Enhancing Capabilities of Numerical Weather Prediction in the Arctic – Tools of the Alertness Project

by Marvin Kähnert, University of Bergen, Bjerknes Centre for Climate Research

The YOPP-endorsed Norwegian Alertness project aims to improve AROME-Arctic – the recent entry-into-service weather forecast model at MET Norway. As an operational convection-permitting model system dedicated to the European Arctic it is one of the core models of the Year of Polar Prediction (YOPP). PhD student Marvin Kähnert employs a number of tools to enhance NWP capabilities in the Arctic as a collaborated effort within Alertness.

Many endeavours in the Arctic, from tourism to transportation to exploitation of natural resources require access to accurate weather forecasts. Yet, numerical weather prediction (NWP) models generally display comparatively low predictive skill at these high latitudes. Particularly, the sparse conventional observation network over the ocean and sea-ice as well as the pronounced impact of unresolved processes, such as surface fluxes, radiation or cloud microphysics on Arctic weather events pose a large challenge for numerical modelling. The YOPP-endorsed Norwegian Alertness project, led by Jørn Kristiansen (The Norwegian Meteorological Institute) and Marius O. Jonassen (UNIS), aims to tackle these key specifically Arctic challenges, while exploiting the opportunities of the Year of Polar Prediction in terms of field campaigns, observations and modelling efforts. The methodological basis of the work within Alertness is formed by the operational forecast systems AROME-Arctic. One dedicated aspect of the Alertness project, supervised by Harald Sodemann (University of Bergen), is to enhance the capabilities and diagnostics of AROME-Arctic. Therefore, a team consisting of members from the University of Bergen, the Nansen Environmental and Remote Sensing Center (NERSC), and The Norwegian Meteorological Institute employs a variety of tools that allow for a deeper insight into the “inner workings” of the NWP models.

In this article, these tools and their utility are briefly introduced.

 

1. Inspecting How the Model Advances from One Time Step to the Next

The essence of a weather forecasting model is to calculate the change of atmospheric variables such as temperature, pressure, or wind in time. In our models, this change can be divided into two main contributions: the model dynamics, and the model physics. The model dynamics refer to the large-scale transport of air masses with the wind that the model is able to fully resolve. The model physics on the other hand represent processes such as turbulence, radiation or cloud physics, that are too small to be resolved by the model grid, even though AROME-Arctic employs a 2.5 km grid spacing. As a consequence, these processes need to be represented by simplified formulations, so called parameterisations.

All of these parameterised processes contribute to the change of i.e. temperature or wind in our model. We refer to these contributions as individual tendencies. Investigating these tendencies enables targeted studies of the otherwise “hidden” activity and interplay of the model’s physics. Fig.1 demonstrates this utility. Shown on the left is the physical tendency (sum of all parameterisation schemes) of temperature close to the surface (lowest model level) during a day with major cloud formation. The physics actively warm most of the model domain (red shading). However, this warming is not uniform, and distinct spatial patterns emerge. These patterns can be attributed to environmental factors, such as the sea ice edge (white line), but also to model-internal factors. We found that boundary layer types play an important role (Fig. fd1b). Boundary layer types help the model to adjust its physical package towards the plethora of atmospheric regimes that it needs to represent, such as a cloud free winter night vs. an autumn storm. Together with Wim de Rooy (Royal Netherlands Meteorological Institute), we use the new perspective obtained from tendency output to investigate the impacts of a new boundary layer package.

2. Inspecting Processes at Single Model Columns with MUSC

Enhancing the capabilities of NWP in the Arctic also includes improving the representations of error-prone processes in the model, such as low-level fog, addressed by Teresa Valkonen (The Norwegian Meteorological Institute), or the stable boundary layer, tackled by Igor Esau (NERSC). Therefore, we employ MUSC (Modèle Unifié Simple Colonne), which is the single column model (SCM) of the HARMONIE-AROME model. A SCM separates a vertical column from the full, three-dimensional model-system, but retains all of the science and algorithms. As only a single vertical column is investigated, calculations are very fast, making MUSC an ideal tool to implement new physical parameterisations or to conduct sensitivity test. Fig. 2 exemplifies the sensitivity of liquid precipitation to one parameter that is buried within the parameterisation schemes for cloud microphysics. MUSC has been set up at the beginning of the project as a virtual machine by Stephen Outten (NERSC), making it very accessible and easy to work with, also for educational purposes.

3. Gaining High-Resolution Insight into the Model with DDH

When working with NWP models, end-users are normally provided with hourly to three-hourly output intervals. Even though today’s NWP models internally calculate with time steps of about 60 seconds or so, the sheer number of grid points in a typical model grid (~10^8) makes it simply not feasible to store every single of these time steps. An elegant solution is provided by the DDH-tool (diagnostics on horizontal domains), developed at Météo-France. The DDH tool allows for model output to be written for every single model time step within a specified sub-domain. This enables highly detailed investigation of process representations in the full, three-dimensional model. Especially the combination of such output with observations from super-sites, such as the one in Sodankylä, Finland, yields great potential for validation and model inter-comparison purposes. The tremendous increase in detail that is achieved by the DDH tool is demonstrated in Fig. 3. DDH reveals much finer structures of the development of relative humidity in the model over the measurements site of Sodankylä. Such high-resolution output enables much more detailed process studies of the stable boundary layer and testing of the new parameterisation scheme than otherwise possible.

Equipped with the tools, we work on improving weather forecasting for high latitudes. Yet, our methodology is by no means restricted to HARMONIE-AROME model or NWP in the Arctic, but can be applied to other NWP models and geographical locations. A paper, demonstrating the utility of the individual tendency output is currently in review at Weather and Forecasting.