Precipitation Retrieval Accuracies for
Geo-Microwave Sounders
David H. Staelin, Fellow, IEEE, and Chinnawat Surussavadee, Student Member, IEEE
Abstract—Only instruments on geostationary or comparable
platforms can view global precipitation at the ~15-minute
intervals necessary to monitor rapidly evolving convective events.
This paper compares the abilities of ten such alternative passive
microwave sensors to retrieve surface precipitation rates and
hydrometeor water paths--five instruments observe various
frequencies from 116 GHz to 429 GHz with a filled-aperture
antenna, and five observe various frequencies from 52 to 191
GHz with a U-shaped aperture synthesis array. The analysis is
based on neural network retrieval methods and 122 global MM5simulated storms that are generally consistent with simultaneous
AMSU observations. Several instruments show considerable
promise for retrieving hydrometeor water paths and 15-minute
average precipitation rates ~1-100 mm/h with spatial resolutions
that vary from ~15 km to ~50 km. This space/time resolution is
potentially adequate to support assimilation into cloud-resolving
numerical weather prediction models.
Index Terms—Geostationary microwave imagers, microwave
precipitation estimation, microwave radiative transfer, aperture
synthesis sounders.
I. INTRODUCTION
O
NE major current remote sensing challenge is to monitor
global precipitation accurately on the time and spatial
scales at which it evolves--e.g., ~10-30 km and 10-30 minutes.
Such a system could not only provide better nowcasting, but
may also permit cloud-scale assimilation of precipitation into
numerical weather prediction (NWP) models so as to improve
both precipitation retrievals and weather forecasts.
Current geostationary (GEO) satellites permit better than
15-minute and 10-km resolution at infrared wavelengths, but
cannot penetrate overlying clouds, while the more accurate
low-earth-orbit (LEO) satellites with cloud-penetrating ~15km microwave resolution generally repeat their observations
only at intervals of hours or more. Although dense radar and
rain gauge networks provide good local coverage, they are too
costly and land-bound to cover most nations and their
surrounding waters. Even the NEXRAD 158-radar system
covers only ~20 percent of the continental United States
within its best-performance 110-km range, and less than 60
percent within 220-km range.
Manuscript received April 11, 2006. This work was supported in part by
NASA under Grant NAG5-13652 and Contract NAS5-31376, and by NOAA
under Contract DG133E-02-CN-0011.
The authors are with the Research Laboratory of Electronics,
Massachusetts Institute of Technology, Cambridge, MA 02139 USA (e-mail:
[email protected]).
Fortunately, practical passive microwave imaging
spectrometers in geostationary orbit could meet this need for
precipitation observations that resolve most storms in both
time and space. Geo-microwave systems have been proposed
for many years [1], but generally without analysis of their
precipitation and hydrometeor retrieval performance, which is
the focus of this paper.
II. INSTRUMENT OPTIONS AND ISSUES
Basic approaches to achieving the required high angular
resolution include: mechanically steered filled-aperture multiband antennas of ~1.2-3 meter diameter, designated as "GEM"
[2], and aperture synthesis systems incorporating hundreds of
antenna feeds and amplifiers, designated as "GeoStar" [3].
Table I relates antenna aperture diameters D and
wavelengths λ to practical diffraction-limited resolutions at
nadir with and without image sharpening. A uniformlyilluminated circular aperture yields beamwidths θB ≅ 1.2 λ/D
radians, while reasonable illumination tapering yields θB ≅
1.3λ/D. Nyquist sampling and image sharpening can yield θB
≅ 0.95λ/D, as illustrated in Fig. 1 for reasonable assumptions
about receiver sensitivity. Image sharpening simply involves
deconvolving the antenna pattern G(θ,φ) from the
observations TA(θ,φ) to yield the estimated angular brightness
temperature distribution TB*(θ,φ):
TB*(θ,φ) = F-1{W•F[TA(θ,φ)]/F[G(θ,φ)]} [K]
(1)
where F and F-1 are the Fourier- and inverse-Fourier-transform
operators, respectively, and W is a weighting function that
maximizes the signal-to-noise ratio, partly by canceling nulls
in F[G(θ,φ)]. F[G] is purely real when G(θ,φ) is an even
function, as assumed here.
