http://hydro.iis.u-tokyo.ac.jp/

GCOM-W1 now on the A-Train

GCOM-W1
Global Change Observation Mission-Water

Taikan Oki, K. Imaoka, and M. Kachi
JAXA/EORC (& IIS/The University of Tokyo)
Mini-Workshop on A-Train Science, March 8th, 2013
The University of Tokyo, Tokyo, Japan• Monitor “Global Change” by continuous (>10yr) and consistent observations.
• Estimate model parameters with satellite observations and products.
GCOM observation
GCOM-W

Satellite/sensor andalgorithm development

Model prediction

GCOM observation
GCOM-CFrequent and long-term(>10yer) globalobservation system
required for earth
environment changemonitoring and prediction

Atmos.

• Surface albedo• Snow ice• Cloud/ aerosol• SST/ LSTInputCarbon cycle

Comparison

Water cycle• Water vapor, cloud,precipitation• Soil moisture• Sea ice, snow
• SST, wind

Prediction results

Climate system model

Radiation budget• Vegetation cover• Primary production• Coastal environment

Cooperation with the climate
model research institutions

ICE

Improveaccuracy

Land Sea

Products orradiancewith• Improvement of parameterization
radiative
about radiation budget and carbon
transfer

cycle, etc. in climate prediction
model.
• Verification and improvement ofprediction of the earth environmentchange including the water cycleby comparison with the satellite
observation.

• Surface temperature• Sea level• Snow and sea ice area
• Environmental change• Rain/drought
distribution• Extreme weather
frequency• Land cover
Air temperature prediction

3 °C

Data
application

Knowledge

Futureprediction

Operational use for fishery, searoute, weather forecast, andclimate monitoring

Understanding of the climate
system and the climatechange (global warming)

Policy design throughprediction improvement2http://hydro.iis.u-tokyo.ac.jp/

3

GCOM-W1 Status 16:39 UT, May 17th, 2012: GCOM-W1 waslaunched at Tanegashima-island.
・・・(1)
 17:02 UT: Separated from H-IIA Rocket. ・・・(2) 17:05 UT: Deployed solar paddles. ・・・(3)Camera （H4）
Camera （H2）(2) Separationfrom H-IIA(1) Launch(3) Solar paddles deploymenthttp://hydro.iis.u-tokyo.ac.jp/

4GCOM-W1 Status
3:43 UT, May 18th, 2012: The antenna of AMSR2 wasdeployed.
・・・(4)
 13:34 UT: AMSR2 started rotation in 4 rpm.
・・・(5) 17:00 UT: The Critical Operation Phase was finished.
Camera （H6）(4) AMSR2 antenna deployment(5) AMSR2 rotation in 4 rpm

http://hydro.iis.u-tokyo.ac.jp/

530km

Step2
700km

670km

10km
Aura

5/28
Cloudsat

5/18 JST
Separation from the rocket

6/24

CALIPSO
Aqua

4km

GCOM-W1

Step3

Step1

6/3
6/29

16km

6Standard Products
ProductsRes.

Accuracy

Range

ReleaseStandardGoal

Global

5-50km

±1.5K

±1.5K

±1.0K (systematic)±0.3K (random)

2.7-340K

Integrated
water vapor

Global, overocean

15km±3.5kg/m2±3.5kg/m2±2.0 kg/m2

0-70kg/m2

Integrated cloudliquid water

Global, overocean

15km±0.10kg/ m2±0.05kg/ m2±0.02kg/ m2

0-1.0kg/m2

Precipitation

Global, except
cold latitude

15km

Ocean ±50%Land ±120%

Ocean ±50%Land ±120%

Ocean ±20%Land ±80%0-20mm h-1

Sea surface
temperature

Global, overocean

50km

±0.8℃

±0.5℃

±0.2℃

-2-35℃

Sea surface
wind speed

Global, overocean

15km±1.5m s-1

±1.0m s-1

±1.0m s-1

0-30m s-1

Sea iceconcentration

Polar region,over ocean

15km±10%±10%±5%0-100%

Snow depth

Land30km

±20cm

±20cm

±10cm

0-100 cm

Soil moisture

Land50km±10%±10%±5%0-40%

Brightness
Temperature

GEOAreas

Research Products: all-weather sea surface wind speed, sea ice moving
vector, sea ice thickness, land hydrological assimilated products, etc.
7Sea Surface Temperature

Sea Surface Wind Speed

Total Precipitable Water

Cloud Liquid Water

Monthly average (July 15 – August 14, 2012)8http://hydro.iis.u-tokyo.ac.jp/

9GCOM-W1 and A-Train
 Continuity from AMSR-E on Aqua
 Stable observation from the same orbit and local observing time.
 Direct cross-calibration with AMSR-E.

