06 Sep 2021
06 Sep 2021
A dataset of microphysical cloud parameters, retrieved from EmissionFTIR spectra measured in Arctic summer 2017
 ^{1}University of Bremen, Institute of Environmental Physics, OttoHahnAllee 1, 28359 Bremen
 ^{2}Leibniz Institute for Tropospheric Research (TROPOS), Permoserstr. 15, 04318 Leipzig
 ^{3}NorthWest Research Associates, Redmond, WA, USA
 ^{1}University of Bremen, Institute of Environmental Physics, OttoHahnAllee 1, 28359 Bremen
 ^{2}Leibniz Institute for Tropospheric Research (TROPOS), Permoserstr. 15, 04318 Leipzig
 ^{3}NorthWest Research Associates, Redmond, WA, USA
Abstract. A dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured in summer 2017 in the Arctic, is presented. Measurements were conducted using a mobile Fouriertransform infrared (FTIR) spectrometer which was carried by the RV Polarstern. This dataset contains retrieved optical depths and effective radii of ice and water, from which the liquid water path and ice water path are calculated. These water paths and the effective radii are compared with derived quantities from a combined cloud radar, lidar and microwave radiometer measurement synergy retrieval, called Cloudnet. Comparing the liquid water paths from the infrared retrieval and Cloudnet shows significant correlations with a standard deviation of 8.60 g · m^{−2}. Although liquid water path retrievals from microwave radiometer data come with a uncertainty of at least 20 g · m^{−2}, a significant correlation and a standard deviation of 5.32 g · m^{−2} between the results of clouds with a liquid water path of at most 20 g · m^{−2} retrieved from infrared spectra and results from Cloudnet can be seen. Therefore, despite its large uncertainty, the comparison with data retrieved from infrared spectra shows that optically thin clouds of the measurement campaign in summer 2017 can be observed well using microwave radiometers within the Cloudnet framework. Apart from this, the dataset of microphysical cloud properties presented here allows to perform calculations of the cloud radiative effects, when the Cloudnet data from the campaign are not available, which was from the 22nd July 2017 until the 19th August 2017. The dataset is published at Pangaea (Richter et al., 2021).
Philipp Richter et al.
Status: open (until 04 Feb 2022)

RC1: 'Comment on essd2021284', Anonymous Referee #1, 02 Nov 2021
reply
Review of manuscript untitled Â«Â A dataset of microphysical cloud parameters, retrieved from EmissionFTIR spectra measured in Arctic summer 2017Â Â»
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General comment : This study is devoted to the description of a new dataset of microphysical cloud parameters from optically thin clouds, retrieved from infrared spectral radiances measured aboard the RV Polarstern in summer 2017 in the Arctic. Cloud optical depths, effective radii of hydrometeors (cloud droplets and ice crystals) as well as liquid and ice water paths are derived from a mobile Fouriertransform infrared spectrometer. The results are compared to those derived from a wellknown synergy based on cloud radar, lidar and microwave radiometer measurements (Cloudnet). The study leans on an invaluable dataset built from observations sampled during one summer, in a region where such measurements are not so common. However, the manuscript often presents the results in a qualitative style without fully investigating the differences between the two datasets. The reader is left without a clear understanding of the significance of the differences in a statistical sense, and whenever it the case, without a clear explanation of why this new dataset would be more reliable. Major comments, described below must be taken into account before publication.
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Major comments :
1/ The word Â«Â significantÂ Â» is used a lot of times along the paper to say Â«Â largeÂ Â», forgetting the quantitative, scientific meaning of that word in a statistical sense. Which hypothesis is tested to confirm that this is really significantÂ ? To which null hypothesis does the pvalue refer to ?
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2/ The authors never explain which variable has been calculated when they mention Â«Â significant correlationsÂ Â». Does it refer to the Pearson correlation coefficientÂ ? The coefficient of determination RÂ²Â ? The Spearmanâ€™s rank correlation coefficientÂ ? In addition, providing Â«Â correlationsÂ Â», even though they are large, does not say anything about the discrepancies, but just mean than the parameters vary together. What are the biases and the rootmeansquare errorsÂ ?
