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Figure 6. Mean raindrop size spectra for liquid and solid precipitation groups (a,c) and mean particle fall speed as a function of particle diameter (b,d). The dashed line refers to measured DSD, while the continuous line refers to corrected DSDs. For better visualization, raindrop size spectra values for D < 1.5 mm are zoomed in the inset plot.

Figure 6 Mean raindrop size spectra for liquid and solid precipitation groups (a,c) and mean particle fall speed as a function of particle diameter (b,d). The dashed line refers to measured DSD, while the continuous line refers to corrected DSDs. For better visualization, raindrop size spectra values for D < 1.5 mm are zoomed in the inset plot.

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Figure 1. (a) Map of study area respect to city of Quito and the Ecuadorian Andean mountain range. (b) Topography of Antisana massif and locations of AWS PRAA, AWS ORE and P8 rain gauge. The Antisana icecap outline was taken from Basantes-Serrano [22]. The elevation was presented in meters above sea level (m a.s.l). (c) Photography of AWS PRAA. (d) Disdrometer Parsivel OTT. (e) Pluviograph Texas electronics TE 525WS with rubber funnel used as a reference for liquid precipitation under low wind speed conditions. To analyze the impact of the wind speed on the measurements of solid and liquid precipitation, a Parsivel OTT? disdrometer was installed near the glacier of Antisana volcano in Ecuador at 4730 m above sea level (m asl) (Figure 1). Disdrometers are a practical alternative due to the low maintenance required, including for harsh conditions. Among the advantages of this device, are: (1) it avoids contact with particles due to its optical method of measurement [16,17]; (2) due to its sensitivity, it can measure events with precipitation less than 0.1 mm h7! witha high temporal resolution [18,19]; (3) it offers the possibility of detecting the type of precipitation [16,20]. These characteristics make the gathered data a valuable information source about the fine-scale structure of precipitation which is currently limited to only the paramo zone (high altitude wet grassland) around 3780 m asl [21].
Figure 1. (a) Map of study area respect to city of Quito and the Ecuadorian Andean mountain range. (b) Topography of Antisana massif and locations of AWS PRAA, AWS ORE and P8 rain gauge. The Antisana icecap outline was taken from Basantes-Serrano [22]. The elevation was presented in meters above sea level (m a.s.l). (c) Photography of AWS PRAA. (d) Disdrometer Parsivel OTT. (e) Pluviograph Texas electronics TE 525WS with rubber funnel used as a reference for liquid precipitation under low wind speed conditions. To analyze the impact of the wind speed on the measurements of solid and liquid precipitation, a Parsivel OTT? disdrometer was installed near the glacier of Antisana volcano in Ecuador at 4730 m above sea level (m asl) (Figure 1). Disdrometers are a practical alternative due to the low maintenance required, including for harsh conditions. Among the advantages of this device, are: (1) it avoids contact with particles due to its optical method of measurement [16,17]; (2) due to its sensitivity, it can measure events with precipitation less than 0.1 mm h7! witha high temporal resolution [18,19]; (3) it offers the possibility of detecting the type of precipitation [16,20]. These characteristics make the gathered data a valuable information source about the fine-scale structure of precipitation which is currently limited to only the paramo zone (high altitude wet grassland) around 3780 m asl [21].
Table 1. Summary of 15 min data series at PRAA station for July 2019-July 2020 monitoring period. P5 and P95 correspond to percentile 5 and 95, respectively. Rain rate corresponds to measured values at Parsivel disdrometer. P05 and P95 are the percentiles 5 and 95, respectively. 2.2. Measuring Devices
Table 1. Summary of 15 min data series at PRAA station for July 2019-July 2020 monitoring period. P5 and P95 correspond to percentile 5 and 95, respectively. Rain rate corresponds to measured values at Parsivel disdrometer. P05 and P95 are the percentiles 5 and 95, respectively. 2.2. Measuring Devices
Figure 2. (a) Monthly precipitation values (blue line) measured at P8 rain gauge for 2005-2014 and 2019-2020 period. The same for air temperature (red line) measured at AWS PRAA station for 2010-2017 and 2019-2020 period. (b) Mean monthly wind speed (green line) at AWS PRAA station for 2012-2016 and 2019-2020 period. Verticals bars represent the monthly standard deviation computed for each cycle. (c) Wind rose displaying wind directions at the PRAA station for the 2019-2020 period.
