Thailand Daily Rainfall and Comparison with TRMM Products
Open Access
- 1 April 2008
- journal article
- Published by American Meteorological Society in Journal of Hydrometeorology
- Vol. 9 (2), 256-266
- https://doi.org/10.1175/2007jhm876.1
Abstract
Daily rainfall data collected from more than 100 gauges over Thailand for the period 1993–2002 are used to study the climatology and spatial and temporal characteristics of Thailand rainfall variations. Comparison of the Thailand gauge (TG) data binned at 1° × 1° with the Global Precipitation Climatology Centre (GPCC) monitoring product shows a small bias (1.11%), and the differences can be reconciled in terms of the increased number of stations in the TG dataset. Comparison of daily TG with Tropical Rainfall Measuring Mission (TRMM) version 6 (V6) 3B42 rain estimates shows improvements over version 5 (V5) in terms of bias and mean absolute difference (MAD). The V5 is computed from the adjusted Geostationary Operational Environmental Satellite (GOES) precipitation index (AGPI) and V6 is computed using the TRMM Multisatellite Precipitation Analysis (TMPA) algorithm. The V6 histogram is much closer to that of TG than V5 in terms of rain fraction and conditional rain rates. Scatterplots show that both versions of the satellite products are deficient in capturing heavy rain events. In terms of detecting rain events, a critical success index (CSI) shows no difference between V6 and V5 3B42. The CSI for V6 is higher for the rainy season than the dry season. These results are generally insensitive to rain-rate threshold and averaging periods. The temporal and spatial autocorrelation of daily rain rates for TG, V6, and V5 3B42 are computed. Autocorrelation function analyses show improved temporal and spatial autocorrelations for V6 compared to TG over V5 with e-folding times of 1, 1, and 2 days, and isotropic spatial decorrelation distances of 1.14°, 1.87°, and 3.61° for TG, V6, and V5, respectively. Rain event statistics show that the V6 3B42 overestimates the rain event durations and underestimates the rain event separations and the event conditional rain rates when compared to TG. This study points to the need to further improve the 3B42 algorithm to lower the false detection rate and improve the estimation of heavy rainfall events.Keywords
This publication has 18 references indexed in Scilit:
- The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor Precipitation Estimates at Fine ScalesJournal of Hydrometeorology, 2007
- Comparison of Near-Real-Time Precipitation Estimates from Satellite Observations and Numerical ModelsBulletin of the American Meteorological Society, 2007
- TRMM Observed Vertical Structure and Diurnal Variation of Precipitation in South AsiaPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2006
- An analysis of the performance of hybrid infrared and microwave satellite precipitation algorithms over India and adjacent regionsRemote Sensing of Environment, 2006
- Comparison of TRMM and water district rain rates over New MexicoAdvances in Atmospheric Sciences, 2006
- Status of TRMM Monthly Estimates of Tropical PrecipitationPublished by Springer Science and Business Media LLC ,2003
- Tropical Rainfall Distributions Determined Using TRMM Combined with Other Satellite and Rain Gauge InformationJournal of Applied Meteorology and Climatology, 2000
- The Global Precipitation Climatology Project (GPCP) Combined Precipitation DatasetBulletin of the American Meteorological Society, 1997
- Global Precipitation Estimates Based on a Technique for Combining Satellite-Based Estimates, Rain Gauge Analysis, and NWP Model Precipitation InformationJournal of Climate, 1995
- Global tropical rain estimates from microwave‐adjusted geosynchronous IR dataRemote Sensing Reviews, 1994