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Roongroj Chokngamwong and Long S. Chiu

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.

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Long S. Chiu and Roongroj Chokngamwong

Abstract

A satellite microwave emission brightness temperature histograms (METH) technique has been applied to Special Sensor Microwave Imager (SSM/I) data taken on board the Defense Meteorological Satellite Program (DMSP) satellites and preprocessed by Remote Sensing Systems (RSS) Co. to produce 21 yr (July 1987–present) of oceanic rainfall products. These rain products are used as input to the Global Precipitation Climatology Project (GPCP) rain maps. Analysis of the METH product using SSM/I version-4 (V4) data shows jumps in vertically polarized 19-GHz brightness temperatures that are attributed to changes in DMSP satellites. A version-6 (V6) SSM/I that corrects for intersatellite differences was released by RSS in 2006. The jumps in the time series are reduced, with most of the changes occurring in the early part of the DMSP F13 data. The bias between RSS V6 and V4 of brightness temperature at 19 and 22 GHz is less than 0.5 K. METH rain rates were reprocessed using V6 data and were analyzed. The 20-yr global mean difference between the METH V4 and V6 is less than 0.3%, with differences as large as 3% in individual years. Trend analyses show increases in the oceanic rain belts, such as the intertropical convergence zone and the South Pacific convergence zone, and in the Bay of Bengal. These rain-rate trends, from both linear trend analysis and empirical mode decomposition analysis, are comparable to the version-2 GPCP analyses but are smaller than those found in the unified microwave ocean retrieval algorithm.

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