scholarly journals Dynamical Properties of Model Output Statistics Forecasts

2008 ◽  
Vol 136 (2) ◽  
pp. 405-419 ◽  
Author(s):  
S. Vannitsem ◽  
C. Nicolis
Keyword(s):  
Space Velocity ◽  
Initial Condition ◽  
Model Output ◽  
Model Errors ◽  
State Dependent ◽  
Dependent Model ◽  
Dynamical Properties ◽  
Model Output Statistics ◽  
The Impact

Abstract The dynamical properties of forecasts corrected using model output statistics (MOS) schemes are explored, with emphasis on the respective role of model and initial condition uncertainties. Analytical and numerical investigations of low-order systems displaying chaos indicate that MOS schemes are able to partly correct the impact of both initial and model errors on model forecasting. Nevertheless the amplitude of the correction is much more sensitive to the presence of (state dependent) model errors, and if initial condition errors are much larger than model uncertainties then MOS schemes become less effective. Furthermore, the amplitude of the MOS correction depends strongly on the statistical properties of the phase space velocity difference between the model and reference systems, such as its mean and its covariance with the model predictors in the MOS scheme. Large corrections are expected when the predictors are closely related to the sources of model errors. The practical implications of these results are briefly discussed.

scholarly journals Dynamical Properties of MOS Forecasts: Analysis of the ECMWF Operational Forecasting System

2008 ◽  
Vol 23 (5) ◽  
pp. 1032-1043 ◽  
Author(s):  
S. Vannitsem
Keyword(s):  
Coastal Zone ◽  
Initial Conditions ◽  
Sensible Heat Flux ◽  
Heat Fluxes ◽  
Initial Condition ◽  
Model Errors ◽  
Free Atmosphere ◽  
Surface Heat Fluxes ◽  
Error Amplification ◽  
Dynamical Properties

Abstract The dynamical properties of ECMWF operational forecasts corrected by a (linear) model output statistics (MOS) technique are investigated, in light of the analysis performed in the context of low-order chaotic systems. Based on the latter work, the respective roles of the initial condition and model errors on the forecasts can be disentangled. For the temperature forecasted by the ECMWF model over Belgium, it is found that (i) the error amplification arising from the presence of uncertainties in the initial conditions dominates the error dynamics of the “free” atmosphere and (ii) the temperature at 2 m can be partly corrected by the use of the (linear) MOS technique (as expected from earlier works), suggesting that model errors and systematic initial condition biases dominate at the surface. In the latter case, the respective amplitudes of the model errors and systematic initial condition biases corrected by MOS depend on the location of the synoptic station. In addition, for a two-observables MOS scheme, the best second predictor is the temperature predicted at 850 hPa in the central part of the country, while for the coastal zone, it is the sensible heat flux entering in the evolution of the surface temperature. These differences are associated with a dominant problem of vertical temperature interpolation in the central and east parts of the country and a difficulty in assessing correctly the surface heat fluxes on the coastal zone. Potential corrections of these problems using higher-resolution models are also discussed.

scholarly journals Estimating and Correcting Global Weather Model Error

2007 ◽  
Vol 135 (2) ◽  
pp. 281-299 ◽  
Author(s):  
Christopher M. Danforth ◽  
Eugenia Kalnay ◽  
Takemasa Miyoshi
Keyword(s):  
Model Integration ◽  
Time Averaging ◽  
A Posteriori ◽  
Model Errors ◽  
Sampling Errors ◽  
Computationally Efficient ◽  
State Dependent ◽  
Weather Model ◽  
Value Decomposition ◽  
The Impact

