Search Results
Abstract
The impact of multiple–Doppler radar data assimilation on quantitative precipitation forecasting (QPF) is examined in this study. The newly developed Weather Research and Forecasting (WRF) model Advanced Research WRF (ARW) and its three-dimensional variational data assimilation system (WRF 3DVAR) are used. In this study, multiple–Doppler radar data assimilation is applied in WRF 3DVAR cycling mode to initialize a squall-line convective system on 13 June 2002 during the International H2O Project (IHOP_2002) and the ARW QPF skills are evaluated for the case. Numerical experiments demonstrate that WRF 3DVAR can successfully assimilate Doppler radial velocity and reflectivity from multiple radar sites and extract useful information from the radar data to initiate the squall-line convective system. Assimilation of both radial velocity and reflectivity results in sound analyses that show adjustments in both the dynamical and thermodynamical fields that are consistent with the WRF 3DVAR balance constraint and background error correlation. The cycling of the Doppler radar data from the 12 radar sites at 2100 UTC 12 June and 0000 UTC 13 June produces a more detailed mesoscale structure of the squall-line convection in the model initial conditions at 0000 UTC 13 June. Evaluations of the ARW QPF skills with initialization via Doppler radar data assimilation demonstrate that the more radar data in the temporal and spatial dimensions are assimilated, the more positive is the impact on the QPF skill. Assimilation of both radial velocity and reflectivity has more positive impact on the QPF skill than does assimilation of either radial velocity or reflectivity only. The improvement of the QPF skill with multiple-radar data assimilation is more clearly observed in heavy rainfall than in light rainfall. In addition to the improvement of the QPF skill, the simulated structure of the squall line is also enhanced by the multiple–Doppler radar data assimilation in the WRF 3DVAR cycling experiment. The vertical airflow pattern shows typical characteristics of squall-line convection. The cold pool and its related squall-line convection triggering process are better initiated in the WRF 3DVAR analysis and simulated in the ARW forecast when multiple–Doppler radar data are assimilated.
Abstract
The impact of multiple–Doppler radar data assimilation on quantitative precipitation forecasting (QPF) is examined in this study. The newly developed Weather Research and Forecasting (WRF) model Advanced Research WRF (ARW) and its three-dimensional variational data assimilation system (WRF 3DVAR) are used. In this study, multiple–Doppler radar data assimilation is applied in WRF 3DVAR cycling mode to initialize a squall-line convective system on 13 June 2002 during the International H2O Project (IHOP_2002) and the ARW QPF skills are evaluated for the case. Numerical experiments demonstrate that WRF 3DVAR can successfully assimilate Doppler radial velocity and reflectivity from multiple radar sites and extract useful information from the radar data to initiate the squall-line convective system. Assimilation of both radial velocity and reflectivity results in sound analyses that show adjustments in both the dynamical and thermodynamical fields that are consistent with the WRF 3DVAR balance constraint and background error correlation. The cycling of the Doppler radar data from the 12 radar sites at 2100 UTC 12 June and 0000 UTC 13 June produces a more detailed mesoscale structure of the squall-line convection in the model initial conditions at 0000 UTC 13 June. Evaluations of the ARW QPF skills with initialization via Doppler radar data assimilation demonstrate that the more radar data in the temporal and spatial dimensions are assimilated, the more positive is the impact on the QPF skill. Assimilation of both radial velocity and reflectivity has more positive impact on the QPF skill than does assimilation of either radial velocity or reflectivity only. The improvement of the QPF skill with multiple-radar data assimilation is more clearly observed in heavy rainfall than in light rainfall. In addition to the improvement of the QPF skill, the simulated structure of the squall line is also enhanced by the multiple–Doppler radar data assimilation in the WRF 3DVAR cycling experiment. The vertical airflow pattern shows typical characteristics of squall-line convection. The cold pool and its related squall-line convection triggering process are better initiated in the WRF 3DVAR analysis and simulated in the ARW forecast when multiple–Doppler radar data are assimilated.
