应用气象学报

2018, 29(5)

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Abstract:

FSS-based Evaluation on Convective Weather Forecasts in North China from High Resolution Models

Tang Wenyuan, Zheng Yongguang, Zhang Xiaowen

2018, 29(5): 513-523. DOI: 10.11898/1001-7313.20180501

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Abstract:
High resolution numerical model has a certain ability to predict the structure and evolution characteristics of the convection system, however, it is not easy to exploit the advantage of high resolution numerical model forecast using traditional verification metrics. Fuzzy verification is a recently developed and popular spatial verification method used for high resolution numerical model. It compares characteristics of the adjacent area of the corresponding point in prediction and observation fields to assess the accuracy of the forecast. Fuzzy verification considers a certain space and time uncertainty instead of completely accurate matching between prediction and observation.The Method of Fraction Skill Score, which is a kind of fuzzy verification, is used to evaluate the convective weather forecast ability in North China from 3 different high-resolution models(including Rapid Analysis and Forecast System GRAPES_Meso, GRAPES_3 km and East China Regional Numerical Model). Seven convective cases in North China with different organization modes caused by different weather systems from July to September 2017 are selected to evaluate the prediction ability of the small and medium scale convection system of high resolution models. Results show that the fraction skill score (FSS) can achieve valuable assessment information when the model prediction has a certain displacement and intensity of deviation. Another dominant advantage of FSS is that it can examine the model forecast skill scale, referring to the smallest window scale over which the forecast output contains useful information. The forecast skill scale is different from the scale of weather system, it represents the scale of spatial displacement deviation. The prediction of radar echo intensity from three models is weaker than the observation, East China Regional Numerical Model is the closest to the observation with echoes below 44 dBZ, and the intensity deviation from GRAPES_3 km is the smallest with echoes above 44 dBZ. Those performances lead to difference of FSS curve between GRAPES_3 km and East China Regional Numerical Model in the case of lower threshold and higher threshold. In order to evaluate the spatial displacement deviation of the model, the frequency (percentile) threshold is adopted, which represents the size change of the verification object. With increasing percentile threshold, GRAPES_3 km forecast skill scale basically maintains at 150-200 km, but the forecast skill scale of East China Regional Numerical Model increases gradually from 100 km to 400 km against the percentile threshold, which suggests that GRAPES_3 km model is more capable of predicting the small scale convective events.

Relationships Between Cloud-to-ground Lightning and Radar Parameters at Naqu of the Qinghai-Tibet Plateau

Meng Qing, Fan Penglei, Zheng Dong, Zhang Yijun, Yao Wen

2018, 29(5): 524-533. DOI: 10.11898/1001-7313.20180502

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Abstract:
Lightning observation may play a key role in the monitoring of deep convection over the Qinghai-Tibet Plateau, especially considering that the wide-range and real-time observation ability of lightning location system. It is firstly necessary to understand the relationship between lightning activity and deep convection features, which, has been rarely concerned in the Qinghai-Tibet Plateau. Using radar data and cloud-to-ground (CG) lightning data during May-September from 2014 to 2015, correlations between CG lightning and radar parameters of thunderstorms are investigated over Naqu, a county in the middle of the Plateau with relatively strong lightning activity. Continuous spatial regions of radar composite reflectivity above 20 dBZ are identified as storm cells at each 6 min radar volume scan, and "matching ellipses" are used to enclose the scope of cells, and then whether CG lightning flashes fall in ellipses or cells is decided. Cells with lightning and located within 30-100 km of radar center are picked out as thunderstorms. Based on 5626 thunderstorm samples, it is summarized that the maximum radar echo, 20 dBZ echo top and 30 dBZ within 5 km of CG flash location exhibit normal distribution, with their peak values ranging from 34 to 41 dBZ, 11 to 15 km, and 8.5 to 12 km, respectively. Meanwhile, the maximum vertical integrated liquid content and the maximum precipitation ice content vertically integrated at 7-11 km both show logarithmic normal distribution. A total of 4719 thunderstorms that possess no less than 30 dBZ reflectivity (a threshold value for the definition of strong reflectivity) are selected for the correlation analysis. Weak correlations between CG lightning frequency and radar parameters are found while are considered as one-to-one relationships. However, correlations enhance prominently when the CG lightning frequency in the thunderstorm increases. The correlation study based on interval segmentations of radar parameters is then made and strong relationships are found, indicating the macroscopic correspondences of CG lightning frequency to the intensity of thunderstorms. The area of composite reflectivity no less than 30 dBZ show the most outstanding correlation with CG lightning frequency among radar parameters which are segmented linearly, with the correlation coefficient being 0.75. Among radar parameters that are segmented according to their logarithms, the logarithm of precipitation ice content accumulated at 7-11 km and in the area with composite reflectivity no less than 30 dBZ are most prominently correlated with CG lightning frequency, with the correlation coefficient being 0.95. Formulas based on linear fittings and power function fittings are all given, while the power function fittings are a little better according to their corresponding correlation coefficient.

