应用气象学报

Dichotomous Weather Forecasts Score Research and a New Measure of Score

Luo Yang, Zhao Wei, Zhai Jingqiu

2009, 20(2): 129-136.

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Abstract:
The significance of forecast estim ate and the principles are discussed. It is assumed that the scores are objective; and moreover they can objectively reflect the forecast level.The scores should be comparable, guiding forecast in the right direction. Several usual methods of dichotomous forecasts score are analyzed, revealing that accuracy and critical success index (CSI) in different areas are incomparable due to the influence of event possibility. It shows that the true skill statistic (TSS) is approach to the probability of detection (POD) when forecasting rare events.When events do not appear but false alarms are made, TSS can't be calculated. Heidke skill score and Girbet skill score make up for the above weaknesses. The three skill scores are all obtained by comparing forecast with random ones, hence there are (n₁₁n₂₂-n₁₂n₂₁) in the three formulas. It can be used as the discriminant for forecast skill. When (n₁₁n₂₂-n₁₂n₂₁)> 0, it indicates that the forecast level is better than that of random forecast, otherwise, it will be worse than it. On the basis of the relationship between event probability and the difficulty of forecast, a new method of score weight is considered and proposed. The essay points out the exiting problems are resulted from improper score weight, leading the score result unreliable and not comparable. The new method of score is based on CSI, and combines CSI of the two event forecasts. The focus of score is laid upon estimating the one with smaller probability in the two events.The principles of forecast score are fulfilled. By comparative analysis, the new method is proved to be superior to other methods, especially on estimating rare events. They can reflect the forecast level and changes more accurately.The advantages are as follows :With the increase of samples, the new score tends to be more stable than other scores in the rare events fo recast, thus leading to a rapid judgement for forecast level. When forecast level is improved, the new score will be able to reflect it correctly and distinctly. The new score is objective, just and real, and is compatible for different seasons and regions. So it is a uniform standard in forecast score.

The Use of Microwave Satellite Data Affected by Cloud in Numerical Forecast of Typhoon

Ren Qiang, Dong Peiming, Xue Jishan

2009, 20(2): 137-146.

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Abstract:
Satellite data is being used in numerical weather prediction and takes up the role of main data source among the observations used. The quality of initial condition and the accuracy of numerical weather forecast are greatly improved by using satellite data. It is extremely important to use the satellite data in the numerical forecast of typhoon for there are few regular observations on the sea. However, only cloud-cleared satellite data is used in most current data assimilation systems because of the complexity to cope with the radiant effect of cloud and rain particles in radiation transfer model. The cloudy and rainy reg ion is always the sensitive area.The observation in these areas implies much information to weather system and has great impacts on the numerical forecast. There is a crucial need to handle the satellite data affected by cloud and rain in numerical weather forecast, but with caution at the same time. At present, the satellite data affected by cloud is examined through the cloud examination method.The No. 0604 typhoon Bilis is taken as a research case. A set of experiments are designed to use the satellite microwave data in cloudy area based on the cloud examination scheme. Scattering index, precipitation probability and precipitation examination are taken for AMS U-A. For AMS U-B, the bias between simulated bright tempe rature and observation of AMS U-B channel 2 and Bennartz scattering index are utilized. Different satellite data affected by cloud is used in data assimilation system by different cloud examinations and channel selection schemes.The screening of satellite data affected by cloud, together with their influences on the numerical forecast of Bilis's three periods, corresponding to formation, maturation and landing, is examined respectively. The result shows that more AMSU-A data is screened by scattering index than precipitation probability and precipitation examination. The scattering index with threshold 15 is suitable for the use of AMSU-A data in regional model. For AMSU-B, the bias between simulated bright temperature and observation of AMSU-B channel 2 performs better than Bennartz scattering index.

Validation of Aerosol Optical Thickness Product over China with MODIS Data Operated at NSMC

Li Xiaojing, Zhang Peng, Zhang Xing ying, Sun Ling, Qi Jin, Zhang Yan

2009, 20(2): 147-156.

