Showing papers by "Eugenia Kalnay published in 2009"

Simultaneous estimation of covariance inflation and observation errors within an ensemble Kalman filter

Abstract: Covariance inflation plays an important role within the ensemble Kalman filter (EnKF) in preventing filter divergence and handling model errors. However the inflation factor needs to be tuned and tuning a parameter in the EnKF is expensive. Previous studies have adaptively estimated the inflation factor from the innovation statistics. Although the results were satisfactory, this inflation factor estimation method relies on the accuracy of the specification of observation error statistics, which in practice is not perfectly known. In this study we propose to estimate the inflation factor and observational errors simultaneously within the EnKF. Our method is first investigated with a low-order model, the Lorenz- 96 model. The results show that the simultaneous approach works very well in the perfect model scenario and in the presence of random model errors or a small systematic model bias. For an imperfect model with large model bias, our algorithm may require the application of an additional method to remove the bias. We then apply our approach to a more realistic high-dimension model, assimilating observations that have errors of different size and units. The SPEEDY model experiments show that the estimation of multiple observation error parameters is successful in retrieving the true error variance for different types of instruments separately. Copyright c ! 2009 Royal Meteorological Society

Accounting for Model Errors in Ensemble Data Assimilation

Abstract: This study addresses the issue of model errors with the ensemble Kalman filter. Observations generated from the NCEP‐NCAR reanalysis fields are assimilated into a low-resolution AGCM. Without an effort to account for model errors, the performance of the local ensemble transform Kalman filter (LETKF) is seriously degraded when compared with the perfect-model scenario. Several methods to account for model errors, including model bias and system noise, are investigated. The results suggest that the two pure bias removal methods considered [Dee and Da Silva (DdSM) and low dimensional (LDM)] are not able to beat the multiplicative or additive inflation schemes used to account for the effects of total model errors. In contrast, when the bias removal methods are augmented by additive noise representing random errors (DdSM1 and LDM1), they outperform the pure inflation schemes. Of these augmented methods, the LDM1, where the constant bias, diurnal bias, and state-dependent errors are estimated from a large sample of 6-h forecast errors, gives the best results. The advantage of the LDM1 over other methods is larger in data-sparse regions than in data-dense regions.

Weight interpolation for efficient data assimilation with the Local Ensemble Transform Kalman Filter

Abstract: We have investigated a method to substantially reduce the analysis computations within the Local Ensemble Transform Kalman Filter (LETKF) framework. Instead of computing the LETKF analysis at every model grid point, we compute the analysis on a coarser grid and interpolate onto a high-resolution grid by interpolating the analysis weights of the ensemble forecast members derived from the LETKF. Because the weights vary on larger scales than the analysis increments, there is little degradation in the quality of the weight-interpolated analyses compared to the analyses derived with the high-resolution grid. The weight-interpolated analyses are more accurate than the ones derived by interpolating the analysis increments. Additional benefit from the weight-interpolation method includes improving the analysis accuracy in the data-void regions, where the standard LEKTF with the high-resolution grid gives no analysis corrections due to a lack of available observations. Copyright © Royal Meteorological Society and Crown Copyright, 2008

Comparison of Local Ensemble Transform Kalman Filter, 3DVAR, and 4DVAR in a Quasigeostrophic Model

Abstract: Local ensemble transform Kalman filter (LETKF) data assimilation, three-dimensional variational data assimilation (3DVAR), and four-dimensional variational data assimilation (4DVAR) schemes are implemented in a quasigeostrophic channel model. Their advantages and disadvantages are compared to assess their use in practical applications. LETKF and 4DVAR, which take into account the flow-dependent errors, outperform 3DVAR under a perfect model scenario. Given the same observations, LETKF produces more accurate analyses than 4DVAR with a 12-h window by effectively correcting the fast-growing errors with the flow-dependent background error covariance. Even though 4DVAR performance benefits substantially from using a longer assimilation window, LETKF is also able to achieve a satisfactory accuracy compared to the 24-h 4DVAR analyses. It is shown that the advantage of the LETKF over 3DVAR is a result of both the ensemble averaging and the information about the “errors of the day” provided by the ensembl...

Observation bias correction with an ensemble Kalman filter

Abstract: This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases. Our approach is to use state-space augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme—the local ensemble transform Kalman filter (LETKF)—to assimilate simulated biased atmospheric infrared sounder brightness temperature observations from 15 channels on the simplified parameterizations, primitive-equation dynamics (SPEEDY) model. The scheme we present successfully reduces both the observation bias and analysis error in perfect-model simulations.