Figure 2. (a) Monthly precipitation values (blue line) measured at P8 rain gauge for 2005-2014 and 2019-2020 period. The same for air temperature (red line) measured at AWS PRAA station for 2010-2017 and 2019-2020 period. (b) Mean monthly wind speed (green line) at AWS PRAA station for 2012-2016 and 2019-2020 period. Verticals bars represent the monthly standard deviation computed for each cycle. (c) Wind rose displaying wind directions at the PRAA station for the 2019-2020 period.
Table 3. Set of density values (in g cm) for each type of hydrometeor in proposed models. For models 1-4, we applied the snow density-diameter relationships reported by Brandes et al. [61] (BRO7) or Yu et al. [62] (YU20), while a constant value was assumed for the other hydrometeors. Model 5 assumed the density of all hydrometeors equals water density (1 g cm~%). Thus, the given values represent the a-parameter along with b = 0 fulfill the power-law expression p = aD". To assess the performance of corrected precipitation (Poo;recteq) With five models, we use the correlation coefficient (r7), and the root mean square error (RMSE) to check vari- ability and residual errors with respect to reference measurements (Preference). We also compared the accumulated Peorrected respect to the accumulated Preference during avail- able records and different wind speed regimes to obtain the relative difference (Diff) (Equation (6)) and the accumulated ratio (Ac,) (Equation (7)) in order to quantify the cumulative deviation. Cc 7 nN Tr" ny
Table 3. Set of density values (in g cm) for each type of hydrometeor in proposed models. For models 1-4, we applied the snow density-diameter relationships reported by Brandes et al. [61] (BRO7) or Yu et al. [62] (YU20), while a constant value was assumed for the other hydrometeors. Model 5 assumed the density of all hydrometeors equals water density (1 g cm~%). Thus, the given values represent the a-parameter along with b = 0 fulfill the power-law expression p = aD". To assess the performance of corrected precipitation (Poo;recteq) With five models, we use the correlation coefficient (r7), and the root mean square error (RMSE) to check vari- ability and residual errors with respect to reference measurements (Preference). We also compared the accumulated Peorrected respect to the accumulated Preference during avail- able records and different wind speed regimes to obtain the relative difference (Diff) (Equation (6)) and the accumulated ratio (Ac,) (Equation (7)) in order to quantify the cumulative deviation. Cc 7 nN Tr" ny
Figure 3. Scheme of processes applied to obtain diameter size distributions (DSD) and precipitation corrected. Then, the records were classified as solid, liquid, and unclassified precipitation using air temperature. Finally, the solid (S) and liquid (R) sets were introduced to be clustered in low (LW), medium (MW), and high (HW) wind speed.
Figure 3. Scheme of processes applied to obtain diameter size distributions (DSD) and precipitation corrected. Then, the records were classified as solid, liquid, and unclassified precipitation using air temperature. Finally, the solid (S) and liquid (R) sets were introduced to be clustered in low (LW), medium (MW), and high (HW) wind speed.
