GSI-Based, Continuously Cycled, Dual-Resolution Hybrid Ensemble–Variational Data Assimilation System for HWRF: System Description and Experiments with Edouard (2014)

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

doi:10.1175/mwr-d-17-0068.1

Cited by 50 publications

(76 citation statements)

References 63 publications

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“…It is found that hybrid DA techniques perform better than the nonhybrid 4DVAR method. Recently, Hamill, Whitaker, Kleist, et al (), Kutty et al (), Lu et al (, ), Schwartz et al (), and Wang () used hybrid DA techniques to study TC and found that the track and intensity prediction of TC initialized with hybrid DA was significantly better than the track forecast generated using only variational method. The result from the present study also matches well with these studies.…”

Section: Discussionmentioning

confidence: 99%

“…Although these studies (Lu et al, . and Lu et al, ) show the positive impact of the flow‐dependent BEC estimated from different hybrid DA techniques on TC analysis and forecast compared to static BEC estimated from nonhybrid DA, there are limited studies over North Indian Ocean (NIO).…”

Section: Introductionmentioning

confidence: 99%

“…Many previous studies using hybrid DA system (Buehner, 2005;Buehner et al, 2010aBuehner et al, , 2010bWang et al, 2008b;Kutty et al, 2018, Wang, 2011, Zhang & Zhang, 2012Wang et al, 2013;Zhang, Huang, & Poterjoy, 2013) have shown that hybrid technique produces better results compared to those obtained from 3DVAR and 4DVAR DA schemes. Although these studies (Lu et al, 2016. andLu et al, 2017) show the positive impact of the flow-dependent BEC estimated from different hybrid DA techniques on TC analysis and forecast compared to static BEC estimated from nonhybrid DA, there are limited studies over North Indian Ocean (NIO).…”

Section: Introductionmentioning

confidence: 99%

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Appraisal of Data Assimilation Techniques for Dynamical Downscaling of the Structure and Intensity of Tropical Cyclones

Malakar

Kesarkar

Bhate

et al. 2020

Earth and Space Science

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The dynamical downscaling technique is used for the understanding of physical mechanisms associated with the atmospheric phenomena. We have developed high‐resolution analysis (6 km) for three tropical cyclones (TCs), namely, Phailin (2013), Nilofar (2014), and Chapala (2015) originated over the North Indian Ocean using the dynamical downscaling approach. The study aimed at the identification of appropriate methodology for generating analysis so that it becomes useful for identifying the role of environmental and internal dynamics on intensification processes and structural changes of TCs. The simulations using Weather Research and Forecasting model and four‐dimensional variational (4DVAR), hybrid three‐dimensional ensemble‐variational (3DEnVAR), and a hybrid four‐dimensional ensemble‐variational (4DEnVAR) data assimilation (DA) techniques are compared. The impact of DA is quantified by comparing errors in position, minimum sea level pressure, and maximum wind speed with the best track data set of India Meteorological Department. The intensities of TCs simulated by three downscaling methods are validated in terms of changes in minimum sea level pressure, maximum surface winds, and boundary layer and middle tropospheric relative humidity. The skills scores, namely, equitable threat score, false alarm ratio, the probability of detection, and biases (BIAS), are calculated to identify the best suitable DA technique. It is found that the hybrid DA techniques improve the overall quality of analysis compared to those developed using only variational DA techniques. The simulation using the hybrid 4DEnVAR DA technique is found to be better for simulation of the track, intensity changes, and structural characteristics of TCs.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Appraisal of Data Assimilation Techniques for Dynamical Downscaling of the Structure and Intensity of Tropical Cyclones

Malakar

Kesarkar

Bhate

et al. 2020

Earth and Space Science

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“…(1) 201111 Nanmadol 2011-08-24_06:00:00-2011-08-27_06:00:00 (2) 201117 Nesat 2011-09-25_06:00:00-2011-09-28_06:00:00 3201120 Banyan 2011-10-10_06:00:00-2011-10-13_06:00:00 (4) 201208 Vicente 2012-07-20_06:00:00-2012-07-23_06:00:00 (5) 201209 Saola 2012-07-29_06:00:00-2012-08-01_06:00:00 (6) 201213 Kai-tak 2012-08-14_06:00:00-2012-08-17_06:00:00 7201307 Soulik 2013-07-08_06:00:00-2013-07-11_06:00:00 (8) 201311 Utor 2013-08-10_06:00:00-2013-08-13_06:00:00 (9) 201319 Usagi 2013-09-17_06:00:00-2013-09-20_06:00:00 (10) 201409 Rammasun 2014-07-14_06:00:00-2014-07-17_06:00:00 (11) 201410 Matmo 2014-07-19_06:00:00-2014-07-22_06:00:00 (12) 201415 Kalmaegi 2014-09-12_06:00:00-2014-09-15_06:00:00 (13) 201510 Linfa 2015-07-05_06:00:00-2015-07-08_06:00:00 (14) 201513 Soudelor 2015-08-02_06:00:00-2015-08-05_06:00:00 (15) 201522 Mujigae 2015-10-01_06:00:00-2015-10-04_06:00:00 * The coordinated universal time was used in this study.…”

