Introduction
Global climate models (GCMs) continuously improve to overcome deficiencies in
climate predictions associated with cloud and precipitation processes
e.g.,. Following suit, observational
studies serve to inform GCM and cloud-resolving model (CRM) activities by
providing the physical understanding for more diverse, climatically
significant global cloud conditions and their associated feedbacks. Cumulus
to deeper convective clouds are associated with high-impact weather events
and act as the engine of global circulation. Subsequently, model
treatments of convection have a profound impact on weather and climate
simulations. For practical reasons, the evaluation of cumulus treatments has
often stressed comparisons against larger-scale, longer-term precipitation
properties, for example, accumulated rainfall products from ground or
spaceborne instruments e.g.,. Thus, a traditional
observational approach in support of convective modeling has been to document
global precipitation variability and improve basic rainfall retrievals.
Nevertheless, improving model capabilities introduces new challenges that
motivate multi-scale, multi-sensor observations to better constrain cloud
microphysics and dynamics closer to the process levels future GCMs attempt to
represent e.g.,.
Recently, the Observations and Modeling of the Green Ocean Amazon
(GoAmazon2014/5) experiment was motivated by demands to gain a better
understanding of aerosol, cloud and precipitation interactions on climate and global circulation . One source of
uncertainty when developing useful precipitation retrievals for model
development is the shortage of long-term surface gauge and disdrometer
observations within tropical regions. Although radar rainfall estimation and
its uncertainty for tropical applications is of primary interest, basic radar
preprocessing, calibration and dual-polarization radar data quality are also
improved with extended surface precipitation records in diverse environments.
Establishing boundaries for tropical precipitation expectations and radar
data quality concepts (self-consistency methods; e.g., )
provides an immediate benefit when interpreting remote radar deployment
datasets including those from the Atmospheric Radiation Measurement
(ARM; ) mobile facility (AMF; ) during
GoAmazon2014/5. Specifically, the Amazon basin offers a unique tropical
perspective on the variability in precipitation, as it receives copious
precipitation across diverse cloud conditions, including wet and dry
seasonal variability interconnected to large-scale shifts in the
thermodynamic forcing and coupled local cloud-scale feedbacks
e.g.,.
Although improving hydrological retrievals is of a practical significance, an
interesting outcome from previous Amazon studies is the labeling of the
Amazon as the “Green Ocean”. This Green Ocean terminology is rooted in
studies such as wherein low cloud condensation nuclei
(CCN) concentrations and high CCN-to-condensation nuclei (CN) ratios over the
Amazon resembled marine environments, distinct from previous continental
expectations. However, this terminology is often extended to include the
unique regional characteristics observed from Amazon convection that span
oceanic to continental cloud extremes in key attributes such as updraft
intensities and propensity for electrification. Specific to convection,
Amazon clouds may initiate under these clean (or lower) CCN conditions or over
a pristine forest, but they may also experience a range of thermodynamical and aerosol
forcing influences that promote changes in cloud properties including
electrification, cloud droplet size distribution and precipitation changes,
or enhanced updraft intensity
e.g.,. As
described by , the prevalence of maritime convective
cloud regimes over a large continent are possibly still underappreciated in
the convective cloud spectrum and its intensity, especially given the
propensity to identify deeper convection over the Amazon having
electrification, arguing continental convective characteristics.
To understand the diversity of convective clouds as well as to constrain
upcoming convective modeling activities from GoAmazon2014/5, it is
informative to explore Amazon cumulus characteristics over this extended
dataset. One motivation for this study is to identify conditions under which
precipitation sampled in the Amazon basin adheres more to oceanic,
maritime and continental characteristics e.g.,. Previous
ARM Tropical Western Pacific (TWP; e.g., ) precipitation
studies have helped identify practical thresholds and composite behaviors for
convection, as well as the strengths/deficiencies for more practical
convective cloud regime segregations under various larger-scale monsoonal and
more oceanic cloud environments
e.g.,. While it is
important to view these Amazon datasets and cloud or larger-scale regime
shifts in the context of global disdrometer observations
e.g.,, it is also useful to determine whether common
remote-sensing platforms (e.g., dual-polarization quantities as from X-band
to S-band radars) are sensitive to these differences.
This study summarizes the precipitation properties collected by the ARM AMF
during GoAmazon2014/5 at the “T3” site located approximately 70 km to the
west of Manaus in central Amazonia, Brazil (3∘12′46.70′′ S,
60∘35′53.0′′ W). The location samples both the local pristine
atmosphere and the possible effects of the Manaus, Brazil, pollution plume.
The T3 site was equipped to capture continuous convective cloud and
precipitation column characteristics from a reconfigured radar wind profiler
coupled with a ground disdrometer. Section describes the
instrumentation, methods and sources for uncertainty in results presented by
this study. Precipitation comparisons from the disdrometer using traditional
drop size distribution (DSD) parameters and dual-polarization quantities are
located in Sect. . Sections and
discuss the properties of the Amazon cumulus
convective and associated stratiform precipitation, including segregations
according to seasonal (wet or dry regime) variability, cloud height and possible
aerosol influences. A summary of the key findings and discussions about
the Amazon as a Green Ocean are provided in the final section.
Dataset and methodology
ARM T3 precipitation and radar wind profiler dataset and processing
Precipitation observations are obtained from two primary instruments, a
second-generation particle size velocity (PARSIVEL) disdrometer
e.g., and a
reconfigured 1290 MHz radar wind profiler
(RWP; ). The collocated sensors
capture surface DSDs, as well as simultaneous profiles for the vertical
velocity and reflectivity factor estimates through the depth of Amazon
clouds. These instruments operated concurrently from September 2014 through
December 2015, a period that captured one wet season (herein, the 5
months defined as December through April) and one dry season (herein,
periods from June through September). Additional information on the AMF
deployment, including details on the larger-scale thermodynamic sampling
throughout the campaign and appropriateness for wet or dry regime separations,
is found in the GoAmazon2014/5 overview by .
PARSIVEL measurements such as estimated DSD parameters and additional derived
radar quantities are determined using 5 min aggregation windows. This
sampling reduces noisiness found in 1 min DSD quantity retrievals, which is
reduced further by selecting 5 min DSDs having
R > 0.5 mmh-1 and total drops > 100. In total,
3852 5 min DSDs were collected during the GoAmazon2014/5 campaign, with 3087
associated with the collocated RWP observations. The total precipitation
associated with the full set of DSD observations is 2597 mm, with
2511 mm associated with collocated RWP observations. Approximately
1500 mm were associated with convective precipitation (RWP
classifications to be discussed in later sections). Processing for the
disdrometer was performed using the open-source PyDSD code
, with standard corrections
e.g.,. Estimated quantities include the
rainfall rate R (mmh-1), rain water content (LWC,
gm-3), measured median volume drop size D0 (mm), and
the mass-weighted mean diameter Dm (mm). Processing also
includes additional parameters of a gamma-fit DSD assumed to be of the form
N(D) = N0Dμexp(-ΛD), having
volume diameter D (mm) equivalent to number concentration N0
(mm-1m-3), shape parameter μ and slope parameter
Λ (mm-1) calculated following a method of higher moments
(second, fourth and sixth moments; e.g., ).
Additional calculations for a normalized DSD intercept parameter
Nw have been adapted following . These are
required to investigate a DSD-based convective–stratiform partitioning scheme
based on disdrometer observations
e.g.,. Dual-polarization radar
quantities including the radar reflectivity factor Z (dBZ),
differential reflectivity factor ZDR (dB), specific
differential phase KDP (degkm-1) and specific
attenuation A (dBkm-1, horizontal polarization) are
estimated for liquid media at 20 ∘C using a T-matrix approach
e.g.,. These estimates assume nonspherical drop
shapes according to the relationship in and standard drop
canting assumptions for S-band (10 cm), C-band (5.45 cm) and
X-band (3.16 cm) wavelengths.
