| 08 Sep 2026
ESA CCI 0.1 degree soil moisture: evaluation and applications for precipitation verification and storm nowcasting
Abstract. The recognized importance of soil moisture (SM) in land-atmosphere interactions has driven efforts to develop higher resolution datasets that better capture such interactions. In this study, ESA Climate Change Initiative MEDIUM RESOLUTION SM (0.1°) and the retrievals from contributing sensors are evaluated over Africa. Daytime land surface temperature (LST) from thermal infrared imagery can identify spatial variability in SM and is used here for evaluation of 0.1° (and reference coarse 0.25°) SM products. Among the different passive (AMSR2, SMAP and SMOS), active (ASCAT), MEDIUM RESOLUTION (merged), and reanalysis (ERA5) SM products evaluated, ASCAT was best at capturing spatial variability at 0.1° (and for most surface conditions at 0.25°). The MEDIUM RESOLUTION 0.1° dataset is dominated by passive sensors and shows poorer spatial correlation than ASCAT SM. ERA5-Land SM showed almost no skill in capturing mesoscale spatial patterns, and ERA5 (which benefits from ASCAT SM data assimilation) also performed comparatively poorly. We discuss two applications of medium resolution SM using the best performing SM dataset. We use 0.1° ASCAT SM to rank different precipitation datasets in their ability to capture medium resolution accumulated rainfall patterns (~12 hrs). Rain-over-Africa (a deep learning-based dataset) that solely relies on thermal infrared radiation clearly outperforms more established products. ERA5 precipitation shows poor skill, which largely explains the poor ERA5 SM estimates. A second application explores the preliminary use of SM in nowcasting convective storms (cores) in western Africa. We find that adding 0.1° ASCAT SM to the baseline nowcasting model (which uses thermal infrared brightness temperatures only) improved the prediction accuracy of the nowcasts at lead time of 4 hrs. Results show that adding SM resulted in an increase in predicted probability values for true events, particularly across regions of dry SM anomalies and high spatial gradients in SM climatology. These results highlight the potential of better prediction of rainfall through incorporation of medium resolution SM, while further work is needed to leverage the advantages of multiple sensors in creating a 0.1° SM dataset for applications that require high temporal resolution information.
Overall assessment
The manuscript addresses a relevant topic by evaluating mesoscale spatial information in soil moisture products and exploring its potential value for assessing precipitation patterns and nowcasting convective storms. The results are potentially useful, but substantial revision is needed to improve the manuscript’s organization and establish a clearer central scientific narrative.
First, dataset descriptions and methodological procedures are not clearly separated. Section 2 introduces the datasets together with some methods, while important methodological details are scattered across Sections 3-5 and interwoven with the results. This makes the study design difficult to follow. A conventional structure comprising Data and Methods, Results, and Discussion would improve clarity. Alternatively, if the manuscript is organized by research task, the methods and results should be clearly distinguished within each task-specific section.
Second, the manuscript combines three distinct components: evaluating soil moisture products, using ASCAT soil moisture changes to assess precipitation patterns, and incorporating ASCAT soil moisture into a convective nowcasting model. Although the product evaluation motivates the selection of ASCAT for both applications, the two applications are largely independent, which weakens the continuity of the manuscript. Their links to the product evaluation and their combined contribution to the study could be explained more clearly. Detailed comments are provided below.
Detailed comments
1. Introduction
2. Methods
This section combines dataset descriptions and methodological procedures without clearly distinguishing between them, making it difficult to follow. I suggest reorganizing it into clearly defined subsections covering: (i) datasets, including product versions, periods used, variables, units, and access links; (ii) preprocessing, including quality control, resampling, temporal matching, and anomaly calculations; and (iii) methods and experimental designs for soil moisture evaluation, precipitation product evaluation, and storm nowcasting. Consolidating the relevant methodological details from Sections 3-5 into this section would help readers understand the workflow and assess the reproducibility of the study.
Please also place the caption for Table 1 above the table, following the journal’s template.
3. Evaluation of 0.1° SM
4. Application of 0.1° SM to evaluation of rainfall
5. Application of 0.1° SM to nowcasting convective storms
6. Discussion
Some interpretations in the Discussion extend beyond the evidence presented. For example, the authors infer from weak mesoscale spatial correlations that errors in ERA5 precipitation explain the limited agreement between ERA5-Land soil moisture and LSTA, and further extend this interpretation to unreliable surface fluxes and potential forecast improvements through increased weighting of ASCAT in data assimilation. However, these processes were not directly evaluated, and alternative contributing factors were not adequately considered. Furthermore, the Discussion does not sufficiently reconcile the limited mesoscale spatial performance of ERA5-Land reported here with its demonstrated utility in previous studies and applications. The stronger spatial consistency of ASCAT soil moisture and RoA precipitation may make them more suitable for certain applications, but it does not establish their overall superiority. The authors should distinguish among spatial pattern representation, magnitude accuracy, temporal coverage, and application-specific requirements, and avoid generalizing rankings obtained at a particular scale and using a particular metric to overall product performance.