07 big skyearth_dlr_7_april_2016

High resolution maps of PWV and 3D reconstruction of atmosphere refractivity

Giovanni Nico

[email protected]

Consiglio Nazionale delle Ricerche (CNR) Istituto per le Applicazioni del Calcolo (IAC)

Bari, Italy

Outline

Interesting features meterological datasets Active vs. passive remote sensing Interaction of e.m. with atmosphere Meteorological databases SAR interferometry (new high-resolution meteorological data?)

GPS tomography

What am I interested in?

Linear features are related to transport of moisture in atmosphere?

Laminar or turbolent flow ?

Are there frontal zones?

Are isolated anomalies related to some specific atmospheric phenomenon?

High resolution image of atmosphere

What am I interested in?

3D images of atmospheric refractivity

Can I identify/retrieve specific patterns ?

Relevance

I could better study/identify different types of clouds:

•Stratiform cloud •Small cumulus clouds •Cumulonimbus •Ice clouds

Deeper knowledge about atmospheric dynamics

Active vs. passive remote sensing

The Sun energy is reflected, for visible wavelengths, or absorbed and re-emited, as it is for thermal infrared.

detect energy when the naturally occurring energy is available

Passive Sensors can only take place during the day (sun) time

Thermal-IR energy can be detected by night or day as long as the amount of energy is large enough to be recorded.

Active vs. passive remote sensing

They provide their own energy source for illumination.

Active Sensors

The sensor emits radiation which is directed toward the target to be investigated. The radiation reflected from that target is detected and measured by the sensor.

The advantages of active sensors is that they can operate at any time of day

Active sensors can be used for examining wavelengths that are not sufficiently provided by the sun, such as microwaves.

Passive remote sensing

The microwave energy recorded by a passive sensor can be:

1. Emitted by the atmosphere

2. Reflected from the surface

3. Emitted from the surface

4. Transmitted from the subsurface

Because the wavelengths are so long, the energy available is quite small compared to optical wavelengths.

Thus, the fields of view must be large to detect enough energy to record a signal. Most passive microwave sensors are therefore characterized by low spatial resolution.

Active remote sensing

As with passive microwave sensing, a major advantage of radar is the capability of the radiation to penetrate through cloud cover and most weather conditions.

Because radar is an active sensor, it can also be used to image the surface at any time, day or night

The two main advantages are:

“All-weather”

“Day and Night”

Because of the fundamentally different way in which an active radar operates compared to the passive sensors a radar image is quite different from images acquired in the visible.

Active vs. passive RS images

Active vs. passive RS images

Interaction with atmosphere

Fevereiro 2012

DEGGE, João Catalão Fernandes [[email protected]]

11

Particles and gases in the atmosphere can affect the incoming light and radiation.

Two mechanisms:

Scattering

Absorption


Scattering occurs when particles or large gas molecules present in the atmosphere interact with and cause the electromagnetic radiation to be redirected from its original path.

How much scattering takes place depends on the wavelength of the radiation, the abundance of particles or gases, and the distance travelled by radiation.

Three types of scattering:

Rayleigh Mie

NonSelective


Rayleigh scattering causes shorter wavelengths of energy to be scattered more than longer wavelengths

Rayleigh scattering is the dominant scattering mechanism in the upper atmosphere.

Mie scattering occurs when the particles in the atmosphere have 0about the same size as the radiation wavelength.

Examples are: Dust, pollen, smoke and water vapor

Mie scattering occurs mostly in the lower portions of the atmosphere where larger particles are more abundant


Nonselective scattering occurs when the particle size is greater than the radiation wavelength.

All wavelengths are scattered about equally.

Water droplets and large dust particles can cause this type of scattering.

This type of scattering causes fog and clouds to appear white to our eyes because blue, green, and red light are all scattered in approximately equal quantities (blue+green+red light = white light)


Absorption: this phenomenon causes molecules in the atmosphere to absorb energy at various wavelengths.

Ozone, carbon dioxide, and water vapor are the three main atmospheric constituents which absorb radiation.

Ozone serves to absorb the harmful (to most living things) ultraviolet radiation from the sun. Without this protective layer in the atmosphere our skin would burn when exposed to sunlight.


Effect of the atmospheric refraction on microwave signal propagation in a horizontally stratified atmosphere in which the refractive index decreases with height: delay in the wave propagation

Royal Observatory of Belgium. GNSS Research Group


Because these gases absorb electromagnetic energy in very specific regions of the spectrum, they influence where (in the spectrum) we can "look" for remote sensing purposes.

