Seasonal Rainfall Prediction Research Articles

Role of Cross-Equatorial Waves in Maintaining Long Periods of Low Convective Activity over Southern Africa

Abstract Periods of low convective activity over southern Africa during the peak rainy season from December to February are known to be due to the northeastward displacement of the tropical temperate trough (TTT) systems from the landmass. In this study, using Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim) data, the authors show that the displacement of the TTT systems during long periods of low convective activity has origins in the Northern Hemisphere. Using standardized area-averaged outgoing longwave radiation (OLR) daily anomalies over southern Africa, long periods of low convective activity are defined as periods of positive OLR anomalies lasting consecutively for 5 or more days with a standard deviation of 1 or more. An eddy streamfunction anomaly composite of the periods of low convective activity shows an upper-level anomalous wave originating in the Northern Hemisphere and extending to southern Africa from the eastern Pacific and displacing the tropical–extratropical cloud bands from the southern African landmass into the southwestern Indian Ocean. The wave train is also seen to generate an anticyclonic anomaly over southern Africa, resulting in suppressed convective activity. Understanding the causes of the long periods of low convective activity will help in improving their predictability and also the predictability of seasonal rainfall over southern Africa.

Predictability of seasonal rainfall, monsoon onset and duration in Indonesia, Philippines and Bangladesh

Predictability of seasonal rainfall, monsoon onset and duration in Indonesia, Philippines and Bangladesh

Simulating the Daily Evolution of West African Monsoon Using High Resolution Regional Cosmo-model: A Case Study of the First Half of 2015 over Nigeria

Understanding the dynamics and variability of the West African Monsoon (WAM) at daily time scales will improve skillful prediction of the onset and evolution of the monsoon and thus would contribute toward food security of Nigeria. This study, therefore, uses high resolution regional COSMO-model, a weather-mode model from the German Weather Service adopted by the Nigerian Meteorological Agency, to study the daily evolution of WAM as well as the ability of the model to predict the daily characteristics of monsoon, for the first half of 2015, over Nigeria. Results show that, qualitatively, the model has the ability to predict the daily evolution of WAM, daily variability of rainfall, which includes the onset of the raining season as well as dry-spells, over Nigeria. The spatial correlations between the observation and the forecast are generally greater than 0.64, implying that the model, though, underestimates the rainfall amount as much as half of the actual amount, it nevertheless proved to have a good representation of the spatial characteristics of the rain over Nigeria. The model shows that the Inter-Tropical Discontinuity (ITD) advances northward, from the Gulf of Guinea (GOG) to the Sahelian region, by about 0.42° per week; and that for the onset of monsoon in Nigeria, the average position of the ITD should be at least 6.7°N and must not retreat south of it in the subsequent average weekly position. In agreement with earlier findings, the model also shows that the African Easterly Jet (AEJ), together with its associated core, is not only a boreal summer element but can also exist during the boreal winter with the same strength in the wind speed. The atmospheric thermodynamic properties, predicted by the model, show that for an onset of the rains, a threshold value of at least 1500 J/Kg of convective available potential energy (CAPE) may be required. The results suggest that COSMO-Model has proved to be a good tool for operational daily weather forecast; therefore, the model could also have potential for seasonal rainfall predictions over Nigeria when run in climate mode.

Ethiopian Seasonal Rainfall Variability and Prediction Using Canonical Correlation Analysis (CCA)

Because Ethiopia’s economy is mainly dependent on rain-fed agriculture, the failure or the goodness of seasonal rainfall is incredibly decisive the country’s socio economic functioning- in particular, food production. As a result, the reliable seasonal rainfall prediction would have several advantages for agricultural activities, water management, health (Malaria control) and drought related disaster mitigation. In this paper an attempt is made to study the variability and predictability of two Ethiopian rainy seasons using statistical methods. Canonical Correlation Analysis (CCA) applied to analyze and predict seasonal rainfall over Ethiopia using global sea surface temperature (SST) predictor data and historical monthly total Ethiopian rainfall and merged both satellite and rain gauge rainfall data predictand data. It is found that in general, ENSO is the main source of predictive skill for Ethiopian seasonal rainfall. This is the case for both the Belg (small rainy season) from February to May and Kiremt (main rainy season) from June to September, during which other, more regional SST in the Atlantic and Indian Ocean also contribute. The objective approach provided by the CAA approach resulted in higher mean skill than the more subjective methods used traditionally by the Ethiopian National Meteorological Agency (NMA) since the late 1980’s.

