scholarly journals Geospatial Trends and Decadal Anomalies in Extreme Rainfall over Uganda, East Africa

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Charles Onyutha
Keyword(s):  
North Atlantic Ocean ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Indicator Method ◽  
Significance Level ◽  
Modes Of Variability ◽  
In Series

Trends and variability in series comprising the mean of fifteen highest daily rainfall intensities in each year were analyzed considering entire Uganda. The data were extracted from high-resolution (0.5° × 0.5°) gridded daily series of the Princeton Global Forcings covering the period 1948–2008. Variability was analyzed using nonparametric anomaly indicator method and empirical orthogonal functions. Possible drivers of the rainfall variability were investigated. Trends were analyzed using the cumulative rank difference approach. Generally, rainfall was above the long-term mean from the mid-1950s to the late 1960s and again in the 1990s. From around 1970 to the late 1980s, rainfall was characterized by a decrease. The first and second dominant modes of variability correspond with the variation in Indian Ocean Dipole and North Atlantic Ocean index, respectively. The influence of Niño 3 on the rainfall variability of some parts of the country was also evident. The southern and northern parts had positive and negative trends, respectively. The null hypothesisH0(no trend) was collectively rejected at the significance level of 5% in the series from 7 out of 168 grid points. The insights from the findings of this study are vital for planning and management of risk-based water resources applications.

scholarly journals A model intercomparison analysing the link between ozone and geopotential height anomalies in January

2007 ◽  
Vol 7 (6) ◽  
pp. 15409-15451 ◽  
Author(s):  
P. Braesicke ◽  
C. Brühl ◽  
M. Dameris ◽  
R. Deckert ◽  
V. Eyring ◽  
...  
Keyword(s):  
Geopotential Height ◽  
Climate Models ◽  
Horizontal Resolution ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Modes Of Variability ◽  
Statistical Framework ◽  
Column Ozone ◽  
Linear Correlations ◽  
Upper Boundary

Abstract. A statistical framework to evaluate the performance of chemistry-climate models with respect to the interaction between meteorology and ozone during northern hemisphere mid-winter, in particularly January, is used. Different statistical diagnostics from four chemistry-climate models (E39C, ME4C, UMUCAM, ULAQ) are compared with the ERA-40 re-analysis. First, we analyse vertical coherence in geopotential height anomalies as described by linear correlations between two different pressure levels (30 and 200 hPa) of the atmosphere. In addition, linear correlations between (partial) column ozone and geopotential height anomalies at 200 hPa are discussed to motivate a simple picture of the meteorological impacts on ozone on interannual timescales. Secondly, we discuss characteristic spatial structures in geopotential height and (partial) column ozone anomalies as given by their first two empirical orthogonal functions. Finally, we describe the covariance patterns between reconstructed anomalies of geopotential height and (partial) column ozone. In general we find good agreement between the models with higher horizontal resolution (E39C, ME4C, UMUCAM) and ERA-40. Some diagnostics seem to be capable of picking up model similarities (either that the models use the same dynamical core (E39C, ME4C), or that they have a high upper boundary (ME4C, UMUCAM)). The methodology allows to identify the leading modes of variability contributing to the overall ozone-geopotential height correlations and points to interesting differences between the chemistry-climate models and ERA-40. Those discrepancies have to be taken into account when providing confidence intervals for climate change integrations.

Understanding the role of water vapor and temperature in easterly wave-related convection

2020 ◽  
Author(s):  
Rosa Vargas Martes ◽  
Angel Adames Corraliza
Keyword(s):  
Water Vapor ◽  
Rainfall Variability ◽  
Large Fraction ◽  
Deep Convection ◽  
Linear Regression Analysis ◽  
Empirical Orthogonal Functions ◽  
Specific Humidity ◽  
Coupling Mechanism ◽  
Orthogonal Functions ◽  
The Relationship

