Modelling suppressed and active convection. Comparing a numerical weather prediction, cloud-resolving and single-column model

2007 ◽  
Vol 133 (626) ◽  
pp. 1087-1100 ◽  
Author(s):  
J. C. Petch ◽  
M. Willett ◽  
R. Y. Wong ◽  
S. J. Woolnough
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Column Model ◽  
Single Column ◽  
Single Column Model ◽  
Active Convection

scholarly journals HARMONIE-AROME single-column tools and experiments

2020 ◽  
Vol 17 ◽  
pp. 255-267
Author(s):  
Emily Gleeson ◽  
Stephen Outten ◽  
Bjørg Jenny Kokkvoll Engdahl ◽  
Eoin Whelan ◽  
Ulf Andrae ◽  
...  
Keyword(s):  
Radiative Transfer ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Cloud Microphysics ◽  
Canonical Model ◽  
Prediction System ◽  
Numerical Weather ◽  
Model Configuration ◽  
Single Column ◽  
Transfer Study

Abstract. The single-column version of the shared ALADIN-HIRLAM numerical weather prediction system, called MUSC, was developed by Météo-France in the 2000s and has a growing user-base in both HIRLAM and ALADIN countries. Tools to derive the required input, to run the experiments and to handle outputs of experiments carried out using MUSC have been developed within the HARMONIE-AROME canonical model configuration of the ALADIN-HIRLAM system are described within and constitute a large portion of this paper. The paper also illustrates the usefulness of the single-column approach for testing and developing HARMONIE-AROME physical parametrizations related to cloud microphysics and radiative transfer. Study cases concerning these physical parametrizations have been included for illustration purposes.

Engaging the Community in the Development of Physics for NWP Models

2020 ◽  
Author(s):  
Ligia Bernardet ◽  
Grant Firl ◽  
Dom Heinzeller ◽  
Laurie Carson ◽  
Xia Sun ◽  
...  
Keyword(s):  
Case Studies ◽  
Numerical Models ◽  
Weather Prediction ◽  
Forecast System ◽  
Linear Processes ◽  
Column Model ◽  
Model Code ◽  
Single Column ◽  
Single Column Model ◽  
The U.S

<p>Contributions from the community (national laboratories, universities, and private companies) have the potential to improve operational numerical models and translate to better forecasts. However, researchers often have difficulty learning about the most pressing forecast biases that need to be addressed, running operational models, and funneling their developments onto the research-to-operations process. Common impediments are lack of access to current and portable model code, insufficient documentation and support, difficulty in finding information about forecast shortcomings and systematic errors, and unclear processes to contribute code back to operational centers. </p><p>The U.S. Developmental Testbed Center (DTC) has the mission of connecting the research and operational Numerical Weather Prediction (NWP) communities. Specifically in the field of model physics, the DTC works on several fronts to foster the engagement of community developers with the Unified Forecast System (UFS) employed by the U.S. National Oceanic and Atmospheric Administration (NOAA).  As a foundational step, the UFS’ operational and developmental physical parameterizations and suites are now publicly distributed through the Common Community Physics Package (CCPP), a library of physics schemes and associated framework that enables their use with various models. The CCPP can be used for physics experimentation and development in a hierarchical fashion, with hosts ranging in complexity from a single-column model driven by experimental case studies to fully coupled Earth system models. This hierarchical capability facilitates the isolation of non-linear processes prior to their integration in complex systems. </p><p>The first public release of a NOAA Unified Forecast System (UFS) application is expected for February 2020, with a focus on the Medium-Range Weather Application. This global configuration uses the CCPP and will be documented and supported to the community. To accompany future public releases, the DTC is creating a catalog of case studies to exemplify the most prominent model biases identified by the US National Weather Service. The case studies will be made available to the community, who will be able to rerun the cases, to test their innovations and document model improvements. </p><p>In this poster we will summarize how we are using the UFS public release, the single-column model, the CCPP, and the incipient catalog of code studies to create stronger connections among the groups that diagnose, develop, and produce predictions using physics suites.</p>

scholarly journals Improving Wind Predictions in the Marine Atmospheric Boundary Layer through Parameter Estimation in a Single-Column Model

2016 ◽  
Vol 145 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Jared A. Lee ◽  
Joshua P. Hacker ◽  
Luca Delle Monache ◽  
Branko Kosović ◽  
Andrew Clifton ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Parameter Estimation ◽  
Atmospheric Boundary Layer ◽  
Weather Prediction ◽  
Sea Surface ◽  
Offshore Wind ◽  
Forecast Errors ◽  
Column Model ◽  
Single Column ◽  
Single Column Model

Abstract A current barrier to greater deployment of offshore wind turbines is the poor quality of numerical weather prediction model wind and turbulence forecasts over open ocean. The bulk of development for atmospheric boundary layer (ABL) parameterization schemes has focused on land, partly because of a scarcity of observations over ocean. The 100-m FINO1 tower in the North Sea is one of the few sources worldwide of atmospheric profile observations from the sea surface to turbine hub height. These observations are crucial to developing a better understanding and modeling of physical processes in the marine ABL. In this study the WRF single-column model (SCM) is coupled with an ensemble Kalman filter from the Data Assimilation Research Testbed (DART) to create 100-member ensembles at the FINO1 location. The goal of this study is to determine the extent to which model parameter estimation can improve offshore wind forecasts. Combining two datasets that provide lateral forcing for the SCM and two methods for determining , the time-varying sea surface roughness length, four WRF-SCM/DART experiments are conducted during the October–December 2006 period. The two methods for determining are the default Fairall-adjusted Charnock formulation in WRF and use of the parameter estimation techniques to estimate in DART. Using DART to estimate is found to reduce 1-h forecast errors of wind speed over the Charnock–Fairall ensembles by 4%–22%. However, parameter estimation of does not simultaneously reduce turbulent flux forecast errors, indicating limitations of this approach and the need for new marine ABL parameterizations.

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