Journal articles on the topic 'Precipitation forecasting Africa'
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1
Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences Discussions 10, no. 8 (August 8, 2013): 10209–30. http://dx.doi.org/10.5194/hessd-10-10209-2013.
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Abstract. The humanitarian crisis caused by the recent droughts (2008–2009 and 2010–2011) in the East African region have illustrated that the ability to make accurate drought predictions with adequate lead time is essential. The use of dynamical model forecasts and drought indices, such as Standardized Precipitation Index (SPI), promises to lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for both rain seasons when evaluated against measurements from the available in-situ stations from East Africa. The October–December rain season has higher skill that the March–May season. ECMWF forecasts add value to the statistical forecasts produced during the Greater Horn of Africa Climate Outlook Forums (GHACOF) which is the present operational product. Complementing the raw precipitation forecasts with SPI provides additional information on the spatial extend and intensity of the drought event.
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Mwangi, E., F. Wetterhall, E. Dutra, F. Di Giuseppe, and F. Pappenberger. "Forecasting droughts in East Africa." Hydrology and Earth System Sciences 18, no. 2 (February 18, 2014): 611–20. http://dx.doi.org/10.5194/hess-18-611-2014.
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Abstract. The humanitarian crises caused by the recent droughts (2008–2009 and 2010–2011) in East Africa have illustrated that the ability to make accurate drought forecasts with sufficient lead time is essential. The use of dynamical model precipitation forecasts in combination with drought indices, such as the Standardized Precipitation Index (SPI), can potentially lead to a better description of drought duration, magnitude and spatial extent. This study evaluates the use of the European Centre for Medium-Range Weather Forecasts (ECMWF) products in forecasting droughts in East Africa. ECMWF seasonal precipitation shows significant skill for March–May and October–December rain seasons when evaluated against measurements from the available in situ stations from East Africa. The forecast for October–December rain season has higher skill than for the March–May season. ECMWF forecasts add value to the consensus forecasts produced during the Greater Horn of Africa Climate Outlook Forum (GHACOF), which is the present operational product for precipitation forecast over East Africa. Complementing the original ECMWF precipitation forecasts with SPI provides additional information on the spatial extent and intensity of the drought event.
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Ratnam, J. V., Takeshi Doi, Willem A. Landman, and Swadhin K. Behera. "Seasonal Forecasting of Onset of Summer Rains over South Africa." Journal of Applied Meteorology and Climatology 57, no. 12 (December 2018): 2697–711. http://dx.doi.org/10.1175/jamc-d-18-0067.1.
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AbstractIn this study, we attempted to forecast the onset of summer rains over South Africa using seasonal precipitation forecasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change, version 2 (SINTEX-F2), seasonal forecasting system. The precipitation forecasts of the 12-member SINTEX-F2 system, initialized on 1 August and covering the period 1998–2015, were used for the study. The SINTEX-F2 forecast precipitation was also downscaled using dynamical and statistical techniques to improve the spatial and temporal representation of the forecasts. The Weather Research and Forecasting (WRF) Model with two cumulus parameterization schemes was used to dynamically downscale the SINTEX-F2 forecasts. The WRF and SINTEX-F2 precipitation forecasts were corrected for biases using a linear scaling method with a 31-day moving window. The results indicate the onset dates derived from the raw and bias-corrected model precipitation forecasts to have realistic spatial distribution over South Africa. However, the forecast onset dates have root-mean-square errors of more than 30 days over most parts of South Africa except over the northeastern province of Limpopo and over the Highveld region of Mpumalanga province, where the root-mean-square errors are about 10–15 days. The WRF Model with Kain–Fritsch cumulus scheme (bias-corrected SINTEX-F2) has better performance in forecasting the onset dates over Limpopo (the Highveld region) compared to other models, thereby indicating the forecast of onset dates over different regions of South Africa to be model dependent. The results of this study are important for improving the forecast of onset dates over South Africa.
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Muofhe, Tshimbiluni Percy, Hector Chikoore, Mary-Jane Morongwa Bopape, Nthaduleni Samuel Nethengwe, Thando Ndarana, and Gift Tshifhiwa Rambuwani. "Forecasting Intense Cut-Off Lows in South Africa Using the 4.4 km Unified Model." Climate 8, no. 11 (November 7, 2020): 129. http://dx.doi.org/10.3390/cli8110129.
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Mid-tropospheric cut-off low (COL) pressure systems are linked to severe weather, heavy rainfall and extreme cold conditions over South Africa. They occur during all the above and often result in floods and snowfalls during the winter months, disrupting economic activities and causing extensive damage to infrastructure. This paper examines the evolution and circulation patterns associated with cases of severe COLs over South Africa. We evaluate the performance of the 4.4 km Unified Model (UM) which is currently used operationally by the South African Weather Service (SAWS) to simulate daily rainfall. Circulation variables and precipitation simulated by the UM were compared against European Centre for Medium-Range Weather Forecast’s (ECMWF’s) ERA Interim re-analyses and GPM precipitation at 24-hour timesteps. We present five recent severe COLs, which occurred between 2016 and 2019, that had high impact and found a higher model skill when simulating heavy precipitation during the initial stages than the dissipating stages of the systems. A key finding was that the UM simulated the precipitation differently during the different stages of development and location of the systems. This is mainly due to inaccurate placing of COL centers. Understanding the performance and limitations of the UM model in simulating COL characteristics can benefit severe weather forecasting and contribute to disaster risk reduction in South Africa.
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Yuan, Xing, Eric F. Wood, Nathaniel W. Chaney, Justin Sheffield, Jonghun Kam, Miaoling Liang, and Kaiyu Guan. "Probabilistic Seasonal Forecasting of African Drought by Dynamical Models." Journal of Hydrometeorology 14, no. 6 (November 22, 2013): 1706–20. http://dx.doi.org/10.1175/jhm-d-13-054.1.
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Abstract As a natural phenomenon, drought can have devastating impacts on local populations through food insecurity and famine in the developing world, such as in Africa. In this study, the authors have established a seasonal hydrologic forecasting system for Africa. The system is based on the Climate Forecast System, version 2 (CFSv2), and the Variable Infiltration Capacity (VIC) land surface model. With a set of 26-yr (1982–2007) seasonal hydrologic hindcasts run at 0.25°, the probabilistic drought forecasts are validated using the 6-month Standard Precipitation Index (SPI6) and soil moisture percentile as indices. In terms of Brier skill score (BSS), the system is more skillful than climatology out to 3–5 months, except for the forecast of soil moisture drought over central Africa. The spatial distribution of BSS, which is similar to the pattern of persistency, shows more heterogeneity for soil moisture than the SPI6. Drought forecasts based on SPI6 are generally more skillful than for soil moisture, and their differences originate from the skill attribute of resolution rather than reliability. However, the soil moisture drought forecast can be more skillful than SPI6 at the beginning of the rainy season over western and southern Africa because of the strong annual cycle. Singular value decomposition (SVD) analysis of African precipitation and global SSTs indicates that CFSv2 reproduces the ENSO dominance on rainy season drought forecasts quite well, but the corresponding SVD mode from observations and CFSv2 only account for less than 24% and 31% of the covariance, respectively, suggesting that further understanding of drought drivers, including regional atmospheric dynamics and land–atmosphere coupling, is necessary.
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Zhang, Sara Q., T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard. "Impact of Assimilated Precipitation-Sensitive Radiances on the NU-WRF Simulation of the West African Monsoon." Monthly Weather Review 145, no. 9 (September 1, 2017): 3881–900. http://dx.doi.org/10.1175/mwr-d-16-0389.1.
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Abstract This work assimilates multisensor precipitation-sensitive microwave radiance observations into a storm-scale NASA Unified Weather Research and Forecasting (NU-WRF) Model simulation of the West African monsoon. The analysis consists of a full description of the atmospheric states and a realistic cloud and precipitation distribution that is consistent with the observed dynamic and physical features. The analysis shows an improved representation of monsoon precipitation and its interaction with dynamics over West Africa. Most significantly, assimilation of precipitation-affected microwave radiance has a positive impact on the distribution of precipitation intensity and also modulates the propagation of cloud precipitation systems associated with the African easterly jet. Using an ensemble-based assimilation technique that allows state-dependent forecast error covariance among dynamical and microphysical variables, this work shows that the assimilation of precipitation-sensitive microwave radiances over the West African monsoon rainband enables initialization of storms. These storms show the characteristics of continental tropical convection that enhance the connection between tropical waves and organized convection systems.
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Ratnam, J. V., S. K. Behera, S. B. Ratna, C. J. de W. Rautenbach, C. Lennard, J. J. Luo, Y. Masumoto, K. Takahashi, and T. Yamagata. "Dynamical Downscaling of Austral Summer Climate Forecasts over Southern Africa Using a Regional Coupled Model." Journal of Climate 26, no. 16 (August 6, 2013): 6015–32. http://dx.doi.org/10.1175/jcli-d-12-00645.1.
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Abstract The prediction skill of dynamical downscaling is evaluated for climate forecasts over southern Africa using the Advanced Research Weather Research and Forecasting (WRF) model. As a case study, forecasts for the December–February (DJF) season of 2011/12 are evaluated. Initial and boundary conditions for the WRF model were taken from the seasonal forecasts of the Scale Interaction Experiment-Frontier Research Center for Global Change (SINTEX-F) coupled general circulation model. In addition to sea surface temperature (SST) forecasts generated by nine-member ensemble forecasts of SINTEX-F, the WRF was also configured to use SST generated by a simple mixed layer Price–Weller–Pinkel ocean model coupled to the WRF model. Analysis of the ensemble mean shows that the uncoupled WRF model significantly increases the biases (errors) in precipitation forecasted by SINTEX-F. When coupled to a simple mixed layer ocean model, the WRF model improves the spatial distribution of precipitation over southern Africa through a better representation of the moisture fluxes. Precipitation anomalies forecasted by the coupled WRF are seen to be significantly correlated with the observed precipitation anomalies over South Africa, Zimbabwe, southern Madagascar, and parts of Zambia and Angola. This is in contrast to the SINTEX-F global model precipitation anomaly forecasts that are closer to observations only for parts of Zimbabwe and South Africa. Therefore, the dynamical downscaling with the coupled WRF adds value to the SINTEX-F precipitation forecasts over southern Africa. However, the WRF model yields positive biases (>2°C) in surface air temperature forecasts over the southern African landmass in both the coupled and uncoupled configurations because of biases in the net heat fluxes.
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Early, Regan, Pablo González-Moreno, Sean T. Murphy, and Roger Day. "Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm." NeoBiota 40 (November 9, 2018): 25–50. http://dx.doi.org/10.3897/neobiota.40.28165.