The principal advantage of filled-aperture systems is their
simplicity and low cost even when many frequency bands, and
channels within bands, are being observed. The principal
disadvantage is that they must be scanned mechanically,
which can impact other instruments. Fortunately a relatively
simple technical solution exists, i.e., use of a small rapidly
tilting subreflector in a Cassegrain configuration with a
relatively large focal length. Such an approach permits 10 or
more beamwidths to be scanned cross-track as the main
reflector scans down-track at only ~0.1 degree per second (the
speed of the minute hand on a watch). With such slow scan
rates open-
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TABLE I
SPATIAL RESOLUTION (KM) AT N ADIR OF GEOSTATIONARY FILLEDAPERTURE AND APERTURE -SYNTHESIS SYSTEMS
Frequency Band (GHz)
Antenna type
2-m 1.3λ/D dish
2-m 0.95λ/D dish
1.2-m 0.95λ/D dish
300-rcvr, λ/2D
600-rcvr, λ/2D
53
118
166
183
380
425
129
96
58
43
72
50
25
42
31
51
50
25
38
28
46
50
25
18
14
23
17
12
20
50
25
instrument. Configurations I and J use 600 receivers to
observe two bands or yield 25-km resolution, respectively,
while H uses 300 receivers near 53 GHz plus 600 near 183
GHz. Multiple channels can be observed within a single band
by frequency switching and time sharing. The principal
advantage of synthesis systems is that they avoid mechanical
scanning, and their principal disadvantages are their very high
power, weight, and cost, in addition to their unproven
performance at scale or above 60 GHz, even on the ground.
III. RETRIEVAL METHOD
Original
5-km
image
o
260 K
220
Original pattern
30 km
180
~22.5 km
Sharpened 30-km
Blurred 30-km
Sharpened pattern
Fig. 1. Original, blurred, and sharpened images using Nyquist sampling and
assuming ∆Trms = 1 K for 30-km steps, which is equivalent to ∆Trms = 6K
for 5-km steps.
loop momentum compensation can readily handle the residual
momentum impact on the satellite. Although the entire earth
cannot be scanned rapidly, all economically significant
precipitation could be scanned at ~15-minute intervals with
~15-km resolution, sufficient to resolve most storm evolution.
More precise mechanically scanned 1.6-meter parabolic
reflectors and sub-millimeter radiometers have flown on the
Microwave Limb Sounder (MLS) on the NASA Aura satellite.
Table I also presents the resolution θB available from
aperture synthesis systems at the same wavelengths. The
assumed antenna array configuration is U-shaped with a
smaller maximum dimension than Y-configurations offering
equivalent resolution [3]; it could also be wrapped around a
rectangular spacecraft, simplifying and perhaps even avoiding
deployment. For operation near 53-GHz the length of each of
the three connected arms is assumed to be D = 2 meters,
yielding a synthesized antenna beamwidth θB measuring λ/2D
radians, peak-to-first-null. This corresponds to 50-km nadir
resolution at 53 GHz and a hypotenuse for the array of ~2.8 m,
larger than the 2-meter filled-aperture antenna diameter
assumed here. Since only 100 equally spaced antennas and
receivers are used in each arm (300 units total), the recovered
image would exhibit aliased images of the earth in a square
grid at angles of 100λ/D ≅ 16.2 degrees, which is slightly less
than the ~17.5-degree angular diameter of the earth. It would
provide a non-aliased square clear zone ~200 pixels across,
exceeding 10,000 km. The resultant aliasing sacrifices some
useless imagery near the terrestrial limb while simplifying the
All sensors are compared here using the same surface
precipitation rate retrieval method, which utilizes three feedforward neural networks [4]. If the first net estimates over 8
mm/h, then the second neural network is used to estimate the
15-minute average precipitation rate; otherwise the third
network is used. The channel radiances were computed using:
1) domain3 of MM5 at 5-km resolution, 2) radiative transfer
computation using TBSCAT [5-6] in its two-stream formulation, together with Mie scattering from spheres of density
F(λ) chosen to match computed scattering cross-sections of
various hydrometeor species, and then 3) Gaussian blurring of
the brightness temperatures with the spatial resolution
indicated in Table II. Each network utilized two inputs per
channel, one observed at the current time and one observed 15
minutes earlier. The three layers of each network had 10, 5,
and 1 neuron, respectively, if there were more than 9 inputs,
and 5, 5, and 1 neuron otherwise. The first two layers used a
tanh(θ) sigmoid function. For each network and task the best
of 100 tested networks was used. For hydrometeor water path
retrievals one feed-forward neural network and one
observation time were used for each hydrometeor species.