Predicting Lightning Activities by a Meso-scale Electrification and Discharge Model

Xu Liangtao, Chen Shuang, Yao Wen, Chen Yun, Zhang Rong

2018, 29(5): 534-545. DOI: 10.11898/1001-7313.20180503

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Abstract:
Using WRF-Electric model coupled with electrification and discharge schemes, experimental predictions are carried out on the regional lightning activity from 2015 to 2017. By establishing a lightning activity prediction verification method, experiment results are classified and evaluated. Taking operational prediction as a reference, the ability to predict regional lightning activity with the numerical model is evaluated objectively. Main problems of the model are identified through verification, which provides a basis for its further improvement.The major region of lightning activity could be predicted well by the meso-scale electrification and discharge model. In the strict point-to-point verification, the CSI of the model prediction almost reach the operational prediction level during the main flood season (June-August). Quantitative verification results over North China also show that the prediction performs best with the forecast time of 6-12 hours. For small-scale thunderstorms, CSI of the model prediction is higher than that of the operational prediction. With expansion of the thunderstorm scale, the model prediction gradually loses its advantage. Therefore, the model is more valuable for predicting localized and small-scale thunderstorms.The range of the lightning activity predicted by the model is small and relatively concentrated, and some scattered lightning activity is often missed. Thus, in the parameterization of discharge, the threshold should be decreased at the initial time of lightning to improve the performance in relatively weaker electrification region. The lightning flash density predicted by the model is obviously greater than observed. To reduce the predicted flash density in the strong electrification area, the amount of neutralization charge of a single lightning should be consistent with the observation in the discharge scheme.CSI is relatively low with both the operational and this model prediction. In some cases, the prediction can achieve a relatively high CSI, but in long-term prediction experiments it's difficult to maintain high score using the strict point-to-point verification method. On the other hand, for weather phenomena with strong randomness in their occurrence position, the predictability is usually poor.Although the model can forecast the lightning activity area well, reaching the level of operational prediction, many problems remain in terms of the flash density forecast. How to parameterize lightning reasonably in a meso-scale model is still unresolved and extremely challenging. Currently, numerical models can predict precipitation successfully, while the ability to quantitatively predict the flash density is far behind. Improvement in lightning parameterization schemes and the selection of relevant thresholds in models relies on new methods and a large number of experiments being conducted.

Quality Control of S-band Polarimetric Radar Measurements for Data Assimilation

Wang Hong, Kong Fanyou, Jung Youngsun, Wu Naigeng, Yin Jinfang

2018, 29(5): 546-558. DOI: 10.11898/1001-7313.20180504

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Abstract:
The polarimetric radar is an important detection device whose measurements can be used for severe convective weather analysis and cloud microphysics progress research. Upgrading the traditional Doppler weather radar to polarimetric radar is a key part of severe convective weather monitoring program of China in the next few years, and the quality control of polarimetric radar measurements is key technical issue of the monitoring program. In Guangdong Province, based on the domestic and international mainstream quality control algorithms and relevant experience, a quality control system is developed for S-band polarimetric radars, to deal with the non-meteorological echo, non-standard blockage and high frequency noise in the radar radial, which have negative impacts on application of polarimetric radar measurements in data assimilation. The system is applied to the typical severe convective weather case in South China monsoon region, including a rainfall case, a severe convection case and a typhoon case in 2017. Evaluation results show that a combination of the hydrometeor classification screening based on fuzzy logic, co-polar cross-correlation coefficient (ρ_HV), signal-to-noise ratio (SNR) and specific differential phase (K_DP) thresholding and despeckling can remove most non-meteorological echoes, and suppress virtual echo caused by anomalous propagation efficiently. Non-meteorological echoes include ground clutter, biological scatters, partial clear-air echo and radiographic noise due to anomalous propagation. A linear interpolation is employed to fill the small gap (the width of which is less than 5°) caused by non-standard blockage. A median filter and radial smooth are found effective in filtering out high frequency noise in the radar radial while maintaining polarimetric radar characteristics. After quality control, the meteorological echo is clearer and more prominent, and accounts for about 40% of valid observation which is defined by reflectivity (Z_H) being larger than -30 dBZ. Z_H of the meteorological echo is larger than 5 dBZ, ρ_HV is larger than 0.8 and less than 1.0, and the differential reflectivity (Z_DR) is between -0.2 and 4 dB. Batch tests are needed to keep the quality control system stable and effective in the further work. And how to combine multiple polarimetric radar measurements to form a three-dimensional gridded product is also another important prerequisite for application of polarimetric radars measurements in the numerical model.

