Dynamical and Microphysical Aspects of Two Distinct Precipitation Systems in the Himalayas With 206.5 MHz Radar and WRF Model

The dynamical and microphysical aspects of two different precipitating systems have been investigated using the ARIES Stratosphere‐Troposphere Radar (ASTRad) facility and further substantiated by Weather Research Forecasting (WRF) model over Manora Peak. The first event (Case‐I) is associated with the southwest Indian summer monsoon that occurred on 4 August 2020, with a vertical extension of 10–12 km and leads to liquid phase precipitation. The second event (Case‐II) linked to the winter western disturbance occurred on 5 February 2021. This precipitating system was developed with a vertical extension of 6–7 km, resulting in both liquid and solid phase precipitation. Such distinct vertical extension of the systems is found to be associated with the thermodynamical conditions and prevailed large‐scale circulations. By analyzing the vertical structure of these systems using three Doppler moments estimated from the ASTRad (equivalent Reflectivity dBZe, Doppler velocity, and Spectral width), maximum dBZe (∼60 dB) is observed in Case‐II, while higher spectral width (>2 m s−1) is associated to Case‐I. The microphysical processes assessed by the WRF model pointed out that Case‐I involved snow accretion on supercooled droplets, leading to graupel and raindrop formation, while in Case–II, solid and liquid precipitation resulted from ice processes, including accretion or autoconversion. These findings highlight the significance of integrating radar and modeling data to understand the dynamical and microphysical evolution of precipitation under the influence of orography in the Himalayan region.


Introduction
The extensive mountain ranges and complex topography of the Himalayas play a significant role in the formation of diverse precipitation and weather systems over the Indian subcontinent (Anders et al., 2006;Das et al., 2006;Pant et al., 2018).The north-western and central Himalayan regions experience complex weather systems, including Indian Summer Monsoon (ISM) and Western Disturbance (WD) synoptic scale phenomena during monsoon and winter seasons, respectively, that bring precipitation over the region (Barros et al., 2004;Barros & Lang, 2003;Bhatt & Nakamura, 2005, 2006;Krishnamurti & Kishtawal, 2000;Lang & Barros, 2002).Apart from synoptic scale weather systems over the region, the height and steepness of the mountainous terrain are crucially linked to the cloud formation and growth of microphysical species (Hobbs et al., 1973;Houze Jr, 2012).The WDs are the eastward-moving extra-tropical low-pressure systems embedded in the subtropical westerly jet streams that, after being intercepted by mountainous terrain, result in heavy precipitation in the form of rain, graupel/hail, and snow (Dimri & Chevuturi, 2014;Dimri et al., 2015;Hatwar et al., 2005).The average rainfall received mainly from these WDs is around 176 mm over the central Indian Himalayan region (Nageswararao et al., 2016).On the other hand, from June to September, the ISM flow brings ample moisture with it, leading to the formation of local cloud systems and causing high-intensity rainfall events (Meese et al., 2018;Pant et al., 2018).The majority of precipitation over the central Himalayan region is received from the ISM with mean long-term average rainfall of about 704 mm (Bookhagen & Burbank, 2010;Mukherjee et al., 2014;Palazzi et al., 2013).These mountainous region also play a vital role in the global water cycle and provides a significant water budget to downstream population in Asia (Viviroli et al., 2007).
The magnitude and spatio-temporal distribution of precipitation received during ISM and WD is strongly controlled by the dynamical structure and microphysical characteristics of precipitation systems (Chawla et al., 2018;Kumar et al., 2014;Murali Krishna et al., 2022;Palazzi et al., 2013;Rawat et al., 2022;Williams et al., 1995).Although the topography of the Himalayan region significantly influences the spatial distribution of precipitation, which can exhibit substantial variations from regional to the local scales, the patterns of precipitation remain poorly understood due to insufficiently dense and unevenly distributed ground-based stations and in situ rain gauge measurements (Anders et al., 2006;Bookhagen & Burbank, 2006, 2010;Das et al., 2006).There have been efforts to integrate rain gauge measurements with satellite-based data sets to provide comprehensive coverage, but they strongly underestimate the detection of snow (Andermann et al., 2011;Rasmussen et al., 2011).Moreover, the atmospheric models oversimplify the complex regional-scale processes and features, including moist convection, cloud microphysical processes, and orographic effects.These factors affect Himalayan precipitation systems with insufficient accuracy associated with biases in models and observations (Ma et al., 2009;Palazzi et al., 2013).However, in the absence of observations over the high-altitude Himalayan stations due to the presence of difficult terrain and rough weather conditions, the limited area models, such as Weather Research and Forecasting (WRF) model, play a vital role in filling gaps in observations and are widely used for meteorological modeling over the region (Navale & Singh, 2020;Rajput et al., 2022;Singh et al., 2016Singh et al., , 2021)).
In the WRF model, cloud processes are resolved by convective parameterization and explicit modeling of cloud processes by microphysics schemes as per their applicability at different spatial scales (Powers et al., 2017;Stensrud, 2007).The microphysical schemes can simulate the microphysical processes from vapor to formation and fallout of the precipitation in close agreement with the observation (Zhu et al., 2021).The microphysics schemes of the WRF model also show a pronounced impact on the model performance, especially during extreme precipitation (Bryan & Morrison, 2012;Chawla et al., 2018;Douluri & Chakraborty, 2021;Houze et al., 2017), as these schemes estimate the heat-moisture tendencies, distribution of hydrometeors, and precipitation fluxes (Morrison et al., 2009;Stensrud, 2007).
The driving force of atmospheric circulation systems over a broad range of scales is mainly dependent on the release of latent heat by different precipitating systems, such as convective and stratiform.The studies of such systems have been suited well with the ground-based Very High Frequency (VHF)/Ultra High Frequency (UHF) wind profilers, used widely in making assessments of the vertical structure of such precipitation systems through spectral moment estimation (Received power, Doppler velocity, and spectral width) at zenith beam (Chu & Lin, 1994;Chu & Song, 1998;Martin Ralph, 1995;Rao et al., 1999;Williams et al., 1995).The vertical Doppler spectrum of the precipitating atmosphere obtained from radar measurements is used to detect hydrometeor terminal velocity during moderate to heavy precipitation (Fukao et al., 1985).Moreover, in the precipitating atmosphere, a thick horizontal layer around the 0°C isotherm in the reflectivity profile is marked as a bright band during stratiform rain and has been of major interest in vertically pointing VHF and UHF radars (Ecklund et al., 1995;Fukao et al., 1985;Rao et al., 1999).The enhancement of reflectivity and the changes in bright band characteristics are mainly due to the scattering of melting ice or snow particles around the 0°C isotherm level, and the factor |K| 2 associated with the complex index of refraction for water (0.93) is more than that of snow (0.18) that can enhance reflectivity by ∼5 dB (Campos et al., 2007;Fabry & Zawadzki, 1995;Rao et al., 1999).
All these factors play an important role in meteorology and precipitation-related natural hazards.Therefore, the understanding of precipitation systems and their interactions with climate over the Himalayan region is essential for comprehending the impacts of climate change on the mountain ecosystem and ensuring water availability in downstream countries (Bolch et al., 2012;Cogley, 2012;Moors et al., 2011).However, the dynamical and microphysical processes associated with precipitation systems over the Himalayan region have been still less explored using both observations and modeling.This manuscript aims to investigate two contrasting precipitation systems observed during monsoon on 4 August 2020 (Case-I) and on 5 February 2021 (Case-II) in winter that occurred over the Himalayas, using a combination of the ASTRad wind profiler and WRF model.Our study focuses on unraveling the complex interplay between dynamical and microphysical processes that shape the precipitation characteristics of these systems.We compare and contrast the key features of two precipitation systems, highlighting their similarities and differences in terms of precipitation intensity, duration, and spatial distribution.Through our analysis, we aim to contribute to a better understanding of the associated mechanisms governing precipitation variability in the Himalayan region using real-time observation made with a 206.5 MHz VHF radar facility (ASTRad) at the site under precipitating conditions.The rest of this paper is organized as follows: Section 2 provides an overview of the study area and the data sources used in the analysis, along with the methodology used for the numerical simulations and data analysis.Section 3 presents the results of the investigation, including a detailed comparison of the two precipitation systems.Finally, Section 4 offers a conclusion of the findings.

