The nature of raindrop size distribution (DSD) is analyzed for wet and dry
spells of the Indian summer monsoon (ISM) in the Western Ghats (WG) region using
Joss–Waldvogel disdrometer (JWD) measurements during the ISM period
(June–September) in 2012–2015. The observed DSDs are fitted with a gamma
distribution. Observations show a higher number of smaller drops in dry spells
and more midsize and large drops in wet spells. The DSD spectra show distinct
diurnal variation during wet and dry spells. The dry spells exhibit a strong
diurnal cycle with two peaks, while the diurnal cycle is not very prominent in
the wet spells. Results reveal the microphysical characteristics of warm rain
during both wet and dry periods. However, the underlying dynamical parameters,
such as moisture availability and vertical wind, cause the differences in
DSD characteristics. The higher moisture and strong vertical winds can provide
sufficient time for the raindrops to grow bigger in wet spells, whereas
higher temperature may lead to evaporation and drop breakup processes in dry
spells. In addition, the differences in DSD spectra with different rain rates
are also observed. The DSD spectra are further analyzed by separating them into
stratiform and convective rain types. Finally, an empirical relationship
between the slope parameter λ and the shape parameter μ is derived by
fitting the quadratic polynomial during wet and dry spells as well as for
stratiform and convective types of rain. The μ–λ relations
obtained in this work are slightly different compared to previous
studies. These differences could be related to different rain microphysics
such as collision–coalescence and breakup.
Introduction
The Western Ghats (WG) is one of the heavy rainfall regions in India. WG
receives a large amount of rainfall (∼6000mm) during the Indian
summer monsoon (ISM) period and references therein. Shallow
convection significantly contributes to monsoon rainfall on the windward
side and deep convection on
the leeward side of the WG. The
rainfall distribution in the WG region is complex, and topography plays a
significant role and references therein. The
rainfall distribution in the WG depends on the area, whether on the mountain's
windward or leeward side. For instance,
showed that rainfall trends are different in the northern and southern parts
of the WG. These different properties correspond to different physical
mechanisms. The intense rainfall on the WG windward side, usually called
orographic precipitation, comes from shallow clouds with long-lasting
convection .
ISM rainfall shows large spatial and temporal variability. It is known
that during active (with a high amount of rainfall) and break (with a little
or no rain) spells of the ISM, there are different behaviors in the formation of
weather systems and large-scale instability. The strength of ISM rainfall
depends on the frequency and duration of active and break spells
. This intra-seasonal oscillation of rainfall is
considered one of the most critical weather variability sources in the Indian
region . Since the earlier studies of
, active and break spells of the ISM have been extensively
studied, especially during the last 2 decades
. The characteristic features of ISM active and break
spells have been widely reported in earlier studies; this includes, for example, their
identification , spatial
distribution , circulation patterns
, vertical wind and
thermal structure , rainfall variability
, and cloud properties
. Even though different dynamical
mechanisms for the observed rainfall distribution during wet and dry spells of
the ISM are well understood, investigations of microphysical processes for rain
formation are still lacking.
Raindrop size distribution (DSD) is a fundamental microphysical property of
precipitation. DSD characteristics are related to processes such as
hydrometeor condensation, coalescence, and evaporation. In addition, the
altitudinal variations in DSD parameters provide the cloud and rain
microphysical processes . These are important
parameters affecting the microphysical processes in the parameterization
schemes of numerical models . Hence, numerous DSD
observations during different types of precipitation, different seasons, and
different intra-seasonal periods at several locations are essential for better
representation of physical processes in the parameterization schemes. As a
result, the numerical model communities continue to improve the simulation of
clouds and precipitation at monsoon intra-seasonal scales by better
representing the microphysical processes through parameterization schemes. In
addition, different DSD characteristics lead to different reflectivity (Z)
and rainfall rate (R) relations. Henceforth, understanding DSD variability
is also vital to improving the reliability and accuracy of quantitative precipitation estimation
from radars and satellites
.
The ISM active and break spells over the WG are nearly identical to the active and
break phases over the core monsoon zone . The
distribution of convective clouds in the WG region exhibits distinct
spatiotemporal variability at intra-seasonal timescales (wet: analogous to
the active period of the ISM, dry: similar to the break period of the ISM) during the
ISM. Recently, studied the characteristics of convective
clouds over the WG using X-band radar, European Center for Medium-Range Weather
Forecasts (ECMWF) interim reanalysis (ERA-Interim), and Tropical Rainfall
Measuring Mission (TRMM) satellite datasets. They showed that the wet spells
are associated with negative geopotential height anomalies at 500 hPa,
negative outgoing longwave radiation (OLR) anomalies, and positive
precipitable water anomalies. All these features promote anomalous
southwesterlies, which enhance convective activity over the WG. In contrast,
positive geopotential height anomalies, positive OLR anomalies, and negative
precipitable water anomalies are observed during the dry spells, which
suppress the convective activity in the Arabian Sea, and hence little to no
rain is seen over the WG during dry periods. These different dynamical properties
affect the convection during wet and dry spells over the WG. However, DSD (often
used to infer the microphysical processes of rain) during wet and dry ISM
periods is poorly addressed, especially in the WG region.
