Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau

Abstract

Manabendra Saharia (2014) Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau, This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005-2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods -arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic meanshow insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.

Similarity and Error Intercomparison of the GPM and Its Predecessor-TRMM Multisatellite Precipitation Analysis Using the Best Available Hourly Gauge Network over the Tibetan Plateau

“…Both products show a decreasing trend from the southeast to the northwest over the plateau, which are generally consistent with other satellite products (e.g., 3B42RT, CMORPH and PERSIANN) [9][10][11]. The average accumulated rainfall in the warm season of 2014 is 347 mm over the whole plateau, and the maximum rainfall primarily occurs in the Himalayan region with a value of 2609 mm.…”

“…Along the Himalayas (i.e., the southern part of the TP), CC was smaller with the mean value less than 0.40. The weaker correlation was attributed to the complex topography in the Himalayas, which might influence the retrieval of satellite precipitation [10]. In spite of higher elevation (i.e., more than 4000 m a.s.l.)…”

“…However, all of the gauge data have undergone strict quality control in three levels by CMA, including: (1) the extreme values' check; (2) internal consistency check; and (3) spatial consistency check [26]. The quality-controlled hourly gauge data are used widely for satellite precipitation estimation and high spatiotemporal gauge-satellite precipitation integration in mainland China [10,15,27]. To our best understanding, this is the best hourly rainfall observational dataset over the study region.…”

“…Efforts have been made to assess the suitability of satellite precipitation products over the TP [1,[7][8][9][10][11]. Previous studies found that the TMPA precipitation products outperform other satellite products (e.g., PERSIANN and CMORPH) across the plateau with lower errors and biases [9,11].…”

China’s 1 km Merged Gauge, Radar and Satellite Experimental Precipitation Dataset

“…The Multi-Radar/Multi-Sensor (MRMS) system [16] has provided a good prototype from which to learn. The second aims to comprehensively use multiple-source measurements related to precipitation and the numerical weather or hydrological models to improve the analyzed or assimilated techniques in regions with sparse data [36]. The parameters of OI will be adjusted when new datasets are introduced to minimize the potential uncertainty brought by them.…”