Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

Elsanabary, Mohamed Helmy; Gan, Thian Yew

doi:10.1175/mwr-d-13-00085.1

Cited by 24 publications

(14 citation statements)

References 39 publications

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“…These three (NAO, ENSO, and IOD) monthly indices have been further averaged over 3 months (seasonal average) to be consistent with our observational datasets (KMO, KSA, and BAH). This is similar to the approach used by Donat et al (2014) and Elsanabary and Gan (2013) to examine the interannual seasonal variability of temperature and precipitation. Furthermore, the longest spatial available dust dataset for the study region was obtained from GEMS (Global and regional Earth-system Monitoring using Satellite and in-situ data, Hollingsworth et al, 2008) developed by ECMWF-IFS (European Centre for Medium-Range Weather Forecasts-Integrated Forecast System, Benedetti et al, 2009Benedetti et al, ) between 2003Benedetti et al, and 2012 in the form of Dust Aerosol Optical Depth at 550 nm (DU).…”

Section: Climate Variabilitymentioning

confidence: 89%

“…where x represents the standardized teleconnection pattern seasonal index, i is the yearly seasonal data point index, and n is the total number of years. The values of N s range between − ∞ to +1, with 1 indicating a perfect relation, N s = 0 indicating that the standardized meteorological seasonal anomaly is only as good as the standardized teleconnection pattern seasonal index average and N s < 0 indicating a weak relation (Elsanabary and Gan, 2013).…”

Section: Climate Variabilitymentioning

confidence: 99%

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Shamals and climate variability in the Northern Arabian/Persian Gulf from 1973 to 2012

Senafi

Anis

2015

Intl Journal of Climatology

145

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This paper presents key results from analysis of surface meteorological observations collected in the Northern Arabian/Persian Gulf (N Gulf; Kuwait, Bahrain, and NE Saudi Arabia), which spans a 40-years period . The first part of this study analyzes climate variability in the N Gulf, and relates them to teleconnection patterns (North Atlantic Oscillation, El Nino Southern Oscillation, and Indian Ocean Dipole). Results of the analysis indicate that during the study period the climate in the region experienced a general trend of increase in temperature (0.8 ∘ C), decrease in barometric pressure (1 mbar), reduction in humidity (6%), and decrease in visibility (9%). Significant correlations were found between the three teleconnection patterns and the meteorological conditions suggesting that seasonal variabilities in air temperature, barometric pressure, and precipitation are closely related to the teleconnection patterns. The second part of this study examines the 40-year variability of Shamal events (strong NW winds that commonly generate significant dust storms). The data suggests that on average Shamal events occur at a rate of 10 events year -1 with 85% of the events occurring during the summer and winter. The number of these events has increased in the past 14 years of the study period. These events resulted in abrupt changes in meteorological conditions: an increase in wind speed of 2.7 m s -1 , a decrease in visibility of 1.7 km, and reduction in humidity of 4.3%. Seasonal variations in temperature (an increase in temperature during summer of 0.8 ∘ C, and a decrease of 1.5 ∘ C during winter) and barometric pressure (a decrease in barometric pressure during summer of 0.6 mbar and an increase of 7.8 mbar during winter) were observed during Shamal events.

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Section: Climate Variabilitymentioning

confidence: 89%

Section: Climate Variabilitymentioning

confidence: 99%

Shamals and climate variability in the Northern Arabian/Persian Gulf from 1973 to 2012

Senafi

Anis

2015

Intl Journal of Climatology

145

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“…Eritrea, Djibouti, northern Ethiopia, northern Uganda, and South Sudan also receive most of their rainfall in the boreal summer. However, with the exception of Nicholson [], forecast models for this season have been produced only for Ethiopia [e.g., Jury , ] or for a region within that country, such as the Blue Nile basin [ Elsanabary and Gan , ].…”

