A Regime-Based Evaluation of TRMM Oceanic Precipitation Biases

Henderson, David S.; Kummerow, Christian D.; Marks, David A.; Berg, Wesley

doi:10.1175/jtech-d-16-0244.1

Cited by 17 publications

(2 citation statements)

References 59 publications

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“…Roca et al () have shown that MCS of 12‐hr duration or longer bring 75% of tropical precipitation, and the subset of these systems traveling 250 km or more bring 60%. Several studies have compiled climatologies of tropical rainfall (Elsaesser & Kummerow, ; Houze et al, ; Zipser et al, ), and Henderson et al () have shown that organized precipitation causes much of the discrepancy between Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and precipitation radar (PR) retrievals during El Niño periods. They have also used TRMM data to show an increase in upper tropospheric (UT) moisture and stratiform rainfall during El Niño periods (Henderson et al, ).…”

Section: Introductionmentioning

confidence: 99%

The Response of Tropical Organized Convection to El Niño Warming

et al. 2019

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Convective organization has a large impact on precipitation and feeds back on larger-scale circulations in the tropics. The degree of this convective organization changes with modes of climate variability like the El Niño-Southern Oscillation (ENSO), but because organization is not represented in current climate models, a quantitative assessment of these shifts has not been possible. Here, we construct multidecade satellite climatologies of occurrence of tropical convective organization and its properties and assess changes with ENSO phase. The occurrence of organized deep convection becomes more concentrated, increasing threefold in the eastern and central Pacific during El Niño and decreasing twofold outside of these regions. Both horizontal extent of the cold cloud shield and convective depth increase in regions of positive sea surface temperature anomaly (SSTa); however, the regions of greatest convective deepening are those of large-scale ascent, rather than those of warmest SSTa. Extent decreases with SSTa at a rate of about 20 km/K, while the SSTa dependence of depth is only about 0.2 K/K. We introduce two values to describe convective changes with ENSO more succinctly: (1) an information entropy metric to quantify the clustering of convective system occurrences and (2) a growth metric to quantify deepening relative to spreading over the system lifetime. Finally, with collocated precipitation data, we see that rainfall attributable to convective organization jumps up to 5% with warming. Rain intensity and amount increase for a given system size during El Niño, but a given rain amount may actually fall with higher intensity during La Niña. Plain Language Summary Tropical storms at an intermediate scale between individualthunderstorms and cyclones or hurricanes are not represented by climate models. As the satellite data record now spans several decades, we can use these observations to understand how large storms change during El Niño periods. Here, we find that warmer waters in the central and eastern Pacific during El Niño cause a dramatic increase in storm occurrence. Storms are more likely to occur near to other storms and to become larger in both the vertical and horizontal. Changes to large-scale winds are more influential on these structural factors than locally warmer ocean water. The hydrological impact of these storms is also important because they bring the majority of precipitation in some regions. We see that a storm of a certain extent brings larger volume and intensity of rain during El Niño. But a certain rain volume may actually fall with greater intensity during La Niño.

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Section: Introductionmentioning

confidence: 99%

The Response of Tropical Organized Convection to El Niño Warming

et al. 2019

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“…Different coefficients are then applied to retrieve precipitation rate as the former is a necessary input to the hydrometeor melting model [4,5]. For passive microwave (PMW) sensors such as the microwave imager (GMI) onboard the same GPM Core Observatory satellite, although precipitation type is not yet in its GPM-GMI Radiometer Precipitation Profiling (GPROF) product yet, some recent research indicate that pregrouping according to different weather systems or atmospheric stabilities (e.g., convective available potential energy, or CAPE) could potentially improve the precipitation rate retrieval [6,7].…”

Section: Introductionmentioning

confidence: 99%

A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements

et al. 2022

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Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

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