Barker D. M., W. Huang, Y. R. Guo, A. J. Bourgeois, and Q. N. Xiao, 2004: A three-dimensional variational data assimilation system for MM5: Implementation and initial results. Mon. Wea. Rev., 132, 897- 914.92a4e98a8f7f6f7ea591bb40cdf39af0http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004MWRv..132..897B%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D20143http://xueshu.baidu.com/s?wd=paperuri%3A%28123ab87ad8ac6211f664815678d7fa67%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004MWRv..132..897B%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D20143&ie=utf-8&sc_us=41340900915828137 |
Benjamin S.G., Coauthors, 2004: An hourly assimilation-forecast cycle: The RUC. Mon. Wea. Rev., 132, 495- 518.10.1175/1520-0493(2004)1322.0.CO;299e98620a0eb9aac9822fca6d5c3a124http%3A%2F%2Fci.nii.ac.jp%2Fnaid%2F10013126454%2Fhttp://ci.nii.ac.jp/naid/10013126454/The Rapid Update Cycle (RUC), an operational regional analysis09“forecast system among the suite of models at the National Centers for Environmental Prediction (NCEP), is distinctive in two primary aspects: its hourly assimilation cycle and its use of a hybrid isentropic09“sigma vertical coordinate. The use of a quasi-isentropic coordinate for the analysis increment allows the influence of observations to be adaptively shaped by the potential temperature structure around the observation, while the hourly update cycle allows for a very current analysis and short-range forecast. Herein, the RUC analysis framework in the hybrid coordinate is described, and some considerations for high-frequency cycling are discussed. A 20-km 50-level hourly version of the RUC was implemented into operations at NCEP in April 2002. This followed an initial implementation with 60-km horizontal grid spacing and a 3-h cycle in 1994 and a major upgrade including 40-km horizontal grid spacing in 1998. Verification of forecasts from the latest 20-km version is presented using rawinsonde and surface observations. These verification statistics show that the hourly RUC assimilation cycle improves short-range forecasts (compared to longer-range forecasts valid at the same time) even down to the 1-h projection. |
Bluestein H. B., M. H. Jain, 1985: Formation of mesoscale lines of precipitation: Severe squall lines in Oklahoma during the spring. J. Atmos. Sci., 42( 16), 1711- 1732.10.1175/1520-0469(1985)0422.0.CO;2fd1696dce164effb0b7fd0536596551dhttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1985JAtS...42.1711Bhttp://adsabs.harvard.edu/abs/1985JAtS...42.1711BFour distinct kinds of severe, mesoscale convective-line development are identified in Oklahoma during the spring based on the analysis of an 11-year period of reflectivity data from the National Severe Storms Laboratory's 10-cm radar in Norman, Oklahoma. The primary classes of fine formation are broken line, back building, broken areal and embedded areal. Each is described in detail, along with illustrative examples. Comparisons are made with other observations and with numerical model simulations. The former two classes of line formation have been previously documented, while the latter two have not. Only the broken-areal squall line has been realistically simulated numerically.The environment for each of the types of line development was determined from data from the standard National Weather Service surface and upper-air networks and from special rawinsonde launches. It was found that broken-line formation tends to occur along cold fronts in a multicell environment, while back building occurs along any boundary in a supercell environment. The former formation is associated with a steering level with respect to cell motion, while the others are not. A steering level with respect to line motion exists around 6 or 7 km MSL in all cases. Cells within back-building squall lines have high relative helicity, like supercells, while cells within broken-line squall lines have low relative helicity. Most lines were oriented approximately 40掳 to the left of the pressure-weighted vertical shear vector in the troposphere, along the pressure-weighted vertical shear vector in the lowest 1 km and at a large angle to the shear somewhere in the lower portion of the middle troposphere. |
Brand es, E. A., R. P. Davies-Jones, B. C. Johnson, 1988: Streamwise vorticity effects on supercell morphology and persistence. J. Atmos. Sci., 45, 947- 963.10.1175/1520-0469(1988)0452.0.CO;24838a2b4fdbe905a848ab45814b6d0bchttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1988JAtS...45..947Bhttp://adsabs.harvard.edu/abs/1988JAtS...45..947BAbstract The structure and steadiness of radar-observed supercell thunderstorms are examined in terms of their particular distribution of vorticity. The data confirm that the vorticity vector in supercells points in the direction of the storm-relative velocity vector and that supercell updrafts contain large positive helicity (V路蠅). The alignment of vorticity and velocity vectors dictates that low pressure associates not only with vorticity but also with helicity. Accelerating pressure gradients and helicity, both thought important for suppressing small-scale features within supercells, may combine with shear-induced vertical pressure gradient forces to organize and maintain the large-scale persistent background updrafts that characterize supercells. Rear downdrafts possess weak positive or negative helicity. Thus, the decline of storm circulation may be hastened by turbulent dissipation when the downdraft air eventually mixes into supercell updrafts. |
Chen, D. H., Coauthors, 2008: New generation of multi-scale NWP system (GRAPES): General scientific design. Chin. Sci. Bull., 53( 22), 3433- 3445.10.1007/s11434-008-0494-z426cc55bf84622f194822ec3b646ebafhttp%3A%2F%2Fwww.cqvip.com%2FMain%2FDetail.aspx%3Fid%3D28719473http://www.cnki.com.cn/Article/CJFDTotal-JXTW200822003.htmA new generation of numerical prediction system GRAPES (a short form of Global/Regional Assimilation and PrEdiction System) was set up in China Meteorological Administration (CMA). This paper focuses on the scientific design and preliminary results of the numerical prediction model in GRAPES, including basic idea and strategy of the general scientific design, multi-scale dynamic core, physical package configuration, architecture and parallelization of the codes. A series of numerical experiments using the real data with horizontal resolutions from 10 to 280 km and idealized experiments with very high resolution up to 100 m are conducted, giving encouraging results supporting the multi-scale application of GRAPES. The results of operational implementation of GRAPES model in some NWP centers are also presented with stress at evaluations of the capability to predict the main features of precipitation in China. Finally the issues to be dealt with for further development are discussed. |
