1 / 28

PHERPP Meeting 　（ 3-5December 2007) WMO (Geneva)

PHERPP Meeting 　（ 3-5December 2007) WMO (Geneva). A sian P recipitation -- H ighly R esolved O bservational D ata I ntegration T owards E valuation of the Water Resources ( APHRODITE ’s Water Resources). Global Environmental Research Fund by the Ministry of Environment, Japan

lambdin
Download Presentation

PHERPP Meeting 　（ 3-5December 2007) WMO (Geneva)

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. PHERPP Meeting　（3-5December 2007) WMO (Geneva) Asian Precipitation -- Highly Resolved Observational Data IntegrationTowards Evaluation of the Water Resources (APHRODITE’s Water Resources) Global Environmental Research Fund by the Ministry of Environment, Japan (Project B062 Approved as a three year project; May 2006 – March 2009) Interim evaluation – A (it may continue 2 more years) • Akiyo Yatagai１, Akio Kitoh２, Kenji Kamiguchi２, Osamu Arakawa2 , • Haruko Kawamoto１, Hisahiro Takashima１, Tsugihiro Watanabe１, • Jumpei Kubota１, Makoto Taniguchi １, Shinjiro Kanae3 • Research Institute for Humanity and Nature (RIHN), Kyoto, Japan • Meteorological Research Institute (MRI), Japan Meteorological Agency (JMA), Tsukuba, Japan • Institute for Industrial Science, University of Tokyo, Japan

  2. Background Regional impact of the global warming is argued by using high-resolution climate models. Do we have sufficient data? • Model validation: High resolution・Accuracy（Quantity) • Statistical downscaling：Long-term data • Climate impact on hydrological resources: Grid precipitation data • Extreme events：High resolution・Accuracy・Long-term data • Hydrological resources over the mountains: grid precipitation data＋snow accumulation＋temperature

  3. Grid precipitation data used for model validation APHRODITE Asia 0.5 1978-2004

  4. Can we use satellite-based daily precipitation data to discuss extreme events? Daily Precipitation for June to August, 2000 at Kagoshima (mm/day) 赤: Radar-AMeDAS 青: GPCP-1DD 緑: TRMM3B42 Satellite-based “observation” underestimates Heavy precipitation compared to rain-gauge-based Observation (Radar-AMeDAS) Satellite based rainfall estimate is not sufficient to validate extreme precipitation events simulated by high resolution models

  5. East Asia rain gauge based analysis of daily precipitation • Daily grid precipitation data • - 0.5 degree • 1978-2003 (Jul.) • Information of “Number of Gauges” • Available fromhttp://www.chikyu.ac.jp/precip/data/EAG.htm • Reference • Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu and S. Yang (2006) JHM (in press)

  6. Strategy to Define Analysis of Daily Precipitation Step 1: Constructing analyzed fields of daily precipitation climatology; Step 2: Computing analyzed fields of ratio of daily observation to daily climatology for the target day; Step 3: Defining analysis of daily precipitation by multiplying the daily climatology with the daily ratio; (Xie et al., 2007)

  7. Purpose of this study • Based on the Xie et al.(2007) algorithm, • collecting more rain-gauge data over Asia, • discussing with potential users, • We create daily grid precipitation data over Asia (mainly gauge-based)

  8. Time series Data Collection Blue：GTS Black：Pre-compiled dataset Red：Individual collection

  9. Small problems in EA V0409 original0.05 deg anal ratioclimatology 0 10 20 30 40 500 1000 2000 3000 0 5000 10000 0 1 10 100 Add Off-line Data Abnormal large values are included over Philippines and South Asia. Some suspicious values are included in the GTS data

  10. Input more rain gauges GTS 920 station V0409 1400 stations V0707 6030 stations

  11. The latest version of daily grid precipitation over the Monsoon Asia (July 1998)

  12. User Email Address Data Download

  13. TRMM Precipitation Radar (PR) Ver.5 Monthly mean rain rate Himalayas China India SE Asia

  14. Himalayas China GAME enhanced EAanal/ GAME EA analysis PR V5 PR V6 SE Asia India

  15. Himalayas China PR V5 India SE Asia PR V5 Rain-gauge (GAME Enhanced) Rain-gauge (GAME Enhanced)

  16. East Asia Analysis Climatology (0.05deg)

  17. Information to the modelers Rain-gauge 0.05deg clim 20km model GPCP1DD Yatagai, Xie and Kitoh (2005)

  18. Trrkey（DSI） IRANIRIMO（気象研究所） INDIAタミールナドゥ農業大学 PHILIPPINESPAGASA(気象庁） Discussion with local policy makers; Data collection and Capacity Building TURKEY トルコ水利庁（DSI）講演 データ提供（or discount）の見返り：共同研究(論文)・指導しに行く・若い学生を学ばせる

