EXPERIMENTS OF ENSEMBLE FORECAST OF TYPHOON TRACK USING BDA PERTURBING METHOD

A new method, BDA perturbing, is used in ensemble forecasting of typhoon track. This method is based on the Bogus Data Assimilation scheme. It perturbs the initial position and intensity of typhoons and gets a series of bogus vortex. Then each bogus vortex is used in data assimilation to obtain initial conditions. Ensemble forecast members are constructed by conducting simulation with these initial conditions. Some cases of typhoon are chosen to test the validity of this new method and the results show that: using the BDA perturbing method to perturb initial position and intensity of typhoon for track forecast can improve accuracy, compared with the direct use of the BDA assimilation scheme. And it is concluded that a perturbing amplitude of intensity of 5 hPa is probably more appropriate than 10 hPa if the BDA perturbing method is used in combination with initial position perturbation.     

APPLICATION OF BDA SCHEME IN TYPHOON TRACK PREDICTION

The MM5, which is the PSU/NCAR mesoscale nonhydrostatic limited-area model, and its adjoining modeling system are used in this paper. Taking T106 analysis data as background field the authors generate an optimal initial condition of a typhoon by using two bogus data assimilation schemes, and conduct some numerical simulating experiments. The results of No.9608 typhoon (Gloria) show that the optimal initial field have some dramatic improvements, such as inaccurate position of typhoon center, weaker typhoon circulation and incomplete inner structure of the typhoon, which are caused by shortage of data over the sea. Some improvements have been made in the track forecast. Through several comparing experiments, the initialization optimized by BDA scheme is found to be more reasonable than GFDL scheme and its typhoon track forecast is better.     

Numerical Simulation of a Landfall Typhoon Using a Bogus Data Assimilation Scheme

A typhoon bogus data assimilation scheme (BDA) using dimension-reduced projection four-dimen-sional variational data assimilation (DRP-4-DVar),called DRP-BDA for short,is built in the Advanced Regional Eta Model (AREM).As an adjoint-free approach,DRP-BDA saves time,and only several minutes are taken for the full BDA process.To evaluate its performance,the DRP-BDA is applied to a case study on a landfall ty-phoon,Fengshen (2008),from the Northwestern Pacific Ocean to Guangdong province,in which the bogus sea level pressure (SLP) is assimilated as a kind of observa-tion.The results show that a more realistic typhoon with correct center position,stronger warm core vortex,and more reasonable wind fields is reproduced in the analyzed initial condition through the new approach.Compared with the control run (CTRL) initialized with NCEP Final (FNL) Global Tropospheric Analyses,the DRP-BDA leads to an evidently positive impact on typhoon track forecasting and a small positive impact on typhoon inten-sity forecasting.Furthermore,the forecast landfall time conforms to the observed landfall time,and the forecast track error at the 36th hour is 32 km,which is much less than that of the CTRL (450 km).     

EXPERIMENTAL STUDY ON A DYNAMIC ASYMMETRICAL TYPHOON INITIALIZATION SCHEME BASED ON 4D-VAR

Axisymmetric bogus vortexes at sea level are usually used in the traditional bogus data assimilation (BDA) scheme. In the traditional scheme, the vortex could not accurately describe the specific characteristics of a typhoon, and the evolving real typhoon is forced to unreasonably adapt to this changeless vortex. For this reason, an asymmetrical typhoon bogus method with information blended from the analysis and the observation is put forward in this paper, in which the impact of the Subtropical High is also taken into consideration. With the fifth-generation Penn State/NCAR Mesoscale Model (MM5) and its adjoint model, a four-dimensional variational data assimilation (4D-Var) technique is employed to build a dynamic asymmetrical BDA scheme to assimilate different asymmetrical bogus vortexes at different time. The track and intensity of six summer typhoons much influenced by the Subtropical High are simulated and the results are compared. It is shown that the improvement in track simulation in the new scheme is more significant than that in the traditional scheme. Moreover, the periods for which the track cannot be simulated well by the traditional scheme can be improved with the new scheme. The results also reveal that although the simulated typhoon intensity in the new scheme is generally weaker than that in the traditional scheme, this trend enables the new scheme to simulate, in the later period, closer-to-observation intensity than the traditional scheme. However, despite the fact that the observed intensity has been largely weakened, the simulated intensity at later periods of the BDA schemes is still very intensive, resulting in overly development of the typhoon during the simulation. The limitation to the simulation effect of the BDA scheme due to this condition needs to be further studied.     

