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Ligia Bernardet, Laurie Carson, and Vijay Tallapragada

Abstract

NOAA/NCEP runs a number of numerical weather prediction (NWP) modeling suites to provide operational guidance to the National Weather Service field offices and service centers. A sophisticated infrastructure, which includes a complex set of software tools, is required to facilitate running these NWP suites. This infrastructure needs to be maintained and upgraded so that continued improvements in forecast accuracy can be achieved. This contribution describes the design of a robust NWP Information Technology Environment (NITE) to support and accelerate the transition of innovations to NOAA operational modeling suites.

Through consultation with and at the request of the NOAA NCEP Environmental Modeling Center, a survey of segments of the national NWP community, and a review of selected aspects of the computational infrastructure of several modeling centers was conducted, which led to the following elements being considered as key for NITE: data management, source code management and build systems, suite definition tools, scripts, workflow management, experiment database, and documentation and training.

The design for NITE put forth by the DTC would make model development by NOAA staff and their external collaborators more effective and efficient. It should be noted that NITE was not designed to work exclusively for a certain modeling suite; instead it transcends the current operational suites and is applicable to the expected evolution in NCEP systems. NITE is particularly important for community engagement in the Next-Generation Global Prediction System, which is expected to be an Earth modeling system including several components.

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Xu Lu, Xuguang Wang, Mingjing Tong, and Vijay Tallapragada

Abstract

A Gridpoint Statistical Interpolation analysis system (GSI)-based, continuously cycled, dual-resolution hybrid ensemble Kalman filter–variational (EnKF-Var) data assimilation (DA) system is developed for the Hurricane Weather Research and Forecasting (HWRF) Model. In this system, a directed moving nest strategy is developed to solve the issue of nonoverlapped domains for cycled ensemble DA. Additionally, both dual-resolution and four-dimensional ensemble–variational (4DEnVar) capabilities are implemented. Vortex modification (VM) and relocation (VR) are used in addition to hybrid DA. Several scientific questions are addressed using the new system for Hurricane Edouard (2014). It is found that dual-resolution hybrid DA improves the analyzed storm structure and short-term maximum wind speed (Vmax) and minimum sea level pressure (MSLP) forecasts compared to coarser, single-resolution hybrid DA, but track and radius of maximum wind (RMW) forecasts do not improve. Additionally, applying VR and VM on the control background before DA improves the analyzed storm, overall track, RMW, MSLP, and Vmax forecasts. Further applying VR on the ensemble background improves the analyzed storm and forecast biases for MSLP and Vmax. Also, using 4DEnVar to assimilate tail Doppler radar (TDR) data improves the analyzed storm and short-term MSLP and Vmax forecasts compared to three-dimensional ensemble–variational (3DEnVar) although 4DEnVar slightly degrades the track forecast. Finally, the new system improves overall RMW, MSLP, and Vmax forecasts upon the operational HWRF, while no improvement on track is found. The intensity forecast improvement during the intensifying period is due to the better analyzed structures for an intensifying storm.

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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

Abstract

The impact of assimilating Global Precipitation Measurement (GPM) Microwave Imager (GMI) clear-sky radiance on the track and intensity forecasts of two Atlantic hurricanes during the 2015 and 2016 hurricane seasons is assessed using the Hurricane Weather Research and Forecasting (HWRF) Model. The GMI clear-sky brightness temperature is assimilated using a Gridpoint Statistical Interpolation (GSI)-based hybrid ensemble–variational data assimilation system, which utilizes the Community Radiative Transfer Model (CRTM) as a forward operator for satellite sensors. A two-step bias correction approach, which combines a linear regression procedure and variational bias correction, is used to remove most of the systematic biases prior to data assimilation. Forecast results show that assimilating GMI clear-sky radiance has positive impacts on both track and intensity forecasts, with the extent depending on the phase of hurricane evolution. Forecast verifications against dropsonde soundings and reanalysis data show that assimilating GMI clear-sky radiance, when it does not overlap with overpasses of other microwave sounders, can improve forecasts of both thermodynamic (e.g., temperature and specific humidity) and dynamic variables (geopotential height and wind field), which in turn lead to better track forecasts and a more realistic hurricane inner-core structure. Even when other microwave sounders are present (e.g., AMSU-A, ATMS, MHS, etc.), the assimilation of GMI still reduces temperature forecast errors in the near-hurricane environment, which has a significant impact on the intensity forecast.

