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The impact of assimilating ground‐based radar reflectivity on the rainband structure and secondary eyewall formation (SEF) of Hurricane Matthew (2016) is investigated within the framework of the Hurricane Weather Research and Forecasting model and its hybrid three‐dimensional ensemble‐variational data assimilation (DA) system. Compared to the control experiment (no radar reflectivity DA), the radar reflectivity DA experiment shows a clear signal of concentric eyewall and eyewall replacement cycle. Results demonstrate that radar reflectivity DA improves the stratiform rainband analysis, resulting in the mid‐level cooling associated with mesoscale descending inflow (MDI). The MDI further contributes to the low‐level acceleration maximum with boundary layer dynamics and triggers new convective updrafts in the SEF region. Momentum budget analysis also suggests that the mean vertical advection of absolute angular momentum plays an important role in the local momentum tendency in the SEF region in Hurricane Matthew (2016). Plain Language Summary: The impact of assimilating radar reflectivity on the secondary eyewall formation (SEF) of Hurricane Matthew (2016) is investigated within the framework of the Hurricane Weather Research and Forecasting model and its hybrid three‐dimensional ensemble‐variational data assimilation (DA) system. Compared to the control experiment (no radar reflectivity DA), the radar reflectivity DA experiment shows a clear signal of secondary eyewall and eyewall replacement cycle. Results demonstrate that radar reflectivity DA improves the stratiform rainband analysis, resulting in mesoscale descending inflow (MDI). The MDI further contributes to the low‐level tangential wind increase in boundary layer and triggers new convective updrafts in the SEF region. Results also suggests that the mean vertical advection of absolute angular momentum plays an important role in the local momentum tendency in the SEF region in Hurricane Matthew (2016). Key Points: Utilizing radar reflectivity data assimilation enhances the analysis of stratiform rainbands associated with mesoscale descending inflow (MDI)Momentum budget analysis indicates that the MDI brings high angular momentum to the boundary layer through the mean vertical advection termThe high angular momentum by the MDI contributes to the low‐level acceleration maximum in the secondary eyewall formation region [ABSTRACT FROM AUTHOR]

The Micro Unmanned Robot Observation Network (MURON) is a planned in-situ observation network over the surface of West Pacific Ocean, and it is designed to sample high spatial and temporal resolution observations of wind and mass fields over the ocean surface. The impacts of MURON observations for Tropical Cyclone (TC) intensity forecast are investigated using Observation System Simulation Experiments. The regional Ensemble Kalman Filter (EnKF) system of Gridpoint Statistical Interpolation is used with the Advanced Research version of the Weather Research and Forecasting model to conduct OSSEs for typhoon Haiyan (2013) while Haiyan goes through rapid intensification. Assimilating MURON observations improves the TC structure and intensity analysis and forecast. The intensity forecast is improved largely due to the correction of initial vorticity and vertical transport of mass flux. The improvement of intensity forecast is attributed largely to the assimilated MURON wind observations when Haiyan is at the tropical disturbance stage, and then by the MURON mass observations when Haiyan enters the tropical storm stage. In addition, our results show that the quality of moisture analysis is sensitive to the choice of the moisture control variable (CV) in the EnKF system. Using the default pseudo relative humidity (PRH) as the moisture CV degrades the accuracy of the moisture analysis. This is likely due to the neglect of updated temperature field during the nonlinear conversion from the PRH CV to the mixing ratio variable and due to the larger deviation of the PRH from Gaussian distribution. The use of mixing ratio moisture CV mitigates these problems. [ABSTRACT FROM AUTHOR]

Short-term spinup for strong storms is a known difficulty for the operational Hurricane Weather Research and Forecasting (HWRF) Model after assimilating high-resolution inner-core observations. Our previous study associated this short-term intensity prediction issue with the incompatibility between the HWRF Model and the data assimilation (DA) analysis. While improving physics and resolution of the model was found to be helpful, this study focuses on further improving the intensity predictions through the four-dimensional incremental analysis update (4DIAU). In the traditional 4DIAU, increments are predetermined by subtracting background forecasts from analyses. Such predetermined increments implicitly require linear evolution assumption during the update, which are hardly valid for rapidly evolving hurricanes. To confirm the hypothesis, a corresponding 4D analysis nudging (4DAN) method, which uses online increments is first compared with the 4DIAU in an oscillation model. Then, variants of 4DIAU are proposed to improve its application for nonlinear systems. Next, 4DIAU, 4DAN and their proposed improvements are implemented into the HWRF 4DEnVar DA system and are investigated with Hurricane Patricia (2015). Results from both the oscillation model and HWRF Model show that 1) the predetermined increments in 4DIAU can be detrimental when there are discrepancies between the updated and background forecasts during a nonlinear evolution; 2) 4DAN can improve the performance of incremental update upon 4DIAU, but its improvements are limited by the overfiltering; 3) relocating initial background before the incremental update can improve the corresponding traditional methods; and 4) the feature-relative 4DIAU method improves the incremental update the most and produces the best track and intensity predictions for Patricia among all experiments. [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]