ASTRad and Data Processing
The ASTRad is operated at 206.5 MHz with 5 MHz bandwidth, has been established at ARIES, Manora peak (29.22°N, 79.27°E; ∼1 ,950m amsl) in the Nainital district of Uttarakhand, which is located in the central Himalayan region of India.It is an active aperture, coherent, and monostatic wind profiler radar comprising an active antenna array, solid-state transceivers, and high-end digital signal processing (DSP) subsystems.The planar phased array consists of 588 number of three-element Yagi-Uda antennae that are placed in a circular aperture of 32 m diameter and arranged in an equilateral triangular grid fashion with an inter-element spacing of 0.7λ, where λ represents the operating wavelength of 1.45 m.The array is mounted on the roof-top of the building (Figure 1a), and each array element is connected to individual Transmit-Receive Modules (TRMs) placed beneath.The TRMs are designed with programmable phase shifters that facilitate electronic beam swinging and provide a peak transmit power of 230 kW in pulsed mode comprising 1 × 10 8 Wm 2 of peak power aperture product.The critical radar parameters such as receiver dynamic range (∼70 dB) and receiver noise figure (maximum 2 dB) can provide Doppler detection up to 145 dBm.The measurement uncertainties associated with the horizontal wind velocity using this radar system is <2.5 m s 1 , while it's in the order of cm s 1 for vertical wind estimates.
The backscattered signal (echo) received through radar is processed using high-end Digital Signal Processing (DSP) units to estimate the mean Doppler, Spectral width, and three-dimensional wind vectors-U (zonal), V (meridional), and W (vertical).In the first step, the received signal is processed online by dividing it into in-phase (I) and quadrature-phase (Q) signals that are coherently integrated to enhance the signal-to-noise ratio (SNR) and stored in binary raw data format.In offline signal processing steps, first, the direct current (DC) offset in the complex time domain signals is removed by averaging the I and Q signals separately and thereafter subtracting the respective mean values.After applying the suitable window function (Hamming), the complex time domain signal is converted to the Doppler power spectrum through Fast Fourier Transform (FFT).Clutter lying around the zero Doppler frequency in the Doppler power spectrum is removed and replaced with interpolated values from adjacent Doppler bins.Further, an incoherent integration is carried out to further improve the signal-to-noise ratio (SNR).The estimation of zeroth, first, and second moments give the received signal power, radial velocity, and spectral width (Barth et al., 1994;Woodman, 1985).In the present study, the data obtained from the ASTRad observations, particularly consisting of zenith beam profiles for Case-I (4 August 2020) and Case-II (5 February 2021), are used, and detailed information on radar experiments conducted during these periods are provided in Table 1.

