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Abstract
A method for correcting the magnetic deviation error from planes using a flux valve heading sensor is presented. This error can significantly degrade the quality of the wind data reported from certain commercial airlines. A database is constructed on a per-plane basis and compared to multiple model analyses and observations. A unique filtering method is applied using coefficients derived from this comparison. Three regional airline fleets hosting the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor were analyzed and binned by error statistics. The correction method is applied to the outliers with the largest deviation, and the wind observational error was reduced by 22% (2.4 kt; 1 kt = 0.51 m s−1), 50% (8.2 kt), and 68% (20.5 kt) for each group.
Abstract
A method for correcting the magnetic deviation error from planes using a flux valve heading sensor is presented. This error can significantly degrade the quality of the wind data reported from certain commercial airlines. A database is constructed on a per-plane basis and compared to multiple model analyses and observations. A unique filtering method is applied using coefficients derived from this comparison. Three regional airline fleets hosting the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) sensor were analyzed and binned by error statistics. The correction method is applied to the outliers with the largest deviation, and the wind observational error was reduced by 22% (2.4 kt; 1 kt = 0.51 m s−1), 50% (8.2 kt), and 68% (20.5 kt) for each group.
Abstract
Tropical storms simulated by a nine-member ensemble of GCM integrations forced by observed SSTs have been tracked by an objective procedure for the period 1980–88. Statistics on tropical storm frequency, intensity, and first location have been produced. Statistical tools such as the chi-square and the Kolmogorov–Smirnov test indicate that there is significant potential predictability of interannual variability of simulated tropical storm frequency, intensity, and first location over most of the ocean basins. The only common point between the nine members of the ensemble is the SST forcing. This implies that SSTs play a fundamental role in model tropical storm frequency, intensity, and first location interannual variability. Although the interannual variability of tropical storm statistics is clearly affected by SST forcing in the GCM, there is also a considerable amount of noise related to internal variability of the model. An ensemble of atmospheric model simulations allows one to filter this noise and gain a better understanding of the mechanisms leading to interannual tropical storm variability.
An EOF analysis of local SSTs over each ocean basin and a combined EOF analysis of vertical wind shear, 850-mb vorticity, and 200-mb vorticity have been performed. Over some ocean basins such as the western North Atlantic, the interannual frequency of simulated tropical storms is highly correlated to the first combined EOF, but it is not significantly correlated to the first EOF of local SSTs. This suggests that over these basins the SSTs have an impact on the simulated tropical storm statistics from a remote area through the large-scale circulation as in observations. Simulated and observed tropical storm statistics have been compared. The interannual variability of simulated tropical storm statistics is consistent with observations over the ocean basins where the model simulates a realistic interannual variability of the large-scale circulation.
Abstract
Tropical storms simulated by a nine-member ensemble of GCM integrations forced by observed SSTs have been tracked by an objective procedure for the period 1980–88. Statistics on tropical storm frequency, intensity, and first location have been produced. Statistical tools such as the chi-square and the Kolmogorov–Smirnov test indicate that there is significant potential predictability of interannual variability of simulated tropical storm frequency, intensity, and first location over most of the ocean basins. The only common point between the nine members of the ensemble is the SST forcing. This implies that SSTs play a fundamental role in model tropical storm frequency, intensity, and first location interannual variability. Although the interannual variability of tropical storm statistics is clearly affected by SST forcing in the GCM, there is also a considerable amount of noise related to internal variability of the model. An ensemble of atmospheric model simulations allows one to filter this noise and gain a better understanding of the mechanisms leading to interannual tropical storm variability.
An EOF analysis of local SSTs over each ocean basin and a combined EOF analysis of vertical wind shear, 850-mb vorticity, and 200-mb vorticity have been performed. Over some ocean basins such as the western North Atlantic, the interannual frequency of simulated tropical storms is highly correlated to the first combined EOF, but it is not significantly correlated to the first EOF of local SSTs. This suggests that over these basins the SSTs have an impact on the simulated tropical storm statistics from a remote area through the large-scale circulation as in observations. Simulated and observed tropical storm statistics have been compared. The interannual variability of simulated tropical storm statistics is consistent with observations over the ocean basins where the model simulates a realistic interannual variability of the large-scale circulation.
