the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Jul 2026
Improving representativeness of microwave radiometer brightness temperatures for data assimilation by complementing cloud detection with cloud clearing
Abstract. This study introduces two new retrievals for ground-based microwave radiometers (MWR) and demonstrates that, together, they solve the misrepresentation issue that makes it challenging to assimilate cloudy-sky brightness temperatures. Multiple studies have shown that assimilating MWR brightness temperatures is beneficial to numerical weather prediction, despite rejecting substantial amounts of valid observations from the assimilation due to the presence of liquid water clouds. Cloud detection complemented with a cloud clearing retrieval makes this rejected data available for direct assimilation.
We scrutinize the introduced cloud detection retrieval and cloud clearing retrieval using multiple approaches. For one, we contextualize the new retrievals with reference retrievals. Secondly, we determine the retrieval sensitivity to instrument errors using a Monte Carlo method. And most importantly, we assess the representativeness of the retrieval products, which is crucial for data assimilation, through observation minus background statistics.
Our analysis reveals that the new cloud detection retrieval predicts less false positive cloudy-sky cases (-13 % over two years) compared to the established reference. Additionally, the cloud-clearing retrieval improves the representativeness between observation and background to the extent that artificial clear-sky and native clear-sky statistics almost match. Considering the minimal instrumentation required, both retrievals perform surprisingly well for elevation angles from zenith down to 4.8 °.
Overall, our findings demonstrate that combining cloud detection and cloud clearing retrievals improves the representativeness between observations and model. This retrieval combination enables the direct assimilation of cloudy-sky brightness temperatures.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2026-2264', Anonymous Referee #2, 29 Jul 2026
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RC2: 'Comment on egusphere-2026-2264', Alan Geer, 31 Jul 2026
Review of “Improving representativeness of microwave radiometer brightness temperatures for data assimilation by complementing cloud detection with cloud clearing”
The wider context of this manuscript is the effort towards assimilating ground-based microwave radiometers in weather forecasting. Ground based radiometers could fill the observation gap in the boundary layer over land, where space-based microwave observations have little information due to lack of radiometric contrast between the lower atmosphere and the surface. The work under review proposes the assimilation of cloud-cleared brightness temperatures, rather than cloud-affected brightness temperatures (all-sky assimilation essentially) or clear-sky assimilation (throwing away locations affected by cloud). The cloud-clearing approach would allow the forward model in data assimilation to model only gas absorption and not the effect of hydrometeors, which can be trickier. Cloud-affected brightness temperatures need to be assimilated with relatively large observation errors, and the expectation is that this reduces the impact of the observations. Instead, the hope is that (a) cloud-cleared brightness temperatures can be assimilated with smaller prescribed errors and hence provide more impact and (b) the observations can be assimilated in cloudy areas where data would otherwise have been thrown away. To support this, the current work trains two neural networks, the first to identify cloud and the second to do cloud clearing. These are evaluated against a number of other existing metrics available from the same ground based radiometer systems, based on a limited time and space domain.
A first major issue with this work is the goal of assimilating cloud-cleared radiances. For over 15 years, global weather centres have been assimilating space-based microwave observations using direct all-sky assimilation. Forward modelling of hydrometeor-affected scenes is relatively accurate and fast, and the largest errors are the forecast errors in the underlying cloud and precipitation, humidity and temperature fields, rather than inaccuracies in the radiative transfer modelling. 4D assimilation methods are able to use this information, including the cloud and precipitation part, to improve the analyses of all atmospheric variables, through the generalised model tracing effect. Further, although the observation errors are large in cloudy areas, signals from displaced cloud are typically even larger, so there is still plenty of useful information to assimilate. Hence, the work under review needs to show greater awareness of progress in space-based all-sky microwave assimilation and it should better justify why assimilation of cloud-cleared radiances makes sense, particularly if the motivation comes specifically from the ground-based context. I wonder whether the real underlying issue is a lack of reliable all-sky forward models (RTTOV, for example, does not support up-looking geometries yet) or because (possibly incorrectly) the main geophysical target for assimilation in the current work is expected to be the humidity and temperature profile of the boundary layer, rather than the clouds. But in a more sophisticated 4D system, cloud and precipitation themselves provide a lot of information, so restricting the information to the temperature and humidity profile is not necessarily the best strategy.
