Toward the camera rain gauge

Abstract

We propose a novel technique based on the quantitative detection of rain intensity from images, i.e., from pictures taken in rainy conditions. The method is fully analytical and based on the fundamentals of camera optics. A rigorous statistical framing of the technique allows one to obtain the rain rate estimates in terms of expected values and associated uncertainty. We show that the method can be profitably applied to real rain events, and we obtain promising results with errors of the order of 625%. A precise quantification of the method's accuracy will require a more systematic and long-term comparison with benchmark measures. The significant step forward with respect to standard rain gauges resides in the possibility to retrieve measures at very high temporal resolution (e.g., 30 measures per minute) at a very low cost. Perspective applications include the possibility to dramatically increase the spatial density of rain observations by exporting the technique to crowdsourced pictures of rain acquired with cameras and smartphones.

Similarities and Improvements of GPM Dual-Frequency Precipitation Radar (DPR) upon TRMM Precipitation Radar (PR) in Global Precipitation Rate Estimation, Type Classification and Vertical Profiling

“…NASA's Jet Propulsion Laboratory's (JPL's) new mission, RainCube, takes advantages of a number of CubeSat satellites and pairs of low-cost Ka band radar system to achieve a more frequent earth observation with higher temporal and spatial resolution [78,79]. Allamano et al have explored the use of mobile video and imagery to capture and analyze precipitation events, showing that crowd-source projects have a promising future in earth observation [79,80].…”

Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras

“…With the distance s ( i ) calculated, the diameter D ( i ) and falling speed v ( i ) of the i th raindrop can be determined via equations and , respectively. Accordingly, the rainfall intensity R (mm/hr) can be estimated based on the D ( i ) and v ( i ) for all the identified rain streaks within the DOF, as well as the density of raindrops in the sampling volume (Allamano et al, ). However, not all rain streaks can be precisely identified; therefore, the derived number and size of raindrops involve an appreciable uncertainty.…”

“…The focal length and F‐number of the lens are 4 mm and 2.2, respectively. Based on preliminary tests and previous studies (Allamano et al, ; Dong et al, ), the focus distance was fixed to 1.0 m to achieve an appropriate DoF, resulting in a near‐focus distance of s n = 0.57 m and a far‐focus distance of s f = 4.29 m. The frame rate was configured to capture one frame per second to achieve a balance between the computational costs of processing and the temporal resolution of the estimated rainfall intensity. The camera is rotatable such that three different backgrounds can be examined (see Figure ).…”

“…Allamano et al () proposed a framework to estimate quantitative and analytical rainfall intensity from camera images based on physical optics from a hydrological perspective. In that study, rain streaks were identified using the technique introduced by Garg and Nayar (), which considers the thresholded grayscale changes in frame triplets as rain streaks.…”

Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data

“…Following Yang and Ng (), in this present study, we consider two forms of crowdsourced data: (i) scattered observations made by individuals using personal smartphones, and (ii) fixed‐point observations made using fixed sensors such as surveillance cameras. The two data types have the potential to provide large volumes of instantaneous rainfall estimates at acceptable levels of accuracy (Allamano et al, ) and are what is meant by crowdsourced data in our analysis.…”