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363
Estimation of Parameters and Eigenmodes of Multivariate Autoregressive Models
, 2001
"... Dynamical characteristics of a complex system can often be inferred from analyses of a stochastic time series model fitted to observations of the system. Oscillations in geophysical systems, for example, are sometimes characterized by principal oscillation patterns, eigenmodes of estimated autoregre ..."
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Cited by 100 (2 self)
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Dynamical characteristics of a complex system can often be inferred from analyses of a stochastic time series model fitted to observations of the system. Oscillations in geophysical systems, for example, are sometimes characterized by principal oscillation patterns, eigenmodes of estimated autoregressive (AR) models of first order. This paper describes the estimation of eigenmodes of AR models of arbitrary order. AR processes of any order can be decomposed into eigenmodes with characteristic oscillation periods, damping times, and excitations. Estimated eigenmodes and confidence intervals for the eigenmodes and their oscillation periods and damping times can be computed from estimated model parameters. As a computationally efficient method of estimating the parameters of AR models from highdimensional data, a stepwise least squares algorithm is proposed. This algorithm computes model coefficients and evaluates criteria for the selection of the model order stepwise for AR models of successively decreasing order. Numerical simulations indicate that, with the least squares algorithm, the AR model coefficients and the eigenmodes derived from the coefficients are estimated reliably and that the approximate 95% confidence intervals for the coefficients and eigenmodes are rough approximations of the confidence intervals inferred from the simulations.
Space and SpaceTime Modeling Using Process Convolutions
"... . A continuous spatial model can be constructed by convolving a very simple, perhaps independent, process with a kernel or point spread function. This approach for constructing a spatial process o#ers a number of advantages over specification through a spatial covariogram. In particular, this proces ..."
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Cited by 77 (4 self)
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. A continuous spatial model can be constructed by convolving a very simple, perhaps independent, process with a kernel or point spread function. This approach for constructing a spatial process o#ers a number of advantages over specification through a spatial covariogram. In particular, this process convolution specification leads to compuational simplifications and easily extends beyond simple stationary models. This paper uses process convolution models to build space and spacetime models that are flexible and able to accomodate large amounts of data. Data from environmental monitoring is considered. 1 Introduction Modeling spatial data with Gaussian processes is the common thread of all geostatistical analyses. Some notable references in this area include Matheron (1963), Journel and Huijbregts (1978), Ripley (1981), Cressie (1991), Wackernagel (1995), and Stein (1999). A common approach is to model spatial dependence through the covariogram c(), so that covariance between any t...
2001: Eddy–zonal flow feedback in the Southern Hemisphere
 J. Atmos. Sci
"... The variability of the zonalmean zonal wind in the Southern Hemisphere is studied using EOF analysis and momentum budget diagnostics of NCEP Reanalysis data (19781997). The leading EOF of the zonalmean zonal wind is well separated from the remaining EOF’s and represents the north/south movement ..."
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Cited by 67 (5 self)
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The variability of the zonalmean zonal wind in the Southern Hemisphere is studied using EOF analysis and momentum budget diagnostics of NCEP Reanalysis data (19781997). The leading EOF of the zonalmean zonal wind is well separated from the remaining EOF’s and represents the north/south movement of the midlatitude jet. Analysis of the momentum budget shows that a positive feedback between the zonalmean wind anomalies and the eddy momentum fluxes accounts for the unusual persistence of EOF1 and plays an important role in the selection of the leading EOF of midlatitude variability. Further analysis also shows a propagating feedback, common to both EOF1 and EOF2, which is responsible for the poleward drift of wind anomalies with time. The observations support the following feedback mechanism: anomalous baroclinic wave activity is generated at the latitude of anomalous temperature gradient which, by thermal wind, coincides with the latitude of the anomalous zonal jet. The net propagation of baroclinic wave activity away from the jet gives momentum fluxes into the jet. This positive feedback is partially offset by lowfrequency, equivalent barotropic eddies which propagate into the jet and remove momentum from it. The bias toward equatorward wave propagation on a sphere contributes to the
Algorithm 808: ARfit  A Matlab package for the estimation of parameters and eigenmodes of multivariate autoregressive models
 ACM TOMS
, 2001
"... ARfit is a collection of Matlab modules for modeling and analyzing multivariate time series with autoregressive (AR) models. ARfit contains modules for fitting AR models to given time series data, for analyzing eigenmodes of a fitted model, and for simulating AR processes. ARfit estimates the parame ..."
