Assimilating All-sky Infrared Radiances from Himawari-8 Using the 3DVar Method for the Prediction of a Severe Storm over North China

Although radar observations capture storm structures with high spatiotemporal resolutions, they are limited within the storm region after the precipitation formed. Geostationary satellites data cover the gaps in the radar network prior to the formation of the precipitation for the storms and their environment. The study explores the effects of assimilating the water vapor channel radiances from Himawari-8 data with Weather Research and Forecasting model data assimilation system(WRFDA) for a severe storm case over north China. A fast cloud detection scheme for Advanced Himawari imager(AHI)radiance is enhanced in the framework of the WRFDA system initially in this study. The bias corrections, the cloud detection for the clear-sky AHI radiance, and the observation error modeling for cloudy radiance are conducted before the data assimilation. All AHI radiance observations are fully applied without any quality control for all-sky AHI radiance data assimilation. Results show that the simulated all-sky AHI radiance fits the observations better by using the cloud dependent observation error model, further improving the cloud heights. The all-sky AHI radiance assimilation adjusts all types of hydrometeor variables, especially cloud water and precipitation snow. It is proven that assimilating all-sky AHI data improves hydrometeor specifications when verified against the radar reflectivity. Consequently, the assimilation of AHI observations under the all-sky condition has an overall improved impact on both the precipitation locations and intensity compared to the experiment with only conventional and AHI clear-sky radiance data.

基于Landsat 8 OLI卫星遥感数据的黄前水库水源地保护区水土流失监测

近年来,为了提升饮用水水源地的综合监管水平,遥感监测技术得到了广泛应用。本文中基于landsat8 OLI卫星遥感数据,采用ENVI、Arc GIS等遥感图像处理技术,研究了黄前水库水源地保护区的植被覆盖度及水土流失分布特征。结果表明:黄前水库饮用水水源地保护区的林草覆盖度较高,占保护区总面积的57.32%;而水土流失较为严重,土壤侵蚀面积占55.30%,其中,以轻度、中度为主。该成果为黄前水库饮用水水源地保护区的水土流失监测和生态环境综合治理提供了技术支撑。

Mapping high-resolution surface shortwave radiation over East Asia with the new generation geostationary meteorological satellite Himawari-8

Surface shortwave radiation(SSR)plays an important role in global energy systems.The new generation of geostationary meteorological satellite Himawari-8,with higher spatiotemporal and spectral resolution,offers a new opportunity to retrieve SSR with higher accuracy.In this study,an improved algorithm was applied to estimate instantaneous,hourly,and daily mean SSR using cloud products from the Advanced Himawari Imager(AHI)onboard the Himawari-8 satellite.The validation against Baseline Surface Radiation Network(BSRN)stations showed a root mean square error(RMSE)of 95.8 W m^(-2) for instantaneous SSR,82.4 W m^(-2) for hourly SSR,and 22.8 W m^(-2) for daily SSR and mean bias error(MBE)of-15.8 W m^(-2),-14.1 W m^(-2),and-6.6 W m^(-2).The validation against China Meteorological Administration(CMA)stations showed a RMSE of 99.5 W m^(-2) and MBE of-8.2 W m^(-2) for hourly SSR and RMSE of 27.7 W m^(-2) and MBE of-3.9 W m^(-2) for daily SSR,which are generally better than the Himawari-8 SSR product.Overall,the improved algorithm performed well on the new-generation geostationary satellite,with high accuracy and efficiency,and would contribute to surface process research and photovoltaic engineering applications.

基于多源数据的山东太阳总辐射对比分析

利用ngstr m-Prescott(A-P)太阳辐射气候学公式和山东122个国家级气象观测站的日照时数资料,估算得到全省站点的日太阳总辐射,与订正系统偏差后的欧洲中期天气预报中心ERA5数据和葵花8卫星太阳总辐射产品进行误差对比,讨论不同数据在山东空间分布上的差异。结果表明:(1)葵花8卫星订正后的太阳总辐射与观测的误差最小,在不同天气条件下表现稳定;A-P公式估算的太阳总辐射与观测的误差和ERA5数据与观测的误差接近,云量越多这两种数据的误差越大,A-P公式估算值在晴天表现较好,ERA5数据在多云天表现较好。(2)相比于葵花8卫星的太阳总辐射分布,A-P公式估算值在山东西部偏低,夏季在全省范围内普遍偏低;而ERA5太阳总辐射往往在低值区偏高、在高值区偏低,另外在威海数值明显偏高。(3)对山东太阳总辐射而言,葵花8卫星数据的准确性最高。

基于葵花8号卫星资料的对流初生预警研究

强对流天气带来的强降水、雷电、大风严重危害着航空飞行安全,因此准确的临近预警强对流对航空工作的顺利运行有较高的实用价值。基于对流初生预警算法,利用华东地区发生的大量对流初生个例对算法进行阈值调整,提高算法适用范围,并将原算法、调整后的算法和SATCAST算法三者对比。结果表明:调整后的算法可以较好的适用于华东地区;相比SATCAST算法在云边缘误报较多,调整后的算法能有效地消除云边缘误报,预警效果更佳。

融合物理信息的热带气旋强度估计

热带气旋强度的精确估计是进行有效强度预测的基础工作,对于灾害预报至关重要.当前基于深度学习的热带气旋强度估计技术展现出了优越的性能,但仍然存在着物理信息融合不足的问题.因此,本文基于深度学习框架,提出一种融合物理信息的热带气旋强度估计模型(physical factor fusion for tropical cyclone intensity estimation,PF-TCIE),来估计西北太平洋的热带气旋强度.PF-TCIE由多通道卫星云图学习分支和物理信息提取分支组成.多通道卫星云图学习分支用于提取热带气旋云系特征,物理信息提取分支用于提取物理因子特征,来约束云系特征的学习.本文数据选用葵花-8卫星资料和ERA-5再分析资料.实验证明,在引入多个通道后,模型的RMSE误差较单通道降低了3.7%.同时,物理信息的引入使模型的误差进一步下降了8.5%.PF-TCIE的RMSE最终达到了4.83 m/s,优于大部分深度学习方法.

