A New Approach for Estimating the Observation Impact in Ensemble–Variational Data Assimilation

2018 ◽  
Vol 146 (2) ◽  
pp. 447-465 ◽  
Author(s):  
Mark Buehner ◽  
Ping Du ◽  
Joël Bédard
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Observation Error ◽  
New Approach ◽  
Regional Prediction ◽  
Observation Impact ◽  
Adjoint Approach ◽  
The Impact

Abstract Two types of approaches are commonly used for estimating the impact of arbitrary subsets of observations on short-range forecast error. The first was developed for variational data assimilation systems and requires the adjoint of the forecast model. Comparable approaches were developed for use with the ensemble Kalman filter and rely on ensembles of forecasts. In this study, a new approach for computing observation impact is proposed for ensemble–variational data assimilation (EnVar). Like standard adjoint approaches, the adjoint of the data assimilation procedure is implemented through the iterative minimization of a modified cost function. However, like ensemble approaches, the adjoint of the forecast step is obtained by using an ensemble of forecasts. Numerical experiments were performed to compare the new approach with the standard adjoint approach in the context of operational deterministic NWP. Generally similar results are obtained with both approaches, especially when the new approach uses covariance localization that is horizontally advected between analysis and forecast times. However, large differences in estimated impacts are obtained for some surface observations. Vertical propagation of the observation impact is noticeably restricted with the new approach because of vertical covariance localization. The new approach is used to evaluate changes in observation impact as a result of the use of interchannel observation error correlations for radiance observations. The estimated observation impact in similarly configured global and regional prediction systems is also compared. Overall, the new approach should provide useful estimates of observation impact for data assimilation systems based on EnVar when an adjoint model is not available.

Impact of the Different Components of 4DVAR on the Global Forecast System of the Meteorological Service of Canada

2007 ◽  
Vol 135 (6) ◽  
pp. 2355-2364 ◽  
Author(s):  
Stéphane Laroche ◽  
Pierre Gauthier ◽  
Monique Tanguay ◽  
Simon Pellerin ◽  
Josée Morneau
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Forecast ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Global Forecast System ◽  
Forecast System ◽  
Medium Range Weather Forecast ◽  
Medium Range ◽  
The Impact

Abstract A four-dimensional variational data assimilation (4DVAR) scheme has recently been implemented in the medium-range weather forecast system of the Meteorological Service of Canada (MSC). The new scheme is now composed of several additional and improved features as compared with the three-dimensional variational data assimilation (3DVAR): the first guess at the appropriate time from the full-resolution model trajectory is used to calculate the misfit to the observations; the tangent linear of the forecast model and its adjoint are employed to propagate the analysis increment and the gradient of the cost function over the 6-h assimilation window; a comprehensive set of simplified physical parameterizations is used during the final minimization process; and the number of frequently reported data, in particular satellite data, has substantially increased. The impact of these 4DVAR components on the forecast skill is reported in this article. This is achieved by comparing data assimilation configurations that range in complexity from the former 3DVAR with the implemented 4DVAR over a 1-month period. It is shown that the implementation of the tangent-linear model and its adjoint as well as the increased number of observations are the two features of the new 4DVAR that contribute the most to the forecast improvement. All the other components provide marginal though positive impact. 4DVAR does not improve the medium-range forecast of tropical storms in general and tends to amplify the existing, too early extratropical transition often observed in the MSC global forecast system with 3DVAR. It is shown that this recurrent problem is, however, more sensitive to the forecast model than the data assimilation scheme employed in this system. Finally, the impact of using a shorter cutoff time for the reception of observations, as the one used in the operational context for the 0000 and 1200 UTC forecasts, is more detrimental with 4DVAR. This result indicates that 4DVAR is more sensitive to observations at the end of the assimilation window than 3DVAR.

Adjoint-Based Observation Impact Estimation with Direct Verification Using Forward Calculation

2018 ◽  
Vol 146 (9) ◽  
pp. 2837-2858 ◽  
Author(s):  
Toshiyuki Ishibashi
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Weather Prediction ◽  
Adjoint Operator ◽  
Estimation Method ◽  
Forecast Model ◽  
Japan Meteorological Agency ◽  
Essential Information ◽  
Observation Impact ◽  
Forward Calculation

