scholarly journals Evaluation of the Simulation of Typhoon Lekima (2019) Based on Different Physical Parameterization Schemes and FY-3D Satellite’s MWHS-2 Data Assimilation

2021 ◽  
Vol 13 (22) ◽  
pp. 4556
Author(s):  
Dongmei Xu ◽  
Xuewei Zhang ◽  
Hong Li ◽  
Haiying Wu ◽  
Feifei Shen ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Background Field ◽  
Weather Research And Forecasting ◽  
Initial Field ◽  
Parameterization Schemes ◽  
Microwave Humidity Sounder ◽  
Radiance Data ◽  
Physical Parameterization

In this study, the case of super typhoon Lekima, which landed in Jiangsu and Zhejiang Province on 4 August 2019, is numerically simulated. Based on the Weather Research and Forecasting (WRF) model, the sensitivity experiments are carried out with different combinations of physical parameterization schemes. The results show that microphysical schemes have obvious impacts on the simulation of the typhoon’s track, while the intensity of the simulated typhoon is more sensitive to surface physical schemes. Based on the results of the typhoon’s track and intensity simulation, one parameterization scheme was further selected to provide the background field for the following data assimilation experiments. Using the three-dimensional variational (3DVar) data assimilation method, the Microwave Humidity Sounder-2 (MWHS-2) radiance data onboard the Fengyun-3D satellite (FY-3D) were assimilated for this case. It was found that the assimilation of the FY-3D MWHS-2 radiance data was able to optimize the initial field of the numerical model in terms of the model variables, especially for the humidity. Finally, by the inspection of the typhoon’s track and intensity forecast, it was found that the assimilation of FY-3D MWHS-2 radiance data improved the skill of the prediction for both the typhoon’s track and intensity.

scholarly journals The Impact of Assimilating Atmospheric Infrared Sounder Observation on the Forecast of Typhoon Tracks

2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Chien-Ben Chou ◽  
Huei-Ping Huang
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Atmospheric Infrared Sounder ◽  
Background Error ◽  
Forecasting System ◽  
Typhoon Tracks ◽  
The Impact ◽  
Decorrelation Scale

This work assesses the effects of assimilating atmospheric infrared sounder (AIRS) observations on typhoon prediction using the three-dimensional variational data assimilation (3DVAR) and forecasting system of the weather research and forecasting (WRF) model. Two major parameters in the data assimilation scheme, the spatial decorrelation scale and the magnitude of the covariance matrix of the background error, are varied in forecast experiments for the track of typhoon Sinlaku over the Western Pacific. The results show that within a wide parameter range, the inclusion of the AIRS observation improves the prediction. Outside this range, notably when the decorrelation scale of the background error is set to a large value, forcing the assimilation of AIRS data leads to degradation of the forecast. This illustrates how the impact of satellite data on the forecast depends on the adjustable parameters for data assimilation. The parameter-sweeping framework is potentially useful for improving operational typhoon prediction.

scholarly journals Assimilation of Himawari-8 imager radiance data with the WRF-3DVAR system for the prediction of Typhoon Soudelor

2021 ◽  
Vol 21 (5) ◽  
pp. 1569-1582
Author(s):  
Feifei Shen ◽  
Aiqing Shu ◽  
Hong Li ◽  
Dongmei Xu ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Inner Core ◽  
Surface Wind ◽  
Three Dimensional ◽  
Background Field ◽  
Japan Meteorological Agency ◽  
The Pacific ◽  
Geostationary Meteorological Satellite ◽  
Typhoon Season ◽  
Radiance Data

Abstract. Himawari-8 is a next-generation geostationary meteorological satellite launched by the Japan Meteorological Agency. It carries the Advanced Himawari Imager (AHI) on board, which can continuously monitor high-impact weather events with high frequency in space and time. The assimilation of AHI radiance data was implemented with the three-dimensional variational data assimilation system (3DVAR) of the Weather Research and Forecasting Model for the analysis and prediction of Typhoon Soudelor (2015) in the Pacific typhoon season. The effective assimilation of AHI radiance data in improving the forecast of the tropical cyclone during its rapid intensification has been realized. The results show that, after assimilating the AHI radiance data under clear-sky conditions, the typhoon position in the background field of the model was effectively corrected compared with the control experiment without AHI radiance data assimilation. It is found that the assimilation of AHI radiance data is able to improve the analyses of the water vapor and wind in a typhoon's inner-core region. The analyses and forecasts of the minimum sea level pressure, the maximum surface wind, and the track of the typhoon are further improved.

scholarly journals Application of ATOVS Radiance with ARW WRF/GSI Data Assimilation System in the Prediction of Hurricane Katrina

