FACULTY

FACULTY

Distinguished University Professor, Department of Atmospheric and Oceanic Science

Awards: WMO/IMO Prize for 2009, see talk “Population and Climate Change: A Proposal” (also in Spanish) ; Member of the National Academy of Engineering (1996); foreign member of the Academia Europaea (2000); Distinguished University Professor, UMD, 2001; Eugenia Brin Professor in Data Assimilation (2008); Doctor Honoris Causa, University of Buenos Aires, 2008; corresponding member of the Argentine National Academy of Physical Sciences (2003); Fellow of AGU (2005), AAAS (2006); UMD-wide Kirwan 2006 Award; former Robert E. Lowry Chair, School of Meteorology, U. of Oklahoma; NASA medal for Exceptional Scientific Achievement; two Department of Commerce gold and one silver medal; and others. Former head of the Goddard Global and Simulation Branch (now GMAO), and former director of the Environmental Modeling Center at NCEP. Her recent work on the impact of land use on climate change (Kalnay and Cai, Nature, 2003), was chosen by Discovery Magazine as one of the top 100 science news of the year (see feature in International Association for Urban Climate newsletter). The Reanalysis paper of 1996 is the most cited paper in all geosciences. Education: License in Meteorology, University of Buenos Aires, 1965. Ph. D., 1971, MIT (under Jule G. Charney). Email: ekalnay@atmos.umd.edu Tel: (301) 405-5370/5391; Fax: (301)314-9482. Address: Department of Atmospheric and Oceanic Science, University of Maryland, 3431 Computer and Space Sciences Bldg., College Park, MD, 20742-2425

From 1987 to 1997, Eugenia Kalnay was the Director of the Environmental Modeling Center (EMC, ex Development Division) of the National Centers for Environmental Prediction (NCEP, ex NMC), National Weather Service (NWS). During those ten years there were major improvements in the NWS models' forecast skill. Many successful projects such as the 50-year NCEP/NCAR Reanalysis, Eta model and data assimilation changes associated with GCIP, seasonal and interannual dynamical predictions, ensemble forecasting, 3-D and 4-D variational data assimilation, advanced quality control, coastal ocean forecasting, were developed. EMC became a pioneer in both the fundamental science and the practical applications of numerical weather prediction.

Current research interests of Dr. Kalnay are in data assimilation, numerical weather prediction, data assimilation, predictability and ensemble forecasting, coupled ocean-atmosphere modeling and climate change. Zoltan Toth and Eugenia Kalnay introduced the breeding method for ensemble forecasting. She is also the author (with Ross Hoffman and Wesley Ebisuzaki) of other widely used ensemble methods known as Lagged Averaged Forecasting and Scaled LAF. Her book, Atmospheric Modeling, Data Assimilation and Predictability (Cambridge University Press, 2003) sold out within a year, is now on its fifth printing and was published in Chinese (2005).

She worked with Drs. Shu-Chih Yang and Ming Cai on ensemble and data assimilation methods on coupled ocean-atmosphere models using breeding (Cai et al, 2003, Yang et al, 2005, 2006, 2008, 2009), on the one and two-way interaction of the ocean and the atmosphere (Peña et al., 2003, Peña and Kalnay, 2004). Kalnay and Cai (2003) proposed a method (Observation minus Reanalysis trends, OMR) to estimate impact of land-cover and land-use change in climate change. The OMR paper was selected by Discovery Magazine as one of the top 100 science news of the year, and many papers have since used OMR to conclude that Green is Cool.

