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Guo-Yuan Lien, Chung-Han Lin, Zih-Mao Huang, Wen-Hsin Teng, Jen-Her Chen, Ching-Chieh Lin, Hsu-Hui Ho, Jyun-Ying Huang, Jing-Shan Hong, Chia-Ping Cheng, and Ching-Yuang Huang

Abstract

The FORMOSAT-7/COSMIC-2 Global Navigation Satellite System (GNSS) Radio Occultation (RO) satellite constellation was launched in June 2019 as a successor of the FORMOSAT-3/COSMIC mission. The Central Weather Bureau (CWB) of Taiwan has received FORMOSAT-7/COSMIC-2 GNSS RO data in real time from the Taiwan Analysis Center for COSMIC. With the global numerical prediction system at CWB, a parallel semioperational experiment assimilating the FORMOSAT-7/COSMIC-2 bending angle data with all other operational observation data has been conducted to evaluate the impact of the FORMOSAT-7/COSMIC-2 data. The first seven-month results show that the quality of the early FORMOSAT-7/COSMIC-2 data has been satisfactory for assimilation. Consistent and significant positive impacts on global forecast skills have been observed since the start of the parallel experiment, with the most significant impact found in the tropical region, reflecting the low-inclination orbital design of the satellites. The impact of the FORMOSAT-7/COSMIC-2 RO data is also estimated using the ensemble forecast sensitivity to observation impact (EFSOI) method, showing an average positive impact per observation similar to other existing GNSS RO datasets, while the total impact is impressive by virtue of its large amount. Sensitivity experiments suggest that the quality control processes built in the Gridpoint Statistical Interpolation (GSI) system for RO data work well to achieve a positive impact by the low-level FORMOSAT-7/COSMIC-2 RO data, while more effort on observation error tuning should be focused to obtain an optimal assimilation performance. This study demonstrates the usefulness of the FORMOSAT-7/COSMIC-2 RO data in global numerical weather prediction during the calibration/validation period and leads to the operational use of the data at CWB.

Open access
Tobias Goecke and Ekaterina Machulskaya

Abstract

We present a detailed evaluation of the turbulence forecast product eddy dissipation parameter (EDP) used at the Deutscher Wetterdienst (DWD). It is based on the turbulence parameterization scheme TURBDIFF, which is operational within the Icosahedral Nonhydrostatic (ICON) numerical weather prediction model used operationally by DWD. For aviation purposes, the procedure provides the cubic root of the eddy dissipation rate ε 1/3 as an overall turbulence index. This quantity is a widely used measure for turbulence intensity as experienced by aircraft. The scheme includes additional sources of turbulent kinetic energy with particular relevance to aviation, which are briefly introduced. These sources describe turbulence generation by the subgrid-scale action of wake eddies, mountain waves, and convection, as well as horizontal shear as found close to fronts or the jet stream. Furthermore, we introduce a postprocessing calibration to an empirical EDR distribution, and we demonstrate the potential as well as limitations of the final EDP-based turbulence forecast by considering several case studies of typical turbulence events. Finally, we reveal the forecasting capability of this product by verifying the model results against one year of aircraft in situ EDR measurements from commercial aircraft. We find that the forecasted EDP performs favorably when compared to the Ellrod index. In particular, the turbulence signal from deep convection, which is accounted for in the EDP product, is advantageous when spatial nonlocality is allowed in the verification procedure.

Open access
Xiaomin Chen, Ming Xue, Bowen Zhou, Juan Fang, Jun A. Zhang, and Frank D. Marks

Abstract

Horizontal grid spacings of numerical weather prediction models are rapidly approaching O(1) km and have become comparable with the dominant length scales of flows in the boundary layer; within such “gray-zones,” conventional planetary boundary layer (PBL) parameterization schemes start to violate basic design assumptions. Scale-aware PBL schemes have been developed recently to address the gray-zone issue. By performing WRF simulations of Hurricane Earl (2010) at subkilometer grid spacings, this study investigates the effect of the scale-aware Shin–Hong (SH) scheme on the tropical cyclone (TC) intensification and structural changes in comparison to the non-scale-aware YSU scheme it is built upon. Results indicate that SH tends to produce a stronger TC with a more compact inner core than YSU. At early stages, scale-aware coefficients in SH gradually decrease as the diagnosed boundary layer height exceeds the horizontal grid spacing. This scale-aware effect is most prominent for nonlocal subgrid-scale vertical turbulent fluxes, in the nonprecipitation regions radially outside of a vortex-tilt-related convective rainband, and from the early stage through the middle of the rapid intensification (RI) phase. Both the scale awareness and different parameterization of the nonlocal turbulent heat flux in SH reduce the parameterized vertical turbulent mixing, which further induces stronger radial inflows and helps retain more water vapor in the boundary layer. The resulting stronger moisture convergence and diabatic heating near the TC center account for a faster inner-core contraction before RI onset and higher intensification rates during the RI period. Potential issues of applying these two PBL schemes in TC simulations and suggestions for improvements are discussed.

