This study analyzes passive and active microwave measurements during the 2003 Wakasa Bay field experiment for understanding of the electromagnetic characteristics of frozen hydrometeors at millimeter-wave frequencies. Based on these understandings, parameterizations of the electromagnetic scattering properties of snow at millimeter-wave frequencies are developed and applied to the hydrometeor profiles obtained by airborne radar measurements. Calculated brightness temperatures and radar reflectivity are compared with the millimeter-wave measurements

Johnson, B.

Kim, Min-Jeong

Meneghini, R.

Roman-Nieves, J. I.

Sekelsky, S. M.

Skofronick-Jackson, G.

Tanelli, S.

Wang, J. R.

NASA Technical Reports Server

Microwave Remote Sensing of Falling Snow 
Min-Jeong Kim, 'J. R. Wang , 'R. Meneghini, 'B. Johnson, 1 
'S. Tanelli, J.I. Roman-Nieves, 'S. M. Sekeisky, and 'G. Skofronick-Jackson 
'NASA Goddard Space Flight Center, Code 975, Greenbelt, MD 20771 
Phone: 30 1-6 14-6089, mi kimoneptune. csfc.nasa.gov 
Atmospheric Radar Science and Engineering group Jet Propulsion Laboratory, 
California Institute of Technology, Pasadena, CA 91 109 
Department of Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003 
2 
1. INTRODUCTION 
Motivation: 
Snowfall is an important component of the Earth's 
precipitation and hydrological cycle. Remote measurements of 
frozen hydrometeor properties have been limited because 
coincident measurements of microphysical and electromagnetic 
properties of snowfall have rarely been available. Ground-based 
radars have been widely used to monitor snowfall intensity, but 
even those measurements are difficult to relate to water 
equivalent of snow because of the vast diversity of snow habits, 
density, and size distributions. Moreover, the spatial coverage 
of radar networks is limited over most regions other than the 
U.S.A., Europe, and Japan. 
Remote measurements at millimeter-wave frequencies can 
overcome this spatialhemporal sampling limitation because 
wavelengths are short enough to interact strongly with frozen 
hydrometeors and opaqueness obscures the variable emission 
from the underlying snow covered surface at millimeter-wave 
frequencies. The NOAA Advanced Microwave Sounding Unit 
(AMSU) has five millimeter-wave channels at 89 GHz, 
15OGHz, and 183.3*1 GHz, 183.3h3 GHz, and 183.3*7 GHz. 
The CloudSat [ 11 satellite, which has 95 GHz radar to measure 
the vertical structure of clouds from space, will be launched in 
summer 2005. The CloudSat will fly in the same orbit with the 
AQUA satellite which has passive microwave measurements at 
frequencies up to 89 GHz. Combining the CloudSat 
observations with AMSR-E and/or AMSU-B observations can 
shed light on snowfall measurements from space. 
Challenge: 
Developing a retrieval algorithm using passive and/or active 
microwave measurements requires parameterization of 
electromagnetic properties of the frozen precipitating particles 
to generate accurate brightness temperatures, radar attenuation, 
and reflectivity. However, microwave measurements of frozen 
hydrometeor properties were limited in the past and the 
electromagnetic characteristics of frozen hydrometeors are still 
poorly understood at present. 
Research Goal: 
This study analyzes passive and active microwave 
measurements during the 2003 Wakasa Bay field experiment 
for understanding of the electromagnetic characteristics of 
frozen hydrometeors at millimeter-wave frequencies. Based on 
these understandings, parameterizations of the electromagnetic 
scattering properties of snow at millimeter-wave frequencies 
are developed and applied to the hydrometeor profiles obtained 
