Estimating daily precipitation climatology by postprocessing high‐resolution reanalysis data

• Published in: International journal of climatology Vol. 43; no. 9; pp. 4151 - 4165
• Main Authors: Du, Yiliang, Wang, Quan J., Su, Chun‐Hsu, Wu, Wenyan, Yang, Qichun
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.07.2023; Wiley Subscription Services, Inc
• Subjects: Bias; bias correction; Climate; Climatology; Daily; Daily precipitation; Density; Distribution; Errors; Estimation; Extreme values; Frequency dependence; Frequency distribution; Gauges; Hydrologic data; Interpolation; Interpolation methods; Locations (working); Mapping; Meteorology; Precipitation; Precipitation data; Quantiles; quantile‐mapping; reanalysis dataset; Spatial data; ungauged location; Australia
• ISSN: 0899-8418; 1097-0088
• DOI: 10.1002/joc.8079

Spatial information of climatological frequency distribution of daily precipitation is highly valuable for a wide range of applications. Accurate estimation of climatology can be made for gauged locations where quality and lengthy observations are available. For ungauged or poorly gauged locations, however, indirect estimation is needed. One approach is to use a gridded daily precipitation dataset derived from interpolating observations. However, gridded daily precipitation data can be subject to large errors when gauge density is low. In addition, most interpolation methods tend to smooth the extreme values and increase the low ones, leading to unrealistic statistical properties and therefore poor estimation of daily climatology. Another approach is to first derive climatology at gauged locations and then interpolate climatology to ungauged locations. While this approach is likely to be more robust than the first approach, low gauge density can still cause significant errors especially in areas of complex terrain. In this study, we develop a method that postprocesses spatially consistent and rich reanalysis data using accurate observations at gauged locations. At an ungauged location, daily precipitation amounts from the reanalysis are bias‐corrected using quantile‐mapping guided by frequency distributions of reanalysis data and observations at a nearby gauged location (reference location). The bias‐corrected precipitation amounts are then used to estimate the climatology for the ungauged location. This method eliminates the need for interpolation and therefore its adverse effects. Special care is taken in quantile‐mapping when extrapolating beyond the range of reanalysis data at the reference location. We evaluate the method at 50 locations in Australia, using the Bureau of Meteorology Atmospheric high‐resolution Regional Reanalysis for Australia (BARRA) and precipitation observation network across Australia. These locations are chosen to represent different climate regions in Australia and have observations to validate the postprocessed reanalysis climatology of daily precipitation. Results show that the postprocessed climatology is consistent with observations, in terms of frequency distribution, high quantiles, probabilities of wet and dry days and their transitions. A new method is introduced to estimate daily precipitation climatology by postprocessing high‐resolution reanalysis data. The method bias‐corrects reanalysis data using information from a nearby gauged location and estimates daily precipitation climatology from the corrected reanalysis data. The method is evaluated at 50 locations across Australia and found to yield daily precipitation climatology estimates that are consistent with observations.