scholarly journals Assimilation of High-Resolution Tropical Cyclone Observations with an Ensemble Kalman Filter Using HEDAS: Evaluation of 2008–11 HWRF Forecasts

2015 ◽  
Vol 143 (2) ◽  
pp. 511-523 ◽  
Author(s):  
Sim D. Aberson ◽  
Altuğ Aksoy ◽  
Kathryn J. Sellwood ◽  
Tomislava Vukicevic ◽  
Xuejin Zhang
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Tropical Cyclone ◽  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Radar Data ◽  
Doppler Radar Data ◽  
Flight Level ◽  
Airborne Doppler Radar ◽  
Aircraft Data

Abstract NOAA has been gathering high-resolution, flight-level dropwindsonde and airborne Doppler radar data in tropical cyclones for almost three decades; the U.S. Air Force routinely obtained the same type and quality of data, excepting Doppler radar, for most of that time. The data have been used for operational diagnosis and for research, and, starting in 2013, have been assimilated into operational regional tropical cyclone models. This study is an effort to quantify the impact of assimilating these data into a version of the operational Hurricane Weather Research and Forecasting model using an ensemble Kalman filter. A total of 83 cases during 2008–11 were investigated. The aircraft whose data were used in the study all provide high-density flight-level wind and thermodynamic observations as well as surface wind speed data. Forecasts initialized with these data assimilated are compared to those using the model standard initialization. Since only NOAA aircraft provide airborne Doppler radar data, these data are also tested to see their impact above the standard aircraft data. The aircraft data alone are shown to provide some statistically significant improvement to track and intensity forecasts during the critical watch and warning period before projected landfall (through 60 h), with the Doppler radar data providing some further improvement. This study shows the potential for improved forecasts with regular tropical cyclone aircraft reconnaissance and the assimilation of data obtained from them, especially airborne Doppler radar data, into the numerical guidance.

scholarly journals Procedures to Improve the Accuracy of Airborne Doppler Radar Data

2002 ◽  
Vol 19 (3) ◽  
pp. 322-339 ◽  
Author(s):  
Brian L. Bosart ◽  
Wen-Chau Lee ◽  
Roger M. Wakimoto
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Correction Method ◽  
Fine Tuning ◽  
Earth Surface ◽  
Doppler Radar Data ◽  
Tilt Error ◽  
Corrected Data ◽  
Flight Level ◽  
Airborne Doppler Radar

Abstract The navigation correction method proposed in Testud et al. (referred to as the THL method) systematically identifies uncertainties in the aircraft Inertial Navigation System and errors in the radar-pointing angles by analyzing the radar returns from a flat and stationary earth surface. This paper extends the THL study to address 1) error characteristics on the radar display, 2) sensitivity of the dual-Doppler analyses to navigation errors, 3) fine-tuning the navigation corrections for individual flight legs, and 4) identifying navigation corrections over a flat and nonstationary earth surface (e.g., ocean). The results show that the errors in each of the parameters affect the dual-Doppler wind analyses and the first-order derivatives in different manners. The tilt error is the most difficult parameter to determine and has the greatest impact on the dual-Doppler analysis. The extended THL method can further reduce the drift, ground speed, and tilt errors in all flight legs over land by analyzing the residual velocities of the earth surface using the corrections obtained in the calibration legs. When reliable dual-Doppler winds can be deduced at flight level, the Bosart–Lee–Wakimoto method presented here can identify all eight errors by satisfying three criteria: 1) the flight-level dual-Doppler winds near the aircraft are statistically consistent with the in situ winds, 2) the flight-level dual-Doppler winds are continuous across the flight track, and 3) the surface velocities of the left (right) fore radar have the same magnitude but opposite sign as their counterparts of right (left) aft radar. This procedure is able to correct airborne Doppler radar data over the ocean and has been evaluated using datasets collected during past experiments. Consistent calibration factors are obtained in multiple legs. The dual-Doppler analyses using the corrected data are statistically superior to those using uncorrected data.

scholarly journals A Generalized Navigation Correction Method for Airborne Doppler Radar Data

2018 ◽  
Vol 35 (10) ◽  
pp. 1999-2017 ◽  
Author(s):  
Huaqing Cai ◽  
Wen-Chau Lee ◽  
Michael M. Bell ◽  
Cory A. Wolff ◽  
Xiaowen Tang ◽  
...  
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Correction Method ◽  
Ground Speed ◽  
Flat Surfaces ◽  
Doppler Radar Data ◽  
Flight Level ◽  
The Cost ◽  
Navigation Errors ◽  
Airborne Doppler Radar