Spatiotemporal Variations of Microwave Land Surface Emissivity(MLSE)over China Derived from Four-Year Recalibrated Fengyun 3B MWRI Data

Microwave Land Surface Emissivity(MLSE)over China under both clear and cloudy sky conditions was retrieved using measurements of recalibrated microwave brightness temperatures(Tbs)from Fengyun-3B Microwave Radiation Imager(FY-3B MWRI),combined with cloud properties derived from Himawari-8 Advanced Himawari Imager(AHI)observations.The contributions from cloud particles and atmospheric gases to the upwelling Tbs at the top of atmosphere were calculated and removed in radiative transfer.The MLSEs at horizontal polarizations at 10.65,18.7,and 36.5 GHz during 7 July 2015 to 30 June 2019 over China showed high values in the southeast vegetated area and low values in the northwest barren,or sparsely vegetated,area.The maximum values were found in the belt area of the Qinling-Taihang Mountains and the eastern edge of the Qinghai-Tibet Plateau,which is highly consistent with MLSEs derived from AMSR-E.It demonstrates that the measurements of FY-3B MWRI Tbs,including its calibration and validation,are reliable,and the retrieval algorithm developed in this study works well.Seasonal variations of MLSE in China are mainly driven by the combined effects of vegetation,rainfall,and snow cover.In tropical and southern forest regions,the seasonal variation of MLSE is small due to the enhancement from vegetation and the suppression from rainfall.In the boreal area,snow causes a significant decrease of MLSE at 36.5 GHz in winter.Meanwhile,the MLSE at lower frequencies experiences less suppression.In the desert region in Xinjiang,increases of MLSEs at all frequencies are observed with increasing snow cover.

智能手机GNSS标准定位性能与精度分析

GNSS接收机定位精度高,但是体积大不便于日常生活的使用。智能手机体积小、便于携带,并内置定位芯片可以为用户进行必要的位置服务。本文利用双频手机小米Mi8和测量型接收机Trimble NetR9在相同环境下进行同步观测。对两者的GNSS原始观测值的质量进行对比分析,通过Android Studio开发平台设计软件实现原始数据的获取和定位解算。结果表明,智能手机的定位结果可以达到水平方向3 m~13 m的定位精度。智能手机的GNSS原始观测值质量不及测量型接收机。它的信噪比更低,多路径影响更大。这是由于其内置天线的设计造成的,但其结果可以满足日常生活的需要,在民用领域有良好的应用前景。

Evaluation of Cloud Top Height Retrievals from China’s Next-Generation Geostationary Meteorological Satellite FY-4A

To evaluate the validity of cloud top height (CTH) retrievals from FY-4A, the first of China's next-generation geostationary meteorological satellite series, the retrievals are compared to those from Himawari-8, CloudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Moderate Resolution Imaging Spectroradiometer (MODIS) operational products from August to October 2017. Regarding CTHs from CloudSat, CALIPSO, and MODIS as truth, the results show that the performance of FY-4A CTH retrievals is similar to that of Himawari-8. Both FY-4A and Himawari-8 retrieve reasonable CTH values for single-layer clouds, but perform poorly for multi-layer clouds. The mean bias error (MBE) shows that the mean value of FY-4A CTH retrievals is smaller than that of Himawari-8 for single-layer clouds but larger for multi-layer clouds. For ice crystal clouds, both FY-4A and Himawari-8 obtain the underestimated CTHs. However, there is a tendency for FY-4A and Himawari-8 to overestimate the CTH values of CloudSat and CALIPSO mainly for low level liquid water clouds. The temperature inversion near the tops of water clouds may result in an overestimation of CTHs. According to the MBE change with altitude, FY-4A and Himawari-8 overestimate the CTHs mainly for clouds below 3 km, and the overestimation is slightly more apparent in Himawari-8 data than that in FY-4A values. As the cloud optical thickness (COT) increases, the CTH bias of FY-4A CTH retrievals gradually decreases. Two typical cases are analyzed to illustrate the differences between different satellites' CTH retrievals in detail.

基于地球静止气象卫星的地表参数遥感研究进展

地表参数定量遥感反演是遥感科学研究的重要环节。21世纪以来,地球静止气象卫星数据在地表参数遥感反演中受到越来越多的重视。本文对利用地球静止气象卫星进行地表参数遥感反演研究的进展进行了综述。文章首先简单介绍了当前正在运行的欧盟Meteosat、美国GOES-R、日本葵花和中国风云静止卫星系统,随后详细总结了不同卫星系统估算各种地表参数的方法。在此基础上,文章对进一步利用静止卫星估算地表参数的研究展开讨论,指出未来的研究应重点关注几个方面:(1)探索和运用新技术提高静止卫星数据获取和处理的效率和精度;(2)融合全球多颗静止气象卫星,同时与极轨卫星融合,生产覆盖全球的长时序地表参数产品;(3)探索地表参数的高效获取方法,对静止气象卫星地表参数产品开展真实性检验,满足地表过程研究和资源环境动态监测对高质量地表参数产品的需求。