the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Assessing sampling and retrieval errors of GPROF precipitation estimates over The Netherlands
Abstract. The Goddard Profiling algorithm (GPROF) converts radiometer observations aboard Global Precipitation Measurement (GPM) constellation satellites to precipitation estimates. Analyzing the accuracy of GPROF’s estimates is vital to further improve the algorithm. Such analyses often use high-quality ground-based estimates as reference with a different spatial resolution. Often, the reference is resampled to match the satellite’s resolution. However, the implemented sampling method to simulate the satellite’s resolution varies amongst studies, which limits the transferability of conclusions. additionally, GPROF combines observations from various sensors and frequency channels, each with its own footprint size. Hence, uncertainties related to sampling are added on top of the uncertainty introduced when converting brightness temperatures to precipitation intensities. The contribution of sampling to the total amount of uncertainty remains unknown.
Here, we quantify the uncertainty related to sampling while analyzing the current performance of GPROF over the Netherlands during a four year period (2017–2020). In this area, shallow and light precipitation frequently occur. Both precipitation types are often subject to research, as both types are difficult to detect with space-borne sensors. Only GPROF estimates based on observations from the conical-scanning radiometers of the GPM constellation are used. We investigate the uncertainty related to sampling by simulating the reference precipitation as satellite footprints that vary in size, geometry, and applied weighting technique. The reference estimates are gauge-adjusted radar precipitation estimates from two ground-based weather radars from the Royal Netherlands Meteorological Institute (KNMI). Echo top heights (ETH) retrieved from the same radars are used to classify the precipitation as shallow, medium, or deep.
The method used to spatially average the reference into a satellite footprint, i.e. using Gaussian weighting or the arithmetic mean, is found to exhibit a minimal influence on the retrieved estimate. The size of the sampled area is found to be the most influential. Still, the effect of using different footprint sizes cannot explain all the differences between the ground- and satellite based precipitation estimates. Additionally, the discrepancies between GPROF and the reference are largest for low ETH, while the relative bias between the different footprint sizes and implemented weighting methods increase with increasing ETH. Lastly, our results do not show a clear difference between coastal simulations and simulations over land. We conclude that the uncertainty introduced by merging different channels and sensors cannot fully explain the errors introduced by the retrieval algorithm. Hence, retrieval errors are found to be more prominent than sampling uncertainties, in particular for shallow and light precipitation.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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Status: closed
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RC1: 'Comment on egusphere-2023-1258', Anonymous Referee #1, 20 Sep 2023
Review of "Assessing sampling and retrieval errors of GPROF precipitation estimates over The Netherlands" by Linda Bogerd et al., submitted to the Atmospheric Measurement Techniques (AMT) Journal.
This manuscript studies the uncertainty of precipitation estimates over the Netherlands computed from radiometers' observations regarding different samplings and retrieval methods.
The precipitation estimates from radiometers are evaluated against ground-based weather radar precipitation estimates using different satellite footprints and sensors.
Finally, the methods and results are clearly described and discussed throughout the manuscript, providing new insights into the performance of the Goddard Profiling algorithm (GPROF).
The manuscript fits the scope of the AMT journal; it is well written -clear and concise- and I consider it a great contribution to the remote sensing community. Congrats!I have a few specific questions/comments to improve clarity.
1. Lines 29-30. What do the authors mean by "Their spatial coverage and representation, however, is limited"? Although the spatial coverage of space-borne sensors is greater than ground-based weather radars and rain gauges, this coverage is still somewhat limited (~885 km), right?
2. Lines 35-42. These paragraphs are confusing. The authors state in Line 36 that one kind of input is the indirect observation of cloud properties. However, Lines 39-42 read that direct observations "are preferred for quantitative applications in meteorology and hydrology as precipitation retrieval from visible and infrared channels is based on cloud to precipitation relations". Maybe the authors mean "the former" in Line 41, but rephrasing may be needed. Please elaborate.
3. Line 64. Consider adding "detection and accuracy OF RAIN RATES" or similar.
4. Line 117. Why this particular threshold? Please discuss setting this threshold to help understand the radar Echo Top Height (ETH) influence in the results.
5. Line 207. Please replace "The first three figures" with "Figures 1-3"
6. Figure 2, p17. These plots are great and depict much information; however, the markers need some improvement. For instance, it is almost impossible to notice the stars in the plot showing the number of observations. I advise using other marker fill styles, such as none for circles, changing the edge colour, or other alternatives. Also, add ETH, e.g., low ETH, med ETH, etc., to the colour scale.
Citation: https://doi.org/10.5194/egusphere-2023-1258-RC1 -
AC1: 'Reply on RC1', Linda Bogerd, 06 Nov 2023
The comments of reviewer #1 are addressed (in italics) below. The line numbers correspond to the initial version of the manuscript.
