Comparative Ground Validation of IMERG and TMPA at Variable Spatiotemporal Scales in the Tropical Andes

Abstract

An initial ground validation of the Integrated Multisatellite Retrievals for GPM (IMERG) Day-1 product from March 2014 to August 2015 is presented for the tropical Andes. IMERG was evaluated along with the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) against 302 quality-controlled rain gauges across Ecuador and Peru. Detection, quantitative estimation statistics, and probability distribution functions are calculated at different spatial (0.18, 0.258) and temporal (1 h, 3 h, daily) scales. Precipitation products are analyzed for hydrometeorologically distinct subregions. Results show that IMERG has a superior detection and quantitative rainfall intensity estimation ability than TMPA, particularly in the high Andes. Despite slightly weaker agreement of mean rainfall fields, IMERG shows better characterization of gauge observations when separating rainfall detection and rainfall rate estimation. At corresponding space-time scales, IMERG shows better estimation of gauge rainfall probability distributions than TMPA. However, IMERG shows no improvement in both rainfall detection and rainfall rate estimation along the dry Peruvian coastline, where major random and systematic errors persist. Further research is required to identify which rainfall intensities are missed or falsely detected and how errors can be attributed to specific satellite sensor retrievals. The satellite-gauge difference was associated with the point-area difference in spatial support between gauges and satellite precipitation products, particularly in areas with low and irregular gauge network coverage. Future satellite-gauge evaluations need to identify such locations and investigate more closely interpixel point-area differences before attributing uncertainties to satellite products.

Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions

“…One plausible explanation for this is the fact that IMERGV03 uses a prelaunch GPM database; the transition to a full GPM database indeed improves the accuracy of IMERGV06B (with 20% decrease in MRE and nearly 4% in CRMSE) relative to TMPAV07. Similarly, Manz and others [28] evaluated IMERG Day-1 against TMPA over Peru and Ecuador and concluded that IMERG performance was worse compared to TMPA in terms of normalized RMSE for the dry coastal region of Peru.…”

Rainfall monitoring network design using conditioned Latin hypercube sampling and satellite precipitation estimates: An application in the ungauged Ecuadorian Amazon

“…GPM, which is the successor of TRMM, provides the next generation of precipitation products at a spatial resolution of 0.1 × 0.1° (~10 × 10 km) since April 2014 (Hou et al ., ). Early assessments of GPM products around the world have shown a better performance compared to TRMM products at different temporal scales (Prakash et al ., ; Tang et al, ; ; Wang et al ., ; Xu et al ., ) and it also has better capabilities to identify local precipitation patterns caused by orographic effects (Sharifi et al ., ; Manz et al ., ; Mayor et al ., ). For the study, monthly estimates of the IMERG Level 3 Final Run research product Version 05 (hereafter IMERG L3) for 2015 were downloaded from the NASA database (https://pmm.nasa.gov/data-access/downloads/gpm).…”

“…Despite this importance, little is known about the amount and variability of precipitation in this region since many areas remain ungauged. Several researchers have stressed the need of a well‐developed monitoring network in the region with a representative spatio‐temporal coverage that would support (a) an adequate characterization of precipitation variability (Morán‐Tejeda et al ., ), (b) the evaluation of satellite precipitation products and streamflow simulations (Manz et al ., ; Zubieta et al ., ) and (c) for a correct assessment of downscaling precipitation techniques (Ulloa et al ., ), among others. To design such monitoring network for the Amazonian region, it is required to take several things into account, such as the lack of prior information, the inherent spatio‐temporal nature of precipitation and the inaccessibility that might make difficult the rain gauge network deployment and data retrieval.…”

Evaluación de productos IMERG V03 y TMPA V7 en la detección de crecidas caso de estudio cuenca del río Cañar

“…El preprocesamiento de la información de lluvia se ha realizado por medio de tres métodos: control de extremos, consistencia interna y consistencia espacial (Shen et al 2010;Manz et al 2017). Para el control extremo se comparan valores horarios máximos con los diarios, de las estaciones automáticas con valores diarios máximos obtenidos de estaciones convencionales.…”

“…La base de datos de lluvia a nivel horario de estaciones automáticas del INAMHI en la cuenca del río Cañar fue la información de referencia para el ejercicio comparativo, ajuste y corrección, tomando como referencias algunas investigaciones planteadas (Nerini et al 2015;Manz et al 2017). La resolución espacio-temporal de productos para este estudio correspondiente IMERGV03 es 0.1° 1 hora y a TMPA es 0.25° 3 horas.…”