AbstractUncertainties in aircraft inertial navigation system and radar-pointing angles can have a large impact on the accuracy of airborne dual-Doppler analyses. The Testud et al. (THL) method has been routinely applied to data collected by airborne tail Doppler radars over flat and nonmoving terrain. The navigation correction method proposed in Georgis et al. (GRH) extended the THL method over complex terrain and moving ocean surfaces by using a variational formulation but its capability over ocean has yet to be tested. Recognizing the limitations of the THL method, Bosart et al. (BLW) proposed to derive ground speed, tilt, and drift errors by statistically comparing aircraft in situ wind with dual-Doppler wind at the flight level. When combined with the THL method, the BLW method can retrieve all navigation errors accurately; however, it can be applied only to flat surfaces, and it is rather difficult to automate. This paper presents a generalized navigation correction method (GNCM) based on the GRH method that will serve as a single algorithm for airborne tail Doppler radar navigation correction for all possible surface conditions. The GNCM includes all possible corrections in the cost function and implements a new closure assumption by taking advantage of an accurate aircraft ground speed derived from GPS technology. The GNCM is tested extensively using synthetic airborne Doppler radar data with known navigation errors and published datasets from previous field campaigns. Both tests show the GNCM is able to correct the navigation errors associated with airborne tail Doppler radar data with adequate accuracy.

scholarly journals Ensemble Kalman Filter Assimilation of Doppler Radar Data with a Compressible Nonhydrostatic Model: OSS Experiments

2005 ◽  
Vol 133 (7) ◽  
pp. 1789-1807 ◽  
Author(s):  
Mingjing Tong ◽  
Ming Xue
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Radial Velocity ◽  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Radar Data ◽  
Relative Role ◽  
Doppler Radar Data ◽  
Ice Microphysics ◽  
Reflectivity Data

Abstract A Doppler radar data assimilation system is developed based on an ensemble Kalman filter (EnKF) method and tested with simulated radar data from a supercell storm. As a first implementation, it is assumed that the forward models are perfect and that the radar data are sampled at the analysis grid points. A general purpose nonhydrostatic compressible model is used with the inclusion of complex multiclass ice microphysics. New aspects of this study compared to previous work include the demonstration of the ability of the EnKF method to retrieve multiple microphysical species associated with a multiclass ice microphysics scheme, and to accurately retrieve the wind and thermodynamic variables. Also new are the inclusion of reflectivity observations and the determination of the relative role of the radial velocity and reflectivity data as well as their spatial coverage in recovering the full-flow and cloud fields. In general, the system is able to reestablish the model storm extremely well after a number of assimilation cycles, and best results are obtained when both radial velocity and reflectivity data, including reflectivity information outside of the precipitation regions, are used. Significant positive impact of the reflectivity assimilation is found even though the observation operator involved is nonlinear. The results also show that a compressible model that contains acoustic modes, hence the associated error growth, performs at least as well as an anelastic model used in previous EnKF studies at the cloud scale. Flow-dependent and dynamically consistent background error covariances estimated from the forecast ensemble play a critical role in successful assimilation and retrieval. When the assimilation cycles start from random initial perturbations, better results are obtained when the updating of the fields that are not directly related to radar reflectivity is withheld during the first few cycles. In fact, during the first few cycles, the updating of the variables indirectly related to reflectivity hurts the analysis. This is so because the estimated background covariances are unreliable at this stage of the data assimilation process, which is related to the way the forecast ensemble is initialized. Forecasts of supercell storms starting from the best-assimilated initial conditions are shown to remain very good for at least 2 h.

Mapping of Airborne Doppler Radar Data

1994 ◽  
Vol 11 (2) ◽  
pp. 572-578 ◽  
Author(s):  
Wen-Chau Lee ◽  
Peter Dodge ◽  
Frank D. Marks ◽  
Peter H. Hildebrand
Keyword(s):  
Doppler Radar ◽  
Radar Data ◽  
Doppler Radar Data ◽  
Airborne Doppler Radar

scholarly journals Analysis of a Tornadic Mesoscale Convective Vortex Based on Ensemble Kalman Filter Assimilation of CASA X-Band and WSR-88D Radar Data

2011 ◽  
Vol 139 (11) ◽  
pp. 3446-3468 ◽  
Author(s):  
Nathan Snook ◽  
Ming Xue ◽  
Youngsun Jung
Keyword(s):  
Kalman Filter ◽  
High Resolution ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Radar Data ◽  
Convective System ◽  
Foundation Engineering ◽  
X Band ◽  
Regional Prediction ◽  
Mesoscale Convective

