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This study investigates the relative impact of assimilating the ground‐based WSR‐88D radar (GBR) and the airborne Tail Doppler Radar (TDR) observations on the analysis and prediction of Hurricane Harvey (2017) during its landfalling stage. Results show that the assimilation of GBR (experiment "DAG") outperforms the assimilation of TDR (experiment "DAT") in multiple aspects. For example, "DAG" produces better predictions in Vmax, radial velocity, brightness temperature, and precipitation than "DAT." The advantages of "DAG" over "DAT" are likely from the better analyzed thermodynamical structures in addition to the better data availability. The average Minimum Sea Level Pressure (MSLP) prediction error is the only aspect of "DAG" that is inferior to "DAT." Diagnostics show that such an inferior performance of MSLP for "DAG" is associated with the systematic bias from the Hurricane Weather Research and Forecasting model. The combined assimilation of both observations (experiment "DAB") shows complementary effects and performs the best overall among all experiments. Plain Language Summary: The Ground‐based WSR‐88D Radar (GBR) and the airborne tail Doppler Radar (TDR) observations have been separately assimilated and evaluated in the analysis and prediction of landfalling hurricanes in the past. This study explores their relative and combined impacts. Using a newly developed, GSI‐based, hourly hybrid three‐dimensional ensemble‐Kalman‐Filter‐Variational data assimilation system for the Hurricane Weather Research and Forecasting, experiments are performed for the landfalling stage of Hurricane Harvey (2017). Results showed that the continuous availability allows GBR to produce better and more persistent improvements in the analysis and the subsequent forecasts than TDR. Investigations show that the improvements are not only in the wind fields but also in the precipitation and brightness temperature predictions. The combined assimilation shows complementary effects and further improves upon GBR alone. Key Points: Both ground‐based radar and airborne radar observations are critical to analyzing and predicting the landfalling of hurricane HarveyThe continuous ground‐based radar observations benefit the analysis and prediction of Harvey more than the intermittent airborne datasetsCombining both observations shows complementary effects and produces Harvey's overall best analysis and predictions [ABSTRACT FROM AUTHOR]

The initial conditions for hurricanes are difficult to improve due to the lack of inner-core observations over the ocean. An enhanced atmospheric motion vectors (AMVs) dataset from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) has recently become available and covers the inner-core region of hurricanes. This study tries to find an optimal data assimilation (DA) configuration to better utilize the observations for the Hurricane Weather Research and Forecasting (HWRF) model with hurricane Irma (2017). The results show that (a) without vortex relocation (VR), the hourly three-dimensional ensemble–variational (3DEnVar) outperforms the 6-hourly 3DEnVar DA configuration in almost all aspects, except for long-term track predictions. The assimilation of inner-core AMVs further improves the corresponding intensity forecasts for both hourly and 6-hourly 3DEnVar DA. (b) The 6-hourly 3DEnVar DA predictions with VR can be significantly improved upon their non-VR counterparts. However, VR can be detrimental to hourly 3DEnVar minimum sea level pressure (MSLP) predictions due to the spuriously enhanced upper-level warm core. The improvements from the assimilation of additional inner-core AMVs are thus limited under hourly VR. Reducing VR frequency can reduce the detrimental effects of hourly 3DEnVar. (c) An updated observation error profile for the enhanced AMVs benefits the hourly 3DEnVar DA more than the 6-hourly 3DEnVar DA. [ABSTRACT FROM AUTHOR]

Diverse observations, such as the High Definition Sounding System (HDSS) dropsonde observations from the Tropical Cyclone Intensity (TCI) program, the Tail Doppler Radar (TDR), Stepped Frequency Microwave Radiometer (SFMR), and flight-level observations from the Intensity Forecasting Experiment (IFEX) program, and the atmospheric motion vectors (AMVs) from the Cooperative Institute for Meteorological Satellite Studies (CIMSS) simultaneously depicted the three-dimensional (3D) structure of Hurricane Patricia (2015). Experiments are conducted to understand the relative impacts of each of these observation types on Patricia's analysis and prediction using the Gridpoint Statistical Interpolation (GSI)-based ensemble-variational data assimilation system for the Hurricane Weather Research and Forecasting (HWRF) Model. In comparing the impacts of assimilating each dataset individually, results suggest that 1) the assimilation of 3D observations produces better TC structure analysis than the assimilation of two-dimensional (2D) observations; 2) the analysis from assimilating observations collected from platforms that only sample momentum fields produces a less improved forecast with either short-lived impacts or slower intensity spinup as compared to the forecast produced after assimilating observations collected from platforms that sample both momentum and thermal fields; and 3) the structure forecast tends to benefit more from the assimilation of inner-core observations than the corresponding intensity forecast, which implies better verification metrics are needed for future TC forecast evaluation. [ABSTRACT FROM AUTHOR]

A hybrid ensemble transform Kalman filter (ETKF)-three-dimensional variational data assimilation (3DVAR) system developed for the Weather Research and Forecasting Model (WRF) was studied for the forecasts of the tracks of two major hurricanes, Ike and Gustav, in 2008 over the Gulf of Mexico. The impacts of the flow-dependent ensemble covariance generated by the ETKF were revealed by comparing the forecasts, analyses, and analysis increments generated by the hybrid data assimilation method with those generated by the 3DVAR that used the static background covariance. The root-mean-square errors of the track forecasts by the hybrid data assimilation (DA) method were smaller than those by the 3DVAR for both Ike and Gustav. Experiments showed that such improvements were due to the use of the flow-dependent covariance provided by the ETKF ensemble in the hybrid DA system. Detailed diagnostics further revealed that the increments produced by the hybrid and the 3DVAR were different for both the analyses of the hurricane itself and its environment. In particular, it was found that the hybrid, using the flow-dependent covariance that gave the hurricane-specific error covariance estimates, was able to systematically adjust the position of the hurricane during the assimilation whereas the 3DVAR was not. The study served as a pilot study to explore and understand the potential of the hybrid method for hurricane data assimilation and forecasts. Caution needs to be taken to extrapolate the results to operational forecast settings. [ABSTRACT FROM AUTHOR]

Six-hourly three-dimensional ensemble variational (3DEnVar) (6H-3DEnVar) data assimilation (DA) assumes constant background error covariance (BEC) during a six-hour DA window and is, therefore, unable to account for temporal evolution of the BEC. This study evaluates the one-hourly 3DEnVar (1H-3DEnVar) and six-hourly 4DEnVar (6H-4DEnVar) DA methods for the analyses and forecasts of hurricanes with rapidly evolving BEC. Both methods account for evolving BEC in a hybrid EnVar DA system. In order to compare these methods, experiments are conducted by assimilating inner core Tail Doppler Radar (TDR) wind for Hurricane Edouard (2014) and by running the Hurricane Weather Research and Forecasting (HWRF) model. In most metrics, 1H-3DEnVar and 6H-4DEnVar analyses and forecasts verify better than 6H-3DEnVar. 6H-4DEnVar produces better thermodynamic analyses than 1H-3DEnVar. Radar reflectivity shows that 1H-3DEnVar produces better structure forecasts. For the first 24–48 h of the intensity forecast, 6H-4DEnVar forecast performs better than 1H-3DEnVar verified against the best track. Degraded 1H-3DEnVar forecasts are found to be associated with background storm center location error as a result of underdispersive ensemble storm center spread. Removing location error in the background improves intensity forecasts of 1H-3DEnVar. [ABSTRACT FROM AUTHOR]