RWP measurement details have been summarized by several recent studies, with
precipitation datasets available at high spatiotemporal resolution of
approximately 15 s and 200 m e.g.,. To
align with the 5 min disdrometer measurements, an RWP profile from the
midpoint of the 5 min window is selected. RWP measurements are typically
stable with respect to Z calibration offsets, with measurements
aligned with those from the surface disdrometer (typically viable to within
2 dB). Echo-top height (ETH) from the RWP is defined as the altitude
at which column Z drops below 10 dBZ. Amazon RWP observations
indicate this relative Z altitude as the approximate height that
mean convective cloud vertical velocity approaches 0 ms-1.
Vertical air velocity retrievals and echo classifications follow techniques
outlined by . For echo classifications, we identify
convective and stratiform regions on the basis of column Z
signatures, velocity properties and/or so-called radar “bright band”
(melting-level) designations for longer wavelengths
e.g.,. In contrast to scanning
radar-based echo designations (e.g., those typically use near-surface
Z ≃ 40–45 dBZ thresholds; e.g.,
), one RWP advantage is that columns exhibiting stronger
vertical air velocity signatures help to differentiate transitional or
elevated convective cells (e.g., instances of
|VV| > 2 ms-1).
ARM T3 aerosol observations and aerosol regime classification
Aerosol regime classifications are based on the combination of number
concentration of particle CN measurements, measurements for the fraction of
particles with diameters less than 70 nm, and carbon monoxide CO and
oxides of nitrogen (NOy) measurements at the T3 location
using instrumentation as described in and the Supplement. The philosophy for this aerosol classification is that each
aerosol measurement builds on the previous when establishing a background
condition (“clean”), with additional support for “polluted” conditions
(e.g., urban, above this background condition) as well as “biomass burning”
conditions attributed on top of “polluted” criteria. One advantage for
using this classification is that this combination of measurements helps
mitigate concerns that precipitation onset will mask ambient aerosol
conditions (e.g., as in including an insoluble CO measurement). Because of
the pronounced shift in aerosol properties seasonally, the methods
subclassify background and polluted air mass types according to
seasonal-specific windows. Classifications are available at 5 min intervals
that align with the available precipitation observations.
Histograms for a-coefficient values from
single-parameter rainfall relationships (a) R(Z),
(b) R(KDP) and
(c) R(A), calculated using the least-squares method
under the assumption of a fixed b coefficient from random sampling
of half of the dataset (5000 times), for the S-band wavelength. The black
vertical lines represent the a coefficient calculated based on the
whole dataset.
As summarized by , clean conditions (typical
background levels) exhibit values of CN < 500 cm-3,
CO < 0.14 ppm and NOy < 1.5 ppbv
during the wet season. In contrast, background levels shift towards values of
CN ≃ 1500 cm-3 (e.g., potentially a 3 or more factor of
difference in CN) for similar CO and NOy thresholds during
the dry season (transitional months fall between wet and dry characteristics). In
this regard, the dry season background conditions reflect regional biomass
burning background levels that might otherwise be considered polluted
conditions during typical wet season months. For this study, sampling
limitations during the GoAmazon2014/5 dry season (lack of available
collocated precipitation and aerosol measurements) requires our use of only
wet season observations to provide a more detailed
aerosol–cloud–precipitation investigation. Under wet season conditions,
polluted regimes are those having CN > 500 cm-3.
Biomass regimes are considered a more stringent polluted classification,
classified as those polluted regimes that also have
CO > 0.14 ppm.
PARSIVEL sampling and rainfall relationship interpretation
Later sections document relationships between estimated radar quantities and
the measured rainfall rate R. These quantities carry instrument
sampling considerations that include catchment uncertainties under convective
conditions e.g.,, additional limitations when compared
to collocated devices e.g., and/or
processing assumptions when applying functional-form DSD parameter fits
e.g.,. Radar quantities as estimated using the
disdrometer are also influenced by raindrop shape and additional assumptions
introducing variability established in previous studies
e.g.,. Given our comparisons among rainfall
accumulations with surface gauge measurements under typical storm
intensities, as well as previous side-by-side performance testing of other
PARSIVEL units, we do not anticipate radar quantity uncertainty falling
outside the variability established by previous studies. For example,
reasonable instrument offsets for radar quantities such as Z may be
on the order of 10–20 % or 1–2 dBZ.
An additional consideration when fitting rainfall relationships is the
representativeness of this dataset, including challenges when attempting to
establish the significance of functional fits. We establish coefficients for
conventional R(Z) relationships of the form
Z=aRb using nonlinear least-squares methods matched over the
entire dataset (or subsets) of Z–R pairs. For lengthier
datasets, it is informative to test variability in coefficients as related to
modest samples drawn from the total population. Since consecutive DSD
observations within precipitation events are nonindependent and correlated
in processes e.g.,, it may be important to consider this
impact of proper spacing among samples when ensuring a reliable
relationship.
As plotted in Fig. , we show histograms for
a-coefficient values from various single parameter rainfall
relationships (radar quantities estimated as in previous sections), assuming
a fixed b coefficient as determined from our complete Amazon dataset
for the S-band wavelength. This example highlights the sensitivity in the
a coefficients as estimated from random half-dataset subsets to the
complete dataset (vertical black line). Assuming a constant
b coefficient of ≃ 1.4 is typically a reasonable assumption to
assist in microphysical interpretation from R(Z)
relationships for size-controlled conditions e.g.,. As
the radar wavelength decreases, the sampling sensitivity depends on the radar
quantity of interest. For example, the differences we observe for Amazon
subsamples are typically to within 5 % of the mean dataset
a-coefficient value with respect to R(Z). Though
not shown in Fig. , a deterioration in performance at shorter
wavelengths is found for R(Z) relationships owing to the
importance of larger diameters to Z estimates and increased
non-Rayleigh scattering. In contrast, a coefficients are found
typically to within 2 % for R(KDP) and
R(A) relations, with improved performance to shorter
wavelengths (more immediate relationship between these quantities and
rainfall rate). Such results help inform basic interpretations of the
significant changes (larger than that expected from subsampling) in these
radar relationship coefficients. The corresponding plots for C-band and
X-band wavelengths are provided in the Supplement (Figs. S1 and S2).
A summary of 5 min DSD parameter breakdowns for
number of DSDs, rain rate R, median volume drop size D0,
normalized DSD intercept parameter Nw, reflectivity Z at
S-band wavelengths, and liquid water content (LWC), filtered according to rainfall rate
intervals, for all, wet and dry seasons for the Amazon (MAO) and the
Southern Great Plains (SGP) sites.
