应用气象学报

2017, 28(2)

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Abstract:

The Statistical Downscaling Method of Summer Rainfall Prediction over the Huang-Huai Valley

Chen Lijuan, Gu Weizong, Bo Zhongkai, Liu Xiangwen

2017, 28(2): 129-141. DOI: 10.11898/1001-7313.20170201

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Abstract:
The statistical downscaling method and predictability of summer rainfall anomaly over the Huang-Huai Valley (SRAHV) is studied based on station precipitation data, NCEP/NCAR reanalysis data and BCC_CSM1.1m hindcasts from 1991 to 2011.Firstly, correlation coefficients between SRAHV and seasonal circulations in troposphere are calculated. In the high troposphere, significant circulation patterns are the South Asia high, the westerly over Eurasia, 200 hPa zonal wind over the southern of South China Sea and the Philippines. In the middle level, significant predictors are blocking high over Ural and west Pacific subtropical high. In the low level, southern anomaly wind over South China is the key factor. These predictors show clearly positive relationship with SRAHV and may lead to more rainfall.Secondly, the performance of BCC_CSM1.1m is diagnosed on the basis of summer hindcast circulations. Skills of 200 hPa and 500 hPa potential heights, 200 hPa zonal wind, 850 hPa meridional wind by BCC_CSM1.1m are relatively high in some key regions which may affect the SRAHV in reasonable physical mechanism. Six key factors are selected based on the consistent anomaly ratio of factors between BCC_CSM1.1m and reanalysis data, as well as the ratio between SRAHV and predictors from reanalysis data. The optimal sub-tree regression (OSR) is used as transfer function in the statistical downscaling model. Six predictors are tested by one-year-out cross validation sample tests. The consistent ratio between observation of SRAHV and prediction is 61%. By deleting dependent factors, three independent predictors (200 hPa potential height over the Ural, 200 hPa potential height over the South Asia high region to South China, 200 hPa zonal wind over South China Sea to South Philippines) are used to make the statistical downscaling model again, and the accuracy is improved to 72%.Further studies show that the predictability of statistical downscaling model comes from the skill of three key predictors by BCC_CSM1.1m, representing the strength of blocking activity over the Ural, the strength and position of the South Asia high, and the strength of west anomaly wind over the west tropical Pacific. When model output show high skill on three factors, skills of downscaling model are also high and predictions of SRAHV are close to observations in the years of 1994, 1995, 1998, 2004 and 2010. In the years of 1991, 1996 and 1997, BCC_CSM1.1m performs poorly especially on west anomaly wind over the west tropical Pacific. The correlation coefficient of west anomaly wind over the west tropical Pacific and SRAHV is 0.55 which passing the test of 0.01 level, indicating BCC_CSM1.1m's important role in the statistical downscaling model, which determines the prediction skill of SRAHV.

An Hourly Standard Ice Thickness Model Using Conventional Meteorological Data with Its Validation

Deng Fangping, Kang Lili, Jiang Yujun, Chu Jinliang, Liu Yan

2017, 28(2): 142-156. DOI: 10.11898/1001-7313.20170202

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Abstract:
The effective monitoring and early warning of ice on transmission lines are required to guarantee the reliable operation of power grid. However, due to the sparse coverage of wire icing observation stations, the regional ice loads can hardly be characterized by in-situ measurements. To solve this problem, an hourly standard ice thickness model using conventional meteorological data has been developed. In this model, the evolution of icing event is divided into different phases, namely accretion phase, persistence phase and shedding phase. During accretion phase, the ice weight increases by glaze and rime-ice accreting on power lines. During persistence phase, there is no change of ice weight. And in the phase of shedding, the ice weight decreases due to melting or sublimation. Each icing event includes at least an accretion phase and a shedding phase, and may also include other accretion, persistence and shedding phases.The simulation consists of three steps. The phase of icing event and the type of ice accretion (or shedding) is determined by hourly meteorological data. According to identified results, the variation of ice weight in the current hour is estimated using different methods:The variation is zero in the phase of persistence; the glaze and rime ice accretion is respectively simulated by adjusting Jones' simple model and Mackinnon model; the melting and sublimation ice is estimated using experimental equations presented by Farzaneh et al. The varied weight of current hour is summed with the ice weight of the previous hour to get the ice thickness of current hour.Using hourly meteorological data from more than 2000 stations located in Zhejiang and neighboring provinces, along with NCEP FNL analysis data, the model is employed to estimate the hourly standard ice thickness with 0.01°×0.01° spatial resolution in Zhejiang Province during periods from 11 Jan to 20 Feb in 2008, and from 11 Jan to 10 Jan in 2013. Furthermore, it is evaluated and validated by the power system fault data, survey data of damaged transmission lines, wire icing observation, and the in-situ wire tension measurements. Results indicate that the model can well capture the influence of weather on icing events, and also well characterize the spatial distribution and the temporal variation of wire icing events. At the wire tension monitoring sites, the simulated hourly standard ice thickness is generally in agreement with measured values, with determination coefficient of 0.5209-0.9287(with a mean value of 0.8093), and root mean square error of 0.1-2.4 mm (with a mean value of 0.8 mm).

