scholarly journals Can an ensemble transform Kalman filter predict the reduction in forecast-error variance produced by targeted observations?

2001 ◽  
Vol 127 (578) ◽  
pp. 2803-2820 ◽  
Author(s):  
S. J. Majumdar ◽  
C. H. Bishop ◽  
B. J. Etherton ◽  
I. Szunyogh ◽  
Z. Toth
Keyword(s):  
Kalman Filter ◽  
Forecast Error ◽  
Error Variance ◽  
Forecast Error Variance ◽  
Targeted Observations ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform

scholarly journals The Gaussian Approach to Adaptive Covariance Inflation and Its Implementation with the Local Ensemble Transform Kalman Filter

2011 ◽  
Vol 139 (5) ◽  
pp. 1519-1535 ◽  
Author(s):  
Takemasa Miyoshi
Keyword(s):  
Kalman Filter ◽  
General Circulation ◽  
Forecast Error ◽  
Grid Point ◽  
Circulation Model ◽  
Error Variance ◽  
Model Errors ◽  
Variable Model ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform

In ensemble Kalman filters, the underestimation of forecast error variance due to limited ensemble size and other sources of imperfection is commonly treated by empirical covariance inflation. To avoid manual optimization of multiplicative inflation parameters, previous studies proposed adaptive inflation approaches using observations. Anderson applied Bayesian estimation theory to the probability density function of inflation parameters. Alternatively, Li et al. used the innovation statistics of Desroziers et al. and applied a Kalman filter analysis update to the inflation parameters based on the Gaussian assumption. In this study, Li et al.’s Gaussian approach is advanced to include the variance of the estimated inflation as derived from the central limit theorem. It is shown that the Gaussian approach is an accurate approximation of Anderson’s general Bayesian approach. An advanced implementation of the Gaussian approach with the local ensemble transform Kalman filter is proposed, where the adaptive inflation parameters are computed simultaneously with the ensemble transform matrix at each grid point. The spatially and temporally varying adaptive inflation technique is implemented with the Lorenz 40-variable model and a low-resolution atmospheric general circulation model; numerical experiments show promising results both with and without model errors.

scholarly journals Soil Moisture Assimilation Using a Modified Ensemble Transform Kalman Filter Based on Station Observations in the Hai River Basin

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Guocan Wu ◽  
Bo Dan ◽  
Xiaogu Zheng
Keyword(s):  
Soil Moisture ◽  
Kalman Filter ◽  
Water Budget ◽  
Forecast Error ◽  
Estimation Accuracy ◽  
Surface Model ◽  
Common Land Model ◽  
Ensemble Transform Kalman Filter ◽  
Analysis Error ◽  
Ensemble Transform

Assimilating observations to a land surface model can further improve soil moisture estimation accuracy. However, assimilation results largely rely on forecast error and generally cannot maintain a water budget balance. In this study, shallow soil moisture observations are assimilated into Common Land Model (CoLM) to estimate the soil moisture in different layers. A proposed forecast error inflation and water balance constraint are adopted in the Ensemble Transform Kalman Filter to reduce the analysis error and water budget residuals. The assimilation results indicate that the analysis error is reduced and the water imbalance is mitigated with this approach.

scholarly journals Regional Ensemble Forecasts Using the Ensemble Transform Technique

2009 ◽  
Vol 137 (1) ◽  
pp. 288-298 ◽  
Author(s):  
Craig H. Bishop ◽  
Teddy R. Holt ◽  
Jason Nachamkin ◽  
Sue Chen ◽  
Justin G. McLay ◽  
...  
Keyword(s):  
Forecast Error ◽  
Initial Perturbation ◽  
Error Variance ◽  
Precipitation Forecast ◽  
Initial Time ◽  
Naval Research ◽  
Ensemble Forecasts ◽  
Breeding Technique ◽  
Forecast Error Variance ◽  
Ensemble Transform

Abstract A computationally inexpensive ensemble transform (ET) method for generating high-resolution initial perturbations for regional ensemble forecasts is introduced. The method provides initial perturbations that (i) have an initial variance consistent with the best available estimates of initial condition error variance, (ii) are dynamically conditioned by a process similar to that used in the breeding technique, (iii) add to zero at the initial time, (iv) are quasi-orthogonal and equally likely, and (v) partially respect mesoscale balance constraints by ensuring that each initial perturbation is a linear sum of forecast perturbations from the preceding forecast. The technique is tested using estimates of analysis error variance from the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) and the Navy’s regional Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) over a 3-week period during the summer of 2005. Lateral boundary conditions are provided by a global ET ensemble. The tests show that the ET regional ensemble has a skillful mean and a useful spread–skill relationship in mass, momentum, and precipitation variables. Diagnostics indicate that ensemble variance was close to, but probably a little less than, the forecast error variance for wind and temperature variables, while precipitation ensemble variance was significantly smaller than precipitation forecast error variance.

