Influence of the Self-Consistent Regional Ensemble Background Error Covariance on Hurricane Inner-Core Data Assimilation with the GSI-Based Hybrid System for HWRF

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

doi:10.1175/jas-d-16-0017.1

Cited by 42 publications

(69 citation statements)

References 14 publications

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“…There exists a potential undesirable impact on the HWRF analysis and forecast when non‐native ensemble is used (e.g. vortex spindown as discussed by Pu et al ., ). Due to the different scales and architectures of the two models, background error covariance estimated by the GFS ensemble may be unable to accurately describe the cross‐variable correlations of HWRF.…”

Section: Methodsmentioning

confidence: 97%

Assimilating GPM hydrometeor retrievals in HWRF: choice of observation operators

Zupanski

2017

Atmospheric Science Letters

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A recent study investigated the capability to assimilate hydrometeor retrievals in the National Oceanic and Atmospheric Administration (NOAA) Hurricane Weather Research and Forecasting (HWRF) system. Hydrometeor retrievals were obtained from a hurricane-specific retrieval that utilizes data from the Global Measurement Mission Microwave Imager. Data assimilation system used in HWRF is the hybrid ensemble-variational Gridpoint Statistical Interpolation (GSI). As a first attempt to assimilate hydrometeor retrievals in HWRF, observation operators for solid and liquid integrated water content were developed using the GSI standard control variables, such as temperature, pressure, and specific humidity, under the assumption that all water vapor in excess of saturation is condensed out. To improve the usefulness of assimilating hydrometeor retrievals in HWRF, this study extends this previous work by introducing new observation operators that (1) directly use hydrometeor species estimated from HWRF microphysics and (2) include total cloud condensate as a control variable. Hurricane Gonzalo (2014) was used to examine the performance of the old and new observation operators on HWRF analysis and subsequent forecasts with three pairs of experiments. Results suggest that when new observation operators are used the analysis is an improved fit to observations and realistic adjustments to control variables are evident as seen in analysis increment. In addition, the forecasts also indicate some improvements in hurricane intensity.

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Section: Methodsmentioning

confidence: 97%

Assimilating GPM hydrometeor retrievals in HWRF: choice of observation operators

Zupanski

2017

Atmospheric Science Letters

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“…, ); (Pu et al. , )). On the other hand, it is short enough to ensure that the intensity error saturation is not reached.…”

Section: Methodsmentioning

confidence: 99%

On the growth of intensity forecast errors in the operational hurricane weather research and forecasting (HWRF) model

Kieu

Keshavamurthy

Tallapragada³

et al. 2018

Quart J Royal Meteoro Soc

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This study examines the growth of tropical cyclone (TC) intensity forecast errors and related intensity predictability for the NOAA operational Hurricane Weather Research and Forecasting (HWRF) model. Using operational intensity forecasts during the 2012 to 2016 seasons, two conditions for a limited range of TC intensity predictability are demonstrated, which include (a) the existence of an intensity error saturation limit, and (b) the dependence of the intensity error growth rate on storm intensity during TC development. By stratifying intensity errors based on different initial intensity bins, it is shown that TC intensity error growth rate is relatively small (∼0.3 kt h −1 ) at the early stage of TC development, but it quickly increases to ∼1 kt h −1 during TC intensification. Of further importance is that the intensity error saturation varies in the range of 14-18 kt in different ocean basins, thus suggesting the potential dependence of the intensity predictability on large-scale environment. Additional idealized experiments with the HWRF model confirm the saturation of intensity errors, even under a perfect model scenario. The existence of the intensity error saturation together with the finding of a faster error growth rate for higher intensity suggests that the TC dynamics possesses an inherent limited predictability, which prevents us from reducing the intensity errors in TC dynamical models below a certain threshold.

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“…As a step to further improve environmental conditions along with the storm-scale enhancement, the CIMSS-AMV is utilized. A common approach to assimilation of multiple data sources is to carry out the assimilation sequentially for each type of observations (see, e.g., Tallapragada et al 2014;Pu et al 2016). Our proposed approach for the TC initialization problem is, however, to combine both the AMV and the TCR-based synthetic vortex structure into a single data tank before assimilation.…”

Section: A Construction Of Tc-synthetic Profilementioning

confidence: 99%

“…Recent implementation of real-time assimilation of targeted observations revealed further that the TC bogussing technique is not always compatible with the assimilation of the inner-core observation (Tallapragada et al 2014;Tong et al 2014;Pu et al 2016). Specifically, the TC bogussing process attempts to generate a new vortex that best fits to observed TC intensity at the surface, whereas the inner-core assimilation tends to modify the TC structure at all levels, which can result in a TC initial intensity different from the best track observation.…”

Section: Introductionmentioning

confidence: 99%

Initializing the WRF Model with Tropical Cyclone Real-Time Reports Using the Ensemble Kalman Filter Algorithm

Du¹,

Ngo‐Duc

Kieu

2017

Pure Appl. Geophys.

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This study presents an approach to assimilate tropical cyclone (TC) real-time reports and the University of Wisconsin-Cooperative Institute for Meteorological Satellite Studies (CIMSS) Atmospheric Motion Vectors (AMV) data into the Weather Research and Forecasting (WRF) model for TC forecast applications. Unlike current methods in which TC real-time reports are used to either generate a bogus vortex or spin-up a model initial vortex, the proposed approach ingests the TC realtime reports through blending a dynamically consistent synthetic vortex structure with the CIMSS-AMV data. The blended dataset is then assimilated into the WRF initial condition, using the local ensemble transform Kalman filter (LETKF) algorithm. Retrospective experiments for a number of TC cases in the north Western Pacific basin during 2013-2014 demonstrate that this approach could effectively increase both the TC circulation and enhance the large-scale environment that the TCs are embedded in. Further evaluation of track and intensity forecast errors shows that track forecasts benefit more from improvement in the large-scale flow at 4-5 day lead times, whereas the intensity improvement is minimal. While the difference between the track and intensity improvement could be due to a specific model configuration, this result appears to be consistent with the recent reports of insignificant impacts of inner-core data assimilation in operational TC models at the long range of 4-5 days. The new approach will be most beneficial for future regional TC models that are directly initialized from very high-resolution global models whose storm initial locations are sufficiently accurate at the initial analysis that there is no need to carry out any artificial vortex removal or filtering steps.

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Influence of the Self-Consistent Regional Ensemble Background Error Covariance on Hurricane Inner-Core Data Assimilation with the GSI-Based Hybrid System for HWRF

Cited by 42 publications

References 14 publications

Assimilating GPM hydrometeor retrievals in HWRF: choice of observation operators

Assimilating GPM hydrometeor retrievals in HWRF: choice of observation operators

On the growth of intensity forecast errors in the operational hurricane weather research and forecasting (HWRF) model

Initializing the WRF Model with Tropical Cyclone Real-Time Reports Using the Ensemble Kalman Filter Algorithm

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