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50
Estimating global black carbon emissions using a topdown Kalman Filter approach*
"... research with independent policy analysis to provide a solid foundation for the public and private decisions needed to mitigate and adapt to unavoidable global environmental changes. Being datadriven, the Program uses extensive Earth system and economic data and models to produce quantitative analy ..."
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research with independent policy analysis to provide a solid foundation for the public and private decisions needed to mitigate and adapt to unavoidable global environmental changes. Being datadriven, the Program uses extensive Earth system and economic data and models to produce quantitative analysis and predictions of the risks of climate change and the challenges of limiting human influence on the environment—essential knowledge for the international dialogue toward a global response to climate change. To this end, the Program brings together an interdisciplinary group from two established MIT research centers: the Center for Global Change Science (CGCS) and the Center for Energy and Environmental Policy Research (CEEPR). These two centers—along with collaborators from the Marine Biology Laboratory (MBL) at Woods Hole and short and longterm visitors—provide the united vision needed to solve global challenges. At the heart of much of the Program’s work lies MIT’s Integrated Global System Model. Through this integrated model, the Program seeks to: discover new interactions among natural and human climate system components; objectively assess uncertainty in economic and climate projections; critically and quantitatively analyze environmental management and policy proposals; understand complex
A Comparison of the Hybrid and EnSRF Analysis Schemes in the Presence of Model Errors due to Unresolved Scales
, 2009
"... A hybrid analysis scheme is compared with an ensemble square root filter (EnSRF) analysis scheme in the presence of model errors as a followup to a previous perfectmodel comparison. In the hybrid scheme, the ensemble perturbations are updated by the ensemble transform Kalman filter (ETKF) and the ..."
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A hybrid analysis scheme is compared with an ensemble square root filter (EnSRF) analysis scheme in the presence of model errors as a followup to a previous perfectmodel comparison. In the hybrid scheme, the ensemble perturbations are updated by the ensemble transform Kalman filter (ETKF) and the ensemble mean is updated with a hybrid ensemble and static backgrounderror covariance. The experiments were conducted with a twolayer primitive equation model. The true state was a T127 simulation. Data assimilation experiments were conducted at T31 resolution (3168 complex spectral coefficients), assimilating imperfect observations drawn from the T127 nature run. By design, the magnitude of the truncation error was large, which provided a test on the ability of both schemes to deal with model error. Additive noise was used to parameterize model errors in the background ensemble for both schemes. In the first set of experiments, additive noise was drawn from a large inventory of historical forecast errors; in the second set of experiments, additive noise was drawn from a large inventory of differences between forecasts and analyses. The static covariance was computed correspondingly from the two inventories. The hybrid analysis was statistically significantly more accurate than the EnSRF analysis. The improvement of the hybrid over the EnSRF was smaller when differences of forecasts and analyses were used to form the random noise and the static covariance. The EnSRF analysis was more sensitive to the size of the ensemble than the hybrid. A series of tests was conducted to understand why the EnSRF performed worse than the hybrid. It was shown that the inferior performance of the EnSRF was likely due to the sampling error in the estimation of the modelerror covariance in the mean update and the lessbalanced EnSRF initial conditions resulting from the extra localizations used in the EnSRF. 1.
Characteristics of target areas selected by the Ensemble Transform Kalman Filter
"... for mediumrange forecasts of highimpact winter weather ..."
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for mediumrange forecasts of highimpact winter weather
Linear Theory for Filtering Nonlinear Multiscale Systems with Model Error
, 2014
"... In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuoustime noisy observations of all components of the slow variables. Mathematically, t ..."
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In this paper, we study filtering of multiscale dynamical systems with model error arising from limitations in resolving the smaller scale processes. In particular, the analysis assumes the availability of continuoustime noisy observations of all components of the slow variables. Mathematically, this paper presents new results on higherorder asymptotic expansion of the first two moments of a conditional measure. In particular, we are interested in the application of filtering multiscale problems in which the conditional distribution is defined over the slow variables, given noisy observation of the slow variables alone. From the mathematical analysis, we learn that for a continuous time linear model with Gaussian noise, there exists a unique choice of parameters in a linear reduced model for the slow variables which gives the optimal filtering when only the slow variables are observed. Moreover, these parameters simultaneously give the optimal equilibrium statistical estimates of the underlying system, and as a consequence they can be estimated offline from the equilibrium statistics of the true signal. By examining a nonlinear test model, we show that the linear theory extends in this nonGaussian, nonlinear configuration as long as we know the optimal stochastic parameterization and the correct observation model. However, when the stochastic parameterization model is inappropriate, parameters chosen for good filter performance may give poor equilibrium statistical estimates and vice versa; this finding is based on analytical and numerical results
On the propagation of information and the use of localization in ensemble Kalman filtering
 J. Atmos. Sci
, 2010
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1 Ensemblebased observation impact estimates using the NCEP GFS
"... The impacts of the assimilated observations on the 24 hour forecasts are estimated with the ensemblebased method proposed by Kalnay et al. using the ensemble Kalman filter (EnKF). This method estimates the relative impact of observations in data assimilation similarly to the adjointbased method pr ..."
