| 05 Dec 2025
Retrieving Tropospheric Temperature and Humidity Profiles Over the Ocean Using Buoy-Based Microwave Radiometers
Abstract. The acquisition of atmospheric temperature and humidity profiles over the sea is strategically vital for meteorological forecasting, marine monitoring, and national security. Achieving their real-time, stable, and routine retrieval under complex sea conditions is a critical and urgent challenge. Traditional retrieval methods rely heavily on large historical datasets. However, marine sounding stations are sparse, making data acquisition challenging. Concurrently, buoy platforms experience wave disturbance, causing real-time variations in zenith angle observations. Without correction, this induces significant random errors in target brightness temperature. To address these issues, this paper proposes a collaborative retrieval method. This method does not rely on historical data and integrates platform attitude information. Our approach uses a multi-objective genetic algorithm to construct a small-scale joint prior database. It also incorporates an attitude error correction model, a pressure-altitude model, and a parallel optimization strategy. This fundamentally eliminates dependence on historical data. It also effectively mitigates systematic errors from buoy attitude, enhances computational efficiency, and enables real-time, routine retrieval of marine atmospheric profiles. Simulation experiments and field tests in Qingdao’s Jiaozhou Bay confirm the results. Under sparse data conditions, the temperature RMSE is 2.08 K, and the humidity RMSE of 20.95 %. This validates the method’s stability and applicability in real marine environments. This research provides a potentially practical pathway for ocean areas with sparse radiosondes for real-time, stable, and routine detection of marine atmospheric parameters.
This paper tackles the complex use of tropospheric microwave radiometers onboard ocean buoys. Due to the continuous sounding and relatively independent operation of this type of operation, they are particularly well suited to operating in areas- such as on the sea surface- where observations tend to be more sparse than on land, yet where observations are still of necessity for improving NWP models, monitoring climate, and other applications. One central issue with the use of radiometric measurement on a bouy platform, however, is the movement of buoy induced by ocean waves which changes the elevation angle (or zenith angle) of the radiometer antenna. As there is a dependence of elevation angle on the level 1 measurements (normally brightness temperature), it is important that this is considered and corrected for in the calibration or retrieval stage when treating buoy-based radiometric observations.
The paper uses an observational database with buoy-based observations to make retrievals of humidity and temperature which could be compared to nearby radiosonde observations. The dataset is one month long which led to 38 profiles which could be used to derive statistics for the performance of the retrieval methodology.
The paper presents an interesting dataset and tackles a relevent problem. I would however reccommend that additional corrections and analyses are performed before the final article be published.
General Comments
The scientific English is mostly very good and the plots that are presented are appropriately sized and clear. My view is that some important information, as well as relevant details about what is actually shown in statistics and plots, is often omitted.
Of particular importance for this work are the attitude sensors on the buoy, from which the roll and pitch angles are found, which are then used to calculate the zenith angle of the antenna. However, there is no inclusion of the accuracy of these sensors, and how accurate we may expect roll and pitch angles, and therefore the zenith angle to be. It is also not discussed how each measurement from the radiometer is treated. When the zenith angle of the radiometer is under constant flux as it is on a sea surface, the integration time of a single observation is very relevant, yet it is not discussed in the paper. For each individual measurement, what zenith angle represents this measurement? The mean over the integration time, or otherwise?
There seems to be a lack of technical detail in certain aspects where statistical comparisons are introduced. For example, the campaign took place from 22nd of August until the 23rd of September 2023. As radiosondes are launched two times per day, one may assume that there are 64 radiosoundings which could be used for comparison to the MWR profiles in the study. However, only 38 valid matchups are found. What is the criterion for matching profiles? Was this decided in advance or to optimise the dataset for this study? How much will the criteria likely reduce the dataset of an operational radiometer, which may be away from land water with potentially more wave activity?
There is also a lack of information in how the retrieval works. There is a mention of constraints according to physical laws such as the lapse rate- but this cannot be considered a physical law for multiple reasons, and I struggle to see how this can be applied in such an algorithm. How the single Pareto solution is found should also be documented. While equations for things like the RMSE are written, equations that are essential for understanding the optimisation framework of the retrieval algorithm are not shown. If these are documented elsewhere, this should be appropriately cited, but if not they should be included in this article.
Specific Comments
Line 11 - something is missing to relate lack of measurements over the sea to microwave radiometers’ ability to do this
Line 16 - You state several times that the algorithm does not rely on historical data but later say that it relies on a limited amount of historical data. Please state clearly in the abstract, and later on, what exactly are the requirements in terms of radiosounding data.
Line 18 - ‘pressure-altitude model’ - you present an unreferenced empirical formula later, is this what you mean or is there something more complicated?
Line 28 - ‘unprecedented challenges’ - this is too strong. Explain concisely and clearly, after introducing that microwave radiometers are able to profile atmospheric temperature and humidity, that their use in the marine environment is further complicated.
Line 31 - citation needed here
Line 36 - sparse distribution of what?
Line 38 - This isn’t necessarily true. OEM/1D-Var methods do not necessarily rely on large amounts of training data. What do you mean by ‘potential interference and correction issues’? The results that you present here also involve a systematic height-dependent correction, which is based on radiosondings, so I am not sure that NOGA-II framework avoids this problem.
