Met Office NERC Cloud model (MONC)

Nicholas Brown; Michele Weiland

doi:10.7488/ds/2343

Met Office NERC Cloud model (MONC)

Research output: Non-textual form › Software

Abstract

Source code for the Met Office NERC Cloud model (MONC) which is an atmospheric model used to study clouds and turbulent flows. It has been shown to scale to over 32768 compute cores and includes both the simulation (computation of raw data prognostic fields) and in-situ data analytics to then convert these to diagnostics. A variety of sample test-cases are included and the README file describes, in detail, how to compile and run the model.

Original language	English
Publisher	EPCC, University of Edinburgh
Media of output	Online
DOIs	https://doi.org/10.7488/ds/2343
Publication status	Published - 30 Apr 2018

Keywords / Materials (for Non-textual outputs)

MONC
HPC
Parallel Code
in-situ analytics
atmospheric model
Large Eddy Simulation
LES

Access to Document

10.7488/ds/2343Licence: All Rights Reserved

http://hdl.handle.net/10283/3071Licence: All Rights Reserved

3 Conference contribution
2 Abstract
2 Article
1 Paper

Massively parallel parcel-based simulation of moist convection
Boeing, S., Gibb, G., Brown, N., Weiland, M. & Dritschel, D. G., 2019. 1 p.
Research output: Contribution to conference › Abstract › peer-review

Open Access
Leveraging MPI RMA to optimise halo-swapping communications in MONC on Cray machines
Brown, N., Bareford, M. & Weiland, M., 24 May 2018, CUG 2018: Stockholm, Sweden, 20-24 May 2018. 12 p.
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Open Access
File
In situ data analytics for highly scalable cloud modelling on Cray machines
Brown, N., Weiland, M., Hill, A. & Shipway, B., 10 Jan 2018, In: Concurrency and Computation: Practice and Experience. 30, 1, 14 p., 4331.
Research output: Contribution to journal › Article › peer-review

Open Access
File

Met Office NERC Cloud model (MONC)
Brown, N. (Creator) & Weiland, M. (Creator), Edinburgh DataShare, 30 Apr 2018
DOI: 10.7488/ds/2343
Dataset

Nick Brown
- Computer Systems
- EPCC - Senior Research Fellow
Person: Academic: Research Active

Cite this

@misc{cc1566da11764aabb9c506acf77d9799,

title = "Met Office NERC Cloud model (MONC)",

abstract = "Source code for the Met Office NERC Cloud model (MONC) which is an atmospheric model used to study clouds and turbulent flows. It has been shown to scale to over 32768 compute cores and includes both the simulation (computation of raw data prognostic fields) and in-situ data analytics to then convert these to diagnostics. A variety of sample test-cases are included and the README file describes, in detail, how to compile and run the model.",

keywords = "MONC, HPC, Parallel Code, in-situ analytics, atmospheric model, Large Eddy Simulation, LES",

author = "Nicholas Brown and Michele Weiland",

year = "2018",

month = apr,

day = "30",

doi = "10.7488/ds/2343",

language = "English",

publisher = "EPCC, University of Edinburgh",

}

TY - ADVS

T1 - Met Office NERC Cloud model (MONC)

AU - Brown, Nicholas

AU - Weiland, Michele

PY - 2018/4/30

Y1 - 2018/4/30

N2 - Source code for the Met Office NERC Cloud model (MONC) which is an atmospheric model used to study clouds and turbulent flows. It has been shown to scale to over 32768 compute cores and includes both the simulation (computation of raw data prognostic fields) and in-situ data analytics to then convert these to diagnostics. A variety of sample test-cases are included and the README file describes, in detail, how to compile and run the model.

AB - Source code for the Met Office NERC Cloud model (MONC) which is an atmospheric model used to study clouds and turbulent flows. It has been shown to scale to over 32768 compute cores and includes both the simulation (computation of raw data prognostic fields) and in-situ data analytics to then convert these to diagnostics. A variety of sample test-cases are included and the README file describes, in detail, how to compile and run the model.

KW - MONC

KW - HPC

KW - Parallel Code

KW - in-situ analytics

KW - atmospheric model

KW - Large Eddy Simulation

KW - LES

U2 - 10.7488/ds/2343

DO - 10.7488/ds/2343

M3 - Software

PB - EPCC, University of Edinburgh

ER -

Met Office NERC Cloud model (MONC)

Abstract

Keywords / Materials (for Non-textual outputs)

Access to Document

Fingerprint

Research output

Massively parallel parcel-based simulation of moist convection

Leveraging MPI RMA to optimise halo-swapping communications in MONC on Cray machines

In situ data analytics for highly scalable cloud modelling on Cray machines

Datasets

Met Office NERC Cloud model (MONC)

Profiles

Nick Brown

Cite this