It is fine to accept the goal of assimilating cloud cleared radiances as the premise for this paper, as long as it is properly contextualised and explained. But a further major problem with the current manuscript is that it is hard to follow, poorly organised, missing critical methodological information, and not fully motivated or explained. I have read it multiple times without being able to understand everything that has been presented. My review is incomplete for this reason. I think the manuscript needs a total rewrite to ensure a better structure and a clear, more complete documentation of the work. I make some illustrative suggestions, but it is ultimately up to the authors to find a way to better communicate their work. This will be a major task.
A final issue is that there is too much extrapolation and over-claiming. The work has demonstrated a method that generates smaller O-B but has not, as stated in the abstract, found the complete solution to the problems of misrepresentation error. We will only know if the proposed methods really work once they have been used in an assimilation system, or validated against high quality and completely independent data, neither of which is done here. Therefore, there should be a clear and neutral presentation of the work has been done, and any wider hopes or hypotheses need to be presented very carefully - typically in the conclusion, where it is OK to look ahead a bit. As part of this, it is vital in the abstract and conclusions to underline that the results are based on a limited sample of in space, time and synoptic states - this might possibly be summarised as “one summer, one location, 35 episodes of scattered cumulus cloud”.
Main points
1) Following the overview comments, in the abstract, introduction and conclusions, please better reflect the wider context of all-sky direct assimilation of space-based microwave radiometers and how the ground-based context differs. Please also try to better motivate and justify the aim of assimilating cloud-cleared radiances in this context.
2) L30-32 - it would be important to have more detail on the state of the art of ground-based radiometer assimilation. In particular, it would be good to detail which of the studies being cited are done in a research context, and which describe a successful operational assimilation system. Also please be precise about the data assimilation methodologies, whether clear-sky or all-sky, 3D or 4D, and the forward models, whether they account for cloud or precipitation. Is the assimilation direct, or is it based on retrieved temperature and humidity profiles? This would help a lot to better motivate the current work.
3) Around line 141, the documentation of the radiative transfer modelling is insufficient. It would be good to say more about many things including the representation of clouds (presumably only the absorption, based on Rayleigh scattering?). Also, it is not clear if the slant path atmosphere is a straight line or ray traced (and how this is done) and the air mass correction needs explaining. In which cases does the radiative transfer modelling including the cloud contribution and which not? Which exact inputs are used to drive the radiative transfer, and from which model? And from this, which datasets are used in which parts of the results section? A table might help summarise the latter aspects more clearly.
4) Section 2.4, on the neural network training, should probably be the largest methodological part of the paper, but instead it is just a few paragraphs. Among the basic missing details are things like the final structure of the neural networks (e.g. how many trainable parameters), and the validation loss for different architectures, helping to understand why a specific configuration was chosen, and how it has been determined that the training converged. There needs to be a more careful discussion of the training data, i.e. the imposed inputs and outputs of the NN. The intended output, cloud-clear radiances for example, is not necessarily exactly the same as the thing the network was trained on, here ERA5-simulated brightness temperatures. This is very clear from the need for internal bias correction, which needs to be fully investigated and documented in the text. The bias correction illustrates that the cloud-clearing neural network is trained precisely to map from observed brightness temperatures to ERA5 simulated brightness temperatures (presumably clear sky - but this is not absolutely clear from the text - see point 3). Therefore it will automatically learn any biases between the observations and ERA5 - this is likely the reason it will not output the observed clear-sky brightness temperature in clear-sky cases.
5) One of the most helpful parts of the presentation is figure 4. It would be much easier to understand the work if this figure were presented and explained earlier on, to put everything into context. A similar figure to illustrate the results, inputs, outputs and validation of the two machine learning models would also be really helpful for understanding.
6) I found myself rather lost in all the results given as recall matrices, which ultimately just compare to other imperfect cloud detection strategies from the same sensors. The authors already illustrate many of the limitations of these other strategies. From the current manuscript, I am not sure what we really learn from all of this. I also do not fully understand the motivation for the cloud detection model - this needs to be made clearer.
