多因子和多尺度合成中国夏季降水预测模型及预报试验

根据青藏高原60个站平均的月积雪深度、热带太平洋Nino 3区月海温和中国160个站月降水量等资料,用小波变换和相关分析,分析了1958～1998年秋冬季青藏高原异常雪盖与El Nino-南方涛动(ENSO)的关系、多时间尺度变化的特征及其与中国夏季降水的相关型式.并取青藏高原积雪和Nino 3区海温的年际变化、年代际变化和线性趋势三种不同时间尺度的小波分量作为预报因子,对我国夏季降水距平作线性回归,建立了相应的预测模型.最后,利用1999～2002年的独立资料进行了预报试验,并在2003年和2004年应用于实际预报.研究表明,青藏高原雪盖与ENSO这两个物理因子彼此具有一定的独立性.它们都是多时间尺度现象,并与中国夏季降水有较好的关系.在不同时间尺度上不仅有不同的相关型式,而且相对贡献也有变化.回归预测模型的拟合情况和预报试验表明,综合考虑前期秋冬季青藏高原雪盖和ENSO这两个物理因子的年际变化、年代际变化和线性趋势作为预报因子建立的预测我国夏季降水距平分布的模型,有一定的预报能力.     

SOME ASPECTS OF THE INTERACTION OF BLOWING SNOW WITH THE ATMOSPHERIC BOUNDARY LAYER

Several possible effects of blowing snow on the atmospheric boundary layer are investigated, mostly within the general framework of the Prairie Blowing Snow Model (PBSM). The processes of snow saltation and suspension are first described. Variations to the drift density profile are tested and the effects of stratification and density variation calculations are evaluated. Despite high density gradients of blowing snow, stratification effects on turbulence and the velocity profiles can generally be neglected. However, with saltating or suspended snow in a constant shear stress layer, part of the shear stress is carried by the particles. A highly simplified, single-phase approach, based on the density variation of the air–snow mixture coupled to a simple turbulent stress–strain relationship, is used to illustrate this. Sublimation rates in a column of blowing snow are calculated using the PBSM and results are compared with those obtained with a modified formulation which incorporates a spectrum of sublimating particles of varying sizes at each height in a steady-state surface boundary layer and different specifications of the ventilation velocity.     

SNOW WETNESS ESTIMATES OF VEGETATED TERRAIN FROM SATELLITE PASSIVE MICROWAVE DATA

The Special Sensor Microwave/Imager (SSM/I) radiometer is a useful tool for monitoring snow wetness on a large scale because water content has a significant effect on the microwave emissions at the snowpack surface. To date, SSM/I snow wetness algorithms, based on statistical regression analysis, have been developed only for specific regions. Inadequate ground-based snow wetness measurements and the non-linearity between SSM/I brightness temperatures (T_Bs) and snow wetness over varied vegetation covered terrain has impeded the development of a general model. In this study, we used a previously developed linear relationship between snowpack surface wetness (% by volume) and concurrent air temperature (°C) to estimate the snow wetness at ground weather stations. The snow condition (snow free, dry, wet or refrozen snow) of each SSM/I pixel (a 37 × 29 km area at 37.0 GHz) was determined from ground-measured weather data and the T_B signature. SSM/I T_Bs of wet snow were then linked with the snow wetness estimates as an input/output relationship. A single-hidden-layer back-propagation (backprop) artificial neural network (ANN) was designed to learn the relationships. After training, the snow wetness values estimated by the ANN were compared with those derived by regression models. Results show that the ANN performed better than the existing regression models in estimating snow wetness from SSM/I data over terrain with different amounts of vegetation cover.     

欧亚春季雪盖对印度洋偶极子的影响

文章研究了欧亚春季雪盖对印度洋偶极子的影响。研究发现,欧亚春季雪盖与印度洋偶极子关系密切,两者之间存在显著的反相关关系。欧亚春季雪盖异常导致夏季赤道印度洋垂直纬向环流以及印度洋和欧亚大陆之间的垂直经向环流发生异常,是欧亚春季雪盖与印度洋偶极子存在反相关关系的主要原因。欧亚春季雪盖异常可能是印度洋偶极子发生的一个重要的外在诱发因子。     

