Showing papers by "Eugenia Kalnay published in 2007"

Estimating and Correcting Global Weather Model Error

Abstract: The purpose of the present study is to explore the feasibility of estimating and correcting systematic model errors using a simple and efficient procedure, inspired by papers by Leith as well as DelSole and Hou, that could be applied operationally, and to compare the impact of correcting the model integration with statistical corrections performed a posteriori. An elementary data assimilation scheme (Newtonian relaxation) is used to compare two simple but realistic global models, one quasigeostrophic and one based on the primitive equations, to the NCEP reanalysis (approximating the real atmosphere). The 6-h analysis corrections are separated into the model bias (obtained by time averaging the errors over several years), the periodic (seasonal and diurnal) component of the errors, and the nonperiodic errors. An estimate of the systematic component of the nonperiodic errors linearly dependent on the anomalous state is generated. Forecasts corrected during model integration with a seasonally dependent estimate of the bias remain useful longer than forecasts corrected a posteriori. The diurnal correction (based on the leading EOFs of the analysis corrections) is also successful. State-dependent corrections using the full-dimensional Leith scheme and several years of training actually make the forecasts worse due to sampling errors in the estimation of the covariance. A sparse approximation of the Leith covariance is derived using univariate and spatially localized covariances. The sparse Leith covariance results in small regional improvements, but is still computationally prohibitive. Finally, singular value decomposition is used to obtain the coupled components of the correction and forecast anomalies during the training period. The corresponding heterogeneous correlation maps are used to estimate and correct by regression the state-dependent errors during the model integration. Although the global impact of this computationally efficient method is small, it succeeds in reducing state-dependent model systematic errors in regions where they are large. The method requires only a time series of analysis corrections to estimate the error covariance and uses negligible additional computation during a forecast. As a result, it should be suitable for operational use at relatively small computational expense.

50th Anniversary of Operational Numerical Weather Prediction

Abstract: The National Centers for Environmental Prediction (NCEP), Air Force Weather Agency (AFWA), Fleet Numerical Meteorology and Oceanography Center (FNMOC), National Weather Association, and American Meteorological Society (AMS) cosponsored a “Symposium on the 50th Anniversary of Operational Numerical Weather Prediction,” on 14–17 June 2004 at the University of Maryland, College Park in College Park, Maryland. Operational numerical weather prediction (NWP) in the United States started with the Joint Numerical Weather Prediction Unit (JNWPU) on 1 July 1954, staffed by members of the U.S. Weather Bureau, the U.S. Air Force and the U.S. Navy. The origins of NCEP, AFWA, and FNMOC can all be traced to the JNWPU. The symposium celebrated the pioneering developments in NWP and the remarkable improvements in forecast skill and support of the nation's economy, well being, and national defense achieved over the last 50 years. This essay was inspired by the presentations from that symposium.

Assessing Predictability with a Local Ensemble Kalman Filter

Abstract: In this paper, the spatiotemporally changing nature of predictability is studied in a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), a state-of-the-art numerical weather prediction model. Atmospheric predictability is assessed in the perfect model scenario for which forecast uncertainties are entirely due to uncertainties in the estimates of the initial states. Uncertain initial conditions (analyses) are obtained by assimilating simulated noisy vertical soundings of the “true” atmospheric states with the local ensemble Kalman filter (LEKF) data assimilation scheme. This data assimilation scheme provides an ensemble of initial conditions. The ensemble mean defines the initial condition of 5-day deterministic model forecasts, while the time-evolved members of the ensemble provide an estimate of the evolving forecast uncertainties. The observations are randomly distributed in space to ensure that the geographical distribution of t...

An implementation of the Local Ensemble Kalman Filter in a quasi geostrophic model and comparison with 3D-Var

Abstract: We perform data assimilation experiments with a widely used quasi-geostrophic channel model and compare the Local Ensemble Kalman Filter (LEKF) with a 3D-Var developed for this model. The LEKF shows a large improvement, especially in correcting the fast growing modes of the analysis errors, with a mean square error equal to about half that of the 3D-Var. The improvement obtained in the analysis is maintained in the forecasts, implying that the system is capable of correcting the initial errors responsible for later forecast error growth. Different configurations of the LEKF are tested and compared. We find that for this system, adding random perturbations after every analysis step is more effective than the standard variance inflation in order to avoid underestimating the background error covariance and the consequent filter divergence. Experiments indicate that optimal results are obtained with a relatively small number of vectors (~30) in the ensemble. The LEKF is characterized by the "localization" of the analysis process over local domains surrounding each gridpoint of the model grid. We find that, when using a fixed number of ensemble vectors, there is an optimal size of the local horizontal domain beyond which the results do not change further.