Abstract
A four-dimensional ensemble-based variational data assimilation (4DEnVar) algorithm proposed in Part I of the 4DEnVar series (denoted En4DVar in Part I, but here we refer to it as 4DEnVar according to WMO conference recommendation to differentiate it from En4DVar algorithm using adjoint model) uses a flow-dependent background error covariance calculated from ensemble forecasts and performs 4DVar optimization based on an incremental approach and a preconditioning algorithm. In Part II, the authors evaluated 4DEnVar with observing system simulation experiments (OSSEs) using the Advanced Research Weather Research and Forecasting Model (ARW-WRF, hereafter WRF). The current study extends the 4DEnVar to assimilate real observations for a cyclone in the Antarctic and the Southern Ocean in October 2007. The authors performed an intercomparison of four different WRF variational approaches for the case, including three-dimensional variational data assimilation (3DVar), first guess at the appropriate time (FGAT), and ensemble-based three-dimensional (En3DVar) and four-dimensional (4DEnVar) variational data assimilations. It is found that all data assimilation approaches produce positive impacts in this case. Applying the flow-dependent background error covariance in En3DVar and 4DEnVar yields forecast skills superior to those with the homogeneous and isotropic background error covariance in 3DVar and FGAT. In addition, the authors carried out FGAT and 4DEnVar 3-day cycling and 72-h forecasts. The results show that 4DEnVar produces a better performance in the cyclone prediction. The inflation factor on 4DEnVar can effectively improve the 4DEnVar analysis. The authors also conducted a short period (10-day lifetime of the cyclone in the domain) of analysis/forecast intercomparison experiments using 4DEnVar, FGAT, and 3DVar. The 4DEnVar scheme demonstrates overall superior and robust performance.
Abstract
A four-dimensional ensemble-based variational data assimilation (4DEnVar) algorithm proposed in Part I of the 4DEnVar series (denoted En4DVar in Part I, but here we refer to it as 4DEnVar according to WMO conference recommendation to differentiate it from En4DVar algorithm using adjoint model) uses a flow-dependent background error covariance calculated from ensemble forecasts and performs 4DVar optimization based on an incremental approach and a preconditioning algorithm. In Part II, the authors evaluated 4DEnVar with observing system simulation experiments (OSSEs) using the Advanced Research Weather Research and Forecasting Model (ARW-WRF, hereafter WRF). The current study extends the 4DEnVar to assimilate real observations for a cyclone in the Antarctic and the Southern Ocean in October 2007. The authors performed an intercomparison of four different WRF variational approaches for the case, including three-dimensional variational data assimilation (3DVar), first guess at the appropriate time (FGAT), and ensemble-based three-dimensional (En3DVar) and four-dimensional (4DEnVar) variational data assimilations. It is found that all data assimilation approaches produce positive impacts in this case. Applying the flow-dependent background error covariance in En3DVar and 4DEnVar yields forecast skills superior to those with the homogeneous and isotropic background error covariance in 3DVar and FGAT. In addition, the authors carried out FGAT and 4DEnVar 3-day cycling and 72-h forecasts. The results show that 4DEnVar produces a better performance in the cyclone prediction. The inflation factor on 4DEnVar can effectively improve the 4DEnVar analysis. The authors also conducted a short period (10-day lifetime of the cyclone in the domain) of analysis/forecast intercomparison experiments using 4DEnVar, FGAT, and 3DVar. The 4DEnVar scheme demonstrates overall superior and robust performance.
Abstract
Applying a flow-dependent background error covariance (𝗕 matrix) in variational data assimilation has been a topic of interest among researchers in recent years. In this paper, an ensemble-based four-dimensional variational (En4DVAR) algorithm, designed by the authors, is presented that uses a flow-dependent background error covariance matrix constructed by ensemble forecasts and performs 4DVAR optimization to produce a balanced analysis. A great advantage of this En4DVAR design over standard 4DVAR methods is that the tangent linear and adjoint models can be avoided in its formulation and implementation. In addition, it can be easily incorporated into variational data assimilation systems that are already in use at operational centers and among the research community.