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Abstract:
The aerosol optical thickness producing software provided by Cooperative Institute for Meteorological Satellite Studies (CIMSS) of University of Wisconsin has been modified and operationally run at NSMC. And the MODIS/AOT product is shared at National MODIS Data Center.The MODIS aerosol optical thickness product has been validated with AERONET Level 2.0 aerosol optical thickness product so that the product can be improved and popularized.The MODIS/AOT product from January 2005 to May 2007 has been matched with L2.0 AOT product from AERONET stations in east Asia during the same period. The spacial average value of the MODIS/AOT within the 10 km distance from the site of AERONET station have been compared with the temporal average value of the AERONET/AOT within 30 minutes period the satellite passing the station. The validation result show that RMSE of all validation AOT samples over land is near 0.25, and about forty-four percent of the test samples meet the expected uncertainty of±0.05±0.20τ.The precision of MODIS/AOT is different according to seasons and areas.Usually, RMSE of MODIS/AOT is smaller at drought season than that at rain season. Cloud is the main factor impacted on the large RMSE at rain season. At south evergreen vegetable area, MODIS/AOT RMSE is better than that of north seasonal changed area.MODIS/AOT at 658nm has obviously systematical over-retrieval since the surface reflectivity of red band is over-estimated. Absolute error of MODIS/AOT at 466nm retrieved with blue band data has about half of the positive value and RMSE of MODIS/AOT at 466nm is higher than that of 658nm. The cause is that MODIS/AOT obtained with blue band is more sensitive for aerosol model used in retrieval process than it done with red band, though the estimated surface reflectivity of blue band has smaller error than that of red band. In a word, error from aerosol model is main cause of high random error of MODIS/AOT at 466nm.Correlation among the surface reflectivity at 0.47, 0.66 μm and 2.1μm bands are discussed using selected 79 items of vegetation spectrum reflectivity observed in China by LRCVES/CMA, in order to analyze effects of surface reflectivity for retrieval MODIS/AOT and get some ideas that could improve the precision of MODIS/AOT.The vegetation spectrum reflectivity is obtained from different vegetations and from different growing period of the same vegetation.The analysis show that there is high linear correlation (86.43%) between reflectivity at 0.47 μm bands and reflectivity at 0.66 μm bands for the vegetations.And linear correlation between vegetation reflectivity at 2.1 μm and vegetation reflectivity at 0.66 μm is 68.58%. Linear correlation between vegetation reflectivity at 2.1 μm and vegetation reflectivity at 0.47 μm is 59.79%. It can be concluded that the scheme which decides vegetation surface reflectivity of two visible bands in algorithm of Collection 5 MODIS aerosol products agrees well with the statistics, and is instrumental for algorithm of AOT retrieval with other similar satellite sensors.

Cloud Classification of the Whole Sky Infrared Image Based on the Fuzzy Uncertainty Texture Spectrum

Sun Xuejin, Liu Lei, Gao Taichang, Zhao Shijun, Liu Jian, Mao Jietai

2009, 20(2): 157-163.

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Abstract:
Clouds play an important role in the earth radiation budget and climate change. Their shape, size, distribution and movement indicate the condition of the atmosphere.Nowadays, cloud amount and cloud height are collected by means of both satellites and ground-based instruments. Satellite cloud images provide global coverage, and these data are used widely in weather forecast. Ground-based cloud images are very local ones which contain more details of clouds.Cloud classification using satellite images has been done for many years, while the study of ground-based cloud classification is still underway. A method using fuzzy uncer tainty texture spectrum and essential information in cloud images is proposed to classify five sky conditions (stratus, cumulus, altocum ulus, cirrus and clear sky) autom atically based on cloud images obtained from the whole sky infrared cloud measuring system (WSICMS).The WSICMS is a ground-based passive sensor that uses an uncooled microbolometer detector array to measure downwelling atmospheric radiance in the 8—14μm wavelength band of the electromagnetic spectrum. It provides a way to identify clouds, obtain clouds distributions and calculate clouds amounts continuously with no difference in sensitivity during day and night. The primary WSICMS components are optical detector, environmental parameter sensors, controller and power component. The optical detector is an uncooled microbometer array containing 320×240 pixels. It obtains nine images at zenith and at each eight orientations under the control of the scan servo system. A whole sky image is accomplished after spelling nine images, water vapor correction and zenith angles correction.The WSICMS locating at Nanjing, China has been working since August 2006. The 200 cloud images according to human observations are selected randomly from these sample sets. Before cloud classification, an appropriate FUTS filter window (7×7) is chosen. Analyses of FUTS of five different sky conditions and same sky condition (cumulus) show that FUTS can serve as a good discriminating tool in cloud classification. Based on above analysis, a supervised classification with minimum distance rule is used to classify sky conditions. The classification accuracy rates of stratus, cumulus, altocumulus, cirrus and clear sky compared with human observations increase sharply after adding essential information in cloud images. Importance of the cloud characteristic is shown in cloud classification. The final classification result are 100%, 100%, 90%, 100% and 100% respectively, the average accuracy rate is 98%. Altostratus, cumulostratus and complex sky conditions are not discussed here. Future work on this project will focus on this. In addition, more particular sample sets should be built up to improve the accuracy of both training and test data.

Application of Cascade Interpolation to GRAPES Model

Chen Fengfeng, Wang Guanghui, Shen Xueshun, Chen Dehui, Hu Jianglin

2009, 20(2): 164-170.

