Intercomparison of precipitation datasets for summer precipitation characteristics over East Asia

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.

Modeling of Mass Balance Variability and Its Impact on Water Discharge from the Urumqi Glacier No. 1 Catchment, Tian Shan, China

“…Snowfall in combination with T2 determines the main source of external mass income to the glacier and also exhibits a strong influence on albedo and thus the energy balance (Equation (1)). RRR prediction is known to be highly error-prone, especially in complex terrain (e.g., [3,89,90]). UG1 has been shown in various studies to be especially sensitive to T2 and to RRR [11,13,16,20].…”

Evaluación de variables meteorológicas modeladas para determinar el clima de ciudades costeras argentinas

“…El Reanalysis no podría ser utilizado para la caracterización climática de ciudades costeras de Argentina sin ser corregido adecuadamente, debido a que los errores producto de las diferencias de escala espacial, los generados por la topografía y la cercanía al mar, generan que la serie sea estadísticamente distinta a los datos observados (Kistler et al, 2001;Rusticucci y Kousky, 2002;Kim, Kim, Boo, Shim y Kim, 2019; Gomes de Lima y Rodrigues Alcântara, 2019). Es importante considerar que los métodos estadísticos aplicados para realizar un downscaling de variables meteorológicas permiten modelar las relaciones estacionarias entre una serie de datos observados a escala local con los provenientes del Reanalysis (Xue, Janjic, Dudhia, Nasicy y De Sales, 2014;Miró Pérez, Estrela Navarro y Olcina Cantos, 2015;Libanda et al, 2020).…”

“…Esto se debe principalmente a que en algunas circunstancias, los datos del Reanalysis tienen diferencias espacio-temporales marcadas que impiden su modelización. Estas hacen referencia a los eventos extremos termo-pluviométricos y a las sobreestimaciones o subestimaciones de los valores en algunas estaciones del año (Haylock, Cawley, Harpham, Wilby y Goodess, 2006;Kim et al, 2019;Gomes de Lima y Rodrigues Alcântara, 2019). Por este motivo, la estación pluviométricamente seca de Concordia tampoco pudo ser determinada en el modelo identificado.…”

“…Esta situación fue demostrada también por otros autores (por ejemplo, Gomes de Lima y Rodrigues Alcântara, 2019). En lo que respecta a la precipitación y a la humedad relativa, las diferencias fueron mayores, sobre todo a escala estacional como también se visualizó en el estudio de Kim et al (2019). Por ello, es importante considerar la incorporación de un mayor número de estaciones meteorológicas dentro de cada cuadrícula del Reanalysis para poder identificar el patrón espacial al que hacen referencia y eliminar los efectos de la topografía (Libanda et al, 2020).…”

Response of the Asian summer Monsoons to a high-latitude thermal forcing: mechanisms and nonlinearities

“…1). Noting the significant discrepancies that arise in the JJA climatological precipitation pattern among the different reanalysis (and also among observational products: Kim et al 2019;Wang et al 2017) we consider that the model reproduces the ASM regimes in an adequate manner.…”