Abstract The adjoint-based observation impact estimation method has been providing essential information to improve data assimilation systems (DASs) in numerical weather prediction (NWP). This paper has two purposes. The first is to verify the four approximations used in the method: iterative construction of the Kalman gain adjoint operator, the tangent linear (TL) approximation of a forecast model, ignoring cross terms of observation impacts, and approximations for incremental DASs. The second is to add new information to our knowledge of observation impacts. For the verification of the adjoint-based method, we use the TL-based observation impact estimation method that can calculate the same quantity as the adjoint-based method without adjoint calculations. Results of these verifications and observation impact estimations performed on the global NWP system of the Japan Meteorological Agency are as follows. First, observation impacts calculated using the adjoint-based method agree well with those from the TL-based method, with the correlation coefficient between them exceeding 0.97. Second, estimated observation impacts are consistent with previous studies in many aspects. There are also system-dependent properties, such as relatively small impacts from GPS radio occultation data above 13 km. Furthermore, new aspects of observation impacts are found: 1) the probability density function of the observation impact agrees well with the scalar theory when giving the experimental value of the observation and the forecast error standard deviations; 2) later observations in the data assimilation window have larger positive impacts; and 3) impacts of AMSU-A in the Southern Hemisphere are more than double those in the Northern Hemisphere.

Inhomogeneous Background Error Modeling for WRF-Var Using the NMC Method

2014 ◽  
Vol 53 (10) ◽  
pp. 2287-2309 ◽  
Author(s):  
Hongli Wang ◽  
Xiang-Yu Huang ◽  
Juanzhen Sun ◽  
Dongmei Xu ◽  
Man Zhang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Control Variable ◽  
Variational Data Assimilation ◽  
Error Modeling ◽  
Error Statistics ◽  
Background Error ◽  
New Approach ◽  
Data Assimilation System ◽  
Assimilation System

AbstractBackground error modeling plays a key role in a variational data assimilation system. The National Meteorological Center (NMC) method has been widely used in variational data assimilation systems to generate a forecast error ensemble from which the climatological background error covariance can be modeled. In this paper, the characteristics of the background error modeling via the NMC method are investigated for the variational data assimilation system of the Weather Research and Forecasting (WRF-Var) Model. The background error statistics are extracted from short-term 3-km-resolution forecasts in June, July, and August 2012 over a limited-area domain. It is found 1) that background error variances vary from month to month and also have a feature of diurnal variations in the low-level atmosphere and 2) that u- and υ-wind variances are underestimated and their autocorrelation length scales are overestimated when the default control variable option in WRF-Var is used. A new approach of control variable transform (CVT) is proposed to model the background error statistics based on the NMC method. The new approach is capable of extracting inhomogeneous and anisotropic climatological information from the forecast error ensemble obtained via the NMC method. Single observation assimilation experiments show that the proposed method not only has the merit of incorporating geographically dependent covariance information, but also is able to produce a multivariate analysis. The results from the data assimilaton and forecast study of a real convective case show that the use of the new CVT improves synoptic weather system and precipitation forecasts for up to 12 h.

One-Dimensional Variational Data Assimilation of SSM/I Observations in Rainy Atmospheres at MSC

2007 ◽  
Vol 135 (1) ◽  
pp. 152-172 ◽  
Author(s):  
G. Deblonde ◽  
J-F. Mahfouf ◽  
B. Bilodeau ◽  
D. Anselmo
Keyword(s):  
Water Vapor ◽  
Data Assimilation ◽  
Variational Data Assimilation ◽  
Scale Model ◽  
Observation Error ◽  
Step Method ◽  
One Dimensional ◽  
Brightness Temperatures ◽  
The Impact ◽  
Assimilation System

Abstract Currently, satellite radiances in the Canadian Meteorological Centre operational data assimilation system are only assimilated in clear skies. A two-step method, developed at the European Centre for Medium-Range Weather Forecasts, is considered to assimilate Special Sensor Microwave Imager (SSM/I) observations in rainy atmospheres. The first step consists of a one-dimensional variational data assimilation (1DVAR) method. Model temperature and humidity profiles are adjusted by assimilating either SSM/I brightness temperatures or retrieved surface rain rates (derived from SSM/I brightness temperatures). In the second step, 1DVAR column-integrated water vapor analyses are assimilated in four-dimensional variational data assimilation (4DVAR). At the Meteorological Service of Canada, such a 1DVAR assimilation system has been developed. Model profiles are obtained from a research version of the Global Environmental Multi-Scale model. Several issues raised while developing the 1DVAR system are addressed. The impact of the size of the observation error is studied when brightness temperatures are assimilated. For two case studies, analyses are derived when either surface rain rate or brightness temperatures are assimilated. Differences in the analyzed fields between these configurations are discussed and shortcomings of each approach are identified. Results of sensitivity studies are also provided. First the impact of observation error correlation between channels is investigated. Second, the size of the background temperature error is varied to assess its impact on the analyzed column-integrated water vapor. Third, the importance of each moist physical scheme is investigated. Finally, the portability of moist physical schemes specifically developed for data assimilation is discussed.

