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Precipitation in Africa

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A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Climatology".

Deadline for manuscript submissions: closed (20 July 2023) | Viewed by 13217

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Special Issue Editors

Dr. Isaac Nooni

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Guest Editor

School of Atmospheric Science and Remote Sensing, Wuxi University, Wuxi 214105, China
Interests: land–atmosphere interactions; climate change; remote sensing; hydrological cycle; extreme events

Dr. Daniel Fiifi Tawia Hagan

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Guest Editor

Department of 3S Integration and Meteorological Applications, School of Geographical Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China
Interests: land–atmosphere interactions; remote sensing; hydrological cycle; climate modeling; hydrological modelling

Dr. William Amponsah

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Guest Editor

Department of Agricultural and Biosystems Engineering/WASCAL Climate Change and Land Use Centre, College of Engineering, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana
Interests: satellite remote sensing of precipitation; hydrological (extreme flood) modelling; forensic and statistical hydrometeorology; climate change; irrigation and agricultural water management

Dr. Nana Agyemang Prempeh

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Guest Editor

Department of Atmospheric and Climate Science, School of Geosciences, University of Energy and Natural Resources, Sunyani P.O. Box 214, Ghana
Interests: climate change; extreme events; climatology; climate modeling

Dr. Benjamin Lamptey

E-Mail Website
Guest Editor

1. School of Earth and Environment, University of Leeds, Leeds, UK
2. WASCAL, Center for Scientific and Industrial Research Secretariat Complex, Accra, Ghana
Interests: numerical weather prediction; climate modeling; climate change

Special Issue Information

Dear Colleagues,

Understanding the characteristics of the water cycle (or hydrological cycle) and processes are pivotal for improving our knowledge on the climate system. This continuous exercise is particularly important regarding essential climate variables such as precipitation.

In recent decades, the African continent has received more attention and priority regarding climate change studies. This awareness has prompted more interest in the continent, with substantial achievements regarding measuring and modelling the African precipitation and the water cycle. These recent achievements also contributed to the surge in African climate databases from multiple sources (i.e., remotely sensed, reanalysis, and modelled datasets) due to the relatively uneven and scarce ground-measurement networks. Such datasets have been instrumental for improving precipitation science in Africa. However, these datasets and their acquisition techniques are consistently being refined and improved to meet both research and application demands.

This Special Issue is focused on broadly defined climatology. We would like to invite research papers presenting innovative approaches for characterizing precipitation and the water cycle (or hydrological cycle) in Africa.

Submissions may be related to the use of interdisciplinary scientific studies devoted (but not limited) to a wide range of topic evaluation and enhancement tools; data independency and multisource estimate uncertainties in the tropics are especially welcome.

Dr. Isaac Nooni
Dr. Daniel Fiifi Tawia Hagan
Dr. William Amponsah
Dr. Nana Agyemang Prempeh
Dr. Benjamin Lamptey
Guest Editors

Manuscript Submission Information

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Keywords

hydrological cycle
precipitation
climatology
climate modeling

Published Papers (7 papers)

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Research

20 pages, 3962 KiB

Open AccessArticle

Nonlinear Trend and Multiscale Variability of Dry Spells in Senegal (1951–2010)

by Noukpo M. Agbazo, Moustapha Tall and Mouhamadou Bamba Sylla

Atmosphere 2023, 14(9), 1359; https://doi.org/10.3390/atmos14091359 - 29 Aug 2023

Cited by 1 | Viewed by 837

Abstract

Dry spells occurring during the rainy season have significant implications for agricultural productivity and socioeconomic development, particularly in rainfed agricultural countries such as Senegal. This study employs various chaos-theory-based tools, including the lacunarity method, rescaled analysis, and the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) method, to investigate the distribution, predictability, and multiscale properties of the annual series of maximum dry spell length (AMDSL) in Senegal during the rainy season. The analysis focuses on 29 stations across Senegal, spanning the period from 1951 to 2010. The findings reveal persistent behavior in the AMDSL across nearly all stations, indicating that predictive models based on extrapolating past time trends could enhance AMDSL forecasting. Furthermore, a well-defined spatial distribution of the lacunarity exponent β is observed, which exhibits a discernible relationship with rainfall patterns in Senegal. Notably, the lacunarity exponent displays a south-to-north gradient for all thresholds, suggesting its potential for distinguishing between different drought regimes and zones while aiding in the understanding of spatiotemporal rainfall variability patterns. Moreover, the analysis identifies five significant intrinsic mode functions (IMFs) characterized by different periods, including interannual, interdecadal, and multidecadal oscillations. These IMFs, along with a nonlinear trend, are identified as the driving forces behind AMDSL variations in Senegal. Among the inter-annual oscillations, a 3-year quasi-period emerges as the primary contributor and main component influencing AMDSL variability. Additionally, four distinct morphological types of nonlinear trends in AMDSL variations are identified, with increasing–decreasing and increasing trends being the most prevalent. These findings contribute to a better understanding of the variability in annual maximum dry spell lengths, particularly in the context of climate change, and provide valuable insights for improving AMDSL forecasting. Overall, this study enhances our comprehension of the complex dynamics underlying dry spell occurrences during the rainy season and presents potential avenues for predicting and managing the AMDSL in Senegal. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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22 pages, 4060 KiB