Several studies have demonstrated the seasonal variations in DSD over the Indian region
e.g.,. However, climatological studies of DSD over
orographic regions are limited, especially in the WG region. Despite its
orography, the rainfall intensity is low (below 10 mmh-1) over
the WG . A few attempts have been made to
understand the DSD characteristics in the WG. For example,
studied the DSD characteristics by fitting
a three-parameter gamma function during the monsoon. They observed a bimodal and
monomodal DSD during low and high rainfall rates, respectively. However, their
study is limited to brightband and non-brightband conditions
only. examined the DSD differences between
coastal (Kochi) and high-altitude (Munnar) stations located in the WG region
and reported larger drops relatively more often at Munnar.
studied the DSD characteristics during different precipitating systems in the
WG region using disdrometer, Micro Rain Radar, and X-band radar
measurements. They noticed different Z–R relations for different
precipitating systems. studied the DSD
differences between mid-altitude (Braemore, 0.4 km above mean sea level) and
high-altitude (Rajamallay, 1.8 km above mean sea level) regions in
the southern WG during brightband events. They observed bimodal DSD at the
mid-altitude station and monomodal DSD at the high-altitude station. However,
their study was confined to stratiform rain only.
DSD studies are inadequate in the WG region with consideration of long-term
datasets. This work is the first to analyze the DSD characteristics and
plausible dynamic and microphysical processes by considering the monsoon
intra-seasonal oscillations (wet and dry spells). The present study brings out
the results of a unique opportunity by analyzing a more extensive dataset and
considering different phases of monsoon intra-seasonal oscillations in the
WG. With this background, the current study attempts to address the following
questions regarding DSD in the WG.
How do DSD characteristics vary during wet and dry spells?
Does wet and dry spell rainfall have a different microphysical origin over the complex terrain?
Does DSD show any diurnal differences like in rainfall distribution during wet and dry spells?
What are the dynamical processes influencing DSD characteristics during wet and dry spells?
What is the best fit for the μ–λ relationship during wet and dry spells?
The paper is organized as follows: details of the instrument and dataset used
are presented in Sect. 2. The methodology adopted for separating rainy days
into wet and dry spells is given in Sect. 3. A brief overview of DSD variation
with topography is in Sect. 4. The characteristics of DSDs during wet and dry
spells and the possible reasons are reported in Sect. 5. The summary of this
study is provided in Sect. 6.
Instrument and datasets
A total of 4 years (June to September; 2012–2015) of Joss–Waldvogel disdrometer (JWD)
measurements at the High Altitude Cloud Physics Laboratory (HACPL; located on the
windward slopes of the WG) in Mahabaleshwar (17.92∘ N,
73.6∘ E; ∼1.4km above mean sea level) are utilized
to understand DSD variations during the wet and dry spells of
the ISM. Figure shows the topography map along with the
disdrometer site (HACPL). The background surface meteorological parameters
like temperature, relative humidity, rainfall accumulation, wind speed, and
wind direction measured with an automatic weather station over the study site can
be found in .
Topographical map of India's Western Ghats generated by using Shuttle Radar Topography Mission (SRTM) data . The location of the disdrometer installed at HACPL is shown with a black circle.
A JWD is an impact-type disdrometer, which measures hydrometeors with sizes
ranging from 0.3 to 5.1 mm and arranges them in 20 channels
. The JWD has a styrofoam cone to measure the diameter
of hydrometeors. Once the hydrometeors hit the 50 cm2 styrofoam cone, a
voltage is induced by downward displacement, which is directly correlated with
drop size. The accuracy of the JWD is 5 % of the measured drop
diameter. Although a JWD is a standard instrument for DSD measurements
, it has several shortcomings, such as noise, sampling
errors, and wind . In addition, the JWD
miscounts raindrops in lower-sized bins, specifically for drop diameters below
1 mm. Effort has been made to overcome this
deficiency by discarding noisy measurements and applying the manufacturer's
error correction matrix. To reduce the sampling error arising from
insufficient drop counts, rain rates less than 0.1 mmh-1 are
discarded. During heavy rain, the JWD underestimates the number of smaller drops; this is
known as disdrometer dead time. To account for the aforementioned error in JWD
estimates, the rain rates during wet and dry spells are analyzed. It is
observed that ∼85 % (90 %) of the rain rates lie below
8 mmh-1 during wet (dry) spells (figure not shown). Using the
noise-limit diagram of ,
investigated the underestimation of small drops by
the JWD. They found that 50 % of the drops below 0.4 mm cannot be
detected by the JWD when the rain rate is above 20 mmh-1. Here, only
4 % (1 %) of the rain rates exceed 20 mmh-1
during wet (dry) spells, and hence the underestimation of small drops by the JWD
is negligible in this region. further demonstrated
that the gamma parameters (such as a normalized intercept parameter)
derived from long-term observations by a JWD and a two-dimensional video
disdrometer (2DVD) are in good agreement. We examined the DSD differences
between the ISM's wet and dry spells using a long-term (four monsoon) dataset in
the present study. So it is appropriate that the undercounting of small drops
does not significantly affect the gamma DSD. Further, the underestimation of
smaller drops for higher rain rates (4 % for wet spells and
1 % for dry spells) may not affect the conclusions, as this work does
not intend to quantify the DSD variations. Instead, it aims to understand the
DSD variability during wet and dry spells over the complex terrain. The
undersized integration period can contribute to DSD's numerical fluctuations,
whereas a longer sampling time may miscount actual physical deviations
. As there is no consensus regarding the JWD sampling
period, we have averaged the JWD measurements into 1 min periods to filter
out these deviations.
A JWD provides rain integral parameters, like raindrop concentration, rain
rate, and reflectivity, at 1 min integration time
. The 1 min DSD measurements are
fitted with a three-parameter gamma distribution, as mentioned in
. Details of the DSDs used in the present study
can be found in and .