7) The results from the cloud clearing are characterised as observation error, but really these are just O-B, and it is not possible to say how the data would work in a real assimilation system. Further, the presentation of O-B results in figures 7 and 8 using a logarithmic colour scale is hard to interpret, especially from a quantitative perspective. Line graphs would probably convey more useful information here. I also wonder, since it is possible to interpolate across cloudy episodes, whether that strategy could be used to generate cloud-cleared retrievals, and what sort of O-B statistics it would generate. Does the new technique bring anything that could not be obtained from the interpolation strategy?
8) Cloud-clearing has a long history and many people have attempted to use cloud-cleared radiances in the past, including in data assimilation. It would be really helpful to review these past studies and how they relate to the current one. Perhaps the birth of cloud clearing is the technique often called N*:
Smith, W.L., 1968. An improved method for calculating tropospheric temperature and moisture from satellite radiometer measurements. Monthly Weather Review, 96(6), pp.387-396.
Cloud cleared radiances were assimilated at NCEP at one stage:
Derber, J.C. and Wu, W.S., 1998. The use of TOVS cloud-cleared radiances in the NCEP SSI analysis system. Monthly Weather Review, 126(8), pp.2287-2299.
In an article of which I am a co-author, we reviewed the idea of cloud-cleared radiance assimilation from the perspective of assimilating cloud-affected IR data, and there are many citations to follow there:
Li, J., Geer, A.J., Okamoto, K., Otkin, J.A., Liu, Z., Han, W. and Wang, P., 2022. Satellite all-sky infrared radiance assimilation: Recent progress and future perspectives. Advances in Atmospheric Sciences, 39(1), pp.9-21.
Minor points
Abstract - “This retrieval combination enables the direct assimilation of cloudy-sky brightness temperatures”. Assimilation of cloud-cleared radiances is not direct assimilation - it is retrieval assimilation.
L104-105 - FOGHAT is introduced here but it is not really clear what relevance this has to the wider work - please explain in the text.
L109-111 - It is not clear from the text whether a degraded radome and a wet radome are synonymous, or different concepts.
L114 - Need to explain what “carefully flagged data” means precisely, to a level someone could attempt to reproduce this study.
L123 - The spatial resolution of ERA5 is definitely not 0.02 degrees, i.e. around 2 km. Also please be careful to distinguish the native resolution (the model spectral and reduced Gaussian grid) which is likely quite different from the archiving resolution and grid.
L125-6 - There is no surface level in the profiles, but there are 2m temperature and humidity estimates which could be used, or (probably less accurate) it can be assumed that the lowest atmospheric level represents the whole layer down to the surface.
L 204-213 - There is a lot of information on instrument errors and their quantisation, but it is unclear where this information is actually used in the current study.
L 249-257 - I didn’t find this introduction helpful as it provided details of method that were only needed later on in the section. Such methodological details belong ideally in the methods section.
Table 5 results - what these “confidence intervals” actually mean is not explained.
Editorial
ECMWF is a medium (not mid) range forecasting organisation - please check the correct name.
L99 Introduce the symbol lambda
Citation: https://doi.org/10.5194/egusphere-2026-2264-RC2
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General comments
This manuscript presents two neural-network-based retrievals for ground-based microwave radiometers: Cloud-RERA5 for cloud detection and Clearing-RERA5 for removing the spectral contribution of cloud liquid water from observed brightness temperatures. The topic is timely and relevant to the broader use of microwave-radiometer observations in data assimilation, particularly at off-zenith viewing angles. The manuscript is generally well organized, and the results indicate that the proposed methods have potential to increase the number of microwave-radiometer observations that can be used under cloudy conditions.
My main concern is the interpretation of the evaluation results. Throughout the manuscript, the terms observation error, representativeness, representativeness error, and O–B statistics are sometimes used interchangeably. However, the analyses presented in this study directly evaluate O–B statistics, which contain contributions from observation error, background error, representativeness error, and observation-operator error. Therefore, a reduction in O–B standard deviation does not necessarily demonstrate a corresponding reduction in observation error alone. I recommend revising the terminology throughout the manuscript and consistently distinguishing between the quantities directly evaluated and their implications for data assimilation.
