Asaminew Teshome

Numerous subseasonal-to-seasonal (S2S) models exist, yet their performance over tropical regions remains poorly understood, leaving forecasters with little guidance on which models best suit operational downscaling. This study evaluates 14 global models from the C3S and NMME initiatives for precipitation prediction over Ethiopia. We utilize Taylor diagrams and spatial bias assessments to categorize model performance across monthly, seasonal, and annual scales, using CHIRPS and ENACTS datasets as references. Our quantitative results reveal substantial inter-model variability, with correlation coefficients ranging from 0.46 to 0.92 and Root Mean Square Error (RMSE) values between 33.46 and 80.89 mm/month. The JMA, ECCC, and ECMWF models consistently emerge as top performers, maintaining high correlations (0.88–0.92) and the lowest RMSE values (33.46–35.84 mm/month). Seasonal analysis demonstrates that these three models effectively capture the spatial climatology of the main rainy season (JJAS) and the ONDJ season. However, the JJAS season exhibits the highest one-standard-deviation spread, indicating increased forecast uncertainty during peak rainfall periods. Conversely, the NMME ensemble tends to overestimate precipitation during the FMAM season. We find that while individual C3S models generally exhibit higher skill, the full NMME ensemble outperforms the C3S ensemble through beneficial error cancellation. Annual skill scores confirm that a parsimonious ensemble of JMA, ECCC, and ECMWF reduces spatial bias more effectively than larger aggregates. Consequently, we recommend this refined subset for operational services and downscaling at the Ethiopian Meteorological Institute (EMI). This selection optimizes predictive capability for Ethiopia’s tropical climate while reducing computational overhead and simplifying data management.

Understanding future rainfall variability is crucial for climate resilience planning in rain-fed agricultural regions like West Shewa in Ethiopia. This study investigated projected rainfall patterns under moderate (SSP245) and high (SSP585) emission scenarios using an ensemble of bias-corrected CMIP6 models: BCC-CSM2-MR, MRI-ESM2-0, and GFDL-ESM4. Future estimates (2030-2089) were analyzed using statistical methods such as the Mann-Kendall trend test, Sen's slope estimator, rainfall anomaly index, and Standardized Precipitation Index (SPI). Results suggest a significant increase in annual and seasonal rainfall, particularly during the Kiremt and Bega seasons in the near term (2030-2059), with more pronounced increases under SSP585. However, the strength and significance of these trends tend to diminish toward the end of the century (2060-2089), indicating potential future instability and uncertainty in rainfall patterns. SPI analyses further highlight the growing frequency of both wet and dry anomalies, especially under the high-emission pathway, suggesting an increased likelihood of extreme hydrological events. These anticipated changes provide both opportunities and challenges: higher rainfall may alleviate drought-related stress, but it may also increase flood risks and disrupt existing water resource management techniques. The findings highlight the necessity of strategies for adaptation such as climate-smart agriculture, early warning systems, and long-term land use planning in West Shewa to protect livelihoods and ecosystems. Overall, this study provides useful localized insight into future climatic scenarios, which can help policymakers and communities prepare for increased climate resilience.

We test methods of postprocessing rainfall forecasts out to 7 days over East Africa.Using the physical forecast models, IFS from ECMWF and GFS from NCEP, we apply several combinations of post-processing techniques to empirically correct the predicted rainfall towards IMERG blended satellite rainfall data. The techniques we apply include a generative adversarial neural network (GAN) model (Harris et al. 2022), isotonic distributional regression (EasyUQ, Walz et al. 2024), EMOS (Gneiting et al. 2005), linear regression, and the kernel density estimate. Other approaches are also considered, however for the purposes of practical operational forecasts, we mainly focus on computationally cheap methods. Because we are comparing against IMERG, our results compare favourably against fully empirical models, such as FuXi and Graphcast, that have been trained to predict ERA5.Being computationally cheap, in an operational forecast cycle on a standard desktop computer, the GAN model can produce spatially correlated 1000 member ensembles from the input forecast data. from which we can display the distribution using a histogram. The other techniques also cheaply produce rainfall distributions. We compare the quality of these distributions using the CRPS, variogram score and reliability diagrams. Biases in the raw rainfall forecasts are most notably reduced over the large lakes, for example Lake Victoria, over mountains, Indian ocean, and other places of high rainfall. We find it difficult to reduce biases in dry regions and over the Congo rainforest.Different empirical modelling methods are optimal for different physical phenomena, and there is no theory for the most accurate model without physical insight. We also observe that it is often possible to improve each of the models with various tweaks. Each of the tested approaches might improve in the future, and the question we are trying to answer is “what is the best practical model available today?”

