Sensitivity of Mesoscale Model Forecast During a Satellite Launch to Different Cumulus Parameterization Schemes in MM5

The identification of the model discrepancy and skill is crucial when a forecast is issued. The characterization of the model errors for different cumulus parameterization schemes (CPSs) provides more confidence on the model outputs and qualifies which CPSs are to be used for better forecasts. Cases of good/bad skill scores can be isolated and clustered into weather systems to identify the atmospheric structures that cause difficulties to the forecasts. The objective of this work is to study the sensitivity of weather forecast, produced using the PSU-NCAR Mesoscale Model version 5 (MM5) during the launch of an Indian satellite on 5th May, 2005, to the way in which convective processes are parameterized in the model. The real-time MM5 simulations were made for providing the weather conditions near the launch station Sriharikota (SHAR). A total of 10 simulations (each of 48 h) for the period 25th April to 04th May, 2005 over the Indian region and surrounding oceans were made using different CPSs. The 24 h and 48 h model predicted wind, temperature and moisture fields for different CPSs, namely the Kuo, Grell, Kain-Fritsch and Betts-Miller, are statistically evaluated by calculating parameters such as mean bias, root-mean-squares error (RMSE), and correlation coefficients by comparison with radiosonde observation. The performance of the different CPSs, in simulating the area of rainfall is evaluated by calculating bias scores (BSs) and equitable threat scores (ETSs). In order to compute BSs and ETSs the model predicted rainfall is compared with Tropical Rainfall Measuring Mission (TRMM) observed rainfall. It was observed that model simulated wind and temperature fields by all the CPSs are in reasonable agreement with that of radiosonde observation. The RMSE of wind speed, temperature and relative humidity do not show significant differences among the four CPSs. Temperature and relative humidity were overestimated by all the CPSs, while wind speed is underestimated, except in the upper levels. The model predicted moisture fields by all CPSs show substantial disagreement when compared with observation. Grell scheme outperforms the other CPSs in simulating wind speed, temperature and relative humidity, particularly in the upper levels, which implies that representing entrainment/detrainment in the cloud column may not necessarily be a beneficial assumption in tropical atmospheres. It is observed that MM5 overestimates the area of light precipitation, while the area of heavy precipitation is underestimated. The least predictive skill shown by Kuo for light and moderate precipitation asserts that this scheme is more suitable for larger grid scale (>30 km). In the predictive skill for the area of light precipitation the Betts-Miller scheme has a clear edge over the other CPSs. The evaluation of the MM5 model for different CPSs conducted during this study is only for a particular synoptic situation. More detailed studies however, are required to assess the forecast skill of the CPSs for different synoptic situations.     

A data-based regional scale autoregressive rainfall-runoff model: a study from the Odra River

This paper aims to compare the performances of multivariate autoregressive (MAR) techniques and univariate autoregressive (AR) methods applied to regional scale rainfall-runoff modelling. We focus on the case study from the upper and middle reaches of the Odra River with its main tributaries in SW Poland. The rivers drain both the mountains (the Sudetes) and the lowland (Nizina Śląska). The region is exposed to extreme hydrologic and meteorological events, especially rain-induced and snow-melt floods. For the analysis, four hydrologic and meteorological variables are chosen, i.e., discharge (17 locations), precipitation (7 locations), thickness of snow cover (7 locations) and groundwater level (1 location). The time period is November 1971–December 1981 and the temporal resolution of the time series is of 1 day. Both MAR and AR models of the same orders are fitted to various subsets of the data and subsequently forecasts of discharge are derived. In order to evaluate the predictions the stepwise procedure is applied to make the validation independent of the specific sample path of the stochastic process. It is shown that the model forecasts peak discharges even 2–4 days in advance in the case of both rain-induced and snow-melt peak flows. Furthermore, the accuracy of discharge predictions increases if one analyses the combined data on discharge, precipitation, snow cover, and groundwater level instead of the pure discharge multivariate time series. MAR-based discharge forecasts based on multivariate data on discharges are more accurate than AR-based univariate predictions for a year with a flood, however, this relation is reverse in the case of the free-of-flooding year. In contrast, independently of the occurrence of floods within a year, MAR-based discharge forecasts based on discharges, precipitation, snow cover, and groundwater level are more precise than AR-based predictions.     

