被动微波遥感反演地表温度研究进展

热红外遥感技术在地表温度反演中已经获得了丰厚的果实,反演精度可达到1 K,然而,大气中云雾和尘埃对热红外遥感探测地表温度影响很大,限制了热红外遥感反演地表温度的应用.相反,被动微波遥感受大气干扰小,可穿透云层获取地表辐射信息,并具有全天候、多极化等特点,在地表温度反演中具有独特的优越性.但是微波信号也受多种因素的影响,其反演地表温度的算法目前尚不成熟,有待进一步研究.根据不同微波辐射计特征,系统讨论并评估了被动微波反演地表温度的经验模型、物理模型以及半经验模型及其发展过程,指出目前研究的难点和缺点,为今后相关研究提供参考.     

"5.10"强对流天气雷达产品特征及临近预报

本文运用多普勒雷达的基本反射率(R)、基本径向速度(V)、垂直积分液态含水量(VIL)等产品对2005年5月10日邯郸发生的强对流天气进行分析,总结了雷达产品特征,得出了一些具有实用价值的结果,为预报员快速判断强对流的潜势提供参考.     

“5．10”强对流天气雷达产品特征及临近预报

本文运用多普勒雷达的基本反射率（R）、基本径向速度（V）、垂直积分液态含水量（VIL）等产品对2005年5月10日邯郸发生的强对流天气进行分析，总结了雷达产品特征，得出了一些具有实用价值的结果，为预报员快速判断强对流的潜势提供参考。     

用短期大风资料推算极值风速的一种方法

根据复合极值分布理论，试用二项—对数正态复合极值分布，利用海上短期实测大风资料求算海面多年一遇极值风速，并以此作为基础值，以沿岸站长年代大风经验公式计算风速为订正值，基础值与订正值的叠加作为海面多年一遇工程设计风速。该方法计算结果与皮尔逊Ⅲ型、泊松—龚贝尔复合极值分布计算结果相近，较单纯由二项—对数正态分布计算稳定度增大。     

规则建筑物真三维摄影测量视图

提出一种用摄影测量方法获取的规则建筑物几何模型和表面纹理，重建真实感３Ｄ图形的方法，用以扩展摄影测量制图系统，适应现代规划、设计领域决策的需要，产生较大比例尺的大区域景观图。     

Weibull分布参数估计方法及其应用

Ｗｅｉｂｕｌｌ分布对于年最大风速、波高、降水量等气候极值具有很高的拟合精度和很强的适应性。本文给出四种Ｗｅｉｂｕｌｌ分布参数估计方法：概率权重矩法（ＰＷＭ）、最小二乘法（ＬＳＭ）、最大似然法（Ｍ－Ｌ）和适线法（ＦＩＴ），并通过统计检验进行拟会精度比较。结果表明：ＰＷＭ、ＬＳＭ和Ｍ－Ｌ法对样本的拟合精度都比较高，其中ＰＷＭ法计算最为简便，ＬＳＭ和Ｍ－Ｌ法采取ａ０的试设法，大大简化了计算过程；经验累积频率公式更适合于Ｗｅｉｂｕｌｌ分布；三参数计算精度较二参数高得多。     