 Atmosphere Snow fall detection by combining CloudSat and AMSR2.
 Validation of cloud liquid water by MODIS, AMSR2, and CloudSat.
 Aerosol-Cloud-Rain science by MODIS, CloudSat, AMSR2, and Aura.

 Land Improvement of soil moisture retrieval by the help of MODIS NDVI.
 Snow cover mapping by MODIS and AMSR2.
 Land-atmosphere CO2 exchange by MODIS, AMSR2, and OCO-2.

 Ocean Validation of AMSR2 sea ice concentration by using MODIS, thin
ice detection by combining MODIS and AMSR2, and advanced sea
ice research by adding the information of surface roughness fromCALIPSO/CloudSat.

http://hydro.iis.u-tokyo.ac.jp/

10

Status of Aqua/AMSR-E
 Instrument characteristics
Multifrequency, dual polarization
microwave radiometer system developed
by JAXA.
Sister instrument of AMSR on ADEOS-II.
Large main reflector with diameter of 1.6m,
which realizes higher spatial resolution.
Channels at 6.9GHz for retrieving SST andsoil moisture content.
Afternoon (1:30pm) equatorial crossing
time, where only AMSR-E is observing.

http://aqua.nasa.gov/animations.html
Caution! Real AMSR-E rotates its
antenna counterclockwise about an axis
parallel to the geocentric direction.

 Mission status
After 9.5-years of observation onboard
Aqua satellite, observation halted inOctober 2011 due to the increase of motor
rotation torque.
Discussions and efforts are being made forpotential slow rotation for cross calibration.
Pre-launch AMSR-E in Tsukuba Space Centerhttp://hydro.iis.u-tokyo.ac.jp/

11

Direct comparison with AMSR-E Orbits and frequency channel sets are almost identical: no corrections areneeded for center frequency, incidence angle, and observing local time. Itenables cross calibration in wide range of Tbs over land, ice, and ocean.
 AMSR-E observations resumed from December 4, 2012 with 2rpm rotation
speed. Geolocation and Tbs are computed by modified software.
 Observation is sparse, but reasonable for global-scale comparison.
 Calibration improvement of 2rpm mode data is underway.

AMSR2 23V Descending

AMSR-E 2rpm 23V Descendinghttp://hydro.iis.u-tokyo.ac.jp/
Tb diff AMSR(2-E)

12

vs. AMSR-E 2rpm mode
Descending

+10K

-10K
Obs. Tb[K]http://hydro.iis.u-tokyo.ac.jp/
Tb diff AMSR(2-E)

13vs. AMSR-E 2rpm mode
Descending

+10K

-10K
Obs. Tb[K]13http://hydro.iis.u-tokyo.ac.jp/

14

GCOM-W1 and A-Train
 Continuity from AMSR-E on Aqua
 Stable observation from the same orbit and local observing time.
 Direct cross-calibration with AMSR-E.

 Atmosphere Snow fall detection by combining CloudSat and AMSR2.
 Validation of cloud liquid water by MODIS, AMSR2, and CloudSat.
 Aerosol-Cloud-Rain science by MODIS, CloudSat, AMSR2, and Aura.

 Land Improvement of soil moisture retrieval by the help of MODIS NDVI.
 Snow cover mapping by MODIS and AMSR2.
 Land-atmosphere CO2 exchange by MODIS, AMSR2, and OCO-2.

 Ocean Validation of AMSR2 sea ice concentration by using MODIS, thin
ice detection by combining MODIS and AMSR2, and advanced sea
ice research by adding the information of surface roughness fromCALIPSO/CloudSat.

http://hydro.iis.u-tokyo.ac.jp/

15Lookup Table based on MHS-CloudSat
Matchups – 4.5 years data, North America
40-65°N, 50-170°W
Land, T2m<0°C
Viewing angle ±10°

- EOF analysis to MHS data:- First 3 PCs – 88.6%, 8.2% and 2.1%of variances- PC3 had the best correlation Coeff to
CloudSat reflectivity- Lookup Table:- Project observed TBs to the first 3PCs
- In the 3-d EOF space, using MHSCloudSat matchups, compute theprobability of snowfall (CloudSat
near-surface dBZe>-15)- Lookup tables for different MHS
viewing angles- Retrieve snowfall probability using theabove lookup table; Use a Z-S relation,
we can retrieve snowfall rate as wellhttp://hydro.iis.u-tokyo.ac.jp/16

Snowfall detection result - C3VP Case – 2007.1.22
Map of Retrieved Snowfall Probability

Along CloudSat Track
CloudSat

Retrieved Snowfall Probability

Use CloudSat to train passive
microwave observations (done MHS,
working on AMSR-E/AMSR2) for
snowfall detection. This example
shows that the trained passive
microwave algorithm correctly
identified snowfall area, and ispotentially able to estimate snowfallintensity as well.

http://hydro.iis.u-tokyo.ac.jp/

17

GCOM-W1 and A-Train

 Continuity from AMSR-E on Aqua

 Stable observation from the same orbit and local observing time.
 Direct cross-calibration with AMSR-E.

