Moving from plot-based to hillslope-scale assessments of savanna vegetation structure with long-range terrestrial laser scanning (LR-TLS)

Reliable quantification of savanna vegetation structure is critical for accurate carbon accounting and biodiversity assessment under changing climate and land-use conditions. Inventories of fine-scale vegetation structural attributes are typically conducted from field-based plots or transects, while large-area monitoring relies on a combination of airborne and satellite remote sensing. Both of these approaches have their strengths and limitations, but terrestrial laser scanning (TLS) has emerged as the benchmark for vegetation structural parameterization – recording and quantifying 3D structural detail that is not possible from manual field-based or airborne/spaceborne methods. However, traditional TLS approaches suffer from similar spatial constraints as field-based inventories. Given their small areal coverage, standard TLS plots may fail to capture the heterogeneity of landscapes in which they are embedded. Here we test the potential of long-range (>2000 m) terrestrial laser scanning (LR-TLS) to provide rapid and robust assessment of savanna vegetation 3D structure at hillslope scales. We used LR-TLS to sample entire savanna hillslopes from topographic vantage points and collected coincident plot-scale (1 ha) TLS scans at increasing distances from the LR-TLS station. We merged multiple TLS scans at the plot scale to provide the reference structure, and evaluated how 3D metrics derived from LR-TLS deviated from this baseline with increasing distance. Our results show that despite diluted point density and increased beam divergence with distance, LR-TLS can reliably characterize tree height (RMSE = 0.25–1.45 m) and canopy cover (RMSE = 5.67–15.91%) at distances of up to 500 m in open savanna woodlands. When aggregated to the same sampling grain as leading spaceborne vegetation products (10–30 m), our findings show potential for LR-TLS to play a key role in constraining satellite-based structural estimates in savannas over larger areas than traditional TLS sampling can provide.     

雅砻江流域主汛期降水长期预报的一种方法

本文使用天气学方法，从影响雅砻江区域主汛期降水量的长期预报有关因子出发，再运用数理统计方法反复筛选，仅取３个主要因子：西部青藏高原８个站冬春积温，北部冬季极涡面积指数，南部冬季印缅槽平均强度，建立逐步回归预报方程。结果显示：对主汛期６～９月降水量预报拟合准确率为１６／１７即约９４％。回归分析中发现：尽管西太平洋副热带高压对盆地西部降水有很大影响，盛夏且与高原积温有较密切相关，但由放大范围长时段的青藏高原积温，对于雅砻江区域主汛期降水量的影响超过西太平洋副热高压，因而在逐步回归过程中，副高因子仍被剔除．     

超级计算机上矩阵乘的并行计算与实现

数值预报系统中经常要用到矩阵乘运算。在分布式超级计算机 (如IBM-SP) 上, 矩阵乘的并行计算需要较多的数据移动, 有效的数据传输对矩阵乘的实现至关重要。该文讨论了两种矩阵乘的并行算法, 一种是基于矩阵的列-行划分方式, 一种是基于矩阵的网格划分方式。在IBM-SP计算机上的实验结果表明, 网格划分的矩阵乘并行算法通讯开销更小, 并行效率更高, 其并行加速比较列-行并行算法改善约10 %。     

东营市森林火险天气等级预报系统

利用9210传输的气象资料，选用国家气象中心T106和HLAFS数值预报产品中的要素作为火险天气因子，根据森林火险原因，建立了东营市森林火险天气等级预报系统。     

数值预报产品在夏季持续高温预报中的释用

利用 1999～ 2 0 0 2年 6～ 8月 96～ 192h日本数值预报产品 85 0hPa气温与吉林省的日平均气温、阶段性高温及极端最高气温 (长春市 )资料 ,通过线性分析 ,找出了日本数值预报产品的 85 0hPa气温与吉林省地面日平均气温、阶段性高温及极端最高气温的对应关系 :吉林省 6～ 8月日平均气温在 96～ 192h的 85 0hPa日本数值预报长春站日平均值上加 6 6～ 4 1℃ ,6～ 8月的极端最高气温在相应的日本数值预报产品上加 9 3～ 13 0℃。     

An Innovative Bias-Correction Approach to CMA-GD Hourly QuantitativePrecipitation Forecasts

This paper proposes a simple and powerful optimal integration (OPI) method for improving hourly quantitative precipitation forecasts (QPFs, 0-24 h) of a single-model by integrating the benefits of different bias- corrected methods using the high-resolution CMA-GD model from the Guangzhou Institute of Tropical and Marine Meteorology of China Meteorological Administration (CMA). Three techniques are used to generate multi-method calibrated members for OPI: deep neural network (DNN), frequency-matching (FM), and optimal threat score (OTS). The results are as follows: (1) The QPF using DNN follows the basic physical patterns of CMA-GD. Despite providing superior improvements for clear-rainy and weak precipitation, DNN cannot improve the predictions for severe precipitation, while OTS can significantly strengthen these predictions. As a result, DNN and OTS are the optimal members to be incorporated into OPI. (2) Our new approach achieves state-of-the-art performances on a single model for all magnitudes of precipitation. Compared with the CMA-GD, OPI improves the TS by 2.5%, 5.4%, 7.8%, 8.3%, and 6.1% for QPFs from clear-rainy to rainstorms in the verification dataset. Moreover, OPI shows good stability in the test dataset. (3) It is also noted that the rainstorm pattern of OPI relies heavily on the original model and that OPI cannot correct for deviations in the location of severe precipitation. Therefore, improvements in predicting severe precipitation using this method should be further realized by improving the numerical model’s forecasting capability.     

亚欧500hPa旬平均形势预报的改进及检验

利用数值预报产品，对原亚欧５００ｈＰａ旬平均形势预报方程作了改进。除对空间和时间尺度上作了改进外，在预报因子的选择上，由亚欧地区的单点相关，扩张至整个北半球的相关；并根据长波天气系统的气候地理分布和遥相关分析等方法，选择关键区作为候选因子，组建了新的形势预报方程。经检验比较，新的形势预报方程高度距平正确和长波系统位置正确率分别提高了０．１５－０．２０和０．１０。     

广州中尺度模式局地要素预报性能分析

该文简要介绍了华南中尺度模式的地表要素的预报诊断方案, 站点要素时间序列预报情况.用适合站点要素时间序列预报的检验方法, 检验并分析了各要素时间序列预报的性能.表明该中尺度模式的地表要素时间序列预报方案是有效的, 预报具有较高的准确性和应用价值.     

经验正交函数在解释数值预报产品中的应用

本文用经验正交函数展开的分析方法，确定了各种数值预报产品物理场中和对应时效的运城日降水量显著相关的物理量要素及关键区域，对关键区域的物理量场使用典型相关分析进行处理，得到了数量较少，而又能代表该关键区物理量分布状况主要信息的新因子，从而建立使用数值预报产品制作短期单站降水预报模式，进行预报效果较好。     

冬半年冷空气过程中期预报业务系统

介绍了一种以数值预报产品为基础，结合预报经验，运用天气学、统计学、能量学诊断、人工智能和延伸预报等多种技术手段，通过综合决策，制作冷空气过程的中期预报方法。