| 12 May 2026
Radar range dependent validation of spaceborne cloud profiling radar precipitation detection: lessons from CloudSat and the Canadian C band network for the EarthCARE era
Abstract. Ground based validation of spaceborne precipitation detection algorithms depends critically on the quality of the reference radar observations, which degrades in a range dependent fashion due to ground clutter contamination and beam geometry effects. This study quantifies that dependence using five years (2006–2010) of coincident CloudSat Cloud Profiling Radar (CPR) overpasses and observations from the King City C band dual polarisation radar in southern Ontario, Canada, supplemented by automated and human METAR observations from twelve Environment and Climate Change Canada (ECCC) weather stations. Across 75,239 matched profiles, the CloudSat precipitation occurrence product achieves a probability of detection (POD) of 54.6 %, a critical success index (CSI) of 48.9 %, and a false alarm ratio (FAR) of 17.7 %. A systematic range bin decomposition reveals anomalously elevated Network Radar Precipitation (NRP) algorithm detection frequencies within 70 km of the King City radar, attributable to ground clutter passing the NRP 480 m vertical extent filter before Doppler discrimination becomes reliable. Excluding profiles within this range raises the POD to 66.8 % and the CSI to 56.3 %. Combining this range filter with the ZCPR > -10 dBZ threshold recommended for the Great Lakes winter precipitation regime further reduces the FAR to 13.8 % (CSI = 58.7 %). The 519 independent METAR comparisons yield a POD of 55.8 % and a low FAR of 9.2 %, confirming the radar based findings through a fully instrument independent pathway and demonstrating that the dominant CloudSat error mode is precipitation detection failure rather than false alarming. The performance degradation relative to the winter only benchmark of Hudak et al. (2008) is shown to arise primarily from near range clutter contamination and year round sampling rather than from algorithmic limitations. Critically, the 70 km threshold is governed by antenna beam geometry rather than radar wavelength and is therefore transferable to validation frameworks employing the new Canadian S band dual polarisation network. Implications for the design of ground validation campaigns for the recently launched EarthCARE Cloud Profiling Radar are discussed.
This manuscript presents an interesting investigation of radar-range-dependent validation of spaceborne precipitation detection using CloudSat CPR observations and a ground-based C-band radar. I appreciate that the author use a substantially larger dataset than previous studies, which provides a useful statistical basis for the analysis. The topic is also timely because the results may provide useful guidance for the validation of EarthCARE CPR observations.
However, I currently have difficulty fully understanding the validation methodology and, particularly, the physical and observational reasons for the large disagreement between CloudSat and the ground radar within approximately 70 km of the radar site. Although the manuscript presents extensive statistical results, I do not think the physical interpretation of these results is sufficiently demonstrated. In particular, the proposed explanation for the 70-km threshold would be much more convincing if it were supported by actual radar examples rather than primarily by statistical evidence. I therefore recommend that the author conduct additional analyses to clarify these issues.
Major Comments
1. Definition of precipitation in the ground-radar observations
It is not sufficiently clear to me how precipitation occurrence is defined from the ground-based C-band radar observations. The manuscript explains the NRP criteria, including the minimum vertical echo extent, but the actual radar sampling and precipitation-detection procedure need to be described more clearly. For example, is precipitation determined from radar reflectivity at a particular altitude using a CAPPI product (e.g., around 2 km), from the lowest available elevation scan, or from the three-dimensional radar volume? What altitude or vertical range of the radar echo is effectively used to determine precipitation occurrence?
This issue is particularly important because the validation results may depend strongly on the type and vertical structure of the precipitation system. For example, a large frontal precipitation system with deep and vertically continuous radar echoes may produce very different validation statistics from shallow clouds producing weak precipitation near the surface. I therefore suggest separating the analysis according to precipitation regime, such as widespread/frontal precipitation, convective precipitation, and shallow/light precipitation, if possible.
More importantly, I suspect that the apparent 70-km threshold may depend strongly on how precipitation is defined from the ground radar observations. The manuscript attributes the poor agreement within 70 km mainly to ground-clutter contamination, but the relationship between the radar sampling geometry, the precipitation-detection algorithm, and the resulting 70-km threshold needs to be demonstrated more clearly.
2. Examples of successful and unsuccessful CloudSat–C-band radar comparisons
The manuscript provides extensive statistics such as POD, FAR, and CSI, but it would be very helpful to show several actual examples of cases in which CloudSat and the C-band radar agree and disagree.
For example, the author could present representative vertical cross sections or PPI/CAPPI images for (1) a hit, (2) a miss, and (3) a false-alarm case, together with the corresponding CloudSat CPR profile. Examples from both inside and outside the 70-km range would be particularly useful.
At present, it is difficult for the reader to understand exactly what kind of radar echoes are responsible for the disagreement. If ground clutter within 70 km is indeed the dominant cause, representative radar observations should be able to demonstrate how the clutter passes the precipitation-detection criteria. Such examples would substantially strengthen the physical interpretation of the statistical results.
3. Large difference from Hudak et al. (2008)
The validation results in this study differ substantially from those reported by Hudak et al. (2008). The manuscript discusses several possible reasons, including differences in sampling period, precipitation characteristics, and near-range clutter contamination. However, I do not think the current discussion fully explains the magnitude of the difference.
As mentioned above, representative case studies would be particularly valuable here. For example, the author could show cases representative of the winter precipitation conditions considered by Hudak et al. (2008) and compare them with summer convective or shallow precipitation cases included in the present dataset. This would help demonstrate why the POD and CSI obtained in the present study are substantially lower than those reported by Hudak et al. (2008), rather than relying mainly on a qualitative explanation.
4. Interpretation of the comparison with METAR observations
Some additional care is needed when interpreting the comparison with surface METAR precipitation observations. A precipitation echo may be detected by CloudSat or the ground radar aloft but may evaporate before reaching the surface, particularly under a dry sub-cloud layer. In such a case, the satellite radar may correctly detect precipitation or virga aloft while the METAR station reports no precipitation at the surface.
Therefore, some of the apparent false alarms in the CloudSat–METAR comparison may not necessarily represent errors in the CloudSat precipitation detection. I suggest examining the vertical radar-reflectivity structure for these cases and, if possible, the thermodynamic environment below the precipitation echo, particularly relative humidity. This would help distinguish genuine CloudSat false alarms from precipitation that evaporates before reaching the surface.
5. Temporal variability of the validation statistics
Since this study uses a relatively long observational dataset, it would also be interesting to examine how the CloudSat–C-band radar validation statistics vary with time rather than presenting only statistics integrated over the entire analysis period.
For example, the author could examine seasonal or annual variations in POD, FAR, and CSI. Such an analysis could help determine whether the validation performance is stable or whether it changes systematically with precipitation regime and season. This would also provide additional evidence for the proposed explanation of the difference between the present results and the winter-only results of Hudak et al. (2008).