scholarly journals The Evolution of Dispersion Spectra and the Evaluation of Model Differences in an Ensemble Estimation of Error Statistics for a Limited-Area Analysis

2006 ◽  
Vol 134 (11) ◽  
pp. 3456-3478 ◽  
Author(s):  
Simona Ecaterina Ştefănescu ◽  
Loïk Berre ◽  
Margarida Belo Pereira
Keyword(s):  
Large Scale ◽  
Model Error ◽  
Small Scale ◽  
Limited Area ◽  
Error Statistics ◽  
Model Framework ◽  
Cold Starting ◽  
Estimation Of Error ◽  
Ensemble Estimation ◽  
Perfect Model

Abstract An ensemble of limited-area forecasts has been obtained by integrating the Aire Limitée Adaptation Dynamique Développement International (ALADIN) limited-area model, in cold-starting mode, from an ensemble of Action de Recherche Petite Echelle Grande Echelle (ARPEGE) global analyses and forecasts. This permits error covariances of the ALADIN 6-h forecast and of the ARPEGE analysis to be estimated. These two fields may be combined in a future ALADIN analysis. The evolution of dispersion spectra is first studied in a perfect model framework. The ARPEGE analysis reduces the large-scale dispersion of the ARPEGE background by extracting some information from observations. Then, the digital filter initialization reduces the small-scale dispersion by removing the noise caused by interpolation of the ARPEGE analysis onto the ALADIN grid. Finally, the ALADIN 6-h forecast strongly increases the small-scale dispersion, in accordance with its ability to represent small-scale processes. Some model error contributions are then studied. The variances of the differences between the ALADIN and ARPEGE forecasts, which are started from the same ARPEGE analysis, are of smaller scale than are the ALADIN and ARPEGE perfect model dispersions. The small-scale part of these ARPEGE–ALADIN model differences is shown to correspond to structures that are represented by ALADIN and not by ARPEGE. Therefore, this part may be added to the ARPEGE analysis dispersion. The residual large-scale part is more ambiguous, but it may be added to the ALADIN dispersion; this may reflect some effects of the coupling inaccuracies, and strengthen (in a future ALADIN analysis) the use of the large-scale information from the ARPEGE analysis.

scholarly journals A Scale-Selective Data Assimilation Approach to Improving Tropical Cyclone Track and Intensity Forecasts in a Limited-Area Model: A Case Study of Hurricane Felix (2007)

2012 ◽  
Vol 27 (1) ◽  
pp. 124-140 ◽  
Author(s):  
Bin Liu ◽  
Lian Xie
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Dynamical Downscaling ◽  
Weather Forecasting ◽  
Forecast Error ◽  
Regional Model ◽  
Small Scale ◽  
Limited Area ◽  
Track Forecast ◽  
Global Models

Abstract Accurately forecasting a tropical cyclone’s (TC) track and intensity remains one of the top priorities in weather forecasting. A dynamical downscaling approach based on the scale-selective data assimilation (SSDA) method is applied to demonstrate its effectiveness in TC track and intensity forecasting. The SSDA approach retains the merits of global models in representing large-scale environmental flows and regional models in describing small-scale characteristics. The regional model is driven from the model domain interior by assimilating large-scale flows from global models, as well as from the model lateral boundaries by the conventional sponge zone relaxation. By using Hurricane Felix (2007) as a demonstration case, it is shown that, by assimilating large-scale flows from the Global Forecast System (GFS) forecasts into the regional model, the SSDA experiments perform better than both the original GFS forecasts and the control experiments, in which the regional model is only driven by lateral boundary conditions. The overall mean track forecast error for the SSDA experiments is reduced by over 40% relative to the control experiments, and by about 30% relative to the GFS forecasts, respectively. In terms of TC intensity, benefiting from higher grid resolution that better represents regional and small-scale processes, both the control and SSDA runs outperform the GFS forecasts. The SSDA runs show approximately 14% less overall mean intensity forecast error than do the control runs. It should be noted that, for the Felix case, the advantage of SSDA becomes more evident for forecasts with a lead time longer than 48 h.

