Abstract. Raindrop size distribution (DSD) information is fundamental in understanding the precipitation microphysics and quantitative precipitation estimation, especially in complex terrain or urban environments which are known for complicated rainfall mechanism and high spatial and temporal variability. In this study, the DSD characteristics of rainy seasons in the Beijing urban area are extensively investigated using 5-year DSD observations from a Parsivel2 disdrometer located at Tsinghua University. The results show that the DSD samples with rain rate < 1 mm h−1 account for more than half of total observations. The mean values of the normalized intercept parameter (log 10Nw) and the mass-weighted mean diameter (Dm) of convective rain are higher than that of stratiform rain, and there is a clear boundary between the two types of rain in terms of the scattergram of log 10Nw versus Dm. The convective rain in Beijing is neither continental nor maritime, owing to the particular location and local topography. As the rainfall intensity increases, the DSD spectra become higher and wider, but they still have peaks around diameter D∼0.5 mm. The midsize drops contribute most towards accumulated rainwater. The Dm and log 10Nw values exhibit a diurnal cycle and an annual cycle. In addition, at the stage characterized by an abrupt rise of urban heat island (UHI) intensity as well as the stage of strong UHI intensity during the day, DSD shows higher Dm values and lower log 10Nw values. The localized radar reflectivity (Z) and rain rate (R) relations (Z=aRb) show substantial differences compared to the commonly used NEXRAD relationships, and the polarimetric radar algorithms R(Kdp), R(Kdp, ZDR), and R(ZH, ZDR) show greater potential for rainfall estimation.

No abstract

No abstract

No abstract

&lt;p&gt;The profile classification module in GPM DPR level-2 algorithm outputs various products &amp;#160;such as rain type classification, melting layer&amp;#160; detection and &amp;#160;identification of&amp;#160; surface snowfall , as well as presence of graupel and hail. Extensive evaluation and validation activities have been performed on these products and have illustrated excellent performance. The latest version of these products is 6X. &amp;#160;With increasing interests &amp;#160;on severe weather &amp;#160;such as hail and &amp;#160;extreme precipitation, in &amp;#160;the next version (version 7), we development a flag to identify hail along the vertical profile using &amp;#160;precipitation type index (PTI).&lt;/p&gt;&lt;p&gt;Precipitation type index (PTI) plays an important role in a couple of algorithms in the profile classification module. PTI is a value calculated for each dual-frequency profile with precipitation observed by GPM DPR. &amp;#160;&amp;#160;DFRm slope, the maximum value of the Zm(Ku) , and &amp;#160;storm top height &amp;#160;are used in calculating PTI. PTI is effective in separating snow and Graupel/Hail &amp;#160;profiles. In version 7, we zoom in further into PTI for &amp;#160;Graupel/ hail profiles and separate &amp;#160;them into graupel and hail profiles with different PTI thresholds. A new Boolean product of &amp;#8220;flagHail&amp;#8221; is a hail only identifier for each vertical profile. &amp;#160;This hail product will be validated with ground radar products and other DPR products from Trigger module of DPR level-2 algorithm.&amp;#160; &amp;#160;In version 7, we make improvements of the surface snowfall algorithm. An adjustment is made accounting for global variability of storm top profiles.. A storm top normalization is introduced to obtain a smooth transition of surface snowfall identification algorithm along varying latitudes globally.&lt;/p&gt;

No abstract