the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 31 May 2024
Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products
Abstract. In the central Arctic, high quality water vapour observations are sparse due to the low density of meteorological stations and uncertainties in satellite remote sensing. Different reanalyses also disagree on the amount of water vapour in the central Arctic. The Multidisciplinary drifting Observatory for the Study of the Arctic Climate (MOSAiC) expedition provides comprehensive observations that are suitable for evaluating satellite products and reanalyses. Radiosonde observations provide high quality water vapour estimates with a high vertical but a low temporal resolution. Observations from the microwave radiometers (MWRs) onboard the research vessel Polarstern complement these observations through high temporal resolution. In this study, we demonstrate the high accuracy of the combination of the two MWRs HATPRO (Humidity and Temperature Profiler) and MiRAC-P (Microwave Radiometer for Arctic Clouds – Passive). For this purpose, we developed new retrievals of integrated water vapour (IWV) and profiles of specific humidity and temperature using a Neural Network approach, including observations from both HATPRO and MiRAC-P to utilize their different water vapour sensitivity. The retrievals were trained with ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and synthetic MWR observations simulated with the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA). We applied the retrievals on the synthetic and real observations and evaluated them with ERA5 and radiosondes launched during MOSAiC, respectively. To assess the benefit of the combination of HATPRO and MiRAC-P compared to single MWR retrievals, we compared the errors with respect to MOSAiC radiosondes and computed the vertical information content of the specific humidity profiles. The root mean squared error (RMSE) of IWV was reduced by up to 15 %. Specific humidity biases and RMSE were reduced by up to 75 and 50 %, respectively. The vertical information content of specific humidity could be increased from 1.7 to 2.4 degrees of freedom. We also computed relative humidity from the retrieved temperature and specific humidity profiles and found that RMSE was reduced from 45 to 15 %. Finally, we show a case study demonstrating the enhanced humidity profiling capabilities compared to the standard HATPRO based retrievals. The vertical resolution of the retrieved specific humidity profiles is still low compared to radiosondes but the case study revealed the potential to resolve major humidity inversions. To which degree the MWR combination detects humidity inversions, also compared to satellites and reanalyses, will be part of future work.
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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
(4051 KB)
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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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- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2024-1301', Anonymous Referee #1, 23 Jun 2024
Comments on manuscript:
Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, by Walbröl et al.
Overview
The work presents retrievals of vertically resolved temperature, humidity, and integrated water vapor (IWV) from combined measurements of 2 microwave radiometers, including frequencies at or higher than 183 GHz. The retrievals were trained with ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and synthetic MWR observations simulated with a radiative transfer code. Retrievals were evaluated against ERA5 and radiosondes launched during MOSAiC. The combined retrievals noticeably improved the IWV and the profiles compared to retrievals where only lower frequencies were used.
General comment
The paper is generally well written and complete in its explanation, my main question is of a general nature and goes back to the broader issue of the advantage of using radiometers vs. reanalysis for profiling. As the authors state in the introduction current reanalysis have difficulties in accurately representing the Arctic winter atmosphere but, in this context the paper does not shed any light on whether this combination of frequencies provides an improvement over the reanalysis. For example, in figure 5, rather than seeing errors related to MWR-MOSAIC and MWR-ERA5, it would be better to show MWR-MOSAIC and ERA5-MOSAIC. This way we can see if the retrievals offer a better representation of the low troposphere in the Arctic compared to ERA5.
Similarly, looking at Fig. 10 b, it appears that ERA5 still does a better job than the synergy to represent the humidity vertical structure. Therefore, my question is, why not just use ERA5? It appears that even after all the improvements shown in this work, radiometric profiles are still not good enough to provide useful vertically resolved humidity. Are they just a smoother version of the reanalysis dataset used to train them? Or they actually improve on the reanalysis biases?