A one-dimensional shallow water model was used for preliminary tests of the En4DVAR scheme. Compared with standard 4DVAR, the En4DVAR converges well and can produce results that are as good as those with 4DVAR but with far less computation cost in its minimization. In addition, a comparison of the results from En4DVAR with those from other data assimilation schemes [e.g., 3DVAR and ensemble Kalman filter (EnKF)] is made. The results show that the En4DVAR yields an analysis that is comparable to the widely used variational or ensemble data assimilation schemes and can be a promising approach for real-time applications.
In addition, experiments were carried out to test the sensitivities of EnKF and En4DVAR, whose background error covariance is estimated from the same ensemble forecasts. The experiments indicated that En4DVAR obtained reasonably sound analysis even with larger observation error, higher observation frequency, and more unbalanced background field.
Abstract
Applying a flow-dependent background error covariance (𝗕 matrix) in variational data assimilation has been a topic of interest among researchers in recent years. In this paper, an ensemble-based four-dimensional variational (En4DVAR) algorithm, designed by the authors, is presented that uses a flow-dependent background error covariance matrix constructed by ensemble forecasts and performs 4DVAR optimization to produce a balanced analysis. A great advantage of this En4DVAR design over standard 4DVAR methods is that the tangent linear and adjoint models can be avoided in its formulation and implementation. In addition, it can be easily incorporated into variational data assimilation systems that are already in use at operational centers and among the research community.
A one-dimensional shallow water model was used for preliminary tests of the En4DVAR scheme. Compared with standard 4DVAR, the En4DVAR converges well and can produce results that are as good as those with 4DVAR but with far less computation cost in its minimization. In addition, a comparison of the results from En4DVAR with those from other data assimilation schemes [e.g., 3DVAR and ensemble Kalman filter (EnKF)] is made. The results show that the En4DVAR yields an analysis that is comparable to the widely used variational or ensemble data assimilation schemes and can be a promising approach for real-time applications.
In addition, experiments were carried out to test the sensitivities of EnKF and En4DVAR, whose background error covariance is estimated from the same ensemble forecasts. The experiments indicated that En4DVAR obtained reasonably sound analysis even with larger observation error, higher observation frequency, and more unbalanced background field.
Abstract
A high-resolution, full-physics model initiated with an idealized tropical cyclone–like vortex is used to simulate and investigate the secondary eyewall formation. The beta skirt axisymmetrization (BSA) hypothesis previously proposed is examined and the roles of axisymmetrizing vortex Rossby waves (VRWs) in the secondary eyewall formation are further investigated. During the formation period, convection outside the inner-core region is organized into an outer spiral rainband. The PV dipoles that are persistently generated by convective updrafts through tilting effect move along the rainband and inward toward inner-core region and are finally axisymmetrized in the preexisting beta skirt region. The formation of the secondary eyewall is preceded by a rapid intensification period, during which vortical hot towers, discrete VRWs, and sheared VRWs dominate the inner-core asymmetric structures. Sheared VRWs are repeatedly emanated from the outer edge of the eyewall and become more concentric when propagating outward, leading to the formation of a weak but nonnegligible secondary circulation near the VRWs’ stagnant radius. The mean tangential flow is accelerated by the low-level convergence associated with the secondary circulation and also by the wave–mean flow interaction mechanism, both of which are elucidated by absolute angular momentum budget calculation. The mean radial gradient of relative vorticity is enhanced across the stagnant radius, causing the extension of beta skirt to outer radii in the lower-tropospheric levels. Results from this study suggest that the stagnant radius mechanism and the BSA mechanism may work cooperatively in the sense that the former helps to establish an extensive beta skirt and the latter takes charge from then on.