A New Index for Surface Sensible Heat Flux over the Tibetan Plateau and Its Possible Impacts on the Rainfall in South China

Dai Yifei, Li Dongliang, Wang Hui

2017, 28(2): 157-167. DOI: 10.11898/1001-7313.20170203

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Abstract:
The intensity of surface sensible heat flux (SH) over the Tibetan Plateau is one of the most significant prior signals for precipitation anomalies in China. However, it's always a challenge to establish an index to describe the strength of SH due to the lack of corresponding observations and the complex topography. Based on the monthly surface sensible heat flux over the Tibetan Plateau which is calculated from observations including air temperature, land surface temperature, near surface wind speed and pressure in 70 meteorological stations provided by China Meteorological Administration (CMA) and normalized difference vegetation index (NDVI) observed by National Oceanic and Atmospheric Administration (NOAA) remote sensing satellites and the season reliant EOF method (SEOF), 4 representative stations are chosen, a new sensible heat index (I_SH) is defined, and then the monthly I_SH from 1982 to 2012 is calculated. By comparing with the average sensible heat in 70 stations which represents the regional averaged sensible heat condition in the mid-eastern Tibetan Plateau, surface heating strength index (B-H) and two indices of Xizang Plateau from National Climate Center, it's concluded that the inter-annual variabilities of I_SH can represent the surface sensible heat in the middle and eastern region of the Tibetan Plateau, and this index has better relationship with two indices of Xizang Plateau than B-H, especially in winter, which mean I_SH reflects geopotential height anomalies respond to surface heat better. Then the relationship between I_SH in spring and the summer rainfall (total precipitation in June and July) in South China from 1982 to 2012 is discussed based on the monthly rainfall data in 92 meteorological stations provided by CMA and the monthly NCEP/NCAR reanalysis data. Results show that I_SH in spring bears significant negative correlations with summer rainfall in South China. The larger index in spring indicates higher geopotential height in mid-low latitudes and lower in high latitudes during next several months, and the positive anomalies in mid-low latitudes transfer from middle troposphere to upper and maintain in 200 hPa, which may lead to the westward extension of subtropical high in 500 hPa, the South Asian high in 200 hPa and westerly wind around 40°N enhancing, whereafter, it may result in the downdraft and southerly wind enhancing, but water vapor convergence subside in South China, moisture can be transported to northern region through South China. And finally, it will cause the reduction of precipitation in South China during the summertime. In addition, negative correlations will become even more significant after removing the influence of sea surface temperature (SST) in regions of Niño3.4, which indicates there may be more complex coupled influence derived from the Tibetan Plateau heating and SST anomalies in Niño regions.

