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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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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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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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M. M. Nageswararao
,
Yuejian Zhu
, and
Vijay Tallapragada

Abstract

Indian summer monsoon rainfall (ISMR) from June to September (JJAS) contributes 80% of the total annual rainfall in India and controls the agricultural productivity and economy of the country. Extreme rainfall (ER) events are responsible for floods that cause widespread destruction of infrastructure, economic damage, and loss of life. A forecast of the ISMR and associated ER events on an extended range (beyond the conventional one-week lead time) is vital for the agronomic economy of the country. In September 2020, NOAA/NCEP implemented Global Ensemble Forecast System, version 12 (GEFSv12) for various risk management applications. It has generated consistent reanalysis and reforecast data for the period 2000–19. In the present study, the Raw-GEFSv12 with day-1–16 lead-time rainfall forecasts are calibrated using the quantile (QQ) mapping technique against Indian Monsoon Data Assimilation and Analysis (IMDAA) for further improvement. The present study evaluated the prediction skill of Raw and QQ-GEFSv12 for ISMR and ER events over India by using standard skill metrics. The results suggest that the ISMR patterns from Raw and QQ-GEFSv12 with (lead) day 1–16 are similar to IMDAA. However, Raw-GEFSv12 has a dry bias in most parts of prominent rainfall regions. The low- to medium-intensity rainfall events from Raw-GEFSv12 is remarkably higher than the IMDAA, while high- to very-high-intensity rainfall events from Raw-GEFSv12 are lower than IMDAA. The prediction skill of Raw-GEFSv12 in depicting ISMR and associated ER events decreased with lead time, while the prediction skill is almost equal for all lead times with marginal improvement after calibration.

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Stephen J. Lord
,
Xingren Wu
,
Vijay Tallapragada
, and
F. M. Ralph

Abstract

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical weather forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the National Centers for Environmental Prediction (NCEP) Global Forecast System version 15 (GFSv15) with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all of the routinely assimilated data and included data from 628 ARR dropsondes, whereas the denial run (DENY) excluded the dropsonde data. Results from 17 intensive observing periods (IOPs) indicate a mixed impact for mean sea level pressure and geopotential height over the Pacific–North American (PNA) region in CTRL compared to DENY. The overall local impact over the U.S. West Coast and Gulf of Alaska for the 17 IOPs is neutral (−0.45%) for integrated vapor transport (IVT), but positive for wind and moisture profiles (0.5%–1.0%), with a spectrum of statistically significant positive and negative impacts for various IOPs. The positive dropsonde data impact on precipitation forecasts over U.S. West Coast domains appears driven, in part, by improved low-level moisture and wind fields at short-forecast lead times. Indeed, data gaps, especially for accurate and unbiased moisture profiles and wind fields, can be at least partially mitigated to improve U.S. West Coast precipitation forecasts.

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Stephen J. Lord
,
Xingren Wu
,
Vijay Tallapragada
, and
F. M. Ralph

Abstract

The impact of assimilating dropsonde data from the 2020 Atmospheric River (AR) Reconnaissance (ARR) field campaign on operational numerical precipitation forecasts was assessed. Two experiments were executed for the period from 24 January to 18 March 2020 using the NCEP Global Forecast System, version 15 (GFSv15), with a four-dimensional hybrid ensemble–variational (4DEnVar) data assimilation system. The control run (CTRL) used all the routinely assimilated data and included ARR dropsonde data, whereas the denial run (DENY) excluded the dropsonde data. There were 17 intensive observing periods (IOPs) totaling 46 Air Force C-130 and 16 NOAA G-IV missions to deploy dropsondes over targeted regions with potential for downstream high-impact weather associated with the ARs. Data from a total of 628 dropsondes were assimilated in the CTRL. The dropsonde data impact on precipitation forecasts over U.S. West Coast domains is largely positive, especially for day-5 lead time, and appears driven by different model variables on a case-by-case basis. These results suggest that data gaps associated with ARs can be addressed with targeted ARR field campaigns providing vital observations needed for improving U.S. West Coast precipitation forecasts.

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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
,
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.

Open access
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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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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