Davies-Jones R., D. W. Burgess, and M. Foster, 1990: Test of helicity as a forecast parameter. Preprints, 16th Conf. on Severe Local Storms, Kananaskis Park, AB, Canada, Amer. Meteor. Soc., 588- 592. |
Dong J. L., M. Xue, 2013: Assimilation of radial velocity and reflectivity data from coastal WSR-88D radars using an ensemble Kalman filter for the analysis and forecast of landfalling hurricane Ike (2008). Quart. J. Roy. Meteor. Soc., 139, 467- 487.10.1002/qj.1970acbe85ed-91aa-4fd0-94e2-c56e7e95ac8e7b14981f348ad8c9788bcca5bc82fe93http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fqj.1970%2Fpdfrefpaperuri:(4e0e18a174b14cb917a4a998c3d35d82)http://onlinelibrary.wiley.com/doi/10.1002/qj.1970/pdfNot Available |
Fujita T., 1955: Results of detailed synoptic studies of squall lines. Tellus, 7( 4), 405- 436.10.1111/j.2153-3490.1955.tb01181.xc27d66c549df976ef6fcd1be26e06b0bhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.2153-3490.1955.tb01181.x%2Fcitedbyhttp://onlinelibrary.wiley.com/doi/10.1111/j.2153-3490.1955.tb01181.x/citedbyNot Available |
Gao J. D., M. Xue, K. Brewster, and K. K. Droegemeier, 2004: A three-dimensional variational data analysis method with recursive filter for Doppler radars. J. Atmos. Oceanic Technol., 21, 457- 469.e485d4c3dc07366bc3f77e8a55f22a67http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004JAtOT..21..457G%26db_key%3DPHY%26link_type%3DABSTRACThttp://xueshu.baidu.com/s?wd=paperuri%3A%28bea7369f63db7495437d458da988726a%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2004JAtOT..21..457G%26db_key%3DPHY%26link_type%3DABSTRACT&ie=utf-8&sc_us=13188604239151258641 |
Hu M., M. Xue, J. D. Gao, and K. Brewster, 2006: 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the Fort Worth, Texas, tornadic thunderstorms. Part II: Impact of radial velocity analysis via 3DVAR. Mon. Wea. Rev., 134, 699- 721.10.1175/MWR3092.15985d2c8-06ac-4764-97ef-5b98d592928197de4b71474781242f9bc2176f727b52http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2006MWRv..134..675Hrefpaperuri:(2a1b9c7a13ebb23ea77ced12a2fbf3d1)http://adsabs.harvard.edu/abs/2006MWRv..134..675HIn this two-part paper, the impact of level-II Weather Surveillance Radar-1988 Doppler (WSR-88D) reflectivity and radial velocity data on the prediction of a cluster of tornadic thunderstorms in the Advanced Regional Prediction System (ARPS) model are studied. Radar reflectivity data are used primarily in a cloud analysis procedure that retrieves the amount of hydrometeors and adjusts in-cloud temperature, moisture, and cloud fields, while radial velocity data are analyzed through a three-dimensional variational (3DVAR) scheme that contains a mass divergence constraint in the cost function. In Part I, the impact of the cloud analysis and modifications to the scheme are examined while Part II focuses on the impact of radial velocity and the mass divergence constraint. The case studied is that of the 28 March 2000 Fort Worth, Texas, tornado outbreaks. The same case was studied by Xue et al. using the ARPS Data Analysis System (ADAS) and an earlier version of the cloud analysis procedure with WSR-88D level-III data. Since then, several modifications to the cloud analysis procedure, including those to the in-cloud temperature adjustment and the analysis of precipitation species, have been made. They are described in detail with examples. The assimilation and predictions use a 3-km grid nested inside a 9-km one. The level-II reflectivity data are assimilated, through the cloud analysis, at 10-min intervals in a 1-h period that ends a little over 1 h preceding the first tornado outbreak. Experiments with different settings within the cloud analysis procedure are examined. It is found that the experiment using the improved cloud analysis procedure with reflectivity data can capture the important characteristics of the main tornadic thunderstorm more accurately than the experiment using the early version of cloud analysis. The contributions of different modifications to the above improvements are investigated. |
Joyce R. J., J. E. Janowiak, P. A. Arkin, and P. P. Xie, 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. Journal of Hydrometeorology, 5( 3), 487- 503.4a3fc2d0005c7912545a662c4155d55ahttp%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Dbibr19%26dbid%3D16%26doi%3D10.1603%252FME14015%26key%3D10.1175%252F1525-7541%282004%29005%3C0487%253ACAMTPG%3E2.0.CO%253B2http://xueshu.baidu.com/s?wd=paperuri%3A%28aca48978d7ce0f0aaef86f9b0a951a4d%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fwww.bioone.org%2Fservlet%2Flinkout%3Fsuffix%3Dbibr19%26dbid%3D16%26doi%3D10.1603%252FME14015%26key%3D10.1175%252F1525-7541%282004%29005%253C0487%253ACAMTPG%253E2.0.CO%253B2&ie=utf-8&sc_us=14659918988861497797 |
Li X., J. Ming, Y. Wang, K. Zhao, and M. Xue, 2013: Assimilation of T-TREC-retrieved wind data with WRF 3DVAR for the short-term forecasting of Typhoon Meranti (2010) near landfall. J. Geophys. Res., 118, 10 361- 10 375.10.1002/jgrd.5081590f01285b704be32a129061cd6171f05http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2Fjgrd.50815%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1002/jgrd.50815/fullFor most operational radar,such as the WSR-88D of the U.S.,and WSR-98D of China,the maximum Doppler velocity range is about 150 km,far less than the maximum range of reflectivity,Z data,typically 460 km.It would thus be advantageous if the reflectivity data could be used to estimate the wind field to alleviate the limitations of wind data from single Doppler radar.An extended Tracking Radar Echo by Correlation (TREC) technique,called T-TREC technique,has been developed recently to retrieve horizontal circulations within tropical cyclones (TCs) from single Doppler radar reflectivity (Z) and radial velocity (V r,when available) data.This study explores,for the first time,the assimilation of T-TREC-retrieved winds for a landfalling typhoon,Meranti (2010),into a convection-resolving model,the WRF. |