The functional form of the gamma distribution assumed for DSD is expressed as
N(D)=N0Dμexp-(3.67+μ)DD0,
where N(D) is the number of drops per unit volume per unit size interval,
N0 (in m-3mm-(1+μ)) is the number concentration
parameter, D (in mm) is the drop diameter, D0 (in mm) is
the median volume diameter, and μ (unitless) is the shape parameter
. The gamma DSD parameters
are calculated using moments proposed by . Here, second,
third, and fourth moments are utilized to estimate gamma parameters. This method
gives relatively fewer errors than other methods over the WG
. The nth-order moment of the gamma distribution
can be calculated as
Mn=∫0∞DnN(D)dD.
The shape parameter, μ, and the slope parameter, λ, are expressed
as
3μ=11-G-4,4λ=M2M3(μ+3),5G=M32M2M4=∫0∞D3N(D)dD2∫0∞D2N(D)dD∫0∞D4N(D)dD.
The other parameters, including the normalized intercept parameter Nw (in
mm-1m-3), mass-weighted mean diameter Dm (in
mm), and liquid water content (LWC; in gmm-3), are
calculated following .
6Dm=∫0∞D4N(D)dD∫0∞D3N(D)dD7LWC=10-3π6ρw∫0∞D3N(D)dD8Nw=44πρw103LWCDm4
Here, ρw is the density of water.
Apart from JWD measurements, the ERA-Interim dataset is
also used to understand the dynamical processes influencing different DSD
characteristics. ERA-Interim provides atmospheric data at different
pressure and time intervals. Here, temperature (K), specific humidity
(kgkg-1), and horizontal and vertical winds at 850 hPa with a
spatial resolution of 0.25∘×0.25∘ at
00:00 UTC are considered during the ISM period of 2012–2015.
Scatter plot of daily accumulated rainfall between the rain gauge and the JWD. The solid grey line indicates the linear regression.
The daily accumulated rainfall collected by the India Meteorological
Department (IMD) rain gauges is used to identify ISM's wet and dry spells. IMD
receives the rainfall accumulations at 08:30 LT
(LT=UTC+5.5h) every day. To examine JWD data quality, the daily
accumulated rainfall measured by the JWD is compared with the daily accumulated
rainfall collected from a rain gauge. For comparison, JWD rainfall
accumulated at 08:30 LT is calculated for all the days during the 2015
monsoon. The daily accumulated rainfall collected by the rain gauge and the JWD above
1 mm is considered for the comparison. A total of 76 d of data
are utilized. Non-availability of data might occur either due to
maintenance activity or due to non-rainy days. Figure shows
the scatter plot of daily accumulated rainfall between the JWD and the rain gauge. The
correlation coefficient is about 0.99 between the two measurements despite
their different physical and sampling characteristics. The JWD measured
rainfall bias is about -0.7 mm, and the root mean square error is about
2.9 mm. These results suggest that the JWD measurements can be
utilized to understand the DSD characteristics during wet and dry spells of
the ISM in the WG region.
Identification of wet and dry spells
proposed an objective methodology to identify wet
and dry spells of the ISM. A long-term (1979–2011), high-resolution
(0.25∘×0.25∘) gridded daily rainfall dataset from
the IMD rain gauge network is used to classify the wet and dry spells of the ISM. The
area-averaged daily rainfall time series is constructed for HACPL in the
Mahabaleshwar (17.75–18∘ N and 73.5–73.75∘ E)
region during the monsoon (1 June to 30 September) for 4 years (2012–2015) as
well as for long-term data. The daily average rainfall difference for four
monsoons and the daily average of the long-term data provide the daily
anomalies. The standard deviation of daily average rainfall is calculated from
long-term data. The standardized anomaly time series is obtained by
normalizing the daily anomalies with corresponding standard deviations.
Events=(Avg. of daily rain-avg. of long term rain)SD of daily rain
These standardized anomaly time series are used to separate the wet and dry
spells. A period in this time series is marked as wet (dry) if the
standardized anomaly exceeds 0.5 (-0.5) for three consecutive days or more
. Figure shows the standardized rainfall
anomalies calculated using Eq. (9). Table shows the number
of wet and dry days for the study period. It is observed that there are more
dry days during the 2012–2015 monsoon, and July has relatively more wet days. A
total of 44 640 (149 760) 1 min raindrop spectra are analyzed during
wet (dry) days for the 2012–2015 ISM.
The standardized rainfall anomaly for the years (a) 2012, (b) 2013, (c) 2014, and (d) 2015 during June–September. The dashed line marks the 0.5 and -0.5 rainfall anomaly.
Total number of wet and dry days during the monsoon (June–September) of 2012–2015.
MonthsWet (no. of days)Dry (no. of days)June1540July1638August046September1035DSD overview – topographic perspective
A single pointwise instrument is not sufficient to address the orographic
impacts on DSD characteristics. One of the difficulties in studying the effect
of orography on DSD properties is the unavailability of many disdrometer
measurements in the WG region. Here an overview of DSD characteristics over
the WG is shown using Global Precipitation Measurement (GPM) mission satellite
products. The GPM level 3 data provide different DSD parameters like
Dm and Nw at a spatial resolution of
0.25∘×0.25∘ from 60∘ S to
60∘ N. The GPM is the first spaceborne dual-frequency
precipitation radar (DPR) that contains the Ku-band at ∼13.6GHz and
Ka-band at ∼35.5GHz. The details of the GPM mission can be found in
, and the dataset used can be found in
.
