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Remote Sensing of Precipitation. World’s Disaster Statistics. ( ( ( There are three major ways to measure precipitation: rain gauges, ground radars and satellites. Other possible ways: Cell phone network signals (Messer, 2007).

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Remote Sensing of Precipitation

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Remote sensing of precipitation

Remote Sensing of Precipitation

World s disaster statistics

World’s Disaster Statistics

Remote sensing of precipitation




There are three major ways to measure precipitation:

rain gauges, ground radars and satellites

  • Other possible ways:

  • Cell phone network signals (Messer, 2007)

Remote sensing of precipitation

  • 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.

Remote sensing of precipitation

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 “multi-spectral” (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.

Remote sensing of precipitation

Ten widely used high-resolution rain datasets

4 ground-based datasets

6 satellite-based datasets

Remote sensing of precipitation

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)

Remote sensing of precipitation

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)

Remote sensing of precipitation

VIS/IR Methods

Part i re profile lwp estimation previous studies of lwp estimation





Part I: re profile & LWP estimation Previous Studies of LWP estimation

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 re profile

Warm rain estimation a quick look of a train observations

Warm RainEstimation 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 observation shows correlation with warm rain

Passive microwave methods

Passive Microwave Methods

Remote sensing of precipitation

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.

Remote sensing of precipitation

Vertical View of Atmospheric Profiles in Microwave Frequency

Remote sensing of precipitation

Basic Relationship Between PMW Frequency and Rainrates

Remote sensing of precipitation


Remote sensing of precipitation

The Advanced Microwave Sounding Unit (AMSU A and B)



Pixel IFOV = 3.3

IFOV Size (Nadir) = 48 km


Pixel IFOV = 1.1

IFOV Size (Nadir) = 16 km

Remote sensing of precipitation

1,2 3 4,5 6,7 SSMI

1,2 3 4,5 6,7 SSMI




The Advanced Microwave Sounding Unit (AMSU A and B)




Pixel IFOV = 3.3

IFOV Size (Nadir) = 48 km


Pixel IFOV = 1.1

IFOV Size (Nadir) = 16 km

Remote sensing of precipitation

Rainfall retrieval using AMSU

Ice Cloud Scattering Parameter

  • Physical retrieval of 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)

Remote sensing of precipitation

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)

Remote sensing of precipitation


Frequency ratio (rr>0/rr≥0) for April 2005.

Upper panel: AMSU retrieval

Bottom panel: SSMI GPROF 6.0


Remote sensing of precipitation


Remote sensing of precipitation

Comparison of SSMI and SSMI/S Sensors characteristics for those specific channels used in the hydrological product generation

Remote sensing of precipitation

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/no-rain

    • RADCAL correction applied to F15 8/06 and forward

Remote sensing of precipitation


Remote sensing of precipitation

TRMM Satellite

Remote sensing of precipitation







Remote sensing of precipitation

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

LIS (Lightning Imaging Sensor)

Launch Date: 11/22/1997

Already achieved 10 yr mission

Remote sensing of precipitation

Major Characteristics of TRMM

Remote sensing of precipitation

Major Characteristics of TRMM

Remote sensing of precipitation

1998-2005 Mean Monthly Rainfall (5°x5°)

Remote sensing of precipitation

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)




TRMM Maximim

TRMM Minimum




Precipitation (mm/month)


























Adler and Wang

Remote sensing of precipitation

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-basedtropical 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

TRMM image from U.S. Navy Tropical Cyclone web site

Hurricane Katrina-2005

TRMM radar (PR) cross-sections of hurricanes available in real-time for operational analysis from TRMM web site

from TRMM web site

Remote sensing of precipitation

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.

Remote sensing of precipitation


  • Understand horizontal & vertical structure of rainfall, its macro- & micro-physical nature, & its associated latent heating

  • Train & calibrate retrieval algorithms for constellation radiometers


  • Provide sufficient global sampling to significantly reduce uncertainties in short-term rainfall accumulations

  • Extend scientific and societal applications

GPM Reference Concept



  • 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

  • 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

  • Precipitation Processing Center

  • Produces global precipitation products

  • Products defined by GPM partners

Remote sensing of precipitation

Ground-based Precipitation Radar

Rain and snow observable characteristics

Rain and SnowObservable 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.

Rainfall rate and drop size distribution function

Rainfall Rate and Drop-Size Distribution Function


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.

Precipitation water content

Precipitation Water Content

The precipitation water content L is independent of the fall speed and is measured in terms of mass/volume

Weather radar

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

Weather radar important parameters

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

Rayleigh scattering

the ratio of the circumference of the sphere to the wavelength


Rayleigh Scattering

Define the scattering size parameter  for a sphere as

For  << 1, scattering is in the Rayleigh region, and  for a sphere or radius ro is given as

n is the refractive index and k the absorption coefficient.

Weather radar equation cont

Weather Radar Equation - cont.

Assuming Rayleigh scattering spheres of diameter D

Introduce the radar reflectivity factor Z, where

where the summation extends over a unit volume,

and N(D)dD is the number of drops per unit volume

of a given diameter.

Weather radar equation cont1

Weather Radar Equation - cont.

After accounting for the scattering volume and the beam pattern, the weather radar range equation is



where  is the pulse duration and  is the beam width.

Weather radar equation cont2

Weather Radar Equation - cont.

  • 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.

where C is a constant determined by radar parameters

and dielectric characteristics of the target

Radar displays

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 intensity-modulate 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.

Remote sensing of precipitation

230 km range PPI

Radar displays cont

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.

Homework due april 20

Homework, Due April 20

  • 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.

Remote sensing of precipitation

AcknowledgementsSome slides used in this lecture were provided by Ralph Ferraro, Daniel VileYudong Tian, Song Yang,

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