Deviation Correction and Assimilation Experiment on L-band Radiosonde Humidity Data

Hao Min, Gong Jiandong, Tian Weihong, Wan Xiaomin

2018, 29(5): 559-570. DOI: 10.11898/1001-7313.20180505

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Abstract:
The radiosonde observation of L band is a kind of conventional data, which plays an important role in weather forecast and numerical forecast. In recent years, with the progress of assimilation technology, the requirement of data precision also improves. It is found that more and more researches and operational forecasting centers are doing detailed classification and analysis for each type of instrument, even in each region and each season, according to the development of numerical forecast data assimilation technology. The method of humidity deviation correction takes the influence of observation pressure, temperature, solar altitude angle and other factors on the observation instrument into account and formulates a targeted deviation correction scheme to improve the data.Based on the analysis of humidity deviation and distribution characteristics of three kinds of L-band radiosonde instruments used in China, an effective correction method suitable for L-band radiosonde instruments in China has been developed and applied in GRAPES assimilation analysis. By improving the assimilation analysis and model prediction results, the observed humidity deviation of 3 kinds of instruments and results of continuous tests show as follows.Three main types of radiosonde instruments are used in China, among which Instrument 32 is most widely used, Instrument 31 and 33 are used at a dozen stations. The deviation of Instrument 32 is smaller than that of Instrument 31. The deviation of Instrument 33 is smaller when the humidity is greater than 60%, and greater than the others below the level of 400 hPa and the humidity is less than 60%.Compared with ECMWF reanalysis of humidity field, there is a dry phenomenon in L-band radiosonde humidity observation. Compared with the control test, the humidity deviation value of various deviation correction schemes is obviously reduced above the level of 500 hPa. The Vomel deviation correction scheme is used in GRAPES assimilation system, and the analytical deviation is reduced by 5%. After the humidity observation is revised, the forecast precipitation is closer to the actual situation, and the test score of forecast precipitation is improved significantly.Through the analysis and comparison of humidity observation deviations of several kinds of radiosonde instruments in China, the evaluation and understanding of the performance of these instruments are deepened, and the revised scheme suitable for radiosonde humidity deviation in China has been developed, which has achieved better application effect in the test. It lays the foundation for the better use of these data in practical applications, and makes an active attempt to better classifying sounding instruments.

Assessment on Systematic Errors of GRAPES_GFS 2.0

Zhang Meng, Yu Haipeng, Huang Jianping, Shen Xueshun, Su Yong, Xue Haile, Yang Zhijian

2018, 29(5): 571-583. DOI: 10.11898/1001-7313.20180506

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Abstract:
The Global and Regional Assimilation and Prediction System(GRAPES) model is set up as a new generation multi-scale universal data assimilation and numerical prediction system in China. The global forecasting system version of GRAPES_GFS 2.0 is formally established in June 2016, and thus a comprehensive assessment on its forecasting capacity is urgently needed. Comparing with NCEP FNL data, the hindcast of a whole year of 2014 and 4 seasonal representative months by GRAPES_GFS 2.0 are analyzed.The systematic error of 500 hPa potential height field is characterized by the obvious gradient and zonal distribution or wave columnar distribution, concentrated in mid and high latitudes. The error shows significant seasonal variation, which is much larger in winter than that in summer of both the north and south hemispheres. Furthermore, compared with the linear growth rate, GRAPES_GFS 2.0 forecast error is lower, and changing trends of errors with height are similar when lead time changes. The distribution of the initial error of 500 hPa temperature field is concentrated in tropics, while along with the growth of the forecast time, the large area of forecast error gradually moves to middle and high latitude areas. Moreover, the zonal average temperature error is mainly negative, while slightly positive near the tropopause of high latitude areas. There is no obvious distribution law of latitudinal wind field error, which is not closely related to latitude, sea land distribution and topography, alternated with west wind error and east wind error. The error of the height field in the tropopause, the temperature field and zonal wind field in the boundary layer and in the tropopause increases rapidly as well.Results above show that the evaluation on the oblique pressure instability of geopotential height field in mid and high latitudes still needs improving. As the low latitude area is dominated by positive pressure structure, the absolute error value with its growth is relatively small. Over-estimated thermal forces in plateau and desert regions result in the large error area of temperature field. The zonal wind field error is similar but may result in meridional wind error. In addition, the performance of the model in boundary layer and tropopause simulation needs improving.

