利用被动微波探测青海湖湖冰物候变化特征

湖冰物候是气候变化的敏感因子,不仅能反映区域气候变化特征,还可以反映区域气候与湖泊相互作用.利用长时间序列(1978—2018年)被动微波遥感18 GHz和19 GHz亮度温度数据、MODIS数据(2000—2018年)、实测湖冰厚度数据(1983—2018年)和气温、风速、降水(雪)数据(1961—2018年),分析...     

Processing NOAA-AVHRR satellite data for sea ice and oceanographic research in the marginal ice zone of the polar seas

This paper describes a method for NOAA-AVHRR satellite data processing in sea ice and oceanographic studies in the polar seas. This method includes geometrical processing to generate gridded and corrected images according to a polar stereographic map, ice and cloud discrimination during summer, and the production of combined sea ice and sea surface temperature imagery for watching marginal ice zone processes.     

基于卫星气候资料的1989-2015年南北极海冰面积变化分析

利用被动微波卫星海冰密集度气候资料,分析了1989-2015年南北极海冰面积和密集度的长期变化趋势。结果表明：研究期内,北极年平均海冰面积减少,南极海冰面积增加,变化趋势分别为-0.569×10⁶ km²·（10a）^-1和0.327×10⁶ km²·（10a）^-1,均通过了0.01水平的显著性检验,两极海冰面积变化趋势表现出明显的"非对称性"。两极总海冰面积出现了下降,变化趋势为-0.242×10⁶ km²·（10a）^-1。年海冰密集度在北极地区普遍减少,而在南极地区的变化趋势存在显著的空间差异,威德尔海、罗斯海北部海冰密集度增加,趋势超过了10%·（10a）^-1,别林斯高晋海、阿蒙森海的海冰密集度出现下降。北极各月海冰面积的变化趋势存在明显的季节差异,7-10月海冰面积减少明显,其中9月减少最显著,趋势为-0.955×10⁶ km²·（10a）^-1。南北极海冰冻结和融化的时间不完全对应,北极融化与冻结时间基本平衡,南极海冰冻结时间明显长于融化时间。南极年内海冰面积的变化幅度大于北极,呈现显著的季节性特征。北极极小海冰面积的变化趋势最显著,达到了-0.636×10⁶ km²·（10a）^-1。南极极大海冰面积出现的时间后移明显,趋势为0.733候·（10a）^-1;极小海冰面积出现的时间非常稳定,没有明显的变化趋势。     

基于CryoSat-2卫星测高数据分析南极海冰厚度的时空变化

利用卫星测高数据能够获取大尺度、长时序的海冰厚度信息。相较于北极,目前南极海冰厚度特别是近期变化信息仍很缺乏。基于2013-2018年的CryoSat-2卫星测高数据,采用最低点高程法和静力平衡方程模型反演了近6年逐月平均海冰厚度并分析其时空变化规律。结果表明：2013-2018年南极海冰厚度整体呈现先上升后下降的趋势,其中,2014-2017年年平均海冰厚度表现为快速变薄。南极较厚的海冰集中在威德尔海西南海域,最大值出现在该海域2014年的7月（6.27 m）。年平均海冰厚度在2017年达到最低值。南极海冰厚度的时空变化研究可为深入研究海冰变化与全球变化的关系提供参考。     

Utilizing microwave communication data for detecting fog where satellite retrievals are challenged

This study shows the potential of microwave communication networks for the detection of fog in challenging conditions where satellite systems are often limited. In a first event, the ability to detect fog using commercial microwave links, at a time when the satellite cannot detect the phenomena due to high-level cloud cover obscuring the ground-level fog, is demonstrated. The ability of the microwave system to rule out ground-level fog at times when the satellite detects a low-lying stratus cloud, but cannot identify whether it is adjacent to the ground or at higher elevations above it, is demonstrated in a second event. The results indicate the operative potential of this technique to assist end users in reliable decision making that have highly important economic and safety implications.     

External field characterization using CHAMP satellite data for induction studies

Knowledge of external inducing source field morphology is essential for precise estimation of electromagnetic (EM) induction response. A better characterization of the external source field of magnetospheric origin can be achieved by decomposing it into outer and inner magnetospheric contributions, which are best represented in Geocentric Solar Magnetospheric (GSM) and Solar Magnetic (SM) reference frames, respectively. Thus we propose a spherical harmonic (SH) model to estimate the outer magnetospheric contribution, following the iterative reweighted least squares approach, using the vector magnetic data of the CHAMP satellite. The data covers almost a complete solar cycle from July 2001 to September 2010, spanning 54,474 orbits. The SH model, developed using orbit-averaged vector magnetic data, reveals the existence of a stable outer magnetospheric contribution of about 7.39 nT. This stable field was removed from the CHAMP data after transforming to SM frame. The residual field in the SM frame acts as a primary source for induction in the Earth. The analysis of this time-series using wavelet transformation showed a dominant 27-day periodicity of the geomagnetic field. Therefore, we calculated the inductive EM C-response function in a least squares sense considering the 27-day period variation as the inducing signal. From the estimated C-response, we have determined that the global depth to the perfect substitute conductor is about 1132 km and its conductivity is around 1.05 S/m.     

