Routine Symbiotic LargeEddy Simulation and Observation Data Bundles of Continental Shallow Convection for Improving Atmospheric Models Core LASSO Team PNNL William Gustafson PI Heng Xiao ID: 914760
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Slide1
https://www.arm.gov/capabilities/modeling
Routine, Symbiotic Large-Eddy Simulation and Observation Data Bundles of Continental Shallow Convection for Improving Atmospheric Models
Core LASSO TeamPNNL: William Gustafson (PI), Heng XiaoBNL: Andrew M. Vogelmann (Co-PI), Satoshi Endo, Tami Toto UCLA: Zhijin LiNat. U of Defense Tech: Xiaoping ChengORNL: Bhargavi KrishnaAdditional ContributionsWayne Angevine & Mariko Oue
Understanding & Modelling Atmospheric Processes, 2
nd
Pan-GASS Meeting
Lorne, Australia, 1 March 2018
Slide2Another set of forcings for LES runs?
Search Google Scholar for “les intercomparison” and in the first 2 pages…
CGILS LES intercomparison (Blossey et al., 2013)GABLS1/3… LES intercomparisons, stable BL (Beare et al., 2006; Moene et al, 2011)
BOMEX LES intercomparison, shallow cumulus
(
Siebesma
et al., 2003)
GASS/EUCLIPSE intercomparison with LES, strato-Cu to shallow-Cu transition (van der Dussen, et al., 2013)DYCOMS-II intercomparison, nonprecipitating marine strato-Cu (Zhu et al., 2005)ARM GCSS SGP, ASTEX, ATEX, RICO, …And now, LASSO: The LES ARM Symbiotic Simulation and Observation workflow
https://
www.arm.gov
/capabilities/modeling
Slide3What makes LASSO special and worth your time?
Ever-growing library
of LES cases to build robust statistical analyses and confidenceFocus on blending observations, forcings, and LES into user-friendly data bundlesSteps back from the tradition of fine-tuned, idealized forcings by employing an ensemble of plausible forcings constrained by observationsBacking of US Department of Energy’s Atmospheric Radiation Measurement (ARM) Facility with a commitment to ongoing production and expansion
https://
www.arm.gov
/capabilities/modeling
Slide4LASSO is an approach with an associated product
Goal:
The LASSO Workflow is designed to complement ARM megasite observations with LES output to support the study of atmospheric processes and the improved parameterization of these processes in atmospheric modelsApproach: Provide a library of cases and use an ensemble of LES combined with observations to simplify data discovery and bridge the gap from point observations to model grid cellsStatus: Completed pilot phase testing and currently implementing the first iteration to routinely run the LES for shallow convection days at SGP, working on expansion options during 2018/2019
https://
www.arm.gov
/capabilities/modeling
Slide5The 1st LASSO implementation
Current focus: shallow convection at ARM’s Southern Great Plains observatoryhttps://www.arm.gov/capabilities/modeling/lasso
25 km
75 km
MWR3C
AERI
Doppler Wind
Lidar
Slide6Core LASSO components
Library of LES simulations for each case, chosen by selected weather type (currently ShCu at SGP)18 days in the library for 2015 & 2016 periods32 more cases selected for 2017
A data bundle for each simulation in the ensemble:Large-scale forcing data & sfc. fluxes to drive the LESLES inputs and outputsSelection of concurrent observations for cloud and boundary layer variablesSkill scores, diagnostics, & plots for evaluating the simulationsBundle Browser interface to find simulations of interesthttp://archive.arm.gov/lassobrowser
https://
www.arm.gov
/capabilities/modeling
Slide7http://
archive.arm.gov/lassobrowser
Slide8Examples of current LASSO usage1) Informing observations and retrievals
Understanding cloud radar sampling and improving scan strategies
Oue, et al. (2016)Comparing HI-SCALE aircraft observations to LASSO simulations Fast, et al. (in preparation)
Simulated Observation
Using Radar Simulator
Model’s Cloud Field
Cloud Fraction Profiles
as a Function of Scan Time
Slide9Examples of current LASSO usage2) Improving PBL & cloud parameterizations
Evaluating the behavior of the MYNN-EDMF PBL & shallow-Cu scheme
Angevine et al. (in review)Using LES results from many cases to inform parameter estimation in CLUBBJeremy McGibbon & Chris Bretherton, U WA (in progress)
Hour CST
Slide10Important LASSO Reports
Recommendations for Implementation of LASSO
Contains recommendations from the LASSO Pilot Phase regarding what should be implemented for operationsWe are still accepting feedback and have not yet locked down the implementationDescription of the LASSO Alpha 2 ReleaseContains technical details about the LASSO data bundles, e.g., skill score descriptions, lists of variablesGustafson, et al., 2017. Recommendations for Implementation of the LASSO Workflow. doi:10.2172/1406259.
Gustafson, et al., 2017: Description of the LASSO Alpha 2 Release. doi:10.2172/1376727.
https://
www.arm.gov
/capabilities/modeling
Slide11Expanding LASSO beyond SGP ShCu
Shallow clouds at SGP is the start, with a heavy focus on getting the machinery in place in addition to the science drivers.
Now, we are considering options for expansion.Science drivers are key for prioritizationPracticality also plays a role
Slide12Discover LASSO!
Top-level webpage:
https://www.arm.gov/capabilities/modelingBundle Browser interface: http://www.archive.arm.gov/lassobrowserE-mail list: http://eepurl.com/bCS8s5Contact: William.Gustafson@pnnl.gov or lasso@arm.govAlpha 1 release available with 2015 cases (5 cases; soon to be re-released using updated Alpha 2 code)Alpha 2 release available with 2016 cases (13 cases)First official production year with 2017 cases available later this year (~32 cases)Looking for feedback as we work through formal implementationStill time to make adjustments—check out LASSO and let us know what you think!
What would you prioritize for expansion?
Slide13Extra…
Slide14Shallow convection can occur in the midst of widely varying conditions
Yellow bar is approximately 300 km long, a commonly used forcing scale.
19-Jun-2016
18-May-2016
16-Jul-2016
Slide15LASSO employs an ensemble of forcings to capture the range of possible conditions
Large-scale forcing datasets generated from 3 sources
Variational Analysis: ARM product, 300 km spatial scaleECMWF IFS model: ~16, 115, & 413 km spatial scalesMultiscale Data Assimilation (MSDA): 75, 150, & 300 km scales; can directly incorporate ARM observations into the analysisHybrid AERI+Raman Lidar T profilesRaman Lidar Qv profilesRWP wind profilesSurface meteorology
https://
www.arm.gov
/capabilities/modeling
Slide16Typical forcing ensemble displays significant differences
Even the sign of the forcing differs between different forcing datasets…
Height [m]Temperature Tendency [K h-1]Vertical Velocity [cm s-1]
Large-Scale
Advective
Tendencies, Ensemble from 25-Jun-2016 17 UTC
Water Vapor Tendency [g (kg h)
-1
]
MSDA
ECMWF
VARANAL
Subsidence
Lifting
Cooling
Warming
Drying
Moistening
Slide17Metrics quantify the range of results to determine which forcing(s) is better
Case = 25-Jun-2016
Better
, best
=(1,1)
Forcing:
VARANAL
ECMWF
MSDA
No LSF
Model:
<100 = WRF
≥100 = SAM
Slide18LASSO’s workflow for aiding research
Simulations packaged into
data bundlesSkill scores, diagnostics, and quick-looksSelected coincident model & obs.
vars
LES domain-averaged statistics and 3-D instantaneous volumes
Input data to reproduce simulations