WRF Model Setup and Experimental Design
In this study, WRF version 4.2.2, comprising advanced physics and numerical schemes, is used for simulating the meteorology and dynamics over the domain shown in Figure 1b.The selection of appropriate physics options is crucial for accurately simulating meteorological fields using the WRF model.In this study, physics options that have been previously evaluated for Indian conditions were utilized (Kumar et al., 2012;Ojha et al., 2016;Singh et al., 2016Singh et al., , 2021)).Specifically, the Goddard scheme was used for short-wave radiation (Chou & Suarez, 1994), while the rapid radiative transfer model was used for longwave radiation (Mlawer et al., 1997).The Thompson microphysics scheme was used (Thompson et al., 2008) for cloud water, rainwater, ice, snow, and graupel mixing ratios.The Kain-Fritsch cumulus parameterization was employed (Kain, 2004) for resolving sub-grid-level processes such as precipitation, latent heat release, and the vertical redistribution of heat and moisture.The unified Noah land-surface model was utilized (Tewari et al., 2004) to parameterize surface processes.The convection scheme was activated only in the outermost domain d01 and was turned off in the nested domains (d02 and d03).
The model used in this study is configured with three different horizontal resolutions: outermost domain d01 (15 km × 15 km), and two nested domains for d02 (5 km × 5 km) and d03 (1 km × km) with two-way nesting.It is also shown in an earlier study that the grid resolution of 1 km × 1 km better resolves the topographic-induced features in meteorology and local circulations over the complex terrain of the central Himalayas (Singh et al., 2021).The initialization and boundary conditions to the WRF model are obtained from the European Center for Medium-Range Weather Forecast (ECMWF) re-analysis version 5 (ERA5).The ERA5 data available at 0.25°× 0.25°horizontal resolution is used here at the temporal resolution of 6 hr (Hersbach et al., 2020).Vertical discretization is performed at 51 unequally spaced eta levels from the surface to the model top at 10 hPa.The simulations are performed from 2-5 August 2020 for Case-I and from 3-6 February 2021 for Case-II.Model output has been stored at 1-hr temporal resolutions, and the first 24 hr of each simulation are excluded as a spin-up period to ensure the model reaches the dynamically balanced state.The model-simulated 2 m temperature (T2), 2 m relative humidity (RH2), and 10 m wind speed (WS10) over the nearest grid point of the observational site are compared with the surface observations and summarized in Table 2.
The comparison for both cases shows that in the d03 simulation, the mean bias (MB) and root mean square error (RMSE) values are quite less for T2 as compared to the benchmarks (±0.5°C) suggested by Emery et al. (2001).RH2 and WS10 also show the MB and RMSE values within the range as discussed in Singh et al. (2021).Overall, the model simulations are in agreement with the observations and well reproduced the meteorological conditions during the study period.So, the simulation results are used here for further analysis.

Results and Discussion
To examine the comparative difference between these precipitating systems, the synoptic scale meteorological conditions are investigated for both cases using model-simulated meteorological fields, including geopotential height and wind fields, along with the surface observations carried out over the site.Further, the dynamical structure and microphysical structural differences are investigated using the ASTRad observations and WRF simulations for both cases.

Synoptic Overview and Meteorological Conditions of the Events
The underlying synoptic weather patterns during the events Case-I and Case-II are shown in Figure 2 using the geopotential height and wind fields from WRF output at 500 hPa.During Case-I of the monsoon season, the synoptic scale flow at 500 hPa shows a northward-shifting trough from the Bay of Bengal (BOB) to the Indo Gangetic Plain (IGP) with a cyclonic circulation.During this period, strong easterly flow is prevailed over the IGP, and the observation site experiences the south-easterly wind flow associated with the BOB branch of the monsoon.These trough and flow conditions are responsible for transporting moisture toward the Himalayas with easterly winds and forming convective systems over the site.
The wintertime precipitation (here Case-II) over the region is associated with the western disturbance.The synoptic meteorological conditions for Case-II, as shown in Figures 2e-2h, clearly depict the propagation of a low-pressure system associated with western disturbance over the site.The strong cyclonic circulation around the low-pressure system could result in a strong upward motion and atmospheric instability for cloud formations and precipitation.The synoptic flow at 500 hPa is westerly, with southwest to northeast tilt.This low-pressure system extends from north Pakistan to Central India, containing cold air pulled from the extra-tropical regions, which could decrease temperature and often lead to liquid or solid precipitation depending on the surface temperature.These weather systems are responsible for the winter rainfall and recharge the Himalayan cryosphere snow cover (Dimri et al., 2015).Further, the diurnal variation of surface temperature, relative humidity, and volumetric water content (VWC) using surface observations carried out over the site and WRF simulated precipitation (liquid and solid) is shown in Figure 3 during both precipitation events.Since the observational data for rainfall was unavailable, VWC, which is the ratio of the volume of water to the unit volume of soil, has been utilized as an indicator of rainfall.
In Case-I, the observations revealed a sudden decline in temperature from 1200 IST until 1500 IST (Figure 3a), which is the period of precipitation event comprising 100% RH.Such a sudden fall in temperature by 3°C and RH of 100% in the afternoon refers to the onset of precipitation through the synoptic-scale flow along with the local convective activities.In the meantime, the VWC shows a sudden rise after 1400 IST, confirming rainfall over the site.WRF simulated accumulated liquid precipitation also shows a sudden rise in values from 1.5 to 3 mm during the event (Figure 3b).However, the solid accumulated precipitation is negligible because the atmospheric temperature is quite warm during the monsoon season.
In Case-II, the fall in temperature is observed from 1000 IST, which continued till 1800 IST, along with the rise and fall of RH (Figure 3c).The rise in VWC starts at 1300 IST and continues till night (2400 IST).A similar rise is also observed in accumulated liquid (0.2-3 mm) and solid precipitation (0.1-1 mm) from 1300 IST until 1800 IST (Figure 3d).Therefore, here, the precipitation event is considered from 1400 to 1800 IST.The occurrence of solid precipitation is linked to low surface temperature (∼3-4°C) associated with a western disturbance which reduces  Earth and Space Science 10.1029/2023EA003213 the likelihood of rainfall and increases the chances of solid precipitation.Further, the spatial distribution of solid and liquid precipitation over the region is also investigated using the WRF for both cases shown in Figure 4.During Case-I, less than 30 mm of liquid precipitation is observed around the site containing a grid box of resolution 0.5°× 0.5°.However, less than 10 mm solid and liquid precipitation is depicted in Case-II.These observations clearly distinguish that Case-I is associated with the convective system developed with moistureladen air mass from synoptic scale flow leading to the rainfall (liquid precipitation) during the summer monsoon.However, Case-II is related to the passage of western disturbance, which brings cold moisture-laden extra-tropical air masses over the region and leads to solid and liquid precipitation forming in support of the existing lower surface temperature (∼4°C) as compared to the monsoon (∼20°C).

