Anexos 1 - 2

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Anexos 1 - 2

ANEXO 1
JOURNAL OF GEOPHYSICAL RESEARCH, VOL. 116, D09204, doi:10.1029/2010JD015335, 2011
Understanding three‐dimensional effects in polarized observations
with the ground‐based ADMIRARI radiometer
during the CHUVA campaign
Alessandro Battaglia,1 Pablo Saavedra,2 Carlos Augusto Morales,3 and Clemens Simmer2
Received 15 November 2010; revised 11 February 2011; accepted 17 February 2011; published 10 May 2011.
[1] Measurements of down‐welling microwave radiation from raining clouds performed
with the Advanced Microwave Radiometer for Rain Identification (ADMIRARI)
radiometer at 10.7–21–36.5 GHz during the Global Precipitation Measurement Ground
Validation “Cloud processes of the main precipitation systems in Brazil: A contribution to
cloud resolving modeling and to the Global Precipitation Measurement” (CHUVA)
campaign held in Brazil in March 2010 represent a unique test bed for understanding
three‐dimensional (3D) effects in microwave radiative transfer processes. While the
necessity of accounting for geometric effects is trivial given the slant observation geometry
(ADMIRARI was pointing at a fixed 30° elevation angle), the polarization signal (i.e.,
the difference between the vertical and horizontal brightness temperatures) shows
ubiquitousness of positive values both at 21.0 and 36.5 GHz in coincidence with high
brightness temperatures. This signature is a genuine and unique microwave signature of
radiation side leakage which cannot be explained in a 1D radiative transfer frame but
necessitates the inclusion of three‐dimensional scattering effects. We demonstrate these
effects and interdependencies by analyzing two campaign case studies and by exploiting a
sophisticated 3D radiative transfer suited for dichroic media like precipitating clouds.
Citation: Battaglia, A., P. Saavedra, C. A. Morales, and C. Simmer (2011), Understanding three‐dimensional effects in
polarized observations with the ground‐based ADMIRARI radiometer during the CHUVA campaign, J. Geophys. Res., 116,
D09204, doi:10.1029/2010JD015335.
1. Introduction
[2] Although three‐dimensional (3D) radiative transfer
(RT) effects within cloudy atmospheres have been theoretically quantified via sophisticated radiative transfer tools
[e.g., Marshak and Davis, 2005], their observation has been
always extremely elusive. The main reason is the enormous
difficulty to perform closure studies with a full characterization of the radiatively important 3D structure of a cloud.
Hence observational studies toward 3D effects have been
statistical in nature, for example, by analyzing satellite
measurements in ways that illustrate dependencies that are
inconsistent with the assumption of 1D RT. Emphasis has
always been put on shortwave solar radiances particularly
for the understanding of the relationship between cloud
albedo, cloud microphysics and cloud structure, which is of
great interest for studies of equilibrium climate and climate
change. This research avenue has been boosted by the rising
number of satellites with increasingly higher spectral and
1
Department of Physics and Astronomy, University of Leicester,
Leicester, United Kingdom.
2
Meteorological Institute, University of Bonn, Bonn, Germany.
3
Instituto de Astronomia, Geofisica e Ciéncias Atmosféricas,
Universidade de São Paulo, São Paulo, Brazil.
Copyright 2011 by the American Geophysical Union.
0148‐0227/11/2010JD015335
spatial resolution and more viewing angles (e.g., the Aerosol
Polarimetric Sensor on board the upcoming GLORY mission [Mishchenko et al., 2007]).
[3] In this paper, we focus on 3D RT in the microwave
region with a specific interest in precipitation, which is
known to have a high spatiotemporal heterogeneity. The
latter represents a caveat for all microwave‐based remote
sensing techniques; already in the late 1970s, Weinman and
Davies [1978] used both analytical and Monte Carlo 3D RT
models to quantify the so‐called nonuniform beam filling
(NUBF) effect in passive microwave retrievals of rain
rate. The beam‐filling effect arises from the assumption of
homogeneous rainfall across the field of view (FOV), coupled with the nonlinear, concave‐downward response of
brightness temperatures (TBs) to rainfall rate. The effect
depends mainly on the footprint dimension, the microwave
frequency under investigation, the cloud type and shape,
and in all cases increases with inhomogeneity and mean
LWP or rain rate [Kummerow, 1998; Lafont and Guillimet,
2004]. NUBF was found to be the main source of error in
retrieved rainfall rate from spaceborne microwave radiometers; an uncertainty of a factor of 2 can exist in the mean
rain rate for a given brightness temperature [Weinman and
Davies, 1978; Lafont and Guillimet, 2004].
[4] The presence of inhomogeneity in the instrument
FOV, and more generally the 3D structure of the system
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BATTAGLIA ET AL.: 3D EFFECTS IN POLARIZED PASSIVE MICROWAVE RADIOMETERS
Figure 1. Site scheme for the CHUVA campaign with the
location of all the instruments. The dash‐dotted line indicates the direction of observation for ADMIRARI (Site 2)
and of the RHI scans of the X band radar (Site 1). The distance between sites 1 and 2 is 7.65 km.
under observation, does not preclude the use of 1D radiative
transfer approaches. In fact the plane‐parallel assumption
does not require homogeneity at distances arbitrarily far
from the FOV of the sensing instrument. For instance, for
pure absorbing atmospheres and Fresnel‐like surfaces, the
radiation sensed by spaceborne passive microwave radiometers originates exclusively from the FOV projected slant
tube. In these cases, 1D independent pixel approximations
work very well, with the simple expedient of taking into
account geometric effects in case of off‐nadir looking
radiometers [Battaglia et al., 2005]. In the slant path (SP)
approximation [Bauer et al., 1998; Roberti et al., 1994] the
structure is horizontally homogeneous while the vertical
profile is reconstructed by using the slant profile defined
by the ray traced from the sensor upward to the TOA for
ground‐based or downward to the surface and then reflected
upward for spaceborne radiometers.
[5] In the presence of scattering media and/or diffusive
surfaces, due to the redirecting of radiation by diffusion
events, radiation sensed by the radiometer may not be
generated within the slant tube of observation. In this case
the horizontal displacement of radiation in directions perpendicular to the viewing direction produces scattering
effects, which are more subtle and difficult to treat. The
study of these effects has matured with the simultaneous
development of 3D RT codes, mainly based on Monte Carlo
techniques [Roberti et al., 1994; Liu et al., 1996; Roberti
and Kummerow, 1999].
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[ 6 ] Kummerow [1998] and Roberti and Kummerow
[1999] noticed that, compared to the correct 3D simulations, the 1D SP modeling introduces mainly random errors
and only minor bias errors. In fact, in 1D SP approximations, radiation remains trapped by construction in the slant
tube: no contribution from outside the tube is allowed,
possibly resulting in nonphysical variations for contiguous
pixels. A 3D radiation field can be depicted as a smoothed
version of the field constructed from many 1D radiation
simulations. The computed DTB = TB [3D] − TB ([1DSP]
leads to large differences (>10 K) for TRMM Microwave
Imager resolutions only at the highest frequency (85.5 GHz,
5 km resolution). Areas with positive DTB are generally
followed immediately by areas with negative DTB, thus
confirming the overall cancelation of the bias.
[7] Only recently, attention has turned toward studies
involving 3D effects in the polarization signal. This has
been fostered by the introduction of full polarimetry in
Monte Carlo RT codes [Battaglia and Mantovani, 2005;
Davis et al., 2005; Battaglia et al., 2007], which are now
capable of treating dichroic media. While spherical particles
are known to produce small polarization signals at microwave frequencies [Liu and Simmer, 1996], preferentially
oriented nonspherical particles are potentially more effective in that respect. Two main scenarios have been studied
so far.
[8] 1. Davis et al. [2007] accurately simulated observations
of 3D midlatitude preferentially oriented cirrus clouds (synthetically generated from 2D observations of the Chilbolton
radar at a resolution of approximately 780 m by 780 m by
110 m) for a variety of viewing geometries corresponding to
operational (Advanced Microwave Sounding Unit AMSU‐B,
Earth Observing System–Microwave Limb Sounder EOS‐
MLS) and proposed (Cloud Ice Water Sub‐millimetre
Imaging Radiometer CIWSIR) high‐frequency spaceborne
radiometers. For the AMSU‐B 190.3 GHz and the CIWSIR
334.65 and 664 GHz channels, they demonstrated the significance of polarization effects for nonspherical particles,
and also of beam‐filling effects with regard both to intensity
and to polarization. They found a good agreement between
3D and the independent pixel approximation (IPA), which
suggests that for slant viewing instruments (with footprint
radii of 5.5 km (CIWSIR) or 16 km (AMSU‐B)) and low
tangent height limb sounding, 3D scattering RT effects do not
have a significant impact and show unequivocal signatures as
well. Their study is purely notional; no (statistical) analysis
with observations has been performed.
[9] 2. Battaglia et al. [2006] studied 3D RT effects in
ground‐based low microwave frequency (10–36 GHz)
radiometric observations of rain. As theoretically proposed
by Czekala and Simmer [1998] and confirmed by Czekala
et al. [2001a], larger drops exhibit negative polarization
differences (PD ≡ TBV − TBH ) in the downwelling microwave
radiation which can be exploited in discriminating between
cloud and rain liquid water [Czekala et al., 2001b]. The
basis for this information is the assumption of a well‐
defined equilibrium shape of raindrops and their orientation
distribution in absence of turbulence and wind shear [e.g.,
Andsager et al., 1999]. Battaglia et al. [2006] demonstrated
that 3D effects tend to modify the distribution of observations in the TB − PD plane, which exhibits a parabolic shape
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Figure 2. ADMIRARI measurements collected during the CHUVA campaign displayed in the TB − PD
plane at (a) 10.7 and (b) 36.5 GHz. The colorbar indicates the number of occurrences on a logarithmic scale.
with a negative PD minimum at intermediate TBs (e.g., see
Figures 2 and 3 of Czekala et al. [2001a] or Figure 4 of
Battaglia et al. [2010]). The 3D effects may alter the
amplitude of the minimum PD and the general slope in the
ascending and descending part of the curve. More subtle
effects like nonzero PDs at nadir and also nonzero third
Stokes vector components may occur. Battaglia et al.
[2006] concluded that a 1D SP approximation is generally
insufficient for scenarios with high rain rates; here the PD
signal is the most affected.
[10] To further advance this second research avenue,
Advanced Microwave Radiometer for Rain Identification
(ADMIRARI) was developed and deployed in different
field campaigns [Battaglia et al., 2009, 2010]. A Bayesian
scheme including 3D RT simulations designed for the
ADMIRARI suite of measurements retrieves simultaneously
water vapor, rain and cloud liquid water paths for the slant
volume under observation.
[11] The goal of this study is to deepen our understanding
of 3D RT effects in passive low‐frequency and polarized
ground‐based observations of microwaves signal. In particular we aim at validating the conjectures and predictions
proposed by notional RT studies with field measurements.
Thanks to their proximity to the target which results in
narrow FOV, ground‐based radiometry has a huge potential
in that respect because polarization features produced by 3D
structures can be observed without having to contend with
NUBF effects, which tend to smooth them out. The March
2010 Global Precipitation Measurement Ground Validation “Cloud processes of the main precipitation systems in
Brazil: A contribution to cloud resolving modeling and to
the Global Precipitation Measurement” (CHUVA) campaign
represents a perfect test bed given the distinct structures
of the observed typical precipitating systems and the measurement setup (section 2). Two situations are investigated
in detail (section 3), which provides excellent examples of
pristine 3D RT effects (section 4). Unique scattering 3D RT
features are identified in the observations and explained by
comparing 3D backward Monte Carlo and 1D SP RT simulations (section 5). Conclusions are drawn in section 6.
2. The CHUVA Field Campaign
[12] As part of CHUVA, several field campaigns will take
place in Brazil (during 2010–2013) to support the Brazilian
activities of the GPM‐Brazil program toward the cooperation between the Brazilian Space Agency‐AEB and NASA
Ground Validation program (GPM/GV). The first campaign,
PRE‐CHUVA took place at the Brazilian Launching Center
of Alcăntara (CLA) in northeastern Brazil from 1 to 25
March 2010 (http://gpmchuva.cptec.inpe.br). According to
CHUVA objectives, in this field experiment the measurements were concentrated to depict the warm rain process
and their transition to the vertically developed tropical
precipitating systems. During the campaign, AEB, the
Brazilian Air Force at CLA, the Space Research Institute
(INPE), University of São Paulo, University of Bonn and
NASA provided several instruments to support the PRE‐
CHUVA campaign. Figure 1 provides the location of the
sensors deployed for this field experiment: X Doppler Dual
Polarization weather radar, automatic weather stations,
radiosondes, disdrometers (JOSS, Parsivel and Thiess),
rain gauges, lidar, the Radiometrics MP3000 Microwave
Radiometer and the ADMIRARI radiometer. For the present study, we focus on measurements taken only by
ADMIRARI and the X band weather radar.