Open AccessArticle

The Phenomenology of West African Coastal Rainfall Events Based on a New Rain Gauge Network over Abidjan (Côte d’Ivoire)

by Modeste Kacou, Eric-Pascal Zahiri, Kouakou Christian Yao, Luc Séguis, Clément Dutremble, Ehouman Serge Koffi, Jean-Louis Perrin, Amidou Dao, Angah Armel Fourier Kodji, Kouamé Fréjus Konan and Kouassi Tandji Tewa

Atmosphere 2023, 14(9), 1322; https://doi.org/10.3390/atmos14091322 - 22 Aug 2023

Viewed by 711

Abstract

In the District of Abidjan, flooding typically occurs suddenly during intense rainfall events. The individual rainfall event provides the basic element for the study. Its analysis is required to develop solutions for managing the impact of extreme rainfall occurrences. Our study proposes to identify individual rainfall events that occurred in the District of Abidjan from a densified network and analyze some of their characteristics related to the amount of rainfall they provided, their duration, and their level of intensity. A total of 1240 individual rainfall events were identified between 2018 and 2021 using a network of 21 rain gauges. Rainfall events were identified based on criteria such as a minimum inter-event time without rainfall of 30 min, a detection threshold of 0.02 mm/5 min, a minimum duration of 30 min applicable to the average hyetograph, and a minimum of 1 mm of rainfall in at least one rain gauge. The analysis of characteristics related to accumulation, intensity, and duration showed that the rainfall events were essentially convective, with an average duration of more than 2 h and a rainfall of 11.30 mm/event. For 70% of the rainfall events of a mixed nature, the convective episodes last up to 33.33% of the total duration of the event and produce an average of 80% of the cumulative rainfall. The 30-min peak intensities generally occur in the first half of the event. Less than 13.5% of the events have peaks greater than 50 mm/h. The probability of observing more than two, three, or four events per day is high in June and October, the core of the two rainy seasons. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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24 pages, 54213 KiB

Open AccessArticle

The Role of Water Vapor Observations in Satellite Rainfall Detection Highlighted by a Deep Learning Approach

by Mónica Estébanez-Camarena, Fabio Curzi, Riccardo Taormina, Nick van de Giesen and Marie-Claire ten Veldhuis

Atmosphere 2023, 14(6), 974; https://doi.org/10.3390/atmos14060974 - 02 Jun 2023

Cited by 1 | Viewed by 1404

Abstract

West African food systems and rural socio-economics are based on rainfed agriculture, which makes society highly vulnerable to rainfall uncertainty and frequent floods and droughts. Reliable rainfall information is currently missing. There is a sparse and uneven rain gauge distribution and, despite continuous efforts, rainfall satellite products continue to show weak correlations with ground measurements. This paper aims to investigate whether water vapor (WV) observations together with temporal information can complement thermal infrared (TIR) data for satellite rainfall retrieval in a Deep Learning (DL) framework. This is motivated by the fact that water vapor plays a key role in the highly seasonal West African rainfall dynamics. We present a DL model for satellite rainfall detection based on WV and TIR channels of Meteosat Second Generation and temporal information. Results show that the WV inhibition of low-level features enables the depiction of strong convective motions usually related to heavy rainfall. This is especially relevant in areas where convective rainfall is dominant, such as the tropics. Additionally, WV data allow us to detect dry air masses over our study area, that are advected from the Sahara Desert and create discontinuities in precipitation events. The developed DL model shows strong performance in rainfall binary classification, with less false alarms and lower rainfall overdetection (FBias

< 2.0

) than the state-of-the-art Integrated MultisatellitE Retrievals for GPM (IMERG) Final Run. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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17 pages, 3208 KiB

