Search Results

You are looking at 1 - 10 of 62 items for

  • Author or Editor: Lu Zhang x
  • Refine by Access: All Content x
Clear All Modify Search
Yinghui Lu and Fuqing Zhang

Abstract

Satellite-based hyperspectral radiometers usually have thousands of infrared channels that contain atmospheric state information with higher vertical resolution compared to observations from traditional sensors. However, the large numbers of channels can lead to computational burden in satellite data retrieval and assimilation. Furthermore, most of the channels are highly correlated and the pieces of independent information contained in the hyperspectral observations are usually much smaller than the number of channels. Principal component analysis (PCA) was used in this research to compress the observational information content contained in the Atmospheric Infrared Sounder (AIRS) channels to a few leading principal components (PCs). The corresponding PC scores were then assimilated into a PCA-based ensemble Kalman filter (EnKF) system. In this proof-of-concept study based on simulated observations, hyperspectral brightness temperatures were simulated using the atmospheric state vectors from convection-permitting ensemble simulations of Hurricane Harvey (2017) as input to the Community Radiative Transfer Model (CRTM). The PCs were derived from a preexisting training dataset of brightness temperatures calculated from convection-permitting simulation over a large domain in the Indian Ocean representing generic atmospheric conditions over tropical oceans. The EnKF increments from assimilating many individual measurements in the brightness temperature space were compared to the EnKF increments from assimilating significantly fewer numbers of leading PCs. Results showed that assimilating about 10–20 leading PCs could yield increments that were nearly indistinguishable to that from assimilating hyperspectral measurements from orders of magnitude larger number of hyperspectral channels.

Free access
Liang Ma, Guoping Zhang, and Er Lu

Abstract

A new classification scheme based on the gradient boosting decision tree (GBDT) algorithm is developed to improve the accuracy of rain area delineation for daytime, twilight, and nighttime modules using Advanced Himawari Imager on board Himawari-8 (AHI-8) geostationary satellite data and the U.S. Geological Survey digital elevation model data. The GBDT algorithm is able to efficiently manage the nonlinear relationships among high-dimensional data without being affected by overfitting problems. The new delineation module utilizes several features related to the physical variables, including cloud-top heights, cloud-top temperatures, cloud water paths, cloud phases, water vapor, temporal changes, and orographic variations. The scheme procedure is as follows. First, we perform extensive experiments to optimize the module parameters such that the equitable threat score (ETS) reaches its maximum value. Then, the GBDT-based modules are trained and classified with the optimum parameters. Finally, validation datasets are applied to test the true performance of the GBDT-based modules. The agreement between the estimations and observations of the ground-based rain gauges is verified. Results show that the ETS values of the GBDT-based modules are 0.42 for the daytime, 0.30 for the twilight period, and 0.32 for the nighttime. The cloud water path and cloud phase features make the most significant contributions to the modules. Comparisons drawn with the two probability-related methods show that our new scheme presents great advantages in terms of statistical scores on the overall performance.

Full access
Peng Lu, Hua Zhang, and Jiangnan Li

Abstract

A new scheme of water cloud optical properties is proposed for correlated k-distribution (CKD) models, in which the correlation in spectral distributions between the gaseous absorption coefficient and cloud optical properties is maintained. This is an extension of the CKD method from gas to cloud by dealing with the gas absorption coefficient and cloud optical properties in the same way.

Compared to the results of line-by-line benchmark calculations, the band-mean cloud optical property scheme can overestimate cloud solar heating rate, with a relative error over 30% in general. Also, the error in the flux at the top of the atmosphere can be up to 20 W m−2 at a solar zenith angle of 0°. However, the error is considerably reduced by applying the new proposed CKD cloud scheme. The physical explanation of the large error for the band-mean cloud scheme is the absence of a spectral correlation between the gaseous absorption coefficient and the cloud optical properties. The overestimation of the solar heating rate at the cloud-top layer could affect the moisture circulation and limit the growth of cloud. It is found that the error in the longwave cooling rate caused by the band-mean cloud scheme is very small. In the infrared, the local thermal emission strongly affects the spectral distribution of the radiative flux, which makes the correlation between the gaseous absorption coefficient and cloud optical properties very weak. Therefore, there is no obvious advantage in emphasizing the spectral correlation between gas and cloud.