The Preconditioning of Minimization Algorithm in GRAPES Global Four-dimensional Variational Data Assimilation System

Zhang Lin, Liu Yongzhu

2017, 28(2): 168-176. DOI: 10.11898/1001-7313.20170204

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Abstract:
Variational data assimilation is a minimization problem of the cost function. Main characteristics of the problem are that the cost function is quadratic or nearly-quadratic and Hessian matrix of the cost function is sparse, symmetric and positive-definite. Iterative methods are suitable for solving this problem, but the calculation of the cost function and its gradient is very expensive, especially in four-dimensional variational data assimilation (4DVar). To find an optimal solution and achieve acceptable convergence rate, it is necessary to precondition the minimization algorithm.GRAPES global 4DVar system is developed for the operational use in China Meteorological Administration (CMA). It solves the minimization using L-BFGS algorithm, which is well known as a practical algorithm for variational data assimilation and originated from the works of Nocedal and Liu et al. It uses the information from the previous m iterations to compute the BFGS matrix which is an approximation to the inverse of Hessian matrix. GRAPES global 4DVar system adopts the incremental approach. In the incremental 4DVar, the inner loop minimization is solved several times with multi-outer-loop updates to find a more accurate solution of the nonlinear problem. It's possible to use information from previous minimization to precondition the next minimization. It is also related to the so-called warm-start of L-BFGS.Preconditioned L-BFGS is introduced and impacts of the preconditioning of L-BFGS on the convergence rate in 4DVar experiments of real observations are evaluated. Firstly, a case study is performed with four inner loop minimizations and 50 iterations during each inner loop minimization. Since the preconditioning works from the second inner loop minimization, nonlinear observation terms in the cost function are compared during 50-200 iterations. Results show the preconditioning of L-BFGS is effective, especially during the second inner loop minimization which uses the information from the first inner loop minimization. The scheme which uses information from the previous day to precondition the 4DVar minimization at the next day is also investigated. Given the small change of Hessian matrix between 6 and 24 hours, it may also be positive to precondition the 4DVar minimization using information from the previous day.Analysis-forecast cycling experiments are also carried out in May 2013. The performance of the preconditioned L-BFGS is consistent, leading to quicker convergence of 4DVar minimization. It is encouraging that the 4DVar run-time is reduced significantly, which is vital to the operational use of GRAPES global 4DVar system in the future.

The Bias Analysis of FY-2G Cloud Fraction in Summer and Winter

Liu Jian, Cui Peng, Xiao Meng

2017, 28(2): 177-188. DOI: 10.11898/1001-7313.20170205

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Abstract:
Evaluation of satellite retrieval cloud fraction is fundamental for good use in operational weather analyses application. Cloud fraction relative biases between FY-2G and Aqua/MODIS data are investigated in order to validate FY-2G cloud fraction. In order to understand the accuracy of FY-2G cloud fraction better, cases contain both clear and cloud pixels in the target area are selected, and 80 matched cases are analyzed. Results show that the cloud fraction of FY-2G has the same distribution pattern with Aqua/MODIS. The mean cloud fraction of FY-2G is 72.81%, and according to MODIS data it is 76.19%. Among 80 selected cases, 45 cases are in June and 35 cases are in December of 2015. In June, the mean cloud fraction of FY-2G and Aqua is 68.12% and 70.78%, respectively. In December, the mean cloud fraction of FY-2G and Aqua is 78.84% and 83.14%. FY-2G's cloud fraction is smaller than that of Aqua. For all cases, there are 79.15% pixels that their absolute relative bias between FY-2G and Aqua is smaller than 15%. In June, there are 77.78% pixels that the absolute relative bias between FY-2G and Aqua is smaller than 15%, while it is 81.24% in December. The cloud fraction correlation coefficient between FY-2G and Aqua is 0.74 through the year, 0.76 in June and 0.72 in December.During daytime, the mean cloud fraction of FY-2G and Aqua is 70.42% and 72.21%, respectively. There are 67.74% pixels that the absolute deviation between FY-2G and Aqua is smaller than 5%. The cloud fraction correlation coefficient between FY-2G and Aqua is 0.754. For night time cases, FY-2G mean cloud fraction is 75.59% and Aqua is 80.81%. The cloud fraction correlation coefficient between FY-2G and Aqua is 0.73. There are 72.34% pixels that their cloud fraction absolute deviation between FY-2G and Aqua is smaller than 5% during nighttime.Results show that the cloud fraction bias between FY-2G and Aqua is mainly caused by cloud detection accuracy. The cloud detection bias between FY-2G and Aqua mainly comes from different satellite observation ability and cloud detection algorithm. Compared with Aqua/MODIS data that has 36 channels with the lowest 0.01°×0.01° nadir spatial resolution, FY-2G has 5 channels with the highest 0.05°×0.05° spatial resolution. FY-2G's cloud detection easily makes mistakes when it has broken cloud, thinner cirrus or not all covered by cloud in the view. At the same time, different data processing methods within data match processing also cause bias between different kinds of satellite data.

