9 Nakamura

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Japan’s GPM Science Team GPM Workshop Marriot Hotel Greenblet, 15 June 2004 Kenji Nakamura HyARC/Nagoya University EORC/JAXA

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GPM is ranked among future missions in the Roadmap of EO scenario in JAXA. GPM science team was established in August 2003. Preliminary evaluation has successfully passed in NASDA (JAXA) in August 2003. GPM/DPR RI (Research Invitation) started in January 2004. T. Oki: land surface, T. Iguchi: DPR algorithm, K. Nakamura: GV Several international workshops were held, e.g.: GPM Asia Workshop (2 and 3 February 2004) organized by T. Iguchi and R. Oki with auspices of CRL and JAXA. Workshop on the Satellite Data Utilization for Water Cycle in Asia (3 and 4 February 2004) organized by K. Nakamura and R. Oki with auspices of MEXT and JAXA. DPR Phase B study completed. The fourth TRMM RA activities has started in April 2004. Two science team meetings in August 2003 and February 2004. One of the CREST (Core Research for Evolutional Science and Technology by the Japan Science and Technology Agency (JST) ) projects: global rain mapping project led by Prof. K. Okamoto, Osaka Prefectural University. (from November 2002) Microwave rain retrieval, ground experiments at Okinawa, Japan, and others.

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IFNET: flood warming project: prototype has started. JMA: TMI data assimilation. Post-GAME (GEWEX Asian Monsoon Experiment) discussion, CEOP (Coordinated Enhanced Observing Period) under WCRP/GEWEX with CLIVAR Koike project (Water Cycle Informatics) Water Cycle Initiative in Japan GEOSS activity

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DPR/GPM science team (31 members + JAXA) K. Nakamura (Nagoya Univ., EORC/JAXA): chair T. Iguchi (NiCT): sub-chair R. Oki (JAXA): project Algorithm group: chaired by T. Iguchi (JAXA) Cal/Val: by S. Shimizu (JAXA) Data: by T. Takeshima (JAXA) Engineering: by K. Furukawa (JAXA) Science and applications: all The first meeting: 26 August 2003 The second meeting: 13 February 2004

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DPR/GPM science team (31 members + JAXA) K. Aonashi (MRI): microwave retrieval Y. Asuma (Hokkaido Univ.): cold region weather system and snow J. Awaka (Hokkaido Tokai Univ.): DPR algorithm T. Iguchi (NiCT): DPR algorithm T. Inoue (JMA): cloud observation K. Iwanani (NIESDP): radar observation H. Uyeda (Nagoya Univ.): Baiu/Meiyu and meso-precipitation system T. Ushio (Osaka Pref. Univ.): DPR observation, lightning H. Ohno (Agriculture): Application to agriculture K. Okamoto (Osaka Pref. Univ.): radar algorithm T. Oki (Univ. Tokyo): hydrology, global water resources T. Ose (JMA): model evaluation Z. Kawasaki (Osaka Univ.): lightning R. Kikuchi: flood warming

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DPR/GPM science team (cont’ed) A. Kitoh (MRI): climate modelling M. Kubota (Tokai Univ.): oceanography T. Koike (Univ. Tokyo): hydrology, water problem, GEO T. Kozu (Shimane Univ.): DPR design, DPR algorithm T. Satoh (Kyoto Univ.): radar engineering S. Shige (Osaka Pref. Univ.): latent heat profile retrieval A. Sumi (Univ. Tokyo): climate modelling N. Takahashi (NiCT): radar rain retrieval Y. Takayabu (Univ Tokyo): tropical meteorology Y. Takeuchi (JMA): weather modelling A. Nakakita (Kyoto Univ.): radar rain and water resources T. Nakazawa (MRI): tropical meteorology and typhoon H. Hanado (NiCT): ground validation M. Fujita (Tokyo Sci. Tech. Univ.): rain retrieval Y. Fujiyoshi (Hokkaido Univ.): snow storms M. Murakami (MRI): cloud physics

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------ More involvement of “operational people”: partly done More involvement of “snow people”: partly done ------ Objectives Make current progress of GPM/DPR be known. Explore new sciences and/or applications. Evaluate the activities. Respond to various activities such as GDWaG and GV. Discussion topics Overall strategy of Japan’s activity Science and applications DPR specification DPR algorithm development GV methods

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Agenda of the second meeting on 13 February 2004 Structure and objective of the science team (K. Nakamura) DPR/GPM project (M. Kojima, JAXA) Mission requirements (K. Furukawa, R. Oki, S. Satoh, JAXA) DPR development (S. Satoh, JAXA) Antenna design for DPR (H. Hanado, CRL) External calibration of DPR (N. Takahashi, CRL) Mission operating system (T. Takeshima, JAXA) Science data production and supporting system (S.Shimizu, JAXA) DPR algorithms (T. Iguchi, CRL) Validation of DPR (K. Nakamura) Surface observation by DPR (S. Seto, CRL) Data application in JMA for weather forecast (Y. Takeuchi, JMA) IFNET project (R.Kikuchi) Discussion Project introduction/Instrument and system development/Algorithm development including cal/val.

