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A procedure to adjust rainfall intensity (RI) measurements to account for the wind-induced measurement bias of traditional catching-type gauges is proposed and demonstrated with an application to a suitable case study. The objective is to demonstrate that adjustment curves derived from numerical simulations and disdrometer measurements allow for a posteriori correction of rainfall time series based on the wind velocity measurements alone. To quantify the impact of wind on long-term records, 1 min RI measurements from a cylindrical tipping-bucket rain gauge installed at the Hong Kong Observatory are adjusted. Catch ratios derived from the instrument's aerodynamic behaviour under varying wind speed and drop size combinations are obtained by fitting computational fluid-dynamic simulation results already available in the literature. Co-located high-resolution wind speed measurements from a cup and vane sensor and drop size distribution measurements from an optical video disdrometer (the 2DVD or two-dimensional video disdrometer) are used to infer the collection efficiency of the gauge as a function of wind speed and RI alone and to adjust raw data from a 4-year dataset (2018–2021) of 1 min RI measurements. Due to the specific local climatology, where strong wind is often associated with intense precipitation, 80 % of the dataset adjustments are limited to 4 % of the observed RI values. This, however, results in a significant amount of available freshwater resources that would be missing from the calculated hydrological and water management budget of the region should the adjustments be neglected. This work raises the need to quantify the impact of the wind-induced bias at other sites where disdrometer data support a characterisation of the relationship between the drop size distribution and the measured RI. Depending on the local rain and wind climatology, the correction may account for a significant portion of the annual rainfall amount. [ABSTRACT FROM AUTHOR]

A procedure to adjust rainfall intensity (RI) measurements to account for the wind-induced measurement bias of traditional catching-type gauges is proposed and demonstrated with application to a suitable case study. The objective is to demonstrate that adjustment curves derived from numerical simulation and disdrometer measurements allows for a-posteriori correction of rainfall time series based on the wind velocity measurements alone. One-minute RI measurements from a cylindrical tipping-bucket rain gauge installed at the Hong Kong Observatory are adjusted to quantify the impact of wind on long-term records. Catch ratios deriving from the instrument aerodynamic behaviour under varying wind speed and drop size combinations are obtained by fitting computational fluid-dynamic simulation results already available in the literature. Colocated high-resolution wind speed measurements from a cup and vane sensor and drop size distribution measurements from an optical video disdrometer (the 2DVD) are used to infer the collection efficiency of the gauge as a function of wind speed and RI alone, and to adjust raw data from a four-year dataset (2018-2021) of one-minute RI measurements. Due to the specific local climatology, where strong wind is often associated with intense precipitation, adjustments are limited to 4% of the RI values in 80% of the dataset. This however results in a significant amount of available freshwater resources that would be missing from the calculated hydrological and water management budget of the region should the adjustments be neglected. This work raises the need of quantifying the impact of the wind-induced bias in other sites where disdrometer data support characterizing the relationship between the drop size distribution and the measured RI. Depending on the local rain and wind climatology the correction may account for a significant portion of the annual rainfall amount. [ABSTRACT FROM AUTHOR]

Transfer functions are generally used to adjust for the wind-induced undercatch of solid precipitation measurements. These functions are derived based on the variation of the collection efficiency with wind speed for a particular type of gauge, either using field experiments or based on numerical simulation. Most studies use the wind speed alone, while others also include surface air temperature and/or precipitation type to try to reduce the scatter of the residuals at a given wind speed. In this study, we propose the use of the measured precipitation intensity to improve the effectiveness of the transfer function. This is achieved by applying optimized curve fitting to field measurements from the Marshall field-test site (CO, USA). The use of a non-gradient optimization algorithm ensures optimal binning of experimental data according to the parameter under test. The results reveal that using precipitation intensity as an explanatory variable significantly reduce the scatter of the residuals. The scatter reduction as indicated by the Root Mean Square Error (RMSE) is confirmed by the analysis of the recent quality controlled data from the WMO/SPICE campaign, showing that this approach can be applied to a variety of locations and catching-type gauges. We demonstrate the physical basis of the relationship between the collection efficiency and the measured precipitation intensity, due to the correlation of large particles with high intensities, by conducting a Computational Fluid-Dynamics (CFD) simulation. We use a Reynolds Averaged Navier-Stokes SST k-ω model coupled with a Lagrangian particle-tracking model. Results validate the hypothesis of using the measured precipitation intensity as a key parameter to improve the correction of wind-induced undercatch. Findings have the potential to improve operational measurements since no additional instrument other than a wind sensor is required to apply the correction. This improves the accuracy of precipitation measurements without the additional cost of ancillary instruments such as particle counters. [ABSTRACT FROM AUTHOR]