The influence of economic policy uncertainty and geopolitical risk on US citizens overseas air passenger travel by regional destination

2020 ◽  
pp. 135481662098119
Author(s):  
James E Payne ◽  
Nicholas Apergis
Keyword(s):  
Economic Policy ◽  
Forecast Error ◽  
Error Variance ◽  
Variance Decomposition ◽  
Air Travel ◽  
Policy Uncertainty ◽  
Economic Policy Uncertainty ◽  
Passenger Travel ◽  
Forecast Error Variance

This research note extends the literature on the role of economic policy uncertainty and geopolitical risk on US citizens overseas air travel through the examination of the forecast error variance decomposition of total overseas air travel and by regional destination. Our empirical findings indicate that across regional destinations, US economic policy uncertainty explains more of the forecast error variance of US overseas air travel, followed by geopolitical risk with global economic policy uncertainty explaining a much smaller percentage of the forecast error variance.

scholarly journals Gain Form of the Ensemble Transform Kalman Filter and Its Relevance to Satellite Data Assimilation with Model Space Ensemble Covariance Localization

2017 ◽  
Vol 145 (11) ◽  
pp. 4575-4592 ◽  
Author(s):  
Craig H. Bishop ◽  
Jeffrey S. Whitaker ◽  
Lili Lei
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Satellite Data ◽  
Localization Length ◽  
Model Space ◽  
Ensemble Size ◽  
Length Scales ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform ◽  
Expanded Ensemble

To ameliorate suboptimality in ensemble data assimilation, methods have been introduced that involve expanding the ensemble size. Such expansions can incorporate model space covariance localization and/or estimates of climatological or model error covariances. Model space covariance localization in the vertical overcomes problematic aspects of ensemble-based satellite data assimilation. In the case of the ensemble transform Kalman filter (ETKF), the expanded ensemble size associated with vertical covariance localization would also enable the simultaneous update of entire vertical columns of model variables from hyperspectral and multispectral satellite sounders. However, if the original formulation of the ETKF were applied to an expanded ensemble, it would produce an analysis ensemble that was the same size as the expanded forecast ensemble. This article describes a variation on the ETKF called the gain ETKF (GETKF) that takes advantage of covariances from the expanded ensemble, while producing an analysis ensemble that has the required size of the unexpanded forecast ensemble. The approach also yields an inflation factor that depends on the localization length scale that causes the GETKF to perform differently to an ensemble square root filter (EnSRF) using the same expanded ensemble. Experimentation described herein shows that the GETKF outperforms a range of alternative ETKF-based solutions to the aforementioned problems. In cycling data assimilation experiments with a newly developed storm-track version of the Lorenz-96 model, the GETKF analysis root-mean-square error (RMSE) matches the EnSRF RMSE at shorter than optimal localization length scales but is superior in that it yields smaller RMSEs for longer localization length scales.

scholarly journals Predicting Water Availability in Water Bodies under the Influence of Precipitation and Water Management Actions Using VAR/VECM/LSTM

Climate ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 144
Author(s):  
Harleen Kaur ◽  
Mohammad Afshar Alam ◽  
Saleha Mariyam ◽  
Bhavya Alankar ◽  
Ritu Chauhan ◽  
...  
Keyword(s):  
Water Management ◽  
Short Term Memory ◽  
Forecast Error ◽  
Error Variance ◽  
Water Levels ◽  
Water Bodies ◽  
Economic Activities ◽  
Management Actions ◽  
Forecast Error Variance ◽  
Water Treatments

Recently, awareness about the significance of water management has risen as population growth and global warming increase, and economic activities and land use continue to stress our water resources. In addition, global water sustenance efforts are crippled by capital-intensive water treatments and water reclamation projects. In this paper, a study of water bodies to predict the amount of water in each water body using identifiable unique features and to assess the behavior of these features on others in the event of shock was undertaken. A comparative study, using a parametric model, was conducted among Vector Autoregression (VAR), the Vector Error Correction Model (VECM), and the Long Short-Term Memory (LSTM) model for determining the change in water level and water flow of water bodies. Besides, orthogonalized impulse responses (OIR) and forecast error variance decompositions (FEVD) explaining the evolution of water levels and flow rates, the study shows the significance of VAR/VECM models over LSTM. It was found that on some water bodies, the VAR model gave reliable results. In contrast, water bodies such as water springs gave mixed results of VAR/VECM.

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