R (mmh-1)
No. DSD
<R> (mmh-1)
<D0> (mm)
<Nw> (m3mm-1)
<Z>(dBZ)
<LWC>(gm-3)
All (total rainfall = 2597 mm for MAO dataset, 694 mm for SGP dataset)
MAO
SGP
MAO
SGP
MAO
SGP
MAO
SGP
MAO
SGP
MAO
SGP
0.5–2
1080
676
1.15
1.17
1.01
0.97
6580
8882
24.1
24.1
0.08
0.08
2–4
582
337
2.86
2.87
1.24
1.26
6525
4718
30.5
30.9
0.17
0.15
4–6
294
148
4.83
4.83
1.34
1.46
7621
4454
33.7
34.7
0.27
0.23
6–10
292
116
7.66
7.56
1.49
1.73
7445
3873
36.5
38.5
0.39
0.34
10–20
339
85
14.61
14.29
1.70
1.95
6913
4333
40.8
42.3
0.69
0.61
20–40
289
61
27.79
28.48
1.90
2.16
6948
4543
44.9
45.9
1.21
1.08
40–60
93
19
48.95
47.89
2.07
2.24
7699
5502
48.4
49.0
2.03
1.82
Wet season (total rainfall = 1245 mm for MAO dataset)
0.5–2
649
1.14
0.99
6892
23.7
0.08
2–4
301
2.88
1.19
7851
30.1
0.17
4–6
148
4.80
1.33
8933
33.5
0.27
6–10
147
7.73
1.49
8295
36.1
0.39
10–20
162
14.78
1.65
8149
40.4
0.72
20–40
147
27.57
1.86
7666
44.7
1.23
40–60
44
49.02
2.04
8547
48.1
2.05
Dry season (total rainfall = 366 mm for MAO dataset)
0.5–2
73
1.19
1.06
4453
24.9
0.08
2–4
33
2.79
1.32
4694
30.9
0.15
4–6
24
4.76
1.29
7417
33.1
0.27
6–10
31
7.72
1.53
5045
37.7
0.38
10–20
34
14.92
1.89
4151
42.5
0.65
20–40
30
28.60
2.13
4275
46.4
1.16
40–60
14
49.35
2.24
5349
49.3
1.93
Radar rainfall and self-consistency relations for the
GoAmazon2014/5 dataset, for the cumulative dataset with all, wet and dry seasons,
and convective and stratiform precipitation based on RWP
classifications. Coefficients estimated at S-, C- and X-band radar
wavelengths.
Wavelength
R(Z) (T=20∘C)
R(KDP) (T=20 ∘C)
R(A) (T=20 ∘C)
R(A) (T=10 ∘C)
S band
All
Z = 343.9R1.4
R = 54.2KDP0.8
R = 2904.2A1.0
R = 2227.6A1.0
Wet season
Z = 329.5R1.4
R = 55.2KDP0.8
R = 2949.6A1.0
R = 2265.1A1.0
Dry season
Z = 388.3R1.4
R = 51.5KDP0.8
R = 2732.3A1.0
R = 2090.5A1.0
Convective
Z = 339.9R1.4
R = 54.6KDP0.8
R = 2895.0A1.0
R = 2219.6A1.0
Stratiform
Z = 385.8R1.4
R = 51.1KDP0.8
R = 2867.1A1.0
R = 2202.0A1.0
C band
All
Z = 289.0R1.4
R = 30.6KDP0.8
R = 287.8A0.9
R = 239.4A0.9
Wet season
Z = 280.6R1.4
R = 31.3KDP0.8
R = 314.4A0.9
R = 258.3A0.9
Dry season
Z = 314.8R1.4
R = 28.5KDP0.8
R = 242.1A0.9
R = 203.4A0.9
Convective
Z = 281.6R1.4
R = 30.7KDP0.8
R = 278.4A0.9
R = 232.3A0.9
Stratiform
Z = 339.8R1.4
R = 29.5KDP0.8
R = 290.6A0.9
R = 239.7A0.9
X band
All
Z = 261.4R1.6
R = 21.5KDP0.8
R = 41.4A0.8
R = 43.0A0.8
Wet season
Z = 239.1R1.6
R = 21.6KDP0.8
R = 42.7A0.8
R = 43.9A0.8
Dry season
Z = 303.2R1.6
R = 21.1KDP0.8
R = 38.2A0.8
R = 40.0A0.8
Convective
Z = 250.2R1.6
R = 21.6KDP0.8
R = 41.0A0.8
R = 43.0A0.8
Stratiform
Z = 318.5R1.6
R = 19.6KDP0.8
R = 40.8A0.8
R = 41.3A0.8
Self-consistency (T=20 ∘C)
S band
All
Z = 45.6 + 10.04log(KDP) + 3.20ZDR
Wet season
Z = 45.7 + 10.10log(KDP) + 3.17ZDR
Dry season
Z = 45.6 + 10.05log(kDP) + 3.16ZDR
C band
All
Z = 43.3 + 10.12log(KDP) + 1.96ZDR
Wet season
Z = 43.3 + 10.18log(KDP) + 1.94ZDR
Dry season
Z = 43.4 + 10.12log(KDP) + 1.82ZDR
X band
All
Z = 38.6 + 9.54log(KDP) + 4.62ZDR
Wet season
Z = 38.7 + 9.54log(KDP) + 4.52ZDR
Dry season
Z = 38.7 + 9.80log(KDP) + 4.89ZDR
Scatter plots of (a) Z,
(b) KDP and (c) A versus rain rate
and overlaid associated relationship fits using the least-squares method for
the Amazon (MAO, solid lines) and SGP-Oklahoma (SGP, dashed lines) sites, for the
S-band wavelength. Density is shown on the color scale.
Summary precipitation results and interpretation for retrieval methods
This section summarizes bulk precipitation properties, rainfall
relationships and basic dual-polarization radar connections for the
GoAmazon2014/5 dataset. A summary of DSD parameter breakdowns for select
quantities, filtered according to rainfall rate intervals, is located in
Table . As one point of comparison to continental expectations,
we include values obtained from a year-long ARM Southern Great Plains (SGP)
PARSIVEL2 deployment (November 2016 through October 2017), processed
similarly to the Amazon datasets. Within these narrowed rainfall rate
intervals, the Amazon precipitation exhibits reduced median drop sizes and
higher drop concentrations. This change is also reflected in lower Z
values and higher LWC for a similar R compared to SGP
observations. Although the 5 min dry season samples are limited, rainfall
rate breakdowns demonstrate the dry season exhibits higher relative
Z and median drop sizes (lower Nw and LWC) compared
to wet season observations. Discrepancies between SGP and the Amazon, as well as
wet–dry separations, are most pronounced at the higher R consistent
with convective cores. This is likely based on the propensity for observing
melting hail in deeper SGP convection and/or observed larger melting
aggregates in stratiform regions trailing convective lines. Both SGP
situations would favor sampling larger drop sizes at the surface.
Single-parameter dual-polarization rainfall relationships at S-, C- and X-band wavelengths
In Fig. , we plot summary dataset scatter plots overlaid with
dual-polarization relationship fits for the S-band wavelength. The
corresponding plots for the C- and X-band wavelengths are provided in the Supplement
(Figs. S3 and S4). A summary of matched rainfall coefficients is
provided in Table . For these tables, b coefficients
were fixed at a characteristic dataset value to facilitate comparison across
regime breakdowns. In Fig. , we overlay the associated fitting
(dashed lines) from SGP-Oklahoma to provide a continental reference. As a
function of radar wavelength, the a-coefficient values decrease as
wavelength decreases (e.g., quantities more closely related to the rainfall
rate). SGP dual-polarization relationships are consistent with previous
studies e.g.,, providing confidence in the
appropriateness of disdrometer processing. Note that minor discrepancies in SGP
R(Z) relations may be related to our filtering of large
drops > 5 mm that disproportionately influence Z
measurements at sites including SGP wherein melting hail is more regularly
observable. Moreover, SGP relations may carry higher a coefficients
(thus, larger discrepancies with the Amazon relationships) than reported in
our Table .
Summarized Amazon relationships follow a tropical expectation (more significant
role for warm-rain processes, e.g., droplet growth via
collision–coalescence), indicating higher concentrations of smaller drops.