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Cited by 43 (2 self)
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ARfit is a collection of Matlab modules for modeling and analyzing multivariate time series with autoregressive (AR) models. ARfit contains modules for fitting AR models to given time series data, for analyzing eigenmodes of a fitted model, and for simulating AR processes. ARfit estimates the parameters of AR models from given time series data with a stepwise least squares algorithm that is computationally efficient, in particular when the data are highdimensional. ARfit modules construct approximate confidence intervals for the estimated parameters and compute statistics with which the adequacy of a fitted model can be assessed. Dynamical characteristics of the modeled time series can be examined by means of a decomposition of a fitted AR model into eigenmodes and associated oscillation periods, damping times, and excitations. The ARfit module that performs the eigendecomposition of a fitted model also constructs approximate confidence intervals for the eigenmodes and their oscillation periods and damping times.
2011), Stratospheric ozone depletion: The main driver of 20th century atmospheric circulation changes in the Southern Hemisphere
 J. Clim
"... The importance of stratospheric ozone depletion on the atmospheric circulation of the troposphere is studied with an atmospheric general circulation model, the Community Atmospheric Model, version 3 (CAM3), for the second half of the twentieth century. In particular, the relative importance of ozone ..."
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Cited by 42 (7 self)
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The importance of stratospheric ozone depletion on the atmospheric circulation of the troposphere is studied with an atmospheric general circulation model, the Community Atmospheric Model, version 3 (CAM3), for the second half of the twentieth century. In particular, the relative importance of ozone depletion is contrasted with that of increased greenhouse gases and accompanying sea surface temperature changes. By specifying ozone and greenhouse gas forcings independently, and performing long, timeslice integrations, it is shown that the impacts of ozone depletion are roughly 2–3 times larger than those associated with increased greenhouse gases, for the Southern Hemisphere tropospheric summer circulation. The formation of the ozone hole is shown to affect not only the polar tropopause and the latitudinal position of the midlatitude jet; it extends to the entire hemisphere, resulting in a broadening of the Hadley cell and a poleward extension of the subtropical dry zones. The CAM3 results are compared to and found to be in excellent agreement with those of the multimodel means of the recent Coupled Model Intercomparison Project (CMIP3) and Chemistry–Climate Model Validation (CCMVal2) simulations. This study, therefore, strongly suggests that most Southern Hemisphere tropospheric circulation changes, in austral summer over the second half of the twentieth century, have been caused by polar stratospheric ozone depletion. 1.
2005: The response of tropospheric circulation to perturbations in lower stratospheric temperature
 J. Climate
"... The response of tropospheric circulation to perturbations in lowerstratospheric temperature ABCDEFB ..."
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Cited by 39 (8 self)
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The response of tropospheric circulation to perturbations in lowerstratospheric temperature ABCDEFB
Creation of the WATCH forcing data and its use to assess global and regional reference crop evaporation over land during the twentieth century
 J. Hydrometeorol
"... The Water and Global Change (WATCH) project evaluation of the terrestrial water cycle involves using land surface models and general hydrological models to assess hydrologically important variables including evaporation, soil moisture, and runoff. Such models require meteorological forcing data, and ..."
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Cited by 38 (1 self)
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The Water and Global Change (WATCH) project evaluation of the terrestrial water cycle involves using land surface models and general hydrological models to assess hydrologically important variables including evaporation, soil moisture, and runoff. Such models require meteorological forcing data, and this paper describes the creation of the WATCH Forcing Data for 1958–2001 based on the 40yr ECMWF ReAnalysis (ERA40) and for 1901–57 based on reordered reanalysis data. It also discusses and analyses modelindependent estimates of reference crop evaporation. Global average annual cumulative reference crop evaporation was selected as a widely adopted measure of potential evapotranspiration. It exhibits no significant trend from 1979 to 2001 although there are significant longterm increases in global average vapor pressure deficit and concurrent significant decreases in global average net radiation and wind speed. The nearconstant global average of annual reference crop evaporation in the late twentieth century masks significant decreases in some regions (e.g., the Murray–Darling basin) with significant increases in others. 1.
