Since more than a decade, several instruments using narrow field of view radiometers and applying time series, snapshots and scanning techniques have been developed to monitor down welling long wave radiations and by consequence cloud cover intensity. For regular monitoring of cloud cover, in atmospheric measurement networks the time series technique has already been used with ceilometric data and now with narrow field of view (NFOV) radiometers. In order to determine the best set of data for sampling frequency, zenith angle and also field of view, we built a numerical simulator calculating interactions between a measurement grid in the approximation of the parallel plan model and binarized cloud fields generated by the "Larges Eddies Simulation" method. We will present results obtained with various sets of radiometers with different geometries facing to cloud fields simulating a wide range of broken clouds conditions. As a conclusion, we provide recommendations for positioning and feature of NFOV radiometers and finally we will discuss potential improvements of these simulation techniques.
Recently developed, the Cloud Infrared Radiometer CIR-7, operates 7 infrared sensors, each with a 6-degree field of view, and spectral range 8-14 μm. The sensors are mounted on a semi-circular band at angles 0, 12, 24, 36, 48, 60 and 72°. A "hemispherical" mosaic of 181 brightness temperature measurements centered on the zenith is obtained by the rotation of the band around the vertical axis, performing 30 scans, every 12° from 0° (North) to 348 °.
We present an algorithm that utilizes the brightness temperatures from the CIR-7 given the precipitable water vapor amounts and vertical profiles of the thermodynamic state of the atmosphere from independent measurements. It estimates the total cloud amount, the amount of low, middle and high clouds, maps out the spatial distribution of the cloud field and determines the vertical distribution of the clouds by computing cloud base heights.
The algorithm is validated through comparisons with well studied ground-based and satellite retrieval techniques. Initial analyses show good cloud amount assessment and spatial mapping abilities. The estimated mean absolute cloud amount difference for day time is 12.3% when compared to the amount of opaque clouds derived with a total sky imager (TSI). For night time, this difference is 19.4% comparing to the effective cloud fraction derived with an atmospheric emitted radiance interferometer (AERI). The vertical distribution understanding is currently limited; however, the amounts of low, middle and high clouds could be determined and studied further.