A Deep Convective Systems Database Derived from the Intercalibrated Meteorological Geostationary Satellite Fleet and the TOOCAN algorithm (2012&ndash;2020)

Fiolleau, Thomas; Roca, Remy

doi:https://doi.org/10.5194/essd-2024-36

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https://doi.org/10.5194/essd-2024-36

Preprints

26 Mar 2024

| 26 Mar 2024

Status: this preprint is currently under review for the journal ESSD.

A Deep Convective Systems Database Derived from the Intercalibrated Meteorological Geostationary Satellite Fleet and the TOOCAN algorithm (2012–2020)

Thomas Fiolleau and Remy Roca

Abstract. We introduce two databases aimed at facilitating the study of deep convective systems (DCS) and their morphological characteristics over the intertropical belt during the period spanning from 2012 to 2020: TOOCAN and CACATOES. The TOOCAN database is constructed using a tracking algorithm called TOOCAN applied on a homogenized GEOring infrared (IR) archive and enables the documentation of the morphological parameters of each DCS throughout its life cycle. The homogenized GEOring IR database has been built from level-1 data of a fleet of geostationary platforms originating from various sources and has been inter-calibrated, spectrally adjusted, and limb darkening corrected, specifically for the high cold cloud onto a common reference, the IR channel of the ScaRaB radiometer on-board Megha-Tropiques. The resulting infrared observations are then homogeneous for Brightness Temperatures (BT) < 240 K with a standard deviation lower than 1.5 K throughout the GEOring. A systematic uncertainty analysis is carried out. First, the radiometric errors are shown to have a little impact on the DCS characteristics and occurrences. We further evaluate the impact of missing data and demonstrate that a maximum of 3 hours of consecutive missing images represents a favorable compromise for maintaining tracking continuity while minimizing the impact on the DCS morphological parameters. However, beyond this temporal threshold, the segmentation of DCS is significantly compromised, necessitating the interruption of the tracking process. The CACATOES database is derived from the TOOCAN database through a post-processing procedure, which involves projecting the morphological parameters of each deep convective system (DCS) onto a 1°x1°-1-day grid. This resultant dataset provides a broader perspective, allowing for an Eulerian analysis of the DCS and facilitating comparisons with auxiliary gridded datasets on the same daily 1° × 1° grid box.

Both the TOOCAN and CACATOES databases are provided on a common netCDF format that is compliant with Climate and Forecast (CF) Convention and Attribute Convention for Dataset Discovery (ACDD) standards.

A total of 15x10⁶ DCS have been identified over the tropical regions and the 9-year period. The analysis of DCS over the tropical oceans and continents reveals a large variety of DCS characteristics and organization. They can last from few hours up to several days, and their cloud shield ranges from 1000 km² to a few millions of km². Oceanic DCS are characterized by a longer lifetime duration and larger shields. Finally, the DCS geographical distribution is in line with previous DCS climatology built from other algorithms and satellite observations.

All datasets can be accessed via the repository under the following data DOI:

TOOCAN database: https://doi.org/10.14768/1be7fd53-8b81-416e-90d5-002b36b30cf8 (Fiolleau and Roca, 2023)
CACATOES database: https://doi.org/10.14768/98569eea-d056-412d-9f52-73ea07b9cdca (Fiolleau and Roca, 2023)

Received: 01 Feb 2024 – Discussion started: 26 Mar 2024

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Thomas Fiolleau and Remy Roca

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RC1:
'Comment on essd-2024-36', Anonymous Referee #1, 24 Apr 2024 reply
Title: A Deep Convective Systems Database Derived from the Intercalibrated Meteorological Geostationary Satellite Fleet and the TOOCAN algorithm (2012-2020)
Author(s): Thomas Fiolleau and Remy Roca
MS No.: essd-2024-36
MS type: Data description paper[
Review Comments: General Comment.
This manuscript describes the database derived from the intercalibrated geostationary satellite constellation (GEOring), processed by the TOOCAN algorithm to produce a historical dataset of the trajectories and characteristics of the mesoscale convective system over the 9 years from 2012 to 2020. The dataset is composed of two databases, the TOOCAN, describing the trajectories, including the radiative and morphological characteristics of the MCS with a 30-minute time step, and the CACATOES, a 1°x1° global monthly file describing the spatial distribution of the DCS density over the tropical belt. This is a very rich dataset and the manuscript describes in detail the procedures used, presents some results and shows some examples. I consider the manuscript suitable for publication in the ESSD after some minor corrections.
Specific Comments:
Line 40 – Rewrite the sentence – difficult to understand the meaning, as well as the verb agreement.

Line 43 – “MCS for a family of deep convective systems with horizontal scales larger than 100 km”. When you mention 100 km horizontal scale, it corresponds to an MCS of roughly with 10000 km². However, you start to look MCS from an area larger than 635 km². Please clarify and justify the choice.

Line 47-50. Please rewrite for better clarity.

Line 53 – Infirm?

Line 57 – Please correct - algorithms…

Line 57 – The justification is not appropriate now, in nowadays, there are the ISCCP H series, the MERGIR, among others.

Line 62 – “ A deep convective system then corresponds to a succession of convective clusters “ Should be in the other way round ?, and why only deep convective, stratiform clouds also is part of MCS.

Line 85 – There is a new paper (Km‐Scale Simulations of Mesoscale Convective Systems Over South America—A Feature Tracker Intercomparison - 10.1029/2023JD040254). This is study is very complementary to the discussions on this manuscript.