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Will Boyles and Matthias Katzfuss

Abstract

The ensemble Kalman filter (EnKF) is a popular technique for data assimilation in high-dimensional nonlinear state-space models. The EnKF represents distributions of interest by an ensemble, which is a form of dimension reduction that enables straightforward forecasting even for complicated and expensive evolution operators. However, the EnKF update step involves estimation of the forecast covariance matrix based on the (often small) ensemble, which requires regularization. Many existing regularization techniques rely on spatial localization, which may ignore long-range dependence. Instead, our proposed approach assumes a sparse Cholesky factor of the inverse covariance matrix, and the nonzero Cholesky entries are further regularized. The resulting method is highly flexible and computationally scalable. In our numerical experiments, our approach was more accurate and less sensitive to misspecification of tuning parameters than tapering-based localization.

Open access
Christopher M. Hartman, Xingchao Chen, Eugene E. Clothiaux, and Man-Yau Chan

Abstract

Recent studies have shown that the assimilation of all-sky infrared (IR) observations can be beneficial for tropical cyclone analyses and predictions. The assimilation of tail Doppler radar (TDR) radial velocity observations has also been shown to improve tropical cyclone analyses and predictions; however, there is a paucity of literature on the impacts of simultaneously assimilating them with all-sky IR brightness temperatures (BTs). This study examines the impacts of assimilating combinations of GOES-16 all-sky IR brightness temperatures, NOAA P-3 TDR radial velocities, and conventional observations from the Global Telecommunications System (GTS) on the analyses and forecasts of Hurricane Dorian (2019). It is shown that including IR and/or TDR observations on top of conventional GTS observations significantly reduces both track and intensity forecast errors. Track errors are reduced the most (25% at lead times greater than 48 h) when TDR and GTS observations are assimilated. In terms of intensity, errors are always lower at lead times greater than 48 h when IR BTs are assimilated. Simultaneously assimilating TDR and IR observations has the potential to further improve the intensity forecast by as much as 37% at a lead time of 48–72 h. The improved intensity forecasts produced by the experiments assimilating all three observation sources are shown to be a result of the competing effects of IR assimilation producing an overly broad area of strong cyclonic circulation and TDR assimilation constraining that circulation to a more realistic size and intensity. Interestingly, the order in which observations are assimilated has nonnegligible impacts on the analyses and forecasts of Dorian.

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David R. Ryglicki, Christopher S. Velden, Paul D. Reasor, Daniel Hodyss, and James D. Doyle

Abstract

Multiple observation and analysis datasets are used to demonstrate two key features of the atypical rapid intensification (ARI) process that occurred in Atlantic Hurricane Dorian (2019): 1) precession and nutations of the vortex tilt and 2) blocking of the impinging upper-level environmental flow by the outflow. As Dorian came under the influence of an upper-level anticyclone, traditional methods of estimating vertical wind shear all indicated relatively low values were acting on the storm; however, high-spatiotemporal-resolution atmospheric motion vectors (AMVs) indicated that the environmental flow at upper levels was actually impinging on the vortex core, resulting in a vertical tilt. We employ a novel ensemble of centers of individual swaths of dual-Doppler radar data from WP-3D aircraft to characterize the precession and wobble of the vortex tilt. This tilting and wobbling preceded a sequence of outflow surges that acted to repel the impinging environmental flow, thereby reducing the shear and permitting ARI. We then apply prior methodology on satellite imagery for distinguishing ARI features. Finally, we use the AMV dataset to experiment with different shear calculations and show that the upper-level cross-vortex flow approaches zero. We discuss the implication of these results with regard to prior works on ARI and intensification in shear.

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Jing-Yi Zhuo and Zhe-Min Tan

Abstract

A deep learning–based method augmented by prior knowledge of tropical cyclones (TCs), called DeepTCNet, is introduced to estimate TC intensity and wind radii from infrared (IR) imagery over the North Atlantic Ocean. While standard deep learning practices have many advantages over conventional analysis approaches and can produce reliable estimates of TCs, the data-driven models informed by machine-readable physical knowledge of TCs could achieve higher performance. To this end, two approaches are explored to develop the physics-augmented DeepTCNet: (i) infusing the auxiliary physical information of TCs into models for single-task learning and (ii) learning auxiliary physical tasks for multitask learning. More specifically, augmented by auxiliary information of TC fullness (a measure of the radial decay of the TC wind field), the DeepTCNet yields a 12% improvement in estimating TC intensity over the nonaugmented one. By learning TC wind radii and auxiliary TC intensity task simultaneously, the model’s wind radii estimation skill is improved by 6% over only learning four wind radii tasks and by 9% over separately learning a single wind radii task. The evaluation results showed that the DeepTCNet is in-line with the Satellite Consensus technique (SATCON) but systematically outperforms the advanced Dvorak technique (ADT) at all intensity scales with an averaged 39% enhancement in TC intensity estimation. The DeepTCNet also surpasses the Multiplatform Tropical Cyclone Surface Wind Analysis technique (MTCSWA) with an average improvement of 32% in wind radii estimation.