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Fall armyworm, Spodopterafrugiperda, is a crop pest native to the Americas, which has invaded and spread throughout sub-Saharan Africa within two years. Recent estimates of 20–50% maize yield loss in Africa suggest severe impact on livelihoods. Fall armyworm is still infilling its potential range in Africa and could spread to other continents. In order to understand fall armyworm’s year-round, global, potential distribution, we used evidence of the effects of temperature and precipitation on fall armyworm life-history, combined with data on native and African distributions to construct Species Distribution Models (SDMs). We also investigated the strength of trade and transportation pathways that could carry fall armyworm beyond Africa. Up till now, fall armyworm has only invaded areas that have a climate similar to the native distribution, validating the use of climatic SDMs. The strongest climatic limits on fall armyworm’s year-round distribution are the coldest annual temperature and the amount of rain in the wet season. Much of sub-Saharan Africa can host year-round fall armyworm populations, but the likelihoods of colonising North Africa and seasonal migrations into Europe are hard to predict. South and Southeast Asia and Australia have climate conditions that would permit fall armyworm to invade. Current trade and transportation routes reveal Australia, China, India, Indonesia, Malaysia, Philippines and Thailand face high threat of fall armyworm invasions originating from Africa.
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Nooni, Isaac Kwesi, Guirong Tan, Yan Hongming, Abdoul Aziz Saidou Chaibou, Birhanu Asmerom Habtemicheal, Gnim Tchalim Gnitou, and Kenny T. C. Lim Kam Sian. "Assessing the Performance of WRF Model in Simulating Heavy Precipitation Events over East Africa Using Satellite-Based Precipitation Product." Remote Sensing 14, no. 9 (April 19, 2022): 1964. http://dx.doi.org/10.3390/rs14091964.
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This study investigated the capability of the Weather Research and Forecasting (WRF) model to simulate seven different heavy precipitation (PRE) events that occurred across East Africa in the summer of 2020. The WRF model outputs were evaluated against high-resolution satellite-based observations, which were obtained from prior evaluations of several satellite observations with 30 stations’ data. The synoptic conditions accompanying the events were also investigated to determine the conditions that are conducive to heavy PRE. The verification of the WRF output was carried out using the area-related root mean square error (RMSE)-based fuzzy method. This method quantifies the similarity of PRE intensity distribution between forecast and observation at different spatial scales. The results showed that the WRF model reproduced the heavy PRE with PRE magnitudes ranging from 6 to >30 mm/day. The spatial pattern from the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification-Climate Data Record (PERSIANN-CCS-CDR) was close to that of the WRF output. The area-related RMSE with respect to observation showed that the error in the model tended to reduce as the spatial scale increased for all the events. The WRF and high-resolution satellite data had an obvious advantage when validating the heavy PRE events in 2020. This study demonstrated that WRF may be used for forecasting heavy PRE events over East Africa when high resolutions and subsequent simulation setups are used.
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Valdés-Pineda, Rodrigo, Juan B. Valdés, Sungwook Wi, Aleix Serrat-Capdevila, and Tirthankar Roy. "Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting." Hydrology 8, no. 4 (December 20, 2021): 188. http://dx.doi.org/10.3390/hydrology8040188.
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The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.
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Noble, Erik, Leonard M. Druyan, and Matthew Fulakeza. "The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part II: Precipitation." Monthly Weather Review 145, no. 1 (December 29, 2016): 215–33. http://dx.doi.org/10.1175/mwr-d-15-0294.1.
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Abstract This paper evaluates the performance of the Weather Research and Forecasting (WRF) Model as a regional atmospheric model over West Africa. It tests WRF’s sensitivity to 64 configurations of alternative parameterizations in a series of 104 twelve-day September simulations during 11 consecutive years, 2000–10. The 64 configurations combine WRF parameterizations of cumulus convection, radiation, surface hydrology, and the PBL. Simulated daily and total precipitation results are validated against Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measuring Mission (TRMM) data. Particular attention is given to westward-propagating precipitation maxima associated with African easterly waves (AEWs). A wide range of daily precipitation validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve time–longitude correlations (against GPCP) of between 0.35 and 0.42 and spatiotemporal variability amplitudes only slightly higher than observed estimates. A parallel simulation by the benchmark Regional Model version 3 achieves a higher correlation (0.52) and realistic spatiotemporal variability amplitudes. The largest favorable impact on WRF precipitation validation is achieved by selecting the Grell–Devenyi convection scheme, resulting in higher correlations against observations than using the Kain–Fritch convection scheme. Other parameterizations have less obvious impacts. Validation statistics for optimized WRF configurations simulating the parallel period during 2000–10 are more favorable for 2005, 2006, and 2008 than for other years. The selection of some of the same WRF configurations as high scorers in both circulation and precipitation validations supports the notion that simulations of West African daily precipitation benefit from skillful simulations of associated AEW vorticity centers and that simulations of AEWs would benefit from skillful simulations of convective precipitation.
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Stein, T. H. M., W. Keat, R. I. Maidment, S. Landman, E. Becker, D. F. A. Boyd, A. Bodas-Salcedo, G. Pankiewicz, and S. Webster. "An Evaluation of Clouds and Precipitation in Convection-Permitting Forecasts for South Africa." Weather and Forecasting 34, no. 1 (February 1, 2019): 233–54. http://dx.doi.org/10.1175/waf-d-18-0080.1.
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Abstract Since 2016, the South African Weather Service (SAWS) has been running convective-scale simulations to assist with forecast operations across southern Africa. These simulations are run with a tropical configuration of the Met Office Unified Model (UM), nested in the Met Office global model, but without data assimilation. For November 2016, convection-permitting simulations at 4.4- and 1.5-km grid lengths are compared against a simulation at 10-km grid length with convection parameterization (the current UM global atmosphere configuration) to identify the benefits of increasing model resolution for forecasting convection across southern Africa. The simulations are evaluated against satellite rainfall estimates, CloudSat vertical cloud profiles, and SAWS radar data. In line with previous studies using the UM, on a monthly time scale, the diurnal cycle of convection and the distribution of rainfall rates compare better against observations when convection-permitting model configurations are used. The SAWS radar network provides a three-dimensional composite of radar reflectivity for northeast South Africa at 6-min intervals, allowing the evaluation of the vertical development of precipitating clouds and of the timing of the onset of deep convection. Analysis of four case study days indicates that the 4.4-km simulations have a later onset of convection than the 1.5-km simulations, but there is no consistent bias of the simulations against the radar observations across the case studies.
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Dutra, E., F. Di Giuseppe, F. Wetterhall, and F. Pappenberger. "Seasonal forecasts of drought indices in African basins." Hydrology and Earth System Sciences Discussions 9, no. 9 (September 28, 2012): 11093–129. http://dx.doi.org/10.5194/hessd-9-11093-2012.
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Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas which often have a very low resilience and limited capabilities to mitigate their effects. This paper tries to assess the predictive capabilities of an integrated drought monitoring and forecasting system based on the Standard precipitation index (SPI). The system is firstly constructed by temporally extending near real-time precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System-Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with forecasted fields as provided by the ECMWF seasonal forecasting system and then is evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. All the datasets show similar patterns in the South and North West Africa, while there is a low correlation in the tropical region which makes it difficult to define ground truth and choose an adequate product for monitoring. The Seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depends strongly on the SPI time-scale, and more skill is observed at larger time-scales. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in two to four months lead time.
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Roy, Tirthankar, Aleix Serrat-Capdevila, Juan Valdes, Matej Durcik, and Hoshin Gupta. "Design and implementation of an operational multimodel multiproduct real-time probabilistic streamflow forecasting platform." Journal of Hydroinformatics 19, no. 6 (August 24, 2017): 911–19. http://dx.doi.org/10.2166/hydro.2017.111.
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Abstract The task of real-time streamflow monitoring and forecasting is particularly challenging for ungauged or sparsely gauged river basins, and largely relies upon satellite-based estimates of precipitation. We present the design and implementation of a state-of-the-art real-time streamflow monitoring and forecasting platform that integrates information provided by cutting-edge satellite precipitation products (SPPs), numerical precipitation forecasts, and multiple hydrologic models, to generate probabilistic streamflow forecasts that have an effective lead time of 9 days. The modular design of the platform enables adding/removing any model/product as may be appropriate. The SPPs are bias-corrected in real-time, and the model-generated streamflow forecasts are further bias-corrected and merged, to produce probabilistic forecasts that are computed via several model averaging techniques. The platform is currently operational in multiple river basins in Africa, and can also be adapted to any new basin by incorporating some basin-specific changes and recalibration of the hydrologic models.
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Soliman, Ibrahim M. A. "Forecasting Model of Wheat Yield in Relation to Rainfall Variability in North Africa Countries." International Journal of Food and Beverage Manufacturing and Business Models 4, no. 2 (July 2019): 1–17. http://dx.doi.org/10.4018/ijfbmbm.2019070101.
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The study investigated the effect of rainfall variations on wheat yield in Morocco as a representative case study of North Africa region. The data were collected for the period 2004– 2015 from 12 meteorological stations. The wheat yield variability range was 79.5%-38.0%. It increased in poor-rain years and the regions of precipitation ≤ 350 mm. The wheat yield showed more significant response to monthly perception changes than the annual. The estimated forecasting model showed that March's rain was the critical month for wheat yield as the elasticity of production was 0.587. April and May showed an elasticity of 0.011 and 0.023, respectively. The estimated response of wheat farm price to grain yield showed that 10% increase in wheat yield would decrease the farm gate price by 4.1%, i.e. poor rainy seasons mean income foregone with the loss of inputs expenses and expansion in imported wheat. A country buffer stock, a regional strategic stock of wheat and supplementary water for irrigation in poor precipitation years are required.
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Dutra, E., F. Di Giuseppe, F. Wetterhall, and F. Pappenberger. "Seasonal forecasts of droughts in African basins using the Standardized Precipitation Index." Hydrology and Earth System Sciences 17, no. 6 (June 28, 2013): 2359–73. http://dx.doi.org/10.5194/hess-17-2359-2013.
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Abstract. Vast parts of Africa rely on the rainy season for livestock and agriculture. Droughts can have a severe impact in these areas, which often have a very low resilience and limited capabilities to mitigate drought impacts. This paper assesses the predictive capabilities of an integrated drought monitoring and seasonal forecasting system (up to 5 months lead time) based on the Standardized Precipitation Index (SPI). The system is constructed by extending near-real-time monthly precipitation fields (ECMWF ERA-Interim reanalysis and the Climate Anomaly Monitoring System–Outgoing Longwave Radiation Precipitation Index, CAMS-OPI) with monthly forecasted fields as provided by the ECMWF seasonal forecasting system. The forecasts were then evaluated over four basins in Africa: the Blue Nile, Limpopo, Upper Niger, and Upper Zambezi. There are significant differences in the quality of the precipitation between the datasets depending on the catchments, and a general statement regarding the best product is difficult to make. The generally low number of rain gauges and their decrease in the recent years limits the verification and monitoring of droughts in the different basins, reinforcing the need for a strong investment on climate monitoring. All the datasets show similar spatial and temporal patterns in southern and north-western Africa, while there is a low correlation in the equatorial area, which makes it difficult to define ground truth and choose an adequate product for monitoring. The seasonal forecasts have a higher reliability and skill in the Blue Nile, Limpopo and Upper Niger in comparison with the Zambezi. This skill and reliability depend strongly on the SPI timescale, and longer timescales have more skill. The ECMWF seasonal forecasts have predictive skill which is higher than using climatology for most regions. In regions where no reliable near-real-time data is available, the seasonal forecast can be used for monitoring (first month of forecast). Furthermore, poor-quality precipitation monitoring products can reduce the potential skill of SPI seasonal forecasts in 2 to 4 months lead time.
