Multivariate Adjustment in the IAU-Based Tropical Cyclone Initialization Scheme in the TRAMS Model

The operational Tropical Regional Atmospheric Model System (TRAMS) model often underestimates the initial typhoon intensity when using the global analysis field as the initial condition. The tropical cyclone (TC) initialization scheme developed based on incremental analysis updates (IAU) technique can help reduce the initial bias. However, the IAU-based TC initialization scheme only adjusted the wind field at the analysis moment, with other variables adjusted implicitly under the constraints of the model according to the gradually inserted wind increment (named “univariate adjustment scheme” hereafter). The univariate adjustment scheme required approximately 3  to reach a dynamic equilibrium state, limiting the use of hourly TC observation information and dissipating too much meaningful short-wave information of the adjustment increment. To reduce the equilibrium adjustment time, this study constructed a multivariate adjustment IAU-based TC initialization scheme by introducing the gradient wind balance and hydrostatic balance as large-scale constraints. The case sensitivity tests of TC Hato (1713) demonstrated that the multivariate adjustment scheme can reduce the IAU relaxation time to 1  and slightly improve TC forecasts. By incorporating the equilibrium assumption as a strong constraint for the TC axisymmetric component, the multivariate adjustment scheme achieved a faster convergence of the model to its equilibrium state, reducing the loss of useful observed information. This conclusion was further confirmed with 12 other TCs. The IAU-based multivariate adjustment initialization scheme developed in this study provides a foundation for 4-D initialization with hourly TC observations.

This paper introduces a relocation scheme for tropical cyclone (TC) initialization in the Advanced Research Weather Research and Forecasting (ARW-WRF) model and demonstrates its application to 70 forecasts of Typhoons Sinlaku (2008), Jangmi (2008), and Linfa (2009) for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings. An efficient and dynamically consistent TC vortex relocation scheme for the WRF terrain-following mass coordinate has been developed to improve the first guess of the TC analysis, and hence improves the tropical cyclone initialization. The vortex relocation scheme separates the first-guess atmospheric flow into a TC circulation and environmental flow, relocates the TC circulation to its observed location, and adds the relocated TC circulation back to the environmental flow to obtain the updated first guess with a correct TC position. Analysis of these typhoon cases indicates that the relocation procedure moves the typhoon circulation to the observed typhoon position without generating discontinuities or sharp gradients in the first guess. Numerical experiments with and without the vortex relocation procedure for Typhoons Sinlaku, Jangmi, and Linfa forecasts show that about 67% of the first-guess fields need a vortex relocation to correct typhoon position errors while eliminates the topographical effect. As the vortex relocation effectively removes the typhoon position errors in the analysis, the simulated typhoon tracks are considerably improved for all forecast times, especially in the early periods as large adjustments appeared without the vortex relocation. Comparison of the horizontal and vertical vortex structures shows that large errors in the first-guess fields due to an incorrect typhoon position are eliminated by the vortex relocation scheme and that the analyzed typhoon circulation is stronger and more symmetric without distortions, and better agrees with observations. The result suggests that the main difficulty of objective analysis methods [e.g., three-dimensional variational data assimilation (3DVAR)], in TC analysis comes from poor first-guess fields with incorrect TC positions rather than not enough model resolution or observations. In addition, by computing the eccentricity and correlation of the axes of the initial typhoon circulation, the distorted typhoon circulation caused by the position error without the vortex relocation scheme is demonstrated to be responsible for larger track errors. Therefore, by eliminating the typhoon position error in the first guess that avoids a distorted initial typhoon circulation, the vortex relocation scheme is able to improve the ARW-WRF typhoon initialization and forecasts particularly when using data assimilation update cycling.

