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- Author or Editor: Jean-Noël Thépaut x
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Abstract
This study investigates the impact of a new satellite-derived wind product over the polar regions on ECMWF's global four-dimensional variational assimilation system. The winds are derived at the University of Wisconsin— Madison by tracking structures in successive swaths from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. The new wind product provides unprecedented coverage of the polar wind field. The data are assimilated subject to cautious quality control at lower levels and over land.
The MODIS winds have a positive impact on medium-range global weather forecasts, particularly over the polar regions, but also elsewhere over the Northern Hemisphere. The mean polar wind analysis can be considerably altered as a result of the MODIS winds assimilation. Sensitivity calculations highlight the importance of polar regions for forecasts in the midlatitudes and indicate how MODIS winds can reduce key analysis errors in this region. The MODIS winds are now assimilated operationally at ECMWF.
Abstract
This study investigates the impact of a new satellite-derived wind product over the polar regions on ECMWF's global four-dimensional variational assimilation system. The winds are derived at the University of Wisconsin— Madison by tracking structures in successive swaths from the polar-orbiting Moderate Resolution Imaging Spectroradiometer (MODIS) on the Terra satellite. The new wind product provides unprecedented coverage of the polar wind field. The data are assimilated subject to cautious quality control at lower levels and over land.
The MODIS winds have a positive impact on medium-range global weather forecasts, particularly over the polar regions, but also elsewhere over the Northern Hemisphere. The mean polar wind analysis can be considerably altered as a result of the MODIS winds assimilation. Sensitivity calculations highlight the importance of polar regions for forecasts in the midlatitudes and indicate how MODIS winds can reduce key analysis errors in this region. The MODIS winds are now assimilated operationally at ECMWF.
Abstract
In this paper, a weak constraint formulation of the digital filter based on the Dolph–Chebyshev window is introduced in a preoperational version of the 4DVAR analysis of Météo-France. The constraint is imposed only on the analysis increments to damp spurious fast oscillations associated with gravity–inertia waves. In the incremental formulation of 4DVAR, the analysis increments are obtained from a global model at a uniform low resolution with a simplified set of physical parameterizations, while the high-resolution forecast is obtained with a model that uses a variable-resolution grid having a higher resolution over France and the complete set of physical parameterizations. Both models have the same vertical resolution. In a set of preliminary experiments using the same background field and the same set of observations, it is shown that the weak constraint imposed only on the low-resolution increments manages to control efficiently the emergence of fast oscillations in the resulting high-resolution forecast while maintaining a closer fit to the observations than is possible if the digital filter initialization is applied externally on the final analysis increments. It is also shown that this weak constraint does not add any significant computer cost to the 4DVAR analysis. Finally, 4DVAR has been cycled over a period of 2 weeks and the results show that, compared to 3DVAR, the initial dynamical imbalances are significantly less in 4DVAR even if no constraint is imposed at all. However, it has been noted that the innovation statistics show a positive impact when a constraint is applied.
Abstract
In this paper, a weak constraint formulation of the digital filter based on the Dolph–Chebyshev window is introduced in a preoperational version of the 4DVAR analysis of Météo-France. The constraint is imposed only on the analysis increments to damp spurious fast oscillations associated with gravity–inertia waves. In the incremental formulation of 4DVAR, the analysis increments are obtained from a global model at a uniform low resolution with a simplified set of physical parameterizations, while the high-resolution forecast is obtained with a model that uses a variable-resolution grid having a higher resolution over France and the complete set of physical parameterizations. Both models have the same vertical resolution. In a set of preliminary experiments using the same background field and the same set of observations, it is shown that the weak constraint imposed only on the low-resolution increments manages to control efficiently the emergence of fast oscillations in the resulting high-resolution forecast while maintaining a closer fit to the observations than is possible if the digital filter initialization is applied externally on the final analysis increments. It is also shown that this weak constraint does not add any significant computer cost to the 4DVAR analysis. Finally, 4DVAR has been cycled over a period of 2 weeks and the results show that, compared to 3DVAR, the initial dynamical imbalances are significantly less in 4DVAR even if no constraint is imposed at all. However, it has been noted that the innovation statistics show a positive impact when a constraint is applied.