Abstract
A high-resolution, full-physics model initiated with an idealized tropical cyclone–like vortex is used to simulate and investigate the secondary eyewall formation. The beta skirt axisymmetrization (BSA) hypothesis previously proposed is examined and the roles of axisymmetrizing vortex Rossby waves (VRWs) in the secondary eyewall formation are further investigated. During the formation period, convection outside the inner-core region is organized into an outer spiral rainband. The PV dipoles that are persistently generated by convective updrafts through tilting effect move along the rainband and inward toward inner-core region and are finally axisymmetrized in the preexisting beta skirt region. The formation of the secondary eyewall is preceded by a rapid intensification period, during which vortical hot towers, discrete VRWs, and sheared VRWs dominate the inner-core asymmetric structures. Sheared VRWs are repeatedly emanated from the outer edge of the eyewall and become more concentric when propagating outward, leading to the formation of a weak but nonnegligible secondary circulation near the VRWs’ stagnant radius. The mean tangential flow is accelerated by the low-level convergence associated with the secondary circulation and also by the wave–mean flow interaction mechanism, both of which are elucidated by absolute angular momentum budget calculation. The mean radial gradient of relative vorticity is enhanced across the stagnant radius, causing the extension of beta skirt to outer radii in the lower-tropospheric levels. Results from this study suggest that the stagnant radius mechanism and the BSA mechanism may work cooperatively in the sense that the former helps to establish an extensive beta skirt and the latter takes charge from then on.
Abstract
The bogus data assimilation (BDA) scheme designed by Zou and Xiao to specify initial structures of tropical cyclones was tested further on the simulation of a landfalling hurricane—Hurricane Fran (1996). The sensitivity of the simulated hurricane track and intensity to the specified radius of maximum wind of the bogus vortex, the resolution of the BDA assimilation model, and the bogus variables specified in the BDA are studied. In addition, the simulated hurricane structures are compared with the available observations, including the surface wind analysis and the radar reflectivity captured onshore during Fran’s landfall.
The sensitivity study of the BDA scheme showed that the simulations of the hurricane track and intensity were sensitive to the size of the specified bogus vortex. Hurricanes with a larger radius of maximum sea level pressure gradient are prone to a more westward propagation. The larger the radius, the weaker the predicted hurricane. Results of the hurricane initial structures and prediction were also sensitive to the bogus variables specified in the BDA. Fitting the model to the bogused pressure data reproduced the hurricane structure of all model variables more efficiently than when fitting it to bogused wind data. Examining the initial conditions produced by the BDA, it is found that the generation and intensity of hurricane warm-core structure in the model initial state was a key factor for the hurricane intensity prediction.
Initialized with the initial conditions obtained by the BDA scheme, the model successfully simulated Hurricane Fran’s landfall, the intensity change, and some inner-core structures. Verified against the surface wind analysis, the model reproduced the distribution of the maximum wind streaks reasonably well. The model-predicted reflectivity field during the landfall of Hurricane Fran resembled the observed radar reflectivity image onshore.
Abstract
The bogus data assimilation (BDA) scheme designed by Zou and Xiao to specify initial structures of tropical cyclones was tested further on the simulation of a landfalling hurricane—Hurricane Fran (1996). The sensitivity of the simulated hurricane track and intensity to the specified radius of maximum wind of the bogus vortex, the resolution of the BDA assimilation model, and the bogus variables specified in the BDA are studied. In addition, the simulated hurricane structures are compared with the available observations, including the surface wind analysis and the radar reflectivity captured onshore during Fran’s landfall.
The sensitivity study of the BDA scheme showed that the simulations of the hurricane track and intensity were sensitive to the size of the specified bogus vortex. Hurricanes with a larger radius of maximum sea level pressure gradient are prone to a more westward propagation. The larger the radius, the weaker the predicted hurricane. Results of the hurricane initial structures and prediction were also sensitive to the bogus variables specified in the BDA. Fitting the model to the bogused pressure data reproduced the hurricane structure of all model variables more efficiently than when fitting it to bogused wind data. Examining the initial conditions produced by the BDA, it is found that the generation and intensity of hurricane warm-core structure in the model initial state was a key factor for the hurricane intensity prediction.
