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Map of the sites selected for the error estimate for solid precipitation measurements through precipitation gauges in the upper Valtellina and upper Valcamonica, in Northern Italy. 

Map of the sites selected for the error estimate for solid precipitation measurements through precipitation gauges in the upper Valtellina and upper Valcamonica, in Northern Italy. 

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Precipitation measurements by rain gauges are usually affected by a systematic underestimation, which can be larger in case of snowfall. The wind, disturbing the trajectory of the falling water droplets or snowflakes above the rain gauge, is the major source of error, but when tipping-bucket recording gauges are used, the induced evaporation due to...

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... each one of the sites shown in Figure 2 and listed in Table 1, selected from those that are still active in the area, freshly-fallen snow water equivalent (SWE) daily measurements were compared to daily precipitation data retrieved from rain gauges nearby (Figure 3). SWE data by depth and density measurements of freshly-fallen snow collected on snowboards are assumed to be reliable because they provide a direct estimate of the real precipitation fallen on the ground [15]. ...

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... The above findings highlight that gauge estimates are generally lower than plain reanalysis estimates in the mountains. It is important to take into account that automated measurements tend to underestimate the amount of precipitation, especially snow, depending on the type of gauges used and the wind speed (Grossi et al., 2017;Kochendorfer et al., 2022). Rasmussen et al. (2012) mentions errors from 20% to 50% for solid precipitation. ...
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... These two signals are correlated with a Spearman rank correlation of = 0.58, ( = 0.0001), which is satisfying considering the lower elevation of the meteorological station and the disturbing effect of measurement errors. In a recent study by Grossi et al. (2017) biases between 15% and 66% were observed in solid precipitations measured at rain gauges belonging to the same region. On the other hand, summer ablation increased steadily with time (i.e., it became more negative) as shown in Fig. 5b, with smaller oscillations around the moving average with respect to . ...
... Thus, snow accumulates in the funnel, whose storage capacity is not usually large, and measurement is delayed until it thaws. Likewise, in hail conditions, much of the precipitation is lost due to bouncing of the hail in the funnel [89][90][91]. ...
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Tipping bucket rain gauges (TBRs) continue to be one of the most widely used pieces of equipment for rainfall monitoring; they are frequently used for the calibration, validation, and downscaling of radar and remote sensing data, due to their major advantages—low cost, simplicity and low-energy consumption. Thus, many works have focused and continue to focus on their main disadvantage—measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks’ operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques, summarizing TBR measurement uncertainties, focusing on calibration and error reduction strategies, discussing the state of the art and providing future perspectives of the technology.
... Thus, 9 of 21 snow accumulates in the funnel, whose storage capacity is not usually large, and measurement is delayed until it thaws. Likewise, in hail conditions, much of the precipitation is lost due to bouncing of the hail in the funnel [90][91][92]. ...
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Tipping bucket rain gauges (TBRs) have been, and apparently will continue to be one of the most widely used pieces of equipment for rainfall monitoring, being frequently used for the calibration, validation and downscaling of radar and remote sensing data, due to their major advantages–low cost, simplicity, and low energy consumption. Thus, many works have focused and continue to focus on their main disadvantage–measurement biases (mainly in wind and mechanical underestimations). However, despite arduous scientific effort, calibration methodologies are not frequently implemented by monitoring networks operators or data users, propagating bias in databases and in the different applications of such data, causing uncertainty in the modeling, management, and forecasting in hydrological research, mainly due to a lack of knowledge. Within this context, this work presents a review of the scientific advances in TBR measurement uncertainties, calibration, and error reduction strategies from a hydrological point of view, by describing different rainfall monitoring techniques in Section 2, summarizing TBR measurement uncertainties in Section 3, focusing on calibration, and error reduction strategies in Section 4, a discussion and perspectives in Section 5, and conclusions in Section 6, providing an overview of the of the state of the art and future perspectives of the technology.
