|Based on the evidence presented in this study, it is difficult to draw a solid conclusion about positive impact from assimilating radar reflectivity. I think it is mainly because the authors are focusing on the one-day QPF, which is claimed to be the operational interest. However, it is well recognized by the literature that the impact of radar data is identified for (very) short-term forecast. I would like to suggest the authors that since this work is the new component for the KENDA system, it is essential to identify and justify the impact of the radar data, even though the impact may only last few hours. Also, I’d think that it is important to explain why additionally assimilating radar data doesn’t seem to gain more benefit than applying LHN. Is this because the information is double used or redundant? Based on these concerns, I suggest major revision for this manuscript. My comments are as follows: |
Based on the verification merits used in this study, it is difficult to justify the impact of radar data. Have the authors consider adopting other verification merits such as POD, FAR, TS and BIAS to identify whether there is clearer signal about the data impact? Or, illustrate the results with a case study (from the 4-day assimilation)?
o Discussion about Fig. 4 is vague. Line13, Page11: “Overall, the correspondence of rad60 …. to observations is equal or better than that of conv60 (red)”. Also, by eye, rad60_nolhn is slightly better than rad60. rad60_Bm tends to have the least rainfall among all the rad60-related experiments shown on Fig. 4 but the authors claim that the impact of the additive inflation cannot be judged. To quantify such statement. I suggest to summarize Fig. 4 in terms of RMS error.
o As mentioned in my general comments, I’d think that it is important to explain why additionally assimilating radar data doesn’t seem to gain benefit than applying LHN. Is this somewhat related to the fact that the SRI product uses the radar reflectivity? The authors can illustrate the result with even a case demonstration. Should we expect a large difference between radar data assimilation and LHN with heavy rainfall events?
Although the authors focus on the impact of radar data on 1-day QPF, it is undeniable that the impact is mostly evident within 6 hours. Therefore, I suggest that the authors should address the impact of radar data on short-range forecast (< 6h).
o It is concluded that the radar data only slightly improve QPF both during the assimilation procedure and for the subsequent forecasts, compared to the assimilation of only conventional data (Line3, Page 18). This sentence should be modified for 1-day QPF.
Why does rapid update (15-min) with a small observation actually lead to a dry condition over northern Italy (Fig. 9)? Fig. 9 should be also compared with results from rad15 with rad15_roe0.5.
Since radar reflectivity is the main focus in this work, the authors should briefly comment or summarize what are the issues/difficulties with assimilation of reflectivity volumes in the introduction.
“Grater” is often used in the text. But, I think the authors meant “greater”.