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Chanh Kieu, Vijay Tallapragada, Da-Lin Zhang, and Zachary Moon

Abstract

This study examines the formation of a double warm-core (DWC) structure in intense tropical cyclones (TCs) that was captured in almost all supertyphoon cases during the 2012–14 real-time typhoon forecasts in the northwestern Pacific basin with the Hurricane Weather Research and Forecasting Model (HWRF). By using an idealized configuration of HWRF to focus on the intrinsic mechanism of the DWC formation, it is shown that the development of DWC in intense TCs is accompanied by a thin inflow layer above the typical upper outflow layer. The development of this thin inflow layer in the lower stratosphere (~100–75 hPa), which is associated with an inward pressure gradient force induced by cooling at the cloud top, signifies intricate interaction of TCs with the lower stratosphere as TCs become sufficiently intense, which has not been examined previously. Specifically, it is demonstrated that a higher-level inflow can advect potentially warm air from the lower stratosphere toward the inner-core region, thus forming an upper-level warm core that is separated from a midlevel one of tropospheric air. Such formation of the upper-level warm anomaly in intense TCs is linked to an episode of intensification at the later stage of TC development. While these results are produced by HWRF, the persistent DWC and UIL features in all HWRF simulations of intense TCs suggest that the lower stratosphere may have significant impacts on the inner-core structures of intense TCs beyond the current framework of TCs with a single warm core.

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Zhaoxia Pu, Shixuan Zhang, Mingjing Tong, and Vijay Tallapragada

Abstract

An initial vortex spindown, or strong adjustment to the structure and intensity of a hurricane’s initial vortex, presents a significant problem in hurricane forecasting, as with the NCEP Hurricane Weather Research and Forecasting Model (HWRF), because it can cause significantly degraded intensity forecasts. In this study, the influence of the self-consistent regional ensemble background error covariance on assimilating hurricane inner-core tail Doppler radar (TDR) observations in HWRF is examined with the NCEP gridpoint statistical interpolation (GSI)-based ensemble–three-dimensional variational (3DVAR) hybrid data assimilation system. It is found that the resolution of the background error covariance term, coming from the ensemble forecasts, has notable influence on the assimilation of hurricane inner-core observations and subsequent forecasting results. Specifically, the use of ensemble forecasting at high-resolution native grids results in significant reduction of the vortex spindown problem and thus leads to improved hurricane intensity forecasting.

Further diagnoses are conducted to examine the spindown problem with a gradient wind balance. It is found that artificial vortex initialization, performed before data assimilation, can cause strong supergradient winds or imbalance in the vortex inner-core region. Assimilation of hurricane inner-core TDR data can significantly mitigate this imbalance by reducing the supergradient effects. Compared with the use of a global ensemble background error term, application of the self-consistent regional ensemble background covariance to inner-core data assimilation leads to better representation of the mesoscale hurricane inner-core structures. It can also result in more realistic vortex structures in data assimilation even when the observational data are unevenly distributed.

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Da-Lin Zhang, Lin Zhu, Xuejin Zhang, and Vijay Tallapragada

Abstract

A series of 5-day numerical simulations of idealized hurricane vortices under the influence of different background flows is performed by varying vertical grid resolution (VGR) in different portions of the atmosphere with the operational version of the Hurricane Weather Research and Forecasting Model in order to study the sensitivity of hurricane intensity forecasts to different distributions of VGR. Increasing VGR from 21 to 43 levels produces stronger hurricanes, whereas increasing it further to 64 levels does not intensify the storms further, but intensity fluctuations are much reduced. Moreover, increasing the lower-level VGRs generates stronger storms, but the opposite is true for increased upper-level VGRs. On average, adding mean flow increases intensity fluctuations and variability (between the strongest and weakest hurricanes), whereas adding vertical wind shear (VWS) delays hurricane intensification and then causes more rapid growth in intensity variability. The stronger the VWS, the larger intensity variability and bifurcation rate occur at later stages. These intensity differences are found to be closely related to inner-core structural changes, and they are attributable to how much latent heat could be released in higher-VGR layers, followed by how much moisture content in nearby layers is converged. Hurricane intensity with higher VGRs is shown to be much less sensitive to varying background flows, and stronger hurricane vortices at the model initial time are less sensitive to the vertical distribution of VGR; the opposite is true for relatively uniform VGRs or weaker hurricane vortices. Results reveal that higher VGRs with a near-parabolic or Ω shape tend to produce smoother intensity variations and more typical inner-core structures.

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Jun A. Zhang, Robert F. Rogers, and Vijay Tallapragada

Abstract

This study evaluates the impact of the modification of the vertical eddy diffusivity (K m) in the boundary layer parameterization of the Hurricane Weather Research and Forecasting (HWRF) Model on forecasts of tropical cyclone (TC) rapid intensification (RI). Composites of HWRF forecasts of Hurricanes Earl (2010) and Karl (2010) were compared for two versions of the planetary boundary layer (PBL) scheme in HWRF. The results show that using a smaller value of K m, in better agreement with observations, improves RI forecasts. The composite-mean, inner-core structures for the two sets of runs at the time of RI onset are compared with observational, theoretical, and modeling studies of RI to determine why the runs with reduced K m are more likely to undergo RI. It is found that the forecasts with reduced K m at the RI onset have a shallower boundary layer with stronger inflow, more unstable near-surface air outside the eyewall, stronger and deeper updrafts in regions farther inward from the radius of maximum wind (RMW), and stronger boundary layer convergence closer to the storm center, although the mean storm intensity (as measured by the 10-m winds) is similar for the two groups. Finally, it is found that the departure of the maximum tangential wind from the gradient wind at the eyewall, and the inward advection of angular momentum outside the eyewall, is much larger in the forecasts with reduced K m. This study emphasizes the important role of the boundary layer structure and dynamics in TC intensity change, supporting recent studies emphasizing boundary layer spinup mechanism, and recommends further improvement to the HWRF PBL physics.