A second concern is that several conclusions are stronger than supported by the available validation data. Comparisons among Cloud-RERA5, STD30, STD6, and LWP-RRS quantify agreement among retrieval algorithms rather than absolute performance, because none of these methods provides independent ground truth. More cautious wording would improve the scientific precision of the manuscript without changing its main findings.
Major comments
L38, L41, L65–68, L423, L439: The term “representativeness” is used with several different meanings throughout the manuscript. The discussion appears to combine subgrid-scale variability, cloud-location errors in the model, systematic cloud-water biases, observation-operator errors, and cloud contamination. These are distinct sources of uncertainty. Please define “representativeness error” more precisely and distinguish it consistently from model error, forecast displacement error, observation-operator error, and instrumental observation error.
L114-115: The description of the quality-control procedure is insufficiently quantitative. The sentence “Carefully flagged data are removed from our analysis” does not specify which flags were applied or how much data were excluded. Please report the number or percentage of observations removed because of precipitation, radome degradation, instrument failure, and calibration issues. Given the extended exclusion period from August 2021 to January 2022, please also discuss whether the remaining dataset is sufficiently representative of the intended evaluation period.
L133-134: The procedure used to extend the ICON-D2 atmospheric profiles to 30 km is not described in sufficient detail. Please explain how the standard-atmosphere profiles were merged with the ICON-D2 profiles, at which altitude or pressure level the transition was applied, and how continuity in temperature, pressure, and humidity was ensured across the transition.
L137, L144–145: The radiative-transfer configuration requires further discussion. First, only pressure, temperature, humidity, and liquid water content are mentioned as atmospheric inputs. Please clarify whether frozen hydrometeors were neglected and justify this assumption, particularly for the higher-frequency HATPRO channels. Second, off-zenith brightness temperatures are computed from a single vertical model column under the assumption of horizontal homogeneity. Please discuss the implications of this approximation for cloudy scenes, where horizontal gradients and cloud displacement may be substantial, and comment on whether it could affect the evaluation of the proposed retrievals.
L165-166: The neural-network development and model-selection procedure is not described in sufficient detail for reproducibility. Please specify the hyperparameters that were explored, their ranges, the validation metric used for selection, and the architecture of the final models. In particular, the phrase “selected the best neural network manually” should be replaced by an objective description of the selection procedure.
L168–174: The bias-correction procedure requires further explanation. Please provide more information on the architecture, inputs, outputs, and training procedure of the internal bias-correction network. The rationale for using a 24 h correction window should also be justified, including whether the retrieval performance is sensitive to this choice. Furthermore, the manuscript states that the correction is forward propagated when no clear-sky observations are available in the next time bin. Please explain how long this propagation is allowed to continue and how the correction is reinitialized following prolonged cloudy periods.
Because the bias correction relies on Cloud-RERA5 to identify clear-sky periods, please also discuss how cloud-detection errors propagate into the bias correction and subsequently into Clearing-RERA5.
L190–194: The definitions and evaluation of the retrieval uncertainties should be clarified. The terms “retrieval error” and “product error” are currently ambiguous. Please specify the reference against which retrieval error is defined and explain how retrieval error and sensitivity to instrument errors are combined.
In addition, Cloud-RERA5 and Clearing-RERA5 are evaluated using different approaches: Monte Carlo perturbations for Cloud-RERA5 and O–B statistics for Clearing-RERA5. Please explain why different methodologies are required and whether the resulting uncertainty estimates are intended to be quantitatively comparable.
L199–203 and throughout Sections 2.6, 3.1.3, and 3.2.2: The interpretation of the O–B standard deviation should be revised throughout the manuscript. O–B statistics contain contributions from observation error, background error, representativeness error, and observation-operator error. Therefore, they do not provide a direct estimate of observation error alone.
The use of O–B standard deviation as a practical indicator of retrieval performance is reasonable, but it should be described explicitly as a proxy for the combined uncertainty reflected in the O–B differences. The statement that forecast errors are “included into the observation error” is potentially misleading. A more accurate interpretation is that the individual error contributions cannot be separated using O–B statistics alone.