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Ratnam, J. V., Swadhin K. Behera, Takeshi Doi, Satyban B. Ratna, and Willem A. Landman. "Improvements to the WRF Seasonal Hindcasts over South Africa by Bias Correcting the Driving SINTEX-F2v CGCM Fields." Journal of Climate 29, no. 8 (April 5, 2016): 2815–29. http://dx.doi.org/10.1175/jcli-d-15-0435.1.
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Abstract In an attempt to improve the forecast skill of the austral summer precipitation over South Africa, an ensemble of 1-month-lead seasonal hindcasts generated by the Scale Interaction Experiment–Frontier Research Center for Global Change (SINTEX-F2v) coupled global circulation model is downscaled using the Weather Research and Forecasting (WRF) Model. The WRF Model with two-way interacting domains at horizontal resolutions of 27 and 9 km is used in the study. Evaluation of the deterministic skill score using the anomaly correlation coefficients shows that SINTEX-F2v has significant skill in precipitation forecasts confined to western regions of South Africa. Dynamical downscaling of SINTEX-F2v forecasts using the WRF Model is found to further improve the skill scores over South Africa. However, larger improvements in the skill scores are achieved when the WRF Model is forced by a form of bias-corrected SINTEX-F2v forecasts. The systematic biases in the original fields of the SITNEX-F2v forecasts are removed by superimposing the SINTEX-F2v 6-hourly anomalies over the ERA-Interim 6-hourly climatological fields. The WRF Model forced by the bias-corrected SINTEX-F2v shows significant skill in the forecast anomalies of precipitation over most parts of South Africa. Interestingly, the WRF Model runs with the bias correction did not help to improve the SINTEX-F2v forecast of 2-m air temperatures. Perhaps this is because of the large biases in the precipitation forecast by the WRF Model driven by the bias-corrected SINTEX-F2v. These results are important for potentially improving seasonal forecasts over South Africa.
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Rauch, Manuel, Jan Bliefernicht, Patrick Laux, Seyni Salack, Moussa Waongo, and Harald Kunstmann. "Seasonal Forecasting of the Onset of the Rainy Season in West Africa." Atmosphere 10, no. 9 (September 8, 2019): 528. http://dx.doi.org/10.3390/atmos10090528.
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Seasonal forecasts for monsoonal rainfall characteristics like the onset of the rainy seasons (ORS) are crucial for national weather services in semi-arid regions to better support decision-making in rain-fed agriculture. In this study an approach for seasonal forecasting of the ORS is proposed using precipitation information from a global seasonal ensemble prediction system. It consists of a quantile–quantile-transformation for eliminating systematic differences between ensemble forecasts and observations, a fuzzy-rule based method for estimating the ORS date and graphical methods for an improved visualization of probabilistic ORS forecasts. The performance of the approach is tested for several climate zones (the Sahel, Sudan and Guinean zone) in West Africa for a period of eleven years (2000 to 2010), using hindcasts from the Seasonal Forecasting System 4 of ECMWF. We indicated that seasonal ORS forecasts can be skillful for individual years and specific regions (e.g., the Guinean coasts), but also associated with large uncertainties. A spatial verification of the ORS fields emphasizes the importance of selecting appropriate performance measures (e.g., the anomaly correlation coefficient) to avoid an overestimation of the forecast skill. The graphical methods consist of several common formats used in seasonal forecasting and a new index-based method for a quicker interpretation of probabilistic ORS forecast. The new index can also be applied to other seasonal forecast variables, providing an important alternative to the common forecast formats used in seasonal forecasting. Moreover, the forecasting approach proposed in this study is not computationally intensive and is therefore operational applicable for forecasting centers in tropical and subtropical regions where computing power and bandwidth are often limited.
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Bischiniotis, Konstantinos, Bart van den Hurk, Brenden Jongman, Erin Coughlan de Perez, Ted Veldkamp, Hans de Moel, and Jeroen Aerts. "The influence of antecedent conditions on flood risk in sub-Saharan Africa." Natural Hazards and Earth System Sciences 18, no. 1 (January 19, 2018): 271–85. http://dx.doi.org/10.5194/nhess-18-271-2018.
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Abstract. Most flood early warning systems have predominantly focused on forecasting floods with lead times of hours or days. However, physical processes during longer timescales can also contribute to flood generation. In this study, we follow a pragmatic approach to analyse the hydro-meteorological pre-conditions of 501 historical damaging floods from 1980 to 2010 in sub-Saharan Africa. These are separated into (a) weather timescale (0–6 days) and (b) seasonal timescale conditions (up to 6 months) before the event. The 7-day precipitation preceding a flood event (PRE7) and the standardized precipitation evapotranspiration index (SPEI) are analysed for the two timescale domains, respectively. Results indicate that high PRE7 does not always generate floods by itself. Seasonal SPEIs, which are not directly correlated with PRE7, exhibit positive (wet) values prior to most flood events across different averaging times, indicating a relationship with flooding. This paper provides evidence that bringing together weather and seasonal conditions can lead to improved flood risk preparedness.
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Agyeman, Richard Yao Kuma, Thompson Annor, Benjamin Lamptey, Emmanuel Quansah, Jacob Agyekum, and Sampson Adu Tieku. "Optimal Physics Parameterization Scheme Combination of the Weather Research and Forecasting Model for Seasonal Precipitation Simulation over Ghana." Advances in Meteorology 2017 (2017): 1–15. http://dx.doi.org/10.1155/2017/7505321.
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Seasonal predictions of precipitation, among others, are important to help mitigate the effects of drought and floods on agriculture, hydropower generation, disasters, and many more. This work seeks to obtain a suitable combination of physics schemes of the Weather Research and Forecasting (WRF) model for seasonal precipitation simulation over Ghana. Using the ERA-Interim reanalysis as forcing data, simulation experiments spanning eight months (from April to November) were performed for two different years: a dry year (2001) and a wet year (2008). A double nested approach was used with the outer domain at 50 km resolution covering West Africa and the inner domain covering Ghana at 10 km resolution. The results suggest that the WRF model generally overestimated the observed precipitation by a mean value between 3% and 64% for both years. Most of the scheme combinations overestimated (underestimated) precipitation over coastal (northern) zones of Ghana for both years but estimated precipitation reasonably well over forest and transitional zones. On the whole, the combination of WRF Single-Moment 6-Class Microphysics Scheme, Grell-Devenyi Ensemble Cumulus Scheme, and Asymmetric Convective Model Planetary Boundary Layer Scheme simulated the best temporal pattern and temporal variability with the least relative bias for both years and therefore is recommended for Ghana.
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Mathivha, Fhumulani, Caston Sigauke, Hector Chikoore, and John Odiyo. "Short-Term and Medium-Term Drought Forecasting Using Generalized Additive Models." Sustainability 12, no. 10 (May 14, 2020): 4006. http://dx.doi.org/10.3390/su12104006.
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Forecasting extreme hydrological events is critical for drought risk and efficient water resource management in semi-arid environments that are prone to natural hazards. This study aimed at forecasting drought conditions in a semi-arid region in north-eastern South Africa. The Standardized Precipitation Evaporation Index (SPEI) was used as a drought-quantifying parameter. Data for SPEI formulation for eight weather stations were obtained from South Africa Weather Services. Forecasting of the SPEI was achieved by using Generalized Additive Models (GAMs) at 1, 6, and 12 month timescales. Time series decomposition was done to reduce time series complexities, and variable selection was done using Lasso. Mild drought conditions were found to be more prevalent in the study area compared to other drought categories. Four models were developed to forecast drought in the Luvuvhu River Catchment (i.e., GAM, Ensemble Empirical Mode Decomposition (EEMD)-GAM, EEMD-Autoregressive Integrated Moving Average (ARIMA)-GAM, and Forecast Quantile Regression Averaging (fQRA)). At the first two timescales, fQRA forecasted the test data better than the other models, while GAMs were best at the 12 month timescale. Root Mean Square Error values of 0.0599, 0.2609, and 0.1809 were shown by fQRA and GAM at the 1, 6, and 12 month timescales, respectively. The study findings demonstrated the strength of GAMs in short- and medium-term drought forecasting.
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Verdin, James, Chris Funk, Gabriel Senay, and Richard Choularton. "Climate science and famine early warning." Philosophical Transactions of the Royal Society B: Biological Sciences 360, no. 1463 (October 24, 2005): 2155–68. http://dx.doi.org/10.1098/rstb.2005.1754.
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Food security assessment in sub-Saharan Africa requires simultaneous consideration of multiple socio-economic and environmental variables. Early identification of populations at risk enables timely and appropriate action. Since large and widely dispersed populations depend on rainfed agriculture and pastoralism, climate monitoring and forecasting are important inputs to food security analysis. Satellite rainfall estimates (RFE) fill in gaps in station observations, and serve as input to drought index maps and crop water balance models. Gridded rainfall time-series give historical context, and provide a basis for quantitative interpretation of seasonal precipitation forecasts. RFE are also used to characterize flood hazards, in both simple indices and stream flow models. In the future, many African countries are likely to see negative impacts on subsistence agriculture due to the effects of global warming. Increased climate variability is forecast, with more frequent extreme events. Ethiopia requires special attention. Already facing a food security emergency, troubling persistent dryness has been observed in some areas, associated with a positive trend in Indian Ocean sea surface temperatures. Increased African capacity for rainfall observation, forecasting, data management and modelling applications is urgently needed. Managing climate change and increased climate variability require these fundamental technical capacities if creative coping strategies are to be devised.
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Pirret, Jennifer S. R., Joseph D. Daron, Philip E. Bett, Nicolas Fournier, and Andre Kamga Foamouhoue. "Assessing the Skill and Reliability of Seasonal Climate Forecasts in Sahelian West Africa." Weather and Forecasting 35, no. 3 (May 6, 2020): 1035–50. http://dx.doi.org/10.1175/waf-d-19-0168.1.