The Ensemble Prediction System (EPS) provided by global weather forecast centres generates vast amounts of data that is crucial for early warnings of extreme weather and climate. However, regional and national meteorological services often face challenges in processing this data efficiently, particularly during regional downscaling and post-processing. Conventional methods of downloading and storing GRIB-format data have become increasingly inefficient and unsustainable. The Strengthening Early Warning Systems for Anticipatory Actions (SEWAA) project aims to address these challenges by exploring the use of cloud native operations and GenAI-cGAN driven post-processing systems. Kerchunk provides a groundbreaking solution for real-time weather data streaming, catering to the transition towards open and free to use cloud-based object storage from global weather forecasting centres. Kerchunk, in conjunction with GRIB index files, enables efficient, real-time access to weather data, fostering more sustainable workflows in weather and climate services, thus strengthening early warning systems. This study developed a workflow for streaming forecast data using Kerchunk with two primary objectives: 1. Using GRIB index files to reduce redundant readings and generate Kerchunk reference files. 2. Through streaming-like access, convert the reference files into virtual Zarr datasets and utilise Dask compute for scalable data handling The methodology utilised recent improvements in the Kerchunk library that integrate GRIB scanning with its index files. This allowed the system to sample subsets of the GRIB corpus instead of processing entire Forecast Model Run Collections (FMRC), significantly optimising performance. The workflow was implemented using cloud-based compute operations via Coiled python library and its service on the Google Cloud Platform. Dask cluster, managed through Coiled, enabled the creation of Zarr virtual datasets for analysis and visualisation. This streaming approach efficiently loads weather data into memory on demand, avoiding unnecessary data downloads and duplication. We validated the solution with NOAA GFS/GEFS datasets stored in AWS S3 bucket as open datasets. The optimised workflow demonstrated remarkable efficiency, requiring only

Understanding seasonal rainfall patterns and variability is crucial for managing water resources, pastoral and agricultural activities in Ethiopia, an especially climate‐vulnerable country. This study examines trends in March–April–May (MAM) rainfall from 1981 to 2022, emphasising the relationships between climate indices, atmospheric circulation patterns, and variations in precipitation. By utilising daily CHIRPS rainfall data and monthly sea surface temperature (SST), sea level pressure (SLP) and other reanalysis datasets, we applied extreme indices analysis, correlation analysis and Student's t‐tests to compare climate factors and rainfall during two distinct periods 1(981–2001 and 2002–2022) and assess changes relative to the 1991–2020 long‐term mean. Our findings reveal a notable shift towards warmer sea surface temperature (SST) phases in key ocean basins, with significant positive correlations (r > 0.45, p < 0.05) between Ethiopian MAM rainfall and SSTs in the Mediterranean, Northwest Pacific, Northern Atlantic and Western Indian Ocean. Precipitation patterns shifted from above‐average to below‐average rainfall, aligning with opposite trends in the tropical central Indian Ocean. A long‐term trend analysis revealed a marked decrease in rainy days across northeast, east, central, south and southeastern Ethiopia during 2002–2022, with an increase in consecutive dry days and a decrease in consecutive wet days, with statistical significance at the 95% confidence level. The period from 2002 to 2022 was characterised by La Niña‐like conditions and a negative Pacific Decadal Oscillation, which had a considerable impact on rainfall patterns. Changes in large‐scale atmospheric circulation reduced moisture transport to Ethiopia, leading to drier conditions. These findings enhance our understanding of Ethiopian rainfall variability and its drivers, crucial for improving early warning systems and developing climate adaptation strategies in the region.