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3/ There is a confusion about the term Â«Â standard deviationÂ Â» that is used along the text (especially in Sect. 5.5) to express the RMSE.
The authors do not use standard quantitative scores widely used by the scientific community to evaluate the performance of an algorithm. What is called â€˜Meanâ€™ seems to be the â€˜Mean Biasâ€™. This mean bias can be close to 0 due to compensation errors. The RMSE (root mean square error) usually gives a complementary information about the evaluation of performance. But what the authors use here, and that is called Â«Â STD (TC)Â Â», does not actually represent the full discrepancy between the retrieval and the true parameter as the RMSE would do. What has been calculated in the paper is the STD of the differences between the retrieval (r_i) and true parameter (t_i), which isÂ :
STD(TC) = \sqrt{\frac{1}{n} \sum ((x_i  \bar{x})Â²)}
where x_i = r_i â€“ t_i, and \bar{x} the average value of the x_i.
What should have been calculated is ratherÂ :
RMSE = \sqrt{\frac{1}{n} \sum ((r_i â€“ t_i)^2)}
which would automatically provide larger values than the Â«Â STD(TC)Â Â» used here.
How much is the RMSE for each retrieved parameterÂ ?
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4/ Standard deviations are given with an accuracy of 2 significant digits after comma, for instance in the abstract. Is it really realisticÂ ?
If I understand what has been calculated, the standard deviations are only dispersions. Did the authors also calculate the uncertainties on the retrieved parametersÂ ? This is a crucial information for the reader interested in using this dataset.
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5/ The methodology is justified in a weird way (e.g. L 41)Â : there are plenty of algorithms based on a similar approach that are freely available. Some of them are actually mentioned later in the paper (MIXCRA, CLARRA, XTRA). Can the authors explain exactly what is new in comparison to other published algorithmsÂ ?
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Specific comments :
L 1012Â : it is not clear in the abstract what is the reference dataset and which one is evaluated in the paper. This sentence gives the impression that the authors aim to evaluate the data on opticall thin clouds measured bu microwave radiometers withing the Cloudnet framework (not from FTIR spectrometer).
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L 13Â : The syntax used here (Â«Â allows to perform[â€¦], which was the case[...]Â Â») is misleading. The calculations of the cloud radiative effects are not performed in this study.
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L 37Â : Â«Â smaller uncertaintyÂ Â»Â : Based on the scientific litterature, how much is itÂ ?
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Fig. 1Â : The ship track is not mentioned in the figure caption.
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L 5660Â : Only 4 lines do not justify a whole section. Sections 2 and 3 should be combined.
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L 102Â : Â«Â accuracy of â‰¥ Â± 5 mÂ Â». This is confusing. Does it mean that the absolute error is larger than 5 mÂ ?
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L 105Â : Do the data from the Vaisala ceilometer and the Cloudnet profiles at least agree for the P106 periodÂ ? It is important to give the bias here as the ceilometer data are used during the entire cruise.
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Sect. 5 is very long. It gives the impression that the paper focuses on the presentation of an algorithm rather than on the description and evaluation of the EMFTIR measurements. Can the authors comment on the main objective of this paperÂ ?
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L 116118Â : What are the main differences between the different algorithmsÂ ?
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L 123Â : Are aerosol optical properties included in the calculations, especially for dust particles in the infrared spectrumÂ ?
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L 138Â : What about the size distribution of ice crystalsÂ ? Is it also prescribedÂ ?
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Table 2Â : Is this table really necessaryÂ ? The extreme values of the spectrum and the number of spatral bins may be enough here.
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Eq. 7Â : What does \nu_n meanÂ ? I had understood that \nu was the mean wave number in each intervall. Why should it be a function of n, defined as a iteration step in Eq. 3Â ?
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Eq. 8Â : Where do these values come fromÂ ? Have the authors perform a sensitivity study to evaluate the influence of S_a^{1} on the final retrieved parametersÂ ?
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L177178Â : It would better to use \sigma_{ice} everywhere, rather than ext(rice). The extinction coefficient of ice crystals should also depend on the temperature as the refractive indices do.