Abstract
The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed a coupled assimilation system that ingests simultaneously ocean and atmospheric observations in a coupled ocean–atmosphere model. Employing the coupled model constraint in the analysis implies that assimilation of an ocean observation has immediate impact on the atmospheric state estimate, and, conversely, assimilation of an atmospheric observation affects the ocean state. In this context, observing system experiments have been carried out withholding scatterometer surface wind data over the period September–November 2013. Impacts in the coupled assimilation system have been compared to the uncoupled approach used in ECMWF operations where atmospheric and ocean analyses are computed sequentially. The assimilation of scatterometer data has reduced the background surface wind root-mean-square error in the coupled and uncoupled assimilation systems by 3.7% and 2.5%, respectively. It has been found that the ocean temperature in the mixed layer is improved in the coupled system, while the impact is neutral in the uncoupled system. Further investigations have been conducted over a case of a tropical cyclone when strong interactions between atmospheric wind and ocean temperature occur. Cyclone Phailin in the Bay of Bengal has been selected since the conventional observing system has measured surface wind speed and ocean temperature at a high frequency. In this case study, the coupled assimilation system outperforms the uncoupled approach, being able to better use the scatterometer measurements to estimate the cold wake after the cyclone.
Abstract
The European Centre for Medium-Range Weather Forecasts (ECMWF) has developed a coupled assimilation system that ingests simultaneously ocean and atmospheric observations in a coupled ocean–atmosphere model. Employing the coupled model constraint in the analysis implies that assimilation of an ocean observation has immediate impact on the atmospheric state estimate, and, conversely, assimilation of an atmospheric observation affects the ocean state. In this context, observing system experiments have been carried out withholding scatterometer surface wind data over the period September–November 2013. Impacts in the coupled assimilation system have been compared to the uncoupled approach used in ECMWF operations where atmospheric and ocean analyses are computed sequentially. The assimilation of scatterometer data has reduced the background surface wind root-mean-square error in the coupled and uncoupled assimilation systems by 3.7% and 2.5%, respectively. It has been found that the ocean temperature in the mixed layer is improved in the coupled system, while the impact is neutral in the uncoupled system. Further investigations have been conducted over a case of a tropical cyclone when strong interactions between atmospheric wind and ocean temperature occur. Cyclone Phailin in the Bay of Bengal has been selected since the conventional observing system has measured surface wind speed and ocean temperature at a high frequency. In this case study, the coupled assimilation system outperforms the uncoupled approach, being able to better use the scatterometer measurements to estimate the cold wake after the cyclone.
Abstract
The excellent forecasts made by ECMWF predicting the devastating landfall of Hurricane Sandy attracted a great deal of publicity and praise in the immediate aftermath of the event. The almost unprecedented and sudden “left hook” of the storm toward the coast of New Jersey was attributed to interactions with the large-scale atmospheric flow. This led to speculation that satellite observations may play an important role in the successful forecasting of this event. To investigate the role of satellite data a number of experiments have been performed at ECMWF where different satellite observations are deliberately withheld and forecasts of the hurricane rerun. Without observations from geostationary satellites the correct landfall of the storm is still reasonably well predicted albeit with a slight timing shift compared to the control forecast. On the other hand, without polar-orbiting satellites (which represent 90% of the volume of currently ingested observations) the ECMWF system would have given no useful guidance 4–5 days ahead that the storm would make landfall on the New Jersey coast. Instead the hurricane is predicted to stay well offshore in the Atlantic and hit the Maine coast 24 h later. If background errors estimated from the ECMWF Ensemble of Data Assimilations (EDA) are allowed to evolve and adapt to the depleted observing system, then some of the performance loss suffered by withholding polar satellite data can be recovered. The use of the appropriate EDA errors results in a more enhanced use of geostationary satellite observations, which partly compensates for the loss of polar satellite data.
Abstract
The excellent forecasts made by ECMWF predicting the devastating landfall of Hurricane Sandy attracted a great deal of publicity and praise in the immediate aftermath of the event. The almost unprecedented and sudden “left hook” of the storm toward the coast of New Jersey was attributed to interactions with the large-scale atmospheric flow. This led to speculation that satellite observations may play an important role in the successful forecasting of this event. To investigate the role of satellite data a number of experiments have been performed at ECMWF where different satellite observations are deliberately withheld and forecasts of the hurricane rerun. Without observations from geostationary satellites the correct landfall of the storm is still reasonably well predicted albeit with a slight timing shift compared to the control forecast. On the other hand, without polar-orbiting satellites (which represent 90% of the volume of currently ingested observations) the ECMWF system would have given no useful guidance 4–5 days ahead that the storm would make landfall on the New Jersey coast. Instead the hurricane is predicted to stay well offshore in the Atlantic and hit the Maine coast 24 h later. If background errors estimated from the ECMWF Ensemble of Data Assimilations (EDA) are allowed to evolve and adapt to the depleted observing system, then some of the performance loss suffered by withholding polar satellite data can be recovered. The use of the appropriate EDA errors results in a more enhanced use of geostationary satellite observations, which partly compensates for the loss of polar satellite data.