Identifying the climate profile of an area is essential for effective water resource management, agricultural planning and disaster preparedness. This study focuses on characterizing rainfall patterns and identifying the variability of Jeldu woreda in Oromia, Ethiopia. Previously and recently, little study tried to show the woreda profile, which did not bases on station data properly. Forty years (1981-2020) of stations’ rainfall and temperature data were utilized. The study area had natural forest, highland and mid land with varying topography. Based on the annual rainfall cycle, the woreda had bi-modal rainfall types, with the peak of the year being July. Statistical methods and python script were applied to identify the climate profile. The results show that main rainy seasonal rainfall starts on average from 12-14 June and ceases averagely around 10-15 October. During second rainy period, the rain starts from 25 February to 10 March. The mean annual rainfall of woreda is 1395.1 mm, and the average maximum temperature is 19.5°. Kiremt contributes the highest percent, which is more than 64 % of the annual total rainfall. The 1987 and 2015 were the driest years, and 1996 and 2010 is the wettest during kiremt, 1987 and 2010 the wettest, and 1999 and 2015 the driest during Belg. Seasonal rainfall had a regular to moderate precipitation concentration over woreda. Identifying the causes of unseasonal rainfall that happened during dry season over the study area can help to decrease weather hazard during the season.

This work was aimed to investigate the factors influencing summer (June-September: JJAS) monsoon rainfall variability over Sudan using monthly ground and Climate Hazard Group Infrared Precipitation with Station data (CHIRPS) from 1980 to 2018. The Mann-Kendall (MK) and sequential MK tests were used for trend analysis, whereas the variations of rainfall patterns were analyzed using Empirical Orthogonal Function. The atmospheric factors affecting JJAS rainfall were investigated, and atmospheric circulation anomalies (CA) related to the wet and dry conditions were assessed. The JJAS rainfall revealed a significant increasing trend during the study period. The wet condition was found with accompanying convergence winds at low levels and divergence in the upper levels, while the dry conditions were due to the weakening of convergence moisture transport. A hot Sea Surface Temperature anomaly over the Eastern Pacific Ocean (Nino3.4) are associated with dry conditions and cold anomalies are associated with wet conditions. There is a negative relation between JJAS rainfall over Sudan and Out-going Longwave Radiation in the central Indian Ocean, equatorial Atlantic Ocean, and eastern. This research extends our knowledge of atmospheric circulations influencing the summer rainfall variability over Sudan, providing a reasonable basis for monitoring and predicting similar conditions in the future.

Recurrent extreme drought and flood in Ethiopia lead to more economic loss. This study examines change and trends of 21 climate extremes of temperature and precipitation over Ethiopia by using indices from the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). The analysis was based on the records of observed meteorological data and the future projected from the CMIP5 model under RCP 4.5 and RCP 8.5 scenarios. The results of the seasonal standardized rainfall anomaly and EOF analysis show a decreasing rainfall in JJAS season and significant variability in the FMAM season. The first mode of EOF in FMAM shows that 49.6% was mostly negative with a high amount of variability. The observed precipitation extreme of annual total precipitation (PRCPTOT), consecutive wet days (CWD), and the number of heavy precipitation days (R10) show a decreasing trend, and consecutive dry days (CDD) shows an increasing trend. Additionally, temperature extremes like tropical nights (TR20) and daily maximum and minimum temperatures show a significantly increasing trend. The projected precipitation extremes of CWD, PRCPTOT, very wet day annual total (R95p), and the number of heavy precipitation days (R10) show a decreasing trend. CDD shows longer periods of dryness and a substantial increase which is conducive to the increase of drought. The projected temperature extremes of the warm spell duration indicator (WSDI), daily maximum temperature (TXx) and daily minimum temperature (TNx), summer days (SU25), and tropical nights (TR20) show an increasing trend, while the diurnal temperature range shows a decreasing trend. The projected changes in temperature and precipitation extremes are likely to have significant negative impacts on various socioeconomic activities over Ethiopia. These results highlight the need for planning and developing effective adaptation strategies for disaster prevention.