Dynamical Structure of the Events From ASTRad
At an operating wavelength of 1.45 m, the ASTRad is sensitive to fluctuations or turbulence in clear air as well as the presence of hydrometeors.Therefore, the ASTRad can be used to study the dynamics of a precipitating system as it provides detailed information on the spatial and temporal distribution of the vertical structure and movement of hydrometeors within the system, which is critical for understanding its behavior and predicting its evolution.By analyzing the radar data, we can easily identify dynamic features such as updrafts and downdrafts, which are important drivers of precipitation formation and intensity.Further, using this information, we can also detect the type of precipitation systems developed in the atmosphere, such as convective storms, stratiform precipitation, and mixed-phase precipitation.In this section, all such investigations are carried out for both cases using the ASTRad.
The amount of backscattered energy returning to the wind profiler is dependent on the contents of the radar resolution volume.If the radar resolution volume contains hydrometeors of size larger than about 0.1 mm in diameter, the backscattering of energy is due to the Rayleigh scattering process, while the Bragg scattering process is responsible for measuring the clear air motion.The main output of the wind profiler radar is a Doppler spectrum describing the distributions of the radial velocities of the scatterers within the radar resolution volume.
Examples of such spectra are shown in Figure 5 that are stacked as a function of height determined from the vertically pointing beam during the rainfall recorded over the site for Case-I (Figure 5a) and Case-II (Figure 5b).Earth and Space Science 10.1029/2023EA003213 The spectrum at each height is determined by performing coherent and incoherent averaging during a dwell time (See Table 1).Negative and positive velocities of the scatterers in the Doppler spectrum represent their movements toward and away from the radar, respectively.The peak amplitude of each Doppler spectrum is denoted by a blue triangle for each height range.
Further, the shape of the Doppler spectra is characterized by the three spectral moments: SNR, Doppler velocity, and spectral width.In previous studies, it had also been concluded that the radar wind profilers operating at the wavelengths ranging between 0.3 and 6 m showed that Rayleigh scattering (η ∝ λ 4 ) from precipitation can equal or exceed the Bragg scattering (η ∝ λ 1/3 ) from clear air (Atlas, 1964;Gossard, 1988Gossard, , 1990;;Röttger et al., 1987) where η is radar reflectivity and λ is an operating wavelength.In this case, the total backscattered power would be the sum of the return from clear air and droplet particles which is given by where, A e is the effective area and the constant value (2ln2) in denominator correspond to Gaussian pattern for an antenna system.P t is the transmitted power, r is the range, and Δr is the range resolution.If radar reflectivity factor Z corresponds to the power returned from a droplet ensemble, then the scatter from the water droplets is given by where 0.93 is complex index of refraction for water.The scatter from refractive index turbulent fluctuations in the clear is given by where C n 2 is the refractive index structure parameter when in an inertial subrange, the turbulent fluctuations of size λ/2 exist.
After combining these wavelength dependencies in Equations 2 and 3, the amount of Rayleigh scattering (Gossard, 1988;Martin Ralph, 1995;Rogers et al., 1993) expressed as the equivalent reflectivity (dBZe = 10 log 10 Z) and can be calculated using the relation as where C n 2 is estimated from the SNR profiles (Ghosh et al., 2003;Jain et al., 1995;VanZandt et al., 1978) et al. (2020).The dBZe is a key parameter related to the number, size, and distribution of hydrometeors in the atmosphere and is used for identifying and characterizing different types of precipitation.Therefore, for detailed investigations on the vertical structure and evolution of precipitating systems, the frequency distribution and temporal evolution of three Doppler spectral moments (dBZe, Doppler velocity, and spectral width) are shown in Figures 6 and 7 for Case-I and Case-II.The altitude levels are represented here from the mean sea level (MSL).For the frequency distribution (shown in Figure 6), 30-min profiles are selected from the vertically pointing beam before (evolving phase), during (mature phase), and after (dissipation phase) the events.
Only those Doppler spectra having widely separated precipitation echoes from clear air echoes have been used here for the analysis and the estimation of moment parameters.The mature phase 30 min profiles are used to ensure that the rain is observed over the site using the maintained log book.These frequency distributions are twodimensional or bivariate histograms constructed by calculating one-dimensional histograms at each height (Scott, 1992;Williams et al., 1995).The color of each pixel represents the occurrence of that parameter at that height.Such frequency distribution clearly illustrates the vertical structure without any information on temporal variability.

Case-I
For Case-I, the frequency distribution of reflectivity is shown in Figures 6a, 6g, and 6m.The distribution of reflectivity during evolving phase ranges with a concentration in occurrences between 20 and 40 dB, which increases up to 50 dB in the lower troposphere during the mature phase and then decreases to 35 dB during the dissipation phase.An increase in dBZe during the mature phase ∼5 km is a manifestation of the radar-bright band peak can be inferred to the bigger drop dominance.The increase in dBZe occurs as the solid hydrometeors cross the 0°C isotherm, which serves as the upper bound of the bright band at approximately 6 km (as shown in Figure 6g and Figure S1 in Supporting Information S1).Above this, the reflectivity decreases with height due to the presence of frozen hydrometeors that is found to decrease the Rayleigh scattering intensity (Marshall & Palmer, 1948;Sekhon & Srivastava, 1971).During the dissipation phase, the maximum concentration of reflectivity ranges between 10 and 40 dB and decreases sharply above 5 km.
The Doppler velocity frequency distribution is shown in the second column (Figures 6b, 6h, and 6n).The Doppler velocities measured by wind profiler radar are the fall velocities of hydrometeors convolved with atmospheric vertical motions.The velocity scale with a maximum concentration in occurrences ranges between 2.5 and 2.5 m s 1 during the evolving phase, from 10 to 2 m s 1 during the mature phase, and confined between 1 and 1 m s 1 during the dissipation phase.During the evolving phase, the updrafts and downdrafts are clearly visible throughout the vertical column.However, during the mature phase, the narrow distribution of velocities is observed below 6 km, and the broader distribution is found above it.Such broader distribution corresponds to the presence of hydrometeors are either ice, snow, or graupel.The narrow distribution of velocities below 6 km depicts the distribution of fall velocities corresponding to the distribution of almost similar raindrop sizes.During the dissipation phase, an increase in the portion of frequency distribution for the downdrafts near zero Doppler velocity infers the weakening of the precipitation system.
The frequency distribution of spectral width is shown in the third column (Figures 6c, 6i, and 6o), which is the proxy for estimating precipitation intensity that provides information about the variability and turbulence strength.In the evolving phase, the vertical profiles of spectral widths show a uniform distribution throughout the atmospheric column ranging between 1 and 3 m s 1 .This indicates that the hydrometeors are continually growing as they move upward.During the mature phase, the narrow frequency distribution of dominant spectral width is observed that ranges from 1 to 3 m s 1 below 6 km and then shows wider distribution with an increase up to 5 m s 1 above 6 km.The source of spectral broadening above 6 km is associated with the increase in the width of the drop size distribution.However, below 6 km, smaller spectral width implies a narrower drop size distribution that could lead to less variability in drop size and velocities (Williams et al., 1995).During the dissipation phase, the sharp decrease in spectral widths is observed for values less than 2 m s 1 except between 6 and 8 km altitude level (where spectral width shows a maximum peak of value about 3 m s 1 ).The higher spectral width observed between 6 and 8 km could be associated with the turbulence and mixing processes caused by the interaction of precipitation particles with the surrounding air.The temporal variation of all these three Doppler spectral moments is shown in Figures 7a, 7c, and 7e.The white patches represent the invalid values of the profiles.Simultaneous large patches are observed in all three spectral moment profiles during the precipitation.However, the distribution of higher spectral widths (>4 m s 1 ) is confined above 6 km contrary to the reflectivity and Doppler velocity indicate the presence of a wide range of hydrometeor particles or a mixture of liquid and frozen particles along with the presence of strong turbulent motion within the cloud system.