Abstract
This study examines atmospheric motion vectors derived by tracking features in image sequences from Meteosat for anomalies during the second satellite eclipse period of 2001. During eclipse periods (March–April; September–October), data from geostationary sensors are prone to anomalies caused by solar stray light entering the radiometer around local midnight. As a result, imagery from geostationary satellites can exhibit local anomalies for certain time slots, and these anomalies can change considerably from one time slot to the next. The effect of these anomalies on atmospheric motion vectors is characterized in this study by investigating the temporal consistency of the data and by monitoring the winds against short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) global model. Atmospheric motion vectors from the Meteosat water vapor channel exhibit spuriously fast winds for certain time slots around local midnight for extended periods around the satellite's eclipse. The anomalies are caused primarily through tracking problems, but height assignment is also affected. Less severe anomalies are found for infrared winds, in agreement with less severe image anomalies for the infrared channel. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and ECMWF quality control are capable of reducing the problem, but anomalous characteristics can still be found in the quality-controlled subsamples. As a result of these findings, atmospheric motion vectors from Meteosat are now excluded from the operational assimilation at ECMWF for certain time slots around the eclipse periods.
Abstract
This study examines atmospheric motion vectors derived by tracking features in image sequences from Meteosat for anomalies during the second satellite eclipse period of 2001. During eclipse periods (March–April; September–October), data from geostationary sensors are prone to anomalies caused by solar stray light entering the radiometer around local midnight. As a result, imagery from geostationary satellites can exhibit local anomalies for certain time slots, and these anomalies can change considerably from one time slot to the next. The effect of these anomalies on atmospheric motion vectors is characterized in this study by investigating the temporal consistency of the data and by monitoring the winds against short-term forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) global model. Atmospheric motion vectors from the Meteosat water vapor channel exhibit spuriously fast winds for certain time slots around local midnight for extended periods around the satellite's eclipse. The anomalies are caused primarily through tracking problems, but height assignment is also affected. Less severe anomalies are found for infrared winds, in agreement with less severe image anomalies for the infrared channel. The European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) and ECMWF quality control are capable of reducing the problem, but anomalous characteristics can still be found in the quality-controlled subsamples. As a result of these findings, atmospheric motion vectors from Meteosat are now excluded from the operational assimilation at ECMWF for certain time slots around the eclipse periods.
Abstract
A set of physical parameterizations has been developed for inclusion in incremental four-dimensional variational assimilation (4D-Var). The goal for this physical package is that it be simple, regular (for the efficiency of the minimization in 4D-Var), and at the same time realistic enough. The package contains a simplified computation of radiative fluxes, vertical turbulent diffusion, orographic gravity waves, deep convection, and stratiform precipitation fluxes. Its tangent-linear and adjoint counterparts have also been developed. The validations of the simplified physical parameterizations and of the tangent-linear model with those included have been done. The importance of regularization (removing some thresholds in physical parameterizations that can affect the range of validity of the tangent-linear approximation), which arises during validation of the tangent-linear model, is assessed.
Abstract
A set of physical parameterizations has been developed for inclusion in incremental four-dimensional variational assimilation (4D-Var). The goal for this physical package is that it be simple, regular (for the efficiency of the minimization in 4D-Var), and at the same time realistic enough. The package contains a simplified computation of radiative fluxes, vertical turbulent diffusion, orographic gravity waves, deep convection, and stratiform precipitation fluxes. Its tangent-linear and adjoint counterparts have also been developed. The validations of the simplified physical parameterizations and of the tangent-linear model with those included have been done. The importance of regularization (removing some thresholds in physical parameterizations that can affect the range of validity of the tangent-linear approximation), which arises during validation of the tangent-linear model, is assessed.