The neural networks were trained on NCEP-initialized
MM5-simulated brightness temperature spectra computed for
122 global storms in all seasons. The NCEP analyses had 1degree resolution at 0Z, 6Z, 12Z, and 18Z for 24 pressure
levels extending to 10 mbar. MM5 was then run with 5-km
resolution for 4-6 hours after initialization so as to coincide
with simultaneous observations by AMSU on NOAA-15, -16,
and -17. These 122 storms represent about half of the original
set of 255 storms that were evaluated, the remainder typically
having been discarded because the NCEP initialization fields
were not sufficiently accurate, as determined using the
concurrent AMSU observations [6]. Since uncertainties in
surface emissivity and brightness can significantly affect
retrieval accuracies, the emissivity of land was assumed
conservatively to be random and uniformly distributed
between 0.91 and 0.97. Ocean emissivity was modeled using
FASTEM [7]. Only 7 percent of the 4 million available pixels
were used for training; these were arranged in a rectangular
grid that was maximally offset from the validation pixel grid.
The precipitation retrieval accuracy predicted using these
techniques is relatively insensitive to reasonable errors in
MM5 and in the radiative transfer model [4].
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IV. RETRIEVAL RESULTS—PREDICTED PERFORMANCE
Table II lists the frequencies in each band for which
precipitation retrieval performance was evaluated, and the
nadir spatial resolution after image sharpening. Table II also
characterizes
ten
promising
alternative
instrument
architectures that were evaluated: five filled-aperture options
(A-E), and five aperture-synthesis options (F-J).
Four
channels are observed in each band, each channel having an
assumed sensitivity ∆Trms of 0.5K at the indicated resolution.
Simulations indicate that rms sensitivities as poor as 1K do not
significantly degrade most precipitation retrievals because
millimeter-wave precipitation signatures are so strong.
Four of the filled-aperture options (A-D) employ a
Cassegrain antenna 2 meters in diameter, small enough to be
readily integrated on an operational GOES satellite, and E
employs one of diameter 1.2 m. Case E yields 72-, 51-, 46-,
23, and 20-km resolution at 118, 166, 183, 380, and 425 GHz,
respectively. The largest aperture synthesis system (F)
measures ~2.8 meters on the diagonal for a U-shaped
configuration operating near 53 GHz with 2-m arms; arm
length is directly proportional to wavelength for fixed spatial
resolution.
The number of antenna/amplifier/mixer RF
assemblies that one can afford or accommodate on the satellite
limits the spatial resolution of aperture synthesis systems. The
nominal set of 300 antenna/amplifier/mixer RF assemblies
assumed for options F and G already severely stresses feasible
cost, weight, and power limits while yielding a synthesized
nadir resolution of ~50 km in any 10-percent spectral band if
we accept some aliasing near the limb. Use of 600 RF
assemblies permits spectral observations in two bands (option
I), or ~25-km resolution (option J), while 900 RF assemblies
permit 50- and 25-km resolution near 53 and 183 GHz (H).