scholarly journals Some Observing System Simulation Experiments with a Hybrid 3DEnVAR System for Storm-Scale Radar Data Assimilation

2014 ◽  
Vol 142 (9) ◽  
pp. 3326-3346 ◽  
Author(s):  
Jidong Gao ◽  
David J. Stensrud
Keyword(s):  
Data Assimilation ◽  
Forecast Error ◽  
Control Variable ◽  
Simulated Data ◽  
Variational Data Assimilation ◽  
Sampling Errors ◽  
Advanced Regional Prediction System ◽  
Sensitivity Experiments ◽  
Variable Approach ◽  
Regional Prediction

A hybrid three-dimensional ensemble–variational data assimilation (3DEnVAR) algorithm is developed based on the 3D variational data assimilation (3DVAR) and ensemble Kalman filter (EnKF) programs with the Advanced Regional Prediction System (ARPS). The method uses the extended control variable approach to combine the static and ensemble-derived flow-dependent forecast error covariances. The method is applied to the assimilation of simulated data from two radars for a supercell storm. Some sensitivity experiments are performed to answer questions about how flow-dependent covariance estimated from the forecast ensemble can be best used in the hybrid 3DEnVAR scheme. When the ensemble size is relatively small (with 5 or 10 ensemble members), it is found that experiments with a weaker weighting value for the ensemble covariance leads to better analysis results. Even when severe sampling errors exist, introducing ensemble-estimated covariances into the variational method still benefits the analysis. For reasonably large ensemble sizes (50–100 members), a stronger relative weighting (>0.8) for the ensemble covariance leads to better analyses from the hybrid 3DEnVAR. In addition, the sensitivity experiments also indicate that the best results are obtained when the number of the augmented control variables is a function of three spatial dimensions and ensemble members, and is the same for all analysis variables.

scholarly journals Intercomparison of Variational Data Assimilation and the Ensemble Kalman Filter for Global Deterministic NWP. Part I: Description and Single-Observation Experiments

2010 ◽  
Vol 138 (5) ◽  
pp. 1550-1566 ◽  
Author(s):  
Mark Buehner ◽  
P. L. Houtekamer ◽  
Cecilien Charette ◽  
Herschel L. Mitchell ◽  
Bin He
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Forecast Model ◽  
Variational Data Assimilation ◽  
Spatial Covariance ◽  
4D Flow ◽  
Background Error ◽  
Single Observation ◽  
The Impact

Abstract An intercomparison of the Environment Canada variational and ensemble Kalman filter (EnKF) data assimilation systems is presented in the context of global deterministic NWP. In an EnKF experiment having the same spatial resolution as the inner loop in the four-dimensional variational data assimilation system (4D-Var), the mean of each analysis ensemble is used to initialize the higher-resolution deterministic forecasts. Five different variational data assimilation experiments are also conducted. These include both 4D-Var and 3D-Var (with first guess at appropriate time) experiments using either (i) prescribed background-error covariances similar to those used operationally, which are static in time and include horizontally homogeneous and isotropic correlations; or (ii) flow-dependent covariances computed from the EnKF background ensembles with spatial covariance localization applied. The fifth variational data assimilation experiment is a new approach called the Ensemble-4D-Var (En-4D-Var). This approach uses 4D flow-dependent background-error covariances estimated from EnKF ensembles to produce a 4D analysis without the need for tangent-linear or adjoint versions of the forecast model. In this first part of a two-part paper, results from a series of idealized assimilation experiments are presented. In these experiments, only a single observation or vertical profile of observations is assimilated to explore the impact of various fundamental differences among the EnKF and the various variational data assimilation approaches considered. In particular, differences in the application of covariance localization in the EnKF and variational approaches are shown to have a significant impact on the assimilation of satellite radiance observations. The results also demonstrate that 4D-Var and the EnKF can both produce similar 4D background-error covariances within a 6-h assimilation window. In the second part, results from medium-range deterministic forecasts for the study period of February 2007 are presented for the EnKF and the five variational data assimilation approaches considered.

