Some experiments with multivariate objective analysis scheme of heights and winds using optimum interpolation

A two-dimensional, multitvariate objective analysis scheme for simultaneous analysis of geopotential height and wind fields has been developed over Indian and adjoining region for use in numerical weather prediction. The height-height correlations calculated using daily data of four July months (1976-1979), are used to derive the other autocorrelations and cross-correlations assuming geostropic relationship. A Gaussian function is used to model the autocorrelation function. Since the scheme is multivariate the regression coefficients (weights) are matrix.Near the equator, the geostrophic approximation relating mass and wind is decoupled in a way similar to Bergman (1979). The objective analyses were made over Indian and adjoining region for 850, 700, 500, 300 and 200 hPa levels for the period from 4 July to 8 July 1979, 12 GMT. The analyses obtained using multivariate optimum in-terpolation scheme depict the synoptic situations satisfactorily. The analyses were also compared with the FGGE ana-lyses (from ECMWF) and also with the station observations by computing the root mean square (RMS) errors and the RMS errors are comparable with those obtained in other similar studies.     

Use of Surface Observations to Estimate Upper Air Humidity for the Objective Analysis of Relative Humidity over Indian Region

In the present study objective analyses of relative humidity (RH) at surface and at the levels of 850,700 and 500 hPa have been made using Gandin’s (1963) optimum interpolation scheme. As the horizontal resolution of the radiosonde stations is rather inadequate for upper air humidity analysis, a scheme has been developed, following Rasmussen (1982) to estimate the upper air RH from the surface observations like surface RH, present weather and cloud cover. The relative humidities at the levels 850, 700 and 500 hPa were related to the surface observations through three separate regression relations. The RH values at 850, 700 and 500 hPa levels were estimated from the surface RH, cloud coverage and present weather using the above regression relations and subsequently the objective analyses at 00 GMT for the period from 4 July to 8 July 1979, were made using these estimated data along with the observed radiosonde data. Objective analyses were also made for the same period using only the radiosonde data for comparison to study the impact of those estimated data. Root mean square errors were computed for all the five days by interpolating RH at the observing stations from the objectively analysed field and comparing them with the actually observed RH to examine how best the analyses (with and without estimated data) fitted the observations. Lastly they were compared with satellite cloud pictures. This study shows that the estimated upper air RH values have positive impact on the analysis of upper air RH and could be used over radiosonde date sparse region and even over oceanic regions.     

A new weighting function for estimating microwave sounding unit channel 4 temperature trends simulated by CMIP5 climate models

A new static microwave sounding unit (MSU) channel 4 weighting function is obtained from using Coupled Model Inter-comparison Project, Phase 5 (CMIP5) historical multimodel simulations as inputs into the fast Radiative Transfer Model for TOVS (RTTOV v10). For the same CMIP5 model simulations, it is demonstrated that the computed MSU channel 4 brightness temperature (T4) trends in the lower stratosphere over both the globe and the tropics using the proposed weighting function are equivalent to those calculated by RTTOV, but show more cooling than those computed using the traditional UAH (University of Alabama at Huntsville) or RSS (Remote Sensing Systems in Santa Rosa, California) static weighting functions. The new static weighting function not only reduces the computational cost, but also reveals reasons why trends using a radiative transfer model are different from those using a traditional static weighting function. This study also shows that CMIP5 model simulated T4 trends using the traditional UAH or RSS static weighting functions show less cooling than satellite observations over the globe and the tropics. Although not completely removed, this difference can be reduced using the proposed weighting function to some extent, especially over the tropics. This work aims to explore the reasons for the trend differences and to see to what extent they are related to the inaccurate weighting functions. This would also help distinguish other sources for trend errors and thus better understand the climate change in the lower stratosphere.     

高,低分辨率模式对典型个例的对比预报试验

利用实时观测资料Ｔ６３模式预报产品资料，对１９９５年７－８月发生在西北地区的３次大雨、暴雨天气过程进行了高、低分痃率数值预报模式的对比预报试验。     

Multivariate Objective Analysis of Wind and Height Fields in the Tropics

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基于迭代重加权最小二乘法的雨滴谱函数拟合研究

传统的雨滴谱函数的拟合方法在不同的降水类型和不同分布函数下,可能存在拟合出来的雨滴谱函数与实际数据差异过大的情况,基于此问题,本文提出一种基于迭代重加权最小二乘法(Iterative Reweighed Least Square, IRLS)的雨滴谱函数拟合方法。利用Parsivel激光雨滴谱仪2019年7—10月在海南安定获得的225组层状云降水样本和110组对流云降水样本数据进行实验,通过不断更新权值,迭代计算,从而求出待估计参数。模拟结果表明了该方法应用在不同降水类型和不同分布函数下,对比阶矩法和最小二乘法得到的拟合优度都是最接近1的。     

分析初值对暴雨数值预报影响的试验

使用统计插值方案，对1990年11月7—9日和1991年7月27—29日两次暴雨过程，形成几种不同资料源和水平分辨率的客观分析初值。通过三重网格预报模式嵌套计算，探讨初值给于我国东部地区48小时暴雨数值预报的影响。     