Development of Nonlinear Regression Model to Estimate OLR Based on FY-3/IRAS

Wu Xiao, Bai Wenguang

2017, 28(2): 189-199. DOI: 10.11898/1001-7313.20170206

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Abstract:
OLR (outgoing longwave radiation) is the radiative energy flux the Earth and atmosphere emit out into the outspace, which is one of three components of the Earth and atmosphere radiative budget system, reflecting the climate and weather characteristics. Since the invention of meteorological satellites, OLR products have been processed for more than 40 years. Numerous methods have been developed to estimate OLR from satellite observations, including the relationship between the window channel brightness temperature of AVHRR and the flux equivalent brightness temperature proposed by Arnald Gruber in 1977 and George Ohring in 1984, regression models relating OLR with narrow band fluxes of window channel and water vapour channel of geostationary meteorological satellites developed by Liu in 1988, the linear and none-linear models relating OLR with satellite multi-channel radiances developed by Enllingson in 1994 and Lee in 2010. At the same time, broadband instruments such as ERBE and CERES on board of NOAA, Nimbus, Terra, Aqua are designed to directly observe OLR from outspace. Due to the high quality, CERES OLR products become the best available data to validate other retrieved OLR products.The IRAS (infrared atmospheric sounder) on board of FY-3 polar meteorological satellites carry 26 channels, among which 20 channels are used to observe radiances at the top of the Earth atmosphere at the wavenumber between 669 cm^-1 and 2666 cm^-1.These narrow band radiances have high relations with the full wavenumber radiative flux (OLR) the Earth and atmosphere emit. Therefore, a formula is derived for calculating OLR with multi-channel radiances of IRAS through infrared radiative transfer simulation. Based on radiances at top of atmosphere simulated with LBLRTM (line by line radiative transfer model) software for 2521 atmospheric profiles and statistical regression, a nonlinear model which relates OLR with multi-channel radiances of FY-3/IRAS are developed. By applying the model into FY-3/IRAS L1 data, the global daily mean OLR and monthly mean OLR data in April 2016 are produced. Comparing the IRAS OLR data with the Aqua/CERES and Terra/CERES OLR products, the root mean square error is 7.5 W·m^-2, the correlation coefficient is 0.98, the mean bias is-0.2 W·m^-2 when comparing the IRAS daily mean OLR with that of CERES. The root mean square error is 2.22 W·m^-2, the correlation coefficient is 0.9982, and the mean bias is-0.2 W·m^-2 when comparing the IRAS monthly mean OLR with that of CERES. The accuracy indicates that both the calibration quality of FY-3/IRAS instruments and the OLR retrieval model all achieve at a high level. In addition, OLR retrieval models used by various satellites since 1970 are also reviewed in brief.

Influence of Vertical Air Motion on the Radar Quantitative Precipitation Estimation