Sensitivity of the Northern Hemisphere climate system to extreme changes in Holocene Arctic sea ice

The extent of seasonal and perennial sea ice changed dramatically through the Late Quaternary and these changes influenced both the ocean and atmosphere by controlling the exchange of energy, moisture and gases between them, and by altering the planetary albedo. Reconstructing the changing patterns of sea ice distribution in the recent past remains one of the outstanding challenges to the paleo-community. To evaluate the importance of these reconstructions we performed sensitivity tests using NCAR's Community Climate Model (CCM3), and a series of prescribed sea ice extents designed to capture the full range of Arctic sea ice variability under interglacial (Holocene) and full glacial (Last Glacial Maximum) boundary conditions. Our simulations indicate that surface temperatures and sea level pressures in winter (DJF) are most sensitive to changes in sea ice, and that these changes are propagated over the surrounding land masses in the North Atlantic, but that equivalent changes in sea ice produce smaller corresponding changes in temperature or sea level pressure in the North Pacific region. A comparison between CLIMAP (Map Chart Series MC-36, Geological Society of America, Boulder, CO, 1981) and a more realistic assessment of LGM sea ice yields dramatic changes in winter temperatures and precipitation patterns across Eurasia. These differences, forced only by changed sea ice conditions, reinforce the need to develop accurate maps of past sea ice to correctly simulate Late Quaternary environments. Such reconstructions also will be essential to validate the next generation of sea ice models.     

Quantitative measurement of the sea ice diatom biomarker IP25 and sterols in Arctic sea ice and underlying sediments: Further considerations for palaeo sea ice reconstruction

The lipid content of sea ice samples collected in 2011 and 2012 from Resolute Passage in the Canadian Arctic Archipelago was measured and compared with related samples obtained from the Amundsen Gulf in 2008. The highly branched isoprenoid (HBI) sea ice biomarker, IP₂₅, was found in sea ice samples from each study, consistent with its formation by diatoms during the spring bloom. Our analysis also revealed the occurrence of a number of sterols in Arctic sea ice and these were rigorously identified and quantified for the first time. Concentrations of IP₂₅ and sterols exhibited some variability between sampling studies, with somewhat higher values in samples from Resolute in 2012 than for the other two datasets, consistent with a general increase in biomass; however, major differences in biomarker concentration between sampling studies were not observed. An estimate of the proportion of Arctic sea ice diatoms that produce IP₂₅ (ca. 1–5%) was obtained by comparison of the concentration of IP₂₅ in the samples with those in laboratory cultures of known HBI-producing diatoms and cell enumeration in selected sea ice samples. The estimate is similar to the proportion of Haslea spp. in the same samples, providing further support to the suggestion that at least some species of the Haslea genus may be responsible for the biosynthesis of IP₂₅ and related HBI diatom lipids in Arctic sea ice and that IP₂₅ is made by a relatively small proportion of sea ice diatoms. In contrast, median sterol/IP₂₅ values were all substantially higher than those in cultures of HBI-producing diatoms, suggesting that sterols are made by the majority of sea ice diatoms. The sterol/IP₂₅ ratio was quite variable between locations and samples, likely as a result of differences in diatom assemblage; however, a comparison of individual and median sterol/IP₂₅ values in sea ice with those from surface sediments from different Arctic regions demonstrated that sterols from sea ice diatoms may, in some cases, have a significant impact on the sedimentary budget. This should be considered carefully for quantitative estimates of palaeo sea ice reconstruction using methods such as the PIP₂₅ index, which are based on the relative concentrations of IP₂₅ and sterols in Arctic marine sediment cores.     