[13] As depicted in Figure 1, ADMIRARI was located at
the Delta village (latitude 2°23.16′S, longitude 44°22.8′W,
Site 2) and it was aligned at southeast of the weather radar
(latitude 2°19.5′S, longitude 44°25.2′W, site 1) at 7.65 km.
Along this radial, several ancillary observations were taken
in the airport (latitude 2°22.6′S, longitude 44°24′W, site 3).
For the campaign, the radar strategy was repeated every
10 min and it was composed of one volume scan with 12
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Figure 3. Measurements from 19 March 2010 at 30° elevation angle. (a) MRR reflectivity in dBZ.
The dashed line corresponds to the range location of the freezing level as identified by the closest radiosounding. (b) Brightness temperature for the three frequencies. Polarization difference at (c) 36, (d) 21,
and (e) 10 GHz. Gray areas indicate rainy periods flagged by the rain sensor collocated with ADMIRARI.
elevations and one range height indicator (RHI) along the
ADMIRARI direction. The volume scans were set to start at
00, 10, 20, 30, 40 and 50 min every hour, while the RHI was
at 06, 16, 26, 36, 46, 56 min. The radar was set to collect
radar reflectivity (Z), Doppler velocity and spectral width,
differential reflectivity (ZDR), differential phase (FDP) and
correlation between horizontal and vertical polarization
(rHV) with gate width of 125 m. The RHI scan strategy
varied from 0 to 90° every 0.5° elevation steps. ADMIRARI
was set to observe at a constant 30° elevation angle in the
direction toward the weather radar, Site 1. ADMIRARI
measurements comprise TBs at vertical and horizontal
polarization at its three frequencies (10.7–21.0–36.5 GHz);
the TBs were complemented by slant reflectivity profiles
observed at 24.1 GHz by a Micro Rain Radar (MRR) [see,
e.g., Peters et al., 2002] at 30° elevation angle with 300 m
range resolution and 31 bins. In addition, rain occurrence
over the radiometer position from a rain sensor, ambient
temperature and pressure as well as internal receiver and
stability temperatures were recorded for quality control.
[14] During the Pre‐CHUVA, three marked weather
regimes could be found based on the radar and gauge measurements: (1) during the first two weeks, dry weather conditions without significant precipitation prevailed; (2) in the
third week, isolated and short warm rain cells were observed;
and (3) finally, the last week was marked by the rainiest period
that included several warm rain events and deep convective
storms with a wide range of intensity and duration. As most of
these raining systems are small and convective, a large variability on the rain gauge and disdrometer accumulation was
found at the three sites, i.e., 250 mm, 200 mm and 270 mm
rain accumulation for 13, 9 and 9 days at site 1 (Radar), site 2
(ADMIRARI) and site 3 (CLA), respectively.
[15] Figure 2 summarizes all ADMIRARI measurements
collected during the CHUVA campaign in the TB − PD
plane. Two features are striking: in Figure 2a, extremely
large TBs at 10.7 GHz hint at extreme events with high optical
thicknesses characteristic for tropical regions (including an
unique event reaching saturation level in the radiometric
signal, a feature observed at this frequency for the first time);
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Figure 4. Radar (top) range height indicator (RHI) and (bottom) plane position indicator (PPI) sequence
for the event of 19 March 2010. The RHI scans are performed every 6 min toward ADMIRARI.
ADMIRARI position (FOV) is indicated by a cross (cone) in the PPI (RHI) plots. The radiometer is
located 7.65 km away from radar.
and in Figure 2b, in the region with TBs close to saturation (i.e., close to ambient temperature) positive PDs are
ubiquitous both at 36.5 (shown) and at 21.0 GHz (not shown).
These features will be discussed in detail in sections 3 and 4.
3. Case Studies
3.1. Scenario from 19 March
[16] The first case analyzed is a 6 min long rain shower,
which occurred on 19 March 2010 around 2045 UTC.
The ADMIRARI observations (i.e., MRR slant reflectivity
profiles, TBs and PDs at 10–21–36 GHz) are depicted in
Figure 3. This case represents typical situations encountered
during the campaign: rain‐bearing cells were forming over
the ocean, were advected inland, and passed ADMIRARI.
ADMIRARI was looking roughly orthogonal to the flow
direction, with the rain cells coming from the northeast
toward the southwest (i.e., roughly following the same line
as the airport pad in Figure 1). The MRR slant reflectivity
profiles clearly identify that this particular event was
observed mostly with the radiometer being outside the rain
cell; this is corroborated by the rain sensor (gray area in
Figures 3 and 6) which did not flag rain during the period
under consideration. This evolution is also confirmed by the
series in the RHIs (Figure 4). Note that the rain shaft is not
very deep along the line of sight of the radiometer, which
partially explains why only the highest frequency is reaching complete saturation, and only for a very short period.
Compared to the MRR profiles, the onset of precipitation
appears to be anticipated in the ADMIRARI measurements,
with all PDs being negative from 2043:54 UTC onward.
Looking at the plane position indicator (PPI) radar image
(Figure 4, bottom), this seems to be caused by a more distant
rain cell (beyond the MRR ranging distance) which was
passing earlier through the ADMIRARI line of sight (circled
in red in Figure 4, bottom left).
[17] The time evolution of the ADMIRARI observed
variables in the TB − PD plane (Figure 5) showcases a
recurrent pattern during the CHUVA campaign. Apart from
variations which can be related to the time evolution of the
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Note that on the exit path, PDs with similar TBs as during the
entering path are characterized by lower absolute values; this
is most probably caused by the fact that the ADMIRARI
FOV is intercepting an increasing cloud component (e.g.,
compare the FOVs in Figure 13) which reduces the PDs,
because of the spherical shapes of cloud droplets. The
36.5 GHz pattern is completely different, with TB reaching
the highest values of 265 K (still 35 K below the ambient
temperature of 27°C) after passing a minimum PD of −6 K
around 170 K. From saturation on, the TBs go back to
the clear‐sky value at 85 K with PDs straddling around 0 K.
The 21 GHz observations have an intermediate behavior.
[18] The observed 36.5 GHz signal evolution is totally
unexpected in a pure 1D world: all the observation pairs
from 2045 UTC onward do not fit Figure 3 of Czekala
[1998] or Figure 4 of Battaglia et al. [2010], which are both
based on 1D simulations without slant path approximations.
The series of 36.5 GHz observations with decreasing TBs and
zero PDs would be associated with a profile only containing
cloud droplets, with lower and lower contents with time. But
this is obviously not the case because the 10 GHz signal shows
significant negative PDs, and the MRR observed reflectivities
are well above the noise level (due to their low signal cloud
droplets are well below the noise level of such an instrument).
The fact that all our observations are performed at 30° introduced significant geometric effects, but this will only partially
explain the observed feature for this instance.
Figure 5. Event of 19 March 2010: time evolution in the
TB − PD plane for the three ADMIRARI frequencies. The
overall duration of the event is 8 min. The colorbar modulates the time passed in minutes from the beginning of the
event at 2042 UTC.
system itself, the 10 GHz time series is as expected: in the
beginning both the TB (the absolute PD) values gradually
increase with the rain cell entering the FOV up to 125 (13) K
and then gradually decrease when the rain cell exits the FOV.
3.2. Scenario From 20 March
[19] The observations from 20 March indicate an extreme
scenario with a first period of observation made from outside the rain cell (until 1007 UTC) and then from inside
the rain cell as depicted by the rain sensor in Figure 6.
The convective cells under observation were much more
intense and larger than those from the previous day as
clearly shown by the RHI radar observations (Figure 7); the
event also lasted much longer (around 45 min). For most
of the time the 21.0 and 36.5 GHz TBs were fully saturated
(around 18 min saturation), even the 10 GHz TBs reached
extraordinary high values up to 280 K (see Figure 6). At
certain instants also the MRR signal is fully attenuated by
the rain cell. A 21 GHz TB around 180 (250) K corresponds
approximately to a slant optical thickness of 1 (2); assuming
the same for the 24.1 GHz MRR frequency, this corresponds
to a 8.5 (17) dB two‐way attenuation. Therefore for instants
when the 21 GHz TBs exceed 250 K the MRR backscattering signal coming from the more distant precipitating
volume will be most likely lost.
[20] The time evolution of the event in the PD − TB plane
(Figure 8) shows a remarkable variability of the PDs at
saturated TBs both at 21.0 and 36.5 GHz with positive
polarization values up to +4 K and +2.1 K, respectively. As
discussed hereafter, these features represent a conundrum
which can only be explained via 3D RT.
4. The 3D Polarized Simulations
of Precipitating Clouds
[21] To understand some of the features observed by
ADMIRARI during CHUVA, we resort to a very simple
box cloud scenario following ideas similar to Battaglia
et al.’s [2006] (see Figure 9). To resemble the situation
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Figure 6. Measurements from 20 March 2010 at 30° elevation angle. (a) MRR reflectivity in dBZ (color
scale coded). (b) Brightness temperature for the three frequencies. Polarization difference at (c) 36, (d) 21,
and (e) 10 GHz. Gray areas indicate rainy periods flagged by the rain sensor.
encountered on 20 March 2010, a Lcx × Lal = 4 × 4 = 16 km2
box with liquid water paths of 20.0 and 3.6 kg/m2 of the rain
and cloud component, respectively, was assumed with a
cloud base located around 2.5 km and a rain column reaching up to 4 km (similar to that shown later on in Figure 15
(top left). This profile matches the radar observations
around 1006 UTC (Figure 7, top middle). Note that later on
the cell developed some hail as evident from the presence of
flare echoes at 1024 and 1030 UTC.
[22] In the lowest levels with rain content of the order of
3 g/m3 (corresponding to 13.8 kg/m2 in the slant path), the
extinction coefficients are around 0.5, 2 and 5 km−1 for
the three ADMIRARI frequencies, with single scattering
albedos ranging from 0.36 to 0.53. TBs and PDs are simulated (Figure 10, only 10.7 and 36.5 GHz) as sensed by an
ADMIRARI‐like radiometer (i.e., with a 3dB beam width of
6.5°) located at different positions inside and outside the
rain shaft with an elevation angle of 30°. The color coding
in Figure 10 quantifies the simulated measurements looking
“southward,” i.e., along the negative y axis. In the following
the position of the radiometer will be identified by two
coordinates: a cross‐ and an along‐ground projected line‐of‐
sight (GP‐LOS) distance. In such a reference frame the rain
shaft is indicated by the thick black rectangle with corners
located at (0,0),(0,4),(−4,4),(−4 km,0 km) in Figure 10
(bottom left); because of the symmetry of the problem all
plots are cut at a cross‐LOS distance equal 2 km, i.e., in the
middle of the cloud.
[23] There are obviously border/edge effects due to the
finite antenna beam width of the radiometer, which causes
the spill‐out of rain shaft–generated radiation in the region
with negative cross‐GP‐LOS distances as well. The effect is
roughly restricted to the conical area identified by the
dashed thick black lines in Figure 10 (bottom left), with the
conical vertex angle being half the radiometer 3dB beam
width. For observation points within such areas, the observed
volume will be nonuniformly filled. An extreme scenario is
achieved when half of the beam is filled by the rain shaft and
half by clear sky (cross‐GP‐LOS distance equal to 0 km).
If the radiometer is located to the north of the rain shaft and
looking toward the shaft at an along‐GP‐LOS distance of
around 4 km, the region affected by NUBF is about 0.75 km
wide. This corresponds roughly to 3 min for a precipitation
cell moving at 20 km/h in the direction orthogonal to the
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Figure 7. Radar RHI sequence for the event of 20 March 2010. The RHI scans are performed every 6 min toward
ADMIRARI. The ADMIRARI FOV is indicated by the blue cone in the top left panel.
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Figure 8. Event of 20 March 2010: time evolution in the
TB − PD plane for the three ADMIRARI frequencies. The
overall duration of the event is 40 min. The colorbar modulates the time passed in minutes from the beginning of the
event at 0950 UTC.
radiometer viewing direction. In the NUBF‐affected region
the radiation field is clearly characterized by a strong gradient. Certainly the situation is extreme due to the unrealistic
sharp edge of the rain shaft assumed, but it is indicative of an
important pitfall of the measurements, as we see later.