Thank you
Citation: https://doi.org/10.5194/egusphere-2024-1301-RC1 - AC1: 'Reply on RC1', Andreas Walbröl, 25 Jul 2024
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AC2: 'Reply on RC1', Andreas Walbröl, 25 Jul 2024
Publisher’s note: this comment is a copy of AC1 and its content was therefore removed.
Citation: https://doi.org/10.5194/egusphere-2024-1301-AC2
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RC2: 'Comment on egusphere-2024-1301', Anonymous Referee #2, 24 Jun 2024
This is a well-written paper that is easy to follow and clearly articulates its goals and its approach to investigating them. Overall, this paper is appropriate and suitable for publication in AMT after some relatively simple issues have been addressed. Unfortunately, I feel that this includes some small data analysis which raises the threshold from minor to major revisions. Still, these revisions should not be difficult to implement.
Line 191: The authors state that they use NNs "because they can deal better with the nonlinear relationship between IWV and TB measurements in the G-band." I feel that this statement needs more supporting justification. What is this comparison being made to (i.e. NNs are better than what other technique?) Why and how are they better? One of the chief advantages of NNs is that, after they have been trained, they are computationally very quick to perform retrievals as compared to physical/OE methods. However, physical methods have a significant advantage in that they are better suited to retrieving observations of conditions that are not represented in the training dataset, which could be important in a changing arctic. I am not suggesting that the authors redo their entire analysis with a brand new retrieval method; however, I feel that they need to more strongly justify why they chose this method.
Line 210: Why was this grid chosen? There is growing awareness that the selection of a retrieval grid has a notable influence on DOF and vertical resolution (see Loveless et al. 2023 doi:10.1109/IGARSS52108.2023.10283250). Did you assess any of the influences that the grid choice may have had on the results?
Lines 265 and onward: I am approaching this problem from the perspective of an observationalist, not a modeler or a strong user of ERA5. With that in mind, it seems intuitive that an NN that is trained on ERA5 will produce profiles that show stronger agreement with ERA5 than with an external data source not just because of the uncertainties of the radiosondes or the spatiotemporal matching issues (though those concerns are important) but because the NN is implicitly including all of the known biases and uncertainties of ERA5. To me, the comparison to radiosondes is more interesting and important than the comparison to ERA5, but it is that reanalysis comparison that gets the stronger focus of attention. One step forward may be stronger justification of why the ERA5 comparisons are important.
Line 305: It sounds like the radiosonde analysis was performed with respect to sondes that had been interpolated to the retrieval grid, and not smoothed to account for the influence of weighting functions. Given that you have averaging kernals for a random subset of the cases, it would be possible to vertically smooth the sondes. This would permit an analysis of the bias and RMSE relative to sondes that would remove the influence of the large vertical gradients and provide a more direct comparison between the statistics calculated relative to sondes versus those from ERA5.
Lines 411 and onward: the DOF is an important quantity for evaluating the information content of a retrieved profile. Also important is the vertical resolution, which is easily obtained by scaling the vertical grid spacing by the inverse of the averaging kernal. As you already have these pieces of information, a small discussion of the changes in the vertical resolution brought on by the synergy would be achievable and relevant to this paper's aims.
Line 432: Why did you opt for radiosonde pressure instead of a hypsometrically-derived pressure profile from the T and specific humidity profiles? This would allow for a self-contained measurement of RH without relying on the relatively-infrequent sonde observations.
Line 448: The paper may be stronger if this particular section is removed. As it stands, it introduces a somewhat counterintuitive example (the synergy makes things worse) then promises to investigate further in a future paper. It may be best if the entire discussion is held to that future paper so that it can be investigated in detail and the nuances can be explained.
Line 486: Here is a good example of why the inclusion of a vertical resolution discussion is important. Improvements are seen in the lowest 1500 m, but I'm guessing that even with the synergy and the non-zenith stares, the true vertical resolution is on the order of 1500 m or worse given the broad weighting functions of the microwave band. Noting the changes in the vertical resolution would help better attribute the causes of these improvments.
