APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges

Yatagai, Akiyo; Kamiguchi, Kenji; Arakawa, Osamu; Hamada, Atsushi; Yasutomi, Natsuko; Kitoh, Akio

doi:10.1175/bams-d-11-00122.1

Cited by 1,550 publications

(1,141 citation statements)

References 21 publications

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“…The spatial distribution of monthly precipitation was evaluated using data from the Asian Precipitation -Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) project (Kamiguchi et al 2010;Yatagai et al 2012). The WRF model provides three-hourly integrated precipitation (mm 3h −1 ); therefore, simulated daily precipitation (mm day −1 ) was obtained by integration of these data and was then averaged to obtain monthly precipitation.…”

Section: Model and Numerical Setupmentioning

confidence: 99%

Effect of Spatial Resolution and Cumulus Parameterization on Simulated Precipitation over South Asia

Sugimoto

Takahashi

2016

SOLA

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This paper evaluates the ability of a regional-scale climate model to simulate precipitation over the South Asian tropical region. Experiments were conducted using three different spatial resolutions, with and without cumulus parameterization (CP), to assess the influence of horizontal mesh size and the CP on regional-scale precipitation. The experiments that used a finer mesh size but no CP improved the spatial distribution of monthly precipitation relative to that in the experiments based on a coarser spatial resolution. Meanwhile, the impact of horizontal mesh size was much less in the experiments that included the CP because an overestimation of precipitation caused by the CP strongly affected the simulation accuracy in these experiments. Regional differences in diurnal variations in precipitation intensity and frequency were captured reasonably well in the pre-monsoon season regardless of the spatial resolution, and both with and without the CP. In contrast, the diurnal characteristics of precipitation were difficult to simulate during the mature monsoon season. During both seasons, those experiments that incorporated the CP tended to predicted a continuous weak precipitation due to the excessive release of convective instability; accordingly, precipitation intensity was weaker, and precipitation frequency greater than in those experiments that did not use the CP.(Citation: Sugimoto, S., and H. G. Takahashi, 2016: Effect of spatial resolution and cumulus parameterization on simulated precipitation over South Asia. SOLA, 12A, 7−12,

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Section: Model and Numerical Setupmentioning

confidence: 99%

Effect of Spatial Resolution and Cumulus Parameterization on Simulated Precipitation over South Asia

Sugimoto

Takahashi

2016

SOLA

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“…Due to some differences found in ''observed'' data sets (Krajewski et al 2000;Nguyen et al 2011), in particular over Asia (Yatagai et al 2005(Yatagai et al , 2012, we used four different gridded observed data sets to validate the model results. The observed data sets are the CPC Merged Analysis of Precipitation (CMAP, Xie and Arkin 1997), the Global Precipitation Climatology Project data set (GPCP, Adler et al 2003), University of East Anglia Climatic Research Unit (CRU, New et al 1999) and Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation of the Water Resources (APHRO-DITE), where CMAP and GPCP have resolution 2.5°, CRU 0.5°and APHRODITE 0.25°.…”

Section: Data Used and Model Setupmentioning

confidence: 99%

Downscaling over Vietnam using the stretched-grid CCAM: verification of the mean and interannual variability of rainfall

2013

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Rainfall over Vietnam is highly variable from north to south, due to the interaction of the monsoonal winds with the terrain. There is high rainfall from April to September, and little rainfall from October to March (except along the central Vietnam coast). In order to study the ability of the Commonwealth Scientific and Industrial Research Organisation stretched-grid Conformal Cubic Atmospheric Model (CCAM) to capture the climatic and interannual variability of rainfall, downscaled simulations at approximately 20 km horizontal resolution over the region were produced for the period 1979-2001. A scaleselective digital filter was used to force the winds, temperature and sea-level pressure from the ERA-Interim reanalysis for length scales greater than about 700 km. For wind and temperature, the forcing is applied for pressuresigma levels above about 0.9. ERA-Interim sea surface temperatures were used over the oceans. The simulations were primarily validated against the gridded Asian Precipitation Highly Resolved Observational Data Integration Toward Evaluation of the Water Resources rainfall dataset and station observations using standard statistical methods. It was found that CCAM reproduces well the amount and spatial variability of rainfall, with an area-averaged bias for the entire study domain of less than 1 mm day -1 ; CCAM is also able to capture the rainfall pattern under different El Niño Southern Oscillation phases reasonably well for the dry season. For interannual variability, the simulation generally performed better for North and Central Vietnam than for South Vietnam, where rainfall variability was overestimated.

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“…chikyu.ac.jp/precip/), version 1101, which covered the monsoon Asia region of 158S-558N, 608-1508E for the period of 1951-2007; and the Tropical Rainfall Measuring Mission (TRMM; http://disc2.nascom.nasa.gov/ dods/3B42_V7_daily) daily precipitation for the period 1998-2014. Since APHRODITE and TRMM produced agreeable rainfall analyses over monsoon Asia (Yatagai et al 2012), both precipitation datasets were merged to create a longer, continuous dataset covering 1951-2014 with a spatial resolution of 0.258 3 0.258. The merger was done by subjecting the two datasets to least squares regression during their overlapping period.…”

Section: B Data Sourcesmentioning

confidence: 99%

The 2011 Great Flood in Thailand: Climate Diagnostics and Implications from Climate Change

2015

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Severe flooding occurred in Thailand during the 2011 summer season, which resulted in more than 800 deaths and affected 13.6 million people. The unprecedented nature of this flood in the Chao Phraya River basin (CPRB) was examined and compared with historical flood years. Climate diagnostics were conducted to understand the meteorological conditions and climate forcing that led to the magnitude and duration of this flood. Neither the monsoon rainfall nor the tropical cyclone frequency anomalies alone was sufficient to cause the 2011 flooding event. Instead, a series of abnormal conditions collectively contributed to the intensity of the 2011 flood: anomalously high rainfall in the premonsoon season, especially during March; record-high soil moisture content throughout the year; elevated sea level height in the Gulf of Thailand, which constrained drainage; and other water management factors. In the context of climate change, the substantially increased premonsoon rainfall in CPRB after 1980 and the continual sea level rise in the river outlet have both played a role. The rainfall increase is associated with a strengthening of the premonsoon northeasterly winds that come from East Asia. Attribution analysis using phase 5 of the Coupled Model Intercomparison Project historical experiments pointed to anthropogenic greenhouse gases as the main external climate forcing leading to the rainfall increase. Together, these findings suggest increasing odds for potential flooding of similar intensity to that of the 2011 flood.

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APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges

Cited by 1,550 publications

References 21 publications

Effect of Spatial Resolution and Cumulus Parameterization on Simulated Precipitation over South Asia

Effect of Spatial Resolution and Cumulus Parameterization on Simulated Precipitation over South Asia

Downscaling over Vietnam using the stretched-grid CCAM: verification of the mean and interannual variability of rainfall

The 2011 Great Flood in Thailand: Climate Diagnostics and Implications from Climate Change

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