Abstract One of the goals of the National Science Foundation Engineering Research Center (ERC) for Collaborative Adaptive Sensing of the Atmosphere (CASA) is to improve storm-scale numerical weather prediction (NWP) by collecting data with a dense X-band radar network that provides high-resolution low-level coverage, and by assimilating such data into NWP models. During the first spring storm season after the deployment of four radars in the CASA Integrated Project-1 (IP-1) network in southwest Oklahoma, a tornadic mesoscale convective system (MCS) was captured by CASA and surrounding Weather Surveillance Radars-1988 Doppler (WSR-88Ds) on 8–9 May 2007. The MCS moved across northwest Texas and western and central Oklahoma; two tornadoes rated as category 1 on the enhanced Fujita scale (EF-1) and one tornado of EF-0 intensity were reported during the event, just to the north of the IP-1 network. This was the first tornadic convective system observed by CASA. To quantify the impacts of CASA radar data in storm-scale NWP, a set of data assimilation experiments were performed using the Advanced Regional Prediction System (ARPS) ensemble Kalman filter (EnKF) system configured with full model physics and high-resolution terrain. Data from four CASA IP-1 radars and five WSR-88Ds were assimilated in some of the experiments. The ensemble contained 40 members, and radar data were assimilated every 5 min for 1 h. While the assimilation of WSR-88D data alone was able to produce a reasonably accurate analysis of the convective system, assimilating CASA data in addition to WSR-88D data is found to improve the representation of storm-scale circulations, particularly in the lowest few kilometers of the atmosphere, as evidenced by analyses of gust front position and comparison of simulated Vr with observations. Assimilating CASA data decreased RMS innovation of the resulting ensemble mean analyses of Z, particularly in early assimilation cycles, suggesting that the addition of CASA data allowed the EnKF system to more quickly achieve a good result. Use of multiple microphysics schemes in the forecast ensemble was found to alleviate underdispersion by increasing the ensemble spread. This work is the first assimilating real CASA data into an NWP model using EnKF.

Finescale Vertical Structure of a Cold Front as Revealed by an Airborne Doppler Radar

2006 ◽  
Vol 134 (1) ◽  
pp. 251-271 ◽  
Author(s):  
Bart Geerts ◽  
Rick Damiani ◽  
Samuel Haimov
Keyword(s):  
Vertical Structure ◽  
Doppler Radar ◽  
Cold Front ◽  
Vertical Plane ◽  
Radar Data ◽  
Doppler Velocity ◽  
Density Current ◽  
Convective Boundary ◽  
Flight Level ◽  
Airborne Doppler Radar

Abstract In the afternoon of 24 May 2002, a well-defined and frontogenetic cold front moved through the Texas panhandle. Detailed observations from a series of platforms were collected near the triple point between this cold front and a dryline boundary. This paper primarily uses reflectivity and Doppler velocity data from an airborne 95-GHz radar, as well as flight-level thermodynamic data, to describe the vertical structure of the cold front as it intersected with the dryline. The prefrontal convective boundary layer was weakly capped, weakly sheared, and about 2.5 times deeper than the cold-frontal density current. The radar data depict the cold front as a fine example of an atmospheric density current at unprecedented detail (∼40 m). The echo structure and dual-Doppler-inferred airflow in the vertical plane reveal typical features such as a nose, a head, a rear-inflow current, and a broad current of rising prefrontal air that feeds the accelerating front-to-rear current over the head. The 2D cross-frontal structure, including the frontal slope, is highly variable in time or alongfront distance. Along this slope horizontal vorticity, averaging ∼0.05 s−1, is generated baroclinically, and the associated strong cross-front shear triggers Kelvin–Helmholtz (KH) billows at the density interface. Some KH billows occupy much of the depth of the density current, possibly even temporarily cutting off the head from its trailing body.

Quadrant Distribution of Tropical Cyclone Inner-Core Kinematics in Relation to Environmental Shear

2014 ◽  
Vol 71 (7) ◽  
pp. 2713-2732 ◽  
Author(s):  
Jennifer C. DeHart ◽  
Robert A. Houze ◽  
Robert F. Rogers
Keyword(s):  
Tropical Cyclone ◽  
Inner Core ◽  
Doppler Radar ◽  
Radar Data ◽  
Low Level ◽  
Strong Convection ◽  
Upper Level ◽  
Airborne Doppler Radar ◽  
The Vertical Distribution ◽  
Left Quadrant

Abstract Airborne Doppler radar data collected in tropical cyclones by National Oceanic and Atmospheric Administration WP-3D aircraft over an 8-yr period (2003–10) are used to statistically analyze the vertical structure of tropical cyclone eyewalls with reference to the deep-layer shear. Convective evolution within the inner core conforms to patterns shown by previous studies: convection initiates downshear right, intensifies downshear left, and weakens upshear. Analysis of the vertical distribution of radar reflectivity and vertical air motion indicates the development of upper-level downdrafts in conjunction with strong convection downshear left and a maximum in frequency upshear left. Intense updrafts and downdrafts both conform to the shear asymmetry pattern. While strong updrafts occur in the eyewall, intense downdrafts show far more radial variability, particularly in the upshear-left quadrant, though they concentrate along the eyewall edges. Strong updrafts are collocated with low-level inflow and upper-level outflow superimposed on the background flow. In contrast, strong downdrafts occur in association with low-level outflow and upper-level inflow.

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