Promoting Satellite Applications in the TPE Water and Energy Cycle Studies: Chance and Challenge Kun...

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Promoting Satellite Applications in the TPE Water and Energy Cycle Studies: Chan

ce and Challenge

Kun YangInstitute of Tibetan Plateau Research

Chinese Academy of Sciences

2nd Third Pole Environment Workshop, 26-28 October 2010, Kathmandu, Nepal

Nearly no stations in west and > 4800 m

CMA stations

Lack of data in TP studies

TP hydro-meteorological studies need

• Radiation

• Soil moisture

• Land fluxes

• Land surface temperature

• Water vapor

• Albedo

• ……Need satellite products!

Verify satellite products before applied

• Satellite products: usually developed, calibrated and validated in lowlands

• TP represents an extreme– High elevation– Low air mass– Low aerosol– ……

• TP provides an opportunity to validate a satellite product’s global applicability

Outline

• Assessment of RS/DA products

• Development of satellite products

• Application of satellite products

• Challenge of satellite applications

Outline

• Assessment of RS/DA products– Radiation budget: GEWEX-SRB and ISCCP-FD– Water vapor: AIRS and MODIS– Albedo: MODIS

Rad observations in Tibet

Yang et al. (2006 GRL)

• GEWEX-SRB V2.5 under-estimates ~50 Wm-2

• Partially due to neglect of elevation effects

SQHGerz

TTHD66 Ave

Obs V2.5Mean

Shortwave Rad: Obs. vs GEWEX-SRB v2.5

Mean bias in Rsw after accounting for elevation effects

Mean Bi as

GEWEX-SRB V2. 5 GEWEX-SRB V2. 81

(Yang et al., 2008 JGR)

Longwave Rad: Obs. vs ISCCP-FD

SQH Gerze Naqu Anduo NPAM Ave

Obs ISCCPDownward LW

Mean Bias: -37 Wm-2

SQH Gerze Naqu Anduo NPAM Ave

Obs ISCCPUpward LW

Mean Bias: -62 Wm-2

SQH Gerze Naqu D66 MS3637 Anduo TTH NPAM D110

Tsfc Obs Tsfc I SCCP

Mean Bi as: - 11K

Tai r Obs Tai r I SCCP

Mean Bi as: - 10K

PW Obs PW I SCCP

Mean Bi as: - 0. 33 cm

Yang et al. (2006 GRL)

Assessment of satellite water vapor

010002000300040005000600070008000

CAMPTibet

PBL Tower

Windprofiler

JICA GPS network

Assessment of MODIS Precipitable Water Vapor

Clear-sky Cloudy

All-sky

StatisticsBias=mean(MOD-GPS)

Std=Standard deviation

RMSE= Root mean square error

NRMSE=100*RMSE/mean(GPS)

Mean=mean(MOD)

MaxDiff=Max(abs(MOD-GPS))

24 GPS Receivers

(By Dr. Lv Ning )

For ground sites > 3000 mMOD-PWV assessment under clear-sky

acorrected HzPWPPPWPW ))/(exp(*)/(* 0

Before Optimization After OptimizationWe propose a formula to correct the large uncertainty for high-altitude regions

(By Dr. Lv Ning )

Qin et al. JMSJ, submitted

AIRS-PWV assessment under clear-sky

Outline

• Assessment of satellite products

• Development of satellite products– Soil moisture and land fluxes– Radiation

Radiation transferin canopy

Interception

Base flow

Infiltrate and Diffuse

Transpira-tion

vqTU lEH

Radiation transferin canopy

Interception

Base flow

Infiltrate and Diffuse

Transpira-tion

Minimization schemeF(Tbobs-Tbsim)

Tg, Tc, Wsfc

Vegetation layerSurface

Surface radiation Vegetation emission

MicrowaveTMI/AMSR/AMSR-E

(6.9/10.6 and 18.7 GHz)Microwave data assimilation

(Yang et al., 2007 JMSJ)

Validation at Tibet site

Surface soil water content

121 131 141 151 161 171 181 191 201 211

156 161 166 171

Observed LDAS SiB2

(Yang et al., 2007 JMSJ)

Assessment of soil moisture estimate at a Mongolian site (Yang et al., 2009 JHM)

LDAS NCEP

An example: 2003 Seasonality of distributed Bowen Ratio

Compared to NCEP, LDAS shows a reasonable seasonal march and regional contrast between eastern Tibet and western Tibet

Outline

• Application of satellite products– Tibet warming trend: elevation dependence– Atmospheric heating sources

• Challenge of satellite data applications

Backgrounds

They concluded “there exists a clear tendency of the surface temperature trends to increase generally as the

site elevation rises “ by analyzing station data from nearly 0 m to 4800 m.

This figure is adopted from Liu and Chen’s paper.

CMA stations

How warming rate depends on elevation?

500m increment

Warming rate

200m increment

Warming rate

Warming rate above 5000 m ?

Based on CMA data

Can MODIS data show the warming dependence on elevation?

MODIS station

Warming rate

Station

(dz=500 m)

Warming rate derived from MODIS data

(Qin et al., 2009)

Outline

• Challenge of satellite applications– Validation issue: Scale match– Application issue: Accuracy

Soil moisture validation: Scale-match validation

4500-4700 m

Cal/Val central Tibet site of SMOS and SMAP soil moisture 39 sets, starting on 30 July 2010

Each SMTMS station: 4 levels 0-3 cm, 20 cm, 40 cm, 100m

Radiation accuracy for glacier and snow surfaces

Palong No.4

Cumulative ablation (mm w.e.)

5/21 6/4 6/18 7/2 7/16 7/30 8/13 8/27

- 0. 2

Rn/ Total H/ Total l E/ Total G/ Total

1.27m / month

SE-Tibet mass and energy balance station

(Lu et al., 2010 JGR)

140 160 180 200 220 240 260Day of Year

SWD-obs SWD-LU

Under-estimated by 100 Wm-2 (from 240 to 140), due to the difficulty to discriminate cloud and snow surface

RS-estimated downward solar radiation

Summary

• Satellite data are very helpful for understanding the status, processes, and modeling in this region

• Need to improve the accuracy of satellite products and to develop new products for this region

Thank you for your attention！

Promoting Satellite Applications in the TPE Water and Energy Cycle Studies: Chance and Challenge Kun...

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