The diversity of small lakes' (size < 50 km) configurations, sizes, surrounding terrain, and land use combined with relative sparsity of observations complicates the observational evaluation of the lake breezes (LB) that are induced by these lakes. In the present article observational data obtained from available documents, data archives, and special projects were surveyed to suggest characterization of the LB features. The observational survey was complemented by conceptual evaluations. A preliminary generalization of the LB intensity and inland penetration in relation to the surrounding land use was inferred. The conceptual evaluation suggested that for a given lake width the prime factor affecting the LB intensity is the magnitude of the surface sensible heat flux over the surrounding land. Cooling related to the lake water temperature was indicated to have usually a secondary effect on the LB intensity for small lakes. Surface observations implied that the onshore penetration of the LB by the early afternoon hours is typically less than the characteristic width of the lake. Lower atmosphere observations indicated that the vertical extent of the LB may reach several hundred meters. Implications of the observed LB features in support of characterization of the real-world vegetation breeze are discussed.
The diversity of small lakes' (size < 50 km) configurations, sizes, surrounding terrain, and land use combined with relative sparsity of observations complicates the observational evaluation of the lake breezes (LB) that are induced by these lakes. In the present article observational data obtained from available documents, data archives, and special projects were surveyed to suggest characterization of the LB features. The observational survey was complemented by conceptual evaluations. A preliminary generalization of the LB intensity and inland penetration in relation to the surrounding land use was inferred. The conceptual evaluation suggested that for a given lake width the prime factor affecting the LB intensity is the magnitude of the surface sensible heat flux over the surrounding land. Cooling related to the lake water temperature was indicated to have usually a secondary effect on the LB intensity for small lakes. Surface observations implied that the onshore penetration of the LB by the early afternoon hours is typically less than the characteristic width of the lake. Lower atmosphere observations indicated that the vertical extent of the LB may reach several hundred meters. Implications of the observed LB features in support of characterization of the real-world vegetation breeze are discussed.
Abstract
TIROS photographs of cloud patterns in the vicinity of the jet stream are examined and compared with surface, upper air, and pilot-report data. It is found that with certain conditions of lighting and satellite attitude the northern edge of the cirrus cloud shield, which lies immediately south of the jet, can be easily identified by a shadow cast by the higher cloud deck on the lower underlying surface. This shadow identifies the cloud structure associated with the jet stream. Differences in texture and pattern also help to identify the northern limits of the high-level cirrus and thus aid in positioning the jet stream.
Abstract
TIROS photographs of cloud patterns in the vicinity of the jet stream are examined and compared with surface, upper air, and pilot-report data. It is found that with certain conditions of lighting and satellite attitude the northern edge of the cirrus cloud shield, which lies immediately south of the jet, can be easily identified by a shadow cast by the higher cloud deck on the lower underlying surface. This shadow identifies the cloud structure associated with the jet stream. Differences in texture and pattern also help to identify the northern limits of the high-level cirrus and thus aid in positioning the jet stream.
Abstract
Idealized twin experiments with the HOPE ocean model have been used to study the ability of sea level data assimilation to correct for errors in a model simulation of the tropical Pacific, using the Cooper and Haines method to project the surface height increments below the surface. This work should be seen in the context of the development of the comprehensive real-time ocean analysis system used at ECMWF for seasonal forecasting, which currently assimilates only thermal data.
Errors in the model simulation from two sources are studied: those present in the initial state and those generated by errors in the surface forcing during the simulation. In the former, the assimilation of sea level data improves the convergence of the model toward its twin. Without assimilation convergence occurs more slowly on the equator, compared to an experiment using only correct surface forcing. With forcing errors present the sea level assimilation still significantly reduces the errors almost everywhere. An exception was in the central equatorial Pacific where assimilation of sea level did not correct the errors. This is mainly due to this region responding rapidly to errors in wind stress forcing and also to relatively large freshwater flux errors imposed here. These lead to errors in the mixed layer salinity, which the Cooper and Haines scheme is not designed to correct. It is argued that surface salinity analyses would strongly complement sea level assimilation here.