Seasonal prediction of East African rainfall

ABSTRACTThe detrimental impacts of climate variability on water, agriculture, and food resources in East Africa underscore the importance of reliable seasonal climate predictions. This study compares several forecasting methods using sea surface temperature (SST) anomalies to predict East African rains with various lead times. It is shown that the forecasts can explain more than 50% of the short rains (boreal autumn) variance across large regions of Ethiopia, Somalia, and Kenya, and reaffirm the importance of the Walker‐like circulation over the Indian Ocean. The forecasts also explain more than 50% of the long rains (boreal spring) over Uganda, Lake Victoria, and western Kenya, and demonstrate the strong connection of the long rains to the SSTs in the southern Atlantic Ocean, Mediterranean Sea, southwestern Indian Ocean, and Arabian Sea. For the unimodal rains in Tanzania, the forecasts explain more than 40% of the rainfall variance over northwestern Tanzania based on SST predictors in the southwestern Indian Ocean and the subtropical North Atlantic. For the South Sudan and Ethiopian Kiremt rains, the forecast skill is less pronounced. The predictor regions (dipoles) for a recently developed method are validated through composite maps of surface and atmospheric reanalysis variables. Lastly, 2010–2011 forecasts for both the long and short rains demonstrate skill in predicting the recent drought suggesting that seasonal climate forecasts can inform natural disaster preparedness and water resources planning in East Africa.

Assessment of climate change impacts on rainfall using large scale climate variables and downscaling models – A case study

Many of the applied techniques in water resources management can be directly or indirectly influenced by hydro-climatology predictions. In recent decades, utilizing the large scale climate variables as predictors of hydrological phenomena and downscaling numerical weather ensemble forecasts has revolutionized the long-lead predictions. In this study, two types of rainfall prediction models are developed to predict the rainfall of the Zayandehrood dam basin located in the central part of Iran. The first seasonal model is based on large scale climate signals data around the world. In order to determine the inputs of the seasonal rainfall prediction model, the correlation coefficient analysis and the new Gamma Test (GT) method are utilized. Comparison of modelling results shows that the Gamma test method improves the Nash–Sutcliffe efficiency coefficient of modelling performance as 8% and 10% for dry and wet seasons, respectively. In this study, Support Vector Machine (SVM) model for predicting rainfall in the region has been used and its results are compared with the benchmark models such as K-nearest neighbours (KNN) and Artificial Neural Network (ANN). The results show better performance of the SVM model at testing stage. In the second model, statistical downscaling model (SDSM) as a popular downscaling tool has been used. In this model, using the outputs from GCM, the rainfall of Zayandehrood dam is projected under two climate change scenarios. Most effective variables have been identified among 26 predictor variables. Comparison of the results of the two models shows that the developed SVM model has lesser errors in monthly rainfall estimation. The results show that the rainfall in the future wet periods are more than historical values and it is lower than historical values in the dry periods. The highest monthly uncertainty of future rainfall occurs in March and the lowest in July.

A probabilistic model for predicting seasonal rainfall in semi-arid lands of northeast Brazil

Abstract. In most of the northeast region of Brazil rainfall is relatively low, presenting significant inter-annual fluctuations, especially when compared to rainfall in other areas of Brazil. Moreover, evaporative rates (like the ones found in the northeast semi-arid region) are too high, sometimes reaching over 2800 mm annually. Owing to such a climate character, very large areas in northeast Brazil are subjected to recurrent droughts. This paper presents a methodology for the prediction of seasonal rainfall in semi-arid lands of northeast Brazil. A total of 72 raingauge stations of Paraiba State, and 84 in Ceará State were employed, all of them distributed in three and seven homogeneous areas, respectively. A rainy season with different subdivisions was established for each homogeneous area. The zi proportions – the ratio between the cumulative rainfall of the first rainy season period and the rain that falls during the whole rainy season were made to fit the Beta probabilistic model used for calculating the second and eighth deciles and the probability of rainfall above the average rainfall for the second period of the rainy season. The performance of the prognostic model for individual stations of Paraíba State in the period 1996–2000 was evaluated. In the period 1996 to 2000, with rainfall above average, the error was less than 20 %. The methodology adopted proved very accurate for forecasting droughts in northeast Brazil.