Table 4. Disdrometer data summary for the 15, 30, and 60 min time scales. Total number of records, errors, records with precipitation, and with precipitation rate >0.01 mm h7! are shown. The records with valid DSD and the common high-quality records containing (1) correct DSD, (2) correct bulk values, and (3) with precipitation > 0.01 mm h~! are also shown. Identified records as solid, liquid and unclassified precipitation are also included. The values in parentheses correspond to percentages of solid, liquid, and unclassified precipitation with regards to the number of records of the high- quality dataset. The precipitation phase selection allowed us to identify 298 records as tation (6%), 159 as solid precipitation (3%). The remaining 4526 va ues (91%) to unclassified precipitation within the interval —1 °C < T, <3 °C. This result due to the recurrent fluctuations of the 0 °C isotherm around the height o accumulation of measurements at 30- and 60-min scales reduced Furthermore, the variability of temperature tends to decrease for hinders to have extended intervals with T, < —1°C and reduced the num with solid precipitation, whereas the number of records above t iquid precipi- corresponded was expected f the site. The the high-quality records. arger time scales which ber of records his threshold increased, explaining the increase of unclassified and liquid precipitation contribution (Table 4). of solid, liquid, and unclassified precipitation with regards to the number of records of the high-
Table 4. Disdrometer data summary for the 15, 30, and 60 min time scales. Total number of records, errors, records with precipitation, and with precipitation rate >0.01 mm h7! are shown. The records with valid DSD and the common high-quality records containing (1) correct DSD, (2) correct bulk values, and (3) with precipitation > 0.01 mm h~! are also shown. Identified records as solid, liquid and unclassified precipitation are also included. The values in parentheses correspond to percentages of solid, liquid, and unclassified precipitation with regards to the number of records of the high- quality dataset. The precipitation phase selection allowed us to identify 298 records as tation (6%), 159 as solid precipitation (3%). The remaining 4526 va ues (91%) to unclassified precipitation within the interval —1 °C < T, <3 °C. This result due to the recurrent fluctuations of the 0 °C isotherm around the height o accumulation of measurements at 30- and 60-min scales reduced Furthermore, the variability of temperature tends to decrease for hinders to have extended intervals with T, < —1°C and reduced the num with solid precipitation, whereas the number of records above t iquid precipi- corresponded was expected f the site. The the high-quality records. arger time scales which ber of records his threshold increased, explaining the increase of unclassified and liquid precipitation contribution (Table 4). of solid, liquid, and unclassified precipitation with regards to the number of records of the high-
Table 5. Comparison between the precipitation phase measured by the disdrometer (DPP) and the classified precipitation in this study. The percentages of contribution of each subgroup (LW, MW HW) regarding liquid and solid precipitation are shown in parenthesis. Table 6. Cluster means of solid and liquid precipitation records estimated at 15 min time scale by using K-means. 1} represents the number of records per group, parentheses are show the contribution for each phase respectively.
Table 5. Comparison between the precipitation phase measured by the disdrometer (DPP) and the classified precipitation in this study. The percentages of contribution of each subgroup (LW, MW HW) regarding liquid and solid precipitation are shown in parenthesis. Table 6. Cluster means of solid and liquid precipitation records estimated at 15 min time scale by using K-means. 1} represents the number of records per group, parentheses are show the contribution for each phase respectively.
Light wind records (RLW + SLW) accounted for the 45% of all analyzed records (Table 6). Moderate wind speed constituted about 35%, and high wind speed records accounted for the remaining 20%. The performance of the disdrometer for the three solid precipitation groups was good and achieved hit rate values greater than 95% (Table 5), suggesting that the low temperature threshold of —1 °C performs well for discriminating solid precipitation. For liquid precipitation the hit rate was 86% for RLW. For RMW this score decreased to 53% together with a reduction of Ty and q. The RHW displayed a hit rate of 20% with a drop in q below 7 g kg~! and Ty near to 0 °C (Table 6). These highly variable conditions during liquid precipitation could limit the disdrometer performance to wind speeds lower than 6 ms~! and RH over 85%.