<p>Easterly Waves (EW) in the Pacific Ocean (PEW) and over Africa (AEW) account for a large fraction of rainfall variability in their respective regions. Although multiple studies have been conducted to better understand EWs, many questions remain regarding their structure, development, and coupling to deep convection. Recent studies have highlighted the relationship between water vapor and precipitation in tropical motion systems. However, EW have not been studied within this context. On the basis of Empirical Orthogonal Functions (EOFs) and a novel plume-buoyancy framework, the thermodynamic processes associated with EW-related convection are elucidated. A linear regression analysis reveals the relationship between temperature, moisture, and precipitation in EW. Temperature anomalies are found to be highly correlated in space and time with anomalies in specific humidity. However, this coupling between temperature and moisture is more robust in AEWs than PEWs. In PEWs moisture accounts for a larger fraction of precipitation variability. Results suggest that the convective coupling mechanism in AEW may differ from the coupling mechanism of PEWs.</p>

scholarly journals Rainfall variability and meteorological drought in the Horn of Africa

MAUSAM ◽  
2021 ◽  
Vol 68 (3) ◽  
pp. 463-474
Author(s):  
Y. WANG ◽  
Z. W. SHILENJE ◽  
P. O. SAGERO ◽  
A. M. NYONGESA ◽  
N. BANDA
Keyword(s):  
Rainfall Variability ◽  
Meteorological Drought ◽  
Research Unit ◽  
Dominant Mode ◽  
Empirical Orthogonal Functions ◽  
Seasonal Rainfall ◽  
Horn Of Africa ◽  
Standard Precipitation Index ◽  
Orthogonal Functions ◽  
Rainfall Characteristics

 Basic rainfall characteristics and drought over the Horn of Africa (HoA) is investigated, from 1901 to 2010. Standard Precipitation Index (SPI) is used to study drought variability, mainly focusing on 3-month SPI. The dominant mode of variability of seasonal rainfall was analyzed by performing Empirical orthogonal functions (EOF) analysis. Gridded data is sourced from Climate Research Unit (CRU), spanning from 1901 to 2010. The HoA experiences predominantly bimodal rainfall distribution in time; March to May (MAM) and October to December (OND). The spatial component of the first eigenvector (EOF1) shows that the MAM and OND seasonal rainfalls are dominated by negative and positive loadings, respectively. The EOF1 explain 34.5% and 58.9% variance of MAM and OND seasonal rainfall, respectively. The EOF2, 3 and 4 are predominantly positive, explaining less than 25% in total of the seasonal rainfall variance in the two seasons. The last two decades experienced the highest negative anomaly, with OND seasonal rainfall showing higher anomalies as compared to MAM season. The OND season recorded 9% more drought events as compared to MAM season. The frequency of occurrence of moderate, severe and extreme dryness was almost the same in the two seasons. These results give a good basis for regional model validation, as well as mapping out drought hotspots and projections studies in the HoA.

scholarly journals Influence of Indian Ocean dipole on rainfall variability and extremes over southern Africa

MAUSAM ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 637-648
Author(s):  
OGWANG B. A. ◽  
ONGOMA V. ◽  
SHILENJE Z. W. ◽  
RAMOTUBEI T. S. ◽  
LETUMA M. ◽  
...  
Keyword(s):  
Indian Ocean ◽  
Southern Africa ◽  
Indian Ocean Dipole ◽  
Rainfall Variability ◽  
Dominant Mode ◽  
Empirical Orthogonal Functions ◽  
Extreme Weather Events ◽  
Orthogonal Functions ◽  
Upper Level ◽  
The Relationship