This is observed with a smaller a coefficient than found for
SGP R(Z) relations and larger a coefficients than
found for SGP R(KDP) and R(A)
relations (as in Fig. ). These changes reflect a significant
change when viewed compared to Amazon dataset sampling arguments found in
Sect. . For Table , R(A)
relationships are also listed for multiple temperature assumptions,
highlighting one explanation for modest variability when attempting to
promote these relations for practical rainfall retrievals
e.g.,. As additional reference for
dual-polarization radar processing and natural calibration concepts,
self-consistency relationships among radar quantities KDP,
Z and ZDR for the various wavelengths have been
provided in Table . In comparison to continental SGP references
for statistical DSD relationships e.g.,,
self-consistency coefficients in Table reinforce the tropical
character of the Amazon precipitation, again consistent with smaller median
drop sizing (e.g., reductions in ZDR or KDP) to
achieve similar estimates of Z.
Rainfall relationships stratified according to wet and dry season conditions
are also found in Table . The wet season indicates lower
a coefficients for R(Z) and higher relative
coefficients for R(KDP) and R(A)
relations. One interpretation is that for similar R values, the wet season
DSDs carry more pronounced tropical precipitation characteristics. A
similar trend is found with seasonal self-consistency relationship
breakdowns. As before, most seasonal breakdowns are reflected as significant
changes in relationship coefficients when compared with the sampling arguments in
Sect. . An exception to this are KDP-based
rainfall relationships that appear least sensitive to this seasonal DSD
variability at X-band wavelengths (the shortest wavelength tested), possibly a reflection
of non-Rayleigh influences on KDP (i.e., the presence/absence of
larger drops) is less important.
Finally, interpreting seasonal differences can be challenging without
mentioning factors including storm intensity changes related to the
larger-scale thermodynamic shifts that alter convective and congestus
frequency, or mid-level moisture (e.g., during GoAmazon2014/5 as in
, cf. Fig. 2). The dry season promotes events that achieve
a higher rainfall rate R, but under convective environments that favor
enhanced evaporation, cooling and subsidence, which are less capable of
sustaining expansive stratiform processes. Wet and transitional month
stratiform precipitation linked to aggregation and associated DSD evolution
processes beneath the melting layer favors lower Nw, higher D0
values for similar Z values e.g.,. The fraction
of stratiform DSDs (count) of the total DSD observations in this dataset for
the wet season is 50.5 %. This fraction decreases to approximately
30.1 % for the dry season. Nevertheless, summary rainfall properties
skew heavily towards convective designations for all seasons, as reported in
Table S1. Seasonal changes will be discussed further in
Sect. in the context of multiparameter DSD breakdowns.
Contoured frequency altitude display (CFAD) histograms for the entire Amazon dataset with confidence intervals, the median
(thick black lines) and 90th (thin black lines) and 95th percentiles (white
lines) for RWP convective and stratiform reflectivity profiles (a, b) and vertical velocity retrievals (c, d). The number of profiles
of each situation is shown as a red line.
Convective–stratiform regimes for rainfall relationships and DSD properties
Isolating contributions from convective and stratiform DSDs is an initial
step for improved rainfall estimates or possible model evaluation
e.g.,. Table is segregated according to
RWP-based convective–stratiform echo classifications. These RWP-based
segregations will be further decomposed in Sect. but
for demonstration purposes are considered a reasonable benchmark when
isolating bulk regime contributions. Cumulative contoured frequency altitude
display histograms e.g.,CFADs with quantile values
(median and 90th and 95th percentiles) for RWP Z and vertical velocity
retrieval profiles are plotted in Fig. . These histograms help
establish these RWP classifications as reasonable; for example, convective
columns (Fig. 3a, c) have monotonically decreasing profiles and stronger
vertical motions, whereas stratiform (Fig. 3b, d) columns emphasize
pronounced radar bright band – or, aggregation process signatures and
weaker composite upwards vertical air velocity signatures
e.g.,. Given that checks for pronounced bright band
signatures are part of the echo classification, that these signatures are
observed is not surprising. Inflation of mid-level downwards motions in
stratiform regions is observed near the freezing level, originating from
contamination within the melting layer on fall speed corrections (e.g.,
enhanced Z from aggregation resulting in overestimates for ice fall
speeds).
In terms of rainfall relationships in Table , convective
relationships demonstrate higher coefficient values for
R(KDP) relations and smaller coefficients for
R(Z) relations. This shift is consistent with convection
favoring high Nw and low D0 for a similar Z or
KDP. R(A) relationships register as those least
influenced by these separations (smallest coefficient shifts), followed by
KDP relationships at the shorter wavelengths. This reduced
coefficient variability reflects on the closer relationship between
A and KDP with rainfall rate, less influenced by the
presence/absence of select larger drop sizes. As complementary examples for
the Amazon datasets, in Fig. we show the corresponding
histograms for Amazon convective and stratiform DSDs in terms of
Nw (Fig. 4a), LWC versus R relationships (Fig. 4b) and
D0 variations with Z (Fig. 4c). For these plots, convection is
noted by red shadings and stratiform is plotted in blue contours. Convection
demonstrates a broader distribution of Nw, LWC and other
quantities of interest. Although there is substantial overlap with stratiform
DSDs, convective DSDs exclusively cover higher extreme parameter spaces.
Histograms associated with RWP classification-based
convective (red) and stratiform (blue) DSDs in terms of
Nw (a), LWC versus R behaviors (b)
and D0 scaling according to Z (c). Density is shown on the color scale.
Histograms associated with RWP classification-based
convective DSDs in terms of ETH (a), temperature at
ETH (b) and Z at 2 km (d) for all, dry,
wet and transitional seasons, as well as the congestus for all the seasons. The
wind rose (c) is also shown for all the seasons.
The frequency for observing a given vertical velocity
(across all levels, > 1 ms-1 in navy, > 3 ms-1 in
dark green, > 5 ms-1 in light green) as a function of a
2 km RWP reflectivity. The number of samples (for
|VV| > 1 ms-1) is displayed as a red line.
Scatter plot of log10(Nw) versus
D0 from PARSIVEL disdrometer, overlaid by the contours representing the
RWP-based classifications for convective (red colors) and stratiform (blue
lines) precipitating columns. The ellipse conveys the two-sigma confidence
interval (dotted line) for those regions containing RWP-based stratiform
DSDs. The convective–stratiform regime segregation concepts in
BR and in TM are presented as a
solid black line and dashed black line, respectively.
Amazon precipitation properties: cumulative dataset characteristics
Convection-permitting models struggle to simultaneously capture convective
and stratiform cloud processes; therefore model–observational comparisons
often emphasize bulk cloud regime segregations and contingent performances to
diagnose issues with cloud model treatments e.g.,.
Although there is no clear line separating convective and stratiform
processes (e.g., for identifying deficiencies in modeled precipitation,
vertical air motions or heating profiles), bulk regime separations introduced
for Sect. are of practical use. Here, we assess how
precipitation depictions from previous campaigns might be useful to constrain
Amazon observations and the sensitivity of radar quantities to those changes.
Precipitating clouds identified by the RWP demonstrate a clear bimodal ETH
distribution (Fig. ), and one that varies according to Amazon
seasons (Fig. 5a). The behaviors are consistent with freezing level
(typically around 5 km above surface) and tropopause-level cloud-top
expectations for tropical convection (Fig. 5b, e.g.,
). Note also that the RWP is not sensitive to
cloud-sized particles; thus actual cloud-top heights (as from collocated
cloud radar references) may extend 2 km or more above these heights.
Sounding-based winds over the T3 site are predominantly easterly (mostly
observed during the dry season) to northeasterly (mostly wet season)
(Fig. 5c). Low-level Z observations (Fig. 5d) illustrate that Amazon
cumuli are often linked to relatively modest values of Z
≃ 35 dBZ. From a practical radar-based classification
perspective that typically utilizes higher Z
≃ 40–45 dBZ thresholds, it follows that standard methods may
necessitate additional texture, peakedness or similar ideas to properly
identify Amazon convection e.g.,.