The GPM estimates Dm and Nw using
the dual-frequency ratio (DFR) method. However, the GPM DPR suffers
from limitations. The DSD parameterization used in the GPM DPR is the gamma
distribution with a constant shape parameter, μ=3. The constant μ introduces errors into the
retrievals. The retrieval of Dm using the DFR method is iterative,
and it has two solutions when the DFR is less than 0
. The
uncertainties in GPM DPR in estimating DSD are detailed in
and . assessed the
DSD measurements from the GPM in the WG region by comparing them with
a ground-based disdrometer. They showed that the seasonal variations in
Dm and Nw are well represented in the GPM
measurements. However, the GPM underestimates Dm and overestimates
Nw compared to the ground-based
disdrometer. also showed that the GPM underestimates
(overestimates) the mean Dm (Nw) during southwest
and northeast monsoons over Gadanki, a semiarid region of southern India. They
showed that the single-frequency algorithm underestimates mean
Dm by ∼0.1mm below 8 mmh-1, and the
underestimation is a little higher at higher rain rates, whereas in the DFR
algorithm, the mean Dm is nearly the same below
8 mmh-1 but underestimated (∼0.1mm) at higher rain
rates. Further, the underestimation is very small for Dm below
1.5 mm. In most cases, the rainfall intensity is below
8 mmh-1 (as discussed in the previous section), and Dm
is below 1.5 mm in the WG region. Hence, it is reasonable to consider
the GPM measurements to present DSD characteristics over the WG.
Box-and-whisker plot of Dm distributions over the ocean, windward side (HACPL), and leeward side of the mountain from GPM measurements. The box represents the data between the first and third quartiles, and the whiskers show the data from the 12.5 and 87.5 percentiles. The horizontal line within the box represents the median value of the distribution.
Three locations (ocean, windward side, and leeward side of WG) are selected to
examine the DSD variations in different topographic regions. The DSD
differences at these three sites can be used to partly infer the effect of orography on
DSD. Figure shows the Dm distribution over the ocean,
windward side, and leeward side of the WG. The Dm distribution is
smaller over the ocean and windward side, whereas Dm shows large
variability on the leeward side. Further, the Dm median value is lower
over the ocean than the windward and leeward sides of the mountain. The smaller
distribution of Dm over the ocean and windward side can be
attributed to shallow clouds and cumulus congestus. The broader distribution and
relatively higher median value of Dm represent the continental
convection on the mountain's leeward side. also
observed the narrow Dm distribution during the Olympic Mountains
Experiment (OLYMPEX) on the Olympic peninsula's windward side.
Results and discussion
The DSD and rain integral parameters during wet and dry spells are examined in
terms of the diurnal cycle and with different types of precipitation (convective and
stratiform). We considered raindrops with diameters less than 1 mm
to be small drops, diameters between 1 and 4 mm to be midsize drops,
and diameters above 4 mm to be large drops.
Raindrop size distribution during wet and dry spells
Figure shows the temporal evolution of the normalized raindrop
concentration during wet and dry spells for smaller and midsize drops. The
concentration of smaller drops (Fig. 5a) is higher during dry periods. The
higher concentration of small drops in dry spells indicates the influence of
orography on rainfall over the WG. In the mountain regions rainfall is produced
when the upslope wind is stronger and moisture availability is high
. In such a situation, the strong orographic wind
enhances cloud droplet growth via condensation, collision, and coalescence
. Further, many small raindrops during dry
spells indicate drop breakup and evaporation processes. For smaller drops, dry
spells exhibit a strong diurnal cycle with a primary maximum in the afternoon
(15:00–19:00 LT) and a secondary peak in the night
(23:00–05:00 LT). also found similar diurnal
features in 15 dBZ echo-top height (ETH) from radar observations during
dry spells. However, such a diurnal cycle is not present in smaller drops
during wet spells. These smaller drops show a slightly higher concentration
during morning (05:00–07:00 LT), representing the oceanic nature of
rainfall .
Diurnal variation in raindrop concentration during wet and dry spells for (a) smaller drops (<1mm) and (b) midsize drops (1–4 mm). The concentration of raindrops within each hour is normalized with the total concentration of raindrops in the respective spells (wet or dry). The black line represents wet spells, and the red line represents dry spells.
For midsize drops (Fig. 5b), the concentration is higher in wet spells than dry
spells. The higher concentration of midsize drops during wet spells could be
due to the collision–coalescence process and
accretion of cloud water by raindrops . This result
suggests that congestus clouds are omnipresent during wet spells. A clear
diurnal cycle can be observed during both spells; however, their strengths
are different. The wet spells exhibit two broad maxima, one in the late
afternoon (14:00–19:00 LT) and the other in the early morning
(05:00–07:00 LT). The dry spells also show two maxima, one in the
late afternoon (14:00–19:00 LT) as in the wet periods, and the other
in the night (23:00–05:00 LT). Such a diurnal cycle is also observed
in rainfall features over the WG . found continuous
rainfall with a double-peak structure of nocturnal and afternoon–evening
maxima in the WG region. observed a
double-peak rainfall pattern in the WG region. They proposed that the morning
peak is related to oceanic convection, while the afternoon peak is associated
with continental convection.
Average DSDs during wet and dry spells.
Figure shows the mean DSDs during wet and dry spells along
with the seasonal mean. Here, N(D) is plotted on a logarithmic scale to
accommodate its large variability. In general, the DSDs during dry spells are
narrower than during wet periods. The DSDs are concave-downward during both
spells. The mean concentration of smaller drops (below 0.9 mm) is
higher and the mean concentration of medium and larger drops is lower in dry
periods. An increased concentration of smaller drops and a decrease in the number of medium
and larger drop concentrations are found in the dry spells compared to the seasonal
mean concentration. This indicates the collision and breakup processes
described by and . In
contrast, low concentrations of smaller drops and an increase in the number
concentration of drops above 0.9 mm diameter are observed in the wet
spells.
