Assimilation of crowd‐sourced surface observations over Germany in a regional weather prediction system

Abstract

Near-surface temperature and humidity observations over Germany, coming on the one hand from the citizen weather station's network Netatmo and on the other hand from synoptic weather stations, were assimilated into the limited are mode of the Icosahedral Nonhydrostatic Model with 2-km resolution (ICON-D2). For that we use the Kilometre-Scale Ensemble Data Assimilation (KENDA) system and a bias-correction approach that improves the assimilation of the observations by taking into account the diurnal cycle of temperature and humidity variables. Our results show that the assimilation of bias-corrected observations from Netatmo stations reduces the forecast error considerably; meanwhile, the assimilation of Netatmo observations without bias correction leads to a strong warm bias with a negative impact on forecast performance.In contrast, for the assimilation of synoptic observations the usage of our bias-correction approach does not lead to any significant decrease in the forecast error, yet reduces the bias for the diurnal cycle of synoptic stations. Overall, it can be concluded that the forecast quality can gain from assimilating Netatmo data, provided an effective bias-correction approach is applied.

A new statistical downscaling approach for short‐term forecasting of summer air temperatures through a fusion of deep learning and spatial interpolation

“…These results indicate that bias‐corrected high spatial resolution summer air temperature forecasts with relatively long forecast lead times can be produced using the proposed DeU‐Net model for operational forecasting. Recently, many researchers have focused on the use of crowdsourced weather stations such as citizen weather stations (Meier et al ., 2017; Sgoff et al ., 2022; Venter et al ., 2020). If these data are used, the proposed statistical downscaling model can be applied to other regions, and its spatial performance can be further improved.…”

The set‐up and evaluation of fine‐scale data assimilation for the urban climate of Amsterdam

“…Other literature rather focuses on operational forecasts with assimilation of citizen observations (4D‐Var). For example, recently, a huge number of NetAtmo crowdsourced weather stations were assimilated in a regional weather model (Sgoff et al ., 2022). Due to ensemble forecasts (local ensemble transform Kalman filter), an observation error and model error could be derived for the quality control of personal weather stations.…”

From proof‐of‐concept to proof‐of‐value: Approaching third‐party data to operational workflows of national meteorological services

“…Nevertheless, 3PD applications are "new" in these well-established domains and the road ahead for the R2O process is unclear, and yet to be explored in collaboration with partners (e.g., EUMETNET members) that are facing similar challenges with these novel 3PD observations. Recent research has shown potential from 3PD to contribute to weather forecasting, concretely to numerical weather prediction (NWP; Hintz et al, 2019a;Nipen et al, 2020), or data assimilation (DA; Hintz et al, 2019b;Sgoff et al, 2022). Also, 3PD observations combined with additional data sources can have a role in nowcasting activities (Nuottokari et al, 2022) and the verification of high-impact weather (Marsigli et al, 2020).…”

“…Recent research has shown potential from 3PD to contribute to weather forecasting, concretely to numerical weather prediction (NWP; Hintz et al ., 2019a; Nipen et al ., 2020), or data assimilation (DA; Hintz et al ., 2019b; Sgoff et al ., 2022). Also, 3PD observations combined with additional data sources can have a role in nowcasting activities (Nuottokari et al ., 2022) and the verification of high‐impact weather (Marsigli et al ., 2020).…”