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[24] To have a deeper understanding of the RT, we restrict
our analysis to a cross‐GP‐LOS distance equal to 2 km along
the double array dash‐dotted line depicted in Figure 10
(bottom left). Thus ADMIRARI “looks” toward the center
of the rain shaft, and we vary the distance from the rain shaft
(Figure 11). With this selection we avoid the NUBF affected
area so that “lateral” NUBF effects do not play any role for
these radiometer viewing positions. The continuous lines in
Figure 11 indicate the ADMIRARI TB and PDs including
all scattering order contributions via a full 3D simulation
(backward Monte Carlo [Battaglia et al., 2007]). The black
diamond line shows the corresponding measurements simulated for a radiometer with a pencil beam via a full 3D
simulation (backward Monte Carlo [Battaglia et al., 2007]).
Thus we can study resolution effects. The dashed red line
provides also the results for a radiometer with a pencil beam
but obtained from a slant path 1D simulation (adapted RT4
code [Evans and Stephens, 1991]). We can draw the following observations.
[25] 1. At 36.5 GHz (10.7 GHz), in the region with the
highest TBs, half (10%) the total radiation has encountered
at least one scattering event (see the difference between
black continuous and red diamond lines in Figure 11 (left)).
Therefore at the higher frequencies the scattered field is
expected to largely affect the radiometer signal; thus 3D
scattering effects are likely to occur.
[26] 2. The emission (i.e., zero order of scattering) term is
very different at the three frequencies because of the different downwelling atmospheric emission (see equations (1)
and (2) of Battaglia et al. [2006] for details). PD[0] (diamond red lines in Figure 11, right) is the result of two
processes: (1) the propagation of radiation in rain that is
vertically polarizing due to the increased absorption of horizontally polarized radiation and (2) the emission of radiation, which is preferentially horizontally polarized. At 21 and
36.5 GHz, due to the presence of a considerable background
emission from behind the rain shaft, the propagation effect
tends to overcome the emission resulting in positive PD[0] s
for all optical thicknesses. Conversely, at 10 GHz, this
happens only for large optical thicknesses, while for thin
media, PD[0] is negative.
[27] 3. The impact of the higher orders of scattering on
PDs is much larger than the impact on TBs. For instance,
at 10 GHz, the PDs may be affected for more than 50% by the
radiation scattered within the observed volume (Figure 11,
top right; compare red diamond and black continuous lines).
[28] 4. The total signal simulated for an ADMIRARI
3dB beam width (6.5 degrees) significantly differs from the
pencil beam only when the along‐GP‐LOS distance from the
rain shaft exceeds 4 km (compare the black continuous and
diamond black dash‐dotted lines in Figure 11, right). This is
due to the NUBF which is responding to the vertical variability of the precipitating cloud. The overall effect is equivalent to spatially smoothing out the PD and TB fields obtained
in the pencil beam configuration along the viewing direction.
[29] 5. In all situations the 1D pencil beam shows smaller
(larger) TBs when the radiometer is well within (far outside)
the rain shaft. The difference is almost imperceptible at
10.7 GHz but can reach values as high as 15–20 K at 36.5 GHz.
[30] 6. There is an extended region outside the rain shaft
with significantly positive PDs (larger than 2K) at 36.5 (and
at 21.0 GHz, not shown). The 1D pencil beam cannot
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Figure 9. Schematic for the rain cloud simulation. Radiances have been computed at the radiometer
location identified by the coordinate (Along GP‐LOS, Cross GP‐LOS). The blue shaded area contains
the rain system which has a vertical but no horizontal structure. The length of the horizontal sides of
the cloud box are Lal and Lcx. Nonshaded areas contain only atmospheric gases. The surface is assumed
to be a blackbody.
reproduce this feature, and PDs are even slightly below zero
in the same region in a 1D approximation. While at 10.7
GHz PDs are always negative and can also achieve
extremely negative values when the radiometer is located
either within or outside of the rain shaft (feature well depicted in Figure 6 and confirmed for the whole set of observations in Figure 2a), there is only a confined region at
36.5 GHz where large negative PDs are reached (Figure 10,
bottom right). This situation is achieved when the radiometer is looking from underneath the rain shaft, having a
small portion of the precipitating cell in the FOV (around
one optical thickness). In that region, 3D simulations generally favor more negative PD values than 1D‐SP.
better insight is provided by analyzing the contribution of
the different orders of scattering to the signal. The zero order
of scattering term (i.e., the emission term, red diamonds in
Figure 11) is perfectly accounted for by a 1D‐SP approximation. These differences must result from higher order of
scattering terms. As visible in Figure 11 (right) the structure
of the PD signal is driven by the first order of scattering. For
scenarios involving horizontally oriented flattened raindrops
the first order of scattering tends to produce negative PDs
(blue squares). Hereafter we generalize the theoretical argument proposed by Battaglia and Simmer [2007, equations
(17)–(21)]: at the surface (z = 0) the jth order of the downwelling TB sensed at a given direction (mr, r) is given by
0
1
½ j1
J V ðr ;z′Þ
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl{
Z
B
C
i
0 #½ j
1 B R #V ðr ;r ;z′Þ h
½ j1
½ j1
dz′ C
B sl e sl
C
Z
ð
;
;
D
ÞT
ð
;
;
z′
Þ
þ
Z
ð
;
;
D
ÞT
ð
;
;
z′
Þ
dW
vv
i
r
i
i
vh
i
r
i
i
i
TV ðr ; r Þ
V
H
j
j
r
B
C
@
A¼B
C;
B
C
½ j1
#½ j
J
ð
;z′
Þ
r
B
C
H
TH ðr ; r Þ
B
C
zfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl
ffl
}|fflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflfflffl
ffl
{
Z h
i
@R
A
#H
½
j1
½
j1
sl ðr ;r ;z′Þ
dz′
Zhv ði ; r ; DÞTV ði ; i ; z′Þ þ Zhh ði ; r ; DÞTH ði ; i ; z′Þ dWi jr j
sl e
5. Discussion
5.1. Anisotropic Scattering Effects
[31] The three last observations described above deserve
further discussion. What is the fundamental cause of the
difference between 1D‐SP and 3D radiative transfer? A
ð1Þ
where Z is the ensemble‐averaged phase matrix (whose elements are expressed in the H‐V basis). The outer integral is
performed over the slant volume and accounts for the propagation effect, i.e., the larger H extinction of the medium,
which tends to produce V‐polarized radiation (positive PDs).
The inner integral accounts for the polarization effect of the
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Figure 10. (left) Brightness temperatures and (right) polarization differences as sensed by an ADMIRARI‐
type radiometer looking “southward” (i.e., downward in the image) at an elevation angle of 30° for
(top) 10.7 GHz and (bottom) 36.5 GHz. The different radiometer viewing positions are identified by two
coordinates: the along‐ and cross‐ground‐projected line‐of‐sight (GP‐LOS) distances.
scattering from all possible incoming directions (mi, i) into
the radiometer viewing direction (mr, r). Note that with
preferentially oriented azimuthally symmetric distributed
hydrometeors the phase matrix depends only on the relative
azimuth difference D = i − r. For the polarization difference at scattering order j we can write
½ j1
PD½ j ðr ; r Þ / hJ V
½ j1
isl hJ H
isl ;
ð2Þ
where the brackets indicate an averaging along the slant
volume. Reverting to the linear basis [T ≡ 0.5(TV + TH),
PD ≡ TV − TH] the contribution of the first order of scattering to polarization will be
Z h
2Z21 ði ; r ; DÞ T ½0 ði ; i Þ
i
þ Z22 ði ; r ; DÞ PD½0 ði ; i Þ dWi i:
PD½1 ðr ; r Þ / h
ð3Þ
In the specific CHUVA setup mr = −0.5 and given the
amplitude of T[0] and PD[0] and the behavior of Z21 and Z22
(not shown) the first term within the integral is dominant.
Thus it is worthy to analyze the dependence of the phase
matrix scattering element Z21 on the incoming direction (in =
acos(mi)) and the relative azimuth difference when mr = −0.5.
Figure 12 (left) depicts a typical behavior for Z21 at 36.5 GHz
(but the same is found at the other ADMIRARI frequencies
with large horizontally oriented raindrops). The azimuth
dependence of the phase matrix elements is the result of two
effects: (1) the dependence of the scattering angle on D
(with the related dependence of polarization on the scattering
angle) and (2) the rotation needed to relate the Stokes parameters of the incident and scattered beams relative to their
meridional planes (details in chapter 1 of Mishchenko et al.
[2000]) which, for instance, accounts for the azimuthal
dependence at in = 0°, 180° in Figure 12. Figure 12 (right)
shows the same element azimuthally averaged, i.e., the phase
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Figure 11. Contribution of different order of scattering (left) to the brightness temperatures and (right) to
the polarization differences for different radiometer positions of Figure 10 along the line with a cross‐
ground‐projected line‐of‐sight (GP‐LOS) distance equal to 2 km (dash‐dotted line in Figure 10, bottom
left) for (top) 10.7 GHz and (bottom) 36.5 GHz.
matrix element used in a 1D approximation where there is
no azimuth dependence of the radiation field.
[32] The major differences between 3D and 1D‐SP are
found when the radiometer is located immediately underneath the rain shaft and when the radiometer is outside of the
rain shaft. The two situations are illustrated in Figure 13.
[33] The first configuration (Figure 13, top) is representative of region I (along‐GP‐LOS distance from the rain
shaft between −4 and −3 km, i.e., inside the rain shaft in
Figure 11). In this case the radiometer is only looking through
rain (the cloud base is at 3 km) and, even at 36.5 GHz, the
signal is not fully saturated. Very negative values for polarization are reached at 36.5 GHz in the 3D simulation. The
1D‐SP approximation is producing lower TBs and higher
PDs. The 1D RT is run on a 1D domain, which is derived
by extending horizontally the domain intercepted by the slant
ADMIRARI volume (Figure 13, red dash‐dotted line rectangle). Therefore, while the radiation emitted within the slant
volume is perfectly accounted for, the scattered radiation
is not. Let us consider here the radiation sensed by the
radiometer scattered once only, and within the ADMIRARI
FOV. The 1D approximation introduces fictitious scattering
events like those illustrated with the red dash‐dotted arrows
in Figure 13, i.e., corresponding to radiation emitted from
outside the sides of the box and emitted downward (side
leakages, positive contribution). Conversely, the 1D approximation is missing the radiation coming from the upper part
of the box in Figure 13 (blue region), for example, the
contributions illustrated with the blue dashed lines (leakages
from the upper part of the rain shaft, negative contribution).
Moreover, part of the radiation coming from the surface is
scattered by the rain medium and therefore the first order of
scattering component is penalized in favor of higher order of
scattering radiation. Overall, in the 1D approximation, there
will be also a loss and a reduction of the radiation traveling upward within the radiometer volume due to radiation
escaping to space (negative leakages to space). In the 1D
approximation the suppression of the upwelling I[0] coming
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Figure 12. (left) Phase function element Z21 for r = 150° for a Marshall and Palmer distributed rain
layer with a rain content of 2.8 g/m3. (right) Azimuthally averaged phase function element hZ21iDF from
Figure 12 (left).
from the right side of the rain shaft represents the most relevant source for the reduction of the negative amplitude of the
PD signal at small optical thickness. In the 3D RT, there is a
surplus of radiation coming from angles i between 0 and 90°
which is scattered back to the radiometer with DF around
zero. For this range of angles, Z12 assumes strongly negative
values (lower left corner in Figure 12, left), thus explaining the
strongly negative PDs in the 3D computations. The leakages
from the top have also a relevant effect since they relate to Z12
values with i close to 180° but with predominant DF values
around 180°. To summarize, in the 1D approximation the
positive leakages from the side are smaller than the negative
leakages from the top and to space. The overall effect is to
reduce TBs. In addition to that, the 1D approximation tends to
favor a radiation field within the radiometer volume characterized by larger orders of scattering; since radiation scattered
many times tends to be unpolarized this also explains the less
negative PDs in region I.
Figure 13. Schematic for understanding 3D effects when the radiometer is (top) underneath the rain
shaft and (bottom) outside of it.
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Figure 14. Same as in Figure 10 but for the (top) 21 and (bottom) 36.5 GHz channels and for a scenario
more appropriate for the 19 March event: Lcx = 1.5 km and Lal = 1.5 km. The magenta line is a guessed
position of the radiometer during the 19 March event.