E. Kalnay co-founded with J. Yorke the Weather/Chaos Group at UMCP, which discovered the presence of low dimensionality in unstable regions of the atmosphere (Patil et al, 2002) and applied this result to develop the Local Ensemble Kalman Filter (Ott et al. 2002, 2004), the Local Ensemble Transform Kalman Filter (Hunt et al., 2007), and its extension to 4 dimensions (Hunt et al., 2004). See papers and publications for new applications of the LETKF. She has also published papers on atmospheric dynamics and convection, use of satellite data, numerical methods, and the atmosphere of Venus. More than a dozen doctoral theses have been completed in this project including former students of Eugenia (DJ Patil, Chris Danforth, Malaquías Peña, Shu-Chih Yang, Takemasa Miyoshi, Pablo Grumman, Matt Hoffman, Hong Li, Junjie Liu, Ji-Sun Kang). Present students include Steve Greybush (data assimilation for Mars, localization and stability in EnKF), Yan Zhou (estimation and correction of model bias in reanalysis), Javier Amezcua (RAW method, continuous EnKF), Steve Penny (LETKF applied to ocean data assimilation), Tamara Singleton (data assimilation in coupled systems). With Inez Fung, Junjie Liu and Ji-Sun Kang developed a system to estimate surface carbon fluxes from the simultaneous assimilation of atmospheric variables and CO2 concentration. With Zhao-xia Pu and Seon Ki Park, she introduced the “quasi-inverse” method of backward integration of atmospheric models and novel applications to targeted observations and data assimilation.

On the occasion of receiving the 54^th WMO/IMO prize, she talked at the NAS on “Population and Climate Change: a Proposal”, where basic facts about population growth and sustainability are reviewed, pointing out that no meaningful discussion of climate change can ignore this ‘elephant in the room’. Successful trends in non-coercive population control, and the alleged economic problems of reducing population are also discussed. She proposes that government agencies encourage development of regional population models coupled with Earth System models to study population and climate change in an objective, scientific way, while at the same time ‘desensitizing’ this taboo subject. A team that includes Eugenia, Matthias Ruth, Ning Zeng, Victor Yakovenko, Jorge Rivas, and other experts is working on the development of a fully coupled natural and human model. The idea is that no Earth System Model can be used to study possible scenarios of climate change without including a two-way coupled Human System Model, since humans dominate the biosphere (see short talk at WCRP).

Malise Cooper Dick, Malise and Eugenia, Washington Post

Seminars at MIT, April 2011: Recent advances in EnKF, Population and Climate Change ; Talk at WCRP, October 2011: Population and Climate Change (15 min).

WMO-62 Executive Council Talk: Chaos-Predictability-EnKF , WMO 15^th Meeting of the Region III, Bogotá, Talk on Predictability: what is scientifically feasible? (in Spanish)

CO2 data assimilation and Reanalysis (Baltimore, Nov 1 2010)

Balance and EnKF Localization (MWR, Greybush et al 2010)

Mars: Hoffman et al., 2010, Eluszkiewicz et al., 2008, Greybush et al., PPT, May 2010

Ocean instabilities explained with breeding (Hoffman et al GRL, 2009, aux. material)

Localization of Variables (Kang et al., 2011, JGR)

Invited talk at MOCA-09 (Data assimilation)

Seminar at NCEP - EnKF: Status and potential (1/6/09)

Handling nonlinearity and non-Gaussianity: Yang-Kalnay-Hunt (in press, MWR); Accelerating spin-up (RIP, Kalnay-Yang, QJRMS, 2010)

Dissertations (2009): Matt Hoffman (Mars, etc.); Ji-Sun Kang (CO2 data assimilation)

Ballabrera et al., 2009: Data assimilation in a coupled system

In the midst of chaos, good predictions

Earth Sciences at 20 years

Simultaneous estimation of inflation and obs errors

Analysis sensitivity to obs and cross-validation

Comparison of methods to deal with model errors in EnKF

International Association for Urban Climate features our work ; Fall et al, 2009: Land-use and Land-cover impact on the US temperature trends; JGR paper on Argentina’s land change

Yang et al., 2009, QJRMS, revised: Coarse analysis by weight interpolation in the LETKF.

Yang et al., 2008, J.of C., revised: Application of coupled breeding to ensemble forecasting and data assimilation

Ensemble Kalman Filter: Current Status and Potential (book Chapter)

Accelerating spin-up in EnKF: Running in Place

Six Lectures in Alghero, MSMM08: 1: (intro predict), 2: Tang/Adj Models-SVs, 3: BV applications, 4: EKF&EnKF, 5: New ideas to improve EnKF, 6: 4D-Var and EnKF

Two lectures in Puerto Rico: 1. Reanalyses; 2. Impact of Land Use on Climate Change

Thesis of Junjie Liu: Adaptive obs, obs sensitivity, obs impact w/o adjoint, and assimilation of humidity. PPT of defense. Forecast sensitivity to observations without adjoint (QJ) .