L267 and Section 3.1: The selection of the LWP threshold of 5 g m⁻² requires stronger quantitative justification. The manuscript explains the qualitative trade-off between probability of detection and false-alarm rate, but it remains unclear why 5 g m⁻² is the preferred threshold. Please show the sensitivity of the relevant performance metrics, and preferably the O–B statistics, to the selected threshold or provide independent evidence supporting this choice.
Section 3.1.1: The Monte Carlo experiment assumes a static Gaussian brightness-temperature bias with σ = 0.5 K. Please justify why this perturbation is representative of realistic instrumental uncertainties. In particular, discuss whether random noise, calibration drift, temporally varying biases, and interchannel error correlations could affect the conclusions.
L302: The statement that “cloud detection is perfect” is too strong. The results show a POD of 100% above the specified LWP range under the assumed perturbation model. The conclusion should be restricted to those experimental conditions.
L315–345: Cloud-RERA5 is used as the shared reference when comparing the cloud-detection retrievals. Since Cloud-RERA5 is itself a retrieval rather than independent ground truth, it may be helpful to clarify that disagreements with Cloud-RERA5 do not necessarily correspond to false detections or missed detections by STD30, STD6, or LWP-RRS. Instead, the reported statistics could be described as reflecting the level of agreement among the retrievals.
L345: For the same reason, the statement that the differences “mostly favor Cloud-RERA5” appears stronger than supported by the comparison. A more neutral interpretation should be adopted unless independent observational validation is available.
L349–351, L365–367: The analysis shows that Cloud-RERA5 and STD30 produce similar clear-sky O–B standard deviations despite selecting different numbers of clear-sky scenes. This is a useful result. However, it does not directly demonstrate that the two methods yield the same (approximated) observation error. It would be more precise to phrase the conclusion in terms of the similarity of the resulting clear-sky O–B statistics.
L353–355: Cases classified as clear sky by the retrievals but cloudy by ICON-D2 were excluded from the analysis. Please report the fraction of observations removed by this criterion and discuss whether this filtering could reduce the apparent differences between Cloud-RERA5 and STD30.
L376–377, L401–408: The evaluation of Clearing-RERA5 relies on linearly interpolated brightness temperatures between the last clear-sky observation before a cloud passage and the first clear-sky observation after it. This is a useful practical reference, but it is not independent ground truth. Convective cloud passages may be associated with systematic evolution in temperature and humidity, violating the linear-interpolation assumption. Please discuss more explicitly how this limitation may affect the inferred retrieval performance.
L401–408: The statement that the remaining residuals can be largely explained by atmospheric variability appears somewhat stronger than the presented evidence. The observed correlation with IWV fluctuations is consistent with this interpretation, but it does not exclude residual retrieval errors or errors in the interpolated reference. It may therefore be helpful to phrase this conclusion more cautiously.
L421–424, L434–437: The reduction in O–B standard deviation after cloud clearing is an important result, but it should not be described directly as a reduction in random observation error. The analysis demonstrates a reduction in cloudy-sky O–B variability after both the observed and simulated brightness temperatures have been transformed into an artificial clear-sky framework. It would therefore be more precise to describe the result in terms of the resulting O–B statistics rather than the observation error itself.
L422–424: Similarly, the statement that the cleared cloudy observations have a representativeness comparable to native clear-sky observations appears stronger than the presented evidence. The results show comparable O–B standard deviations, but they do not isolate or directly estimate representativeness error.
L419–420: Please also explain why the cleared O–B standard deviation is occasionally smaller than the native clear-sky value. This may be relevant for interpreting whether the cloud-clearing transformation changes variability unrelated to cloud liquid water.
L446–450: Several statements in the Conclusions could be moderated. In particular, the claim that Cloud-RERA5 “identifies all clouds relevant for data assimilation” is not supported by an independent cloud-truth dataset. It would be more accurate to describe the retrieval performance under the evaluation conditions used in this study.
L458–460: The statement that cloud clearing “could double the effect of assimilating zenith MWR TB observations” appears somewhat speculative because no data-assimilation experiments are presented. The reduction in O–B variability suggests potential benefits for data assimilation, but the actual impact on the analysis and forecast should be evaluated in future assimilation experiments.
Minor comments
L44–46: Please clarify that observation-error inflation and VarQC reduce observational influence through different statistical mechanisms.