INVESTIGATION ON EFFECTS OF INITIAL SCHEMES FOR BINARY TYPHOONS ROKE AND SONCA IN 2011

Based on the Tropical Region Atmospheric Modeling System for South China Sea (TRAMS), Typhoon Roke (1115) and Sonca (1116) in 2011 which have large forecast errors in numerical operation prediction, have been selected for research focusing on the initial scheme and its influence on forecast. The purpose is to find a clue for model improvement and enhance the performance of the typhoon model. Several initialization schemes have been designed and the corresponding experiments have been done for Typhoon Roke and Sonca. The results show that the forecast error of both typhoons’ track and intensity are less using the initial scheme of relocation and bogus just for the weak Typhoon Sonca, compared with using the scheme for both typhoons. By analysis the influence of the scheme on weak typhoon vortex circulation may be the reason that leads to the improvement. All weak typhoons in 2011 to 2012 are selected for tests. It comes to the conclusion that the initial scheme of relocation and bogus can reduce the error of track and intensity forecast. Besides, the height of cloud top in typhoon vortex constructed by bogus is too high according to weak typhoon. It is feasible to develop a bogus which is suitable for weak typhoon.     

The Effects of Assimilating Satellite Brightness Temperature and Bogus Data on the Simulation of Typhoon Kalmaegi (2008)

Observational and bogus satellite data are directly assimilated into the Weather Research and Forecasting (WRF) model in simulations of Typhoon Kalmaegi (2008). The data assimilation is performed using the Radiative Transfer for TIROS-N Operational Vertical Sounder (RTTOV) model and the three-dimensional variational data assimilation (3DVAR) technique, with satellite observations taken from the National Oceanic and Atmospheric Administration-16 (NOAA-16) Advanced TIROS Vertical Sounder (ATOVS) system composed of the High-resolution Infrared Radiation Sounder (HIRS), the Advanced Microwave Sounding Unit-A (AMSU-A), and the Advanced Microwave Sounding Unit-B (AMSU-B). Data assimilation experiments are initialized at three different times. Improvements in the numerical simulation of the typhoon are discussed in the context of wind, temperature, pressure, and geopotential fields. The results indicate that assimilation of satellite data can improve both the representation of the initial conditions and the subsequent simulation of the typhoon. Different satellite data have different impacts on the typhoon track. In these simulations, data from AMSU-A play a greater role in improving the simulation of the typhoon than data from AMSU-B or HIRS. Assimilation of satellite data significantly affects the simulation of the subtropical high and the steering of the typhoon by the environmental flow. The subtropical high is enhanced and extends westward in the data assimilation experiments. The background flow therefore steers the typhoon more westward, improving the simulated typhoon track. Although direct assimilation of satellite brightness temperature improves the simulated environmental conditions, it does not significantly improve the simulated intensity of the typhoon. By contrast, initializing the typhoon simulation using bogus data in tandem with satellite data improves not only the environmental conditions but also the simulated inner-core structure of the typhoon. Assimilation of both types of data therefore improves the simulation of both the typhoon track and the typhoon intensity. The results of these experiments offer new insight into improving numerical simulations of typhoons.     

THE EFFECTS OF BOGUS TYPHOON AND OBSERVED OCEANIC DATA ON THE ABILITY OF T_(213)L_(31) TO PREDICT TC TRACK

The abilities of typhoon (TC) track prediction by a medium-range forecast model T213L31 at National Meteorological Center are analyzed and its ability to improve its TC forecasts is discussed. The results show that about 57% of the TCs could be predicted by T213L31 but the initial position errors are large.The 43% area without the prediction of TC tracks is concentrated between 13°N and 20°N and east of 120°E and lack of conventional observation data is the main reason for the absence of TC prediction in this area.The adding of bogus TC could improve the ability of TC track prediction when there is no TC vortex in the analysis field, but could only have positive effects on the short-range TC track prediction when there is TC vortex in the T213L31 analysis field.     

