A Vortex Relocation Scheme for Tropical Cyclone Initialization in Advanced Research WRF

Abstract This paper introduces a relocation scheme for tropical cyclone (TC) initialization in the Advanced Research Weather Research and Forecasting (ARW-WRF) model and demonstrates its application to 70 forecasts of Typhoons Sinlaku (2008), Jangmi (2008), and Linfa (2009) for which Taiwan’s Central Weather Bureau (CWB) issued typhoon warnings. An efficient and dynamically consistent TC vortex relocation scheme for the WRF terrain-following mass coordinate has been developed to improve the first guess of the TC analysis, and hence improves the tropical cyclone initialization. The vortex relocation scheme separates the first-guess atmospheric flow into a TC circulation and environmental flow, relocates the TC circulation to its observed location, and adds the relocated TC circulation back to the environmental flow to obtain the updated first guess with a correct TC position. Analysis of these typhoon cases indicates that the relocation procedure moves the typhoon circulation to the observed typhoon position without generating discontinuities or sharp gradients in the first guess. Numerical experiments with and without the vortex relocation procedure for Typhoons Sinlaku, Jangmi, and Linfa forecasts show that about 67% of the first-guess fields need a vortex relocation to correct typhoon position errors while eliminates the topographical effect. As the vortex relocation effectively removes the typhoon position errors in the analysis, the simulated typhoon tracks are considerably improved for all forecast times, especially in the early periods as large adjustments appeared without the vortex relocation. Comparison of the horizontal and vertical vortex structures shows that large errors in the first-guess fields due to an incorrect typhoon position are eliminated by the vortex relocation scheme and that the analyzed typhoon circulation is stronger and more symmetric without distortions, and better agrees with observations. The result suggests that the main difficulty of objective analysis methods [e.g., three-dimensional variational data assimilation (3DVAR)], in TC analysis comes from poor first-guess fields with incorrect TC positions rather than not enough model resolution or observations. In addition, by computing the eccentricity and correlation of the axes of the initial typhoon circulation, the distorted typhoon circulation caused by the position error without the vortex relocation scheme is demonstrated to be responsible for larger track errors. Therefore, by eliminating the typhoon position error in the first guess that avoids a distorted initial typhoon circulation, the vortex relocation scheme is able to improve the ARW-WRF typhoon initialization and forecasts particularly when using data assimilation update cycling.

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

The Open Atmospheric Science Journal ◽

10.2174/1874282300903010013 ◽

2009 ◽

Vol 3 (1) ◽

pp. 13-28 ◽

Cited By ~ 7

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.

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

Geoscientific Model Development ◽

10.5194/gmd-12-4031-2019 ◽

2019 ◽

Vol 12 (9) ◽

pp. 4031-4051 ◽

Cited By ~ 1

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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Tropical Cyclone Initialization with a Spherical High-Order Filter and an Idealized Three-Dimensional Bogus Vortex

Monthly Weather Review ◽

10.1175/2009mwr2943.1 ◽

2010 ◽

Vol 138 (4) ◽

pp. 1344-1367 ◽

Cited By ~ 34

Author(s):

In-Hyuk Kwon ◽

Hyeong-Bin Cheong

Keyword(s):