Full access
Feng Lu, Xiaohu Zhang, and Jianmin Xu

Abstract

An automatic image navigation algorithm for Feng Yun 2 (FY2) spin-stabilized geosynchronous meteorological satellites was determined at the National Satellite Meteorological Center (NSMC) of the China Meteorological Administration (CMA). This paper derives the parameters and coordinate systems used in FY2 image navigation, with an emphasis on attitude and misalignment parameters. The solution to the navigation model does not depend on any landmark matching.

The time series dataset of the satellite orientation with respect to the center line of the earth’s disk contains information on the two components of the attitude (orientation of the satellite spin axis) and the roll component of the misalignment. With this information, the two attitude components can be solved simultaneously, expressed as declination and right ascension (with diurnal variation in the fixed earth coordinate system) and the roll component of the misalignment (with no diurnal variation).

In each spin cycle, the satellite views the sun and earth. The position of the sun is detected and used to align earth observation pixels in the scan line together with an angle subtended at the satellite by the sun and earth (β). With satellite position and attitude known, the β angle can be calculated and predicted with sufficient accuracy. Next, the image is assembled. Prediction of the β angle takes an important role in the image formation process, as imperfect β angle prediction may cause east–west shift and image deformation. In the image registration process of FY2, both the east–west shift and the image deformation are compensated for.

The above-mentioned solution to the navigation model requires accurate knowledge of astronomical parameters and coordinate systems. The orbital, attitude, misalignment, and β angle parameters are produced automatically and routinely without any manual operation. Image navigation accuracy for the FY2 geosynchronous meteorological satellite approaches 5 km at the subsatellite point (SSP).

Full access
Xu Zhang, Youyu Lu, and Keith R. Thompson

Abstract

Satellite observations of sea level and surface wind from the tropical Pacific Ocean, and their relationship to the Madden–Julian oscillation (MJO), are analyzed using a combination of statistical techniques and a simple, physically based model. Wavenumber–frequency analysis reveals that sea level variations at the equator contain prominent eastward-propagating signals as the intraseasonal Kelvin waves. The component of sea level variation that is coherent with the MJO (η MJO) is concentrated in a narrow strip along the equator between 150°E and 110°W. To explain the physical forcing of η MJO, the component of zonal wind stress that is coherent with the MJO is also calculated. It is shown that is strongest in the western Pacific, but the MJO accounts for a higher percentage of the wind variance in the central equatorial Pacific. A simple linear model of the Kelvin waves, based on a first-order wave equation forced by and with a linear damping term included, successfully reproduces η MJO. It is also shown that zonal wind variations to the east of the date line act to increase the apparent propagation speeds of the Kelvin waves.

Full access
Wilfried Brutsaert, Lei Cheng, and Lu Zhang

Abstract

A generalized implementation of the complementary principle was applied to estimate global land surface evaporation and its spatial distribution. The single parameter in the method was calibrated as a function of aridity index, mainly on the basis of runoff and precipitation data for 524 catchments in different parts of the world. The spatial distribution of annual evaporation from Earth’s land surfaces for 2001–13 was then calculated at a spatial resolution of 0.5°, by means of an available global net radiation dataset (commonly referred to as CERES SYN1deg-Day) and a global forcing dataset (referred to as CRU-NCEP v7) for near-surface temperature, humidity, wind speed, and air pressure. The results are shown to agree with reliable previous estimates by more elaborate methods. The global average evaporation for 2001–13 was found to be 472.65 mm a−1 or 36.96 W m−2. The present method should allow not only future updates but also retroactive historical analyses with routine data of net radiation, near-surface air temperature, humidity, wind speed, and precipitation; its main advantage is that the environmental aridity is deduced from atmospheric conditions and requires no knowledge of surface characteristics, such as soil moisture, vegetation, and terrain, which are highly variable and often difficult to quantify at larger spatial scales. Because they are strictly measurement based, the results can serve also as a reality check for different aspects of climate and related models.

Free access
Yunji Zhang, Xingchao Chen, and Yinghui Lu

Abstract

There are ongoing efforts to establish an ensemble data assimilation and prediction system for tropical cyclones based on the finite-volume cubed-sphere (FV3) dynamic core with the capability to assimilate satellite all-sky infrared and microwave observations. To complement the system developments and improve our understanding of the assimilation of all-sky infrared and microwave observations, this study assesses their potential impacts on the analysis of Hurricane Harvey (2017) through examinations of the structure and dynamics of the ensemble-based correlations as well as single observation data assimilation experiments, using an ensemble forecast generated by a global-to-regional nested FV3-based model. It is found that different infrared and microwave channels are sensitive to different types of hydrometeors within different layers of the atmosphere, and the correlations vanish beyond 200 km in the region covered by cloud or abundant hydrometeors. The spatial correlations between brightness temperatures and model states will adjust the structure and intensity of the hurricane in the model so that the simulated hurricane will better fit the “observed” brightness temperatures. In general, these results show how assimilating infrared and microwave together can improve the analyses of tropical cyclone intensity and structure, which may lead to improved intensity forecasts.

