Monitoring of the W ater Cycle and Clim ate Variation by the Earth Observation Satellites Misako KACHI Manager for GCOM-W Research/ GPM Applications Earth Observation Research Center (EORC) Japan Aerospace Exploration Agency (JAXA) Dec. 4, 2015@COP21 Japan Pavilion Side Event, Paris 2
Role of Observation from “Space” “Observations” have played important role in addressing climate change issues Capturing current status of the Earth and monitoring its variations Contributing to climate models Satellite will provide reference to evaluate climate models and their forecasts/ predictions It also improve precipitation process in the model Why from space? Global (wide-area), repetitive, and uniform observation Information can be shared simultaneously by broadcasting. Robust against disasters (stable), and homogeneous all over the world . 3
JAXA’s Current and High Resolution GOSAT-2 W ide Sw ath Future Satellite & ( JFY2 0 1 7 ) ( Optical) ( JFY2 0 1 9 ( TBD) ) Sensor Activities Earth CARE/ CPR GCOM-C ( JFY2 0 1 7 ) DAI CHI -2 ( JFY2 0 1 6 ) ( ALOS-2 ) ( 2 0 1 4 ) DAI CHI ( ALOS) ( JFY2 0 0 5 ) GPM/ DPR ( 2 0 1 4 ) Aqua/AMSR-E ( JFY2 0 0 2 ) ADEOS- Ⅱ ( JFY2 0 0 2 ) TRMM/PR JERS-1 SHI ZUKU ( JFY1 9 9 7 ) MOS ( JFY1 9 9 1 ) ( GCOM-W ) I BUKI ( GOSAT) ( 2 0 1 2 ) ( JFY1 9 8 6 ) ( 2 0 0 9 ) 4
Importance of Water Cycle Precip. over W ater land Vapor Snow Rain Snow Depth Cloud Liquid W ater Land Sea Radiation Cover Surf. Budget W ind Precip. over ocean Sea Soil Surface Sea I ce m oisture Tem p. (Oki and Kanae, Science , 2006) GCOM GCOM GPM -W -C 5
Example(1) El Niño/ La Niña Monitor Sea Surface Temperature (SST) SST differences from Climatology (anomaly) Variation of SST anomaly of NINO.3 6
How El Niño Developed? Sea Surface Temperature (SST) SST differences from Climatology (anomaly) AMSR2 SST animation from Jul, 2012 to Nov. 2015 7
El Niño Changes Rainfall Distribution Drought in I ndonesia causes Severe Forest Fire 8
Example(2) Global Rainfall Monitor Rapidly changing precipitation phenomena need frequent observations JAXA provides hourly rainfall product in 0.1x0.1deg lat/ lon grid in global (60N-60S) by merging multi-satellites’ microwave radiometers and geostationary infrared cloud moving vector information Processed and distributed in near real time basis (about 4- hour after observations) Example of application will be shown by Mr. Konami. Cham pi Patricia Olaf http:/ / sharaku.eorc.jaxa.jp/ GSMaP 9
From 4-hour Delay to Realtime To reduce latency of GSMaP to respond users Using data that is available within 0.5-hour (GMI, AMSR2 direct receiving data, AMSU direct receiving data and MTSAT) to produce GSMaP at 0.5-hr before. Applying 0.5-hour forward extrapolation (future direction) by cloud moving vector to produce GSMaP at current hour (GSMaP_NOW). Area: Geostationary satellite Himawari area Grid size: 0.1-degree Average: Hourly Update: every 30-min Data is freely available after simple registration http:/ / sharaku.eorc.jaxa.jp/ GSMaP_ NOW 10
Example (3) Drought Monitoring June 1-15, 2012 June 16-31, 2012 May 1-15, 2012 May 16-31, 2012 August 1-15, 2012 August 16-31, 2012 July 1-15, 2012 July 16-31, 2012 WindSat AMSR2 October 1-15, 2012 October 16-31, 2012 September 1-15, 2012 September 16-30, 2012 Soil Moisture Snow Area Dry Snow 300 200 150 120 100 90 70 30 0 [%] Anom aly Ratio by MODI S Wet W et Snow by Microw ave Dry 11
Example (4) Sea Ice Monitoring Sea Ice is one of indicator of climate status Shrinkage of sea ice extent in the Arctic is one of the major climate change issues Sea Ice Concentration 1980’s 1990’s 2000’s 2012 2007 Previous record ： 4.25 mil. sq km (Sep..24 2007) AMSR2 Sep.16,2012 Arctic Sea Ice Extent Minimum record SIE ： 3.49 mil. km 2 (Sep. 16, 2012) 12
More examples will be shown by Prof. Enomoto’s presentation 13
Constrains and Conditions Satellite data is unique, but not enough necessary to be combined with other data necessary to be transformed into information easy to understand. Timely delivery and continuity of satellite data are not guaranteed Just a single satellite can not provide timely delivery of data in response to disasters Continuous observation is essential for climate monitoring In order to overcome these constraints International collaborations between space agencies and various discipline GEO, CEOS, UNESCO, ADB, JICA, local agencies, ... Combine the in-situ data with satellite data, and utilize numerical models and forecasts 14
Summary Combination of multi-satellite data, ground observation, and numerical models provides us more “information” than single observation. Assuring continuity of observations, both from space and ground, is essential to archive data for corresponding to climate change issues. To reduce impacts and risks of extreme weather events and related water hazards, more collaboration among different disciplines are needed. 15
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