Fabian Lenartz

Uncertainty forecast from 3-D super-ensemble multi-model combination: validation and calibration

Measurements collected during the Recognized Environmental Picture 2010 experiment (REP10) in the Ligurian Sea are used to evaluate 3-D super-ensemble (3DSE) 72-hour temperature predictions and their associated uncertainty. The 3DSE reduces the total Root-Mean-Square Difference by 12 and 32% respectively with reference to the ensemble mean and the most accurate of the models when comparing to regularly distributed surface temperature data. When validating against irregularly distributed in situ observations, the 3DSE, ensemble mean and most accurate model lead to similar scores. The 3DSE temperature uncertainty estimate is obtained from the product of a posteriori model weight error covariances by an operator containing model forecast values. This uncertainty prediction is evaluated using a criterion based on the 2.5th and 97.5th percentiles of the error distribution. The 3DSE error is found to be on average underestimated during the forecast period, reflecting (i) the influence of ocean dynamics and (ii) inaccuracies in the a priori weight error correlations. A calibration of the theoretical 3DSE uncertainty is proposed for the REP10 scenario, based on a time-evolving amplification coefficient applied to the a posteriori weight error covariance matrix. This calibration allows the end-user to be confident that, on average, the true ocean state lies in the −2/+2 3DSE uncertainty range in 95% of the cases.

Data Interpolating Variational Analysis for the Generation of Atmospheric Pollution Maps at Various Scales

Ordinary kriging is a widely used method to estimate the spatial distribution of atmospheric pollutants at all scales. However, more sophisticated strategies exist. For local mapping, where one often focuses on pollutants with a high spatio-temporal variability, such as nitrogen dioxide or black carbon, land use regression models are commonly used. In epidemiological research, several model reviews have already been published on this topic Hoek et al. (A review of land-use regression models to assess spatial variation of outdoor air pollution. Atmos Environ 42:7561–7578, 2008); Gaines et al. (A review of intraurban variations in particulate air pollution: Implications for epidemiological research. Atmos Environ 39:6444–6462, 2005). For regional mapping, de- and retreading procedures also make use of ancillary variables, such as the population density or the land use, to take into account the local characteristics of the sampling sites before and after the actual interpolation. Due to their low computational cost, these techniques can be implemented operationally Janssen et al. (Spatial interpolation of air pollution measurements using CORINE land cover data. Atmos Environ 42:4884–4903, 2008). In this study we introduce DIVA, a variational inverse method, originally designed for oceanographic applications, that allows one to take into account some new constraints. As it is based on a finite-element approach, physical boundaries such as buildings are naturally taken into account since they actually define the domain of interest. Another useful feature is the possibility to consider an advection field and hence propagate the information in the preferred direction. Finally, this technique also allows one to attribute a different weight to each available measurement, according to the quality of the data, so that heterogeneous data sources, consisting for example of monitoring network, passive sampler and mobile device values, can be used simultaneously and consistently. The model will be tested for two situations: the mapping of NO2 in the Walloon Region and the air pollution assessment of year 2012 in Antwerp. Results will be qualitatively compared with those of operational models: an ordinary kriging method run at AwAC by Bonvalet et al. (Validation of a geostatistical interpolation model using measurement of particulate matter concentration, Matinee des chercheurs a l’Universite de Mons 2013) and a detrended kriging run at ISSeP and originally implemented by Merbitz (Untersuchung und Modellierung der raumzeitlichen Variabilitat urbaner und regionaler Feinstaubkonzentrationen. Ph.D. thesis 2013) for the first case, and the RIO-IFDM-OSPM modelling system for the second case as implemented by Maiheu et al. (Luchtkwaliteitsmodellering Ringland, Studie uitgevoerd in opdracht van Stramien cvba en Ring genootschap vzw 2015/RMA/R/13 2015).