Light wind records (RLW + SLW) accounted for the 45% of all analyzed records (Table 6). Moderate wind speed constituted about 35%, and high wind speed records accounted for the remaining 20%. The performance of the disdrometer for the three solid precipitation groups was good and achieved hit rate values greater than 95% (Table 5), suggesting that the low temperature threshold of —1 °C performs well for discriminating solid precipitation. For liquid precipitation the hit rate was 86% for RLW. For RMW this score decreased to 53% together with a reduction of Ty and q. The RHW displayed a hit rate of 20% with a drop in q below 7 g kg~! and Ty near to 0 °C (Table 6). These highly variable conditions during liquid precipitation could limit the disdrometer performance to wind speeds lower than 6 ms~! and RH over 85%.
snow, and wet graupel (F5). This was more frequent in RHW, RMW, and SHW subgroups (Table 7) because wind speeds were higher than 5.92 ms~'. Filters also removed particles of solid precipitation and RHW that passed through the sampling area with D < 2 mm and velocities lower than 2 ms~! as a consequence of splashing effects. Inspecting the DSDs (Figure 4), there were scarce and probably unrealistic particles with D > 10 mm and v <1ms~! and few particles with D > 20 mm or D < 0.025 mm, which caused low percentages of particles removed (<0.001%). Hence, the removal produced by filters F1, F2, and F4 were negligible for both precipitation phases. Figure 4. Scattergram of accumulated particle size distributions for each precipitation groups: solid with light wind (SLW at first row), solid with moderate wind (SMW at second row), solid with high wind (SHW at third row), liquid with light wind (RLW at fourth rows), liquid with moderate wind (RMW at fifth row) and liquid with high wind (RHW at sixth row). The original DSDs measured are shown in first column; corrected DSDs correspond to second column. Third column shows the bins affected by filters/corrections applied. Colorbar represents the logarithm of absolute values (Logg) of DSDagig including their sign. Red bins correspond to particles added and blue bins correspond to particles removed. Solid lines indicate the fall velocity—diameter relationships for each type of hydrometeor: raindrops (black), wet snow (red), snow (blue), lump graupel (green), graupel (brown), soft hail (light blue), lump hail (purple), and fresh hail (dark purple).
snow, and wet graupel (F5). This was more frequent in RHW, RMW, and SHW subgroups (Table 7) because wind speeds were higher than 5.92 ms~'. Filters also removed particles of solid precipitation and RHW that passed through the sampling area with D < 2 mm and velocities lower than 2 ms~! as a consequence of splashing effects. Inspecting the DSDs (Figure 4), there were scarce and probably unrealistic particles with D > 10 mm and v <1ms~! and few particles with D > 20 mm or D < 0.025 mm, which caused low percentages of particles removed (<0.001%). Hence, the removal produced by filters F1, F2, and F4 were negligible for both precipitation phases. Figure 4. Scattergram of accumulated particle size distributions for each precipitation groups: solid with light wind (SLW at first row), solid with moderate wind (SMW at second row), solid with high wind (SHW at third row), liquid with light wind (RLW at fourth rows), liquid with moderate wind (RMW at fifth row) and liquid with high wind (RHW at sixth row). The original DSDs measured are shown in first column; corrected DSDs correspond to second column. Third column shows the bins affected by filters/corrections applied. Colorbar represents the logarithm of absolute values (Logg) of DSDagig including their sign. Red bins correspond to particles added and blue bins correspond to particles removed. Solid lines indicate the fall velocity—diameter relationships for each type of hydrometeor: raindrops (black), wet snow (red), snow (blue), lump graupel (green), graupel (brown), soft hail (light blue), lump hail (purple), and fresh hail (dark purple).
