Hailei Liu

Evaluation of MODIS water vapour products over China using radiosonde data

Radiosonde data collected from 83 stations in China from January to December 2012 were used to evaluate Moderate Resolution Imaging Spectroradiometer (MODIS) near-infrared (NIR) and thermal infrared (IR) total precipitable water vapour (PWV) products. The results indicate that MODIS NIR PWV products shows better agreement with radiosonde data than with IR PWV products, with the correlation coefficients up to 0.95. The root mean square errors (RMSEs) of NIR PWV range from 2 to 8 mm with different stations, which shows significant regional differences over China. The mean RMSE is about 5.03 mm (~35%) with a positive deviation of 2.56 mm (~18%), indicating the occurrence of a slight overestimation. Moreover, MODIS IR PWV during night-time has a better agreement with radiosonde PWV than that during daytime. The mean RMSE of IR PWV during daytime was ~6.02 mm (~42%), with a positive deviation of 1.54 mm (~11%). The mean RMSE of IR PWV during night-time was ~5.81 mm (~40%), with a negative deviation of approximately −0.04 mm (~0.25%). Both the NIR and IR PWV products during daytime tend to be higher than radiosonde PWV.

A New Angle-Based Spectral Index and Its Application in Drought Monitoring

An angle-based drought spectral index (ABDI) was proposed aimed to drought monitoring based on Near Infrared (NIR, 858 nm) and Shortwave Infrared (SWIR, 1240 and 1640 nm) bands of the Moderate Resolution Imaging Spectrometer (MODIS). The Shortwave Angle Slope Index (SASI) is reviewed and analyzed. United States Geological Survey (USGS) spectral datasets were employed to validate the ability of ABDI to estimate soil and vegetation moisture. Six-year(2002-2007) time series of Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI) and ABDI was created and analyzed for drought assessment and monitoring within the region of Nanchong in Sichuan Province, using MODIS history data and meteorological data. The investigation of a six-year history of MODIS NDVI, NDWI and ABDI indicates that a strong relationship exists among NDWI, ABDI and 2006 drought conditions of eastern Sichuan Basin. And the proposed ABDI had a stronger response to regional drought than NDVI and NDWI, which can be a practical method of drought monitoring in both accuracy and efficiency.

Atmospheric correction and land surface temperature retrieval method for FY-3 IR observations

Land surface temperature (LST) is a key variable for studies of global or regional land surface processes, energy and water cycle, and thus, has important applications in various areas. However, LST retrieval is a difficult subject and a challenge issue due to complex interactions between land surface and atmosphere. In this study, atmospheric correction and LST retrieval methods are briefly reviewed first. Then, based on the NASA ASTER-WVS (Water Vapor Scaling) method, a new atmospheric correction and LST retrieval method for the FY-3 VIRR with only two thermal IR (TIR) window channels is developed, referred to as FY-3 -WVS method. Some tests, including using MODIS data, numerical simulation for FY-3 sensor, the synchronous measurements for the radiative calibration of the FY-1D and FY-3 radiometers in Qinghai Lake, respectively, for the FY-3 -WVS algorithm have been carried out. All the results show that although the performance of FY-3 VIRR less well than other similar existing sensors, the RMSE in temperature is less than 1.0K.

Land Surface Temperature and Emissivity Estimation from MODIS Observations

Land surface temperature (LST) is a key variable for studying global or regional land surface processes, energy and water cycle, and thus, has important applications in various areas. LST retrieval, however, is a difficult subject and a challenge issue due to complex interactions between land surface and atmosphere. Based on the water vapor dependent (WVD) and the extended water vapor dependent (EWVD) algorithms, a new approach for separating and determining LST and land surface emissivity (LSE) using only two MODIS thermal infrared (TIR) channels is proposed. In the algorithm, both WVD and EWVD algorithms are used to get first guess estimates of LST and at-surface brightness temperature, which are used to determine LSE, first. Then, the LST is recalculated from the surface radiance combined with the estimated LSE. Compared with the MODIS LST and LSE products, the root mean square error (RMSE) of LST and LSE retrieved by our algorithm is 0.62K, and 0.008, respectively. The advantages of the proposed algorithm is discussed briefly. The preliminary results show that the new algorithm is able to provide an accurate estimation of LST and emissivity from MODIS data. And compared with the current MODIS algorithm, our algorithm is more easy to be implemented.