Section: Seasonal Predictionmentioning

confidence: 99%

Climate and climatic variability of rainfall over eastern Africa

Nicholson

2017

Reviews of Geophysics

547

425

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This review examines several aspects of the climate of eastern Africa. The climatic commonality throughout the region is the frequent occurrence of drought severe enough to incapacitate the population. Because of recent droughts and evidence of disastrous, long‐term climatic change, the region has become a major focus of meteorological research. This review covers six relevant topics: climatic regionalization, seasonal cycle, intraseasonal variability, interannual variability, recent trends, and seasonal forecasting. What emerges is a markedly different view of the factors modulating rainfall, the dynamics associated with the seasons, and the character of teleconnections within the region and the interrelationships between the various rainy seasons. Some of the most important points are the following. (1) The paradigm of two rainy seasons resulting from the biannual equatorial passage of the Intertropical Convergence Zone is inadequate. (2) The “long rains” should not be treated as a single season, as character, causal factors, and teleconnections are markedly different in each month. (3) The long rains have been declining continuously in recent decades. (4) The Madden‐Julian Oscillation has emerged as a factor in interannual and intraseasonal variability, but the relative strength of Pacific and Indian Ocean anomalies plays a major role in the downward trend. (5) Factors governing the short rains are nonstationary. (6) Droughts have become longer and more intense and tend to continue across rainy seasons, and their causes are not adequately understood. (7) Atmospheric variables provide more reliable seasonal forecasts than the factors traditionally considered in forecast models, such as sea surface temperatures and El Niño–Southern Oscillation.

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“…One of the widely used tools to investigate the periodic pattern of time series is the wavelet transform analysis [35]. In this method, called continuous wavelet transform (CWT), the time series is decomposed in the time and frequency domains identifying the dominant frequency as well as its temporal variation [35][36][37] and it has been frequently used to shed some light on the complex characteristics of hydro-climatic variables [8,[38][39][40][41][42][43]. Another representation of a wavelet spectrum, called the Global Wavelet Power Spectrum (GWPS), can also be used to determine the dominating time scales within a time series, whereby the coefficients of a power spectrum for one scale are averaged over the length of the entire time series [36].…”

Section: Time Series Analysismentioning

confidence: 99%

Lake Volume Data Analyses: A Deep Look into the Shrinking and Expansion Patterns of Lakes Azuei and Enriquillo, Hispaniola

Moknatian

Piasecki

2019

Hydrology

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This paper presents the development of an evenly spaced volume time series for Lakes Azuei and Enriquillo both located on the Caribbean island of Hispaniola. The time series is derived from an unevenly spaced Landsat imagery data set which is then exposed to several imputation methods to construct the gap filled uniformly-spaced time series so it can be subjected to statistical analyses methods. The volume time series features both gradual and sudden changes the latter of which is attributed to North Atlantic cyclone activity. Relevant cyclone activity is defined as an event passing within 80 km and having regional monthly rainfall averages higher than a threshold value of 87 mm causing discontinuities in the lake responses. Discontinuities are accounted for in the imputation algorithm by dividing the time series into two sub-sections: Before/after the event. Using leave-p-out cross-validation and computing the NRMSE index the Stineman interpolation proves to be the best algorithm among 15 different imputation alternatives that were tested. The final time series features 16-day intervals which is subsequently resampled into one with monthly time steps. Data analyses of the monthly volume change time series show Lake Enriquillo’s seasonal periodicity in its behavior and also its sensitivity due to the occurrence of storm events. Response times feature a growth pattern lasting for one to two years after an extreme event, followed by a shrinking pattern lasting 5–7 years returning the lake to its original state. While both lakes show a remarkable long term increase in size starting in 2005, Lake Azuei is different in that it is much less sensitive to storm events and instead shows a stronger response to just changing seasonal rainfall patterns.

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Wavelet Analysis of Seasonal Rainfall Variability of the Upper Blue Nile Basin, Its Teleconnection to Global Sea Surface Temperature, and Its Forecasting by an Artificial Neural Network

Cited by 24 publications

References 39 publications

Shamals and climate variability in the Northern Arabian/Persian Gulf from 1973 to 2012

Shamals and climate variability in the Northern Arabian/Persian Gulf from 1973 to 2012

Climate and climatic variability of rainfall over eastern Africa

Lake Volume Data Analyses: A Deep Look into the Shrinking and Expansion Patterns of Lakes Azuei and Enriquillo, Hispaniola

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