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# Diabatic heating of mesoscale convective cloud systems from synergistic satellite data

Xiaoting Chen, Claudia Stubenrauch, Giulio Mandorli

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Xiaoting Chen, Claudia Stubenrauch, Giulio Mandorli. Diabatic heating of mesoscale convective cloud systems from synergistic satellite data. International Radiation Symposium, Jun 2024, Hangzhou, China. pp.012024, 10.1088/1755-1315/1522/1/012024. [hal-05393700](https://hal.science/hal-05393700v1)

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# Diabatic heating of mesoscale convective cloud systems from synergistic satellite data

To cite this article: Xiaoting Chen et al 2025 IOP Conf. Ser.: Earth Environ. Sci. 1522 012024

View the [article online](#) for updates and enhancements.

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# Diabatic heating of mesoscale convective cloud systems from synergistic satellite data

Xiaoting Chen¹*, Claudia Stubenrauch¹ and Giulio Mandorli¹

¹Laboratoire de Météorologie Dynamique/Institut Pierre-Simon Laplace, (LMD/IPSL), Sorbonne Université, Ecole Polytechnique, CNRS, Paris, France

*E-mail: xiaoting.chen@lmd.ipsl.fr

**Abstract.** This study investigates the relationship between latent and radiative heating of tropical mesoscale convective systems (MCSs). Therefore, we expanded heating rates derived from active sensors, which have a sparse sampling, by applying artificial neural networks on cloud properties from IR sounder data and atmospheric and surface properties from meteorological reanalyses. With the help of a cloud system reconstruction, we determined the MCSs and their size. The radiative enhancement of mature MCSs rises with their released latent heat. Furthermore, larger, more organized MCSs generally heat the atmosphere more than smaller MCSs for a similar rain intensity. The projection of the MCS properties onto a plane of vertically integrated latent heating (LP) and radiative enhancement (ACRE) reveals that the MCSs tend to converge towards a mean LP-ACRE value during their life cycle, with larger initial LP-ACRE values requiring longer lifetimes to reach this settling point.

## 1 Introduction

Upper tropospheric (UT) clouds are most abundant in the tropics and often form as cirrus anvils from convective outflow, building mesoscale systems (MCS). While latent heat is released into the atmosphere by the precipitating parts of these MCSs, the long-lasting anvils play also a crucial role in modulating the Earth's energy budget and heat transport.

CIRS (Clouds from IR Sounders) data, derived from measurements taken by the Atmospheric Infrared Sounder (AIRS) onboard NASA's Aqua satellite, sensitive to cirrus, provide cloud height and emissivity horizontal structure, because these instruments are along-track scanners with wide swaths, providing about 70% horizontal coverage at a specific local time (LT) in tropical regions. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar and CloudSat radar observations (Stephens et al., 2018) provide the vertical structure of clouds, in particular their radiative heating rates ($Q_{rad}$), though only along narrow nadir tracks (about 1km broad, 2500 km apart).

For a complete 3D view, these heating rate profiles have been extended in space and time by Stubenrauch et al. (2021), using artificial neural network (ANN) regression on CIRS cloud properties and ERA-Interim atmospheric and surface properties (Dee et al., 2011). In order to link these to the latent heat, we used latent heating profiles from the Tropical Rainfall Measuring Mission (TRMM). While TRMM provides a diurnal sampling, the instantaneous spatial coverage overlap with AIRS at a specific LT is only about 3%. Therefore, we expanded these latent heating rates (LH) across the AIRS swaths, again using ANN regression (Chen et al., 2024).

By applying in addition a cloud system identification (Stubenrauch et al., 2023), we are able to study the relationship between LH and $Q_{rad}$ at the scale of MCSs.

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Content from this work may be used under the terms of the Creative Commons Attribution 4.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
## 2 Data and Methods

### 2.1 Input data

CIRS cloud top pressure and emissivity (Stubenrauch et al., 2017) from AIRS are available at 1:30 AM and 1:30 PM LT, at a spatial resolution of about 13.5 km at nadir. Atmospheric and surface properties (e.g., temperature, pressure, humidity profiles) from ERA-Interim reanalysis data (Dee et al., 2011) have been added by interpolating to the same local times.