Initialized with the initial conditions obtained by the BDA scheme, the model successfully simulated Hurricane Fran’s landfall, the intensity change, and some inner-core structures. Verified against the surface wind analysis, the model reproduced the distribution of the maximum wind streaks reasonably well. The model-predicted reflectivity field during the landfall of Hurricane Fran resembled the observed radar reflectivity image onshore.
Abstract
Numerical experiments have been conducted to examine the impact of multisatellite data on the initialization and forecast of the rapid weakening of Hurricane Lili (in 2002) from 0000 UTC to landfall in Louisiana on 1300 UTC 3 October 2002. Fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) 4DVAR sensitivity runs were conducted separately with QuikSCAT surface winds, the Geostationary Operational Environmental Satellite-8 (GOES-8) cloud drift–water vapor winds, and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) temperature–dewpoint sounding data to investigate their individual impact on storm track and intensity. The results were compared against a simulation initialized from a Global Forecast System background interpolated to the MM5 grid. Assimilating QuikSCAT surface wind data improves the analyzed outer-core surface winds, as well as the inner-core low-level temperature and moisture fields. Substantial adjustments of winds are noted on the periphery of the hurricane by assimilating GOES-8 satellite-derived upper-level winds. Both track forecasts initialized at 1200 UTC 2 October 2002 with four-dimensional variational data assimilation (4DVAR) of QuikSCAT and GOES-8 show improvement compared to those initialized with the model background. Assimilating Aqua MODIS sounding data improves the outer-core thermodynamic features. The Aqua MODIS data has a slight impact on the track forecast, but more importantly shows evidence of impacting the model intensity predicting by retarding the incorrect prediction of intensification. All three experiments also show that bogusing of an inner-core wind vortex is required to depict the storm’s initial intensity.
To properly investigate Lili’s weakening, data assimilation experiments that incorporate bogusing vortex, QuikSCAT winds, GOES-8 winds, and Aqua MODIS sounding data were performed. The 4DVAR satellite-bogus data assimilation is conducted in two consecutive 6-h windows preceding Lili’s weakening. Comparisons of the results between the experiments with and without satellite data indicated that the satellite data, particularly the Aqua MODIS sounding information, makes an immediate impact on the hurricane intensity change beyond normal bogusing procedures. The track forecast with the satellite data is also more accurate than just using bogusing alone. This study suggests that dry air intrusion played an important role in Lili’s rapid weakening. It also demonstrates the potential benefit of using satellite data in a 4DVAR context—particularly high-resolution soundings—on unusual cases like Hurricane Lili.
Abstract
Numerical experiments have been conducted to examine the impact of multisatellite data on the initialization and forecast of the rapid weakening of Hurricane Lili (in 2002) from 0000 UTC to landfall in Louisiana on 1300 UTC 3 October 2002. Fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) 4DVAR sensitivity runs were conducted separately with QuikSCAT surface winds, the Geostationary Operational Environmental Satellite-8 (GOES-8) cloud drift–water vapor winds, and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) temperature–dewpoint sounding data to investigate their individual impact on storm track and intensity. The results were compared against a simulation initialized from a Global Forecast System background interpolated to the MM5 grid. Assimilating QuikSCAT surface wind data improves the analyzed outer-core surface winds, as well as the inner-core low-level temperature and moisture fields. Substantial adjustments of winds are noted on the periphery of the hurricane by assimilating GOES-8 satellite-derived upper-level winds. Both track forecasts initialized at 1200 UTC 2 October 2002 with four-dimensional variational data assimilation (4DVAR) of QuikSCAT and GOES-8 show improvement compared to those initialized with the model background. Assimilating Aqua MODIS sounding data improves the outer-core thermodynamic features. The Aqua MODIS data has a slight impact on the track forecast, but more importantly shows evidence of impacting the model intensity predicting by retarding the incorrect prediction of intensification. All three experiments also show that bogusing of an inner-core wind vortex is required to depict the storm’s initial intensity.