As documented by (Figs. 6 and 8), convection passing
over T3 follows a diurnal cycle with peak cloud frequency around local
13:00–14:00 LT. A shift in peak frequency to later afternoon is found
within the dry season, whereas wet season deeper convection exhibits a
secondary peak in cloud frequency (related to mesoscale convective systems)
during the overnight hours. Congestus clouds (loosely precipitating clouds
having ETHs between 4.5 and 9 km) demonstrate a similar diurnal
pattern across all Amazon seasons. The frequency of all precipitating clouds
(congestus and deeper) increases substantially for the Amazon wet season. Of
additional note, the precipitation originating from congestus or possible
shallower forms of tropical organized cloud systems (as defined solely on an
RWP-based ETH < 9 km in ) is nontrivial for this
Amazon dataset (accumulations as reported in Table S1 in the Supplement).
As plotted in Fig. , we show the frequency for observing various
levels of vertical air motions within an RWP column as additional reference
to the convective character of these clouds. Displays present these
frequencies as a function of a lower-level RWP Z
(≃ 2 km). To lower ranges of Z
(< 35 dBZ), we observe a stable percentage of columns having
vertical air motions around 1 ms-1. This may also be viewable as
the baseline uncertainty regarding RWP-based vertical velocity retrievals. As
Z increases above 35 dBZ, we observe a rapid increase in the
frequency of stronger updrafts and downdrafts, indicative of the increasing
contributions from convective clouds sharing these relative Z
levels. As Z is stronger, the likelihood of sampling deeper clouds
(and therefore the additional chance to observe a stronger velocity in those
columns) will also increase as a function of Z. The results in
Fig. also provide some guidance in convective–stratiform
classification methods for scanning radars that use low-level Z thresholds
e.g.,. Specifically, low-level Z exceeding a
40 dBZ value (or higher) is a reasonable designation of convection in
the
absence of vertical velocity measurements.
Scatter plots of log10(Nw) versus
D0 and LWC versus D0 for BR-based stratiform DSDs (density in colors).
DSDs identified as convective and stratiform by the RWP are shown in (a, c) and (b, d).
Scatter plots of log10(Nw) versus
D0 and LWC versus D0, overlaid by the contours representing the
RWP-based classifications for convective (red colors) and stratiform (blue
lines) precipitating columns and for ETH > 9 km (a, c) and
ETH < 9 km (b, d) situations.
Scatter plots of log10(Nw) versus
D0 and LWC versus D0 for dry (a, d), wet (b, e) and
transitional (c, f) seasons (density in colors).
Disdrometer convective–stratiform segregation: alignment with RWP signatures
In Fig. , a convective–stratiform regime segregation concept is
shown, with the solid line as reference to a DSD-based classification
following (herein BR). In this Nw versus D0
space, BR proposed that tropical maritime convective precipitation observed
at Darwin, Australia, falls to the right of the solid black line in
Fig. . In terms of thresholds, for this dataset the DSDs best
aligned with falling on either side of the BR line correspond to those having
a rainfall rate threshold of 13 mmh-1, or a Z value of
40 dBZ. In Fig. , we also overlay the contours of the
RWP-based classifications for convective (red colors) and stratiform (blue
lines) precipitating columns. The ellipse in Fig. indicates the
two-sigma confidence interval for those regions containing stratiform DSDs
based on the RWP classification.
RWP-based classifications indicate that substantial DSDs may be attributed to
convective classifications left of this BR line. These are associated with
the RWP identifying congestus or shallower convective cloud columns, as based
on velocity signatures. However, the Amazon dataset supports bulk BR findings
for deeper tropical convection in that precipitation to the right of the BR
line is exclusive to convective designations. Since BR was developed using a
Darwin monsoonal dataset, we anticipate that the study included modest convective
diversity, including congestus clouds, and clouds with maritime, continental
and deeper convective properties (those supporting additional graupel
growth). Darwin may exhibit even more intense “Break” (e.g., more
continental characteristics) convective cell periods and associated DSD
changes interspersed with maritime tropical “Active” monsoonal conditions
than what is observed over the Amazon
e.g.,.
However, it appears the use of BR would minimize the contributions from congestus
or shallower organized convective precipitation found under Amazon
conditions.
More recently, highlighted limitations for imposing BR
concepts when characterizing oceanic precipitation observed over ARM TWP ground disdrometers at Manus Island (Papua New Guinea) and equatorial
Indian Ocean Gan (Addu Atoll, Maldives). (herein TM) proposed a unique
oceanic convective–stratiform segregation having origins in LWC and D0
space. One justification for this change was to isolate DSD clusters
exhibiting the higher concentrations of smaller drops consistent with
oceanic convective clouds favoring warm-rain processes/collision–coalescence
over mixed-phase and/or stratiform particle growth. The TM classification is
simple to implement since it overlaps within the BR space as a line of
constant log10(Nw) ≃ 3.85 m-3mm-1. As
plotted in Fig. , we consider only the DSDs that would fall to
the left of the BR separation line (e.g., those that follow a traditional BR
stratiform designation). For this figure, the DSDs identified as belonging to
convective or stratiform (based on the RWP definitions) are then subset
according to the left and right panels, respectively. When populations from
the Amazon DSDs exhibit more oceanic qualities (residing above the dashed TM
line), contributions to the histograms (Fig. 8a, c) are typically associated
with RWP convection signatures. Similarly, DSDs identified as stratiform by
the RWP (Fig. 8b, d) follow those residing below the TM criteria for
oceanic-like stratiform precipitation. Overall, bulk Amazon precipitation
carries several hybrid characteristics as found from previous ARM tropical
DSD studies.
Cumulative precipitation properties according to cloud regime and season
Extending the previous analysis into cloud regimes, in Fig. we
separate Amazon precipitation according to ETH values above and below
9 km. This choice follows the discussion from Fig.
and is assumed to be a reasonable proxy to also help separate statistical
congestus from deeper convective events. These plots include combined
convective precipitation (e.g., stronger updraft–downdraft regions) as well
as associated trailing stratiform DSDs and/or decaying convection.
As shown in Fig. , deeper cumulus clouds are associated with an
additional maritime continental DSD properties as is similar to Darwin
studies, with fewer observations residing above TM recommendations for
possible oceanic characteristics. Deeper convective and stratiform DSDs as
designated by the RWP exhibit more frequent DSD examples having larger median
drop sizes. In contrast, DSDs associated with ETH < 9 km carry
DSD properties most similar to TM oceanic characteristics, having
corresponding stratiform DSDs that favor smaller median drop sizing than
deeper column counterparts. While tempting to attribute these oceanic
ETH < 9 km DSD characteristics solely to weak, isolated congestus
clouds, inspection of the events reveals oceanic DSDs are often associated
with widespread convective lines and/or widespread convective cells (to be
further discussed).
Scatter plots of log10(Nw) versus
D0 and LWC versus D0 for RWP-based stratiform DSDs (density on the color scale)
and
for ETH < 9 km (a, c) and
ETH > 9 km (b, d) DSDs. The overlaid black contours
represent the RWP-based classifications for stratiform clouds with bright band
precipitating columns.
