Ground validation ofTRMM 3B43 V7precipitation estimates over Colombia. Part I: Monthly and seasonal timescales

Abstract

In this study, we validate precipitation estimates remotely sensed by the Tropical Rainfall Measuring Mission (TRMM) at monthly and seasonal timescales, during the period 1998-2015, by calculating and analyzing diverse error metrics between the 3B43 V7 product and in situ measurements from 1,180 rain gauges over Colombia, of which at least 987 are fully independent of TRMM. We explore the existence of spatiotemporal patterns to assess the performance of 3B43 V7 over the five major natural regions of Colombia: Caribbean, Pacific, Andes, Orinoco and Amazon. The results show that 3B43 V7 product is able to capture the phase of the annual cycle of monthly mean precipitation, but the performance is not good for the amplitude, in particular over the Andes and Pacific regions owing to complex climatic and topographic conditions. In general, 3B43 V7 exhibits good performance in the low-lying and plain Amazon, Orinoco and Caribbean regions. Over the Andes region, characterized by complex topography, overestimation errors are identified [root mean squared error (RMSE) ≥83.59 mm•month −1 and relative bias (BIAS) ≥4.69%], whereas the extremely wet rainfall regime of the Pacific region is largely underestimated (RMSE ≥253.52 mm •month −1 and BIAS ≤−11.75%). These errors are greater during the wet seasons when the metrics reach worse scores than those reported in similar studies worldwide. Occurrence analyses showed that 3B43 V7 misses very frequent light rainfall events and less frequent but very heavy storms, which contribute to the overall underestimation (overestimation) observed over the Pacific (Andes) region. The error characteristics identified and quantified in this study confirm the well-documented limitations of remote precipitation sensing and constitute a warning about major challenges that complex climatic and physiographic features can impose on satellite rainfall missions.

Assessment of Precipitation Variability and Trends Based on Satellite Estimations for a Heterogeneous Colombian Region

“…This study evaluates the efficacy of the CHIRPS v2.0 database through statistical comparisons with rain gauge information provided by IDEAM. Similar to previous research [11,[14][15][16][17][18][19][20], our results indicate that CHIRPS v2.0 preserves important rainfall characteristics such as mean and seasonality at monthly and annual timescales. The bias and error metrics agree with the results of authors such as Katsanos et al [62], Urrea et al [11], Paredes Trejo et al [63], Dinku et al [20], and Alemu and Bawoke [48], among others.…”

“…These conclusions are similar to those from Cruz-Roa et al [13], where they created rainfall maps through cokriging using ground data and elevation. Researchers have widely used satellite rainfall datasets for case studies in Colombia in recent years [14][15][16][17][18][19][20]. Baez-Villanueva et al [21] analyzed the temporal and spatial evaluation of satellite rainfall data over Chile, Brazil, and Colombia using six different estimates: TRMM 3B42v7, TRMM 3B42RT, CHIRPSv2, CMORPHv1, PERSIANN-CDR, and MSWEPv2.…”

Colombian climatology in CMIP5/CMIP6 models: Persistent biases and improvements

“…For temperature, both [31]. Moreover, satellite products like TRMM (considered here as the reference dataset for precipitation) exhibit biases in Colombia, with overestimations in the Andes and underestimations in the Pacific region [81]. Therefore, we also compare model simulations with respect to climatological annual cycles from IDEAM gauges located across the country.…”

Towards a Mechanistic Understanding of Precipitation Over the Far Eastern Tropical Pacific and Western Colombia, One of the Rainiest Spots on Earth

“…We acknowledge that GPM products suffer from calibration and scale issues (0.1° × 0.1° grid size; Tang et al., 2020) and that there are sites like Lloró or other places on Earth showing long‐term rain gauge records exceeding this record‐breaking GPM‐based estimate. Regardless of the inherited uncertainties of the remote sensing precipitation products (Vallejo‐Bernal et al., 2020), the region offshore of the Colombian Pacific coast shows a striking, broad, coherent area of precipitation maximum.…”