A number of rain events recorded from May to September 2013 at the Hong Kong International Airport (HKIA) have been selected to investigate the performance of post-processing algorithms used to calculate the Rainfall Intensity (RI) from Tipping-Bucket Rain Gauges (TBRGs). We assumed a drop counter catching-type gauge as a working reference and compared rainfall intensity measurements with two calibrated TBRGs operated at a time resolution of 1 min. The two TBRGs differ in their internal mechanics, one being a traditional single-layer dual-bucket assembly, while the other has two layers of buckets. The drop counter gauge operates at a time resolution of 10 s, while the time of tipping is recorded for the two TBRGs. The post-processing algorithms employed for the two TBRGs are based on the assumption that the tip volume is uniformly distributed over the inter-tip period. A series of data of an ideal TBRG is reconstructed using the virtual time of tipping derived from the drop counter data. From the comparison between the ideal gauge and the measurements from the two real TBRGs the performance of different post-processing and correction algorithms are statistically evaluated over the set of recorded rain events. The improvement obtained by adopting the inter-tip time algorithm in the calculation of the RI is confirmed. However, by comparing the performance of the real and ideal TBRGs, the beneficial effect of the inter-tip algorithm is shown to be relevant for the mid-low range of rainfall intensity values (where the sampling errors prevail), while its role vanishes with increasing the RI, in the range where the mechanical errors prevail. [ABSTRACT FROM AUTHOR]

This presentation describes the results obtained by the application of an innovative environmental monitoring technique able to estimate rainfall intensity in real time by processing the satellite microwave link (SML) signal.The field experimentation performed in the city of Genoa (Italy) between November 2016 and March 2019 was aimed to evaluate the performance of a new rainfall monitoring technique based on the exploitation of SML and the use of simple low power sensors (denominated SRS) to measure the electromagnetic field attenuation. The satellite that has been used during our work, Turksat 42° E, belongs to the plethora of satellites operating for television and radio channel broadcasting. The analysis took advantage of the corrected reference measurements made by two calibrated TBRG sensors and three SRS sensors installed at convenient locations. The dynamic calibration of the rain gauges was carried out at the WMO-CIMO Lead Centre "B.Castelli" on Precipitation Intensity by using an automatic calibration rig and the measurements have been processed with advanced algorithms to reduce counting errors. The experimental setup allowed a full characterization of the microwave signal trends as a function of different precipitation. Overall, the results showed that the sensitivity of the microwave field measured by the SRS sensors to the reference rain observation has been confirmed in terms of the linear correlation. The adoption of a generalized electromagnetic model, such as ITU-R P.838, can be used to compute rainfall measurement starting from SML signal attenuation. The comparison between the rainfall amounts measured over a 10-min time interval showed a variable level of agreement between the point-scale observation of the TBRGs and the SRS sensors. The field research undertaken in this analysis supports preliminary indications that the SML technique implemented by SRS can be used to provide information on the average rainfall amounts measured along the rainy portion of the link path. In that regard, a crucial information is represented by the altitude of the troposphere melting layer that should be known in order to implement the SML technique to monitor the precipitation field in real-time.References:Colli, M., Stagnaro, M., Caridi, A., Lanza, L. G., Randazzo, A., Pastorino, M.,Caviglia, D. D., Delucchi, A., 2018. A Field Assessment of a Rain Estimation System Based on Satellite-to-Earth Microwave Links. IEEE Transactions on Geoscience and Remote Sensing, final manuscript accepted, published online 2018. [ABSTRACT FROM AUTHOR]

Catching-type precipitation gauges are affected by wind-induced undercatch due to the modification of the airflow around the gauge body. The wind speed-up and updraft above the gauge orifice induces variations of the particle trajectories and, in some cases, leads them out of the measuring area; this produces an underestimation in precipitation measurements. The aerodynamic behaviour depends on the gauge geometry and impacts on the hydrometeors trajectory and on the resulting collection performance.This study presents the results of the experiments conducted jointly by the University of Genoa and Politecnico di Milano (POLIMI) in the wind tunnel facility available at POLIMI within the Italian project PRIN 20154WX5NA "Reconciling precipitation with runoff: the role of understated measurement biases in the modelling of hydrological processes". The objective is to quantify the effect of the aerodynamics on the particle trajectories above the gauge orifice of three different precipitation gauge shapes. The cylindrical and "chimney" shapes, typical of most common catching type gauges, were tested as well as the performance of an aerodynamic gauge with inverted conical shape. Water droplets were generated above and upstream of the gauge collector using a calibrated nozzle and a precision dispensing pump. A special backlight system was prepared in order to ensure the capturing of the drop trajectories while traveling close to the collector by means of a high-speed camera (maximum speed in full resolution 600 frames per second, maximum frame rate 204100 fps), equipped with 1600x1600 CMOS sensor and optical lens The size of the dispensed droplets and the associated velocity were measured for different wind speed conditions. Trajectories were detected at 10 and 1000 frames per second. The results are presented in terms of deviations of the drop trajectory against the undisturbed one for each geometry and wind speed. [ABSTRACT FROM AUTHOR]