The observations of bright band carried out simultaneously with X-and Ka-band radars for the first time over the Indian region have been examined to reveal various contrasting characteristics of bright band at the two wavelengths. The study reports the bright band observations on September 12-13, 2015 at millimeter and centimeter wavelengths and brings out a comparative analysis of the bright band features (e.g., intensity, thickness, height, etc.) under three different rain conditions ranging from very light (<0.1 mm/hr) to light (0.1-3 mm/hr) to heavy (3-5 mm/hr). It is seen that the bright band region at Ka-band is always narrower and situated at a higher altitude than at X-band frequency. Our analysis shows that at Ka-band frequency, the polarimetric fields like LDR can be utilized to detect and determine the bright band features using an appropriate selection of a threshold value of LDR, which is found to be −22 dB in this study and could be associated reasonably with the top and bottom heights of the bright band. This study explores the potential of both radars, particularly the Ka-band radar for probing the bright band effect and estimating its features which would be helpful to improve the quantitative estimates of precipitation.

Abstract. Cloud radar reflectivity profiles can be an important measurement for the investigation of cloud vertical structure (CVS). However, extracting intended meteorological cloud content from the measurement often demands an effective technique or algorithm that can reduce error and observational uncertainties in the recorded data. In this work, a technique is proposed to identify and separate cloud and nonhydrometeor echoes using the radar Doppler spectral moments profile measurements. The point and volume targetbased theoretical radar sensitivity curves are used for removing the receiver noise floor and identified radar echoes are scrutinized according to the signal decorrelation period. Here, it is hypothesized that cloud echoes are observed to be temporally more coherent and homogenous and have a longer correlation period than biota. That can be checked statistically using ∼ 4 s sliding mean and standard deviation value of reflectivity profiles. The above step helps in screen out clouds critically by filtering out the biota. The final important step strives for the retrieval of cloud height. The proposed algorithm potentially identifies cloud height solely through the systematic characterization of Z variability using the local atmospheric vertical structure knowledge besides to the theoretical, statistical and echo tracing tools. Thus, characterization of high-resolution cloud radar reflectivity profile measurements has been done with the theoretical echo sensitivity curves and observed echo statistics for the true cloud height tracking (TEST). TEST showed superior performance in screening out clouds and filtering out isolated insects. TEST constrained with polarimetric measurements was found to be more promising under high-density biota whereas TEST combined with linear depolarization ratio and spectral width perform potentially to filter out biota within the highly turbulent shallow cumulus clouds in the convective boundary layer (CBL). This TEST technique is promisingly simple in realization but powerful in performance due to the flexibility in constraining, identifying and filtering out the biota and screening out the true cloud content, especially the CBL clouds. Therefore, the TEST algorithm is superior for screening out the low-level clouds that are strongly linked to the rainmaking mechanism associated with the Indian Summer Monsoon region's CVS.

Abstract.One of the key parameters that must be included in the analysis of atmospheric constituents (gases and 11 particles) and clouds is the vertical structure of the atmosphere. Therefore high-resolution vertical profile 12 observations of the atmospheric targets are required for both theoretical and practical evaluation and as inputs to 13 increase accuracy of atmospheric models. Cloud radar reflectivity profiles can be an important measurement for the 14 investigation of cloud vertical structure in a resourceful way. However, extracting intended meteorological cloud 15 content from the overall measurement often demands an effective technique or algorithm that can reduce error and

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