An investigation of microphysics and subgrid‐scale variability in warm‐rain clouds using the A‐Train observations and a multiscale modeling framework

Takahashi, H.; Lebsock, Matthew; Suzuki, Kentaroh; Stephens, Graeme L.; Wang, Minghuai

doi:10.1002/2016jd026404

Cited by 22 publications

(15 citation statements)

References 46 publications

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“…In addition to the collision-coalescence process due to the convective updraft, two other processes determine raindrop growth, that is, accretion of bulk cloud water and self-collection among raindrops [35]. The updraft is generally weaker over ocean than over land [36]. The strength of the updrafts affects the height at which collisions between different sized droplets occur.…”

Section: Engineering and Mathematical Topics In Rainfall 78mentioning

confidence: 99%

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Seasonal and Diurnal Variations of Vertical Profile of Precipitation over Indonesian Maritime Continent

Marzuki¹,

Hashiguchi²,

Vonnisa³

et al. 2018

Engineering and Mathematical Topics in Rainfall

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This chapter presents variabilities in the vertical structure of precipitation over the Indonesian maritime continent (IMC), which were inferred from the gradients of the radar reflectivity (dBZ) below the freezing level and were gathered from the latest 2A25 TRMM-Precipitation Radar product over a 17-year time span (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014). In general, the downward increasing (DI) pattern of dBZ toward the surface is more dominant than the downward decreasing (DD) pattern, which has a ratio of 1.3. The DI is frequently observed over the ocean, and the higher prevailing rain top heights over land are associated with DD, in most cases. Shallow convective rains have the largest ratio of DI to DD (>4), followed by deep convective rains. The largest ratio is observed during DecemberJanuary-February (DJF) when wetter conditions are dominant over the IMC, which is a favorable condition for raindrop growth. The stratiform rains show a dominant DD in which the ratio of DD to DI is greater than 1.6 for each season. The spatial distribution of the stratiform gradient is more complex than that of convective rain and does not show a robust land-ocean contrast. The diurnal variation in the reflectivity gradient for stratiform rain is less pronounced. With convective rain, DD is more dominant in the afternoon and evening over a large island, indicates a decrease in the raindrop concentration due to the evaporation and updraft associated with the intense convection.

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Section: Engineering and Mathematical Topics In Rainfall 78mentioning

confidence: 99%

“…Therefore, a greater concentration of small-sized-raindrops will be observed at the surface. On the other hand, smaller raindrops can fall and monotonically grow in weaker updraft condition [36].…”

Section: Engineering and Mathematical Topics In Rainfall 78mentioning

confidence: 99%

Seasonal and Diurnal Variations of Vertical Profile of Precipitation over Indonesian Maritime Continent

Marzuki¹,

Hashiguchi²,

Vonnisa³

et al. 2018

Engineering and Mathematical Topics in Rainfall

View full text Add to dashboard Cite

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“…On the other hand, only the CRM could satisfactorily capture the cloud‐to‐rain transition process observed in the satellite data. One of the causes of this systematic difference could be attributed to the treatment of precipitation in the model (Takahashi et al, ; Wang et al, ), that is, diagnostic (hereinafter “DIAG”) and prognostic (hereinafter “PROG”) approaches. For example, although small drizzle drops, which do not reach the surface within a single‐model time step, are suspended in the real atmosphere (e.g., Wood, ), the DIAG method assumes that all the diagnosed rainwater precipitates to the surface within a single time step.…”