TABLE II
FREQUENCIES USED IN COMPARING 10 INSTRUMENT OPTIONS
Approximate Frequencies (GHz)
A B C D
52.8, 53.6, 54.4, 55.5 @ 50 km
118.75±0.5, ±1.15, ±1.5, ±2.05 @ 45 km
• • • •
166 @ 30 km; 183.31±1, ±3, ±7 @ 30/25 km • • • •
380.2±1.5, ±4, ±9, ±18 @ 15 km
•
•
424.76±0.6, ±1, ±1.5, ±4 @ 15 km
• •
E F G H I J
•
• • •
•
•
•
•
•
•
•
Precipitation-rate retrieval images (mm/h) for four
representative precipitation types are presented in Fig. 2 for
instrument configurations D, E, H, I, and J (arranged left to
right). The left-most images are the corresponding MM5
simulations. From top to bottom the precipitation corresponds
to a strong front over France (1/2/03; ~50N/5E), a typhoon
(12/8/02;
~15N/145E),
stratiform
rain
(12/14/02;
~40N/125W), and oceanic warm rain (11/16/02; ~50N/35W).
The images suggest that even the 600-receiver aperture
synthesis systems I and J cannot surpass the performance of
the inexpensive 1.2-meter diameter GEM option E. System I is
handicapped primarily by its 50-km resolution, and J uses only
a single band that is less sensitive to snow and graupel. The
1.2-m GEM (E) is aided primarily by its four-band
configuration and its ~22-km resolution above 375 GHz. The
2-meter GEM (D) generally delivers even better performance,
particularly at higher latitudes where the spatial resolution
degrades somewhat, and is comparable to the 900-receiver
system H.
Table III presents the rms errors in 15-minute average
retrieved surface precipitation rate averaged over the same 122
storms, but using an offset grid of pixels, ~2200 pixels per
storm and ~269,000 total. The statistics are computed over
octaves of surface precipitation rate, as defined by MM5. The
table also presents rms retrieval errors for instantaneous
graupel, snow, and rain water paths, and for the sum of these
three paths.
TABLE III
RMS RAIN AND HYDROMETEOR RETRIEVAL ERRORS
MM5
Instrument Configuration
Octave range:
Rate (mm/h)
A
B
C
D
E
F
G
H
I
or Path (mm)
Precipitation
1-2 mm/h
1.7 1.8 1.8 1.6 1.7 1.6 1.8 1.7 1.5
4-8 mm/h
4.2 4.2 4.0 4.0 4.1 4.5 4.4 4.1 3.9
32-64 mm/h
25 25 24 26 25 27 27 24 24
Snow path S
0.5-1 mm
0.16 0.14 0.15 0.14 0.18 0.42 0.38 0.13 0.27
2-4 mm
0.53 0.46 0.48 0.46 0.57 1.4 0.81 0.45 0.74
8-16 mm
2.5 2.0 2.1 2.1 1.6 4.7 4.4 2.1 3.3
Graupel path P
0.25-0.5 mm 0.36 0.38 0.38 0.38 0.36 0.47 0.42 0.36 0.42
2-4 mm
1.8 1.8 1.8 1.8 1.8 2.2 2.2 1.8 2.1
8-16 mm
4.4 4.1 4.1 3.9 4.7 6.8 6.0 4.0 5.6
Rain water R
0.25-0.5 mm
0.33 0.33 0.33 0.33 0.32 0.34 0.30 0.31 0.28
2-4 mm
1.6 1.6 1.6 1.6 1.7 1.7 1.8 1.6 1.6
16-32 mm
12 12 12 12 12 12 12 11 11
S+P+R path
0.062-0.125 mm 0.06 0.06 0.06 0.06 0.07 0.34 0.22 0.04 0.09
1-2 mm
0.50 0.45 0.49 0.45 0.55 0.88 0.80 0.40 0.60
16-32 mm
7.6 7.5 7.2 7.0 7.8 10.3 9.8 6.7 8.8
J
1.7
4.3
23
0.42
1.2
4.0
0.42
2.0
4.9
0.27
1.6
11
0.31
0.76
7.5
V. DISCUSSION
Comparison of many retrieved precipitation images,
including those illustrated in Fig. 2 for a strong front, a
typhoon, stratiform rain, and warm rain, suggests that the 4band 14-km resolution GEM configuration D (2-m dish) and
the 900-receiver system H yield the greatest fidelity of those
instruments studied. In no case does the 600-receiver 53/118GHz 50-km aperture synthesis spectrometer (I) or the 53-GHz
25-km resolution aperture synthesis spectrometer (J) yield
images superior to GEM E(1.2-m dish), although the
performance becomes more comparable for heavy convective
events. These outcomes reflect the weaknesses of lowresolution (50-km) systems (I) and single-band systems (J).