scholarly journals Filtering the Stochastic Skeleton Model for the Madden–Julian Oscillation

2016 ◽  
Vol 144 (2) ◽  
pp. 501-527 ◽  
Author(s):  
Nan Chen ◽  
Andrew J. Majda
Keyword(s):  
Large Scale ◽  
Warm Pool ◽  
Model Error ◽  
Small Scale ◽  
Fast Wave ◽  
Madden Julian Oscillation ◽  
Computationally Efficient ◽  
Skeleton Model ◽  
Spatially Extended ◽  
Novel Strategy

Abstract The filtering and prediction of the Madden–Julian oscillation (MJO) and relevant tropical waves is a contemporary issue with significant implications for extended range forecasting. This paper examines the process of filtering the stochastic skeleton model for the MJO with noisy partial observations. A nonlinear filter, which captures the inherent nonlinearity of the system, is developed and judicious model error is included. Despite its nonlinearity, the special structure of this filter allows closed analytical formulas for updating the posterior states and is thus computationally efficient. A novel strategy for adding nonlinear observational noise to the envelope of convective activity is designed to guarantee its nonnegative property. Systematic calibration based on a cheap single-column version of the stochastic skeleton model provides a practical guideline for choosing the parameters in the full spatially extended system. With these column-tuned parameters, the full filter has a high overall filtering skill for Rossby waves but fails to recover the small-scale fast-oscillating Kelvin and moisture modes. An effectively balanced reduced filter involving a simple fast-wave averaging strategy is then developed, which greatly improves the skill of filtering the moisture modes and other fast-oscillating modes and enhances the total computational efficiency. Both the full and the reduced filters succeed in filtering the MJO and other large-scale features with both homogeneous and warm pool cooling/moistening backgrounds. The large bias in filtering the solutions by running the perfect model with noisy forcing is due to the noise accumulation, which indicates the importance of including judicious model error in designing filters.

Limited-area model error statistics over Western Africa: Comparisons with midlatitude results

2006 ◽  
Vol 132 (614) ◽  
pp. 213-230 ◽  
Author(s):  
Thibaut Montmerle ◽  
Jean-Philippe Lafore ◽  
Loïk Berre ◽  
Claude Fischer
Keyword(s):  
Model Error ◽  
Limited Area ◽  
Error Statistics ◽  
Limited Area Model ◽  
Area Model ◽  
Western Africa

scholarly journals Orographic Effects and Evaporative Cooling along a Subtropical Cold Front: The Case of the Spectacular Saharan Dust Outbreak of March 2004

2012 ◽  
Vol 140 (8) ◽  
pp. 2520-2533 ◽  
Author(s):  
Gregor Gläser ◽  
Peter Knippertz ◽  
Bernd Heinold
Keyword(s):  
Large Scale ◽  
Cold Front ◽  
Evaporative Cooling ◽  
Dust Emission ◽  
Leading Edge ◽  
Saharan Dust ◽  
Small Scale ◽  
Limited Area ◽  
Sensitivity Experiment ◽  
High Winds

Abstract On 2 March 2004 a marked upper-level trough and an associated surface cold front penetrated into the Sahara. High winds along and behind this frontal system led to an extraordinary, large-scale, and long-lived dust outbreak, accompanied by significant precipitation over parts of Algeria, Tunisia, and Libya. This paper uses sensitivity simulations with the limited-area model developed by the Consortium for Small-Scale Modeling (COSMO) together with analysis data and surface observations to test several hypotheses on the dynamics of this case proposed in previous work. It is demonstrated that air over central Algeria is cooled by evaporation of frontal precipitation, substantially enhancing winds at the leading edge of the cold front. This process is supported by very dry low-level air in the lee of the Atlas Mountains associated with a foehn situation. Flattening the mountain chain in a sensitivity experiment, however, has complex effects on the wind. While reduced evaporative cooling weakens the front, the elimination of the orographic blocking accelerates its penetration into the Sahara. The simulations also indicate high winds associated with a hydraulic jump at the southern slopes of the Tell Atlas. Feeding the simulated winds into a dust emission parameterization reveals reduced emissions on the order of 20%–30% for suppressed latent heating and even more when effects of the increased precipitation on soil moisture are considered. In the experiment with the Atlas removed, effects of the overall increase in high winds are compensated by an increase in precipitation. The results suggest that a realistic representation of frontal precipitation is an important requisite to accurately model dust emission in such situations.

