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- Author or Editor: Istvan Szunyogh x
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Abstract
Model error is the component of the forecast error that is due to the difference between the dynamics of the atmosphere and the dynamics of the numerical prediction model. The systematic, slowly varying part of the model error is called model bias. This paper evaluates three different ensemble-based strategies to account for the surface pressure model bias in the analysis scheme. These strategies are based on modifying the observation operator for the surface pressure observations by the addition of a bias-correction term. One estimates the correction term adaptively, while another uses the hydrostatic balance equation to obtain the correction term. The third strategy combines an adaptively estimated correction term and the hydrostatic-balance-based correction term. Numerical experiments are carried out in an idealized setting, where the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model is integrated at resolution T62L28 to simulate the evolution of the atmosphere and the T30L7 resolution Simplified Parameterization Primitive Equation Dynamics (SPEEDY) model is used for data assimilation. The results suggest that the adaptive bias-correction term is effective in correcting the bias in the data-rich regions, while the hydrostatic-balance-based approach is effective in data-sparse regions. The adaptive bias-correction approach also has the benefit that it leads to a significant improvement of the temperature and wind analysis at the higher model levels. The best results are obtained when the two bias-correction approaches are combined.
Abstract
Model error is the component of the forecast error that is due to the difference between the dynamics of the atmosphere and the dynamics of the numerical prediction model. The systematic, slowly varying part of the model error is called model bias. This paper evaluates three different ensemble-based strategies to account for the surface pressure model bias in the analysis scheme. These strategies are based on modifying the observation operator for the surface pressure observations by the addition of a bias-correction term. One estimates the correction term adaptively, while another uses the hydrostatic balance equation to obtain the correction term. The third strategy combines an adaptively estimated correction term and the hydrostatic-balance-based correction term. Numerical experiments are carried out in an idealized setting, where the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) model is integrated at resolution T62L28 to simulate the evolution of the atmosphere and the T30L7 resolution Simplified Parameterization Primitive Equation Dynamics (SPEEDY) model is used for data assimilation. The results suggest that the adaptive bias-correction term is effective in correcting the bias in the data-rich regions, while the hydrostatic-balance-based approach is effective in data-sparse regions. The adaptive bias-correction approach also has the benefit that it leads to a significant improvement of the temperature and wind analysis at the higher model levels. The best results are obtained when the two bias-correction approaches are combined.
Abstract
This study investigates the efficiency of the major operational global ensemble forecast systems of the world in capturing the spatiotemporal evolution of the forecast uncertainty. Using data from 2015, it updates the results of an earlier study based on data from 2012. It also tests, for the first time on operational ensemble data, two quantitative relationships to aid in the interpretation of the raw ensemble forecasts. One of these relationships provides a flow-dependent prediction of the reliability of the ensemble in capturing the uncertain forecast features, while the other predicts the 95th percentile value of the magnitude of the forecast error. It is found that, except for the system of the Met Office, the main characteristics of the ensemble forecast systems have changed little between 2012 and 2015. The performance of the UKMO ensemble improved in predicting the overall magnitude of the uncertainty, but its ability to predict the dominant uncertain forecast features was degraded. A common serious limitation of the ensemble systems remains that they all have major difficulties with predicting the large-scale atmospheric flow in the long (longer than 10 days) forecast range. These difficulties are due to the inability of the ensemble members to maintain large-scale waves in the forecasts, which presents a stumbling block in the way of extending the skill of numerical weather forecasts to the subseasonal range. The two tested predictive relationships were found to provide highly accurate predictions of the flow-dependent reliability of the ensemble predictions and the 95th percentile value of the magnitude of the forecast error for the operational ensemble forecast systems.