枣庄市夏季降水预报集成系统

应用多元决策加权法,把６－９月份５种短期预报方法以不同的权重进行集成,对本地区３个降水量级作出较准确和客观的概率预报,效果较好。     

Spatial Interpolation of Daily Precipitation in China: 1951--2005

Climate research relies heavily on good quality instrumental data; for modeling efforts gridded data are needed. So far, relatively little effort has been made to create gridded climate data for China. This is especially true for high-resolution daily data. This work, focuses on identifying an accurate method to produce gridded daily precipitation in China based on the observed data at 753 stations for the period 1951--2005. Five interpolation methods, including ordinary nearest neighbor, local polynomial, radial basis function, inverse distance weighting, and ordinary kriging, have been used and compared. Cross-validation shows that the ordinary kriging based on seasonal semi-variograms gives the best performance, closely followed by the inverse distance weighting with a power of 2. Finally the ordinary kriging is chosen to interpolate the station data to a 18 km×18 km grid system covering the whole country. Precipitation for each 0.5o×0.5o latitude-longitude block is then obtained by averaging the values at the grid nodes within the block. Owing to the higher station density in the eastern part of the country, the interpolation errors are much smaller than those in the west (west of 100oE). Excluding 145 stations in the western region, the daily, monthly, and annual relative mean absolute errors of the interpolation for the remaining 608 stations are 74%, 29%, and 16%, respectively. The interpolated daily precipitation has been made available on the internet for the scientific community.     

Simulation of Indian summer monsoon: experiments with SSTs

Summary Hindcasts for the Indian summer monsoons (ISMs) of 2002 and 2003 have been produced from an ensemble of numerical simulations performed with a global model by changing SST. Two sets of ensemble simulations have been produced without vegetation: (i) by prescribing the weekly observed SST from ECMWF (European Centre for Medium Range Weather Forecasting) analyses, and (ii) by adding weekly SST anomalies (SSTA) of April to the climatological SST during the simulation period from May to August. For each ensemble, 10 simulations have been realized with different initial conditions that are prepared from ECMWF data with five each from April and May analyses of both the years. The predicted June–July monsoon rainfall over the Indian region shows good agreement with the GPCP (observed) pentad rainfall distribution when 5 member ensemble is taken from May initial conditions. The All-India June–July simulated rainfall time series matches favourably with the observed time series in both the years for the five member ensemble from May initial condition but drifts away from observation with April initial conditions. This underscores the role of initial conditions in the seasonal forecasting. But the model has failed to capture the strong intra-seasonal oscillation in July 2002. Heating over equatorial Indian Ocean for June 2002 in a particular experiment using 29th May 12 GMT as initial conditions shows some intra-seasonal oscillation in July 2002 rainfall, as in observation. Further evaluation of the seasonal simulations from this model is done by calculating the empirical orthogonal functions (EOFs) of the GPCP rainfall over India. The first four EOFs explain more than 80% of the total variance of the observed rainfall. The time series of expansion coefficients (principal components), obtained by projecting on the observed EOFs, provide a better framework for inter-comparing model simulations and their evaluation with observed data. The main finding of this study is that the All-India rainfall from various experiments with prescribed SST is better predicted on seasonal scale as compares to prescribed SST anomalies. This is indicative of a possible useful seasonal forecasts from a GCM at least for the case when monsoon is going to be good. The model responses do not differ much for 2002 and 2003 since the evolution of SST during these years was very similar, hence July rainfall seems to be largely modulated by the other feedbacks on the overall circulation.     

Retrieval Single-Doppler Radar Wind with Variational Assimilation Method-Part I: Objective Selection of Functional Weighting Factors

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An investigation of flux-variance methods and universal functions applied to three land-use types in unstable conditions

Fluxes calculated from three flux-variance methods, which depend upon three different forms of the normalized standard deviation functions (referred to as universal functions) of the surface atmospheric stability have been tested and compared with measurements for temperature and water vapour. The flux measurements were made over a fully leafed deciduous forest, a leafless deciduous forest and over a wetland region during the summer. The first method (referred to as the variance method) allows for certain constants, which are associated with the universal functions, to vary with land-use type and the scalar for which the flux is computed; the second method uses the form of the universal function developed by Tillman, which depends upon two constants; and the third method, known as the Wyngaard method, is the simplest and depends upon one constant only. Flux estimates from the variance method yield the best agreement with observations over the three hand-use types and the Wyngaard method appears to yield estimates that are quite comparable. The measurements for the univeral functions agree better with the Wyngaard function for temperature and better with the Tillman function for water vapour, although both show some scatter. The simplest formula developed by Wyngaard may be considered adequate for computing fluxes of temperature and water vapour from their variances. The main advantage of the formulae is to help define an upper limit for actual fluxes.     