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Abstract:
For the numerical weather predicting model (NWM) based on semi-Lagrange scheme, it is not economical to apply the conventional point-by-point approach based on Cartesian product of one-dimensional Lagrange interpolation polynomials to evaluate up-stream variables at each integration time step. It takes O(N³) operations to calculate each point. The bigger the N value is, the more accurate the calculation may become. However, it involves too much calculation. When the method of a Cascade of one-dimensional interpolation of the entire data is employed, it requires only O(N) operations. The so-called Cascade method is a highly efficient means of carrying out the grid-to-grid interpolations required by a high-order semi-Lagrangian model. It goes like follows: The intersection points between the regular Eularian and curvilinear Lagrangian meshes form hybrid coordinate lines, and some variables of the intermediate points and the target point of the Lagrangian mesh can be interpolated by using one-dimensional curvilinear Lagrange interpolation method. First, the values of all intermediate points are interpolated. Then, the values of the target points are interpolated from the evaluated intermediate values step by step.The interpolation of the target points is not isolated because the adjoining target point uses shared some intermediate points. Some intermediate results can be repeatedly utilized so that it reduces the amount of computation in interpolation process.GRAPES (Global/Regional Assimilation and Prediction System) is a new generation of numerical weather prediction system of China developed by Research Center for Numerical Meteorological Prediction of CAMS (Chinese Academy of Meteorological Sciences) of CMA (China Meteorological Administration). It is designed based on the scheme using two time-level semi-Implicit time integration and semi-Lagrangian backward trajectories. It is also a fully compressible, non-hydrostatic grid model using latitude and longitude, as well as terrain-following height vertical coordinate. The model variables are staggered in two-dimension horizontal space in the form of an Arakawa-C grid. According to the designing principles of softw are engineering, GRAPES is a standardized, modularized, and coding infrastructure system. As far as the big numerical predicting models are concerned, the parallel computing becomes a necessary feature of them. The parallel computation of GRAPES is realized by means of decomposing zone in latitude and longitude directions. In order to parallelize Cascade interpolation code conveniently, the independent variables like distance on the curves need to be calculated in individual subsections instead of those from the start point.When the Cascade interpolation is applied in GRAPES model, predicting models are tested based on different horizontal grid resolutions such as those of 50km (720×360) and 100km (360×180). There are 31 vertical levels altogether.The timing of interpolating upstream points is monitored on the IBM-1600 cluster in CMA.The results of tests show that Cascade interpolation can significantly reduce computer running time by about 30%, compared with the conventional Cartesian interpolation, without affecting the accuracy of predicting models.

Results from Measurements of Large Aperture Scintillometer over Different Surfaces

Lu Li, Liu Shaomin, Xu Ziwei, Wang Jiemin, Li Xiaowen

2009, 20(2): 171-178.

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Abstract:
For surface flux measurements, large aperture scintillometer (LAS) has become more and more popular in recent years. Compared with traditional observation techniques, it can measure surface fluxes on a larger scale (500 m-10 km). At present, LAS observation is not common in China. Beijing Normal University, associated with other institutes, has carried out several short term measurements of LAS at Xiao tangshan (Beijing, 2002, 2004). A long-term LAS site has also been constructed at Miyun (Beijing) in June, 2006. Sensible heat flux (H) calculation with the LAS data of above measurements shows that the beam height of LAS and wind speed are sensitive factors for sensible heat flux measurement (H_LAs), zeroplane displacement height is crucial unless the beam height is much lager than it, and Bowen ratio needs to be determined accurately over wet surface, while air temperature, air pressure and aerodynamic roughness length are not sensitive for H_LAs. There are two key points of H_LAS calculation under stable condition. First, there is much less agreement on the form which universal stability function f_T should take. In this study, the f_T function proposed by Andreas (as in the LAS manual) is used. Further more, since the iteration process of H_LAS can not be convergence when low wind speed and very stable conditions appearing at night, ψ_m≥-5 is specified. Second, the scintillometer is unable to determine the sign of the heat flux. Richardson Number R_i can be used to determine atmospheric stability and fix the sign of H_LAS. Besides, the sunrise-sunset time and net radiation could be used for this purpose if there is no wind and air temperature profiles. Therefore, a calculation scheme of 24-hour sensible heat flux observed by LAS is obtained after settling the above two key points. According to the observations mentioned above, LAS can measure surface fluxes both over homogeneous and heterogeneous surfaces. The daily and monthly variation of H_LAs is analyzed. And the observation differences between eddy covariance system and LAS, need to be studied further combined with footprint model and energy unbalance of eddy covariance system observation.

