Transcript Precipitable Water & Precipitation

Precipitable Water &
Precipitation
GEOG 4140/6140
Satellite Meteorology/Climatology
Precipitable water
 Total amount of water vapor in a column or
some portion of a column of the
atmosphere, measured as if it all fell to the
ground as precipitation (mm/inches)
WV and Precipitable Water
 Precipitable water is just the integral of
water vapor in a vertical column through
the atmosphere, and is
 usually in cm (or g/cm2). Water vapor is
often the mixing ratio at some level in the
atmosphere, but is a general term not as
precise as PW.
Sensing Precipitable water (IR
sounders)
 Water vapor/IR sounders
• Spectral region: 6-9 m
– 6.3-6.7 m: mid-upper troposphere
– 7.3 m:
low-mid troposphere
– 8.3 m:
surface
• Water vapor channel
– 6.3-6.7 m (600-300mb)
– Used to estimate RH under clear sky conditions (not
total precipitable water)
Estimates dependent on knowledge of temperature profile
Sensing Precipitable water
• TOVS/VAS
– Can get moisture at lower levels
– Using 8-9m spectral region
GOES precipitable water
Sensing Precipitable water
(Microwave)
 Microwave estimates
• SSM/I… 22.235 GHz (water vapor)
– Low-level (below 700mb)
– Only works over ocean... too much variation in
surface emissivity over land
• SSM/T and SSM/T2 (sounder)
– DMSP
– 7 channels around 50 GHz (SSM/T)
– 5 channels, mostly near 183GHz (SSM/T2)
Precipitable water (Microwave)
 Microwave estimates
• AMSU (NOAA-15)
– 20 bands
– AMSU - A : 15 channels around 50-80 GHz
– AMSU - B : 5 channels around 183 GHz
– Vertical profiles
– Over land and ocean
– http://amsu.cira.colostate.edu/
Satellite and Radar - How Radar
Works
 http://www.comet.ucar.edu/nsflab/web/re
mote/1221.htm
Microwave spectrum
Microwave sounder frequencies
Channel SSM/T
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
50.5H
53.2H
54.35H
54.9H
58.4V
58.825V
59.4V
MSU
50.30R
53.74R
54.96R
57.95R
AMSU-A
23.8R
31.4R
50.3R
52.8R
53.6R
54.4R
54.9R
55.5R
57.2R
57.29±.217R
57.29±.322±.048R
57.29±.322±.022R
57.29±.322±.010R
57.29±.322±.0045R
89.0R
AMSU-B SSM/I SSM/T-2
89.0R
150.0R
183.3±1R
183.3±3R
183.3±7R
19.35H
19.35V
22.235V
37.0H
37.0V
85.5H
85.5V
183.3±3R
183.3±1R
183.3±7R
91.7R
150R
Precipitation Measurement from Space
Rain measurement systems radar
Advantages:
 Excellent space and time resolution
 Observations in real time
Disadvantages:
 Little coverage over oceans or remote
regions
 Signal calibration
 Corrections required for beam filling,
bright band, anomalous propagation,
attenuation, etc.
 Z-R relationship
 Expensive to operate
Z-R Relationships
Rain rate
R

Radar reflectivity
Z

Volume extinction  ext
D
N(D)
U ( D)
Qe ( D /  )
K

3
N
(
D
)
U
(
D
)
D
dD

6
K  N ( D ) D6 dD
   N ( D )Qe ( D /  ) D 2 dD
Drop Diameter
Dropsize distribution
T erminalfall velocity
Extinctionefficiency
Dielectricconstantof water
Marshall-Palmer (1948): Z=200 R1.6
U.S. WSR-88D Frequency of Rainfall Occurrence for 1998 - 2000
Mountains/Data Voids
Satellites Offer Perfect Complement
Range Issues
Limited Offshore Coverage
Precipitation Measurement from
Satellite
 “Old school” methods
• Visible and infrared techniques
– Cloud indexing
– Life history
– Bispectral
– Cloud model
• Passive microwave
– Absorption-based approaches
– Scattering-based approaches
Precipitation

“New School” methods
• Active Microwave
– Space-Based Radar
– Combined Radar/Passive Microwave
• Merged/Blended Techniques
• Future
– Constellations of Passive MW Satellites
– Dual-Frequency Radar Systems/High Frequency Passive
Microwave
– Passive/Active Microwave at Geosynchronous Orbit???
VIS/IR Techniques