Hyperspectral Satellite Remote Sensing of Dust Aerosol Based on SVD Method

Satellite remote sensing of dust aerosol depth is quite significant for practical application. In this paper, airborne dust AOD is retrieved from the hyperspectral observed data of the Atmospheric Infra-Red Sounder (AIRS) by using Singular Value Decomposition (SVD) method which is first proposed by L Kuser in 2011. According to the analysis, 8.8-12 infrared observation can be used for dust aerosol retrieval. This method took advantage of the spectral shape of dust extinction and surface and atmospheric influence over the total 8.8–12μm window band. Though the proper linear combination of the singular vectors, dust signal was finally distinguish from the influence of surface emissivity and gas absorption. Then dust AOD of Beijing areas was retrieved to validate this method. As a result, the inversion by using SVD is good with ground-based observations of Aerosol Observation Network (AERONET) data, where their correlation coefficient is 0.9891. In contrast to the traditional physical methods, this method takes advantage of the statistics without losing the physical meaning.

THE development towards the optical lattice clock of Strontium atom in National Time Service Center

At present the optical clock with accuracy and stability achieves to 10-18 level, which could be the next generation of time and frequency standard. This paper gives an introduction of the progress of researching on the optical lattice clock of Strontium atom in NTSC (National Time Service Center). We realize the (5s5s)1S0—(5s5p)1P1 cooling (blue MOT) and (5s5s)1S0—(5s5p)3P1 cooling (red MOT) in succession for the optical lattice clock, and loading these cold atoms in a lattice composed by a magic wave-length is studied. By using our narrow line-width diode laser and the fiber femtosecond optical frequency comb, we precisely measure the absolute frequencies of the inter-combination transitions (5s5s)1S0—(5s5p)3P1 of four isotopes of Strontium referenced to an H maser.

Nonlinear Cross Prediction Analysis of Water Vapor Time Series with Fractal Interpolation

A nonlinear cross prediction method is taken to analyze change characteristics of water vapor time series. Fractal interpolation method is used to deal with remote sensing data. The cross prediction error is used to detect the sign of coming rainstorm, which forecast the occurrence of heavy rain, the experiment result shows the fractal interpolation is effective for preprocessing satellite remote sensing data.

The application of symbolic dynamics in rainstorm prediction

Rainstorm is one of the major natural disasters in the world and lead huge losses of national economy and peoples life and property every year. The prediction of rainstorms is very difficult using the current methods because of the atmospheric systems complexity and non-linearity. In recent years, nonlinear science has developed rapidly and nonlinear time series analysis has been widely used in many scientific and technological fields. Symbolic dynamics is a branch of nonlinear science and have gradually become a tool of time series analysis. In this paper the symbolic dynamics method was introduced in detail, the process including interpolation, de-noising, segmentation, symbolization, coding and statistical analysis. Using symbolic dynamics to explore storm event has some certain significance, the entropy curves of a large number of heavy rain events were analyzed and the entropy reached its minimum before most of the heavy rainfall. So this characteristic can be treated as the symptom the prediction of rainstorms. In this paper 158 global rainstorm events are used to test symbolic dynamics method. The results show that 107 rainstorms events appear obvious symptom and the prediction accuracy reach 67.7%. So it is valuable in monitoring and forecasting heavy rain events.

The Preliminary Analysis of Guizhou Short-Term Climate Change Characteristics Using the Information Theory

The regional climate change in Guizhou area is special and complex. In this study, the information theory was used for the nonlinear time series analysis of Guizhou short-term climate change. It was found that, the greatest impact element on the annual average air temperature was pressure, followed by sunshine duration, low cloud cover, etc., and the same in spring and autumn. The sunshine duration was the greatest impact element on the summer air temperature, followed by relative humidity, and low cloud cover, and so on. In winter, the effect order of meteorological elements on the surface air temperature was not very clear, relative humidity and sunshine duration were the two more important factors.

A Neural Network Based Algorithm for the Retrieval of Precipitable Water Vapor from MODIS Data

A neural network (NN) based algorithm for retrieval of precipitable water vapor (PWV) from the Moderate Resolution Imaging Spectroradiometer (MODIS) radiance is proposed. A multilayer feedforward neural network (MFNN) is selected, in which the at-sensor brightness temperature, the surface emissivity of MODIS chs. 31 and 32, and the land surface temperature (LST) are input variables, and PWV is the output variable. The input parameters for the MFNN are mainly based on the radiative transfer simulation with MODTRAN 4.0 code and the latest global assimilation data. The algorithm is applied to retrieval of the PWV over northeast area in china using MODIS data. Compared with the MODIS PWV products, the RMSE of the PWV retrieved by our algorithm is 0.45g/cm2. Furthermore, a comparison of our retrieval PWVs with radiosonde data is carried out. The results show that the MFNN-based retrieval algorithm for PWV is robust and efficient.