Emergent Properties of Convection in OTREC and PREDICT

Raymond, David J.; Fuchs-Stone, Zeljka

doi:10.1029/2020jd033585

Cited by 19 publications

(18 citation statements)

References 110 publications

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“…OTREC data are treated as in Raymond and Fuchs-Stone (2021). Dropsonde data are analyzed by a three-dimensional variational scheme (3DVar; López-Carrillo & Raymond, 2011), which inputs only dropsondes; no background analysis field is incorporated.…”

Section: Otrec Data Analysismentioning

confidence: 99%

Weak Temperature Gradient Modeling of Convection in OTREC

Raymond

Fuchs-Stone

2021

J Adv Model Earth Syst

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Section: Otrec Data Analysismentioning

confidence: 99%

Weak Temperature Gradient Modeling of Convection in OTREC

Raymond

Fuchs-Stone

2021

J Adv Model Earth Syst

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“…The recent Organization of Tropical East Pacific Convection (OTREC) and PRE‐Depression Investigation of Cloud‐systems in the Tropics (PREDICT) field campaigns have shown that heavy precipitation does not scale with large values of CAPE, but rather with strong moisture convergence, large values of saturation fraction and small values of both instability index and deep convective inhibition (Fuchs‐Stone et al ., 2020; Raymond and Fuchs‐Stone, 2021). Even though Suhas and Zhang (2015) pointed out that this is because CAPE only shows a good correlation with convective precipitation when it leads precipitation by at least 3 hrs, they also found a better correlation of convective precipitation with moisture convergence than with CAPE.…”

Section: Introductionmentioning

confidence: 99%

Characteristics of convective precipitation over tropical Africa in storm‐resolving global simulations

Becker

Bechtold

Sandu

2021

Quart J Royal Meteoro Soc

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We analyse how the representation of deep convection affects the characteristics of convective precipitation over tropical Africa in global storm-resolving simulations with the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS). The simulations consist in 48 hr forecasts initiated daily at 0000 UTC for a 40-day period in August and September 2016, at ∼9 and 4 km resolution. We find that results agree best with the satellite retrieval Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG) when a term for the integrated and scaled total advective moisture tendency is added to the convective instability closure. This allows for a better coupling of the convective parametrization to the mesoscale dynamics and improves precipitation intensity (increased), precipitating area fraction (decreased), the size of individual deep convective systems (decreased) and their propagation (westward), even though the duration of rain events remains underestimated. With explicit deep convection, the duration of rain events is more realistic, but precipitation intensity is significantly overestimated, and extreme rain events (>400 mm⋅day −1 ) are three times more likely than in GPM IMERG, causing biases in the organization of deep convection. In particular, individual convective systems are too small, hindering the formation of mesoscale convective systems. These findings are also valid for two other regions: the Maritime Continent and the contiguous United States. Our results suggest that deep convection is not completely resolved at a resolution of 9 or even 4 km, and that some scale-aware parametrization is still needed for global numerical weather prediction.

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“…This study exclusively uses OTREC dropsonde in situ observations UCAR/NCAR Earth Observing Laboratory (2019); Fuchs-Stone et al (2020); Raymond and Fuchs-Stone (2021); Vömel et al (2021) while a 3D variational analysis produced using those dropsondes serves as a 3D proxy of the observations. The 3D-var analysis is calculated by a penalty function minimization Raymond and López Carrillo (2011); López which produces a gridded data set where the interpolated values in between dropsondes satisfy the mass continuity equation.…”

Section: Methodsmentioning

confidence: 99%

“…The 3D-var analysis is calculated by a penalty function minimization Raymond and López Carrillo (2011); López which produces a gridded data set where the interpolated values in between dropsondes satisfy the mass continuity equation. This 3D-var approach was used in many previous studies of convection Gjorgjievska and Raymond (2014); Fuchs-Stone et al (2020); Raymond and Fuchs-Stone (2021).…”

Section: Methodsmentioning

confidence: 99%

“…Deep Convective Inhibition (DCIN), defined as the mean saturated moist entropy from 1.5 to 2 km minus the mean moist entropy from 0 to 1 km in the boundary layer, was also shown to play a significant role in convective development Sentić et al (2015); Fuchs-Stone et al ( 2020); Raymond and Fuchs-Stone (2021). Convection develops when DCIN is small or negative.…”

Section: Methodsmentioning

confidence: 99%

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On the impact of dropsondes on the EC IFS model (Cy47r1) analysis of convection during OTREC

Sentić

Bechtold

Fuchs-Stone

et al. 2021

Preprint

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Abstract. The Organization of Tropical East Pacific Convection (OTREC) field campaign, conducted August through October 2019, focuses on studying convection in the East Pacific and the Caribbean. An unprecedented number of dropsondes were deployed (648) during 22 missions to study the region of strong sea surface temperature (SST) gradients in the East Pacific region, the region just off the coast of Columbia, and in the uniform SST region in the southwest Caribbean. The dropsondes were assimilated in the European Center for Medium Range Weather Forecast model. This study quantifies departures, observed minus the model value of a variable, in dropsonde denial experiments, and studies time series of convective variables: saturation fraction which measures moisture, and instability index and deep convective inhibition which quantify atmospheric stability and boundary layer stability to convection, respectively. Departures are small whether dropsondes are assimilated or not, except in a special case of a precursor to tropical storm Ivo where wind departures are significantly larger when dropsondes are not assimilated. Departures are larger in cloudy regions compared to cloud free regions when comparing a vertically integrated departure with a cloudiness estimation. Above mentioned variables are all well represented by the model when com- pared to observations, with some systematic deviations in and above the boundary layer. Time series of these variables show artificial convective activity in the model, in the East Pacific region off the coast of Costa Rica, which we hypothesize occurs due to overestimation of moisture content in that region.

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Emergent Properties of Convection in OTREC and PREDICT

Cited by 19 publications

References 110 publications

Weak Temperature Gradient Modeling of Convection in OTREC

Weak Temperature Gradient Modeling of Convection in OTREC

Characteristics of convective precipitation over tropical Africa in storm‐resolving global simulations

On the impact of dropsondes on the EC IFS model (Cy47r1) analysis of convection during OTREC

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