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L 205Â : As a consequence, the variance of rice is written by this convention \sigma_{rice}. To avoid confusion with the extinction coefficient of ice crystals, the authors may want to note this latter differently, for example \alpha_{ice}(r_{ice}).
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Table 3Â : What does the Â«Â maximum m testcasesÂ Â» meanÂ ? It has not been defined here.
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L 220Â : Â«Â SignificantÂ Â» does not mean Â«Â largeÂ Â», but has a precise statistical meaning. To confirm that a correlation is significant, the authors must perform a statistical test and provide the values of the result of this test.
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L 220221Â : I am not sure if I correctly understand this sentence. What are the given uncertaintiesÂ ?
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L 229230Â : How much are the results sensitive to the choice of the threshold of f_{ice}Â ? If we choose thresholds at 0.8 / 0.2, are the results significantly differentÂ ?
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L 233Â : I donâ€™t get this point. Here, \bar{r} has been calculated from the knowledge of r_{liq}, r_{ice} and f_{ice}. How can it be Â«Â estimated independentlyÂ Â»Â ? Do the authors rather want to say that \bar{r} results from a compensation of errors in the cloud parameters used for its calculationÂ ?
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L 237Â : This should be said before when A is introduced for the first time.
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L 244Â : Are the authors comparing the same variables (Â«Â called standard deviationsÂ Â») that what is used in the litterature (LÃ¶hnert and Crewell, 2013)Â ?
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Fig. 4Â : This caption is not very explicitÂ ? What is represented exactlyÂ ?
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L 274Â : The parameter Â«Â hÂ Â» has not been defined. Are Â«Â hÂ Â» and Â«Â \Delta \epsilonÂ Â» equalÂ ?
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L 282283Â : Please comment those values. They seem extremely large to me. Does it suggest that the effective radii and liquid/ice water contents cannot be estimated by this approachÂ ?
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L 286Â : What do the authors mean by the Â«Â standard deviations of r_{ice}Â Â»Â ? Is it a std on the parameter Â«Â r_{ice}Â Â» or the std on a difference as it is the case along the paperÂ ?
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L 290291Â : This turns out to be only a partial conclusion. In the case of hollow columns for example, the retrieval is particularly bad in almost half of the cases, but it is not mentioned here.
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L 294Â : What are Â«Â differentials of IWPÂ Â»Â ? Are they simply differencesÂ ?
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Tables 5, 6, 7Â : Â«Â Difference of r_{ice}/IWP/ \tau_{ice}Â Â». What are the reference parametersÂ ?
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Fig 5Â : Are the histograms normalized by the total number of occurrencesÂ ? And also by the width of the bars/intervalls on \tauÂ ?
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Fig 5Â : The authors said before that the algorithm was not used when the total optical depth of the cloud was lower than 6. Why are there values for \tau_{liq} > 6Â ? It such values are removed from the analysis, how are the results modifiedÂ ?
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Fig. 6Â : It seems that in 2000 cases, there is no IWC. Does it mean that there are 1000 occurrences of pure liquid cloudsÂ ?
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Fig 7Â : How many cases correspond to the criteria set for the plot (optical depths > 0.1)Â ?
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L 301Â : This is not the place for this. It is said later in a specific section.
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L 350Â : Do the authors conclude that the geometry of ice crystals was incorrectÂ ?
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L 354Â and followingÂ : This is a very strange way to write differences between two datasets. In the litterature, when we write Â«Â m Â± sÂ Â», it stands for a mean value m and a dispersion value, generally expressed by the standard deviation s. If we would rather to express a confidence intervall around m, it is usually written m Â± s/ \sqrt{n}, where n is the number of values in the dataset. When comparing two datasets, it is common to use the mean bias (MB) and the RMSE, but they are never written as MB Â± RMSE has the second one does not stand for a dispersion around the first one. Both are statistical variables expressing the discrepancies between a model distribution and a reference or observed value. In this section and the next ones, the way the values are given is very confusing.
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Fig. 10Â : Values donâ€™t seem correlated and the r parameter is indeed very low. Are the data derived from TCWnet really reliableÂ ?