Line 88 – I think it is important to define what the author calls a cluster of pixels. Later in the text I noticed that you are looking at the 4 neighbouring pixels and the diagonal pixels. It should be clearly defined what you are tracking. I also suggest talking about the RDT algorithm, which also uses a variable threshold to follow the MCS structure. What are the basic differences between the two methods? Also, I see some similarities of the TOOCAN with the Fortracc, is the TOOCAN based in this tracking with the variable threshold?

Line 113 - Sometime the split is a natural characteristic of the MCS, as for instance the supercells that regularly split in two different systems.

Line 120 – “MCS shield growth rate has been carried out (Elsaesser et al. 2022)” . Several other papers earlier discussed the growth rate, one example is the Machado and Laurent 2004 (https://doi.org/10.1175/1520-0493(2004)132<0714:TCSAEO>2.0.CO;2.).

Table I – correct Feng etal.

Page 145 – “This paper presents a such DCS database” – rewrite the sentence.

Page 185 – “The decrease of the ScaRaB data availability does not impact the quality of the corrections from the end of 2018”. If there is no effect, why not apply the correction once a month to have a more homogeneous dataset?

Page 188 – Authors should discuss the larger Bias of Meteosat-7.

Page 197 – How does the interpolation take into account differences in satellite resolution? How is the change in resolution with latitude zenith angle taken into account? How do you deal with missing data, are they extrapolated? How does the different resolution affect the size of the MCS? Is the 55N to 55S window too large for an acceptable satellite viewing angle?

Figure 2 – “…in the range [180 K-235 K] between 2012 and 2020” why the range is different from the SCARAB correction [180 K-240 K]

Line 225 – “The arrivals of GOES-16” – replace by launched;

Line 250 - Could you explain why the cloud cluster associated with the tropical cyclone was dropped from the track? Several times the MCS evolved from a tropical storm to a hurricane. In this case, does TOOCAN consider the MCS dissipated?

Line 278 - There is an almost linear relationship between size (the threshold) and lifecycle. Your methodology uses the 1.5-hour lifecycle for the different thresholds. For 190K, 1.5 hours is a high requirement, but for 235K it is common.

Line 280 - How do you deal with semi-transparent clouds? For pixels where the Tir does not correspond exactly to the cloud top, the 3D threshold procedure will not work correctly because it contradicts the basic principle of the 3D threshold? Wouldn't it be better to follow the corrected cloud top?

Line 282 – “to resolve the unphysical split and merge issues.” There are split and merge in real life – Explain what do you consider unphysical?

Line 287 – How the algorithm handles missing images. With overlapping, you would normally have to use image extrapolation or a dynamic overlap threshold. How does TOOCAN use the overlap threshold and what value is used?

Line 304 - Maybe I didn't understand correctly, but this is not a good example to show, because the advanced feature of TOOCAN is the 3D threshold to avoid splits and merges. However, looking at system A, it seems to be a unique MCS, but TOOCAN produced several MCS that merge (you said there was no merge) at 1200. Also, B appears to be a single MCS, but it changes to C. Could you provide a better explanation for these merges and splits we see in this image? It seems that TOOCAN solves many split and merge situations, but it continues to have split and merge in smaller numbers.

Figure 7 - I did not understand how a missing image could increase the bias in the number population of short lived MCS. It is also interesting to see that the 2 hour missing image has the largest bias and 4 hours no bias, for short lived MCS. I fully understand this for systems larger than about 8 hours, but for shorter lifetimes I don't.

Line 365 – Perhaps it was in this part of the manuscript that I understood the above problem. Actually, this is not a missing image, it is the replication of the same image. So it is not really a missing image, it represents that the MCS was frozen. Please clarify.

Line 402 – “propagated distance, cold cloud maximum extent…)” – please do not use … and add all variables – maybe in a table – it is an important information to the users.

Line 405 – “thresholds, eccentricity, instantaneous velocity…)” Same comment.

Line 407 – “. A quality flag lower than 11110 is a good compromise to keep DCS little impacted by imagery issues”. Please clarify what 11110 means.

Figure 10 - It would be more appropriate to present this figure on the basis of the whole year, since not including the missing months, mainly during the dry season, will have an impact on the number of MCS/day..

Line 532 – “The CACATOES 1°x1°-1day global monthly files” please clarify what is 1day in this sentence.

Line 535 – Is the TOOCAN algorithm available? It will be important for the user to have access to the algorithm that produced the dataset.

Reply
Citation: https://doi.org/10.5194/essd-2024-36-RC1

Thomas Fiolleau and Remy Roca

Data sets

TOOCAN Database V2.08 – Tracking Of Organized Convection Algorithm using a 3-dimensional segmentation Thomas Fiolleau and Remy Roca https://doi.org/10.14768/1be7fd53-8b81-416e-90d5-002b36b30cf8

CACATOES database V1.04 Thomas Fiolleau and Remy Roca https://doi.org/10.14768/98569eea-d056-412d-9f52-73ea07b9cdca

GEOgrid_coldcloud : a 2012-2020 global homogenized infrared dataset from a fleet of geostationary satellites Thomas Fiolleau and Remy Roca https://doi.org/10.14768/93f138f5-a553-4691-96ed-952fd32d2fc3

Thomas Fiolleau and Remy Roca

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Short summary

This paper presents a tropical deep convective system database over the 2012–2020 period and built from a cloud tracking algorithm called TOOCAN, which has been applied on homogenized infrared observation from a fleet of geostationary satellites. This database aims at analyzing the tropical deep convective systems, the evolution of their associated characteristics along their life cycle, their organization and their importance in the hydrological and energy cycle...


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