The role of weather models in mitigation of tropspheric delay for SAR Interferometry.ppt

July 27, 2011 1

The role of numerical weather models (NWM) in mitigation of tropospheric delay for SAR Interferometry

Shizhuo Liu1, Agnes Mika2, Wenyu Gong3, Franz Meyer3, Ramon Hanssen1, Don Morton3 and Peter Webley3

1 Delft institute of Earth Observation and Space Systems (DEOS), the Netherlands2 BMT AGROSS, the Netherlands3 University of Alaska Fairbanks, United States

Department of Earth Observation and Space Systems (DEOS), Aerospace engineering

InSAR WRF

Delay observed by repeat-pass SAR Interferometry

07/27/11 2

€

Dp,qt1, t2 = Dp - Dq( )

t1- Dp - Dq( )

t2

• Temporal difference:

• Spatial difference:

€

DpDt = Dp

t1 - Dpt2

€

Dpq = Dp

t - Dqt

p q

t1 t2

observed delay: (spatio-temporal difference)

€

Dpt1

€

Dpt2

€

Dqt1

€

Dqt2

Spatial characteristics of delay in InSAR

07/27/11 3

8 ERS1/2 tandem interferograms over Groningen, the Netherlands

a b c d

e f g h

trend: c, e, h

local anomaly: a, g

trend+anomaly: b, d, f Trend + Variation (water vapor)

mm

Delay in mountainous regions

07/27/11 4

p

q

Atmospheric-only interferogram Hawaii topography

h

trend + variation+ vertical stratification

mmm

Studies of regions with different climates

07/27/11 5

Netherlands

Hawaii

Mexico City

Lake Moore, WA

Forecasting setup

• WRF (ver 3.1): includes non-hydrostatic dynamics;• Spatial domains: 27, 9, 3, 1 km ;• Spin-up time: 12-16 hours ;• Initial-boundary condition: FNL data (100 km, 6

hours);• Land topography data: SRTM (90 m);• Land-use data (MODIS 20-category);• Microphysics: Morrison 2-moment• Vertical levels: 28 (10 under 2km)

07/27/11 6

InSAR - WRFInSAR (35-day)

Hawaii (case No.1)

07/27/11 7

InSAR WRF InSAR - WRF

€

σinsar =19.4mm

s wrf = 23.8mm

s diff =11.4mm

WRF

Foster JGRL, vol. 33, 2006

mm

Hawaii (case No.2)

07/27/11 8

InSAR (35-day) WRF InSAR - WRF


€

σinsar =16.9mm

s wrf =14.2mm

s diff =10.5mm

Topography of Mexico City

07/27/11 9

m

Mexico City (case No.1)

07/27/11 10

InSAR (35-day) WRF InSAR-WRF

InSAR WRF InSAR-WRF

€

σinsar = 7.6mm

s wrf = 7.6mm

s diff = 4.6mm

mm

Mexico City (case No.2)

07/27/11 11



€

σinsar =11.7mm

s wrf = 9.5mm

s diff = 5.9mm

Inconsistency (case No.3)

07/27/11 12


€

σinsar = 8.0mm

s wrf = 7.4mm

s diff = 9.4mm

Cross-validation with MERIS

07/27/11 13

WRFInSAR MERIS

mm

Flat regions

07/27/11 14

Netherlands (9 cases)

07/27/11 15


€

σinsar = 6.9mm

s wrf = 4.0mm

s diff = 5.4mmNo.1

€

σinsar = 5.3mm

s wrf =1.7mm

s diff = 5.8mmNo.2

€

σinsar = 4.2mm

s wrf = 2.4mm

s diff = 5.5mmNo.3

mm

Netherlands

07/27/11 16

€

σinsar = 4.4mm

s wrf = 2.1mm

s diff = 4.7mm

€

σinsar = 5.0mm

s wrf = 2.8mm

s diff = 3.8mm

€

σinsar = 3.7mm

s wrf =1.1mm

s diff = 3.7mm


No.4

No.5

No.6

Netherlands

07/27/11 17

€

σinsar = 3.4mm

s wrf = 0.9mm

s diff = 3.4mm

€

σinsar = 3.8mm

s wrf = 2.1mm

s diff = 3.4mm

€

σinsar = 4.0mm

s wrf = 2.0mm

s diff = 3.7mm


No.7

No.8

No.9

Southwest Australia (5 cases)

