scholarly journals Predictability of June–September Rainfall in Ethiopia

2007 ◽  
Vol 135 (2) ◽  
pp. 628-650 ◽  
Author(s):  
Diriba Korecha ◽  
Anthony G. Barnston
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
Southern Oscillation ◽  
Summer Rainfall ◽  
Rainfall Anomaly ◽  
Seasonal Rainfall ◽  
Multivariate Statistical ◽  
Local Climate ◽  
Rainfall Prediction ◽  
Enso Predictability

Abstract In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction.

The Southern Oscillation Index as a predictor of seasonal rainfall in the arable areas of the inland Australian subtropics

1993 ◽  
Vol 44 (6) ◽  
pp. 1337 ◽  
Author(s):  
JS Russell ◽  
IM McLeod ◽  
MB Dale ◽  
TR Valentine
Keyword(s):  
Southern Oscillation Index ◽  
Southern Oscillation ◽  
Linear Trend ◽  
Summer Rainfall ◽  
Seasonal Rainfall ◽  
Four Seasons ◽  
Eastern Australia ◽  
Australian Continent ◽  
Ace Algorithm ◽  
Linear Trends

A detailed study has been carried out in four regions in the subtropics of Eastern Australia to determine the relationship between the Southern Oscillation Index (SOI) and subsequent seasonal rainfall. The period studied was from 1915 to 1991 for 3-monthly periods of spring (SON), summer (DJF), autumn (MAM) and winter (JJA). The 3-monthly prior SOI values were plotted against seasonal rainfall of the four regions and four seasons. These data were widely scattered but with a linear trend showing increased seasonal rainfall as the SOI increased. Linear trends were plotted for each season and region. Comparisons were made between the use of the ACE algorithm, which transforms the SOI and rainfall data, and the use of linear trends. Polynomials were used to calculate equations for each region and season, but only spring and summer produced satisfactory ACE functions. Estimates were made of spring and summer rainfall relative to prior SOI values for each region. While the SOI as a predictor of rainfall broadly estimates spring and summer rainfall, this variable has limited usefulness on its own. One of the options available with the ACE program is that additional independent variables can be added as required. Current research suggests that sea surface temperature data from specific ocean areas surrounding the Australian continent is the most useful additional variable at present. However the complexity of such an analysis is greatly increased.

scholarly journals Long-Lead Seasonal Prediction of China Summer Rainfall Using an EOF–PLS Regression-Based Methodology*,+

2016 ◽  
Vol 29 (5) ◽  
pp. 1783-1796 ◽  
Author(s):  
Wen Xing ◽  
Bin Wang ◽  
So-Young Yim
Keyword(s):  
Air Temperature ◽  
Dynamic Models ◽  
Principal Component ◽  
Summer Rainfall ◽  
Seasonal Prediction ◽  
Rainfall Anomaly ◽  
Pls Regression ◽  
Rainfall Prediction ◽  
Forecast Validation ◽  
Forecast Models

Abstract Considerable year-to-year variability of summer rainfall exposes China to threats of frequent droughts and floods. Objective prediction of the summer rainfall anomaly pattern turns out to be very challenging. As shown in the present study, the contemporary state-of-the-art dynamical models’ 1-month-lead prediction of China summer rainfall (CSR) anomalies has insignificant skills. Thus, there is an urgent need to explore other ways to improve CSR prediction. The present study proposes a combined empirical orthogonal function (EOF)–partial least squares (PLS) regression method to offer a potential long-lead objective prediction of spatial distribution of CSR anomalies. The essence of the methodology is to use PLS regression to predict the principal component (PC) of the first five leading EOF modes of CSR. The preceding December–January mean surface temperature field [ST; i.e., SST over ocean and 2-m air temperature (T2m) over land] is selected as the predictor field for all five PCs because SST and snow cover, which is reflected by 2-m air temperature, are the most important factors that affect CSR and because the correlation between each mode and ST during winter is higher than in spring. The 4-month-lead forecast models are established by using the data from 1979 to 2004. A 9-yr independent forward-rolling prediction is made for the latest 9 yr (2005–13) as a strict forecast validation. The pattern correlation coefficient skill (0.32) between the observed and the 4-month-lead predicted patterns during the independent forecast period of 2005–13 is significantly higher than the dynamic models’ 1-month-lead hindcast skill (0.04), which indicates that the EOF–PLS regression is a useful tool for improving the current seasonal rainfall prediction. Issues related to the EOF–PLS method are also discussed.