Stochastic Simulation for Dry and Wet Spell

Wang Shiqi, Zhu Yeping, Li Shijuan

2009, 20(2): 179-185.

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Abstract:
Rainfall models are the most important component in stochastic weather generator. Two-state, firstorder Markov chain model is generally applied to simulate rainfall occurrence.The monthly statistics of time series of dry and wet days simulated by the model shows it may work well, but it is not satisfying when focusing on the persistent drought or prolonged wet in the series, although the difference between the simulated monthly mean of rainy days and the actually observed one are not marked.A stochastic model of dry and wet spells (DWS) is described, in which defined stochastic variables are the length of dry or wet spells, numbering in days, other than dry and wet day state. It is obvious that the variable itself has expressed the persistency of rainy or drought weather. Data modeling method is applied too. The related techniques include designing an algorithm for obtaining observed data of dry and wet spells from history records of daily rainfall; constructing empirical distribution function of the length of dry and wet spells monthly, and creating the parameter tables mapping the accumulated frequency distribution monthly; deriving a stochastic sampling formula for generating a dry or wet spell based on direct sampling principle and an algorithm of daily weather (dry or wet) on computer based on Monte Carlo simulation technique with previous sampling formula and parameter tables. Dry and wet spell simulation has been implemented using Java language. Users can select some run time parameters, for example, the name of observed location, the thread value for rainy day, and so on.Model validation test are done using history data from three locations, Beijing, Taiyuan and Zhengzhou. 100 years of rainfall data are generated for each location with the help of DWS simulator respectively. Its statistic items monthly includes : maximum of spell, mean of spell, variance of spell and mean number of rainy days. The mean absolute deviation of simulated value from observed one for all statistical items are about 1.8—2.0, 0.1—0.4, 0.4—0.6, 0.08—0.09 and 0.2—0.4, respectively.The t-tests are done in order to detect significant differences between observed and simulated value for maximum, mean and variance. No significant differences are found at α=0.01. For comparison betw een dry and wet spell model and two-state, first-order Markov chain model, the same statistics are obtained by running Markov chain model. Results indicate that the accuracy of dry and wet spell model is higher than two-state, first-order Markov chain for all statistical items, especially for maximum dry spells.Although dry and wet spell model is available and better than two-state, first-order Markov chain, its weakness is that the parameters in dry and wet spell model are more than those in Markov chain model, lacking in aesthetic feeling of mathematics.

The Agricultural Drought and Flood Index and Its Operational Application to Monitoring and Early-warning in Jianghuai Area

Ma Xiaoqun, Wu Wenyu, Zhang Hui

2009, 20(2): 186-194.

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Abstract:
An accumulated humidity index is introduced as agricultural drought and flood indicator and can be applied to operation. The index bases on the relative humidity index, replacing evapotranspiration with the crop water requirements, and the influence of former drought and flood status to current ones is considered too. Each component's time efficiency and effect weight varies on different temperature condition.The effect weight of every ten days are also different.The reference evapotranspiration is estimated and calibrated using temperature data of actual measurement, and errors are eliminated to meet application requirement, thus FAO Penman-Monteith is available for operation. The macroscopical crop water requirements is obtained by compositive crop coefficients in different region, also the grades of drought and flood index of a ten-day period in the humid and semi-humid zones are established.Used in agricultural drought and flood monitoring, and the qualitative coincident between this index and the soil moisture indices reaches 80%—90%, and quantitative coincident percentage is 60%—70%. The soil moisture data of longer serial stations are more consistent with accumulated humidity index than with regional survey result; normal and relative normal drought and flood grade leads to the highest coincident rate, drought the second, while flood leads to a relative low result. Since there is great difference between the soil moisture and cumulated humidity index in the monitoring aging and means, 10%—20% errors can be considered acceptable. Considering the index of the coincident ratio for stations, every grade samples proportion of total and its coincident ratio, the accumulated humidity index reflects soil moisture status in general.This index is used in agricultural drought and flood early-warning, the reliability of which is influenced by medium-term forecast.It contains former droughts and flood facts, making the trend relatively accurate on the whole. Considering the operation requirement, the early-warning precision still needs improvement. On one hand, the day-to-day data can be obtained by analyzing information of middle-term precipitation course prediction; on the other hand, statistical method is used to predict the index on basis of temporal characteristics analysis, to adjust the error of precipitation prediction, and improve the accuracy of agricultural drought and flood early-warning.The agricultural drought and flood are primary agrometeorological disasters in China, so the monitoring and the early-warning operation is a key agrometeorological job. At present, there are indexes on agricultural drought and flood such as soil moisture index, remote sensing index and meteorological index. But considering the complexity of agricultural drought and flood, the above indexes each has advantage and weakness, therefore strengthening research on compositive technique for the agricultural drought and flood is necessary.