To properly investigate Lili’s weakening, data assimilation experiments that incorporate bogusing vortex, QuikSCAT winds, GOES-8 winds, and Aqua MODIS sounding data were performed. The 4DVAR satellite-bogus data assimilation is conducted in two consecutive 6-h windows preceding Lili’s weakening. Comparisons of the results between the experiments with and without satellite data indicated that the satellite data, particularly the Aqua MODIS sounding information, makes an immediate impact on the hurricane intensity change beyond normal bogusing procedures. The track forecast with the satellite data is also more accurate than just using bogusing alone. This study suggests that dry air intrusion played an important role in Lili’s rapid weakening. It also demonstrates the potential benefit of using satellite data in a 4DVAR context—particularly high-resolution soundings—on unusual cases like Hurricane Lili.
Abstract
An ensemble-based four-dimensional variational data assimilation (En4DVAR) algorithm and its performance in a low-dimension space with a one-dimensional shallow-water model have been presented in Part I. This algorithm adopts the standard incremental approach and preconditioning in the variational algorithm but avoids the need for a tangent linear model and its adjoint so that it can be easily incorporated into variational assimilation systems. The current study explores techniques for En4DVAR application in real-dimension data assimilation. The EOF decomposed correlation function operator and analysis time tuning are formulated to reduce the impact of sampling errors in En4DVAR upon its analysis. With the Advanced Research Weather Research and Forecasting (ARW-WRF) model, Observing System Simulation Experiments (OSSEs) are designed and their performance in real-dimension data assimilation is examined. It is found that the designed En4DVAR localization techniques can effectively alleviate the impacts of sampling errors upon analysis. Most forecast errors and biases in ARW are reduced by En4DVAR compared to those in a control experiment. En3DVAR cycling experiments are used to compare the ensemble-based sequential algorithm with the ensemble-based retrospective algorithm. These experiments indicate that the ensemble-based retrospective assimilation, En4DVAR, produces an overall better analysis than the ensemble-based sequential algorithm, En3DVAR, cycling approach.
Abstract
An ensemble-based four-dimensional variational data assimilation (En4DVAR) algorithm and its performance in a low-dimension space with a one-dimensional shallow-water model have been presented in Part I. This algorithm adopts the standard incremental approach and preconditioning in the variational algorithm but avoids the need for a tangent linear model and its adjoint so that it can be easily incorporated into variational assimilation systems. The current study explores techniques for En4DVAR application in real-dimension data assimilation. The EOF decomposed correlation function operator and analysis time tuning are formulated to reduce the impact of sampling errors in En4DVAR upon its analysis. With the Advanced Research Weather Research and Forecasting (ARW-WRF) model, Observing System Simulation Experiments (OSSEs) are designed and their performance in real-dimension data assimilation is examined. It is found that the designed En4DVAR localization techniques can effectively alleviate the impacts of sampling errors upon analysis. Most forecast errors and biases in ARW are reduced by En4DVAR compared to those in a control experiment. En3DVAR cycling experiments are used to compare the ensemble-based sequential algorithm with the ensemble-based retrospective algorithm. These experiments indicate that the ensemble-based retrospective assimilation, En4DVAR, produces an overall better analysis than the ensemble-based sequential algorithm, En3DVAR, cycling approach.
Abstract
Initialization of the hurricane vortex in weather prediction models is vital to intensity forecasts out to at least 48 h. Airborne Doppler radar (ADR) data have sufficiently high horizontal and vertical resolution to resolve the hurricane vortex and its imbedded structures but have not been extensively used in hurricane initialization. Using the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation (3DVAR) system, the ADR data are assimilated to recover the hurricane vortex dynamic and thermodynamic structures at the WRF model initial time. The impact of the ADR data on three hurricanes, Jeanne (2004), Katrina (2005) and Rita (2005), are examined during their rapid intensification and subsequent weakening periods before landfall.
With the ADR wind data assimilated, the three-dimensional winds in the hurricane vortex become stronger and the maximum 10-m winds agree better with independent estimates from best-track data than without ADR data assimilation. Through the multivariate incremental structure in WRF 3DVAR analysis, the central sea level pressures (CSLPs) for the three hurricanes are lower in response to the stronger vortex at initialization. The size and inner-core structure of each vortex are adjusted closer to observations of these attributes. Addition of reflectivity data in assimilation produces cloud water and rainwater analyses in the initial vortex. The temperature and moisture are also better represented in the hurricane initialization.