Abstract
This study investigates the efficiency of the major operational global ensemble forecast systems of the world in capturing the spatiotemporal evolution of the forecast uncertainty. Using data from 2015, it updates the results of an earlier study based on data from 2012. It also tests, for the first time on operational ensemble data, two quantitative relationships to aid in the interpretation of the raw ensemble forecasts. One of these relationships provides a flow-dependent prediction of the reliability of the ensemble in capturing the uncertain forecast features, while the other predicts the 95th percentile value of the magnitude of the forecast error. It is found that, except for the system of the Met Office, the main characteristics of the ensemble forecast systems have changed little between 2012 and 2015. The performance of the UKMO ensemble improved in predicting the overall magnitude of the uncertainty, but its ability to predict the dominant uncertain forecast features was degraded. A common serious limitation of the ensemble systems remains that they all have major difficulties with predicting the large-scale atmospheric flow in the long (longer than 10 days) forecast range. These difficulties are due to the inability of the ensemble members to maintain large-scale waves in the forecasts, which presents a stumbling block in the way of extending the skill of numerical weather forecasts to the subseasonal range. The two tested predictive relationships were found to provide highly accurate predictions of the flow-dependent reliability of the ensemble predictions and the 95th percentile value of the magnitude of the forecast error for the operational ensemble forecast systems.
Abstract
Previously developed techniques that have been used to extract envelopes of Rossby wave packets are based on the assumption of zonally propagating waves. In this note a method that does not require such an assumption is proposed. The advantages of the new technique, both on analytical and real-world examples, are demonstrated.
Abstract
Previously developed techniques that have been used to extract envelopes of Rossby wave packets are based on the assumption of zonally propagating waves. In this note a method that does not require such an assumption is proposed. The advantages of the new technique, both on analytical and real-world examples, are demonstrated.
Abstract
The paper investigates the processes that drive the spatiotemporal evolution of baroclinic transient waves in the Martian atmosphere by a simulation experiment with the Geophysical Fluid Dynamics Laboratory (GFDL) Mars general circulation model (GCM). The main diagnostic tool of the study is the (local) eddy kinetic energy equation. Results are shown for a prewinter season of the Northern Hemisphere, in which a deep baroclinic wave of zonal wavenumber 2 circles the planet at an eastward phase speed of about 70° Sol−1 (Sol is a Martian day). The regular structure of the wave gives the impression that the classical models of baroclinic instability, which describe the underlying process by a temporally unstable global wave (e.g., Eady model and Charney model), may have a direct relevance for the description of the Martian baroclinic waves. The results of the diagnostic calculations show, however, that while the Martian waves remain zonally global features at all times, there are large spatiotemporal changes in their amplitude. The most intense episodes of baroclinic energy conversion, which take place in the two great plain regions (Acidalia Planitia and Utopia Planitia), are strongly localized in both space and time. In addition, similar to the situation for terrestrial baroclinic waves, geopotential flux convergence plays an important role in the dynamics of the downstream-propagating unstable waves.
Abstract
The paper investigates the processes that drive the spatiotemporal evolution of baroclinic transient waves in the Martian atmosphere by a simulation experiment with the Geophysical Fluid Dynamics Laboratory (GFDL) Mars general circulation model (GCM). The main diagnostic tool of the study is the (local) eddy kinetic energy equation. Results are shown for a prewinter season of the Northern Hemisphere, in which a deep baroclinic wave of zonal wavenumber 2 circles the planet at an eastward phase speed of about 70° Sol−1 (Sol is a Martian day). The regular structure of the wave gives the impression that the classical models of baroclinic instability, which describe the underlying process by a temporally unstable global wave (e.g., Eady model and Charney model), may have a direct relevance for the description of the Martian baroclinic waves. The results of the diagnostic calculations show, however, that while the Martian waves remain zonally global features at all times, there are large spatiotemporal changes in their amplitude. The most intense episodes of baroclinic energy conversion, which take place in the two great plain regions (Acidalia Planitia and Utopia Planitia), are strongly localized in both space and time. In addition, similar to the situation for terrestrial baroclinic waves, geopotential flux convergence plays an important role in the dynamics of the downstream-propagating unstable waves.
