JIANG Jinhao,LIU Hailei,WANG Yizhu,et al.Precipitable Water Vapor Retrieval Using Himawari-8 Satellite Observations over Tibetan Plateau[J].Journal of Chengdu University of Information Technology,2022,37(05):494-500.[doi:10.16836/j.cnki.jcuit.2022.05.002]
基于Himawari-8卫星数据的青藏高原大气可降水量反演算法研究
- Title:
- Precipitable Water Vapor Retrieval Using Himawari-8 Satellite Observations over Tibetan Plateau
- 文章编号:
- 2096-1618(2022)05-0494-07
- 关键词:
- 青藏高原; Himawari-8; 大气可降水量; 神经网络; GFS
- Keywords:
- Tibetan Plateau; Himawari-8; precipitable water vapor; neural network; GFS
- 分类号:
- TP301.6
- 文献标志码:
- A
- 摘要:
- 大气可降水量对降水形成、水循环、物质能量交换、天气和气候变化均有重要影响。青藏高原的水汽分布、输送特征是重要的高原气象科学问题。新一代静止气象卫星能够获取更高时间、空间和光谱分辨率的观测数据，为高时空分辨率PWV探测提供良好的机遇。利用Himawari-8卫星的水汽和分裂窗区通道亮温、全球天气预报系统(GFS)的PWV预报场和其他辅助数据(卫星观测角、海拔高度、经纬度和时间信息)，构建一种基于神经网络的青藏高原PWV快速反演算法。结果表明，神经网络模型估算的PWV与地基全球定位系统(GPS)PWV的相关系数、均方根误差和偏差分别为0.957、1.33 mm和-0.004 mm。相比之下，未包含GFS PWV模型反演的PWV相关系数、均方根误差和偏差分别为0.943、1.52 mm和0.01 mm，意味着引入GFS PWV预报场可以有效提高模型的反演精度。该方法适用于其他低水汽环境下的PWV反演，也可用于其他静止气象卫星数据。
- Abstract:
- Precipitable water vapor(PWV)plays an important role in precipitation formation, water cycle, energy exchange, weather and climate change. The characteristics of water vapor distribution and transportation over the Tibetan Plateau are important issues in plateau meteorology. The new generation of geostationary meteorological satellites can obtain observation data with higher temporal, spatial and spectral resolution, which provides a good opportunity for PWV retrieval with high temporal and spatial resolution. In this study, a fast neural network based algorithm for PWV retrieval over the Tibetan Plateau was proposed. The inputs of model mainly include the water vapor absorption and split window channels brightness temperature of Advanced Himawari Imagers(AHI)onboard Himawari-8, Global Forecast System(GFS)PWV forecasts and other auxiliary data(satellite observation angle, elevation, longitude and latitude, and time information). The correlation coefficient(R),root mean square error(RMSE)and Bias of PWV estimated by neural network model with GPS(Global Positioning System)PWV are 0.957,1.33 mm and -0.004 mm, respectively. In contrast, the R, RMSE and Bias of the model without GFS PWV are 0.943,1. 52 mm and 0.01 mm. This indicates that the introduction of GFS PWV can improve the inversion accuracy. The proposed method is suitable for PWV retrieval in other area with low water vapor content and can be also used for other geostationary meteorological satellite data.
参考文献/References:
[1] Zveryaev I I,RP Allan.Water vapor variability in the tropics and its links to dynamics and precipitation[J].Journal of Geophysical Research-Atmospheres,2005,110(D21).
[2] Dessler A E,Zhang Z,Yang P.Water-vapor climate feedback inferred from climate fluctuations,2003-2008[J].Geophysical Research Letters,2008.35(20):293-210.
[3] Ji D B,Shi J C,Xiong C,et al.A total precipitable water retrieval method over land using the combination of passive microwave and optical remote sensing[J].Remote Sensing of Environment,2017,191:313-327.
