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)

中文翻译：

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来自相互校准气象静止卫星群和 TOOCAN 算法的深对流系统数据库（2012-2020）

摘要。我们介绍了两个旨在促进2012年至2020年期间热带带深对流系统（DCS）及其形态特征研究的数据库：TOOCAN和CACATOES。 TOOCAN 数据库是使用一种名为 TOOCAN 的跟踪算法构建的，该算法应用于均质 GEOring 红外 (IR) 档案，并能够记录每个 DCS 在其整个生命周期中的形态参数。均质化的 GEOring IR 数据库是根据来自各种来源的一组对地静止平台的 1 级数据构建的，并经过相互校准、光谱调整和临边变暗校正，特别针对公共参考上的高冷云， Megha-Tropiques 上 ScaRaB 辐射计的红外通道。所得红外观测结果在亮度温度 (BT) < 240 K 下是均匀的，整个 GEOring 的标准偏差低于 1.5 K。进行了系统的不确定性分析。首先，辐射误差对 DCS 特性和发生情况影响不大。我们进一步评估了丢失数据的影响，并证明最多 3 小时的连续丢失图像代表了保持跟踪连续性同时最大限度地减少对 DCS 形态参数的影响的有利折衷。然而，超过这个时间阈值，DCS 的分割就会受到严重影响，从而需要中断跟踪过程。 CACATOES 数据库通过后处理程序从 TOOCAN 数据库衍生而来，其中涉及将每个深对流系统 (DCS) 的形态参数投影到 1°x1°-1 天的网格上。由此产生的数据集提供了更广阔的视角，允许对 DCS 进行欧拉分析，并有助于与同一日常 1° × 1° 网格盒上的辅助网格数据集进行比较。 TOOCAN 和 CACATOES 数据库均采用通用 netCDF 格式提供，该格式符合气候与预报 (CF) 公约和数据集发现属性公约 (ACDD) 标准。在热带地区和 9 年期间总共发现了 15x10 ⁶ DCS。对热带海洋和大陆 DCS 的分析揭示了 DCS 的多种特征和组织。它们可持续数小时至数天，云屏蔽范围从1000 km ²到数百万km ²。海洋 DCS 的特点是使用寿命更长和防护罩更大。最后，DCS 地理分布与之前根据其他算法和卫星观测建立的 DCS 气候学一致。所有数据集均可通过以下数据 DOI 下的存储库访问：

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