This manuscript studies the uncertainty of precipitation estimates over the Netherlands computed from radiometers' observations regarding different samplings and retrieval methods.
The precipitation estimates from radiometers are evaluated against ground-based weather radar precipitation estimates using different satellite footprints and sensors.
Finally, the methods and results are clearly described and discussed throughout the manuscript, providing new insights into the performance of the Goddard Profiling algorithm (GPROF).
The manuscript fits the scope of the AMT journal; it is well written -clear and concise- and I consider it a great contribution to the remote sensing community. Congrats!We highly appreciate these kind words and would like to thank the reviewer for the time to provide feedback on our manuscript.
I have a few specific questions/comments to improve clarity.
1. Lines 29-30. What do the authors mean by "Their spatial coverage and representation, however, is limited"? Although the spatial coverage of space-borne sensors is greater than ground-based weather radars and rain gauges, this coverage is still somewhat limited (~885 km), right?
Indeed, the coverage of radiometers is still limited. However, this sentence refers to all spaceborne sensors, including geostationary satellites. Additionally, although the coverage might be limited, the radiometers have a global representation in contrast to the local representation of ground-based radars.2. Lines 35-42. These paragraphs are confusing. The authors state in Line 36 that one kind of input is the indirect observation of cloud properties. However, Lines 39-42 read that direct observations "are preferred for quantitative applications in meteorology and hydrology as precipitation retrieval from visible and infrared channels is based on cloud to precipitation relations". Maybe the authors mean "the former" in Line 41, but rephrasing may be needed. Please elaborate.
Direct observations are preferred as these observations provide information regarding precipitation. The sensors capable of measuring these direct characteristics, however, are typically found on low Earth orbit (LEO) satellites due to their frequency channels. In contrast, channels operating within the visible (VIS) and infrared (IR) frequency ranges can be accommodated on geostationary satellites, but these frequencies only provide information about cloud properties. We will revise the aforementioned lines to enhance clarity.3. Line 64. Consider adding "detection and accuracy OF RAIN RATES" or similar.
We appreciate the suggestion and we will rewrite line 64 as follows: “detection of precipitation and accuracy of precipitation intensity”4. Line 117. Why this particular threshold? Please discuss setting this threshold to help understand the radar Echo Top Height (ETH) influence in the results.
The threshold is chosen to ensure light precipitation rates are also detected, a precipitation type that frequently occurs in the Netherlands. A drawback of this low threshold is discussed in lines 119-122.5. Line 207. Please replace "The first three figures" with "Figures 1-3"
We appreciate the suggestion and we will rewrite line 207 as follows: “GPROF’s performance and some first results concerning the influence of sampling are shown in Figs. 1-3”6. Figure 2, p17. These plots are great and depict much information; however, the markers need some improvement. For instance, it is almost impossible to notice the stars in the plot showing the number of observations. I advise using other marker fill styles, such as none for circles, changing the edge colour, or other alternatives. Also, add ETH, e.g., low ETH, med ETH, etc., to the colour scale.
The suggestion is appreciated and will be implemented to improve the clarity of Fig. 2.Citation: https://doi.org/10.5194/egusphere-2023-1258-AC1
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AC1: 'Reply on RC1', Linda Bogerd, 06 Nov 2023
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RC2: 'Comment on egusphere-2023-1258', T.H.M. Rientjes, 23 Oct 2023
This interesting study is well developed and executed. The topic of estimating/comparing precipitation/rainfall by different remote sensing-based data sources is quite challenging. My comments mostly are textual where further detailing and description of used terms is advised to increase readability.
- AC2: 'Reply on RC2', Linda Bogerd, 06 Nov 2023
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2023-1258', Anonymous Referee #1, 20 Sep 2023
Review of "Assessing sampling and retrieval errors of GPROF precipitation estimates over The Netherlands" by Linda Bogerd et al., submitted to the Atmospheric Measurement Techniques (AMT) Journal.
This manuscript studies the uncertainty of precipitation estimates over the Netherlands computed from radiometers' observations regarding different samplings and retrieval methods.
The precipitation estimates from radiometers are evaluated against ground-based weather radar precipitation estimates using different satellite footprints and sensors.
Finally, the methods and results are clearly described and discussed throughout the manuscript, providing new insights into the performance of the Goddard Profiling algorithm (GPROF).
The manuscript fits the scope of the AMT journal; it is well written -clear and concise- and I consider it a great contribution to the remote sensing community. Congrats!I have a few specific questions/comments to improve clarity.
1. Lines 29-30. What do the authors mean by "Their spatial coverage and representation, however, is limited"? Although the spatial coverage of space-borne sensors is greater than ground-based weather radars and rain gauges, this coverage is still somewhat limited (~885 km), right?
