Impact of surface observations on simulation of rainfall over NCR Delhi using regional background error statistics in WRF-3DVAR model

In this study, efforts are made to improve the simulation of heavy rainfall events over National Capital Region (NCR) Delhi during 2010 summer monsoon, using additional observations from automatic weather stations (AWS). Two case studies have been carried out to simulate the relative humidity, wind speed and precipitation over NCR Delhi in 48-h model integrations; one from 00UTC, August 20, 2010, and the other from 00UTC, September 12, 2010. Several AWS installed over NCR Delhi in the recent past provide valuable surface observations, which are assimilated into state-of-the-art weather research and forecasting (WRF) model using the three-dimensional variational data assimilation (3DVAR). The quality of background error statistics (BES) is a key component in successful 3DVAR data assimilation in a mesoscale model. In this study, the domain-dependent regional background error statistics (RBS) are estimated using National Meteorological Center method in the months of August and September 2010 and then compared with the global background error statistics (GBS) in the WRF model. The model simulations are analyzed and validated against AWS and radiosonde observations to quantify the impact of RBS. The root mean square differences in the spatial distributions of precipitation, relative humidity and wind speed at the surface showed significant differences between both the global and regional BES. Similar differences are also observed in the vertical distributions along the latitudinal cross section at 28.5°N. Model-simulated fields are analyzed at five different surface stations and one upper air station located in NCR Delhi. It is found that in 24-h model simulation, the RBS significantly improves the model simulations in case of precipitation, relative humidity and wind speed as compared to GBS.     

利用3维变分方法同化多普勒天气雷达资料的试验研究

以美国新近研发的天气研究预报模式(WRF)配置的3维变分同化系统WRF 3D-Var为平台,针对2003年7月4-5日的一次暴雨过程进行了一组同化多普勒天气雷达径向风和回波强度资料的试验研究.其中同化径向风资料的方法是先用两步变分法由多普勒资料反演出水平风,再同化反演风场;同化雷达回波强度资料的方法是通过雷达回波强度与雨水混合比的经验关系以暖雨过程为约束的同化方法.试验结果表明同化雷达资料后对降水预报有显著的正作用.其中单独同化雷达回波强度资料的正作用在前3～6 h效果非常显著;单独同化反演的水平风场对降水预报的正作用能持续12 h;二者同时同化对提前9 h的降水预报改进更加明显.     

Simulation of heavy rainfall events over Indian monsoon region using WRF-3DVAR data assimilation system

We present the results of the impact of the 3D variational data assimilation (3DVAR) system within the Weather Research and Forecasting (WRF) model to simulate three heavy rainfall events (25–28 June 2005, 29–31 July 2004, and 7–9 August 2002) over the Indian monsoon region. For each event, two numerical experiments were performed. In the first experiment, namely the control simulation (CNTL), the low-resolution global analyses are used as the initial and boundary conditions of the model. In the second experiment (3DV-ANA), the model integration was carried out by inserting additional observations in the model’s initial conditions using the 3DVAR scheme. The 3DVAR used surface weather stations, buoy, ship, radiosonde/rawinsonde, and satellite (oceanic surface wind, cloud motion wind, and cloud top temperature) observations obtained from the India Meteorological Department (IMD). After the successful inclusion of additional observational data using the 3DVAR data assimilation technique, the resulting reanalysis was able to successfully reproduce the structure of convective organization as well as prominent synoptic features associated with the mid-tropospheric cyclones (MTC). The location and intensity of the MTC were better simulated in the 3DV-ANA as compared to the CNTL. The results demonstrate that the improved initial conditions of the mesoscale model using 3DVAR enhanced the location and amount of rainfall over the Indian monsoon region. Model verification and statistical skill were assessed with the help of available upper-air sounding data. The objective verification further highlighted the efficiency of the data assimilation system. The improvements in the 3DVAR run are uniformly better as compared to the CNTL run for all the three cases. The mesoscale 3DVAR data assimilation system is not operational in the weather forecasting centers in India and a significant finding in this study is that the assimilation of Indian conventional and non-conventional observation datasets into numerical weather forecast models can help improve the simulation accuracy of meso-convective activities over the Indian monsoon region. Results from the control experiments also highlight that weather and regional climate model simulations with coarse analysis have high uncertainty in simulating heavy rain events over the Indian monsoon region and assimilation approaches, such as the 3DVAR can help reduce this uncertainty.     

Atmosphere data assimilation system for the Siberian region with the WRF-ARW model and three-dimensional variational analysis WRF 3D-Var

The system of the cyclic assimilation of data on atmospheric conditions used in the West Siberian Administration for Hydrometeorology and Environmental Monitoring is described. It is based on the WRF-ARW mesoscale atmospheric model and on the WRF 3D-Var system of the three-dimensional variational analysis of data. The system is verified when the first approximation data (6-hour forecast) and WRF-ARW forecasts with the lead time up to 24 hours are compared with the observational data. The problems of assimilation of observations from the AMSU-A and AIRS satellite instruments are considered. The effect of using AMSU-A and AIRS for the analysis in the Novosibirsk region is estimated. The experiments demonstrated that the cyclic data assimilation system operates successfully. The AMSU-A observations improve the quality of analyses and forecasts in winter. In summer the impact of satellite observations on the forecast skill scores is ambiguous. Good short-term forecasts are provided by the initial conditions obtained using the system of detailing of the NCEP large-scale analysis.     

GSI-3DVAR背景误差协方差水平相关特征尺度对地面自动气象站资料同化的适应性研究

基于华东地区3 km分辨率WRF (Weather Research and Forecasting) 模式和高密度地面自动气象站(AWS)观测,研究GSI-3DVAR同化系统的R_HZSCL对AWS观测的地面温度和风观测同化的敏感性。结果表明:运用GSI-3DVAR同化地面AWS观测时,R_HZSCL的取值较为敏感;选取合适的R_HZSCL能有效改进地面分析场精度,相较于背景场地面温度和地面矢量风差(VWD) RMSE均可减小35％以上。当R_HZSCL过大会导致温度高、低值中心的影响范围过大,风分析场较为平滑,无法反映出中小尺度环流结构。但R_HZSCL过小则会使得温度分析场增加误差,并导致风分析场出现虚假大风。观测密度稀疏化的敏感性试验结果表明,地面温度场及风场所适应的最优R_HZSCL皆随着观测密度的增大而相应减小。     

Impact of terrain complexity on the turbulence drag coefficient: A case study from the Indian Himalayan region

The aerodynamic drag coefficient (C_D) is conjectured to change (or remains almost uniform) with the horizontal wind speed (U) over a flexible (or fixed) surface element, which is represented with the surface roughness (z₀). This conjecture is tested for the near neutral atmospheric turbulence (i.e. when surface stability z/L is almost equal to 0, where z is the measurement height and L is Obukhov length) of monsoon and winter season at an on-slope and a ridge-top site in the Indian Himalaya, wherein the ridge-top site is associated with a higher degree of sensitivity to the roughness element and terrain attributes. This hypothesis is successfully verified for two conditions, (i) the monsoon period observations of ridge-top site are found to have higher z₀ due to vegetative growth than the winter period for flows having similar terrain signature, and (ii) the monsoon and winter period observations of on-slope site are noted to have similar z₀ for flows having signature of steep terrain. Subsequently, constants (i.e. a and b) of the power-law relationships between C_D and U (i.e. C_D = aU^b), as a function of z₀, are optimized. It is noted that the relationship between C_D and U has higher sensitivity towards the terrain slope than the vegetative growth.     

Impact of nonlocal boundary-layer diffusion scheme on forecasts over Indian region

Summary The results of incorporating a nonlocal boundary-layer diffusion scheme in a forecast model over Indian region are discussed. The simple formulation of atmospheric boundary layer height in the nonlocal diffusion scheme is examined in detail to understand how far the model simulated boundary layer height is realistic. Analyses of the temporal and spatial variability of the boundary height for three cases representing premonsoon, active monsoon and post monsoon conditions over Indian region show that it is comparable with the observational evidence. Further, for a case of active monsoon condition over Indian region, comparison of precipitation forecasts with the nonlocal scheme and the control local boundary-layer scheme clearly indicated that the model run with the nonlocal scheme is significantly more accurate in forecasting the intense precipitation locations. Received November 16, 2001 Revised December 28, 2001     

雷达反射率资料的三维变分同化研究

应用天气研究和预报模式(WRF)三维变分系统中一种新的雷达反射率资料间接同化方法来进行反射率资料的三维变分同化研究,评估雷达反射率资料对夏季短时定量降水预报的作用.该方法不直接同化雷达反射率资料,而是同化由反射率资料反演出的雨水和估计的水汽.以2009年夏季北京地区发生的4次强降水过程为例,考察了北京市气象局业务运行的快速更新循环同化预报系统对京津冀地区雷达网的雷达反射率资料的同化性能以及雷达反射率资料和径向风资料同时同化的效果.数值试验结果表明:(1)同化反演雨水或水汽都能改善降水预报,但同化反演水汽对降水预报效果的改善起了更重要的作用;(2)同化反射率资料能极大地提高短时降水预报的效果,其稳定的正面效果可以延伸到6h的预报时效,而同化径向风资料不能得到稳定的正效果;(3)同化雷达资料时,应用快速更新循环同化预报系统是提高短时定量降水预报的一个有效途径.     

三维变分同化中不同物理量对台风预报的影响

采用NMC方法统计了2006年9月的背景误差协方差,利用Bogus资料对台风进行了初始化,并对2006年“桑美”台风进行了同化试验。结果发现,同化不同的Bogus资料,所得到的台风初始场各不相同,对台风预报的影响也各不相同。对海平面气压同化,可以使台风强度明显加强,形成成熟的暖心结构;基于风速同化,对台风强度的改变较弱,对暖心结构的改进不是很明显。在同化海平面气压和风速的基础上,针对相对湿度的同化,在一定程度上可以改善台风强度预报,有利于提高台风路径的预报精度。     

Adaptive observations in the context of 4D-Var data assimilation

Summary The design of adaptive observations strategies must account for the particular properties of the data assimilation method. A new adjoint sensitivity approach to the targeted observations problem is proposed in the context of four-dimensional variational data assimilation (4D-Var). The method is based on a periodic update of the adjoint sensitivity field that takes into account the interaction between time distributed adaptive and routine observations. Information provided by all previously located observations is used to identify best locations for new targeted observations. Adaptive observations at distinct instants in time are selected in a sequential manner such that the method is only suboptimal. The selection algorithm proceeds backward in time and requires only one additional adjoint model integration in the assimilation window. Therefore, the method is very efficient and is suitable for practical applications. A comparative performance analysis is presented using the traditional adjoint sensitivity method as well as the total energy singular vectors technique as alternative adaptive strategies. Numerical experiments are performed in the twin experiments framework using a two-dimensional global shallow water model in spherical coordinates and an explicit Turkel-Zwas discretization scheme. Data from a NASA 500mb analysis valid for 00Z 16 Mar 2001 6h obtained with the GEOS-3 model was used to specify the geopotential height at the initial time and the initial velocities were obtained from a geostrophic balance. Numerical results show that the new adaptive observations approach is a promising method for targeted observations and its implementation is feasible for large scale atmospheric models.     

集合变分混合同化背景误差协方差流依赖性分析

通过单点观测试验的方法,对集合变分混合同化背景误差协方差的流依赖特征、流依赖性影响因子、产生原因,以及集合预报方法对流依赖性的影响进行了研究。结果表明:由于引入了集合信息,集合变分混合同化的分析增量与天气系统的分布有关,具有非均匀、各向异性的特征;这种流依赖特征对混合系数敏感,当集合协方差所占权重很小时,分析增量仍呈现出均匀、各向同性特征;混合同化背景误差协方差的流依赖特征不仅与集合样本有关,还与构造集合协方差的ETKF方法有关,只引入与环流形势密切相关的集合样本并不能使分析增量表现出显著的流依赖性,集合样本和ETKF方法共同作用才能将流依赖信息引入到混合协方差中,使分析增量出现流依赖特征;不同集合预报方法对混合协方差的流依赖特征有显著影响,考虑初值和物理过程的超级集合,以及在超级集合样本上再进行ETKF更新扰动后样本构造的混合协方差流依赖特征更加显著。     

Impact of surface meteorological observations on RAMS forecast of monsoon weather systems over the Indian region

Summary An attempt has been made to study the impact of surface meteorological observations on the Regional Atmospheric Modelling System (RAMS) simulation of a monsoon depression and two low pressure systems. The surface observations are blended with the GEWEX Asian Monsoon Experiment (GAME) gridded analyses for these cases. In one set of experiments the model is run in 12 hour nudging mode initially and then in forecast mode using GAME gridded data without incorporating surface observations. In another set of experiments surface data are incorporated to enhance the signature of the systems in the large scale GAME analyses and nudging is applied initially for twelve hours. Subsequently the model is run in forecast mode to see the temporal and spatial evolution of different meteorological features associated with the systems. It is found that inclusion of the surface data has in general enhanced the signature of the systems in the analysis and subsequently shows improvement in the forecast of sea-level pressure, geopotential, wind field, etc. and the associated forecast of heavy rainfall, in particular. To make a quantitative comparison of the predicted rainfall with the observed one, equitable threat score and bias are calculated for different threshold values of rainfall. It is clearly noted that inclusion of surface data has improved the precipitation forecast over the Indian land mass as indicated by the equitable threat score and bias for all the threshold rainfall categories.     

FY-4A卫星云导风观测误差优化及同化效果影响研究

为了推进FY-4A卫星资料在数值模式中的实际应用,本研究选择云导风产品作为研究对象,首先统计了FY-4A高层水汽通道和红外通道云导风的观测误差,进一步基于WRFDA(Weather Research and Forecasting model Data Assimilation system)系统,利用默认观测误差和新...     

Impact of assimilation of INSAT-3D retrieved atmospheric motion vectors on short-range forecast of summer monsoon 2014 over the South Asian region

The Weather Research and Forecasting (WRF) model and its three-dimensional variational data assimilation system are used in this study to assimilate the INSAT-3D, a recently launched Indian geostationary meteorological satellite derived from atmospheric motion vectors (AMVs) over the South Asian region during peak Indian summer monsoon month (i.e., July 2014). A total of four experiments were performed daily with and without assimilation of INSAT-3D-derived AMVs and the other AMVs available through Global Telecommunication System (GTS) for the entire month of July 2014. Before assimilating these newly derived INSAT-3D AMVs in the numerical model, a preliminary evaluation of these AMVs is performed with National Centers for Environmental Prediction (NCEP) final model analyses. The preliminary validation results show that root-mean-square vector difference (RMSVD) for INSAT-3D AMVs is ～3.95, 6.66, and 5.65 ms^?1 at low, mid, and high levels, respectively, and slightly more RMSVDs are noticed in GTS AMVs (～4.0, 8.01, and 6.43 ms^?1 at low, mid, and high levels, respectively). The assimilation of AMVs has improved the WRF model of produced wind speed, temperature, and moisture analyses as well as subsequent model forecasts over the Indian Ocean, Arabian Sea, Australia, and South Africa. Slightly more improvements are noticed in the experiment where only the INSAT-3D AMVs are assimilated compared to the experiment where only GTS AMVs are assimilated. The results also show improvement in rainfall predictions over the Indian region after AMV assimilation. Overall, the assimilation of INSAT-3D AMVs improved the WRF model short-range predictions over the South Asian region as compared to control experiments.     

Ultraviolet radiation in the background region over Northeast China

Measurements of broadband global solar and ultraviolet radiation (UV-KZ) by a Kipp & Zonen radiometer from 2005 to 2011 at three sites in the background region over Northeast China were used to investigate the characteristics of the temporal variability of UV-KZ radiation in Northeast China. The highest annual mean daily values of UV-KZ (0.53 MJ m^?2 d^?1) were observed at Sanjiang and Changbai Mountain. The lowest value (0.49 MJ m^?2 d^?1) was measured at Hailun due to the high aerosol burden and the long path of solar radiation. The diurnal variation in UV-KZ radiation on four clear days near the equinoxes and solstices exhibited bell-shaped curves, with a maximum at approximately noon. There was little difference in UV-KZ radiation between representative spring and autumn days except that the amount of UV-KZ radiation during the spring was higher than that during the autumn. The relationship between the maximum values of UV-KZ radiation and clearness index can be accurately demonstrated with three polynomial equations. An empirical estimation model suited for all weather conditions was developed using measurements collected at Hailun. The slope of the linear regression between the measured and modeled UV-KZ radiation was approximately 1, the intercept of the linear regression equation was near zero, and the relative error of the equation was less than 8.5 %. These validation results suggest that this model can accurately estimate the UV-KZ radiation based on more conventional measured radiation data. The empirical estimation model can also serve as a valuable method for the study of ecological processes in other regions.     

Impact of Northwest Pacific anticyclone on the Indian summer monsoon region

Influence of northwest (NW) Pacific anticyclone on the Indian summer monsoon (ISM), particularly over the head Bay of Bengal and monsoon trough region, is investigated. Strong NW Pacific anticyclone during summer induces negative precipitation anomalies over the head Bay of Bengal and Gangetic Plain region. Westward extension of moisture divergence and dry moisture transport from NW Pacific associated with anticyclone (ridge) and local Hadley cell-induced subsidence are responsible for these negative precipitation anomalies. The impact is maximum when the anticyclone and Indian Ocean basin warming co-occur. This contributes significantly to year-to-year variability of ISM.     

The 3D-Var Data Assimilation Experiments on a Dense Fog Event over the Central Plain of China

Nonhydrostatic mesoscale WRF and its 3D-Var system are used to study a dense fog event occurring in 13-14 January 2006. Three different observation data sets including GTS （Global Telecommunication System）, AMDAR （Aircraft Meteorological Data Relay） data, and 9210 data are assimilated into the initial analysis fields in experiments. Experiments with three different assimilation time intervals （1, 3, and 6 h） are also carried out. Three experiments with different data sets have all modified the temperature and humidity field of initial fields, and therefore show an obvious positive effect on fog simulation. Further study indicates that the humidity and stability of boundary layer are improved obviously in assimilation experiments, although different data sets make different contribution to the analysis fields. The multi-time assimilation cycle experiments show that the analysis increment in experiment with l-h interval is more realistic than that with 3- and 6-h intervals.     

Decrease in evaporation over the Indian monsoon region: implication on regional hydrological cycle

Using surface observations from 58 widely distributed stations over India, a highly significant (99.9 %) decreasing trend of pan evaporation (E_pan) of 9.24 mm/a/a is calculated for 1971 to 2010. This constitutes a ~10 % reduction of E_pan over the last four decades. While E_pan is decreasing during the wet summer monsoon season (JJAS), as well as during the dry rest of the year, the rate of decrease during the dry season is much larger than that during the wet season. Apart from increasing solar dimming, surface winds are also persistently decreasing over the Indian sub-continent at the rate of ?0.02 m/s/a resulting in ~40 % reduction over the last four decades. Based on PenPan model, it is shown that both the above factors contribute significantly to the decreasing trend in E_pan. On a continental scale, annual mean potential evaporation (E_p) is larger than rainfall (P or E_p-P > 0, moisture divergence) indicating that India is water-limited. However, during wet monsoon P > E_p (or E_p-P < 0, moisture convergence) indicating that India is energy-limited during this season. Long term data shows that annually E_p-P follows a significant decreasing trend indicating that water limitation is decreasing with time. This is largely due to stronger decreasing trend of E_p-P during the dry season compared to weaker increasing trend of E_p-P during the wet monsoon season. The scatter plot of E_p-P versus E_p also conveys that the decrease in E_p leads to increase in moisture convergence in wet season and decrease in moisture divergence in dry season.     

Intercomparison of the performance of MM5/WRF with and without satellite data assimilation in short-range forecast applications over the Indian region

The present study is conducted to verify the short-range forecasts from mesoscale model version5 (MM5)/weather research and forecasting (WRF) model over the Indian region and to examine the impact of assimilation of quick scatterometer (QSCAT) near surface winds, spectral sensor microwave imager (SSM/I) wind speed and total precipitable water (TPW) on the forecasts by these models using their three-dimensional variational (3D-Var) data assimilation scheme for a 1-month period during July 2006. The control (without satellite data assimilation) as well as 3D-Var sensitivity experiments (with assimilating satellite data) using MM5/WRF were made for 48 h starting daily at 0000 UTC July 2006. The control run is analyzed for the intercomparison of MM5/WRF short-range forecasts and is also used as a baseline for assessing the MM5/WRF 3D-Var satellite data sensitivity experiments. As compared to the observation, the MM5 (WRF) control simulations strengthened (weakened) the cross equatorial flow over southern Arabian sea near peninsular India. The forecasts from MM5 and WRF showed a warm and moist bias at lower and upper levels with a cold bias at the middle level, which shows that the convective schemes of these models may be too active during the simulation. The forecast errors in predicted wind, temperature and humidity at different levels are lesser in WRF as compared to MM5, except the temperature prediction at lower level. The rainfall pattern and prediction skill from day 1 and day 2 forecasts by WRF is superior to MM5. The spatial distribution of forecast impact for wind, temperature, and humidity from 1-month assimilation experiments during July 2006 demonstrated that on average, for 24 and 48-h forecasts, the satellite data improved the MM5/WRF initial condition, so that model errors in predicted meteorological fields got reduced. Among the experiments, MM5/WRF wind speed prediction is most benefited from QSCAT surface wind and SSM/I TPW assimilation while temperature and humidity prediction is mostly improved due to latter. The largest improvement in MM5/WRF rainfall prediction is due to the assimilation of SSM/I TPW. The assimilation of SSM/I wind speed alone in MM5/WRF degraded the humidity and rainfall prediction. In summary the assimilation of satellite data showed similar impact on MM5/WRF prediction; largest improvement due to SSM/I TPW and degradation due to SSM/I wind speed.