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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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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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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
,
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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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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Erin B. Munsell
,
Jason A. Sippel
,
Scott A. Braun
,
Yonghui Weng
, and
Fuqing Zhang

Abstract

The governing dynamics and uncertainties of an ensemble simulation of Hurricane Nadine (2012) are assessed through the use of a regional-scale convection-permitting analysis and forecast system based on the Weather Research and Forecasting (WRF) Model and an ensemble Kalman filter (EnKF). For this case, the data that are utilized were collected during the 2012 phase of the National Aeronautics and Space Administration’s (NASA) Hurricane and Severe Storm Sentinel (HS3) experiment. The majority of the tracks of this ensemble were successful, correctly predicting Nadine’s turn toward the southwest ahead of an approaching midlatitude trough, though 10 members forecasted Nadine to be carried eastward by the trough. Ensemble composite and sensitivity analyses reveal the track divergence to be caused by differences in the environmental steering flow that resulted from uncertainties associated with the position and subsequent strength of a midlatitude trough.

Despite the general success of the ensemble track forecasts, the intensity forecasts indicated that Nadine would strengthen, which did not happen. A sensitivity experiment performed with the inclusion of sea surface temperature (SST) updates significantly reduced the intensity errors associated with the simulation. This weakening occurred as a result of cooling of the SST field in the vicinity of Nadine, which led to weaker surface sensible and latent heat fluxes at the air–sea interface. A comparison of environmental variables, including relative humidity, temperature, and shear yielded no obvious differences between the WRF-EnKF simulations and the HS3 observations. However, an initial intensity bias in which the WRF-EnKF vortices are stronger than the observed vortex appears to be the most likely cause of the final intensity errors.

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Lei Zhu
,
Qilin Wan
,
Xinyong Shen
,
Zhiyong Meng
,
Fuqing Zhang
,
Yonghui Weng
,
Jason Sippel
,
Yudong Gao
,
Yunji Zhang
, and
Jian Yue

Abstract

The current study explores the use of an ensemble Kalman filter (EnKF) based on the Weather Research and Forecasting (WRF) Model to continuously assimilate high-resolution Doppler radar data during the peak-intensity stage of Tropical Cyclone (TC) Vicente (2012) before landfall. The WRF-EnKF analyses and forecasts along with the ensembles initialized from the EnKF analyses at different times were used to examine the subsequent evolution, three-dimensional (3D) structure, predictability, and dynamics of the storm. Vicente was an intense western North Pacific tropical cyclone that made landfall around 2000 UTC 23 July 2012 near the Pearl River Delta region of Guangdong Province, China, with a peak 10-m wind speed around 44 m s−1 along with considerable inland flooding after a rapid intensification process. With vortex- and dynamics-dependent background error covariance estimated by the short-term ensemble forecasts, it was found that the WRF-EnKF could efficiently assimilate the high temporal and spatial resolution 3D radar radial velocity to improve the depiction of the TC inner-core structure of Vicente, which in turn improved the forecasts of the track and intensity along with the associated heavy precipitation inland. The ensemble forecasts and sensitivity analyses were further used to explore the leading dynamics that controlled the prediction and predictability of track, intensity, and rainfall during and after its landfall. Results showed that TC Vicente’s intensity and precipitation forecasts were largely dependent on the initial relationship between TC intensity and location and the initial steering flow.

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