Tropical Cyclone ◽

Three Dimensional ◽

Analysis Data ◽

High Order ◽

Positive Anomaly ◽

Order Filter ◽

Intensity Prediction ◽

Tangential Wind ◽

Tropical Cyclone Initialization ◽

Disturbance Field

Abstract A tropical cyclone initialization method with an idealized three-dimensional bogus vortex of an analytic empirical formula is presented for the track and intensity prediction. The procedure in the new method consists of four steps: the separation of the disturbance from the analysis, determination of the tropical cyclone domain, generation of symmetric bogus vortex, and merging of it with the analysis data. When separating the disturbance field, an efficient spherical high-order filter with the double-Fourier series is used whose cutoff scale can be adjusted with ease to the horizontal scale of the tropical cyclone of interest. The tropical cyclone domain is determined from the streamfunction field instead of the velocities. The axisymmetric vortex to replace the poorly resolved tropical cyclone in the analysis is designed in terms of analytic empirical functions with a careful treatment of the upper-layer flows as well as the secondary circulations. The geopotential of the vortex is given in such a way that the negative anomaly in the lower layer is changed into positive anomaly above the prescribed pressure level, which depends on the intensity of the tropical cyclone. The geopotential is then used to calculate the tangential wind and temperature using the gradient wind balance and the hydrostatic balance, respectively. The inflow and outflow in the tropical cyclone are constructed to resemble closely the observed or simulated structures under the constraint of mass balance. The bogus vortex is merged with the disturbance field with the use of matching principle so that it is not affected except near the boundary of tropical cyclone domain. The humidity of the analysis is modified to be very close to the saturation in the lower layers near the tropical cyclone center. The balanced bogus vortex of the present study is completely specified on the basis of four parameters from the Regional Specialized Meteorological Center (RSMC) report and the additional two parameters, which are derived from the analysis data. The initialization method was applied to the track and the intensity (in terms of central pressure) prediction of the TCs observed in the western North Pacific Ocean and East China Sea in 2007 with the use of the Weather Research and Forecasting (WRF) model. No significant initial jump or abrupt change was seen in either momentum or surface pressure during the time integration, thus indicating a proper tropical cyclone initialization. Relative to the results without the tropical cyclone initialization and the forecast results of RSMC Tokyo, the present method presented a great improvement in both the track and intensity prediction.

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An Impact Assessment of GPS Radio Occultation Data on Prediction of a Rapidly Developing Cyclone over the Southern Ocean*

Monthly Weather Review ◽

10.1175/mwr-d-14-00024.1 ◽

2014 ◽

Vol 142 (11) ◽

pp. 4187-4206 ◽

Cited By ~ 10

Author(s):

Shu-Ya Chen ◽

Tae-Kwon Wee ◽

Ying-Hwa Kuo ◽

David H. Bromwich

Keyword(s):

Data Assimilation ◽

Southern Ocean ◽

Large Scale ◽

Radio Occultation ◽

Three Dimensional ◽

Wrf Model ◽

The Other ◽

Gps Radio Occultation ◽

The Antarctic ◽

The Impact

Abstract The impact of global positioning system (GPS) radio occultation (RO) data on an intense synoptic-scale storm that occurred over the Southern Ocean in December 2007 is evaluated, and a synoptic explanation of the assessed impact is offered. The impact is assessed by using the three-dimensional variational data assimilation scheme (3DVAR) of the Weather Research and Forecasting (WRF) Model Data Assimilation system (WRFDA), and by comparing two experiments: one with and the other without assimilating the refractivity data from four different RO missions. Verifications indicate significant positive impacts of the RO data in various measures and parameters as well as in the track and intensity of the Antarctic cyclone. The analysis of the atmospheric processes underlying the impact shows that the assimilation of the RO data yields substantial improvements in the large-scale circulations that in turn control the development of the Antarctic storm. For instance, the RO data enhanced the strength of a 500-hPa trough over the Southern Ocean and prevented the katabatic flow near the coast of East Antarctica from an overintensification. This greatly influenced two low pressure systems of a comparable intensity, which later merged together and evolved into the major storm. The dominance of one low over the other in the merger dramatically changed the track, intensity, and structure of the merged storm. The assimilation of GPS RO data swapped the dominant low, leading to a remarkable improvement in the subsequent storm’s prediction.

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

Remote Sensing ◽

10.3390/rs13224556 ◽

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.

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

Advances in Meteorology ◽

10.1155/2011/803593 ◽

2011 ◽

Vol 2011 ◽

pp. 1-10 ◽

Cited By ~ 1

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.