To study the differences in DSD during wet and dry spells with rain rate,
the N(D) distribution is compared at different rain rates, as shown in
Fig. . Here, N(D) is plotted on a logarithmic scale. A
significant difference in N(D) is found between wet and dry spells. The
contours are shifted to higher rain rates and higher diameters in the wet
spells. This indicates that the number of midsize drops in the range 1–2 mm is
higher in wet spells than in dry spells for the same rain rate. This is more
pronounced at lower rain rates below 10 mmh-1. Further, the
raindrop concentration in the range 1–2 mm increases as the rain rate
increases between 5 and 15 mmh-1 during wet periods. At higher
rain rates (above 10 mmh-1), the number of smaller and midsize drops is
higher in the wet spells than in the dry periods. However, this difference
decreases gradually as the rain rate increases. At above 30 mmh-1,
both the periods show a similar distribution of N(D) (not shown). However,
for larger drops above 4.5 mm, the concentration is higher in wet
spells than dry periods for all rain rate intervals (not shown).
The variation in N(D) as a function of D at different rain rates for (a) wet and (b) dry spells.
Histograms of (a)Dm, (b)log10(Nw), (c)λ, and (d)μ for wet and dry spells. (e–h) Same as (a–d), but for stratiform rain. (i–l) Same as (a–d), but for convective rain. Here, the black and red lines represent wet and dry spells, respectively.
Figure presents histograms of Dm,
log10(Nw), λ, and μ during wet and dry
spells. The histograms of Dm are positively skewed during both
wet and dry periods (Fig. 8a). The distribution of Dm is broader
in dry spells. The Dm varies from 0.42 to 4.8 mm, with
a maximum at ∼1.2mm during wet periods, whereas it ranges from
0.4 to 5 mm, with a maximum at ∼0.8mm during dry
spells. For Dm below 1 mm, the dry spell distribution
is higher than for wet spells. This finding indicates the predominance of
smaller drops during dry spells. The mean, standard deviation, and skewness of
Dm are provided in Table . The mean
Dm is 1.3 mm, and its standard deviation is 0.38 during
wet spells, whereas the mean Dm is 0.9 mm, and its
standard deviation is 0.37 during dry spells. A relatively large number of
small drops reduce Dm in dry spells, while fewer smaller drops
and relatively more midsize drops increase Dm in wet
periods. The histograms of log10(Nw) are negatively skewed
during both wet and dry spells (Fig. 8b). The log10(Nw)
shows an inverse relation with Dm and is varied from 0.52 to
5.11 during wet spells and from 0.50 to 5.43 during dry periods. The histogram
of log10(Nw) peaks at 3.9 during wet periods; however, it
shows a bimodal distribution during dry spells that peaks at 3.9 and 5. This
finding is consistent with . They analyzed 0 dBZ ETH,
which represents the cloud-top height, and observed a bimodal distribution,
which peaks at 3 and 6.5 km during dry periods. The large standard
deviation indicates the large variations in Dm and
Nw during both wet and dry periods. The histograms of λ
and μ are shown in Fig. 8c and d. Generally, λ represents the
truncation of the DSD tail and μ indicates the breadth of DSD. If λ is
small, the DSD tail is extended to larger diameters and vice versa. The
positive (negative) μ indicates the concave-downward (upward) shape for the
DSD. The zero value of μ represents the exponential shape for DSD
. The λ shows positive values during wet and
dry spells. The occurrence of λ is higher below 10 mm-1
during wet periods, indicating the broader spectrum of raindrops, whereas it
is distributed up to 20 mm-1 during dry spells. The extension of
λ towards higher values represents the higher occurrence of smaller
drops during both periods. Relatively smaller λ and Nw
in wet spells indicate that the tail of DSD extends to large raindrop
sizes. The μ is positive during both wet and dry spells, indicating the
concave-downward shape of DSD.
Mean, standard deviation, and skewness of the DSD parameters in wet and dry spells.
Numerous studies have been carried out to understand DSDs during different
types of convection and within a convective system . These studies showed that the combined dynamical (stratiform and
convective) and microphysical processes occurring in a precipitating system
cause differences in observed DSD. Therefore, to understand the effect of
dynamical processes on different DSD characteristics during wet and dry
spells, the precipitation events are classified into stratiform and convective
types. Several rain classification schemes are proposed in the literature using
different instruments, like a disdrometer, radar, and/or a profiler
. In this work, precipitating
systems are classified as stratiform and convective based on the
criterion. Even though several other classification
schemes are in the literature, it is the most widely used classification criterion
for stratiform and convective rainfall. The main purpose here is to understand
the DSD differences between convective and stratiform (rain that does not
fall under the convective category) rain systems. For rain type
classification, considered five consecutive
2 min DSD samples. However, 10 successive 1 min DSD samples
are considered to classify rainfall as stratiform and convective in this
work. If the mean rain rate of 10 successive DSD samples is greater than
0.5 mmh-1 and if the standard deviation is less than
1.5 mmh-1, then the precipitation is classified as stratiform;
otherwise, it is classified as convective.
Mean, standard deviation, and skewness of the DSD parameters in stratiform rain for wet and dry spells.