Case-II
In Case-II, the occurrence of graupel/hail logged from the log book is maintained over the site.In addition, Figures 3d and 4d also confirm it with existence of solid precipitation, and Figure 5b shows a Doppler velocity of about 12-15 m s 1 which is the typical fall velocity of these hydrometeors (Spek et al., 2008;Wang & Qiao, 2020).From now it is referred to as a hailstorm.The vertical structure of such a hailstorm is analyzed in Figure 6, similar to the case-I corresponding before, during, and after the hailstorm.Before the hailstorm, the maximum occurrence of reflectivity (Figures 6d,6j,and 6p) ranges between 10 and 40 dB within 3 km while increasing from 40 to 55 dB between 3 and 5 km altitude level, and at higher altitudes, reflectivity values are confined between 40 and 50 dB.A similar vertical variation of reflectivity is observed during the hailstorm with a shift of 10 dB and reaches a maximum value of 65 dB between 3 and 6 km altitude level.After the hailstorm, the reflectivity profiles still show high values between 30 and 55 dB at the lower levels (below 4 km) and uniform distribution of values around 50 dB above 5 km.The median profiles of dBZe in all three stages show a peak around 3 km and decrease with decreasing the height.The 0°C isotherm is estimated ∼3.3 km for Case-II as shown in Figure S1 in Supporting Information S1.The dBZe peak and 0°C isotherm depict a relative altitudinal shift of 2 km due to the seasonal contrast between Case-I and Case-II.
The frequency distribution of Doppler velocity (Figures 6e, 6k, and 6q) before the hailstorm is confined with strong downdrafts (>1 m s 1 ) and weak updraft motions (<0.5 m s 1 ) up to a 7 km altitude level.However, during the hailstorm, fall velocities are at their maximum (∼12 m s 1 ), and a broad distribution of downdrafts ranging between 5 and 10 m s 1 is observed below 6 km, then velocities decrease above 6 km.After the hailstorm, Doppler velocities are confined around zero Doppler and show uniform distribution due to melting and evaporating hailstones.
The frequency distribution of spectral width (Figures 6f, 6l, and 6r) before the hailstorm represents the narrower distribution of values between 0 and 1 m s 1 below 4 km and wider distribution between 4 and 7 km altitude levels ranging from 1 to 2 m s 1 .Above 7 km, spectral width shows a decreasing trend of values less than 1 m s 1 .During the hailstorm, the vertical variation of spectral width is similar using the log book maintained, but the distribution becomes much wider with a slight shift of 0.5 m s 1 as compared to before the hailstorm.After the hailstorm, the distribution in spectral width is not changed significantly and follows an almost similar pattern formed during the hailstorm period.

Earth and Space Science
10.1029/2023EA003213 Also, a significant change is observed in the temporal variation of spectral width (Figure 7f) as compared to Case-I.The increase in spectral width after a hailstorm reflects a significant change in the size and shape of hydrometeors through melting, fragmentation, and sublimation.The significant increment in the temporal variation of reflectivity and Doppler velocity (Figures 7b and 7d) during the event is not observed simultaneously with spectral width.Even spectral width has significantly increased between 2 and 3 m s 1 throughout the vertical column after the hailstorm event, that is, after 1800 IST.For both cases, during mature phase (Figures 7c and 7d), few red color patches are dominant at higher altitudes in Doppler velocity profiles that could be due to aliasing in Doppler spectrum due to inadequate spectral processing technique especially at higher altitudes, where noise fluctuations are dominated over the real atmospheric signal.
In conclusion, the variation in reflectivity, Doppler velocity and spectral width observed in radar measurements for Case-I and Case-II clearly indicate the significant difference in the vertical structure of these two precipitation systems.Reflectivity profiles suggested that for Case-I, the melting level is observed about 5 km while it is found below 3 km for Case-II.Additionally, the seasonal time series variation of 0°C isotherm level from ERA5 reanalysis data set in Figure S1 in Supporting Information S1 also shows close agreement with ASTRad observations.However, the Doppler velocity and spectral width profiles suggested differences in size, shape, and motion of hydrometeors involved during both the events.In Case-I, the narrower spectral width indicates the similar fall speed that could be associated to raindrops within the radar resolution volume, while the wider spectral width in Case-II depicts the complex motion patterns with wider variations in their fall velocities that could be due to the presence of large irregularly shaped hailstones.Additionally, in Case-II, there might be the coexistence of different types of hydrometeors, such as graupel and rain, that can further increase the range of fall velocities within the radar volume.