Numerical Study on the Impacts of the Bogus Data Assimilation and Sea Spray Parameterization on Typhoon Ducts

The Weather Research and Forecasting model version 3.2 (WRF v3.2) was used with the bogus data assimilation (BDA) scheme and sea spray parameterization (SSP), and experiments were conducted to assess the impacts of the BDA and SSP on prediction of the typhoon ducting process induced by Typhoon Mindule (2004). The global positioning system (GPS) dropsonde observations were used for comparison. The results show that typhoon ducts are likely to form in every direction around the typhoon center, with the main type of ducts being elevated duct. With the BDA scheme included in the model initialization, the model has a better performance in predicting the existence, distribution, and strength of typhoon ducts. This improvement is attributed to the positive effect of the BDA scheme on the typhoon’s ambient boundary layer structure. Sea spray affects typhoon ducts mainly by changing the latent heat (LH) flux at the air-sea interface beyond 270 km from the typhoon center. The strength of the typhoon duct is enhanced when the boundary layer under this duct is cooled and moistened by the sea spray; otherwise, the typhoon duct is weakened. The sea spray induced changes in the air-sea sensible heat (SH) flux and LH flux are concentrated in the maximum wind speed area near the typhoon center, and the changes are significantly weakened with the increase of the radial range.     

EXPERIMENTAL STUDIES WITH AN OPERATIONAL NUMERICAL MODEL FOR PREDICTION OF TYPHOON TRACKS OVER THE SOUTH CHINA SEA REGION

Experiments for short range forecasting of typhoon tracks over the South China Sea Region were carried out using limited-area numerical model recently developed by the authors.Due to the shortage of sounding data over the sea, typhoon circulations are usually obtained incompletely from objective analysis, or with significant deviation of the centre from reality.Therefore a set of schemes for typhoon initialization are proposed here to construct a bogus typhoon with the circulation being consistent with the physical processes included in the limited-area model.Based on the schemes bogus data over the model grids are created and analysed together with conventional observational data.Then the normal model initialization scheme is issued to further modulate the model typhoon with the numeriCal modeel.Some experiments of sensitivity in various aspects are conducted for further improvement of typhoon track predictions.The experimental results for a number of typhoon landing in southern China in 1993 and 1994 shows that the limited-area model is capable of predicting typhoon tracks in the southern China region.     

The effect of three-dimensional variational data assimilation of QuikSCAT data on the numerical simulation of typhoon track and intensity

In this paper, the three-dimensional variational data assimilation scheme (3DVAR) in the mesoscalemod el version 5 (MM5) of the US Pennsylvania State University/National Center for Atmospheric Research is used to study the effect of assimilating the sea-wind data from QuikSCAT on the prediction of typhoon track and intensity. The case of Typhoon Dujuan (2003) is first tested and the results show appreciable improvements. Twelve other cases in 2003 are then evaluated. The assimilation of the QuikSCAT data produces significant impacts on the structure of Dujuan in terms of the horizontal and vertical winds, sealevel pressure and temperature at the initial time. With the assimilation, the 24-h (48-h) track prediction of 11 (10) out of the 12 typhoons is improved. The 24-h (48-h) prediction of typhoon intensity is also improved in 10 (9) of the 12 cases. These experiments therefore demonstrate that assimilation of the QuikSCAT sea-wind data can increase the accuracy of typhoon track and intensity predictions through modification of the initial fields associated with the typhoon.     