Figure 8e–h present histograms of Dm,
log10(Nw), λ, and μ during stratiform rain
events in wet and dry spells. The mean, standard deviation, and skewness of
these parameters are provided in Table . The histograms of
Dm (Fig. 8e) are positively skewed during stratiform rain events
in both the spells. The Dm is broader in stratiform rain for dry
spells, and it varies between 0.38 and 2.77 mm with a maximum near
0.42–0.58 mm. The distribution of Dm shows higher
frequency below 0.6 mm in dry spells. This finding indicates the
presence of more smaller raindrops in stratiform rain for dry
spells. The Dm varies from 0.42 to 2.48 mm with a
maximum near 1–1.4 mm during stratiform rain in wet periods. The
Dm distribution is higher in wet spells above 1 mm,
indicating the dominance of midsize and/or larger drops. The histogram of
log10(Nw) (Fig. 8f) is positively skewed in the wet spells
and negatively skewed in the dry periods for stratiform rain. The distribution
is narrower in wet periods and broader in dry spells. The distribution peaks
between 3 and 3.6 during wet spells, whereas it peaks at 5 during dry
spells. The distribution of λ (Fig. 8g) is broader in stratiform
rain events during both wet and dry periods. The distribution varies from 1.2
to 52 mm-1 with a mode at 10 mm-1 in stratiform rain
for wet spells. This result further supports the presence of midsize drops in
wet periods. The distribution of λ shows higher occurrences above
15 mm-1 during dry spells, indicating the truncation of DSD at
relatively smaller drop diameters. The histograms of μ (Fig. 8h) show a
concave-downward shape for DSDs during stratiform rain events in both wet and
dry spells.
Figure 8i–l show the distribution of Dm,
log10(Nw), λ, and μ during convective rain
events in wet and dry spells. The Dm histograms are positively
skewed in convective rain during both wet and dry spells (Fig. 8i). In
convective rain, the distribution of Dm is broader in wet
spells. It can be seen that the presence of small drops is higher in dry
spells, even in convective rain. The distribution of
log10(Nw) shows an inverse relation with Dm in
convective rain (Fig. 8j). The log10(Nw) is negatively
skewed in wet spells, whereas it is positively skewed in dry spells. The
distribution of λ (Fig. 8k) indicates larger drops in convective rain
compared to stratiform rain in both wet and dry spells. The histograms of
μ (Fig. 8l) show the concave-downward shape of DSDs in convective rain for
both wet and dry spells. The mean, standard deviation, and skewness of these
parameters are provided in Table .
Mean, standard deviation, and skewness of the DSD parameters in convective rain for wet and dry spells.
Several points can be noted from the above discussion.
The maximum value for mean Dm and the largest standard deviation are for convective rain in wet spells.
The maximum value for log10(Nw) and higher standard deviation are observed during stratiform rain in dry spells.
A considerable difference is found in Dm and log10(Nw) during stratiform rain in dry and wet periods. However, this difference is small in convective rain.
There are distinct differences in λ and μ for stratiform rain during wet and dry spells.
The above results indicate that rainfall over the WG is associated with warm
rain processes during wet and dry spells. The microphysical processes in warm
rain include rain evaporation, accretion of cloud water by raindrops, and rain
sedimentation .
observed the predominance of larger cloud droplets in warm clouds during wet
spells over the Amazon. Similarly, showed that larger
Dm is associated with mixed-phase clouds during dry periods
over the Amazon. Recently, showed that cumulus congestus is
higher during wet spells, and shallow clouds are dominant during dry periods
in the WG region. Thus, the larger Dm may be due to cumulus
congestus during wet spells. The differences in Dm during wet
and dry spells might occur at the cloud formation stage and/or during
the descent of precipitation particles to the ground. The microphysical and dynamical
processes during the descent of precipitation particles are responsible for
the spatial–temporal variability in Dm. The dominant dynamical processes that affect
Dm are updrafts, downdrafts, and advection by horizontal
winds. To understand the dynamical mechanisms leading to different
microphysical processes during wet and dry periods, we have analyzed
temperature, specific humidity, and horizontal and vertical winds for the 2012–2015
monsoon. Figure shows the anomalies in specific humidity
(kgkg-1, shading), temperature (K, contours), and
horizontal winds (vectors) at 850 hPa derived from the ERA-Interim
dataset. This pressure level is selected, as the temperature anomaly and
moisture availability aid the growth of active convection. The daily
00:00 UTC ERA-Interim data for 10 years (2006–2015) are considered to
find anomalies. Seasonal averages are calculated for different atmospheric
parameters, and the anomalies are estimated as the difference between the wet and dry
period mean and the seasonal mean. Here, positive anomalies in specific humidity
(temperature) represent an increase in moisture content (heating), and
a negative anomaly represents a decrease in specific humidity (cooling). It is
observed that the temperature over the west coast of India
(including the study region) is cooler in wet spells than dry periods. This figure also
shows that the anomalous winds are maritime and continental during wet and
dry spells, respectively. The anomalous winds coming from the oceanic region
bring more moisture (positive anomalies in specific humidity) over the WG during
wet spells, whereas the anomalous winds coming from the continent bring dry
(negative anomalies in specific humidity) air during dry spells. The thermal
gradient between the WG and surrounding regions and the availability of more
moisture favor active convection in the wet spells, whereas positive
temperature anomalies in the dry spell can lead to evaporation of raindrops,
which can subsequently break the drops, thereby leading to smaller-diameter
drops.
Spatial distribution of anomalies in specific humidity (kgkg-1, shading), temperature (K, contours), and horizontal winds (vectors) at 850 hPa during wet and dry spells in the monsoon for 2012–2015. Here, positive anomalies in specific humidity (temperature) represent an increase in moisture content (heating), and a negative anomaly represents a decrease in moisture (cooling). The black dot represents the observational site.
The mean profile of omega for wet and dry spells.