[34] Conversely, when the radiometer is in the region II
(along‐GP‐LOS distance from the rain shaft between −0.5
and 8 km in Figure 11) the leakages from the side play the
most important role. In fact the signal is now close to saturation and the volume effectively contributing to the radiometer signal is confined to a region at most few hundred of
meters within the rain shaft (so the leakages from the top
have no relevance at all). The situation is reversed from
the former case with the 1D approximation TBs exceeding
the ones computed accounting for the full 3D structure. The
radiation corresponding to these side leakages is generally
characterized by incoming polar angle in slightly above 90°
and by DF around 180°. The scattering phase function for
those angles is significantly negative (center upper part in
Figure 12, left). The absence of such radiation in the real 3D
world does produce the positive PDs we actually observe. In
a 1D‐SP RT on the other hand all the profiles having along‐
GP‐LOS distances from the rain shaft in the range [−2.5, 8]
km look quite similar and tend to have TBs approaching the
ones characteristic of a blackbody, thus unpolarized.
[35] In summary, there is an azimuthal anisotropy of the
radiation field within the radiometer FOV when looking
at a rain shaft from outside at slant angles. This anisotropy,
in combination with the peculiar structure of the phase
function elements of preferentially horizontally oriented
raindrops (Z21 is predominantly negative when azimuthally
averaged), produces the puzzling positive PDs, which are
ubiquitous in our CHUVA observations. On the other hand,
in a 1D approximation the radiation field has no azimuthal
dependence, modifications of the radiation fields caused
by horizontal variability cannot be accounted for, and the
observed positive PDs cannot be reproduced in a simulation
framework because of the behavior of hZ21iDF. The simultaneous collocated observations tend to exclude other possible explanations of the phenomenon of positive PDs
observed at 36.5 GHz. For instance, a huge amount of
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Figure 15. (top left) Hydrometeor profile considered to match the case observed on 19 March 2010.
(top right and bottom) The spatial evolution in the TB − PD space is shown when moving from within
to the outside of the rain shaft for different cross‐GP‐LOS distances as indicated in the legend. Each line
is traveled counterclockwise. The magenta line corresponds to simulated observations in correspondence
to the magenta path shown in Figure 14.
cloud water located beyond a small amount of horizontally
oriented raindrops could potentially produce positive polarization at 35 GHz via differential extinction but such scenario is excluded by the simultaneous large negative PDs at
10.6 GHz and by the high MRR reflectivities.
5.2. NUBF Effects
[36] An additional complication is added when NUBF
situations are present. Let us reconsider the time evolution
shown in Figure 5. Again we resort to a simple square rain
shaft with sides equal Lal = 1.5 km and Lcx = 2 km similar to
that observed at 2048 UTC according to the RHI profile
(Figure 4, top right) and to the MRR reflectivity (Figure 3a)
to interpret the measurements. Similarly to Figure 10, simulated TBs and PDs for the 21 and 36.5 GHz channels
are shown in Figure 14. The vertical hydrometeor profile
here assumed is plotted in Figure 15 (top left). Figure 15
(top right and bottom) depicts the change in the TB − PD
plane when moving the radiometer observation point along
the radiometer viewing direction with an along‐GP‐LOS
distance from the rain shaft from −1.45 km to 10 km, i.e.,
passing from inside to outside the rain along the line of
sight. Different lines correspond to different positions relative to the rain shaft border in the direction orthogonal to the
radiometer line of sight, as indicated by the legend. When
considering radiometer locations distant from the rain shaft
edge (i.e., those labeled with a cross‐GP‐LOS distance
equal to 1 km) the counterclockwise transition from the
point with along‐GP‐LOS distance from the rain shaft equal
to −1.45 km to that with along‐GP‐LOS distance equal 8 km
can be interpreted as if a cell with stationary rain has passed
over the radiometer and has moved away along the line
of sight of the radiometer. Such patterns (black lines in
Figure 15) qualitatively resemble the temporal evolution of
TB − PDs measured at the three frequencies (Figure 5). At
36.5 GHz, there is a “spatial accumulation point” for TB
∼280 K and PD ∼0; that is, there is no spatial variability in
the simulated signal for observation points located from the
rain shaft edge up to an along‐GP‐LOS distance from the
rain shaft of about 6 km. At such far distance the radiometer
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starts sensing the decrease in rain content at around 4 km
altitude and the presence of cloud droplets as well (Figure 15,
top left). Similarly, in Figure 5, there is a “temporal accumulation point,” with measurements dwelling at the same
location in the 36.5 GHz TB − PD plane between 2045 and
2047 UTC at TB ∼ 270 K. Assuming that the storm is moving
at 8 m/s, this will produce a movement of 1.5 km in 3 min
(and of 4 km for the whole duration of the event), which is
inconsistent with the former length estimate (4 times larger).
It is therefore very likely that the storm did not move along
the line of sight of the radiometer but on the other hand
crossed its FOV. This is in agreement with the two consecutive PPI images of Figure 4 which suggest movement of
the rain cells from the east to west. In this case, in Figure 15,
instead of dwelling on the continuous black line, the measurements would have progressively jumped on the other
symbol lines (circle, then crosses, then dots), undergoing a
strong TB gradient. This actually also better explains the
presence of near‐null PDs in the 36.5 GHz channel for TBs
below 250 K. The accumulation of measurements in such
region of the TB − PD plane is likely to be the result of NUBF
effects and to occur when precipitating systems are migrating out of the radiometer FOV.
[37] To verify our assumption, according to our auxiliary
observations for the 19 March event, a sequence of positions
of ADMIRARI relative to our simulated rain shaft has been
assumed (magenta line in Figure 14, bottom). Note that the
sharp change of direction is a pure graphical artefact; the
problem is indeed symmetric respect to the vertical line
where cross‐GP‐LOS is equal to 0.75 km. The radiometer
is first under the rain shaft, and then the rain cell is moving away from the radiometer and exiting its FOV, which
is consistent with the MRR observation (Figure 3a), rain
shaft going away from the radiometer) and with the PPI
image (Figure 4, cell crossing the FOV from east to west).
The magenta lines in Figure 15 correspond to this possible
solution; these three patterns resemble the ones observed in
Figure 5 with all the limitations of the case.
[38] It is important to note that in the 10.7 GHz channel
the along line of sight optical thicknesses of precipitating
media are generally small, PDs are linearly decreasing with
TBs, so that, in the TB − PD plane, points corresponding to
NUBF scenes fall in the same region of uniform beam filled
(UBF) scenes (Figure 15, top right) and the structure of the
TB − PD curve does not suffer significant changes. Because
of the nonlinearities between the radiometer signal and the
retrieved quantities (cloud and rain integrated water path)
such ambiguities introduce additional uncertainties in the
retrieval. In addition to that, at 21.0 and 36.5 GHz the pronounced concave upward behavior of the TB − PD curves
permits the exploration of new regions in the TB − PD plane
uncovered by UBF scenes (e.g., blue crosses in Figure 15,
bottom right). The inclusion of NUBF effects is therefore
mandatory to cover the full range of observations in the
PDs space and to decrease the residuals in any Bayesian‐
type retrieval.
6. Conclusions
[39] The CHUVA GPM/GV campaign has been a unique
opportunity in understanding 3D effects related to microwave ground‐based polarimetric observations of rain sys-
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tems. During CHUVA, many events were observed by
ADMIRARI, all of them characterized by an overwhelming
3D structure with small localized rain elements surrounded
by clear air. The occurrence of warm rain events and the
simultaneous acquisitions of collocated X band RHI scans
greatly facilitated the RT interpretation. By investigating
some case studies, we can draw the following conclusions
for polarimetric ground‐based radiometer observations.
[40] 1. Geometric 3D effects are, obviously, always affecting measurements performed at slant angles. They can be
easily accounted for by adopting 1D slant path approximations.
[41] 2. In heavy rain, for radiometers with frequencies in
the 10–36 GHz region, the scattered component represents a
large fraction (increasing with frequency) of the total signal;
for instance, at 36 GHz, raindrops are both absorbing and
scattering microwave radiation with single scattering albedos easily exceeding 0.5. The overall power detected by
the radiometer is therefore the result both of emission and of
scattering processes within the FOV of the radiometer, and
3D scattering effects are likely to occur.
[42] 3. The PD signal is particularly sensitive to scattered
radiation with the first order of scattering contributing crucially to the overall signal. The polarization property of the
scattered radiation is driven by the Z12 phase matrix element
of the scattering medium along the radiometer line of sight.
For perfectly oriented spheroids like raindrops and for
radiometer observations at 30° elevation angle, this term can
assume both positive and negative values, depending on the
relative geometry of the incoming/outgoing radiation, with
a strong dependence on the relative azimuth. Radiation
fields with strong azimuthal inhomogeneities (like those produced by side leakages) can produce large departures from the
polarization signals produced when adopting 1D SP approximations (which inherently assume azimuthal symmetric radiation fields).
[43] 4. Observations of PDs as high as +4 K and +2.5 K at
36.5 GHz and 21.0 GHz, respectively, in combination with
almost saturated TB s are clear 3D scattering fingerprints.
As a consequence, the interpretation of PD signals for the
21 and 36.5 GHz channels is utterly difficult at large optical
thicknesses because they are heavily influenced by the 3D
structure of the system. This poses serious problems when
interpreting the PD results for instance in the implementation of ADMIRARI‐like physically based schemes tailored
to retrieve integrated cloud and rain water paths.
[44] 5. Due to the smaller footprint, ground‐based observations are potentially less affected by NUBF than spaceborne observations and therefore more suited for pristine
studies of the radiation field. However, when the precipitating system is tall (e.g., in tropical environments) and it is
located far away from the radiometer position, NUBF can
play a relevant role as well. Because of the nonlinear
response of PDs with TBs and of both these quantities with
the variables to be retrieved (cloud and rain integrated water
path), NUBF is difficult to disentangle. Its effect is twofold:
either it bears ambiguities (i.e., measurements with the same
PDs and TBs but corresponding to different microphysical
states) or it favors the occurrence of PDs and TB unpredicted
by UBF scenarios.
[45] The current analysis stresses thorny issues related to
3D RT effects present in physically based schemes aimed
at retrieving integrated cloud and rain water paths from
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ADMIRARI‐like observations. The introduction of highly
resolved (500 m resolution or less) cloud model runs (tailored to the different synoptic conditions experienced during
the measurement field campaigns) coupled with full 3D RT
models represents the most rigorous modus operandi for the
foundation of a RT database suited for a Bayesian retrieval
scheme. This is the strategy we are currently pursuing for
an optimal interpretation of ADMIRARI observations.
[46] Acknowledgments. The authors would like to thank the NASA
GPM/GV program for funding the participation of ADMIRARI in the
CHUVA campaign, the Brazilian GPM/GV counterpart for logistic assistance and cooperation during the experiment, and the access to auxiliary data.
We are also grateful to C. Kummerow for useful discussions during the
field campaign and afterward and to the reviewers for their comments.
The ADMIRARI project has been funded by the Deutsche Forschungsgemeinshaft (DFG) under grant BA 3485/1‐1. The authors are grateful
for the financial support provided by the Brazilian Space Agency‐AEB during the CHUVA field campaign at Alcantara, Brazil. One of the authors
(C.A. Morales) was also partially supported by FAPESP grant 2009/
15235‐8 and Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior‐CAPES. A. Battaglia was funded for his travels by the NCEO
Mission Support funding.
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J. Geophys. Res., 99(D8), 16,707–16,716, doi:10.1029/94JD01150.
Weinman, J. A., and R. Davies (1978), Thermal microwave radiances from
horizontally finite clouds of hydrometeors, J. Geophys. Res., 83(C6),
3099–3107, doi:10.1029/JC083iC06p03099.
A. Battaglia, Department of Physics and Astronomy, University of
Leicester, University Road, LE1 7RH Leicester, UK. ([email protected])
C. A. Morales, Instituto de Astronomia, Geofisica e Ciéncias Atmosféricas,
Universidade de São Paulo, Rua do Matão, 1226, São Paulo, Brazil.
([email protected])
P. Saavedra and C. Simmer, Meteorological Institute, University of Bonn,
Auf dem Hügel 20, D‐53121 Bonn, Germany. (pablosaa@uni‐bonn.de;
csimmer@uni‐bonn.de)
17 of 17
ANEXO 2
Cursos de Treinamento do Projeto CHUVA
Curso de Fortaleza.
O treinamento durante a campanha do CHUVA em Fortaleza valerá como disciplina no Mestrado Acadêmico em Ciências
Físicas Aplicadas (MCFA) da UECE. O nome da disciplina é "Tópicos de Meteorologia e Climatologia I", que tem conteúdo
variável como ementa. De acordo com a duração do curso, que foi de 30h/a, ele somará 2 créditos ao currículo dos
alunos, conforme decidido em reunião do colegiado do MCFA.
Relatório de Avaliação do Curso
“Sensoriamento Remoto e dos Processos de Formação da Precipitação”
Com 110 alunos inscritos, o curso originalmente previsto para o auditório da FUNCEME,
foi transferido para o Instituto Aldy Mentor, que tem se tornado parceiro da FUNCEME na
realização de nossos treinamentos.