基于MODIS积雪产品的高亚洲融雪末期雪线高度遥感监测

以2001—2016年逐日MODIS积雪产品为主要数据源,在高亚洲区域发展了大尺度融雪末期雪线高度的遥感提取方法,并对其2001—2016年的时空变化特征进行了分析。提取方法首先对逐日的MODIS积雪覆盖率产品进行去云处理,获得积雪覆盖日数（SCD）数据集;并用冰川年物质平衡观测数据、融雪末期Landsat数据对提取终年积雪的MODIS SCD阈值进行率定;最后以MODIS SCD提取的终年积雪面积结合地形“面积—高程”曲线实现大尺度融雪末期雪线高度信息的提取。结果表明：① 高亚洲融雪末期雪线高度的空间异质性较强,总体上呈南高北低的纬度地带性分布规律;并因受山体效应的影响,雪线高度由高海拔地区向四周呈环形逐渐降低的特点。② 高亚洲2001—2016年融雪末期雪线高度总体上表现为明显的增加趋势。在744个30 km的监测格网中,24.2%的格网雪线高度呈显著增加,而仅0.9%的格网呈显著下降。除兴都库什、西喜马拉雅外,其他地区雪线高度均表现为升高趋势,显著上升的地区主要分布在天山、喜马拉雅中东部和念青唐古拉山等,其中以东喜马拉雅升高最为显著（8.52 m yr ^-1）。③ 夏季气温是影响高亚洲融雪末期雪线高度变化的主要因素,两者具有显著的正相关关系（R = 0.64,P < 0.01）。     

南海北部底层沉积物捕获器中海雪和生物粪粒组成及来源探究

Sediment traps are an important tool for studying the source, composition and sedimentation processes of sinking particulate matter in the ocean. An in situ observational mooring(TJ-A-1) is located in the northern South China Sea(20.05°N, 117.42°E) at a water depth of 2 100 m and equipped with two sediment traps deployed at 500 m and 1 950 m. Samples were collected at 18-day intervals, and 20 samples were obtained at both depths from May 2014 to May 2015. Large amounts of fecal matter and marine snow were collected in the lower trap. The fluxes of marine snow and fecal pellets exhibited a fluctuating decrease between May 2014 and early August 2014 and then stabilized at a relatively low level. Scanning electron microscopy observations revealed that the main components of the marine snow and fecal pellets were diatoms, coccolithophores, radiolarians, and other debris, all of which are planktons mostly produced in photic zone. Used in conjunction with the particle collection range estimates from the lower trap and data on ocean surface chlorophyll, these marine snow and fecal pellets were related to the lateral transport of deep water and not vertical migrations from overlying water column. Moreover, the source area might be southwest of Taiwan.     

1961—2017年新疆积雪期时空变化特征及其与气象因子的关系

利用新疆89个地面站逐日积雪深度观测资料,研究探讨了1961—2017年新疆区域积雪期、积雪初日、积雪终日的时空变化规律,分析了北疆和天山山区积雪期的年代际和周期变化特征及其与气温、降水的关系.结果表明:新疆各地积雪期、积雪初日和终日存在明显的差异,积雪期以天山为界北多南少;从空间分布看,天山山区和新疆北部阿勒泰、塔城...     

基于GPS新型L5信号的地表雪深反演研究

利用GPS多路径反射信号测量地表雪深具有全天候和高时空分辨率的特点,因此其可作为一种代替气象站监测雪深的新手段。然而,先前大多数研究仅使用了GPS L1和L2C波段信噪比数据探测积雪深度。为验证新型的L5信号在雪深反演方面的优越性,本文阐述了GPS-R技术反演雪深的原理,利用Lomb-Scargle周期图法所处理的受积雪表层影响的信噪比数据计算了频谱振幅强度,通过获取频谱特征值与天线高度的关系求解雪深值,最后分别与L1反演结果和实测雪深数据进行了对比。试验结果表明：与现有的GPS-R测量雪深结果相比,利用新型的L5反射信号反演地表雪深的精度更佳;采用GPS-R技术探测雪深对把握测站区域内的雪深变化情况和淡水资源储量具有重要价值。     

我国南方西南和中东部区域两次持续性低温雨雪过程与关键环流系统的关系研究

利用NCEP再分析资料和国家气象信息中心提供的753站逐日气温和降水资料,对比分析了我国南方西南和中东部区域两次持续10 d以上的低温雨雪过程,结果表明:(1)两次过程中欧亚大陆中高纬东亚大槽均加深,但环流形势有差异。西南过程呈现"北高南低"形势,关键脊区在贝加尔湖,而中东部区域过程"北高南低"和"西高东低"形势共存,关键脊区从乌拉尔山延伸至贝加尔湖。两次过程异常的环流与北大西洋向东传播的波列有关。(2)西南过程关键脊区提前过程3 d发展并东移至贝加尔湖,形成稳定形势;而中东部区域过程关键脊区提前过程一周发展,在开始日达最强。两次过程均伴随蒙古高压东移南压使地面降温,500 hPa关键脊区超前蒙古高压2 d变化。西南过程降温主要受到冷平流和绝热冷却影响,而中东部区域过程主要受到冷平流的影响。(3)西南过程水汽来自孟加拉湾,只受南支槽支配。中东部区域过程水汽来自孟加拉湾、南海和西太平洋,由南支槽和西太平副热带高压的共同影响。两次过程水汽正收支主要来自南边界。