A number of satellite rainfall estimation techniques
based on Vis/IR have been developed.
• Cloud indexing methods
• Bispectral methods
• Life-history methods
• Cloud model-based techniques
VIS/IR rainfall estimates
VIS reflectivity
Brighter (thicker clouds)
Dark
 heavier rainfall
 no rain
IR brightness temperature
Colder (deeper clouds)
Warm
 heavier rainfall
 no rain
NIR brightness temperature
|TNIR-TIR|~0 (large drops or ice)  rain more likely
|TNIR-TIR|>0 (small water drops)  no rain
Cloud Indexing
Logic: The techniques rely on VIS/IR data to characterize a cloud type or
temperature which is then related to rainfall via empirical relationships.
Different methods have been used to calibrate the indices to give rainfall
estimates.
• Barrett (1970)
Cloud indexing
Cloud Indexing
 Follansbee (1973)
• Lower spatial resolution, but daily estimates
(compared to Barrett)
• Fractional coverage of a state by
– cumulonimbus
(r = 1”/day)
– nimbostratus
(r = 0.25”/day)
– cumulus congestus (r = 0.02”/day)
• Works well with convective precip.
Cloud Indexing
• Arkin (1979), Arkin and Meisner (1987)**********
– Tropical precipitation for climatological purposes
– Similar to K-R, but uses GOES IR
– GOES Precipitation Index (GPI)
– Fraction of pixels colder than 235K
– GPI = 3 f
t,
– f is fractional coverage of IR pixels < 235K over a large domain (e.g. 50 x
50 km or larger_
– Delta t is the number of hours over which f is compiled
• In general...
– VIS works best in tropics and warm season extratropics, dominated by
convection
– IR works best in mid-latitudes
– Good temporal sampling from GEO orbit
– It is appropriate for tropical precipitation over 2.5 deg x 2.5 deg and
for monthly totals..
GPI Performance
Arkin (1979)
Bispectral Methods
T
• The logic...
– Rain: cold/bright clouds
– No rain: warm/dark (or no) clouds
– Put together
• Dittberner and Vonder Haar
(1973), Lovejoy and Austin (1979),
Tsonis and Isaac (1985), Muench
(1981)
NR
R
B
GOES Multispectral Rainfall Algorithm
(GMSRA)
Rain indicator:
VIS: a  0.40
NIR: re (eff. radius)  15 m
OR
T11-T6.7: Negative for deep
convective cores (T11< 230K)
http://orbit-net.nesdis.noaa.gov/arad/ht/ff/gmsra.html
Rain amount:
R = probability of rain(T11) * mean rain rate (T11) * RH correction
factor * growth correction factor
Life-History Methods
• The logic...
– Rain rate a function of its stage in life cycle
– A major problem is that often cirrus anvils of neighboring
clouds screen the cloud life cycle leading to underestimates
early in the day and overestimates towards the evening.
•
Griffith-Woodley (Griffith et al. 1978)
– Entirely automated
– Clouds (colder than 253K) followed through life cycle to determine max. area
(Am)
– Radar echo area (Ae) is estimated from Am
– Precipitation is a function of:
– Rain rate [function of Ae/Ae(max)]
– Echo area (Ae)
– Time interval between images
– Empirical factor (a function of CTT)
Life-History Methods
• Stout, Martin and Skidar (1979)
– Rain rate peaks when cloud area is most rapidly
growing
– R = a0A + a1(dA/dt)
• Negri et. al. (1984) have simplified the technique
eliminating cloud tracking and producing a
precipitation scheme that treats each cloud as if
existing only in one image.
Cloud Model Methods

Logic:
Cloud models are used to improve estimation results by
trying to build the physics of clouds into the retrieval process.
•
Gruber (1973) first introduced a cumulus convection parameterization to relate
fractional cloud cover to rainrate.
– R = Qc/tL
– Q is moisture necessary for saturation
– tL is lifetime of a convective storm
– c is fractional cloud cover
•
Wylie (1979) used a cloud model to adjust calibration coefficients.
•
The Convective Stratiform Technique (CST) (Adler and Negri 1988) relies upon a
1D cloud model to relate cloud top temperature (CTT) to rainrate and rain area. Local
minima in the IR temperature are sought and screened to eliminate thin,
nonprecipitating cirrus. Precipitation is assigned to convective areas by means of the
cloud model. To every other element colder than the stratiform threshold a fixed
stratiform rainrate of 2 mm/h is assigned.
Cloud Model Methods
• Scofield and Oliver (1977)
– Uses enhanced, half hourly GOES VIS/IR
– Useful for flash-flood forecasting
– Steps 1. Is the cloud convective? 2. Outline area of convective cloud 3. Assign a
preliminary rainfall rate based on CTT 4. Increase rain rates for overshooting tops,
cloud merger, saturated environment (5.) Correction factors for “speed of storm
factor” and “moisture correction” factor-(model derived)
• S-O “look-alikes”
– Tropical cyclones (Spayd and Scofield, 1977)
– Extratropical cyclones (Scofield and Spayd, 1984)
– Automated (Martin and Howland, 1986)
• NOTE: These methods were originally developed for a particular location and their
adaptation to other areas of the globe or climate regimes is not trivial (e.g. Marrocu et
al. 1993).
Unconventional Channels

Split Window Channels- These techniques rely upon the possibility of
detecting non-precipitating cirrus and low-level cumulus clouds using the 11
and 12 μm channels.
•
Kurino (1997) has used the 11 μm brightness temperature, the difference
between 11 and 12 μm, and the difference between 11 and 6.7 μm. The
second parameter serves the purpose of removing thin cirrus
contributions. The water vapor channel is used for extracting deep
convective clouds associated to heavy rainfall..

