Factors Governing Cellular Convection in Orographic Precipitation

Kirshbaum, Daniel J.; Durran, Dale R.

doi:10.1175/1520-0469(2004)061<0682:fgccio>2.0.co;2

Cited by 91 publications

(85 citation statements)

References 25 publications

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“…We also use the total diabatic heating [Wright and Fueglistaler, 2013] and the moist Froude number (Fr m ) as diagnostic tools for flow over topography [Reeves and Lin, 2006]. To calculate the Fr m , we used constant meridional flow (v = 5 m/s), mountain height (h = 5 km), and the saturated Brunt-Väisälä Frequency (N m ) [Kirshbaum and Durran, 2004;Reeves and Lin, 2006] when a negative N m layer above topography is observed, latent heat is strong, and the Fr m is negative. As a result, the statically unstable layer allows moisture to flow over the terrain (i.e., unblocked).…”

Section: Methodsmentioning

confidence: 54%

The neglected Indo‐Gangetic Plains low‐level jet and its importance for moisture transport and precipitation during the peak summer monsoon

Acosta

Huber

2017

Geophysical Research Letters

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Accurately simulating the Indo‐Asian monsoon (IAM) using atmospheric general circulation models (AGCMs) is challenging but crucial. This study uses reanalysis products European Centre of Medium‐Range Forecast Interim reanalysis, Japanese Reanalysis year 55, and High Asia Reanalysis to highlight an easterly, low‐level barrier jet along the Indo‐Gangetic Plain (referred from here as IG LLJ), which we identify as the primary moisture transport mechanism for the northeastern branch of the IAM. We show that the NCAR family of AGCMs (Community Atmospheric Model (CAM)) does not capture this circulation until 1/2° or greater spatial horizontal resolution is used. The IG LLJ develops due to a persistent low‐pressure system centered over the Ganges basin and is enhanced by the Himalayas. Using diabatic heating rates and the moist Froude number as diagnostics, we find that in CAM, this branch of the IAM displays two different dynamical regimes as a function of resolution. At low resolution, the atmosphere near the Himalayas is statically unstable, diabatic heating is strong, and the moisture flow is southwesterly from the Arabian Sea and moves over the terrain (unblocked). At high resolution, the moist static stability near the Himalayan Mountains is stable, diabatic heating is weak, and the flow primarily enters easterly from the Bay of Bengal and moves parallel to the terrain (blocked). During the summer season, the low‐resolution CAM is locked into the unblocked mode, which has serious implications for interpreting topography‐monsoon interactions. For a broader context, we demonstrate that more than half of the CMIP5 models do not capture the IG LLJ, which further highlights model‐data mismatch across the IAM region.

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Section: Methodsmentioning

confidence: 54%

The neglected Indo‐Gangetic Plains low‐level jet and its importance for moisture transport and precipitation during the peak summer monsoon

Acosta

Huber

2017

Geophysical Research Letters

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“…Note, however, that the model is not foreseen to simulate purely convection. Such embedded convection mainly occurs when lifting is locally enhanced at mid-and upper tropospheric levels leading to a decrease of thermal stability by the release of the latent heat of condensation (e. g., Kirshbaum and Durran, 2004;Kirshbaum and Smith, 2008;Cannon 5 et al, 2012). Convection in general involves several complex processes that make simulation a difficult task.…”

Section: Embedded Convectionmentioning

confidence: 99%

Flood-Related Extreme Precipitation in Southwestern Germany: Development of a Two-Dimensional Stochastic Precipitation Model

Ehmele¹,

Kunz²

2018

Preprint

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Abstract. Various application fields, such as insurance industry risk assessments for the design of flood protection systems, require reliable precipitation statistics in high spatial resolution, including estimates for events with high return periods. Observations from point stations, however, lack of spatial representativeness, especially over complex terrain, and do not reliably represent the heavy tail of the distribution function. This paper presents a new method for stochastically simulating precipitation fields based on a linear theory of orographic precipitation and additional functions that consider synoptically driven rainfall The model is applied for the stochastic simulation of heavy rainfall over the complex terrain of Southwest Germany. It is shown that the model, despite its simplicity, yields reliable precipitation fields. Differences between observed and simulated 10 rainfall statistics are small, being in the order of only ±10% for return periods of up to 1,000 years.

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“…The spatial scale of trigger mechanisms in OP may range from a few hundred metres (small convective cells; e.g. Kirshbaum and Durran, 2005;Kirshbaum and Smith 2009) to a few kilometres (in stably stratified cases; e.g. Smith and Barstad, 2004), scales that are present in most complex terrain formations.…”

Section: Introductionmentioning

confidence: 99%

“…Awareness of the role played by intensification of precipitation due to enhanced cloud liquid water in embedded cellular convective cells is one of the legacies of MAP (e.g. Yuter and Houze, 2003;Kirshbaum and Durran, 2005). The IMPROVE campaigns addressed the microphysical side of OP.…”

Section: Introductionmentioning

confidence: 99%

Orographic precipitation across an island in southern Norway: model evaluation of time‐step precipitation

Barstad

Caroletti

2012

Quart J Royal Meteoro Soc

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Observations of high spatio-temporal resolution from a precipitation network across Stord Island, located off the west coast of southern Norway, are compared to state-of-the-art numerical model simulations. The 12 week long observation period shows a clear orographic precipitation signal across the 10-15 km wide island (peak elevation 750 m). The model experiment designed to capture this signal is run with 9-3-1 km nested grid and results are compared with observations at different accumulation intervals. The total amount of precipitation over the 12 week period is underpredicted, even for the 1 km grid. The maximum precipitation intensity, however, is slightly overpredicted. Time-step (5 s) precipitation from the model is also compared with observed intensities at the highest possible temporal resolution permitted by the rain collection method. The observations indicate that most of the precipitation is formed at intensities from 5 to 20 mm h −1 . A smaller fraction of the precipitation is formed with intensities >20 mm h −1 . The simulated precipitation at the 3 km grid did not reproduce at the correct intensities. The 1 km grid showed an improved tendency to produce the precipitation at the right intensities, but had too high maximum intensities. A test simulation where the intermediate grid had no cumulus parametrization was performed. Even though effects such as undercatchment and unresolved terrain influenced, it was concluded that the test run performed better than the control run. The investigation concluded that, in general, a 1 km grid is sufficient for capturing the most intensive precipitation event in a satisfying way.

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Factors Governing Cellular Convection in Orographic Precipitation

Cited by 91 publications

References 25 publications

The neglected Indo‐Gangetic Plains low‐level jet and its importance for moisture transport and precipitation during the peak summer monsoon

The neglected Indo‐Gangetic Plains low‐level jet and its importance for moisture transport and precipitation during the peak summer monsoon

Flood-Related Extreme Precipitation in Southwestern Germany: Development of a Two-Dimensional Stochastic Precipitation Model

Orographic precipitation across an island in southern Norway: model evaluation of time‐step precipitation

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