Abstract The purpose of the present study is to explore the feasibility of estimating and correcting systematic model errors using a simple and efficient procedure, inspired by papers by Leith as well as DelSole and Hou, that could be applied operationally, and to compare the impact of correcting the model integration with statistical corrections performed a posteriori. An elementary data assimilation scheme (Newtonian relaxation) is used to compare two simple but realistic global models, one quasigeostrophic and one based on the primitive equations, to the NCEP reanalysis (approximating the real atmosphere). The 6-h analysis corrections are separated into the model bias (obtained by time averaging the errors over several years), the periodic (seasonal and diurnal) component of the errors, and the nonperiodic errors. An estimate of the systematic component of the nonperiodic errors linearly dependent on the anomalous state is generated. Forecasts corrected during model integration with a seasonally dependent estimate of the bias remain useful longer than forecasts corrected a posteriori. The diurnal correction (based on the leading EOFs of the analysis corrections) is also successful. State-dependent corrections using the full-dimensional Leith scheme and several years of training actually make the forecasts worse due to sampling errors in the estimation of the covariance. A sparse approximation of the Leith covariance is derived using univariate and spatially localized covariances. The sparse Leith covariance results in small regional improvements, but is still computationally prohibitive. Finally, singular value decomposition is used to obtain the coupled components of the correction and forecast anomalies during the training period. The corresponding heterogeneous correlation maps are used to estimate and correct by regression the state-dependent errors during the model integration. Although the global impact of this computationally efficient method is small, it succeeds in reducing state-dependent model systematic errors in regions where they are large. The method requires only a time series of analysis corrections to estimate the error covariance and uses negligible additional computation during a forecast. As a result, it should be suitable for operational use at relatively small computational expense.

scholarly journals Using Singular Value Decomposition to Parameterize State-Dependent Model Errors

2008 ◽  
Vol 65 (4) ◽  
pp. 1467-1478 ◽  
Author(s):  
Christopher M. Danforth ◽  
Eugenia Kalnay
Keyword(s):  
Singular Value Decomposition ◽  
Coupled System ◽  
Singular Value ◽  
Model Errors ◽  
Sampling Errors ◽  
Empirical Correction ◽  
State Dependent ◽  
Dependent Model ◽  
Value Decomposition ◽  
Lorenz 96

Abstract The purpose of the present study is to use a new method of empirical model error correction, developed by Danforth et al. in 2007, based on estimating the systematic component of the nonperiodic errors linearly dependent on the anomalous state. The method uses singular value decomposition (SVD) to generate a basis of model errors and states. It requires only a time series of errors to estimate covariances and uses negligible additional computation during a forecast integration. As a result, it should be suitable for operational use at a relatively small computational expense. The method is tested with the Lorenz ’96 coupled system as the truth and an uncoupled version of the same system as a model. The authors demonstrate that the SVD method explains a significant component of the effect that the model’s unresolved state has on the resolved state and shows that the results are better than those obtained with Leith’s empirical correction operator. The improvement is attributed to the fact that the SVD truncation effectively reduces sampling errors. Forecast improvements of up to 1000% are seen when compared with the original model. The improvements come at the expense of weakening ensemble spread.

Model forecast error correction based on the Local Dynamical Analog method: an example application to the ENSO forecast

2020 ◽  
Author(s):  
Zhaolu Hou ◽  
Bin Zuo ◽  
Shaoqing Zhang ◽  
Fei Huang ◽  
Ruiqiang Ding ◽  
...  
Keyword(s):  
Error Correction ◽  
Forecast Error ◽  
Forecast Errors ◽  
Model Errors ◽  
Model Forecast ◽  
New Approach ◽  
Correct Model ◽  
State Dependent ◽  
Dependent Model ◽  
Scalar Time Series

<p>Numerical forecasts always have associated errors. Analogue correction methods combine numerical simulations with statistical analyses to reduce model forecast errors. However, identifying appropriate analogues remains a challenging task. Here, we use the Local Dynamical Analog (LDA) method to locate analogues and correct model forecast errors. As an example, an ENSO model forecast error correction experiment confirms that the LDA method locates more dynamical analogues of states of interest and better corrects forecast errors than do other methods. This is because the LDA method ensures similarity of the initial states and the evolution of both states. In addition, the LDA method can be applied using a scalar time series, which reduces the complexity of the dynamical system. Model forecast error correction using the LDA method provides a new approach to correcting state-dependent model errors and can be readily integrated with other advanced models.</p>

scholarly journals Thalamic state influences timing precision in the thalamocortical circuit

2021 ◽  
Author(s):  
Clarissa J Whitmire ◽  
Yi J Liew ◽  
Garrett B. Stanley
Keyword(s):  
White Noise ◽  
Cortical Neurons ◽  
Spike Timing ◽  
Sensory Stimuli ◽  
Temporal Precision ◽  
Timing Precision ◽  
State Dependent ◽  
Sensory Encoding ◽  
The Impact

