Eugenia Kalnay

Bio: Eugenia Kalnay is an academic researcher from University of Maryland, College Park. The author has contributed to research in topics: Data assimilation & Ensemble Kalman filter. The author has an hindex of 61, co-authored 259 publications receiving 52574 citations. Previous affiliations of Eugenia Kalnay include Goddard Space Flight Center & Eötvös Loránd University.

Accelerating the spin-up of Ensemble Kalman Filtering

Abstract: Ensemble Kalman Filter (EnKF) may have a longer spin-up time to reach its asymptotic level of accuracy than the corresponding spin-up time in variational methods (3D-Var or 4D-Var). During the spin-up EnKF has to fulfill two independent requirements, namely that the ensemble mean be close to the true state, and that the ensemble perturbations represent the ‘errors of the day’. As a result, there are cases, such as radar observations of a severe storm, or regional forecast of a hurricane, where EnKF may spin-up too slowly to be useful. A heuristic scheme is proposed to accelerate the spin-up of EnKF by applying a no-cost Ensemble Kalman Smoother, and using the observations more than once in each assimilation window during spin-up in order to maximize the initial extraction of information. The performance of this scheme is tested with the Local Ensemble Transform Kalman Filter (LETKF) implemented in a quasi-geostrophic model, which requires a very long spin-up time when initialized from random initial perturbations from a uniform distribution. Results show that with the new ‘running in place’ (RIP) scheme the LETKF spins up and converges to the optimal level of error faster than 3D-Var or 4D-Var, even in the absence of any prior information. Additional computations (2 to 12 iterations for each assimilation window) are only required during the initial spin-up, since the scheme naturally returns to the original LETKF after spin-up is achieved. RIP also accelerates spin-up when the initial perturbations are drawn from a well-tuned 3D-Var background-error covariance, rather than being uniform noise, and fewer iterations and RIP cycles are required than in the case without such prior information. Copyright © 2010 Royal Meteorological Society

Use of the breeding technique to estimate the structure of the analysis "errors of the day"

Abstract: A 3D-variational data assimilation scheme for a quasi-geostrophic channel model (Morss, 1998) is used to study the structure of the background error and its relation- ship to the corresponding bred vectors. The "true" evolution of the model atmosphere is defined by an integration of the model and "rawinsonde observations" are simulated by ran- domly perturbing the true state at fixed locations. Case studies using different observational densities are considered to compare the evolution of the Bred Vectors to the spatial structure of the background error. In addition, the bred vector dimension (BV-dimension), defined by Patil et al. (2001) is applied to the bred vectors. It is found that after 3-5 days the bred vectors develop well organized structures which are very similar for the two dif- ferent norms (enstrophy and streamfunction) considered in this paper. When 10 surrogate bred vectors (corresponding to different days from that of the background error) are used to describe the local patterns of the background error, the ex- plained variance is quite high, about 85-88%, indicating that the statistical average properties of the bred vectors represent well those of the background error. However, a subspace of 10 bred vectors corresponding to the time of the background error increased the percentage of explained variance to 96- 98%, with the largest percentage when the background errors are large. These results suggest that a statistical basis of bred vectors collected over time can be used to create an effective constant background error covariance for data assimilation with 3D- Var. Including the "errors of the day" through the use of bred vectors corresponding to the background forecast time can bring an additional significant improvement.

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.

Maturity of Operational Numerical Weather Prediction: Medium Range

Abstract: In 1939 Rossby demonstrated the usefulness of the linearized perturbation of the equations of motion for weather prediction and thus made possible the first successful numerical forecasts of the weather by Charney et al. In 1951 Charney wrote a paper on the science of numerical weather prediction (NWP), where he predicted with remarkable vision how NWP would evolve until the present. In the 1960's Lorenz discovered that the chaotic nature of the atmosphere imposes a finite limit of about two weeks to weather predictability. At that time this fundamental discovery was “only of academic interest” and not really relevant to operational weather forecasting, since at that time the accuracy of even a 2-day forecast was rather poor. Since then, however, computer-based forecasts have improved so much that Lorenz's limit of predictability is starting to become attainable in practice, especially with ensemble forecasting, and the predictabilty of longer-lasting phenomena such as El Nino is beginning to be ...