Section: Number Typhoon Cases Simulation Periodsmentioning

confidence: 99%

“…Several typhoon initialization methods exist to eliminate the uncertainty of the initial values, such as building a bogus vortex method [2][3][4][5], bogus data assimilation method [6][7][8][9][10], and a dynamical initialization method [11][12][13]. The uncertainty of the models' physical representation is another important source of simulation error, and many studies have focused on improvements to the model's physical processes for better simulation of tropical cyclones.…”

Section: Introductionmentioning

confidence: 99%

Combinatorial Optimization for WRF Physical Parameterization Schemes: A Case Study of Three-Day Typhoon Simulations over the Northwest Pacific Ocean

Gong

Gan

et al. 2019

Atmosphere

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Quantifying a set of suitable physics parameterization schemes for the Weather Research and Forecasting (WRF) model is essential for obtaining highly accurate typhoon forecasts. In this study, a systematic Tukey-based combinatorial optimization method was proposed to determine the optimal physics schemes of the WRF model for 15 typhoon simulations over the Northwest Pacific Ocean, covering all available schemes of microphysics (MP), cumulus (CU), and planetary boundary layer (PBL) physical processes. Results showed that 284 scheme combination searches were sufficient to find the optimal scheme combinations for simulations of track (km), central sea level pressure (CSLP, hPa), and 10 m maximum surface wind (10-m wind, m s−1), compared with the 700 sets of full combinations (i.e., 10 MP × 7 CU × 10 PBL). The decrease in the typhoon simulation error (i.e., root mean square error between simulation and observations) with this optimal scheme combination was 34%, 33.92%, and 25.67% for the track, CSLP, and 10-m wind, respectively. Overall, the results demonstrated that the optimal scheme combination yields reasonable results, and the Tukey-based optimization method is very effective and efficient in terms of computational resources.

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Probabilistic high‐impact rainfall forecasts from landfalling tropical cyclones using Warn‐on‐Forecast system

Yussouf

Jones

Skinner

2020

Quart J Royal Meteoro Soc

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Intense rainfall and flash flooding from landfalling tropical cyclones (LTC) can have devastating impacts on human life and property in coastal areas. The years 2017 and 2018 are examples of how the North Atlantic LTCs can create widespread destruction in the United States. Better preparedness is needed to mitigate the impact from the violent LTCs and can be achieved by improving the accuracy of forecasts and increased lead‐time of guidance products. However, predicting the fine‐scale details of rain bands in LTC is very challenging. This study attempts to elucidate the potential of National Severe Storms Laboratory's convective‐scale ensemble analysis and prediction system, known as the Warn‐on‐Forecast System (WoFS), in improving 0–6 hr probabilistic intense rainfall forecasts from three recent LTCs in the United States. Results indicate that the frequent 15 min assimilation cycling can accurately analyse the small‐scale details from the LTC rain bands in the WoFS analyses. The WoFS 0–6 hr ensemble forecasts initialized from those analyses represent the location, intensity and spatial distribution of intense rainfall (with the potential to cause flash flooding) as well as low‐level rotation with reasonably good accuracy. The continuous flow of the frequently updated WoFS rainfall guidance has the potential to aid operational forecasters in issuing watches, warnings, and short‐term forecast products of life‐threatening LTC with higher spatial and temporal specificity.

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GSI-Based, Continuously Cycled, Dual-Resolution Hybrid Ensemble–Variational Data Assimilation System for HWRF: System Description and Experiments with Edouard (2014)

Cited by 50 publications

References 63 publications

Appraisal of Data Assimilation Techniques for Dynamical Downscaling of the Structure and Intensity of Tropical Cyclones

Appraisal of Data Assimilation Techniques for Dynamical Downscaling of the Structure and Intensity of Tropical Cyclones

Combinatorial Optimization for WRF Physical Parameterization Schemes: A Case Study of Three-Day Typhoon Simulations over the Northwest Pacific Ocean

Probabilistic high‐impact rainfall forecasts from landfalling tropical cyclones using Warn‐on‐Forecast system

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