scholarly journals Spatial Coherence of Tropical Rainfall at the Regional Scale

2007 ◽  
Vol 20 (21) ◽  
pp. 5244-5263 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward ◽  
Pierre Camberlin
Keyword(s):  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Frequency Of Occurrence ◽  
Tropical Regions ◽  
Mean Intensity ◽  
Northwestern India ◽  
Rainfall Occurrence ◽  
Rainfall Frequency ◽  
Daily Time

Abstract This study examines the spatial coherence characteristics of daily station observations of rainfall in five tropical regions during the principal rainfall season(s): the Brazilian Nordeste, Senegal, Kenya, northwestern India, and northern Queensland. The rainfall networks include between 9 and 81 stations, and 29–70 seasons of observations. Seasonal-mean rainfall totals are decomposed in terms of daily rainfall frequency (i.e., the number of wet days) and mean intensity (i.e., the mean rainfall amount on wet days). Despite the diverse spatiotemporal sampling, orography, and land cover between regions, three general results emerge. 1) Interannual anomalies of rainfall frequency are usually the most spatially coherent variable, generally followed closely by the seasonal amount, with the daily mean intensity in a distant third place. In some cases, such as northwestern India, which is characterized by large daily rainfall amounts, the frequency of occurrence is much more coherent than the seasonal amount. 2) On daily time scales, the interstation correlations between amounts on wet days always fall to insignificant values beyond a distance of about 100 km. The spatial scale of daily rainfall occurrence is larger and more variable among the networks. 3) The regional-scale signal of the seasonal amount is primarily related to a systematic spatially coherent modulation of the frequency of occurrence.

scholarly journals Seasonal Predictability and Spatial Coherence of Rainfall Characteristics in the Tropical Setting of Senegal

2006 ◽  
Vol 134 (11) ◽  
pp. 3248-3262 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward
Keyword(s):  
Interannual Variability ◽  
General Circulation ◽  
Degrees Of Freedom ◽  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Occurrence Frequency ◽  
Seasonal Rainfall ◽  
Mean Intensity ◽  
Rainfall Occurrence

Abstract This study examines space–time characteristics of seasonal rainfall predictability in a tropical region by analyzing observed data and model simulations over Senegal. Predictability is analyzed in terms of the spatial coherence of observed interannual variability at the station scale, and within-ensemble coherence of general circulation model (GCM) simulations with observed sea surface temperatures (SSTs) prescribed. Seasonal mean rainfall anomalies are decomposed in terms of daily rainfall frequency and daily mean intensity. The observed spatial coherence is computed from a 13-station network of daily rainfall during the July–September season 1961–98 in terms of (i) interannual variability of a standardized anomaly index (i.e., the average of the normalized anomalies of each station), (ii) the external variance (i.e., the fraction of common variance among stations), and (iii) the number of spatiotemporal degrees of freedom. Spatial coherence of interannual anomalies across stations is found to be much stronger for seasonal rainfall amount and daily occurrence frequency, compared with daily mean intensity of rainfall. Combinatorial analysis of the station observations suggests that, for occurrence and seasonal amount, the empirical number of spatial degrees of freedom is largely insensitive to the number of stations considered, and is between 3 and 4 for Senegal. For daily mean intensity, by contrast, each station is found to convey almost independent information, and the number of degrees of freedom would be expected to increase for a denser network of stations. The GCM estimates of potential predictability and skill associated with the SST forcing are found to be remarkably consistent with those inferred from the observed spatial coherence: there is a moderate-to-strong skill at reproducing the interannual variations of seasonal amounts and rainfall occurrence, whereas the skill is weak for the mean intensity of rainfall. Over Senegal during July–September, it is concluded that (i) regional-scale seasonal amount and rainfall occurrence frequency are predictable from SSTs, (ii) daily mean intensity of rainfall is spatially incoherent and largely unpredictable at the regional scale, and (iii) point-score estimates of seasonal rainfall predictability and skill are subject to large sampling variability.

scholarly journals Weather Types and Rainfall over Senegal. Part I: Observational Analysis

