Abstract
Understanding precipitation extremes over Monsoon Asia is vital for water resource management and hazard mitigation, but there are many gaps and uncertainties in observations in this region. To better understand observational uncertainties, this study uses a high-resolution validation dataset to assess the consistency of the representation of annual daily precipitation maxima (Rx1day) over land in 13 observational datasets from the Frequent Rainfall Observations on Grids (FROGS) database. The FROGS datasets are grouped into three categories: in situ-based and satellite-based with and without corrections to rain gauges. We also look at three sub-regions: Japan, India and the Maritime Continent based on their different station density, orography and coastal complexity. We find broad similarities in spatial and temporal distributions among in situ-based products over Monsoon Asia. Satellite products with correction to rain gauges show better general agreement and less inter-product spread than their uncorrected counterparts. However, this comparison also reveals strong sub-regional differences that can be explained by the quantity and quality of rain gauges. High consistency in spatial and temporal patterns are observed over Japan, which has a dense station network, while large inter-product spread is found over the Maritime Continent and India, which have sparser station density. We also highlight that while corrected satellite products show improvement compared to uncorrected products in regions of high station density (e.g. Japan) they have mixed success over other regions (e.g., India and the Maritime Continent). In addition, the length of record available at each station can also affect the satellite correction over these poorly sampled regions. Results of the additional comparison between all considered datasets and the sub-regional high-resolution dataset remain the same, indicating that the overall quality of the station network has implications for the reliability of the in situ-based products derived and also the satellite products that use a correction to in situ data. Given these uncertainties in observations, there is no single best dataset for assessment of Rx1day in Monsoon Asia. In all cases we recommend users understand how each dataset is produced in order to select the most appropriate product to estimate precipitation extremes to fit their purpose.
Highlights
Asia is home to about 60% of the world’s population and is the largest and most populous continent in the world (Hijioka et al, 2014)
Over regions poorly sampled by stations, how well the correction to in situ acts depends on the length of record available and this can lead to regional contrasts but generally we find minor improvements in the representation of climatological Rx1day between the corrected and uncorrected version of the satellite products over such regions, which implies that does poor station coverage affect the representation of precipitation extremes in in situ-based datasets but it has clear impact in most satellite products that rely on ground networks
This study focused on the robustness of 1-day precipitation annual maxima (Rx1day) over Monsoon Asia by comparing the climatological value of Rx1day across multiple observational precipitation products and exploring the influence of the underlying station density and the correction methods that satellites use to estimate precipitation
Summary
Asia is home to about 60% of the world’s population and is the largest and most populous continent in the world (Hijioka et al, 2014). The FROGS database contains a variety of observational daily gridded precipitation datasets that have all been interpolated onto a common 1◦x1◦ grid These products differ in their data sources (e.g., in situ, satellite and blended sources) and the methods by which they are produced, and they have different spatial coverage (regional to global). The long-term TRMM on-board radar had obvious advantages for detecting the heavy precipitation that is associated with distinct orographic features and coastal effects (Shige et al, 2013, 2015, 2017) Other products such as CMORPH (Xie et al, 2017) and version 6 of the Global Precipitation Mission (GPM) Integrated Multi-Satellite Retrievals for GPM (IMERG) (Huffman et al, 2019, 2020) use a “morphing” based approach to estimate precipitation. Sparse and intermittent PMW observations are used to derive instantaneous rain rates, which are combined with motion vectors to derive a detailed two-dimensional rain rate structure that covers every location
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