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  • Author or Editor: Daren Lu x
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Juan Huo and Daren Lu

Abstract

Naked-eye observation of cloud cover has widely resisted automation. Replacement of human observation by instruments is an inexorable trend for the development of ground-based macroscopic cloud observation. In this paper, cloud covers from an all-sky imager (ASI) are compared with those from a meteorological observer (MO) through field experiments performed at three sites in China. The correlation coefficient between ASI and MO is 0.77 for all cases. The ASI cloud fractions have great agreement with MO for clear sky, overcast sky, and sky loaded with low- and middle-level clouds. About 78% of the ASI cases had deviations between ±1 tenth compared to MO cloud cover. High-level cloud (or aerosol) is the main reason causing this difference. It is partially due to MO, who takes aerosol as high, thin cloud. Another reason might be that ASI made a wrong estimation for high-level cloud (or aerosol) because of its detector and the cloud-determination algorithm. Distinguishing high, thin cloud from aerosol is a challenge, and is the main problem that needs to be resolved for future developments of ASI. A new, improved method is discussed at the end of this paper.

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Juan Huo and Daren Lu

Abstract

The threshold method is commonly used to determine cloud in a sky image. This paper evaluates the method by numerical simulation and shows that the aerosol optical depth (AOD) is a key factor that influences the accuracy. Particularly when the visibility is low, a single threshold method is inappropriate. To improve the accuracy of cloud determination from low-visibility sky images, an integrated cloud-determination algorithm is presented that is based on the fast Fourier transform, symmetrical image features, and threshold methods. The preliminary comparison tests show that the new integrated method improves the ability to determine cloud under lower-visibility conditions.

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Ling Zhang, Daren Lu, Shu Duan, and Jinli Liu

Abstract

An observational method has been proposed to sample radar echo with high range resolutions using a ground-based meteorological radar. Utilizing this method, the rainfall echo data with a high range resolution of 125 m was obtained by using an X-band meteorological radar. The analysis of rain nonuniformity strength using these high-resolution radar data shows that the nonuniformity is significant and, even in an instantaneous field of view (IFOV) of 1 km, the reflectivity excursion above 10 dB is common. The simulation of the nonuniform beam filling (NUBF) error of the path-integrated attenuation (PIA) measured by the spaceborne radar has been also implemented using these data. The results show that the PIA encounters mainly underestimation and cannot be neglected; even in 0.5-km IFOV the underestimation can reach up to 50%. The correlation analyses show that the relative PIA error and the standard deviation of rain rate have a power-law relationship with quite good correlation, which might be used to partially correct this error. The simulation also shows that it is very important to use the high-resolution data in studying the NUBF error of the next-generation spaceborne radar with a higher across-beam resolution (e.g., below 3 km).

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Jinhuan Qiu, Xiangao Xia, Jianghui Bai, Pucai Wang, Xuemei Zong, and Daren Lu

Abstract

A method is proposed to simultaneously calibrate shortwave (0.3–4 μm) global, direct, and scattering solar irradiance (GSI, DSI, and SSI, respectively) measurements. The method uses the World Radiation Reference (WRR) as a calibration standard and on-site radiation measurements as inputs. Two simple but effective techniques are used in the calibration. The first is to scale SSI and GSI detection sensitivities under overcast skies, which is based on the assumption that SSI should be equal to GSI if DSI is completely scattered and absorbed. The second is a new method to retrieve aerosol optical thickness (AOT), using the ratio of horizontal DSI (HDSI) to GSI measurements under clear and clean conditions. Thereafter, retrieved AOTs are used to drive a radiative transfer model to calculate atmospheric transmittance and then a ratio of GSI to the transmittance. Deviation of this ratio to the WRR is regarded as an indicator of GSI uncertainty, and the calibration transfer coefficient is derived as the WRR ratio. The method is applied to calibrate radiation measurements at Xianghe, China, during 2005. It is estimated from the derived transfer coefficients on 36 clear and clean days that uncertainties of DSI, GSI, and SSI measurements are within −4.0% to 2.9%, −5.9% to 2.4%, and −6.1% to 4.9%, respectively. The calibration is further validated based on comparisons of AOT at 750 nm retrieved from HDSI/GSI to Aerosol Robotic Network (AERONET) AOT products. The maximum deviation between two AOT products is 0.026. The unique advantage of this method lies in its potential applications in correcting historic radiation measurements and monitoring radiometer performance.

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