Dynamical Structure Using WRF
To further understand the atmospheric processes driving the formation, development, and evolution of these precipitation systems, the temporal evolution of vertical wind, reflectivity, and specific humidity profiles is investigated in Figure 8 from WRF outputs.Here, the high-resolution simulations performed for half an hour resolution are utilized to investigate the dynamical structure of the events.
In Case-I, prior to the initiation of the precipitation (before 1200 IST), the high reflectivity (Figure 8a) values (between 30 and 50 dB) are confined below 6 km with relatively weaker vertical wind (0.1 m s 1 ) and the structure of the precipitation system extended vertically up to 12 km.Earth and Space Science 10.1029/2023EA003213 RAJPUT ET AL.
In the meantime, near the surface, moist air of magnitude ∼15 g kg 1 (Figure 8b) is present.As the land heats up from strong solar radiation during the day, the convective available potential energy (CAPE) increases from 800 to 1200 J kg 1 , as shown in Figure 9.Such an increment in CAPE and moisture availability during the day hours creates an environment conducive to convective development.As the day progresses (after 1200 IST), CAPE is further increased up to 1500 J kg 1 , and the formation of the strongest updrafts (from 0.1 to 0.4 m s 1 ) leads to carrying out moisture-laden air from the lower levels to the higher altitudes.Hence, an increase in reflectivity (between 50 and 60 dB) is observed with a significant gradient, indicating the presence of strong updrafts and moisture convergence.As the precipitating system weakens and dissipates (after 1400 IST), the reflectivity and updrafts start to decrease; however, the availability of moisture content and CAPE values (∼2000 J kg 1 ) are still higher.The sustained moisture supply and high CAPE values create favorable atmospheric conditions allowing convective instability to persist even after the dissipation of the system.
In Case-II (Figure 8c), before 1200 IST, the vertical wind profile shows stable atmospheric conditions with minimal vertical motions (0.005-0.2 m s 1 ).In winter, the atmosphere is generally calm, cold and dry, with limited moisture content; therefore, specific humidity values (Figure 8d) at the surface are much lower (∼4 g kg 1 ) than Case-I and resulting in negligible reflectivity.In the meantime, CAPE values are also observed to be lower than 300 J kg 1 .After 1200 IST, the specific humidity values >5 g kg 1 are observed at the surface (in Figure 8d) and the vertical wind with a magnitude of 0.1-0.2m s 1 is confined below 6 km which is strong enough for the transport of moisture from the surface to higher altitudes.It further leads to an increase in reflectivity up to 50 dB and indicates the development of convective activity.Between 1400 and 1800 IST, the strong updrafts of 0.2 m s 1 bring abundant moisture-laden air to the elevated levels which is crucial for forming and growing solid hydrometeors and contribute to higher reflectivity greater than 40 dB.In the meantime, a significant variation in CAPE values is not observed while shows a constant variation about 300 J kg 1 .However, over extra-tropical regions, variation in CAPE values is observed between 400-900 J kg 1 for hailstorms (Kulkarni et al., 2015;Tudurí & Ramis, 1997).It means that there are some localized factors, such as the presence of complex mountain terrain, that can trigger dynamic instability (from orographic lifting mechanism) as well as the transport of moisture flux that influence the development and intensity of convective activity.As the system dissipates after 1730 IST, the vertical wind gradually decreases in intensity.As a result, reflectivity values also tend to decrease and help to understand the availability and distribution of moisture.
To further investigate the roles played by both large-scale and local forcings during the precipitation event over the site, the distribution of the transport of moisture flux at the lower troposphere (900-700 hPa) and midtroposphere (600-400 hPa) before and during the events is shown in Figure 10 from WRF outputs.The distribution of moisture flux in Case-I before and during the event (Figures 10a-10d) at lower and mid-troposphere clearly indicates that the whole vertical column is filled with high moisture content and supported by both local and large-scale monsoonal circulation systems.However, in Case-II (Figures 10e-10h), the high moisture content is available only in the lower troposphere and almost negligible in the mid-troposphere.It clearly indicates the transport of moisture at lower levels from the nearby IGP region supported by the local orographic lifting mechanism and seen as a belt of high moisture along the black contour line only.
All these results from the WRF outputs suggested that Case-I precipitating system is formed under the influence of both local and large-scale monsoon system with a vertical extension of 12 km altitude while Case-II precipitating system is developed under the influence of local orographic lifting mechanism with a vertical extension of Additionally, comparison of radar reflectivity estimated from the WRF model and ASTRad (as shown in Figure S2 in Supporting Information S1) demonstrates good agreement.Results indicates that WRF and ASTRad observations are in agreement for large values of reflectivity (>35 dBZ) in Case-II as compared to Case-I.This could be associated with the larger proportion of solid phase hydrometeors extending from surface to its vertical extension.However, smaller values of reflectivity between 30 and 45 dBZ correlated well for both data sets.Overall, the comparison between both is quite reasonable for the target precipitation systems.

