N. Pierdicca1, F. Rocca2, P. Basili3, S. Bonafoni3, G. Carlesimo4, D. Cimini5, P. Ciotti4, R. Ferretti4, F.S. Marzano1, V. Mattioli3, M. Montopoli4, R. Notarpietro6, D. Perissin7, E. Pichelli4, B. Rommen8, S. Reising9, G. Venuti2

1Sapienza Univ. of Rome, 2Politechnic of Milan, 3University of Perugia, 4Univ. of L'Aquila, 5IMAA-CNR, 6Politechnic of Turin, 7Chinese Univ. of Hong Kong, 8ESA/ESTEC9Colorado State University

SYNERGIC USE OF EO, NWP AND GROUND SYNERGIC USE OF EO, NWP AND GROUND BASED MEASUREMENTS FOR THE BASED MEASUREMENTS FOR THE MITIGATION OF VAPOUR ARTEFACTS IN MITIGATION OF VAPOUR ARTEFACTS IN SAR INTERFEROMETRYSAR INTERFEROMETRY

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IntroductionIntroduction SAR interferometric maps displays not only very small terrain motions, but

also the so called Atmospheric Phase Screen, i.e., the excess path delay mainly associated to the variable columnar amount of water vapor at the time of the passage, that delays the e.m. wave.

The atmospheric contribution to the interferograms is a major artifact to be corrected to get reliable motion estimates.

By PS multipass techniques and for long term displacements APS’s can be retrieved and could provide a valuable information on the atmospheric conditions at very good spatial resolution

Note that interferometric measurements are doubly differential as they are time differences referred to a single point in each image, that is constrained to be stationary in time.

Outline I will briefly review the METAWAVE (Mitigation of Electromagnetic

Transmission errors induced by Atmospheric Water Vapor Effects) ESA sponsored study.

We focus on the exercise to assimilate APS derived information on water vapor within Numerical Weather Prediction models

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METAWAVE projectMETAWAVE project

Funded by ESA/ESTEC

Objectives Use any additional information to correct, at list partially

(mitigate), the atmospheric WV artefacts in InSAR

Assess usefulness of InSAR for atmospheric applications and particularly weather prediction

Requirements for InSAR are very demanding: resolution order of 100 m

thematic accuracy order of mm ZWD (0.16 mm IWV)

Timeliness

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Project activitiesProject activities During the METAWAVE project several techniques have been

exploited to map path delay due to water vapor: Numerical Weather Prediction (NWP) models Earth Observation products Ground based microwave radiometers and GPS receivers Data interpolation and downscaling processing techniques Tomographic techniques

Two experiments were set up Rome area: regional scale applications and ground based microwave

radiometers (including Colorado State University CMR for 3-D tomography)

Como area: local scale applications by exploiting a dense network of GPS receivers

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Regional scale: Rome experiment setupRegional scale: Rome experiment setup

10 km

Radiosonde launch 8 launched from Sapienza, 6 successful 4 Daytime, 2 nighttime 75 from nearby operational station

5 km

Radiometers and LIDARS One 2-channel + one 4-channel LIDAR nearby Two other 4-channel to form a

triangle

Colosseo

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Local scale: Como experiment setupLocal scale: Como experiment setup

Regional operational GPS network

Intermediate GPS network

Local network

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From PS to APSFrom PS to APS

On land, numerous scatterers exist that maintain their scattering characteristics very stable in time (the Persistent Scatterers: PS).

Once these points are detected, their apparent motion can be recorded from the phase of the radar returns with millimeter precision.

The atmosphere, particularly due to the high water vapour spatial and temporal variability, introduces an unknown delay in the signal propagation (the Atmospheric Phase Screen: APS).

Under certain hypothesis and using a huge number of interferograms the apparent motion of the PS’s due to the APS‘s can be singled out wrt to real displacements and APS’s can be estimated

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Samples of APS’s of PS’s Samples of APS’s of PS’s

Winter Summer

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The APS and the atmosphereThe APS and the atmosphere Atmospheric path delay L can be converted into (columnar) Integrated

Water Vapor (IWV) and passed to the meteorologists.

InSAR could become a tool for high resolution water vapour retrieval and provide routinely water vapour maps to be assimilated into high resolution Numerical Weather Prediction (NWP) models.