Li X., J. Ming, M. Xue, Y. Wang, and K. Zhao, 2015: 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). J. Geophys. Res., 120, 4017- 4039.10.1002/2014JD02270618d95b0a-7ce3-4b95-bd79-e56af7d760c04ecfdc2c56093598d2e9771bbdaa8b4chttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1002%2F2014JD022706%2Fepdfrefpaperuri:(11aca886dd686394db00bd1c7964d561)http://onlinelibrary.wiley.com/doi/10.1002/2014JD022706/epdfProper dynamic equation constraints in data assimilation (DA) systems can help improve balance of analyzed atmospheric state. The formulation of ensemble‐variational DA algorithms allows for easy incorporation of such constraints, but their impacts within such DA systems have been little studied. A dynamic constraint based on the steady momentum equations is incorporated into the WRF (Weather Research and Forecasting) hybrid ensemble‐three‐dimensional variational (3DVar) (En3DVar) DA system as a weak constraint. The constraint aims at improving the coupling and balance among wind and thermodynamic state variables, especially when few state variables are directly observed. The scheme is applied to the assimilation of radar T‐TREC (Typhoon‐Tracking Radar Echo by Correlation) winds at a convection‐allowing resolution, for landfalling typhoon, Chanthu (2010), when it was within the range of a coastal radar. Parallel experiments using the 3DVar and En3DVar with and without the dynamic constraint are run to examine the impact of the constraint. The flow‐dependent ensemble covariance used in En3DVar helps to update unobserved pressure and temperature fields in a dynamically more consistent way compared to the static covariance; the added dynamic constraint produces more accurate pressure within the typhoon. The pressure field improved by the dynamic constraint also leads to better temperature and moisture analyses within the variational minimization through flow‐dependent cross covariance. En3DVar analysis with the dynamic constraint produces the best intensity forecast for the typhoon, in terms of the minimum sea level pressure and maximum surface wind speed. Additional sensitivity experiments examine the impact of the weight of the dynamic constraint. |
Newton C.W., 1950: Structure and mechanism of the prefrontal squall line. J. Atmos. Sci., 7, 210- 222.10.1175/1520-0469(1950)0072.0.CO;26d053f29757bb140bd0f4dbb58b3ce35http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1950JAtS....7..210Nhttp://adsabs.harvard.edu/abs/1950JAtS....7..210NAbstract Using upper-air soundings taken by the Thunderstorm Project and surface and serological data available through teletype distribution, a detailed three-dimensional analysis of a prefrontal squall line is presented and certain new observational features of squall-line structure are shown. It is shown that squall-line thunderstorms appear in some cases to form first over the cold-front surface and subsequently move into the warm sector. Serial ascents taken in such a case show that there is a distinct cold front at the forward edge of the thunderstorm area, which coincides with the squall line observed at the ground. It is suggested that the squall-line activity can be accounted for partly as a result of this front, and partly by the continuous generation of new thunderstorms as a result of convergence-divergence patterns produced by the vertical transfer of horizontal momentum in pre-existent thunderstorms. This is augmented by solenoidal circulations due to unbalance between the “thermal wind” and... |
Newton, C W., 1966: Circulations in large sheared cumulonimbus. Tellus, 18, 699- 713.10.1111/j.2153-3490.1966.tb00291.x1e648a1125b060493414df0d00071a70http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1111%2Fj.2153-3490.1966.tb00291.x%2Fabstracthttp://onlinelibrary.wiley.com/doi/10.1111/j.2153-3490.1966.tb00291.x/abstractABSTRACT The structure of a cumulonimbus cloud subjected to vertical shear is interpreted in light of the horizontal forces acting upon it, and of the varying thermodynamic influences in its different parts. In-cloud horizontal velocities depart greatly from those in the environment, and the forms assumed by draft columns (updrafts typically leaning in a sense opposing the vertical shear) vary with the shear, vertical motion, and speed of storm movement. The cumulonimbus is viewed as an ensemble of air elements which have undergone varying degrees of mixing with the environment, penetrating upward to different heights. Some of the air in the updraft rises into stratospheric towers, then descends as a vigorous downdraft which, because of mixing-in of heat and of air having no initial vertical momentum, dies out in the upper troposphere. This air, together with air reaching the upper troposphere in the less buoyant outskirts of the updraft, feeds the expanding anvil plume. A separate downdraft in the lower part of the cloud, originating from middle levels where the wet-bulb potential temperature is low, continually regenerates the updraft though mechanical lifting. Estimates of the air and water budgets of squall-line thunderstorms are given. |
Parrish D. F., J. C. Derber, 1992: The national meteorological center's spectral statistical-interpolation analysis system. Mon. Wea. Rev., 120, 1747- 1763.10.1175/1520-0493(1992)1202.0.CO;24214772c1895942c49e785d413b108e0http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1992MWRv..120.1747Phttp://adsabs.harvard.edu/abs/1992MWRv..120.1747PAt the National Meteorological Center (NMC), a new analysis system is being extensively tested for possible use in the operational global data assimilation system. This analysis system is called the because the spectral coefficients used in the NMC spectral model are analyzed directly using the same basic equations as statistical (optimal) interpolation. Results from several months of parallel testing with the NMC spectral model have been very encouraging. Favorable features include smoother analysis increments, greatly reduced changes from initialization, and significant improvement of 1-5-day forecasts. Although the analysis is formulated as a variational problem, the objective function being minimized is formally the same one that forms the basis of all existing optimal interpolation schemes. This objective function is a combination of forecast and observation deviations from the desired analysis, weighted by the invent of the corresponding forecast- and observation-error covariance matrices. There are two principal differences in how the SSI implements the minimization of this functional as compared to the current OI used at NMC. First, the analysis variables are spectral coefficients instead of gridpoint values. Second, all observations are used at once to solve a single global problem. No local approximations are made, and there is no special data selection. Because of these differences, it is straightforward to include unconventional data, such as radiances, in the analysis. Currently temperature, wind, surface pressure, mixing, ratio, and Special Sensor Microwave/lmager (SSM/I) total precipitable water can be used as the observation variables. Soon to be added are the scatterometer surface winds. This paper provides a detailed description of the SSI and presents a few results. |