Abstract
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Abstract
Current ensemble-based Kalman filter (EnKF) algorithms are not robust to gross observation errors caused by technical or human errors during the data collection process. In this paper, the authors consider two types of gross observational errors, additive statistical outliers and innovation outliers, and introduce a method to make EnKF robust to gross observation errors. Using both a one-dimensional linear system of dynamics and a 40-variable Lorenz model, the performance of the proposed robust ensemble Kalman filter (REnKF) was tested and it was found that the new approach greatly improves the performance of the filter in the presence of gross observation errors and leads to only a modest loss of accuracy with clean, outlier-free, observations.
Abstract
Packets of Rossby waves play an important role in the transfer of kinetic energy in the extratropics. The ability to locate, track, and detect changes in the envelope of these wave packets is vital to detecting baroclinic downstream development, tracking the impact of the analysis errors in numerical weather forecasts, and analyzing the forecast effects of targeted weather observations. In this note, it is argued that a well-known technique of digital signal processing, which is based on the Hilbert transform, should be used for extracting the envelope of atmospheric wave packets. This technique is robust, simple, and computationally inexpensive. The superiority of the proposed algorithm over the complex demodulation technique (the only technique previously used for this purpose in atmospheric studies) is demonstrated by examples. The skill of the proposed algorithm is also demonstrated by tracking wave packets in operational weather analyses from the National Centers for Environmental Prediction (NCEP) and analyzing the effects of targeted observations from the 2000 Winter Storm Reconnaissance (WSR00) field program.
Abstract
Packets of Rossby waves play an important role in the transfer of kinetic energy in the extratropics. The ability to locate, track, and detect changes in the envelope of these wave packets is vital to detecting baroclinic downstream development, tracking the impact of the analysis errors in numerical weather forecasts, and analyzing the forecast effects of targeted weather observations. In this note, it is argued that a well-known technique of digital signal processing, which is based on the Hilbert transform, should be used for extracting the envelope of atmospheric wave packets. This technique is robust, simple, and computationally inexpensive. The superiority of the proposed algorithm over the complex demodulation technique (the only technique previously used for this purpose in atmospheric studies) is demonstrated by examples. The skill of the proposed algorithm is also demonstrated by tracking wave packets in operational weather analyses from the National Centers for Environmental Prediction (NCEP) and analyzing the effects of targeted observations from the 2000 Winter Storm Reconnaissance (WSR00) field program.
Abstract
This paper evaluates a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter (LETKF) data assimilation scheme. The assimilation strategy includes a mechanism to select the radiance observations that are assimilated at a given grid point and an ensemble-based observation bias-correction technique. Numerical experiments are carried out with a reduced (T62L28) resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The observations used for the evaluation of the assimilation strategy are AMSU-A level 1B brightness temperature data from the Earth Observing System (EOS) Aqua spacecraft. The assimilation of these observations, in addition to all operationally assimilated nonradiance observations, leads to a statistically significant improvement of both the temperature and wind analysis in the Southern Hemisphere. This result suggests that the LETKF, combined with the proposed data assimilation strategy for the assimilation of satellite radiance observations, can efficiently extract information from radiance observations.
Abstract
This paper evaluates a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter (LETKF) data assimilation scheme. The assimilation strategy includes a mechanism to select the radiance observations that are assimilated at a given grid point and an ensemble-based observation bias-correction technique. Numerical experiments are carried out with a reduced (T62L28) resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The observations used for the evaluation of the assimilation strategy are AMSU-A level 1B brightness temperature data from the Earth Observing System (EOS) Aqua spacecraft. The assimilation of these observations, in addition to all operationally assimilated nonradiance observations, leads to a statistically significant improvement of both the temperature and wind analysis in the Southern Hemisphere. This result suggests that the LETKF, combined with the proposed data assimilation strategy for the assimilation of satellite radiance observations, can efficiently extract information from radiance observations.
