Development of a rain‐on‐snow detection algorithm using passive microwave radiometry

Currently observed climate warming in the Arctic has numerous consequences. Of particular relevance, the precipitation regime is modified where mixed and liquid precipitation can occur during the winter season leading to rain‐on‐snow (ROS) events. This phenomenon is responsible for ice crust formation, which has a significant impact on ecosystems (such as biological, hydrological, ecological and physical processes). The spatially and temporally sporadic nature of ROS events makes the phenomenon difficult to monitor using meteorological observations. This paper focuses on the detection of ROS events using passive microwave (PMW) data from a modified brightness temperature (T_B) gradient approach at 19 and 37 GHz. The approach presented here was developed empirically for observed ROS events with coincident ground‐based PMW measurements in Sherbrooke, Quebec, Canada. It was then tested in Nunavik, Quebec, with the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E). We obtained a detection accuracy of 57, 71 and 89% for ROS detection for three AMSR‐E grid cells with a maximum error of 7% when considering all omissions and commissions with regard to the total number of AMSR‐E passes throughout the winter period. Copyright © 2016 John Wiley & Sons, Ltd.     

地基探测与NCEP GFS模式预报云量在ARM SGP站点对比

使用大气辐射测量实验(Atmospheric Radiation Measurements:ARM)在美国南部大平原站点(Southern Great Plains:SGP)长时间序列(2001 2010年)的地基主动遥感云(Active Remote Sensing of Clouds:ARSCL)和美国国家环境预报中心(National Centers for Environmental Prediction:NCEP)全球预报系统(Global Forecast System:GFS)模式预报资料,对比分析了两者云量在不同时间尺度内(年际、月份和季节)的差异。结果表明,GFS模式预报总云量为83.8%,略高于地基观测结果(78.1%);两者总云量差异在秋季最大(8.8%),春季最小(2.2%)。在低垂直高度分辨率(≥3 km)时,地基探测低云、中云和高云的云量分别为46.1%、43.5%和61.2%;模式预报三类云的云量均要高于地基探测的云量,差异分别为9.6%、17.2%和9.1%。但是,在高垂直分辨率(250 m)时,地基探测云量在大多数高度层上要高于模式预报结果。这应该是两种资料廓线中有云出现的高度层数目存在差异引起的。地基观测和GFS模式预报同时表明,SGP站点上空云量垂直廓线呈现双峰结构,在边界层附近(1 km)和上对流层区域(8-12 km)云量较大,2-3 km高度范围内云量较小。在春夏秋冬四个季节内,两种资料在低层边界层附近的最大云量偏差分别为9.5%、8.8%、7.8%和11.2%。     

Impact of sub-pixel heterogeneity on modelled brightness temperature for an agricultural region

Knowledge of sub-pixel heterogeneity, particularly at the passive microwave scale, can improve the brightness temperature (and ultimately the soil moisture) estimation. However, the impact of surface heterogeneity (in terms of soil moisture, soil temperature and vegetation water content) on brightness temperature in an agricultural setting is relatively unknown. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) provided an opportunity to evaluate sub-pixel heterogeneity at the scale of a Soil Moisture Ocean Salinity (SMOS) or the Soil Moisture Active Passive (SMAP) radiometer footprint using field measured data. The first objective of this study was to determine if accounting for surface heterogeneity reduced the error between estimated brightness temperature (T_b) and T_b measured by SMOS. It was found that when accounting for variation in surface soil moisture, temperature and vegetation water content within the pixel footprint, the error between the modelled T_b and the measured T_b was less than if a homogeneous pixel were modelled. The correlation between the surface parameters and the error associated with not accounting for surface heterogeneity were investigated. It was found that there was low to moderate correlation between the error and the coefficient of variance associated with the measured soil moisture, soil temperature and vegetation volumetric water content during the field campaign. However, it was found that the correlations changed depending on the stage of vegetation growth and the amount of time following a precipitation event. At the start of the field campaign (following a precipitation event), there was strong correlation between the error and all three surface parameters (r ≥ 0.75). Following a precipitation event close to the middle of the field campaign (during which there was rapid growth in vegetation), there was strong correlation between the error and the variability in vegetation water content (r = 0.89), moderate correlation with soil moisture (r = 0.61) and low correlation with soil temperature (r = 0.26).     

