Search Results

You are looking at 1 - 10 of 16 items for

  • Author or Editor: Yonghui Weng x
  • Refine by Access: All Content x
Clear All Modify Search
Fuqing Zhang
and
Yonghui Weng

Abstract

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.

Full access
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.

Full access
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.

Full access
Yunji Zhang
,
Zhiyong Meng
,
Fuqing Zhang
, and
Yonghui Weng
Full access
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.

Full access
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.

Full access
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.

Full access
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.

Full access
Xin Zhang
,
Xiang-Yu Huang
,
Jianyu Liu
,
Jonathan Poterjoy
,
Yonghui Weng
,
Fuqing Zhang
, and
Hongli Wang

Abstract

This paper presents the development of a single executable four-dimensional variational data assimilation (4D-Var) system based on the Weather Research and Forecasting (WRF) Model through coupling the variational data assimilation algorithm (WRF-VAR) with the newly developed WRF tangent linear and adjoint model (WRFPLUS). Compared to the predecessor Multiple Program Multiple Data version, the new WRF 4D-Var system achieves major improvements in that all processing cores are able to participate in the computation and all information exchanges between WRF-VAR and WRFPLUS are moved directly from disk to memory. The single executable 4D-Var system demonstrates desirable acceleration and scalability in terms of the computational performance, as demonstrated through a series of benchmarking data assimilation experiments carried out over a continental U.S. domain. To take into account the nonlinear processes with the linearized minimization algorithm and to further decrease the computational cost of the 4D-Var minimization, a multi-incremental minimization that uses multiple horizontal resolutions for the inner loop has been developed. The method calculates the innovations with a high-resolution grid and minimizes the cost function with a lower-resolution grid. The details regarding the transition between the high-resolution outer loop and the low-resolution inner loop are introduced. Performance of the multi-incremental configuration is found to be comparable to that with the full-resolution 4D-Var in terms of 24-h forecast accuracy in the week-long analysis and forecast experiment over the continental U.S. domain. Moreover, the capability of the newly developed multi-incremental 4D-Var system is further demonstrated in the convection-permitting analysis and forecast experiment for Hurricane Sandy (2012), which was hardly computationally feasible with the predecessor WRF 4D-Var system.

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

Abstract

The dynamics and predictability of the intensification of Hurricane Edouard (2014) are explored through a 60-member convection-permitting ensemble initialized with an ensemble Kalman filter that assimilates dropsondes collected during NASA’s Hurricane and Severe Storm Sentinel (HS3) investigation. The 126-h forecasts are initialized when Edouard was designated as a tropical depression and include Edouard’s near–rapid intensification (RI) from a tropical storm to a strong category-2 hurricane. Although the deterministic forecast was very successful and many members correctly forecasted Edouard’s intensification, there was significant spread in the timing of intensification among the members of the ensemble.

Utilizing composite groups created according to the near-RI-onset times of the members, it is shown that, for increasing magnitudes of deep-layer shear, RI onset is increasingly delayed; intensification will not occur once a critical shear threshold is exceeded. Although the timing of intensification varies by as much as 48 h, a decrease in shear is observed across the intensifying composite groups ~6–12 h prior to RI. This decrease in shear is accompanied by a reduction in vortex tilt, as the precession and subsequent alignment process begins ~24–48 h prior to RI. Sensitivity experiments reveal that some of the variation in RI timing can be attributed to differences in initial intensity, as the earliest-developing members have the strongest initial vortices regardless of their environment. Significant sensitivity and limited predictability exists for members with weaker initial vortices and/or that are embedded in less conducive environments, under which the randomness of moist convective processes and minute initial differences distant from the surface center can produce divergent forecasts.

Full access