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Impact of Lidar Data Assimilation on Low-Level Wind Shear Simulation at Lanzhou Zhongchuan International Airport, China: A Case Study

Atmosphere ◽

10.3390/atmos11121342 ◽

2020 ◽

Vol 11 (12) ◽

pp. 1342

Author(s):

Lanqian Li ◽

Ningjing Xie ◽

Longyan Fu ◽

Kaijun Zhang ◽

Aimei Shao ◽

...

Keyword(s):

Data Assimilation ◽

Wind Shear ◽

Three Dimensional ◽

Wrf Model ◽

Lidar Data ◽

Positive Role ◽

Low Level ◽

Wind Lidar ◽

The Impact ◽

Doppler Wind Lidar

Doppler wind lidar has played an important role in alerting low-level wind shear (LLW). However, these high-resolution observations are underused in the model-based analysis and forecasting of LLW. In this regard, we employed the Weather Research and Forecasting (WRF) model and its three-dimensional variational (3D-VAR) system to investigate the impact of lidar data assimilation (DA) on LLW simulations. Eight experiments (including six assimilation experiments) were designed for an LLW process as reported by pilots, in which different assimilation intervals, assimilation timespans, and model vertical resolutions were examined. Verified against observations from Doppler wind lidar and an automated weather observing system (AWOS), the introduction of lidar data is helpful for describing the LLW event, which can represent the temporal and spatial features of LLW, whereas experiments without lidar DA have no ability to capture LLW. While lidar DA has an obviously positive role in simulating LLW in the 10–20 min after the assimilation time, this advantage cannot be maintained over a longer time. Therefore, a smaller assimilation interval is favorable for improving the simulated effect of LLW. In addition, increasing the vertical resolution does not evidently improve the experimental results, either with or without assimilation.

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Intercomparison and Coupling of Ensemble and Four-Dimensional Variational Data Assimilation Methods for the Analysis and Forecasting of Hurricane Karl (2010)

Monthly Weather Review ◽

10.1175/mwr-d-13-00394.1 ◽

2014 ◽

Vol 142 (9) ◽

pp. 3347-3364 ◽

Cited By ~ 16

Author(s):

Jonathan Poterjoy ◽

Fuqing Zhang

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Hybrid System ◽

Ensemble Kalman Filter ◽

Computational Cost ◽

Wrf Model ◽

Variational Data Assimilation ◽

Weather Research And Forecasting ◽

Forecast Errors ◽

Verification Methods

This study examines the performance of ensemble and variational data assimilation systems for the Weather Research and Forecasting (WRF) Model. These methods include an ensemble Kalman filter (EnKF), an incremental four-dimensional variational data assimilation (4DVar) system, and a hybrid system that uses a two-way coupling between the two approaches (E4DVar). The three methods are applied to assimilate routinely collected data and field observations over a 10-day period that spans the life cycle of Hurricane Karl (2010), including the pregenesis disturbance that preceded its development into a tropical cyclone. In general, forecasts from the E4DVar analyses are found to produce smaller 48–72-h forecast errors than the benchmark EnKF and 4DVar methods for all variables and verification methods tested in this study. The improved representation of low- and midlevel moisture and vorticity in the E4DVar analyses leads to more accurate track and intensity predictions by this system. In particular, E4DVar analyses provide persistently more skillful genesis and rapid intensification forecasts than the EnKF and 4DVar methods during cycling. The data assimilation experiments also expose additional benefits of the hybrid system in terms of physical balance, computational cost, and the treatment of asynoptic observations near the beginning of the assimilation window. These factors make it a practical data assimilation method for mesoscale analysis and forecasting, and for tropical cyclone prediction.