A Method of Short-time Strong Rainfall Forecasting During Pre-rainy Season in Fujian Based on ECMWF Productions

Hong Wei, Zheng Yulan

2018, 29(5): 584-595. DOI: 10.11898/1001-7313.20180507

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Abstract:
Distribution features of model variables accompanied with short-time strong rainfall events are investigated based on hourly precipitation data from 1605 automatic weather stations and ECMWF 0.125°×0.125° fine grid model products, and a method of short-time strong rainfall forecasting based on threshold determination is established. Results show that short-time strong rainfall occur more frequently in Fujian inland area and less in Fujian coastal area during pre-rainy season, and the diurnal variation exhibits double peaks with the notable one at 1700 BT and the inapparent one at 0500 BT. The box difference index is useful to check whether a variable could differentiate short-time strong rainfall events well. The box difference index of humidity variables like specific humidity at 925 hPa and total column water vapor are most prominent followed by K index and convective available potential energy (CAPE) which shows these variables have good performances in distinguishing short-time strong rainfall events. Some variables like temperature difference between 850 hPa and 500 hPa and temperature change in 24 h at 500 hPa perform poorly in differentiating short-time strong rainfall events.The minimum threshold method based on the minimum values of variables after eliminating outliers works well in judging short-time strong rainfall events, which could decrease vacancy forecast rate effectively through increasing missing forecast rate appropriately compared with adopting real minimum values as threshold. In the key area (25.9°-27.1°N, 116.4°-117.4°E), TS (threaten score) of validation set in 2016 with 12 h interval reaches 0.5 at daytime and 0.3 at nighttime just based on the minimum threshold method. Revising threshold of variables with high box difference index values could improve the accuracy with the nighttime TS of validation set in 2016 increasing from 0.3 to 0.34. TS of 2016 is relatively lower compared with that of 2014-2015, and the cause may be that short-time strong rainfalls happen much more frequently in 2016 which is a very strong El Niño year.To establish a potential forecast model of short-time strong rainfall during pre-rainy season, Fujian is divided into grids of 1°×1°, and minimum threshold method is applied in each grid followed by threshold revise of variables with high box difference index values. This model could analyze all kinds of variables comprehensively besides those considered by weather forecasters. TS with 12 h interval at daytime mainly ranges from 0.3 to 0.5 while TS at nighttime is relatively lower. TS in inland area is much better than coastal area both at daytime and nighttime mainly because short-time strong rainfall occurs more frequently in inland area than coastal area during pre-rainy season climatologically.

Observational Analysis of Summer Atmospheric Downward Longwave Radiation at 4 Sites on the Tibetan Plateau

