radar and lidar sensor for precipitation method measurement

8/12/2019 Radar and Lidar Sensor for Precipitation Method Measurement

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GROUP MEMBERS:

Khairil Ali MizamAzierahNurul AthirahEmiliaNur Ritasha

REMOTE SENSINGTECHNIQUES FORPRECIPITATION

MEASUREMENTUSINGRADAR/LIDAR

SENSOR

REMOTE SENSINGFOR HYDROLOGYAND WATER

RESOURCES


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RADAR SENSOR FOR

PRECIPITATIONMETHOD

MEASUREMENT


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Sense precipitation by using electromagnetic

radiation (examines inside of the cloud) Wavelength: 3cm10cm microwave pulse (large

cloud water droplets, raindrops, hail, snow particles,and other solid forms of precipitation reflect emittedradiation)

Sends out signals into the atmosphere. If anyprecipitation is present, the radar signal scatteredback to the RADAR transmitter (also called RADARechoes) used to produce RADAR images.

The precipitation intensity is measured by the

strength of the echo in the units of decibels dbZ.Larger/numerous particles reflect waves with greaterintensity than smaller/fewer particles.

An image showing precipitation intensity is called a"reflectivity image."

Basic Concept


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Sensing Precipitation & CloudsWith RADAR

Principles:

Use a relationship between radar reflectivity

factor Z (or Ze) and the rainfall rate, Rr(mm/hour) in the form (called Z-Rrelationships)

Where A and b are constants depending onthe type of rains.


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Sensing Precipitation & CloudsWith RADAR (cont)


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TROPICAL RAINFALLMEASURING MISSION(TRMM)


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BACKGROUND

Joint mission between NASA and theJapan Aerospace Exploration Agency

(JAXA).

Launched on November 27, 1997 fromTanegashima, Japan

Designed to monitor and measure rainfall

which covers tropical and sub-tropicalregions of the earth.



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SPECIFICATIONS

Orbit: 350 km

Inclination Angle: 35

Non-sun-synchronous

Revisit Frequency: 11-12 hours

Track Speed: 6.9 km/s

Area covered: 35N to 35S


Reference: TRMM 2009


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INSTRUMENTS ON BOARD

TRMM Microwave Imager (TMI)

Precipitation Radar (PR)

Visible and Infrared Scanner (VIRS)

Cloud and Earths Radiant Energy System(CERES)

Lightning Imaging Sensor (LIS)



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TRMM MICROWAVE IMAGER

(TMI)

9-channel passive microwave radiometer

Frequencies: 10.65, 19.35, 21.3, 37, 85.5 GHz

Horizontal and vertical polarizations Reads rainfall, water vapor, and cloud

water

Scan Geometry

Swath: 758.5 km Off-nadir: 52.8 Incident Angle

Conical Scan: 130



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PRECIPITATION RADAR (PR)

Active Rain Radar

Frequency: 13.8 GHz

Scan Geometry:

Nadir

Spatial Resolution: 4.3 km

Range Resolution: 250 m

Swath: 215 km



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VISIBLE INFRARED SCANNER

(VIRS)

5-channel visible and infrared passiveradiometer

Wavelengths: 0.6-12m Reads brightness and temperature

Scan Geometry

Swath: 720 km

IFOV: 2.11 km nadir Radiometric Properties:

Channels 1 and 2 read solar energy

Channels 3-5 read thermal energy



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TROPICAL RAINFALL


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TRMM precipitation radar


GLOBAL PRECIPITATION


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GLOBAL PRECIPITATIONMEASUREMENT (GPM)



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BACKGROUND International network

of satellites that

provide the next-generation globalobservations of rainand snow data forevery three hours.

Joint mission betweenNASA and JAXA(Japan AerospaceExploration Agency).


Illustration of the GPM satelliteconstellation.



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SPECIFICATIONS

Orbit: 407 km

Inclination Angle: 65

Non-sun-synchronous

Revisit Frequency: 3 hours

Track Speed: 7.2 km/s

Area covered: Entire globe




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GPM MICROWAVE IMAGER

(GMI)

13-channel passive microwaveradiometer

Frequencies: 10-183 GHz Horizontal and vertical polarizations

New high frequency channels toimprove ice and snow measurements

Reads rainfall, water vapor, cloudwater, ice and snow

Scan Geometry

Swath: 885 km

Off-nadir: 52.8 Incident Angle




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DUAL FREQUENCY RADAR (DFR)Active Rain Radar operating at two frequencies


Frequency: 13.8 GHz(KuPR)

Scan Geometry:

Nadir

Spatial Resolution: 5 km

Range Resolution: 250m

Swath: 215 km

Frequency: 35.5 GHz(KaPR)

Scan Geometry:

Nadir

Spatial Resolution: 5 km

Range Resolution: 250 -500 m

Swath: 245 km

Data collected from the KuPR and KaPR units will provide 3-Dobservations of rain and will also provide an accurate estimation

of rainfall rate



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APPLICATIONS

Precipitation Monitoring

Flood and Landslide Potential

Global Climatology

Tropical Storm Monitoring

Fire Detection




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PRECIPITATION

MONITORING



FLOOD

POTENTIAL




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LANDSLIDE

POTENTIAL



TROPICAL STORM

MONITORING




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FIRE MONITORING



TRMM VS GPM


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TRMM VS GPM

TRMM vs. GPM

General TRMM GPM

Orbit Altitude 350 km 407 km

Inclination Angle 35 65

Revisit Frequency 11-12 hrs 3hrs

Track Speed 6.9 km/s 7.2 km/s

Coverage Tropics Global

Microwave Imager

Swath 758.5 km 885 km

Incident Angle 52.8 52.8

Number of Channels 9 13

Precipitation RadarSwath 215 km 245 km

Number of Channels 1 2

Spatial Resolution 4.3 km 5 km

Range Resolution 250 m 250, 500 m

CONCLUSION


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TRMM data greatly expanded knowledgeof global hydrology but it is limited incoverage area

Errors associated with TRMM data

GPM provide more frequent andaccurate data with better technology. Italso expand the coverage area fromArtic Circle to Antartic Circle.

CONCLUSION


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LIDAR SENSOR FOR

PRECIPITATIONMETHODMEASUREMENT

JOURNAL (LIDAR)


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Lidar-Based Estimation of Small-

Scale Rainfall: Empirical EvidenceBy: P.ALewandowski et al.

From: JOURNAL OF ATMOSPHERIC AND OCEANICTECHNOLOGY

Year: 2008

JOURNAL (LIDAR)

JOURNAL (LIDAR)


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Instruments

Disdrometer (in-situ)Scanning Elastic

Lidar

JOURNAL (LIDAR)

JOURNAL (LIDAR)


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Instrument: Scanning

Elastic LidarElastic backscattering is designed to determine the distribution and

properties of atmospheric particulates.

Elasticrefers to scattering in which no energy is lost by the photons, so

that the detected light is at the same wavelength as the emitted light.

The system is entirely computer controlled using PC cards to control

the motors, the laser, the digitizers, and other auxiliary equipment such

as GPS.

Lidar can be operated remotely and autonomously, using pre-

programmed sequences that only require an operator to start.

JOURNAL (LIDAR)

JOURNAL (LIDAR)


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Methodology: Inversion

MethodThe inversion algorithm transforms measured quantities(the intensity of the backscattered light as a function of

distance) into rainfall amounts.

Determination ofthe extinction

coefficients fromraw Lidar data

Calculation of thecorresponding rain

rates.

Step 1 Step 2

JOURNAL (LIDAR)

JOURNAL (LIDAR)


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Step 1: Determination of

Existence Coefficient(1)

(2)

(3&4)

(5)

(6)

JOURNAL (LIDAR)

JOURNAL (LIDAR)


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Step 2:Calculating Rain Rates

Where,

(8)

(9)

(10)

(11)

(12)

( )

JOURNAL (LIDAR)


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Results

Upper graph presents

the full time series of

Lidar backscattered

power measured

directly over the

disdrometer during the

event on 5 Oct 2005 in

Iowa City, IA.

Bottom graph shows a

time series of full-rangelidar profiles (color

coded)

( )

JOURNAL (LIDAR)


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ResultsFrom Eq (13) parameters C3 and C4 are determined by fitting the

Lidar data and result is presented in following formula:

( )

JOURNAL (LIDAR)


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Results

Mean fractional difference (fractional difference between

the Lidar and the disdrometer rain rates for the entire

sample with values above the detection limit of 0.1 mm

h)

( )

JOURNAL (LIDAR)


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Conclusion

Lidar-based estimates of rainfall at small spatial and

temporal scales might be the most accurate of all

instruments available in compared with tipping-bucket

rain gauges, optical and mechanical disdrometers andradar.

A concurrent application of Lidar and a limited number

of disdrometers capable of mapping horizontal

distribution of rainfall with high spatial (~5 m) andtemporal (~1min) resolution.

( )


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Edward H. Bair, Robert E. Davis,David C. Finnegan, Adam L.

LeWinter, Ethan Guttmann, and JeffDozier


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Measure accurate snowfallmeasurements in windy areas.

Effective snow depth mappingover a small study area ofseveral hundred m2

LiDAR also produces dense point cloudsbydetectingfallingandblowing hydrometeors during storms


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Riegl LMS-Z390i

IR withwavelength 1.55

m

Capable of 360azimuthaland

80 elevationalcoverage at 0.09angularincrements

Mounted on a

steel platform,approximately 7m above snow-free ground.

Automaticallyscans every houror every 15 min.


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Produces file in 3dd formatwhich thenconverted to ASCII format contain (X,Y,Z) andrelative intensities (0-1) for each detection.

From the coordinates, we recorded the numberof detections in the sample volumeand theassociated scan time.


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Beam Geometry

Illustrationduring readingbeing taken.


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Daily manual SWE and summed lidar counts, hourly fromMarch 2011-April 2012.

The correlation coefficient r=0.58.


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Daily manual SWE and summed lidar counts, 15 min fromFeb-April 2012.

The correlation coefficient r=0.73.


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1. It is shown that, in addition of snow depth

mapping, LiDAR can also be used for snowmass flux estimation.

2. Found a good empirical agreement betweenLiDAR counts in sample volume, summedover one day, and manually weighed SWE.

radar and lidar sensor for precipitation method measurement

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