Wind Measuring Accuracy of L-band Radar-digital Radiosonde System Through the Intercomparison with GPS Data

Yao Wen, Ma Ying, Huang Bingxun, Guo Yatian

2009, 20(2): 195-202.

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Abstract:

GPS Sounding and Wind Measuring System(GSWMS)is a new generation of sounding system, which measures wind speed and wind direction by GPS technology. It will be the major developing trend of sounding because of positioning accuracy and characteristic of weather independent. L-band Radar and Electrical Radiosonde System(LRERS)has been employed widely at eighty stations. The data acquisition rate, accuracy, reliability, and automation have improved significantly. But the accuracy of LRERS measuring wind, the differences between its precision of measuring wind and GSWMS, and the potential of this system still needs further analysis. LRERS has designed experiments to compare 59-701 radar system with Vaisala RS-80 system. But the results are not accurate enough because of frequency interference, clock synchronization, different data processing methods and so on. In order to handle this problem, a new radiosonde with GPS function has been developed, the data of which, such as temperature, pressure, humidity, latitude, longitude and height, are received by GPS receiver through L-band radar. Real-time position and wind data are received and processed by means of L-band radar with just measuring wind at the same time. Then the wind and height data can be obtained by a strict dynamic intercomparison, due to precise time synchronization and consistent calculating methods. Thus the basic differences between these two wind profiles and comparability in the practical sounding application can be obtained.23 electrical radiosondes with GPS function model has been set free which are traced by L-band radar in Shanghai and Nanjing from the last ten days of May to the first ten days of June. The analyzed result of 23 intercomparison data demonstrates that the accuracy indicators for GSWMS relative positioning is attained to meter grade, which can show the pendulum details of wind caused by radiosonde's spiral rise. The accuracy of LRERS can not reach meter grade, but it can also show the pendulum trend after handled by 3-points smoothing. Generally, after 30-point suitable smoothing to eliminate the pendulum phenomenon, two fine upper-air wind profiles from the two independent systems of LRERS and GSWMS are quite consistent when the elevation of radar is not too low and the radiosonde is near to the station. It reveals that the wind-finding accuracy of the LRERS may reach that of the GSWMS. However, when the balloon ascends to the high layer of low wind speed and is far away from the station, the wind measuring accuracy of the LRERS is not as good as that of GSWMS and much more smoothing is required for the original raw data from the radar. The intercomparison analysis shows that the operation of LRERS at the sounding network has much potential to be mined. Original location data should be explored sufficiently to provide upper air wind data approaching GPS position.

Statistical Characteristics of Clutter and Improvements of Ground Clutter Identification Technique with Doppler Weather Radar

Jiang Yuan, Liu Liping, Zhuang Wei

2009, 20(2): 203-213.

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Abstract:
Radar echoes caused by non-meteorological targets significantly affect radar data quality, and contaminated bins by ground clutter should be identified and eliminated before precipitation can be quantitatively estimated from radar data. An automatic algorithm for ground clutter detection is developed and examined. The algorithm is based on fuzzy logic, using volume scanning radar raw data. It uses some statistics to highlight clutter characteristics, such as shallow vertical extent, high spatial variability, and low radial velocities. A value that quantifies the possibility of each bin being affected by clutter is derived, and then certain impacts can be eliminated when this factor exceeds acertain threshold. The ground clutter points in sample data are distinguished empirically. In order to reduce the identified inaccuracy of the precipitation echoes with least infections on the ground clutter identified veracity, the optimal membership functions are determined by analyzing statistic the precipitation and ground clutter with the critical success index (CSI) based on the standard ground clutter and precipitation data. CSI is obtained based on the identified veracity through all samples includes clutters and precipitation of each function performs. The performance of this algorithm (M_OP) is compared against that of the original one such as China currently available membership function (M_CH) and American membership function (M_AM) by testing with statistical analysis, individual cases analysis, and inaccurate result analysis methods. Satisfactory results are obtained from an exhaustive evaluation of this algorithm, especially in the cases where anomalous propagation plays an important role. It turns out six characteristic parameters including T_DBZ, G_DBZ, S_PIN, M_DVE, M_DSW, S_DVE can retrieve precipitation echo and clutters well. Radial velocity used in algorithm shows it is good for echo classifying, it will reduce the possibility of identifying the precipitation echo to clutter. The membership functions got from CSI show better result than the original one, especially in distinguishing the precipitation echo from clutter. The algorithm performs well, but the result isn't hundred-percent correct yet. Through individual case analysis, it's found out the cause for the wrong classifying is echo intensity's horizontal texture and velocity's range unfold which is unavoidable, but it proves velocity data can improve the echo classifying result too. Radar data quality control is a complicated question, just using radar data is not enough to reach a perfect outcome. Satellite or automatic weather station data can be imported to make the result more authentic. And the most effective work on radar data quality control is to combine the manual work to the algorithm, through which all kinds of data problems recognized by auto algorithm can be solved. Radar echo classifying is still a key point in radar data quality control, radar data quality will not be totally exact until the radar echo characteristic is acknowledged and the right way to work it out is chosen, and that will have great effect on the application of radar data.