In this exploratory study, a series of perturbations to the land surface model (LSM) component of the Weather Research and Forecasting (WRF) Model was developed to investigate the sensitivity of forecasts of severe thunderstorms and heavy precipitation at 4-km grid spacing and whether such perturbations could improve ensemble forecasts at this scale. The perturbations (generated using a combination of perturbing fixed parameters and using separate schemes, one of which-Noah-MP-is new among the WRF modeling community) were applied to a 10-member ensemble including other mixed physics parameterizations and compared against an identically configured ensemble that did not include the LSM perturbations to determine their impact on probabilistic forecasts. A third ensemble using only the LSM perturbations was also configured. The results from 14 (in total) 36-h ensemble forecasts suggested the LSM perturbations resulted in systematic improvement in ensemble dispersion and error characteristics. Lower-tropospheric temperature, moisture, and wind fields were all improved, as were probabilistic precipitation forecasts. Biases were not systematically altered, although some outlier members are present. Examination of near-surface temperature and mixing ratio fields, surface energy fluxes, and soil fields revealed tendencies caused by certain perturbations. A case study featuring tornadic supercells illustrated the physical causes of some of these tendencies. The results of this study suggest LSM perturbations can sample a dimension of model error not yet sampled systematically in most ensembles and should be included in convection-allowing ensembles. [ABSTRACT FROM AUTHOR]

Object-based verification of deterministic forecasts from a convection-allowing ensemble for the 2009 NOAA Hazardous Weather Testbed Spring Experiment is conducted. The average of object attributes is compared between forecasts and observations and between forecasts from subensembles with different model dynamics. Forecast accuracy for the full ensemble and the subensembles with different model dynamics is also evaluated using two object-based measures: the object-based threat score (OTS) and the median of maximum interest (MMI). Forecast objects aggregated from the full ensemble are generally more numerous, have a smaller average area, more circular average aspect ratio, and more eastward average centroid location than observed objects after the 1-h lead time. At the 1-h lead time, forecast objects are less numerous than observed objects. Members using the Advanced Research Weather Research and Forecasting Model (ARW) have fewer objects, more linear average aspect ratio, and smaller average area than members using the Nonhydrostatic Mesoscale Model (NMM). The OTS aggregated from the full ensemble is more consistent with the diurnal cycles of the traditional equitable threat score (ETS) than the MMI because the OTS places more weight on large objects, while the MMI weights all objects equally. The group of ARW members has higher OTS than the group of NMM members except at the 1-h lead time when the group of NMM members has more accurate maintenance and evolution of initially present precipitation systems provided by radar data assimilation. The differences between the ARW and NMM accuracy are more pronounced with the OTS than the MMI and the ETS. [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]

A hybrid ensemble transform Kalman filter (ETKF)–optimum interpolation (OI) analysis scheme is described and compared with an ensemble square root filter (EnSRF) analysis scheme. A two-layer primitive equation model was used under perfect-model assumptions. A simplified observation network was used, and the OI method utilized a static background error covariance constructed from a large inventory of historical forecast errors. The hybrid scheme updated the ensemble mean using a hybridized ensemble and static background-error covariance. The ensemble perturbations in the hybrid scheme were updated by the ETKF scheme. The EnSRF ran parallel data assimilation cycles for each member and serially assimilated the observations. The EnSRF background-error covariance was estimated fully from the ensemble. For 50-member ensembles, the analyses from the hybrid scheme were as accurate or nearly as accurate as those from the EnSRF, depending on the norm. For 20-member ensembles, the analyses from the hybrid scheme were more accurate than analyses from the EnSRF under certain norms. Both hybrid and EnSRF analyses were more accurate than the analyses from the OI. Further reducing the ensemble size to five members, the EnSRF exhibited filter divergence, whereas the analyses from the hybrid scheme were still better than those updated by the OI. Additionally, the hybrid scheme was less prone to spurious gravity wave activity than the EnSRF, especially when the ensemble size was small. Maximal growth in the ETKF ensemble perturbation space exceeded that in the EnSRF ensemble perturbation space. The relationship of the ETKF ensemble variance to the analysis error variance, a measure of a spread–skill relationship, was similar to that of the EnSRF ensemble. The hybrid scheme can be implemented in a reasonably straightforward manner in the operational variational frameworks, and the computational cost of the hybrid is expected to be much less than the EnSRF in the operational settings. [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]