Atmospheric River Reconnaissance Observation Impact in the Navy Global Forecast System

2020 ◽  
Vol 148 (2) ◽  
pp. 763-782 ◽  
Author(s):  
Rebecca E. Stone ◽  
Carolyn A. Reynolds ◽  
James D. Doyle ◽  
Rolf H. Langland ◽  
Nancy L. Baker ◽  
...  
Keyword(s):  
North American ◽  
Forecast Error ◽  
Error Variance ◽  
Observation Error ◽  
Atmospheric Rivers ◽  
Atmospheric River ◽  
The North ◽  
Beneficial Impact ◽  
Observation Impact ◽  
The Impact

Abstract Atmospheric rivers, often associated with impactful weather along the west coast of North America, can be a challenge to forecast even on short time scales. This is attributed, at least in part, to the scarcity of eastern Pacific in situ observations. We examine the impact of assimilating dropsonde observations collected during the Atmospheric River (AR) Reconnaissance 2018 field program on the Navy Global Environmental Model (NAVGEM) analyses and forecasts. We compare NAVGEM’s representation of the ARs to the observations, and examine whether the observation–background difference statistics are similar to the observation error variance specified in the data assimilation system. Forecast sensitivity observation impact is determined for each dropsonde variable, and compared to the impacts of the North American radiosonde network. We find that the reconnaissance soundings have significant beneficial impact, with per observation impact more than double that of the North American radiosonde network. Temperature and wind observations have larger total and per observation impact than moisture observations. In our experiment, the 24-h global forecast error reduction from the reconnaissance soundings can be comparable to the reduction from the North American radiosonde network for the field program dates that include at least two flights.

scholarly journals Assimilation of Wind Profiler Data in the Canadian Meteorological Centre’s Analysis Systems

2005 ◽  
Vol 22 (8) ◽  
pp. 1181-1194 ◽  
Author(s):  
Judy S. St-James ◽  
Stéphane Laroche
Keyword(s):  
Data Assimilation ◽  
Winter Season ◽  
Variational Data Assimilation ◽  
Radiosonde Data ◽  
Horizontal Wind ◽  
Observation Error ◽  
The Impact ◽  
Thinning Process ◽  
Vertical Correlation ◽  
Profiler Data

Abstract Real-time horizontal wind observations from the National Oceanic and Atmospheric Administration’s (NOAA’s) Profiler Network (NPN) are assessed in preparation for their assimilation in the Canadian Meteorological Centre (CMC) analysis systems. As a first step, radiosonde winds from 20 stations were compared to the central U.S. profiler stations over the 2001/02 winter season. It was found that profilers are at least as good as conventional radiosonde data. The 2001/02 winter season data were also used to examine the vertical correlation structure of the observation error for profilers. Using a statistical analysis of innovations, the observation error standard deviation of the wind components is estimated as 2.2 m s−1 and the vertical correlation length is approximately 500 m. These results suggest that the data are vertically correlated because they are available every 250 m. Therefore, a thinning process is proposed in which one out of three data are selected in the vertical for each station. Since January 2004, a close monitoring of NPN profiler data revealed significant errors at some stations in the lower and upper troposphere. Consequently, a monthly blacklist of NPN profilers is built based on data from the previous month. A data impact study with both the three-dimensional variational data assimilation (3DVAR) and four-dimensional variational data assimilation (4DVAR) analysis systems was conducted using data from the 2003/04 winter season in which the vertical thinning was tested. It was found that the vertical thinning improves slightly the 6-h forecast error, especially in the 4DVAR over the central United States in which 6 times more profilers are assimilated. The impact of the vertical thinning is found to be neutral in the 3DVAR. Also, the impact of profiler data is significant over the central U.S. domain compared to a control run with the only difference being the addition of profiler data. These results were sufficiently good to implement NPN profilers in both the CMC global and regional analysis systems with the thinning process in fall of 2004.

scholarly journals Cloud and Precipitation Parameterizations in Modeling and Variational Data Assimilation: A Review

2007 ◽  
Vol 64 (11) ◽  
pp. 3766-3784 ◽  
Author(s):  
Philippe Lopez
Keyword(s):  
Data Assimilation ◽  
General Circulation ◽  
Large Scale ◽  
Weather Prediction ◽  
General Circulation Models ◽  
Variational Data Assimilation ◽  
Current Status ◽  
General Question ◽  
Observation Error ◽  
Moist Processes

Abstract This paper first reviews the current status, issues, and limitations of the parameterizations of atmospheric large-scale and convective moist processes that are used in numerical weather prediction and climate general circulation models. Both large-scale (resolved) and convective (subgrid scale) moist processes are dealt with. Then, the general question of the inclusion of diabatic processes in variational data assimilation systems is addressed. The focus is put on linearity and resolution issues, the specification of model and observation error statistics, the formulation of the control vector, and the problems specific to the assimilation of observations directly affected by clouds and precipitation.

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