Those areas of the spectrum which are not severely influenced by atmospheric absorption and thus, are useful to remote sensors, are called atmospheric windows.

Meteorological stations

Meteorological databases

MODIS (Moderate-Resolution Imaging Spectroradiometer)

spatial resolution of 1x1

1 day-time acquisition

36 spectral bands

0.4 – 15.0 m

The MODIS PWV product represents the total atmospheric column water vapor

Satellite Terra (1999) Satellite Aqua (2002)

MOD05 Terra product

spatial resolution of 5x5

2 day and night acquisitions

MOD07 Terra product



AVHRR (Advaced Very High Resolution Radiometer)

spatial resolution of 1.1x1.1 km at nadir

ground swath of about 2000 km

6-8 acquisitions per day (by combining two operational satellites)

Five channels

• C1 0.58 – 0.68 m • C2 0.73 – 1.1 m • C3 3.6 – 3.9 m • C4 10.3 – 11.3 m • C5 11.5 – 12.5 m

AVHRR images can be used to get an overview of the general atmospheric situation, the position of frontal zones and the type of cloud cover

Cold cirrus clouds Warmer medium and lower level clouds

Combination of channels 1, 2 and 4


Meteosat

spatial resolution of 5x5 km at nadir

1 acquisition per half an hour

Three channels

• C1 0.5 – 0.9 m • C2 5.7 – 7.1 m • C3 10.5 – 12.5 m

Water Vapor


It will carry the Flexible Combined Imager (FCI) with a spatial resolution of 1–2 km

at the sub-satellite point and 16 channels (8 in the thermal band), and an infrared

sounder (IRS) that will be able to provide unprecedented information on

horizontally, vertically, and temporally (four-dimensional; 4-D) resolved water

vapor and temperature structures of the atmosphere.

Humidity and temperature profiles will be generated on the vertical hybrid-sigma

coordinates of the ECMWF forecast system (91 levels)

Meteosat Third Generation (MTG)


Global Atmospheric Models:

ERA-Interim (European Center for Medium-Range Weather Forecasts – ECMWF) North American Regional Reanalysis (NARR) Modern Era-Retrospective Analysis for Research and Application (MERRA)

Global and regional reanalysis of atmospheric data provide estimates of atmospheric variables several time a day at different pressure levels.


ERA-Interim is a atmospheric reanalysis of the ECMWF, following ERA-40. It provides estimates of temperature, water vapor partial pressure, and geopotential height along 37 pressure levels, on a global 0.7° grid, at 0:00, 6:00, 12:00 and 18:00 UTC daily, from 1989 to present.

NARR is a regional model that provides estimates of temperature, water vapor partial pressure, and geopotential height along 29 pressure levels, on a Northern Hemisphere Lambert Conformal Conic grid centered on the USA, at 0:00, 3:00, 6:00, 9:00, 12:00, 15:00, 18:00 and 21:00 UTC daily, from 1979 to the present.

MERRA is a global reanalysis, providing temperature, water vapor partial pressure and geopotential height along 42 pressure levels, on a global grid (0.5° along longidute and 0.75° along latitude), at 0:00, 6:00, 12:00, and 18:00 UTC daily, from 1979 to present.


The geopotential height is defined to compensate for the decrease of gravitational attraction with the geometric height z, as

zR

zRH

e

e

where Re = 6536.766 is the mean Earth radius

TeRH

e s100

The partial pressure e of water vapor is computed from the relative humidity RH and temperature

Interested people can search for the Clausius-Clapeyron equation giving the saturation partial water vapor pressure es

Numerical Weather Models (NWMs)

The Weather Reseach & Forecasting (WRF) model can be used to generate 3D field of atmosphere temperature, pressure, geopotential, water vapor fraction and liquid water.

Spatial resolution 1kmx1km

NWMs atmospheric phase delay

h

hydwethydwetatm dhNNRRR0

6

cos

10


GPS stations overlaid to delay map


Stratified atmosphere? Turbolent atmosphere?


SAR interferometry

WRF

Atmospheric signal in radar interferometric phase images?