Seasonal rainfall predictability over the Lake Kariba catchment area

The Lake Kariba catchment area in southern Africa has one of the most variable climates of any major river basin, with an extreme range of conditions across the catchment and through time. Marked seasonal and interannual fluctuations in rainfall are a significant aspect of the catchment. To determine the predictability of seasonal rainfall totals over the Lake Kariba catchment area, this study used the low-level atmospheric circulation (850 hPa geopotential height fields) of a coupled ocean-atmosphere general circulation model (CGCM) over southern Africa, statistically downscaled to gridded seasonal rainfall totals over the catchment. This downscaling configuration was used to retroactively forecast the 3-month rainfall seasons of September-October-November through February-March-April, over a 14-year independent test period extending from 1994. Retroactive forecasts are produced for lead times of up to 5 months and probabilistic forecast performances evaluated for extreme rainfall thresholds of the 25th and 75th percentile values of the climatological record. The verification of the retroactive forecasts shows that rainfall over the catchment is predictable at extended lead-times, but that predictability is primarily found for austral mid-summer rainfall. This season is also associated with the highest potential economic value that can be derived from seasonal forecasts. A forecast case study of a recent extreme rainfall season (2010/11) that lies outside of the verification period is presented as evidence of the statistical downscaling system’s operational capability.Keywords: Lake Kariba catchment, coupled ocean-atmosphere model, statistical downscaling, seasonal forecasting, economic value

The Predictability of Rainfall over the Greater Horn of Africa. Part I: Prediction of Seasonal Rainfall

Abstract The predictability of each of the three rainy seasons affecting the Horn of Africa is examined using multiple linear regression and cross validation. In contrast to most other empirical forecast models, atmospheric dynamics are emphasized. Two geographical sectors are considered: the summer rainfall region with a single rainfall peak in the boreal summer and an equatorial rainfall region with rainy seasons in both the boreal spring and the boreal autumn. These two seasons are termed the “long rains” and “short rains,” respectively, in much of East Africa. Excellent predictability is noted 5 months in advance for both regions during the boreal autumn season and 2 months in advance for the summer season in the summer rainfall region. There is also excellent predictability for the short rains of the equatorial region 2 months in advance. Two notable findings are that atmospheric variables generally provide higher forecast skill than surface variables, such as sea surface temperatures and sea level pressure, and that ENSO and the Indian Ocean dipole provide less forecast skill than atmospheric variables associated with them. As in other studies, the results show that the spring predictability barrier limits the lead time for the forecasting of spring and summer rainfall.

The influence of stochasticism on Indian summer monsoon rainfall and its impact on prediction

Output from a multi-millennial control simulation of the CSIRO Mark 2 coupled model has been used to investigate quantitatively the relation between the Indian summer monsoon rain and El Nino/Southern Oscillation events. A moving window correlation between these two features revealed marked interannual and multi-decadal variability with the correlation coefficient varying between −0.8 and +0.2. This suggests that current observations showing a decline in this correlation are due to natural climatic variability. A scatter diagram of the anomalies of the Indian summer monsoon rainfall and NINO 3.4 surface temperature showed that in almost 40 % of the cases ENSO events were associated with rainfall anomalies opposite to those implied by the climatological correlation coefficient. Case studies and composites of global distributions of surface temperature and rainfall anomalies for El Nino (or La Nina) events highlight the opposite rainfall anomalies over India that can result from very similar ENSO surface temperature anomalies. Composite differences are used to demonstrate the unique sensitivity of Indian summer monsoon rainfall anomalies to ENSO events. The problem of predicting such anomalies is discussed in relation to the fact that time series of the monsoon rainfall, both observed and simulated, consist of white noise. Based on the scatter diagram it is concluded that in about 60 % of the cases seasonal or annual prediction of monsoon rainfall based on individual ENSO events will result in the correct outcome. Unfortunately, there is no way, a priori, of determining for a given ENSO event whether the correct or a rogue prediction will result. Analysis of the present model’s results suggest that this is an almost world-wide problem for seasonal predictions of rainfall.