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L 358Â : Â«Â means and standard deviations for LWP and r_{liq} are shownÂ Â». In Table 9 caption, the text seems to indicate that the given values are means and standard deviations of differences. Which one is correctÂ ?
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Sect. 6.1Â : This small subsection is confusing and not very rigorous. Do the values given here significantly (e.g. in its statiscal sense, meaning using a statistical test) differ from the values obtained for the testcasesÂ ?
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L 318Â : Â«Â there a less casesÂ Â»Â : How manyÂ ? Which fraction does it representÂ ?
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Tables 8, 9Â : Do Â«MeanÂ Â» and Â«Â STDÂ Â» stand for the mean and standard deviation of the parameter â€˜IWPâ€™, â€˜r_{ice}â€™ or the standard deviation of the discrepancies between the variables retrieved from the TCWnet and CloudnetÂ ? In this latter case, it would be better to use the mean bias and the RMSE.
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Tables 8, 9Â : What has been tested exactly by the pvalueÂ (never mentioned in the text)Â ? To which null hypothesis does the statistical test correspondÂ ? What do the authors conclude with such valuesÂ ?
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L 368Â : Â«Â significant correlationÂ Â»Â : the authors may rather want to say that the correlation coefficient is large enough. The statistical significance can then be discussed using the statistical test (and the associated pvalue under a specified null hypothesis).
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L 405406Â : The error is as large as the threshold on LWP. Can we say something about the agreement of the two datasets in this caseÂ ?
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L 407410Â : No statistical test has been performed nor discussed. It is therefore impossible to say anything about the significance.
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L 409Â : Â«Â too smallÂ Â», Â«Â overestimatedÂ Â»Â : this is very qualitative. By how muchÂ ? Are the differences larger than the uncertaintiesÂ ?
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L 414417Â : The paper underlines that the results on r_{ice}, r_{liq} and IWP are different from those derived by Cloudnet. Is it worth publishing such results if the values significantly differÂ ? Which dataset is reliableÂ ?
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Technical comments :
The syntax is often incorrect and there are a lot a typos in the current version. The text needs to be checked very carefully, and ideally be corrected by a native speaker.
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L 51Â : A closing parenthesis is missing here.
L 55Â : Replace Â«Â is providedÂ Â» by Â«Â are providedÂ Â».
Sect. 3Â : The authors regularly switch from the present to the past tense and vice versa. Please keep only one.
L 64Â : Replace Â«Â hasÂ Â» by Â«Â hadÂ Â»
L 66Â : Replace Â«Â has a movable mirror which givesÂ Â» by Â«Â has a movable mirror givingÂ Â».
Fig 3Â : What does Emissivity (1) meanÂ ? If Â«Â 1Â Â» is only used to say that the emissivity is a dimensionless variable, it is better to remove it.
L 81Â : Replace Â«Â of high temperatureÂ Â» by Â«Â at high temperatureÂ Â».
L 82Â : interferograms
L 83Â : procedure
Some acronyms are not defined in the text, e.g. OCEANET (L9091), HATPRO (L. 94).
L 101Â : Replace Â«Â Informations â€¦ areÂ Â» by Â«Â Information â€¦ isÂ Â».
L 127Â : Replace Â«Â An vertically inhomogeniousÂ Â» by Â«Â A vertically inhomogeneousÂ Â».
L 131Â : singlescattering albedo
L 131Â : different wavenumbers
L 133Â : Replace Â«Â temperature dependedÂ Â» by Â«Â temperature dependentÂ Â».
L 138Â : Â«were chosen in a wayÂ Â».
L 146Â : Â«Â stepsÂ Â»
L 149, 169, 184, 193, 322Â : Please avoid starting a sentence by the final dot of the previous equation.
L 150Â : Replace Â«Â inverse covariancesÂ Â» by Â«Â inverse covariance matrixÂ Â».
Eq. 6Â : Remove the square on x_{n+1}
Eq. 7Â : x should be a vector, as defined by Eq. 3.