by airborne radar measurements. Calculated brightness 
tempcraturcs and radar reflectivity are conpared with the 
millimeter-wave measurements. 
2. OBSERVATIONS 
Wakasa bay field experiment: 
The U.S. AMSR-E and Japanese AMSR teams implemented 
a field experiment for JadFeb 2003 over Wakasa Bay, Japan 
[2]. During the field experiment, the NASA P-3 aircraft carried 
the dual frequency precipitation radar (PR-2) operating at 14 
and 35 GHz; a cloud radar operating at 94 GHz (ACR); a 
passive microwave sensor (PSR) that simulates the AMSR 
observations; a high frequency passive Microwave Imaging 
Radiometer (MIR) covering from 90 to 340 GHz; and an 
upward looking radiometer at 21 and 37 GHz (AMMR). The 
Japanese contribution to the experiment consisted of a dual- 
polarized ground-based radar and supporting surface 
observations that allow us to put the aircraft observations into 
the larger meteorological context. 
Wakasa Bay, on the eastern end of the Sea of Japan, has 
fairly predictable cold air outbreaks in which cold air from the 
Eurasian continent blows over the relatively warm Sea of 
Japan. These storms generally produce rainfall near the 
surface, where warm boundary layer air mixes with the cold air 
aloft. The depth of the rain layer, however, is typically very 
shallow and in some cases, the snowfall reaches all the way to 
the surface. 
There were 12 flights during this field experiment between 
January 14 and February 3,2003 but only one case, on January 
29", had heavy dry snow precipitation. Since the dielectric 
constant of liquid water is much different from air and ice, the 
https://ntrs.nasa.gov/search.jsp?R=20050210088 2019-08-29T20:44:17+00:00Z
melting snow (air, ice, and water mixture) exhibits more 
complicated electromagnetic (EM) behavior than dry snow (air 
and ice mixture). As a first step to understand the EM 
scattering properties of snowfall, we therefore confine this 
study to dry snow retrieva! d y .  The air temperature near the 
ground measured at the Fukui airport observation site during 
the field experiment showed that the air temperature dropped 
below 0°C after 23 UTC January 28" so that we assume 
snowfall to be dry. 
Microwave measurements: 
Figure 1 shows the PR-2 measured radar reflectivity at 14 and 
35 GHz and the ACR measured radar reflectivity at 95 GHz 
over the ocean between 03 UTC and 04 UTC in January 29", 
2003. It has been reported that the center of low pressure that 
has been just west of Hokkaido had moved northwards to the 
middle of Sakhalin Island and it deepened to 979 mb. Strong 
northwesterly flow over all of the Sea of Japan produced 
extensive areas of snow off the coast during the day. Measured 
radar reflectivities up to 32 dBZ in the 14 GHz channel of PR-2 
indicate moderate snowfall rates. 
It is noted from Figure 1 that the radar reflectivities decrease 
with frequency, especially in the region where snowfall 
intensity is strong. Figure 2 shows the dual frequency ratio 
(DFR) between 14 GHz and 35 GHz (upper panel) and between 
14 GHz and 95 GHz (lower panel). It is seen that the DFR has 
negative values near cloud top indicating the particles are small 
and backscattering properties are in Rayleigh regime. In 
14 GHZ 
35 GHz 
95 GHz 
e> I_ i 
-10 -5 0 5 10 15 . 20 25 30dB7. 
~~ 
Figure 1 .  The PR-2 measured radar reflectivity at 14 GHz (Ku band) 
and 35 GHz (Ka band) and the ACR measured radar reflectivity at 95 
GHz (W band) along the nadir between 03:18 UTC in January 291h, 
2003. 
- .  . 
contrast, the DFR shows large positive values where snowfall 
intensity is strong, especially near cloud base. This may be 