These data have been collocated for the training of the neural networks to expand

(1) the radiative heating rates: for the period 2007-2010, per footprint, with cloud vertical structure (2B-GEOPROF, Mace et al., 2009), radiative heating rates (2B-FLXHR, Henderson et al., 2013) and precipitation rates (2C-PRECIP-COLUMN, Haynes et al., 2009) derived from CloudSat-CALIPSO observations, and

(2) the latent heating rates: for the period 2004-2013, per 0.5°latitude × longitude grid, with Spectral Latent Heating rates (SLH, version 06, Shige et al., 2009) derived from TRMM observations.

Given the different orbits of the satellites and swath widths of the instruments, temporal and spatial overlap between these datasets is limited, as illustrated in Fig. 1. However, by developing ANN models to predict LH and $Q_{rad}$ from active sensor data using physical variables provided by CIRS and ERA-Interim, increases the effective coverage of all data to 70 % and allows therefore to study the horizontal structures of LH and $Q_{rad}$ as well as to combine them at the scale of MCSs.

<figure><img src="image_5.png" /></figure>

**Figure 1:** Illustration of the initial data used for this analysis: IR sounders provide the horizontal structure (UT cloud systems on the upper left, with convective core, cirrus and thin cirrus according to cloud emissivity), while active sensors provide the vertical structure: radiative heating rates from CloudSat radar and CALIPSO lidar (dark blue tracks on the upper right) and latent heating rates from TRMM radar (lower map, 3 % overlap in time and space with AIRS at 1:30 AM LT in yellow, within a time window of ± 20 minutes, for a given day). Adapted from Stubenrauch et al. (2021) and Chen et al. (2024).

### 2.2 ANN prediction models

Since the ANNs are designed to produce reliable mean values, and given the highly skewed, non-Gaussian distributions of $Q_{rad}$ and LH, we have identified scenes for the ANN development, in particular those scenes with a small occurrence but for which we do not want to have large biases, like convective cores. Different scene types were identified for the $Q_{rad}$ and LH ANN regression development: eight for $Q_{rad}$ (Cb, Ci and thin Ci, mid- and low-level clouds, clear sky, separately over ocean and over land) and twelve for LH (no rain, light rain, heavy rain, separately for high- and for mid- and low-level clouds and separately over ocean and over land).

For the rain intensity scene identification, the CloudSat precipitation data have been expanded by an ANN classification into three categories: no rain, light rain (< 5 mm h⁻¹), and heavy rain (> 5 mm h⁻¹).

For the regression models, we used ANNs with two hidden layers (64 neurons for $Q_{rad}$, 32 neurons for LH). Mean Squared Error (MSE) was used as the loss function, and Mean Absolute Error (MAE)
was used for evaluation, with early stopping to prevent overfitting. Hyperparameters such as learning rate and batch size were tuned, but the variations had minimal impact. Also, the choice of the variables, from a certain basic set of about 25 variables on, did not affect very much the results anymore. The final configurations included 40/41 input variables for the prediction of the longwave (LW) / shortwave (SW) heating rates (Table 1, Stubenrauch et al., 2021) and 27 variables for the prediction of the latent heating rates (Table 1, Chen et al., 2024).

After training with randomly chosen 60 % of the collocated data (and further 20 % used for improving the loss), the LH and $Q_{rad}$ predictions were evaluated against the 20 % data left (Chen et al., 2024; Stubenrauch et al., 2021).