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Abstract Seasonal climate forecasts have the potential to support planning decisions and provide advanced warning to government, industry, and communities to help reduce the impacts of adverse climatic conditions. Assessing the reliability of seasonal forecasts, generated using different models and methods, is essential to ensure their appropriate interpretation and use. Here we assess the reliability of forecasts for seasonal total precipitation in Sahelian West Africa, a region of high year-to-year climate variability. Through digitizing forecasts issued from the regional climate outlook forum in West Africa known as Prévisions Climatiques Saisonnières en Afrique Soudano-Sahélienne (PRESASS), we assess their reliability by comparing them to the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) project observational data over the past 20 years. The PRESASS forecasts show positive skill and reliability, but a bias toward lower forecast probabilities in the below-normal precipitation category. In addition, we assess the reliability of seasonal precipitation forecasts for the same region using available global dynamical forecast models. We find all models have positive skill and reliability, but this varies geographically. On average, NCEP’s CFS and ECMWF’s SEAS5 systems show greater skill and reliability than the Met Office’s GloSea5, and in turn than Météo-France’s Sys5, but one key caveat is that model performance might depend on the meteorological situation. We discuss the potential for improving use of dynamical model forecasts in the regional climate outlook forums, to improve the reliability of seasonal forecasts in the region and the objectivity of the seasonal forecasting process used in the PRESASS regional climate outlook forum.
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Achugbu, Ifeanyi C., Jimy Dudhia, Ayorinde A. Olufayo, Ifeoluwa A. Balogun, Elijah A. Adefisan, and Imoleayo E. Gbode. "Assessment of WRF Land Surface Model Performance over West Africa." Advances in Meteorology 2020 (August 7, 2020): 1–30. http://dx.doi.org/10.1155/2020/6205308.
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Simulations with four land surface models (LSMs) (i.e., Noah, Noah-MP, Noah-MP with ground water GW option, and CLM4) using the Weather Research and Forecasting (WRF) model at 12 km horizontal grid resolution were carried out as two sets for 3 months (December–February 2011/2012 and July–September 2012) over West Africa. The objective is to assess the performance of WRF LSMs in simulating meteorological parameters over West Africa. The model precipitation was assessed against TRMM while surface temperature was compared with the ERA-Interim reanalysis dataset. Results show that the LSMs performed differently for different variables in different land-surface conditions. Based on precipitation and temperature, Noah-MP GW is overall the best for all the variables and seasons in combination, while Noah came last. Specifically, Noah-MP GW performed best for JAS temperature and precipitation; CLM4 was the best in simulating DJF precipitation, while Noah was the best in simulating DJF temperature. Noah-MP GW has the wettest Sahel while Noah has the driest one. The strength of the Tropical Easterly Jet (TEJ) is strongest in Noah-MP GW and Noah-MP compared with that in CLM4 and Noah. The core of the African Easterly Jet (AEJ) lies around 12°N in Noah and 15°N for Noah-MP GW. Noah-MP GW and Noah-MP simulations have stronger influx of moisture advection from the southwesterly monsoonal wind than the CLM4 and Noah with Noah showing the least influx. Also, analysis of the evaporative fraction shows sharp gradient for Noah-MP GW and Noah-MP with wetter Sahel further to the north and further to the south for Noah. Noah-MP-GW has the highest amount of soil moisture, while the CLM4 has the least for both the JAS and DJF seasons. The CLM4 has the highest LH for both DJF and JAS seasons but however has the least SH for both DJF and JAS seasons. The principal difference between the LSMs is in the vegetation representation, description, and parameterization of the soil water column; hence, improvement is recommended in this regard.
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Figueroa, Silvio N., José P. Bonatti, Paulo Y. Kubota, Georg A. Grell, Hugh Morrison, Saulo R. M. Barros, Julio P. R. Fernandez, et al. "The Brazilian Global Atmospheric Model (BAM): Performance for Tropical Rainfall Forecasting and Sensitivity to Convective Scheme and Horizontal Resolution." Weather and Forecasting 31, no. 5 (September 21, 2016): 1547–72. http://dx.doi.org/10.1175/waf-d-16-0062.1.
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Abstract This article describes the main features of the Brazilian Global Atmospheric Model (BAM), analyses of its performance for tropical rainfall forecasting, and its sensitivity to convective scheme and horizontal resolution. BAM is the new global atmospheric model of the Center for Weather Forecasting and Climate Research [Centro de Previsão de Tempo e Estudos Climáticos (CPTEC)], which includes a new dynamical core and state-of-the-art parameterization schemes. BAM’s dynamical core incorporates a monotonic two-time-level semi-Lagrangian scheme, which is carried out completely on the model grid for the tridimensional transport of moisture, microphysical prognostic variables, and tracers. The performance of the quantitative precipitation forecasts (QPFs) from two convective schemes, the Grell–Dévényi (GD) scheme and its modified version (GDM), and two different horizontal resolutions are evaluated against the daily TRMM Multisatellite Precipitation Analysis over different tropical regions. Three main results are 1) the QPF skill was improved substantially with GDM in comparison to GD; 2) the increase in the horizontal resolution without any ad hoc tuning improves the variance of precipitation over continents with complex orography, such as Africa and South America, whereas over oceans there are no significant differences; and 3) the systematic errors (dry or wet biases) remain virtually unchanged for 5-day forecasts. Despite improvements in the tropical precipitation forecasts, especially over southeastern Brazil, dry biases over the Amazon and La Plata remain in BAM. Improving the precipitation forecasts over these regions remains a challenge for the future development of the model to be used not only for numerical weather prediction over South America but also for global climate simulations.
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Paeth, Heiko, Robin Girmes, Gunter Menz, and Andreas Hense. "Improving Seasonal Forecasting in the Low Latitudes." Monthly Weather Review 134, no. 7 (July 1, 2006): 1859–79. http://dx.doi.org/10.1175/mwr3149.1.
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Abstract Seasonal forecast of climate anomalies holds the prospect of improving agricultural planning and food security, particularly in the low latitudes where rainfall represents a limiting factor in agrarian production. Present-day methods are usually based on simulated precipitation as a predictor for the forthcoming rainy season. However, climate models often have low skill in predicting rainfall due to the uncertainties in physical parameterization. Here, the authors present an extended statistical model approach using three-dimensional dynamical variables from climate model experiments like temperature, geopotential height, wind components, and atmospheric moisture. A cross-validated multiple regression analysis is applied in order to fit the model output to observed seasonal precipitation during the twentieth century. This model output statistics (MOS) system is evaluated in various regions of the globe with potential predictability and compared with the conventional superensemble approach, which refers to the same variable for predictand and predictors. It is found that predictability is highest in the low latitudes. Given the remarkable spatial teleconnections in the Tropics, a large number of dynamical predictors can be determined for each region of interest. To avoid overfitting in the regression model an EOF analysis is carried out, combining predictors that are largely in-phase with each other. In addition, a bootstrap approach is used to evaluate the predictability of the statistical model. As measured by different skill scores, the MOS system reaches much higher explained variance than the superensemble approach in all considered regions. In some cases, predictability only occurs if dynamical predictor variables are taken into account, whereas the superensemble forecast fails. The best results are found for the tropical Pacific sector, the Nordeste region, Central America, and tropical Africa, amounting to 50% to 80% of total interannual variability. In general, the statistical relationships between the leading predictors and the predictand are physically interpretable and basically highlight the interplay between regional climate anomalies and the omnipresent role of El Niño–Southern Oscillation in the tropical climate system.
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Ummenhofer, Caroline C., Alexander Sen Gupta, Matthew H. England, and Chris J. C. Reason. "Contributions of Indian Ocean Sea Surface Temperatures to Enhanced East African Rainfall." Journal of Climate 22, no. 4 (February 15, 2009): 993–1013. http://dx.doi.org/10.1175/2008jcli2493.1.
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Abstract Links between extreme wet conditions over East Africa and Indian Ocean sea surface temperatures (SST) are investigated during the core of the so-called short rain season in October–November. During periods of enhanced East African rainfall, Indian Ocean SST anomalies reminiscent of a tropical Indian Ocean dipole (IOD) event are observed. Ensemble simulations with an atmospheric general circulation model are used to understand the relative effect of local and large-scale Indian Ocean SST anomalies on above-average East African precipitation. The importance of the various tropical and subtropical IOD SST poles, both individually and in combination, is quantified. In the simulations, enhanced East African “short rains” are predominantly driven by the local warm SST anomalies in the western equatorial Indian Ocean, while the eastern cold pole of the tropical IOD is of lesser importance. The changed East African rainfall distribution can be explained by a reorganization of the atmospheric circulation induced by the SST anomalies. A reduction in sea level pressure over the western half of the Indian Ocean and converging wind anomalies over East Africa lead to moisture convergence and increased convective activity over the region. The pattern of large-scale circulation changes over the tropical Indian Ocean and adjacent landmasses is consistent with an anomalous strengthening of the Walker cell. The seasonal cycle of various indices related to the SST and the atmospheric circulation in the equatorial Indian Ocean are examined to assess their potential usefulness for seasonal forecasting.
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MacLeod, David, and Cyril Caminade. "The Moderate Impact of the 2015 El Niño over East Africa and Its Representation in Seasonal Reforecasts." Journal of Climate 32, no. 22 (October 31, 2019): 7989–8001. http://dx.doi.org/10.1175/jcli-d-19-0201.1.
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Abstract El Niño–Southern Oscillation (ENSO) has large socioeconomic impacts worldwide. The positive phase of ENSO, El Niño, has been linked to intense rainfall over East Africa during the short rains season (October–December). However, we show here that during the extremely strong 2015 El Niño the precipitation anomaly over most of East Africa during the short rains season was less intense than experienced during previous El Niños, linked to less intense easterlies over the Indian Ocean. This moderate impact was not indicated by reforecasts from the ECMWF operational seasonal forecasting system, SEAS5, which instead forecast large probabilities of an extreme wet signal, with stronger easterly anomalies over the surface of the Indian Ocean and a colder eastern Indian Ocean/western Pacific than was observed. To confirm the relationship of the eastern Indian Ocean to East African rainfall in the forecast for 2015, atmospheric relaxation experiments are carried out that constrain the east Indian Ocean lower troposphere to reanalysis. By doing so the strong wet forecast signal is reduced. These results raise the possibility that link between ENSO and Indian Ocean dipole events is too strong in the ECMWF dynamical seasonal forecast system and that model predictions for the East African short rains rainfall during strong El Niño events may have a bias toward high probabilities of wet conditions.
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Tompkins, Adrian M., and Laura Feudale. "Seasonal Ensemble Predictions of West African Monsoon Precipitation in the ECMWF System 3 with a Focus on the AMMA Special Observing Period in 2006." Weather and Forecasting 25, no. 2 (April 1, 2010): 768–88. http://dx.doi.org/10.1175/2009waf2222236.1.