Extreme weather events; floods and droughts are common in southern Africa (SA) consisting of 8 countries (Botswana, Namibia, South Africa, Lesotho, Swaziland, Mozambique, Zimbabwe, parts of Angola and Zambia). This study examines the linkage between the SA October-December (OND) rainfall, the Indian Ocean Dipole (IOD) and the South Atlantic Oscillation Dipole (SAOD). Empirical Orthogonal Functions (EOF) technique is used to establish the dominant mode of variability of OND rainfall, as correlation analysis is applied to quantify the relationship between the indices; IOD [Dipole Mode Index (DMI)], SAOD Index (SAODI) and OND rainfall variability. Results show that the dominant mode of variability of OND rainfall exhibits a dipole pattern over SA and there exists a significant correlation at 95% confidence level between the area average OND rainfall (rainfall index (RFI)) and DMI, with a correlation coefficient of -0.3. The relationship between the mean SA OND rainfall and the positive phase of IOD varies greatly in space, ranging from one country to another. Further analysis of the dry and wet of SAOND rainfall years reveal that wet years are associated with convergence at  surface level (850 hPa) and divergence at upper level (200 hPa), depicting rising motion in the region, whereas dry years are associated with divergence at low level and convergence at upper level, implying descending motion. The study recommends further research on a reduced spatial scale, for instance at a country level to ascertain the effect of IOD on individual country’s weather. This will help in accurate monitoring of the evolution of IOD events to improve quality of seasonal weather forecasts in the region.

scholarly journals Trend and spatial variability of 1-day extreme rainfall from 1980 - 2015: study at the adminisitrative area of UPT PSDA Pasuruan

2019 ◽  
Vol 8 (1) ◽  
pp. 29
Author(s):  
Indarto Indarto
Keyword(s):  
Spatial Variability ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Statistical Test ◽  
Rainfall Event ◽  
Rainfall Data ◽  
Rainfall Events ◽  
Significance Level ◽  
Main Input ◽  
Crossing Test

This study aims to analyze trends,  shift and spatial variability of extreme-rainfall in the area of UPT PSDA Pasuruan. The daily rainfall data from 64 stations from 1980 until 2015 were used as main input. The 1-day extreem rainfall data is determined as the maximum annual of 24-hour rainfall events.  The statistical  analysis using Mann-Kendall, Rank-Sum, and Median Crossing Test using significance level α = 0,05. The spatial variability of extrem rainfall data is described using average annual 24-hour rainfall during the periods of record. Each station is represented by one value. The values are then interpolated using IDW interpolation methods to maps the spatial variability of extreem rainfall event.  The results show the value of statistical test for each stations that show the existing  trend, shift, or randomness of data. The result also produce thematic maps show the spatial variability of extreme rainfall and the value of each trend.

Extreme rainfall and drought events in Tamil Nadu, India

2020 ◽  
Vol 80 (3) ◽  
pp. 175-188
Author(s):  
R Rajkumar ◽  
CS Shaijumon ◽  
B Gopakumar ◽  
D Gopalakrishnan
Keyword(s):  
Tamil Nadu ◽  
Daily Rainfall ◽  
India Meteorological Department ◽  
Extreme Rainfall ◽  
Drought Severity ◽  
Extreme Rainfall Events ◽  
Rainfall Events ◽  
Significance Level ◽  
Spatio Temporal ◽  
Temporal Dimensions

In the present study, we examined the exposure of the Tamil Nadu region, India, to droughts and extreme rainfall events using the Standardised Precipitation Evapotranspiration Index (SPEI) and a classification scheme based on daily rainfall. We used high-resolution temperature and rainfall observations from the India Meteorological Department for the period 1951-2016. The robustness of the results was tested using the Mann-Kendall trend (M-K) test and the Kolmogorov-Smirnov (K-S) test. During the study period, there were statistically significant increasing trends in drought area (90% significance level), maximum drought intensity (99% significance level) and maximum drought severity (99% significance level) over the Tamil Nadu region. There has also been an increase in the frequency and intensity of heavy rainfall events in recent years. The spatio-temporal dimensions of this study suggest an increasing exposure of this semi-arid, rain shadow region to severe droughts and extreme rainfall events in recent decades. The results provide sufficient grounds to substantiate the necessity of immediate interventions at the policy level.

scholarly journals Influence of South Atlantic Sea Surface Temperatures on Rainfall Variability and Extremes over Southern Africa

2008 ◽  
Vol 21 (24) ◽  
pp. 6498-6520 ◽  
Author(s):  
C. J. R. Williams ◽  
D. R. Kniveton ◽  
R. Layberry
Keyword(s):  
High Resolution ◽  
Southern Africa ◽  
Climate Model ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
South Atlantic ◽  
Sea Surface ◽  
Rainfall Extremes ◽  
Central South