<figure><img src="image_6.png" /><figcaption>**Figure 2:** Comparison of predicted (dashed) and observed (solid) radiative heating (FLXHR, 2007–2010) and latent heating (SLH, 2004–2013) for high-level clouds within 30°N–30°S. Upper panels show LW heating rates for three cloud types determined by cloud emissivity ($< 0.5, 0.5-0.95, > 0.95$, respectively). Lower panels show latent heating rates for the same cloud types. Shaded areas represent $\pm 67.5\%$ of the standard deviation, approximately corresponding to the quartiles.</figcaption></figure>

As an illustration, we present in Fig. 2 LW and latent heating rates from ANN-predicted and from collocated data for three types of high-level clouds (cloud top pressure < 440 hPa). For both original and predicted data, the mean values for each cloud type align closely. However, while the variability within the cloud types is similar for predicted and original LW heating rates (upper panels), the variability of the predicted latent heating rates is underestimated, in particular in the lower part of the troposphere (lower panels). This may be explained by LH released by lower clouds underneath which are not identified by CIRS, because the Infrared sounders are passive instruments. Considering the variability towards negative values, the ANN regressions seem also to have difficulties to identify the evaporating in the lower parts of the troposphere, in particular in the case of stratiform precipitation.

Yet, Fig. 2 reveals specific vertical patterns for UT clouds:

1. In the LW panels, opaque clouds warm the lower atmosphere while cooling the layers above. Cirrus and in particular thin cirrus warms the UT by intercepting LW radiation from below.

2. In the LH panels, opaque clouds generate strong latent heating (on average about $25 \text{ K day}^{-1}$) around 450 hPa on average. In contrast, cirrus clouds produce on average only a small latent heating of
about 2 K day⁻¹. Non-precipitating thin cirrus, as expected, show no latent heating, except for a small peak (0.7 K day⁻¹) around 850 hPa, which is linked to low clouds beneath the cirrus (Fig. S2 in Chen et al. (2024)), as CIRS primarily identifies the cirrus in these cases.

The difference in the quality between the ANN-derived $Q_{rad}$ and LH results can be explained by the coarser spatial resolution for the LH training, not sufficient input information as well as by the very non-Gaussian, skewed distribution of precipitation, in space and time. Yet, we show in the following section that the horizontal fields of latent and radiative heating match well.

### 3 Diabatic heating patterns

The heating fields have been reconstructed across the entire tropics for the period 2004–2018 by applying the ANN prediction models on the CIRS and ERA-Interim data (Chen et al., 2024; Stubenrauch et al., 2021). The final diabatic heating dataset at 1:30 AM and 1:30 PM is gridded to 0.5°latitude × longitude. We determined the vertical integrals of latent heat (LP) and the atmospheric cloud radiative effect (ACRE), which show the heating within the whole atmospheric column. ACRE is given by the difference between the vertical integrals of $Q_{rad}$ between cloud and clear sky, using the CIRS cloud detection.

<figure><img src="image_7.png" /><figcaption>**Figure 3:** Geographical maps of vertically integrated diabatic heating, for a period of 10 years (2008-2018), (a) January and (b) July. The colorbars represent the structure of expanded ACRE from CIRS-ML-CALIPSO-Cloudsat, while contours indicate the distribution of expanded LP from CIRS-ML-TRMM at 1h30 AM/PM, at a spatial resolution of 0.5°.</figcaption></figure>

Figure 3 shows the horizontal structures of LP and ACRE across the tropics during boreal winter (January) and boreal summer (July), averaged over a 10-year period (2008–2018). The zones of strong convection correspond to large values of LP, and it is interesting to note that these are also zones of large ACRE. The regions with the highest LP and ACRE values shift seasonally, following the sun: (1) January: The Inter-Tropical Convergence Zone (ITCZ) is located in the Southern Hemisphere, with strong convection over the maritime continent and the western Pacific, South America and South Africa. (2) July: The ITCZ is located in the Northern Hemisphere, with intense convection especially over the Indian continent (Monsoon), the eastern Pacific and Africa.

These results indicate further that our ANN-derived heating effectively captures the seasonal cycle characteristics and reveals a strong connection between LP and ACRE.

### 4 Diabatic Heating of MCSs

In order to study the relationship between precipitation intensity, given by LP, and radiative enhancement, given by ACRE, within the MCSs, we first need to identify the MCSs and determine their properties.

