Influence of Wind-Induced Effects on Laser Disdrometer Measurements: Analysis and Compensation Strategies

Nowadays, laser disdrometers constitute a very appealing tool for measuring surface precipitation properties, by virtue of their capability to estimate not only the rainfall amount and intensity, but also the number, the size and the velocity of falling drops. However, disdrometric measures are affected by various sources of error being some of them related to environmental conditions. This work presents an assessment of Thies Clima laser disdrometer performance with a focus on the relationship between wind and the accuracy of the disdrometer output products. The 10-min average rainfall rate and total rainfall accumulation obtained by the disdrometer are systematically compared with the collocated measures of a standard tipping bucket rain gauge, the FAK010AA sensor, in terms of familiar statistical scores. A total of 42 rainy events, collected in a mountainous site of Southern Italy (Montevergine observatory), are used to support our analysis. The results show that the introduction of a new adaptive filtering in the disdrometric data processing can reduce the impact of sampling errors due to strong winds and heavy rain conditions. From a quantitative perspective, the novel filtering procedure improves by 8% the precipitation estimates with respect to the standard approach widely used in the literature. A deeper examination revealed that the signature of wind speed on raw velocity-diameter spectrographs gradually emerges with the rise of wind strength, thus causing a progressive increase of the wrongly allocated hydrometeors (which reaches 70% for wind speed greater than 8 m s−1). With the aid of reference rain-gauge rainfall data, we designed a second simple methodology that makes use of a correction factor to mitigate the wind-induced bias in disdrometric rainfall estimates. The resulting correction factor could be applied as an alternative to the adaptive filtering suggested by this study and may be of practical use when dealing with disdrometric data processing.

[1]  Terry Lucke,et al.  Rain Drop Measurement Techniques: A Review , 2016 .

[2]  Santiago Beguería,et al.  Comparison of precipitation measurements by OTT Parsivel 2 and Thies LPM optical disdrometers , 2017 .

[3]  A. Macke,et al.  Measurement of solid precipitation with an optical disdrometer , 2007 .

[4]  T. Cochrane,et al.  Comparison of three types of laser optical disdrometers under natural rainfall conditions , 2020, Hydrological sciences journal = Journal des sciences hydrologiques.

[5]  A. Taskinen,et al.  Operational correction of daily precipitation measurements in Finland , 2016 .

[6]  O. Rodriguez-Hernandez,et al.  Wind Turbulence Intensity at La Ventosa, Mexico: A Comparative Study with the IEC61400 Standards , 2018, Energies.

[7]  Witold F. Krajewski,et al.  Assessment of the Thies optical disdrometer performance , 2011 .

[8]  R. Fraile,et al.  Aerosol size distribution in precipitation events in León, Spain , 2010 .

[9]  Ground validation of oceanic snowfall detection in satellite climatologies during LOFZY , 2010 .

[10]  Witold F. Krajewski,et al.  Daily estimates of rainfall, water runoff, and soil erosion in Iowa , 2006 .

[11]  L. Lanza,et al.  Non-parametric analysis of one-minute rain intensity measurements from the WMO Field Intercomparison , 2012 .

[12]  Matthias Wächter,et al.  Characterization of wind turbulence by higher‐order statistics , 2012 .

[13]  Alexis Berne,et al.  Correction of raindrop size distributions measured by Parsivel disdrometers, using a two-dimensional video disdrometer as a reference , 2014 .

[14]  Luca G. Lanza,et al.  Investigation of the Wind-Induced Airflow Pattern Near the Thies LPM Precipitation Gauge , 2021, Sensors.

[15]  F. Rubel,et al.  Correction of Daily Rain Gauge Measurements in the Baltic Sea Drainage Basin , 1999 .

[16]  D. Harrison,et al.  The Disdrometer Verification Network (DiVeN): a UK network of laser precipitation instruments , 2019, Atmospheric Measurement Techniques.

[17]  M. Stoffel,et al.  Automated precipitation monitoring with the Thies disdrometer: biases and ways for improvement , 2020 .

[18]  Road Weather Information System in Finland , 2001 .

[19]  Two months of disdrometer data in the Paris area , 2018 .

[20]  Jinfu Liu,et al.  The analysis of turbulence intensity based on wind speed data in onshore wind farms , 2018, Renewable Energy.

[21]  L. Lanza,et al.  Parameterization of the Collection Efficiency of a Cylindrical Catching-Type Rain Gauge Based on Rainfall Intensity , 2020, Water.

[22]  Antonio Martinez-Millana,et al.  Disdrometer Performance Optimization for Use in Urban Settings Based on the Parameters that Affect the Measurements , 2020, Symmetry.

[23]  E. Gorgucci,et al.  Influence of Disdrometer Type on Weather Radar Algorithms from Measured DSD: Application to Italian Climatology , 2018, Atmosphere.

[24]  Mario Montopoli,et al.  Validation of GPM Rainfall and Drop Size Distribution Products through Disdrometers in Italy , 2021, Remote. Sens..

[25]  Haonan Chen,et al.  Statistical characteristics of raindrop size distribution during rainy seasons in the Beijing urban area and implications for radar rainfall estimation , 2019, Hydrology and Earth System Sciences.