Abstract It is generally agreed that changing climate variability, and the associated change in climate extremes, may have a greater impact on environmentally vulnerable regions than a changing mean. This research investigates rainfall variability, rainfall extremes, and their associations with atmospheric and oceanic circulations over southern Africa, a region that is considered particularly vulnerable to extreme events because of numerous environmental, social, and economic pressures. Because rainfall variability is a function of scale, high-resolution data are needed to identify extreme events. Thus, this research uses remotely sensed rainfall data and climate model experiments at high spatial and temporal resolution, with the overall aim being to investigate the ways in which sea surface temperature (SST) anomalies influence rainfall extremes over southern Africa. Extreme rainfall identification is achieved by the high-resolution microwave/infrared rainfall algorithm dataset. This comprises satellite-derived daily rainfall from 1993 to 2002 and covers southern Africa at a spatial resolution of 0.1° latitude–longitude. Extremes are extracted and used with reanalysis data to study possible circulation anomalies associated with extreme rainfall. Anomalously cold SSTs in the central South Atlantic and warm SSTs off the coast of southwestern Africa seem to be statistically related to rainfall extremes. Further, through a number of idealized climate model experiments, it would appear that both decreasing SSTs in the central South Atlantic and increasing SSTs off the coast of southwestern Africa lead to a demonstrable increase in daily rainfall and rainfall extremes over southern Africa, via local effects such as increased convection and remote effects such as an adjustment of the Walker-type circulation.

scholarly journals An Empirical Seasonal Rainfall Forecasting Model for the Northeast Region of Brazil

Water ◽  
2021 ◽  
Vol 13 (12) ◽  
pp. 1613
Author(s):  
Rodrigo Lins da Rocha Júnior ◽  
David Duarte Cavalcante Pinto ◽  
Fabrício Daniel dos Santos Silva ◽  
Heliofábio Barros Gomes ◽  
Helber Barros Gomes ◽  
...  
Keyword(s):  
Time Series ◽  
Southern Oscillation ◽  
Rainfall Variability ◽  
Northern Region ◽  
Empirical Orthogonal Functions ◽  
Rainfall Forecasting ◽  
Orthogonal Functions ◽  
Northeast Region ◽  
Teleconnection Patterns ◽  
The North

The Northeast region of Brazil (NEB) is characterized by large climate variability that causes extreme and long unseasonal wet and dry periods. Despite significant model developments to improve seasonal forecasting for the NEB, the achievement of a satisfactory accuracy often remains a challenge, and forecasting methods aimed at reducing uncertainties regarding future climate are needed. In this work, we implement and assess the performance of an empirical model (EmpM) based on a decomposition of historical data into dominant modes of precipitation and seasonal forecast applied to the NEB domain. We analyzed the model’s performance for the February-March-April quarter and compared its results with forecasts based on data from the North American Multi-model Ensemble (NMME) project for the same period. We found that the first three leading precipitation modes obtained by empirical orthogonal functions (EOF) explained most of the rainfall variability for the season of interest. Thereby, this study focuses on them for the forecast evaluations. A teleconnection analysis shows that most of the variability in precipitation comes from sea surface temperature (SST) anomalies in various areas of the Pacific and the tropical Atlantic. The modes exhibit different spatial patterns across the NEB, with the first being concentrated in the northern half of the region and presenting remarkable associations with the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM), both linked to the latitudinal migration of the intertropical convergence zone (ITCZ). As for the second mode, the correlations with oceanic regions and its loading pattern point to the influence of the incursion of frontal systems in the southern NEB. The time series of the third mode implies the influence of a lower frequency mode of variability, probably related to the Interdecadal Pacific Oscillation (IPO). The teleconnection patterns found in the analysis allowed for a reliable forecast of the time series of each mode, which, combined, result in the final rainfall prediction outputted by the model. Overall, the EmpM outperformed the post-processed NMME for most of the NEB, except for some areas along the northern region, where the NMME showed superiority.

