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Yonghui Weng and Fuqing Zhang

Abstract

Through a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system, the impact of assimilating airborne radar observations for the convection-permitting analysis and prediction of Hurricane Katrina (2005) is examined in this study. A forecast initialized from EnKF analyses of airborne radar observations had substantially smaller hurricane track forecast errors than NOAA’s operational forecasts and a control forecast initialized from NCEP analysis data for lead times up to 120 h. Verifications against independent in situ and remotely sensed observations show that EnKF analyses successfully depict the inner-core structure of the hurricane vortex in terms of both dynamic (wind) and thermodynamic (temperature and moisture) fields. In addition to the improved analyses and deterministic forecast, an ensemble of forecasts initiated from the EnKF analyses also provided forecast uncertainty estimates for the hurricane track and intensity.

Also documented here are the details of a series of data thinning and quality control procedures that were developed to generate superobservations from large volumes of airborne radial velocity measurements. These procedures have since been implemented operationally on the NOAA hurricane reconnaissance aircraft that allows for more efficient real-time transmission of airborne radar observations to the ground.

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Fuqing Zhang and Yonghui Weng

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Performance in the prediction of hurricane intensity and associated hazards has been evaluated for a newly developed convection-permitting forecast system that uses ensemble data assimilation techniques to ingest high-resolution airborne radar observations from the inner core. This system performed well for three of the ten costliest Atlantic hurricanes: Ike (2008), Irene (2011), and Sandy (2012). Four to five days before these storms made landfall, the system produced good deterministic and probabilistic forecasts of not only track and intensity, but also of the spatial distributions of surface wind and rainfall. Averaged over all 102 applicable cases that have inner-core airborne Doppler radar observations during 2008–2012, the system reduced the day-2-to-day-4 intensity forecast errors by 25%–28% compared to the corresponding National Hurricane Center’s official forecasts (which have seen little or no decrease in intensity forecast errors over the past two decades). Empowered by sufficient computing resources, advances in both deterministic and probabilistic hurricane prediction will enable emergency management officials, the private sector, and the general public to make more informed decisions that minimize the losses of life and property.

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Jonathan Poterjoy, Fuqing Zhang, and Yonghui Weng

Abstract

Atmospheric data assimilation methods that estimate flow-dependent forecast statistics from ensembles are sensitive to sampling errors. This sensitivity is investigated in the context of vortex-scale hurricane data assimilation by cycling an ensemble Kalman filter to assimilate observations with a convection-permitting mesoscale model. In a set of numerical experiments, airborne Doppler radar observations are assimilated for Hurricane Katrina (2005) using an ensemble size that ranges from 30 to 300 members, and a varying degree of covariance inflation through relaxation to the prior. The range of ensemble sizes is shown to produce variations in posterior storm structure that persist for days in deterministic forecasts, with the most substantial differences appearing in the vortex outer-core wind and pressure fields. Ensembles with 60 or more members converge toward similar axisymmetric and asymmetric inner-core solutions by the end of the cycling, while producing qualitatively similar sample correlations between the state variables. Though covariance relaxation has little impact on model variables far from the observations, the structure of the inner-core vortex can benefit from a more optimal tuning of the relaxation coefficient. Results from this study provide insight into how sampling errors may affect the performance of an ensemble hurricane data assimilation system during cycling.

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Yunji Zhang, Zhiyong Meng, Fuqing Zhang, and Yonghui Weng
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Jason A. Sippel, Scott A. Braun, Fuqing Zhang, and Yonghui Weng

Abstract

This study utilizes ensemble Kalman filter (EnKF) observing system simulation experiments (OSSEs) to analyze the potential impact of assimilating radial velocity observations of hurricanes from the High-altitude Imaging Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a new Doppler radar mounted on the NASA Global Hawk unmanned airborne system that flies at roughly 19-km altitude and has the benefit of a 25–30-h flight duration, which is 2–3 times that of conventional aircraft. This research is intended as a proof-of-concept study for future assimilation of real HIWRAP data. The most important result from this research is that HIWRAP data can potentially improve hurricane analyses and prediction. For example, by the end of a 12-h assimilation period, the analysis error is much lower than that in deterministic forecasts. As a result, subsequent forecasts initialized with the EnKF analyses also improve. Furthermore, analyses and forecasts clearly benefit more from a 12-h assimilation period than for shorter periods, which highlights a benefit of the Global Hawk's potentially long on-station times.