In Fig. , we show this cloud segregation according to dry, wet
and transitional months (here, “transitional” implies May, October and
November properties that share qualities of both wet and dry seasons). The dry
season conditions (Fig. 10a, d) skew towards bulk precipitation properties
associated with the deeper convective clouds from above. These properties
follow an isolated, stronger convective cell expectation for dry season
precipitation that also includes an absence of DSDs associated with
detrained stratiform precipitation processes (e.g., low Nw,
larger D0) as discussed in the following section. In contrast, wet
season DSD characteristics (Fig. 10b, e) follow previous tropical and
oceanic expectations, with additional excursions into DSD contributions
associated with the convective core modes (right of BR).
Stratiform precipitation properties associated with Amazon convective events
Stratiform precipitation within the Amazon is commonly observed during the
wet season and transitional months, associated with the detrained regions
from deeper convective cells or cell dissipation. Increased stratiform
precipitation frequency during the wet season is attributed to factors
including the seasonal change in midlevel moisture and reductions in wet
season convective inhibition more supportive of convective initiation and
prevalence. Recalling Fig. 8b and d, stratiform DSDs as identified by the RWP are
often the same as combining thoughts from BR/TM recommendations. This
statement is further confirmed consulting cumulative and fractional
convective precipitation as in Table S1 in the Supplement. In
Fig. , we present the composite DSD properties as reported in
Fig. , exclusive to RWP-indicated stratiform properties.
Contours overlaid in Fig. indicate those DSD regions
designated as having a bright band signature in the column. As for the left
panels in Fig. (ETH > 9 km), locations with
profiles exhibiting clear bright band signatures correspond well with BR
expectations for stratiform precipitation; for example, these would often
represent the DSDs within more developed precipitation trailing deeper
convective cells and mesoscale convective systems e.g.,.
Lower echo-top stratiform characteristics (ETH < 9 km) indicate
two unique clusters. The first cluster represents observations associated
with aggregation processes that produce stronger melting layer signals,
similar to examples with an ETH > 9 km. These observations are found
under wet season conditions (50 % of the available DSDs), and are
less common under dry season conditions (30 % of the available
DSDs). Initially, this supports an argument that enhanced wet season moisture
influences sustained stratiform development, ice growth (deposition) and
eventual aggregation processes. The second cluster is associated with smaller
median drop sizes and higher relative number concentrations. This represents
the more prevalent stratiform mode for lower-top dry season observations, and
is equally frequent for wet season observations. This cluster argues for less
developed stratiform processes, either owing to the lack of mid-level
moisture in dry season profiles, or consistent with widespread,
weaker wet season congestus (e.g., reduced inhibition resulting in larger areas having
weaker updraft intensity).
Implications of convective–stratiform partitioning
Previous sections indicate that RWP and hybrid BR–TM classifications
faithfully differentiate congestus and deeper convective DSDs from stratiform
DSDs. Table S1 in the Supplement reports the total convective precipitation
and fractional convective precipitation for this GoAmazon2014/5 dataset.
These values are estimated according to segregations from BR methods, a
hybrid BR–TM combination, the RWP classification, and a simple rainfall rate
R > 10 mmh-1 threshold. Table S1 in the Supplement
has also been segregated according to wet/dry and transitional season
component behaviors.
Scatter plots of ZDR versus
10log10(KDP/Z) and Z versus ZDR for
the various regimes, deep convection, congestus, stratiform with bright band
and stratiform without bright band identified by the RWP classifications
(a, b). The ellipses convey the two-sigma confidence interval for
corresponding regimes. The wet (shaded ellipses) and dry (ellipses) season
segregations are presented in (c, d).
For the Amazon dataset, both TM–BR and RWP methods attribute approximately
half of the total precipitation (convective plus stratiform) to possible
congestus or shallower cloud regimes, as defined by our definitions with an
ETH < 9 km. Moreover, we observe that the fractional
convective precipitation is higher for those methods adding additional
complexity to the classification. Convective fractions suggest differences to
within ≃ 10 %. Seasonal breakdowns confirm that the wet
season and transitional months are more dominated by stratiform rainfall,
with transitional months suggesting the largest share of stratiform
precipitation. Overall, fractional convective contributions are high
(exceeding 80 %), but the strong agreement between RWP and BR–TM
gives confidence that traditional radar segregations would report lower
convective fractions owing to incorrect attribution of congestus or
shallower-topped precipitation systems.
Scatter plots of log10(Nw) versus
D0 and LWC versus D0 for RWP-based convective DSDs, for dry (a, d), wet (b, e) and transitional (c, f) seasons. The
contours represent the congestus (ETH < 9 km, blues) and deep
(ETH > 9 km, reds) convective DSDs.
It is possible to check whether dual-polarization radar quantities are
sensitive to apparent variations among congestus, deeper convection and
associated stratiform precipitation properties. In Fig. , we
show the (Z, ZDR) scatter plot as well as
(KDP-Z-ZDR) self-consistency curve behaviors for
various regimes identified by the RWP; the lower panels in Fig.
illustrate the wet and dry season segregations. For all panels in
Fig. , we present X-band dual-polarization estimates calculated
from T-matrix scattering, as radar quantities at these shorter wavelengths
should be more sensitive to lower rainfall rate conditions. The radar
quantities are presented in terms of their associated two-sigma confidence
regions (ellipses). Since radars routinely perform separate ETH and/or bright
band designation checks, the demonstrations in Fig. are not a
true reference for what is possible from a robust radar echo classification
methodology. However, Fig. suggests substantial overlap
between these cloud precipitation regimes when placed in this
dual-polarization context. This would suggest X-band or longer-wavelength
radars would not be sufficient constraints for regime classifications without
additional information. The most pronounced contrasts are those observed
between wet and dry seasons, wherein the dry season favors the larger extremes
for all dual-polarization radar quantities, associated with the contributions
of larger drops.
Averaged RWP-based convective DSDs for congestus
(ETH < 9 km) and deep (ETH > 9 km) DSDs (a),
for convective and stratiform DSDs depending on the BR
separation (b), and for those having Z (at
surface) < 35 dBZ and
Z > 35 dBZ (c), for all, dry and wet seasons.
Scatter plots of log10(Nw) versus
D0 and LWC versus D0 for RWP-based convective DSDs under the wet season
(a, b), as well as for only congestus convective DSDs (d, e), contouring the clean (blues) and polluted (reds) conditions. The
corresponding composite median and 90th and 95th percentile RWP Z
profile behaviors under the clean (blue) and polluted (red) conditions are
shown in (c) and (f).
Amazon precipitation properties: the Green Ocean characteristics
The Amazon wet season has been highlighted for its copious precipitation
owing to factors including enhanced moisture and reduced convective
inhibition (CIN). One additional consideration is that these conditions,
possibly when coupled with cleaner atmospheric aerosol profiles, may promote
the so-called Green Ocean or oceanic cloud and precipitation
characteristics. In contrast, dry season convective conditions migrate
towards enhanced convective available potential energy (CAPE) and stronger
CIN that may promote stronger convective events, initiating within more
polluted atmospheric states closer to continental regimes. Other recent
Amazon studies indicate that the convection that initiates during the Amazon
dry season exhibits more intense vertical air motions and precipitation
properties e.g.,.
The Amazon Green Ocean: when do we observe oceanic behaviors?
As shown in Fig. , we extend the previous analysis found in
Fig. to a seasonal comparison between deeper clouds
(ETH > 9 km, reds) and congestus or shallower convection
(ETH < 9 km, blues). To simplify, stratiform DSD components
(as identified by the RWP) have been removed from this figure. Although all
DSDs are assumed to be convective, it is instructive to focus on DSDs in
Fig. located to the right of the BR separation line, as those
DSDs correspond to the most confident convective conditions having a typical
rainfall rate R > 13 mmh-1. As also in
Table , convective dry season DSDs carry fewer drops, but larger
median drop sizes. Physically, this corresponds well with expectations that
stronger updrafts in the dry season should promote larger droplet sizes as a
consequence of mixed-phase growth. Wet season characteristics are noticeably
shifted towards higher number concentrations, with lower-relative LWC. This
is consistent with the anticipated changes towards more oceanic and/or
tropical warm-rain processes and cleaner and/or weaker updraft events. For
dual-polarization radar studies, these characteristics are consistent with
dry season convection exhibiting larger values in ZDR or
KDP for a similar value of Z, noting surface conditions
may also be modified slightly from the conditions sampled aloft from radar.
