Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data AssimilationSource: Monthly Weather Review:;2018:;volume 146:;issue 012::page 4155Author:Tong, Mingjing
,
Sippel, Jason A.
,
Tallapragada, Vijay
,
Liu, Emily
,
Kieu, Chanh
,
Kwon, In-Hyuk
,
Wang, Weiguo
,
Liu, Qingfu
,
Ling, Yangrong
,
Zhang, Banglin
DOI: 10.1175/MWR-D-17-0380.1Publisher: American Meteorological Society
Abstract: AbstractThis study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble?variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18?24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown.
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contributor author | Tong, Mingjing | |
contributor author | Sippel, Jason A. | |
contributor author | Tallapragada, Vijay | |
contributor author | Liu, Emily | |
contributor author | Kieu, Chanh | |
contributor author | Kwon, In-Hyuk | |
contributor author | Wang, Weiguo | |
contributor author | Liu, Qingfu | |
contributor author | Ling, Yangrong | |
contributor author | Zhang, Banglin | |
date accessioned | 2019-09-19T10:04:46Z | |
date available | 2019-09-19T10:04:46Z | |
date copyright | 6/4/2018 12:00:00 AM | |
date issued | 2018 | |
identifier other | mwr-d-17-0380.1.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4261287 | |
description abstract | AbstractThis study evaluates the impact of assimilating high-resolution, inner-core reconnaissance observations on tropical cyclone initialization and prediction in the 2013 version of the operational Hurricane Weather Research and Forecasting (HWRF) Model. The 2013 HWRF data assimilation system is a GSI-based hybrid ensemble?variational system that, in this study, uses the Global Data Assimilation System ensemble to estimate flow-dependent background error covariance. Assimilation of inner-core observations improves track forecasts and reduces intensity error after 18?24 h. The positive impact on the intensity forecast is mainly found in weak storms, where inner-core assimilation produces more accurate tropical cyclone structures and reduces positive intensity bias. Despite such positive benefits, there is degradation in short-term intensity forecasts that is attributable to spindown of strong storms, which has also been seen in other studies. There are several reasons for the degradation of intense storms. First, a newly discovered interaction between model biases and the HWRF vortex initialization procedure causes the first-guess wind speed aloft to be too strong in the inner core. The problem worsens for the strongest storms, leading to a poor first-guess fit to observations. Though assimilation of reconnaissance observations results in analyses that better fit the observations, it also causes a negative intensity bias at the surface. In addition, the covariance provided by the NCEP global model is inaccurate for assimilating inner-core observations, and model physics biases result in a mismatch between simulated and observed structure. The model ultimately cannot maintain the analysis structure during the forecast, leading to spindown. | |
publisher | American Meteorological Society | |
title | Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data Assimilation | |
type | Journal Paper | |
journal volume | 146 | |
journal issue | 12 | |
journal title | Monthly Weather Review | |
identifier doi | 10.1175/MWR-D-17-0380.1 | |
journal fristpage | 4155 | |
journal lastpage | 4177 | |
tree | Monthly Weather Review:;2018:;volume 146:;issue 012 | |
contenttype | Fulltext |