◽  
Model Errors ◽  
Mean Square Errors

Abstract This study examines the performance of a hybrid ensemble-variational data assimilation system (E3DVar) that couples an ensemble Kalman filter (EnKF) with the three-dimensional variational data assimilation (3DVar) system for the Weather Research and Forecasting (WRF) Model. The performance of E3DVar and the component EnKF and 3DVar systems are compared over the eastern United States for June 2003. Conventional sounding and surface observations as well as data from wind profilers, aircraft and ships, and cloud-tracked winds from satellites, are assimilated every 6 h during the experiments, and forecasts are verified using standard sounding observations. Forecasts with 12- to 72-h lead times are found to have noticeably smaller root-mean-square errors when initialized with the E3DVar system, as opposed to the EnKF, especially for the 12-h wind and moisture fields. The E3DVar system demonstrates similar performance as an EnKF, while using less than half the number of ensemble members, and is less sensitive to the use of a multiphysics ensemble to account for model errors. The E3DVar system is also compared with a similar hybrid method that replaces the 3DVar component with the WRF four-dimensional variational data assimilation (4DVar) method (denoted E4DVar). The E4DVar method demonstrated considerable improvements over E3DVar for nearly all model levels and variables at the shorter forecast lead times (12–48 h), but the forecast accuracies of all three ensemble-based methods (EnKF, E3DVar, and E4DVar) converge to similar results at longer lead times (60–72 h). Nevertheless, all methods that used ensemble information produced considerably better forecasts than the two methods that relied solely on static background error covariance (i.e., 3DVar and 4DVar).

scholarly journals A composite state method for ensemble data assimilation with multiple limited-area models

2015 ◽  
Vol 67 (1) ◽  
pp. 26495 ◽  
Author(s):  
Matthew Kretschmer ◽  
Brian R. Hunt ◽  
Edward Ott ◽  
Craig H. Bishop ◽  
Sabrina Rainwater ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Limited Area ◽  
Composite State ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
State Method

Parameter estimation for ocean biogeochemical component in a global model using Ensemble Kalman Filter: a twin experiment

2021 ◽  
Author(s):  
Tarkeshwar Singh ◽  
Francois Counillon ◽  
Jerry F. Tjiputra ◽  
Mohamad El Gharamti
Keyword(s):  
Parameter Estimation ◽  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Earth System Model ◽  
System Model ◽  
Earth System ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Parameter Values

<p>Ocean biogeochemical (BGC) models utilize a large number of poorly-constrained global parameters to mimic unresolved processes and reproduce the observed complex spatio-temporal patterns. Large model errors stem primarily from inaccuracies in these parameters whose optimal values can vary both in space and time. This study aims to demonstrate the ability of ensemble data assimilation (DA) methods to provide high-quality and improved BGC parameters within an Earth system model in idealized twin experiment framework.  We use the Norwegian Climate Prediction Model (NorCPM), which combines the Norwegian Earth System Model with the Dual-One-Step ahead smoothing-based Ensemble Kalman Filter (DOSA-EnKF). The work follows on Gharamti et al. (2017) that successfully demonstrates the approach for one-dimensional idealized ocean BGC models. We aim to estimate five spatially varying BGC parameters by assimilating Salinity and Temperature hydrographic profiles and surface BGC (Phytoplankton, Nitrate, Phosphorous, Silicate, and Oxygen) observations in a strongly coupled DA framework – i.e., jointly updating ocean and BGC state-parameters during the assimilation. The method converges quickly (less than a year), largely reducing the errors in the BGC parameters and eventually it is shown to perform nearly as well as that of the system with true parameter values. Optimal parameter values can also be recovered by assimilating climatological BGC observations and challenging sparse observational networks. The findings of this study demonstrate the applicability of the approach for tuning the system in a real framework.</p><p> </p><p><strong>References</strong>:</p><p>Gharamti, M. E., Tjiputra, J., Bethke, I., Samuelsen, A., Skjelvan, I., Bentsen, M., & Bertino, L. (2017). Ensemble data assimilation for ocean biogeochemical state and parameter estimation at different sites. Ocean Modelling, 112, 65-89.</p>

scholarly journals A Nonvariational Consistent Hybrid Ensemble Filter

2015 ◽  
Vol 143 (12) ◽  
pp. 5073-5090 ◽  
Author(s):  
Craig H. Bishop ◽  
Bo Huang ◽  
Xuguang Wang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Forecast Error ◽  
Model Space ◽  
High Rank ◽  
Covariance Model ◽  
Ensemble Data Assimilation ◽  
Error Covariance ◽  
Ensemble Data ◽  
Ensemble Transform Kalman Filter

Abstract A consistent hybrid ensemble filter (CHEF) for using hybrid forecast error covariance matrices that linearly combine aspects of both climatological and flow-dependent matrices within a nonvariational ensemble data assimilation scheme is described. The CHEF accommodates the ensemble data assimilation enhancements of (i) model space ensemble covariance localization for satellite data assimilation and (ii) Hodyss’s method for improving accuracy using ensemble skewness. Like the local ensemble transform Kalman filter (LETKF), the CHEF is computationally scalable because it updates local patches of the atmosphere independently of others. Like the sequential ensemble Kalman filter (EnKF), it serially assimilates batches of observations and uses perturbed observations to create ensembles of analyses. It differs from the deterministic (no perturbed observations) ensemble square root filter (ESRF) and the EnKF in that (i) its analysis correction is unaffected by the order in which observations are assimilated even when localization is required, (ii) it uses accurate high-rank solutions for the posterior error covariance matrix to serially assimilate observations, and (iii) it accommodates high-rank hybrid error covariance models. Experiments were performed to assess the effect on CHEF and ESRF analysis accuracy of these differences. In the case where both the CHEF and the ESRF used tuned localized ensemble covariances for the forecast error covariance model, the CHEF’s advantage over the ESRF increased with observational density. In the case where the CHEF used a hybrid error covariance model but the ESRF did not, the CHEF had a substantial advantage for all observational densities.

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