Evaluation of Satellite Precipitation Products over the Central of Vietnam

Transcription

1 Evaluation of Satellite Precipitation Products over the Central of Vietnam Long Trinh-Tuan (1), Jun Matsumoto (1,2), Thanh Ngo-Duc (3) (1) Department of Geography, Tokyo Metropolitan University, Japan. (2) Department of Coupled Ocean-Atmosphere-Land Processes Research, JAMSTEC, Japan. (3) Department of Space and Aeronautics, University of Science and Technology of Hanoi, Vietnam 8 th IPWG & 5 th IWSSM Joint Workshop, Bologna city, Italy October 4th, 2016

2 Content Introduction Data, Location & Method Evaluation Satellite precipitation data (SPD) on rainfall season over Central Highland & Central Coast of Vietnam. Base on regional scale. Base on grid scale. Evaluation of SPD in case Heavy Rainfall Events caused by: Southwest Monsoon Northeast Monsoon (Cold Surge) Tropical cyclone

3 Introduction The limitation of rain gauge stations, radar data, in complex topography (mountain, coast area ) has been seen. Satellite remote sensing can provide the spatial precipitation data over large areas in a temporally continuous way. Real time rainfall production are very necessary for applications. There have not been many studies that evaluate satellite rainfall data for complicated regions in Vietnam. Aims of present study: How is performance of SPD in complex terrain? To evaluate the capacity of hydrological applications and water management, in heavy rainfall cases.

4 Location, Data sources and Method Central Coast Annual rainfall average (left) and contribution (%) of summer rainfall (center) and autumn rainfall (right) for period over Central Vietnam by VnGP. Central Highland Topography (shaded,m) of Indochina Peninsula and surrounding regions. Rain Gauge stations Network of Central Coast (red circle) and Central Highland (blue circle) The difference (largest-lowest (mm/d) and range (%)) among 4 SPD in JJA (left) and SON(right) Location: Central Coast and Central Highland Satellite data: CMORPH, GSMaP, PERSIANN, TRMM Obs data: Rain gauge station, VnGP Period time: (JJA in Central Highland and SON in Central Coast)

5 Data Satellite rainfall data CMORPH version 1 (3hourly, 0.25 degree) : Precipitation estimates are solely based on MW data. IR data are only used to derive a cloud motion field to propagate precipitation in higher spatial and temporal resolution. GSMaP_MVK: V (hourly, 0.1 degree): The algorithm follows three main steps: 1) retrieval of precipitation rate from PMW data using a Kalman filter approach (Ushio et al. 2009), 2) propagation of the estimated precipitation rate using the same procedure as CMORPH, and 3) refinement of precipitation data based on the relationship between the IR brightness temperature and surface precipitation rates. PERSIANN: CDR (3hourly, 0.25 degree): A relationship between IR and precipitation rate is established using an artificial neural network. The network is additionally trained with MV data. The actual precipitation estimates are solely based on instantaneous IR observations. TRMM 3B42_v7 (3hourly, 0.25 degree) : MW-based estimations are merged and calibrated, and subsequently combined with IR-based estimates. The combined approximation is then rescaled using monthly CAMS and GPCP data. Product Provider Spatial resolution Temporal resolution Temporal converage CMORPH NOAA-CPC hour Since 1998, Jan GSMaP JAXA/EORC hour Since 2000 Mar PERSIANN University of Arizona hour Since 2000 Mar TRMM NASA hour Since 1998 Jan Observation data: -Rain gauge stations: (from NHMS): 12 stations in Central Coast and 7 stations in Central Highland were used -VnGP (*) Vietnam Gridded Precipitation dataset was created from 481 rainn gause station in Vietnam using Spheremap technique. (daily, 0.25, 0.1 degree) (Nguyen-Xuan et al., SOLA, 2016, accepted) (*)

6 Data & Method Periods: using Vietnamese Standard Time (12 UTC or 19 LTC) Spatial scales: regional scale, grid scale Temporal scales: daily to monthly Use statistical index (bias, RMSE ratio, correlation coefficient) Use Probably of Detection (POD), False of Alarm (FAR), and Heidke skill score (HSS) to evaluate the performance of capturing heavy rainfall events (in different thresholds). Bias=!!!!!!!!!!!! 1 Categorical validation statistics with A= number of hits, B= number of false, C= number of misses and D= number of correct negatives RMSE=!!!!(!!!!! )!! Name Formula Perfect Score Probability of detection A/(A+C) 1 RMSE ratio =!"#$! False alarm ratio B/(A+B) 0 Heidke Skill Score HSS=2(AD - BC)/((A + C)(C + D) + (A+B)(B+D)) 1

