Uncertainties in TRMM‐Era multisatellite‐based tropical rainfall estimates over the Maritime Continent

Abstract

This study investigates the regional and seasonal rainfall rate retrieval uncertainties within nine state‐of‐the‐art satellite‐based rainfall products over the Maritime Continent (MC) region. The results show consistently larger differences in mean daily rainfall among products over land, especially over mountains and along coasts, compared to over ocean, by about 20% for low to medium rain rates and 5% for heavy rain rates. However, rainfall differences among the products do not exhibit any seasonal dependency over both surface types (land and ocean) of the MC region. The differences between products largely depends on the rain rate itself, with a factor 2 difference for light rain and 30% for intermediate and high rain rates over ocean. The rain‐rate products dominated by microwave measurements showed less spread among themselves over ocean compared to the products dominated by infrared measurements. Conversely, over land, the rain gauge‐adjusted post–real‐time products dominated by microwave measurements produced the largest spreads, due to the usage of different gauge analyses for the bias corrections. Intercomparisons of rainfall characteristics of these products revealed large discrepancies in detecting the frequency and intensity of rainfall. These satellite products are finally evaluated at subdaily, daily, monthly, intraseasonal, and seasonal temporal scales against high‐quality gridded rainfall observations in the Sarawak (Malaysia) region for the 4 year period 2000–2003. No single satellite‐based rainfall product clearly outperforms the other products at all temporal scales. General guidelines are provided for selecting a product that could be best suited for a particular application and/or temporal resolution.

Interpretation of El Niño–Southern Oscillation‐related precipitation anomalies in north‐western Borneo using isotopic tracers

“…A spectral analysis of the merged δ 18 O time series demonstrates a dominant cycle spanning a period of between 10 and 70 days (Figure ). This is consistent with the pronounced ISV modes of Sarawak rainfall, which span periods of 20 to 90 days (Kanamori, Yasunari, & Kuraji, ; Rauniyar et al., ). Moerman et al () also found that the day‐to‐day δ 18 O variability was correlated more strongly with the TRMM rainfall retrieved within the 2.5° × 2.5° longitude/latitude box than with the local precipitation.…”

“…Original data are available online at http://disc.gsfc.nasa.gov/datacollection/TRMM_3B42_V7.shtml. At daily or longer time scale, the TRMM 3B42 has been found to moderately reproduce observed area averaged precipitation over Sarawak region (Rauniyar, Protat, & Kanamori, ).…”

Evaluation of regional climate model simulated rainfall over Indonesia and its application for downscaling future climate projections

“…These spatial and temporal resolutions are higher than most available precipitation products and CHIRPS data have been used by previous studies focusing on the MCR (Hermawan et al ., ; Rustiana et al ., ). While recent comparisons indicate that no single dataset is coherently better in all of the precipitation parameters analyzed (Rauniyar et al ., ), CHIRPS outperforms other datasets at the seasonal scale and offers the best spatial distribution of daily values. We believe CHIRPS data provide the best estimates of observed rainfall for the goals of this project.…”

Factorial inferential grid grouping and representativeness analysis for a systematic selection of representative grids

“…A key advantage of FIGGRA is a quantitative analysis of heterogeneities under nonstationarities based on systematic classification. Due to this advantage, FIGGRA may be an advanced and reliable alternative algorithm for Earth imagery classification and the associated diverse studies or practices [Jiang and Shekhar, 2017;Rauniyar et al, 2017;Schwenk et al, 2017]. In addition, FIGGRA-based spatial/temporal heterogeneity analysis may facilitate improvement of various quantitative analysis approaches for investigating many problems in Earth and space sciences, e.g., monitoring network design [Mishra et al, 2016;Gleason et al, 2017], urban ecology [Bardhan et al, 2016], representative-days selection [Rife et al, 2013], O 3 distribution detection [Parrish et al, 2016], spatial tracking or navigation [Fuchs et al, 2015;Palmer et al, 2016], tsunamis modeling [Grawe and Makela, 2015], atmospheric process analyses [Weisz et al, 2015], seafloor venting detection [Smart et al, 2017], sporadic E propagation [Ghosh and Berkey, 2015], or eco-system analysis [Zhang et al, 2017].…”