Forty-eight-hour forecasts are conducted to evaluate the impact of ADR data using the Advanced Research Hurricane WRF (AHW), a derivative of the Advanced Research WRF (ARW) model. Assimilation of ADR data improves the hurricane-intensity forecasts. Vortex asymmetries, size, and rainbands are also simulated better. Hurricane initialization with ADR data is quite promising toward reducing intensity forecast errors at modest computational expense.
Abstract
Initialization of the hurricane vortex in weather prediction models is vital to intensity forecasts out to at least 48 h. Airborne Doppler radar (ADR) data have sufficiently high horizontal and vertical resolution to resolve the hurricane vortex and its imbedded structures but have not been extensively used in hurricane initialization. Using the Weather Research and Forecasting (WRF) three-dimensional variational data assimilation (3DVAR) system, the ADR data are assimilated to recover the hurricane vortex dynamic and thermodynamic structures at the WRF model initial time. The impact of the ADR data on three hurricanes, Jeanne (2004), Katrina (2005) and Rita (2005), are examined during their rapid intensification and subsequent weakening periods before landfall.
With the ADR wind data assimilated, the three-dimensional winds in the hurricane vortex become stronger and the maximum 10-m winds agree better with independent estimates from best-track data than without ADR data assimilation. Through the multivariate incremental structure in WRF 3DVAR analysis, the central sea level pressures (CSLPs) for the three hurricanes are lower in response to the stronger vortex at initialization. The size and inner-core structure of each vortex are adjusted closer to observations of these attributes. Addition of reflectivity data in assimilation produces cloud water and rainwater analyses in the initial vortex. The temperature and moisture are also better represented in the hurricane initialization.
Forty-eight-hour forecasts are conducted to evaluate the impact of ADR data using the Advanced Research Hurricane WRF (AHW), a derivative of the Advanced Research WRF (ARW) model. Assimilation of ADR data improves the hurricane-intensity forecasts. Vortex asymmetries, size, and rainbands are also simulated better. Hurricane initialization with ADR data is quite promising toward reducing intensity forecast errors at modest computational expense.
Abstract
The purpose of this study is to investigate the performance of 3DVAR radar data assimilation in terms of the retrievals of convective fields and their impact on subsequent quantitative precipitation forecasts (QPFs). An assimilation methodology based on the Weather Research and Forecasting (WRF) model three-dimensional variational data assimilation (3DVAR) and a cloud analysis scheme is described. Simulated data from 25 Weather Surveillance Radar-1988 Doppler (WSR-88D) radars are assimilated, and the potential benefits and limitations of the assimilation are quantitatively evaluated through observing system simulation experiments of a dryline that occurred over the southern Great Plains. Results indicate that the 3DVAR system is able to analyze certain mesoscale and convective-scale features through the incorporation of radar observations. The assimilation of all possible data (radial velocity and reflectivity factor data) results in the best performance on short-range precipitation forecasting. The wind retrieval by assimilating radial velocities is of primary importance in the 3DVAR framework and the storm case applied, and the use of multiple-Doppler observations improves the retrieval of the tangential wind component. The reflectivity factor assimilation is also beneficial especially for strong precipitation. It is demonstrated that the improved initial conditions through the 3DVAR analysis lead to improved skills on QPF.