Development and Application of the Doppler Weather Radar 3-D Digital Mosaic System

Wang Hongyan, Liu Liping, Wang Gaili, Zhuang Wei, Zhang Zhiqiang, Chen Xiaolei

2009, 20(2): 214-224.

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Abstract:
Today, most radar sites of the CINRAD have been established, and there is good condition to transmit radar base data to the regional center. To fully utilize the advantage of the Doppler weather radar network, and improve the capability of mesoscale disaster weather early warning, study about weather radar 3-D mosaic has been made in recent years, and the Doppler weather radar 3-D digital mosaic system is developed for the first time in China based on these research results. It introduces the design, system structure, main function modules, data process flow, and corresponding algorithms of the system, analysis software performance, practicality and reliability of the mosaic results, study methods to discriminate two important factors affect the mosaic results.The system includes the following modules: Base data loading, data time matching, data quality controlling, coordinates conversion of single site base data to Cartesian coordinates, reflectivity mosaic for all sites in the region, and the generation of series of derived products. It can provide quality controlled base data, 3-D reflectivity grid data of single site, 3-D mosaic reflectivity and some derived products base on mosaic base data, which are useful not only for operational work, but also for scientific research. It can run real time for the region with around fifteen radars, at intervals about 6 minutes, with the horizontal resolution of about 1 km, and at least 20 vertical height levels.Operational running on trial proves that the system is steady. Case study results show that the 3-D mosaic result with high time and spatial resolution is reliable, it provides advantage for analyzing mesoscale and small-scale severe weather, and supplies data basis for developing now-casting and some other works. Besides, the observation errors and position errors are two important cases which influence the mosaic results, and they can be determined easily by analyzing outputs of the system itself. The system is running on trial currently. It's planned to upgrade the system for business, after adding some functions and useful derived products in the near future.

Temperature and Solar Radiation's Amendment of a Canyon Reservoir with Its Application

Tuo Youcai, Deng Yun, Liang Ruifeng

2009, 20(2): 225-231.

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Abstract:
Taken the influence of a canyon reservoir surrounding terrain on meteorological factors into account, based on the previous research, the regression remainder method is used to calculate temperature including orographic influence. And a simplified algorithm is applied to calculate solar radiation on the slope land of parallel mountain ridge. Taking Ertan reservoir of Yalong River valley as example, the change regularity of temperature and solar radiation under terrain influence is studied. The river bank slope is about 30 degree, and the main trend of this reservoir is S-N. There are 8 observation stations around reservoir, providing data of annual sunlight average percentage and the temperature. According to the terrain and the trend, Ertan reservoir is divided into 8 groups including 9 points from reservoir tail to reservoir head. Suppose that the temperature is divided into four revisal components-latitude, longitude, altitude and microtopography, and the multivariate linear regression equation is applied to be fit. The equation is qualified because that the value of F is over 35, and the multiple correlation coefficients are over 0.98. The results show that the parameter b₁ has negative value in the whole year, the temperature of each month decreases with the latitude increasing, and t its value is larger in winter and spring than that in summer and autumn and its annual variation is obvious; the second parameter b₂ has positive value in the year except for May, June, September, and generally the temperature of each month increases with the longitude, and its annual amplitude is very large; the value of the last parameter b₃ keeps positive during the year, which means that the temperature of each month decreases with the altitude increasing, but its value is smaller compared with the parameter b₁ and b₂. The application results show that the test precision could reach 0.5℃ by the multivariate regression equation, and the temperature data of Yanbian weather station in the same period of time has verified the result by the equation. The prediction results of temperature in Ertan reservoir show that temperature increases along the river, and the difference of annual temperature between reservoir tail and reservoir head is 1.6℃, which is in consistent with the variation characteristics of climate. Compared with flatland, there are changes on solar radiation of Ertan reservoir due to mountain masking. Besides, different gradients exert influence on solar radiation that Ertan reservoir received. Compared with that of the non-correction annual solar radiation, the corrected is reduced by 9%, which is 15 W/m². And the change of the solar radiation is greater with the increase of slope. The temporal and spatial variations of the solar radiation in the mountain area is revealed, which is contributed by the interaction of the solar declination and geographic latitude and terrain slope. As far as the slope 20 of and 50 dagree concerned, the annual change can reach 56 W/m², and its relative change amplitude reaches 34%. The new method agrees with the actual phenomenon of the mountain.