Influence of Atmospheric Teleconnections on Interannual Variability of Boreal Fires

2020 ◽  
Author(s):  
Zhiyi Zhao ◽  
Zhongda Lin ◽  
Fang Li
Keyword(s):  
Interannual Variability ◽  
Large Scale ◽  
Driving Forces ◽  
Southern Oscillation ◽  
Burned Area ◽  
East Atlantic ◽  
Local Climate ◽  
Atmospheric Teleconnection ◽  
Teleconnection Patterns ◽  
Global Carbon

<p>Wildfires are common in boreal forests around the world and strongly affect regional ecosystem processes and global carbon cycle. Previous studies have suggested that local climate is a dominant driver of boreal fires. However, the impacts of large-scale atmospheric teleconnection patterns on boreal fires and related physical processes remain largely unclear. This study investigates the influence of nine leading atmospheric teleconnection modes and El Niño-Southern Oscillation (ENSO) on the interannual variability of simultaneous summer fires in the boreal regions based on 1997-2015 GFED4s burned area, NCEP/NCAR atmospheric reanalysis, and HadISST sea surface temperature. Results show that ENSO has only a weak effect on boreal fires, distinct from its robust influence on the tropical fires. Instead, the interannual variability of burned area in the boreal regions is significantly regulated by five teleconnection patterns. Specifically, East Pacific-North Pacific (EP/NP) and East Atlantic/West Russia (EA/WR) patterns affect the burned area in North America, North Atlantic Oscillation (NAO) and East Atlantic (EA) patterns for Asia, and the Pacific-North American (PNA) pattern for Europe. Related to the teleconnections, the larger burned area is attributable to warmer surface by an anomalous high-pressure above and drier surface due to less moisture transport from the neighboring oceans. The results improve our understanding of driving forces of interannual variability of boreal fires and then regional and global carbon budgets.</p>

Subseasonal Predictions of Regional Summer Monsoon Rainfall over Tropical Asian Oceans and Land

2015 ◽  
Vol 28 (24) ◽  
pp. 9583-9605 ◽  
Author(s):  
Xiangwen Liu ◽  
Song Yang ◽  
Jianglong Li ◽  
Weihua Jie ◽  
Liang Huang ◽  
...  
Keyword(s):  
Lead Time ◽  
Large Scale ◽  
Southern China ◽  
Intraseasonal Oscillation ◽  
Summer Rainfall ◽  
Summer Monsoon Rainfall ◽  
Forecast Skill ◽  
Boreal Summer ◽  
Regional Rainfall ◽  
Local Rainfall

Abstract Subseasonal predictions of the regional summer rainfall over several tropical Asian ocean and land domains are examined using hindcasts by the NCEP CFSv2. Higher actual and potential forecast skill are found over oceans than over land. The forecast for Arabian Sea (AS) rainfall is most skillful, while that for Indo-China (ICP) rainfall is most unskillful. The rainfall–surface temperature (ST) relationship over AS is characterized by strong and fast ST forcing but a weak and slow ST response, while the relationships over the Bay of Bengal, the South China Sea (SCS), and the India subcontinent (IP) show weak and slow ST forcing, but apparently strong and rapid ST response. Land–air interactions are often less noticeable over ICP and southern China (SC) than over IP. The CFSv2 forecasts reasonably reproduce these observed features, but the local rainfall–ST relationships often suffer from different degrees of unrealistic estimation. Also, the observed local rainfall is often related to the circulation over limited regions, which gradually become more extensive in forecasts as lead time increases. The prominent interannual differences in forecast skill of regional rainfall are sometimes associated with apparent disparities in forecasts of local rainfall–ST relationships. Besides, interannual variations of boreal summer intraseasonal oscillation, featured by obvious changes in frequency and amplitude of certain phases, significantly modulate the forecasts of rainfall over certain regions, especially the SCS and SC. It is further discussed that the regional characteristics of rainfall and model’s deficiencies in capturing the influences of local and large-scale features are responsible for the regional discrepancies of actual predictability of rainfall.

scholarly journals Development and Assessment of Localized Seasonal Rainfall Prediction Models: Mapping and Characterizing Rift Valley Fever Hotspot Areas in the Southern and Southeastern Ethiopia

2018 ◽  
Vol 11 (5) ◽  
pp. 102
Author(s):  
Ephrem Weledekidane
Keyword(s):  
East Africa ◽  
Rift Valley ◽  
Rift Valley Fever ◽  
Large Scale ◽  
Prediction Models ◽  
Rainfall Anomaly ◽  
Seasonal Rainfall ◽  
Valley Fever ◽  
Average Rainfall ◽  
Early Indicators

