Raindrop size distribution characteristics in summer of a nival glacial zone in eastern Tianshan, Central Asia

Abstract

Precipitation is a key component of the hydrological cycle, which is critical to understanding its formation and evolution. In this study, based on the observation data of the PWS100 located at the meteorological observation site at the terminal of Urumqi Glacier No. 1, eastern Tianshan Mountains, the statistical characteristics of the summer raindrop size distribution (DSD) were analyzed, and the DSD characteristics of five different rainfall rates(R) and two rainfall types (convective and stratiform) were investigated for the daytime and nighttime. The average raindrop spectral width was the largest in class III (1 < R < 5 mm h−1). The result showed that the raindrop concentration increased with the rainfall rate. The maximum raindrop concentration was at class IV (5 < R < 10 mm h−1), when the raindrop diameter was higher than 1.74 mm. The small and medium size raindrops played a dominant role in precipitation composition in the head watershed of the Urumqi River, contributing 98% of the total raindrop. The convective precipitation at the headwaters was divided into continental clusters. The stratiform/convective Dm-log10Nw was characterized by a large mass-weighted mean diameter Dm = 1.523/2.608, and a generalized intercept log10Nw = 2.841/3.469. N(D) of convective precipitation was significantly different between the daytime and nighttime, while that of stratiform precipitation was almost the same. The constraint relationship between R-Dm and R-log10Nw of these two precipitation types was deduced, the exponent of the R-log10Nw relationship of the two precipitation types was negative, and the Dm value of stratiform precipitation tended to be stable at a higher rainfall rate (1–2 mm). Finally, we deduced the power-law relationship between radar reflectivity (Z) and rain rate (R) [Z = A*Rb] for stratiform and convective precipitation at the headwaters. Z = 698.8R2.0 was for stratiform, and Z = 47.1R2.0 was for convective. These results, for the first time, offer insights into the microphysical nature of precipitation in the head watershed of the Urumqi River during the summer and provide essential information that could be useful for precipitation retrievals based on weather radar observations.

Raindrop Size Distribution Characteristics of Heavy Precipitation Events Based on a PWS100 Disdrometer in the Alpine Mountains, Eastern Tianshan, China

“…Meanwhile, the low frequency of observation and lack of data limit researchers' in-depth understanding of precipitation patterns in mountain areas. A recent study showed that the DSD characteristics and microphysical parameters in the alpine region were quite different from those in other areas of the Tianshan Mountains, monsoon regions, and arid regions [42].…”

“…The PWS100 and Geonor T-200B are installed in the meteorological observation field, A recent study showed a high correlation between the two instruments (0.8, n ≥ 731), which ensures that the observations from the PWS100 are valid [42]. The PWS100's built-in algorithm assumes that raindrops are deformed when they fall, and its internal code will exclude particles with unusual velocities [55].…”

“…Only part of the D m -log 10 N w data appear in the maritime convective clusters region, and less data are observed for the continental convective clusters. A recent study showed that convective precipitation (rainfall intensity: 5.87 mm h −1 ) at the headwaters of the Urumqi River can be regarded as continental-like clusters [42]. However, in the present study, the log 10 N w mean of the entire dataset computed in Figure 3 is roughly in the maritime-like clusters, and the D m value is marginally higher, indicating that the D m value of the heavy precipitation events in the Tianshan region is higher than that for the maritime-like clusters.…”

“…Figure 9 shows the Z-R fitting curves and relational expressions of the three heavy precipitation patterns in the study area. In order to facilitate comparison, the mid-latitude convective precipitation relationship (solid green line) Z = 300R 1.4 proposed by Fulton et al [57]; the Z-R relationship (solid burgundy line) Z = 47.1R 2.0 proposed by Chen et al [42] through the study of DSD in the headwaters; and the power law relationship (solid orange line) Z = 53.7R 1.7 of Moto convective precipitation over the Tibetan Plateau proposed by Wang et al [58] are all superimposed on Figure 9.…”

“…Figure 9 shows the Z-R fitting curves and relational expressions of the three heavy precipitation patterns in the study area. In order to facilitate comparison, the mid-latitude convective precipitation relationship (solid green line) Z = 300R 1.4 proposed by Fulton et al [57]; the Z-R relationship (solid burgundy line) Z = 47.1R 2.0 proposed by Chen et al [42] through the study of DSD in the headwaters; and the power law relationship (solid orange line) Z = 53.7R 1.7 of Moto convective precipitation over the Tibetan Plateau proposed by Compared to the standard empirical relation Z = 300R 1.4 , similarly to the Z-R relationship for the convective precipitation of the headwaters of the Urumqi River [42], the fitted power law relationships of heavy rain and heavy snow in this work have lower coefficients A (10 and 228.7, respectively) and higher indexes b (2.6 and 2.1, respectively), while the opposite is true for hail events (A = 551.5, b = 1.3). The relationship of Z = 200R 1.6 was recommended in mid-latitude areas for stratiform rain [60], it has a higher coefficient and a lower index than heavy rain, and a lower coefficient than hail and heavy snow, indicating that, under the same rainfall rates, heavy snow and heavy rain have lower radar reflectivity than standard empirical relation and the M-P relationship.…”