O perfil dos alunos foi bastante diverso, envolvendo, de graduandos a doutores, bem
como de áreas diversas, tais como: física, meteorologia, oceanografia, geografia e
ciências ambientais.
Do total de inscritos, 92 alunos compareceram, o que corresponde a 84% do previsto.
Destes 92 alunos, 98% tiveram freqüência, em pelo menos 80% das aulas.
Sessenta e cinco destes 92 alunos, ou seja, 71% responderam ao questionário de
avaliação, cujo modelo encontra-se em anexo.
Resultado da Avaliação
Quanto ao Curso
Na primeira questão, foram atribuídas notas de 1 a 4, correspondendo aos conceitos: 1Ruim, 2-Razoável, 3-Bom e 4-Muito Bom. Nesta questão foram avaliados os seguintes
itens: Temas abordados, Professores, Carga horária, Organização do curso, Auditório e
Infra-estrutura.
A nota média dada aos itens é mostrada na Tabela 1, a seguir. De modo geral, o curso foi
muito bem avaliado, com nota média entre Bom e Muito Bom.
Tabela 1 – Nota média do curso, por item avaliado.
Item
Nota média
Temas abordados
3,74
Professores
3,78
Carga horária
3,32
Organização do curso
3,72
Auditório e Infra-estrutura
3,74
A Figura 1 mostra os percentuais das notas dadas a cada um dos itens. O que se observa
é que a maior parte dos alunos avaliou como Muito Bom, principalmente, os itens: Temas
abordados, Professores, Organização do curso e Auditório e Infra-estrutura.
Em relação ao quesito Carga Horária, a nota média mais baixa, próxima do conceito Bom,
pode ter explicação na diversidade de opiniões apresentadas: para alguns, a carga
horária foi insuficiente, e para outros, foi demais, tornando as aulas cansativas (vide
Tabela 2).
90%
Percentual das Respostas
80%
70%
Temas Abordados
Professores
Carga Horária
60%
Organização do Curso
50%
40%
30%
20%
10%
0%
Ruim
Razoável
Bom
Muito Bom
Avaliação
Figura 1 – Avaliação do curso.
Tabela 2 – Observações dos alunos em relação aos itens avaliados.
Itens
Temas abordados
Professores
Carga horária
Observações
- Bem comentados.
- Abordar relacionando com outros assuntos e trazendo
essa realidade para os acontecimentos no oceano.
- Todos maravilhosos.
- Com boa didática.
- Excelente.
- Alguns bons, outros ruins.
- Seria bom um pouco mais de tempo.
- Muito compacto.
- Bem distribuída.
- Melhor com maior carga horária.
- Muita informação X pouco tempo.
- Intensidade exagerada.
- Muito cansativo.
- Poderia ter sido maior.
- Orientar o professor sobre a utilização da mídia.
- Todos de parabéns.
- Faltou material didático.
- Controle e tela de datashow.
- Problemas de som, computador e cadeiras quebradas.
- Às vezes muito frio.
- Melhorar áudio e datashow.
14%
12%
10%
8%
6%
4%
2%
Ferramentas para Previsão
Imediata Utilizando Radar e
Satélites
Eletrif icação das Nuvens
Estimativa de Precipitação por
Satélite e Radar
Satélites Meteorológicos e a
Observação em Microondas
Radar Princípios Básicos
Microf ísica das Nuvens
A Parametrização de Nuvens e
Convecção
Camada Limite Planetára e o
Processo de Convecção
Princípios Básicos de Modelagem
em Alta Resolução
0%
Camada Limite Planetária:
Conceitos Básicos
Percentual de Seleção de Tema
Quanto aos Temas Abordados
O resultado da pesquisa em relação aos temas que despertaram maior atenção dos
alunos é mostrado na Figura 2, a seguir. Desde que foram permitidas múltiplas escolhas,
houve um total de 312 seleções, sendo que o tema que mais despertou a atenção dos
alunos foi: Camada Limite Planetária: Conceitos Básicos, com 13% dos votos. Contudo,
como mostrado na Figura 2, praticamente todos os temas mostraram-se atraentes para os
alunos.
Temas
Figura 2 – Percentual de seleções de temas de maior interesse.
Ainda em relação aos temas abordados, 97% dos alunos afirmaram que eles serão úteis
em seus estudos; 3% dos alunos se abstiveram desta questão.
Quanto aos Comentários
Alguns dos alunos transcreveram comentários em sua avaliação. A maioria destes
comentários traz elogios ao curso, porém, também indicam aspectos que podem ser
melhorados, tais como a carga horária, a existência de material de apoio ou “didático”,
como mencionado. Por outro lado é gratificante perceber nesses comentários que o curso
despertou o interesse para o experimento e para os temas, servindo de apoio para os
estudos atuais, e abrindo possibilidades para o futuro.
Tabela 3 – Comentários dos alunos.
-
-
-
-
-
-
Em geral o curso foi muito bom, mas, se a carga horária fosse um pouco maior seria
melhor.
A programação do curso foi muito bem feita, pois foi possível focar no melhor de cada
área de conhecimento (física, oceanografia, etc.)
Tive oportunidade de ver assuntos da área a qual faço parte, sendo abordados de formas
diferentes. Conhecendo e aprofundando informações relevantes no exercício de meus
trabalhos.
Considero de extrema importância esse período conceitual. Gostaria de aprofundar mais
nessas áreas de microfísica de nuvens modelagem numérica.
Curso muito bem organizado, professores muito bem preparados, porém, deveriam focar
inicialmente seus slides a ligação do seu tema com o projeto CHUVA.
Excelente idéia de conciliar os estudantes em um projeto tão conceituado e promissor
para as diversas áreas.
A idéia deste curso foi muito boa, e boa parte do que foi abordado será de grande
proveito, ainda abriu a mente para outros rumos. Parabéns à organização.
Parabéns pela iniciativa foi um ótimo curso.
O curso foi muito bem organizado e de conteúdo muito atrativo. Apesar de estar na área
de instrumentação eu me interessei muito pelos sistemas de radares.
Como bom oceanógrafo, gostaria de ter visto mais sobre interação oceano-atmosfera,
mas, como o curso é baseado no Projeto CHUVA eu achei os temas abordados bem
pertinentes.
O curso serviu para rever conceitos e entender a campanha no seu sentido e em seu
desenvolvimento.
O curso apesar de breve foi bem técnico e as abordagens foram bastante precisas. Todos
os temas abordados serão de grande importância para mim, Agradeço a oportunidade.
Gostei muito do curso, principalmente da parte de radar meteorológico. Gostaria de
trabalhar nessa parte de pesquisa.
Possibilitou o acesso a informações muito interessantes, de uma forma didática,
aumentando o interesse pela área de estudo.
Poderia haver mais cursos como esse ao longo do ano, ou pelo menos todo ano.
Excelente curso e parabéns pela organização. Os temas foram ministrados de forma
bastante didática, o que possibilitou o entendimento para pessoas que não são
exatamente da área de meteorologia, como eu.
Gostaria de parabenizar a organização do curso, pelo incentivo em disseminar o
conhecimento sobre os temas, fazendo uma análise construtiva do desenvolvimento da
região.
Sempre é bom aprender!
Parabéns pelo curso e professores. Preciso participar dos experimentos.
O curso foi muito interessante, de grande importância, mas, um pouco cansativo.
Na busca por uma melhor qualificação acadêmica o curso se torna notável.
Palestras bastante úteis e uma ótima estrutura.
Como graduando em oceanografia, senti-me satisfeito com o conteúdo. Poderei usar
deste aprendizado em meu futuro trabalho de conclusão de curso (TCC) e talvez investir
numa pós-graduação em Sensoriamento Remoto.
Em geral, achei o curso bastante proveitoso, sendo, principalmente uma ferramenta para
aprofundar os conhecimentos vistos em sala de aula.
Deveria haver seminários de aprofundamento dos temas.
Servirá de base para os estudos que farei no mestrado.
Ótima idéia do curso e os professores escolhidos são exemplares.
Poderia ter sido melhor se tivesse material didático.
Anexo – O questionário de avaliação aplicado
Curso
Sensoriamento Remoto e
Modelagem dos Processos de Formação da Precipitação
Nome (opcional) _________________________________________________________________
Dê a nota, considerando a seguinte pontuação:
Nota
1
2
3
4
Item
Temas abordados
Professores
Carga horária
Avaliação
Ruim
Razoável
Bom
Muito bom
Nota
Observação
Marque com (x) o tema ou os temas que despertaram mais a sua atenção e você gostaria de
aprofundar seus conhecimentos:
(
(
(
(
(
(
(
(
(
(
) Camada Limite Planetária: Conceitos Básicos
) Princípios básicos da Modelagem em Alta Resolução
) Camada Limite Planetária e o Processo de Convecção
) A Parametrização de Nuvens e Convecção
) Microfísica das Nuvens
) Radar Princípios Básicos
) Satélites Meteorológicos e a Observação em Microondas
) Estimativa de Precipitação por Satélite e Radar
) Eletrificação das Nuvens
) Ferramentas para Previsão Imediata Utilizando Radar e Satélites
Os temas abordados serão úteis para você? (
) sim
(
) não
Comentários:
______________________________________________________________________________
______________________________________________________________________________
______________________________________________________________________________
______________________________________________________________________________
_____________________________________________________________________________
N.
Nome Completo
Instituição
1
Adriano Correia de Marchi
UFAL
2
Alan Zeno Martins Viana
UFC
3
Ana Luzia de F. Lacerda
UFC-Labomar
4
André de Sena Pinheiro
UECE
5
Angélica Silva de Oliveira
FUNCEME
6
Antônio Geovan de Araújo Holanda Guerra
UECE
7
Antonio Tavares Bittencourt
UECE
8
Arthur Costa Tomaz de Souza
UECE
9
Augusto César de O. Freitas
UFC
10 Aurélio Wildson Teixeira de Noronha
UECE
11 Bruno Nogueira Catunda
UFC
12 Bruno Pires Sombra
FUNCEME
13 Cíntia Carolina Mota Menezes
UECE
14 Clodoaldo Campos dos Santos
UECE
15 Cristiano da Silva Rocha
UFC
16 Davison Lucas Mendes Viana
UECE
17 Daysiane Barbosa Brandão
UFC-Labomar
18 Dhemetryo de Freitas Cassundé de Oliveira
UFC
19 Diogenes Passos Fontenele
UFC
20 Domingo Cassain Sales
UECE
21 Ednardo Moreira Rodrigues
UECE
22 Évila Pinheiro Damasceno
UFC-Labomar
23 Felipe Viana Pimentel
UECE
24 Fernanda Oliveira
UFC-Labomar
25 Fiamma Eugênia Lemos Abreu
UFC-Labomar
26 Francisco Elineldo Maia Pinheiro
Defesa Civil Fortaleza
27 Francisco Franklin Sousa Rios
UECE
28 Gabrielle Melo Fernandes
UFC-Labomar
29 Gaia Tavares Machado
UFC-Labomar
30 Giullian Nícola Lima dos Reis
UFC-Labomar
31 Gláucia Miranda Lopes Barbieri
UFC
32 Heitor Flávio de Albuquerque Gentil Neto
UFC-Labomar
33 Heládio Gonçalves Nepomuceno
UECE
34 Iliana Maria da Silva Gomes
UFC
35 Iohanna Bezerra Rodrigues
UFC
36 Italo Gois Miranda
UFC-Labomar
N.
Nome Completo
Instituição
37 Jacques Servain
IRD
38 Janne Kelly Lima Rabelo
UECE
39 João Bosco Passos Accioly Filho
FUNCEME
40 Jonathan Alencar da Silva
UECE
41 Jorge Felipe Gomes Rocha
UFC
42 José Airton Bezerra Viana Filho
UFC-Labomar
43 José Cavalcante de Oliveira Filho
UFC-Labomar
44 José Gabriel Barbosa Neto
UFC
45 Jose Madson de Oliveira Filho
UFC
46 José Marcelo Rodrigues Pereira
FUNCEME
47 José Nacizo Holanda Luciano Júnior
UECE
48 Kleber Melo Oliveira
UFC
49 Kurtis François Bastos
IBAMA
50 Larissa Plutarco
UFC-Labomar
51 Lauro Pessoa Maia Junior
UECE
52 Leonardo Hislei Uchôa Monteiro
UFC-Labomar
53 Levi Mendes Franklin
UECE
54 Liana Pacheco Bittencourt
UFC-Labomar
55 Luidhy Santana da Silva
UECE
56 Luiz Martins de Araújo Júnior
UECE
57 Marcos Wender Santiago Marinho
UECE
58 Marcus Vinicius de Abreu Avila
UFC-Labomar
59 Maria Cibele Torres Lemos
UFC-Labomar
60 Maria Jocilandia Mendes Vasconcelos
UECE
61 Maria Valdete Lira
FUNCEME
62 Mariany Sousa Cavalcante
UFC-Labomar
63 Mario Rodrigues Pinto de Sousa Filho
FUNCEME
64 Michaela de Sousa Costa
UFC-Labomar
65 Natalia Paiva Castro
UFC
66 Natanael Vieira de Sousa
UECE
67 Nayanna Cris M. Chaves
UFC-Labomar
68 Paulo José dos Santos
FUNCEME
69 Paulo Ricardo Bardou Barbieri
FUNCEME
70 Pedro Silveira Calixto
UFC
71 Priscila Lima Pereira
FUNCEME
72 Raquel Almeida Bezerra Rodrigues
UFC-Labomar
N.