07/27/11 18


€

σinsar =1.9mm

s wrf = 0.8mm

s diff =1.9mm

€

σinsar = 3.2mm

s wrf =1.9mm

s diff = 4.0mm

€

σinsar =1.8mm

s wrf = 0.9mm

s diff =1.9mm

No.1

No.2

No.3

Southwest Australia

07/27/11 19


€

σinsar = 5.4mm

s wrf = 3.6mm

s diff = 6.2mm

€

σinsar = 5.7mm

s wrf = 4.4mm

s diff = 7.8mm

No.4

No.5

Variograms of delay

07/27/11 20

Netherlands Australia

Distance [km]

InSAR

WRF

Results review

• In mountainous regions, topography dependent delay is well predicted by WRF in most cases. In these cases, 40% to 50% delay reduction can be achieved. However, its reliability is not 100% (80%)

• In flat regions, delay prediction by WRF is unrealistic and hardly bring significant delay reduction

• Moreover, the spatio-temporal delay variation predicted by WRF is underestimated at all spatial scales

07/27/11 21

Model tuning

• Initial boundary conditions: FNL -> ECMWF (50 km) ;

• Longer spin-up time: 12 hours -> 24 hours ;

• Vertical levels: 28 -> 40 (30 below ABL) ;

07/27/11 22

ECMWF versus FNL

07/27/11 23

Mexico City (case No.3) same model settings

Netherlands (case No.2)

07/27/11 24

InSAR ECMWF(WRF) InSAR-ECMWF

FNL(WRF) InSAR-FNL

Netherlands (case No.9)

07/27/11 25

InSAR ECMWF(WRF) InSAR-ECMWF

FNL(WRF) InSAR-FNL

Longer spin-up time and more vertical levels

07/27/11 26

Hawaii Mexico City

Netherlands Australia

InSAR

WRF tuned

WRF original

07/27/11 27

• NWM (numerical weather models) work for topography-dependent delay when topography variation is significant (> 2000 km)

- max 50% RMS reduction with ; - a reliability of 80% (improvement for 4 out of 5) ;• NWM fail for lateral variation of water vapor at small scales (< 50 km) - always underestimation ; - max 30% reduction ; - a poor reliability (improvement for 2 out of 14)

The low reliability of NWM for flat regions excludes it from operational tools for delay mitigation in SAR Interferometry. For mountainous

regions, delay correction could go wrong as well, users should be careful and critical

Conclusions

Thank you !

07/27/11 28

Is the weather model generally bad for delay prediction ?

07/27/11 29

MERIS WRFAbsolute delay:

Mean delay

07/27/11 30

Recommendations

• To improve the reliability of NWM it is necessary to include more meteorological observations with high spatial density

• Hindcasting using observations after satellite acquisitions would be also useful to constrain NWM aiming to increase its reliability

07/27/11 31

Tropospheric delay experienced by MW

07/27/11 32

hydrostatic (gas components) wet (water vapor) cloud droplets

€

Dpt1 = Nhydro

t1ò ds + Nwett1ò ds + Ndroplet

t1ò ds

absolute delay due to troposphere:

hydrostatic: long wavelength spatial gradient(pressure, temperature), i.e., trend

wet/cloud: significant spatial variation, i.e., local variation

Numerical forecasting for delay mitigation

07/27/11 33

Earth’s surface

NWMprediction

dh

z(h)

(T, e, P)

P: total air pressuree: water vapour pressureT: air temperature

x

y

wetchydrostati NN

T

ek

T

ek

T

PkN

23'21 ++=

Constants (Davis et al., 1985)

Refractivity

€

Dp,qt1 ,t2 is obtained by taking temporal

and spatial difference in sequence

Hawaii (case No.3)

07/27/11 34



€

σinsar =11.5mm

s wrf =10.2mm

s diff =14.9mm

Hawaii (case No.4)

07/27/11 35


InSAR WRF InSAR-WRF

€

σinsar =12.0mm

s wrf = 6.8mm

s diff =13.3mm

Technology

The role of weather models in mitigation of tropspheric delay for SAR Interferometry.ppt