Transcript Rain and Snow Observable Characteristics

Remote Sensing of
Precipitation
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World’s Disaster Statistics
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There are three major ways to measure precipitation:
rain gauges, ground radars and satellites
(cpc.ncep.noaa.gov) (www.roc.noaa.gov)
(cpc.ncep.noaa.gov)
Other possible ways:
• Cell phone network signals (Messer, 2007)
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• Weather radars and rain gauges (primary source of
rainfall) are typically restricted to populated areas on the
Earth and can only extend out over water bodies 150 km
or so.
Satellite-based methodologies serve to fill in these
huge data voids, especially over unpopulated
regions and oceans.
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Satellite-based rainfall estimation methods
• Satellite rainfall retrievals are generally categorized
into LEO and GEO.
• Retrieval algorithms are typically classified on their
observing spectrum (VIS, IR, PMW, AMW) or “multispectral” (i.e., use of one or more of these individual
spectrums).
• If the methodology uses multiple satellites or other
information such as radar or gauges is classified as a
“blended” technique.
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Ten widely used high-resolution rain datasets
4 groundbased
datasets
6 satellitebased
datasets
Spatial Temporal
resolution resolution
Dataset
Full name
Sensor platforms
References
CPC-UNI
NOAA CPC Unified Daily Gauge
Analysis
0.25○
24 h
gauges
Chen et al. 2008
CPC
NOAA CPC near-real-time daily
precipitation analysis
0.25○
24 h
gauges
Higgins et al.,
2000
STIV
NOAA NCEP Stage IV data
4-km
1h
NEXRAD and gauges
Lin and Mitchell,
2005
PRISM
Parameter-elevation Regression
on Idependent Slopes Model
4-km
monthly
gauges
Daly et al. 1994;
Daly et al. 2002
GSMaP
Global Satellite Mapping of
Precipitation (GSMaP MVK+
Version 4.8.4)
0.1○
1h
IR, SSM/I, TRMM,
AMSU-B, AMSR-E
Okamoto et al.,
2005; Kubota et
al., 2007
3B42
TRMM Multi-satellite Precipitation
Analysis research product 3B42
Version 6
0.25○
3h
IR, SSM/I, TRMM,
AMSU-B, AMSR-E,
gauges
Huffman et al.,
2007
3B42RT
TRMM Multi-satellite Precipitation
Analysis Real-time experimental
product 3B42RT
0.25○
3h
IR, SSM/I, TRMM,
AMSU-B, AMSR-E
Huffman et al.,
2007
CMORPH
NOAA Climate Prediction Center
(CPC) MORPHing technique
0.25○
3h
IR, SSM/I, TRMM,
AMSU-B, AMSR-E
Joyce et al.,
2004
PERSIANN
Precipitation Estimation from
Remotely Sensed Information
using Artificial Neural Networks
0.25○
3h
IR, TRMM
Hsu et al, 1997;
Sorooshian et
al., 2000
NRL
Naval Research Laboratory's
blended technique
○
3h
IR, VIS, SSM/I,
TRMM, AMSU-B,
AMSR-E
Turk and Miller,
2005
0.25
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Winter -- Gauge and radar-based estimates are similar and
have small biases.
Satellite-based data underestimated in the West.
Total biases (mm) for DJF 2006 (2005-06 winter)
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Summer -- Gauge and radar-based estimates are similar and
have small biases.
Satellite-based data overestimated in the central U.S.
Total biases (mm) JJA 2006 (2006 summer)
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VIS/IR Methods
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Part I: re profile & LWP estimation
Previous Studies of LWP estimation
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LWP   re
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re(h)
re
re
re(h)
Problem : Assume vertically constant re. re is retrieved from
single NIR channel and weighted toward cloud top.
• Overestimate LWP when re increased with height (IreP)
• Underestimate LWP when re decreased with height (DreP)
• Chang and Li’s linear Re profile (re1-top, re2-base) retrieval
using 1.6µm, 2.1µm, and 3.7µm, and LWP estimation with
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re profile
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Warm Rain Estimation
A quick look of A-Train observations
• 20:55~23:35 UTC at 01/06/08 over eastern pacific
• AMSR-E misses the shallow warm rain, MODIS cloud
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observation shows correlation with warm rain
Passive Microwave Methods
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Passive Microwave (PMW) Techniques