We show, in Fig. , congestus and deep convective full DSD
averages for convective conditions as in Fig. . Average DSDs
are also provided for those observations found to the right of the BR
separation line, as well as those DSDs having
Z > 35 dBZ. Overall, composite behaviors emphasize that
dry season convective precipitation (and into convective core regions) is
skewed towards an increased presence of larger drops, and toward parameter
spaces favoring higher LWC for a similar D0. In contrast to wet season
properties, Amazon dry season precipitation conditions are not consistent
with TM oceanic findings (shift towards DSDs having increased larger drops),
though they do support that the updrafts in the dry season are stronger.
The Amazon Green Ocean: role of pollution in oceanic signatures?
Overall, the primary shift in precipitation properties for the Amazon
coincides with changes in the larger-scale seasonal shifts in thermodynamics
and aerosol conditions. In this respect, it is difficult to differentiate
relative controls, especially given sampling limits of our Amazon
precipitation dataset during the dry season. However, the frequent wet season
convective instances (removing the more obvious stratiform contributions)
offer some opportunity to test whether we observe any sensitivity to
background aerosol conditions and/or other environmental conditions when
promoting so-called oceanic DSD properties.
As plotted in Fig. , we show the set of convective DSDs
observed during the wet season, identifying the relative clean (blues) and
polluted (reds) aerosol conditions. The bottom panels illustrate the convective DSDs associated with column
ETH < 9 km. Figure c and f show a
composite median and the 90th and 95th percentile RWP Z profile under the
clean and polluted conditions, respectively. For simplicity, polluted regimes
in our study combine the more stringent (but, in this dataset, the more
frequent) biomass polluted classification with standard polluted
designations. During this campaign, a total of 82 clean and 61 polluted
events were collected having at least one 5 min convective DSD, with 66
clean events registering an ETH < 9 km DSD and 46 polluted events
with a ETH < 9 km DSD.
The mean thermodynamic conditions are sampled from the morning 12:00 UTC
radiosondes. For this dataset, clean events record a mean (standard
deviation) most unstable convective available potential energy (MUCAPE) of 2124 (1100) Jkg-1K-1,
most unstable convective inhibition (MUCIN) of -34 (42) Jkg-1K-1 and average
0–5 km RH of 83 (6) %. Polluted events are slightly more
favorable to deeper convection, in recording a higher mean MUCAPE of 2567
(1176) Jkg-1K-1, with a MUCIN of -35
(36) Jkg-1K-1 and RH of 80 (7) %, respectively.
Histograms of MUCAPE and MUCIN are shown in the Supplement (Fig. S5). Both
clean and polluted events share a similar mean freezing level height at
approximately 4.8 km. Overall, it is still important to suggest the
polluted cases should be more conducive to deeper events based on the
available dataset. For the ETH < 9 km panels, mean clean
(polluted) environments appear less favorable, with MUCAPE of 1993
(2388) Jkg-1K-1, MUCIN of -36
(-38) Jkg-1K-1 and RH of 83 (81) %. Standard
deviations for clean (polluted) values are similar as ETH > 9 km
convection.
Scatter plots of log10(Nw) versus
D0 and LWC versus D0 for wet season DSDs on the ambient wind
directions, northeasterly–east-southeasterly (NE – blues; ESE – reds) and
east–east-northeast (E – oranges; ENE – greens).
As indicated in Fig. , cleaner regime convective precipitation
during the wet season agrees well with oceanic expectations as reported by TM
and discussions above. Cumulative polluted regime convective results are less
consistent with oceanic expectations, but there is overlap emphasizing DSDs
associated with ETH < 9 km columns. Deeper ETH > 9 km
polluted convective observations (deeper convection properties) are those
most skewed towards the dry season and/or the least oceanic behaviors, including
hints of stratiform-type DSD excursions. Inevitably, some DSD contamination
could follow from convective-to-stratiform transitional columns in the
strongest events as well, for example those featuring sloped updrafts having
stronger vertical motions aloft overhanging a stratiform-type downdraft in
the column below
Bulk clean and polluted contrasts are potentially visible on the composite
Z profiles, with cleaner regime composites demonstrating an
increasing Z profile (Z weighted towards increasing
contributions from larger drops) towards the surface. One explanation is that
these cleaner profiles are more routinely associated with collisional growth
process contributions influencing Z profiles over evaporation and/or
breakup process influences on radar signatures (e.g., evaporation and/or
breakup acting to reduce Z, perhaps not observable with available
larger drops to RWP wavelengths). These profile behaviors are pronounced for
the ETH < 9 km observations that should minimize mixed-phase
process influences. In contrast, the polluted regime profiles indicate
similar and/or larger Z values aloft to approximately 3.0 km
above ground level, with Z profiles peaking and/or decrease in magnitude below
these altitudes.
One explanation for the polluted profile characteristics in
Fig. is that more prominent mixed-phase particle process
contributions are acting within these convective columns. Since these
polluted events demonstrate more favorable mean thermodynamic conditions that
favor stronger convective updrafts, it is possible that an updraft
enhancement partially elicits such a transition. A similar response may also
be attributed to the proposed role of aerosols in following invigoration
arguments e.g.,. For example, recent Amazon aircraft
studies as in indicate changes such as an absence of liquid
within growing convective cumulus during polluted conditions, and/or
differences in the relative formation and altitudes of ice particles. Regardless
of process path, the suggestion is that polluted convective columns would be
those that potentially promote added ice depositional growth (resulting in
fewer but larger ice particles at the expense of additional liquid). Such
physical arguments could help explain the similar or larger Z
magnitude aloft (larger ice sizing, offsetting density), coupled with a
modest melting enhancement followed by a reduction in Z below
5 km. A reduced number of particles under this scenario would also
reduce collisional growth below the freezing level compared to the cleaner
profiles. Overall, surface DSD properties in Fig. suggest that
cleaner aerosol conditions are associated with enhanced oceanic DSD
properties (a.k.a., in agreement with select Green Ocean statements).
However, it is nonobvious whether these lesser oceanic conditions (especially
within the deeper cores having fewer samples) were the consequence of the
aerosol conditions or the shift in the environmental conditions that tracked
the change in aerosol.
The Amazon Green Ocean: an alternate explanation
It is useful to determine whether we can better deconvolve environmental
influences from aerosol and find those more important to the prevalence of oceanic
precipitation characteristics. In Fig. , we show wet season
DSDs contingent on the ambient wind directions, with relative breakdowns
according to the northeasterly–east-southeasterly (NE–ESE) and
east–east-northeast (E–ENE) directional pairings. First, the specific
NE–ESE and E–ENE pairings were selected for having similar DSD sample
sizes. Second, these wind orientations may also be viewed as relevant with
respect to the Manaus pollution plume (e.g., E and ENE flows over T3 are
arguably the more polluted relative to the Manaus location).