scholarly journals Seasonal Predictability and Change of Large-Scale Summer Precipitation Patterns over the Northeast United States

2019 ◽  
Vol 20 (7) ◽  
pp. 1275-1292 ◽  
Author(s):  
Kuk-Hyun Ahn ◽  
Scott Steinschneider
Keyword(s):  
United States ◽  
Large Scale ◽  
North Atlantic Ocean ◽  
Rainfall Variability ◽  
Storm Track ◽  
Daily Rainfall ◽  
Positive Trend ◽  
Low Frequencies ◽  
Northeast United States ◽  
Dry Conditions

Abstract This study examines space–time patterns of summer daily rainfall variability across the Northeast United States, with a focus on historical trends and the potential for long-lead predictability. A hidden Markov model based on daily data is used to define six weather states that represent distinct patterns of rainfall across the region, and composites are used to examine atmospheric circulation during each state. The states represent the occurrence of region-wide dry and wet conditions associated with a large-scale ridge and trough over the Northeast, respectively, as well as inland and coastal storm tracks. There is a positive trend in the frequency of the weather state associated with heavy, regionwide rainfall, which is mirrored by a decreasing trend in the frequency of stationary ridges and regionwide dry conditions. The frequency of state occurrences is also examined for historical Northeast droughts. Two primary drought types emerge that are characterized by region-wide dry conditions linked to a persistent ridge and an eastward-shifted storm track associated with light precipitation along the coastline. Finally, composites of May sea surface temperature anomalies (SSTAs) prior to summers with high and low frequencies of each weather state are used to assess long-lead predictability. These composites are compared against similar composites based on regional anomalies in low streamflow conditions [June–August 7-day low flows (SDLFs)]. Results indicate that springtime SSTs, particularly those in the Caribbean Sea and tropical North Atlantic Ocean, provide some predictability for summers with above-average precipitation and SDLFs, but SSTs provide little information on the occurrence of drought conditions across the Northeast.

Genesis of Hurricane Julia (2010) within an African Easterly Wave: Sensitivity Analyses of WRF-LETKF Ensemble Forecasts

2014 ◽  
Vol 71 (9) ◽  
pp. 3180-3201 ◽  
Author(s):  
Stefan F. Cecelski ◽  
Da-Lin Zhang
Keyword(s):  
Deep Convection ◽  
Empirical Orthogonal Functions ◽  
Sensitivity Analyses ◽  
Tropical Cyclogenesis ◽  
Tropospheric Temperature ◽  
Orthogonal Functions ◽  
Ensemble Forecasts ◽  
Low Level ◽  
Modes Of Variability ◽  
Two Parameters

Abstract In this study, the predictability of tropical cyclogenesis (TCG) is explored by conducting ensemble sensitivity analyses on the TCG of Hurricane Julia (2010). Using empirical orthogonal functions (EOFs), the dominant patterns of ensemble disagreements are revealed for various meteorological parameters such as mean sea level pressure (MSLP) and upper-tropospheric temperature. Using the principal components of the EOF patterns, ensemble sensitivities are generated to elucidate which mechanisms drive the parametric ensemble differences. The dominant pattern of MSLP ensemble spread is associated with the intensity of the pre–tropical depression (pre-TD), explaining nearly half of the total variance at each respective time. Similar modes of variance are found for the low-level absolute vorticity, though the patterns explain substantially less variance. Additionally, the largest modes of variability associated with upper-level temperature anomalies closely resemble the patterns of MSLP variance, suggesting interconnectedness between the two parameters. Sensitivity analyses at both the pre-TD and TCG stages reveal that the MSLP disturbance is strongly correlated to upper-tropospheric temperature and, to a lesser degree, surface latent heat flux anomalies. Further sensitivity analyses uncover a statistically significant correlation between upper-tropospheric temperature and convective anomalies, consistent with the notion that deep convection is important for augmenting the upper-tropospheric warmth during TCG. Overall, the ensemble forecast differences for the TCG of Julia are strongly related to the processes responsible for MSLP falls and low-level cyclonic vorticity growth, including the growth of upper-tropospheric warming and persistent deep convection.

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