A finite-element treatment of sea ice dynamics for different ice rheologies

In this paper the effects of four different rheologies on the evolution of a large-scale sea ice pack are determined and compared. Two rheologies are of viscous-plastic form, and two are viscous fluid relations. The initial pack domain is rectangular, and the motion is driven by wind stress and resisted by ocean drag. Two adjacent edges are rigid shore boundaries, and the other two are free boundaries at open water which move during the pack motion, so that the pack domain changes in time. Two different forms of boundary conditions at the rigid shore edges are considered, which also influence the evolution. The governing equations are solved numerically using a finite-element method, and, unlike previous numerical treatments, no artificial viscosity is incorporated to stabilise the algorithm near interfaces between converging and diverging flow. Instability arises when any tensile stress is abruptly cut-off when diverging flow is initiated, and an alternative view is offered. © 1998 John Wiley & Sons, Ltd.     

基于FY-3 MWRI数据的北极海冰密集度反演研究

以ASI算法（ARTIST sea ice algorithm）为基础, 得到基于风云3C气象卫星（FY-3C）微波辐射计（MWRI）数据的纯水与纯冰系点值, 利用插值方法确定基于FY-3 MWRI数据的ASI海冰密集度计算公式, 采用大津法（Otsu算法）得到基于MWRI数据的天气滤波器阈值。以2016年1月数据为例, 对北极海冰密集度进行反演, 并与美国国家冰雪数据中心（NSIDC）以及德国不莱梅大学提供的海冰密集度产品进行对比验证。结果表明： 基于MWRI数据得到的1月平均海冰面积以及平均密集度均介于二者之间, 其中平均密集度与不莱梅产品更接近, 仅相差1.310%。与风云卫星空间分辨率为250 m的中分辨率光谱成像仪（MERSI）数据得到的结果进行对比, 发现二者的海冰外缘线基本一致, MERSI数据得到的海冰密集度以及海冰面积比MWRI数据得到的结果分别高出5.029%、 9.318%。因此, 应用该方法可有效推进MWRI数据反演北极海冰密集度, 进而监测北极海冰分布和变化。     

环渤海海冰弯曲强度的试验测试及特性分析

在渤海冰区油气开发中,海冰物理力学性质对海洋结构设计、海冰动力学过程均有重要影响.目前,随着渤海冰区锥体海洋平台的增加,对海冰弯曲强度的研究具有重要意义.对环渤海沿岸9个测点的海冰弯曲强度进行了现场和室内测试,同时对影响海冰弯曲强度的海冰盐度、温度进行了测试;分析了莱州湾、辽东湾西岸和辽东湾东岸等不同测点海冰弯曲强度的...     

High resolution aerosol data from MODIS satellite for urban air quality studies

The Moderate Resolution Imaging Spectroradiometer (MODIS) provides daily global coverage, but the 10 km resolution of its aerosol optical depth (AOD) product is not suitable for studying spatial variability of aerosols in urban areas. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was developed for MODIS which provides AOD at 1 km resolution. Using MAIAC data, the relationship between MAIAC AOD and PM_2.5 as measured by the 27 EPA ground monitoring stations was investigated. These results were also compared to conventional MODIS 10 km AOD retrievals (MOD04) for the same days and locations. The coefficients of determination for MOD04 and for MAIAC are R² =0.45 and 0.50 respectively, suggested that AOD is a reasonably good proxy for PM_2.5 ground concentrations. Finally, we studied the relationship between PM_2.5 and AOD at the intra-urban scale (?10 km) in Boston. The fine resolution results indicated spatial variability in particle concentration at a sub-10 kilometer scale. A local analysis for the Boston area showed that the AOD-PM_2.5 relationship does not depend on relative humidity and air temperatures below ～7 °C. The correlation improves for temperatures above 7–16 °C. We found no dependence on the boundary layer height except when the former was in the range 250–500 m. Finally, we apply a mixed effects model approach to MAIAC aerosol optical depth (AOD) retrievals from MODIS to predict PM_2.5 concentrations within the greater Boston area. With this approach we can control for the inherent day-to-day variability in the AOD-PM_2.5 relationship, which depends on time-varying parameters such as particle optical properties, vertical and diurnal concentration profiles and ground surface reflectance. Our results show that the model-predicted PM_2.5 mass concentrations are highly correlated with the actual observations (out-of-sample R² of 0.86). Therefore, adjustment for the daily variability in the AOD-PM_2.5 relationship provides a means for obtaining spatially-resolved PM_2.5 concentrations.     

