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Sinclair Chinyoka, Thierry Hedde, and Gert-Jan Steeneveld

Abstract

This study improves surface wind predictions in an unresolved valley using an artificial neural network (ANN). Forecasting winds in complex terrain with a mesoscale model is challenging. This study assesses the quality of 3-km wind forecasts by the Weather Research and Forecasting (WRF) Model and the potential of postprocessing by an ANN within the 1–2-km-wide Cadarache valley in southeast France. Operational wind forecasts for 110 m above ground level and the near-surface vertical potential temperature gradient with a lead time of 24–48 h were used as ANN input. Observed horizontal wind components at 10 m within the valley were used as targets during ANN training. We use the directional accuracy (DACC45, wind direction error ≤ 45°) and mean absolute error to evaluate the WRF direct model output and the ANN results. By postprocessing, the score for DACC45 improves from 56% in the WRF direct model output to 79% after applying the ANN. Furthermore, the ANN performed well during the day and night, but poorly during the morning and afternoon transitions. The ANN improves the DACC45 at 10 m even for poor WRF forecasts (direction bias ≥ 45°) from 42% to 72%. A shorter lead time and finer grid spacing (1 km) showed negligible impact, which suggests that a 3-km grid spacing and a 24–48-h lead time is effective and relatively cheap to apply. We find that WRF performs well in near-neutral conditions and poorly in other atmospheric stability conditions. The ANN posttreatment consistently improves the wind forecast for all stability classes to a DACC45 of about 80%. The study demonstrates the ability to improve Cadarache valley wind forecasts using an ANN as postprocessing for WRF daily forecasts.

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Peter Christiaan Kalverla, Gert-Jan Duine, Gert-Jan Steeneveld, and Thierry Hedde

Abstract

In the winter of 2012/13, the Katabatic Winds and Stability over Cadarache for the Dispersion of Effluents (KASCADE) observational campaign was carried out in southeastern France to characterize the wind and thermodynamic structure of the (stable) planetary boundary layer (PBL). Data were collected with two micrometeorological towers, a sodar, a tethered balloon, and radiosoundings. Here, this dataset is used to evaluate the representation of the boundary layer in the Weather Research and Forecasting (WRF) Model. In general, it is found that diurnal temperature range (DTR) is largely underestimated, there is a strong negative bias in both longwave radiation components, and evapotranspiration is overestimated. An illustrative case is subjected to a thorough model-physics evaluation. First, five PBL parameterization schemes and two land surface schemes are employed. A marginal sensitivity to PBL parameterization is found, and the sophisticated Noah land surface model represents the extremes in skin temperature better than does a more simple thermal diffusion scheme. In a second stage, sensitivity tests for land surface–atmosphere coupling (through parameterization of z 0h/z 0m), initial soil moisture content, and radiation parameterization were performed. Relatively strong surface coupling and low soil moisture content result in a larger sensible heat flux, deeper PBL, and larger DTR. The larger sensible heat flux is not supported by the observations, however. It turns out that, for the selected case, a combination of subsidence and warm-air advection is not accurately simulated, but this inaccuracy cannot fully explain the discrepancies found in the WRF simulations. The results of the sensitivity analysis reiterate the important role of initial soil moisture values.

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Gert-Jan Steeneveld and Jordi Vilà-Guerau de Arellano

Abstract

Numerical weather prediction models have become widespread tools that are accessible to a variety of communities, ranging from academia and the national meteorological services to commercial weather providers, wind and solar energy industries, and air quality modelers. Mesoscale meteorological models that are used to refine relatively coarse global weather forecasts to finer atmospheric scales have become mainstream. The wide use of mesoscale meteorological models also generates new requirements in undergraduate education concerning the knowledge and application of these models. In this paper, we present teaching strategies, course outcomes, student activities, impacts, and reflections on the possible future direction of the graduate-level atmospheric modeling course using the Weather Research and Forecasting (WRF) Model at Wageningen University, the Netherlands. This information is based on 15 years of experience in teaching the course and the continuous implementation of new educational techniques to adapt to students’ needs and improve their chances in their academic careers and the atmospheric sciences job market.

