scholarly journals Implementation of a dynamic equation constraint based on the steady state momentum equations within the WRF hybrid ensemble-3DVar data assimilation system and test with radar T-TREC wind assimilation for tropical Cyclone Chanthu (2010)

2015 ◽  
Vol 120 (9) ◽  
pp. 4017-4039 ◽  
Author(s):  
Xin Li ◽  
Jie Ming ◽  
Ming Xue ◽  
Yuan Wang ◽  
Kun Zhao
Keyword(s):  
Steady State ◽  
Tropical Cyclone ◽  
Data Assimilation ◽  
Dynamic Equation ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Equation Constraint

The Influence of Shallow Convection on Tropical Cyclone Track Forecasts

2012 ◽  
Vol 140 (7) ◽  
pp. 2188-2197 ◽  
Author(s):  
Ryan D. Torn ◽  
Christopher A. Davis
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Weather Research And Forecasting ◽  
Shallow Convection ◽  
Western Atlantic ◽  
Temperature Bias ◽  
Appropriate Treatment ◽  
Model Predictions ◽  
Data Assimilation System ◽  
Assimilation System

ABSTRACT Accurate tropical cyclone (TC) track forecasts depend on having skillful numerical model predictions of the environmental wind field. Given that wind and temperature are related through thermal wind balance, structural errors in the processes that determine the tropical temperature profile, such as shallow convection, can therefore lead to biases in TC position. This paper evaluates the influence of shallow convection on Advanced Hurricane Weather Research and Forecasting Model (AHW) TC track forecasts by cycling an ensemble data assimilation during a 1-month period in 2008 where cumulus convection is parameterized on the coarse-resolution domain using the Kain–Fritsch scheme or the modified Tiedtke scheme, which contains a more appropriate treatment of oceanic shallow convection. Short-term forecasts with the Kain–Fritsch scheme are characterized by a 1-K, 700-hPa temperature bias over much of the western Atlantic Ocean, which is attributed to a lack of shallow convection within that scheme. In turn, the horizontal gradients in this temperature bias are associated with wind biases in the region where multiple TCs move during this period. By contrast, the Tiedtke scheme does not suffer from this temperature bias, thus the wind biases are smaller. AHW forecasts initialized from the data assimilation system that uses the Tiedtke scheme have track errors that are up to 25% smaller than forecasts initialized from the data assimilation system that uses Kain–Fritsch.

scholarly journals Impact of Assimilating Aircraft Reconnaissance Observations on Tropical Cyclone Initialization and Prediction Using Operational HWRF and GSI Ensemble–Variational Hybrid Data Assimilation

2018 ◽  
Vol 146 (12) ◽  
pp. 4155-4177 ◽  
Author(s):  
Mingjing Tong ◽  
Jason A. Sippel ◽  
Vijay Tallapragada ◽  
Emily Liu ◽  
Chanh Kieu ◽  
...  
Keyword(s):  
Tropical Cyclone ◽  
Data Assimilation ◽  
Inner Core ◽  
Positive Impact ◽  
Background Error ◽  
Hybrid Data Assimilation ◽  
The Impact ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Tropical Cyclone Initialization

Abstract This 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.

scholarly journals DASSO: a data assimilation system for the Southern Ocean that utilizes both sea-ice concentration and thickness observations

2021 ◽  
pp. 1-6
Author(s):  
Hao Luo ◽  
Qinghua Yang ◽  
Longjiang Mu ◽  
Xiangshan Tian-Kunze ◽  
Lars Nerger ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Southern Ocean ◽  
Sea Ice ◽  
Coupled Model ◽  
Sea Ice Concentration ◽  
Model Free ◽  
Antarctic Sea Ice ◽  
Ice Concentration ◽  
Data Assimilation System ◽  
Assimilation System

Abstract To improve Antarctic sea-ice simulations and estimations, an ensemble-based Data Assimilation System for the Southern Ocean (DASSO) was developed based on a regional sea ice–ocean coupled model, which assimilates sea-ice thickness (SIT) together with sea-ice concentration (SIC) derived from satellites. To validate the performance of DASSO, experiments were conducted from 15 April to 14 October 2016. Generally, assimilating SIC and SIT can suppress the overestimation of sea ice in the model-free run. Besides considering uncertainties in the operational atmospheric forcing data, a covariance inflation procedure in data assimilation further improves the simulation of Antarctic sea ice, especially SIT. The results demonstrate the effectiveness of assimilating sea-ice observations in reconstructing the state of Antarctic sea ice, but also highlight the necessity of more reasonable error estimation for the background as well as the observation.

scholarly journals Use of Microwave Radiances from Metop-C and Fengyun-3 C/D Satellites for a Northern European Limited-area Data Assimilation System

2021 ◽  
Author(s):  
Magnus Lindskog ◽  
Adam Dybbroe ◽  
Roger Randriamampianina
Keyword(s):  
Data Assimilation ◽  
Positive Impact ◽  
Weather Prediction ◽  
Implementation Process ◽  
Satellite System ◽  
Limited Area ◽  
Global Navigation Satellite ◽  
Microwave Radiances ◽  
Data Assimilation System ◽  
Assimilation System

AbstractMetCoOp is a Nordic collaboration on operational Numerical Weather Prediction based on a common limited-area km-scale ensemble system. The initial states are produced using a 3-dimensional variational data assimilation scheme utilizing a large amount of observations from conventional in-situ measurements, weather radars, global navigation satellite system, advanced scatterometer data and satellite radiances from various satellite platforms. A version of the forecasting system which is aimed for future operations has been prepared for an enhanced assimilation of microwave radiances. This enhanced data assimilation system will use radiances from the Microwave Humidity Sounder, the Advanced Microwave Sounding Unit-A and the Micro-Wave Humidity Sounder-2 instruments on-board the Metop-C and Fengyun-3 C/D polar orbiting satellites. The implementation process includes channel selection, set-up of an adaptive bias correction procedure, and careful monitoring of data usage and quality control of observations. The benefit of the additional microwave observations in terms of data coverage and impact on analyses, as derived using the degree of freedom of signal approach, is demonstrated. A positive impact on forecast quality is shown, and the effect on the precipitation for a case study is examined. Finally, the role of enhanced data assimilation techniques and adaptions towards nowcasting are discussed.

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