Cloud base height (CBH) is a critical input to short-term solar forecasting algorithms, yet CBH measurements are difficult to obtain. Existing methods to detect CBH include radiosondes, ceilometers, and the stereographic method. However, these methods are deficient for intra-hour forecasting due to high costs or low temporal resolution. While satellite images could overcome these limitations, only the cloud top height can be determined from the thermal IR channel. We describe the integration of a cloud shadow speed sensor (CSS) with angular cloud speed from a sky imager to determine CBH. Furthermore, an improved methodology to determine cloud motion vectors from the CSS is presented, which offers lower noise and greater accuracy and stability than existing methods. Two months at the UC San Diego campus were used for validation against measurements from meteorological aerodrome reports (METAR) and an on-site ceilometer. Typical daily root mean square differences (RMSD) are 126 m which corresponds to 16.9% of the observed CBH. Normalized RMSD remains below 30% for all days. The daily bias is usually less than 80 m which suggests that the method is robust and that most of the RMSD is driven by short-term random fluctuations in CBH. Unlike sky image stereography the present method can be applied to measurements at a single site making it widely applicable.

Abstract. A camera model and associated automated calibration procedure for stationary daytime sky imaging cameras is presented. The specific modeling and calibration needs are motivated by remotely deployed cameras used to forecast solar power production where cameras point skyward and use 180° fisheye lenses. Sun position in the sky and on the image plane provides a simple and automated approach to calibration; special equipment or calibration patterns are not required. Sun position in the sky is modeled using a solar position algorithm (requiring latitude, longitude, altitude and time as inputs). Sun position on the image plane is detected using a simple image processing algorithm. The performance evaluation focuses on the calibration of a camera employing a fisheye lens with an equisolid angle projection, but the camera model is general enough to treat most fixed focal length, central, dioptric camera systems with a photo objective lens. Calibration errors scale with the noise level of the sun position measurement in the image plane, but the calibration is robust across a large range of noise in the sun position. Calibration performance on clear days ranged from 0.94 to 1.24 pixels root mean square error.

A method for retrieving cloud optical depth (τc) using a UCSD developed ground-based sky imager (USI) is presented. The radiance red-blue ratio (RRBR) method is motivated from the analysis of simulated images of various τc produced by a radiative transfer model (RTM). From these images the basic parameters affecting the radiance and red-blue ratio (RBR) of a pixel are identified as the solar zenith angle (θ0), τc, solar pixel angle/scattering angle (vs), and pixel zenith angle/view angle (vz). The effects of these parameters are described and the functions for radiance, Iλ (τc; θ0;vs;vz), and RBR.τc; θ0;vs; vz/ are retrieved from the RTM results. RBR, which is commonly used for cloud detection in sky images, provides non-unique solutions for τc, where RBR increases with τc up to about τc = 1 (depending on other parameters) and then decreases. Therefore, the RRBR algorithm uses the measured Imeasλ (vs;vz), in addition to RBRmeas (vs;vz), to obtain a unique solution for τc. The RRBR method is applied to images of liquid water clouds taken by a USI at the Oklahoma Atmospheric Radiation Measurement (ARM) program site over the course of 220 days and compared against measurements from a microwave radiometer (MWR) and output from the Min et al. (2003) method for overcast skies. τc values ranged from 0 to 80 with values over 80, being capped and registered as 80. A τc RMSE of 2.5 between the Min et al. (2003) method and the USI are observed. The MWR and USI have an RMSE of 2.2, which is well within the uncertainty of the MWR. The procedure developed here provides a foundation to test and develop other cloud detection algorithms.