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The impacts of the assimilated observations on the 24 hour forecasts are estimated with the ensemblebased method proposed by Kalnay et al. using the ensemble Kalman filter (EnKF). This method estimates the relative impact of observations in data assimilation similarly to the adjointbased method proposed by Langland and Baker but without using the adjoint model. It is implemented with the National Centers for Environmental Prediction (NCEP) Global Forecasting System (GFS) EnKF which has been used as a part of operational global data assimilation system at NCEP since May 2012. The result quantifies the overall positive impacts of the assimilated observations and the relative importance of the satellite radiance observations compared to other types of observations especially for the moisture variables. The method is also used to identify the cause of local forecast failure cases in the 24 hr forecasts. Data denial experiments of the observations identified as producing a negative impact observation sets reduce the forecast errors as estimated, validating the impact estimate. 3 1.
A General Strategy for PhysicsBased Model Validation Illustrated with Earthquake Phenomenology, Atmospheric Radiative Transfer, and Computational Fluid Dynamics
, 710
"... Ref. [1] is also available in preprint form at URL ..."
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High Resolution Modeling of Typhoon Morakot (2009): Vortex Rossby Waves and Their Role in Extreme Precipitation over Taiwan
, 2011
"... A highresolution nonhydrostatic numerical model, the Advanced Regional Prediction System (ARPS), is used to simulate Typhoon Morakot (2009) as it made landfall over Taiwan and produced recordbreaking rainfall totals. In particular, the mesoscale structure of the typhoon is investigated, with empha ..."
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A highresolution nonhydrostatic numerical model, the Advanced Regional Prediction System (ARPS), is used to simulate Typhoon Morakot (2009) as it made landfall over Taiwan and produced recordbreaking rainfall totals. In particular, the mesoscale structure of the typhoon is investigated, with emphasis on its associated deep convection, the development of inner rainbands near the center, and the resultant intense rainfall over western Taiwan. Simulations at 15 and 3 km grid spacing revealed that, following the decay of the initial inner eyewall, a new, much larger eyewall develops as the typhoon made landfall over Taiwan. Relatively large amplitude wave structures develop in the outer eyewall, and these waves are identified as vortex Rossby waves (VRWs) based on the wave characteristics and their similarity to VRWs identified in previous modeling and observational studies. Moderate to strong vertical shear over the typhoon system produced a persistent wavenumber 1 asymmetric structure during the landfall period, with upward motion and
Track and Intensity Forecasting of Hurricanes: Impact of ConvectionPermitting Resolution and Global Ensemble Kalman Filter Analysis on 2010 Atlantic Season Forecasts
, 2012
"... Twicedaily 48h tropical cyclone (TC) forecasts were produced for the fall 2010 Atlantic hurricane season using the Advanced Research core of theWeather Research and Forecasting (WRFARW)model on a large 4km grid covering much of the northern Atlantic. WRF forecasts initialized from operational Gl ..."
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Twicedaily 48h tropical cyclone (TC) forecasts were produced for the fall 2010 Atlantic hurricane season using the Advanced Research core of theWeather Research and Forecasting (WRFARW)model on a large 4km grid covering much of the northern Atlantic. WRF forecasts initialized from operational Global Forecast System (GFS) analyses based on the gridpoint statistical interpolation (GSI) threedimensional variational data assimilation (3DVAR) system and from experimental global ensembleKalman filter (EnKF) analyses, and corresponding global GFS forecasts were intercompared. For the track, WRF forecasts show improvement over GFS forecasts using either set of initial conditions (ICs). The EnKFinitialized GFS and WRF are also better than the corresponding GSIinitialized forecasts, but the difference is not always statistically significant. At all lead times, the WRF track errors are comparable to or smaller than the National Hurricane Center (NHC) official track forecast error, with those of the EnKF WRF being smallest. For weaker TCs, more improvement comes from the model (resolution) than from the ICs. For hurricane intensity TCs, EnKF ICs produce better track forecasts than GSI ICs, with the best forecast coming fromWRF at most lead times. For intensity, EnKF ICs consistently outperformGSI ICs in both models for weaker TCs. For hurricanestrength TCs, EnKF ICs produce forecasts statistically indistinguishable from GSI ICs in either model. For all TCs combined, WRF produces about half the error of the corresponding GFS simulation beyond 24 h, and at 36 and 48 h, the errors are smaller than those from NHC official forecasts. The improvement is even greater for hurricanestrength TCs. Overall, theWRF forecasts initialized with EnKF ICs have the smallest intensity error, and the difference is statistically significant compared to the GFS forecasts.
Observation Bias Correction with an Ensemble Kalman Filter
, 2007
"... This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases relative to the observation operator in use. Our approach is to use statespace augmentation to estimate satellite biases as part of the ensemble data assimilation procedu ..."
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This paper considers the use of an ensemble Kalman filter to correct satellite radiance observations for state dependent biases relative to the observation operator in use. Our approach is to use statespace augmentation to estimate satellite biases as part of the ensemble data assimilation procedure. We illustrate our approach by applying it to a particular ensemble scheme, the Local Ensemble Transform Kalman Filter (LETKF), to assimilate simulated biased AIRS brightness temperature observations on the Simplified Parameterizations, primitivEEquation DYnamics (SPEEDY) model. The bias parameters estimated by LETKF successfully reduce both the observation bias and analysis error. 1