## 8 m Temperature  
### 9 m Temperature  
BF Background Forecast  
BGK The Temperature  
standard deviation. A noticeable reduction in residuals and spread is observed after each assimilation cycle, demonstrating an improvement in forecast accuracy and a decrease in uncertainty. In particular, the distinct discontinuity around hour 3 reflects the direct impact of the second assimilation cycle, which further corrects the forecast and reduces the bias. Moreover, a reduced increase in forecast error is evident between the second and third assimilation cycles compared to the period between the first and second cycles. This behavior indicates not only the corrective effect of cyclic data assimilation on the model state but also its positive role in limiting error propagation in subsequent forecast hours.  
To assess the effectiveness of the cyclic data assimilation approach for the Meteorones observations, Figure A2 shows the vertical profiles of temperature (T), relative humidity (RH), and wind vector orientation (expressed as cosine similarity) to evaluate the impact of sequential data assimilation using a 3 DVar scheme. The comparison is made between the open loop (OL) simulation, which does not assimilate any observations (blue line), and the assimilation runs, which include the assimilation of Meteorone profiles (black line) with different observation error settings. The left panels display the forecast at 00 UTC after assimilating the first observation profile collected at 21 UTC (red and pink lines), while the right panels show the analysis at 00 UTC after assimilating the second profile collected at 00 UTC. The results highlight how the information from the first assimilation cycle is effectively retained in the model forecast, improving alignment with observations compared to the OL simulation. The subsequent assimilation at 00 UTC further refines the model state, demonstrating a cumulative improvement, particularly visible in relative humidity and wind orientation profiles. The sensitivity test, represented by the red (small error) and pink (large error) lines, indicates that even when assigning a smaller observation error, the assimilation system successfully integrates the observational data without degrading the solution, confirming the robustness of the setup. Overall, the figure demonstrates how cyclic variational assimilation contributes not only to correcting the model state at analysis time but also to reducing forecast errors and preserving improvements across successive assimilation cycles. This evaluation was carried out on a set of cases during the demonstrator phase to verify that such impacts are consistently observed across different situations. Naturally, the degree to which the assimilation effect is retained from one cycle to the next may vary slightly depending on the type of event and the prevailing atmospheric dynamics.  
Figure A2: **(A,B)**: Example of vertical profiles comparing the Open Loop (OL) simulation with forecasts after a first data assimilation (left column: **A,C,E**) and with the analysis (right column: **B,D,F**). **(A,B)** Temperature profiles (T, in \({}^{\circ}\)C) as a function of pressure (p, in hPa). (**C,D**) Relative Humidity profiles (RH, in %) versus pressure. (**E,F**) wind vector orientation expressed as cosine similarity. Lines represent the Metedrone observations (black), the OL (blue), and different assimilation experiments (red and pink lines show two different simulations performed with different observational errors).