Water Vapor Channel-The potential of using the water vapor channel of
geostationary satellites is indicated by observations of “warm water vapor
pixels” (Tjemkes et al. 1997) over deep convective clouds. (related to the
presence of stratospheric water vapor and its amount)

The near IR 3.9 μm channel of GOES-8/9 satellites is also interesting for
rainfall estimation.
• Vicente (1996) - The idea is that the 11 μm channel is more sensitive to
the presence of ice while the 3.9 μm is more sensitive to the presence of
water vapor.
Lightning-IR Based Estimates
Logic:
Morales and Anagnostou (2003)
Lightning measurement that is associated with
ice aloft can provide better identification of the
convective area, which could contribute to
improving precipitation estimation using
VIS/IR techniques
•Lightning from an experimental long-range very
low frequency radio receiver network named the
Sferics Timing and Ranging Network.
•Parameterizations for delineating the total rain
area and its convective portion as well as
convective and stratiform rain-rate relationships
are obtained for lightning (LTG) and lightningfree (NLTG) clouds.
•Still being evaluated for performance
Precipitation—IR/VIS vs. MW
Physical Robustness:

•
•
•
Microwave radiances are sensitive to moisture
throughout the cloud, particularly rainfall and ice
Passive microwave frequencies precipitation particles are
the main source of the attenuation (Large ice particles) or
of increase of signal received due to the emission of
many raindrops
IR/VIS data reflect cloud-top conditions only and thus
are more weakly related to actual rainfall rates over a
wider range of conditions than MW radiances.
Precipitation—IR/VIS vs. MW
Space/Time Resolution

•
•
•
•
IR/VIS data are available at 4 km/1km resolution
(GOES) on geostationary platforms, allowing
looks in many locations every 15 minutes—
suitable for extreme precipitation events at short
time scales
MW Low spatial resolution (~5 – 25+ km)
MW poor time resolution
MW sensors have beamfilling issues with rain
and land
Passive microwave precipitation signal
• Most directly linked to
surface precipitation
• Over cold (water) surfaces
only
• All types of surfaces
• More indirect
Excellent reference: http://www.nrlmry.navy.mil/~kuciausk/esis/
Emission and Scattering
Approaches


Low frequency channels (e.g. 19 GHz)
• ocean only because emssivity of land ~ 1
• Warm microwave brightness temperature due to emission of many
raindrops the more emission the heavier the rain
• Cool microwave brightness temperature no rain
High frequency channels (e.g. 85.5Ghz)
• land and ocean
• Cold microwave brightness temperature due to scattering from large ice
particles the more the scattering the heavier the rain
• Warm microwave brightness temperature no rain
• Principle of passive microwave rainfall from polar orbiting satellite
Scattering vs absorption
– < 22 GHz:
– 22-60 GHz:
– > 60 GHz:
mostly absorption
scattering and absorption
mostly scattering by ice
Precipitation amount/rate
• Notice...
– No rain (D=0), TBTS
– Rain increases ( decreases) TB approaches TA
TB  T A
a
D
TB
TA
TS

T

1   S  1  (1   ) 

TA


  exp(  a D)
is the absorption coefficient
is the depth of the rain layer
is the brightness temperature
recorded by the radiometer
is the air temperature
is the ground temperature
is the ground emissivity
is the transmittance of the rain layer
2