Abstract
An observing system experiment, simulating a surface-only observing network representative of the 1930s, is carried out with three- and four-dimensional variational data assimilation systems (3D-VAR and 4D-VAR) and an ensemble-based data assimilation system (EnsDA). It is found that 4D-VAR and EnsDA systems produce analyses of comparable quality and that both are much more accurate than the analyses produced by the 3D-VAR system. The EnsDA system also produces useful estimates of analysis error, which are not directly available from the variational systems.
Abstract
An observing system experiment, simulating a surface-only observing network representative of the 1930s, is carried out with three- and four-dimensional variational data assimilation systems (3D-VAR and 4D-VAR) and an ensemble-based data assimilation system (EnsDA). It is found that 4D-VAR and EnsDA systems produce analyses of comparable quality and that both are much more accurate than the analyses produced by the 3D-VAR system. The EnsDA system also produces useful estimates of analysis error, which are not directly available from the variational systems.
Abstract
A four-dimensional (4D) variational assimilation (4DVAR) seeks an optimal balance between observations scattered in time and space over a finite 4D analysis volume and a priori information. In some cases, 4DVAR is able to closely fit both observations and the a priori initial estimate by making very small changes to the initial conditions that correspond to those rapidly growing perturbations that have large amplitude at the observation locations and times. Some observations may occur at locations and times for which the amplitudes of rapidly growing perturbations are not large. To fit such data, larger changes to the initial conditions are necessary. Such cases may result in amplification of the analysis increments away from the observation locations. This situation occurs generally for surface data, because of the damping effect of surface exchange processes. These interactions are seen in experiments using single observations.
To further explore the impact of surface data in 4DVAR, experiments were conducted with and without ERS-1 C-band measurements of backscatter. As expected and in contrast to conventional approaches, the impact is not confined to the lower troposphere and the analysis increments are balanced. The study focuses on the case of a small intense North Atlantic storm that struck the coast of Norway on New Year's Day 1992. The scatterometer data have a significant, apparently positive, impact on the 4DVAR analysis in this case. The example using scatterometer data also demonstrates the ease with which 4DVAR assimilates nonstandard data, which have a complex, highly nonlinear relationship with the model variables.
Abstract
A four-dimensional (4D) variational assimilation (4DVAR) seeks an optimal balance between observations scattered in time and space over a finite 4D analysis volume and a priori information. In some cases, 4DVAR is able to closely fit both observations and the a priori initial estimate by making very small changes to the initial conditions that correspond to those rapidly growing perturbations that have large amplitude at the observation locations and times. Some observations may occur at locations and times for which the amplitudes of rapidly growing perturbations are not large. To fit such data, larger changes to the initial conditions are necessary. Such cases may result in amplification of the analysis increments away from the observation locations. This situation occurs generally for surface data, because of the damping effect of surface exchange processes. These interactions are seen in experiments using single observations.
To further explore the impact of surface data in 4DVAR, experiments were conducted with and without ERS-1 C-band measurements of backscatter. As expected and in contrast to conventional approaches, the impact is not confined to the lower troposphere and the analysis increments are balanced. The study focuses on the case of a small intense North Atlantic storm that struck the coast of Norway on New Year's Day 1992. The scatterometer data have a significant, apparently positive, impact on the 4DVAR analysis in this case. The example using scatterometer data also demonstrates the ease with which 4DVAR assimilates nonstandard data, which have a complex, highly nonlinear relationship with the model variables.
Abstract
This study investigates and quantifies in detail the spatial correlations of random errors in atmospheric motion vectors (AMVs) derived by tracking structures in imagery from geostationary satellites. A good specification of the observation error is essential to assimilate any kind of observation for numerical weather prediction in a near-optimal way. For AMVs, height assignment, tracking of similar cloud structures, or quality control procedures may introduce spatially correlated errors.
The spatial structure of the error correlations is investigated based on a 1-yr dataset of pairs of collocations between AMVs and radiosonde observations. Assuming spatially uncorrelated sonde errors, the spatial AMV error correlations are obtained over dense sonde networks. Results for operational infrared and water vapor wind datasets from Meteosat-5 and -7, Geostationary Operational Environmental Satellite-8 and -10 (GOES-8 and -10), and Geostationary Meteorological Satellite-5 (GMS-5) are presented.
