Statistically Extrapolated Nowcasting of Summertime Precipitation over the Eastern Alps

This paper presents a new multiple linear regression(MLR) approach to updating the hourly, extrapolated precipitation forecasts generated by the INCA(Integrated Nowcasting through Comprehensive Analysis) system for the Eastern Alps.The generalized form of the model approximates the updated precipitation forecast as a linear response to combinations of predictors selected through a backward elimination algorithm from a pool of predictors. The predictors comprise the raw output of the extrapolated precipitation forecast, the latest radar observations, the convective analysis, and the precipitation analysis. For every MLR model, bias and distribution correction procedures are designed to further correct the systematic regression errors. Applications of the MLR models to a verification dataset containing two months of qualified samples,and to one-month gridded data, are performed and evaluated. Generally, MLR yields slight, but definite, improvements in the intensity accuracy of forecasts during the late evening to morning period, and significantly improves the forecasts for large thresholds. The structure–amplitude–location scores, used to evaluate the performance of the MLR approach,based on its simulation of morphological features, indicate that MLR typically reduces the overestimation of amplitudes and generates similar horizontal structures in precipitation patterns and slightly degraded location forecasts, when compared with the extrapolated nowcasting.     

主客观融合定量降水预报方法及平台实现

随着天气预报业务现代化的发展,预报员面临气象数据爆发增长、服务前端需求不断提高以及客观预报技术广泛应用带来的挑战。传统以手工绘制降水落区为主的定量降水预报(QPF)技术流程已经不能帮助预报员在更高层面体现附加值。为支撑预报员在QPF流程中的核心作用,设计和开发了主客观融合QPF平台。该平台从海量预报数据选取、多源QPF集成、QPF调整和订正、格点化处理和服务产品制作五个方面帮助预报员控制数字化预报全流程。发展了多项关键技术支持平台的智能化,包括多模式QPF数据集构建技术、多模式QPF集成技术、QPF预报场调整和订正技术以及格点场后处理技术。基于MICAPS4系统,实现了主客观融合QPF平台的主要功能,发布了"QPF Master Blender 1.0"版本,并在2017年5月投入业务应用,取得良好反馈和效果。最后,对平台的未来发展进行了展望,包括发展数值模式检验评估工具支持预报员做出最优判断,研究多尺度模式信息的融合技术等。     

汉江丹江口流域水文气象预报系统

汉江丹江口流域水文气象预报系统在GIS技术的支持下,以水文气象监测网、定量降水估算、定量降水预报、洪水预报技术为基础,通过雷达估算降水技术、中尺度数值模式预报技术获取高时空分辨率的降水信息输入水文模型来进行水文气象预报。以Web形式为基础的汉江丹江口流域水文气象预报系统平台在2010年7月以及2011年9月汉江丹江口两次洪水过程中及时、准确地显示了流域实况降水、预报降水,准确地预报了洪水入库过程。目前系统已成功移植到三峡区间、清江水布垭、淮河王家坝、漳河水库等流域开展汛期试验与服务,取得了较好的应用效果。     

An Objective Approach for Prediction of Daily Summer Monsoon Rainfall over Orissa (India) due to Interaction of Mesoscale and Large-scale Synoptic Systems

Orissa State, a meteorological subdivision of India, lies on the east coast of India close to north Bay of Bengal and to the south of the normal position of the monsoon trough. The monsoon disturbances such as depressions and cyclonic storms mostly develop to the north of 15° N over the Bay of Bengal and move along the monsoon trough. As Orissa lies in the southwest sector of such disturbances, it experiences very heavy rainfall due to the interaction of these systems with mesoscale convection sometimes leading to flood. The orography due to the Eastern Ghat and other hill peaks in Orissa and environs play a significant role in this interaction. The objective of this study is to develop an objective statistical model to predict the occurrence and quantity of precipitation during the next 24 hours over specific locations of Orissa, due to monsoon disturbances over north Bay and adjoining west central Bay of Bengal based on observations to up 0300 UTC of the day. A probability of precipitation (PoP) model has been developed by applying forward stepwise regression with available surface and upper air meteorological parameters observed in and around Orissa in association with monsoon disturbances during the summer monsoon season (June-September). The PoP forecast has been converted into the deterministic occurrence/non-occurrence of precipitation forecast using the critical value of PoP. The parameters selected through stepwise regression have been considered to develop quantitative precipitation forecast (QPF) model using multiple discriminant analysis (MDA) for categorical prediction of precipitation in different ranges such as 0.1–10, 11–25, 26–50, 51–100 and >100 mm if the occurrence of precipitation is predicted by PoP model. All the above models have been developed based on data of summer monsoon seasons of 1980–1994, and data during 1995–1998 have been used for testing the skill of the models. Considering six representative stations for six homogeneous regions in Orissa, the PoP model performs very well with percentages of correct forecast for occurrence/non-occurrence of precipitation being about 96% and 88%, respectively for developmental and independent data. The skill of the QPF model, though relatively less, is reasonable for lower ranges of precipitation. The skill of the model is limited for higher ranges of precipitation. accepted September 2006     