Enhancing the predictive capabilities for tropical cyclones (TCs) necessitates comprehensive investigations into their predictability. This study employs convection-allowing ensemble forecasts using the Hurricane Weather Research and Forecasting model, integrating perturbed initial conditions to examine error growth and initial condition error-related predictability (IE-predictability) of TCs. Distinctively, our research concentrates on how the predictability of TC track and intensity is influenced by initial errors in various zones: (1) the inner core and outer rainbands (0–350 km), (2) the near environment (350–1300 km), and (3) the far environment (1300–3500 km). Contrary to many prior studies, our findings suggest that the most critical region for initial errors affecting TC track forecasts is within the TC inner structures. For Typhoon Chan-hom (2020), characterized by significant track forecast discrepancies, the initial inaccuracies in the inner core and outer rainbands substantially affect the TC's proximate environment and track, more so than errors in the near and far environments. Regarding TC intensity, the inner core emerges as the most sensitive area. The surface wind configuration of the TC inner core at larger scales (wavenumbers 0–2) maintains predictability for over three days, whereas its structure at finer scales is only predictable for a few hours. The forecasting of tropical cyclones (TCs) has seen significant advancements over recent decades. A critical question arises: to what extent can we further enhance the accuracy of TC predictions? Addressing this requires in-depth exploration of TC predictability. In our study, we utilized convection-allowing ensemble forecasts based on the Hurricane Weather Research and Forecasting (HWRF) model, incorporating perturbed initial conditions to analyze the development of forecast errors and the predictability related to these initial errors in TCs. A novel aspect of this research is its emphasis on the impact of initial errors in different regions on the predictability of TC track and intensity. Our key findings are twofold: (1) The region most impacted by initial errors for TC track predictions may not always be the surrounding environment. Instead, errors within the TC's inner core and outer rainbands, owing to their pronounced interplay, can lead to significantly greater track forecast errors compared to those in the near and far environments. (2) As for TC intensity, the inner core emerges as the most susceptible region. The large-scale components (wavenumbers 0–2) of the TC's inherent vortex flow can be forecasted accurately for over four days, largely due to the predictability of the synoptic-scale environment. In contrast, the smaller-scale components (wavenumbers >2) demonstrate predictability for only a few hours. • The primary sensitive area for initial errors affecting TC track uncertainties may be within the TC. • Uncertainties in TC intensity are particularly sensitive to initial errors in the TC's inner core. • The source of TC intensity predictability for a few days is the predictable environment. [ABSTRACT FROM AUTHOR]

In this study, the ensemble of three-dimensional variational data assimilation (En3DVar) method for convective-scale weather is adopted and evaluated using an idealized supercell storm simulated by the Weather Research and Forecasting (WRF) Model. Synthetic radar radial velocity, reflectivity, satellitederived cloud water path (CWP), and total precipitable water (TPW) data are produced from the simulated supercell storm and then these data are assimilated into another WRF Model run that starts with no convection. Two types of experiments are performed. The first assimilates radar and satellite CWP data using a perfect storm environment. The second assimilates additional TPW data using a storm environment with dry bias. The first set of experiments indicates that incorporating CWP and radar data into the assimilation leads to a much faster initiation of supercell storms than found using radar data alone. Assimilating CWP data primarily improves the analyses of nonprecipitating hydrometeor variables. The results from the second set of experiments demonstrate the critical importance of the storm environment. When using the biased storm environment, assimilation of CWP and radar data enhances the analyses, but the forecast skill rapidly decreases over the subsequent 1-h forecast. Further experiments show that assimilating theTPWdata has a large impact on storm environment that is essential to the accuracy of the storm forecasts. In general, the combination of radar data and satellite data within the En3DVar results in better analyses and forecasts than when only radar data are used, especially for an imperfect storm environment. [ABSTRACT FROM AUTHOR]

A coupled ensemble square root filter-three-dimensional ensemble-variational hybrid (EnSRF-En3DVar) data assimilation (DA) system is developed for the operational Rapid Refresh (RAP) forecasting system. The En3DVar hybrid system employs the extended control variable method, and is built on the NCEP operational gridpoint statistical interpolation (GSI) three-dimensional variational data assimilation (3DVar) framework. It is coupled with an EnSRF system for RAP, which provides ensemble perturbations. Recursive filters (RF) are used to localize ensemble covariance in both horizontal and vertical within the En3DVar. The coupled En3DVar hybrid system is evaluated with 3-h cycles over a 9-day period with active convection. All conventional observations used by operational RAP are included. The En3DVar hybrid system is run at ⅓ of the operational RAP horizontal resolution or about 40-km grid spacing, and its performance is compared to parallel GSI 3DVar and EnSRF runs using the same datasets and resolution. Short-term forecasts initialized from the 3-hourly analyses are verified against sounding and surface observations. When using equally weighted static and ensemble background error covariances and 40 ensemble members, the En3DVar hybrid system outperforms the corresponding GSI 3DVar and EnSRF. When the recursive filter coefficients are tuned to achieve a similar height-dependent localization as in the EnSRF, the En3DVar results using pure ensemble covariance are close to EnSRF. Two-way coupling between EnSRF and En3DVar did not produce noticeable improvement over one-way coupling. Downscaled precipitation forecast skill on the 13-km RAP grid from the En3DVar hybrid is better than those from GSI 3DVar analyses. [ABSTRACT FROM AUTHOR]