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Japan’s strategy Japan’s contribution to GPM is via DPR observation. The core satellite is the hub of GPM TRMM: PR and TMI comparison improved the accuracy. WCRP Satellite WG emphasizes cross-calibration, overlapping. Japan will contribute for the full utilization of DPR. Japan’s GV will put emphasis on the DPR validation/evaluation. Full utilization of TRMM GV experiences Space oriented verification Radiometrically consistent way Assumed precipitation system model approprieteness DPR, GMI (+AMSU type sounder?) Full utilization of quasi-simultaneous observation Direct-mirror echo, SRT (Ku, Ka, delta), …

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TRMM heritage/experience Simple comparison is never enough. Precise and comprehensive precipitation system measurement is required Microwave radiometer, DPR, … Algorithm specific validation, dependent on each algorithm DPR Level 2 product: a few number instead of many products may be preferable. Maybe like 2A25+21+23: Ka-Ku wide swath with narrow swath flag Spatial/temporal variation of rain relevant to PR-ground instantaneous comparison

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Algorithm development 　TRMM PR has made a new paradigm emerged for a single-wavelength radar Improved accuracy of measurement: pattern, qualitatively  quantitatively Accurate measurement may be more required from sciences than from applications. Z-R method with SRT: N+1 data Originally, attenuating radiowave frequency is a compromise, but it enabled SRT technique applicable. DPR Data: 2N Zm's at 2N range bins + two Zs's Products: 2N x No and Do How to use other two data --> determining two parameters along each ray Calibration correction Water vapor, cloud attenuation correction Beam filling correction Others

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Verification of the algorithm Simulation needs basic data/test bed data NiCT’s Okinawa Observatory: Prof. Uyeda’s group in HyARC/NU will make data set for interesting events. This could be a prototype. Experiments Upward Ka, Ka and X-band radars with other instrument – the first experiment has just finished. Downward Ka and Ku band radar -- needs an airbonre system Testing algorithm Ku-band only algorithm Ka-band only algorithm PR algorithm verification by DPR result --> reanalysis of TRMM PR products

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Accuracy is required from climate research rather than from flood prediction and river control. El Nino/La Nina precipitation Detection of global precipitation change associated with global warming Error covariance  data assimilation forecast score 10 % is required from climate change detection. Snow rate retrieval accuracy is crucial for DPR/GPM Direct comparison with climate model results

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Summary of the workshop on the Satellite Data Utilization for Water Cycle in Asia Participations and presentations from: Indonesia, Malaysia, Philippine, Mongolia, Nepal, Vietnam, Mynmar, Lao P.D.R., Bangladesh, Korea, Japan Topics referred: Flood (flash/slow), El Nino/Na Nina, Drought, Forest fire Snow melting, Glacier Long-term/shor-term Short: Flood, typhoon Long: ENSO, drought, snow melting, glacier Satellite data utilization Short-term weather forecast Long-term monitoring: not much mentioned. However, maybe the most important impact of satellite observation  Data: Price, Processing skill, User friendly format

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Nowcasting -- monitoring Seasonal prediction statistical method -- need long-term data dynamical -- need deep science ----- What is the current utilization of satellite data on water cycle? --- Done at least partly. What is the expectation for the future satellite data on water cycle? --- Conceptually and partly done. The utilization for water resources is poorly described. Some aspects of the wide water cycle issues are described. What is the possible partnership for satellite data utilization? --- Yet.

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Global Cloud Resolving Model: NICAM (Nonhydrostatic ICosahedral Atmospheric Model) Satoh,M., Tomita,H., Nasuno,T., Iga,S.-I., Miura,H. (Frontier Research System for Global Change) Use of the Earth Simulator Δx=3.5km grid interval using the icosahedral grid Nonhydrostatic model with explicit cloud physics The Earth Simulator Icosahdral grid

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Roles of satellite data in model development For validation of numerical models Comparison of Hierarchical structure of cloud cluster Determination of parameters in cloud microphysics models： cloud amount, precipitation, cloud water, cloud ice Diurnal cycle Analyze model results in the same method as satellite data Improvement of conventional cloud parameterization for GCM Problems for modelers Understanding of algorithm of satellite data Definition of convective clouds vs. stratiform clouds Cloud water vs .cloud ice：what is the property of cloud ice Comparison method Statistical method/Deterministic method: short range NWP in local area Access to enormous data Collaboration between modelers and satellite group Use of TRMM data for model validation, before waiting for GPM To organize data user group

PART I. Introduction(Mirror Image) : 

PART I. Introduction (Mirror Image) TRMM PR observed rainfall reflectivity cross section Schematic diagram of the mirror image

PART II. Characteristics of Mirror Image(Comparison of mirror image over ocean and land surface): 

PART II. Characteristics of Mirror Image (Comparison of mirror image over ocean and land surface)

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PART II. Characteristics of Mirror Image (Incidence angle effect)

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Characteristics of Mirror Image (Rain Type and Rain Rate)

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(Storm Height Effect)-----Continue

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Comparison Results (Estimation Method) PIA Zmm Ze Zd 2Aj-n As-j Zem Comparison 1C21 2A25 2Aj-n Hj Rain Top Path Integrated Attenuation (PIA) Surface

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Comparison Results (Averaged Profiles) At 3 km Zmm<Zem Zmm>Zem Zmm<Zem Zmm>Zem Shallower system Deeper system

PART IV. Mirror Image of Ka-band Radar(The Contoured Frequency by Altitude Diagrams): 

PART IV. Mirror Image of Ka-band Radar (The Contoured Frequency by Altitude Diagrams)

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PART IV. Mirror Image of Ka-band Radar (Histograms and Available Mirror image Count)

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PART IV. Mirror Image of Ka-band Radar (Model Results) PR case 35 GHz radar case

Summary: 

Summary The Ka-band radar expands the dynamic range of the MI method from 4 - 30 mm/h (25 percent) to 0.6 - 30 mm/h. (about 75 percent) The rainge where Ka-band rain estimate (attenuation estimate) and Ku-band rain estimate overlap is very narrow.