定量降水预报与水文模型耦合的中小流域汛期洪水预报试验

预见期内的降水量直接影响着洪水预报的精度,预见期愈长,预见期内的降雨对预报值影响愈大,预见期内的降雨越来越被人们重视。本文以湖北省漳河水库流域为例,利用中尺度数值模式(AREM模式)的预报降雨信息,作为洪水预见期内的降雨,输入新安江模型,对流域2008年汛期典型洪水过程进行预报测试,结果表明考虑预见期内的降雨相对于未考虑预见期降雨对洪水预报结果提高具有明显的优势。研究表明中尺度暴雨模式预报技术在水文预报学的应用具有广阔的发展空间。     

ON USE OF LHN METHOD TO ASSIMILATE THE INTENSIFIED SURFACE PRECIPITATIONS FOR GRAPES_MESO MODEL INITIALIZATION

The quantitative precipitation forecast (QPF) in very-short range (0-12 hours) has been investigated in this paper by using a convective-scale (3km) GRAPES_Meso model. At first, a latent heat nudging (LHN) scheme to assimilate the hourly intensified surface precipitation data was set up to enhance the initialization of GRAPES_Meso integration. And then based on the LHN scheme, a convective-scale prediction system was built up in considering the initial “triggering” uncertainties by means of multi-scale initial analysis (MSIA), such as the three-dimensional variational data assimilation (3DVAR), the traditional LHN method (VAR0LHN3), the cycling LHN method (CYCLING), the spatial filtering (SS) and the temporal filtering (DFI) LHN methods. Furthermore, the probability matching (PM) method was used to generate the QPF in very-short range by combining the precipitation forecasts of the five runs. The experiments for one month were carried out to validate the MSIA and PM method for QPF in very-short range. The numerical simulation results showed that: (1) in comparison with the control run, the CYCLING run could generate the smaller-scale initial moist increments and was better for reducing the spin-up time and triggering the convection in a very-short time; (2) the DFI runs could generate the initial analysis fields with relatively larger-scale initial increments and trigger the weaker convections at the beginning time (0-3h) of integration, but enhance them at latter time (6-12h); (3) by combining the five runs with different convection triggering features, the PM method could significantly improve the QPF in very-short range in comparison to any single run. Therefore, the QPF with a small size of combining members proposed here is quite prospective in operation for its lower computation cost and better performance.     

Quantitative precipitation forecasts: a statistical adaptation of model outputs through an analogues sorting approach

Medium-term quantitative precipitation forecasts (QPFs) up to several days ahead are required to issue early flood warnings and to allow optimum operation of hydraulic structures or reservoirs. This paper describes an approach which can be seen as an adaptation of deterministic meteorological model outputs. It involves searching for a sample of past situations similar to the current one from a long meteorological archive. The analogy is considered in terms of general circulation patterns over a window covering western Europe. For this restricted sample of days similar to the day at hand, the corresponding sample of observed daily precipitation is extracted for each catchment. The rainfall to be observed during the current day is assumed to follow the same distribution, known from this empirical sample. This provides a probabilistic forecast expressed, for example, by a central quantile and a confidence range. This paper describes the many choices underlying the optimisation of this approach: choice of predictor variables to characterise a meteorological situation, choice of similarity criterion between two situations, criterion for performance evaluation between two versions of the algorithm, etc. This method was calibrated over about 50 catchments located in France, Italy and Spain, using a meteorological and hydrological archive running from 1953 to 1996. Comparisons carried out over a validation sample (1995–1996) with three poor-man methods prove the interest of this approach, in a perfect prognosis context. In real-time operation, the use of forecast instead of observed predictor variables, essentially geopotential fields, produces only a minor decrease in performance. The use of the single-valued central quantile supplemented by the confidence interval provided a QPF that has proved effective and informative on the potential for extreme values.     

ECMWF的QPF短期预报性能在新疆的评估

为有效掌握ECMWF模式的短期定量降水预报(QPF)产品在新疆地区的时空预报规律,为客观模式订正算法上提供有效依据,利用频率统计、非参数检验法、累积分布函数、公正预兆得分(ETS)等技术方法,从多角度深入分析了2016年9月—2019年12月12～36 h的QPF产品在新疆地区151个气象观测站点的预报性能。研究表明:QPF和实况降水观测(SPO)两组数据还存在很大差距,3 h-QPF在2.4 mm和24 h-QPF在11.0 mm以下存在明显的空报,3 h-QPF在16.5 mm和24 h-QPF在52.5 mm以上的大降水完全无预报能力,QPF中雨预报效果最好。新疆大雨以上降水主要发生在山脉附近,24 h-QPF产品在天山中脉和伊犁河谷地区的大降水预报比其他地区更具有指导意义。3 h-QPF在1天中8个时间的小、中、大雨的QPF频率在时间趋势上都与SPO存在相反情况。大雨以上的QPF与SPO是一个非线性、不确定的对应关系,建议先建立漏报模型,再进行频率匹配会更有效。