Geostationary Satellite Observation of Precipitable Water Vapor Using an Empirical Orthogonal Function (EOF) based Reconstruction Technique over Eastern China
نویسندگان
چکیده
Water vapor, as one of the most important greenhouse gases, is crucial for both climate and atmospheric studies. Considering the high spatial and temporal variations of water vapor, a timely and accurate retrieval of precipitable water vapor (PWV) is urgently needed, but has long been constrained by data availability. Our study derived the vertically integrated precipitable water vapor over eastern China using Multi-functional Transport Satellite (MTSAT) data, which is in geostationary orbit with high temporal resolution. The missing pixels caused by cloud contamination were reconstructed using an Empirical Orthogonal Function (EOF) decomposition method over both spatial and temporal dimensions. GPS meteorology data were used to validate the retrieval and the reconstructed results. The diurnal variation of PWV over eastern China was analyzed using harmonic OPEN ACCESS Remote Sens. 2015, 7 5880 analysis, which indicates that the reconstructed PWV data can depict the diurnal cycle of PWV caused by evapotranspiration and local thermal circulation.
منابع مشابه
Trends in Tropospheric Humidity from 1970 to 2008 over China from a Homogenized Radiosonde Dataset
Radiosonde humidity data provide the longest record for assessing changes in atmospheric water vapor, but they often contain large discontinuities because of changes in instrumentation and observational practices. In this study, the variations and trends in tropospheric humidity (up to 300 hPa) over China are analyzed using a newly homogenized radiosonde dataset. It is shown that the homogeniza...
متن کاملEstimation and Analysis of Precipitable Water Vapor Using GPS Data and Satellite Altimeter
Determination of water vapor in the atmosphere plays an important role in forecasting weather conditions and precipitation studies. For this reason, it is very important to study the tropospheric delay, especially the wet component, which is due to the presence of water vapor in the atmosphere. In this paper, the amount of water vapor was estimated by altimeter satellite radiometer and GPS data...
متن کاملCalculating of Radiosonde Precipitable water using MODIS Satellite images in Goorganrood basin
Deficiency of atmospheric water vapor profile data is one of most important problems in the flood hazard researches for areas flooding such as Goorganrood basin, because of no radiosonde stations. With the aim of radiosonde data generation retrieved radiance MODIS data, after Geometric and radiometric corrections, on 21 and 8 august 2005 from MODIS-Level 1, In order to make spatial TPW maps of ...
متن کاملA Comparative Study of Sea Level Reconstruction Techniques Using 20 Years of Satellite Altimetry Data
Sea level reconstructions extend spatially dense data sets, such as those from satellite altimetry, by decomposing the data set into basis functions and fitting those functions to in situ tide gauge measurements with a longer temporal record. We compare and evaluate two methods for reconstructing sea level through an idealized study. The compared sea level reconstruction methods differ in the t...
متن کاملAnalysis of temporal and spatial correlation between precipitable water vapor retrievals from AIRS satellite sensor and 29 synoptic station measurements in Iran
Precipitable Water Vapor (PWV) is one of the most important quantities in meteorology and climate studies. PWV in Earth's atmosphere can be measured by Sun-photometer, the Atmospheric Infrared Sounder (AIRS), and radiosonde from surface, atmosphere and space-based systems, respectively. In this paper, we use PWV measured by Sun-photometer located in Institute for Advanced Studies in Basic Scien...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- Remote Sensing
دوره 7 شماره
صفحات -
تاریخ انتشار 2015