Artificial neural network coupled with wavelet transform for estimating snow water equivalent using passive microwave data

Snow Water Equivalent (SWE) is an important parameter in hydrologic engineering involving the streamflow forecasting of high-elevation watersheds. In this paper, the application of classic Artificial Neural Network model (ANN) and a hybrid model combining the wavelet and ANN (WANN) is investigated in estimating the value of SWE in a mountainous basin. In addition, k-fold cross validation method is used in order to achieve a more reliable and robust model. In this regard, microwave images acquired from Spectral Sensor Microwave Imager (SSM/I) are used to estimate the SWE of Tehran sub-basins during 1992–2008 period. Also for obtaining measured SWE within the corresponding Equal-Area Scalable Earth-Grid (EASE-Grid) cell of SSM/I image, approach of Cell-SWE extraction using height–SWE relations is applied in order to reach more precise estimations. The obtained results reveal that the wavelet-ANN model significantly increases the accuracy of estimations, mainly because of using multi-scale time series as the ANN inputs. The Nash–Sutcliffe Index (NSE) for ANN and WANN models are respectively 0.09 and 0.44 which shows a firm improvement of 0.35 in NSE parameter when WANN is applied. Similar trend is observed in other parameters including RMSE where the value is 0.3 for ANN and 0.07 for WANN.     

Where the ice meets the sea - high resolution paleoenvironmental studies in the northern North Atlantic

Andrews, J. T., Austin, W. E. N., Bergsten, H. & Jennings, A. E. (eds), 1996: Late Quaternary Paleoceanography of the North Atlantic Margins     

On the design of paleoenvironmental data networks for estimating large-scale patterns of climate

Guidelines are determined for the spatial density and location of climatic variables (temperature and precipitation) that are appropriate for estimating the continental- to hemispheric-scale pattern of atmospheric circulation (sea-level pressure). Because instrumental records of temperature and precipitation simulate the climatic information that is contained in certain paleoenvironmental records (tree-ring, pollen, and written-documentary records, for example), these guidelines provide useful sampling strategies for reconstructing the pattern of atmospheric circulation from paleoenvironmental records. The statistical analysis uses a multiple linear regression model. The sampling strategies consist of changes in site density (from 0.5 to 2.5 sites per million square kilometers) and site location (from western North American sites only to sites in Japan, North America, and western Europe) of the climatic data. The results showed that the accuracy of specification of the pattern of sea-level pressure: (1) is improved if sites with climatic records are spread as uniformly as possible over the area of interest; (2) increases with increasing site density-at least up to the maximum site density used in this study; (3) is improved if sites cover an area that extends considerably beyond the limits of the area of interest. The accuracy of specification was lower for independent data than for the data that were used to develop the regression model; some skill was found for almost all sampling strategies.     

Atmospheric correction for sea surface temperature retrieval from single thermal channel radiometer data onboard Kalpana satellite

An atmospheric correction method has been applied on sea surface temperature (SST) retrieval algorithm using Very High Resolution Radiometer (VHRR) single window channel radiance data onboard Kalpana satellite (K-SAT). The technique makes use of concurrent water vapour fields available from Microwave Imager onboard Tropical Rainfall Measuring Mission (TRMM/TMI) satellite. Total water vapour content and satellite zenith angle dependent SST retrieval algorithm has been developed using Radiative Transfer Model [MODTRAN ver3.0] simulations for Kalpana 10.5–12.5 μm thermal window channel. Retrieval of Kalpana SST (K-SST) has been carried out for every half-hourly acquisition of Kalpana data for the year 2008 to cover whole annual cycle of SST over Indian Ocean (IO). Validation of the retrieved corrected SST has been carried out using near-simultaneous observations of ship and buoys datasets covering Arabian Sea, Bay of Bengal and IO regions. A significant improvement in Root Mean Square Deviation (RMSD) of K-SST with respect to buoy (1.50–1.02 K) and to ship datasets (1.41–1.19 K) is seen with the use of near real-time water vapour fields of TMI. Furthermore, comparison of the retrieved SST has also been carried out using near simultaneous observations of TRMM/TMI SST over IO regions. The analysis shows that K-SST has overall cold bias of 1.17 K and an RMSD of 1.09 K after bias correction.     

Surface studies of water isotopes in Antarctica for quantitative interpretation of deep ice core data