In Fig. , we highlight evidence of oceanic-type DSD behaviors
according to most wind directions. The fractional polluted versus
clean DSD breakdowns along these directions are as follows: NE:
57 % clean and 43 % polluted; ENE: 68 % clean and
32 % polluted; E: 94 % clean and 6 % polluted; ESE:
91 % clean and 9 % polluted. Following Fig. ,
it is found that the larger DSD outlier populations (e.g., convective DSDs
found to be least oceanic when compared with TM) are observed for NE and
ESE wind directions, and therefore should not be as influenced by a possible
Manaus pollution plume. Note, most polluted events sampled during the wet
season were attributed to biomass classifications (e.g., local aerosol
sources), which may explain NE flows as those most polluted. As expected from
discussions above, slightly stronger 12:00 UTC MUCAPE (SD) values are also
found along the NE and ENE directions (2207 (1325)
and 2131 (934) Jkg-1K-1, respectively) that are associated
with bulk polluted events, while the weakest potential forcing conditions are
found with the ESE and E flows (2089 (1241) and
1766 (1035) Jkg-1K-1, respectively). Nevertheless, these
local thermodynamic controls associated with wind direction are far less
pronounced than previous polluted–clean contrasts. The histograms for MUCAPE
and MUCIN as a function of wind direction are found in the Supplement
(Fig. S6).
The DSDs observed along NE wind flows reflect the least oceanic
characteristics in this dataset, favoring low Nw–D0 pairings
typical of dry season convection (also carrying Z profiles similar
to in Fig. , not shown). Again, these NE flows reflect the most
polluted wind components, and directions associated with the larger mean
convective forcing parameters associated with higher values towards the tail
of the MUCAPE distributions (Fig. S6 in the Supplement). In that regard, a
reduced presence for oceanic-type DSDs was not unexpected. However, the
pronounced absence of oceanic DSD characteristics along NE flows is far more
noteworthy than when contrasted to previous clean–polluted criteria, and not
immediately in line with mean thermodynamic values. From event inspection,
most nonoceanic DSD characteristics were associated with isolated, deeper
convective cell events, or widespread convective events still demonstrating
deeper cloud ETH. Widespread, shallower convective events or organized
shallow systems (possible Amazon warm-rain dominant systems as observed over
oceans; e.g., ) were not favored, compared with other wind
components. Again, this change may be attributed to the frequency of higher
MUCAPE at the tail of the NE distribution (Fig. S6
in the Supplement).
Additional outlier DSD populations (including several events having numerous
oceanic DSD properties) are observed according to ESE wind directions
(relatively clean). These DSDs reflect the presence of deeper convective DSDs
(to the right of the BR separation line) that exhibit high concentrations of
larger relative drop sizes. These regions, although not typical of TM oceanic
examples, are also not consistent with Amazon dry season characteristics
(having a relatively higher triplet of LWC, D0 and Nw). As in NE
flow examples, the basic radiosonde parameter checks and aerosol forcing
controls associated with these events are in line with the other wind
components. However, histograms in Fig. S6 in the Supplement do show a
similar enhancement for the frequency of higher MUCAPE values towards the
tail of the distribution.
As far as potential explanations for why these outliers cluster according to
particular wind directions compared to other environmental factors, it is
important to note that while Amazon convection timing follows a
well-established diurnal cycle over T3, 12:00 UTC radiosondes and
associated parameters (those typically closest to earlier convective
initiation) may not be completely representative of the important
larger-scale conditions (e.g., South Atlantic Convergence Zone (SACZ)
positioning, influences on the Amazon basin during the wet season;
). For one example, wet season sea-breeze intrusion and
associated statistical cloud enhancements (as determined by satellite) into
the Amazon basin orient tangential to a NE–SW axis over the T3. This
sea-breeze front passage is in phase with this T3 diurnal precipitation cycle
(e.g., see composite convective evolution as in ). It is
possible that similar forms of dynamical or moisture enhancements, for
example SACZ drivers of frontal intrusions, as well as river breeze
influences e.g.,, would not be
completely captured by our morning radiosonde observations (given their
timing). However, these larger-scale features may promote enhancements
sufficient to spark possible changes in cloud initiation and subsequent
precipitation properties.
From inspection of events according to wind directions, ESE events tended to
emphasize widespread organized convective events exhibiting copious rainfall
along a NE–SW orientation (with winds flowing from ESE preceding those
lines), having a shallower ETH < 9 km. Timing for these events was
near or just following the afternoon diurnal maximum (18:00–20:00 UTC).
One suggestion is that the oceanic DSDs tended to be associated with these
shallower but widespread convective events initiated or enhanced by
sea breeze, Kelvin waves or other influences. As the conditions are also
clean, this is also consistent with shallower, oceanic forms of organized
convection. These combined concepts and possible SACZ influences on these
events are the subject of ongoing research. In contrast, NE events most often
reflected deeper events (ETH > 9 km) with less evidence for forms
of NE–SW linear or shallow cloud organization for animations of the
widespread events. Deeper clouds would be consistent with pollution arguments
as above, but these breakdowns speak to the complexities of these studies.
Conclusions
This study summarizes Amazon precipitation properties collected during the
unique, multi-year GoAmazon2014/5 campaign. Emphasis was placed on cumulative
campaign precipitation properties and relationships that may benefit
potential hydrological applications and radar-based precipitation data
product development, as well as connections relevant to future Amazon
convective model evaluation. The study also explored Amazon precipitation
properties from the perspective of possible Green Ocean convective
characteristics, including possible thermodynamic and aerosol forcing
influences that may be influential to observations of oceanic-like
precipitation.
Amazon rainfall and radar self-consistency relationships demonstrate tropical
characteristics as compared to continental SGP references, associated with
radar quantities (in both convective and stratiform contexts) that sample
higher relative concentrations of smaller drops. Typically, this indicates a
reduced role for convective mixed-phase and/or graupel growth, as well as
stratiform aggregation processes in the Amazon. These tropical precipitation
characteristics are more pronounced within the wet seasons than dry season
events, with dry season events favoring the presence of larger drop sizes as
a suggested consequence of stronger event updrafts under more favorable
thermodynamic conditions. Although it is difficult to differentiate wet–dry
regimes exclusively using radar quantities, our analysis suggests Z,
ZDR and KDP would exhibit larger values within dry
season events and deeper convective cores therein.
Coupled RWP and disdrometer Amazon T3 precipitation breakdowns confirm the
overall findings of previous ARM campaign BR and TM studies on tropical
convective to oceanic-type cloud and precipitation breakdowns. Amazon
precipitation is varied and often found to straddle maritime continental
behaviors as seen in previous studies, with DSD excursions into the more
oceanic examples presented from ARM Manus and Gan deployments. As before, the
separations between wet and dry seasons are pronounced, with most oceanic DSD
conditions observed during the wet season. The strongest convective
behaviors, as well as events having a marked absence of stratiform
precipitation, are observed during the Amazon dry season.
Considering deeper versus congestus properties, Amazon congestus clouds are
attributed to the more oceanic precipitation behaviors found in our dataset.
When exploring Green Ocean themes, our analysis was not able to
demonstrate that either aerosol conditions or enhanced local convective
forcing parameters were strongly associated with the presence/absence of an
oceanic character of the congestus and deeper precipitation. Rather, the more
pronounced separation was found when segregating by wind direction, which may
reflect that our initial options for thermodynamic or aerosol controls are
all unable to deconvolve a more subtle change important to an enhanced DSD
signature. However, there is evidence to support that aerosol or other early
morning forcing factors within the wet season are not significantly different
to promote these differences. Rather, episodic to frequent larger-scale Amazon basin
(e.g., SACZ, sea breeze) or river forcing controls and
associated enhancements may require future investigation to determine their
importance to the apparent oceanic nature of the clouds and eventual
precipitation. Other factors including the possible role of aerosol sizing
e.g., on updraft and precipitation enhancements for Amazon
convection are also a topic of future consideration.