[4] Jimenez-Munoz J C,Sobrino J A,Skokovic D,et al.Land Surface Temperature Retrieval Methods From Landsat-8 Thermal Infrared Sensor Data[J].Ieee Geoscience and Remote Sensing Letters,2014,11(10):1840-1843.
[5] Vermote E F,Saleous N,Justice C O.Atmospheric correction of MODIS data in the visible to middle infrared: first results[J].Remote Sensing of Environment,2002,83(1):97-111.
[6] Li Z L,Tang B H,Wu H,et al.Satellite-derived land surface temperature: Current status and perspectives[J].Remote Sensing of Environment,2013,131:14-37.
[7] Liu H L,Tang S H,Zhang S L,et al.Evalution of MODIS water Vapour Products over China using radiosonde data[J].International Journal of Remote Sensing,2015,36(2):680-690.
[8] Vaquero-Martinez J,Anton M,Galisteo J,et al.Inter-comparison of integrated water vapor from satellite instruments using reference GPS data at the Iberian Peninsula[J].Remote Sensing of Environment,2018,204:729-740.
[9] Liu H L,Tang S H,Hu J Y,et al.An improved physical split-window algorithm for precipitable water vapor retrieval exploiting the water vapor channel observations[J]. Remote Sensing of Environment,2017,194:366-378.
[10] 王永前,施建成,曾巧林,等.TRMM/VIRS热红外通道陆地大气可降水汽总量分裂窗反演[J].遥感学报,2016,20(1):94-102.
[11] Alshawaf F,Fuhrmann T,Knopfler A,et al.Accurate Estimation of Atmospheric Water Vapor Using GNSS Observations and Surface Meteorological Data[J].Ieee Transactions on Geoscience and Remote Sensing,2015,53(7):3764-3771.
[12] Niell A E,Coster A J,Solheim F S,et al.Comparison of Measurements of Atmospheric Wet Delay by Radiosonde, Water Vapor Radiometer, GPS, and VLBI[J].Journal of Atmospheric and Oceanic Technology,2001,18(6):830-850.
[13] 黄荣辉.青藏高原对我国及世界气候环境的影响[J].地球科学信息,1988(6):25-27.
[14] 徐祥德.青藏高原“敏感区”对我国灾害天气气候的影响及其监测[J].中国工程科学,2009,11(10):96-107.
[15] 敬文琪, 崔园园, 刘瑞霞,等.影响长江中下游夏季降水的青藏高原水汽抽吸作用和水汽路径的定量化研究[J].高原气象,2017,36(4):900-911.
[16] 刘屹岷,燕亚菲,吕建牟,等.基于CloudSat/CALIPSO卫星资料的青藏高原云辐射及降水的研究进展[J].大气科学,2018,42(4):847-858.
[17] 卢乃锰,范锦龙,刘健,等.卫星遥感在青藏高原大气科学研究中的应用前景[J].气象科技进展,2013,3(3):29-33.
[18] Lee Y L,Li Z L,Li J,et al.Evaluation of the GOES-R ABI LAP Retrieval Algorithm Using the GOES-13 Sounder[J].Journal of A tmospheric and Oceanic Technology,2014,31(1):3-19.
[19] Guillory A R,Jedlovec G J,Fuelberg H E.A Technique for Deriving Column-integrated Water Content Using VAS Split-Window Data[J].Journal of Applied Meteorology and Climatology,1993,32(7):1226-1241.
[20] Suggs R J,Jedlovec G J,Guillory A R.Retrieval of Geophysical Parameters from GOES:Evaluation of a Split-Window Technique[J].Journal of Applied Meteorology,1998,37(10):1205-1227.
[21] Schmit T J,Gunshor M M,Menzel W P,et al.Introducing the next-generation Advanced Baseline Imager on goes-R[J].Bulletin of the American Meteorological Society,2005,86(8):1079-1096.