Real aperture radar systems

Synthetic Aperture Radar (SAR)

h S

Range (R)

Azi

mu

th (

Az)

Azimuth aperture

Azimuth aperture

beamwidth

Radar frequencies

RADAR acronim for RAdio Detection And Ranging

SAR = Synthetic Aperture Radar (Radar ad apertura sintetica)

Band name Frequency (GHz) Wavelength (cm)

P 0.3-1 30 – 100

S 1-2 15 – 30

L 2-4 7.5 – 15

C 4-8 3.8 – 7.5

X 8 - 12.5 2.4 – 3.8

Ku 12.5 – 18 1.7 – 2.4

K 18 – 26.5 1.1 – 1.7

Ka 26.5 - 40 0.8 – 1.1

W > 50 < 0.6

SAR sensor

ALOS-2

RADARSAT-2, SENTINEL

COSMO-SKY-MED, TERRASAR-X

GROUND-BASED SAR

GPS (20180 km) L-band

Sentinel-1 (693 km) C-band

ALOS-2 (628 km) L band

CSK (620 km) X-band

11000 km

Ionosphere = dispersive medium

Propagation delay in atmosphere

Synthetic Aperture Radar (SAR) Interferometry (InSAR)

Spaceborne radar satellites

• Simultaneously • Spaced in time

• Hi-res topography • Motions • Crustal deformation • Atmosphere

Multiple observations of surface

Applications

InSAR data acquisition

z

baseline

slant range

The baseline is the distance between “time coregistered” orbits

SAR interferometry

DD

42

2

(MASTER) S1(t)

Pixel = A1ei1

D1 D2

Pixel = A2ei2

12

4DD

(SLAVE) S2(t)

InSAR Geometry - height

For =2: Height ambiguity B=200m h 2= 43.7 m

Topographic phase

Differential SAR interferometry (DInSAR)

After interferogram flattening, the interferometric phase contains both altitude and motion contributions:

If there is a

DTM Phase DTM

contribution

Differential Interferogram

DInSAR Applications

Terrain displacements (earthquakes, landslides, subsidences, glaciers, etc…

InSAR phase contributions

topo noise atmo Displacement k

The atmospheric contribution

Longer wavelength microwave radiation can penetrate through cloud cover, haze, dust, as the longer wavelengths are not susceptible to atmospheric scattering.

Radiation travel path can be affected by atmospheric humidity, temperature and pressure

Two SAR images not simultaneous, can be affected differently by the atmosphere with consequences on the interferometric phase.

BUT

Wf

N

T

Pk

T

Pk

T

Pksn ed

4.131.401)(

22

v3

v21

vacatm

atm dsdsn(s)

ZTDFdzn

H

0

0atm 1sin

1

Propagation delay in atmosphere

Precipitable Water Vapour and InSAR

Let us suppose to have an interferogram corrected for topography

)()()()(, M

atm

S

atm

M

def

S

def

SM tttttt

where tdtdef

4)(

tPWV

Mtatm

14)(

cos

1M is the mapping function

'

23

6

2

10

kT

kR

m

vOH

The precipitable water vapor is the total amount of water vapor

In a vertical column of the atmosphere if it would all

condense


If terrain deformation can be neglected, InSAR can provide maps of PWV temporal changes

A set of independent measurements of PWV by a network of permanent GPS stations can be used to calibrate InSAR measurements. Each station measures the mean PWV in a circular area with a radius of about 3.8 km depending on the cut-off angle set in the GPS processing. The idea is to use GPS estimates of PWV at the acquisition times of master and slave SAR images to compute an independent set of PWV.

rSM MttPWV

4,


Precipitable Water Vapour and InSAR La

titu

de

[deg

]

Longitude [deg] GPS stations

PW

V [

mm

]

30/08/2009 04/10/2009


Longitude [deg]

Lati

tud

e [d

eg]

GPS stations

P

WV

[m

m]

04/10/2009 08/11/2009


Longitude [deg]

Lati

tud

e [d

eg]

GPS stations

P

WV

[m

m]

12/04/2009 17/05/2009


Longitude [deg]

Lati

tud

e [d

eg]

GPS stations

P

WV

[m

m]

21/06/2009 26/07/2009


Longitude [deg]

Lati

tud

e [d

eg]

GPS stations

P

WV

[m

m]

26/07/2009 30/08/2009


A refinement of the PWV can be obtained by accurately estimating the mean vertical temperature used to compute the constant .