An application of hybrid downscaling model to forecast summer precipitation at stations in China

A pattern prediction hybrid downscaling method was applied to predict summer (June–July–August) precipitation at China 160 stations. The predicted precipitation from the downscaling scheme is available one month before. Four predictors were chosen to establish the hybrid downscaling scheme. The 500-hPa geopotential height (GH5) and 850-hPa specific humidity (q85) were from the skillful predicted output of three DEMETER (Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction) general circulation models (GCMs). The 700-hPa geopotential height (GH7) and sea level pressure (SLP) were from reanalysis datasets. The hybrid downscaling scheme (HD-4P) has better prediction skill than a conventional statistical downscaling model (SD-2P) which contains two predictors derived from the output of GCMs, although two downscaling schemes were performed to improve the seasonal prediction of summer rainfall in comparison with the original output of the DEMETER GCMs. In particular, HD-4P downscaling predictions showed lower root mean square errors than those based on the SD-2P model. Furthermore, the HD-4P downscaling model reproduced the China summer precipitation anomaly centers more accurately than the scenario of the SD-2P model in 1998. A hybrid downscaling prediction should be effective to improve the prediction skill of summer rainfall at stations in China.

Assessment of Spatial Rainfall Variability over the Lower Mississippi River Alluvial Valley

Abstract A large portion of the lower Mississippi River alluvial valley (LMRAV) relies on irrigation from the regional alluvial aquifer for crop sustainability, which is expensive both in terms of water resources and farmer expenditures because of the large volume of water necessary to maintain crop production. As a result, knowledge of the seasonal frequency and distribution of precipitation over the LMRAV is critical for water resources management, the development of irrigation strategies, and economic planning. This project addresses the need for a detailed assessment of regional precipitation patterns through the use of rotated principal component analysis (RPCA) of high-resolution gridded radar-derived rainfall data, which provides quantification of the spatial and temporal characteristics of rainfall over the LMRAV from 1996 to 2011. Results of the project show that precipitation depths over the LMRAV are generally lower and more variable than adjacent eastern areas throughout the year, although there is substantial variability between seasons. This pattern seems to be influenced more by variations during the cool season (January–March), which has a higher overall precipitation depth and lower spatial variability than the warm season (July–September). Results further indicate that warm season rainfall is generally lower and less predictable over the LMRAV as compared to the cool season, which may be detrimental to regional water resources since irrigation planning and permitting is heavily based on seasonal rainfall predictions.

Long-Lead Prediction Skill of Indian Summer Monsoon Rainfall Using Outgoing Longwave Radiation (OLR): an Application of Canonical Correlation Analysis

There is a statistical linkage between tropical outgoing longwave radiation (OLR) and all-India summer monsoon rainfall (AISMR). A positive and significant correlation is observed over the surrounding areas of northeast Australia and the Arafura Sea during the months of January and February (J&F) which drops down as the lead month decreases. The OLR index as an area average over the surrounding areas of northeast Australia and the Arafura Sea is found to have 0.4 correlation with AISMR. The index is also found to be strongly correlated with the Indian monsoon index. In view of the teleconnection pattern, the OLR index is used for the development of statistical models using the concept of linear regression (LR) and canonical correlation analysis (CCA). Potential of CCA over LR is observed for the prediction of seasonal rainfall over the northwest, west central and over the whole country as well. The seasonal rainfall predictability basically comes from the months of June and September.

Downscaling of a global climate model for estimation of runoff, sediment yield and dam storage: A case study of Pirapama basin, Brazil