Citation: https://doi.org/10.5194/egusphere-2024-1301-RC2 -
AC3: 'Reply on RC2', Andreas Walbröl, 25 Jul 2024
Dear reviewer,
than you for the detailed comments on the manuscript and the suggested additional analyses. Please find our response in the reply to reviwer one as we coomposed all responses into a single document.
Best regards,
Andreas
Citation: https://doi.org/10.5194/egusphere-2024-1301-AC3
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AC3: 'Reply on RC2', Andreas Walbröl, 25 Jul 2024
Interactive discussion
Status: closed
-
RC1: 'Comment on egusphere-2024-1301', Anonymous Referee #1, 23 Jun 2024
Comments on manuscript:
Combining low and high frequency microwave radiometer measurements from the MOSAiC expedition for enhanced water vapour products, by Walbröl et al.
Overview
The work presents retrievals of vertically resolved temperature, humidity, and integrated water vapor (IWV) from combined measurements of 2 microwave radiometers, including frequencies at or higher than 183 GHz. The retrievals were trained with ERA5 data from the European Centre for Medium-Range Weather Forecasts (ECMWF) and synthetic MWR observations simulated with a radiative transfer code. Retrievals were evaluated against ERA5 and radiosondes launched during MOSAiC. The combined retrievals noticeably improved the IWV and the profiles compared to retrievals where only lower frequencies were used.
General comment
The paper is generally well written and complete in its explanation, my main question is of a general nature and goes back to the broader issue of the advantage of using radiometers vs. reanalysis for profiling. As the authors state in the introduction current reanalysis have difficulties in accurately representing the Arctic winter atmosphere but, in this context the paper does not shed any light on whether this combination of frequencies provides an improvement over the reanalysis. For example, in figure 5, rather than seeing errors related to MWR-MOSAIC and MWR-ERA5, it would be better to show MWR-MOSAIC and ERA5-MOSAIC. This way we can see if the retrievals offer a better representation of the low troposphere in the Arctic compared to ERA5.
Similarly, looking at Fig. 10 b, it appears that ERA5 still does a better job than the synergy to represent the humidity vertical structure. Therefore, my question is, why not just use ERA5? It appears that even after all the improvements shown in this work, radiometric profiles are still not good enough to provide useful vertically resolved humidity. Are they just a smoother version of the reanalysis dataset used to train them? Or they actually improve on the reanalysis biases?
Thank you
Citation: https://doi.org/10.5194/egusphere-2024-1301-RC1 - AC1: 'Reply on RC1', Andreas Walbröl, 25 Jul 2024
-
AC2: 'Reply on RC1', Andreas Walbröl, 25 Jul 2024
Publisher’s note: this comment is a copy of AC1 and its content was therefore removed.
Citation: https://doi.org/10.5194/egusphere-2024-1301-AC2
-
RC2: 'Comment on egusphere-2024-1301', Anonymous Referee #2, 24 Jun 2024
This is a well-written paper that is easy to follow and clearly articulates its goals and its approach to investigating them. Overall, this paper is appropriate and suitable for publication in AMT after some relatively simple issues have been addressed. Unfortunately, I feel that this includes some small data analysis which raises the threshold from minor to major revisions. Still, these revisions should not be difficult to implement.
Line 191: The authors state that they use NNs "because they can deal better with the nonlinear relationship between IWV and TB measurements in the G-band." I feel that this statement needs more supporting justification. What is this comparison being made to (i.e. NNs are better than what other technique?) Why and how are they better? One of the chief advantages of NNs is that, after they have been trained, they are computationally very quick to perform retrievals as compared to physical/OE methods. However, physical methods have a significant advantage in that they are better suited to retrieving observations of conditions that are not represented in the training dataset, which could be important in a changing arctic. I am not suggesting that the authors redo their entire analysis with a brand new retrieval method; however, I feel that they need to more strongly justify why they chose this method.