•
Microwave energy
can penetrate clouds, in
particular, cirrus clouds
• Frequencies from 6
GHz to 190 GHz on
most PWM sensors.
• Below 20 GHz,
emission by
precipitation-size drops
dominates and ice
particles above the rain
layer are nearly
transparent.
• Above 60 GHz, ice
scattering dominates
and the radiometers
cannot sense the rain
drops below the freezing
layer.
Cumulonimbus
Emission – freq’s <40 GHz
Scattering – freq’s >60 GHz
0C
0C
Nimbostratus
0C
0C
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20
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Vertical View of Atmospheric Profiles in Microwave Frequency
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Basic Relationship Between PMW Frequency and Rainrates
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NOAA / AMSU-B
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The Advanced Microwave Sounding Unit
(AMSU A and B)
AMSU-B
AMSU-A:
AMSU-B - MHS:
Pixel IFOV = 3.3
Pixel IFOV = 1.1
IFOV Size (Nadir) = 48 km
IFOV Size (Nadir) = 16 km 25
The Advanced Microwave Sounding Unit
(AMSU A and B)
1,2 3 4,5
6,7
SSMI
AMSU-B
1-5
AMSU-A:
AMSU-B - MHS:
Pixel IFOV = 3.3
Pixel IFOV = 1.1
IFOV Size (Nadir) = 48 km
IFOV Size (Nadir) = 16 km 26
6-7
Rainfall retrieval using AMSU
Ice Cloud Scattering
Parameter
 Physical retrieval of
B
A
ice water
path (IWP) and particle size (De)
using AMSU-B 89 and 150 GHz:
• De ~ (89)/(150)
• IWP ~ De*(/(89,150))
 IWP to rain rate based on limited
cloud model data and comparisons
with in situ data:
RR = A0 + A1*IWP + A2*IWP2
(from Zhao and Weng, 2002)
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AMSU Rain-Rate Scattering Approach
• Advantages:
• Availability of three NOAA POES satellites spaced
approximately 4 h apart with a spatial resolution of 16 km at
nadir (Metop-A is also incorporated with the same capabilities).
• Wider swath than SSM/I sensors.
• Moisture channels (not available in SSMI)
• Weaknesses:
• Lack of low frequency channels with appropriate spatial
resolution.
• Inability to retrieve rain that has little or no ice (only scattering
is available).
• Cross-scan characteristics of the instrument (different
footprints for different local zenithal angles).
• Mixed polarization (SSMI V,H polarization)
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AMSU
Frequency ratio
(rr>0/rr≥0) for
April 2005.
SSMI – GPROF 6.0
Upper panel:
AMSU retrieval
Bottom panel:
SSMI GPROF
6.0
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DOD SSM/I
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Comparison of SSMI and SSMI/S Sensors characteristics
for those specific channels used in the hydrological
product generation
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SSM/I Precipitation Product
• Develop empirical fits
between SSM/I F15 and
SSMI/S F16 during
period of close overpass
times (3/06 – 2/07)
– All channels
– Stratify via
land/ocean; rain/norain
– RADCAL correction
applied to F15 8/06
and forward
TASSM / I  chan,sfctype,rain  chan,sfctype,rain *TASSMI / S
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HOW TO CONTINUE WITH THE
MONTHLY DATA SET?
USE THE HERITAGE OF THE
EXISTING ALGORITHMS
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NASA TRMM
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TRMM Satellite
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TMI
PR
reflectivity
profiles
TMI
footprints
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TRMM Sensors
Precipitation radar (PR):
13.8 GHz
4.3 km footprint
0.25 km vertical res.
215 km swath
Microwave radiometer (TMI):
10.7, 19.3, 21.3, 37.0
85.5 GHz (dual polarized
except for 21.3 V-only)
10x7 km FOV at 37 GHz
760 km swath
Visible/infrared radiometer
(VIRS):
0.63, 1.61, 3.75, 10.8, and
12mm
at 2.2 km resolution
Additional EOS instruments:
CERES (Cloud & Earth Radiant
Energy System) 720 km swath
Launch Date: 11/22/1997
LIS
(Lightning
Sensor)
Already
achievedImaging
10 yr mission
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Major Characteristics of TRMM
Orbit
Circlar ( Non-Sunsynchronous)
Altitude
350 km
Inclination
35 degree
Precipitation Radar (PR)
Instruments
TRMM µ wave Imag er (TMI)
Visible and Infrared Scanner (VIR S)
Clouds and th e Earth Radian t Energy System (CERES)
Lightning Imagi ng Sensor (LIS)
Tra cking & S/C Operation