Section: Introductionmentioning

confidence: 99%

Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations

Michibata

Suzuki

Sekiguchi

et al. 2019

J Adv Model Earth Syst

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A comprehensive two‐moment microphysics scheme is incorporated into the MIROC6‐SPRINTARS general circulation model (GCM). The new scheme includes prognostic precipitation for both rain and snow and considers their radiative effects. To evaluate the impacts of applying different treatments of precipitation and the associated radiative effect, we perform climate simulations employing both the traditional diagnostic and new prognostic precipitation schemes, the latter also being tested with and without incorporating the radiative effect of snow. The prognostic precipitation, which maintains precipitation in the atmosphere across multiple time steps, models the ratio of accretion to autoconversion as being approximately an order of magnitude higher than that for the diagnostic scheme. Such changes in microphysical process rates tend to reduce the cloud water susceptibility as the autoconversion process is the only pathway through which aerosols can influence rain formation. The resultant anthropogenic aerosol effect is reduced by approximately 21% in the prognostic precipitation scheme. Modifications to the microphysical process rates also change the vertical distribution of hydrometeors in the manner that increases the fractional occurrence of single‐layered warm clouds by 38%. The new scheme mitigates the excess of supercooled liquid water produced by the previous scheme and increases the total mass of ice hydrometeors. Both characteristics are consistent with CloudSat/CALIPSO retrievals. The radiative effect of snow is significant at both longwave and shortwave (6.4 and 5.1 W/m2 in absolute values, respectively) and can alter the precipitation fields via energetic controls on precipitation. These results suggest that the prognostic precipitation scheme, with its radiative effects incorporated, makes an indispensable contribution to improving the reliability of climate modeling.

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“…To identify the source of warm precipitation biases and fundamentally improve the model representation of precipitation, there is a growing interest in the GCM community to study precipitation and relevant processes with a particular emphasis on their process-level characteristics (Baker & Peter, 2008;Geoffroy et al, 2008;Takahashi et al, 2017;Zhao et al, 2016). This emerging trend of research is also facilitated by recent progress of satellite observations, particularly given the emergence of the A-Train constellation that simultaneously observes clouds and precipitation with multiple platforms and sensors (L'Ecuyer & Jiang, 2010;Stephens et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

A Multimodel Study on Warm Precipitation Biases in Global Models Compared to Satellite Observations

et al. 2017

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The cloud‐to‐precipitation transition process in warm clouds simulated by state‐of‐the‐art global climate models (GCMs), including both traditional climate models and a high‐resolution model, is evaluated against A‐Train satellite observations. The models and satellite observations are compared in the form of the statistics obtained from combined analysis of multiple‐satellite observables that probe signatures of the cloud‐to‐precipitation transition process. One common problem identified among these models is the too‐frequent occurrence of warm precipitation. The precipitation is found to form when the cloud particle size and the liquid water path (LWP) are both much smaller than those in observations. The too‐efficient formation of precipitation is found to be compensated for by errors of cloud microphysical properties, such as underestimated cloud particle size and LWP, to an extent that varies among the models. However, this does not completely cancel the precipitation formation bias. Robust errors are also found in the evolution of cloud microphysical properties from nonprecipitating to drizzling and then to raining clouds in some GCMs, implying unrealistic interaction between precipitation and cloud water. Nevertheless, auspicious information is found for future improvement of warm precipitation representations: the adoption of more realistic autoconversion scheme in the high‐resolution model improves the triggering of precipitation, and the introduction of a sophisticated subgrid variability scheme in a traditional model improves the simulated precipitation frequency over subtropical eastern ocean. However, deterioration in other warm precipitation characteristics is also found accompanying these improvements, implying the multisource nature of warm precipitation biases in GCMs.

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An investigation of microphysics and subgrid‐scale variability in warm‐rain clouds using the A‐Train observations and a multiscale modeling framework

Cited by 22 publications

References 46 publications

Seasonal and Diurnal Variations of Vertical Profile of Precipitation over Indonesian Maritime Continent

Seasonal and Diurnal Variations of Vertical Profile of Precipitation over Indonesian Maritime Continent

Prognostic Precipitation in the MIROC6‐SPRINTARS GCM: Description and Evaluation Against Satellite Observations

A Multimodel Study on Warm Precipitation Biases in Global Models Compared to Satellite Observations

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