Ruan Zheng, Li Tao, Jin Long, Li Feng, Ge Runsheng

2017, 28(2): 200-208. DOI: 10.11898/1001-7313.20170207

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Abstract:
The radar quantitative precipitation estimation (QPE) is one of the main purpose of weather radar application. QPE products are applied very well due to wide space coverage, good precision and high spatial and temporal resolution of precipitation information. Main influencing factors cause differences between the QPE from radar and ground observation include the accuracy of the radar reflectivity, inconsistent of spatial and temporal between the radar and surface observation, and complex precipitation particle raindrop spectrum distribution. Air vertical motion effect in precipitation system and its temporal variation of random fluctuation is another important factor. Raindrop spectral distribution is considered with the development of radar QPE in recent years, and its falling speed can be achieved at the same time from PARSIVEL disdrometer. The air vertical motion acquired from data of PARSIVEL can be used to analyze its influence to the radar QPE. Using PARSIVEL data from the Southern China Monsoon Rainfall Experiment (SCMREX) during May and June 2014 at Yangjiang, Guangdong Province, several precipitation processes are analyzed, including 5 stratiform cloud (SC) precipitation events and 6 convective cloud (CC) precipitation events. The vertical air motion is retrieved and their influences on the QPE precision for both SC and CC are analyzed.The vertical air motion influencing value for 5 SC events are between-0.18 mm·h^-1 and-1.05 mm·h^-1, ranging from 13.61% to 13.99%. The vertical air motion influencing value for the six CC events are between 5.44 mm·h^-1 and 24.81 mm·h^-1, ranging from-38.59% to 25.92%. The influence on CC is greater than that on SC. PARSIVEL observation is applied to estimate the A and b coefficient in Z-R relation. The average deviation estimates SC under stationary atmospheric condition is 10.9% and 9.2% under non-stationary atmospheric condition. The vertical air motion effect partly offset by the uncertainty of the estimated precipitation Z-R relation. Average deviations of radar QPE are 25.5%, 51.2% under the stationary and non-stationary atmospheric conditions. After considering the raindrop spectrum distributions, the error of radar QPE is mainly from the vertical air motion. The deviation of QPE is related to data duration, shorter data usually lead to greater deviation. Simulation experiments are also carried out using PARSIVEL data to investigate this influence.

Quality Control of Temperature and Humidity Profile Retrievals from Ground-based Microwave Radiometer

Fu Xinshu, Tan Jianguo

2017, 28(2): 209-217. DOI: 10.11898/1001-7313.20170208

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Abstract:
Ground-based microwave radiometer profiler (MWR) can continuously retrieve thermodynamic profiles of the atmosphere on a minute time scale. These profiles are important supplements to radiosonde observations. Since the presence of liquid water on the profiling radiometer, radio frequency interference and other events can cause errors, quality control procedures that can detect data-quality problems are of critical importance. QC procedures are proposed for ground-based MWR-retrieved atmospheric profiles of temperature and humidity using 10-year radiosonde soundings (January 2004 to December 2014) at Baoshan Station in Shanghai and observations from MP-3000A MWR (January 2012 to May 2014) at Shanghai Expo Station. QC procedures include three types of checks:Climatological test, time consistency test, and vertical consistency test. Climatological check aims to verify if the values of instantaneous data are within climatology range limits. Thresholds are set based on the seasonally varying statistics of 10-year radiosonde soundings. The time consistency check focus on the change rate of instantaneous data. If the difference between the current instantaneous value with the previous one exceeds a specific limit, the current value will be flagged as doubtful. The test is applied level by level, and the profile with suspected data at more than one level is flagged as doubtful. Vertical consistency checks operate on dT_h and contain two tests: A test for the plausible value check of dT_hand a test for the standard deviation of dT_h (σ (dT_h)). dT_h (unti:℃· (100 m)^-1) is defined as the ratio of temperature difference to height difference at consecutive levels. The plausible value check of dT_h is proposed to verify if there are spikes in a profile. The test for (σ (dT_h)) is used to sort profiles with excessive level-to-level fluctuations throughout a sounding. Results show that QC methods can effectively identify various suspected observations, such as climatological outliers, temporal inconsistency and excessive vertical fluctuations in profiles. The frequency of suspected temperature and humidity profiles detected in rainy days is much higher than that in other weather conditions. The accuracy of MWR retrieved temperature and vapor density profiles is improved after applying these QC procedures. Correlation coefficients of both temperature and humidity observations between the MWR and radiosonde increase and corresponding root mean square error (RMSE) decrease after QC, especially those in rainy days. QC procedures for MWR-retrieved thermodynamic profiles are also proposed, through which various suspected profiles can be flagged. The accuracy of MWR retrievals is improved significantly after applying QC procedures.