Polar ice cores are unique climate archives. Indeed, most of them have a continuous stratigraphy and present high temporal resolution of many climate variables in a single archive. While water isotopic records (δD or δ¹⁸O) in ice cores are often taken as references for past atmospheric temperature variations, their relationship to temperature is associated with a large uncertainty. Several reasons are invoked to explain the limitation of such an approach; in particular, post-deposition effects are important in East Antarctica because of the low accumulation rates. The strong influence of post-deposition processes highlights the need for surface polar research programs in addition to deep drilling programs. We present here new results on water isotopes from several recent surface programs, mostly over East Antarctica. Together with previously published data, the new data presented in this study have several implications for the climatic reconstructions based on ice core isotopic data: (1) The spatial relationship between surface mean temperature and mean snow isotopic composition over the first meters in depth can be explained quite straightforwardly using simple isotopic models tuned to d-excess vs. δ¹⁸O evolution in transects on the East Antarctic sector. The observed spatial slopes are significantly higher (～ 0.7–0.8‰·°C^?1 for δ¹⁸O vs. temperature) than seasonal slopes inferred from precipitation data at Vostok and Dome C (0.35 to 0.46‰·°C^?1). We explain these differences by changes in condensation versus surface temperature between summer and winter in the central East Antarctic plateau, where the inversion layer vanishes in summer. (2) Post-deposition effects linked to exchanges between the snow surface and the atmospheric water vapor lead to an evolution of δ¹⁸O in the surface snow, even in the absence of any precipitation event. This evolution preserves the positive correlation between the δ¹⁸O of snow and surface temperature, but is associated with a much slower δ¹⁸O-vs-temperature slope than the slope observed in the seasonal precipitation. (3) Post-deposition effects clearly limit the archiving of high-resolution (seasonal) climatic variability in the polar snow, but we suggest that sites with an accumulation rate of the order of 40 kg.m^?2.yr^?1 may record a seasonal cycle at shallow depths.     

渤海海冰动力学中的粘弹塑性本构模型

在粘塑性海冰本构模型的基础上,将Kelvin-Voigt粘弹性本构理论引入到海冰动力学中,进而建立了粘弹塑性海冰本构模型.该模型可较好地反映渤海海冰在小应变和小应变率条件下的粘弹性力学行为,同时还考虑了大应变率下的海冰粘塑性力学行为.对渤海辽东湾海冰进行了48h数值模拟.结果表明:粘弹塑性海冰本构方程较Hibler的粘塑性本构模型可更好地处理渤海的冰间相互作用,提高海冰数值模拟的计算精度.     

基于随机森林算法的青藏高原AMSR2被动微波雪深反演

青藏高原因其复杂的地形地势和和积雪分布使得多种雪深算法未达到理想的精度。基于新一代被动微波数据AMSR2（Advanced Microwave Scanning Radiometer 2）, 应用随机森林算法（Random Forest, RF）将亮温（Brightness Temperature, BT）和亮温差（Brightness Temperature Difference, BTD）作为参数输入, 并将高程和纬度参数引入雪深反演模型中, 经过模拟退火算法进行有效反演因子筛选, 构建了基于随机森林算法的青藏高原雪深反演模型。结果表明： 与AMSR2全球雪深产品相比, 随机森林算法的拟合优度（R²）由0.41提升至0.60, 均方根误差（Root Mean Square Error, RMSE）由7.36 cm降至4.88 cm, 偏差（BIAS）由3.24 cm减小至-0.16 cm, 随机森林雪深反演模型在青藏高原的精度更高; 青藏高原平均海拔超过4 000 m, 当海拔大于青藏高原平均海拔时, 随机森林算法的反演效果最差, 但RMSE仅为3.78 cm, BIAS仅为-0.09 cm; 高原南部（25° ~ 30° N）因其复杂的地势和相对较少的气象站点使得反演效果较差, RMSE为5.94 cm, BIAS为-0.39 cm; 青藏高原的主要土地覆盖类型为草地, 随机森林算法在草地的RMSE约为3 cm, BIAS接近0 cm。     

基于AMSR2被动微波积雪参量高精度反演方法研究

以新疆为研究区域建立了被动微波遥感积雪深度高精度反演模型,采用高空间和时间分辨率AM SR2被动微波遥感数据(2012年11月-2015年3月逐日数据),结合研究区域海拔高度、坡度、坡向、沙漠,荒漠和地表粗糙度等地形、地貌特征,考虑冰川、水体、林地等地表覆盖类型和不同季节的新雪、干雪和湿雪等积雪属性的微波辐射特征,以决策树阈值法为基础,通过采集样本分类建立起多种雪深判识阈值,在此基础上建立AMSR2高精度积雪深度反演综合模型,分类分析不稳定积雪和冰川信息,从而实现雪深在60 cm以内的积雪深度AMSR2反演的主要原理、思路及方法,并对模型的反演结果跟台站实测或者野外观测积雪值以时间和空间角度进行检验.结果表明:该综合模型能够定量判识研究区域复杂地形地貌条件下的1~60 cm积雪厚度,检验的复相关系数为0.74~0.88,均方根误差为2.92~6.14 cm,平均绝对偏差指数为3~4 cm,雪深误差5 cm的精度为91%~94%,雪深误差2.5cm的精度为81%~87%.