Therefore, we first reconstruct UT cloud systems by merging adjacent grid cells, which are covered by more than 90 % CIRS clouds with similar top pressure < 350 hPa. Since ubiquitous tropical thin cirrus is
connected to the MCSs, only grid cells with cloud emissivity > 0.2 are considered. MCSs are then defined as UT cloud systems with at least one convective core (grid cells with cloud emissivity > 0.98) and the presence of precipitation. The convective core fraction, which is the ratio of the core size to the overall MCS size, can be used as an indicator of the system's maturity (Stubenrauch et al., 2023). Mature MCSs are defined by a ratio between 0.4–0.6. Since for the cloud system reconstruction the gaps between the AIRS swaths have been filled (Protopapadaki et al., 2017), but the ANN-derived variables are only given within these swaths, only systems with an overlap by more than 70 % are kept for the following analysis.

## 4.1 ACRE modulation by MCS size

First, we want to isolate the effect of convective organization. Therefore, we compare for each LP interval, representing similar rain intensity, the ACRE of mature MCSs between larger, more organized systems and smaller, less organized systems. For this specific study we only use MCSs which consist at least of four grid cells, as Chen et al. (2024) have shown that LP is more reliable when averaged over an area of more than 1°. Furthermore, the identification of maturity is also more certain as one needs several grid cells to determine well the core fraction. For the separation between small and large MCSs, we choose for each LP interval the 40 % smallest and 40 % largest MCSs. The intervals have been chosen to compare similar statistics.

Figure 4 clearly shows that large, more organized mature MCSs produce on average a larger ACRE than smaller, less organized mature MCSs of similar rain intensity. The average effect is about 10 W m⁻².

<figure><img src="image_8.png" /></figure>

**Figure 4:** Relationship between radiative enhancement (ACRE) and precipitation intensity (LP), averaged over mature MCSs with convective core fractions between 0.4 and 0.6. Results are shown separately for small MCSs (below the 40th percentile) and large MCSs (above the 60th percentile), considering MCSs larger than 4 pixels (1° × 1°). Data over ocean observed at 1:30 LT, from 2004–2018, between 25°N and 25°S. There are about 100 MCSs for the computation of each mean value and standard error.

## 4.2 MCS properties as function of LP and ACRE

A recent Lagrangian humidity and energy budget analysis of MCSs within the LP-ACRE space by Masunaga and Takahashi (2024) has revealed that the MCSs converge during their evolution to a settling point with LP of about 700 W m⁻² and ACRE of about 75 W m⁻².

We want to test with our data and methods, if we can see a similar behaviour. Therefore, we use the convective core fraction within the MCS again as a proxy for the MCS life stage. For this study we use all MCSs, because if we exclude MCSs with less than four grid cells, we bias the statistics by eliminating many MCSs in the developing stage.
Figure 5 displays several MCS properties projected onto the LP-ACRE plane, using the CIRS-ML dataset and the CIRS MCS identification. The size of the systems is similar to the ones used by Masunaga and Takahashi (2024). However, the MCS and life stage identifications are different: We merge MCSs by similar height and only one grid cell of the MCS needs to include precipitation, while the MCSs by Masunaga and Takahashi (2024) include precipitation in all cells and heavy precipitation in at least one grid cell. We also use a statistical approach to identify their life stage instead of following the systems in time.

<figure><img src="image_9.png" /><figcaption>**Figure 5:** Averages of (a) core fraction, (b) MCS size, (c) MCS emissivity, and (d) minimum longwave cooling above the MCS core as function of LP and ACRE released by precipitating MCSs over ocean, for the period 2004-2018, spanning 25°N to 25°S, at 1:30 LT. LP and ACRE are from CIRS-ML, while the MCS properties are derived from the CIRS dataset. Each square corresponds to a specific LP-ACRE interval.</figcaption></figure>