explained by the combination of (1) attenuation caused by snow 
and liquid cloud water and (2) non-Raleigh backscattering 
effects in the snow at the higher frequencies. 
dBZ(14GHz) - dBZ(35GHz) 
It should be noted that the DFR shows strong positive values 
up to the cloud top in region marked "A" in Figure 2. This 
shows that there is much stronger attenuation here than in other 
regions of the storm. The Doppler velocity measured by 14 
GHz radar shown in Figure 3 indicates that the convection was 
very strong with vertical velocity of - 4 . 8 d s  in the region. The 
strong vertical motion may be responsible for large amounts of Figure 2. Dual frequency ratio (DFR) (a) for 14 GHz and 35 
supercooled cloud water and for increases in the particle size GHz and (b) for 14 GHz and 95 GHz along the nadir between 
distribution. Both effects can give rise to a large DFR ratio. 03:18 UTC in January 2gth, 2003. 
Figure 4 shows the MIR measured brightness temperature 
(TB)s at frequencies between 89 GHz and 340 GHz. The TB 14 GHz Doppler Velocity 
depression at frequencies greater than 183 GHz is generally 
well correlated with the ACR and PR-2 radar reflectivity. 
However, the 89 GHz channel shows high TBs in many regions 
of the precipitation, indicating the presence of cloud water. 
4 
5 3  
5 2  
I1 
I "  P-.. : L 7 3' The retrievals of cloud liquid water with the surface reference 0 0 5  1 0  1 5  2 0  2 5  30 3 5  4 0  45m/s 
technique (sRT) E3i using l4 GHz, 35 GHz, and 95 GHz radar Figure 3. Doppler velocity measured by PR-2 at 14 GHz along 
reflectivity are ongoing. Retrieved cloud liquid water will be the nadir between 03: 8 UTC in January 29th, 2003. 
considered in radiative transfer calculations for comparison 
with the MIR measured TBs. 
240 
g220 
k 
200 
30. 
Figure 5. The snow particle size distributions measured by 
Kanazawa University at the ground truth site (Fukui Airport, 25-  I 36.14N, 136.22 E) during the field experiment at (a) 01 UTC, $20. 
B 10. 
8 
z 
15. 
(b) 02 UTC, (c) 03 UTC, and (d) 04 UTC in January 29", 2003. 
It is noted that the snow particle size distributions such as 5 ,  
mean and maximum sizes do not change with time/snowfall 
1801 , !l=:rl , , , 1 
yo , 
1.18 1.2 1.22 1.24 1.26 
Figure 4. The MIR brightness temperatures (TB) measured at 
89, 150,183*1,183~3,183*7 GHz along the nadir between 
03: 18 UTC in January 29Ih, 2003. 
Hexagonal Column 
Planar Rorene w/ 4 arms 
Spatial Roaene )w 6 arms 
where D is the large dimension of a snow particle and Do is the 
median mass diameter and assumed to have a fixed value of 2.8 
mm based on the observations shown in Figure 4. 
3. THEORETICAL. CALCULATIONS 
The scattering properties depend on the morphology and 
orientation of snow crystals with respect to the direction of 
observations. Because of the variegated shapes of snowflakes, 
calculation of the mm-wave scattering properties is a daunting 
effort. Nonetheless [4] computed the scattering properties of 
several idealized shapes of frozen hydrometeors, such as those 
shown in Fig. 1, using the Discrete Dipole Approximation 
(DDA) described by [5] at frequencies (14, 35, 95, 140, 183, 
220, and 340 GHz). 
Snow microphysics measurements: 
The snow microphysics variables were measured by 
Kanazawa University at the ground truth site during the field 
experiment. Figure 4 shows snow particle size distributions 
(PSD) between 01-04 UTC in January 29'h, 2003. 
am.!.r[XMhlLh B y I 4 B p l m - o e 4 - o ~ d l l )  D i a n u k  WliibuUO. pia-mm-Oae_dim*l811 
m 16 1 
14 eca 
=xa E 6 4w 1- 
z m  
I r n  
0 
o z 4 r 1 1 0 1 2 1 4  0 2 . L I 1 1 1 2 1 ,  
