High‐resolution satellite‐gauge merged precipitation climatologies of the Tropical Andes

Abstract

Satellite precipitation products are becoming increasingly useful to complement rain gauge networks in regions where these are too sparse to capture spatial precipitation patterns, such as in the Tropical Andes. The Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (TPR) was active for 17 years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014) and has generated one of the longest single-sensor, high-resolution, and high-accuracy rainfall records. In this study, high-resolution (5 km) gridded mean monthly climatological precipitation is derived from the raw orbital TPR data (TRMM 2A25) and merged with 723 rain gauges using multiple satellite-gauge (S-G) merging approaches. The resulting precipitation products are evaluated by cross validation and catchment water balances (runoff ratios) for 50 catchments across the Tropical Andes. Results show that the TPR captures major synoptic and seasonal precipitation patterns and also accurately defines orographic gradients but underestimates absolute monthly rainfall rates. The S-G merged products presented in this study constitute an improved source of climatological rainfall data, outperforming the gridded TPR product as well as a rain gauge-only product based on ordinary Kriging. Among the S-G merging methods, performance of inverse distance interpolation of satellite-gauge residuals was similar to that of geostatistical methods, which were more sensitive to gauge network density. High uncertainty and low performance of the merged precipitation products predominantly affected regions with low and intermittent precipitation regimes (e.g., Peruvian Pacific coast) and is likely linked to the low TPR sampling frequency. All S-G merged products presented in this study are available in the public domain.

Two-Step Downscaling of Trmm 3b43 V7 Precipitation in Contrasting Climatic Regions With Sparse Monitoring: The Case of Ecuador in Tropical South America

“…This is consistent with [32], where the climatological maps showed that western Ecuador is underestimated by TRMM. This behavior was improved by the step 2 methods, where P RKT and P RKTC showed to be better products than those which did not apply residual kriging correction, reducing both RMSE and PBI AS.…”

“…The results revealed that the performance of the method is related to the density of the station network. Furthermore, NDVI was used as an auxiliary variable in [32], where a very large area of almost 4 countries (Colombia, Ecuador, Brazil and Peru) was covered, where TRMM was merged with 273 in situ stations. However, climatological maps were generated, making it still necessary to treat time series at finer temporal scales over the study area.…”

Multiregional Satellite Precipitation Products Evaluation over Complex Terrain

“…Mean annual precipitation of all rain gauges in the region is 713 mm, while the maximum reported area-average daily rainfall in the study period is 540 mm. Precipitation patterns are controlled by the interaction of synoptic-scale atmospheric currents and the complex Andean topography (Manz et al 2016). The Pacific coastline is arid and experiences precipitation less than 100 mm yr 21 as a result of the cold von Humboldt current (Manz et al 2016), which increases toward the Andes mountainous range because of strong topographic gradients that result in pronounced orographic effects (Espinoza et al 2015;Espinoza Villar et al 2009;Bookhagen and Strecker 2008).…”

Satellite-Based Rainfall Retrieval: From Generalized Linear Models to Artificial Neural Networks

“…Several other studies (e.g., [13,45,46]) showed that, during the daytime, satellite-based rainfall retrievals benefit from additionally considering VIS channels. However, when taking VIS channels into account, two separate algorithms need to be used for day-and nighttime with the nighttime algorithm exhibiting lower skill.…”

“…Often, different data sources are combined to enhance the QPE accuracy, e.g., ground-based radar and gauges (e.g., [9,10]), ground-based radar and satellite radiometer (e.g., [11]), satellite radiometer and gauges (e.g. [7,12]), spaceborne radar and gauges (e.g., [13]), spaceborne radar and satellite radiometer (e.g., [14,15]), or spaceborne radar, satellite radiometer, and gauges (e.g., [16]). Each precipitation measurement instrument has its specific set of strengths and limitations.…”