A major difficulty is associated to the differential nature of the APS. APS's provide an insuperable high resolution mapping of the atmospheric path delay differences (i.e., in time and space) over stable PS’s, but they do not furnish absolute values.

This difficulty can be overcome by relating on external information providing suitable climatological values in order to provide the reference atmospheric signal associated to the master SAR image of the interferometric stack which cannot be known using SAR data only.

ionoraincloudvapourdry

S

ATMO LLLLLdsnL )1( 15.0 LIWV

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From APS to water vapourFrom APS to water vapour DInSAR interferometric phase contains DISPLacement phase and excess path

ATMOspheric delay L differentiated wrt time i and j and referred to point x0

For a steady (or known motion) surface, DISPL =0 (or known) and an image sequence provides the atmospheric delay (APS) in each point x wrt a unique master j=M, with arbitrary unknown consti=/4iM (x0):

L has a dry and a wet component, the latter proportional to the Integrated Water Vapour (IWV)

One could derive the “absolute” atmospheric delay at time i, and than the wet contribution proportional to IWV, assuming the master contribution is known by EXTernal sources (e.g., NWP, EO products):

The associated error variance is a combination of APS error and EXTernal source error, which can be significant:

L=

APS+ EXT

)()(4

)()()( 00 xxLxxx ijATMO

ijDISPL

ijijij

iMii constxLxLxAPS )()()(

iiEXTMii IWPconstxLxAPSxL )()()(

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From APS to water vapourFrom APS to water vapour Alternatively, by averaging many APS’s and corresponding EXTernal

information :

Again using an EXTernal source one can estimate the master contribution and than the actual “absolute” atmospheric delay from APS by:

Which is more reliably since the associated error variance depends on error variance of the mean L field provided by the EXTernal source, which is much smaller than that of an individual LEXT field:

L=

APS+ MeanAPS+

MeanEXT

There still an ambiguity due to const which can be removed by relying on EXTernal sources provided by NWP or independent EO products (e.g. MERIS).

Absolute path delay, or its wet component proportional to IWV, can be assimilated into NWP.

constxLxLMeanxAPSMean Mii )()]([)]([

constxLMeanxAPSMeanxAPSxL EXTiiii )]([)]([)()(

15.0 )4/( vapourLIWV

)()()( iMii constxLxLxAPS

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The water vapour maps from APSThe water vapour maps from APS

The local circulation in the urban area of Rome was studied using the high-resolution Mesoscale Model (MM5) and Weather Research and Forecasting (WRF) model.

These are well established fully compressible non-hydrostatic NWP models, reaching resolution of 1 km or higher.

A long sequence of ASAR images has been processed to derive APS maps, subsequently converted to absolute IWV maps by using as climatological background and bias (const) correction the MERIS IWV products.

Basically InSAR provides small scale components of the vapour field, whereas the large scale (including mean) of traditional meteo data is retained

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The water vapour maps from APSThe water vapour maps from APS

APS turned into absolute IWV (left panel), known except a constant, using MM5 as external background and then embedded into MM5 map (right panel)

Note higher map resolution inside the SAR frame

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Assimilation experimentAssimilation experiment

MM5: 4 domains 2 way nested, 1km resolution for inner domain, 33 levels

Initialization: warm start to assimilate APS derived IPWV on 3/10/2008 at 9:30 UTC, being the ECMWF initial conditions only available at synoptic hours

MM5 nested domains

Questions: Are the NWP outputs sensitive to the assimilation of APS small

scale features ? Is the effect positive ?

MERIS used as completely independent background to produce IWV from APS

Case study during the Metawave experiment in Rome to be compared to ground truth (October 3, 2008)

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Assimilation of InSAR derived IWVAssimilation of InSAR derived IWV

levels

vtop dQ

g

PPIWV sup

min))(())((2

1)()(

2

1 11 xHyOxHyxxBxxJ oTobTb

xb is the atmospheric “background” status

B and O are the covariance matrix of background field and observation errors

H(x) is the observation operator and Qv is the mixing ratio [g/kg]

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MM5 assimilation resultsMM5 assimilation results

Interpolated rain gauges

MM5 + APSMM5 without APS

MM5 (1 km) hourly rain rate field at 17:00 UTC compared to interpolated raingauge (right panel) at 20:00 UTC

SAR assimilation (top-right) doesn’t correct MM5 time anticipation (3h) of maximum rain.