The coastal zone of northeastern Brazil is characterized by intense human activities and by large settlements and also experiences high soil losses that can contribute to environmental damage. Therefore, it is necessary to build an integrated modeling-forecasting system for rainfall-runoff erosion that assesses plans for water availability and sediment yield that can be conceived and implemented. In this work, we present an evaluation of an integrated modeling system for a basin located in this region with a relatively low predictability of seasonal rainfall and a small area (600 km2). The National Center for Environmental Predictions – NCEP’s Regional Spectral Model (RSM) nested within the Center for Weather Forecasting and Climate Studies – CPTEC’s Atmospheric General Circulation Model (AGCM) were investigated in this study, and both are addressed in the simulation work. The rainfall analysis shows that: (1) the dynamic downscaling carried out by the regional RSM model approximates the frequency distribution of the daily observed data set although errors were detected in the magnitude and timing (anticipation of peaks, for example) at the daily scale, (2) an unbiased precipitation forecast seemed to be essential for use of the results in hydrological models, and (3) the information directly extracted from the global model may also be useful. The simulated runoff and reservoir-stored volumes are strongly linked to rainfall, and their estimation accuracy was significantly improved at the monthly scale, thus rendering the results useful for management purposes. The runoff-erosion forecasting displayed a large sediment yield that was consistent with the predicted rainfall.

An analytical study of hindcasts from general circulation models for Indian summer monsoon rainfall

In this paper, precipitation outputs from retrospective seasonal forecasts made by nine General Circulation Models (GCMs) are used to investigate historical Indian summer monsoon seasonal rainfall variability and predictability over India. The observed data is taken from the India Meteorological Department whereas GCMs are obtained from the International Research Institute for Climate and Society, Columbia University, the National Center for Environmental Prediction, and the Japan Agency for Marine Earth Science and Technology. The study focusses on June–September precipitation hindcasts initialized from the 1 May. First, the mean climatology, variance of interannual variability (IAV), and long-term trends for the nine GCMs were evaluated. Then Empirical Orthogonal Function (EOF) is used to extract major climate modes and spectral analyses method is used to investigate the temporal properties of the leading principal components. It is found that the models are able to reproduce the climatology and IAV to varying degrees. The EOF and spectral analyses of models hindcast reveal that these models are capable of predicting the observed precipitation variability to some extent. In order to study the remote response, the correlation co-efficient between model predicted rainfall and sea surface temperature (SST) have been calculated. The results suggest that the models show exaggerated remote response to ENSO SST forcing and the Indian Ocean Dipole Mode index has less predictive skill compared to ENSO. The correlation values between the model predicted Monsoon Hadley Index (MHI) and observed MHI reveals that only a few of them could exhibit large scale circulation features well.

Extracting Subseasonal Scenarios: An Alternative Method to Analyze Seasonal Predictability of Regional-Scale Tropical Rainfall

Abstract Current seasonal prediction of rainfall typically focuses on 3-month rainfall totals at regional scale. This temporal summation reduces the noise related to smaller-scale weather variability but also implicitly emphasizes the peak of the climatological seasonal cycle of rainfall. This approach may hide potentially predictable signals when rainfall is lower: for example, near the onset or cessation of the rainy season. The authors illustrate such a case for the East African long rains (March–May) on a network of 36 stations in Kenya and north Tanzania from 1961 to 2001. Spatial coherence and potential predictability of seasonal rainfall anomalies associated with tropical sea surface temperature (SST) anomalies clearly peak during the early stage of the rainy season (in March), while the largest rainfall (in April and May) is far less spatially coherent; the latter is shown to contain a large noise component at the station scale that characterizes interannual variability of the March–May seasonal total amounts. Combining the empirical orthogonal function of both interannual and subseasonal variations with a fuzzy k-means clustering is shown to capture the most spatially coherent subseasonal “scenarios” that tend to filter out the noisier variations of the rainfall field and emphasize the most consistent signals in both time and space. This approach is shown to provide insight into the seasonal predictability of long dry spells and heavy daily rainfall events at local scale and their subseasonal modulation.

Indigenous knowledge in seasonal rainfall prediction in Tanzania: A case of the South-western Highland of Tanzania

This paper describes how farmers in the South-Western Highland of Tanzania predict rainfall using local environmental indicators and astronomical factors. The perceptions of the local communities on conventional weather and climate forecasts were also assessed. The study was conducted in Rungwe and Kilolo districts in Mbeya and Iringa regions respectively. Participatory rural appraisal methods, key informant interviews and focus group discussions were used in data collection and the collected data was analyzed using statistical package for social science. It has been found that plant phenology is widely used by local communities in both districts in seasonal rainfall forecasting. Early and significant flowering of Mihemi (Erythrina abyssinica) and Mikwe (Brachystegiaspeciformis) trees from July to November has been identified to be one of the signals of good rainfall season. The behaviour of Dudumizi bird has been singled out as one of the best indicator for rainfall. Both Indigenous Knowledge specialists and TMA experts have predicted 2009/2010 rainfall season to feature normal to above normal rainfall. Systematic documentation and subsequent integration of Indigenous Knowledge into conventional weather forecasting system is recommended as one of the strategy that could help to improve the accuracy of seasonal rainfall forecasts under a changing climate. Key words: Climate variability, seasonal forecasting, El Nino, Dudumizi, indigenous knowledge.