Abstract
This paper investigates the effects of spatial filtering on the ensemble-based estimate of the background error covariance matrix in an ensemble-based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari–Cohn filter, which uses a fifth-order kernel function with a fixed localization length. Numerical experiments are carried out with the 40-variable Lorenz-96 model. The results of the experiments show that the nonparametric approach provides a more accurate estimate of the background error covariance matrix than the Gaspari–Cohn filter with any localization length. It is also shown that the Gaspari–Cohn filter tends to provide more accurate estimates of the covariance with shorter localization lengths. However, the analyses obtained by using longer localization lengths tend to be more accurate than those produced by using short localization lengths or the nonparametric approach. This seemingly paradoxical result is explained by showing that localization with longer localization lengths produces filtered estimates whose time mean is the most similar to the time mean of both the unfiltered estimate and the true covariance. This result suggests that a better metric of covariance filtering skill would be one that combined a measure of closeness to the sample covariance matrix for a very large ensemble with a measure of similarity between the climatological averages of the filtered and sample covariance.
Abstract
This paper investigates the effects of spatial filtering on the ensemble-based estimate of the background error covariance matrix in an ensemble-based Kalman filter (EnKF). In particular, a novel kernel smoothing method with variable bandwidth is introduced and its performance is compared to that of the widely used Gaspari–Cohn filter, which uses a fifth-order kernel function with a fixed localization length. Numerical experiments are carried out with the 40-variable Lorenz-96 model. The results of the experiments show that the nonparametric approach provides a more accurate estimate of the background error covariance matrix than the Gaspari–Cohn filter with any localization length. It is also shown that the Gaspari–Cohn filter tends to provide more accurate estimates of the covariance with shorter localization lengths. However, the analyses obtained by using longer localization lengths tend to be more accurate than those produced by using short localization lengths or the nonparametric approach. This seemingly paradoxical result is explained by showing that localization with longer localization lengths produces filtered estimates whose time mean is the most similar to the time mean of both the unfiltered estimate and the true covariance. This result suggests that a better metric of covariance filtering skill would be one that combined a measure of closeness to the sample covariance matrix for a very large ensemble with a measure of similarity between the climatological averages of the filtered and sample covariance.
Abstract
This study uses the local ensemble transform Kalman filter to assimilate Atmospheric Infrared Sounder (AIRS) specific humidity retrievals with pseudo relative humidity (pseudo-RH) as the observation variable. Three approaches are tested: (i) updating specific humidity with observations other than specific humidity (“passive q”), (ii) updating specific humidity only with humidity observations (“univariate q”), and (iii) assimilating the humidity and the other observations together (“multivariate q”). This is the first time that the performance of the univariate and multivariate assimilation of q is compared within an ensemble Kalman filter framework. The results show that updating the humidity analyses by either AIRS specific humidity retrievals or nonhumidity observations improves both the humidity and wind analyses. The improvement with the multivariate-q experiment is by far the largest for all dynamical variables at both analysis and forecast time, indicating that the interaction between the specific humidity and the other dynamical variables through the background error covariance during data assimilation process yields more balanced analysis fields. In the univariate assimilation of q, the humidity interacts with the other dynamical variables only through the forecast process. The univariate assimilation produces more accurate humidity analyses than those obtained when no humidity observations are assimilated, but it does not improve the accuracy of the zonal wind analyses. The 6-h total column precipitable water forecast also benefits from the improved humidity analyses, with the multivariate q experiment having the largest improvement.
