基于NSIDC海冰产品的FY北极海冰数据集优化

发布时间：2019-03-11 13:24

【摘要】：北极海冰对全球气候起着非常重要的调制作用,海冰范围是海冰监测的基本参数。近40年,北极地区持续变暖,北极海冰显著减少,进而引发北极自然环境恶化、北半球极端天气频发、全球海平面上升等一系列环境和气候问题。准确获取北极海冰范围及其演变趋势,确定海冰变化对全球气候系统的响应,是研究和预测全球气候变化趋势的关键之一。Has ISST和OISST海冰数据集在海冰监测中应用最为广泛,可为北极地区长时间序列海冰变化研究提供基础数据,但这2套数据集空间分辨率相对较低,应用于北极关键区对中国气候响应研究方面存在很大的局限,为解决这一问题和弥补国内海冰监测微波遥感数据的空白,2011年6月27日,国家卫星气象中心(National Satellite Meteorological Center,NSMC)发布了FY(Fengyun,FY)北极海冰数据集,该数据集利用搭载在FY卫星上的微波成像仪(Microwave Radiation Imager,MWRI)数据,使用Enhance NASA Team算法制作,该算法利用前向辐射传输模型模拟北极地区4种海表类型(海水、新生冰、一年冰和多年冰)在不同大气条件下MWRI辐射亮温,进而得到每种大气条件下0~100%的海冰覆盖度查找表(海冰覆盖度每次增加1%),通过观测值与模拟值的比对得到海冰覆盖度,由该数据集计算得到的北极海冰范围在大部分区域与实际情况相符。该产品虽已进行通道间匹配误差修正和定位精度偏差订正,但由于其搭载的微波成像仪(Microwave Radiation Imager,MWRI)天线长度有限,造成传感器探测到的地物回波信号相对较弱,难以区分海冰和近岸附近的陆地,影响了该数据集的精度和应用。为解决这一问题,本文基于美国冰雪中心(National Snow and Ice Data Center,NSIDC)发布的海冰产品对FY海冰数据集进行优化,NSIDC产品利用判断矩阵对海岸线附近的像元进行识别,并对误差像元进行不同程度的修正,由NSIDC产品计算得到的北极海冰范围与实际情况更为符合。数据集优化大大提高了FY海冰数据集的精度,研究结果表明,优化后FY海冰数据集与NSIDC产品相关系数高达0.9997,且二者日、月、年平均最大海冰范围偏差仅为3.5%、1.9%、0.9%,且FY海冰数据集优化过程对其较好的空间分异特征无明显影响。该数据集可正确地反映北极海冰范围及其变化情况,且海岸线附近海冰的分布情况更准确,可为北极海冰变化研究提供可靠的基础数据。
[Abstract]:Arctic sea ice plays a very important role in modulating global climate. Sea ice range is the basic parameter of sea ice monitoring. In the past 40 years, the Arctic region has continued to warm and the Arctic sea ice has decreased significantly, which leads to the deterioration of the Arctic natural environment, the frequent occurrence of extreme weather in the Northern Hemisphere, and a series of environmental and climatic problems such as global sea level rise and so on. Accurately obtaining the Arctic sea ice range and its evolution trend and determining the response of sea ice change to the global climate system is the key to study and predict the global climate change trend. Has ISST and OISST sea ice data sets are the most widely used in sea ice monitoring. It can provide basic data for the study of sea ice change of long time series in the Arctic region, but the spatial resolution of these two sets of data sets is relatively low, and the application of these two sets of data sets to the study of the climate response of China to the key areas of the Arctic has great limitations. To address this problem and fill the gaps in domestic microwave remote sensing data for sea ice monitoring, the National Satellite Meteorological Center (National Satellite Meteorological Center,NSMC) released the FY (Fengyun,FY) Arctic Sea Ice data set on June 27, 2011. The data set is based on the microwave imager (Microwave Radiation Imager,MWRI data on the FY satellite and is made using the Enhance NASA Team algorithm, which uses forward radiative transfer models to simulate four sea surface types (sea water, new ice) in the Arctic. One-year ice and multi-year ice) the brightness temperature of MWRI radiation under different atmospheric conditions, and then get a 100% sea ice coverage lookup table under each atmospheric condition (the sea ice coverage increases by 1% each time). The sea ice coverage is obtained by comparing the observed values with the simulated values. The Arctic sea ice range calculated from the data set is consistent with the actual situation in most areas. Although the product has carried out the correction of inter-channel matching error and positioning accuracy deviation, but because of its limited antenna length of microwave imager (Microwave Radiation Imager,MWRI), the echo signal of ground objects detected by the sensor is relatively weak. It is difficult to distinguish sea ice from land near shore, which affects the accuracy and application of the data set. In order to solve this problem, this paper optimizes the FY sea ice data set based on the sea ice product released by the Ice and Snow Center of the United States (National Snow and Ice Data Center,NSIDC), and the NSIDC product uses the judgment matrix to identify the pixels near the coastline. The error pixels are corrected to some extent, and the Arctic sea ice range calculated by NSIDC products is more consistent with the actual situation. The accuracy of FY sea ice data set is greatly improved by data set optimization. The results show that the correlation coefficient between FY sea ice data set and NSIDC product is up to 0.9997, and the daily, monthly and annual maximum sea ice range deviation between the two data sets is only 3.5%. 1.9%, 0.9%, and the optimization process of FY sea ice data set had no obvious effect on its good spatial differentiation characteristics. The data set can accurately reflect the Arctic sea ice range and its variation, and the distribution of sea ice near the coastline is more accurate, which can provide reliable basic data for the study of Arctic sea ice change.
【作者单位】：山东科技大学测绘科学与工程学院海岛(礁)测绘技术国家测绘地理信息局重点实验室;中国科学院遥感与数字地球研究所中国科学院数字地球重点实验室遥感科学国家重点实验室;国家气候中心国家卫星气象中心中国气象局;
【基金】：中国气象局气候变化专项(CCSF201502) 遥感科学国家重点实验室自由探索/青年人才项目“基于地形自相似理论的湖泊水储量遥感估算方法研究”(Y6Y00200KZ) 国家自然科学基金应急管理项目“近30年青藏高原湖泊水面变化及其区域气候效应”(41440010)
【分类号】：P731.15;P715.7

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