... In particular, products such as IMERG, MERRA-2, or ERA sometimes show a certain underestimation of the snowpack, which makes these instruments unreliable for the purposes of a detailed analysis of snow depths [15,16]. Similarly, it is also complex for heated rain gauges to be able to count the snow water equivalent (SWE), since there are interactions with the wind that can generate underestimates in quantity when not properly shielded by special devices [17][18][19]. Precisely in relation to the possibility of snowfall being blown away by the wind, there are studies evaluating the preferential accumulation of wind-borne snow in mountain or glacial environments [20]. Because of these problems in accurately counting snow, recent efforts are being made to create models that can simulate snow cover and in some cases, predict it in relation to other climatic variables [21]. ...
... In this case, on the other hand, data were obtained from weather stations equipped with sonar to assess the depth of the snow and from manual weather stations, where the measurements were taken in the presence of the operator. Data from heated rain gauges were deliberately discarded because they are very often inaccurate due to the strong winds in a mountain environment, which generate significant underestimates when counting the snowpack thickness [17,27]. Moreover, in this part of the Apennines in central Italy, detailed snow cover studies had never been carried out, given the fragmentary and scarce data. ...
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... Snow measuring systems based on the quantification of precipitation must have heated systems, producing evaporation losses when precipitation is weak. In addition, strong winds decrease the snow collected in snow gauges when wind shields are not installed, causing losses of ~20%-50% (Grossi et al., 2017;Masuda et al., 2019;Rasmussen et al., 2012). Similar values have been found in the Cantabrian Mountains, where the underestimation of snow precipitation using the Spanish Meteorological Agency (AEMET) network is similar to that in the Pyrenees, but less than those in other areas of the Iberian Peninsula (Buisán et al., 2017(Buisán et al., , 2022. ...
... Another source of uncertainties is the measurement of precipitation itself since wind and evaporation can lead to an underestimation of precipitation (Rodda and Dixon, 2012;Grossi et al., 2017). However, the errors are generally higher for solid precipitation than for liquid precipitation and largest at windy sites (above 50 %) (Rodda and Dixon, 2012;Grossi et al., 2017). ...
... Another source of uncertainties is the measurement of precipitation itself since wind and evaporation can lead to an underestimation of precipitation (Rodda and Dixon, 2012;Grossi et al., 2017). However, the errors are generally higher for solid precipitation than for liquid precipitation and largest at windy sites (above 50 %) (Rodda and Dixon, 2012;Grossi et al., 2017). Since we use measurements from AWS2 in the valley, snowfall is less frequent. ...
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... mountainous areas), which may cause erroneous performance assessments especially in cases of localized and severe events, and a too low fraction of heated rain-gauges, which may cause an underestimated and delayed detection of precipitation in case of snowfalls (e.g. Grossi et al., 2017;Mair et al., 2016). On the other hand, surface temperature T A B L E 1 RMSE and correlation index (R) computed for the three parameterizations of the soil temperature at 12.00 m depth against the measurements in Potsdam station, Germany sensors amount to about 1000 and include data from the high-resolution regional network Dewetra as well as from the SYNOP stations (that are not assimilated within SPHERA). ...
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... Because of slow falling, snow hydrometers are more susceptible to deflection by wind-induced turbulence around the gauge, making snowfall measurements prone to large systematic errors (Rasmussen et al., 2012). In windy conditions, the underestimation of snowfall accumulation frequently ranges from 20 % to 50 % or even higher and additionally depends on other variables, such as exposure and the type of rain gauge (Rasmussen et al., 2012;Buisán et al., 2017;Grossi et al., 2017). Other systematic error sources are related to physical processes, such as evaporation from a bucket, wetting, and splashing. ...
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... On the other hand, (Berndt andHaberlandt, 2018, Ly et al., 2011) argue that, topographical impact was indispensable for only temperature reconstruction at all temporal resolutions and station densities, but it was less clear for daily to monthly P. Furthermore, all spatial interpolation techniques can perform poorly in regions with insufficient high-elevation data, due to inaccurate estimation of local lapse rates (Ruelland, 2020). Considering all above reviews, it can be concluded that the basin average observed P estimates are underestimated owing to the under-catch P and unevenly distributed lowdensity (approx. 1 gauge/2235 km 2 ) climate station network especially at high altitude areas which are the main active parts of the basins (Azmat, 2015, Grossi et al., 2017, Ranzi et al., 2003, Winiger et al., 2005. Moreover, the P increases with elevation up to 4500 m in Himalaya mountainous range and then start to decrease (Immerzeel et al., 2015) whereas the climatic stations are installed up to 3000 m elevation. ...
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