Abstract

Precipitation data in the Global Precipitation Climatology Project (GPCP) and in four reanalysis datasets, ERA-Interim, MERRA, NCEP/NCAR, and JRA, are compared against the CPC Merged Precipitation (CMAP) in the cyclostationary empirical orthogonal function (CSEOF) space to evaluate these datasets in representing the summer precipitation characteristics over East Asia. CSEOF analysis is applied to each dataset, and regression analysis is performed in the CSEOF space with the CMAP data as the target. The regression analysis establishes one-to-one correspondence between the CSEOF loading vectors of the target variable and those of the predictors, i.e., GPCP and the four reanalysis datasets. The loading vectors of the GPCP data coincide almost exactly with those of the CMAP data, i.e., the two observation-based precipitation datasets represent practically identical summer precipitation characteristics over East Asia. The reanalysis datasets also reproduce the first five CSEOF modes reasonably; however, performance of NCEP/NCAR is notably lower than others. The re-constructed precipitation using the first five regressed CSEOF modes of the reanalysis datasets are well correlated with that of the CMAP data with reasonably large correlation coefficients, suggesting that these reanalysis precipitation products reliably simulate the major summer precipitation characteristics in East Asia. All of the four reanalysis products commonly show noticeable errors in representing the summer rainfall over the mid-latitude ocean to the south of Japan, the tropical western Pacific, tropical/subtropical regions including the Indochina Peninsula, India, the Maritime Continent, and regions of complex terrain especially those characterized by strong orographic slopes around the Tibetan Plateau. The errors over the regions of complex orography and coastal lines may be partially due to the inability of reanalysis models in simulating the effects of complex terrain and the lack of observations in these sparsely populated regions.

Highlights

  • Observation-based and reanalysis precipitation datasets play critical roles in climate research

  • The temporal variations of the cyclostationary loading vectors (CSLVs) of the first cyclostationary empirical orthogonal function (CSEOF) mode show that Global Precipitation Climatology Project (GPCP), and the four reanalysis datasets closely agree with CPC Merged Analysis of Precipitation (CMAP) throughout the 24 pentads (Supplemental Movie 1)

  • This study has evaluated the observation-based GPCP and four reanalysis precipitation datasets against the observationbased CMAP data for the variability associated with each CSEOF mode as well as for the first two statistical moments

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Summary

Introduction

Observation-based and reanalysis precipitation datasets play critical roles in climate research. Previous studies (Prakash et al 2014; Kim et al 2015a; Kim and Park 2016) found that precipitation characteristics in observation-based gridded datasets often vary widely among them This is an important concern because model evaluation against observational and/or reanalysis data is critical for model developments/improvements and in the application of climate model data to impact assessments via bias correction (Kim et al 2015a), for example. The essence of the approach used in this study is to evaluate individual CSEOF modes in order to assess how well reanalysis models perform in reproducing them This approach allows us to evaluate a dataset in terms of the accuracy of physical and dynamical processes that produced the field in the dataset.

Datasets
CSEOF analysis
Regression analysis in CSEOF space
Raw statistics
Comparison of individual CSEOF modes
Reconstructed CSEOF modes
Variability not included in the first five modes
Discussions and conclusions
Full Text
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