2008 ◽  
Vol 21 (2) ◽  
pp. 266-287 ◽  
Author(s):  
Vincent Moron ◽  
Andrew W. Robertson ◽  
M. Neil Ward ◽  
Ousmane Ndiaye
Keyword(s):  
Rainfall Variability ◽  
Regional Scale ◽  
Daily Rainfall ◽  
Weather Type ◽  
Seasonal Rainfall ◽  
Boreal Summer ◽  
Frequency Of Occurrence ◽  
Weather Types ◽  
Western Sahel ◽  
Type Frequency

Abstract A k-means cluster analysis is used to summarize unfiltered daily atmospheric variability at regional scale over the western Sahel and eastern tropical North Atlantic during the boreal summer season [July–September (JAS)] 1961–98. The analysis employs zonal and meridional regional wind fields at 925, 700, and 200 hPa from the European Centre for Medium-Range Weather Forecasts reanalyses. An eight-cluster solution is shown to yield an integrated view of the complex regional circulation variability, without the need for explicit time filtering. Five of the weather types identified characterize mostly typical phases of westward-moving wave disturbances, such as African easterly waves (AEWs), and persistent monsoon surges, while the three others describe mostly different stages of the seasonal cycle. Their temporal sequencing describes a systematic monsoonal evolution, together with considerable variability at subseasonal and interannual time scales. Daily rainfall occurrence at 13 gauge stations in Senegal is found to be moderately well conditioned by the eight weather types, with positive rainfall anomalies usually associated with southerly wind anomalies at 925 hPa. Interannual variability of daily rainfall frequency is shown to depend substantially on the frequency of occurrence of weather types specific to the beginning and end of the JAS season, together with the number of persistent monsoon surges over the western Sahel. In contrast, year-to-year changes in the frequency of the weather types mostly associated with westward-moving waves such as AEWs are not found to influence seasonal frequency of occurrence of daily rainfall substantially. The fraction of seasonal rainfall variability related to weather-type frequency is found to have a strong relationship with tropical Pacific sea surface temperatures (SSTs): an El Niño (La Niña) event tends to be associated with a higher (lower) frequency of dry weather types during early and late JAS season with enhanced trade winds over the western Sahel, together with lower (higher) prevalence of persistent monsoon surges. The component of seasonal rainfall variability not related to weather-type frequency is characterized by changes in rainfall probability within each weather type, especially those occurring in the core of the JAS season; it exhibits a larger decadal component that is associated with an SST pattern previously identified with recent observed trends in Sahel rainfall.

scholarly journals Frequência e Intensidade Pluviométrica e a Correlação com El Niño-Oscilação Sul na Mesorregião Noroeste Paranaense, Brasil

2020 ◽  
Vol 13 (4) ◽  
pp. 1537
Author(s):  
Nathan Felipe da Silva Caldana ◽  
Luiz Gustavo Batista Ferreira ◽  
Marcelo Augusto De Aguiar e Silva
Keyword(s):  
Extreme Events ◽  
El Niño ◽  
El Nino ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
La Niña ◽  
La Nina ◽  
Rainfall Frequency ◽  
Agricultural Planning ◽  
Daily Time