An Interpretation Scheme on Numerical Products of Prediction System Based upon Full-scale Information

Yang Chengyin, Wang Hanjie, Zhou Lin, Zhao Sux uan

2009, 20(2): 232-239.

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Abstract:
It is difficult to improve the accuracy of short-term numerical climate prediction in the scale of a month or so at present. A nonlinear interpretation model that uses full-scale information of the numerical products is proved to be effective in raising the accuracy of shot-term climate prediction. Besides, the interpretation method is the only way to interpret the grid data of numerical system to stations. Nowadays, most interpretive approaches are based on the MOS (Model Output Statistics). Some potential imperfectness of this method lay in the facts that it can't use full-scale information synthetically and effectively, besides some systematic errors. The change of mean meteorological element may not be reflected from the grid data around the stations, however, it may have some uncertain connections with other grid data beyond the station. If the factors of two dimensions which reflect full-scale information are used, the result of prediction may be improved. Canonical variables are constituted using CCA-BP method, which mainly represents covariance between factors and the predictive variables. The relativity between new-formed composed factors and the predictive variables increases significantly, hence a nonlinear regression model is developed by BPNN method. However, the canonical variables may be influenced by some local change greatly. Thus the canonical variables in the predictive equation should be examined through long-term stability test, to use those stable and effective ones. The interpretation scheme is used to establish the predictive equations for 10-day average temperature and rainfall anomalous percentage in October of Pingtan and Fuzhou, China. The 4-year forecast experiment results indicate that interpretation improves the numerical prediction products remarkably and the prediction accuracy increases evidently. According to the evaluating index, the CCA-BP-BPNN (abridged as C-B method below) is superior to the interpolation method and gives a new way of interpretation prediction of the short-term climate prediction products. Statistics of 4-year prediction results using the present C-B method from 2002-2005 shows, both the prediction accuracy (TT) and anomalous relationship coefficients (ACC) of temperature and precipitation of Pingtan and Fuzhou increase comparing with the interpolation method results and the model outputs of MM5, while the mean absolute errors (MAE) and mean square-root errors (MSE) decrease.The 23 years long dataset used in the experiment makes the monthly prediction variables a sample size of 23 and the 10-day prediction variables a sample size of 69. It is statistically acceptable but not as good as expected. For the interpretation prediction, longer the data series brings better results. Besides, selection and composition of the factors are endless jobs for regression model establishment. With the accumulation of numerical prediction products and the factor optimization procedure used in the CCA-BP-BPNN model improvement, the forecast accuracy will be increased in the future. Limited by the shortage of data and capability of computing, only 2 southeast coastal stations are involved in the test, but the results are satisfactory. However, the adaptability of this model in northern and western region with great climatic variability still needs validating with a mass of case studies.

Diagnosis Analysis on Relationship Between the Atmospheric Heat Source in the Northwest Pacific Ocean and the Meiyu of Mid-lower Reaches of the Yangtze

Mao Wenshu, Gong Yuanfa, Peng Jun, Li Guoping

2009, 20(2): 240-246.

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Abstract:
Based on the precipitation of Meiyu at 25 stations of mid-lower reaches of the Yangtze and the daily reanalysis geopotential height, wind field, humidity field and surface pressure field data (2.5°×2.5°) from 1954 to 2001, the relationship between the atmospheric heat source in the North Pacific Ocean and the abnormality of Meiyu precipitaion of mid-lower reaches of the Yangtze is investigated in terms of the Z index methold, empirical orthogonal function (EOF), composite analysis, singular value decomposition (SVD), and so on.The results of Z index methold indicate that there are notable multi (shored) Meiyu precipitation on mid-lower reaches of the Yangtze during the Meiyu period. There are 8 years in which the Meiyu precipitation is abnormally more and 6 years in which the Meiyu precipitation is abnormally less. The first mode of EOF shows the ENSO mode of the atmospheric heat source of North Pacific Ocean on mid-lower reaches of the Yangtze during the Meiyu period which express specifically the positive (negative) correlation between the atmospheric heat source in the mid-east Pacific Ocean and the north Pacific Ocean.The second mode of EOF shows the abnormal mode of the atmospheric heat source on the mid-lower reaches of the Yangtze during the Meiyu period which represents specifictly the positive (negative) correlation between the atmospheric heat source in the equatorial Pacific Ocean and the north Pacific Ocean of the middle-high latitudes. The results of SVD show that there are notable correlation between the Meiyu precipitation in the good (poor) Meiyu year and the atmospheric heat source in the Pacific Ocean. Through Monte-Carlo test at 95%, it shows there is positive (negative) correlation between the atmospheric heat source in the western (eastern) Pacific Ocean and the precipitation on mid-lower reaches of the Yangtze. When the SSTA around the Philippine island ocean and in the north-west Pacific Ocean are marked increasing (decreasing), convective activity is vigorous (weaken), the atmospheric heat in the north-west Pacific Ocean is increasing (decreasing), so the atmospheric heat in the eastern Pacific Ocean is decreasing (increasing), while the precipitation is marked increasing (decreasing) on mid-lower reaches of Yangtze river, and vice verse. The results of composite analysis and correlation analysis and SVD are identical congruent.