Usually Tm is obtained by a linear regression with the surface temperature Ts


'

23610 k

T

kR

m

v

How can we estimate the absolute PWV?

Relative PWV InSAR

17/05/09 – 12/04/09

Absolute PWV WRF model 12/04/09

Absolute PWV 17/05/09

What can we do with InSAR estimates of PWV?

InSAR interpolation to 10km x 10km grid

Assimilation of InSAR PWV maps in NWMs

Spatial distribution of the cumulative difference in the water vapor mixing ratio (QVAPOR) in g/kg (positive mean increase

with respect to the analysis field)

QVAPOR vertical profile before and after the data assimilation

Assimilation of InSAR PWV maps in NWMs

Cumulative difference of hydrometeors in mm Hydrometeors vertical profile

before and after data assimilation

Sentinel-1 High-resolution mapping of PWV on a regional scale

Footprint of all swaths for each segment (S1 and S2) and the network of GPS permanent

stations


Phase contributions due to the temporal change of the dry and ionospheric components of refractivity have been removed!!! Differences with respect to

100kmx100km SAR interferograms (e.g. Envisat)


wetNASWD

GNSS (Global Navigation Satellite System) tomography


PWVdr

T

Pk

T

PkZSWD w 2

v3

v2

1610

K2 = 71.6 k mb-1

K3 = 3.747 105 k2 mb-1

Z-1 = empirical inverse wet compressibility factor

SWD observations

unknown refractivity

3D tomographic grid model

wet

M

wet

MNN

voxM

N voxvoxobsobsaa

aa

N

N

.

SWD

SWD

1

1111

1


The A matrix is filled using a ray tracing algorithm to measure the sub-path distance travelled by the total SWD in each voxel

The main drawback of GNSS tomography formulation is its ill-posedeness resulting from the sub-optimal coverage of the grid model due to he GPS geometry properties the A matrix is not invertible due the large amount of zeros values ini correspondence of the empty voxels mainly in the lowerr part of the tomographic part of the tomographic model.

To overcome this problem a set of constraints or additional information concerning the grid model are added

wetNB

A

0

SWD


Each row of matrix B contains a constraint imposed to the tomographic model

The most common ones are weighted averages using horizontal and vertical smoothing functions or the inverse distance from the neighbors in each horizontal layer

Another useful contraint sets refractivity values to zero above a given height of the troposphere or impose that they follow an atmospheric standard profile.

Can we use meteorological databases to properly fill the A matrix?


0

TT NASWDPAPAPANN 1

00

wet

If a first guess solution N0 of refractivity is available, a solution can be provided by the damped least square method

P = vector with the weight of each SWD observation

P0 = error covariance matrix of the a priori solution N0

GPS tomography

GPS tomography + SAR interferometry

wetINSAR

GPS

INSAR

GPS

.N

B

A

A

0

SWD

SWD

Units: g/m3 Doy 230 (17/8) 11:30

wetMODIS

GPS

MODIS

GPS

.N

B

A

A

0

SWD

SWD

GPS tomography + MODIS

Radiosonde: Strong vertical variability

Tomography: Smoother and smaller vertical variability

A few references + contacts for data

P. Benevides, G. Nico, J. Catalão, and P. Miranda, “Bridging InSAR and PS Tomography: A New Differential Geometrical Constraint,” IEEE Transactions on Geoscience and Remote Sensing, 54(2), 697–702, 2016. P. Mateus, G. Nico, and J. Catalão, “Maps of PWV Temporal Changes by SAR Interferometry: A Study on the Properties of Atmosphere’s Temperature Profiles,” IEEE Geoscience and Remote Sensing Letters, 11(12), 2065–2069, 2014. P. Mateus, G. Nico, R. Tomé, J. Catalão, and P. Miranda, “Experimental Study on the Atmospheric Delay Based on GPS, SAR Interferometry, and Numerical Weather Model Data,” IEEE Transactions on Geoscience and Remote Sensing, 51(1), 6–11, 2013. P. Mateus, G. Nico, and J. Catalão, “Can spaceborne SAR interJ. Catalão, ferometry be used to study the temporal evolution of PWV?” Atmospheric Research, vol. 119, no. 0, pp. 70–80, 2013.

João Catalão, University of Lisbon, [email protected]

Giovanni Nico, CNR-IAC, [email protected]

mailto:[email protected]

mailto:[email protected]

Science

07 big skyearth_dlr_7_april_2016