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Lars van Galen, Oscar Hartogensis, Imme Benedict, and Gert-Jan Steeneveld

Abstract

We report on redesigning the undergraduate course in synoptic meteorology and weather forecasting at Wageningen University (the Netherlands) to meet the current-day requirements for operational forecasters. Weather strongly affects human activities through its impact on transportation, energy demand planning, and personal safety, especially in the case of weather extremes. Numerical weather prediction (NWP) models have developed rapidly in recent decades, with reasonably high scores, even on the regional scale. The amount of available NWP model output has sharply increased. Hence, the role and value of the operational weather forecaster has evolved into the role of information selector, data quality manager, storyteller, and product developer for specific customers. To support this evolution, we need new academic training methods and tools at the bachelor’s level. Here, we present a renewed education strategy for our weather forecasting class, called Atmospheric Practical, including redefined learning outcomes, student activities, and assessments. In addition to teaching the interpretation of weather maps, we underline the need for twenty-first-century skills like dealing with open data, data handling, and data analysis. These skills are taught using Jupyter Python Notebooks as the leading analysis tool. Moreover, we introduce assignments about communication skills and forecast product development as we aim to benefit from the internationalization of the classroom. Finally, we share the teaching material presented in this paper for the benefit of the community.

Full access
Stephan T. Kral, Joachim Reuder, Timo Vihma, Irene Suomi, Kristine F. Haualand, Gabin H. Urbancic, Brian R. Greene, Gert-Jan Steeneveld, Torge Lorenz, Björn Maronga, Marius O. Jonassen, Hada Ajosenpää, Line Båserud, Phillip B. Chilson, Albert A. M. Holtslag, Alastair D. Jenkins, Rostislav Kouznetsov, Stephanie Mayer, Elizabeth A. Pillar-Little, Alexander Rautenberg, Johannes Schwenkel, Andrew W. Seidl, and Burkhard Wrenger

Abstract

The Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer Program (ISOBAR) is a research project investigating stable atmospheric boundary layer (SBL) processes, whose representation still poses significant challenges in state-of-the-art numerical weather prediction (NWP) models. In ISOBAR ground-based flux and profile observations are combined with boundary layer remote sensing methods and the extensive usage of different unmanned aircraft systems (UAS). During February 2017 and 2018 we carried out two major field campaigns over the sea ice of the northern Baltic Sea, close to the Finnish island of Hailuoto at 65°N. In total 14 intensive observational periods (IOPs) resulted in extensive SBL datasets with unprecedented spatiotemporal resolution, which will form the basis for various numerical modeling experiments. First results from the campaigns indicate numerous very stable boundary layer (VSBL) cases, characterized by strong stratification, weak winds, and clear skies, and give detailed insight in the temporal evolution and vertical structure of the entire SBL. The SBL is subject to rapid changes in its vertical structure, responding to a variety of different processes. In particular, we study cases involving a shear instability associated with a low-level jet, a rapid strong cooling event observed a few meters above ground, and a strong wave-breaking event that triggers intensive near-surface turbulence. Furthermore, we use observations from one IOP to validate three different atmospheric models. The unique finescale observations resulting from the ISOBAR observational approach will aid future research activities, focusing on a better understanding of the SBL and its implementation in numerical models.

Open access
Janet Barlow, Martin Best, Sylvia I. Bohnenstengel, Peter Clark, Sue Grimmond, Humphrey Lean, Andreas Christen, Stefan Emeis, Martial Haeffelin, Ian N. Harman, Aude Lemonsu, Alberto Martilli, Eric Pardyjak, Mathias W Rotach, Susan Ballard, Ian Boutle, Andy Brown, Xiaoming Cai, Matteo Carpentieri, Omduth Coceal, Ben Crawford, Silvana Di Sabatino, Junxia Dou, Daniel R. Drew, John M. Edwards, Joachim Fallmann, Krzysztof Fortuniak, Jemma Gornall, Tobias Gronemeier, Christos H. Halios, Denise Hertwig, Kohin Hirano, Albert A. M. Holtslag, Zhiwen Luo, Gerald Mills, Makoto Nakayoshi, Kathy Pain, K. Heinke Schlünzen, Stefan Smith, Lionel Soulhac, Gert-Jan Steeneveld, Ting Sun, Natalie E Theeuwes, David Thomson, James A. Voogt, Helen C. Ward, Zheng-Tong Xie, and Jian Zhong
Open access