Liu Mengqi, Zheng Xiangdong, Zhao Chunsheng

2018, 29(5): 596-608. DOI: 10.11898/1001-7313.20180508

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Abstract:
The summer downward longwave radiation (L_↓) observed in Naqu, Lhasa, Nyingchi and Ali is analyzed. The averaged L_↓ at 4 sites are 299, 319, 368 and 305 W·m^-2, respectively. L_↓ is lower in the local morning and subsequently increases significantly in the afternoon. The mean diurnal variation at Naqu and Ali is about 30 W·m^-2, while it's 9 and 19 W·m^-2 in Lhasa and Nyingchi respectively. Based on solar shortwave radiation observations, a method to determine the daytime sky cloud-coverage index (cloud fraction, C_F) is presented by the solar radiation comparisons between the empirically calculation for cloud-free situation and the observed. C_F of -5% to 5% is assumed as daytime cloud-free situation to test the suitability of 10 empirical formula of L_↓ on the Plateau. It shows that the empirical formula of Ångström (1915) is most suitable for the Nyingchi where the vapor pressure is high, while the empirical formula of Konzelmann (1994) is most suitable for Naqu, Lhasa and Ali. Errors of the calculated daytime cloud-free L_↓ from the observed at Naqu, Lhasa, Nyingchi and Ali are 2.1%, -0.27%, -0.89% and 0.94%. The cloud-induced L_↓ enhancement effect (measured L_↓ minus the calculated cloud-free values given the surface temperature and humidity) clearly shows that the mean L_↓ enhancement effect at Naqu, Lhasa, Nyingchi and Ali are 30.8, 22.1, 38.8 and 15.6 W·m^-2 with the median values of 24.4, 17.3, 42.7 and 6.8 W·m^-2. With the increase of artificially visual total cloud amount, the increasing trend of L_↓ enhancement is obvious, especially when the cloud amount increase from less than 20% to 70% and above, the corresponding L_↓ enhancement effects rapidly increases from above 20 to more than 50 W·m^-2 at all the 4 sites. Given the same visual cloud mount, the L_↓ enhancement effects induced by the low clouds in Lhasa and Ali are obviously higher than those induced by the total cloud. The effect from cloud coverage and height on the L_↓ enhancement is further confirmed by the aerosol lidar cloud base height at zenith direction and the C_F. The decreasing cloud base height (no available cloud base height data in Lhasa) corresponding to increasing trends of C_F and L_↓ enhancement is detected. The L_↓ enhancement effects are only about 5 W·m^-2 with cloud-free condition, but they may rise to 60 W·m^-2 when C_F is above 90% (the average cloud base height is less than 3.5 km). Given the fixed cloud base height, the L_↓ enhancement obviously increases with the growth of C_F. C_F, significantly more than the zenith cloud base height, which affects the enhancement of L_↓ on the Tibetan Plateau.

Radiative Effects of Aerosols in Different Areas of Beijing

Li Deping, Cheng Xinghong, Sun Zhian, Wang Liming, Zhang Benzhi, Zhang Tianming

2018, 29(5): 609-618. DOI: 10.11898/1001-7313.20180509

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Abstract:
Shortwave radiation fluxes on the ground at four stations in Beijing are calculated using the second Sun-Edward-Slingo radiative transfer (SES2) model and cloud and vapor data from European Centre for Medium-Range Weather Forecasts reanalysis dataset (ECMWF-thin) with 0.125°×0.125° spatial resolution from January 2013 to October 2015. And impacts of aerosol on surface radiation on synoptic scale in clear-sky and cloudy days in the urban and suburb of Beijing are respectively analyzed based on the difference between the modeled global horizontal irradiances (GHI) with the inclusion of cloud and vapor and the corresponding observations. Spatial-temporal variation characteristics of quantificational aerosol radiative effects on the synoptic scale in the urban (polluted area) and at Shangdianzi Regional Background Station (clean area) are preliminarily studied in different haze pollution episodes. Quantitative models between deduction ratios of GHI caused by aerosol and PM₁₀ and PM_2.5 concentrations in the urban and suburb of Beijing are established. Results show that aerosol radiative effects on the synoptic scale in the urban are about twice of that at Shangdianzi Station, and those in south and west are larger than other areas. GHI reductions caused by aerosol fall in 146.23-180.99 W·m^-2 in clear-sky days and 202.11-217.02 W·m^-2 in cloudy days, and differences of GHI deduction in different districts in clear sky are larger than those in cloudy days. Aerosol radiative effects on the synoptic scale in autumn and winter when concentrations of PM₁₀ and PM_2.5 are higher in the same period are obviously lager than those in spring and summer. Reduction ratios of GHI in southern suburbs of Beijing in autumn and winter are 10%-20% higher than those in spring and summer during 2013-2015. Linear relationship between reduction ratios of GHI and direct radiation (DIR) and AOD in the urban and suburb of Beijing are found and impacts of AOD on DIR are larger than GHI, especially in the urban with heavy haze. Additionally, impacts of PM_2.5 concentration on GHI and DIR cannot be ignored. Results above have certain scientific and practical application values for better understanding the interaction between aerosol and meteorological conditions such as solar radiation, and improvements of refined assessments and forecasts of solar energy resources.