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Jun A. Zhang, David S. Nolan, Robert F. Rogers, and Vijay Tallapragada

Abstract

As part of the Hurricane Forecast Improvement Project (HFIP), recent boundary layer physics upgrades in the operational Hurricane Weather Research and Forecasting (HWRF) Model have benefited from analyses of in situ aircraft observations in the low-level eyewall region of major hurricanes. This study evaluates the impact of these improvements to the vertical diffusion in the boundary layer on the simulated track, intensity, and structure of four hurricanes using retrospective HWRF forecasts. Structural metrics developed from observational composites are used in the model evaluation process. The results show improvements in track and intensity forecasts in response to the improvement of the vertical diffusion. The results also demonstrate substantial improvements in the simulated storm size, surface inflow angle, near-surface wind profile, and kinematic boundary layer heights in simulations with the improved physics, while only minor improvements are found in the thermodynamic boundary layer height, eyewall slope, and the distributions of vertical velocities in the eyewall. Other structural metrics such as warm core anomaly and warm core height are also explored. Reasons for the structural differences between the two sets of forecasts with different physics are discussed. This work further emphasizes the importance of aircraft observations in model diagnostics and development, endorsing a developmental framework for improving physical parameterizations in hurricane models.

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Masahiro Sawada, Zaizhong Ma, Avichal Mehra, Vijay Tallapragada, Ryo Oyama, and Kazuki Shimoji

Abstract

The impact of the assimilation of high spatial and temporal resolution atmospheric motion vectors (AMVs) on tropical cyclone (TC) forecasts has been investigated. The high-resolution AMVs are derived from the full disk scan of the new generation geostationary satellite Himawari-8. Forecast experiments for three TCs in 2016 in a western North Pacific basin are performed using the National Centers for Environmental Prediction (NCEP) operational Hurricane Weather Research and Forecasting Model (HWRF). Two different ensemble–variational hybrid data assimilation configurations (using background error covariance created by global ensemble forecast and HWRF ensemble forecast), based on the Gridpoint Statistical Interpolation (GSI), are used for the sensitivity experiments. The results show that the inclusion of high-resolution Himawari-8 AMVs (H8AMV) can benefit the track forecast skill, especially for long-range lead times. The diagnosis of optimal steering flow indicates that the improved track forecast seems to be attributed to the improvement of initial steering flow surrounding the TC. However, the assimilation of H8AMV increases the negative intensity bias and error, especially for short-range forecast lead times. The investigation of the structural change from the assimilation of H8AMV revealed that the following two factors are likely related to this degradation: 1) an increase of inertial stability outside the radius of maximum wind (RMW), which weakens the boundary layer inflow; and 2) a drying around and outside the RMW. Assimilating H8AMV using background error covariance created from HWRF ensemble forecast contributes to a significant reduction in negative intensity bias and error, and there is a significant benefit to TC size forecast.

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Zhan Zhang, Jun A. Zhang, Ghassan J. Alaka Jr., Keqin Wu, Avichal Mehra, and Vijay Tallapragada

Abstract

A statistical analysis is performed on the high-frequency (3⅓ s) output from NOAA’s cloud-permitting, high-resolution operational Hurricane Weather Research and Forecasting (HWRF) Model for all tropical cyclones (TCs) in the North Atlantic Ocean basin over a 3-yr period (2017–19). High-frequency HWRF forecasts of TC track and 10-m maximum wind speed (Vmax) exhibited large fluctuations that were not captured by traditional low-frequency (6 h) model output. Track fluctuations were inversely proportional to Vmax, with average values of 6–8 km. The Vmax fluctuations were as high as 20 kt (10.3 m s−1) in individual forecasts and were a function of maximum intensity, with a standard deviation of 5.5 kt (2.8 m s−1) for category-2 hurricanes and smaller fluctuations for tropical storms and major hurricanes. The radius of Vmax contracted or remained steady when TCs rapidly intensified in high-frequency HWRF forecasts, consistent with observations. Running-mean windows of 3–9 h were applied at synoptic times to smooth the high-frequency HWRF output to investigate its utility to operational forecasting. Smoothed high-frequency HWRF output improved Vmax forecast skill by up to 8% and produced a more realistic distribution of 6-h intensity change when compared with low-frequency, instantaneous output. Furthermore, the high-frequency track forecast output may be useful for investigating characteristics of TC trochoidal motions.

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