Estimating observation impact without adjoint model in an ensemble Kalman filter

Abstract: We propose an ensemble sensitivity method to calculate observation impacts similar to Langland and Baker (2004) but without the need for an adjoint model, which is not always available for numerical weather prediction models. The formulation is tested on the Lorenz 40-variable model, and the results show that the observation impact estimated from the ensemble sensitivity method is similar to that from the adjoint method. Like the adjoint method, the ensemble sensitivity method is able to detect observations that have large random errors or biases. This sensitivity could be routinely calculated in an ensemble Kalman filter, thus providing a powerful tool to monitor the quality of observations and give quantitative estimations of observation impact on the forecasts. Copyright © 2008 Royal Meteorological Society

The NCEP/NCAR 40-Year Reanalysis Project

Abstract: The NCEP and NCAR are cooperating in a project (denoted “reanalysis”) to produce a 40-year record of global analyses of atmospheric fields in support of the needs of the research and climate monitoring communities. This effort involves the recovery of land surface, ship, rawinsonde, pibal, aircraft, satellite, and other data; quality controlling and assimilating these data with a data assimilation system that is kept unchanged over the reanalysis period 1957–96. This eliminates perceived climate jumps associated with changes in the data assimilation system. The NCEP/NCAR 40-yr reanalysis uses a frozen state-of-the-art global data assimilation system and a database as complete as possible. The data assimilation and the model used are identical to the global system implemented operationally at the NCEP on 11 January 1995, except that the horizontal resolution is T62 (about 210 km). The database has been enhanced with many sources of observations not available in real time for operations, provided b...

The ERA-Interim reanalysis: configuration and performance of the data assimilation system

Abstract: ERA-Interim is the latest global atmospheric reanalysis produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). The ERA-Interim project was conducted in part to prepare for a new atmospheric reanalysis to replace ERA-40, which will extend back to the early part of the twentieth century. This article describes the forecast model, data assimilation method, and input datasets used to produce ERA-Interim, and discusses the performance of the system. Special emphasis is placed on various difficulties encountered in the production of ERA-40, including the representation of the hydrological cycle, the quality of the stratospheric circulation, and the consistency in time of the reanalysed fields. We provide evidence for substantial improvements in each of these aspects. We also identify areas where further work is needed and describe opportunities and objectives for future reanalysis projects at ECMWF. Copyright © 2011 Royal Meteorological Society

Global Consequences of Land Use

Abstract: Land use has generally been considered a local environmental issue, but it is becoming a force of global importance. Worldwide changes to forests, farmlands, waterways, and air are being driven by the need to provide food, fiber, water, and shelter to more than six billion people. Global croplands, pastures, plantations, and urban areas have expanded in recent decades, accompanied by large increases in energy, water, and fertilizer consumption, along with considerable losses of biodiversity. Such changes in land use have enabled humans to appropriate an increasing share of the planet’s resources, but they also potentially undermine the capacity of ecosystems to sustain food production, maintain freshwater and forest resources, regulate climate and air quality, and ameliorate infectious diseases. We face the challenge of managing trade-offs between immediate human needs and maintaining the capacity of the biosphere to provide goods and services in the long term.

Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change

Abstract: Drafting Authors: Neil Adger, Pramod Aggarwal, Shardul Agrawala, Joseph Alcamo, Abdelkader Allali, Oleg Anisimov, Nigel Arnell, Michel Boko, Osvaldo Canziani, Timothy Carter, Gino Casassa, Ulisses Confalonieri, Rex Victor Cruz, Edmundo de Alba Alcaraz, William Easterling, Christopher Field, Andreas Fischlin, Blair Fitzharris, Carlos Gay García, Clair Hanson, Hideo Harasawa, Kevin Hennessy, Saleemul Huq, Roger Jones, Lucka Kajfež Bogataj, David Karoly, Richard Klein, Zbigniew Kundzewicz, Murari Lal, Rodel Lasco, Geoff Love, Xianfu Lu, Graciela Magrín, Luis José Mata, Roger McLean, Bettina Menne, Guy Midgley, Nobuo Mimura, Monirul Qader Mirza, José Moreno, Linda Mortsch, Isabelle Niang-Diop, Robert Nicholls, Béla Nováky, Leonard Nurse, Anthony Nyong, Michael Oppenheimer, Jean Palutikof, Martin Parry, Anand Patwardhan, Patricia Romero Lankao, Cynthia Rosenzweig, Stephen Schneider, Serguei Semenov, Joel Smith, John Stone, Jean-Pascal van Ypersele, David Vaughan, Coleen Vogel, Thomas Wilbanks, Poh Poh Wong, Shaohong Wu, Gary Yohe