Rift Valley Fever disease has been recognized as being among permanent threats for the sustainability of livestock production in Ethiopia, owing to shared boarders with RVF endemic countries in East Africa. Above-normal and widespread rainfall have outweighed as immediate risk factor that facilitated historical outbreaks of the disease in the East Africa. The objective of the present study, thus, was to develop prospective localized seasonal rainfall anomaly prediction models, and assess their skills as early indicators to map high risk localized rift valley fever disease outbreak areas (hotspots) over the southern and southeastern part of Ethiopia. 21 years of daily rainfall data; for five meteorological stations, was employed in diagnosing existences of any anomalous patterns of rainfall, along with a cumulative rainfall analysis to determine if there were ideal conditions for potential flooding. The results indicated that rainfall in the region is highly variable; with non-significant trends, and attributed to be the results of the effects of large-scale climatic-teleconnection. The moderate to strong positive correlations found between the regional average rainfall and large scale teleconnection variables (r ≥ 0.48), indicated some potentials for early prediction of seasonal patterns of rainfall. Accordingly, models developed, based on the regional average rainfall and emerging developments of El Niño/Southern Oscillation and other regional climate forcings, showed maximum skills (ROC scores ≥ 0.7) and moderate reliability. Deterministically, most of the positive rainfall anomaly patterns, corresponding to El Niño years, were portrayed with some skills. The study demonstrated that localized climate prediction models are invaluable as early indicators to skillfully map climatically potential RVF hotspot areas.

scholarly journals Simulation of Daily Rainfall Data using Articulated Weather Generator Model for Seasonal Prediction of ENSO-Affected Zones in Indonesia

2020 ◽  
Vol 52 (2) ◽  
pp. 143
Author(s):  
Andung Bayu Sekaranom ◽  
Emilya Nurjani ◽  
Rika Harini ◽  
Andi Syahid Muttaqin
Keyword(s):  
Large Scale ◽  
Southern Oscillation ◽  
Daily Rainfall ◽  
Regression Function ◽  
Seasonal Prediction ◽  
Weather Generator ◽  
Rainfall Data ◽  
Rainfall Simulation ◽  
Rainfall Prediction ◽  
Student’S T

Synthetic rainfall simulation using weather generator models is commonly used as a substitute at locations with incomplete or short rainfall data. It incorporates a method that can be developed into forecasts of future rainfall. This study was designed to modify a rainfall prediction system based on the principles of weather generator models and to test the validity of the modelling results. It processed the data collected from eight rain stations in zones affected by El-Nino Southern Oscillation (ENSO). A large-scale predictor, that is, SST prediction data in the Nino 3.4 region over the Pacific Ocean was used as the influencing variable in projecting rainfall for the following six months after the predefined dates. Rainfall data from weather stations and SST in 1960-2000 were analyzed to identify the effects of ENSO and build a statistical model based on the regression function. Meanwhile, the model was validated using the data from 2001 to 2007 by backtesting six months in a row. The analysis results showed that the model could simulate both low rainfall in the dry season and high one in the rainy season. Validation by the student's t-test confirmed that the six-month synthetic rain data at nearly all observed stations was homogenous. For this reason, the developed model can be potentially used as one of the season prediction systems.  

Downscaling Climate Information

2016 ◽  
Author(s):  
Rasmus Benestad
Keyword(s):  
Climate Change ◽  
Large Scale ◽  
Climate Models ◽  
Southern Oscillation ◽  
Global Climate ◽  
Global Climate Model ◽  
Global Climate Models ◽  
Local Climate ◽  
Skill Scores ◽  
The Right