Applications of LETKF: adaptive observations, information content, observation sensitivity and assimilation of humidity: Junjie Liu thesis defense PPT.

Tellus A (Oct07): 4D-Var or EnKF?, Discussion by Gustafsson, Response to Discussion

LETKF with realistic observations (Dr. Hong Li defense PPT); Hong Li dissertation; Simultaneous estimation of inflation and observational errors

Lidar Workshop: Adaptive observations

AMS 2007 presentations (ppt.pdf): Li-Kalnay-Online-Estimation-Inflation&ObErrors, Liu-Kalnay-AdaptiveObservations, Liu-AssimHumidity, Li-AIRSretrievals, Li-Model-Errors, Yang-QGcomparison-4DVarEnKFHybrid3DVar, Kalnay-ArakawaSymposium (Breeding-A simple tool for complex dynamics);Kalnay-Li-Miyoshi:Inflation-ObErrorsEstimation (extended abstract)

Featured article in the International Association for Urban Climate newsletter; AMS-2007SummaryPoster on impact of land-use on temperature trends, JGR NEW paper, Lim-Cai-Kalnay-Zhou (2007, JAMC-revised), GRL Lim-Cai-Kalnay-Zhou paper on the the impact of land-type on surface warming (in press), AGU-2005PPT, Nature Kalnay-Cai 2003paper on impact of land use on climate change (pdf), Corrigendum (pdf),CorrectedFig2, Corrected Fig3, Suppl Fig1, SupplFig2, Suppl Fig3,Response to comments , ChristianScience Monitor article (A parking lot effect?), letter to CSM editor from Kalnay and Cai, Related paper by Zhou, Dickinson, et al

4DVar or EnKF?(submitted to Tellus), 4DVar or EnKF (ppt-pdf), AdaptiveObservations, Corazza et al 2007 (NPG), LocalEnsemble Kalman Filter (Ott et al, 2004, Tellus); 4-DimensionalEnsemble KalmanFilter, Tellus, 2004 http://math.gmu.edu/~tsauer/fourdvar;LEKFexperimentswith the NCEP global model (Szunyogh et al, Tellus, 2005, in press). Hunt (2005, link), Harlim and Hunt (2006, link)

Estimating and correcting model errors(JAS 07), Estimating and correcting model errors (Danforth et al ESSIC seminar ppt), Estimating and correcting model errors (Danforth et al, MWR-2007). Defense presentation

IUGG 2003 Sushi lecture; ECMWF 2002 Predictability Book: Ensemble forecasting and data assimilation: two problems with the same solution? See also 50^thNWP Symposium. SAMSI Talk: 4D-Var or EnKF.

RISE 2004: Synchronization and data assimilation (PPT), JAS (2006) paper , RISE 2002: Evans et al (2004) BAMS Lorenz model is predictable, pdf version.

AMS 2004 extended abstracts: Land-use and climate change, Initialization of unstable coupled systems, Lifespan ofcoupled anomalies, Bred and Singular vectors and data assimilation, Regional Reanalysis, Local Ensemble Kalman Filter, 4D-Ensemble Kalman Filter,

50thNWPSymposium: Ensemble forecasting anddata assimilation, two problems with the same solution?, LEKF at UMd (Szunyogh et al 2005)

MOS, Perfect Prog and Reanalysis (Marzban, Sandgathe, Kalnay, MWR 2006)

Inverse 3D-VAR to precondition 4Dvar (Park and Kalnay, GRL, 2004 , Kalnay et al, 2000)

Breeding in a coupled system: Yang et al.(2007, subm MWR); Yang et al (JClim, 2006); Yang ESSIC Seminar 2006; Shu-ChihYang thesis; MIT-Seminar 2005; Pena and Kalnay, NPG,2004, Shu-Chih Yang, Kalnay and Cai (PPT); Bred Vectors of the Cane-Zebiak model (Cai et al), (Powerpoint)