Sensory signals from the outside world are transduced at the periphery, passing through thalamus before reaching cortex, ultimately giving rise to the sensory representations that enable us to perceive the world. The thalamocortical circuit is particularly sensitive to the temporal precision of thalamic spiking due to highly convergent synaptic connectivity. Thalamic neurons can exhibit burst and tonic modes of firing that strongly influence timing within the thalamus. The impact of these changes in thalamic state on sensory encoding in the cortex, however, remains unclear. Here, we investigated the role of thalamic state on timing in the thalamocortical circuit of the vibrissa pathwayin the anesthetized rat. We optogenetically hyperpolarized thalamus while recording single unit activity in both thalamus and cortex. Tonic spike triggered analysis revealed temporally precise thalamic spiking that was locked to weak white-noise sensory stimuli, while thalamic burst spiking was associated with a loss in stimulus-locked temporal precision. These thalamic state dependent changes propagated to cortex such that the cortical timing precision was diminished during the hyperpolarized (burst biased) thalamic state. While still sensory driven, the cortical neurons became significantly less precisely locked to the weak white-noise stimulus. The results here suggests a state dependent differential regulation of spike timing precision in the thalamus that could gate what signals are ultimately propagated to cortex.

scholarly journals Comparing impacts of parameter and spatial data uncertainty for a grid-based distributed watershed model

2016 ◽  
Vol 18 (6) ◽  
pp. 961-974 ◽  
Author(s):  
Younggu Her ◽  
Conrad Heatwole
Keyword(s):  
Land Cover ◽  
Parameter Uncertainty ◽  
Input Data ◽  
Hydrologic Model ◽  
Data Uncertainty ◽  
Model Parameters ◽  
Model Output ◽  
Model Errors ◽  
Land Cover Data ◽  
The Impact

Parameter uncertainty in hydrologic modeling is commonly evaluated, but assessing the impact of spatial input data uncertainty in spatially descriptive ‘distributed’ models is not common. This study compares the significance of uncertainty in spatial input data and model parameters on the output uncertainty of a distributed hydrology and sediment transport model, HYdrology Simulation using Time-ARea method (HYSTAR). The Shuffled Complex Evolution Metropolis (SCEM-UA) algorithm was used to quantify parameter uncertainty of the model. Errors in elevation and land cover layers were simulated using the Sequential Gaussian/Indicator Simulation (SGS/SIS) techniques and then incorporated into the model to evaluate their impact on the outputs relative to those of the parameter uncertainty. This study demonstrated that parameter uncertainty had a greater impact on model output than did errors in the spatial input data. In addition, errors in elevation data had a greater impact on model output than did errors in land cover data. Thus, for the HYSTAR distributed hydrologic model, accuracy and reliability can be improved more effectively by refining parameters rather than further improving the accuracy of spatial input data and by emphasizing the topographic data over the land cover data.

Intergroup Threat and Outgroup Attitudes

2013 ◽  
Vol 44 (5) ◽  
pp. 311-319 ◽  
Author(s):  
Marco Brambilla ◽  
David A. Butz
Keyword(s):  
Gay Men ◽  
Social Group ◽  
Welfare Recipients ◽  
Intergroup Attitudes ◽  
Social Policies ◽  
Intergroup Threat ◽  
Economic Threat ◽  
The Impact ◽  
General Climate

Two studies examined the impact of macrolevel symbolic threat on intergroup attitudes. In Study 1 (N = 71), participants exposed to a macrosymbolic threat (vs. nonsymbolic threat and neutral topic) reported less support toward social policies concerning gay men, an outgroup whose stereotypes implies a threat to values, but not toward welfare recipients, a social group whose stereotypes do not imply a threat to values. Study 2 (N = 78) showed that, whereas macrolevel symbolic threat led to less favorable attitudes toward gay men, macroeconomic threat led to less favorable attitudes toward Asians, an outgroup whose stereotypes imply an economic threat. These findings are discussed in terms of their implications for understanding the role of a general climate of threat in shaping intergroup attitudes.

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