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Akiyo yatagai
- 1. QUANTITATIVE ESTIMATION OF OROGRAPHICPRECIPITATION OVER THE HIMALAYAS BY USING APHRODITE’S DENSE NETWORK OF RAIN-GAUGES AND TRMM/PR Akiyo Yatagai （Solar-Terrestrial Environment Laboratory, Nagoya University, Japan)
- 2. ASIAN PRECIPITATION --HIGHLY RESOLVED OBSERVATIONAL DATA INTEGRATION TOWARDS EVALUATION OF THE WATER RESOURCES (APHRODITE’S WATER RESOURCES) Project Term: I) May 2006 – March 2009, II) April 2009 – March 2011 Yatagai et al. (2009, SOLA), Yatagai et al. (2012, BAMS)
- 3. APHRODITE’S ANALYSIS FLOW Yatagaiet al. (2009, SOLA), Yatagai et al. (2012, BAMS)
- 4. FOR FURTHER IMPROVEMENT Input daily precipitation data Input more data Improve quality control (QC) algorithm Algorithm Analysis method (interpolation) Analysis strategy (ratio/anomaly interpolation) Better climatology Rain-gauge climatology GIS technique for ungauged region (Daly et al., 1994; Hijiman et al.,2001) Rain-gauge + TRMM/PR
- 5. INPUT MORE RAINGAUGES APHRODITE’s water resources project Russia Yatagai et al. (2009)
- 6. INFORMATION TO THEMODELERS Yatagai, Xie and Kitoh (2005) GPCP1DD Rain-gauge 0.05deg clim 20km model
- 7. Yatagai et al.(2005) Xie et al. (2007) based on GHCN and PRISM (China)
- 8.
- 9. 0.05 degree Yatagai andKawamoto (2008)
- 10. METHOD (STRATEGY) CompareTRMM/PR with rain-gauge observation as a climatology (10 year average) Compare at 0.05 degree grid box (5.5 km square box) Concerning about precipitation type (rain/snow) →compare according to month →compare according to the altitude
- 11. MEAN PRECIPITATION ANDDIFFERENCE Rain Gauge PR (NSR) Difference Diff/RG Jan 18.5 13.1 -5.4 0.29 Feb 16.6 24.4 +7.8 0.47 Mar 24.1 30.9 +6.8 0.28 April 28.8 48.9 +20.0 0.69 May 58.3 88.4 +30.0 0.51 Jun 160.7 148.7 -12.0 0.07 Jul 312.0 209.3 -102.7 0.33 Aug 288.2 180.0 -108.6 0.38 Sep 184.8 133.7 -51.1 0.28 Oct 59.2 49.2 -10.0 0.17 Nov 10.8 8.6 -1.9 0.18 Dec 9.3 6.7 -2.7 0.28 Compared with the RG observations, the PR significantly underestimated precipitation by 28–38% in summer (July–September), and overestimated in spring. Yatagai and Kawamoto (2008)
- 12. CORRELATION AT DIFFERENTLEVELS FOR ANNUAL PRECIPITATION Level (m) N R A B ALL 2695 0.786 0.697 124.210 Z >=4,500 24 0.436 0.136 118.175 3,000> Z >=4,500 127 0.638 0.398 127.305 1,500 > Z >=3,000 241 0.762 0.653 266.799 1,000 > Z >=1,500 153 0.489 0.364 904.554 500 > Z >=1,000 268 0.749 0.790 89.459 250 > Z >= 500 647 0.769 0.775 4.826 0 > Z >= 250 1231 0.817 0.660 155.795 Regression {PR}=A*{RG}+B Yatagai and Kawamoto (2008)
- 13. CORRELATIONS AT DIFFERENTELEVATION Yatagai and Kawamoto (2008)
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- 15. COMPARISON FOR APRIL PRunderestimates RG ? higher than 1,500 m a.s.l PR over estimates RG ? lower than 1,500 m a.s.l. PR over estimates RG in Feb (250- 1000 level) March (0-1000 level) April (0- 1500 level) Yatagai and Kawamoto (2008)
- 16. N NE ESE S SW W NW 1 degree
- 18. Summary • The PRdata acquired by the TRMM over 10 years of observation are used to show the monthly rainfall patterns over the Himalayas. • To validate and adjust these patterns, we used APHRODITE’s dense network of rain gauges to measure daily precipitation over Nepal, Bangladesh, Bhutan, Pakistan, India, Myanmar, and China. • We then compared TRMM/PR and RG data in 0.05-degree grid cells (an approximately 5.5-km mesh). Compared with the RG observations, the PR significantly underestimated precipitation by 28–38% in summer (July–September). • Significant correlation between TRMM/PR and RG data was found for all months, but the correlation is relatively low in winter. The relationship is investigated for different elevation zones, and the PR was found to underestimate RG data in most zones, except for certain zones in February (250–1000m), March (0–1000m), and April (0–1500m). • Monthly PR climatology was adjusted on the basis of monthly regressions between the two sets. • The RG adjusted PR climatology was filtered which considers the aspect of slopes to remove random noise. • The finally available data show clearer double rain-band along the Himalayas and show no penetrated (interpolated) precipitation to dry Tibet region.
- 19. References • Yatagai, A.,Xie, P., Kitoh, A. (2005): Utilization of a new gauge-based daily preciptiation dataset over monsoon Asia for validation of the daily precipitation cliamtology simulated by the MRI/JMA 20-km-mesh AGCM, SOLA, 1, 193-196, doi:10.2151/sola.2005-050. • Yatagai, A., and H. Kawamoto (2008): Quantitative estimation of orographic precipitation over the Himalayas by using TRMM/PR and a dense network of rain gauges, SPIE, 7148-11, doi:10.1117/12.811943. • Yatagai, A., O. Arakawa and K. Kamiguchi, H. Kawamoto, M. I. Nodzu, A. Hamada (2009): A 44-year daily gridded precipitation dataset for Asia based on a dense network of rain gauges, SOLA, 5, 137-140, doi:10.2151/sola.2009-035. • Yatagai, A., K. Kamiguchi, O. Arakawa, A. Hamada, N. Yasutomi and A. Kitoh (2012): APHRODITE: Constructing a Long-term Daily Gridded Precipitation Dataset for Asia based on a Dense Network of Rain Gauges, Bulletin of American Meteorological Society, 93,1401-1415, • doi:http://dx.doi.org/10.1175/BAMS-D-11-00122.1. • Yatagai, A., T. N. Krishnamurti, V. Kumar, A. K. Mishra and A. Simon (2014): Use of APHRODITE rain-gauge based precipitation and TRMM3B43 products for improving Asian monsoon seasonal precipitation forecasts, J. Climate , 27, 1062-1069.
- 20. Data used inold version GHCN China Input more gauges FAO ASEAN India Bhutan Nepal Bangladesh Myanmar Pakistan Raingauges used in this study Orography 0.05 degree QC Yatagai and Kawamoto (2008)
- 21. Annual Precipitation June-SeptemberPrecipitation 2695 stations 2695 stations Annual precipitation: r=0.786 (N>=10) {PR}=0.697*{RG}+124.2 Monsoon precipitation: r=0.764 (N>=10) {PR}=0.586*{RG}+118.0 Yatagai and Kawamoto (2008)