Precipitation amount/rate
• Ocean
– Surface emissivity ( 0.4-0.6)
– TB, rain-free about 140-170K
– TB increases significantly with rain rate
– Rain looks “warm” over a “cold” surface
• Land
– Surface emissivity ( 0.8-0.95)
– Difficult to determine rain rate; little difference between
rain and land TB
• Difficulties
– Rain rate related to a, but we “see” aD
– Cloud drops and water vapor do add to 
Precipitation amount/rate
• Absorption schemes
– Wilheit (1977, 1991, et al. 1994), Kummerow 2001, 2004
– Used radiative transfer model to estimate TB at lower frequencies (e.g.
19GHz)
• Scattering schemes (Spencer 1983b, Grody 1991, Adler et al. 1994,
Ferraro and Marks 1995)
– Ice scattering creates Tb depression
• Multi-Channel Inversion Techniques (Olson 1989, Mugnai et al.
1993; Kummerow and Giglio 1994; Smith et al. 1994; Petty 1994).
– A physically based rainfall retrieval, based upon detailed modeling of the
sensor response to precipitation profiles,
Special Sensor Microwave Imager (SSM/I)
{
NOAA
SI = a0 + a1T19V + a2T22V + a3T22V2 - T85V
algorithm: R = a SIb
http://orbit-net.nesdis.noaa.gov/arad2/
PRODUCT: RAIN RATE (mm/hr)
DATA FOR JULIAN DATE 2002145 SATELLITE F15 IN ASCENDING NODE
Advanced Microwave Sounding Unit
(AMSU)
AMSU-A (~50 km spatial resolution):
AMSU-B (~17 km spatial resolution):
Tropical Rainfall Measuring Mission (TRMM)
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 12 m
at 2.2 km resolution
Lightning Imaging Sensor (LIS)
Cloud & Earth Radiant Energy System
(CERES)
A Quick Overview of the TRMM
Algorithms
Instrument
PR
TMI-Emission
TMI-Scattering
Physics
Z-R Relationship from radar
reflectivity profile, total path
attenuation
Thermal emission from rain,
cloud water
Scattering of background
radiation by precipitation ice
Advantages
Active
sensor, can retrieve
vertical profile (useful in
evaluating rain type for
applying Z-R)
High resolution (4-5 km)
Low
frequency channels
(10-19 GHz) are a
radiometrically direct
estimate of the verticallyintegrated rain water content
Insensitive to DSD
High
Disadvantages
Sensitive
Low
Ice
to assumption of
DSD
2.2 cm wavelengthuncertainty in correcting for
attenuation
Minimum detectable signal
~17 dBZ
Narrow swath
resolution leads to
serious beam filling effects
Sensitive to the freezing
level assumption, assumed
vertical profile of the rain
layer
Resolution
scattering-rain rate
relationship must be derived
(usu. empirically); varies
regionally and by system
type
Many combinations of ice
PSDs/densities/ vertical
hydrometeor profiles can
lead to similar Tbs
Subject to artifacts
TRMM Radar/Passive-Combined
Logic:
Provides the vertical structure of rainfall (rates and drop-size-distribution
parameters) based upon the TRMM microwave imager (TMI) and the TRMM
radar (PR), within the PR swath.

Invert the radar measurements for every likely value of the drop-size-distribution
(DSD) shape parameters (see Haddad et al, 1997b)
The resulting rainfall estimates are used to produce the corresponding expected
brightness temperatures, which are then compared to the actual passive
measurements to decide which DSD shape parameter value was most likely

Known Deficiencies

•
•
•
In the first "radar-only" inversion step, the algorithm does not take into account the
possible presence of ice particles and their effect on the measured radar reflectivity
Because the algorithm tries to select between various DSD's, it uses many different
radar-rain relations (one for each assumption about the shape of the DSD)
The algorithm uses average relations to infer the expected 10.7, 19, 21, and 37 GHz
brightness temperatures, based on the estimated rain rate (rather than the estimated
radar attenuation).
Global Precipitation Climatology Project (GPCP)
http://orbit-net.nesdis.noaa.gov/arad/gpcp/
Weighted average of rain estimates from:
IR (GPI), SSM/I, TOVS, rain gauges
Products available from GPCP @ 2.5° monthly resolution:
monthly average rain rate
4-, 8-hour lag correlations of rain
rate
standard deviation of instantaneous rain rate
frequency of rain
sampling error for the monthly rain rate estimate
fractional rainy area
algorithm error for the monthly rain rate estimate number of available samples
GPM Reference Concept
Through more accurate, frequent (3-hourly), global, high spatial resolution, and
microphysically detailed measurements of precipitation made possible by GPM
Constellation Mission: Improved weather, climate, and hydrometeorological
prediction
Core Satellite
•
•
•
•
•
•
•
•
•
Dual Frequency Radar
Multi-frequency Radiometer
H2-A Launch
TRMM-like Spacecraft
Non-Sun Synchronous Orbit
~65° Inclination
~400 - 500 km Altitude
~4 km Horizontal Resolution (Maximum)
250 m Vertical Resolution
Constellation Satellites
• Multiple Satellites with
Microwave Radiometers
• Aggregate Revisit Time,
3 Hour goal
• Sun-Synchronous Polar Orbits
• ~600 km Altitude
Precipitation Validation Sites
• Global Ground Based Rain Measurement
Global Precipitation Processing Center
• Capable of Producing Global Precip Data Products as
Defined by GPM Partners