hmqmnl 
an*r a f h l i o n  wla-Dm.QIw.& &I, 
D.nulwn(rnnIl 
an*. Dl.Wb"Ih iQnmOlB_m.UF.-rd.I) m 
,m 
1- 1 I r m  
f ,ma 8..  
p ,200 
f e J  
4m 
rm 
0 
0 1 . 6 l  
_ _  . .___.  
D 2 4  6 & 1 0 1 2 1 .  
Wn*ulmnl C.mnul-1 
Figure 6. Snow crystal model employed in this study. (a) 
hexagonal column, (b) planar rosette with four arms, and (c) 
spatial rosettes with six arms. 
Snow mass profile determined by radar reflectivity: 
snow water content 
profiles are derived using the PR-2 measured 14 GHz radar 
reflectivity assuming that at this frequency attenuation by cloud 
is negligible. Backscattering cross sections calculated by the 
DDA methods for different snow particle shapes (Fig.6) are 
employed. It is shown that the derived snow water content 
varies with the particle habit. Particles with larger mass for the 
same large dimension show less snow water content for a given 
radar reflectivity. 
From the PSDs shown in Eqn.(l), 
57 
SCOW Water Content (arm') minimum at 02 UTC (weak snowfall) to a maximum at 04 UTC 
(heavy snowfall). Based on these observations, the best fit 
gamma PSD is derived and is given by 
Figure 7. Snow water content derived with the DDA results for 
three different snow crystal shapes. The PSD in Eqn.(l) was 
assumed in the derivation. 
1 (1) (-3.67 + p ) D  N(D) = NOD" exp( 
DO Calculation of brightness temperature: 
Figure 8 shows- the TBs calculated with the Eddington 
radiative transfer model. Coincident sounding data measured 
during the field experiment provided the temperature, pressure, 
and relative humidity profiles as well as snow profiles derived 
from the 14 GHz radar reflecitivity are considered in the 
radiative transfer calculations. The DDA calculated scattering 
properties are employed for each habit. It is noted that 
calculated TBs for hexagonal columns are much colder than the 
calculated TBs for spatial bullets. This is because snow water 
content derived from 14 GHz radar reflectivity is much higher 
for hexagonal columns than for aggregates like rosettes. REFERENCES 
The results show that for most of the radiometer frequencies [l] Stephens, G. L., G.  D. Vane, R. J. Boain, G. G. Mace, K. 
cold TBs are well correlated with large values of the 14GHz Sassen, Z. Wang, A. J. Illingworth, E. J. O’Connor, W. B. 
radar reflectivitity factor. It is noted that the high 89 GHz and Rossow, S.L.Durden, S .  D. Miller, R. T. Austin, A. Benedetti, 
150 GHz TBs observed in MIR measurements in precipitating C. Mitrescu, 2002: The CloudSat mission and the A-Train, Bull. 
cloud (Figure 4) could not be simulated. This may be caused by Am. Meteor. SOC,  1771-1790. 
the fact that cloud liquid water was not considered in the 
calculations. It is expected that the addition of liquid cloud [2] Wilheit, T. T., C. Kumerow, R. Ferraro, R. Austin, R. 
water will increase the TBs. Bennartz, E. Im, and T. Bell, 2002: AMSR rainfall validation 
implementation strategy 
2 8 0 1 1 1  Izttp:!/ruin.utnros.colostate.edu/Wakasa 
[3] Meneghini, R., T. Iguchi, T. Kozu, L. Liao, K. Okamoto, J. 
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[4] Kim, M.-J. 2004: A Physical model to estimate snowfall 
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Time 
Figure 7. The Eddington radiative transfer model calculated 
brightness temperatures at MIR frequencies Results are shown 
in solid lines for hexagonal columns (Fig 6(a)), in dashed lines 
for planar rosettes (Fig 6(b)), and in dotted lines for spatial 
rosettes (Fig.6(c)). 


Microwave Remote Sensing of Falling Snow

Microwave Remote Sensing of Falling Snow

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