微波消解-电感耦合等离子体质谱法测定海泡石中的稀土元素

海泡石是一种纤维状含水的富镁硅酸盐黏土矿,其中的稀土元素含量在1×10~(-7)～1×10~(-5)之间,目前还没有建立海泡石中稀土元素的国家标准分析方法。测定岩石中的稀土元素主要是采用电感耦合等离子体质谱法(ICP-MS),样品前处理一般采用封闭溶矿和碱熔,但这两种处理方法耗时较长,效率不高。本文通过比较硝酸-氢氟酸-过氧化氢、硝酸-氢氟酸、硝酸-过氧化氢三种样品前处理方法,确定使用硝酸-氢氟酸溶矿,然后进行微波消解同时赶去氢氟酸,避免氢氟酸与稀土元素生成难溶的氟化物,再采用ICP-MS法测定15种稀土元素的含量。由于海泡石中的镁含量较高,为降低基体效应,以~(103)Rh和~(185)Re作内标补偿基体效应和校正灵敏度漂移,各元素测定值的准确性显著提高,回收率为91. 2%～110. 9%,检出限为0. 002～0. 011μg/L,精密度≤2. 79%。本方法与封闭酸溶ICP-MS法的分析结果吻合较好,且用酸量少(7 mL),溶矿效率高(1 h),检出限更低。     

2000-2014年西藏雅鲁藏布江流域积雪时空变化分析及对气候的响应研究

利用2000-2014年MODIS逐日无云积雪产品对雅鲁藏布江流域积雪特征的空间分布及变化、积雪随高程变化的规律进行了分析,并采用被动微波数据SMMR （1979-1987年）和SSM/I （1988-2008年）以及中国地面降水和气温0.5°×0.5°日值格点数据集,研究了雅鲁藏布江流域关键积雪参数对气候要素的响应等。结果表明：流域下游积雪日较大且变化剧烈;流域整体上呈显著减少的趋势;积雪日随高程的上升而增加;流域内降水呈不显著的增加趋势,而气温呈显著的增加趋势,最高气温对积雪变化影响最大;气温对积雪终日的影响明显高于积雪初日;在积雪消融期降水的增多促进了积雪的消融。     

微波辐射计在对流天气预报中的应用

选取2016—2018年每年4—9月份RPG-HATPRO型42通道微波辐射计观测的不稳定指数参数(K、SI、CAPE、LI)及水汽参数(IWV、LWP),研究得出各参数触发雷雨大风、短时强降水的阈值条件为K>37℃、SI<-1℃、IWV>60 kg/m~2、LWP>400 g/m~2,而LI、CAPE无法对3种天气类型进行区分。利用费舍判别分析方法,将不稳定指数参数及水汽参数作为预报因子,建立预报方程并进行检验,结果表明:二级判别方程预测对流天气的准确率为76%,可以作为预报对流天气的辅助工具;多级判别方程不能很好地区分3种天气类型,但将其作为修正后的二级判别方程使用,能提高对流天气的测中概率。     

Latitudinal and Scan-dependent Biases of Microwave Humidity Sounder Measurements and Their Dependences on Cloud Ice Water Path