This study investigates the impact of tropical cyclone (TC) initialization methods on TC intensity prediction under a framework coupling the Weather Research and Forecasting Model with the TC Centered-Local Ensemble Transform Kalman Filter (WRF TCC-LETKF). While the TC environments are constrained by assimilating the same environmental observations, two different initialization strategies, assimilating real dropsonde observations (the DP experiment) and synthetic axisymmetric surface wind structure (the VT experiment), are employed to construct the TC inner-core structure. These two experiments have distinct results on predicting the rapid intensification (RI) of Typhoon Megi (2010), which can be attributed to their different convective burst (CB) development. In DP, the assimilation of the dropsondes helps establish a realistic TC structure with asymmetry information, leading to scattered CB distribution and persistent RI with abundant moisture supply. In VT, assimilating the axisymmetric surface wind structure spins up the TC efficiently. However, the initially excessive CB coverage causes a too-early high-level warm core, and the reduced moisture supply hinders RI. The forecast results imply that if the TC structure is initialized using a scheme considering only the axisymmetric vortex structure, the RI potential can possibly be underestimated due to the inability to represent the realistic asymmetric structure. Finally, assimilation of both the real and synthetic data can be complementary, giving a strong TC initially that undergoes a longer RI period.

A tropical cyclone initialization method with an idealized three-dimensional bogus vortex of an analytic empirical formula is presented for the track and intensity prediction. The procedure in the new method consists of four steps: the separation of the disturbance from the analysis, determination of the tropical cyclone domain, generation of symmetric bogus vortex, and merging of it with the analysis data. When separating the disturbance field, an efficient spherical high-order filter with the double-Fourier series is used whose cutoff scale can be adjusted with ease to the horizontal scale of the tropical cyclone of interest. The tropical cyclone domain is determined from the streamfunction field instead of the velocities. The axisymmetric vortex to replace the poorly resolved tropical cyclone in the analysis is designed in terms of analytic empirical functions with a careful treatment of the upper-layer flows as well as the secondary circulations. The geopotential of the vortex is given in such a way that the negative anomaly in the lower layer is changed into positive anomaly above the prescribed pressure level, which depends on the intensity of the tropical cyclone. The geopotential is then used to calculate the tangential wind and temperature using the gradient wind balance and the hydrostatic balance, respectively. The inflow and outflow in the tropical cyclone are constructed to resemble closely the observed or simulated structures under the constraint of mass balance. The bogus vortex is merged with the disturbance field with the use of matching principle so that it is not affected except near the boundary of tropical cyclone domain. The humidity of the analysis is modified to be very close to the saturation in the lower layers near the tropical cyclone center. The balanced bogus vortex of the present study is completely specified on the basis of four parameters from the Regional Specialized Meteorological Center (RSMC) report and the additional two parameters, which are derived from the analysis data. The initialization method was applied to the track and the intensity (in terms of central pressure) prediction of the TCs observed in the western North Pacific Ocean and East China Sea in 2007 with the use of the Weather Research and Forecasting (WRF) model. No significant initial jump or abrupt change was seen in either momentum or surface pressure during the time integration, thus indicating a proper tropical cyclone initialization. Relative to the results without the tropical cyclone initialization and the forecast results of RSMC Tokyo, the present method presented a great improvement in both the track and intensity prediction.

A nudging scheme for humidity fields is implemented in the Advanced Hurricane Weather Research and Forecasting model (WRF) for tropical cyclone (TC) initialization. The scheme improves TC simulation by enhancing the TC humidity profile in deep-convection regions, where it uses satellite Fengyun 2 cloud-top brightness temperatures as a judging criterion. The impacts of the nudging on predicting TC intensity and structure are evaluated through the simulation of TC Khanun (2005) during its movement toward landfall at the coast of Zhejiang Province, China. During the nudging, the humidity distributions at the TC's inner core and along its outer spiral rainbands, where deep convections occur, are both enhanced. As a result, the intensity of the vortex is enhanced, being more consistent to the best-track data from the China Meteorological Administration. Specifically, the nudging modifies the simulated distribution of humidity according to convective activities captured by the satellite and therefore adjusts the development of deep convection in the model, which then influences the intensity and size of TC vortex through diabatic heating. During WRF simulation, the TC vortex initialized from the humidity nudging is dynamically and thermodynamically balanced with the background field, favoring a steady development of the vortex's intensity and structure. Because of the better simulation of TC inner core and outer spiral rainbands, the WRF simulation skills of TC intensity and track are improved.