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A 3DVAR-Based Dynamical Initialization Scheme for Tropical Cyclone Predictions*

Weather and Forecasting ◽

10.1175/waf-d-10-05066.1 ◽

2012 ◽

Vol 27 (2) ◽

pp. 473-483 ◽

Cited By ~ 8

Author(s):

Shengjun Zhang ◽

Tim Li ◽

Xuyang Ge ◽

Melinda Peng ◽

Ning Pan

Keyword(s):

Tropical Cyclone ◽

Data Assimilation ◽

Global Analysis ◽

Three Dimensional ◽

Preliminary Test ◽

Intensity Change ◽

Observation System ◽

Wind Fields ◽

Warm Core ◽

Initialization Scheme

Abstract A combined tropical cyclone dynamic initialization–three-dimensional variational data assimilation scheme (TCDI–3DVAR) is proposed. The specific procedure for the new initialization scheme is described as follows. First, a first-guess vortex field derived from a global analysis will be spun up in a full-physics mesoscale regional model in a quiescent environment. During the spinup period, the weak vortex is forced toward the observed central minimum sea level pressure (MSLP). The so-generated balanced TC vortex with realistic MSLP and a warm core is then merged into the environmental field and used in the subsequent 3DVAR data assimilation. The observation system simulation experiments (OSSEs) demonstrate that this new TC initialization scheme leads to much improved initial MSLP, warm core, and asymmetric temperature patterns compared to those from the conventional 3DVAR scheme. Forecasts of TC intensity with the new initialization scheme are made, and the results show that the new scheme is able to predict the “observed” TC intensity change, compared to runs with the conventional 3DVAR scheme or the TCDI-only scheme. Sensitivity experiments further show that the intensity forecasts with knowledge of the initial MSLP and wind fields appear more skillful than do the cases where the initial MSLP, temperature, and humidity fields are known. The numerical experiments above demonstrate the potential usefulness of the proposed new initialization scheme in operational applications. A preliminary test of this scheme with a navy operational model shows encouraging results.

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A Study on Assimilation of CYGNSS Wind Speed Data for Tropical Convection during 2018 January MJO

Remote Sensing ◽

10.3390/rs12081243 ◽

2020 ◽

Vol 12 (8) ◽

pp. 1243 ◽

Cited By ~ 3

Author(s):

Xuanli Li ◽

John R. Mecikalski ◽

Timothy J. Lang

Keyword(s):

Wind Speed ◽

Tropical Cyclone ◽

Data Assimilation ◽

Positive Impact ◽

Specular Reflection ◽

Surface Wind ◽

Wrf Model ◽

Precipitation Forecast ◽

Wind Speed Data ◽

The Impact

The National Aeronautics and Space Administration (NASA) Cyclone Global Navigation Satellite System (CYGNSS) mission was launched in December 2016. CYGNSS provides ocean surface wind speed retrieval along specular reflection tracks at an interval resolution of approximately 25 km. With a median revisit time of 2.8 h covering a ±35° latitude, CYGNSS can provide more frequent and accurate measurements of surface wind over the tropical oceans under heavy precipitation, especially within tropical cyclone cores and deep convection regions, than traditional scatterometers. In this study, CYGNSS v2.1 Level 2 wind speed data were assimilated into the Weather Research and Forecasting (WRF) model using the WRF Data Assimilation (WRFDA) system with hybrid 3- and 4-dimensional variational ensemble technology. Case studies were conducted to examine the impact of the CYGNSS data on forecasts of tropical cyclone (TC) Irving and a westerly wind burst (WWB) during the Madden–Julian oscillation (MJO) event over the Indian Ocean in early January 2018. The results indicate a positive impact of the CYGNSS data on the wind field. However, the impact from the CYGNSS data decreases rapidly within 4 h after data assimilation. Also, the influence of CYGNSS data only on precipitation forecast is found to be limited. The assimilation of CYGNSS data was further explored with an additional experiment in which CYGNSS data was combined with Global Precipitation Mission (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) hourly precipitation and Advanced Scatterometer (ASCAT) wind vector and were assimilated into the WRF model. A significant positive impact was found on the tropical cyclone intensity and track forecasts. The short-term forecast of wind and precipitation fields were also improved for both TC Irving and the WWB event when the combined satellite data was assimilated.

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