The Characteristic of Wire Icing in Shaanxi Province

Wu Suliang, Cai Xinling, He Xiaoai, Mao Mingce

2009, 20(2): 247-251.

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Abstract:
Wire icing data at Baoji, Huashan, Luochuan, Wuqi and Yulin in Shaanxi observatories from 1980 to 2005 are analyzed to study the characteristics. When glaze and rime happen at the same time, it is taken as glime. Analyzing results shows Huashan has the most icing days annually, 40.3 days on average, while other observatories have 0.5 to 4.2 icing days on average. In Shaanxi, glaze is the most, rime is the second and glime is the least, taking the ratio of 55.2%, 27.9% and 16.9%, respectively. The icing days mainly concentrate from November to March. Huashan has the most icing days in March. Baoji and Wuqi have the most icing days in December. Luochuan and Yulin have the most icing days in January. The average equivalent diameters of glaze, rime and glime of Shaanxi are between 10 to 25 mm. The maximum equivalent diameter is 78 mm. The average masses are between 86 to 236g·m^-3. The average densities are among 0.22 to 0.34 g·cm³, with glime being the biggest, rime the smallest. The meridional average equivalent diameters, average masses and ave rage densities of are bigger than those zonal ones. The maximum mass of Baoji, Huashan, Luochuan, Wuqi and Yulin are 13, 1290, 94, 25 g·m^-1 and 25 g·m^-1, respectively.

Statistical Model of the Relationship Between Atmospheric Visibility and PM_2.5 in Shenzhen

Lin Yun, Sun Xiangming, Zhang Xiaoli, Huang Xiaofeng, He Lingyan, Zeng Liwu

2009, 20(2): 252-256.

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Abstract:
With the development of economy, the level of air quality in main cities of China has experienced a continuously deteriorating process. Pearl River Delta Region are confronting with the disturbance of more and more haze weather especially. As one of the cities with the most serious haze problem in the region, Shenzhen experiences 231 haze days in 2007 according to the definition of haze that visual range is lower than ten kilometers and relative humidity is not higher than 80% at the same time. The degradation of atmospheric visibility is mainly caused by the extinction effect of aerosol particles, especially of fine particles, including scattering by inorganic components and absorption by black carbon. The average concentration of fine particle (particle whose aerodynamic diameter is lower than 2.5 μm) in Shenzhen is as high as 53.4±37.3 μg·m^-3 in 2007, which is a little lower than other main cities in China but two times higher than American national standard enacted by USEPA. Accordingly, the average visibility is as low as 13.4±9.3 km, and shows the same seasonal variation as fine particle concentration. However, few studies on the quantitative relationship between visibility and fine particles in Chinese cities are reported in the literature. Based on the analysis of the extinction mechanisms and relevant influential factors, statistic models are developed for describing the relationship between visibility and fine particles in the urban air of Shenzhen using multiple regression techniques. The data includes visual range acquired by visual observation, fine particles concentration generated by TEOM 1400a (an online instrument for monitoring the concentration of fine particles) and relative humidity (RH). All the data are monitored simultaneously in the year of 2007, and abnormal values are excluded before regression analysis. f_RH is used to eliminate the light extinction of humidity to particles, and four usual forms of it are discussed too. Multiple linear and nonlinear regression methods are used for regression analysis and the initial values of parameters come from literatures for nonlinear regression. Finally, a power function form of f_RH containing underlying physical mechanism of particles' extinction is selected to reflect humidity's effect due to good agreement and the compact form. The complete model expression is given at the same time. The correlative coefficient between the observed visibility values and the reconstructed visibility values using the best model is 0.40 for the 1-hour average data. The model for 24-hour average data is also established in the same form. The correlative coefficient reaches as high as 0.73, and the deviation of the reconstructed values is small, so the model can properly reflect a good relationship between visibility and fine particles concentration. In addition, the expression of extinction efficiency changing with relative humidity demonstrates the similar increasing patterns in existing study, and reasonably describes the relationship between extinction efficiency and relative humidity in Shenzhen.

Vol.20, NO.2, 2009