Nome Completo
Instituição
73 Raul Fritz Bechtel Teixeira
FUNCEME
74 Rayza Ponce Leon Araruna
UFC-Labomar
75 Renan Gomes Crisóstomo da Silveira
UFC-Labomar
76 Rigoberto Soares do Nascimento
UECE
77 Rodolfo Teixeira Alves
UFC-Labomar
78 Rodrigo Alves Patricio
UECE
79 Roger Barreto Magalhães
Defesa Civil Fortaleza
80 Samuel Galvão de Souza
UECE
81 Samuellson Lopes Cabral
UFC
82 Simony Maia Vieira
UECE
83 Suany Campos da Silva
UECE
84 Sullyandro Oliveira Guimarães
UECE
85 Thaysa Portela de Carvalho
UFC-Labomar
86 Thiago Valério de Araújo
UFC-Labomar
87 Tyhago Aragão Dias
UECE
88 Victor Peixoto N. Cordeiro
UFC-Labomar
89 Vinícius Milanez Couto
UECE
90 Vinicius Oliveira
FUNCEME
91 Wagner Luiz Barbosa Melciades
FUNCEME
92 Wersângela Duaví
UFC-Labomar
Curso Belém
Curso: Sensoriamento Remoto e Modelagem dos processos de formação da precipitação – O PROJETO CHUVA
Horário
9:00 às 12:00
Dia 2/6
I) O Projeto CHUVA
II) Satélites
Meteorológicos e a
observação em
microondas.
Dia 3/6
9/6
10/6
IV) Princípios básicos
da Modelagem em
alta resolução
vI) O Uso do GPS na
Meteorologia
VIII) Radar de dupla
polarização
(David - UEA)
(Marc Adrian
Schneebeli - INPE)
(Henrique Barbosa IFUSP)
16/6
17/6
21/6
22/6
XII) Camada Limite
Planetária: conceitos
básicos
XIV) Camada Limite
Planetária e o Processo
de Convecção
Gilberto Fisch (IAE-DCTA) Gilberto Fisch (IAE-DCTA
(Luiz Machado-INPE)
14:00 às 17:00
III) Ferramentas para
Previsão imediata
utilizando radar e
satélites
(Luiz Machado-INPE)
V) A parametrização
de nuvens e
convecção
(Henrique Barbosa IFUSP)
VII) Microfísica das
nuvens
IX) Eletrificação das
nuvens
(Carlos Morales – IAGUSP)
(Carlos Morales – IAGUSP)
X) Estimativa de
precipitação por
satélite e Radar
XI) Introduction to the
LIDAR technique
(Riad Bourayou – INPE)
(Frederico Angelis –
INPE)
XIII) (David Fitzjarrald –
SUNY)
XV) (David Fitzjarrald –
SUNY)
I ) O Projeto CHUVA – Descrição dos objetivos, instrumentação e das campanhas e os resultados preliminares.
II) Satélites Meteorológicos e a observação em microondas. – Descrição sobre os principais satélites em órbita, o sistema de
observação por satélites para a década 2010-2020, os principais canais e o uso para conhecer a estrutura e a microfísica da nuvem, a
observação por microondas, o espectro de absorção na faixa das microondas, características dos canais.
III) Ferramentas para Previsão imediata utilizando radar e satélites. – Características observacionais das células de chuva,
conceitos sobre previsão imediata, parâmetros previsores observados por satélite e radar, deslocamento dos sistemas, o fortracc,
separação entre convectivo e estratiforme, previsão de descargas elétricas e evolução dos campos de umidade.
IV) Princípios básicos da Modelagem em alta resolução: Equações básicas da atmosfera. Operator splitting. Taylor e diferenças
finitas. Teorema de Nyquist. Média de Reynolds. Convergência e estabilidade de soluções numéricas. Erro de truncamento, dispersão e
difusão numérica. Aplicação a equação de advecção-difusão: Foward Euler, Implicit e Runge-Kutta.
V) Princípios básicos da Modelagem em alta resolução: Equações básicas da atmosfera. Operator splitting. Taylor e diferenças finitas.
Teorema de Nyquist. Média de Reynolds. Convergência e estabilidade de soluções numéricas. Erro de truncamento, dispersão e difusão
numérica. Aplicação a equação de advecção-difusão: Foward Euler, Implicit e Runge-Kutta.
VI) O uso do GPS (GNSS) na Meteorologia – Conversão de atraso dos sinais de GNSS em água precipitável, metodologias para processar
dados GNSS, campos de vapor d'água em 3D usando GNSS, relações convecção profunda/vapor d'água observadas com GNSS.
VII) Microfísica das nuvens – Transformação de fase; Convecção e mistura; aerossóis e CCN; formação e crescimento de gotículas,
gotas e cristais de gelo; e características de precipitação
VIII) Radar de dupla polarização –
IX) Eletrificação das nuvens – Modelos conceituais de eletrificação das nuvens; processos de eletrificação; características dos
relâmpagos; sistemas de medição e distribuição de raios no Brasil
X) Estimativa de precipitação por satélite e radar: Conceitos básicos de sensoriamento remoto, espectro eletromagnético, sensores
infravermelho, sensores de microondas passivo, estimativa de precipitação por satélites, estimativa de precipitação por radar.
XI) Introduction to the LIDAR technique- Principle of measurement; relevant laser light / particle interactions; Inversion schemes of
the ill-posed problem; advanced corrections of the signals; tropospheric aerosol sensing; review of environmental sensing; active
networks and systems.
XII) Camada Limite Planetária: conceitos básicos - Características da CLP e suas camadas, balanço de radiação (componentes) e de
energia, fluxos turbulentos de calor sensível e latente, instrumentação micrometorológica (resposta rápida e lenta)
XIII) Landscape and precipitation in the eastern Amazon Basin I - Large scale inflow and precipitation patterns (seasonal and
downstream of lower atmosphere variation, moisture convergence, precipitation recycling and types).
XIV) Camada Limite Planetária e o Processo de Convecção - Evapotranspiração, transporte vertical de calor latente, topo da CLP e
base das nuvens, processos de convecção livre e forçada
XV) Landscape and precipitation in the eastern Amazon Basin II - Mesoscale factors on boundary layer flows and precipitation (Sea
and river Breeze, coastal convergences, topographic effects, cloud patterns, land cover influences), Observational problems with
observing precipitation and climatic parameters in remote areas (surface in situ, satellite and radar data).
O Curso de Belém também contou como crédito do Programa de Pós-Graduação em Ciências Ambientais da UFPA.
Lista de Inscritos no Curso do CHUVA-Belém.
Nº Nome Email Instituição Telefone
1 JULIA CLARINDA PAIVA COHEN [email protected] UFPA 9132017255
2 TARCÍSIO MIRANDA DO AMARAL NETO [email protected] UFPA 88763743
3 ALBERT RICHARD MORAES LOPES [email protected] UFPA 87118416
4 LUCIANO DA SILVA BORGES [email protected] UFPA 32017255
5 BERNARDINO SIMÕES NETO [email protected] SIPAM 32430460
6 IVAN BITAR FIUZA DE MELLO [email protected] UFPA (91)32721254
7 QUEZIA LEANDRO DE MOURA [email protected] UFPA 88247380
8 FLÁVIO AUGUSTO FARIAS D'OLIVEIRA [email protected] UFPA 32283426
9 LILIANE FERREIRA DO ROSÁRIO [email protected] UFPA 91-91994908
10 JAKELINE DA SILVA VIANA [email protected] SIPAM 33662292
11 MARCELA MACHADO POMPEU [email protected] UFPA 32451575
12 JOAO DE ATHAYDES SILVA JUNIOR [email protected] OUTRAS 88327585
13 RAIMUNDO NONATO NASCIMENTO AARÃO JUNIOR [email protected] UFPA9188030210
14 LAURA SUÉLLEN LISBOA FERREIRA [email protected] UFPA 81968544
15 ILLELSON RAFAEL DA SILVA BARBOSA [email protected] UFPA 91 82483006
16 LETICIA LORENA BRAGA AMORIM [email protected] UFPA 32784993
17 PAULA KYZANY CARVALHO MORAES [email protected] UFPA 82777434
18 LUDMILA MONTEIRO DA SILVA TANAKA [email protected] OUTRAS (92)81165215
19 MIRLEN TÁSSIA FILGUEIRA DA SILVA [email protected] SIPAM 32695087
20 RENATA SILVA DE LOUREIRO [email protected] SIPAM 32780523
21 DAVID NOGUEIRA DOS SANTOS [email protected] SIPAM 91-82536354
22 DOUGLAS BATISTA DA SILVA FERREIRA [email protected] SIPAM 32263118
23 KÉZIA MONTEIRO ARAÚJO [email protected] OUTRAS 91 32797993
24 JORGE LUÍS MACHADO LOPES [email protected] SIPAM 3366-2285
25 FELIPE DO SOUTO DE SÁ GILLE [email protected] DECEA 814956336
26 AYLCI NAZARÉ FERREIRA DE BARROS [email protected] INMET 91 32434599
27 ANTONIO GUILHERME SOARES CAMPOS [email protected] OUTRAS 91 32041150
28 MARIA ODINÉA BRITO BARRA [email protected] INMET 91 32434599
29 JOSIANE SARMENTO DOS SANTOS [email protected] UFPA 91-32362171
30 MAURÍCIO DO NASCIMENTO MOURA [email protected] UFPA 83417758
31 ANDREIA CAMPOS TAVARES [email protected] OUTRAS 32634024
32 RAMON DIEGO NASCIMENTO DA SILVA [email protected] UFPA 91 83276679
33 ANTONIO SERGIO CUNHA FREIRE [email protected] UFPA 8156-8584
34 ANTONIO SERGIO CUNHA FREIRE [email protected] UFPA 8156-8584
35 LUCIANA DANIELLE ANTUNES MONTEIRO [email protected] UFPA 91 32337865
36 WANJA JANAYNA DE MIRANDA LAMEIRA [email protected] OUTRAS 91 3075161
37 IRENE CRISTINA PEREIRA CORRÊA [email protected] UFPA (91)32432957
38 MARCO ANTONIO VIEIRA FERREIRA [email protected] SIPAM9132530721
39 GABRIELLE MATOS BOUÇÃO [email protected] UFPA 88905912
40 LAURE MADELEINE DENTEL [email protected] UFPA 9132266946
41 TATIANE BATISTA DA SILVA SANTIAGO [email protected] UFPA 9132273549
42 BRENDA SANTOS SIQUEIRA [email protected] UFPA (91)32225342
43 WELDE MORAES GALVÃO [email protected] UFPA (91)81011533
44 JACI MARIA BILHALVA SARAIVA [email protected] SIPAM 33662281
45 MARGARETE PESSOA DA MOTA [email protected] OUTRAS 91 32499757
46 VANIA CARLA DIAS MARTINS [email protected] UFPA 32387816
47 VANIA CARLA DIAS MARTINS [email protected] UFPA 32387816
48 CÁSSIA CAMILA SILVA DA SILVA [email protected] UFPA (91)81050023
49 SIRLENE DE LIMA CASTRO [email protected] UFPA (91)82528788
50 ADRIANO SOUSA [email protected] OUTRAS 9132105140
51 LÚCIA CARDOSO DA PAIXÃO [email protected] UFPA 91 8102 4541
52 CÉZAR AUGUSTO REIS DA FONSECA BORGES [email protected] UFPA82330200
53 WANDA MARIA DO NASCIMENTO RIBEIRO [email protected] UFPA
54 GUNDISALVO PIRATOBA MORALES [email protected] OUTRAS 91 81215466
55 ROSARIA RODRIGUES FERREIRA [email protected] UFPA 9188393996
56 CINTIA LIMA DOS SANTOS [email protected] UFPA 9196323772
57 FRANCISCO ALVES DOS SANTOS NETO [email protected] UFPA(91)32792312
58 DÉBORAH LORENA DAVIS NASCIMENTO DUARTE [email protected] 3246-4916
59 SUZANE CRUZ DE AQUINO [email protected] UFPA 09132494298
60 THIAGO MELO SOUZA [email protected] UFPA 32465096
61 LUAN ROOSEWEL COSTA NUNES [email protected] UFPA 3245-3655
62 GLAYSON FRANCISCO BEZERRA DAS CHAGAS [email protected] 92-82446404
63 DÉBORAH LORENA DAVIS NASCIMENTO DUARTE [email protected] 32464916
64 FABIANO SOARES ANDRADE [email protected] OUTRAS 9182083934
65 PÂMELA LORENA RIBEIRO ÁVILA [email protected] UFPA 82449825
66 MICHEL JUNIOR LISBOA RODRIGUES [email protected] UFPA 8236-5932
67 AMANDA NASCIMENTO PINHEIRO [email protected] UFPA 92368348
68 SAMYR BARATA CHEBLY [email protected] UFPA 9132634914
69 ABNOÃ DA COSTA E COSTA [email protected] UFPA 84698196
70 ROSARIA RODRIGUES FERREIRA [email protected] UFPA 9188393996
71 MAGNO DE JESUS SIQUEIRA REIS [email protected] OUTRAS 92086040
72 MARINA LOPES DE SOUZA [email protected] UFPA 91664135
73 RENATA SENA VIANA [email protected] UFPA 32454400
74 HELDER JOSE FARIAS DA SILVA [email protected] UFPA 9132487356
75 JEYMISON MARGADO BEZERRA [email protected] UFPA 9182797892