Abstract
Idealized twin experiments with the HOPE ocean model have been used to study the ability of sea level data assimilation to correct for errors in a model simulation of the tropical Pacific, using the Cooper and Haines method to project the surface height increments below the surface. This work should be seen in the context of the development of the comprehensive real-time ocean analysis system used at ECMWF for seasonal forecasting, which currently assimilates only thermal data.
Errors in the model simulation from two sources are studied: those present in the initial state and those generated by errors in the surface forcing during the simulation. In the former, the assimilation of sea level data improves the convergence of the model toward its twin. Without assimilation convergence occurs more slowly on the equator, compared to an experiment using only correct surface forcing. With forcing errors present the sea level assimilation still significantly reduces the errors almost everywhere. An exception was in the central equatorial Pacific where assimilation of sea level did not correct the errors. This is mainly due to this region responding rapidly to errors in wind stress forcing and also to relatively large freshwater flux errors imposed here. These lead to errors in the mixed layer salinity, which the Cooper and Haines scheme is not designed to correct. It is argued that surface salinity analyses would strongly complement sea level assimilation here.
Abstract
High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.
Abstract
High-resolution (3 km) time-lagged (initialized every 3 h) multimodel ensembles were produced in support of the Hydrometeorological Testbed (HMT)-West-2006 campaign in northern California, covering the American River basin (ARB). Multiple mesoscale models were used, including the Weather Research and Forecasting (WRF) model, Regional Atmospheric Modeling System (RAMS), and fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). Short-range (6 h) quantitative precipitation forecasts (QPFs) and probabilistic QPFs (PQPFs) were compared to the 4-km NCEP stage IV precipitation analyses for archived intensive operation periods (IOPs). The two sets of ensemble runs (operational and rerun forecasts) were examined to evaluate the quality of high-resolution QPFs produced by time-lagged multimodel ensembles and to investigate the impacts of ensemble configurations on forecast skill. Uncertainties in precipitation forecasts were associated with different models, model physics, and initial and boundary conditions. The diabatic initialization by the Local Analysis and Prediction System (LAPS) helped precipitation forecasts, while the selection of microphysics was critical in ensemble design. Probability biases in the ensemble products were addressed by calibrating PQPFs. Using artificial neural network (ANN) and linear regression (LR) methods, the bias correction of PQPFs and a cross-validation procedure were applied to three operational IOPs and four rerun IOPs. Both the ANN and LR methods effectively improved PQPFs, especially for lower thresholds. The LR method outperformed the ANN method in bias correction, in particular for a smaller training data size. More training data (e.g., one-season forecasts) are desirable to test the robustness of both calibration methods.
Abstract
The most significant precipitation events in California occur during the winter and are often related to synoptic-scale storms from the Pacific Ocean. Because of the terrain characteristics and the fact that the urban and infrastructural expansion is concentrated in lower elevation areas of the California Central Valley, a high risk of flooding is usually associated with these events. In the present study, the area of interest was the American River basin (ARB). The main focus of the present study was to investigate methods for Quantitative Precipitation Forecast (QPF) improvement by estimating the impact that various microphysical schemes, planetary boundary layer (PBL) schemes, and initialization methods have on cold season precipitation, primarily orographically induced. For this purpose, 3-km grid spacing Weather Research and Forecasting (WRF) model simulations of four Hydrometeorological Test bed (HMT) events were used. For each event, four different microphysical schemes and two different PBL schemes were used. All runs were initialized with both a diabatic Local Analysis and Prediction System (LAPS) “hot” start and 40-km eta analyses.
To quantify the impact of physical schemes, their interactions, and initial conditions upon simulated rain volume, the factor separation methodology was used. The results showed that simulated rain volume was particularly affected by changes in microphysical schemes for both initializations. When the initialization was changed from the LAPS to the eta analysis, the change in the PBL scheme and corresponding synergistic terms (which corresponded to the interactions between different microphysical and PBL schemes) resulted in a statistically significant impact on rain volume. In addition, by combining model runs based on the knowledge about their impact on simulated rain volume obtained through the factor separation methodology, the bias in simulated rain volume was reduced.