Approaches for modeling longitudinal dispersion in pore-networks

The purpose of this study is to quantify the dispersivity in the longitudinal direction by upscaling pore scale mixing over a network domain and to verify the dispersivity with that obtained through the more rigorous upscaling technique, the Brownian particle tracking model (BPTM). We model a porous medium with a network of pore-units that are comprised of pore bodies and bonds of finite volume. Such a pore-unit is assumed to be a mixing cell with the steady state flow condition for a single fluid. Dispersivity can be obtained by solving the mixing cell model (MCM) for the concentration in each pore-unit and by averaging the concentrations for a large number of pore units (as a function of time and space). A minimal size of network that ascertains an asymptotic value of dispersivity was determined and verified with large size pore networks. This numerically computed dispersivity is compared with the results from the BPTM for the same porous medium and flow conditions. We show that the dispersivity obtained from the MCM is equally reliable for the heterogeneous pore-networks and can be estimated as a function of pore size heterogeneity. For homogeneous networks with the MCM, the iteration time step plays an important role. On the other hand, for networks with the BPTM, the assumption of intra-bond velocity profile affects the results.     

Effective macroscopic transport parameters between parallel plates with constant concentration boundaries

A macroscopic transport model is developed, following the Taylor shear dispersion analysis procedure, for a 2D laminar shear flow between parallel plates possessing a constant specified concentration. This idealized geometry models flow with contaminant dissolution at pore-scale in a contaminant source zone and flow in a rock fracture with dissolving walls. We upscale a macroscopic transient transport model with effective transport coefficients of mean velocity, macroscopic dispersion, and first-order mass transfer rate. To validate the macroscopic model the mean concentration, covariance, and wall concentration gradient are compared to the results of numerical simulations of the advection–diffusion equation and the Graetz solution. Results indicate that in the presence of local-scale variations and constant concentration boundaries, the upscaled mean velocity and macrodispersion coefficient differ from those of the Taylor–Aris dispersion, and the mass transfer flux described by the first-order mass transfer model is larger than the diffusive mass flux from the constant wall. In addition, the upscaled first-order mass transfer coefficient in the macroscopic model depends only on the plate gap and diffusion coefficient. Therefore, the upscaled first-order mass transfer coefficient is independent of the mean velocity and travel distance, leading to a constant pore-scale Sherwood number of 12. By contrast, the effective Sherwood number determined by the diffusive mass flux is a function of the Peclet number for small Peclet number, and approaches a constant of 10.3 for large Peclet number.     

基于启发式图搜索的遥感影像道路半自动提取

启发式图搜索法用于线状目标识别的原理是：用图结构表示边缘点和边缘段，根据启发函数计算顶点权值，在图的路径上建立相应的代价函数，通过在图中搜索对应的最小代价的通道以找到最优路径。图搜索法是一种全局最优方法，它在受噪声影响较大时效果仍然较好。文中使用了启发式图搜索法（A*算法）实现了道路的半自动跟踪。它的基本思路是：首先利用自适应平滑滤波算子进行道路信息增强，然后对传统的道路数学模型进行了进一步的扩展，突出了对道路几何特性和辐射特性的描述，并依此构建图搜索的代价函数，实现了基于启发式图搜索法A*算法的道路半自动跟踪。经实验证明，该方法进行遥感影像的道路半自动提取效果较好。     

海岸海洋潮流模拟可视化与虚拟现实建模

潮流数值模拟的科学可视化与虚拟现实是海岸海洋科学研究重要的现代技术手段之一。首先从地理信息系统的角度对潮流数值模拟系统的数据模型进行了论述，提出了适合水文数值模拟的对象模型方法；其次探讨了空间流场的可视化技术，阐述了虚拟现实在潮流数值模拟中的作用；最后设计并实现了一个海岸海洋潮流模拟虚拟现实的原型软件系统VRO－cean,并在南黄海辐射沙洲等的潮流数值模拟试验中进行了实际对比检验。     

VLBI、SLR、GPS综合数据处理方案研究

按参与平差观测值类型的选取不同，对VLBI、SLR、GPS综合处理方案进行了分类。给出了各类方案的平差模型，结合当前我国三种技术的实测情况，分析了各种方案的优缺点，提出了较为可行的综合处理方案。     

地下管网空间数据模型的建立与应用

城市综合地下管网系统作为城市地理信息系统的重要组成部分是城市科学规划，管理和辅助决策等工作的重要工具，本文结合昆明市综合地下管网信息系统设计和开发的实际工作，探讨了地下管网空间数据模型的建立和应用。     

谱模式T63L9正规模初值化方案及试验

推导了全球谱模式T63L9的正规模,对求得的垂直和水平正规模做了分析,与其他文献进行了比较.在此基础上,为其资料四维同化系统研制了一套合适的绝热非线性正规模初值化方案,并进行了一系列试验.分析表明:方案的研制是成功的,它有效地消除了模式早期积分中虚假的高频振荡,明显改进了短期预报的效果;初值化不仅对随后的一次预报有明显的改进,而且通过同化循环,提高了整个资料同化和预报的质量.