The relationship between differences in microwave humidity sounder(MHS)–channel biases which represent measured brightness temperatures and model-simulated brightness temperatures, and cloud ice water path(IWP) as well as the influence of the cloud liquid water path(LWP) on the relationship is examined. Seven years(2011–17) of NOAA-18 MHS-derived measured brightness temperatures and IWP/LWP data generated by the NOAA Comprehensive Large Array-data Stewardship System Microwave Surface and Precipitation Products System are used. The Community Radiative Transfer Model, version2.2.4, is used to simulate model-simulated brightness temperatures using European Center for Medium-Range Weather Forecasts reanalysis data as background fields. Scan-angle deviations of the MHS window channel biases range from-1.7 K to1.0 K. The relationships between channels 2, 4, and 5 biases and scan angle are symmetrical about the nadir. The latitudedependent deviations of MHS window channel biases are positive and range from 0–7 K. For MHS non-window channels,the latitudinal deviations between measured brightness temperatures and model-simulated brightness temperatures are larger when the detection height is higher. No systematic warm or cold deviations are found in the global spatial distribution of difference between measured brightness temperatures and model-simulated brightness temperatures over oceans after removing scan-angle and latitudinal deviations. The corrected biases of five different MHS channels decrease differently with respect to the increase in IWP. This decrease is stronger when LWP values are higher.     

基于散射计风场数据的台风强度诊断方法——以海洋二号卫星数据为例

本文提出了一种基于散射计风场数据的台风定强方法。该方法定义了台风所在海区的一个圆形区域上的风速平均值来对台风进行定强,并使用海洋二号卫星散射计历史数据对该方法进行了验证。验证结果表明,该台风定强方法可以克服散射计反演台风风速过低的问题。与台风强度监测的历史记录比较的结果显示,在台风强度低于"强台风"这一强度等级时,该方法能够有效估算台风强度。     

一维综合孔径微波辐射计遥感海面温度的敏感性分析

一维综合孔径微波辐射计能够有效提高观测的空间分辨率,其观测入射角通常在0°~55°范围内变化。为了开发适用于一维综合孔径微波辐射计的海面温度反演算法,需要评估其观测亮温对海洋大气环境要素的敏感性。利用海面发射率模型和大气辐射传输模型,构建了适用于一维综合孔径微波辐射计的微波海洋大气辐射传输模式,研究了C波段垂直和水平极化微波辐射亮温在不同入射角下对海洋大气环境要素的敏感性变化情况,并定量计算了相应的敏感系数。结果表明:垂直和水平极化亮温对海洋大气环境要素的敏感性表现出不同的特性。随着入射角的增大,垂直极化亮温对海面温度的敏感性增强,对海面风场的敏感性相对减弱;水平极化亮温则相反。由大气水汽含量和云液态水含量误差引入的垂直和水平极化亮温误差随入射角增大而增大,但是,即使在55°的大入射角下垂直和水平极化亮温误差仍小于0.12 K。对于海面温度反演精度优于1 K的要求,一维综合孔径微波辐射计的测温精度需优于0.6 K。研究结果对于一维综合孔径微波辐射计海面温度反演算法的研究和载荷设计具有一定的理论指导意义。     

Verification and recalibration of HY-2A microwave radiometer brightness temperature

HY-2A is the first one of the Chinese HY-2 ocean satellite series carrying a microwave radiometer(RM)to measure sea surface temperature,sea surface wind speed,atmospheric water vapor,cloud liquid water content,and rain rate.We verified the RM level 1B brightness temperature(T B)to retrieve environmental parameters.In the verification,TB that simulated using the ocean-atmosphere radiative transfer model(RTM)was used as a reference.The total bias and total standard deviation(SD)of the RM level 1B TB,with reference to the RTM simulation,ranged-20.6-4.38 K and 0.7-2.93 K,respectively.We found that both the total bias and the total SD depend on the frequency and polarization,although the values for ascending and descending passes are different.In addition,substantial seasonal variation of the bias was found at all channels.The verification results indicate the RM has some problems regarding calibration,e.g.,correction of antenna spillover and antenna physical emission,especially for the 18.7-GHz channel.Based on error analyses,a statistical recalibration algorithm was designed and recalibration was performed for the RM level 1B TB.Validation of the recalibrated TB indicated that the quality of the recalibrated RM level 1B TB was improved significantly.The bias of the recalibrated T B at all channels was reduced to<0.4 K,seasonal variation was almost eradicated,and SD was diminished(i.e.,the SD of the 18.7-GHz channel was reduced by more than 0.5K).