by NASA Tracking and Data Relay Satellite System (TDRSS)
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Major Characteristics
of TRMM
TRMM Sensor Specifications
Observa tion Band
Horizontal Resolution at Nadir
(cross track x do wn track km)
Swath
Width
(km)
Scan
Angle (°)
TMI
10.7 CHz
19.4 GHz
21.3 GHz
37.0 GHz
85.5 GHz
38.3 x 63.2
18.4 x 30.4
16.5 x 27.2
9.7 x 16.0
4.4 x 7.2
790
± 65
PR
13.8 GHz
4.3
220
± 17
VIRS
0.63 m
1.6
3.75
10.8
12.0
0.3 - 0.5 m (shortwave)
8 - 12 m (long wave)
0.3 -> 50 m (total range)
2
2
2
2
2
720
± 46
CERES
LIS
0.7774 m
25
4
± 82
590
± 41
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1998-2005 Mean Monthly Rainfall (5°x5°)
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Original TRMM Climate Question: How much is it raining in
the Tropics (especially over the ocean)?
Nine-year TRMM Zonal Average (Ocean [1998-2006])
From 2A12 (TMI[passive microwave]), 2A25 (PR[radar]), and 2B31 (TMI&PR)
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TRMM Mean
TRMM Maximim
TRMM Minimum
200
Precipitation (mm/month)
180
160
140
120
100
80
60
40
20
0
-40
-35
-30
-25
-20
-15
-10
-5
0
5
10
15
20
25
30
35
40
Latitude
Adler and Wang
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TRMM Data Used for Hurricane/Typhoon Monitoring
TRMM TMI data used by U.S. and international
weather agencies for tropical cyclone detection,
location and intensity estimation--600 TRMM-based
tropical cyclone “fixes”per year
TRMM orbit advantageous for tropical cyclone
monitoring--it is always in tropics, sampling best in
10-35º latitude storm band. TMI resolution twice as
good as operational sensors, about same as AMSR.
Precessing orbit provides off-time observations
relative to sun-synchronous microwave observations.
Hurricane Katrina-2005
from TRMM web site
Hurricane Katrina
TRMM image from
U.S. Navy Tropical
Cyclone web site
TRMM radar (PR) crosssections of hurricanes
available in real-time for
operational analysis from
TRMM web site
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TRMM Precipitation Radar Views Typhoon Etau
Only Space-Based Instrument that Provides Vertical Structure in Tropical Rain Systems
13-km tall hot towers
Intense convective rains in deep eyewall
towers power intensification of Etau,
through latent heat release.
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GPM Reference Concept
OBJECTIVES
• Understand horizontal &
Core
vertical structure of rainfall, its
macro- & micro-physical nature,
& its associated latent heating
• Train & calibrate retrieval
algorithms for constellation
radiometers
Constellation
OBJECTIVES
• Provide sufficient global
sampling to significantly
reduce uncertainties in shortterm rainfall accumulations
• Extend scientific and societal
applications
Core Satellite
• TRMM-like spacecraft (NASA)
• H2-A rocket launch (NASDA)
• Non-sun-synchronous orbit
~ 65° inclination
~400 km altitude
• Dual frequency radar (NASDA)
K-Ka Bands (13.6-35 GHz)
~ 4 km horizontal resolution
~250 m vertical resolution
• Multifrequency radiometer (NASA)
10.7, 19, 22, 37, 85, (150/183 ?) GHz
V&H
Precipitation Processing Center
• Produces global precipitation products
• Products defined by GPM partners
Constellation Satellites
• Pre-existing operational-
experimental & dedicated
satellites with PMW radiometers
• Revisit time
3-hour goal at ~90% of time
• Sun-synch & non-sun- synch
orbits
600-900 km altitudes
Precipitation Validation Sites for Error Characterization
• Select/globally distributed ground validation “Supersites” (research
quality radar, up looking radiometer-radar-profiler system,
raingage-disdrometer network, & T-q soundings)
• Dense & frequently reporting regional raingage networks
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Ground-based Precipitation Radar
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Rain and Snow
Observable Characteristics
Precipitation rate - R (intensity)
is the volume flux of precipitation through a
horizontal area. In cgs units, R is
expressed as cm3 cm-2 sec-1. However, R
is usually expressed in mm/h. R is
sometimes called the rainfall rate or
equivalent rainfall rate.
R varies from trace amounts up to several
hundred mm/h. R for snow tends to be
about 0.1 Rrain.
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Rainfall Rate and Drop-Size Distribution
Function