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Abstract The West Africa monsoon precipitation of the ECMWF operational Seasonal Forecast System (SYS3) is evaluated at a lead time of 2–4 months in a 49-yr hindcast dataset, with special attention paid to the African Monsoon Multidisciplinary Analysis (AMMA) special observation period during 2006. In both the climatology and the year 2006 the SYS3 reproduces the progression of the West Africa monsoon but with a number of differences, most notably a southerly shift of the precipitation in the main monsoon months of July and August and the lack of preonset rainfall suppression and sudden onset jump. The model skill at predicting summer monsoon rainfall anomalies has increased in recent years indicating improvements in the ocean analysis since the 1990s. Examination of other model fields shows a widespread warm sea surface temperature (SST) bias exceeding 1.5 K in the Gulf of Guinea throughout the monsoon months in addition to a cold bias in the North Atlantic, which would both tend to enhance rainfall over the Gulf of Guinea coast at the expense of the monsoon rainfall over the Sahel. Seasonal forecasts were repeated for 2006 using the same release of the atmospheric forecast model forced by observed SSTs, and the monsoon rainfall reverts to its observed position, indicating the importance of the SST biases. A lack of stratocumulus off the west coast of Africa in SYS3 was hypothesized as a possible cause of the systematic rain and SST biases. Two more sets of ensembles were thus conducted with atmospheric model upgrades designed to tackle radiation, deep convection, and turbulence deficiencies. While these enhancements improve the simulation of stratocumulus significantly, it is found that the improvement in the warm SST bias is limited in scope to the southern cold tongue region. In contrast, the changes to the representation of convection cause an increase in surface downwelling shortwave radiation that, combined with latent heat flux changes associated with the wind stress field, increases the SST warm bias on and to the north of the equator. Thus, while the precipitation shortfall in the Sahel is reduced with the new physics, the overestimated rainfall of SYS3 in the coastal region is further enhanced, degrading the model systematic errors overall in the West Africa region. Finally, the difference in the systematic biases between the coupled and uncoupled systems was noted to be an impediment to the development of seamless forecasting systems.
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Schlueter, Andreas, Andreas H. Fink, Peter Knippertz, and Peter Vogel. "A Systematic Comparison of Tropical Waves over Northern Africa. Part I: Influence on Rainfall." Journal of Climate 32, no. 5 (February 5, 2019): 1501–23. http://dx.doi.org/10.1175/jcli-d-18-0173.1.
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Abstract Low-latitude rainfall variability on the daily to intraseasonal time scale is often related to tropical waves, including convectively coupled equatorial waves, the Madden–Julian oscillation (MJO), and tropical disturbances (TDs). Despite the importance of rainfall variability for vulnerable societies in tropical Africa, the relative influence of tropical waves for this region is largely unknown. This article presents the first systematic comparison of the impact of six wave types on precipitation over northern tropical Africa during the transition and full monsoon seasons, using two satellite products and a dense rain gauge network. Composites of rainfall anomalies in the different datasets show comparable modulation intensities in the West Sahel and at the Guinea Coast, varying from less than 2 to above 7 mm day−1 depending on the wave type. African easterly waves (AEWs) and Kelvin waves dominate the 3-hourly to daily time scale and explain 10%–30% locally. On longer time scales (7–20 days), only the MJO and equatorial Rossby (ER) waves remain as modulating factors and explain about up to one-third of rainfall variability. Eastward inertio-gravity waves and mixed Rossby–gravity (MRG) waves are comparatively unimportant. An analysis of wave superposition shows that low-frequency waves (MJO, ER) in their wet phase amplify the activity of high-frequency waves (TD, MRG) and suppress them in the dry phase. The results stress that more attention should be paid to tropical waves when forecasting rainfall over northern tropical Africa.
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Glotfelty, Timothy, Diana Ramírez-Mejía, Jared Bowden, Adrian Ghilardi, and J. Jason West. "Limitations of WRF land surface models for simulating land use and land cover change in Sub-Saharan Africa and development of an improved model (CLM-AF v. 1.0)." Geoscientific Model Development 14, no. 6 (June 3, 2021): 3215–49. http://dx.doi.org/10.5194/gmd-14-3215-2021.
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Abstract. Land use and land cover change (LULCC) impacts local and regional climates through various biogeophysical processes. Accurate representation of land surface parameters in land surface models (LSMs) is essential to accurately predict these LULCC-induced climate signals. In this work, we test the applicability of the default Noah, Noah-MP, and Community Land Model (CLM) LSMs in the Weather Research and Forecasting (WRF) model over Sub-Saharan Africa. We find that the default WRF LSMs do not accurately represent surface albedo, leaf area index, and surface roughness in this region due to various flawed assumptions, including the treatment of the MODIS woody savanna land use and land cover (LULC) category as closed shrubland. Consequently, we developed a WRF CLM version with more accurate African land surface parameters (CLM-AF), designed such that it can be used to evaluate the influence of LULCC. We evaluate meteorological performance for the default LSMs and CLM-AF against observational datasets, gridded products, and satellite estimates. Further, we conduct LULCC experiments with each LSM to determine if differences in land surface parameters impact the LULCC-induced climate responses. Despite clear deficiencies in surface parameters, all LSMs reasonably capture the spatial pattern and magnitude of near-surface temperature and precipitation. However, in the LULCC experiments, inaccuracies in the default LSMs result in illogical localized temperature and precipitation changes. Differences in thermal changes between Noah-MP and CLM-AF indicate that the temperature impacts from LULCC are dependent on the sensitivity of evapotranspiration to LULCC in Sub-Saharan Africa. Errors in land surface parameters indicate that the default WRF LSMs considered are not suitable for LULCC experiments in tropical or Southern Hemisphere regions and that proficient meteorological model performance can mask these issues. We find CLM-AF to be suitable for use in Sub-Saharan Africa LULCC studies, but more work is needed by the WRF community to improve its applicability to other tropical and Southern Hemisphere climates.
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Salakpi, Edward E., Peter D. Hurley, James M. Muthoka, Adam B. Barrett, Andrew Bowell, Seb Oliver, and Pedram Rowhani. "Forecasting vegetation condition with a Bayesian auto-regressive distributed lags (BARDL) model." Natural Hazards and Earth System Sciences 22, no. 8 (August 23, 2022): 2703–23. http://dx.doi.org/10.5194/nhess-22-2703-2022.
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Abstract. Droughts form a large part of climate- or weather-related disasters reported globally. In Africa, pastoralists living in the arid and semi-arid lands (ASALs) are the worse affected. Prolonged dry spells that cause vegetation stress in these regions have resulted in the loss of income and livelihoods. To curb this, global initiatives like the Paris Agreement and the United Nations recognised the need to establish early warning systems (EWSs) to save lives and livelihoods. Existing EWSs use a combination of satellite earth observation (EO)-based biophysical indicators like the vegetation condition index (VCI) and socio-economic factors to measure and monitor droughts. Most of these EWSs rely on expert knowledge in estimating upcoming drought conditions without using forecast models. Recent research has shown that the use of robust algorithms like auto-regression, Gaussian processes, and artificial neural networks can provide very skilled models for forecasting vegetation condition at short- to medium-range lead times. However, to enable preparedness for early action, forecasts with a longer lead time are needed. In a previous paper, a Gaussian process model and an auto-regression model were used to forecast VCI in pastoral communities in Kenya. The objective of this research was to build on this work by developing an improved model that forecasts vegetation conditions at longer lead times. The premise of this research was that vegetation condition is controlled by factors like precipitation and soil moisture; thus, we used a Bayesian auto-regressive distributed lag (BARDL) modelling approach, which enabled us to include the effects of lagged information from precipitation and soil moisture to improve VCI forecasting. The results showed a ∼2-week gain in the forecast range compared to the univariate auto-regression model used as a baseline. The R2 scores for the Bayesian ARDL model were 0.94, 0.85, and 0.74, compared to the auto-regression model's R2 of 0.88, 0.77, and 0.65 for 6-, 8-, and 10-week lead time, respectively.
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Fitzpatrick, Rory G. J., Caroline L. Bain, Peter Knippertz, John H. Marsham, and Douglas J. Parker. "The West African Monsoon Onset: A Concise Comparison of Definitions." Journal of Climate 28, no. 22 (November 15, 2015): 8673–94. http://dx.doi.org/10.1175/jcli-d-15-0265.1.
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Abstract The onset of the West African monsoon (WAM) marks a vital time for local and regional stakeholders. While the seasonal progression of monsoon winds and the related migration of precipitation from the Guinea Coast toward the Sudan/Sahel is apparent, there exist contrasting man-made definitions of what the WAM onset means. Broadly speaking, onset can be analyzed regionally, locally, or over a designated intermediate scale. There are at least 18 distinct definitions of the WAM onset in publication, with little work done on comparing observed onset from different definitions or comparing onset realizations across different datasets and resolutions. Here, nine definitions have been calculated using multiple datasets of different metrics at different resolutions. It is found that mean regional onset dates are consistent across multiple datasets and different definitions. There is low interannual variability in regional onset, suggesting that regional seasonal forecasting of the onset provides few benefits over climatology. In contrast, local onsets show high spatial, interannual, and interdefinition variability. Furthermore, it is found that there is little correlation between local onset dates and regional onset dates across West Africa, implying a disharmony between regional measures of onset and the experience on a local scale. The results of this study show that evaluation of seasonal monsoon onset forecasts is far from straightforward. Given a seasonal forecasting model, it is possible to simultaneously have a good and a bad prediction of monsoon onset simply through selection of the onset definition and observational dataset used for comparison.
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Jones, Charles, Jon Gottschalck, Leila M. V. Carvalho, and Wayne Higgins. "Influence of the Madden–Julian Oscillation on Forecasts of Extreme Precipitation in the Contiguous United States." Monthly Weather Review 139, no. 2 (February 1, 2011): 332–50. http://dx.doi.org/10.1175/2010mwr3512.1.
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Abstract Extreme precipitation events are among the most devastating weather phenomena since they are frequently accompanied by loss of life and property. This study uses reforecasts of the NCEP Climate Forecast System (CFS.v1) to evaluate the skill of nonprobabilistic and probabilistic forecasts of extreme precipitation in the contiguous United States (CONUS) during boreal winter for lead times up to two weeks. The CFS model realistically simulates the spatial patterns of extreme precipitation events over the CONUS, although the magnitudes of the extremes in the model are much larger than in the observations. Heidke skill scores (HSS) for forecasts of extreme precipitation at the 75th and 90th percentiles showed that the CFS model has good skill at week 1 and modest skill at week 2. Forecast skill is usually higher when the Madden–Julian oscillation (MJO) is active and has enhanced convection occurring over the Western Hemisphere, Africa, and/or the western Indian Ocean than in quiescent periods. HSS greater than 0.1 extends to lead times of up to two weeks in these situations. Approximately 10%–30% of the CONUS has HSS greater than 0.1 at lead times of 1–14 days when the MJO is active. Probabilistic forecasts for extreme precipitation events at the 75th percentile show improvements over climatology of 0%–40% at 1-day lead and 0%–5% at 7-day leads. The CFS has better skill in forecasting severe extremes (i.e., events exceeding the 90th percentile) at longer leads than moderate extremes (75th percentile). Improvements over climatology between 10% and 30% at leads of 3 days are observed over several areas across the CONUS—especially in California and in the Midwest.
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Cloux, Sara, Daniel Garaboa-Paz, Damián Insua-Costa, Gonzalo Miguez-Macho, and Vicente Pérez-Muñuzuri. "Extreme precipitation events in the Mediterranean area: contrasting two different models for moisture source identification." Hydrology and Earth System Sciences 25, no. 12 (December 20, 2021): 6465–77. http://dx.doi.org/10.5194/hess-25-6465-2021.