2009 ◽  
Vol 3 (1) ◽  
pp. 13-28 ◽  
Author(s):  
J. Xu ◽  
S. Rugg ◽  
M. Horner ◽  
L. Byerle
Keyword(s):  
Data Assimilation ◽  
Hurricane Katrina ◽  
Three Dimensional ◽  
Wrf Model ◽  
Vortex Center ◽  
Atmospheric Flow ◽  
Infrared Observation ◽  
Westward Movement ◽  
Radiance Data ◽  
The Impact

In this study, we evaluated the impact of directly assimilating radiance on Hurricane Katrina forecasts over the Gulf of Mexico in the southeastern United States in August 2005. The ATOVS (i.e., The Advanced Television and Infrared Observation Satellite (TIROS)-N Operational Vertical Sounder) radiance data, the Gridpoint Statistical Interpolation (GSI) three-dimensional variational analysis (3DVAR) system, and the Advanced Research WRF (ARW WRF) model were employed. The results in a series of experiments show that after radiance data assimilation, the intensity and structure of initial fields including atmospheric flow, temperature and moisture have been modified somehow, especially with instruments using microwave bands such as AMSU-A/B. An anomalous southward pressure gradient has been added behind the hurricane center, which made the easterly flow go through the initial vortex center, accelerating westward movement of the hurricane. All data assimilation experiments obtain a similar forecast for the hurricane track before 36 h of model integration. After 36 h, the hurricane tracks in AMSU-A/B experiments are closer to the best track, but the tracks in HIRS3 and control experiments have a bigger error. However, we note that the improvement is limited, all assimilation experiments did not properly depict the deepening of the hurricane center around 1800 UTC 28 August.

Comparison of Two-Moment Bulk Microphysics Schemes in Idealized Supercell Thunderstorm Simulations

2011 ◽  
Vol 139 (4) ◽  
pp. 1103-1130 ◽  
Author(s):  
Hugh Morrison ◽  
Jason Milbrandt
Keyword(s):  
Three Dimensional ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Cold Pool ◽  
Fall Velocity ◽  
Surface Precipitation ◽  
Single Category ◽  
Supercell Thunderstorm ◽  
Drop Size Distributions ◽  
Reduced Surface

Idealized three-dimensional supercell simulations were performed using the two-moment bulk microphysics schemes of Morrison and Milbrandt–Yau in the Weather Research and Forecasting (WRF) model. Despite general similarities in these schemes, the simulations were found to produce distinct differences in storm structure, precipitation, and cold pool strength. In particular, the Morrison scheme produced much higher surface precipitation rates and a stronger cold pool, especially in the early stages of storm development. A series of sensitivity experiments was conducted to identify the primary differences between the two schemes that resulted in the large discrepancies in the simulations. Different approaches in treating graupel and hail were found to be responsible for many of the key differences between the baseline simulations. The inclusion of hail in the baseline simulation using the Milbrant–Yau scheme with two rimed-ice categories (graupel and hail) had little impact, and therefore resulted in a much different storm than the baseline run with the single-category (hail) Morrison scheme. With graupel as the choice of the single rimed-ice category, the simulated storms had considerably more frozen condensate in the anvil region, a weaker cold pool, and reduced surface precipitation compared to the runs with only hail, whose higher terminal fall velocity inhibited lofting. The cold pool strength was also found to be sensitive to the parameterization of raindrop breakup, particularly for the Morrison scheme, because of the effects on the drop size distributions and the corresponding evaporative cooling rates. The use of a more aggressive implicit treatment of drop breakup in the baseline Morrison scheme, by limiting the mean–mass raindrop diameter to a maximum of 0.9 mm, opposed the tendency of this scheme to otherwise produce large mean drop sizes and a weaker cold pool compared to the hail-only run using the Milbrandt–Yau scheme.

scholarly journals Combinational Optimization of the WRF Physical Parameterization Schemes to Improve Numerical Sea Breeze Prediction Using Micro-Genetic Algorithm

2021 ◽  
Vol 11 (23) ◽  
pp. 11221
Author(s):  
Ji Won Yoon ◽  
Sujeong Lim ◽  
Seon Ki Park
Keyword(s):  
Genetic Algorithm ◽  
Wind Speed ◽  
Land Surface ◽  
Wrf Model ◽  
Longwave Radiation ◽  
Sea Breeze ◽  
Meteorological Variables ◽  
Parameterization Schemes ◽  
Micro Genetic Algorithm ◽  
Physical Parameterization