This study makes improvements to the tropical cyclone (TC) initialization method introduced by Nguyen and Chen (i.e., the NC2011 scheme). The authors found that prescribing sea level pressure associated with the initial vortex using a modified Fujita formula has very little impact on the vortex structure and intensity during a series of 1-h model integration and relocation. On the other hand, inserting an artificial warm core makes the vortex spin up much faster. When a warm core is inserted during the initial spinup process, the computational time required for model initialization is reduced by ½–⅓. Because prescribed sea level pressure is not required to spin up the vortex, information on vortex size, such as radius of maximum wind, is no longer needed. The performance of the improved NC2011 scheme with an initial prescribed warm core during the initial spinup process is tested for typhoons that made landfall over southern China or Vietnam in 2006. Before landfall, these storms were over the open ocean where conventional data were sparse, without special observations. Two sets of model runs, with (NC2011-CTRL) and without (CTRL) vortex initialization, are performed for comparison. The initial and time-dependent boundary conditions are from the NCEP Final Analyses (FNL). There are twelve 48-h simulations in each run set. Results show that the vortex initialization improves TC track and intensity simulations.

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A new Tropical Cyclone (TC) initialization method with the structure adjustable bogus vortex was applied to the forecasts of track, central pressure, and wind intensity for the 417 TCs observed in the Western North Pacific during the 3-year period of 2005–2007. In the simulations the Final Analyses (FNL) with 1° × 1° resolution of National Center for Environmental Prediction (NCEP) were incorporated as initial conditions. The present method was shown to produce improved forecasts over those without the TC initialization and those made by Regional Specialized Meteorological Center Tokyo. The average track (central pressure, wind intensity) errors were as small as 78.0 km (11.4 hPa, 4.9 m s−1) and 139.9 km (12.4 hPa, 5.5 m s−1) for 24-h and 48-h forecasts, respectively. It was found that the forecast errors are almost independent on the size and intensity of the observed TCs because the size and intensity of the bogus vortex can be adjusted to fit the best track data. The results of this study indicate that a bogus method is useful in predicting simultaneously the track, central pressure, and intensity with accuracy using a dynamical forecast model.

Understanding the dynamics of the flow-dependent forecast error covariance across the air–sea interface is beneficial toward revealing the potential influences of strongly coupled data assimilation on tropical cyclone (TC) initialization in coupled models, and the fundamental dynamics associated with TC air–sea interactions. A 200-member ensemble of convection-permitting forecasts from a coupled atmosphere–ocean regional model is used to investigate the forecast error covariance across the oceanic and atmospheric domains during the rapid intensification of Hurricane Florence (2018). Forecast uncertainties in both atmospheric and oceanic domains, from an Eulerian perspective, increase with forecast lead time, mainly from TC displacement errors. In a storm-relative framework, the ensemble forecast uncertainties in both domains are predominantly caused by differences in the simulated storm intensity and structure. The largest ensemble spread in the atmospheric pressure, temperature, and wind fields can be found within the TC inner-core region. Alternatively, the largest ensemble spread in the upper-ocean currents and temperature fields are located along the cold wake behind the storm. Cross-domain ensemble correlations between simulated atmospheric (oceanic) observations and oceanic (atmospheric) state variables in the storm-relative coordinates are highly anisotropic, variable dependent, and ultimately driven by the dynamics of TC air–sea interactions. Meaningful and dynamically consistent cross-domain ensemble correlations suggest that it is possible to use atmospheric and oceanic observations to simultaneously update state variables associated with the coupled ocean–atmosphere prediction of TCs using strongly coupled data assimilation. Sensitivity experiments demonstrate that at least 60–80 ensemble members are required to represent physically consistent cross-domain correlations and minimize sampling errors.