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Abstract. Concern about heavy precipitation events has increasingly grown in the last years in southern Europe, especially in the Mediterranean region. These occasional episodes can result in more than 200 mm of rainfall in less than 24 h, producing flash floods with very high social and economic losses. To better understand these phenomena, a correct identification of the origin of moisture must be found. However, the contribution of the different sources is very difficult to estimate from observational data; thus numerical models are usually employed to this end. Here, we present a comparison between two methodologies for the quantification of the moisture sources in two flooding episodes that occurred during October and November 1982 in the western Mediterranean area. A previous study, using the online Eulerian Weather Research and Forecasting (WRF) Model with water vapor tracer (WRF-WVT) model, determined the contributions to precipitation from moisture evaporated over four different sources: (1) the western Mediterranean, (2) the central Mediterranean, (3) the North Atlantic Ocean and (4) the tropical and subtropical Atlantic and tropical Africa. In this work we use the offline Lagrangian FLEXPART-WRF model to quantify the role played by these same sources. Considering the results provided by WRF-WVT as “ground truth”, we validated the performance of the FLEXPART-WRF. Results show that this Lagrangian method has an acceptable skill in identifying local (western Mediterranean) and medium-distance (central Mediterranean and North Atlantic) sources. However, remote moisture sources, like tropical and subtropical areas, are underestimated by it. Notably, for the October event, the tropical and subtropical area reported a relative contribution 6 times lower than with the WRF-WVT. In contrast, FLEXPART-WRF overestimates the contribution of some sources, especially from North Africa. These over- and underestimates should be taken into account by other authors when drawing conclusions from this widely used Lagrangian offline analysis.
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Noble, Erik, Leonard M. Druyan, and Matthew Fulakeza. "The Sensitivity of WRF Daily Summertime Simulations over West Africa to Alternative Parameterizations. Part I: African Wave Circulation." Monthly Weather Review 142, no. 4 (March 27, 2014): 1588–608. http://dx.doi.org/10.1175/mwr-d-13-00194.1.
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Abstract The performance of the NCAR Weather Research and Forecasting Model (WRF) as a West African regional-atmospheric model is evaluated. The study tests the sensitivity of WRF-simulated vorticity maxima associated with African easterly waves to 64 combinations of alternative parameterizations in a series of simulations in September. In all, 104 simulations of 12-day duration during 11 consecutive years are examined. The 64 combinations combine WRF parameterizations of cumulus convection, radiation transfer, surface hydrology, and PBL physics. Simulated daily and mean circulation results are validated against NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) and NCEP/Department of Energy Global Reanalysis 2. Precipitation is considered in a second part of this two-part paper. A wide range of 700-hPa vorticity validation scores demonstrates the influence of alternative parameterizations. The best WRF performers achieve correlations against reanalysis of 0.40–0.60 and realistic amplitudes of spatiotemporal variability for the 2006 focus year while a parallel-benchmark simulation by the NASA Regional Model-3 (RM3) achieves higher correlations, but less realistic spatiotemporal variability. The largest favorable impact on WRF-vorticity validation is achieved by selecting the Grell–Devenyi cumulus convection scheme, resulting in higher correlations against reanalysis than simulations using the Kain–Fritch convection. Other parameterizations have less-obvious impact, although WRF configurations incorporating one surface model and PBL scheme consistently performed poorly. A comparison of reanalysis circulation against two NASA radiosonde stations confirms that both reanalyses represent observations well enough to validate the WRF results. Validation statistics for optimized WRF configurations simulating the parallel period during 10 additional years are less favorable than for 2006.
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Schwitalla, Thomas, Kirsten Warrach-Sagi, Volker Wulfmeyer, and Michael Resch. "Near-global-scale high-resolution seasonal simulations with WRF-Noah-MP v.3.8.1." Geoscientific Model Development 13, no. 4 (April 21, 2020): 1959–74. http://dx.doi.org/10.5194/gmd-13-1959-2020.
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Abstract. The added value of global simulations on the convection-permitting (CP) scale is a subject of extensive research in the earth system science community. An increase in predictive skill can be expected due to advanced representations of feedbacks and teleconnections in the ocean–land–atmosphere system. However, the proof of this hypothesis by corresponding simulations is computationally and scientifically extremely demanding. We present a novel latitude-belt simulation from 57∘ S to 65∘ N using the Weather Research and Forecasting (WRF)-Noah-MP model system with a grid increment of 0.03∘ over a period of 5 months forced by sea surface temperature observations. In comparison to a latitude-belt simulation with 45 km resolution, at CP resolution the representation of the spatial-temporal scales and the organization of tropical convection are improved considerably. The teleconnection pattern is very close to that of the operational European Centre for Medium Range Weather Forecasting (ECMWF) analyses. The CP simulation is associated with an improvement of the precipitation forecast over South America, Africa, and the Indian Ocean and considerably improves the representation of cloud coverage along the tropics. Our results demonstrate a significant added value of future simulations on the CP scale up to the seasonal forecast range.
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Heinzeller, Dominikus, Diarra Dieng, Gerhard Smiatek, Christiana Olusegun, Cornelia Klein, Ilse Hamann, Seyni Salack, Jan Bliefernicht, and Harald Kunstmann. "The WASCAL high-resolution regional climate simulation ensemble for West Africa: concept, dissemination and assessment." Earth System Science Data 10, no. 2 (April 23, 2018): 815–35. http://dx.doi.org/10.5194/essd-10-815-2018.
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Abstract. Climate change and constant population growth pose severe challenges to 21st century rural Africa. Within the framework of the West African Science Service Center on Climate Change and Adapted Land Use (WASCAL), an ensemble of high-resolution regional climate change scenarios for the greater West African region is provided to support the development of effective adaptation and mitigation measures. This contribution presents the overall concept of the WASCAL regional climate simulations, as well as detailed information on the experimental design, and provides information on the format and dissemination of the available data. All data are made available to the public at the CERA long-term archive of the German Climate Computing Center (DKRZ) with a subset available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.pangaea.de/10.1594/PANGAEA.880512). A brief assessment of the data are presented to provide guidance for future users. Regional climate projections are generated at high (12 km) and intermediate (60 km) resolution using the Weather Research and Forecasting Model (WRF). The simulations cover the validation period 1980–2010 and the two future periods 2020–2050 and 2070–2100. A brief comparison to observations and two climate change scenarios from the Coordinated Regional Downscaling Experiment (CORDEX) initiative is presented to provide guidance on the data set to future users and to assess their climate change signal. Under the RCP4.5 (Representative Concentration Pathway 4.5) scenario, the results suggest an increase in temperature by 1.5 ∘C at the coast of Guinea and by up to 3 ∘C in the northern Sahel by the end of the 21st century, in line with existing climate projections for the region. They also project an increase in precipitation by up to 300 mm per year along the coast of Guinea, by up to 150 mm per year in the Soudano region adjacent in the north and almost no change in precipitation in the Sahel. This stands in contrast to existing regional climate projections, which predict increasingly drier conditions. The high spatial and temporal resolution of the data, the extensive list of output variables, the large computational domain and the long time periods covered make this data set a unique resource for follow-up analyses and impact modelling studies over the greater West African region. The comprehensive documentation and standardisation of the data facilitate and encourage their use within and outside of the WASCAL community.
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Kuik, F., A. Lauer, J. P. Beukes, P. G. Van Zyl, M. Josipovic, V. Vakkari, L. Laakso, and G. T. Feig. "The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study." Atmospheric Chemistry and Physics Discussions 15, no. 5 (March 10, 2015): 7309–63. http://dx.doi.org/10.5194/acpd-15-7309-2015.
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Abstract. South Africa has one of the largest industrialized economies in Africa. Emissions of air pollutants are particularly high in the Johannesburg-Pretoria metropolitan area, the Mpumalanga Highveld and the Vaal Triangle, resulting in local air pollution. This study presents and evaluates a setup for conducting modeling experiments over southern Africa with the Weather Research and Forecasting model including chemistry and aerosols (WRF-Chem), and analyzes the contribution of anthropogenic emissions to the total black carbon (BC) concentrations from September to December 2010. The modeled BC concentrations are compared with measurements obtained at the Welgegund station situated ca. 100 km southwest of Johannesburg. An evaluation of WRF-Chem with observational data from ground-based measurement stations, radiosondes, and satellites shows that the meteorology is modeled mostly reasonably well, but precipitation amounts are widely overestimated and the onset of the wet season is modeled approximately 1 month too early in 2010. Modeled daily mean BC concentrations show a good temporal correlation with measurements, but the total BC concentration is underestimated in the model by up to 50%. Sensitivity studies with anthropogenic emissions of BC and co-emitted species turned off show that anthropogenic sources can contribute up to 100% to BC concentrations in the industrialized and urban areas, and anthropogenic BC and co-emitted species together up to 60% to PM1 levels. Particularly the co-emitted species contribute significantly to the aerosol optical depth (AOD). Furthermore, in areas of large scale biomass burning atmospheric heating rates are increased through absorption by BC up to about the 600 hPa level.
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Kuik, F., A. Lauer, J. P. Beukes, P. G. Van Zyl, M. Josipovic, V. Vakkari, L. Laakso, and G. T. Feig. "The anthropogenic contribution to atmospheric black carbon concentrations in southern Africa: a WRF-Chem modeling study." Atmospheric Chemistry and Physics 15, no. 15 (August 12, 2015): 8809–30. http://dx.doi.org/10.5194/acp-15-8809-2015.
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Abstract. South Africa has one of the largest industrialized economies in Africa. Emissions of air pollutants are particularly high in the Johannesburg-Pretoria metropolitan area, the Mpumalanga Highveld and the Vaal Triangle, resulting in local air pollution. This study presents and evaluates a setup for conducting modeling experiments over southern Africa with the Weather Research and Forecasting model including chemistry and aerosols (WRF-Chem), and analyzes the contribution of anthropogenic emissions to the total black carbon (BC) concentrations from September to December 2010. The modeled BC concentrations are compared with measurements obtained at the Welgegund station situated ca. 100 km southwest of Johannesburg. An evaluation of WRF-Chem with observational data from ground-based measurement stations, radiosondes, and satellites shows that the meteorology is modeled mostly reasonably well, but precipitation amounts are widely overestimated and the onset of the wet season is modeled approximately 1 month too early in 2010. Modeled daily mean BC concentrations show a temporal correlation of 0.66 with measurements, but the total BC concentration is underestimated in the model by up to 50 %. Sensitivity studies with anthropogenic emissions of BC and co-emitted species turned off show that anthropogenic sources can contribute up to 100 % to BC concentrations in the industrialized and urban areas, and anthropogenic BC and co-emitted species together can contribute up to 60 % to PM1 levels. Particularly the co-emitted species contribute significantly to the aerosol optical depth (AOD). Furthermore, in areas of large-scale biomass-burning atmospheric heating rates are increased through absorption by BC up to an altitude of about 600hPa.
