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.

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

Lagged average forecasting, an alternative to Monte Carlo forecasting

Abstract: In order to use the information present in past observations and simultaneously to take advantage of the benefits of stochastic dynamic prediction we formulate the lagged average forecast (LAF) method. In a LAF, just as in a Monte Carlo forecast (MCF), sample statistics are calculated from an ensemble of forecasts. Each LAF ensemble member is an ordinary dynamical forecast (ODF) started from the initial conditions observed at a time lagging the start of the forecast period by a different amount. These forecasts are averaged at their proper verification times to obtain an LAF. The LAF method is operationally feasible since the LAF ensemble members are produced during the normal operational cycle. To test the LAF method, we use a two-layer, f-plane, highly truncated spectral model, forced by asymmetric Newtonian heating of the lower layer. In the experiments, a long run is generated by the primitive equation version of the model which is taken to represent nature, while forecasts are made by the quasigeostrophic version of the model. On the basis of forecast skill, the LAF and MCF are superior to the ODF; this occurs principally because ensemble averaging hedges the LAF and MCF toward the climate mean. The LAF, MCF and ODF are all improved when tempered by a simple regression filter; this procedure yields different weights for the different members of the LAF ensemble. The tempered LAF is the most skillful of the forecast methods tested. The LAF and MCF can provide a priori estimates of forecast skill because there is a strong correlation between the dispersion of the ensemble and the loss of predictability. In this way the time at which individual forecasts lose their skill can be predicted. The application of the LAF method to more realistic models and to monthly or seasonally averaged forecasts is briefly discussed. DOI: 10.1111/j.1600-0870.1983.tb00189.x

Balance and Ensemble Kalman Filter Localization Techniques

Abstract: In ensemble Kalman filter (EnKF) data assimilation, localization modifies the error covariance matrices to suppress the influence of distant observations, removing spurious long-distance correlations. In addition to allowing efficient parallel implementation, this takes advantage of the atmosphere’s lower dimensionality in local regions. There are two primary methods for localization. In B localization, the background error covariance matrix elements are reduced by a Schur product so that correlations between grid points that are far apart are removed. In R localization, the observation error covariance matrix is multiplied by a distance-dependent function, so that far away observations are considered to have infinite error. Successful numerical weather prediction depends upon well-balanced initial conditions to avoid spurious propagation of inertial-gravity waves. Previous studies note that B localization can disrupt the relationship between the height gradient and the wind speed of the analysis...

Four-dimensional ensemble Kalman filtering

Abstract: Ensemble Kalman filteringwas developed as away to assimilate observed data to track the current state in a computational model. In this paper we showthat the ensemble approach makes possible an additional benefit: the timing of observations, whether they occur at the assimilation time or at some earlier or later time, can be effectively accounted for at low computational expense. In the case of linear dynamics, the technique is equivalent to instantaneously assimilating data as they are measured. The results of numerical tests of the technique on a simple model problem are shown.

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