The manuscript titled 'Key factors for quantitative precipitation nowcasting using ground weather radar data based on deep learning' presented a thorough analysis of different schemes to approach precipitation nowcasting problems using deep-learning techniques. The different schemes were tested using ground weather radar data over South Korea. In recent days, multiple works have explored the application of deep-learning algorithms for quantitative precipitation forecasting. Exploring endless options and schemes is necessary to understand better the feasibility of using these methods in an operational scenario. I appreciate the authors' effort in conducting a systematic analysis and discussions. Some parts of the manuscripts are still hard to understand and not very clear.

Major comments:

1. Data imbalance: The major problem in precipitation nowcasting is the lack of representation of intense precipitation due to data imbalance. Did the authors try to consider this problem in their analysis? 
2. Data: Do the authors consider datasets with overlap when training is done? If t1-tn is used as input and tn+1 to tn+m is the forecast, is tn+1 - tn+n used as input for another sample?
3. Equations 2 and 3: The Mean Absolute error and mean bias equation are not normalized. Missing 1/n
4. Line 271 and Section 4.2.3: Adding a dummy zero variable to input causes sparsity. Adding white noise is a better idea. But, I feel that the entire part (Section 4.2.3) does not add much to the paper. It just lengthens the paper. I will suggest the authors remove that part. 
5. Table 4 and others: Persistence is not explained previously in the manuscript.

Minor comments:

Figure 2: Why is dBZ converted to rain rate? Why not just train the model for reflectivity values? 

Figure 3: It is better to mark the study region on the map. 

Line 229: Why is leaky relu not used for U-net and only used for ConvLSTM? 

Table 2: Why is SU-120-60 or RU-120-60 not considered for analysis? Please justify this in the text.

Table 4: Is there a reason why the best bias value is not highlighted? Just curious. 

Figure 8 and 10: The thresholds should be 5 mm/h. Please check the captions. 

Mu comments are in the attached pdf file.