To understand the effect of updrafts and downdrafts on Dm
variability, the omega (vertical motion in pressure coordinates) field is analyzed
for the region 17–18∘ N and
73–74∘ E. Figure shows the vertical profile
of omega during wet and dry spells. Here, negative values of omega represent
updrafts and vice versa. The mean vertical winds are negative in wet spells,
indicating updrafts, whereas the mean vertical winds are small and positive,
indicating downdrafts during dry spells. The updrafts do not allow the smaller
drops to fall, which are carried aloft, where they can fall out later. Hence,
the smaller drops have enough time to grow through the collision–coalescence process
to form midsize or large-size drops. Therefore, medium- or large-size
drops increase at the expense of smaller drops, which leads to larger
Dm during wet spells, whereas the downward flux of raindrops
increases due to the downdrafts, which causes smaller drops to reach the
surface. The large density of smaller drops decreases Dm during
dry spells.
Diurnal variation of the mean rain rate (mmh-1) for wet and dry spells.
Distribution of Dm at different rain rates for wet and dry spells. The horizontal line within the box represents the median value. The boxes represent data between the first and third quartiles, and the whiskers show data from the 12.5 to 87.5 percentiles. Black represents wet spells, and red represents dry spells.
Summary of DSD characteristics for wet and dry spells in the WG region.
The diurnal variation in the mean rain rate during wet and dry spells is shown in
Fig. . The mean rain rate is higher during wet periods
throughout the day. The relatively lower rain rates are due to a higher
concentration of smaller drops during dry spells. The diurnal variation in
the rain rate shows a bimodal distribution during both wet and dry spells. The
primary maximum is in afternoon hours and the secondary maximum is during
morning hours. The raindrop concentration increases monotonically (refer
Fig. 5), with an increase in rain rate for all the drop sizes during dry
spells. This finding indicates that the increase in the rain rate is responsible
for the rise in both the concentration and raindrop size during dry spells. However,
in wet periods, the concentration of smaller drops is constant throughout the
day, and the increase in rain rate is due to the rise in the concentration and
size of midsize raindrops. This further indicates that the collision and
coalescence processes and deposition of water vapor onto the cloud drops are
responsible for the increased concentration (afternoon and early-morning hours) of
midsize raindrops during wet spells. In addition, the raindrop diameter
depends on the rain rate, which varies between wet and dry spells. The
Dm distribution during wet and dry spells at different rain
rates is shown in Fig. . The Dm is higher in
wet spells than dry spells below 10 mmh-1. This could be due to
the deposition of water vapor and accretion of cloud water on raindrops. This
result in larger Dm during wet spells compared to dry spells. At
higher rain rates (above 20 mmh-1), the Dm distribution
remains the same during both spells. This is due to equilibrium of DSD by
collision, coalescence, and breakup mechanisms, as described in
and . So, it is
evident that the dynamical mechanisms underlying the microphysical processes
cause the differences in DSD characteristics during wet and dry spells. The
distinct DSD features during ISM's wet and dry spells over the WG are summarized
in Fig. .
Implications of DSD during wet and dry spells: <inline-formula><mml:math id="M288" display="inline"><mml:mi mathvariant="italic">μ</mml:mi></mml:math></inline-formula>–<inline-formula><mml:math id="M289" display="inline"><mml:mi mathvariant="italic">λ</mml:mi></mml:math></inline-formula> relation
The gamma distribution is widely used in microphysical parameterization
schemes in numerical models to describe various DSDs. However, μ is
often considered to be constant. found that
μ plays a vital role in determining sedimentation and microphysical growth
rates. In this context, the microphysical properties of clouds and
precipitation are sensitive to variations in μ. Several researchers showed
that μ varies during the precipitation . proposed an empirical
μ–λ relationship using 2DVD data collected in Florida. They
examined the μ–λ relation with different rain types. These
μ–λ relations are useful in reducing the bias in estimating rain
parameters from remote sensing measurements . Recent
studies have demonstrated variability in the μ–λ relation for
different types of rain and geographical locations
. Hence, it
is necessary to derive different μ–λ relations based on local DSD
observations.
Comparison of μ–λ relations derived in the present study with other orographic precipitation regions.
StudyClimatic regimeμ–λ relationPresent studyWet spells over the WGλ=0.0359μ2+0.802μ+2.22Present studyDry spells over the WGλ=0.0138μ2+1.151μ+1.198Present studyStratiform precipitationλ=0.0022μ2+0.933μ+1.86Present studyConvective precipitationλ=0.0069μ2+0.576μ+2.42Summer season in Taiwanλ=0.0235μ2+0.472μ+2.394Winter season in Taiwanλ=-0.0135μ2+1.006μ+3.48Summer season, Tibetan Plateauλ=-0.0044μ2+0.764μ-0.49Oklahomaλ=-0.02μ2+0.902μ-1.718Typhoons in northern Taiwanλ=0.0433μ2+1.039μ+1.477Floridaλ=0.0365μ2+0.735μ+1.935
An empirical μ–λ relationship is derived for both wet and dry
spells. The DSDs with a rain rate less than 5 mmh-1 are excluded to
minimize the sampling errors. In addition, only total drop counts above 1000
are considered in the analysis, as proposed by
. Figure shows the μ–λ
relation for wet and dry spells, and the corresponding polynomial least-square
fits are shown as solid lines. The fitted μ–λ relations for wet
and dry spells are given as follows.