Microphysical Characteristics of the Events
The high resolution WRF simulations are further utilized to explore the microphysical characteristics of the events.Microphysical characteristics refer to the properties and processes associated with hydrometeor species, such as size, shape, density, composition, and their interactions.In order to analyze the regions where specific microphysical processes are dominant and their variation with respect to altitude, the vertical cross-section of reflectivity and the mixing ratio of five microphysical species (cloud droplet, snow, rain, ice, and graupel) is shown in Figure 11.The simulated hydrometeors and reflectivity are extracted 1 hr before the event along the cyan line represented in Figure 4a for Case-I and in Figure 4c for Case-II from domain d03.
In Case-I, the high reflectivity of about 45-55 dB (Figures 11a and 11b) is associated with the high rainwater mixing ratio (0.5-1.5 g kg 1 ) extended from the surface to ∼ 6 km altitude.The 0°C isotherm is observed about 6 km above the surface (black dashed line), which almost separates the hydrometeors species distribution solid phase (above) and liquid phase (below).The lower mixing ratio of solid-phase hydrometeors such as snow, ice, and graupel (<0.05 g kg 1 ) indicates the dominance of mixed-phase cloud processes.The solid phase precipitation is almost non-existent during Case-I at the surface.However, the 0°C isotherm is shifted below (∼3 km) for Case-II (Figures 11c and 11d), which resulted in both liquid (rain and cloud droplets) and solid (graupel) phase hydrometeors.The existing precipitating hydrometeors show high reflectivity (∼35 dB) adjacent to the surface, which is comparatively lower than Case-I.The qualitative verification of the solid phase precipitation (graupel/ hail) is logged over the site (maintained log book).The dominant solid phase hydrometeors mixing ratio confirms that Case-II is associated with the cold phase cloud processes.Purnell and Kirshbaum (2018) also suggested the cold rain process for the orographically enhanced low-level clouds from the synthesis of observations and model simulations.Figures 11b and 11d show that the raindrops falling on the Earth's surface are formed from the existing high cloud water and small fractions by melting snow and graupel during Case-I.The solid precipitation (snow and graupel) dominates in Case-II, and the existing liquid precipitation at the surface could be supported by melting of the solid precipitation crossing the 0°C isotherm.In both cases, ice crystals are formed 2-4 km above the melting level.The distribution of snow (Figure 11b) in a vertical column (above 6 km) about the 20 km spatial range demonstrates the accretion of snow on supercooled droplets to grow by riming and form graupels and rain.
In Case-II (from Figure 11d), the main microphysical process responsible for the formation of rain and graupel is found to associate with ice comprising accretion or autoconversion processes.The vertical variation of reflectivity and microphysical species clearly indicates the large vertical extension (8-10 km) of clouds during the monsoon, such as deep convective clouds.Although, in the absence of free convection with lower surface heating, the vertical extension of the winter precipitation system is about 6-8 km during the western disturbance representing orographically lifted clouds by strong westerly/north-westerly flow over the region as discussed earlier.A striking feature is that the simulations manage to reproduce the mixed (Case-I) and cold phase (Case-II) cloud processes as identified by the ASTRad observations.It is also worth noting here that radar moments cannot differentiate raindrops from melting graupel or melting snow or both, while the WRF can simulate it perfectly.
To further understand these results, the time series of vertically integrated column densities of hydrometeors and day-averaged vertical profiles of the mixing ratios for each of the hydrometeors simulated over the site are shown in Figure 12.The column density is computed in each interested grid point for each hydrometeor by multiplying the atmospheric density, mixing ratio of the hydrometeor, and thickness of the atmospheric layer and summing over all model levels.For Case-I, the timing of the precipitation peak (Figure 3a) almost coincides with peaks of rain, cloud, snow, and graupel particles (Figure 12a).The maximum column density of around 2 kg m 2 is observed for rain and cloud for the precipitating hour.The mean vertical profiles of the mixing ratios for each hydrometeor over the site (Figure 13a) show the peak maximum of snow, graupel, and cloud particles above the 6 km altitude range.The peak maximum (∼0.10 g kg 1 ) of rain particles is at around 5 km altitude level (below the melting level) where the sharp vertical gradients of snow and graupel are present, and then only rain particles reach the surface level.However, above 8 km, the mixing ratio of snow is the highest (>0.05 g kg 1 ) compared to the cloud and graupel particles.The more efficient formation of graupel particles is directly linked to the presence of a larger amount of snow particles above 6 km that accrete supercooled droplets to grow by riming and form graupels.These sharp vertical gradients and simultaneous increase in rain particles clearly indicate the conversion of snow and graupel to rain below their respective peaks, demonstrating the mixed-phase cloud process.
For Case-II, in Figure 12b, the peaks of graupel, cloud, and rain coincided at 1600 and 1700 IST, similar to the precipitation peaks observed in Figure 3c.Rain and graupel particles are the most prevalent hydrometeor present during precipitation events with respective maximum values of around 0.15 kg m 2 and 0.10 kg m 2 over the site.However, the presence of ice crystals is negligible for both events.The averaged vertical profile of the mixing ratios of hydrometeors (Figure 13b) demonstrates the peak value (0.03 g kg 1 ) of graupel particles at 6 km altitude level and similar peak shifted at 5 km altitude level for rain where the sharp vertical gradients of graupel particles are present.It could be attributed that the graupel particle continues to grow larger and heavier during the riming process, where more supercooled liquid water droplets collide and freeze onto the graupel particles.The heat energy released during the freezing process is transferred to the graupel particle, raising its temperature.As a result, the graupel particle begins to melt.However, they can't fully melt until the air temperature is consistently  above freezing.These vertical profiles also indicate that both graupel and rain particles reach the surface level.
The mixing ratios of snow and ice particles are almost negligible throughout the vertical column.
All such investigations on microphysical characteristics of Case-I and Case-II precipitating systems show almost similar variations of melting levels (about 6 km for Case-I and about 3 km for Case-II) and distribution of hydrometeors with precipitation peaks as speculated from the ASTRad observations.In addition, the vertical distribution of hydrometeors clearly confirms the reaching of graupel particles with rain at the surface during Case-II hailstorm event and suggests the riming process for the formation of graupel and solid precipitation, indicating the cold-phase cloud process for the event.In Case-I, the presence of peak values of all hydrometeors except rain above the melting level confirmed the mixed-phase cloud process that is responsible for the liquid precipitation for this event.Such information cannot be extracted by using only radar observations.