Abstract
This study uses the local ensemble transform Kalman filter to assimilate Atmospheric Infrared Sounder (AIRS) specific humidity retrievals with pseudo relative humidity (pseudo-RH) as the observation variable. Three approaches are tested: (i) updating specific humidity with observations other than specific humidity (“passive q”), (ii) updating specific humidity only with humidity observations (“univariate q”), and (iii) assimilating the humidity and the other observations together (“multivariate q”). This is the first time that the performance of the univariate and multivariate assimilation of q is compared within an ensemble Kalman filter framework. The results show that updating the humidity analyses by either AIRS specific humidity retrievals or nonhumidity observations improves both the humidity and wind analyses. The improvement with the multivariate-q experiment is by far the largest for all dynamical variables at both analysis and forecast time, indicating that the interaction between the specific humidity and the other dynamical variables through the background error covariance during data assimilation process yields more balanced analysis fields. In the univariate assimilation of q, the humidity interacts with the other dynamical variables only through the forecast process. The univariate assimilation produces more accurate humidity analyses than those obtained when no humidity observations are assimilated, but it does not improve the accuracy of the zonal wind analyses. The 6-h total column precipitable water forecast also benefits from the improved humidity analyses, with the multivariate q experiment having the largest improvement.
Abstract
The propagation of the effect of targeted observations in numerical weather forecasts is investigated, based on results from the 2000 Winter Storm Reconnaissance (WSR00) program. In this field program, nearly 300 dropsondes were released adaptively at selected locations over the northeast Pacific on 12 separate flight days with the aim of reducing the risk of major failures in severe winter storm forecasts over the United States. The data impact was assessed by analysis–forecast experiments carried out with the T62 horizontal resolution, 28-level version of the operational global Medium Range Forecast system of the National Centers for Environmental Prediction.
In some cases, storms that reached the West Coast or Alaska were observed in an earlier phase of their development, while at other times the goal was to improve the prediction of storms that formed far downstream of the targeted region. Changes in the forecasts were the largest when landfalling systems were targeted and the baroclinic energy conversion was strong in the targeted region.
As expected from the experience accumulated during the 1999 Winter Storm Reconnaissance (WSR99) program, downstream baroclinic development played a major role in propagating the influence of the targeted data over North America. The results also show, however, that predicting the location of significant changes due to the targeted data in the forecasts can be difficult in the presence of a nonzonal large-scale flow. The strong zonal variations in the large-scale flow over the northeast Pacific during WSR00 did not reduce the positive forecast effects of the targeted data. On the contrary, the overall impact of the dropsonde data was more positive than during WSR99, when the large-scale flow was dominantly zonal on the flight days. This can be attributed to the improved prediction of the large-scale flow that led to additional improvements in the prediction of the synoptic-scale waves.
Abstract
The propagation of the effect of targeted observations in numerical weather forecasts is investigated, based on results from the 2000 Winter Storm Reconnaissance (WSR00) program. In this field program, nearly 300 dropsondes were released adaptively at selected locations over the northeast Pacific on 12 separate flight days with the aim of reducing the risk of major failures in severe winter storm forecasts over the United States. The data impact was assessed by analysis–forecast experiments carried out with the T62 horizontal resolution, 28-level version of the operational global Medium Range Forecast system of the National Centers for Environmental Prediction.
In some cases, storms that reached the West Coast or Alaska were observed in an earlier phase of their development, while at other times the goal was to improve the prediction of storms that formed far downstream of the targeted region. Changes in the forecasts were the largest when landfalling systems were targeted and the baroclinic energy conversion was strong in the targeted region.
As expected from the experience accumulated during the 1999 Winter Storm Reconnaissance (WSR99) program, downstream baroclinic development played a major role in propagating the influence of the targeted data over North America. The results also show, however, that predicting the location of significant changes due to the targeted data in the forecasts can be difficult in the presence of a nonzonal large-scale flow. The strong zonal variations in the large-scale flow over the northeast Pacific during WSR00 did not reduce the positive forecast effects of the targeted data. On the contrary, the overall impact of the dropsonde data was more positive than during WSR99, when the large-scale flow was dominantly zonal on the flight days. This can be attributed to the improved prediction of the large-scale flow that led to additional improvements in the prediction of the synoptic-scale waves.