TAMDAR Observation Assimilation in WRF 3D-Var and Its Impact on Hurricane Ike (2008) Forecast

This study evaluates the impact of atmospheric observations from the Tropospheric Airborne Meteorological Data Reporting (TAMDAR) observing system on numerical weather prediction of hurricane Ike (2008) using three-dimensional data assimilation system for the Weather Research and Forecast (WRF) model (WRF 3D-Var). The TAMDAR data assimilation capability is added to WRF 3D-Var by incorporating the TAMDAR observation operator and corresponding observation processing procedure. Two 6-h cycling data assimilation and forecast experiments are conducted. Track and intensity forecasts are verified against the best track data from the National Hurricane Center. The results show that, on average, assimilating TAMDAR observations has a positive impact on the forecasts of hurricane Ike. The TAMDAR data assimilation reduces the track errors by about 30 km for 72-h forecasts. Improvements in intensity forecasts are also seen after four 6-h data assimilation cycles. Diagnostics show that assimilation of TAMDAR data improves subtropical ridge and steering flow in regions along Ike’s track, resulting in better forecasts.     

IMPROVED SCHEME OF AXISYMMETRIC TYPHOON BOGUS MODEL AND ITS IMPACT ON NUMERICAL SIMULATION OF TYPHOON NOCKTEN （NO.0405）

There is distinct difference in the tangential wind profile between different typhoons in the western North Pacific. At present, only two parameters, maximum wind and radius of maximum wind, are used in NCAR-AFWA bogus for MM5 mesoscale numerical model. As a result, sometimes the outer structure of typhoon cannot be described accurately. The tangential wind profile of NCAR-AFWA bogus is improved by introducing radii of 25.7 m/s and 15.4 m/s, and then the track and intensity of Typhoon Nockten (No.0425) are simulated. The results show that the simulations of track and intensity of typhoon both have been improved by simultaneously introducing the radii in the tangential wind profile of typhoon bogus. At the same time, there is improvement in the gale wind range of the typhoon simulated.     

ASSIMILATION OF REAL OBSERVATIONAL DATA WITH THE GSI-HYBRID DATA ASSIMILATION SYSTEM TO IMPROVE TYPHOON FORECAST

A hybrid GSI (Grid-point Statistical Interpolation)-ETKF (Ensemble Transform Kalman Filter) data assimilation system has been recently developed for the WRF (Weather Research and Forecasting) model and tested with simulated observations for tropical cyclone (TC) forecast. This system is based on the existing GSI but with ensemble background information incorporated. As a follow-up, this work extends the new system to assimilate real observations to further understand the hybrid scheme. As a first effort to explore the system with real observations, relatively coarse grid resolution (27 km) is used. A case study of typhoon Muifa (2011) is performed to assimilate real observations including conventional in-situ and satellite data. The hybrid system with flow-dependent ensemble covariance shows significant improvements with respect to track forecast compared to the standard GSI system which in theory is three dimensional variational analysis (3DVAR). By comparing the analyses, analysis increments and forecasts, the hybrid system is found to be potentially able to recognize the existence of TC vortex, adjust its position systematically, better describe the asymmetric structure of typhoon Muifa and maintain the dynamic and thermodynamic balance in typhoon initial field. In addition, a cold-start hybrid approach by using the global ensembles to provide flow-dependent error is tested and similar results are revealed with those from cycled GSI-ETKF approach.     

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.     

Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an Ensemble Kalman Filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution

Extending an earlier study, the best track minimum sea level pressure (MSLP) data are assimilated for landfalling Hurricane Ike (2008) using an ensemble Kalman filter (EnKF), in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS (Global Forecast System) analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity (Z) only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind (Vr) data (assimilated at 10-min intervals), but assimilating MSLP at 60-min intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly (noticeably) larger positive impact than Vr(Z) data. When Vr or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.     

Blacklist Design of AMDAR Temperature Data and Their Application in the CMA-GFS

Blacklist methods are used in the CMA Global Forecasting System (CMA-GFS) to improve the application of aircraft temperature data to numerical weather prediction in the Northern Hemisphere and the tropics. In this paper, the ERA5 re-analysis data are used to analyze aircraft temperature observation errors of each aircraft and a blacklist is established using pre-quality controls and threshold methods. The blacklist-filtered and blacklisted aircraft temperature data are then applied to the four-dimensional variational assimilation system, respectively, and an assimilation cycle forecast for the period from September 1 to 30, 2019 is carried out. The results show an uneven distribution in the global aircraft blacklist data. After the application of the blacklist methods, the RMSE of geopotential height and temperature analysis field decrease in the vertical direction by a maximum of ~ 1.5 gpm at 200 hPa and ~ 0.15 K at 250 hPa, respectively. Overall, the blacklist methods of aircraft temperature data improve the analysis and forecast in the CMA-GFS.     