scholarly journals A multiscale ocean data assimilation approach combining spatial and spectral localisation

Ocean Science ◽  
2019 ◽  
Vol 15 (2) ◽  
pp. 443-457 ◽  
Author(s):  
Ann-Sophie Tissier ◽  
Jean-Michel Brankart ◽  
Charles-Emmanuel Testut ◽  
Giovanni Ruggiero ◽  
Emmanuel Cosme ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Spectral Domain ◽  
Small Scale ◽  
Error Statistics ◽  
The North ◽  
Ocean Data Assimilation ◽  
Wide Range ◽  
Spatial Localisation ◽  
Operational Systems

Abstract. Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems, which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localisation algorithm in ensemble data assimilation systems), so that the large-scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in the assimilation systems of the Copernicus Marine Environment Monitoring Service, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large-scale components of the signal by an explicit observational constraint applied directly on the large scales and to restrict the use of spatial localisation to the small-scale components of the signal. To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the Jason tracks), assimilated in a 1/4∘ model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral domain and the spectral localisation provides consistent ensemble estimates of the state of the system (in the spectral domain or after backward transformation to the spatial domain). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.

scholarly journals A multiscale ocean data assimilation approach combining spatial and spectral localisation

2018 ◽  
Author(s):  
Ann-Sophie Tissier ◽  
Jean-Michel Brankart ◽  
Charles-Emmanuel Testut ◽  
Giovanni Ruggiero ◽  
Emmanuel Cosme ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Large Scale ◽  
Spectral Space ◽  
Small Scale ◽  
Localization Algorithm ◽  
Error Statistics ◽  
Ocean Data Assimilation ◽  
Spectral Localization ◽  
Wide Range ◽  
Spatial Localisation

Abstract. Ocean data assimilation systems encompass a wide range of scales that are difficult to control simultaneously using partial observation networks. All scales are not observable by all observation systems which is not easily taken into account in current ocean operational systems. The main reason for this difficulty is that the error covariance matrices are usually assumed to be local (e.g. using a localization algorithm in ensemble data assimilation systems), so that the large scale patterns are removed from the error statistics. To better exploit the observational information available for all scales in CMEMS assimilation systems, we investigate a new method to introduce scale separation in the assimilation scheme. The method is based on a spectral transformation of the assimilation problem and consists in carrying out the analysis with spectral localisation for the large scales and spatial localisation for the residual scales. The target is to improve the observational update of the large scale components of the signal by an explicit observational constraint applied directly on the large scales, and to restrict the use of spatial localisation to the small scale components of the signal. To evaluate our method, twin experiments are carried out with synthetic altimetry observations (simulating the JASON tracks), assimilated in a 1/4° model configuration of the North Atlantic and the Nordic Seas. Results show that the transformation to the spectral space and the spectral localization provides consistent ensemble estimates of the state of the system (in the spectral space,or after backward transformation to the spatial space). Combined with spatial localisation for the residual scales, the new scheme is able to provide a reliable ensemble update for all scales, with improved accuracy for the large scale; and the performance of the system can be checked explicitly and separately for all scales in the assimilation system.

scholarly journals Meteorological uncertainty and rainfall downscaling

2007 ◽  
Vol 14 (3) ◽  
pp. 193-199 ◽  
Author(s):  
J. von Hardenberg ◽  
L. Ferraris ◽  
N. Rebora ◽  
A. Provenzale
Keyword(s):  
Large Scale ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Limited Area ◽  
Forecast Uncertainty ◽  
Downscaling Method ◽  
Synoptic Conditions ◽  
Hydrological Applications ◽  
Stochastic Downscaling

Abstract. We explore the sources of forecast uncertainty in a mixed dynamical-stochastic ensemble prediction chain for small-scale precipitation, suitable for hydrological applications. To this end, we apply the stochastic downscaling method RainFARM to each member of ensemble limited-area forecasts provided by the COSMO-LEPS system. Aim of the work is to quantitatively compare the relative weights of the meteorological uncertainty associated with large-scale synoptic conditions (represented by the ensemble of dynamical forecasts) and of the uncertainty due to small-scale processes (represented by the set of fields generated by stochastic downscaling). We show that, in current operational configurations, small- and large-scale uncertainties have roughly the same weight. These results can be used to pinpoint the specific components of the prediction chain where a better estimate of forecast uncertainty is needed.