Abstract
The purpose of this study is to investigate the performance of 3DVAR radar data assimilation in terms of the retrievals of convective fields and their impact on subsequent quantitative precipitation forecasts (QPFs). An assimilation methodology based on the Weather Research and Forecasting (WRF) model three-dimensional variational data assimilation (3DVAR) and a cloud analysis scheme is described. Simulated data from 25 Weather Surveillance Radar-1988 Doppler (WSR-88D) radars are assimilated, and the potential benefits and limitations of the assimilation are quantitatively evaluated through observing system simulation experiments of a dryline that occurred over the southern Great Plains. Results indicate that the 3DVAR system is able to analyze certain mesoscale and convective-scale features through the incorporation of radar observations. The assimilation of all possible data (radial velocity and reflectivity factor data) results in the best performance on short-range precipitation forecasting. The wind retrieval by assimilating radial velocities is of primary importance in the 3DVAR framework and the storm case applied, and the use of multiple-Doppler observations improves the retrieval of the tangential wind component. The reflectivity factor assimilation is also beneficial especially for strong precipitation. It is demonstrated that the improved initial conditions through the 3DVAR analysis lead to improved skills on QPF.
Abstract
The impact of satellite-derived wind observations on the prediction of a mid–Pacific Ocean cyclone during the North Pacific Experiment (NORPEX, 14 Jan–27 Feb 1998) is assessed using a four-dimensional variational (4DVAR) approach in which a nonhydrostatic version of the Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) serves as a strong constraint. The satellite-derived wind observations are retrieved through an automated tracking algorithm using water vapor visible, and infrared imagery from the operational Geostationary Meteorological Satellite-5 (GMS-5) and Geostationary Operational Environmental Satellite-9 (GOES-9) over the North Pacific basin. For the case studied, it is found that the amount of satellite wind data is much greater in the upper troposphere than in the lower troposphere.
The 4DVAR assimilation of the satellite wind observations is carried out on a single domain with 90-km horizontal resolution. Incorporation of satellite wind observations was found to increase the cyclonic zonal wind shear and the cross-front temperature gradient associated with the simulated cyclone. However, the improvement in the intensity of the simulated cyclone measured by the central sea level pressure is marginal using the same assimilation model. Increasing the forecast model resolution by nesting a 30-km resolution domain yields a more significant impact of the satellite-derived wind data on the cyclone intensity prediction. The GMS-5 satellite winds (upstream data) are found to have more influence on the quality of the cyclone development than the GOES-9 satellite winds (downstream data). An adjoint sensitivity study confirms that the most sensitive region is located upstream of the cyclone, and that the cyclone is more sensitive to the lower rather than the upper atmosphere. Therefore, it is anticipated that larger impacts on cyclone prediction in the mid–Pacific Ocean will occur when a greater or equal amount of satellite wind observations are made available for the lower troposphere as are available for the upper levels.
Abstract
The impact of satellite-derived wind observations on the prediction of a mid–Pacific Ocean cyclone during the North Pacific Experiment (NORPEX, 14 Jan–27 Feb 1998) is assessed using a four-dimensional variational (4DVAR) approach in which a nonhydrostatic version of the Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) serves as a strong constraint. The satellite-derived wind observations are retrieved through an automated tracking algorithm using water vapor visible, and infrared imagery from the operational Geostationary Meteorological Satellite-5 (GMS-5) and Geostationary Operational Environmental Satellite-9 (GOES-9) over the North Pacific basin. For the case studied, it is found that the amount of satellite wind data is much greater in the upper troposphere than in the lower troposphere.
The 4DVAR assimilation of the satellite wind observations is carried out on a single domain with 90-km horizontal resolution. Incorporation of satellite wind observations was found to increase the cyclonic zonal wind shear and the cross-front temperature gradient associated with the simulated cyclone. However, the improvement in the intensity of the simulated cyclone measured by the central sea level pressure is marginal using the same assimilation model. Increasing the forecast model resolution by nesting a 30-km resolution domain yields a more significant impact of the satellite-derived wind data on the cyclone intensity prediction. The GMS-5 satellite winds (upstream data) are found to have more influence on the quality of the cyclone development than the GOES-9 satellite winds (downstream data). An adjoint sensitivity study confirms that the most sensitive region is located upstream of the cyclone, and that the cyclone is more sensitive to the lower rather than the upper atmosphere. Therefore, it is anticipated that larger impacts on cyclone prediction in the mid–Pacific Ocean will occur when a greater or equal amount of satellite wind observations are made available for the lower troposphere as are available for the upper levels.
