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TCP-GAN的热带气旋路径预测

准确预测热带气旋路径对于中国沿海地区的防灾减灾具有重要作用.卫星数据是预测热带气旋的重要手段,针对现有方法生成的卫星图像不够清晰,很难准确判断热带气旋云系的轮廓,提出了一种基于TCP-GAN的热带气旋路径预测方法.采用了生成对抗网络,并加入了感知损失,使得生成的图像更加细致.模型在不同输入图像序列长度(2、4和6)下进行试验,结果表明,当长度为4时,此时的路径误差是最小的,约为45.36 km.此外,进行了滚动预测,以验证模型在12、18和24 h的预测性能,提出的热带气旋路径预测模型生成的图像能够很好地描述云系的细致纹理结构,预测的路径误差相比于同类方法也更小.

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.

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.

融合DEM和遥感影像的黄土区断裂构造识别

断裂构造具有控制矿床发育的作用,其发育区域常为矿产资源潜在区和地质灾害频发区。因此,断裂构造的位置识别解译工作对矿产勘查和灾害预警等有着重要的实用价值和科学意义。文章以宝鸡北部黄土区为研究区,以DEM数据和Landsat 8 OLI为数据源,采用DEM数据山体阴影渲染、水文分析、坡度分析等方法,并结合Landsat 8 OLI遥感影像主成分分析方法,解译出研究区13条断裂构造。研究结果表明:通过DEM数据处理及分析地表形态变化,可以增强黄土覆盖严重的断裂构造特征,提高断裂构造的解译精度;该区以断裂构造为主,主要走向为北北东向、北东向、北西向和东西向;在黄土覆盖区断裂构造解译中,水文分析和主成分分析方法可更好地辅助野外地质构造调查,与已有区域地质调查研究成果保持一致。该研究可用于特殊地质地貌区的断裂构造提取,为区域地质调查和构造运动演化分析提供参考依据。

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.

基于主成分回归及遥感的贵阳市林分郁闭度估测

以贵阳市为研究区,以Landsat-8 OLI为遥感信息源,通过偏相关分析,选择了与郁闭度相关的12个遥感因子作为自变量因子。为克服自变量因子间严重的多重共线性和模型的不稳定性,采用主成分回归分析法建立郁闭度估测模型,并对模型进了检验及精度验证。结果表明,回归方程调整后的R2=0.756,模型的拟合效果较好,说明模型在数学上是可行的;利用15个实测样地对模型进行精度验证,估测精度的平均水平为78.80%,说明该模型可为区域林分郁闭度估测提供参考。