Pu Z. X., X. L. Li, and J. Z. Sun, 2009: Impact of airborne Doppler radar data assimilation on the numerical simulation of intensity changes of Hurricane Dennis near a landfall. J. Atmos. Sci., 66, 3351- 3365.10.1175/2009JAS3121.1d2d5fdcdc56cee83d1ede0841e455b7chttp%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009JAtS...66.3351Phttp://adsabs.harvard.edu/abs/2009JAtS...66.3351PAccurate forecasting of a hurricane's intensity changes near its landfall is of great importance in making an effective hurricane warning. This study uses airborne Doppler radar data collected during the NASA Tropical Cloud Systems and Processes (TCSP) field experiment in July 2005 to examine the impact of airborne radar observations on the short-range numerical simulation of hurricane track and intensity changes. A series of numerical experiments is conducted for Hurricane Dennis (2005) to study its intensity changes near a landfall. Both radar reflectivity and radial velocity-derived wind fields are assimilated into the Weather Research and Forecasting (WRF) model with its three-dimensional variational data assimilation (3DVAR) system. Numerical results indicate that the radar data assimilation has greatly improved the simulated structure and intensity changes of Hurricane Dennis. Specifically, the assimilation of radar reflectivity data shows a notable influence on the thermal and hydrometeor structures of the initial vortex and the precipitation structure in the subsequent forecasts, although its impact on the intensity and track forecasts is relatively small. In contrast, assimilation of radar wind data results in moderate improvement in the storm-track forecast and significant improvement in the intensity and precipitation forecasts of Hurricane Dennis. The hurricane landfall, intensification, and weakening during the simulation period are well captured by assimilating both radar reflectivity and wind data. |
Rotunno R., J. B. Klemp, and M. L. Weisman, 1988: A theory for strong, long-lived squall lines. J.Atmos. Sci., 45, 463- 485.10.1175/1520-0469(1988)0452.0.CO;27340669ce79a156a8c5d08b0f5e87b54http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1988JAtS...45..463Rhttp://adsabs.harvard.edu/abs/1988JAtS...45..463RNot Available |
Sheng C. Y., Y. F. Pu, and S. T. Gao, 2006: Effect of Chinese Doppler radar data on nowcasting output of mesoscale model. Chinese J. Atmos. Sci., 30( 1), 93- 107. (in Chinese)10.1016/S1003-6326(06)60040-X507969e49fb67eac1f8f2e2bdf494e49http%3A%2F%2Fen.cnki.com.cn%2Farticle_en%2Fcjfdtotal-dqxk200601007.htmhttp://en.cnki.com.cn/article_en/cjfdtotal-dqxk200601007.htmThe Advanced Regional Prediction System(ARPS) is a multi-scale model developed by the Center for(Analysis) and Prediction of Storms(CAPS),the complex cloud analysis system in the ARPS data analysis system(ARPSDAS or ADAS) can construct a three-dimensional cloud in the initial field with Doppler radar reflectivity,cloud base and fraction,and satellite image data to adjust the initial cloud and rain water,and ADAS still can adjust the initial wind field directly with radar radial velocity.In order to test the effect of Doppler radar data on mesoscale model,a North China torrential rain event is studied with Chinese Doppler radar data only.Four control experiments are conducted.The 1st experiment is performed without Chinese Doppler radar data analysis,the 2nd is done with Doppler radar reflectivity only to construct a three-dimensional cloud analysis,the 3rd is conducted with radar radial velocity only to adjust the initial wind field,and the last experiment is done with both radar reflectivity and radial velocity.The main results are as follows: 1) The wind components u,v,and w in the initial field adjusted by Doppler radial velocity can be modified under 10 km near the radar observing range,and the most drastic modification of u,v happens in the central troposphere.2) Assimilation of Doppler radar reflectivity can increase the cloud and moisture contents in the strong reflectivity region of the initial field.The adjustment of the water vapor mixture ratio(q_(v)) is mainly below 3 km, the rainfall mixture ratio(q_(r)) below 4 km,cloud vapor mixture ratio(q_(c)) in the troposphere(below 10 km level) and cloud ice(q_(i)) and snow(q_(s)) mixture ratio at around 49 km level in the tro-(posphere.) Diabatic initialization of ADAS will adjust the temperature to balance the cloud microphysical adjustment.3) The comparison between simulated hourly rainfall and observations shows that the assimilation of both reflectivity and radial velocity can improve rainfall forecasting significantly,especially within 3 hours;while the comparison of simulated hourly streamline fields shows that radar radial velocity can increase the mesoscale wind circles in the initial fields and mitigate the model's spin-up time significantly.4) Comparison between radar radial velocity and reflectivity assimilation on the initial field and the simulation shows that Doppler radar radial velocity assimilation can mainly improve the initial wind field while radar reflectivity assimilation can increase the cloud and water contents in the initial field and adjust the temperature.Simulated 6-h rainfall indicates that radar reflectivity assimilation has greater positive effects than radial velocity on the rainfall simulation,although adding radial velocity data results in further improvement in the rain simulation. |
Shi L. J., X. F. Xu, B. Li, H. P. Yang, and F. W. Xu, 2009: Application of Doppler radar data to the landfalling Typhoon Saomai simulation. Journal of Applied Meteorological Science, 20( 3), 257- 266. (in Chinese)33d7cf9879e5fd1f478d66a689f08861http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTOTAL-YYQX200903002.htmhttp://en.cnki.com.cn/Article_en/CJFDTOTAL-YYQX200903002.htmThe mesoscale model ARPS and its data analyzing system ARPS-3DVar developed by CAPS of Oklahoma university has a good potential to utilize in China.Using ARPS and its 3DVar assimilation system, the Doppler weather radar(CINRAD-SA) reflectivity and radial velocity are assimilated.In order to test the effects of Doppler radar data on the initial field and on the forecast field,numeric study is carried out on super typhoon Saomai(0608) which lands at east China and causes a large damage.Comparison between experiments with and without radar data assimilation shows that Doppler radar assimilation can help obtain more realistic precipitation,wind and reflectivity structures within 6-hour initial time windows.The radar assimilation by ARPS-3DVar has the ability to improve the forecast on the mesoscale rain cell position and intensity.The improvement on typhoon track forecast is due to the effective adjustment of the typhoon vortex and eye structure by radar data assimilation.The result of precipitation forecast is improved significantly,mainly because of the physical quantities in assimilation test displaying typical characteristics of mesoscale system.However,there are some inadequate aspects still needing improvements in the stimulation of typhoon intensity. |