Line 53 - You talk about research from the past 20 years and then mention research from almost 50 years ago as if this follows on from the other two.
Line 61 - One alternative (if feasible) is mechanical stabilisation such as Schnitt et al. (2024). If not used, please justify why and quantify the residual pointing variability.
Line 63 - You mention MWRs as if they are introduced for the first time here. I don’t think this is the case. Please rearrange the paragraphs so that this is in the correct place or adapt this paragraph to what has come before it
Line 70 - What is meant by flexible buoy technology?
Line 85 - Do you have a citation for this radiometer?
Line 90 - The measurements are not technically used to detect oxygen, atmospheric oxygen concentrations are usually one of the assumed quantities for detecting temperature profiles in this frequency band.
Line 101 - a zoomed out photograph of the buoy, or some kind of graphic showing the design would help the reader to visualise how it looks here
Line 112 - ‘microwave radiation equation’ - do you mean radiative transfer equation?
Line 113 - ‘improves retrieval accuracy’ - it would be interesting to see how much of a difference this makes when compared to retrievals with no correction at all. I would think that this correction is vital for the accuracy, but perhaps if zenith angle perturbation is not too large, the correction is not necessary.
Line 113 - please also discuss the error of attitude sensor and how this affects the zenith angle error. Was a pointing angle calibration of the radiometer made when it was deployed to ensure that when the buoy is horizontal, that the antenna is pointing with a zenith angle of 0o?
Line 131 - ‘liquid nitrogen calibrated’ - was this done on board the buoy or previously on land?
Line 137 - please state the instrument details for the university of Wyoming data (including resolution and measurement accuracy)
Line 147 - please reference the ITU-R model. Why was this used as opposed to the previously mentioned ones?
Line 155 - ‘The resulting ...’ everything from here is not necessary
Line 157 - This section has the same name as the preceeding section. The article goes from explaining which model is used for calculating absorption coefficients to trying to explain the mechanism through which microwave radiation is present at the surface. Many sentences in this section are oddly phrased, misleading, or incorrect. Take :
"In the microwave frequency band (300 MHz–3000 GHz), electromagnetic waves exhibit both wave-like and particle-like properties." In most literature, microwave frequencies are said to be between 300 MHz and 300 GHz (not 3 THz), particle-wave duality is not applicable to microwaves alone, and it is not particularly relevent to the research presented.
Please combine this sub-section with the previous sub-section (3.2 and 3.3) and ensure that the section is clear, informative and factually correct.
Line 188 - ‘obtain the normal distribution patterns for temperature and humidity’- do you mean to say normally distributed errors?
Line 189 - ‘deviating from the normal rage were excluded’ - what do you consider normal range? was this the two sigma mentioned earlier?
Line 190 - ‘inconsistent measurement data at different altitudes’ - Not sure what is meant by this - that the profile is not a straight line?
196 - Plots to show what the temperature and humidity distributions look like would be useful here. Also, describe the skewness and excess kurtosis, and your considered thresholds of these values to consider a distribution normally distributed. You state that atmospheric temperature followed a ‘perfect’ normal distribution, please state what you consider to be ‘perfectly’ normal.
204 - Is the NOGA-II algorithm used elsewhere? Could you please reference this if so.
207 - ‘it implements essential enhancements in...’ - this phrasing is very imprecise and is reminiscent of AI language. Please rephrase.
209 - ‘For the first time it achieves high-precision temperature and humidity profile inversion...’ - I’m not sure you can consider measurement error of 2 K (for temperature) and 20% (for RH) high precision. Radiometers are not designed to produced highly precise measurements of temperature and humidity- their advantages are continuous remote soundings as opposed to precision measurments of atmospheric quantities.
210 - for each of the three items mentioned here, please describe in detail how each of the steps is done. Please in particular, as highlighted above, describe what ‘physical laws’ are used as constraints. Please also describe the processing of the level 1 data and the attitude sensor data.
216 - ‘suitable for real-time maritime applications’ - can you reference these requirements?
258 - Which data is included in the systematic bias correction? all data or only that which you include in the stats?
286 - Figure 4a - I would expect the retrieved profile to be smooth and the radiosonde to have more curves. How do you explain that the opposite is true? The zoom on both graphs appears to not be very zoomed in and show different curves than on the main graph. The feature shown on the 2a zoom and 2d, where at 5km the RH has a difference of around 1% does not appear to be present on the large graph. Why? Please also explain the criteria for valid comparisons.
305 - Did you also retrieve the liquid water path? Please mention this if so
309 - I am not sure why you mention the mesosphere here
315 - If the retrievals in these figures have been systematically bias corrected, why do the lines not entirely agree? I do not see what the point is in showing bias corrected results if the reference period for the bias correction is the same as the results being presented (it is not clear from reading the article whether this is the case or not). Also the zoom on figure 5b appears again to show a different curve than the main plot.
343- ‘In the upper troposphere (2-10 km)’ - the whole of this range should not be taken as the upper troposphere. I woulds suggest that the upper troposphere is typically around 6-12km at these latitudes.
345 - what values of RMSE are required for what purpose for marine monitoring?
Technical Corrections
Line 27 - change ‘voyage’ to ‘travel’
References
Schnitt, Sabrina, et al. "Ground-and ship-based microwave radiometer measurements during EUREC 4 A." Earth System Science Data 16.1 (2024): 681-700.