Line 210: Why was this grid chosen? There is growing awareness that the selection of a retrieval grid has a notable influence on DOF and vertical resolution (see Loveless et al. 2023 doi:10.1109/IGARSS52108.2023.10283250). Did you assess any of the influences that the grid choice may have had on the results?
Lines 265 and onward: I am approaching this problem from the perspective of an observationalist, not a modeler or a strong user of ERA5. With that in mind, it seems intuitive that an NN that is trained on ERA5 will produce profiles that show stronger agreement with ERA5 than with an external data source not just because of the uncertainties of the radiosondes or the spatiotemporal matching issues (though those concerns are important) but because the NN is implicitly including all of the known biases and uncertainties of ERA5. To me, the comparison to radiosondes is more interesting and important than the comparison to ERA5, but it is that reanalysis comparison that gets the stronger focus of attention. One step forward may be stronger justification of why the ERA5 comparisons are important.
Line 305: It sounds like the radiosonde analysis was performed with respect to sondes that had been interpolated to the retrieval grid, and not smoothed to account for the influence of weighting functions. Given that you have averaging kernals for a random subset of the cases, it would be possible to vertically smooth the sondes. This would permit an analysis of the bias and RMSE relative to sondes that would remove the influence of the large vertical gradients and provide a more direct comparison between the statistics calculated relative to sondes versus those from ERA5.
Lines 411 and onward: the DOF is an important quantity for evaluating the information content of a retrieved profile. Also important is the vertical resolution, which is easily obtained by scaling the vertical grid spacing by the inverse of the averaging kernal. As you already have these pieces of information, a small discussion of the changes in the vertical resolution brought on by the synergy would be achievable and relevant to this paper's aims.
Line 432: Why did you opt for radiosonde pressure instead of a hypsometrically-derived pressure profile from the T and specific humidity profiles? This would allow for a self-contained measurement of RH without relying on the relatively-infrequent sonde observations.
Line 448: The paper may be stronger if this particular section is removed. As it stands, it introduces a somewhat counterintuitive example (the synergy makes things worse) then promises to investigate further in a future paper. It may be best if the entire discussion is held to that future paper so that it can be investigated in detail and the nuances can be explained.
Line 486: Here is a good example of why the inclusion of a vertical resolution discussion is important. Improvements are seen in the lowest 1500 m, but I'm guessing that even with the synergy and the non-zenith stares, the true vertical resolution is on the order of 1500 m or worse given the broad weighting functions of the microwave band. Noting the changes in the vertical resolution would help better attribute the causes of these improvments.
Citation: https://doi.org/10.5194/egusphere-2024-1301-RC2 -
AC3: 'Reply on RC2', Andreas Walbröl, 25 Jul 2024
Dear reviewer,
than you for the detailed comments on the manuscript and the suggested additional analyses. Please find our response in the reply to reviwer one as we coomposed all responses into a single document.
Best regards,
Andreas
Citation: https://doi.org/10.5194/egusphere-2024-1301-AC3
-
AC3: 'Reply on RC2', Andreas Walbröl, 25 Jul 2024
Peer review completion
Journal article(s) based on this preprint
Data sets
Simulated microwave brightness temperatures based on two radiosoundings performed during the MOSAiC expedition Andreas Walbröl https://doi.org/10.5281/zenodo.11092210
Neural Network predictions and ERA5 reference of integrated water vapour, and temperature and specific humidity profiles based on simulated microwave radiometer observations Andreas Walbröl https://doi.org/10.5281/zenodo.10998146
Information content estimation output for specific humidity profiles Andreas Walbröl https://doi.org/10.5281/zenodo.10997692
ERA5 based training, validation and evaluation data for retrievals combining 22-58 GHz with 175-340 GHz microwave radiometer measurements during MOSAiC Andreas Walbröl and Mario Mech https://doi.org/10.5281/zenodo.10997365
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Hannes J. Griesche
Mario Mech
Susanne Crewell
Kerstin Ebell
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(4051 KB) - Metadata XML