Abstract
Abstract. Due to the discretized nature of rain, the measurement of a continuous precipitation rate by disdrometers is subject to statistical sampling errors. Here, Monte Carlo simulations are employed to obtain the precision of rain detection and rate as a function of disdrometer collection area and compared with World Meteorological Organization guidelines for a 1 min sample interval and 95 % probability. To meet these requirements, simulations suggest that measurements of light rain with rain rates R ≤ 0.50 mm h−1 require a collection area of at least 6 cm × 6 cm, and for R = 1 mm h−1, the minimum collection area is 13 cm × 13 cm. For R = 0.01 mm h−1, a collection area of 2 cm × 2 cm is sufficient to detect a single drop. Simulations are compared with field measurements using a new hotplate device, the Differential Emissivity Imaging Disdrometer. The field results suggest an even larger plate may be required to meet the stated accuracy, likely in part due to non-Poissonian hydrometeor clustering.
Highlights
Ground-based precipitation sensors are commonly used to validate remotely sensed precipitation measurement systems, including satellite (TRMM), WSR-88D radar measurements (Kummerow et al, 2000; Fulton et al, 1998), and numerical weather prediction models (Colle et al, 2005) aimed at hydrology, agriculture, transportation, and recreation applications (WMO, 2018; Estévez et al, 2011; Campbell and Langevin, 1995; Brun et al, 1992)
Collection areas smaller than approximately 6 cm × 6 cm meet World Meteorological Organization (WMO) standards for R ≤ 0.50 mm h−1, but a collection area of over 10 cm × 10 cm is required for R > 1 mm h−1
The intersection between 95th and 5th percentile bounds and WMO accuracy criteria occurs in larger collection areas as R increases
Summary
Ground-based precipitation sensors are commonly used to validate remotely sensed precipitation measurement systems, including satellite (TRMM), WSR-88D radar measurements (Kummerow et al, 2000; Fulton et al, 1998), and numerical weather prediction models (Colle et al, 2005) aimed at hydrology, agriculture, transportation, and recreation applications (WMO, 2018; Estévez et al, 2011; Campbell and Langevin, 1995; Brun et al, 1992). They compared different co-located precipitation sensors and found large absolute and relative errors (32 %–44 %) for all instruments when precipitation rate R < 0.3 mm h−1, but the relative errors were approximately 10 % when R > 1.5 mm h−1 Despite these observations, they found that optical and hotplate disdrometers can more accurately detect light precipitation compared to weighing gauges, despite having comparatively small sampling areas, typically 50 cm, where the VRG101 weighing gauge sampling area is 400 cm. We employ a Monte Carlo approach (Liu et al, 2012, 2018; Jameson and Kostinski, 1999, 2001a, 2002) to stochastically generate raindrops based on canonical size distributions aimed at determining the minimum required disdrometer collection area and sampling frequency for precise measurement of precipitation rates between 0.01 and 10 mm h−1. The required square width W found in this work for the same parameters is 13 cm
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