AMS-2002: Use of Breeding in DataAssimilation (Corazza et al, 2002); Lyapunov and BredVectors (Kalnay et al 2002), (Powerpoint); Low dimensionality paper (Patil et al, PRL)

Breeding and the errors of the day (Corazza et al, 2003); Keeping the bred vectors young (Powerpoint)

One-Way and Two-Way ocean-atmosphere coupling (Pena et al); Lifespan of coupled anomalies (Pena et al, JoC, 2004)

Ensemble forecasting and data assimilation seminar at NCAR (powerpoint)

Atmospheric Modeling, Data Assimilation and Predictability (comments on the book); Reviews (JMA), (BAMS), (SPLANC), QJRMS (2003, p2442), Contents and first chapter of the book available from the publisher; Book typos and corrections (sorry!) ; A few more typos in Chapter 6

AOSC614: Syllabus for METO614, SPEEDY (Junjie Liu Tutorial), SPEEDY files (Junjie), book typos and corrections, a few more typos, Ch1_Intro&Overview, Ch2_1 GoverningEqs, Ch2_2 EqsMotionSphere, Ch2_3 WaveOscillations, Ch2_4 FilteringApprox, Ch2_5 SWE-Filtering, Ch2_6 VerticalCoords; Ch3_1 PDEsWellPosed, Ch3-2-1 FiniteDiffsStab, Ch3-2-2Leap-FrogTableSemiimplicit,Williams(2009),RAWfilter&Homework,RAWFigure,RAWfilter on SPEEDY model (Amezcua et al, MWR), Ch3-3-1&2:Space discretization-Spectral models, Ch3-3-3&4Hong, Ch3-3-5Hong, Ch3-3-5&6-3-4JLiu, Nested models BC (Martini), Ch4JLiu, Data assimilation lectures (Chapter 5): Intro-to-DataAssim1, Intro-to-DataAssim2, Steve Greybush: toy DA model examples, 5-4-4DVarShu-Chih, 5-5EnKF-Hong, Recent Advances In EnKF (JCSDA/NCEP seminar). Takemasa Miyoshi’s LETKF, SPEEDY: Google code. Junjie Liu Data Assimilation package: Tutorial, codes and data, Miyoshi 3D-Var doc, Amezcua Lorenz63 model-LETKF; Chapter 6 (Predictability): 1: (intro predict), 2: Tang/Adj Models-SVs, 3: BV applications; Dresden: BV-SV-LV-4D-Var; 6-1; 6-2; 6-3; QGSV;6.4;6.5;PalmerENStm, NotesFromPalmerENS,

AOSC630: outline, notes-1 (review of prob., Bayes),Class-1(Ji-Sun Kang), Class-2(Debra Baker), notes-2 (exploratory), notes-3 (param. prob. distr.), notes-4 (hypothesis testing), Guayaquil Table, notes-5 (regression), notes-6 (regression), notes-7 (multiple regression), notes-8 (statistical prediction), ANOVA, notes-9 (MOS, adaptive regression/KF), notes-10 (time series, Markov chains), Xueetal2000 (Markov prediction of SST), M. Pena (applications), Antolik (MOS), notes-11 (time series, AR, ARMA), notes-12 (time series, frequency, Fourier transf.), notes-13 (time filters, Lanczos), Lanczos code (matlab, Greybush), Krasnopolsky-2011 (neural networks), TianleYuan (applic of neuralnet), notes-14 (intro to EOFs), EOF code (matlab, Greybush), EOF example from Mars (Greybush), notes-15 (Coupled Fields, SVD), notes-16 (cluster analysis, Hong Li), Huug vanden Dool: {figures (ppt), figures (pdf), EOFPPT, EOT2-procedures (PPT), notes 1, notes 2a, notes2b, notes3, notes4a, notes4b, notes5(PPT), notes6, EOT2-procedures, CPCseasonal}, Webster-Hoyos (application of wavelets to statistical forecasting), Wavelets (Tangborn 2010), Malaquías Peña: Ensembles 101