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Milind Sharma, Robin L. Tanamachi, Eric C. Bruning, and Kristin M. Calhoun

Abstract

We demonstrate the utility of transient polarimetric signatures (Z DR and K DP columns, a proxy for surges in a thunderstorm updraft) to explain variability in lightning flash rates in a tornadic supercell. Observational data from a WSR-88D and the Oklahoma lightning mapping array are used to map the temporal variance of polarimetric signatures and VHF sources from lightning channels. It is shown, via three-dimensional and cross-sectional analyses, that the storm was of inverted polarity resulting from anomalous electrification. Statistical analysis confirms that mean flash area in the Z DR column region was 10 times smaller than elsewhere in the storm. On an average, 5 times more flash initiations occurred within Z DR column regions, thereby supporting existing theory of an inverse relationship between flash initiation rates and lightning channel extent. Segmentation and object identification algorithms are applied to gridded radar data to calculate metrics such as height, width, and volume of Z DR and K DP columns. Variability in lightning flash rates is best explained by the fluctuations in Z DR column volume with a Spearman’s rank correlation coefficient value of 0.72. The highest flash rates occur in conjunction with the deepest Z DR columns (up to 5 km above environmental melting level) and largest volumes of Z DR columns extending up to the −20°C level (3 km above the melting level). Reduced flash rates toward the end of the analysis are indicative of weaker updrafts manifested as low Z DR column volumes at and above the −10°C level. These findings are consistent with recent studies linking lightning to the interplay between storm dynamics, kinematics, thermodynamics, and precipitation microphysics.

Open access
Robert G. Nystrom, Steven J. Greybush, Xingchao Chen, and Fuqing Zhang

Abstract

The tropical cyclone (TC) surface-exchange coefficients of enthalpy (C k) and momentum (C d) at high wind speeds have been notoriously challenging to estimate. This difficulty arises from many factors, including the difficulties in collecting observations within the turbulent TC boundary layer, and the complex coupled physical interactions between the TC boundary layer and ocean surface, which are challenging to accurately model. Motivated by recent studies highlighting the limited practical predictability of TC intensity as a result of uncertainty in the physical representation of the air–sea fluxes of momentum and enthalpy at high wind speeds, we investigate the potential to estimate the surface enthalpy and momentum exchange coefficients through ensemble data assimilation. Significant ensemble correlations between tangential wind, radial wind, and simulated infrared brightness temperatures with parameters controlling the enthalpy and momentum exchange coefficients suggest potential to use all-sky satellite and/or airborne radial velocity observations to estimate these unknown parameters. Using a series of observing system simulation experiments (OSSEs), simulated infrared brightness temperature observations, and a known truth, we demonstrate some potential for simultaneous state and parameter estimation with an ensemble-based data assimilation system to converge toward the correct known parameter values. In all OSSEs with either one or multiple unknown parameters, the initial parameter errors are reduced through simultaneous model state and parameter estimation. However, challenges still exist in converging to the precise true parameter values, as state errors during rapid intensification can project onto the parameter estimates.

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Muhammad Naufal Razin and Michael M. Bell

Abstract

Hurricane Ophelia (2005) underwent an unconventional eyewall replacement cycle (ERC) as it was a category-1 storm located over cold sea surface temperatures near 23°C. The ERC was analyzed using airborne radar, flight-level, and dropsonde data collected during the Hurricane Rainband and Intensity Change Experiment (RAINEX) intensive observation period on 11 September 2005. Results showed that the spinup of the secondary tangential wind maximum during the ERC can be attributed to the efficient convergence of absolute angular momentum by the midlevel inflow of Ophelia’s dominantly stratiform rainbands. This secondary tangential wind maximum strongly contributed to the azimuthal mean tangential wind field, which is conducive for increased low-level supergradient winds and corresponding outflow. The low-level supergradient forcing enhanced convergence to form a secondary eyewall. Ophelia provides a unique example of an ERC occurring in a weaker storm with predominantly stratiform rainbands, suggesting an important role of stratiform precipitation processes in the development of secondary eyewalls.

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