10Wet spell λ=0.0359μ2+0.802μ+2.2211Dry spell λ=0.0138μ2+1.151μ+1.198
The above equations represent the fact that the smaller the value of λ (higher rain
rates), the smaller the value of μ in both spells. Thus, the DSDs tend to
be more concave-downward with an increase in the rain rate. This finding suggests
a higher fraction of small and midsize drops and a lower fraction of larger
drops, reflecting less evaporation of smaller drops and more drop breakup
processes. However, the fitted μ–λ relation exhibits a large
difference between wet and dry spells. Comparing Eqs. (10) and (11), one can
observe that the coefficient of the linear term is smaller in wet spells than that
of dry spells. Hence, for a given μ, the dry spells have higher λ
compared to the wet spells. Further, Dm is higher during wet
spells than dry spells for a given rainfall rate due to the different
microphysical mechanisms discussed above (Fig. ). This leads
to higher μ in wet spells than dry spells, which indicates that different
microphysical mechanisms lead to different μ–λ relations. Hence,
it is apparent that a single μ–λ relation cannot reliably
represent the observed phenomenon during different monsoon phases.
Scatter plots of μ–λ values obtained from gamma DSD for (a) wet and (b) dry spells. The solid line indicates the least-square polynomial fit for the μ–λ relation.
Further, μ–λ relationships are derived for convective and
stratiform rain as follows.
12Convective rainλ=0.0069μ2+0.576μ+2.4213Stratiform rain λ=0.0022μ2+0.933μ+1.86 fitted μ–λ relations for summer and
winter rainfall over northern Taiwan. derived an
empirical μ–λ relation over the Tibetan
Plateau. analyzed μ–λ relations over
Oklahoma. Different μ–λ relations are derived for different
weather systems over northern Taiwan . The
μ–λ relationship obtained in this work differs from
, , and
. The differences in μ–λ relations could
be attributed to several factors like geographical location, microphysical
processes, rain rate, and type of instrument. To explore the plausible effect
of rainfall rate, μ–λ relations are compared with previous
studies for rain rates below 5 mmh-1as
in and above 5 mmh-1as
in (figure not shown). It is observed that μ–λ
relations in this work differ from previous studies at both rain rates. Further, the slope of the μ–λ relationship is higher over
the WG than in previous studies. This shows that the wet and dry spells have a higher
μ than previous studies for the same λ, indicating that the underlying
microphysical processes are different over the complex orographic region of the
WG. Further, Dm in the present study is higher than in previous
studies e.g.,. The different Dm
distributions lead to different μ values . Thus,
relatively higher Dm values could contribute to higher μ for
the same λ values in the present study. Hence, the differences in
μ–λ relations compared to previous studies may be related to different
rain microphysics (such as collision–coalescence, breakup). In addition,
, and used 2DVD
measurements, whereas JWD data are utilized in this work. The different
instruments can have different sensitivities, which can also affect
μ–λ relations. The μ–λ relationships derived for the
current study are compared with the other orographic precipitation and are
provided in Table . It is clear that μ–λ
relations vary in different types of rainfall and climatic regimes.
Conclusions
The raindrop spectra measured by a JWD are analyzed to understand the DSD
variations during wet and dry spells of the ISM over the WG. Observational results
indicate that the DSDs are considerably different during wet and dry
periods. In addition, the DSD variability is studied with stratiform and
convective rain during wet and dry spells. Key findings are listed below.
A high concentration of smaller drops is always present in the WG region, indicating shallow convection dominance.
The DSD over the WG shows distinct diurnal features. The dry spells exhibit a strong diurnal cycle with a double peak during late afternoon and nighttime for smaller and midsize drops, whereas this diurnal cycle is weak for smaller drops in wet spells.
Small Dm and large Nw characterize the DSDs over the WG. The Nw shows a bimodal distribution during dry spells. This bimodality is weak in wet spells. The distribution of λ shows the dominance of small drops in dry spells and midsize drops in wet spells.
The thermal gradient between the WG and surrounding regions, higher availability of water vapor, and strong vertical winds favor the formation of cumulus congestus, which are responsible for the presence of midsize to larger drops during wet spells.
The empirical relation between μ and λ shows a significant difference between wet and dry spells. The different microphysical mechanisms lead to different μ–λ relations.
It is evident from this study that, even though warm rain is predominant,
the dynamical mechanisms underlying the microphysical processes are different,
which causes the difference in observed DSD characteristics during wet and dry
spells.
Data availability
The disdrometer data are archived at IITM and are available
from the corresponding author (skd_ncu@yahoo.com) for research
collaboration. GPM and ERA-Interim datasets were respectively downloaded from
https://pmm.nasa.gov/data-access/downloads/gpm and https://apps.ecmwf.int/datasets/data/interim-full-daily/levtype=pl/.
Author contributions
UVMK and SKD designed, analyzed, and prepared the paper. SKD, UVMK, GSE, and UB proposed the methodology. GSE, SMD, and GP contributed to the discussion of the paper.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
The authors are thankful to the director at IITM for his support. The authors would like to acknowledge the technical and administrative staff of the High Altitude Cloud Physics Laboratory (HACPL), Mahabaleshwar, for maintaining the disdrometer. The authors acknowledge the India Meteorological Department (IMD) for the provision of the rainfall dataset. The authors also acknowledge JAXA (Japan) and NASA (USA) for providing GPM data (https://pmm.nasa.gov/data-access/downloads/gpm, last access: 30 November 2018). The authors would like to acknowledge the European Centre for Medium-Range Weather Forecasts (ECMWF) for providing the ERA-Interim dataset. The paper benefitted from comments and suggestions provided by the editor and the anonymous reviewers
Review statement
This paper was edited by Jayanarayanan Kuttippurath and reviewed by two anonymous referees.
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