Open AccessArticle

Present and Future of Heavy Rain Events in the Sahel and West Africa

by Inoussa Abdou Saley and Seyni Salack

Atmosphere 2023, 14(6), 965; https://doi.org/10.3390/atmos14060965 - 31 May 2023

Viewed by 1390

Abstract

Gridding precipitation datasets for climate information services in the semi-arid regions of West Africa has some advantages due to the limited spatial coverage of rain gauges, the limited accessibility to in situ gauge data, and the important progress in earth observation and climate modelling systems. Can accurate information on the occurrence of heavy precipitation in this area be provided using gridded datasets? Furthermore, what about the future of heavy rain events (HRE) under the shared socioeconomic pathways (SSP) of the Inter-Sectoral Impact Model Intercomparison Project (i.e., SSP126 and SSP370)? To address these questions, daily precipitation records from 17 datasets, including satellite estimates, interpolated rain gauge data, reanalysis, merged products, a regional climate model, and global circulation models, are examined and compared to quality-controlled in situ data from 69 rain gauges evenly distributed across West Africa’s semi-arid region. The results show a consensus increase in the occurrence of HRE, between observational and gridded data. All datasets showed three categories of HRE every season, but these categories had lower intensities and an overstated frequency of occurrence in gridded datasets compared to in situ rain gauge data. Eight out of 17 databases (~47%) show significant positive trends and only one showed a significant negative trend, indicating an increase in HRE for all categories in this region. The future evolution of HRE considered under the shared socioeconomic pathways SSP1-2.6 and SSP3-7.0, showed a trend toward the intensification of these events. In fact, the mean of the ensemble of the models showed significant changes toward higher values in the probability distribution function of the future HRE in West Africa, which may likely trigger more floods and landslides in the region. The use of gridded data sets can provide accurate information on the occurrence of heavy precipitation in the West African Sahel. However, it is important to consider the representation of heavy rain events in each data set when monitoring extreme precipitation, although in situ gauge records are preferred to define extreme rainfall locally. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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25 pages, 3279 KiB

Open AccessArticle

A Comparison of the Statistical Downscaling and Long-Short-Term-Memory Artificial Neural Network Models for Long-Term Temperature and Precipitations Forecasting

by Noé Carème Fouotsa Manfouo, Linke Potgieter, Andrew Watson and Johanna H. Nel

Atmosphere 2023, 14(4), 708; https://doi.org/10.3390/atmos14040708 - 12 Apr 2023

Cited by 2 | Viewed by 1989

Abstract

General circulation models (GCMs) run at regional resolution or at a continental scale. Therefore, these results cannot be used directly for local temperatures and precipitation prediction. Downscaling techniques are required to calibrate GCMs. Statistical downscaling models (SDSM) are the most widely used for bias correction of GCMs. However, few studies have compared SDSM with multi-layer perceptron artificial neural networks and in most of these studies, results indicate that SDSM outperform other approaches. This paper investigates an alternative architecture of neural networks, namely the long-short-term memory (LSTM), to forecast two critical climate variables, namely temperature and precipitation, with an application to five climate gauging stations in the Lake Chad Basin. Lake Chad is a data scarce area which has been impacted by severe drought, where water resources have been influenced by climate change and recent agricultural expansion. SDSM was used as the benchmark in this paper for temperature and precipitation downscaling for monthly time–scales weather prediction, using grid resolution GCM output at a 5 degrees latitude × 5 degrees longitude global grid. Three performance indicators were used in this study, namely the root mean square error (RMSE), to measure the sensitivity of the model to outliers, the mean absolute percentage error (MAPE), to estimate the overall performance of the predictions, as well as the Nash Sutcliffe Efficiency (NSE), which is a standard measure used in the field of climate forecasting. Results on the validation set for SDSM and test set for LSTM indicated that LSTM produced better accuracy on average compared to SDSM. For precipitation forecasting, the average RMSE and MAPE for LSTM were 33.21 mm and 24.82% respectively, while the average RMSE and MAPE for SDSM were 53.32 mm and 34.62% respectively. In terms of three year ahead minimum temperature forecasts, LSTM presents an average RMSE of 4.96 degree celsius and an average MAPE of 27.16%, while SDSM presents an average RMSE of 8.58 degree celsius and an average MAPE of 12.83%. For maximum temperatures forecast, LSTM presents an average RMSE of 4.27 degree celsius and an average MAPE of 11.09 percent, while SDSM presents an average RMSE of 9.93 degree celsius and an average RMSE of 12.07%. Given the results, LSTM may be a suitable alternative approach to downscale global climate simulation models’ output, to improve water management and long-term temperature and precipitations forecasting at local level. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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26 pages, 19193 KiB

Open AccessEditor’s ChoiceArticle

Evaluating CMIP6 Historical Mean Precipitation over Africa and the Arabian Peninsula against Satellite-Based Observation

by Isaac Kwesi Nooni, Faustin Katchele Ogou, Abdoul Aziz Saidou Chaibou, Francis Mawuli Nakoty, Gnim Tchalim Gnitou and Jiao Lu