Comparison of Relative Humidity Data Between L-band and 59-701 Sounding System

Yao Wen, Ma Ying, Gao Lina

2017, 28(2): 218-226. DOI: 10.11898/1001-7313.20170209

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Abstract:
The importance of consistency of upper-air observation reports in serial of space and time is well recognized by users and upper-air observation operators. It is the most effective way that the systematic differences between observations of pre and post updating radiosondes can be obtained using the method of direct intercomparison. To achieve that, two datasets are used to analyze effects of pre and post radiosondes updating on the relative humidity (RH) observed by 59-701 sounding system and L-band sounding system. The sensor for humidity measurement changes from goldbeaters skin to a carbon hygristor. One-month RH data of these two different sounding systems are compared directly to analyze the consistency of pre and post updating.Average RH biases between 59-701 and L-band sounding system from 1000 hPa to 200 hPa specified isobaric surface are analyzed. Results reveal that without considering the radiosonde errors upon the place, the season of the year, the time of the day, the RH of L-band is obviously lower than that of 59-701 sounding system in China, and differences are increased with altitudes. The RH bias of two sounding systems is less than 5% near the surface, but the value reaches more than 20% at 200 hPa. It shows that the RH bias of two sounding systems in winter is greater than that in summer, but differences with height are not the same in detail. Whether winter or summer, several stations reflect the problem that differences of average RH bias are much larger than the overall average RH bias, which shows that the difference change of average RH bias is not only related with the ambient temperature change, but also related to the change of RH. This phenomenon reflect possible "wet hysteresis loop" effect of humidity sensors. The difference of humidity components performance is not obvious between the type 59 radiosondes from Taiyuan and Shanghai. The effect of solar radiation on humidity components of these two types of radiosondes is also not obvious.Integrating comparison results, it shows that the RH bias between 59-701 and L-band sounding systems isn't affected by the type 59 radiosonde manufacturers, and RH observations are all affected by the ambient temperature, lag and wet hysteresis. But the influence details in different situations should be study further, and these results can be used as reference of correction by related departments.

Three-dimensional Numerical Simulation of Side Flash on Buildings

Tan Yongbo, Zhang Xin, Xiang Chunyan, Xia Yanling, Ma Xiao

2017, 28(2): 227-236. DOI: 10.11898/1001-7313.20170210

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Abstract:
Lightning is among the top ten kinds of natural disasters. Observations show that the connecting of downward leader and upward leader happen on the corner of tall buildings, a few take place on the side of buildings, but the damage of side flash shouldn't be ignored. As the side of the lightning protection is relatively weak, the side flash brings huge shock wave and strong electromagnetic radiation, causing great threat to buildings and human securities. Therefore, it is necessary to discuss processes and causes of side flash.On the basis of the existing leader developing random pattern of three-dimensional near-ground lightning, keeping other model settings unchanged, by changing initial potential of downward leader and geometrical property, many model studies on the development of occurrence of side flash are carried out, and statistics of the probability of side flash in various circumstances are performed. Results show that electric field strength of the top corner of the building is an important condition for the occurrence of side flash. When the downward leader is close to the building and below the height of the building, the value of electric field will reach the triggering threshold value, and the side flash is prone to take place. Moreover, initial potential of the downward leader and geometrical properties (height and width) of the building are important factors affecting the probability of side flash occurrence. When initial potential of the downward leader is between-9 MV and-3 MV, the probability of side flash increases at first and then decreases. When the initial potential of downward leader is-4.5 MV, the probability of side flash reaches a maximum. When the height of the building is between 50 m and 150 m, the probability of side flash increases at first and then decreases. When the height of the building is 100 m, the probability of side flash reaches a maximum. When the width of the building is between 30 m and 70 m, the probability of side flash decreases with the increase of the width. When the width of the building is 30 m, the probability of side flash reaches a maximum.These results are beneficial for the three-dimensional simulation of different connecting behavior and modifications of the electric field at the building corner, as well as the research on causes and influencing factors of side flash.

Indicators and Risk of Spring Corn Waterlogging Disaster in Jianghan and West Region of Jiangnan

