On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia
AuthorNguyen, P-L; Bador, M; Alexander, LV; Lane, TP; Funk, CC
Source TitleFrontiers in Climate
PublisherFrontiers Media SA
University of Melbourne Author/sLane, Todd
AffiliationSchool of Earth Sciences
Document TypeJournal Article
CitationsNguyen, P. -L., Bador, M., Alexander, L. V., Lane, T. P. & Funk, C. C. (2020). On the Robustness of Annual Daily Precipitation Maxima Estimates Over Monsoon Asia. Frontiers in Climate, 2, https://doi.org/10.3389/fclim.2020.578785.
Access StatusOpen Access
Open Access URLPublished version
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.
- Click on "Export Reference in RIS Format" and choose "open with... Endnote".
- Click on "Export Reference in RIS Format". Login to Refworks, go to References => Import References