Skamarock, W. C., Coauthors, 2008: Description of the advanced research WRF version 3,Rep. NCAR/TN-475++ STR, Natl. Cent. Atmos. Res., Boulder, Colo., 125 pp. |
Sun J., N. A. Crook, 1997: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part I: Model development and simulated data experiments. J. Atmos. Sci., 54( 12), 1642- 1661.10.1175/1520-0469(1997)0542.0.CO;2fd50222b-cffe-44b3-98b2-5a4c3f4de7124d65fad43479cedc983061dbc7fe9168http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1997jats...54.1642srefpaperuri:(3442f4439a64c47314ac768eea6c1f42)http://adsabs.harvard.edu/abs/1997jats...54.1642sThe purpose of the research reported in this paper is to develop a variational data analysis system that can be used to assimilate data from one or more Doppler radars. In the first part of this two-part study, the technique used in this analysis system is described and tested using data from a simulated warm rain convective storm. The analysis system applies the 4D variational data assimilation technique to a cloud-scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields are determined by minimizing a cost function, defined by the difference between both radar observed radial velocities and reflectivities (or rainwater mixing ratio) and their model predictions. The adjoint of the numerical model is used to provide the sensitivity of the cost function with respect to the control variables.Experiments using data from a simulated convective storm demonstrated that the variational analysis system is able to retrieve the detailed structure of wind, thermodynamics, and microphysics using either dual-Doppler or single-Doppler information. However, less accurate velocity fields are obtained when single-Doppler data were used. In both cases, retrieving the temperature field is more difficult than the retrieval of the other fields. Results also show that assimilating the rainwater mixing ratio obtained from the reflectivity data results in a better performance of the retrieval procedure than directly assimilating the reflectivity. It is also found that the system is robust to variations in the Z-qrelation, but the microphysical retrieval is quite sensitive to parameters in the warm rain scheme. The technique is robust to random errors in radial velocity and calibration errors in reflectivity. |
Sun J., N. A. Crook, 1998: Dynamical and microphysical retrieval from Doppler radar observations using a cloud model and its adjoint. Part II: Retrieval experiments of an observed Florida convective storm. J. Atmos. Sci., 55( 4), 835- 852.10.1175/1520-0469(1998)0552.0.CO;21cebba5079f4d1ed94947fe25caff633http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F1998JAtS...55..835Shttp://adsabs.harvard.edu/abs/1998JAtS...55..835SAbstract The variational Doppler radar analysis system developed in part I of this study is tested on a Florida airmass storm observed during the Convection and Precipitation/ Electrification Experiment. The 3D wind, temperature, and microphysical structure of this storm are obtained by minimizing the difference between the radar-observed radial velocities and rainwater mixing ratios (derived from reflectivity) and their model predictions. Retrieval experiments are carried out to assimilate information from one or two radars. The retrieved fields are compared with measurements of two aircraft penetrating the storm at different heights. The retrieved wind, thermodynamical, and microphysical fields indicate that the minimization converges to a solution consistent with the input velocity and rainwater fields. The primary difference between using single-Doppler and dual-Doppler information is the reduction of the peak strength of the storm on the order of 10% when information from only one radar is provided. ... |
Sun J. Z., H. L. Wang, 2013a: Radar data assimilation with WRF 4D-Var. Part II: comparison with 3D-Var for a squall line over the U.S. great plains. Mon. Wea. Rev., 141, 2245- 2264.10.1175/MWR-D-12-00169.1538e5faecb2932e495e33206610790c3http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013MWRv..141.2245Shttp://adsabs.harvard.edu/abs/2013MWRv..141.2245SNot Available |
Sun J. Z., H. L. Wang, 2013b: WRF-ARW variational storm-scale data assimilation: current capabilities and future developments. Advances in Meteorology, 2013, 81591010.1155/2013/815910113bea3ec9985284346a4beb3c03cfdbhttp%3A%2F%2Fwww.oalib.com%2Fpaper%2F3066673http://www.oalib.com/paper/3066673The variational radar data assimilation system has been developed and tested for the Advanced Research Weather Research and Forecasting (WRF-ARW) model since 2005. Initial efforts focused on the assimilation of the radar observations in the 3-dimensional variational framework, and recently the efforts have been extended to the 4-dimensional system. This article provides a review of the basics of the system and various studies that have been conducted to evaluate and improve the performance of the system. Future activities that are required to further improve the system and to make it operational are also discussed. 1. Introduction In the past two decades active research was conducted on the development of techniques to initialize storm-scale numerical prediction models. It has been recognized that the success will critically depend on the optimal use of the national operational WSR-88D radar network that covers the United States with single Doppler coverage in most areas. Although the network provides observations at a resolution that is able to resolve atmospheric convection, they are only limited to radial wind and reflectivity. Therefore several early studies focused on the feasibility of retrieving meteorological fields from these single Doppler observations. Techniques with different complexities have been developed which aim at obtaining the unobserved meteorological variables such as 3-dimensional (3D) wind, temperature, and microphysical fields from the radar observations of radial velocity and reflectivity (e.g., [1鈥5]). The techniques that make use of a numerical model in a data assimilation (DA) context received particular attention because they combine the retrieval, initialization, and forecast in one system. The first radar DA system for the storm-scale was developed based on the 4-dimensional variational data assimilation (4D-Var) technique and a boundary layer fluid dynamics model for the retrieval of the 3D wind and temperature [1]. This system, known as VDRAS (Variational Doppler Radar Analysis System), was later expanded to include microphysical retrieval, as well as short-term forecasts initialized by these retrieved fields [6鈥9]. Another variational-based radar DA system was developed by Gao et al. [4] using a 3-dimensional variational data assimilation (3D-Var) technique in the framework of the ARPS (Advanced Research and Prediction System [10]) model. A so-called 3.5-dimensional variational radar data assimilation based on Navy鈥檚 COAMPS (The Coupled Ocean/Atmosphere Mesoscale Prediction System) was developed and demonstrated |
Sun J. Z., H. L. Wang, W. X. Tong, Y. Zhang, C.-Y. Lin, and D. M. Xu, 2016: Comparison of the impacts of momentum control variables on high-resolution variational data assimilation and precipitation forecasting. Mon. Wea. Rev., 144, 149- 169.10.1175/MWR-D-14-00205.1d95f35b7ffda06becfffa318c1f65b06http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2016MWRv..144..149Shttp://adsabs.harvard.edu/abs/2016MWRv..144..149SNot Available Not Available |
Wang H. L., J. Z. Sun, X. Zhang, X.-Y. Huang, and T. Auligné, 2013: Radar data assimilation with WRF 4D-Var. Part I: system development and preliminary testing. Mon. Wea. Rev., 141, 2224- 2244.10.1175/MWR-D-12-00168.14a20e2777c23da71385ce70e50cf0025http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2013MWRv..141.2224Whttp://adsabs.harvard.edu/abs/2013MWRv..141.2224WThe major goal of this two-part study is to assimilate radar data into the high-resolution Advanced Research Weather Research and Forecasting Model (ARW-WRF) for the improvement of short-term quantitative precipitation forecasting (QPF) using a four-dimensional variational data assimilation (4D-Var) technique. In Part I the development of a radar data assimilation scheme within the WRF 4D-Var system (WRF 4D-Var) and the preliminary testing of the scheme are described. In Part II the performance of the enhanced WRF 4D-Var system is examined by comparing it with the three-dimensional variational data assimilation system (WRF 3D-Var) for a convective system over the U.S. Great Plains. The WRF 4D-Var radar data assimilation system has been developed with the existing framework of an incremental formulation. The new development for radar data assimilation includes the tangent-linear and adjoint models of a Kessler warm-rain microphysics scheme and the new control variables of cloud water, rainwater, and vertical velocity and their error statistics. An ensemble forecast with 80 members is used to produce background error covariance. The preliminary testing presented in this paper includes single-observation experiments as well as real data assimilation experiments on a squall line with assimilation windows of 5, 15, and 30 min. The results indicate that the system is able to obtain anisotropic multivariate analyses at the convective scale and improve precipitation forecasts. The results also suggest that the incremental approach with successive basic-state updates works well at the convection-permitting scale for radar data assimilation with the selected assimilation windows. |
Xiao Q. N., Y.-H. Kuo, J. Z. Sun, W.-C. Lee, E. Lim, Y.-R. Guo, and D. M. Barker, 2005: Assimilation of Doppler radar observations with a regional 3DVAR system: Impact of Doppler velocities on forecasts of a heavy rainfall case. J. Appl. Meteor., 44, 768- 788.48d2987eb3fdb0b31c810262fbb60553http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JApMe..44..768X%26db_key%3DPHY%26link_type%3DABSTRACThttp://xueshu.baidu.com/s?wd=paperuri%3A%28c07a1e750fc8ddfe5e2c4b8798fda70b%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2005JApMe..44..768X%26db_key%3DPHY%26link_type%3DABSTRACT&ie=utf-8&sc_us=6356000078318155908 |
Xiao Q. N., J. Z. Sun, 2007: Multiple-radar data assimilation and short-range quantitative precipitation forecasting of a squall line observed during IHOP_2002. Mon. Wea. Rev., 135, 3381- 3404.0fc6229af8053a2e010082b72963cea6http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2007MWRv..135.3381X%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D19888http://xueshu.baidu.com/s?wd=paperuri%3A%2894178d319f279c20c7f2d7d1ecb57c27%29&filter=sc_long_sign&tn=SE_xueshusource_2kduw22v&sc_vurl=http%3A%2F%2Fadsabs.harvard.edu%2Fcgi-bin%2Fnph-data_query%3Fbibcode%3D2007MWRv..135.3381X%26db_key%3DPHY%26link_type%3DABSTRACT%26high%3D19888&ie=utf-8&sc_us=13718852669626556646 |
Xie P. P., A. Y. Xiong, 2011: A conceptual model for constructing high-resolution gauge-satellite merged precipitation analyses. J. Geophys. Res., 116,D21106, doi: 10.1029/2011 JD016118.10.1029/2011JD016118b7a7f803c2139fb3e7756afba19785a6http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011JD016118%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2011JD016118/fullAbstract Top of page Abstract 1.Introduction 2.Data 3.Removing Bias in the Satellite Estimates 4.Combining Gauge Data With Bias-Corrected Satellite Estimates 5.Summary and Future Work Acknowledgments References Supporting Information [1] A conceptual model has been developed to create high-resolution precipitation analyses over land by merging gauge-based analysis and CMORPH satellite estimates using data over China for a 5 month period from April to September 2007. A two-step strategy is adopted to remove the bias inherent in the CMORPH satellite precipitation estimates and to combine the bias-corrected satellite estimates with the gauge analysis. First, bias correction is performed for the CMORPH estimates by matching the probability density function (PDF) of the satellite data with that of the gauge analysis using colocated data pairs over a spatial domain of 5lat/lon centering at the target grid box and over a time period of 30 days, ending at the target date. The spatial domain is expanded wherever necessary over gauge-sparse regions to ensure the collection of a sufficient number of gauge-satellite data pairs. The bias-corrected CMORPH precipitation estimates are then combined with the gauge analysis through the optimal interpolation (OI) technique, in which the bias-corrected CMORPH is used as the first guess while the gauge data are used as the observations to modify the first guess over regions with station coverage. Error statistics are computed for the input gauge and satellite data to maximize the performance of the high-resolution merged analysis of daily precipitation. Cross-validation tests and comparisons against independent gauge observations demonstrate feasibility and effectiveness of the conceptual algorithm in constructing merged precipitation analysis with substantially removed bias and significantly improved pattern agreements compared with those of the input gauge and satellite data. |
Xie Y. F., A. E. MacDonald, 2012: Selection of momentum variables for a three-dimensional variational analysis. Pure Appl. Geophys., 169, 335- 351.10.1007/s00024-011-0374-36e32115fc5524abeaa497b2a765926d6http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2012PApGe.169..335Xhttp://adsabs.harvard.edu/abs/2012PApGe.169..335XThree choices of control variables for meteorological variational analysis (3DVAR or 4DVAR) are associated with horizontal wind: (1) streamfunction and velocity potential, (2) eastward and northward velocity, and (3) vorticity and divergence. This study shows theoretical and numerical differences of these variables in practical 3DVAR data assimilation through statistical analysis and numerical experiments. This paper demonstrates that (a) streamfunction and velocity potential could potentially introduce analysis errors; (b) A 3DVAR using velocity or vorticity and divergence provides a natural scale dependent influence radius in addition to the covariance; (c) for a regional analysis, streamfunction and velocity potential are retrieved from the background velocity field with Neumann boundary condition. Improper boundary conditions could result in further analysis errors; (d) a variational data assimilation or an inverse problem using derivatives as control variables yields smoother analyses, for example, a 3DVAR using vorticity and divergence as controls yields smoother wind analyses than those analyses obtained by a 3DVAR using either velocity or streamfunction/velocity potential as control variables; and (e) statistical errors of higher order derivatives of variables are more independent, e.g., the statistical correlation between U and V is smaller than the one between streamfunction and velocity potential, and thus the variables in higher derivatives are more appropriate for a variational system when a cross-correlation between variables is neglected for efficiency or other reasons. In summary, eastward and northward velocity, or vorticity and divergence are preferable control variables for variational systems and the former is more attractive because of its numerical efficiency. Numerical experiments are presented using analytic functions and real atmospheric observations. |
Xue M., D. H. Wang, J. D. Gao, K. Brewster, and K. K. Droegemeier, 2003: The advanced regional prediction system (ARPS), storm-scale numerical weather prediction and data assimilation. Meteor. Atmos. Phys., 82, 139- 170.10.1007/s00703-001-0595-64cab6cb2342b233c1b67deb658994032http%3A%2F%2Fwww.springerlink.com%2Findex%2FCG08L17MKCFAHA14.pdfhttp://www.springerlink.com/index/CG08L17MKCFAHA14.pdfIn this paper, we first describe the current status of the Advanced Regional Prediction System of the Center for Analysis and Prediction of Storms at the University of Oklahoma. A brief outline of future plans is also given. Two rather successful cases of explicit prediction of tornadic thunderstorms are then presented. In the first case, a series of supercell storms that produced a historical number of tornadoes was successfully predicted more than 8 hours in advance, to within tens of kilometers in space with initiation timing errors of less than 2 hours. The general behavior and evolution of the predicted thunderstorms agree very well with radar observations. In the second case, reflectivity and radial velocity observations from Doppler radars were assimilated into the model at 15-minute intervals. The ensuing forecast, covering a period of several hours, accurately reproduced the intensification and evolution of a tornadic supercell that in reality spawned two tornadoes over a major metropolitan area. These results make us optimistic that a model system such as the ARPS will be able to deterministically predict future severe convective events with significant lead time. The paper also includes a brief description of a new 3DVAR system developed in the ARPS framework. The goal is to combine several steps of Doppler radar retrieval with the analysis of other data types into a single 3-D variational framework and later to incorporate the ARPS adjoint to establish a true 4DVAR data assimilation system that is suitable for directly assimilating a wide variety of observations for flows ranging from synoptic down to the small nonhydrostatic scales. |
Yang Y., C. J. Qiu, and J. D. Gong, 2006: Physical initialization applied in WRF-Var for assimilation of Doppler radar data. Geophys. Res. Lett., 33,L22807, doi: 10.1029/2006 GL027656.10.1029/2006GL027656369f029c8383d0d9f2bdf1ccc33eb4edhttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2006GL027656%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2006GL027656/fullWRF-Var is further developed with Physical Initialization (PI) to assimilate Doppler radar radial velocity and reflectivity observations. In this updated 3D-Var, specific humidity and vertical velocity are first derived from reflectivity with PI, and then the model fields of specific humidity are replaced by the modified ones, finally, the estimated vertical velocity is added to the cost-function of the existing WRF-Var (version 2.0) as a new observation type, and radial velocity observations are assimilated directly by the method afforded by WRF-Var. It is tested with a rainfall event that occurred in Hubei province near the Yangtze River on 19 June 2002. Results show that the updated 3D-Var shows better capability to forecast the precipitation than the WRF-Var does, and the forecast reflectivity field correlates well with the observations for about 4-h prediction period. |