Change of Dry and Wet Climate and Its Influence on Forest Fire in the Great Xing'an Mountains

Li Xiufen, Guo Zhaobin, Zhao Huiying, Zhu Haixia, Wang Ping, Zhai Mo

2018, 29(5): 619-629. DOI: 10.11898/1001-7313.20180510

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Abstract:
It's of immense importance to understand characteristics of dry and wet climate condition change in forest region of the Great Xing'an Mountains, and reveal its influence on forest fire pattern, which can provide scientific basis for forest fire management and forest resource protection in this region. Based on standardized precipitation index(SPI) in the Great Xing'an Mountains from 1974 to 2016, using statistical analysis and comparative analysis method, effects of different dry and wet scenarios on the number of forest fires and burned areas are systematically analyzed. And similarities and differences of different drought grade effects on forest fires are discussed. From 1974 to 2016, The annual climate of the Great Xing'an Mountains in Heilongjiang shows wetting trends, with several obvious stages. The annual fluctuation of SPI in seasonal scale is larger, and all of them show wetting trends. The precipitation in summer plays a decisive role in the change of annual dry-wet climate conditions. The forest fire frequency and burned areas are basically accordant with the grade of dry and wet climate. However, the number of forest fires is more closely related to the dry and wet climate condition. On annual scale, SPI value is negatively correlated with the number of fires, reaching 0.05 significant level. However, SPI value shows a weak negative correlation with the natural logarithm of the total burned areas, not passing the significant test. On seasonal scale, there is a significant negative correlation of SPI to the number of forest fires and the natural logarithm of burned areas. But the seasonal difference is great, and it's most significant in spring, followed by autumn, and relatively weak in summer. SPI in different seasons is negatively correlated with the number of annual forest fires and the natural logarithm of burned areas. Dry and wet climate has effects on the forest fires in lag period, and it's found that SPI in the previous winter contributes most to the number of forest fires. SPI can not only better reflect dry and wet conditions of regional climate, but also indicate the possibility of forest fire and the relative change of burned areas well. It can provide a scientific basis for forest fire prediction and management.

Design and Implementation of Surface Meteorological Data Statistical Processing System

Sun Chao, Huo Qing, Ren Zhihua, Liu Zhen, Xiao Weiqing, Xu Yongjun

2018, 29(5): 630-640. DOI: 10.11898/1001-7313.20180511

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Abstract:
Statistical products of surface meteorological data (SMD) are among the most-frequently-used data in meteorological research and operations. As the improvement of surface meteorological observation system over China, statistics of SMD have encountered problems such as large number of sites, wide variety of elements, and complexity of statistical strategy. With typical features of big data, it's possible for SMD to serve more precise and efficient operations nowadays, which is obviously beyond the capability of traditional serial processing framework.Aiming at precise and efficient statistic processing of data from more than 60000 surface weather stations, a statistical processing system for SMD is built based on big data technology. Compared to traditional serial processing framework, efficiency of the system has increased by more than 10 times and more statistics and function are provided, such as fast calculation, rolling update of statistical values according to late-arriving data and corrected information, and arbitrary time scale statistics. Storm distributed flow processing technology is applied in the system to realize efficient statistical calculations. Big data message transmission and cache technology are also applied to ensure the system's high efficiency and stability. Modular design framework ensures strong extensibility of the system, based on which statistics, quality control and evaluation algorithms are extended to varieties of data, e.g., upper-air, radiation, oceanic and aircraft measurements. The system is deployed at national meteorology department and its products are synchronously applied at the provincial level, for this layout ensures data consistency.The system is incorporated into China Integrated Meteorological Information Sharing System (CIMISS) and become its core data processing framework. The system provides more than 800 real-time multi-scale SMD statistical values to serve meteorological users and the public through CIMISS data unified service interface since January 2017. Based on data access logs, monthly access of daily SMD statistics reach 19.51 million times in 2017, ranking the 3th among over 400 data or products, playing important roles in weather monitoring, forecasting and warning, meteorological decision, public service and climate research.In the future, the technical framework and algorithm module of the system will be integrated into the processing pipeline of meteorological large data cloud platform, with further optimization of the computational topology for full use of computing resources, which can increase convergence time for distributed node processing results. To further improve the efficiency of statistical processing, the launching mechanism of this operation can be changed from periodic to automatic scheduling based on the trigger of observed data integrity.

Vol.29, NO.5, 2018