2. Lines 35-42. These paragraphs are confusing. The authors state in Line 36 that one kind of input is the indirect observation of cloud properties. However, Lines 39-42 read that direct observations "are preferred for quantitative applications in meteorology and hydrology as precipitation retrieval from visible and infrared channels is based on cloud to precipitation relations". Maybe the authors mean "the former" in Line 41, but rephrasing may be needed. Please elaborate.
3. Line 64. Consider adding "detection and accuracy OF RAIN RATES" or similar.
4. Line 117. Why this particular threshold? Please discuss setting this threshold to help understand the radar Echo Top Height (ETH) influence in the results.
5. Line 207. Please replace "The first three figures" with "Figures 1-3"
6. Figure 2, p17. These plots are great and depict much information; however, the markers need some improvement. For instance, it is almost impossible to notice the stars in the plot showing the number of observations. I advise using other marker fill styles, such as none for circles, changing the edge colour, or other alternatives. Also, add ETH, e.g., low ETH, med ETH, etc., to the colour scale.
Citation: https://doi.org/10.5194/egusphere-2023-1258-RC1 -
AC1: 'Reply on RC1', Linda Bogerd, 06 Nov 2023
The comments of reviewer #1 are addressed (in italics) below. The line numbers correspond to the initial version of the manuscript.
This manuscript studies the uncertainty of precipitation estimates over the Netherlands computed from radiometers' observations regarding different samplings and retrieval methods.
The precipitation estimates from radiometers are evaluated against ground-based weather radar precipitation estimates using different satellite footprints and sensors.
Finally, the methods and results are clearly described and discussed throughout the manuscript, providing new insights into the performance of the Goddard Profiling algorithm (GPROF).
The manuscript fits the scope of the AMT journal; it is well written -clear and concise- and I consider it a great contribution to the remote sensing community. Congrats!We highly appreciate these kind words and would like to thank the reviewer for the time to provide feedback on our manuscript.
I have a few specific questions/comments to improve clarity.
1. Lines 29-30. What do the authors mean by "Their spatial coverage and representation, however, is limited"? Although the spatial coverage of space-borne sensors is greater than ground-based weather radars and rain gauges, this coverage is still somewhat limited (~885 km), right?
Indeed, the coverage of radiometers is still limited. However, this sentence refers to all spaceborne sensors, including geostationary satellites. Additionally, although the coverage might be limited, the radiometers have a global representation in contrast to the local representation of ground-based radars.2. Lines 35-42. These paragraphs are confusing. The authors state in Line 36 that one kind of input is the indirect observation of cloud properties. However, Lines 39-42 read that direct observations "are preferred for quantitative applications in meteorology and hydrology as precipitation retrieval from visible and infrared channels is based on cloud to precipitation relations". Maybe the authors mean "the former" in Line 41, but rephrasing may be needed. Please elaborate.
Direct observations are preferred as these observations provide information regarding precipitation. The sensors capable of measuring these direct characteristics, however, are typically found on low Earth orbit (LEO) satellites due to their frequency channels. In contrast, channels operating within the visible (VIS) and infrared (IR) frequency ranges can be accommodated on geostationary satellites, but these frequencies only provide information about cloud properties. We will revise the aforementioned lines to enhance clarity.3. Line 64. Consider adding "detection and accuracy OF RAIN RATES" or similar.
We appreciate the suggestion and we will rewrite line 64 as follows: “detection of precipitation and accuracy of precipitation intensity”4. Line 117. Why this particular threshold? Please discuss setting this threshold to help understand the radar Echo Top Height (ETH) influence in the results.
The threshold is chosen to ensure light precipitation rates are also detected, a precipitation type that frequently occurs in the Netherlands. A drawback of this low threshold is discussed in lines 119-122.5. Line 207. Please replace "The first three figures" with "Figures 1-3"
We appreciate the suggestion and we will rewrite line 207 as follows: “GPROF’s performance and some first results concerning the influence of sampling are shown in Figs. 1-3”6. Figure 2, p17. These plots are great and depict much information; however, the markers need some improvement. For instance, it is almost impossible to notice the stars in the plot showing the number of observations. I advise using other marker fill styles, such as none for circles, changing the edge colour, or other alternatives. Also, add ETH, e.g., low ETH, med ETH, etc., to the colour scale.
The suggestion is appreciated and will be implemented to improve the clarity of Fig. 2.Citation: https://doi.org/10.5194/egusphere-2023-1258-AC1
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AC1: 'Reply on RC1', Linda Bogerd, 06 Nov 2023
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RC2: 'Comment on egusphere-2023-1258', T.H.M. Rientjes, 23 Oct 2023
This interesting study is well developed and executed. The topic of estimating/comparing precipitation/rainfall by different remote sensing-based data sources is quite challenging. My comments mostly are textual where further detailing and description of used terms is advised to increase readability.
- AC2: 'Reply on RC2', Linda Bogerd, 06 Nov 2023
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Hidde Leijnse
Aart Overeem
Remko Uijlenhoet
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
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