What are the local consequences of a global climate change? This question is important for proper handling of risks associated with weather and climate. It also tacitly assumes that there is a systematic link between conditions taking place on a global scale and local effects. It is the utilization of the dependency of local climate on the global picture that is the backbone of downscaling; however, it is perhaps easiest to explain the concept of downscaling in climate research if we start asking why it is necessary. Global climate models are our best tools for computing future temperature, wind, and precipitation (or other climatological variables), but their limitations do not let them calculate local details for these quantities. It is simply not adequate to interpolate from model results. However, the models are able to predict large-scale features, such as circulation patterns, El Niño Southern Oscillation (ENSO), and the global mean temperature. The local temperature and precipitation are nevertheless related to conditions taking place over a larger surrounding region as well as local geographical features (also true, in general, for variables connected to weather/climate). This, of course, also applies to other weather elements. Downscaling makes use of systematic dependencies between local conditions and large-scale ambient phenomena in addition to including information about the effect of the local geography on the local climate. The application of downscaling can involve several different approaches. This article will discuss various downscaling strategies and methods and will elaborate on their rationale, assumptions, strengths, and weaknesses. One important issue is the presence of spontaneous natural year-to-year variations that are not necessarily directly related to the global state, but are internally generated and superimposed on the long-term climate change. These variations typically involve phenomena such as ENSO, the North Atlantic Oscillation (NAO), and the Southeast Asian monsoon, which are nonlinear and non-deterministic. We cannot predict the exact evolution of non-deterministic natural variations beyond a short time horizon. It is possible nevertheless to estimate probabilities for their future state based, for instance, on projections with models run many times with slightly different set-up, and thereby to get some information about the likelihood of future outcomes. When it comes to downscaling and predicting regional and local climate, it is important to use many global climate model predictions. Another important point is to apply proper validation to make sure the models give skillful predictions. For some downscaling approaches such as regional climate models, there usually is a need for bias adjustment due to model imperfections. This means the downscaling doesn’t get the right answer for the right reason. Some of the explanations for the presence of biases in the results may be different parameterization schemes in the driving global and the nested regional models. A final underlying question is: What can we learn from downscaling? The context for the analysis is important, as downscaling is often used to find answers to some (implicit) question and can be a means of extracting most of the relevant information concerning the local climate. It is also important to include discussions about uncertainty, model skill or shortcomings, model validation, and skill scores.

scholarly journals Use of Teleconnections to Predict Western Australian Seasonal Rainfall Using ARIMAX Model

Hydrology ◽  
2020 ◽  
Vol 7 (3) ◽  
pp. 52
Author(s):  
Farhana Islam ◽  
Monzur Alam Imteaz
Keyword(s):  
Prediction Models ◽  
Southern Oscillation ◽  
Moving Average ◽  
South Coast ◽  
Seasonal Rainfall ◽  
North Coast ◽  
The South ◽  
Rainfall Prediction ◽  
Auto Regressive ◽  
Mlr Model

Increased demand for engineering propositions to forecast rainfall events in an area or region has resulted in developing different rainfall prediction models. Interestingly, rainfall is a very complicated natural system that requires consideration of various attributes. However, regardless of the predictability performance, easy to use models have always been welcomed over the complex and ambiguous alternatives. This study presents the development of Auto–Regressive Integrated Moving Average models with exogenous input (ARIMAX) to forecast autumn rainfall in the South West Division (SWD) of Western Australia (WA). Climate drivers such as Indian Ocean Dipole (IOD) and El Nino Southern Oscillation (ENSO) were used as predictors. Eight rainfall stations with 100 years of continuous data from two coastal regions (south coast and north coast) were selected. In the south coast region, Albany (0,1,1) with exogenous input DMIOct–Nino3Nov, and Northampton (0,1,1) with exogenous input DMIJan–Nino3Nov were able to forecast autumn rainfall 4 months and 2 months in advance, respectively. Statistical performance of the ARIMAX model was compared with the multiple linear regression (MLR) model, where for calibration and validation periods, the ARIMAX model showed significantly higher correlations (0.60 and 0.80, respectively), compared to the MLR model (0.44 and 0.49, respectively). It was evident that the ARIMAX model can predict rainfall up to 4 months in advance, while the MLR has shown strict limitation of prediction up to 1 month in advance. For WA, the developed ARIMAX model can help to overcome the difficulty in seasonal rainfall prediction as well as its application can make an invaluable contribution to stakeholders’ economic preparedness plans.

Integrasi Prediksi Musim dengan Model Simulasi Tanaman untuk Penentuan Waktu Tanam Padi

2020 ◽  
Vol 42 (2) ◽  
pp. 99
Author(s):  
Elza Surmaini ◽  
Tri Wahyu Hadi ◽  
Kasdi Subagyono ◽  
M. Ridho Syahputra
Keyword(s):  
Simulation Model ◽  
Lead Time ◽  
Rice Yield ◽  
Crop Productivity ◽  
Seasonal Rainfall ◽  
Second Step ◽  
Planting Time ◽  
Crop Simulation Model ◽  
Rainfall Prediction ◽  
Crop Simulation