TOPOGRAPHIC DEPENDENT HORIZONTAL LOCALIZATION SCALE SCHEME IN GRAPES-MESO HYBRID EN-3DVAR ASSIMILATION SYSTEM

Based on the GRAPES-MESO hybrid En-3DVAR (Ensemble three-dimension hybrid data assimilation for Global/Regional Assimilation and Prediction system) constructed by China Meteorological Administration, a 7-day simulation (from 10 July 2015 to 16 July 2015) is conducted for horizontal localization scales. 48h forecasts have been designed for each test, and seven different horizontal localization scales of 250, 500, 750, 1000, 1250, 1500 and 1750 km are set. The 7-day simulation results show that the optimal horizontal localization scales over the Tibetan Plateau and the plain area are 1500 km and 1000 km, respectively. As a result, based on the GRAPES-MESO hybrid En-3DVAR, a topography-dependent horizontal localization scale scheme (hereinafter referred to as GRAPES-MESO hybrid En-3DVAR-TD-HLS) has been constructed. The data assimilation and forecast experiments have been implemented by GRAPES-MESO hybrid En-3DVAR, 3DVAR and GRAPES-MESO hybrid En-3DVAR-TD-HLS, and then the analysis and forecast field of these three systems are compared. The results show that the analysis field and forecast field within 30h of GRAPES-MESO hybrid En-3DVAR-TD-HLS are better than those of the other two data assimilation systems. Particularly in the analysis field, the root mean square error (RMSE) of u_wind and v_wind in the entire vertical levels is significantly less than that of the other two systems. The time series of total RMSE indicate, in the 6-30h forecast range, that the forecast result of En-3DVAR-TD-HLS is better than that of the other two systems, but the En-3DVAR and 3DVAR are equivalent in terms of their forecast skills. The 36-48h forecasts of three data assimilation systems have similar forecast skill.     

VERIFICATION OF TYPHOON FORECASTS BY THE GRAPES MODEL

Four landfalling typhoon cases in 2005 were selected for a numerical simulation study with the Global/Regional Assimilation and Prediction System (GRAPES) model. The preliminary assessment results of the performance of the model, including the predictions of typhoon track, landfall time, location and intensity, etc., are presented and the sources of errors are analyzed. The 24-hour distance forecast error of the typhoon center by the model is shown to be about 131 km, while the 48-hour error is 252 km. The model was relatively more skilful at forecasts of landfall time and locations than those of intensity at landfall. On average, the 24-hour forecasts were slightly better than the 48-hour ones. An analysis of data impacts indicates that the assimilation of unconventional observation data is essential for the improvement of the model simulation. The model could also be improved by increasing model resolution to simulate the mesoscale and fine scale systems and by improving methods of terrain refinement processing.     

A New Typhoon Bogus Data Assimilation and its Sampling Method: A Case Study

In this study,the authors introduce a new bogus data assimilation method based on the dimension-reduced projection 4-DVar,which can resolve the cost function directly in low-dimensional space.The authors also try a new method to improve the quality of samples,which are the base of dimension-reduced space projection bogus data assimilation (DRP-BDA).By running a number of numerical weather models with different model parameterization combinations on the typhoon Sinlaku,the authors obtained two groups of samples with different spreads and similarities.After DRP-BDA,the results show that,compared with the control runs,the simulated typhoon center pressure can be deepened by more than 20 hPa to 30 hPa and that the intensity can last as long as 60 hours.The mean track error is improved after DRP-BDA,and the structure of the typhoon is also improved.The wind near the typhoon center is enhanced dramatically,while the warm core is moderate.     

Improvement in Background Error Covariances Using Ensemble Forecasts for Assimilation of High-Resolution Satellite Data

Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated.