Using a mesoscale ensemble to predict forecast error and perform targeted observation

Using NCEP short range ensemble forecast（SREF） system,demonstrated two fundamental on-going evolutions in numerical weather prediction（NWP） are through ensemble methodology.One evolution is the shift from traditional single-value deterministic forecast to flow-dependent（not statistical） probabilistic forecast to address forecast uncertainty.Another is from a one-way observation-prediction system shifting to an interactive two-way observation-prediction system to increase predictability of a weather system.In the first part,how ensemble spread from NCEP SREF predicting ensemble-mean forecast error was evaluated over a period of about a month.The result shows that the current capability of predicting forecast error by the 21-member NCEP SREF has reached to a similar or even higher level than that of current state-of-the-art NWP models in predicting precipitation,e.g.,the spatial correlation between ensemble spread and absolute forecast error has reached 0.5 or higher at 87 h（3.5 d） lead time on average for some meteorological variables.This demonstrates that the current operational ensemble system has already had preliminary capability of predicting the forecast error with usable skill,which is a remarkable achievement as of today.Given the good spread-skill relation,the probability derived from the ensemble was also statistically reliable,which is the most important feature a useful probabilistic forecast should have.The second part of this research tested an ensemble-based interactive targeting（E-BIT） method.Unlike other mathematically-calculated objective approaches,this method is subjective or human interactive based on information from an ensemble of forecasts.A numerical simulation study was performed to eight real atmospheric cases with a 10-member,bred vector-based mesoscale ensemble using the NCEP regional spectral model（RSM,a sub-component of NCEP SREF） to prove the concept of this E-BIT method.The method seems to work most effective for basic atmospheric state variables,moderately effective for convective instabilities and least effective for precipitations.Precipitation is a complex result of many factors and,therefore,a more challenging field to be improved by targeted observation.

The Effects of Land Surface Process Perturbations in a Global Ensemble Forecast System

Atmospheric variability is driven not only by internal dynamics, but also by external forcing, such as soil states, SST, snow, sea-ice cover, and so on. To investigate the forecast uncertainties and effects of land surface processes on numerical weather prediction, we added modules to perturb soil moisture and soil temperature into NCEP＇s Global Ensemble Forecast System （GEFS）, and compared the results of a set of experiments involving different configurations of land surface and atmospheric perturbation. It was found that uncertainties in different soil layers varied due to the multiple timescales of interactions between land surface and atmospheric processes. Perturbations of the soil moisture and soil temperature at the land surface changed sensible and latent heat flux obviously, as compared to the less or indirect land surface perturbation experiment from the day-to-day forecasts. Soil state perturbations led to greater variation in surface heat fluxes that transferred to the upper troposphere, thus reflecting interactions and the response to atmospheric external forcing. Various verification scores were calculated in this study. The results indicated that taking the uncertainties of land surface processes into account in GEFS could contribute a slight improvement in forecast skill in terms of resolution and reliability, a noticeable reduction in forecast error, as well as an increase in ensemble spread in an under-dispersive system. This paper provides a preliminary evaluation of the effects of land surface processes on predictability. Further research using more complex and suitable methods is needed to fully explore our understanding in this area.

The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships,while limited by the capability of climate models.A bias correction methodology in this study was proposed and performed on raw products from two climate models,the First Institute Oceanography Earth System Model(FIOESM)and the National Centers for Environmental Prediction(NCEP)Climate Forecast System(CFS),to improve 60 days predictions for Arctic sea ice.Both models were initialized on July 1,August 1,and September 1 in 2018.A 60-day forecast was conducted as a part of the official sea ice service,especially for the ninth Chinese National Arctic Research Expedition(CHINARE)and the China Ocean Shipping(Group)Company(COSCO)Northeast Passage voyages during the summer of 2018.The results indicated that raw products from FIOESM underestimated sea ice concentration(SIC)overall,with a mean bias of SIC up to 30%.Bias correction resulted in a 27%improvement in the Root Mean Square Error(RMSE)of SIC and a 10%improvement in the Integrated Ice Edge Error(IIEE)of sea ice edge(SIE).For the CFS,the SIE overestimation in the marginal ice zone was the dominant features of raw products.Bias correction provided a 7%reduction in the RMSE of SIC and a 17%reduction in the IIEE of SIE.In terms of sea ice extent,FIOESM projected a reasonable minimum time and amount in mid-September;however,CFS failed to project both.Additional comparison with subseasonal to seasonal(S2S)models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.

Evaluation of a regional climate model for atmospheric simulation over Arctic river basins

Evaluation on a regional climate model was made with five-month atmospheric simulations over the Arctic river basins. The simulations were performed with a modified mesoscale model, Polar MM5 coupled to the NCAR Land Surface Model （LSM） to illustrate the skill of the coupled model （Polar MM5＋LSM） in simulating atmospheric circulation over the Arctic river basins. Near-surface and upper-air observations were used to verify the simulations. Sensitivity studies between the Polar MM5 and Polar MM5＋LSM simulations revealed that the coupled model could improve the forecast skill for surface variables at some sites. In addition, the extended evaluations of the coupled model simulations on the North American Arctic domain during December 15, 2002 to May 15, 2003 were carried out. The time series plots and statistics of the observations and Polar MM5＋LSM simulations at six stations for near-surface and vertical profiles at 850 hPa and 500 hPa were analyzed. The model was found capable of reproducing the observed atmospheric behavior in both magnitude and variability, especially for temperature and near-surface wind direction.

T213降水预报订正系统的建立与研究

目前T213降水预报存在一定程度上的系统性误差,为了更好地使用T213降水预报产品,减小系统性误差对主观预报的影响,利用一种统计学方法可以对T213降水预报进行订正,减小T213降水预报的系统性误差。通过对2004年6—11月订正前后的T213降水预报进行统计学和天气学检验分析,检验该订正系统的订正效果。结果表明,订正后的降水预报的预报偏差B值有了显著改善,其他统计检验量也有了不同程度的提高;订正后雨带的位置和轮廓更加接近降水实况。

全球数值模式中的台风初始化Ⅰ： 方案设计

由于缺少大量有效的观测资料,台风初始化对数值天气预报业务模式而言,仍然是一个悬而未决的难题。中国国家气象中心自从1996年将台风数值预报系统投入业务运行以来,一直使用经验的人造bogus涡旋台风初始化技术。实际上,不同时期的台风有着不同的环流结构,即使同一个台风在不同的生命期也具有不同的结构特征,而这些结构特征的差异并不能依靠现有的bogus涡旋技术体现出来,这种主观方法的统一性与台风在时空上的差异性形成了强烈的反差。最近,基于国家气象中心全球资料分析同化-预报循环系统,设计和发展了一套新的台风初始化业务方案,它主要由初始涡旋形成、涡旋重定位和涡旋调整3部分过程组成。相比于业务中使用的人造bogus涡旋台风初始化方案,新方案在很大程度上减少了人为因素对台风涡旋结构的影响,而更多地是依靠数值模式自身的动力和物理过程来协调约束产生三维空间的涡旋结构。应用新方案,文中对生成于西北太平洋的2006年0605号台风格美(Kaemi)进行了数值试验,初步分析表明,新方案在实现台风涡旋环流结构的初始化方面效果较好,同时,对台风格美多个时次的预报结果也显示,相比于业务使用的bogus方案而言,新方案对台风路径平均预报误差有了大幅度的降低。

全球数值模式中的台风初始化Ⅱ： 业务应用

中国国家气象中心基于全球数值模式、采用人造bogus涡旋初始化方案建立起来的台风数值预报业务系统是独立于全球资料分析同化—预报循环过程来运行的。实际上,台风涡旋是全球数值预报中一个重要的、密不可分的中尺度系统,台风的正确描述会对周围的大尺度形势预报产生积极正面的影响,而这种影响反过来也会反馈到大尺度环流对台风移向移速的变化上,陈旧的台风初始化方案和不合理的台风数值预报业务系统流程已经严重阻碍了中国台风数值预报业务水平的进一步发展。最近,中国国家气象中心利用新开发的台风初始化方案、基于全球数值模式建立了新一代的台风数值预报业务系统,相比于旧系统而言,新系统巧妙地将台风初始化过程与全球资料分析同化—预报循环系统融合在了一起,这使得改进目前台风路径数值预报效果成为可能。应用新的台风数值预报系统,文中对2006年生成于西北太平洋23个不同类型的台风进行了连续数值试验,统计分析表明,新系统在改进台风路径预报效果上有了不俗的表现,相比于业务使用的人造bogus涡旋方案而言,120 h预报时效内的平均路径误差有了十几公里到上百公里的下降。该系统在2007年台风季节投入了实时试验运行,并取得了令人惊喜的预报效果。

Regional and Global Land Data Assimilation Systems: Innovations,Challenges, and Prospects

Since the North American and Global Land Data Assimilation Systems(NLDAS and GLDAS) were established in2004, significant progress has been made in development of regional and global LDASs. National, regional, projectbased, and global LDASs are widely developed across the world. This paper summarizes and overviews the development, current status, applications, challenges, and future prospects of these LDASs. We first introduce various regional and global LDASs including their development history and innovations, and then discuss the evaluation, validation, and applications(from numerical model prediction to water resources management) of these LDASs. More importantly, we document in detail some specific challenges that the LDASs are facing: quality of the in-situ observations, satellite retrievals, reanalysis data, surface meteorological forcing data, and soil and vegetation databases; land surface model physical process treatment and parameter calibration; land data assimilation difficulties; and spatial scale incompatibility problems. Finally, some prospects such as the use of land information system software, the unified global LDAS system with nesting concept and hyper-resolution, and uncertainty estimates for model structure,parameters, and forcing are discussed.

Quality Control and Evaluation of the Observed Daily Data in the North American Soil Moisture Database

The North American Soil Moisture Database (NASMD) was initiated in 2011 to assemble and homogenize in situ soil moisture measurements from 32 observational networks in the United States and Canada encompassing more than 1800 stations. Although statistical quality control (QC) procedures have been applied in the NASMD, the soil moisture content tends to be systematically underestimated by in situ sensors in frozen soils, and using a single maximum threshold (i.e., 0.6 m3 m-3) may not be sufficient for robust QC because of the diverse soil textures in North America. In this study, based on the in situ soil porosity and North American Land Data Assimilation System phase 2 (NLDAS-2) Noah soil temperature, the simple automated QC method is revised to supplement the existing QC approach. This revised QC method is first validated based on the assessment at 78 of the Soil Climate Analysis Network (SCAN) stations where the manually checked data are available, and is then applied to all stations in the NASMD to produce a more strict quality-controlled dataset. The results show that the revised automated QC procedure can flag the spurious and erroneous soil moisture measurements for the SCAN stations, especially for those located in high altitudes and latitudes. Relative to station measurements in the original NASMD, the quality-controlled data show a slightly better agreement with the manually checked soil moisture content. It should be noted that this quality-controlled dataset may be over-flagged for some valid soil moisture measurements due to potential errors of the soil temperature and soil porosity data, and validation in this study is limited by the availability of benchmark soil moisture data. The updated QC and additional validation will be desirable to boost confidence in the product when high-quality data become available in the future.

Evolution of the 2015/16 El Nio and historical perspective since 1979

The 2015/16 El Nio developed from weak warm conditions in late 2014 and NINO3.4 reached 3℃ in November 2015. We describe the characteristics of the evolution of the 2015/16 El Nio using various data sets including SST, surface winds,outgoing longwave radiation and subsurface temperature from an ensemble operational ocean reanalyses, and place this event in the context of historical ENSO events since 1979. One salient feature about the 2015/16 El Nio was a large number of westerly wind bursts and downwelling oceanic Kelvin waves(DWKVs). Four DWKVs were observed in April-November 2015 that initiated and enhanced the eastern-central Pacific warming. Eastward zonal current anomalies associated with DWKVs advected the warm pool water eastward in spring/summer. An upwelling Kelvin wave(UWKV) emerged in early November 2015 leading to a rapid decline of the event. Another outstanding feature was that NINO4 reached a historical high(1.7℃), which was 1℃(0.8℃) higher than that of the 1982/83(1997/98) El Nio . Although NINO3 was comparable to that of the 1982/83 and 1997/98 El Nio , NINO1+2 was much weaker. Consistently, enhanced convection was displaced 20 degree westward, and the maximum D20 anomaly was about 1/3.1/2 of that in 1997 and 1982 near the west coast of South America.

2015/2016厄尔尼诺事件的演变过程及与1979年来历次厄尔尼诺事件的比较

2015/2016厄尔尼诺事件从2014年年底的一个弱暖事件发展而来.到2015年11月,NINO3.4区SST异常达到了3.0℃.利用海表温度、海表风场、长波辐射及次表层海温等数据,本文系统地描述了2015/2016厄尔尼诺事件的演变特征,并与1979年以来历次厄尔尼诺事件进行了比较.2015/2016厄尔尼诺事件一个显著的特征在于有大量的西风爆发事件和下沉开尔文波动发生.在2015年4~11月间,共观测到4次下沉开尔文波动.这些下沉开尔文波动激发并加强了赤道中、东太平洋海表温度的增暖.此外,下沉开尔文波动伴随着的东向海表平流在春、夏季将暖池区暖水东移,而于2015年11月出现的一次上升开尔文波动开启了此次厄尔尼诺事件急速消亡的序幕.2015/2016厄尔尼诺事件的另一个显著特征是NINO4区的海表温度异常创造了历史新高(1.7℃),比1982/1983(1997/1998)厄尔尼诺事件时高出了1℃(0.8℃).尽管NINO3区的海表温度异常与1982/1983和1997/1998厄尔尼诺事件时相当,但NINO1+2的海表温度异常却低了1℃.与此相应的,是对流增强区域向西移动了20个经度,且在南美洲西海岸处的最大D20异常仅仅是1997年和1982年的1/3到1/2.