76 CINTIA LIMA DOS SANTOS [email protected] UFPA 9182592580
77 REGINALDO BERNARDES PACHECO [email protected] OUTRAS 91-99668860
78 PAULO JOSÉ LOBÃO FADUL [email protected] OUTRAS (91)92079694
79 DAYSE DA COSTA FERREIRA [email protected] UFPA (91)81873078
80 NELTON CAVALCANTE LUZ [email protected] UFPA 81134552
81 VERENA DE FATIMA DAS CHAGAS [email protected] UFPA (91)88151109
82 VIVIANE SÁ DE PAIVA PEREIRA [email protected] UFPA 32554836
83 ANA ROSA BAGANHA BARP [email protected] UFPA 32245266
84 ALLYSON ALLENNON PINHEIRO DO ROSARIO [email protected] UFPA81869294
85 DIONES DA SILVA LOPES [email protected] UFPA 32821047
86 DIONES DA SILVA LOPES [email protected] UFPA 32821047
87 SEBASTIÃO FRANCISCO DA CONCEIÇÃO MOUTINHO
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88 VALERRY HENRIQUE BARROS GARCIA [email protected] OUTRAS(91)82874996
89 JOENY SANTOS FARIAS [email protected] UFPA 32546822
90 CARMEN SANTANA COSTA TORRES [email protected] OUTRAS 32921382
91 FABRÍCIO MARTINS SILVA [email protected] UFPA 91 3231-6046
92 RONALDO DA SILVA RODRIGUES [email protected] OUTRAS 32581792
93 ADIANE ALCÂNTARA E SILVA [email protected] UFPA 82352561
94 PEDRO COELHO DE REZENDE NETO [email protected] UFPA 88697114
95 ADRIANO SOUZA DA ROCHA [email protected] UFPA 9132482850
96 BARBARA SUELEN VALVERDE ROTTERDAM DE [email protected] SIPAM 81057260
97 DEYSIANE NONATO QUARESMA [email protected] UFPA 9132553101
98 ADRIANA ALVES DE CARVALHO [email protected] UFPA 32665610
99 FERNANDA ALVES PAZ [email protected] UFPA 91-81088109
100 FERNANDA ALVES PAZ [email protected] UFPA 91-81088109
101 SOYANNA MARA COSTA BAHIA [email protected] UFPA 9132263723
102 DANIELLE DO SOCORRO NUNES CAMPINAS [email protected] 32351794
103 NATALIA DRIELLY FERREIRA PINHEIRO [email protected] UFPA82746267
104 CAMILO DOS REIS PEDROSO [email protected] UFPA 91-32574416
105 CHARLES MARTINS GOMES [email protected] UFPA 81295259
106 SIMONE PINTO MEIRELES MATOS [email protected] UFPA 91 99059778
107 ALCIONE SANTOS DE SOUZA [email protected] OUTRAS 91-32632160
108 ANA LETICIA MELO DOS SANTOS [email protected] UFPA 91 32384573
109 CASSIO ROGERIO GRAÇAS DOS SANTOS [email protected] UFPA 30875789
110 CLEBERSON MARQUES SERRÃO [email protected] UFPA 32427551
111 DENILSON FREITAS ALMEIDA [email protected] OUTRAS 9111-6746
112 LAYRSON DE JESUS MENEZES GONÇALVES [email protected] UFPA32636981
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114 MARIA JOSE DE SOUSA TRINDADE [email protected] OUTRAS 32490985
115 IÊDO SOUZA SANTOS [email protected] OUTRAS 091-84472501
116 BRUNO NAZARENO PRAZERES DE MIRANDA [email protected] SIPAM82702060
117 AGIRLAYNE DE SOUZA REIS [email protected] OUTRAS 3263 0824
118 SUELLEN CRISTINA LAVAREDA DO NASCIMENTO [email protected] 91161661
119 ANTONIO JOSÉ DA SILVA SOUSA [email protected]/MAIL OUTRAS91-81479975
120 LILIAN DE SOUZA DO CARMO [email protected] UFPA 32233617
121 MAYANY SOARES SALGADO [email protected] UFPA 81171632
122 YVIG SILVA DE MOURA [email protected] SIPAM 32479884
123 WHAVERTON PIRES SALDANHA [email protected] OUTRAS 8167-1864
124 DANILO FRAZÃO SOUSA [email protected] UFPA 81964001
125 CARLA DANIELE SILVA BORCEM [email protected] UFPA 8222-4665
126 CARMEN SILVIA DE OLIVEIRA E SILVA [email protected] OUTRAS82363960
127 JOSE DE ARIMATEIA RODRIGUES DO REGO [email protected] UFPA 32290256
128 THIAGO MOREIRA CARDOSO [email protected] OUTRAS 91-81572836
129 ELLEN SIANY SAMPAIO LIMA [email protected] UFPA 91-32313748
130 LUCIANA MARTINS FREIRE [email protected] UFPA 93 81235573
131 EDNA MARIA SOUZA DE OLIVEIRA [email protected] OUTRAS 32555818
132 MARIA DAS GRAÇAS JAQUES RODRIGUES [email protected] UFPA09187454707
133 AUGUSTO CESAR DE MAGALHÃES CHAVES [email protected] 09132017883
134 LARISSA VEIGA DA SILVA CORDOVIL [email protected] UFPA 91-32640189
135 MONICA DE OLIVEIRA COSTA [email protected] OUTRAS 32555818
136 MONICA DE OLIVEIRA COSTA [email protected] OUTRAS 32555818
137 DDDDDDDDDDD DDDDDDDDDD@DDD UFPA dddd
138 CARLA DANIELE FURTADO DA COSTA [email protected] UFPA 81586126
139 JOELSON DE JESUS CORRÊA DA SILVA [email protected] UFPA 81727361
140 JAMILLE FERREIRA GUIMARÃES [email protected] UFPA 82540514
141 HEYDE GONÇALVES GOMES [email protected] UFPA 09181905799
142 MARCO ANTONIO VIEIRA FERREIRA [email protected] SIPAM9132530721
143 DENIS CONRADO DA CRUZ [email protected] OUTRAS 091-81055609
144 LAISA FARIA VIANA [email protected] UFPA 81142040
145 ANA MARIA MOREIRA FERNANADES [email protected] OUTRAS82131593
146 BELTO KLESIO FURTADO DE SOUZA [email protected] OUTRAS 91 32436004
147 GILSON SARMENTO CASTRO [email protected] UFPA 30328133
148 CARLYLE RIBEIRO LIMA [email protected] UFPA 84078261
149 LUANA OLIVEIRA DA CONCEIÇÃO [email protected] UFPA 9181478853
150 JOSIANE AMANDA GOMES MIRANDA [email protected] SIPAM32579632
151 ROSIELLE SOUZA PEGADO [email protected] UFPA 32264160
152 OTÁVIO AUGUSTO DIAS CÂMARA [email protected] OUTRAS 32411996
153 WELINGTON AOOD DA SILVA [email protected] UFPA 83361837
154 MARCOS LUCÍDIO MARTINS BATISTA [email protected] OUTRAS32499571
155 MARIA JOSE DE SOUSA TRINDADE [email protected] OUTRAS 32490985
156 ISRAEL MOURA SERRA NETO [email protected] UFPA 82307720
157 MARIANA NEVES CRUZ [email protected] UFPA 30881704
158 PAMELLA OLIMPIA ANDRADE MAIA [email protected] UFPA 32070865
159 BIANCA DE NAZARE FONSECA DOS REIS PIRES [email protected] 91 81891336
160 RAQUEL GONÇALVES BECHARA [email protected] OUTRAS 9181455638
161 HUGO RAFAEL LINS DE SOUZA [email protected] UFPA 82699849
162 EDUARDO IVAN DE MIRANDA DOURADO [email protected] OUTRAS91-32310778
163 BRUNA CHAVES BRASILEIRO [email protected] UFPA 32332261
164 ANA ALICE SILVA FERNANDES [email protected] UFPA 87279356
165 ANTONIO JOSÉ DA SILVA SOUSA [email protected] OUTRAS91-32451420
166 JOÃO PAULO OLIVEIRA DA COSTA MACHADO [email protected] 09183068585
167 JONAS SOUSA COELHO [email protected] OUTRAS 32523891
168 BRIAN JONES XAVIER DE ALMEIDA [email protected] OUTRAS 91 81062369
169 RODRIGO DA SILVA [email protected] OUTRAS 9388025865
170 JOSÉ DE PAULO ROCHA DA COSTA [email protected] UFPA 32492760
171 CARLOS ALBERTO BORGES GUIMARÃES JUNIOR [email protected] 82940094
172 CARLOS SIMÕES PEREIRA [email protected] SIPAM 91 32575286
173 VANDA MARIA SALES DE ANDRADE [email protected] SIPAM9132350263
174 CLÊNIA RODRIGUES ALCÂNTARA [email protected] OUTRAS (83)33334219
175 MADLENE NUNES CARDOSO [email protected] UFPA 81504051
176 RENATA KELEN CARDOSO CÂMARA [email protected] UFPA 9181113777
177 LEILA SHEILA SILVA LISBOA [email protected] OUTRAS 81025650
178 JOSIAS BATISTA DOS SANTOS [email protected] UFPA(91)87159053
179 SIGLEA SANNA DE FREITAS CHAVES [email protected] OUTRAS 09181453120
180 ROSILENE SILVA DE LOUREIRO [email protected] OUTRAS9181562401
181 NATALIA PINHEIRO DA COSTA [email protected] OUTRAS 32380253
182 MAISSA LUDYMILLA CARVALHO PONTES [email protected] UFPA82144340
183 MICLEIDE DE SOUZA NASCIMENTO [email protected] OUTRAS 8130-9454
184 LEANDRO JOSE TAVARES SANTANA [email protected] OUTRAS32449727
185 VÍVIAN DE FÁTIMA VALE DE OLIVEIRA [email protected] OUTRAS32786171
186 NEUMA TEIXEIRA DOS SANTOS [email protected] UFPA 32318333
187 NATÁLIA FABIANA CANTUÁRIA DA SILVA [email protected] UFPA8192-7012
188 MARIA DE FÁTIMA PINHEIRO CORREA [email protected] OUTRAS91-83697792
189 DANIELA DOS SANTOS ANANIAS [email protected] UFPA 88569935
190 ADRIANA COSTA DOS SANTOS [email protected] OUTRAS 32467143
191 RAÍSA NICOLE CAMPOS CARDOSO [email protected] OUTRAS 32292014
192 DANIELA DOS SANTOS ANANIAS [email protected] UFPA 88569935
193 FRANCISCO [email protected] INMET +5154450136
194 PÂMELLA SUYLY GOMES LOPES [email protected] UFPA 32449557
195 ELINETE DO NASCIMENTO ALMEIDA [email protected] OUTRAS (91)32310146
196 ALEXSANDRA CHRISTINE BORGES DE QUEIROZ [email protected] OUTRAS(91 32553353
197 JÉSSICA DE CÁSSIA RODRIGUES MIRANDA [email protected] UFPA 83172208
198 RICARDO FONSECA DE LIMA [email protected] OUTRAS 81892919
199 JACQUELINE BELO MORAES [email protected] UFPA 3227-5002
200 JULIANA MARIA PINHEIRO SILVA [email protected] UFPA 3227-5002
201 LUANA KARINE SARAIVA ARAÚJO MATOS [email protected] OUTRAS81719803
202 MARCIA CORREA CURSINO [email protected] OUTRAS 82648959
203 MARIA DO CARMO FELIPE DE OLIVEIRA [email protected] UFPA 3201-7985
204 JOSÉ HENRIQUE CATTANIO [email protected] UFPA 32018158
205 GALDINO VIANA MOTA [email protected] UFPA 91-91127287
206 HELEN KAROLLYNE LIMA DE LIMA [email protected] UFPA 32483904
207 MARCELLE FERNANDA SANTOS CORRÊA [email protected] OUTRAS87069217
208 DOUGLAS GASPARETTO [email protected] OUTRAS 91-81376367
209 LAYANA ROBERTA DE SOUZA MELO [email protected] UFPA(91)87152572
210 SIONE VALENTE PINTO [email protected] UFPA (91)82532721
211 GISELLE NERINO BRITO DE SOUZA [email protected] UFPA 32785693
212 SUZAN LETICIA SANTIAGO PINHEIRO [email protected] 32634333
213 SUZAN LETICIA SANTIAGO PINHEIRO [email protected] 32634333
214 PATRÍCIA MALCHER CHAVES [email protected] UFPA 88712647
215 RICHARD DE NIXON RAIOL LEÃO [email protected] OUTRAS 81656536
216 JOSÉ DANILO DA COSTA SOUZA FILHO [email protected] UFPA 32632366
217 LUCINEUSA DA COSTA BORGES [email protected] OUTRAS 84046362
218 WAGNER LEITE DOS SANTOS [email protected] UFPA 3228-4416
219 PAULO ROBERTO DE ARAUJO VIANA JUNIOR [email protected] UFPA(91)91341880
220 PAMELA DE OLIVEIRA BATISTA [email protected] UFPA 32535693
221 KAMILA SOUZA SANTOS [email protected] UFPA 81598056
222 FÁBIO ENRICO ATAIDE LAMEIRA [email protected] UFPA 82937880
223 MARCELA GONÇALVES PEREIRA [email protected] OUTRAS 32234879
224 MARCELA GONÇALVES PEREIRA [email protected] OUTRAS 32234879
225 RITA DE CASSIA IGLESIAS DA SILVA [email protected] UFPA 82085551
226 PAMELLA OLIMPIA ANDRADE MAIA [email protected] UFPA 32070865
227 RAÍSA NICOLE CAMPOS CARDOSO [email protected] OUTRAS 32292014
228 JESSICA CAMILA REIS CAMPOS [email protected] UFPA 82145662
229 RAFAEL RODRIGUES SACRAMENTO [email protected] UFPA09132570407
230 ANDRESSA AZAMBUJA [email protected] OUTRAS 81568698
231 JULIANA ISE DE SOUSA E SOUSA [email protected] OUTRAS91-82064942
Relatório de Avaliação do Curso
“Sensoriamento Remoto e Modelagem dos Processos de Formação da
Precipitação”
Junho 2011 – Belém, Pará
O curso do Projeto Chuva "Sensoriamento Remoto e Modelagem dos
Processos de Formação da Precipitação" foi realizado com a participação de
especialistas nacionais e internacionais nas áreas de sensoriamento remoto por
satélite, radar e Lidar, descargas elétricas, microfísica das nuvens, camada
limite e modelagem em alta resolução. Esses tópicos correspondem as linhas de
pesquisa que estão sendo estudadas no marco do Projeto CHUVA.