Table 7. Mean percentages of removed particles by each filter applied in each group respect to measured DSDs. To account the changes of DSDs after filters/corrections, the mean logNorm value and the mean number of particles before and after corrections were included. In effect, the corrections removed around 5% of the particles in the DSDs of the RLW group and promote the particles to align around the empirical curves employed. The particles of resulting DSDs present two regions of preference, one zone with particles with D < 6mm around the rain curve and another with D < 4mm around the wet snow curve. Particles with D > 10 mm and v < 6 ms~! were removed, and a small fraction of particles (<1%) with v > 8 ms~! were aligned with the soft hail curve. In the RMW group, the corrections removed 11% of the total particles and generated a distribution similar to RLW. The particles with v > 6 ms~! were aligned around the rain curve, whereas particles with v <6ms~! aligned around wet snow and wet graupel curves. The DSDs of the RHW group were the most affected by the filters, particularly, F5 removed 19% of fast particles due to margin faller effects enhanced by the wind speed increase [24,71]. Therefore, the corrected DSD was narrow with diameter—velocity of particles concentrated up to D < 4mm and v< 6ms_!. Convercely the DSINDc of colid nrecinitation had a hroader diameter cnectnim (1) < 1) mm)
Table 7. Mean percentages of removed particles by each filter applied in each group respect to measured DSDs. To account the changes of DSDs after filters/corrections, the mean logNorm value and the mean number of particles before and after corrections were included. In effect, the corrections removed around 5% of the particles in the DSDs of the RLW group and promote the particles to align around the empirical curves employed. The particles of resulting DSDs present two regions of preference, one zone with particles with D < 6mm around the rain curve and another with D < 4mm around the wet snow curve. Particles with D > 10 mm and v < 6 ms~! were removed, and a small fraction of particles (<1%) with v > 8 ms~! were aligned with the soft hail curve. In the RMW group, the corrections removed 11% of the total particles and generated a distribution similar to RLW. The particles with v > 6 ms~! were aligned around the rain curve, whereas particles with v <6ms~! aligned around wet snow and wet graupel curves. The DSDs of the RHW group were the most affected by the filters, particularly, F5 removed 19% of fast particles due to margin faller effects enhanced by the wind speed increase [24,71]. Therefore, the corrected DSD was narrow with diameter—velocity of particles concentrated up to D < 4mm and v< 6ms_!. Convercely the DSINDc of colid nrecinitation had a hroader diameter cnectnim (1) < 1) mm)
Figure 5. Boxplot of logNorm values for solid and liquid precipitation groups. White circles represent the median of the values for each subgroup while isolated points represent the outliers.
Figure 5. Boxplot of logNorm values for solid and liquid precipitation groups. White circles represent the median of the values for each subgroup while isolated points represent the outliers.
Figure 7. Mean particles’ contribution per diameter and per fall velocity for liquid (a,b) and solid precipitation groups (c,d). The percentages were derived from the total particles of each DSD before being averaged. Dashed line refers to measured DSDs, while continuous line refers to the corrected DSDs.
Figure 7. Mean particles’ contribution per diameter and per fall velocity for liquid (a,b) and solid precipitation groups (c,d). The percentages were derived from the total particles of each DSD before being averaged. Dashed line refers to measured DSDs, while continuous line refers to the corrected DSDs.
Table 8. Coefficient of correlation (r?), RMSE, relative difference (Diff) and accumulated ratio (Ac;) between corrected precipitation following the five density models and TPB precipitation for 15, 30, and 60 min timescales. Scores are displayed for low (RLW), moderate (RMW), and high wind speed (RHW) groups and for all liquid precipitation records.
Table 8. Coefficient of correlation (r?), RMSE, relative difference (Diff) and accumulated ratio (Ac;) between corrected precipitation following the five density models and TPB precipitation for 15, 30, and 60 min timescales. Scores are displayed for low (RLW), moderate (RMW), and high wind speed (RHW) groups and for all liquid precipitation records.
Figure 8. Boxplot of contribution (%) of each type of hydrometeor in solid and liquid precipitation groups. The acronyms represent: raindrops (R), wet snow (WS), lump graupel (LG), graupel (G), snow (S), lump hail (LH), fresh hail (FH), and soft hail (SH). TPB suggesting that the 15 min timescale seems to be too short to derivate precipitation properly in the study site.
Figure 8. Boxplot of contribution (%) of each type of hydrometeor in solid and liquid precipitation groups. The acronyms represent: raindrops (R), wet snow (WS), lump graupel (LG), graupel (G), snow (S), lump hail (LH), fresh hail (FH), and soft hail (SH). TPB suggesting that the 15 min timescale seems to be too short to derivate precipitation properly in the study site.
Table 8. Cont.
Table 8. Cont.
