Long‐Term Precipitation Estimates Generated by a Downscaling‐Calibration Procedure Over the Tibetan Plateau From 1983 to 2015

Abstract

The World Meteorological Organization stipulates a minimum of 30 years of historical data is needed to obtain meaningful results in climatological research. However, large numbers of studies have explored downscaling approaches based on the TRMM Multi-Satellite Precipitation Analysis (TMPA) data, which span only from 1998 to the present, to obtain the precipitation estimates (~1-km resolution). The main aim of the present study was to develop a new method for obtaining long-term (>30 years) precipitation estimates at~1-km resolution and to apply that method to a region with complex topography, the Tibetan Plateau. First, PERSIANN-CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record) data were used for downscaling. Considering the characteristics of the PERSIANN-CDR data, a new downscaling-calibration procedure utilizing a combination of a spatial data mining downscaling algorithm (Cubist) and a geographical ratio analysis calibration method was proposed. We found that (1) both the original PERSIANN-CDR data (Bias~40.79%) and the downscaled results before calibration (Bias~26.78%) overestimated the precipitation compared with ground observations; (2) the final downscaled results based on the PERSIANN-CDR data after calibration were close to the ground observations (Bias~5%); (3) compared to the results interpolated based on the PERSIANN-CDR data (E <−1.0), both the downscaling procedure and calibration procedure contributed significantly to the accuracy of the final downscaled results (E~0.83). These findings suggest that the proposed downscaling-calibration procedure has great potential as an approach for retrieving long-term precipitation estimates (~1-km resolution) over the Tibetan Plateau.

Mapping 30-m Resolution Bioclimatic Variables During 1991–2020 Climate Normals for Hubei Province, the Yangtze River Middle Reaches

“…It is noticed that the downscaling procedure hardly affected the accuracy of its products, with only a very slight accuracy improvement for temperature variables and decrease for precipitation observed, similar to the study [63] but being different from others [31], [32] [51]. This may firstly attribute to the quality of original data [65] which has already considered the auxiliary data used by us, allowing the high consistency of the spatial variability between original and downscaled data. Additionally, for the pixels of original data at or around the weather stations, when corresponding fine pixels of auxiliary data exhibit low spatial heterogeneity, downscaling may not cause substantial variations.…”

The evaluation and downscaling‐calibration of IMERG precipitation products at sub‐daily scales over a metropolitan region

“…With the presumption that relationships between precipitation and predict variables are spatially homogeneous, exponential models (e.g., Immerzeel et al, 2009), multiple linear regression models (e.g., Jia et al, 2011) and the random forest‐based regression models (e.g., Ma, He, et al, 2018) have been explored. Considering the spatial variation of precipitation and land surface characteristics across the study area, geographically moving window weight disaggregation models (Ma, He, et al, 2019; Ma, Tan, et al, 2018; Ma, Xu, He, et al, 2020; Xu et al, 2015a, 2015b), spatial data mining downscaling algorithm (Ma, Ghent, et al, 2019; Ma, Shi, et al, 2017; Ma, Zhou, et al, 2018), and spatiotemporal disaggregation calibration algorithm (Ma, Xu, Zhu, et al, 2020) have been developed. These efforts have greatly enriched the application of statistical downscaling techniques in the field of hydrometeorology.…”

SMPD: a soil moisture-based precipitation downscaling method for high-resolution daily satellite precipitation estimation

“…There are two major categories of downscaling methods: statistical downscaling and dynamical downscaling (Maraun et al, 2010;Tang et al, 2016). Statistical downscaling methods are mainly conducted by building the explanatory ability of the precipitation spatial distribution with fine-scale predictors, including topographic, geographic, atmospheric and vegetation variables, with the use of traditional regression methods (Xu et al, 2015;Ma et al, 2019b;Mei et al, 2020), optimal interpolation techniques (Shen et al, 2014;Chao et al, 2018), multidata fusion (Rozante et al, 2020;Ma et al, 2021), spatial data mining algorithm (called cubist) (Ma et al, 2017a, b), geographical ratio analysis (Duan and Bastiaanssen, 2013;Ma et al, 2019a) and machine learning algorithms (He et al, 2016;Baez-Villanueva et al, 2020;Min et al, 2020). Due to their convenience and efficiency, these approaches are dominant in precipitation spatial downscaling research (Abdollahipour et al, 2021).…”