the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 14 Nov 2024
Relationship between latent and radiative heating fields of Tropical cloud systems using synergistic satellite observations
Abstract. In order to investigate the relationship between latent and radiative heating (LH, RH), particularly within mesoscale convective systems (MCSs), we used synergistic satellite-derived data from active instruments. Given the sparse sampling of these observations, we expanded the Spectral LH profiles derived from the Tropical Rain Measurement Mission (TRMM-SLH) by applying artificial neural network regressions on Clouds from InfraRed Sounder data and meteorological reanalyses, following a similar approach as for the expansion of the RH profiles. The zonal averages of vertically integrated LH (LP) at 1:30 AM and PM LT align well with those from the full diurnal sampling of TRMM–SLH over ocean. For Upper Tropospheric (UT) clouds releasing large latent heat, the surface temperature has a larger impact on the atmospheric cloud radiative effect (ACRE) in dry than in humid environments, while for lower clouds, producing relatively small latent heat, humidity plays a large role in enhanced ACRE. The distribution of UT clouds in the LP–ACRE plane shows a very large spread in ACRE for small LP, which is gradually reduced towards larger LP. The mean ACRE per MCS increases with LP, ranging from 50 to 115 W m-2. As expected, the shapes of the LH profiles of mature MCSs show that larger, more organized MCSs have a larger contribution of stratiform rain than the smaller MCSs. Convective organization enhances the mean ACRE of the MCS by up to 20 W m-2.
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Status: open (until 26 Dec 2024)
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RC1: 'Comment on egusphere-2024-3434', Anonymous Referee #1, 03 Dec 2024
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Latent heating and radiative heating are two major diabatic heating sources of the atmosphere. However, the observation of global latent and radiative heating profiles are difficult to obtain. Active sensors onboard satellites provide a way to measure these profiles, such as the products from TRMM and GPM. However, TRMM and GPM satellites are low-orbit satellites thus the spatiotemporal coverage of the heating profile data are limited. The previous work from the author(s) have applied a ML technique to extend the radiative heating profile to greater coverage. This paper does similar thing but for latent heating (LH), with further analysis of LH profiles for different surface conditions, different environmental conditions and MCS characteristics. One of the main conclusions is that although the mean values show good agreement, the ML-expanded latent heating profiles show much smaller variability than the target data. This is different from the ML-expanded radiative heating profiles. The authors made a further analysis and suggested that the data are only robust for coarse-resolution larger than 2.5deg.
Although some results of this paper are interesting and this expanded dataset would be useful for scientific community, I feel that the values and limitations need to be carefully elaborated and better demonstrated. One major concern I have is that since the authors explicitly suggest that “our ML-expanded LH dataset is suitable at scales larger than about 2.5◦”, how robust can we trust the results for MCSs in the size of around 1x1deg, or 100kmx100km, as the authors show in Fig. 13 to 15?
Besides this question, I also have some specific comments listed below:
Line 24: as RH is usually referred as relative humidity, maybe change the abbreviation (e.g. Qrad)
Line 61: maybe need a little bit more introduction on CRE and ACRE, for example, from which data source did you obtain the CRE/ACRE data (clear-sky and full-sky radiation)? Sometimes the authors state CRE and radiative heating profiles together (even the units in Fig.5 have K/day and W/m2 on the same panel, this confuses me a bit), do they represent similar effect?
Fig.1: more details of the figure are needed: what do the different blue colors mean? what are the thin and thick swaths?
Line 160: maximum should be minimum
Line 162: what are the thresholds in your rain intensity categorization?
Line 164: How do you define UT clouds in this paper?
Line 192: I am not an expert of machine learning, but 20 iterations looks a lot to me if you don't see any improvement in the loss function. Is it true that it is still not overfitting after 20 iterations without improvement?
Line 247: Is there any explanation of the two peaks of LH profiles over ocean?
Fig. 5: which variables are the left and right y axes corresponding to?
Fig. 5: why is the SW radiative effect is almost zero? My direct intuition is that when cloud exists, shortwave radiative effect should be pretty negative.
Fig. 5: The ‘pink solid line’ mentioned in the caption is not shown in the figure.
Line 334: do they need to be neighboring grid cells or only by cloud height to be merged into the same system?
Line 335: I don’t quite understand this sentence "the size ... is computed... by the number...". Is there anything missing?
Fig. 9: this is another place that I don’t quite understand what this paper use, is it CRE or radiative heating?
Line 364: not sure I understand this statement. Are you comparing MCS intensity relations with MCS size and opacity? or comparing radiative heating profile with LH profiles?
Line 372-373: from where did you obtain this conclusion that LP is more reliable over ocean?
Fig. 14: this is an interesting result that LP-ACRE relation shows no distinction between developing, mature and dissipating stages, but is dependent on MCS sizes. Not sure how to understand/explain this.
Line 457: What are ‘the larger ones’ refer to?
Citation: https://doi.org/10.5194/egusphere-2024-3434-RC1
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