R 
6

N (D) D 3 V(D) dD
0
Where
N(D)dD - the number of drops per unit volume with
diameters between D and D + dD,
V
- the fall velocity of drops of size D.
For snow, D is the melted diameter of a drop, and
R is the equivalent rainfall rate.
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Precipitation Water Content


L 
w
6

N (D) D3 dD
0
The precipitation water content L is independent of the fall
speed and is measured in terms of mass/volume
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Weather Radar
Radar - acronym for RADio Detection and Ranging
Main components are:
• Transmitter which generates short pulses of
electromagnetic energy
• Antenna which focuses the energy into a narrow
beam
• Receiver which detects that portion of the transmitted
energy that has been reflected (scattered) by objects
with refractive characteristics different from air
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Weather Radar:
Important Parameters
• Peak Power - Pt - (instantaneous power in a pulse)
10 < Pt < 5000 kW
• Radio frequency - 
Radio wavelength -  - (c/
3 < GHz (1 GHz = 109 sec-1)
(wavelengths from 1 to 30 cm)
• Pulse repetition frequency (fr) (PRF)
200 < fr < 2000 sec-1
• Pulse duration - 
0.1 <  < 5 µsec
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Rayleigh Scattering
Define the scattering size parameter  for a sphere as
2r
 

the ratio of the circumference of
the sphere to the wavelength
For  << 1, scattering is in the Rayleigh region, and 
for a sphere or radius ro is given as
5 2 6
  64 4  r o

(m 2  1)
and m  n  ik
where  
2
(m  2)
n is the refractive index and k the absorption coefficient.
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Weather Radar Equation - cont.
Assuming Rayleigh scattering spheres of diameter D
Pr  Pt
G 2 5
4 3 r 4 2
2

D6
Introduce the radar reflectivity factor Z, where
Z 

v
D
6


0
N (D)D 6 dD
where the summation extends over a unit volume,
and N(D)dD is the number of drops per unit volume
of a given diameter.
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Weather Radar Equation - cont.
After accounting for the scattering volume and the beam
pattern, the weather radar range equation is
Pr
Pt G 2 2  2 Z 
3 c 



2
 r 2 
1024 ln 2 




radar
target
where  is the pulse duration and  is the beam
width.
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Weather Radar Equation - cont.
10 log Pr
 10 log Z  20 log r  C
where C is a constant determined by radar parameters
and dielectric characteristics of the target
• Power in decibels is related to the reflectivity factor
as measured on the decibel scale
• Pr - measured in milliwatts, 10 log Pr is the power in
dBm (decibels relative to a milliwatt
• Z is measured in mm6/m3 and 10 log Z is the
reflectivity factor in dBz.
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Radar Displays
PPI - Plan position Indicator
Maps the received signals on polar coordinates in
plan view. The antenna scans 360° at fixed elevation
angle. At every azimuth the voltage output of the
receiver as a function of range is used to intensitymodulate a tube with polar coordinates (Rogers and
Yau, 1989). This produces a plan view of the
distribution of precipitation.
Without careful calibration, PPI records are only
useful to show the location and time occurrence of
precipitation.
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230 km range PPI
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Radar Displays - cont.
RHI - Range Height Indicator
This display is generated when the antenna scans in
elevation with fixed azimuth, thereby showing the
details of the vertical structure of precipitation.
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Homework, Due April 20
1. Read the following articles and write a review of
earth radiation budget (ERB) retrieval and
advancement of our knowledge in ERB:
•
•
•
Wielicki, B. A., R. D. Cess, M. D. King, D. A. Randall, and E. F. Harrison, 1995: Mission to
Planet Earth: Role of clouds and radiation in climate. Bull. Amer. Meteor. Soc., 76, 2125–
2153.
Li, Z., L. Moreau, A. Arking, 1997: On solar energy disposition, A perspective from
observation and modeling, Bull. Amer. Meteor. Soc., 78, 53-70
Li, Z., 2004, On the solar radiation budget and cloud absorption anomaly debate, In
"Observation, Theory, and Modeling of the Atmospheric Variability", (ed. Zhu), World
Scientific Pub. Co., p437-456.
2. Read the following articles and write a summary of
passive microwave (PMW) remote sensing of
precipitation (5 pages)
•
•
Xie, P., and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on
gauge observations, satellite estimates, and numerical model output. Bull. Amer. Meteor.
Soc., 78, 2539–2558.
Olson, W.S., C.D. Kummerow, S. Yang, G.W. Petty, W.-K. Tao, T.L. Bell, S.A. Braun, Y.
Wang, S.E. Lang, D.E. Johnson, and C. Chiu, 2006: Precipitation and latent heating
distributions from satellite passive microwave radiometry. Part I: Method and uncertainties.
J. Appl. Meteor., 45, 702-720.
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Acknowledgements
Some slides used in this lecture were
provided by Ralph Ferraro, Daniel Vile
Yudong Tian, Song Yang,
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