Figure 1. We combine shape-changing interfaces with spatial augmented reality. Shape-changing interfaces physically render coarse shapes and slow movements. Spatial augmented reality is used to display arbitrary textures, objects, and fast motion, directly onto the shape-changing device. This allows us to take advantage of natural depth cues and occlusion. We demonstrate the combination of the two techniques in three applications, i. e. a weather app with waves rendered physically and virtually (left) and a game with position of important game elements emphasized through shape change (center). We show capabilities of enriching shape-changing interfaces with spatial AR, e. g. the ability to render arbitrary objects and shadows (right).

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A new method based on the Transformer model is proposed for post‐processing of numerical weather prediction (NWP) forecasts of 2 m air temperature. The Transformer is a machine learning (ML) model based on self‐attention, which extracts information about which inputs are most important for the prediction. It is trained using time series input from NWP variables and crowd‐sourced 2 m air temperature observations from more than 1000 private weather stations (PWSs). The performance of the new post‐processing model is evaluated using both observational data from PWSs and completely independent observations from the Danish Meteorological Institute (DMI) network of surface synoptic observations (SYNOP) stations. The performance of the Transformer model is compared against the raw NWP forecast, as well as against two benchmark post‐processing models; a linear regression (LR) model and a neural network (NN). The results evaluated using PWS observations show an improvement in the 2 m temperature forecasts with respect to both bias and standard deviation (STD) for all three post‐processing models, with the Transformer model showing the largest improvement. The raw NWP forecast, LR, NN and Transformer model have a bias and STD of 0.34 and 1.96°C, 0.03 and 1.63°C, 0.10 and 1.53°C and 0.02 and 1.13°C, respectively. The corresponding results using DMI SYNOP stations also show improved forecasts, where the Transformer model performs better than both the raw NWP forecast and the two benchmark models. However, a dependence on distance to the coast and cold temperatures is observed.

<p>Numerical weather prediction (NWP) models are known to exhibit systematic errors, especially for near-surface variables such as air temperature. This is partly due to deficiencies in the physical formulation of the model dynamics and the inability of these models to successfully handle sub-grid phenomena. Forecasts that better match the locally observed weather can be obtained by post-processing NWP model output using local meteorological observations. Here, we have implemented a non-linear post-processing model based on machine learning techniques with the aim of post-processing near-surface air temperature forecasts from a global coarse-resolution model in order to produce localized forecasts. The model is trained on observational from a network of private weather stations and forecast data from the global coarse-resolution NWP model. Independent data is used to assess the performance of the model and the results are compared with the performance of the raw NWP model output. Overall, the non-linear machine learning post-processing method reduces the bias and the standard deviation compared to the raw NWP forecast and produces a forecast that better match the locally observed weather.</p>

<p>It is a well-known fact that numerical weather prediction (NWP) models exhibit systematic errors, especially for near-surface variables. Reasons for this are, among other, the inability of these models to successfully handle sub-grid phenomena and shortcomings in the physical formulation of the model dynamics. Even though high-resolution regional NWP models usually have a spatial resolution of a few kilometers (or even finer) they generally exhibit local biases due to unresolved topography and obstacles. In order to obtain more local and site-specific forecasts post-processing methods can be used. Here, we have implemented a Transformer Neural Network model for post-processing 48-hour forecasts of 2 m temperature and relative humidity. The observational data used in this study consist of observations of 2 m air temperature and relative humidity from a network of private weather stations (PWSs). All in all, data from more than 1,000 locations are used. Forecast data from the Global Forecast System (GFS) model – such as temperature, relative humidity, wind speed and direction, radiation fluxes and upper level model fields – are also used as input to the model. The model is trained on 1.5 years of observational and forecast data and the performance is evaluated using an independent validation dataset of PWSs. We find that the Transformer post-processing model reduces the bias and standard deviation compared to the raw NWP forecast for a majority of stations. Furthermore, the model is validated on completely independent data from the Danish Meteorological Institute’s (DMI’s) observational network, where good results were obtained. Overall, the Transformer model produces forecasts that better match the locally observed weather.</p>