Discovery of Climate Indices Using Clustering
 In Proc. of the 9th ACM SIGKDD Int’l Conference on Knowledge Discovery and Data Mining
, 2003
"... To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earth’s oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eig ..."
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Cited by 36 (12 self)
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To analyze the effect of the oceans and atmosphere on land climate, Earth Scientists have developed climate indices, which are time series that summarize the behavior of selected regions of the Earth’s oceans and atmosphere. In the past, Earth scientists have used observation and, more recently, eigenvalue analysis techniques, such as principal components analysis (PCA) and singular value decomposition (SVD), to discover climate indices. However, eigenvalue techniques are only useful for finding a few of the strongest signals. Furthermore, they impose a condition that all discovered signals must be orthogonal to each other, making it difficult to attach a physical interpretation to them. This paper presents an alternative clusteringbased methodology for the discovery of climate indices that overcomes these limitations and is based on clusters that represent regions with relatively homogeneous behavior. The centroids of these clusters are time series that summarize the behavior of the ocean or atmosphere in those regions. Some of these centroids correspond to known climate indices and provide a validation of our methodology; other centroids are variants of known indices that may provide better predictive power for some land areas; and still other indices may represent potentially new Earth science phenomena. Finally, we show that cluster based indices generally outperform SVD derived indices, both in terms of area weighted correlation and direct correlation with the known indices.
2011: Using a stochastic kinetic energy backscatter scheme to improve MOGREPS probabilistic forecast skill.Mon
 Wea. Rev
"... Understanding model error in stateoftheart numerical weather prediction models and representing its impact on flowdependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscalepropagating errors c ..."
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Cited by 34 (6 self)
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Understanding model error in stateoftheart numerical weather prediction models and representing its impact on flowdependent predictability remains a complex and mostly unsolved problem. Here, a spectral stochastic kinetic energy backscatter scheme is used to simulate upscalepropagating errors caused by unresolved subgridscale processes. For this purpose, stochastic streamfunction perturbations are generated by autoregressive processes in spectral space and injected into regions where numerical integration schemes and parameterizations in the model lead to excessive systematic kinetic energy loss. It is demonstrated how output from coarsegrained highresolution models can be used to inform the parameters of such a scheme. The performance of the spectral backscatter scheme is evaluated in the ensemble prediction system of the European Centre for MediumRange Weather Forecasts. Its implementation in conjunction with reduced initial perturbations results in a better spread–error relationship, more realistic kineticenergy spectra, a better representation of forecasterror growth, improved flowdependent predictability, improved rainfall forecasts, and better probabilistic skill. The improvement is most pronounced in the tropics and for largeanomaly events. It is found that whereas a simplified scheme assuming a constant dissipation rate already has some positive impact, the best results are obtained for flowdependent formulations of the unresolved processes. 1.
2004), Prediction of monsoon rainfall and river discharge on 1530 day time scales
 Bull. Am. Meteorol. Soc
"... A new physically based prediction scheme for 20–30day variability shows promise in monsoon regions for agricultural planning, disaster mitigation, and flood forecasting. During the summer of 2002, drought conditions persisted over India from midJune to midJuly following a laterthanaverage arriv ..."
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Cited by 30 (2 self)
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A new physically based prediction scheme for 20–30day variability shows promise in monsoon regions for agricultural planning, disaster mitigation, and flood forecasting. During the summer of 2002, drought conditions persisted over India from midJune to midJuly following a laterthanaverage arrival of seasonal monsoon rain. By the end of that summer, the average summer Indian rainfall totaled 711 mm or 19% below normal. With the exception of the area near the foothills of the Himalayas in the very north of India, where aboveaverage rainfall occurred, most of India was in deficit, with the state of Rajasthan 64 % below normal. Figure 1 provides a geographical reference for the locations discussed in the text. The summer of 2002 was the sixth driest in 130 yr (Table 1).