Dependence of estimated precipitation frequency and intensity on data resolution

Precipitation frequency (F) and intensity (I) are important characteristics that climate models often fail to simulate realistically. Their estimates are highly sensitive to the spatial and temporal resolutions of the input data and this further complicates the comparison between models and observat...

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Bibliographic Details
Published in:Climate dynamics 2018-05, Vol.50 (9-10), p.3625-3647
Main Authors: Chen, Di, Dai, Aiguo
Format: Article
Language:English
Subjects:
Climate models
Climatology
Computer simulation
Data
Data processing
Datasets
Distribution
Earth and Environmental Science
Earth Sciences
Extremely low frequencies
Geophysics/Geodesy
High frequency
Hydrologic data
Meteorology & Atmospheric Sciences
Methods
Oceanography
Precipitation
Precipitation (Meteorology)
Precipitation data
Precipitation variations
Probability theory
Rainfall intensity
Resolution
Satellites
Seasonal variation
Seasonal variations
Spatial distribution
Temporal resolution
Tropical environments
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Summary:Precipitation frequency (F) and intensity (I) are important characteristics that climate models often fail to simulate realistically. Their estimates are highly sensitive to the spatial and temporal resolutions of the input data and this further complicates the comparison between models and observations. Here, we analyze 3-hourly precipitation data on a 0.25° grid from two satellite-derived datasets, namely TRMM 3B42 and CMORPH_V1.0, to quantify this dependence of the estimated precipitation F and I on data resolution. We then develop a simple probability-based relationship to explain this dependence, and examine the spatial and seasonal variations in the estimated F and I fields. As expected, precipitation F (I) increases (decreases) with the size of the grid box or time interval over which the data are averaged, but the magnitude of this change varies with location, and is strongest in the tropics and weakest in the subtropics. Our simple relationship can quantitatively explain this dependence of the estimated F and I on the spatial or temporal resolution of the input data. This demonstrates that large differences in the estimated F and I can arise purely from the differences in the spatial or temporal resolution of the input data. The results highlight the need to have similar resolution in comparing two datasets or between observations and models. Our estimates show that extremely low frequencies (
ISSN:0930-7575
1432-0894
DOI:10.1007/s00382-017-3830-7