Retrieval single-doppler radar wind with variational assimilation method-part i: objective selection of functional weighting factors

In variational problem, the selection of functional weighting factors (FWF) is one of the key points for discussing many relevant studies. To overcome arbitrariness and subjectivity of the empirical selecting methods used widely at present, this paper tries to put forward in optimal objective selecting method of FWF. The focus of the study is on the weighting factors optimal selection in the variation retrieval single-Doppler radar wind field with the simple adjoint models. Weighting factors in the meaning of minimal variance are calculated out with the matrix theory and the finite difference method of partial differential equation. Experiments show that the result is more objective comparing with the factors obtained with the empirical method.     

基于卷积门控循环单元神经网络的临近预报方法研究

基于天气雷达资料的外推预报是灾害天气0～2h临近预报基础,本文以业务应用为目标,应用广东省2015-2018年11部新一代多普勒雷达反射率拼图资料,研究了基于卷积门控循环单元神经网络ConvGRU的临近预报方法,采用多损失函数加权与分级加权的策略,基于ConvGRU框架建立三层自编码模型(Encoder-Decoder...     

坡面散射辐射的分布特征及其计算模式

本文应用安装在经纬仪上的天空辐射表所观测的坡面散射辐射资料,对散射辐射的各向异性问题作了较为详尽的分析。揭示出坡面散射辐射随坡向、坡度变化的基本规律。文中还对前人所提出的计算坡面散射辐射的各种模式进行了检验、评述。最终提出一种通用性较强的坡面散射辐射通量密度的计算模式。其物理意义和计算精度都较国外同类模式优越。     

Spectral and anisotropic corrections for GMS satellite data

Using the radiative transfer simulation, the sampling study about the spectral and anisotropic corrections for GMS satellite data is carried out. The conversion factor and the anisotropic reflectance factor in inversion process of broadband radiation fluxes have been obtained for various underlying surface scenes in clear sky and for the case of overcast sky. The results demonstrate that the consideration of spectral and anisotropic corrections is essential for the earth radiation budget research using satellite data. The mean conversion factors for GMS are between 2.54 and 5.30. The values of the conversion factor are different for various observation angles, especially in cases of ocean, vegeta-tion cover and wet soil surface. The error of retrieving broadband radiance without considering the difference of ob-servation geometry is about 5.5%-15% for ocean, 4.5%-10% for various land surfaces. The calculated anisotropic factors for ocean and cloud scenes are in good agreement with those estimated from Nimbus-7. For Land, desert and snow scenes, the calculated values in backward scattering direction are smaller than the measured.     

基于台站降水资料对不同空间内插方法的比较

如何把离散的气象台站资料通过合适的空间内插方法转变成规则的网格数据,对于气候变化分析和模拟研究具有重要的意义.作者利用中国区域160个常用台站10年降水观测资料为例,分别采用克里格(Kriging)插值、反距离加权、Delaunay三角剖分线性插值、双谐样条(Biharmonic Spline)插值和Cressman客观分析等几种常见的空间内插方法,较为系统地分析和比较了这几种内插方法插值结果之间的异同,对其优缺点和适用范围进行了适当讨论,并采用不同的网格分辨率和影响半径对Cressman客观分析方法做了进一步分析比较.结果表明:在台站分布密集的区域不同空间内插方法之间差异较小,在台站分布稀疏的区域则差异较大;与更加密集的台站观测资料的比较显示,自动调节影响半径的Cressman客观分析方法与双谐样条插值方法误差相对较小.     

Objective time-scale-dependent homogenization of early instrumental temperature series

Summary An objective method for homogenization of early (pre ∼ 1850) instrumental near surface air temperature records is developed. The method is grounded on the same variational principle as used in the objective analysis of meteorological fields; i.e. the method consists of a statistically optimal spatial-coherence-based adjustment of nearby station temperature records. The adjustments are made for several different ranges of time scales, where the wavelet transform is used for the decomposition. The method takes into account that early instrumental temperature records are supposed to contain observational disturbances which are more or less smooth functions of time as a result of summations of numerous sources of biases. The method differs from traditional homogenization techniques in that corrections are not only made for a discrete number of abrupt or linear changes, but for continuously changing errors. The power of the method is illustrated with an example of homogenization of three very long temperature records from Sweden. Received August 31, 2000 Revised October 22, 2001     

The response of climatic jump in summer in north china to global warming

To reveal climatic variation over North China, the climatic jumps in summer in Beijing are analyzed using the data of precipitation of summer (June, July, August) during the period of 1841-1993, in which those missed before 1950 were reconstructed by the stepwise regression method with minimum forecast error. The climatic jumps at different scales are analyzed using different diagnostic methods with different decade (10-100 years) windows. Some new methods and ideas are proposed. The variance difference, the linear tendency difference, and the difference of power spectral distribution between the samples before and after the period at the moving point in the center of the series are compared with other methods (for example, Mann-Kendall test, t-test, and accumulative anomaly etc.). Considering the differences among the statistics above, a synthetic jump index is also proposed in order to get the definite jump points in the moving series. The results show that the climatic jumps in the area occurred in the 1890’s, the 1910s and the 1920s, and mostly in the 1920s, which suggests that the local climatic jumps in North China have a simultaneous response to the global warming in the hundred-year scales.