Response to the discussion on "4-D-Var or EnKF?" by Nils Gustafsson

Abstract: 3 pages, 1 figure.-- Original paper "4-D-Var or ensemble Kalman filter?" available at http://digital.csic.es/handle/10261/15532

Comparison between Local Ensemble Transform Kalman Filter and PSAS in the NASA finite volume GCM: perfect model experiments

Abstract: This paper explores the potential of Local Ensemble Transform Kalman Filter (LETKF) by comparing the performance of LETKF with an operational 3D-Var assimilation system, Physical-Space Statistical Analysis System (PSAS), under a perfect model scenario. The comparison is carried out on the finite volume Global Circulation Model (fvGCM) with 72 grid points zonally, 46 grid points meridionally and 55 vertical levels. With only forty ensemble members, LETKF obtains an analysis and forecasts with lower RMS errors than those from PSAS. The performance of LETKF is further improved, especially over the oceans, by assimilating simulated temperature observations from rawinsondes and conventional surface pressure observations instead of geopotential heights. An initial decrease of the forecast errors in the NH observed in PSAS but not in LETKF suggests that the PSAS analysis is less balanced. The observed advantage of LETKF over PSAS is due to the ability of the forty-member ensemble from LETKF to capture flow-dependent errors and thus create a good estimate of the true background uncertainty. Furthermore, localization makes LETKF highly parallel and efficient, requiring only 5 minutes per analysis in a cluster of 20 PCs with forty ensemble members.

Simple Doppler Wind Lidar adaptive observation experiments with 3D‐Var and an ensemble Kalman filter in a global primitive equations model

Abstract: [1] Through simple Observing System Simulation Experiments, we compare several adaptive observation strategies designed to subsample Doppler Wind Lidar (DWL) observations along satellite tracks, and examine the effectiveness of two data assimilation schemes, 3D-Var and the Local Ensemble Transform Kalman Filter (LETKF) With respect to sampling strategies, our results show that the LETKF-based ensemble spread method is superior to the other strategies tested, namely, use of a uniform distribution, the climatological spread strategy, or use of a random distribution, and is close to the ideal result obtained assuming that the true forecast error is known With 10% DWL observations from the ensemble spread strategy, both 3D-Var and LETKF attain about 90% of the impact that 100% DWL wind profile coverage would provide However, when the adaptive DWL observations coverage is reduced to 2%, 3D-Var becomes less effective than the LETKF assimilation scheme

Impact of satellite-based data on FGGE general circulation statistics

Abstract: The NASA Goddard Laboratory for Atmospheres (GLA) analysis/forecast system was run in two different parallel modes in order to evaluate the influence that data from satellites and other FGGE observation platforms can have on analyses of large scale circulation; in the first mode, data from all observation systems were used, while in the second only conventional upper air and surface reports were used. The GLA model was also integrated for the same period without insertion of any data; an independent objective analysis based only on rawinsonde and pilot balloon data is also performed. A small decrease in the vigor of the general circulation is noted to follow from the inclusion of satellite observations.

Improved analyses and forecasts with airs retrievals using the local ensemble transform Kalman filter

Abstract: The Local Ensemble Transform Kalman Filter (LETKF) (Hunt et al. 2006) is an efficient data assimilation scheme of the square root ensemble Kalman filter family. It has been implemented to assimilate simulated observations in the NCEP GFS model (Szunyogh et al. 2005), and in the NASA fvGCM model (Liu et al. 2006). The results from LETKF are much better than those from 3DVAR in a perfect model scenario. With real data, LETKF has been shown to be superior to the operational NCEP SSI (operational 3DVAR) by Szunyogh et al 2006 when applied on the NCEP GFS model at T62L28 resolution, and verified against the NCEP T254/L64 analysis using all available operational observations. Unlike other square-root schemes that solve the Kalman filter equations in observation space (Anderson 2001, Bishop et al. 2001, Whitaker et al. 2004), LETKF solves the equations locally in model space. In this way, LETKF can utilize parallel computation and is more efficient when assimilating satellite observations, the number of which can be much larger than the number of degrees of freedom in the model. The Atmospheric Infrared Sounder (AIRS) was lunched on EOS Aqua in 2002. Some positive impacts on global analysis and forecast have been found in 3DVAR (Marshall et al. 2006, Chahine et al. 2006). Since the LETKF analysis was shown to be better than 3DVAR analysis when using all operational observations except radiances, we now assess the impact of adding AIRS retrievals. In this study we use the same system as Szunyogh et al (2006) assimilating real nonradiance observations on the NCEP GFS, and add AIRS temperature retrievals provided by Chris