<p><strong>Abstrak.</strong> Penyesuaian waktu tanam merupakan upaya dengan biaya yang paling efisien untuk meningkatkan produktivitas, menstabilkan, bahkan meningkatkan ketahanan pangan. Integrasi prediksi curah hujan musim dengan model simulasi tanaman dapat digunakan untuk memberikan rekomendasi waktu tanam padi dengan hasil yang optimal. Dua tahap analog digunakan untuk memprediksi curah hujan harian untuk satu musim tanam. Analog tahap pertama untuk memprediksi curah hujan harian untuk 120 hari. Tahap kedua mencari satu analog terbaik prediksi sekuens curah hujan 120 hari. Basis data hasil tanaman padi periode 1982-2009 dengan interval harian dibangun menggunakan model simulasi tanaman. Rekomendasi waktu tanam ditentukan berdasarkan perubahan hasil dibandingkan dengan waktu tanam awal. Hasil penelitian menunjukkan bahwa prediksi curah hujan musim dengan lead time 6-9 bulan menggunakan metode downscaling dengan dua tahap analog dapat memperpanjang lag prediksi 2 bulan sebelum tanam sehingga dapat digunakan untuk peringatan dini. Integrasi prediksi curah hujan musim dengan model simulasi tanaman dapat memberikan informasi selang waktu tanam yang berpotensi untuk mendapatkan hasil yang lebih tinggi. Prediksi waktu tanam dalam bentuk selang waktu diperlukan petani , karena berbagai faktor non teknis yang menyebabkan penanaman tidak dapat dilakukan pada rekomendasi waktu tertentu. Informasi tersebut dapat digunakan oleh pengambil kebijakan dan penyuluh untuk rekomendasi kepada petani tentang waktu tanam dengan hasil padi yang lebih tinggi.</p><p><br /><em><strong>Abstract</strong></em>. Adapting planting time is a very cost-efficient way to increase crop productivity and stabilise or even increase food security. Linking seasonal rainfall prediction with crop simulation model is used to evaluate planting date with optimal rice yield. We used a two step analogue method. The first step is to predict 30 daily rainfall analogues for the next 120 days. The second step is to look for best analogue of 120 day rainfall prediction. Daily planting dates were simulated within 1982-2009 using crop simulation model. The second step is to determine the best analoque for the 120 day sequence. Planting time recommendation is adjusted using the difference between the earliest and later planting dates.The result concluded that 6-9 lead time seasonal rainfall prediction using two step analogue could increase lead time 2 months prior to planting time, therefore can be use for early warning. Linking season rainfall prediction with crop simulation model to adjust interval of planting time that provide higher rice yield. Farmers need that interval, due to non-technical factors are caused crop could not planted timely as recommended. In addition, the recommendation of planting time should be used by decision makers and extension workers to recommend appropriate planting time with higher yield to the farmers.</p>

scholarly journals Seasonal Predictability of Summer Rainfall over South America

2018 ◽  
Vol 31 (20) ◽  
pp. 8181-8195 ◽  
Author(s):  
Rodrigo J. Bombardi ◽  
Laurie Trenary ◽  
Kathy Pegion ◽  
Benjamin Cash ◽  
Timothy DelSole ◽  
...  
Keyword(s):  
South America ◽  
Spatial Resolution ◽  
Large Scale ◽  
Climate Models ◽  
Southern Oscillation ◽  
Austral Summer ◽  
Summer Rainfall ◽  
Forecast Skill ◽  
Seasonal Predictability ◽  
Temporal Scales

The seasonal predictability of austral summer rainfall is evaluated in a set of retrospective forecasts (hindcasts) performed as part of the Minerva and Metis projects. Both projects use the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) coupled to the Nucleus for European Modelling of the Ocean (NEMO). The Minerva runs consist of three sets of hindcasts where the spatial resolution of the model’s atmospheric component is progressively increased while keeping the spatial resolution of its oceanic component constant. In the Metis runs, the spatial resolution of both the atmospheric and oceanic components are progressively increased. We find that raw model predictions show seasonal forecast skill for rainfall over northern and southeastern South America. However, predictability is difficult to detect on a local basis, but it can be detected on a large-scale pattern basis. In addition, increasing horizontal resolution does not lead to improvements in the forecast skill of rainfall over South America. A predictable component analysis shows that only the first predictable component of austral summer precipitation has forecast skill, and the source of forecast skill is El Niño–Southern Oscillation. Seasonal prediction of precipitation remains a challenge for state-of-the-art climate models. Positive benefits of increasing model resolution might be more evident in other atmospheric fields (i.e., temperature or geopotential height) and/or temporal scales (i.e., subseasonal temporal scales).

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