L65: Consider replacing “representativeness of MWR TBs and ICON-D2” with “consistency between MWR observations and ICON-D2 simulations.”
L99: Please briefly explain the role of the IR-radiometer when it is first introduced.
L109: Please clarify what is meant by uncertainty. If instrumental errors are intended, measurement uncertainty or instrument error may be more appropriate.
L111: Please briefly describe the method of Löffler (2024).
L116–117: Please define more specifically what is meant by consistent, stable, and climatologically representative.
L123: Please verify the stated ERA5 horizontal resolution 0.02 degree.
L123–126: Please clarify whether the ERA5 profiles were interpolated onto regular 25-hPa pressure levels.
L124: Please explain which numerical instability is avoided by truncating the profiles at 30 km.
L125: Please revise the wording, as ERA5 provides surface variables separately and the current phrasing may be misleading.
L135: Consider replacing “independent model reference” with “independent model dataset” or “independent evaluation dataset.”
L146: Please briefly justify neglecting antenna beam width and receiver bandwidth.
L161: Please specify which dimensionality-reduction methods were tested.
L171: The statement that the differences "should ideally be zero" appears too strong.
L180: Consider replacing "response to instrument errors" with "sensitivity to instrument errors."
L181: Please clarify the relevance of Szegedy et al. (2013), which primarily concerns adversarial perturbations, to realistic microwave-radiometer instrument errors.
L210–216: Please distinguish representativeness errors from geometric effects (e.g., beam width, beam tracing, and viewing geometry), as these appear to be discussed together.
L223–240: Please clarify whether the benchmark retrievals were used in their original form or retuned for the present dataset.
232–240: Please discuss how the zenith-only benchmark methods limit the evaluation of off-zenith retrievals.
L238–240: Please justify the selected LWP threshold (5 g m⁻²) or provide an appropriate reference.
L252: Consider replacing "impact on the approximated observation error" with "impact on the O–B statistics."
L252: Please clarify what is meant by "conceptually works."
L259–262: Please clarify whether all available MWR channels or only the channels used by Cloud-RERA5 were included in the analysis.
L265–267: Consider replacing "ideal threshold" with "selected threshold" or "practical threshold", as the threshold reflects a trade-off rather than a unique optimum.
L276–281: Please justify the use of 100 Monte Carlo realizations and explain why this number was considered sufficient.
L273–295: Please define POD_local when it is first introduced, rather than only when its mathematical definition is given.
L315: Please explain why the detailed comparison is restricted to summer 2021.
L326–332: Please provide quantitative support for the explanation of increased false detections (e.g., the effects of temporal averaging and humidity-dependent thresholds).
L313–345: Please confirm that identical quality-control criteria were applied to all retrievals before comparison.
L353–358: Please clarify whether the reported cloud-cover fractions were calculated after all exclusion criteria had been applied.
L362–363: Please define native clear-sky STD when it is first introduced.
L353–365: Please report the absolute number of retained clear-sky samples.
L362–365: Please support the statement that the differences are negligible with a quantitative criterion.
L376–380: Please explain how cloud passages were identified.
L377–380: Please justify the selected duration and LWP thresholds.
L380–384: Please report the number of cases in different LWP ranges.
L396–404: Please clarify whether the IWV retrieval is independent of the brightness temperatures used by Clearing-RERA5.
L405–408: Please replace "yields plausible cleared TBs" with a more objective statement.
L389–394: Please briefly discuss the increased variance above approximately 54 GHz.
L413–414: Please define native clear-sky, native cloudy-sky, and artificial clear-sky more explicitly when these terms are first introduced.
L410–413: Please clarify which parts of the ICON-D2 state were modified during cloud clearing.
L424–433: Please discuss the origin of the increased O–B standard deviation in the upper V-band.
L463–465: Please clarify what is meant by “low requirements for additional instrumentation.”
L452–453: Consider replacing “pioneers the potential” with a more objective expression.
L458–465: Please distinguish the demonstrated results from the expected implications for future data assimilation.
L466–470: Please mention that part of the evaluation relies on retrieval-to-retrieval comparisons and interpolated brightness temperatures rather than independent observational truth.