It reduces overestimation (>18 mm/h vs observed 16 mm/h) produced by MM5 without SAR (top-left)

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WRF assimilation resultsWRF assimilation results

Interpolated raingauges

WRF + APSWRF without APS

WRF time anticipation only 1 hour in both cases

SAR assimilation (top-right) again better reproduces the observed rainrate values

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ConclusionsConclusions The METAWAVE project has been quickly reviewed A strategy to assimilate InSAR APS into NWP has been

illustrated It was shown that, using APS, NWP forecasts can be

improved, albeit slightly. More can be done: Running models at higher resolution to retain the high frequency

structures of InSAR data. Assimilating directly the path delay L instead of IWV Making the warm start more reliable by using local observations Avoiding the 3DVAR geostrophyc adjustment of the meteorological

fields to the large scale

Future availability of low revisit time SAR’s (e.g., Sentinel 1) could ring the change for the use of InSAR for meteorological purposes

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Triple collocationTriple collocation

xtx )( ytsy y

)( ztsz z

It is useful to validate measuring systems when none of them are immune from errors (apart biases).

Suppose three measurement systems X, Y, and Z measuring a true variable t (IWV in our case).

It is assumed that systems X and Y can resolve smaller scales than system Z by introducing r2 as the variance common to these smaller scales.

Variable t with variance 2 refers to the large scale features of the observed field and has

By collecting observations collocated in time and space, from a statistical analysis it is possible to estimate error variances and scaling factors

x, y , z are random zero-mean observation errors Errors have variances x

2, y2, z

2 sy, sz are scaling factors

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Triple collocation resultsTriple collocation results We applied the triple collocation techniques to data collected

during METAWAVE in Rome We identified GPS (X) and MERIS (Y) as the systems providing

IWV at the smaller scale, whereas MM5 (Z) is supposed to be less resolved

Assuming different hypothesis for r2 we found the following:

r2(cm2) sy sz x(cm) y(cm) z(cm) (cm)

0.001 0.990 0.865 0.099 0.062 0.165 0.438

0.002 0.990 0.869 0.104 0.070 0.161 0.436

0.003 0.990 0.874 0.109 0.077 0.157 0.435

0.004 0.990 0.879 0.113 0.083 0.153 0.434

0.005 0.990 0.883 0.118 0.089 0.149 0.433

0.010 0.990 0.908 0.137 0.113 0.127 0.427

MERIS exhibits smaller random error (≈0.8 mm IWV or 5 mm L) wrt

GPS (≈1.1 mm IWV, 7 mm L) and calibration factor similar to GPS (0.99)

MM5 shows larger error (≈ 1.5 mm) and underestimation of IWV (0.88)

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Project rationalesProject rationales

As for no sudden, long term, ground motion, multi-pass technique can mitigate path delay artifacts and provide APS to meteorologists, to be exploited for other applications.

For sudden, short term, ground motion and/or traditional (few passes) InSAR, two frameworks are identified: Regional scale applications

• Relaxed spatial resolution (goal 1 km), integration of many data sources by interpolation/downscaling, and NWP model assimilation

Local scale applications• Small coverage, support of ground based systems (e.g., GPS,

radiometers)

Regional scale Local scale

Long term

Averaging possible. Are APS useful for WV

mapping ?

Need for high-resolution nearby PS estimates

Short term Averaging not possible.Need for non-SAR APS estimates !

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Pisa

Livorno

Migliarino

Arno

Serchio

R1R2

r

PS

PSVS VS VS VS

SAR and Permanent Scatterers (PS)SAR and Permanent Scatterers (PS)

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PS displacement time seriesPS displacement time series

Non linear motion or noise or

atmospheric delay

residuals (APS)

Linear motion

[mm

]

1 rad≈5mm

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Atmosphere properties from APSAtmosphere properties from APS

Semivariogram of the APS’s derived from a huge number of interferograms, providing an insight into the spatial distribution of the water vapour at small scale (Ferretti et al.).

An example of using APS for atmospheric studies,