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Peer-Review Record

An Improved Spatial–Temporal Downscaling Method for TRMM Precipitation Datasets in Alpine Regions: A Case Study in Northwestern China’s Qilian Mountains

Remote Sens. 2019, 11(7), 870; https://doi.org/10.3390/rs11070870
by Lei Wang 1,2, Rensheng Chen 1,*, Chuntan Han 1,2, Yong Yang 1, Junfeng Liu 1, Zhangwen Liu 1, Xiqiang Wang 1, Guohua Liu 1,2 and Shuhai Guo 1,2
Reviewer 1: Anonymous
Reviewer 2: Anonymous
Remote Sens. 2019, 11(7), 870; https://doi.org/10.3390/rs11070870
Submission received: 4 March 2019 / Revised: 3 April 2019 / Accepted: 6 April 2019 / Published: 10 April 2019
(This article belongs to the Section Remote Sensing Image Processing)

Round 1

Reviewer 1 Report

In this paper, the authors have proposed a method to downscale satellite-based annual precipitation at basin scale. This study is interesting, but needs a major revision.


In title, “Trmm” must be replaced with “TRMM”.

Among 40 point observations, how many observations were used for calibration and validation?

It is not clear that the coefficients (a, b, c,....) in equations 4, 5, and 6 have same (or different) values.

Fig. 10, if six stations were considered for validation then number of points in scatter should be 6 only for annual precipitation. If it would be the case, then the statistics would be not significant to conclude.

How authors decided which input parameters need to be taken into account for downscaling for each year?

How “accuracy” is defined in Figure 12?

Why authors have chosen multiple regression for downscaling? It needs to be justified as several methods have been reported for this purpose.

Why downscaled (e.g., fine resolution) precipitation at annual scale is relevant to the study area? Since temporal scale is too coarse (i.e., annual), fine spatial resolution precipitation dataset would be needed in which application?

One paragraph highlighting the performance of TMPA product as compared to other TRMM-era precipitation products at global and regional scales need to be included.

Few sentences about GPM-era precipitation product (IMERG), having finer spatial and temporal resolutions than TMPA, should also be included.

Author Response

Point 1: In title, “Trmm” must be replaced with “TRMM”.

 

Response 1: Thanks for your suggestion. I am sorry for my careless, and you are right. We have re-edited in the manuscript.

Point 2: Among 40 point observations, how many observations were used for calibration and validation?


Response 2: Thanks for your comment. There are 34 observations were used for calibration and 6 observations were used for validation. The calibration and validation had been explained in Datasets and Methodology, and the corresponding part of manuscripts had been re-edited.


Author Response File: Author Response.docx

Reviewer 2 Report

The manuscript titled “An improved spatial-temporal downscaling method for TRMM precipitation datasets in alpine regions: a case study in northwestern China's Qilian Mountains” is an interesting paper focused on the presentation and evaluation of a method for downscaling TRMM precipitation. Methodology adopted, presentation of results and conclusions are nicely presented.

However, prior to its final acceptance , I would encourage the authors to consider the following issues in Introduction.

Lines 52-55: Please do consider inclusion of CHIRPS (Climate Hazards Group Infrared Precipitation with Station) data with an analysis of about 5km and Global Precipitation Measurement (GPM) high-resolution product with an analysis of about 10km.

Line 60-62: Apart from the suggested methods, authors should also consider to include the following based on the use of neural network approach as referred in:

Retalis A., Tymvios F., Katsanos D. and Michaelides S., 2017, Downscaling CHIRPS precipitation data: an artificial neural networks modelling approach. International Journal of Remote Sensing, 38(13), 3943-3959, http://dx.doi.org/10.1080/ 01431161.2017.1312031.

Alexakis, D.D. and Tsanis, I.K., 2016, Comparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS data, Environ Earth Sci.,  75, 1077, https://doi.org/10.1007/s12665-016-5883-z


Author Response

Point 1: Lines 52-55: Please do consider inclusion of CHIRPS (Climate Hazards Group Infrared Precipitation with Station) data with an analysis of about 5km and Global Precipitation Measurement (GPM) high-resolution product with an analysis of about 10km.

 

Response 1: Thanks for your comment.

The CHIRPS is the high resolution (0.05°×0.05°) precipitation datasets which assimilated climate prediction system (CFSv2) prediction data, TRMM 3B42TIR (Thermal Infrared) band data and base station data.

The GPM is the successor of the Tropical rainfall Measuring Mission (TRMM), and it estimated global precipitation at resolution of 0.1°×0.1° by combining GPM with several other satellite precipitation datasets.

The spatial resolution of these two precipitation products is higher than TRMM, ERA-Interim. It should be discussed in the introduction. Therefore, the discussions on these two precipitation products have been added in the Introduction and Results. Please see line 54-59, line 82-85 and line 392-399.

 

 

Point 1: Line 60-62: Apart from the suggested methods, authors should also consider to include the following based on the use of neural network approach as referred in:

 

Retalis A., Tymvios F., Katsanos D. and Michaelides S., 2017, Downscaling CHIRPS precipitation data: an artificial neural networks modelling approach. International Journal of Remote Sensing, 38(13), 3943-3959, http://dx.doi.org/10.1080/ 01431161.2017.1312031.

 

Alexakis, D.D. and Tsanis, I.K., 2016, Comparison of multiple linear regression and artificial neural network models for downscaling TRMM precipitation products using MODIS data, Environ Earth Sci.,  75, 1077, https://doi.org/10.1007/s12665-016-5883-z

 

 

Response 2: Thanks for your comment. I have read two articles carefully. The neural network approach mentioned in the literatures is also one of the commonly downscaling methods. Therefore, the discussion about neural network approach has been added to the Introduction. Please see line 70-77.


Author Response File: Author Response.docx

Round 2

Reviewer 1 Report

The authors have revised the manuscript satisfactorily.

Reviewer 2 Report

The revised manuscript titled “An improved spatial-temporal downscaling method for TRMM precipitation datasets in alpine regions: a case study in northwestern China's Qilian Mountains” is an interesting paper focused on the presentation and evaluation of a method for downscaling TRMM precipitation. Methodology adopted, presentation of results and conclusions are nicely presented. Concluding, the manuscript could be accepted for publication. 

This manuscript is a resubmission of an earlier submission. The following is a list of the peer review reports and author responses from that submission.


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