  19. Russia East Asia Middle East Monsoon Asia

  20. Middle East version and model validation mm/mon mm/day Rain gauge-based climatology(upper） TRMM/PR composite（lower） For December Yatagai, Kimura, Kitoh, Watanabe (2006) Proceedings for International Symposium on Water and Land Management for Sustainable Irrigated Agriculture, Turkey Yatagai, Xie and Alpert (2007) Kitoh, Yatagai and Alpert (2007)

  21. Number of data is decreasing…?

  22. Russia and Central Asia Grid product Observation of former USSR • NCDC dataset 9813 is useful. • Collections of rain gauges (Groisman and Rankova, 2001; Bogdanova et al., 2002)

  23. 気候値差し替え実験 データ作成における、基準とする気候値の影響を調べるため、モデルの気候値に 差し替えて解析を行った EA_clim model_clim Model_clim – EA_clim 解析に使う 参照気候値 解析値の気候値 ある日の降水量 • 作成されたデータの気候値は、必ずしも元の気候値に似るわけではない • モデル気候値で解析したものの方が、元の気候値の水平解像度が高いため、空間的に細かい構造を持っている。 • 基準となる気候値の空間分布が大切であることが分かる

  24. Intercomparison with other data

  25. ブラウザで年月日を指定するとGoogle Earthが立ち上がり、日降水量を地図表示する。 EAだけではなく、他の降水データや再解析による大気データの重ね合わせの描画も可能

  26. Summary • We upgraded EA analysis (Xie et al., 2007) by inputting more rain-gauges over the monsoon Asia (1978-2003). • We created daily grid precipitation data over the Middle East. • QC (using TRMM etc) is on the way. • We are collecting observation data from Russia as well as the Central Asia. • We start validating of model results as well as satellite products. • We are going to create long-term data (1961-, 1930s-) • 0.25 degree products will be created in the next version. • Collaborations are welcome!

  27. Publications • Yatagai, A. (2007): Interannual variation of summertime precipitation over the Qilian Mountains in Northwest China, Bulletin of Glaciological Research, 24, 1-11. • Yatagai, A. and Xie, P, 2006: Utilization of a rain-gauge-based daily precipitation dataset over Asia for validation of precipitation derived from TRMM/PR and JRA-25. SPIE 0604-53, doi:10.1117/12.723829. • Xie, P., A. Yatagai, M. Chen, T. Hayasaka, Y. Fukushima, C. Liu and Y. Song (2007): A Gauge-Based Analysis of Daily Precipitation over East Asia. J. Hydrometeor. 8, 607-627. • Yatagai, A., N. Yamazaki and T. Kurino (2007): The products and validation of GAME reanalysis and JRA-25 Part 1: Surface Fluxes, Hydrological Processes, 21, 2061-2072. • Kitoh, A. and S. Kusunoki (2007): East Asian summer monsoon simulation by a 20-km mesh AGCM. Climate Dynamics (in press). • Yatagai, A., P. Xie and P. Alpert (2007): Development of a daily grid precipitation data set: Toward evaluation of global warming effects on water resources in the East Mediterranean, Advance in Geophysics (submitted, after 1st review). • Geethalakshmi, V., A. Yatagai, K. Palanisamy and C. Umetsu (2007): Impact of ENSO and the Indian Ocean Dipole on the Northeast Monsoon Rainfall of Tamil Nadu state in India. Hydrological Processes (Submitted, after 2nd review). • Kitoh, A.,A. Yatagai, P. Alpert (2007): First evidence that the ancient “Fertile Crescent” will disappear in this century, Suisui Hydrological Research Letter (submitted).

  28. Daily precipitation Daily precipitation Daily precipitation Daily precipitation Daily precipitation Figure 6: Derivation of probability density function (PDF) of the daily precipitation. The histogram shows the original data, which is fitted by a Gamma distribution function. Figure 6: Derivation of probability density function (PDF) of the daily precipitation. The histogram shows the original data, which is fitted by a Gamma distribution function. Figure 6: Derivation of probability density function (PDF) of the daily precipitation. The histogram shows the original data, which is fitted by a Gamma distribution function. Figure 6: Derivation of probability density function (PDF) of the daily precipitation. The histogram shows the original data, which is fitted by a Gamma distribution function. Figure 6: Derivation of probability density function (PDF) of the daily precipitation. The histogram shows the original data, which is fitted by a Gamma distribution function. 研究プロジェクトの枠組み サブテーマ１（総合地球環境学研究所） 日降水量グリッドデータの作成 サブテーマ２（気象研究所） 気候モデルの日降水量の検証 データの収集 • データセット公開 • 温暖化実験モデルの降水データへの信頼性情報の付加 • 地域の水資源政策への提言 気候モデルの日降水量の検証 グリッドデータ作成 日降水量気候値 • 雨量計データの収集 • 山岳降水を表現した気候値の作製 • 衛星を利用したアルゴリズムの開発 • 捕捉率の補正 • ・各地域の水問題・水資源利用情報、温暖化影響評価 日降水グリッド値 • 高解像度モデルの降水量検証 • 気候モデルの降水量の変動特性の検証 • 極値の出現特性の検証 • 観測の空白域のモデルによる推定 観測の空白域の降水特性

More Related