A New Method for Generating Initial Condition Perturbations in a Regional Ensemble Prediction System: Blending

2014 ◽  
Vol 142 (5) ◽  
pp. 2043-2059 ◽  
Author(s):  
Yong Wang ◽  
Martin Bellus ◽  
Jean-Francois Geleyn ◽  
Xulin Ma ◽  
Weihong Tian ◽  
...  
Keyword(s):  
Large Scale ◽  
Dynamical Downscaling ◽  
Ensemble Prediction ◽  
Small Scale ◽  
Lateral Boundary Condition ◽  
Limited Area ◽  
Initial Condition ◽  
Prediction System ◽  
Ensemble Prediction System ◽  
Blending Method

Abstract A blending method for generating initial condition (IC) perturbations in a regional ensemble prediction system is proposed. The blending is to combine the large-scale IC perturbations from a global ensemble prediction system (EPS) with the small-scale IC perturbations from a regional EPS by using a digital filter and the spectral analysis technique. The IC perturbations generated by blending can well represent both large-scale and small-scale uncertainties in the analysis, and are more consistent with the lateral boundary condition (LBC) perturbations provided by global EPS. The blending method is implemented in the regional ensemble system Aire Limitée Adaptation Dynamique Développement International-Limited Area Ensemble Forecasting (ALADIN-LAEF), in which the large-scale IC perturbations are provided by the European Centre for Medium-Range Weather Forecasts (ECMWF-EPS), and the small-scale IC perturbations are generated by breeding in ALADIN-LAEF. Blending is compared with dynamical downscaling and breeding over a 2-month period in summer 2007. The comparison clearly shows impact on the growth of forecast spread if the regional IC perturbations are not consistent with the perturbations coming through LBC provided by the global EPS. Blending can cure the problem largely, and it performs better than dynamical downscaling and breeding.

scholarly journals On the Predictability and Error Sources of Tropical Cyclone Intensity Forecasts

2016 ◽  
Vol 73 (9) ◽  
pp. 3739-3747 ◽  
Author(s):  
Kerry Emanuel ◽  
Fuqing Zhang
Keyword(s):  
Tropical Cyclone ◽  
Large Scale ◽  
Forecast Skill ◽  
Error Growth ◽  
Tropical Cyclone Intensity ◽  
Model Framework ◽  
Cyclone Intensity ◽  
Perfect Model ◽  
Tropical Cyclone Intensity Forecasts ◽  
Initial Intensity

Abstract The skill of tropical cyclone intensity forecasts has improved slowly since such forecasts became routine, even though track forecast skill has increased markedly over the same period. In deciding whether or how best to improve intensity forecasts, it is useful to estimate fundamental predictability limits as well as sources of intensity error. Toward that end, the authors estimate rates of error growth in a “perfect model” framework in which the same model is used to explore the sensitivities of tropical cyclone intensity to perturbations in the initial storm intensity and large-scale environment. These are compared to estimates made in previous studies and to intensity error growth in real-time forecasts made using the same model, in which model error also plays an important role. The authors find that error growth over approximately the first few days in the perfect model framework is dominated by errors in initial intensity, after which errors in forecasting the track and large-scale kinematic environment become more pronounced. Errors owing solely to misgauging initial intensity are particularly large for storms about to undergo rapid intensification and are systematically larger when initial intensity is underestimated compared to overestimating initial intensity by the same amount. There remains an appreciable gap between actual and realistically achievable forecast skill, which this study suggests can best be closed by improved models, better observations, and superior data assimilation techniques.

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