Winds from all five datasets show statistically significant spatial error correlations for distances up to about 800 km, with little difference between satellites, channels, or vertical levels. Even broader correlations are found for tropical regions. The correlations exhibit considerable anisotropic structures with, for instance, longer correlation scales in the south–north direction for the υ-wind component, and are comparable to error correlations for short-term forecasts. The study estimates the spatially correlated part of the annual mean AMV wind component error for high-level Northern Hemisphere winds to be about 2.7–3.5 m s−1. Some seasonal variation is found for these errors with larger values in winter. The findings have a number of important implications for the use of AMVs in data assimilation.
Abstract
This study investigates and quantifies in detail the spatial correlations of random errors in atmospheric motion vectors (AMVs) derived by tracking structures in imagery from geostationary satellites. A good specification of the observation error is essential to assimilate any kind of observation for numerical weather prediction in a near-optimal way. For AMVs, height assignment, tracking of similar cloud structures, or quality control procedures may introduce spatially correlated errors.
The spatial structure of the error correlations is investigated based on a 1-yr dataset of pairs of collocations between AMVs and radiosonde observations. Assuming spatially uncorrelated sonde errors, the spatial AMV error correlations are obtained over dense sonde networks. Results for operational infrared and water vapor wind datasets from Meteosat-5 and -7, Geostationary Operational Environmental Satellite-8 and -10 (GOES-8 and -10), and Geostationary Meteorological Satellite-5 (GMS-5) are presented.
Winds from all five datasets show statistically significant spatial error correlations for distances up to about 800 km, with little difference between satellites, channels, or vertical levels. Even broader correlations are found for tropical regions. The correlations exhibit considerable anisotropic structures with, for instance, longer correlation scales in the south–north direction for the υ-wind component, and are comparable to error correlations for short-term forecasts. The study estimates the spatially correlated part of the annual mean AMV wind component error for high-level Northern Hemisphere winds to be about 2.7–3.5 m s−1. Some seasonal variation is found for these errors with larger values in winter. The findings have a number of important implications for the use of AMVs in data assimilation.
Several new types of satellite instrument will provide improved measurements of Earth's hydrological cycle and the humidity of the atmosphere. In an effort to make the best possible use of these data, the modeling and assimilation of humidity, clouds, and precipitation are currently the subjects of a comprehensive research program at the European Centre for Medium-Range Weather Forecasts (ECMWF). Impacts on weather prediction and climate reanalysis can be expected. The preparations for cloud and rain assimilation within ECMWF's four-dimensional variational data assimilation system include the development of linearized moist physics, the development of fast radiative transfer codes for cloudy and precipitating conditions, and a reformulation of the humidity analysis scheme.
Results of model validations against in situ moisture data are presented, indicating generally good agreement—often to within the absolute calibration accuracy of the measurements. Evidence is also presented of shortcomings in ECMWF's humidity analysis, from the operational data assimilation and forecasting system in 2002, and from the recently completed ERA-40 reanalysis project. Examples are shown of biases in the data and in the model that lead to biased humidity analyses. Although these biases are relatively small, they contribute to an overprediction of tropical precipitation and to an overly intense Hadley circulation at the start of the forecast, with rapid adjustments taking place during the first 6–12 h. It is shown that with an improved humidity analysis this long-standing “spindown” problem can be reduced.
Several new types of satellite instrument will provide improved measurements of Earth's hydrological cycle and the humidity of the atmosphere. In an effort to make the best possible use of these data, the modeling and assimilation of humidity, clouds, and precipitation are currently the subjects of a comprehensive research program at the European Centre for Medium-Range Weather Forecasts (ECMWF). Impacts on weather prediction and climate reanalysis can be expected. The preparations for cloud and rain assimilation within ECMWF's four-dimensional variational data assimilation system include the development of linearized moist physics, the development of fast radiative transfer codes for cloudy and precipitating conditions, and a reformulation of the humidity analysis scheme.
Results of model validations against in situ moisture data are presented, indicating generally good agreement—often to within the absolute calibration accuracy of the measurements. Evidence is also presented of shortcomings in ECMWF's humidity analysis, from the operational data assimilation and forecasting system in 2002, and from the recently completed ERA-40 reanalysis project. Examples are shown of biases in the data and in the model that lead to biased humidity analyses. Although these biases are relatively small, they contribute to an overprediction of tropical precipitation and to an overly intense Hadley circulation at the start of the forecast, with rapid adjustments taking place during the first 6–12 h. It is shown that with an improved humidity analysis this long-standing “spindown” problem can be reduced.