Neo-fuzzy neuron model for seasonal rainfall forecast: A case study of Ceara's eight homogenous regions

The knowledge about the seasonal rainfall in some Brazilian regions is essential for agriculture and the adequate management of water resources. For this purpose, linear and nonlinear models are commonly used for seasonal rainfall prediction, while some of them are based on Artificial Neural Networks, demonstrating great potential as shown in literature. According to this tendency, this work presents a rainfall seasonal forecast model based on a neuro-fuzzy technique called Neo-Fuzzy Neuron Model. Improved performance by using this approach has been obtained in terms of reduced root mean square error RMSE and increased correlation between predicted and real output when compared with dynamic downscaling model using the Regional Spectral Model. Experimental results show the effectiveness of the proposed method in predictions regarding the first four trimesters from year 2002 up to the current one.

Seasonal prediction of the Indian summer monsoon rainfall using canonical correlation analysis of the NCMRWF global model products

ABSTRACTIn this study, canonical correlation analysis (CCA) has been used to statistically downscale the seasonal predictions of the Indian summer monsoon rainfall (ISMR) from a global spectral model. An extensive diagnostic study of the global model products and observed data for the period 1981–2008 indicates that while the predictions of rainfall anomalies have poor skill, the mean flow patterns are brought out reasonably well by the model. The model precipitation is found to be more strongly dependent on sea surface temperature over the Nino regions in the Pacific Ocean. However, the observed precipitation has a stronger links to winds at 850 hPa near the Somali coast than is evident in the model. On the basis of correlation maps, potential model predictors (specific humidity and zonal and meridional winds over different regions at different levels) are chosen for CCA for the prediction of ISMR. Using leave‐three‐out cross‐validation technique, canonical coefficients are computed using 25 years data (as training period) for CCA model. With this, predictions from the CCA model have also been prepared for the period of 1981–2005 to evaluate the performance. In addition to the above, predictions are made for four independent years (2006–2009). An improvement in skill of the composite forecasts (obtained using all the predictors) in terms of interannual variability is noticed over some parts of east‐ and northeast India as well as many parts of peninsular region especially over west coast of India. Copyright © 2012 Royal Meteorological Society

Seasonal Rainfall Prediction Skill over South Africa: One- versus Two-Tiered Forecasting Systems

Abstract Forecast performance by coupled ocean–atmosphere or one-tiered models predicting seasonal rainfall totals over South Africa is compared with forecasts produced by computationally less demanding two-tiered systems where prescribed sea surface temperature (SST) anomalies are used to force the atmospheric general circulation model. Two coupled models and one two-tiered model are considered here, and they are, respectively, the ECHAM4.5–version 3 of the Modular Ocean Model (MOM3-DC2), the ECHAM4.5-GML–NCEP Coupled Forecast System (CFSSST), and the ECHAM4.5 atmospheric model that is forced with SST anomalies predicted by a statistical model. The 850-hPa geopotential height fields of the three models are statistically downscaled to South African Weather Service district rainfall data by retroactively predicting 3-month seasonal rainfall totals over the 14-yr period from 1995/96 to 2008/09. Retroactive forecasts are produced for lead times of up to 4 months, and probabilistic forecast performance is evaluated for three categories with the outer two categories, respectively, defined by the 25th and 75th percentile values of the climatological record. The resulting forecast skill levels are also compared with skill levels obtained by downscaling forecasts produced by forcing the atmospheric model with simultaneously observed SST in order to produce a reference forecast set. Downscaled forecasts from the coupled systems generally outperform the downscaled forecasts from the two-tiered system, but neither of the two systems outscores the reference forecasts, suggesting that further improvement in operational seasonal rainfall forecast skill for South Africa is still achievable.