scholarly journals Developing long-term single-column model/cloud system–resolving model forcing data using numerical weather prediction products constrained by surface and top of the atmosphere observations

2004 ◽  
Vol 109 (D1) ◽  
Author(s):  
Shaocheng Xie
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Cloud System ◽  
Numerical Weather ◽  
Column Model ◽  
Single Column ◽  
Single Column Model ◽  
Model Cloud

scholarly journals Continuous Single-Column Model Evaluation at a Permanent Meteorological Supersite

2012 ◽  
Vol 93 (9) ◽  
pp. 1389-1400 ◽  
Author(s):  
R. A. J. Neggers ◽  
A. P. Siebesma ◽  
T. Heus
Keyword(s):  
Case Studies ◽  
General Circulation ◽  
Column Model ◽  
Parameterization Schemes ◽  
Numerical Predictions ◽  
Single Column ◽  
Weather And Climate ◽  
Single Column Model ◽  
Process Level

Uncertainties in numerical predictions of weather and climate are often linked to the representation of unresolved processes that act relatively quickly compared to the resolved general circulation. These processes include turbulence, convection, clouds, and radiation. Single-column model (SCM) simulation of idealized cases and the subsequent evaluation against large-eddy simulation (LES) results has become an often used and relied on method to obtain insight at process level into the behavior of such parameterization schemes; benefits of SCM simulation are the enhanced model transparency and the high computational efficiency. Although this approach has achieved demonstrable success, some shortcomings have been identified; among these, i) the statistical significance and relevance of single idealized case studies might be questioned and ii) the use of observational datasets has been relatively limited. A recently initiated project named the Royal Netherlands Meteorological Institute (KNMI) Parameterization Testbed (KPT) is part of a general move toward a more statistically significant process-level evaluation, with the purpose of optimizing the identification of problems in general circulation models that are related to parameterization schemes. The main strategy of KPT is to apply continuous long-term SCM simulation and LES at various permanent meteorological sites, in combination with comprehensive evaluation against observations at multiple time scales. We argue that this strategy enables the reproduction of typical long-term mean behavior of fast physics in large-scale models, but it still preserves the benefits of single-case studies (such as model transparency). This facilitates the tracing and understanding of errors in parameterization schemes, which should eventually lead to a reduction of related uncertainties in numerical predictions of weather and climate.

scholarly journals Effects of model resolution and parameterizations on the simulations of clouds, precipitation, and their interactions with aerosols

2018 ◽  
Vol 18 (1) ◽  
pp. 13-29 ◽  
Author(s):  
Seoung Soo Lee ◽  
Zhanqing Li ◽  
Yuwei Zhang ◽  
Hyelim Yoo ◽  
Seungbum Kim ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Aerosol Concentration ◽  
Cloud System ◽  
Model Resolution ◽  
Substantial Impact ◽  
Numerical Weather ◽  
Saturation Process ◽  
Microphysical Processes

Abstract. This study investigates the roles played by model resolution and microphysics parameterizations in the well-known uncertainties or errors in simulations of clouds, precipitation, and their interactions with aerosols by the numerical weather prediction (NWP) models. For this investigation, we used cloud-system-resolving model (CSRM) simulations as benchmark simulations that adopt high-resolution and full-fledged microphysical processes. These simulations were evaluated against observations, and this evaluation demonstrated that the CSRM simulations can function as benchmark simulations. Comparisons between the CSRM simulations and the simulations at the coarse resolutions that are generally adopted by current NWP models indicate that the use of coarse resolutions as in the NWP models can lower not only updrafts and other cloud variables (e.g., cloud mass, condensation, deposition, and evaporation) but also their sensitivity to increasing aerosol concentration. The parameterization of the saturation process plays an important role in the sensitivity of cloud variables to aerosol concentrations. while the parameterization of the sedimentation process has a substantial impact on how cloud variables are distributed vertically. The variation in cloud variables with resolution is much greater than what happens with varying microphysics parameterizations, which suggests that the uncertainties in the NWP simulations are associated with resolution much more than microphysics parameterizations.

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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