多源卫星高度计海浪资料在中国近海的验证及应用

Studies of offshore wave climate based on satellite altimeter significant wave height(SWH) have widespread application value. This study used a calibrated multi-altimeter SWH dataset to investigate the wave climate characteristics in the offshore areas of China. First, the SWH measurements from 28 buoys located in China's coastal seas were compared with an Ifremer calibrated altimeter SWH dataset. Although the altimeter dataset tended to slightly overestimate SWH, it was in good agreement with the in situ data in general. The correlation coefficient was 0.97 and the root-mean-square(RMS) of differences was 0.30 m. The validation results showed a slight difference in different areas. The correlation coefficient was the maximum(0.97) and the RMS difference was the minimum(0.28 m) in the area from the East China Sea to the north of the South China Sea.The correlation coefficient of approximately 0.95 was relatively low in the seas off the Changjiang(Yangtze River) Estuary. The RMS difference was the maximum(0.32 m) in the seas off the Changjiang Estuary and was0.30 m in the Bohai Sea and the Yellow Sea. Based on the above evidence, it is confirmed that the multialtimeter wave data are reliable in China's offshore areas. Then, the characteristics of the wave field, including the frequency of huge waves and the multi-year return SWH in China's offshore seas were analyzed using the23-year altimeter wave dataset. The 23-year mean SWH generally ranged from 0.6–2.2 m. The greatest SWH appeared in the southeast of the China East Sea, the Taiwan Strait and the northeast of the South China Sea.Obvious seasonal variation of SWH was found in most areas; SWH was greater in winter and autumn than in summer and spring. Extreme waves greater than 4 m in height mainly occurred in the following areas: the southeast of the East China Sea, the south of the Ryukyu Islands, the east of Taiwan-Luzon Island, and the Dongsha Islands extending to the Zhongsha Islands, and the frequency of extreme waves was 3%–6%. Extreme waves occurred most frequently in autumn and rarely in spring. The 100-year return wave height was greatest from the northwest Pacific seas extending to southeast of the Ryukyu Islands(9–12 m), and the northeast of the South China Sea and the East China Sea had the second largest wave heights(7–11 m). For inshore areas, the100-year return wave height was the greatest in the waters off the east coast of Guangdong Province and the south coast of Zhejiang Province(7–8 m), whereas it was at a minimum in the area from the Changjiang Estuary to the Bohai Sea(4–6 m). An investigation of sampling effects indicates that when using the 1°×1°grid dataset, although the combination of nine altimeters obviously enhanced the time and space coverage of sampling, the accuracy of statistical results, particularly extreme values obtained from the dataset, still suffered from undersampling problems because the time sampling percent in each 1°×1°grid cell was always less than33%.     

CLIMATOLOGICAL FEATURES OF RAPIDLY INTENSIFYING (RI) TROPICAL CYCLONES IN NW PACIFIC WEST OF 135°E1

Tropical cyclones which rapidly intensify (ΔV≥ 20 m/s in 24 h)in the Northwest Pacific Ocean west of 135°E could have adverse influence on oceanic and coastal economic activities in China, 71% of which land in China. Rapid intensification is mostly seen east and northeast of the Luzon Island. It is much correlated with sea surface temperature(≥28℃)and upper air conditions, such as enhanced subtropical high, onset of Southwest monsoon surge, invasion of modest cold air, and Tropical Upper Tropospheric Trough(TUTT) etc. Abovementioned processes enhance inflows in the low level and deep convection in the area of inner core. Statistics of satellite pixels have confirmed that rapidly intensifying tropical cyclones are marked by a sharp increase in the inner core convection and stable or slowly-increasing deep convection in outer region. Non-rapidly intensifying tropical cyclones have only constant or decreased deep convection in inner core and outer region. The sharp increasing of deep convection in the inner core and the rapid warming in its upper level is a forewarning of rapid intensification of tropical cyclones.     

Improving evapotranspiration estimation models through quantitative watershed parameter analysis and remote sensing applications

Numerous models had been developed to predict the annual evapotranspiration (ET) in vegetated lands across various spatial scales. Fu's (Scientia Atmospherica Sinica, 5, 23–31) and Zhang's (Water Resources Research, 37, 701–708) ET simulation models have emerged as highly effective and have been widely used. However, both formulas have the non-quantitative parameters (m in Fu's model and w in Zhang's model). Based on the collected 1789 samples from global long-term hydrological studies, this study discovered significant relations between m (or w) and vegetation coverage or greenness in collected catchments. Then, we used these relations to qualify the parameters in both Zhang's and Fu's models. Results show that the ET estimation accuracies of Fu's (or Zhang's) model are significantly improved by about 13.49 mm (or 6.74 mm) for grassland and cropland, 38.52 mm (or 29.84 mm) for forest and shrub land (coverage<40%), 19.74 mm (or 16.17 mm) for mixed land (coverage<40%), respectively. However, Zhang's model shows higher errors compared with Fu's model, especially in regions with high m (or w) values, such as those with dense vegetations or P/E₀ (annual precipitation to annual potential ET) smaller than 1.0. Additionally, this study also reveals that for regions with vegetation cover less than 40%, the annual ET is not only determined by vegetation types, but also relates to the sizes of vegetation-covered areas. Conversely, for regions with vegetation cover more than 40%, the annual ET is mainly determined by the vegetation density rather than vegetation types or vegetation coverage. Thus, linking m (or w) parameters with vegetation greenness allows leveraging remote sensing for forest management in data-scarce areas, safeguarding regional water resources. This study pioneers integrating vegetation-related indices with basin parameters, advocating for their crucial role in more effective hydrological modelling.