Zeng M. J., B. Zhang, J. L. Zhou, W. L. Wang, and H. X. Mei, 2014: Quantitative evaluation for GPS/PWV data assimilation in heavy precipitation events. Journal of the Meteorological Sciences, 34( 1), 77- 86. (in Chinese)3c6edf55e1ea251217d7524f8896d170http%3A%2F%2Fen.cnki.com.cn%2FArticle_en%2FCJFDTotal-QXKX201401012.htmhttp://en.cnki.com.cn/Article_en/CJFDTotal-QXKX201401012.htmBased on WRF model and the three-dimensional variation data assimilation system( WRFDA),using PWV data of Jiangsu GPS observation network,and regional radiosonde and surface data respectively,this paper carried out a series of assimilation experiments of two regional heavy rain events occurred in southern Jiangsu on August 25,2011 and Anhui- Jiangsu on August 1,2008 in order to compare and analyze the quantitative effects of GPS / PWV,sounding and surface data assimilation on heavy precipitation forecast. The results showed that sounding and surface meteorological data assimilation affected the dynamic and thermal field,and formed the strong convergence with upward motion and the thermal instability near the rain center,then improved directly the distribution structure and the intention characteristics of heavy rain,so they played the decisive role in success or failure of numerical simulation. GPS / PWV assimilation enhanced and organized better the water vapor condition on the basis of sounding and surface data assimilation,and provided significant improvements in the intensity and the position of heavy rain center. |
Zhang F. Q., Y. J. Weng, J. A. Sippel, Z. Y. Meng, and C. H. Bishop, 2009: Cloud-resolving hurricane initialization and prediction through assimilation of Doppler radar observations with an ensemble Kalman filter. Mon. Wea. Rev., 137, 2105- 2125.10.1175/2009MWR2645.10936158aba1f1858af596fef36a13452http%3A%2F%2Fadsabs.harvard.edu%2Fabs%2F2009MWRv..137.2105Zhttp://adsabs.harvard.edu/abs/2009MWRv..137.2105ZThis study explores the assimilation of Doppler radar radial velocity observations for cloud-resolving hurricane analysis, initialization, and prediction with an ensemble Kalman filter (EnKF). The case studied is Hurricane Humberto (2007), the first landfalling hurricane in the United States since the end of the 2005 hurricane season and the most rapidly intensifying near-landfall storm in U.S. history. The storm caused extensive damage along the southeast Texas coast but was poorly predicted by operational models and forecasters. It is found that the EnKF analysis, after assimilating radial velocity observations from three Weather Surveillance Radars-1988 Doppler (WSR-88Ds) along theGulf coast, closely represents the best-track position and intensity of Humberto. Deterministic forecasts initialized from the EnKF analysis, despite displaying considerable variability with different lead times, are also capable of predicting the rapid formation and intensification of the hurricane. These forecasts are also superior to simulations without radar data assimilation or with a threedimensional variational scheme assimilating the same radar observations. Moreover, nearly all members from the ensemble forecasts initialized with EnKF analysis perturbations predict rapid formation and intensification of the storm. However, the large ensemble spread of peak intensity, which ranges from a tropical storm to a category 2 hurricane, echoes limited predictability in deterministic forecasts of the storm and the potential of using ensembles for probabilistic forecasts of hurricanes. |
Zhao K., M. Xue, 2009: Assimilation of coastal Doppler radar data with the ARPS 3DVAR and cloud analysis for the prediction of Hurricane Ike (2008). Geophys. Res. Lett.,36, doi: 10.1029/2009GL038658.10.1029/2009GL038658acd63ea3f0d4c40ae09acd4c77ae2d43http%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2009GL038658%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2009GL038658/fullAbstract Top of page Abstract 1.Introduction 2.Method and Experimental Design 3.Results of Experiments 4.Summary Acknowledgments References [1] The impact of radar data on the analysis and prediction of the structure, intensity, and track of landfalling Hurricane Ike (2008), at a cloud-resolving resolution, is examined. Radial velocity (Vr) and reflectivity (Z) data from coastal radars are assimilated over a 6-h period before Ike landfall, using the ARPS 3DVAR and cloud analysis package through 30-min assimilation cycles. Eighteen-hour predictions were made. All 4 experiments that assimilate radar data produce better structure, intensity and precipitation forecasts than that from operational GFS analysis. The improvement to the track forecast lasts for the entire 18 hours while that to intensity prediction lasts about 12 hours. The Vr data help improve the track forecast more while reflectivity data help improve intensity forecast most. Best results are obtained when both Z and Vr data are assimilated. |
Zhao K., X. F. Li, M. Xue, B. J.-D. Jou, and W.-C. Lee, 2012: Short-term forecasting through intermittent assimilation of data from Taiwan and mainland China coastal radars for Typhoon Meranti (2010) at landfall. J. Geophys. Res., 117,D06108, doi: 10.1029/2011JD017109.10.1029/2011JD017109d0f084bd27db38edfea47485f3c40b9ahttp%3A%2F%2Fonlinelibrary.wiley.com%2Fdoi%2F10.1029%2F2011JD017109%2Ffullhttp://onlinelibrary.wiley.com/doi/10.1029/2011JD017109/fullRadial velocity (Vr) and reflectivity (Z) data from eight coastal operational radars of mainland China and Taiwan are assimilated for the first time using the ARPS 3DVAR and cloud analysis package for Pacific Typhoon Meranti of 2010. It is shown that the vortex-scale circulations of Meranti can be adequately established after only 2 hourly assimilation cycles while additional cycles provide more details for subvortex-scale structures. Subsequent 12 h forecasts of typhoon structure, intensity, track, and precipitation are greatly improved over the one without radar data assimilation. Vr data lead to a larger improvement to the intensity and track forecasts than Z data, while additional Z data further improve the precipitation forecast. Overall, assimilating both Vr and Z data from multiple radars gives the best forecasts. In that case, three local rainfall maxima related to typhoon circulations and their interactions with the complex terrain in the southeast China coastal region are also captured. Assimilating radar data at a lower 3 or 6 hourly frequency leads to a weaker typhoon with larger track forecast errors compared to hourly frequency. An attempt to assimilate additional best track minimum sea level pressure data is also made; it results in more accurate surface pressure analyses, but the benefit is mostly lost within the first hour of forecast. Assimilating data from a single Doppler radar with a good coverage of the typhoon inner core region is also quite effective, but it takes one more cycle to establish circulation analyses of similar quality. The forecasts using multiple radars are still the best. |