Figure 9. Comparison of liquid precipitation series for different time-scales (columns) and for low (RLW), medium (RMW) and high wind speed group (RHW). Values correspond to TPB measurements (black dashed line), Parsivel measurements (yellow line) and corrected precipitation with density model 1 (M1 in red line) and 5 (M5 in blue line). In each plot, a scatterplot is included of Parsivel, M4 and M5 respect to TPB as reference. The dashed gray line shows the slope m = 1.
Figure 9. Comparison of liquid precipitation series for different time-scales (columns) and for low (RLW), medium (RMW) and high wind speed group (RHW). Values correspond to TPB measurements (black dashed line), Parsivel measurements (yellow line) and corrected precipitation with density model 1 (M1 in red line) and 5 (M5 in blue line). In each plot, a scatterplot is included of Parsivel, M4 and M5 respect to TPB as reference. The dashed gray line shows the slope m = 1.
Table 9. Root mean square error (RMSE), relative difference (Diff), and accumulated ratio (Ac;) between precipitation corrected with five density models and Parsivel measurements for three timescales. Scores are displayed for low (SLW), moderate (SMW), and high wind speed (SHW) groups of solid precipitation. between precipitation corrected with five density models and Parsivel measurements for three
Table 9. Root mean square error (RMSE), relative difference (Diff), and accumulated ratio (Ac;) between precipitation corrected with five density models and Parsivel measurements for three timescales. Scores are displayed for low (SLW), moderate (SMW), and high wind speed (SHW) groups of solid precipitation. between precipitation corrected with five density models and Parsivel measurements for three
Figure 10. Comparison of liquid precipitation series at 60 min scale for low (RLW), medium (RMW) and high wind speed (RHW) groups. Values correspond to TPB measurements (black dashed line), Parsivel measurements (yellow line) and corrected precipitation with density model 1 (M1 in red line) and 5 (M5 in blue line). Scatterplots of Parsivel, M4 and M5 respect to TPB as reference were included. Dashed gray line shows m = 1 slope. For comparison purposes, we show only records without any precipitation one hour before to guarantee that the TPB was empty.
Figure 10. Comparison of liquid precipitation series at 60 min scale for low (RLW), medium (RMW) and high wind speed (RHW) groups. Values correspond to TPB measurements (black dashed line), Parsivel measurements (yellow line) and corrected precipitation with density model 1 (M1 in red line) and 5 (M5 in blue line). Scatterplots of Parsivel, M4 and M5 respect to TPB as reference were included. Dashed gray line shows m = 1 slope. For comparison purposes, we show only records without any precipitation one hour before to guarantee that the TPB was empty.
Table 9. Cont.
Table 9. Cont.
Figure 11. Measured values in disdrometer (black dashed line), and corrected solid precipitation according to density models 1 (M1 in yellow line), 4 (M4 in red line), and 5 (M5 in blue line) for low (SLW), medium (SMW) and high wind speed (SHW) groups for different time-scales (columns). The TPB values (green line) were included just for reference due to the limitations of this device prevent to measurement properly solid and unclassified precipitation. Scatterplots of M1, M4 and M5 respect to Parsivel were included. Dashed gray line and dashed light-purple line show m = 1 and m = 4 slopes, respectively.
Figure 11. Measured values in disdrometer (black dashed line), and corrected solid precipitation according to density models 1 (M1 in yellow line), 4 (M4 in red line), and 5 (M5 in blue line) for low (SLW), medium (SMW) and high wind speed (SHW) groups for different time-scales (columns). The TPB values (green line) were included just for reference due to the limitations of this device prevent to measurement properly solid and unclassified precipitation. Scatterplots of M1, M4 and M5 respect to Parsivel were included. Dashed gray line and dashed light-purple line show m = 1 and m = 4 slopes, respectively.
Table A1. Mean meteorological conditions for rainfall, mixed and snowfall reported by disdrometer concerning liquid anc solid precipitation records.
Table A1. Mean meteorological conditions for rainfall, mixed and snowfall reported by disdrometer concerning liquid anc solid precipitation records.
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