7 Comparison 4 satellite rainfall estimates and VnGP JJA and SON rainfall of period (mm/day) BIAS (Satellite Data - VnGP)

8 Comparison 4 satellite rainfall estimates and VnGP in regional mean Central Highland Jun JulAug Jun JulAug Jun JulAug VnGP GSMaP TRMM Jun JulAug SepOctNov SepOctNov Jun JulAug 2004 Central Coast CMORPH PERSIANN Jun JulAug VnGP GSMaP TRMM SepOctNov 2003 SepOctNov Jun JulAug 2007 CMORPH PERSIANN SepOctNov 2004 SepOctNov SepOctNov 2007 Monthly scale Central Highland CMORPH GSMaP CORR ME ratio MAE ratio RMSE ratio PERSIANN TRMM Daily scale Central Coast CMORPH GSMaP Central Highland PERSIANN TRMM CORR ME ratio MAE ratio RMSE ratio CMORPH GSMaP Central Coast PERSIANN TRMM CMORPH GSMaP PERSIANN TRMM

9 Central Highland Central Coast Spatial Correlation Daily - scale Monthly - scale RMSE ratio base on threshold

10 Central Highland Central Coast Cumulative Density Function of JJA and SON daily rainfall at each grid box over Central Highland and Central Coast for VnGP and the satellite precipitation datasets.

11 Validation of SPD in Heavy Rainfall Events Percentage of heavy rainfall days in JJA and SON (VnGP) % %

12 Southwest monsoon case + CMORPH: seriously underestimated + PERSIANN: low correlation + GSMaP & TRMM: good Northeast Monsoon case + CMORPH & TRMM: good + GSMaP: some stations underestimated + PERSIANN: seriously underestimation and low correlation Tropical cyclone case + PERSIANN: underestimation + TRMM: good performance Scatter plot of daily rainfall from rain gauge stations versus four satellite products base on (a) Southwest Monsoon in VCH (7 stations), Northeast Monsoon and Tropical cyclone in VCC (12 stations) & Radar plot correlation, Bias, RMSE

13 The POD, FAR and HSS of SPD for rainfall thresholds ranging from 20mm to 100mm Southwest Monsoon Northeast Monsoon Tropical cyclone + TRMM had the best performance with high POD, HSS in all thresholds + CMORPH and PERSIANN had the worst performance in Southwest and Northwest monsoon respectively.

14 In regional & grid scales Summary & Conclusion The performance of satellite-derived precipitation products was significantly different, depending on geographical location and the causes of rainfall. In Central Highland and Central Coast, TRMM possessed the best performance in terms of monthly scale, which had not only high spatial correlation but also high temporal correlation, lowest error. GSMaP and CMORPH, however had a good correlation and were able to capture the patterns of rainfall characteristics, remarkably underestimated the regional rainfall amount, specifically in Central Highland. PERSIANN showed its low-quality performance in estimating and its instability when applied to regions, especially in complex topography such as Central Highland and Central Coast.

15 In Heavy Rainfall Events Case Summary & Conclusion The result indicated a better performance of estimates in Tropical cyclone case than in Southwest Monsoon or Northeast Monsoon. CMORPH and PERSIANN had relatively strong underestimation in Southwest monsoon and Northeast monsoon respectively. TRMM had the best performance with high POD, HSS in all thresholds and thus were deemed to be reliable and highly potential for hydrological applications. On the other hand, CMORPH and GSMaP which had high correlation could be useful for application after adjustment.

16 Summary & Conclusion TRMM possessed the best performance in terms of monthly scale, which had not only high spatial correlation but also high temporal correlation. This could be due to the use of MW combined with IR and PR in addition to the re-correction, which was conducted by GPCP. Despite of the correction of the station observation data, PERSIANN showed its low-quality performance in estimating and its instability when applied to regions, which have complex topography such as VCH and VCC (Dinku et al., 2010), (Thiemig et al., 2012). This was believed to be caused by the only use of IR information. GSMaP and CMORPH, however had a good correlation and were able to capture the patterns of rainfall characteristics, remarkably underestimated the regional rainfall amount, specifically in VCH. The reason for this could be explained based on the fact that GSMaP and CMORPH were mainly built from MW data. GSMaP has shown the better performance in comparison with previous version in coastal area.

17 Thank you for your attention!