O número de participantes por aula é mostrada na Tabela 1, a seguir. De modo
geral, as aulas contaram com uma média de 72 participantes.
Tabela 1 – Número de participantes por aula
Aulas
Satélites Meteorológicos e a observação em microondas
Ferramentas para Previsão imediata utilizando radar e satélites
Princípios básicos da Modelagem em alta resolução
A parametrização de nuvens e convecção
O Uso do GPS na Meteorologia
Microfísica das nuvens
Radar de dupla polarização
Eletrificação das nuvens
Estimativa de precipitação por satélite e radar
Introduction to the LIDAR technique
Camada Limite Planetária: conceitos básicos
Landscape and precipitation in the eastern Amazon Basin I
Camada Limite Planetária e o Processo de Convecção
Landscape and precipitation in the eastern Amazon Basin II
Participantes
122
88
81
71
89
76
77
53
70
50
67
63
58
44
Trinta e três alunos responderam ao questionário de avaliação, cujo modelo
encontra-se em anexo.
Resultado da Avaliação
Quanto ao Curso
Na primeira questão, foram atribuídas notas de 1 a 4, correspondendo aos
conceitos: 1-Ruim, 2-Razoável, 3-Bom e 4-Muito Bom. Nesta questão foram
avaliados os seguintes itens: Temas abordados, Professores, Carga horária,
Organização do curso, Auditório e Infra-estrutura.
A nota média dada aos itens é mostrada na Tabela 2, a seguir. De modo geral, o
curso foi muito bem avaliado, com nota média entre Bom e Muito Bom.
Tabela 2 – Nota média do curso, por item avaliado.
Item
Temas abordados
Professores
Carga horária
Nota Média
3,79
3,61
3,09
3,21
3,09
A Figura 1 mostra os percentuais das notas dadas a cada um dos itens. O que
se observa é que a maior parte dos alunos avaliou como Muito Bom,
principalmente, os itens: Temas abordados e Professores.
Os itens: Carga horária, Organização do curso e Auditório e Infra-estrutura foram
maiormente avaliados com a qualificação Bom.
Em relação ao quesito Carga Horária, próxima do conceito Bom, pode ter
explicação na diversidade de opiniões apresentadas: para alguns, a carga
horária foi suficiente, e para outros, deveria ter sido maior devido à importância
de cada item (vide Tabela 3).
Também próximo ao conceito Bom, o item relacionado ao Auditório e
Infraestrutura, o qual em algumas palestras resultou apertado e inadequado em
detrimento da apresentação da aula.
90%
Temas abordados
80%
Professores
Carga horária
Percentual das Respostas
70%
60%
50%
40%
30%
20%
10%
0%
Muito Bom
Bom
Razoável
Ruim
Avaliação
Figura 1 – Avaliação do curso.
Tabela 3 – Observações dos alunos em relação aos itens avaliados.
Itens
Temas abordados
Professores
Carga horária
Observações
- Muito relevantes.
- Muito específicos.
- Foram ótimos os temas abordados.
- Com conhecimentos fantásticos.
- A maioria foi muito bem.
- Excelente.
- Algumas palestras em Inglês.
- Deveria ser maior (mais aulas).
- Foi suficiente e palpável.
- Para a importância de cada item foi insuficiente.
- Poderia circular resumos das palestras.
- Nada a reclamar, satisfatória.
- Trocas de auditórios.
- Auditório apertado e inadequado na palestra de LIDAR
- Nada a reclamar.
Quanto aos Temas Abordados
O resultado da pesquisa em relação aos temas que despertaram maior atenção
dos alunos é mostrado na Figura 2, a seguir. Desde que foram permitidas
múltiplas escolhas, houve um total de 156 seleções, sendo que o tema que mais
despertou a atenção dos alunos foi: Estimativa de Precipitação por Satélite e
Radar, com 15,38% dos votos. Contudo, como mostrado na Figura 2,
praticamente todos os temas mostraram-se atraentes para os alunos.
18%
Percentual de Seleção de Tema
16%
14%
12%
10%
8%
6%
4%
2%
Ferramentas para
Previsão Imediata
Utilizando Radar e
Satélites
Eletrificação das
Nuvens
Estimativa de
Precipitação por
Satélite e Radar
Satélites
Meteorológicos e a
Observação em
Microondas
Radar Princípios
Básicos
Microfísica das
Nuvens
A Parametrização de
Nuvens e Convecção
Camada Limite
Planetária e o
Processo de
Convecção
Princípios básicos da
Modelagem em Alta
Resolução
Camada Limite
Planetária: Conceitos
Básicos
0%
Temas
Figura 2 – Percentual de seleções de temas de maior interesse.
Ainda em relação aos temas abordados, o 100% dos alunos afirmaram que eles
serão úteis em seus estudos.
Quanto aos Comentários
Alguns dos alunos transcreveram comentários em sua avaliação. A maioria
destes comentários traz elogios ao curso, porém, também indicam aspectos que
podem ser melhorados, tais como a carga horária, a existência de resumos das
palestras ou a eleição de auditórios mais adequados para as apresentações dos
professores, como mencionado. Por outro lado é gratificante perceber nesses
comentários que o curso despertou o interesse para o experimento e para os
temas, servindo de apoio para os estudos atuais, e abrindo possibilidades para o
futuro.
Tabela 4 – Comentários dos alunos
- O projeto CHUVA foi uma boa aprendizagem que deu para ter uma boa visão e ver na
prática a funcionalidade.
- Boas palestras, bons temas, mas com horários matutinos de encontro com o horário de
nossas aulas.
- Um curso de suma importância, porém senti falta da parte mais prática apesar de não
saber dos procedimentos para participar das coletas de dados. Mas no geral o curso foi
excelente e tenho certeza que ira contribuir bastante para futuras pesquisas.
- O curso foi excelente porém em alguns temas pelo fato de serem muito específicos não
tive aproveitamento positivo.
- Como para os alunos do mestrado este curso substitui parte de uma disciplina, talvez
teria sido muito mais proveitoso se tivesse havido um acompanhamento do professor.
- Sugiro um curso de extensão sobre “Linhas de Inestabilidade”
-Gostaria de ter maiores conhecimentos na área de geoprocessamiento e sensoriamento
remoto.
- Somente os horários, acredito que eles prejudicaram um pouco algumas matérias.
- O curso além de proporcionar-me peso curricular, será de grande ajuda nas atividades
de pesquisa acadêmica que desempenho. Buscou-se uma abordagem simples dos temas, já que
o público-alvo não era exclusivamente de meteorologistas, o que facilitou a compreensão.
Resultado satisfatório!
- O curso de maneira geral foi muito proveitoso, uma oportunidade para absorver os
conteúdos apresentados.
- O curso foi muito bom, participei de todas as aulas. Estas foram muito boas adquiri o
conhecimento que esperava sobre todos os assuntos abordados.
- Os avanços que estão ocorrendo em nossa faculdade são notáveis. O projeto CHUVA
tenha sido uma “porta de abertura” para outros.
- O curso atingiu o seu objetivo maior a meu ver que é acima de tudo difundir o
conhecimento nessas áreas afins, com palestrantes diversos de outros institutos e
universidades.
- Gostaria de participar de um curso mais detalhado sobre microfísica das nuvens e
Satélites Meteorológicos e a observação em microondas.
- Este tipo de ciclo de palestra poderia ser ministrado mais vezes (pelo menos uma vez
cada dois anos) para mostrar para os alunos, principalmente de graduação que realmente pode
ser trabalhado na área de meteorologia. Além de incentivar na pesquisa.
- Parabéns pela iniciativa do curso e pela oportunidade que deram, não só aos alunos
mas as pessoas ligadas a área de Meteorologia de assistirem excelentes palestras com
magníficos professores.
- Foi de grande importância este curso. Notamos o empenho dos professores para
conosco, alunos, em nos proporcionar esse grande momento de aprendizagem.
- Este curso foi lucrativo pois deu para ter una visão mais ampla da Meteorologia e as
áreas de atuação e a própria interação entre os demais colegas meteorologistas e conhecer os
“nomes famosos” do curso.
- Temas relevantes e professores excelentes.
- Professores com conhecimentos fantásticos.
Anexo – O questionário de avaliação aplicado
Curso
Sensoriamento Remoto e
Modelagem dos Processos de Formação da Precipitação
Nome (opcional) _________________________________________________________________
Dê a nota, considerando a seguinte pontuação:
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aprofundar seus conhecimentos:
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(
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) Camada Limite Planetária: Conceitos Básicos
) Princípios básicos da Modelagem em Alta Resolução
) Camada Limite Planetária e o Processo de Convecção
) A Parametrização de Nuvens e Convecção
) Microfísica das Nuvens
) Radar Princípios Básicos
) Satélites Meteorológicos e a Observação em Microondas
) Estimativa de Precipitação por Satélite e Radar
) Eletrificação das Nuvens
) Ferramentas para Previsão Imediata Utilizando Radar e Satélites
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Anexos 1 - 2

Transcrição

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