Conclusions
The dynamical and microphysical aspects of the two precipitating systems that occurred during the ISM and WD has been investigated here comprehensively using ground-based observations such as the ASTRad and WRF model simulations over the central Himalayan site.In the beginning, the investigation of synoptic-scale meteorological conditions at 500 hPa, surface observations, and spatial distribution of solid and liquid precipitation was carried out for these two cases.These results depicted that Case-I is associated with a convective system formed by a moisture-laden air mass from the synoptic-scale flow, leading to liquid precipitation during the ISM.
In contrast, Case-II is linked to the passage of a western disturbance, bringing cold moisture-laden extra-tropical air masses and resulting in both solid and liquid precipitation.Then ASTRad observations were utilized to investigate the vertical dynamical structure of these two cases after computing three Doppler spectral moments (reflectivity, Doppler velocity, and spectral width).These results clearly explained the melting levels, bright band signature, size, and distribution of hydrometeors for both case studies.Further, WRF model simulations at halfhour resolution for the d03 domain were utilized to explore the dynamical and microphysical processes responsible for the evolution of these precipitating systems.Additionally, the comparison between ASTRad derived reflectivity and WRF simulated reflectivity were found reasonably comparable for the target precipitation systems.The key findings of this study are as follows: 1.The ASTRad observations confirmed the presence of freezing hydrometeors in Case-II from the sharp decline in reflectivity profiles below 3 km and wider distribution of Doppler velocity and spectral width in comparison of Case-I, followed by narrow distribution of Doppler velocity below 6 km. 2. The WRF outputs highlighted that in Case-I, the availability of moisture content, increase in convective available potential energy (CAPE), and strong updrafts played significant roles in the formation and extension of the precipitation system up to 12 km altitude.In Case-II, the presence of localized factors such as complex mountain terrain played a significant role in the formation and intensity of convective activity up to 6-8 km altitude level by triggering the local orographic lifting mechanism for transporting moisture flux over the site.3. The analysis of the microphysical characteristics of the precipitation events suggested that Case-I exhibited characteristics of a mixed-phase cloud process, with high reflectivity associated with a high rainwater mixing ratio extending from the surface to an altitude of approximately 6 km and snow and graupel above it.Case-II represented cold-phase cloud processes, with a shift in the 0°C isotherm below approximately 3 km altitude.4. The ASTRad observations and WRF model simulations captured almost similar variations of melting levels (about 6 km for Case-I and about 3 km for Case-II) and dynamical evolution of precipitation systems with hydrometeors peaks.Therefore, integrating the ground-based measurements along with the modeled data presents a valuable methodology for conducting comprehensive analyses of weather phenomena in regions devoid of observational data, such as the Himalayas.
In conclusion, due to the lack of observational data, this manuscript has examined the contrasting microphysical differences between two precipitating cases using numerical experiments with the WRF model.However, a detailed investigation using observational data sets of these differences is crucial.While the model demonstrates the ability to reproduce meteorological fields similar to the earlier studies conducted over the region (Singh et al., 2021), its performance is influenced by the representation of microphysics and other cloud processes (Fan et al., 2017;Gao et al., 2016;Morrison et al., 2020).Additionally, the unresolved challenge of sharp topographic variations at 1 km resolution in the Himalayan region affects cloud processes and remains an area of limited exploration.Further research is needed to address the complexities arising from diverse topography (Rajput Earth and Space Science 10. 1029/2023EA003213 et al., 2022) ) and to improve the microphysics representation in the model for better precipitation simulations in the Himalayas.Moreover, the drop size distribution in precipitating systems and their evolution as the precipitation shifts from convective to stratiform mode may also need to investigate, and obtain quantitative estimates using other surface instruments like co-located disdrometer at the profiler site.

Figure 1 .
Figure 1.(a) Site view of the ASTRad and (b) WRF model simulated domains (d01; black, d02; magenta, and d03; red) along with the topographic height.The white triangle represents the observational site.

Figure 2 .
Figure 2. Geopotential height (shading) and wind field (black vectors) variation at 500 hPa before and during the event in domain d01.A green triangle in each panel denotes the observational site.

Figure 3 .
Figure 3.Diurnal variation of observed surface meteorological parameters and accumulated liquid (black) and solid (red) precipitation from the WRF model over the observational site.

Figure 4 .
Figure 4. Spatial distribution of the liquid and solid phase precipitation simulated by the WRF model in domain d03.The red box denotes the area of interest in a grid resolution of 0.5°× 0.5°around the site (white triangle).The cyan line represents the selected region for the vertical cross-section of microphysical species in the further section.

Figure 5 .
Figure 5. Stacked Doppler spectra from the ASTRad for (a) Case-I and (b) Case-II.The blue triangle represents the peak amplitude in each Doppler spectrum.

Figure 6 .
Figure 6.Frequency distribution of Doppler spectral moments (Equivalent reflectivity, Doppler velocity, spectral width) during (a) evolving phase (30 min profiles before the event) (b) mature phase (30 min profiles during the event), (c) and dissipation phase (30 min profiles after the event) over the site for Case-I and Case-II.The solid black line represents the median profile and horizontal dashed gray line shows the 0°C isotherm.

Figure 7 .
Figure 7.The temporal variation of Doppler spectral moments (Equivalent reflectivity, Doppler velocity, spectral width) over the site for Case-I and Case-II.The two vertical black dotted line represents the duration of the events and horizontal dotted gray line shows the 0°C isotherm.

Figure 8 .
Figure 8. Temporal evolution of the vertical profiles of vertical wind, reflectivity, and specific humidity.

Figure 9 .
Figure 9. Temporal variation of convective available potential energy (CAPE) for both cases.

Figure 10 .
Figure10.Distribution of the transport of moisture flux at lower troposphere (900-700 hPa) and mid-troposphere (600-400 hPa) before and during the events.The black and magenta lines represent the 300 and 2,500 m height.

Figure 11 .
Figure 11.Vertical cross-section of reflectivity and microphysical species (Cloud droplet, snow, rain, ice, and graupel).The black dotted line represents the 0°C isotherm line.The brown shaded area indicates the topography.

Figure 12 .
Figure 12.Temporal variation of the hourly column density of microphysical species for the events.

Figure 13 .
Figure13.The vertical profiles of microphysical species over the site for both events.

Table 2
The Mean (± Standard Deviation) Values of the 2 m Temperature (T2; °C), 2 m Relative Humidity (RH2; %), and Wind Speed (WS10; m s 1 ) From WRF Simulations Near the Observational Grid Point and Surface Observations for Case-I and Case-II Along With Mean Bias (MB) and Root Mean Square Error (RMSE) RAJPUT ET AL.
. The required technical parameters other than Table 1 used to estimate C n 2 from the ASTRad are provided in Jaiswal