Restricted access
Biao Zhang, Yiru Lu, William Perrie, Guosheng Zhang, and Alexis Mouche

Abstract

We have developed C-band compact polarimetry geophysical model functions for RADARSAT Constellation Mission ocean surface wind speed retrieval. A total of 1594 RADARSAT-2 images acquired in quad-polarization SAR imaging mode were collocated with in situ buoy observations. This dataset is first used to simulate compact polarimetric data and to examine their dependencies on radar incidence angle and wind vectors. We find that right circular transmit, right circular receive (RR-pol) radar backscatters are less sensitive to incidence angles and wind directions but are more dependent on wind speeds, compared to right circular transmit, horizontal receive (RH-pol), right circular transmit, vertical receive (RV-pol), and right circular transmit, left circular receive (RL-pol). Subsequently, the matchup data pairs are used to derive the coefficients of the transfer functions for the proposed compact polarimetric geophysical model (CMOD) functions, and to validate the associated wind speed retrieval accuracy. Statistical comparisons show that the retrieved wind speeds from CMODRH, CMODRV, CMODRL, and CMODRR are in good agreement with buoy measurements, with root-mean-square errors of 1.38, 1.51, 1.47, and 1.25 m s−1, respectively. The results suggest that compact polarimetry is a good alternative to linear polarization for wind speed retrieval. CMODRR is more appropriate to retrieve high wind speeds than CMODRH, CMODRV or CMODRL.

Restricted access
Jingzhe Sun, Zhengyu Liu, Feiyu Lu, Weimin Zhang, and Shaoqing Zhang

Abstract

Recent studies proposed leading averaged coupled covariance (LACC) as an effective strongly coupled data assimilation (SCDA) method to improve the coupled state estimation over weakly coupled data assimilation (WCDA) in a coupled general circulation model (CGCM). This SCDA method, however, has been previously evaluated only in the perfect model scenario. Here, as a further step toward evaluating LACC for real world data assimilation, LACC is evaluated for the assimilation of reanalysis data in a CGCM. Several criteria are used to evaluate LACC against the benchmark WCDA. It is shown that despite significant model bias, LACC can improve the coupled state estimation over WCDA. Compared to WCDA, LACC increases the globally averaged anomaly correlation coefficients (ACCs) of sea surface temperature (SST) by 0.036 and atmosphere temperature at the bottom level (T s) by 0.058. However, there also exist regions where WCDA outperforms LACC. Although the reduction in the anomaly root-mean-square error (RMSE) is not as consistently clear as the increase in ACC, LACC can largely correct the biased model climatology.

Free access
Yongqiang Zhang, Francis H. S. Chiew, Lu Zhang, and Hongxia Li

Abstract

This paper explores the use of the Moderate Resolution Imaging Spectroradiometer (MODIS), mounted on the polar-orbiting Terra satellite, to determine leaf area index (LAI), and use actual evapotranspiration estimated using MODIS LAI data combined with the Penman–Monteith equation [remote sensing evapotranspiration (ERS)] in a lumped conceptual daily rainfall–runoff model. The model is a simplified version of the HYDROLOG (SIMHYD) model, which is used to estimate runoff in ungauged catchments. Two applications were explored: (i) the calibration of SIMHYD against both the observed streamflow and ERS, and (ii) the modification of SIMHYD to use MODIS LAI data directly. Data from 2001 to 2005 from 120 catchments in southeast Australia were used for the study. To assess the modeling results for ungauged catchments, optimized parameter values from the geographically nearest gauged catchment were used to model runoff in the ungauged catchment. The results indicate that the SIMHYD calibration against both the observed streamflow and ERS produced better simulations of daily and monthly runoff in ungauged catchments compared to the SIMHYD calibration against only the observed streamflow data, despite the modeling results being assessed solely against the observed streamflow data. The runoff simulations were even better for the modified SIMHYD model that used the MODIS LAI directly. It is likely that the use of other remotely sensed data (such as soil moisture) and smarter modification of rainfall–runoff models to use remotely sensed data directly can further improve the prediction of runoff in ungauged catchments.

Full access