Yang Hongyi, Huo Zhiguo, Yang Jianying, Zhang Guixiang, Wu Li, Fan Yuxian

2017, 28(2): 237-246. DOI: 10.11898/1001-7313.20170211

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Abstract:
Current precipitation process and antecedent precipitation have important influences on spring corn waterlogging disaster, and therefore establishing level indicators of spring corn waterlogging disaster is of great scientific significance on real-time dynamic disaster monitoring, early warning and risk assessment. Analyzing the risk of spring corn waterlogging disaster provides technical support to regional preventing disasters and reducing damages, adjusting plantation structure, establishing agricultural insurance countermeasures, as well as business development, service and application of waterlogging disaster monitoring. It ensures national food security and guarantees sustained and steady development of agriculture production. Taking spring corn in Jianghan and west region of Jiangnan as research foci, data of different growth stages at 57 stations in the study area from 1961 to 2012 are investigated, which consist of daily precipitation data, spring corn growth period data and waterlogging disaster data. Current process precipitation and antecedent precipitation's influence on spring corn waterlogging disaster is quantitatively analyzed by using multivariate linear regression analysis, and hereby, "equivalent precipitation" is established. Based on normal distribution Lilliefors test and t-distribution interval estimation method, equivalent precipitation indicator thresholds of different waterlogging disaster levels during different growth stages are calculated. Then, spring corn waterlogging disaster level indicators during different growth stages are determined by thresholds, and verified by independent samples. On this basis, spring corn waterlogging disaster risk index of each station is calculated using risk assessment method based on information diffuse theory. Main results are as follows. First, in the study area, precipitation of current process and the first two ten-day have significant influence on spring corn waterlogging disaster with weight factors coefficients being 0.725, 0.171 and 0.104, respectively. Second, the equivalent precipitation indicator thresholds of spring corn waterlogging disaster of light, moderate and severe level are 56, 93 mm (without severe level) in seeding-jointing stage; 65, 104, 161 mm in jointing-tasseling stage; and 74, 115, 182 mm in tasseling-maturing stage. The spring corn waterlogging disaster level indicators can well reflect actual disaster situation, and there is high consistency between verification result and history record. Third, the risk of spring corn waterlogging disaster is relatively low during seeding-jointing stage and jointing-tasseling stage, by contrast tasseling-maturing stage is a high-risk period during which high-risk areas mainly include Enshi, southwest of Yichang, southwest of Jingzhou, and north of Zhangjiajie.

Methods of Accumulated Temperature During Rice Growing Stage in Heilongjiang Province

Zhu Haixia, Li Xiufen, Wang Ping, Li Yuguang, Wang Ming, Wang Qiujing, Jiang Lixia

2017, 28(2): 247-256. DOI: 10.11898/1001-7313.20170212

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Abstract:
The accumulated temperature is an important index for regional thermal resource to be valued and development process of crops to be evaluated. Taking rice for example, based on the research of temperature coefficient and change of diurnal temperature, a new method of accumulated temperature is explored and studied with biological significance, so heat excessive and fewness could be shown accurately during growing stage of rice. The result shows that using daily extreme temperature, sub-sine simulation method and correct formula can simulate the diurnal variation of temperature for meteorological stations, such as Fuyu, Fujin, Muling and Harbin; temperature coefficients of rice are sectionally simulated above 30℃ and under 20℃ with Curve Equation Method. Results are extended for rice temperature coefficient. Temperature coefficients between three fundamental points of temperature are simulated. It could be as virtual quantification to three fundamental points of temperature. Hourly and daily equivalent temperature are got by combining temperature coefficient with simulated and corrected temperature of 24 h, and accumulated equivalent temperature is achieved during growing stage for rice. The method is preferable to the early method which values the accumulated temperature with daily mean temperature because it could not overlook positive role of temperature of part hours on rice at a low temperature condition, and it could not exaggerate positive role of temperature of part hours on rice at a high temperature condition. The method shows that different rice growing stages is various reaction of changing temperature. Furthermore, continuous quantification of heat resources is achieved during growing of rice. Accuracy is increased for equivalent accumulated temperature during the growing of rice. Taking Harbin city for example, the stage is main period of vigorous growth because daily equivalent temperature is close to daily mean temperature, and even is above daily mean temperature in June and July. In the last 55 years, the accumulated temperature increase significantly by 92℃·d/(10 a); they are sharp periods of that in the 1970s and the 1990s, with the climate change trend rate 359℃·d/(10 a) and 559℃·d/(10 a). From the 2000s to now, heat resource is full. Three fundamental points and diurnal changes of the temperature are taken in studying methods of accumulated equivalent temperature, so accuracy is increased for computation of accumulated equivalent temperature, and could show heat difference in the time and space.

Vol.28, NO.2, 2017