GEM configuration D responds better than E to smaller
convective cells, and therefore facilitates assimilation of such
small cells into cloud-resolving NWP models. Successful
assimilation would permit retrieval accuracies still greater than
those predicted here for pixel-based methods that do not use
model knowledge. For example, models reflect the effects of
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Fig. 2. Comparisons of MM5-simulated surface precipitation rates (left-most image) with 950-km images retrieved by four-band GEM's with 2-m (D) and 1.2-m
(E) Cassegrain antennas, by a 900-receiver 53/183-GHz GeoStar system with 50/25-km resolution (H), and by 600-receiver 50- and 25-km resolution GeoStar
configurations operating at 53/118 and 53 GHz, respectively (I and J). From top to bottom the images correspond to a strong front over France (1/2/03;
~50N/5E), a typhoon (12/8/02; ~15N/145E), stratiform rain (12/14/02; ~40N/125W), and oceanic warm rain (11/16/02; ~50N/35W); the units are mm/h.
virga, whereas cell-top observations generally do not. Also,
cloud-resolving models that position cell tops correctly are
more likely to position surface precipitation correctly, and cell
tops are readily observed by GEM.
In contrast to the clear differences between instruments
shown in Fig. 2, the rms surface-precipitation-rate and rainwater-path errors presented in Table III are surprisingly
indifferent to instrument frequencies or spatial resolution
because these errors are dominated by the space/time offsets
between surface precipitation and the cell-top hydrometeor
populations sensed by satellite. For example, snow at 10 km
altitude requires more than 20 minutes to reach the ground.
As a result of these space/time offsets, slightly blurry
observations of convective events are nearly as useful
numerically as precise ones except for small isolated cells that
may be unresolved and therefore unseen, but such isolated
cells do not significantly alter the statistics. The snow and
graupel water path retrieval accuracies presented in Table III
are more sensitive to instrument configuration because these
hydrometeors are located at altitudes sensed directly by the
satellite; the higher frequencies, multiple spectral bands, and
superior spatial resolution favor the GEM options here relative
to the 600-receiver GeoStar options.
Fortunately, despite these offset issues, GEM geostationary
microwave observations of precipitation would provide an
unprecedented ability to monitor the evolution of precipitation
with ~15-km, 15-minute resolution, as suggested by Fig. 2.
Moreover, it appears that continuous monitoring of
precipitation along with temperature and humidity structures
(retrieved using the same GEM or GeoStar channels) may
permit assimilation into cloud-resolving NWP models. Such
assimilation should significantly improve the quality of
surface precipitation rate retrievals, partly because the effects
of virga should be better reflected in the results, as would the
time/space offsets currently responsible for much of the pixelbased rms error.
Therefore successful cloud-resolving
assimilation could significantly increase the impact of
geostationary microwave observations on future global
precipitation monitoring and numerical weather prediction
systems; such assimilation research is underway.
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