This study aims to improve the performance of the Weather Research and Forecasting (WRF) model in the sea breeze circulation using the micro-Genetic Algorithm (micro-GA). We found the optimal combination of four physical parameterization schemes related to the sea breeze system, including planetary boundary layer (PBL), land surface, shortwave radiation, and longwave radiation, in the WRF model coupled with the micro-GA (WRF-μGA system). The optimization was performed with respect to surface meteorological variables (2 m temperature, 2 m relative humidity, 10 m wind speed and direction) and a vertical wind profile (wind speed and direction), simultaneously for three sea breeze cases over the northeastern coast of South Korea. The optimized set of parameterization schemes out of the WRF-μGA system includes the Mellor–Yamada–Nakanishi–Niino level-2.5 (MYNN2) for PBL, the Noah land surface model with multiple parameterization options (Noah-MP) for land surface, and the Rapid Radiative Transfer Model for GCMs (RRTMG) for both shortwave and longwave radiation. The optimized set compared with the various other sets of parameterization schemes for the sea breeze circulations showed up to 29 % for the improvement ratio in terms of the normalized RMSE considering all meteorological variables.

scholarly journals A THREE-DIMENSIONAL AEROSOL VARIATIONAL DATA ASSIMILATION SYSTEM FOR AIRCRAFT AND SURFACE OBSERVATIONS

2018 ◽  
Vol XLII-3 ◽  
pp. 2215-2218
Author(s):  
Z. Zang ◽  
X. Pan ◽  
W. You ◽  
Y. Liang
Keyword(s):  
Data Assimilation ◽  
Local Time ◽  
Three Dimensional ◽  
Surface Concentration ◽  
Variational Data Assimilation ◽  
Weather Research And Forecasting ◽  
Aircraft Observations ◽  
Data Assimilation System ◽  
Surface Observations ◽  
Assimilation System

A three-dimensional variational data assimilation system is implemented within the Weather Research and Forecasting/Chemistry model, and the control variables consist of eight species of the Model for Simulation Aerosol Interactions and Chemistry scheme. In the experiments, the three-dimensional profiles of aircraft speciated observations and surface concentration observations acquired during the California Research at the Nexus of Air Quality and Climate Change field campaign are assimilated. The data assimilation experiments are performed at 02:00 local time 2 June 2010, assimilating surface observations at 02:00 and aircraft observations from 01:30 to 02:30 local time. The results show that the assimilation of both aircraft and surface observations improves the subsequent forecasts. The improved forecast skill resulting from the assimilation of the aircraft profiles persists a time longer than the assimilation of the surface observations, which suggests the necessity of vertical profile observations for extending aerosol forecasting time.

scholarly journals Vortex Initialization in the NCEP Operational Hurricane Models

Atmosphere ◽  
2020 ◽  
Vol 11 (9) ◽  
pp. 968
Author(s):  
Qingfu Liu ◽  
Xuejin Zhang ◽  
Mingjing Tong ◽  
Zhan Zhang ◽  
Bin Liu ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Background Field ◽  
Initial Field ◽  
The Future ◽  
Background Fields ◽  
Vortex Relocation ◽  
Hwrf Model ◽  
Vortex Initialization

This paper describes the vortex initialization (VI) currently used in NCEP operational hurricane models (HWRF and HMON, and possibly HAFS in the future). The VI corrects the background fields for hurricane models: it consists of vortex relocation, and size and intensity corrections. The VI creates an improved background field for the data assimilation and thereby produces an improved analysis for the operational hurricane forecast. The background field after VI can be used as an initial field (as in the HMON model, without data assimilation) or a background field for data assimilation (as in HWRF model).

scholarly journals A radar reflectivity operator with ice-phase hydrometeors for variational data assimilation (version 1.0) and its evaluation with real radar data

2019 ◽  
Vol 12 (9) ◽  
pp. 4031-4051 ◽  
Author(s):  
Shizhang Wang ◽  
Zhiquan Liu
Keyword(s):  
Data Assimilation ◽  
Three Dimensional ◽  
Wrf Model ◽  
Radar Data ◽  
Poor Quality ◽  
Variational Data Assimilation ◽  
Radar Reflectivity ◽  
Background Error ◽  
Background Fields ◽  
Ice Phase

Abstract. A reflectivity forward operator and its associated tangent linear and adjoint operators (together named RadarVar) were developed for variational data assimilation (DA). RadarVar can analyze both rainwater and ice-phase species (snow and graupel) by directly assimilating radar reflectivity observations. The results of three-dimensional variational (3D-Var) DA experiments with a 3 km grid mesh setting of the Weather Research and Forecasting (WRF) model showed that RadarVar was effective at producing an analysis of reflectivity pattern and intensity similar to the observed data. Two to three outer loops with 50–100 iterations in each loop were needed to obtain a converged 3-D analysis of reflectivity, rainwater, snow, and graupel, including the melting layers with mixed-phase hydrometeors. It is shown that the deficiencies in the analysis using this operator, caused by the poor quality of the background fields and the use of the static background error covariance, can be partially resolved by using radar-retrieved hydrometeors in a preprocessing step and tuning the spatial correlation length scales of the background errors. The direct radar reflectivity assimilation using RadarVar also improved the short-term (2–5 h) precipitation forecasts compared to those of the experiment without DA.

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