Yonghui Weng

Cloud-Resolving Hurricane Initialization and Prediction through Assimilation of Doppler Radar Observations with an Ensemble Kalman Filter

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...

Predicting Typhoon Morakot’s Catastrophic Rainfall with a Convection-Permitting Mesoscale Ensemble System

Abstract This study examines the prediction and predictability of the recent catastrophic rainfall and flooding event over Taiwan induced by Typhoon Morakot (2009) with a state-of-the-art numerical weather prediction model. A high-resolution convection-permitting mesoscale ensemble, initialized with analysis and flow-dependent perturbations obtained from a real-time global ensemble data assimilation system, is found to be able to predict this record-breaking rainfall event, producing probability forecasts potentially valuable to the emergency management decision makers and the general public. Since all the advanced modeling and data assimilation techniques used here are readily available for real-time operational implementation provided sufficient computing resources are made available, this study demonstrates the potential and need of using ensemble-based analysis and forecasting, along with enhanced computing, in predicting extreme weather events like Typhoon Morakot at operational centers.

Predicting Hurricane Intensity and Associated Hazards: A Five-Year Real-Time Forecast Experiment with Assimilation of Airborne Doppler Radar Observations

AbstractPerformance 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 comp...

The Effects of Sampling Errors on the EnKF Assimilation of Inner-Core Hurricane Observations

AbstractAtmospheric 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 corre...

Advanced Data Assimilation for Cloud-Resolving Hurricane Initialization and Prediction

Data assimilation aims to decrease errors in initial conditions of numerical weather prediction models, which are a primary source of uncertainty in hurricane prediction. This study examines the performance of three advanced techniques that assimilate inner-core, high-resolution Doppler radar observations for cloud-resolving hurricane initialization and forecasting for Hurricane Katrina.

Dynamics and Predictability of Hurricane Nadine (2012) Evaluated through Convection-Permitting Ensemble Analysis and Forecasts

AbstractThe 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 tra...

Development of an Efficient Regional Four-Dimensional Variational Data Assimilation System for WRF

AbstractThis 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 ...

Ensemble Kalman Filter Assimilation of Simulated HIWRAP Doppler Velocity Data in a Hurricane

AbstractThis 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 ...

Observing Strategy and Observation Targeting for Tropical Cyclones Using Ensemble-Based Sensitivity Analysis and Data Assimilation

AbstractAn 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 th...

Predictability of Tropical Cyclone Intensity Evaluated through 5-yr Forecasts with a Convection-Permitting Regional-Scale Model in the Atlantic Basin

AbstractThe 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...