 Atmosphere Snow fall detection by combining CloudSat and AMSR2.
 Validation of cloud liquid water by MODIS, AMSR2, and CloudSat.
 Aerosol-Cloud-Rain science by MODIS, CloudSat, AMSR2, and Aura.

 Land Improvement of soil moisture retrieval by the help of MODIS NDVI.
 Snow cover mapping by MODIS and AMSR2.
 Land-atmosphere CO2 exchange by MODIS, AMSR2, and OCO-2.

 Ocean Validation of AMSR2 sea ice concentration by using MODIS, thin ice
detection by combining MODIS and AMSR2, and advanced sea ice
research by adding the information of surface roughness fromCALIPSO/CloudSat.

Global Phenology Monitoring using Vegetation Optical Depth (VOD) from AMSR-E• Microwave provides enhanced temporal fidelity, especially over cloud dominated regions.
• VOD provides synergistic canopy biomass & water content information relative to optical-IR greenness data.
VOD & NDVI Phenology Metrics 2004-2007

MODIS LAI & AMSR-E VOD Correlation 2003-2008

Jones et al. 2012, Rem. Sens. Environ. 123

Jones et al. 2011, Rem. Sens. Environ. 155• Temperature constrained
ecoregions show earlier VOD startof season (SOS)• Water constrained areas show
VOD SOS delay relative to NDVI• Strong VOD agreementwith LAI, EVI & NDVI.

• VOD lag increases with woody
biomass coverR-value

-1.0

-.75

-.5-.25.25.5.75

1.0

AMSR-E VOD, MODIS NDVI & Tower C-flux Seasonality
Jones et al. 2012, Rem. Sens. Environ. 123

VODNDVI

GPPReco• VOD tracks canopy biomass change & seasonal shifts in land-atmosphere carbon fluxes.

Provided by Dr. John S. Kimball of the University of Montana

ClimaticConstraints
on PrimaryProduction

http://hydro.iis.u-tokyo.ac.jp/

19

GCOM-W1 and A-Train

 Continuity from AMSR-E on Aqua

 Stable observation from the same orbit and local observing time.
 Direct cross-calibration with AMSR-E.

 Atmosphere Snow fall detection by combining CloudSat and AMSR2.
 Validation of cloud liquid water by MODIS, AMSR2, and CloudSat.
 Aerosol-Cloud-Rain science by MODIS, CloudSat, AMSR2, and Aura.

 Land Improvement of soil moisture retrieval by the help of MODIS NDVI.
 Snow cover mapping by MODIS and AMSR2.
 Land-atmosphere CO2 exchange by MODIS, AMSR2, and OCO-2.

 Ocean Validation of AMSR2 sea ice concentration by using MODIS, thin
ice detection by combining MODIS and AMSR2, and advanced sea
ice research by adding the information of surface roughness fromCALIPSO/CloudSat.

http://hydro.iis.u-tokyo.ac.jp/20Comparing AMSR2 sea ice concentration with MODIS visible/IR images

http://hydro.iis.u-tokyo.ac.jp/21

GCOM-W1 and A-Train Ocean (cont’d) Detailed analysis of SST stratification by using MODIS skin
SST, AMSR2 subskin SST, and Argo/buoy/ship bulk SST, incomparison with AMSR2 wind, and MTSAT solar radiation
with a 1-D simple ocean model.
 Air-sea exchange of CO2 by using MODIS Chl-a, AMSR2 SSTand winds, and OCO-2 CO2.
 Studies on wind-wave field and surface roughness bycombining AMSR2 wind with data from CALIPSO/CloudSat.

http://hydro.iis.u-tokyo.ac.jp/

22

An example of statistical model for sea surface pCO2
derived from remotely-sensed SST, SSHA, Chl-a, and SSS

Liu et al. (20**)

http://hydro.iis.u-tokyo.ac.jp/

23Summary GCOM-W1 and AMSR2 are in good shape.
 GCOM-W1 “SHIZUKU”
 Launched on May 18, 2012 (JST).
 Joined A-Train constellation.
 Completed the initial checkout phase on August 10, 2012.

 AMSR2
 Started continuous observation from July 3, 2012 (JST).
 Calibration and validation activities are ongoing.
 Preliminary products were already made available to PIs andcollaborating agencies.
 Product release to public will be 8-months and 12-months after
launch for brightness temperatures and geophysical parameters.
 GCOM Data Providing Service at http://gcom-w1.jaxa.jp.
 Seeking various opportunities on the A-Train

 Will participate in GPM constellation.

A-Train
GCOM-W1

Thank You!

http://hydro.iis.u-tokyo.ac.jp/25