Abstract
The most significant precipitation events in California occur during the winter and are often related to synoptic-scale storms from the Pacific Ocean. Because of the terrain characteristics and the fact that the urban and infrastructural expansion is concentrated in lower elevation areas of the California Central Valley, a high risk of flooding is usually associated with these events. In the present study, the area of interest was the American River basin (ARB). The main focus of the present study was to investigate methods for Quantitative Precipitation Forecast (QPF) improvement by estimating the impact that various microphysical schemes, planetary boundary layer (PBL) schemes, and initialization methods have on cold season precipitation, primarily orographically induced. For this purpose, 3-km grid spacing Weather Research and Forecasting (WRF) model simulations of four Hydrometeorological Test bed (HMT) events were used. For each event, four different microphysical schemes and two different PBL schemes were used. All runs were initialized with both a diabatic Local Analysis and Prediction System (LAPS) “hot” start and 40-km eta analyses.
To quantify the impact of physical schemes, their interactions, and initial conditions upon simulated rain volume, the factor separation methodology was used. The results showed that simulated rain volume was particularly affected by changes in microphysical schemes for both initializations. When the initialization was changed from the LAPS to the eta analysis, the change in the PBL scheme and corresponding synergistic terms (which corresponded to the interactions between different microphysical and PBL schemes) resulted in a statistically significant impact on rain volume. In addition, by combining model runs based on the knowledge about their impact on simulated rain volume obtained through the factor separation methodology, the bias in simulated rain volume was reduced.
Abstract
As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing.
A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980–2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF’s utilization of anisotropic background error covariances that may vary in time.
Abstract
As a first step toward coupled ocean–atmosphere data assimilation, a parallelized ensemble filter is implemented in a new stochastic hybrid coupled model. The model consists of a global version of the GFDL Modular Ocean Model Version 4 (MOM4), coupled to a statistical atmosphere based on a regression of National Centers for Environmental Prediction (NCEP) reanalysis surface wind stress, heat, and water flux anomalies onto analyzed tropical Pacific SST anomalies from 1979 to 2002. The residual part of the NCEP fluxes not captured by the regression is then treated as stochastic forcing, with different ensemble members feeling the residual fluxes from different years. The model provides a convenient test bed for coupled data assimilation, as well as a prototype for representing uncertainties in the surface forcing.
A parallel ensemble adjustment Kalman filter (EAKF) has been designed and implemented in the hybrid model, using a local least squares framework. Comparison experiments demonstrate that the massively parallel processing EAKF (MPPEAKF) produces assimilation results with essentially the same quality as a global sequential analysis. Observed subsurface temperature profiles from expendable bathythermographs (XBTs), Tropical Atmosphere Ocean (TAO) buoys, and Argo floats, along with analyzed SSTs from NCEP, are assimilated into the hybrid model over 1980–2002 using the MPPEAKF. The filtered ensemble of SSTs, ocean heat contents, and thermal structures converge well to the observations, in spite of the imposed stochastic forcings. Several facets of the EAKF algorithm used here have been designed to facilitate comparison to a traditional three-dimensional variational data assimilation (3DVAR) algorithm, for instance, the use of a univariate filter in which observations of temperature only directly impact temperature state variables. Despite these choices that may limit the power of the EAKF, the MPPEAKF solution appears to improve upon an earlier 3DVAR solution, producing a smoother, more physically reasonable analysis that better fits the observational data and produces, to some degree, a self-consistent estimate of analysis uncertainties. Hybrid model ENSO forecasts initialized from the MPPEAKF ensemble mean also appear to outperform those initialized from the 3DVAR analysis. This improvement stems from the EAKF’s utilization of anisotropic background error covariances that may vary in time.