L 162163Â : Correct asÂ : Â«Â we assume that all retrievals [â€¦] correctly convergedÂ .Â Â»
L 164Â : Â«Â informationÂ Â».
L 166Â : x should be replaced by x_a.
L 177Â : Â«Â extinction coefficientÂ Â»
L 210Â : Replace Â«Â homogenousÂ Â» by Â«Â homogeneousÂ Â».
L 218Â : Â«Â mean deviationsÂ Â»Â : do the authors use this term instead of the widely used Â«Â mean biasesÂ Â».
L 219Â : Â«Â true cloud parametersÂ Â».
L 219Â : Â«Â the standard deviationsÂ Â».
L 230Â : there are two verbs in the sentence â€˜isâ€™ and â€˜areâ€™. The sentence must be reformulated.
L 238Â : Â«Â retrievedÂ Â»
L 249Â : Add a Â«Â thatÂ Â»Â : Â«Â so that it matchesÂ Â».
L 251Â : Â«Â humidityÂ Â».
L 274Â and L 276 : Replace Â«Â differential quotientÂ Â» by Â«Â partial derivativeÂ Â».
L 275Â : Remove Â«Â asÂ Â».
L 277278Â : Some parentheses are not at the right place or are missing in all expressions.
L 282Â : Make two sentences here. Â«Â . This gives...Â Â».
Tables 5, 6, 7Â : Â«Â bullet rosettesÂ Â».
Fig. 5, 6, 7Â : Replace Â«Â plotÂ Â» by Â«Â panelÂ Â» in the figure captions.
Fig 7Â : Replace Â«Â distributinÂ Â» by Â«Â distributionÂ Â». Correct Â«Â the optical depths isÂ Â» by Â«Â the optical depths are.Â Â»
L 308Â : Replace Â«Â is shownÂ Â» by Â«Â are shownÂ Â».
L 308309Â : Â«Â Similar for ...Â Â»Â : Please make a sentence.
Fig. 8Â : Replace Â«Â StatisticsÂ Â» by Â«Â histogramÂ Â».
Fig. 9Â : Replace Â«Â divided by the chosen ice particle shapeÂ Â» by Â«Â for each ice particle shapeÂ Â».
L 327Â : Replace Â«Â are the spectral windowsÂ Â» by Â«Â is the spectral windowÂ Â».
L 330Â : Â«Â intransparentÂ Â»Â : Do you want to say Â«Â opaqueÂ Â»Â ?
L 334Â : Replace Â«Â resultÂ Â» by Â«Â resultsÂ Â».
L 334Â : Replace Â«Â whereÂ Â» by Â«Â whenÂ Â».
L 336Â : bullet rosettes.
L 336Â : I see a small fraction of hollow columns, spheroÃ¯ds and spheres. Have they been removed in this analysisÂ ?
L 338Â : Add a Â«Â byÂ Â»Â : Â«Â This is motivated by the following.Â Â».
L 338Â : Â«Â The results of [â€¦] show thatÂ Â».
L 339Â : Â«Â and \bar{r} can be seen thatÂ Â».
L 340Â : Â«Â with a too small r_{ice} and a too large r_{liq}.
L 361Â : Replace Â«Â isÂ Â» by Â«Â areÂ Â».
L 363364Â : I canâ€™t understand this sentence. Please reformulate.
L 380Â : Â«Â r_{liq} thus improvesÂ Â»Â : this syntax is incorrect. The algorithm improves the retrieval of r_{liq}.
L 382Â : accessibility
L 388Â : Remove Â«Â in this publicationÂ Â».
L 403Â : Add a Â«Â thatÂ Â» at the end of the sentence.
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Philipp Richter et al.
Data sets
Microphysical Cloud Parameters and water paths retrieved from EMFTIR spectra, measured during PS106 and PS107 in Arctic summer 2017 Richter, Philipp; Palm, Mathias; Weinzierl, Christine; Notholt, Justus https://doi.pangaea.de/10.1594/PANGAEA.933829
Model code and software
Total Cloud Water retrieval Richter, Philipp https://doi.org/10.5281/zenodo.4621127
Philipp Richter et al.
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