Nevertheless, a similar LP-ACRE area around about 700 W m⁻² and 85 W m⁻², respectively, as in Masunaga and Takahashi (2024), clearly emerges with large MCS size and small emissivity, as demonstrated in Fig. 5b and c. These correspond to MCSs in the last part of their life stage, given by a small core fraction (Fig. 5a). MCSs with a larger core fraction and smaller size (in the first part of their life stage) occupy the LP-ACRE space at the border, while the MCS in the second part of their life stage (smaller core fraction and larger size) occupy the LP-ACRE space closer to the settling point. Figure 6 shows that these averages result from a broad scattering in the LP-ACRE space of MCSs in the developing stage and a narrower distribution of MCSs in the dissipating stage. The large spread of the MCS properties in the developing stage is linked to a spread in the atmospheric environment, as shown by Chen et al. (2024).

It is interesting to note that the average optical depth of the MCS cores, indicated by the LW cooling above the core (see also Chen et al. (2024)), does not show this evolution behavior but shows a steady increase from small to large LP and ACRE. Since the averages are obtained from MCSs with values broadly scattered in the LP-ACRE plane in the developing phase and less scattered around the settling point in the dissipating phase, the MCSs with large LP-ACRE, in the developing phase, have very dense cores, while the ones with low ACRE have less dense or smaller cores than the ones with larger ACRE.

Though our method is only a statistical one and misses the time dimension, the MCSs which start with large LP and ACRE move towards smaller LP and ACRE, while the ones at small ACRE are the
ones with smaller cores and move less in this plane.

<figure><img src="image_10.png" /><figcaption>**Figure 6:** Density distribution in the LP-ACRE plane for the MCSs presented in Fig. 5, shown separately for the (a) first part of the life stage (core fraction > 0.6) and (b) later part of the life stage (core fraction < 0.4).</figcaption></figure>

# 5 Conclusions and Outlook

This article presents the relationship between vertically integrated latent and radiative heating within MCSs, using data expanded by artificial neural network regression to overcome sparse sampling. In terms of averages, both the predicted radiative and latent heating rates closely match those of the original data. However, the predicted range of the latent heating rates is narrower than the ones of the original data, likely due to limited input data and the coarse 0.5°spatial scale at which the data were trained. Nevertheless, Chen et al. (2024) have shown that the monthly averages of the vertically integrated latent heat of the expanded LH at 1:30 AM and PM align well with those from TRMM-SLH over ocean, when averaged over grid cells larger than 2.5°.

The reconstructed 3D radiative heating fields, together with other cloud variables, span the period of 2004-2018, at observation times of 1:30 AM and 1:30 PM, and are available at: https://gewex-utcc-proes.aeris-data.fr/. At present, we are preparing a combined dataset of ACRE and LP to be distributed.

The horizontal structure indicates strong connections between rain intensity, given by vertically integrated latent heating (LP) and radiative enhancement, given by ACRE.

On average, the ACRE of MCSs rises with their produced LP. Furthermore, larger MCSs generally produce a larger ACRE at similar rain intensity (LP). This enhancement may reaches on average about 10 W m⁻²and may be linked to convective organization.

The projection of MCS size onto the LP-ACRE plane reveals a similar settling area as in Masunaga and Takahashi (2024), though our methods are very different. The averages of the MCS properties in the LP-ACRE plane are built from MCSs in different life stages, with the spread reducing during the life stage and the MCSs moving closer to this settling area. This indicates that MCSs tend to converge towards a mean LP-ACRE, and an MCS which starts at large LP-ACRE reaches this point after a longer life time than one which is already closer to this mean LP-ACRE.

Future research should directly consider the atmospheric environment around the MCSs and their time evolution. This can be done by combining the CIRS-ML data with MCS parameters from other datasets like the ones of Takahashi et al. (2021) or Fiolleau and Roca (2013). The MCS properties projected on the LP-ACRE plane could also help to evaluate climate models.

# 6 Acknowledgements

This work has been supported by the French National Centre for Scientific Research (CNRS), the China Scholarship Council (CSC) program (Grant No. 202208070008) and the Centre National d'Études Spatiales (CNES).
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