[22] Bessho K,Date K,Hayashi M,et al.An Introduction to Himawari-8/9-Japan’s New-Generation Geostationary Meteorological Satellites[J].Journal of the Meteorological Society of Japan, 2016,94(2):151-183.
[23] Schmit T J,Li J,Lee S J,et al.Legacy Atmospheric Profiles and Derived Products From GOES-16: Validation and Applications[J].Earth and Space Science, 2019,6(9):1730-1748.
[24] Di D,Ai Y F,Li J,et al.Geostationary satellite-based 6.7 mu m band best water vapor information layer analysis over the Tibetan Plateau[J].Journal of Geophysical Research-Atmospheres,2016,121(9):4600-4613.
[25] Wu Y,Zhang F,Wu K,et al.Best Water Vapor Information Layer of Himawari-8-Based Water Vapor Bands over East Asia[J].Sensors,2020,20(8):1-16.
[26] Liu H L,Zhou Q,Zhang S L,et al.Estimation of Summer Air Temperature over China Using Himawari-8 AHI and Numerical Weather Prediction Data[J].Advances in Meteorology,2019(1):1-10.
[27] Lee Y,Han D,Ahn M H,et al.Retrieval of Total Precipitable Water from Himawari-8 AHI Data: A Comparison of Random Forest,Extreme Gradient Boosting,and Deep Neural Network[J].Remote Sensing,2019,11(15):1-18.
相似文献/References:
[1]薛志航,邓 创,孙 一.青藏高原上空臭氧时空分布特征[J].成都信息工程大学学报,2018,(04):464.[doi:10.16836/j.cnki.jcuit.2018.04.017]
XUE Zhi-hang,DENG Chuang,SUN Yi.Temporal and Spatial Distribution Characteristics ofOzone over the Tibetan Plateau[J].Journal of Chengdu University of Information Technology,2018,(05):464.[doi:10.16836/j.cnki.jcuit.2018.04.017]
[2]吴 钩,白爱娟.青藏高原季风环流情况与中亚季风降水特征分析[J].成都信息工程大学学报,2016,(01):76.
WU Gou,BAI Ai-juan.Analysis on the Characteristics of Tibetan Plateau's
Monsoon Circulation and Central Asia's Rainfall[J].Journal of Chengdu University of Information Technology,2016,(05):76.
[3]梁嘉颖,陈权亮.青藏高原多种对流层顶的时空分布特征对比分析[J].成都信息工程大学学报,2020,35(01):69.[doi:10.16836/j.cnki.jcuit.2020.01.010]
LIANG Jiaying,CHEN Quanliang.Analysis of Temporal and Spatial Distribution Characteristics of Multiple Tropopauses over the Tibetan Plateau[J].Journal of Chengdu University of Information Technology,2020,35(05):69.[doi:10.16836/j.cnki.jcuit.2020.01.010]
[4]骆开怡,陈权亮.青藏高原上空平流层水汽的时空演变特征[J].成都信息工程大学学报,2022,37(04):422.[doi:10.16836/j.cnki.jcuit.2022.04.010]
LUO Kaiyi,CHEN Quanliang.Temporal and Spatial Evolution Characteristics of Stratospheric Water Vapor over the Tibetan Plateau[J].Journal of Chengdu University of Information Technology,2022,37(05):422.[doi:10.16836/j.cnki.jcuit.2022.04.010]
[5]曾 剑,张 强,张 宇,等.青藏高原高寒草甸的空气动力学粗糙度特征[J].成都信息工程大学学报,2022,37(04):429.[doi:10.16836/j.cnki.jcuit.2022.04.011]
ZENG Jian,ZHANG Qiang,ZHANG Yu,et al.The Characteristics of Aerodynamic Aoughness Length of Alpine Meadows on the Qinghai-Tibet Plateau[J].Journal of Chengdu University of Information Technology,2022,37(05):429.[doi:10.16836/j.cnki.jcuit.2022.04.011]
备注/Memo
收稿日期:2022-05-11
基金项目:国家自然科学基金资助项目(42030107)