Abstract
In situ measurements made in cold (−35° to −60°C) cirrus clouds over southern Germany in March 1994 are presented. The clouds appeared to be in an early stage of their life cycle and their properties in many ways resemble those reported for ice fogs. Crystal concentrations were high (median 2.5 cm−3, STP) and sizes small with a diameter of mean mass of typically 16 μm. The cloud on 18 March presents an interesting case for modeling studies of cirrus formation. On that particular day, the bulk properties of the cloud appeared to be connected to wave structures in the vertical wind field consistent with the Brunt–Väisälä frequency, which gave a corresponding wavelength of 40–50 km. Furthermore, analyses of potential temperature and vertical wind suggested that the vertical displacement producing these clouds was less than 100 m. Size distribution measurements of interstitial particles and crystal residues (particles remaining after evaporation of the crystals) show that small aerosol particles (diameters <0.5 μm) participate in the nucleation of cirrus crystals at low temperatures. Because the aerosol in this small size range is readily perturbed by anthropogenic activity, it is important to study the link between upper tropospheric aerosol properties and cirrus cloud microphysics. While the observations presented here are not adequate to quantify this link, they pave the way for modeling studies and would be interesting to compare to cirrus observations from cleaner regions.
Abstract
In situ measurements made in cold (−35° to −60°C) cirrus clouds over southern Germany in March 1994 are presented. The clouds appeared to be in an early stage of their life cycle and their properties in many ways resemble those reported for ice fogs. Crystal concentrations were high (median 2.5 cm−3, STP) and sizes small with a diameter of mean mass of typically 16 μm. The cloud on 18 March presents an interesting case for modeling studies of cirrus formation. On that particular day, the bulk properties of the cloud appeared to be connected to wave structures in the vertical wind field consistent with the Brunt–Väisälä frequency, which gave a corresponding wavelength of 40–50 km. Furthermore, analyses of potential temperature and vertical wind suggested that the vertical displacement producing these clouds was less than 100 m. Size distribution measurements of interstitial particles and crystal residues (particles remaining after evaporation of the crystals) show that small aerosol particles (diameters <0.5 μm) participate in the nucleation of cirrus crystals at low temperatures. Because the aerosol in this small size range is readily perturbed by anthropogenic activity, it is important to study the link between upper tropospheric aerosol properties and cirrus cloud microphysics. While the observations presented here are not adequate to quantify this link, they pave the way for modeling studies and would be interesting to compare to cirrus observations from cleaner regions.
Abstract
The number of tornadoes reported in the United States is believed to be less than the actual incidence of tornadoes, especially prior to the 1990s, because tornadoes may be undetectable by human witnesses in sparsely populated areas and areas in which obstructions limit the line of sight. A hierarchical Bayesian model is used to simultaneously correct for population-based sampling bias and estimate tornado density using historical tornado report data. The expected result is that F2–F5 compared with F0–F1 tornado reports would vary less with population density. The results agree with this hypothesis for the following population centers: Atlanta, Georgia; Champaign, Illinois; and Des Moines, Iowa. However, the results indicated just the opposite in Oklahoma. It is hypothesized that the result is explained by the misclassification of tornadoes that were worthy of F2–F5 rating but were classified as F0–F1 tornadoes, thereby artificially decreasing the number of F2–F5 and increasing the number of F0–F1 reports in rural Oklahoma.
Abstract
The number of tornadoes reported in the United States is believed to be less than the actual incidence of tornadoes, especially prior to the 1990s, because tornadoes may be undetectable by human witnesses in sparsely populated areas and areas in which obstructions limit the line of sight. A hierarchical Bayesian model is used to simultaneously correct for population-based sampling bias and estimate tornado density using historical tornado report data. The expected result is that F2–F5 compared with F0–F1 tornado reports would vary less with population density. The results agree with this hypothesis for the following population centers: Atlanta, Georgia; Champaign, Illinois; and Des Moines, Iowa. However, the results indicated just the opposite in Oklahoma. It is hypothesized that the result is explained by the misclassification of tornadoes that were worthy of F2–F5 rating but were classified as F0–F1 tornadoes, thereby artificially decreasing the number of F2–F5 and increasing the number of F0–F1 reports in rural Oklahoma.
