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A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model

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A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model. / Bassett, Richard; Young, Paul; Blair, Gordon; Samreen, Faiza; Simm, William.

In: Journal of Geophysical Research, Vol. 125, No. 7, e2019JD031286, 16.04.2020.

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@article{82b1e5dcd4e64f4a83d5edb3bef89761,
title = "A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model",
abstract = "The Weather Research and Forecasting (WRF) community model is widely used to explore cross‐scale atmospheric features. Although WRF uncertainty studies exist, these usually involve ensembles where different physics options are selected (e.g., the boundary layer scheme) or adjusting individual parameters. Uncertainty from perturbing initial conditions, which generates internal model variability (IMV), has rarely been considered. Moreover, many off‐line WRF research studies generate conclusions based on a single model run without addressing any form of uncertainty. To demonstrate the importance of IMV, or noise, we present a 4‐month case study of summer 2018 over London, UK, using a 244‐member initial condition ensemble. Simply by changing the model start time, a median 2‐m temperature range or IMV of 1.2 °C was found (occasionally exceeding 8 °C). During our analysis, episodes of high and low IMV were found for all variables explored, explained by a relationship with the boundary condition data. Periods of slower wind speed input contained increased IMV, and vice versa, which we hypothesis is related to how strongly the boundary conditions influence the nested region. We also show the importance of IMV effects for the uncertainty of derived variables like the urban heat island, whose median variation in magnitude is 1 °C. Finally, a realistic ensemble size to capture the majority of WRF IMV is also estimated, essential considering the high computational overheads (244 members equaled 140,000 CPU hours). We envisage that highlighting considerable IMV in this repeatable manner will help advance best practices for the WRF and wider regional climate modeling community.",
keywords = "ensemble, initial conditions, internal model variability (IMV), regional climate model (RCM), uncertainty, Weather Research and Forecasting (WRF)",
author = "Richard Bassett and Paul Young and Gordon Blair and Faiza Samreen and William Simm",
year = "2020",
month = apr
day = "16",
doi = "10.1029/2019JD031286",
language = "English",
volume = "125",
journal = "Journal of Geophysical Research",
issn = "0148-0227",
publisher = "American Geophysical Union",
number = "7",

}

RIS

TY - JOUR

T1 - A Large Ensemble Approach to Quantifying Internal Model Variability Within the WRF Numerical Model

AU - Bassett, Richard

AU - Young, Paul

AU - Blair, Gordon

AU - Samreen, Faiza

AU - Simm, William

PY - 2020/4/16

Y1 - 2020/4/16

N2 - The Weather Research and Forecasting (WRF) community model is widely used to explore cross‐scale atmospheric features. Although WRF uncertainty studies exist, these usually involve ensembles where different physics options are selected (e.g., the boundary layer scheme) or adjusting individual parameters. Uncertainty from perturbing initial conditions, which generates internal model variability (IMV), has rarely been considered. Moreover, many off‐line WRF research studies generate conclusions based on a single model run without addressing any form of uncertainty. To demonstrate the importance of IMV, or noise, we present a 4‐month case study of summer 2018 over London, UK, using a 244‐member initial condition ensemble. Simply by changing the model start time, a median 2‐m temperature range or IMV of 1.2 °C was found (occasionally exceeding 8 °C). During our analysis, episodes of high and low IMV were found for all variables explored, explained by a relationship with the boundary condition data. Periods of slower wind speed input contained increased IMV, and vice versa, which we hypothesis is related to how strongly the boundary conditions influence the nested region. We also show the importance of IMV effects for the uncertainty of derived variables like the urban heat island, whose median variation in magnitude is 1 °C. Finally, a realistic ensemble size to capture the majority of WRF IMV is also estimated, essential considering the high computational overheads (244 members equaled 140,000 CPU hours). We envisage that highlighting considerable IMV in this repeatable manner will help advance best practices for the WRF and wider regional climate modeling community.

AB - The Weather Research and Forecasting (WRF) community model is widely used to explore cross‐scale atmospheric features. Although WRF uncertainty studies exist, these usually involve ensembles where different physics options are selected (e.g., the boundary layer scheme) or adjusting individual parameters. Uncertainty from perturbing initial conditions, which generates internal model variability (IMV), has rarely been considered. Moreover, many off‐line WRF research studies generate conclusions based on a single model run without addressing any form of uncertainty. To demonstrate the importance of IMV, or noise, we present a 4‐month case study of summer 2018 over London, UK, using a 244‐member initial condition ensemble. Simply by changing the model start time, a median 2‐m temperature range or IMV of 1.2 °C was found (occasionally exceeding 8 °C). During our analysis, episodes of high and low IMV were found for all variables explored, explained by a relationship with the boundary condition data. Periods of slower wind speed input contained increased IMV, and vice versa, which we hypothesis is related to how strongly the boundary conditions influence the nested region. We also show the importance of IMV effects for the uncertainty of derived variables like the urban heat island, whose median variation in magnitude is 1 °C. Finally, a realistic ensemble size to capture the majority of WRF IMV is also estimated, essential considering the high computational overheads (244 members equaled 140,000 CPU hours). We envisage that highlighting considerable IMV in this repeatable manner will help advance best practices for the WRF and wider regional climate modeling community.

KW - ensemble

KW - initial conditions

KW - internal model variability (IMV)

KW - regional climate model (RCM)

KW - uncertainty

KW - Weather Research and Forecasting (WRF)

U2 - 10.1029/2019JD031286

DO - 10.1029/2019JD031286

M3 - Journal article

VL - 125

JO - Journal of Geophysical Research

JF - Journal of Geophysical Research

SN - 0148-0227

IS - 7

M1 - e2019JD031286

ER -