Atmosphere 2023, 14(3), 607; https://doi.org/10.3390/atmos14030607 - 22 Mar 2023

Cited by 7 | Viewed by 2506

Abstract

This study evaluated the historical precipitation simulations of 49 global climate models (GCMs) of the Coupled Model Intercomparison Project Phase 6 (CMIP6) in reproducing annual and seasonal precipitation climatology, linear trends, and their spatial correlation with global SST across Africa and the Arabian Peninsula during the period of 1980–2014, using Global Precipitation Climatology Centre (GPCP) data as a reference. Taylor’s diagram was used to quantify the strengths and weaknesses of the models in simulating precipitation. The CMIP6 multi-mean ensemble (MME) and the majority of the GCMs replicated the dominant features of the spatial and temporal variations reasonably well. The CMIP6 MME outperformed the majority of the individual models. The spatial variation of the CMIP6 MME closely matched the observation. The results showed that at annual and seasonal scales, the GPCP and CMIP6 MME reproduced a coherent spatial pattern in terms of the magnitude of precipitation. The humid region received >300 mm and the arid region received <50 mm across Africa and the Arabian Peninsula. The models from the same modeling centers replicated the precipitation levels across different seasons and regions. The CMIP6 MME and the majority of the individual models overestimate (underestimate) in humid (arid and semi-arid)-climate zones. The annual and pre-monsoon seasons (i.e., DJFMA) were better replicated in the CMIP6 GCMs than in the monsoon-precipitation model (MJJASON). The CMIP6 MME (GPCP) showed stronger wetting (drying) trends in the northern hemisphere. In contrast, a strong drying trend in the CMIP6 MME and a weak wetting trend in the GPCP were shown in the Southern Hemisphere. The CMIP6 MME captures the spatial pattern of linear trends better than individual models across different climate zones and regions. The relationship between precipitation and sea-surface temperature (SST) exhibited a high spatial correlation (−0.80 and 0.80) with large variability across different regions and climate zones. The GPCP (CMIP6 MME) exhibited a heterogenous (homogeneous) spatial pattern, with higher correlation coefficients recorded in the CMIP6 MME than in the GPCP in all cases. Individual models from the same modeling centers showed spatial homogeneity in correlation values. The differences exhibited by the individual GCMs highlight the significance of each model’s unique dynamics and physics; however, model selection should be considered for specific applications. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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14 pages, 3355 KiB

Open AccessArticle

Climatic and Vegetation Response Patterns over South Africa during the 2010/2011 and 2015/2016 Strong ENSO Phases

by Lerato Shikwambana, Kanya Xongo, Morwapula Mashalane and Paidamwoyo Mhangara

Atmosphere 2023, 14(2), 416; https://doi.org/10.3390/atmos14020416 - 20 Feb 2023

Cited by 1 | Viewed by 2847

Abstract

El Niño-Southern Oscillation (ENSO) is a significant climate phenomenon on Earth due to its ability to change the global atmospheric circulation which influences temperature and precipitation across the globe. In this study, we investigate the responses of climatic and vegetation parameters due to two strong ENSO phases, i.e., La Niña (2010/2011) and El Niño (2015/2016) in South Africa. The study aims to understand the influence of strong seasonal ENSO events on climatic and vegetation parameters over South Africa. Remote sensing data from the Global Precipitation Measurement (GPM), Moderate Resolution Imaging Spectroradiometer (MODIS), Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) and Atmospheric Infrared Sounder (AIRS) was used. The relationship between precipitation, temperature, and Normalized Difference Vegetation Index (NDVI) were studied using Pearson’s correlation. Comparison between the La Niña, neutral year, and El Niño periods showed two interesting results: (1) higher precipitation from the south coast to the east coast of South Africa, with some low precipitation in the interior during the La Niña and El Niño periods, and (2) a drop in precipitation by ~46.6% was observed in the southwestern parts of South Africa during the La Niña and El Niño events. The study further showed that wind speed and wind direction were not impacted by strong ENSO events during the MAM, JJA and SON seasons, but the DJF season showed varying wind speeds, especially on the west coast, during both ENSO events. Overall, the Pearson’s correlation results clearly showed that the relationship between climatic parameters such as precipitation, temperature, and vegetation parameters such a NDVI is highly correlated while other parameters, such as wind speed and direction, are not. This study has provided new insights into the relationship between temperature, precipitation, and NDVI in South Africa; however, future work will include other climatic and vegetation parameters such as relative humidity and net longwave radiation. Full article

(This article belongs to the Special Issue Precipitation in Africa)

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