the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Sensitivity of thermodynamic profiles retrieved from ground-based microwave and infrared observations to additional input data from active remote sensing instruments and numerical weather prediction models
Abstract. Accurate and continuous estimates of the thermodynamic structure of the lower atmosphere are highly beneficial to meteorological process understanding and its applications, such as weather forecasting. In this study, the Tropospheric Remotely Observed Profiling via Optimal Estimation (TROPoe) physical retrieval is used to retrieve temperature and humidity profiles from various combinations of input data collected by passive and active remote sensing instruments, in-situ surface platforms, and numerical weather prediction models. Among the employed instruments are Microwave Radiometers (MWRs), Infrared Spectrometers (IRS), Radio Acoustic Sounding Systems (RASS), ceilometers, and surface sensors. TROPoe uses brightness temperatures and/or radiances from MWRs and IRSs, as well as other observational inputs (virtual temperature from the RASS, cloud base height from the ceilometer, pressure, temperature, and humidity from the surface sensors) in a physical-iterative retrieval approach. This starts from a climatologically reasonable profile of temperature and water vapor, with the radiative transfer model iteratively adjusting the assumed temperature and humidity profiles until the derived brightness temperatures and radiances match those observed by the MWRs and/or IRSs instruments within a specified uncertainty, as well as within the uncertainties of the other observations, if used as input. In this study, due to the uniqueness of the dataset that includes all the above-mentioned sensors, TROPoe is tested with different observational input combinations, some of which also include information higher than 4 km above ground level (agl) from the operational Rapid Refresh numerical weather prediction model. These temperature and humidity retrievals are assessed against independent collocated radiosonde profiles under non-cloudy conditions to assess the sensitivity of the TROPoe retrievals to different input combinations.
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Status: final response (author comments only)
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RC1: 'Comment on amt-2023-263', Anonymous Referee #1, 14 Feb 2024
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2023-263/amt-2023-263-RC1-supplement.pdf
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AC1: 'Reply on RC1', Laura Bianco, 02 Apr 2024
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2023-263/amt-2023-263-AC1-supplement.pdf
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AC1: 'Reply on RC1', Laura Bianco, 02 Apr 2024
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RC2: 'Comment on amt-2023-263', Anonymous Referee #2, 27 Feb 2024
This is a well-written paper that investigates an important issue in ground-based remote sensing: what improvements arise when additional datastreams are included in the retrieval? For years now, TROPoe and its antecedent AERIoe have included RAP or RUC profiles in the calculations, but the improvement in the retrieval has not been fully quantified. This paper is of the appropriate scope and novelty for inclusion in AMT. I have a few corrections and suggestions that will help improve the readability and utility of this paper, but these should be easy to address. None of these issues rise beyond the level of minor corrections.
Most significant issue:
Sound propagates differently at different times of day, and the uncertainties introduced into the RASS observations by horizontal winds are also going to have a strong diurnal cycle. Because of that, one might assume that the biases and MAEs exhibited here are not constant throughout the day, but instead have a noticeable diurnal cycle. It may be that there’s an insufficient number of radiosondes, especially at non-daytime hours, to fully investigate this. Regardless, the possible diurnal impact should be discussed, even if it’s not anticipated to be an important issue and can easily be dismissed.ÂMinor comments:
1. One small issue I had while reading this paper was understanding how the RASS was integrated into the retrieval. The TROPoe retrieval works in T and q space, and it's relatively easy to understand how the RAP profiles would be able to be included as they exist (or can easily be converted) into those variables. However, RASS is measuring Tv which is a unique variable for the retrieval. How does the retrieval address this? Is it just calculated at the end of each iteration from the interim T/q profile and compared to the RASS observations?
2. When I read line 76, noting that the radiosondes were interpolated to the TROPoe grid, I wondered why the sondes weren't smoothed with the retrieval averaging kernal instead. I see that was addressed later in Line 195. Still, I wonder if this is the best approach. Would it be better to smooth the sondes, calculate the differences as a function of height, and then interpolate the vertical profile of the differences to a common grid to facilitate the analysis between different combinations of instruments?Â
3. In certain instances, the additional observation vector entries degrade the profiles, either by increased bias or MAE. This is an interesting finding that ought to be discussed more. What is causing this, how consistent is it (are a couple of retrievals dragging everything down, or are most of the retrievals behaving similarly)?
Technical corrections:
Line 42: this sentence is somewhat awkwardly phrased and would be easier to interpret if rewritten.
Line 43: I don't think it's quite correct to state that in situ sensors only provide point measurements. Surface meteorology stations like ASOS only provide observations at a single point fixed in 3D space, that is true. However, aircraft-based observations like AMDAR are in situ but producing moderately-dense vertical profiles and the sheer density of these observations next to major airports produces a multidimensional web of observations.Â
Line 54 and elsewhere: the name of the radiosonde is the Vaisala RS-41, not Vaisala-41.
Line 96: it is somewhat unclear what the upper range of the IRS is: why does it range from 3000-5000 cm^-1? Did the two IRSes each have a different spectral range?Â
Line 440: necessarily, not necessaryÂLine 444: cloudy conditions, not cloudy-conditions.ÂLine 454: Based on my experience as an AMT author, making data available by request may be insufficient for their standards. It may be good to identify an archive where these data may be stored and given a DOI.ÂCitation: https://doi.org/10.5194/amt-2023-263-RC2 -
AC2: 'Reply on RC2', Laura Bianco, 02 Apr 2024
The comment was uploaded in the form of a supplement: https://amt.copernicus.org/preprints/amt-2023-263/amt-2023-263-AC2-supplement.pdf
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AC2: 'Reply on RC2', Laura Bianco, 02 Apr 2024
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