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Yunji Zhang, Zhiyong Meng, Fuqing Zhang, and Yonghui Weng

Abstract

The practical predictability of tropical cyclone (TC) intensity in terms of mean absolute forecast error with respect to different conditions at forecast initialization was explored through convection-permitting hindcasts of all Atlantic storms during the 2008–12 hurricane seasons using the Weather Research and Forecasting (WRF) Model. Averaged over a total of 2190 simulations, the day 1–5 performance of these WRF hindcasts was comparable to two operational regional-scale hurricane prediction models used by the National Hurricane Center (NHC) but was slightly inferior to the NHC official forecasts. It was found that the prediction accuracy of TC intensity, both at the initialization time and the targeted forecast hours, was strongly correlated with the TC intensity. On average, for both the WRF hindcasts and the NHC official forecasts, stronger intensities and larger intensity variations led to larger forecast errors. A number of synoptic-scale environmental parameters, such as vertical wind shear, sea surface temperature (SST), and the underlying surface condition (land vs sea), affected the intensity forecast errors of TCs, in part due to their influence on intensity changes, while other thermodynamic environmental parameters, such as moisture and instability, had relatively minor effects. The accuracy of the intensity prediction was also found to be sensitive to the translation speed of the TCs. A moderate TC translation speed of 11–15 knots (kt; 1 kt = 0.51 m s−1) corresponded to the largest intensity errors during forecast lead times less than 60 h, while the slowest translation speed (<7 kt) was associated with the largest errors after the 60-h forecast lead time.

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Baoguo Xie, Fuqing Zhang, Qinghong Zhang, Jonathan Poterjoy, and Yonghui Weng

Abstract

An ensemble Kalman filter data assimilation system for the Weather Research and Forecasting Model is used with ensemble-based sensitivity analysis to explore observing strategies and observation targeting for tropical cyclones. The case selected for this study is Typhoon Morakot (2009), a western Pacific storm that brought record-breaking rainfall to Taiwan. Forty-eight hours prior to making landfall, ensemble sensitivity analysis using a 50-member convection-permitting ensemble predicts that dropsonde observations located in the southwest quadrant of the typhoon will have the highest impact on reducing the forecast uncertainty of the track, intensity, and rainfall of Morakot. A series of observing system simulation experiments (OSSEs) demonstrate that assimilating synthetic dropsonde observations located in regions with higher predicted observation impacts will, on average, lead to a better rainfall forecast than in regions with smaller predicted impacts. However, these OSSEs also suggest that the effectiveness of the current-generation ensemble-based tropical cyclone targeting strategies may be limited. The limitations may be due to strong nonlinearity in the governing dynamics of the typhoon (e.g., moist convection), the accuracy of the ensemble background covariance, and the projection of individual dropsonde observations to the complicated targeted sensitivity vectors from the ensemble.

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Jason A. Sippel, Fuqing Zhang, Yonghui Weng, Lin Tian, Gerald M. Heymsfield, and Scott A. Braun

Abstract

This study utilizes an ensemble Kalman filter (EnKF) to assess the impact of assimilating observations of Hurricane Karl from the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a new Doppler radar on board the NASA Global Hawk unmanned airborne system, which has the benefit of a 24–26-h flight duration, or about 2–3 times that of a conventional aircraft. The first HIWRAP observations were taken during NASA’s Genesis and Rapid Intensification Processes (GRIP) experiment in 2010. Observations considered here are Doppler velocity (Vr) and Doppler-derived velocity–azimuth display (VAD) wind profiles (VWPs). Karl is the only hurricane to date for which HIWRAP data are available. Assimilation of either Vr or VWPs has a significant positive impact on the EnKF analyses and forecasts of Hurricane Karl. Analyses are able to accurately estimate Karl’s observed location, maximum intensity, size, precipitation distribution, and vertical structure. In addition, forecasts initialized from the EnKF analyses are much more accurate than a forecast without assimilation. The forecasts initialized from VWP-assimilating analyses perform slightly better than those initialized from Vr-assimilating analyses, and the latter are less accurate than EnKF-initialized forecasts from a recent proof-of-concept study with simulated data. Likely causes for this discrepancy include the quality and coverage of the HIWRAP data collected from Karl and the presence of model error in this real-data situation. The advantages of assimilating VWP data likely include the ability to simultaneously constrain both components of the horizontal wind and to circumvent reliance upon vertical velocity error covariance.

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Fuqing Zhang, Yonghui Weng, Jason A. Sippel, Zhiyong Meng, and Craig H. Bishop

Abstract

This study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along the Gulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a three-dimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes.

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Christopher Melhauser, Fuqing Zhang, Yonghui Weng, Yi Jin, Hao Jin, and Qingyun Zhao

Abstract

This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.

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