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Al-Najjar, Hassan, Gokmen Ceribasi, Emrah Dogan, Mazen Abualtayef, Khalid Qahman, and Ahmed Shaqfa. "Stochastic time-series models for drought assessment in the Gaza Strip (Palestine)." Journal of Water and Climate Change 11, S1 (October 19, 2020): 85–114. http://dx.doi.org/10.2166/wcc.2020.330.
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Abstract The Eastern Mediterranean region of the Middle East and North Africa (MENA) is experiencing patterns of major drought due to the effects of rising temperatures and falling precipitation levels. The multiscale drought evaluation Standardized Precipitation Evapotranspiration Index (SPEI) reveals evolving and severe drought from North Africa and the Sinai desert toward the Middle East. While there has been a period without drought between 1970 and 1990, the severity and frequency of drought increased considerably after 1990. Current drought conditions in the Eastern Mediterranean region of MENA are moderate to severe with a 60–100% likelihood of occurrence, according to time parameters. The Gaza Strip is especially vulnerable to the consequences of increasing drought because it is situated in the vicinity of the Sinai Desert; therefore, a downscaled study of drought in the region is essential to implement mitigation measures for the sustainable management and planning of coastal aquifer and agricultural activities in the Gaza Strip. Considerable availability of precipitation time series from various meteorological stations helped provide a local drought study for the Gaza Strip, in accordance with the Standardized Precipitation Index (SPI). The stochastic time-series model of (4,0,1) (5,1,1)12 shows a robust simulator for modeling and forecasting the future trend of precipitation at the nine meteorological stations. In terms of correlation accuracy, the model achieves a correlation (r) of approximately 93–97% in the calibration range and a correlation (r) of about 92–99% in the validation range. In terms of measuring the difference between the values, the root mean squared error (RMSE) of the model results shows that the RMSE was between 7–21 in the calibration range and 11–21 in the validation range. The model reveals a slightly stable trend in precipitation patterns at the northern meteorological stations of Beit Hanon, Beit Lahia, Shati, and Remal. However, declining precipitation tendency was recorded at the southern meteorological stations of Mughraka, Nussirat, Beir Al-Balah, Khanyounis, and Rafah. The SPI-based drought assessment implies that the precipitation annual threshold levels at SPI = 0 drop territorially from 474 mm in the north to about 250 mm in the south of the Gaza Strip. In this study, a representative 12-month local scale SPI12 at an annual precipitation threshold level of 370 mm was formulated to address the drought conditions in the Gaza Strip. Standing on the outputs of the local SPI12 scale might signify that the region of the Gaza Strip risks drought status with an incidence likelihood varying from 8% in the north to 100% in the south. Regular drought is prevalent in the northern governorates, but the hazards of extreme and severe drought are high in the southern areas with an incidence risk of about 83%. Sequentially, southern governorates of Rafah and Khanyounis experience chronic annual drought, while the return period of drought is reported to be every 9–12 years in the northern governorates of the Gaza Strip. The rain-fed years of 1998 and 2010 reported the worst periods of drought, while the period of 2016 showed a good droughtless water balance. Overall, the no-drought status might define the prospective conditions in the governorates of North Gaza, Gaza, and central Gaza over the next 20 years, while Rafah and Khanyounis are anticipated to be under normal to severe drought conditions.
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Shukla, S., A. McNally, G. Husak, and C. Funk. "A seasonal agricultural drought forecast system for food-insecure regions of East Africa." Hydrology and Earth System Sciences 18, no. 10 (October 2, 2014): 3907–21. http://dx.doi.org/10.5194/hess-18-3907-2014.
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Abstract. The increasing food and water demands of East Africa's growing population are stressing the region's inconsistent water resources and rain-fed agriculture. More accurate seasonal agricultural drought forecasts for this region can inform better water and agropastoral management decisions, support optimal allocation of the region's water resources, and mitigate socioeconomic losses incurred by droughts and floods. Here we describe the development and implementation of a seasonal agricultural drought forecast system for East Africa (EA) that provides decision support for the Famine Early Warning Systems Network's (FEWS NET) science team. We evaluate this forecast system for a region of equatorial EA (2° S–8° N, 36–46° E) for the March-April-May (MAM) growing season. This domain encompasses one of the most food-insecure, climatically variable, and socioeconomically vulnerable regions in EA, and potentially the world; this region has experienced famine as recently as 2011. To produce an "agricultural outlook", our forecast system simulates soil moisture (SM) scenarios using the Variable Infiltration Capacity (VIC) hydrologic model forced with climate scenarios describing the upcoming season. First, we forced the VIC model with high-quality atmospheric observations to produce baseline soil moisture (SM) estimates (here after referred as SM a posteriori estimates). These compared favorably (correlation = 0.75) with the water requirement satisfaction index (WRSI), an index that the FEWS NET uses to estimate crop yields. Next, we evaluated the SM forecasts generated by this system on 5 March and 5 April of each year between 1993 and 2012 by comparing them with the corresponding SM a posteriori estimates. We found that initializing SM forecasts with start-of-season (SOS) (5 March) SM conditions resulted in useful SM forecast skill (> 0.5 correlation) at 1-month and, in some cases, 3-month lead times. Similarly, when the forecast was initialized with midseason (i.e., 5 April) SM conditions, the skill of forecasting SM estimates until the end-of-season improved (correlation > 0.5 over several grid cells). We also found these SM forecasts to be more skillful than the ones generated using the Ensemble Streamflow Prediction (ESP) method, which derives its hydrologic forecast skill solely from the knowledge of the initial hydrologic conditions. Finally, we show that, in terms of forecasting spatial patterns of SM anomalies, the skill of this agricultural drought forecast system is generally greater (> 0.8 correlation) during drought years (when standardized anomaly of MAM precipitation is below 0). This indicates that this system might be particularity useful for identifying drought events in this region and can support decision-making for mitigation or humanitarian assistance.
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González-Rojí, Santos J., Martina Messmer, Christoph C. Raible, and Thomas F. Stocker. "Sensitivity of precipitation in the highlands and lowlands of Peru to physics parameterization options in WRFV3.8.1." Geoscientific Model Development 15, no. 7 (April 7, 2022): 2859–79. http://dx.doi.org/10.5194/gmd-15-2859-2022.
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Abstract. The performance of the Weather Research and Forecasting (WRF) model version 3.8.1 at convection-permitting scale is evaluated by means of several sensitivity simulations over southern Peru down to a grid resolution of 1 km, whereby the main focus is on the domain with 5 km horizontal resolution. Different configurations of microphysics, cumulus, longwave radiation, and planetary boundary layer schemes are tested. For the year 2008, the simulated precipitation amounts and patterns are compared to gridded observational data sets and weather station data gathered from Peru, Bolivia, and Brazil. The temporal correlation of simulated monthly accumulated precipitation against in situ and gridded observational data show that the most challenging regions for WRF are the slopes along both sides of the Andes, i.e. elevations between 1000 and 3000 m above sea level. The pattern correlation analysis between simulated precipitation and station data suggests that all tested WRF setups perform rather poorly along the northeastern slopes of the Andes during the entire year. In the southwestern region of the domain the performance of all setups is better except for the driest period (May–September). The results of the pattern correlation to the gridded observational data sets show that all setups perform reasonably well except along both slopes during the dry season. The precipitation patterns reveal that the typical setup used over Europe is too dry throughout the entire year, and that the experiment with the combination of the single-moment 6-class microphysics scheme and the Grell–Freitas cumulus parameterization in the domains with resolutions larger than 5 km, suitable for East Africa, does not perfectly apply to other equatorial regions such as the Amazon basin in southeastern Peru. The experiment with the Stony Brook University microphysics scheme and the Grell-Freitas cumulus parameterization tends to overestimate precipitation over the northeastern slopes of the Andes, but enforces a positive feedback between the soil moisture, air temperature, relative humidity, mid-level cloud cover and, finally, precipitation. Hence, this setup provides the most accurate results over the Peruvian Amazon, and particularly over the department of Madre de Dios, which is a region of interest because it is considered a biodiversity hotspot of Peru. The robustness of this particular configuration of the model is backed up by similar results obtained during wet climate conditions observed in 2012.
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Kolstad, Erik W., C. Ole Wulff, Daniela I. V. Domeisen, and Tim Woollings. "Tracing North Atlantic Oscillation Forecast Errors to Stratospheric Origins." Journal of Climate 33, no. 21 (November 1, 2020): 9145–57. http://dx.doi.org/10.1175/jcli-d-20-0270.1.
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AbstractThe North Atlantic Oscillation (NAO) is the main driver of weather variability in parts of Eurasia, Greenland, North America, and North Africa on a range of time scales. Successful extended-range NAO predictions would equate to improved predictions of precipitation and temperature in these regions. It has become clear that the NAO is influenced by the stratosphere, but because this downward coupling is not fully reproduced by all forecast models the potential for improved NAO forecasts has not been fully realized. Here, an analysis of 21 winters of subseasonal forecast data from the European Centre for Medium-Range Weather Forecasts monthly forecasting system is presented. By dividing the forecasts into clusters according to their errors in North Atlantic Ocean sea level pressure 15–30 days into the forecasts, we identify relationships between these errors and the state of the stratospheric polar vortex when the forecasts were initialized. A key finding is that the model overestimates the persistence of both the negative NAO response following a weak polar vortex and the positive NAO response following a strong polar vortex. A case in point is the sudden stratospheric warming in early 2019, which was followed by five consecutive weeks of an overestimation of the negative NAO regime. A consequence on the ground was temperature predictions for northern Europe that were too cold. Another important finding is that the model appears to misrepresent the gradual downward impact of stratospheric vortex anomalies. This result suggests that an improved representation and prediction of stratosphere–troposphere coupling in models might yield substantial benefits for extended-range weather forecasting in the Northern Hemisphere midlatitudes.
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Insua-Costa, Damián, Gonzalo Miguez-Macho, and María Carmen Llasat. "Local and remote moisture sources for extreme precipitation: a study of the two catastrophic 1982 western Mediterranean episodes." Hydrology and Earth System Sciences 23, no. 9 (September 24, 2019): 3885–900. http://dx.doi.org/10.5194/hess-23-3885-2019.