Analysis sensitivity calculation in an ensemble Kalman filter

Abstract: Analysis sensitivity indicates the sensitivity of an analysis to the observations, which is complementary to the sensitivity of the analysis to the background. In this paper, we discuss a method to calculate this quantity in an Ensemble Kalman Filter (EnKF). The calculation procedure and the geometrical interpretation, which shows that the analysis sensitivity is proportional to the analysis error and anti-correlated with the observation error, are experimentally verified with the Lorenz 40-variable model. With the analysis sensitivity, the cross-validation in its original formulation can be efficiently computed in EnKFs, and this property can be used in observational quality control. Idealized experiments based on a simplified-parametrization primitive equation global model show that the information content (the trace of the analysis sensitivity f any subset of observations) qualitatively agrees with the actual observation impact calculated from much more expensive data-denial experiments, not only for the same type of dynamical variable, but also for different types of dynamical variables. Copyright © 2009 Royal Meteorological Society

Application of Coupled Bred Vectors to Seasonal-to-Interannual Forecasting and Ocean Data Assimilation

Abstract: Coupled bred vectors (BVs) generated from the NASA Global Modeling and Assimilation Office (GMAO) coupled general circulation model are designed to capture the uncertainties related to slowly varying coupled instabilities. Two applications of the BVs are investigated in this study. First, the coupled BVs are used as initial perturbations for ensemble-forecasting purposes. Results show that the seasonal-to-interannual variability forecast skill can be improved when the oceanic and atmospheric perturbations are initialized with coupled BVs. The impact is particularly significant when the forecasts are initialized from the cold phase of tropical Pacific SST (e.g., August and November), because at these times the early coupled model errors, not accounted for in the BVs, are small. Second, the structure of the BVs is applied to construct hybrid background error covariances carrying flow-dependent information for the ocean data assimilation. Results show that the accuracy of the ocean analyses is impro...

Comparison of observed and simulated cyclone frequency distribution as determined by an objective method

Abstract: We have designed an objective scheme to diagnose the statistics of extratropical cyclones, cyclogenesis and cyclolysis. It uses as much as possible the same criteria which are used for subjective analyses and allows an evaluation of model performance not captured by standard model diagnostics. Examples of possible applications to forecast and simulation experiments are presented in this paper.

Comparison of Methods Used to Generate Probabilistic Quantitative Precipitation Forecasts over South America

Abstract: In this work, the quality of several probabilistic quantitative precipitation forecasts (PQPFs) is examined. The analysis is focused over South America during a 2-month period in the warm season. Several ways of generating and calibrating the PQPFs have been tested, using different ensemble systems and single-model runs. Two alternative calibration techniques (static and dynamic) have been tested. To take into account different precipitation regimes, PQPF performance has been evaluated over two regions: the northern part of South America, characterized by a tropical regime, and the southern part, where synoptic-scale forcing is stronger. The results support the adoption of such area separation, since differences in the precipitation regimes produce significant differences in PQPF performance. The more skillful PQPFs are the ones obtained after calibration. PQPFs derived from the ensemble mean also show higher skill and better reliability than those derived from the single ensemble members. The performance of the PQPFs derived from both ensemble systems is similar over the southern part of the region; however, over the northern part the superensemble approach seems to achieve better results in both reliability and skill. Finally, the impact of using Climate Prediction Center morphing technique (CMORPH) estimates to calibrate the precipitation forecast has been explored since the more extensive coverage of this dataset would allow its use over areas where the rain gauge coverage is insufficient. Results suggest that systematic biases present in the CMORPH estimates produce only a slight degradation of the resulting PQPF.

Use of breeding to detect and explain instabilities in the global ocean

Abstract: [1] The breeding method of Toth and Kalnay finds the perturbations that grow naturally in a dynamical system like the atmosphere or the ocean. Here breeding is applied to a global ocean model forced by reanalysis winds in order to identify instabilities of weekly and monthly timescales. This study extends the method to show how the energy equations for the bred vectors can be derived with only very minimal approximations and used to assess the physical mechanisms that give rise to the instabilities. Tropical Instability Waves in the tropical Pacific are diagnosed, confirming the existence of bands of both baroclinic and barotropic energy conversions indicated earlier by Masina et al. and others. In the South Atlantic Convergence Zone, the bred vector energy analysis shows that there is kinetic to potential ocean eddy energy conversion, suggesting that the growing instabilities found in this area are forced by the wind.

Univariate and Multivariate Assimilation of AIRS Humidity Retrievals with the Local Ensemble Transform Kalman Filter

Abstract: This study uses the local ensemble transform Kalman filter to assimilate Atmospheric Infrared Sounder (AIRS) specific humidity retrievals with pseudo relative humidity (pseudo-RH) as the observation variable. Three approaches are tested: (i) updating specific humidity with observations other than specific humidity (“passive q”), (ii) updating specific humidity only with humidity observations (“univariate q”), and (iii) assimilating the humidity and the other observations together (“multivariate q”). This is the first time that the performance of the univariate and multivariate assimilation of q is compared within an ensemble Kalman filter framework. The results show that updating the humidity analyses by either AIRS specific humidity retrievals or nonhumidity observations improves both the humidity and wind analyses. The improvement with the multivariate-q experiment is by far the largest for all dynamical variables at both analysis and forecast time, indicating that the interaction between the s...