A method of initializing tropical cyclones in high-resolution numerical models is developed by modifying a data assimilation system, the NRL atmospheric variational data assimilation system (NAVDAS), which was designed for general mesoscale weather prediction using a three-dimensional variational (3DVAR) analysis with intermittent updates. The method includes the following three upgrades to overcome difficulties resulting from tropical cyclone initialization with the NAVDAS analysis. First, synthetic observation soundings are generated on 9 vertical levels at 49 points for strong storms (v max > 23.1 m s−1) and 41 points for weak storms around each cyclone center to supplement the observations used by the analysis. Secondly, a vortex relocation method for nested grids is developed to correct the cyclone position in the background fields of the analysis for each nested mesh. Lastly, the 3DVAR analysis is modified to gradually reduce the horizontal length scale and geostrophic coupling constraint near the center of a tropical cyclone for minimizing the problems introduced by improper covariances and coupling constraint used in the analysis. The synthetic observations significantly improve the intensity and structure of the analysis and the track forecast. The vortex relocation significantly improves the first guess background, avoiding the large analysis corrections that would be needed to correct cyclone position, and reducing the imbalance introduced by such large analysis increments. The modifications to the analysis length scale and geostrophic coupling constraint successfully improve the inner core analysis, providing a tighter circulation, and reducing the underestimate of the mass field gradient. Among the three upgrades, the vortex relocation provides the largest improvement to the tropical cyclone initialization and forecast.

This study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble–variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18–24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.

A new scheme, termed Vortex Initialization with the Assimilation of Retrieved Variables (VIRV), is presented to improve the initialization of regional numerical model for Tropical Cyclone (TC) prediction. In this scheme, the horizontal winds in Planetary Boundary Layer (PBL) and the sea level pressure (SLP), retrieved from Quick Scatterometer (QuikSCAT) data obtained using a modified University of Washington Planetary Boundary Layer (UWPBL) model, are assimilated with a cycled three-dimensional variational (3DVAR) technique to produce the initialized analysis. The procedures of retrieval are implemented under the joint dynamical constraints of the gradient wind, secondary circulation, and thermal stratification. Moreover, in order to improve the analysis of TC intensity, the roughness parameterization in the UWPBL model was modified for the case of strong surface wind. The sensitivities of the structure, intensity, and track of TC to the VIRV are then examined by two numerical experiments for TC Bilis (2006) and TC Fung-wong (2008). The maximum Wind Speed (MWS) and minimum Sea Level Pressure (MSLP) retrieved from the QuikSCAT data obtained using the modified UWPBL model show more agreement with the observations relative to those derived from the analysis of the National Center for Environmental Prediction (NCEP global model). The analysis of TC intensity cfm enhanced using VIRV by modifying the low-level (upper-level) convergence (divergence), vertical shear of horizontal wind, transportation of moisture. Significant improvement on 48-h TC simulation is identified in the MWS, with 22.8% error reduction. In particular, the Modification of Roughness Parameterization (MRP) enhanced the simulation of MWS by 6.9%. Finally, the VIRV also reduces the simulation error in the �

Introduction Over the past decade, non‐vitamin K antagonist oral anticoagulants (NOACs) have replaced warfarin as the initial strategy for stroke prevention in non‐valvular atrial fibrillation. However, ischemic strokes occurring in patients taking NOACs are becoming increasingly more frequent. In this study, we aimed to determine the clinical, echocardiographic, and neuroimaging markers associated with developing ischemic strokes in patients taking NOACs for atrial fibrillation. Methods Among atrial fibrillation patients on NOACs with and without ischemic stroke, brain MRIs were reviewed for the presence of neuroimaging markers of small vessel disease. Transthoracic echocardiography reports were reviewed to obtain left atrial volumes, presence of left ventricular hypertrophy, ejection fractions, and presence of wall motion abnormalities. Clinical and radiologic variables with significant differences between groups were entered into a multivariable regression model to determine predictors of ischemic stroke. In the ischemic stroke group, a Cox regression analysis was constructed to determine predictors of ischemic stroke recurrence during follow‐up. Results 112 patients with ischemic stroke and 94 controls (free of ischemic stroke) were included in the study. The mean age (77±9 years vs. 74±11 years, p = 0.13) and proportion of patients with CHA2DS2‐VASc>2 were similar between controls and ischemic stroke patients (82% vs. 83%, p = 0.86). The variables that were significantly different between groups included apixaban use, dabigatran use, prior cerebrovascular events, hemoglobin A1c (HbA1c), left ventricular hypertrophy, left atrial volume index, and severe white matter hyperintensities. After multivariable adjustment, prior cerebrovascular events (aOR 23.86, 95% CI [6.02–94.48]), HbA1c levels (aOR 2.36, 95% CI [1.39–3.99]), left ventricular hypertrophy (aOR 2.73, 95% CI [1.11–6.71]) and left atrial volume index (aOR 1.05, 95% CI [1.01–1.08]) increased the risk of stroke, whereas apixaban use appeared to decrease the risk (aOR 0.38, 95% CI [0.16–0.92]). Among patients with ischemic stroke, there were 103 survivors, 82 (80%) of which had follow‐up data. The median follow‐up duration for these patients was 2.6 (0.5–4.7) years. Of these 82 patients, 11 (13%) experienced an ischemic stroke during the follow‐up period. Malignancy was associated with ischemic stroke recurrence (aHR 4.90, 95% CI [1.35–18.42]) after adjustment for age and chronic renal failure. Conclusion Prior cerebrovascular events, diabetes, left ventricular hypertrophy, and left atrial size are associated with the development of ischemic stroke among NOAC users. Future studies should aim to investigate nonpharmacological stroke prevention strategies in patients with the vascular risk factors and echocardiographic risk markers identified in the present study.

Issues on the initialization and simulation of tropical cyclones (TCs) have been studied here based on four-dimensional variational data assimilation. In particular, experiments have been carried out to assess 1) what the most critical parameters for the so-called bogus data assimilation are and 2) how the current procedures for the bogus data assimilation can be further improved. It is shown that the assimilation of wind fields is more successful than that of pressure fields in improving the initial structure and prediction of TCs. It is emphasized that the geostrophic adjustment favors the pressure field to adjust to the wind field because the TC vortex is much smaller than the radius of Rossby deformation. The results suggest that a better initial condition in the wind field is critical to the simulation of TCs. Experiments from this study also show that inclusion of the initial TC movement in the data assimilation window can help improve the track prediction, particularly during the early integration period. This method is able to shed light on the improvement of TC simulation based on the bogus data assimilation. In all, the results add a theoretical interpretation of the importance of the wind field to the sea level pressure field in terms of geostrophic adjustment, as well as a time dimension of the bogus data assimilation, by assimilating a movable vortex in the four-dimensional variational data assimilation.

The Geophysical Fluid Dynamics Laboratory (GFDL) hurricane initialization algorithm is implemented in the community fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5). This work is applied to the MM5-based Regional Data Assimilation and Prediction System model (RDAPS), the Korea Meteorological Administration's regional forecast model. The bogus procedure starts by initializing the winds within the bogus area. The main difficulty lies in the generation of other variables, such as humidity, temperature, geopotential, etc., which are dynamically consistent with the prescribed wind. However, it was found that there is a simple and practical way of tropical cyclone (TC) initialization. It is achieved by the use of the built-in function of MM5, the four-dimensional data assimilation (FDDA). In order to do so, a miniature RDAPS is constructed. After the initialization of wind within the filter region, all other variables are generated by the mod...