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Abstract. Floods and flash floods are frequent in the south of Europe resulting from heavy rainfall events that often produce more than 200 mm in less than 24 h. Even though the meteorological conditions favourable for these situations have been widely studied, there is a lingering question that still arises: what humidity sources could explain so much precipitation? To answer this question, the regional atmospheric Weather Research and Forecasting (WRF) model with a recently implemented moisture tagging capability has been used to analyse the main moisture sources for two catastrophic flood events that occurred during the autumn of 1982 (October and November) in the western Mediterranean area, which is regularly affected by these types of adverse weather episodes. The procedure consists in selecting a priori potential moisture source regions for the extreme event under consideration, and then performing simulations using the tagging technique to quantify the relative contribution of each selected source to total precipitation. For these events we study the influence of four possible potential sources: (1) evaporation in the western Mediterranean; (2) evaporation in the central Mediterranean; (3) evaporation in the North Atlantic; and (4) advection from the tropical and subtropical Atlantic and Africa. Results show that these four moisture sources explain most of the accumulated precipitation, with the tropical and subtropical input being the most relevant in both cases. In the October event, evaporation in the western and central Mediterranean and in the North Atlantic also had an important contribution. However, in the November episode tropical and subtropical moisture accounted for more than half of the total accumulated rainfall, while evaporation in the western Mediterranean and North Atlantic played a secondary role and the contribution of the central Mediterranean was almost negligible. Therefore, remote sources were crucial: in the October event they played a similar role to local sources, whereas in the November case they were clearly dominant. In both episodes, long-distance moisture transport from the tropics and subtropics mostly occurred in mid-tropospheric layers, via well-defined moisture plumes with maximum mixing ratios at medium levels.
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Arsenault, Kristi R., Shraddhanand Shukla, Abheera Hazra, Augusto Getirana, Amy McNally, Sujay V. Kumar, Randal D. Koster, et al. "The NASA Hydrological Forecast System for Food and Water Security Applications." Bulletin of the American Meteorological Society 101, no. 7 (July 1, 2020): E1007—E1025. http://dx.doi.org/10.1175/bams-d-18-0264.1.
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Abstract Many regions in Africa and the Middle East are vulnerable to drought and to water and food insecurity, motivating agency efforts such as the U.S. Agency for International Development’s (USAID) Famine Early Warning Systems Network (FEWS NET) to provide early warning of drought events in the region. Each year these warnings guide life-saving assistance that reaches millions of people. A new NASA multimodel, remote sensing–based hydrological forecasting and analysis system, NHyFAS, has been developed to support such efforts by improving the FEWS NET’s current early warning capabilities. NHyFAS derives its skill from two sources: (i) accurate initial conditions, as produced by an offline land modeling system through the application and/or assimilation of various satellite data (precipitation, soil moisture, and terrestrial water storage), and (ii) meteorological forcing data during the forecast period as produced by a state-of-the-art ocean–land–atmosphere forecast system. The land modeling framework used is the Land Information System (LIS), which employs a suite of land surface models, allowing multimodel ensembles and multiple data assimilation strategies to better estimate land surface conditions. An evaluation of NHyFAS shows that its 1–5-month hindcasts successfully capture known historic drought events, and it has improved skill over benchmark-type hindcasts. The system also benefits from strong collaboration with end-user partners in Africa and the Middle East, who provide insights on strategies to formulate and communicate early warning indicators to water and food security communities. The additional lead time provided by this system will increase the speed, accuracy, and efficacy of humanitarian disaster relief, helping to save lives and livelihoods.
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Sazib, Nazmus, Iliana Mladenova, and John Bolten. "Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data." Remote Sensing 10, no. 8 (August 11, 2018): 1265. http://dx.doi.org/10.3390/rs10081265.
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Soil moisture is considered to be a key variable to assess crop and drought conditions. However, readily available soil moisture datasets developed for monitoring agricultural drought conditions are uncommon. The aim of this work is to examine two global soil moisture datasets and a set of soil moisture web-based processing tools developed to demonstrate the value of the soil moisture data for drought monitoring and crop forecasting using the Google Earth Engine (GEE). The two global soil moisture datasets discussed in the paper are generated by integrating the Soil Moisture Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions’ satellite-derived observations into a modified two-layer Palmer model using a one-dimensional (1D) ensemble Kalman filter (EnKF) data assimilation approach. The web-based tools are designed to explore soil moisture variability as a function of land cover change and to easily estimate drought characteristics such as drought duration and intensity using soil moisture anomalies and to intercompare them against alternative drought indicators. To demonstrate the utility of these tools for agricultural drought monitoring, the soil moisture products and vegetation- and precipitation-based products were assessed over drought-prone regions in South Africa and Ethiopia. Overall, the 3-month scale Standardized Precipitation Index (SPI) and Normalized Difference Vegetation Index (NDVI) showed higher agreement with the root zone soil moisture anomalies. Soil moisture anomalies exhibited lower drought duration, but higher intensity compared with SPIs. Inclusion of the global soil moisture data into the GEE data catalog and the development of the web-based tools described in the paper enable a vast diversity of users to quickly and easily assess the impact of drought and improve planning related to drought risk assessment and early warning. The GEE also improves the accessibility and usability of the earth observation data and related tools by making them available to a wide range of researchers and the public. In particular, the cloud-based nature of the GEE is useful for providing access to the soil moisture data and scripts to users in developing countries that lack adequate observational soil moisture data or the necessary computational resources required to develop them.
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Lin, Hai, Gilbert Brunet, and Jacques Derome. "Forecast Skill of the Madden–Julian Oscillation in Two Canadian Atmospheric Models." Monthly Weather Review 136, no. 11 (November 1, 2008): 4130–49. http://dx.doi.org/10.1175/2008mwr2459.1.
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Abstract The output of two global atmospheric models participating in the second phase of the Canadian Historical Forecasting Project (HFP2) is utilized to assess the forecast skill of the Madden–Julian oscillation (MJO). The two models are the third generation of the general circulation model (GCM3) of the Canadian Centre for Climate Modeling and Analysis (CCCma) and the Global Environmental Multiscale (GEM) model of Recherche en Prévision Numérique (RPN). Space–time spectral analysis of the daily precipitation in near-equilibrium integrations reveals that GEM has a better representation of the convectively coupled equatorial waves including the MJO, Kelvin, equatorial Rossby (ER), and mixed Rossby–gravity (MRG) waves. An objective of this study is to examine how the MJO forecast skill is influenced by the model’s ability in representing the convectively coupled equatorial waves. The observed MJO signal is measured by a bivariate index that is obtained by projecting the combined fields of the 15°S–15°N meridionally averaged precipitation rate and the zonal winds at 850 and 200 hPa onto the two leading empirical orthogonal function (EOF) structures as derived using the same meridionally averaged variables following a similar approach used recently by Wheeler and Hendon. The forecast MJO index, on the other hand, is calculated by projecting the forecast variables onto the same two EOFs. With the HFP2 hindcast output spanning 35 yr, for the first time the MJO forecast skill of dynamical models is assessed over such a long time period with a significant and robust result. The result shows that the GEM model produces a significantly better level of forecast skill for the MJO in the first 2 weeks. The difference is larger in Northern Hemisphere winter than in summer, when the correlation skill score drops below 0.50 at a lead time of 10 days for GEM whereas it is at 6 days for GCM3. At lead times longer than about 15 days, GCM3 performs slightly better. There are some features that are common for the two models. The forecast skill is better in winter than in summer. Forecasts initialized with a large amplitude for the MJO are found to be more skillful than those with a weak MJO signal in the initial conditions. The forecast skill is dependent on the phase of the MJO at the initial conditions. Forecasts initialized with an MJO that has an active convection in tropical Africa and the Indian Ocean sector have a better level of forecast skill than those initialized with a different phase of the MJO.
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Hu, Zhiyuan, Jianping Huang, Chun Zhao, Qinjian Jin, Yuanyuan Ma, and Ben Yang. "Modeling dust sources, transport, and radiative effects at different altitudes over the Tibetan Plateau." Atmospheric Chemistry and Physics 20, no. 3 (February 7, 2020): 1507–29. http://dx.doi.org/10.5194/acp-20-1507-2020.
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Abstract. Mineral dust plays an important role in the climate of the Tibetan Plateau (TP) by modifying the radiation budget, cloud macro- and microphysics, precipitation, and snow albedo. Meanwhile, the TP, with the highest topography in the world, can affect intercontinental transport of dust plumes and induce typical distribution characteristics of dust at different altitudes. In this study, we conduct a quasi-global simulation to investigate the characteristics of dust source contribution and transport over the TP at different altitudes by using a fully coupled meteorology–chemistry model, the Weather Research and Forecasting model with chemistry (WRF-Chem), with a tracer-tagging technique. Generally, the simulation reasonably captures the spatial distribution of satellite-retrieved dust aerosol optical depth (AOD) at different altitudes. Model results show that dust particles are emitted into atmosphere through updrafts over major desert regions and then transported to the TP. The East Asian dust (mainly from the Gobi and Taklamakan deserts) is transported southward and is lifted up to the TP, contributing a mass loading of 50 mg m−2 at a height of 3 km and 5 mg m−2 at a height of 12 km over the northern slope of the TP. Dust from North Africa and the Middle East are concentrated over both of the northern and southern slopes below 6 km, where mass loadings range from 10 to 100 and 1 to 10 mg m−2 below 3 km and above 9 km, respectively. As the dust is transported to the north and over the TP, mass loadings are 5–10 mg m−2 above a height of 6 km. The dust mass flux carried from East Asia to the TP is 7.9 Tg yr−1, mostly occurring at heights of 3–6 km. The dust particles from North Africa and the Middle East are transported eastward following the westerly jet and then are carried into the TP at the west side with dust mass fluxes of 7.8 and 26.6 Tg yr−1, respectively. The maximum mass flux of the North African dust mainly occurs at 0–3 km (3.9 Tg yr−1), while the Middle Eastern dust occurs at 6–9 km (12.3 Tg yr−1). The dust outflow occurs on the east side (−17.89 Tg yr−1) and south side (−11.22 Tg yr−1) of the TP, with a peak value (8.7 Tg yr−1) at 6–9 km. Moreover, the dust (by mass) is concentrated within the size range of 1.25–5.0 µm and the dust (by particle number) is concentrated in the size range of 0.156–1.25 µm. Compared with other aerosols, the dust contributes to more than 50 % of the total AOD over the TP. The direct radiative forcing induced by the dust is −1.28 W m−2 at the top of the atmosphere (cooling), 0.41 W m−2 in the atmosphere (warming), and −1.68 W m−2 at the surface (cooling). Our quantitative analyses of the dust contributions from different source regions and the associated radiative forcing can help us to better understand the role of dust on the climate over the TP and surrounding regions.
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Bercos-Hickey, Emily, Terrence R. Nathan, and Shu-Hua Chen. "On the Relationship between the African Easterly Jet, Saharan Mineral Dust Aerosols, and West African Precipitation." Journal of Climate 33, no. 9 (May 1, 2020): 3533–46. http://dx.doi.org/10.1175/jcli-d-18-0661.1.
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AbstractThe relationship between the African easterly jet (AEJ), Saharan mineral dust (SMD) aerosols, and West African precipitation (WAP) is examined using European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim) data, the NASA Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), and the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) for July–September 1998–2017. The spatial orientation and structure of AEJs in different SMD–WAP environments are compared. In dustier years, the AEJ is farther east and stronger, rotates clockwise, and has larger zonal and vertical shears. In wetter years, the AEJ is farther north, has a shorter zonal extent, and has larger meridional shear. These changes to the AEJ are a response to the combined effects of the SMD and WAP on the thermal field, which is confirmed through sensitivity tests carried out with the Weather Research and Forecasting Model coupled to an interactive dust model.