O clima do planeta está mudando em frequência e magnitude de seus eventos atmosféricos. As atividades agrícolas são altamente dependentes do clima. Mesmo com avanços da tecnologia e da pesquisa, a variabilidade da produção no estado do Paraná é afetada pelas variáveis climáticas, sendo a precipitação o elemento mais importante para as regiões de clima tropical e subtropical. Para o planejamento agrícola e para as demais atividades humanas, conhecer o clima é importante para minimizar os riscos das atividades e garantir resultados satisfatórios. A Mesorregião Noroeste Paranaense é a área mais quente e seca do Estado do Paraná carecendo de estudos que contribuam para a tomada de decisão na região. Sendo assim, o objetivo deste trabalho foi analisar a variabilidade pluviométrica, a intensidade e a frequência da precipitação na MRNOP. Para isso utilizou-se as escalas temporais anual, mensal e diária com o recorte temporal de 1976 a 2018. Por meio dos resultados, identificou-se grande discrepância nas alturas pluviométricas anuais, mensais e diárias, com parte expressiva dos eventos extremos e maiores alturas de precipitação ocorrendo em períodos de El Niño e neutralidade, enquanto os períodos secos predominam em condições de La Niña. Rainfall Frequency And Intensity And Relation With El Niño-Southern Oscilation In The Northwest Mesoregion Of Parana State, BrazilA B S T R A C TThe planet's climate is changing in frequency and magnitude of its atmospheric events. Agricultural activities are highly climate dependent. Even with advances in technology and research, agricultural production is affected by climatic variables. For agricultural planning and for other human activities, knowing the climate is important to minimize the risks of activities and ensure satisfactory results. The northwest Paraná mesoregion is the warmest and driest area in the State of Paraná and lacks studies that contribute to decision making in the region. Therefore, the objective of this work was to analyze rainfall variability, intensity and frequency of precipitation in MRNOP, as well as assessing the association between rainfall totals and extremes with the occurrence of the El Niño-Southern Oscilation – ENSO. variability mode. The annual, monthly and daily time scales were used from 1976 to 2018. Thematic maps were developed with interpolation and regressions and graphs of box graphics for an analysis of the variability. A large discrepancy was identified at annual, monthly and daily rainfall heights, with significant part of extreme events and higher precipitation heights occurring in El Niño periods and neutrality, while dry periods predominate under La Niña conditions.Keywords: climate risk, extreme events; sea surface temperature.

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

2013 ◽  
Vol 26 (8) ◽  
pp. 2580-2600 ◽  
Author(s):  
Vincent Moron ◽  
Pierre Camberlin ◽  
Andrew W. Robertson
Keyword(s):  
Rainy Season ◽  
Early Stage ◽  
Regional Scale ◽  
Spatial Coherence ◽  
Daily Rainfall ◽  
Seasonal Prediction ◽  
East African ◽  
Seasonal Predictability ◽  
Potential Predictability ◽  
Noise Component

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.

scholarly journals The Wet and Dry Spells across India during 1951–2007

2010 ◽  
Vol 11 (1) ◽  
pp. 26-45 ◽  
Author(s):  
Nityanand Singh ◽  
Ashwini Ranade
Keyword(s):  
Climate Change ◽  
Rainfall Intensity ◽  
Total Duration ◽  
Daily Rainfall ◽  
Annual Rainfall ◽  
Change Scenario ◽  
Monsoon Period ◽  
Northwestern India ◽  
Area Of Interest ◽  
Dry Spells

Abstract Characteristics of wet spells (WSs) and intervening dry spells (DSs) are extremely useful for water-related sectors. The information takes on greater significance in the wake of global climate change and climate-change scenario projections. The features of 40 parameters of the rainfall time distribution as well as their extremes have been studied for two wet and dry spells for 19 subregions across India using gridded daily rainfall available on 1° latitude × 1° longitude spatial resolution for the period 1951–2007. In a low-frequency-mode, intra-annual rainfall variation, WS (DS) is identified as a “continuous period with daily rainfall equal to or greater than (less than) daily mean rainfall (DMR) of climatological monsoon period over the area of interest.” The DMR shows significant spatial variation from 2.6 mm day−1 over the extreme southeast peninsula (ESEP) to 20.2 mm day−1 over the southern-central west coast (SCWC). Climatologically, the number of WSs (DSs) decreases from 11 (10) over the extreme south peninsula to 4 (3) over northwestern India as a result of a decrease in tropical and oceanic influences. The total duration of WSs (DSs) decreases from 101 (173) to 45 (29) days, and the duration of individual WS (DS) from 12 (18) to 7 (11) days following similar spatial patterns. Broadly, the total rainfall of wet and dry spells, and rainfall amount and rainfall intensity of actual and extreme wet and dry spells, are high over orographic regions and low over the peninsula, Indo-Gangetic plains, and northwest dry province. The rainfall due to WSs (DSs) contributes ∼68% (∼17%) to the respective annual total. The start of the first wet spell is earlier (19 March) over ESEP and later (22 June) over northwestern India, and the end of the last wet spell occurs in reverse, that is, earlier (12 September) from northwestern India and later (16 December) from ESEP. In recent years/decades, actual and extreme WSs are slightly shorter and their rainfall intensity higher over a majority of the subregions, whereas actual and extreme DSs are slightly (not significantly) longer and their rainfall intensity weaker. There is a tendency for the first WS to start approximately six days earlier across the country and the last WS to end approximately two days earlier, giving rise to longer duration of rainfall activities by approximately four days. However, a spatially coherent, robust, long-term trend (1951–2007) is not seen in any of the 40 WS/DS parameters examined in the present study.

Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

2021 ◽  
Author(s):  
Samuele Segoni ◽  
Minu Treesa Abraham ◽  
Neelima Satyam ◽  
Ascanio Rosi ◽  
Biswajeet Pradhan
Keyword(s):  
Sigma Model ◽  
Confusion Matrix ◽  
Regional Scale ◽  
Geographical Location ◽  
Daily Rainfall ◽  
Rain Gauge ◽  
Integrated System ◽  
Rainfall Thresholds ◽  
Skill Scores ◽  
Landslide Forecasting

<p>SIGMA (Sistema Integrato Gestione Monitoraggio Allerta – integrated system for management, monitoring and alerting) is a landslide forecasting model at regional scale which is operational in Emilia Romagna (Italy) for more than 20 years. It was conceived to be operated with a sparse rain gauge network with coarse (daily) temporal resolution and to account for both shallow landslides (typically triggered by short and intense rainstorms) and deep seated landslides (typically triggered by long and less intense rainfalls). SIGMA model is based on the statistical distribution of cumulative rainfall values (calculated over varying time windows), and rainfall thresholds are defined as the multiples of standard deviation of the same, to identify anomalous rainfalls with the potential of triggering landslides.</p><p>In this study, SIGMA model is applied for the first time in a geographical location outside of Italy, i.e. Kalimpong town in India. The SIGMA algorithm is customized using the historical rainfall and landslide data of Kalimpong from 2010 to 2015 and has been validated using the data from 2016 to 2017. The model was validated by building a confusion matrix and calculating statistical skill scores, which were compared with those of the state-of-the-art intensity-duration rainfall thresholds derived for the region.</p><p>Results of the comparison clearly show that SIGMA performs much better than the other models in forecasting landslides: all instances of the validation confusion matrix are improved, and all skill scores are higher than I-D thresholds, with an efficiency of 92% and a likelihood ratio of 11.28. We explain this outcome mainly with technical characteristics of the site: when only daily rainfall measurements from a spare gauge network are available, SIGMA outperforms other approaches based on peak measurements, like intensity – duration thresholds, which cannot be captured adequately by daily measurements. SIGMA model thus showed a good potential to be used as a part of the local Landslide Early Warning System (LEWS).</p>

scholarly journals A synthetic view of rainfall intensification in the West African Sahel

2022 ◽  
Author(s):  
Guillaume Chagnaud ◽  
Geremy Panthou ◽  
Theo Vischel ◽  
Thierry Lebel
Keyword(s):  
Decadal Variability ◽  
Regional Scale ◽  
Daily Rainfall ◽  
Extreme Value Distribution ◽  
Extreme Rainfall ◽  
West African ◽  
Rainfall Regime ◽  
The West ◽  
West African Sahel ◽  
African Sahel

Abstract The West African Sahel has been facing for more than 30 years an increase in extreme rainfalls with strong socio-economic impacts. This situation challenges decision-makers to define adaptation strategies in a rapidly changing climate. The present study proposes (i) a quantitative characterization of the trends in extreme rainfalls at the regional scale, (ii) the translation of the trends into metrics that can be used by hydrological risk managers, (iii) elements for understanding the link between the climatology of extreme and mean rainfall. Based on a regional non-stationary statistical model applied to in-situ daily rainfall data over the period 1983-2015, we show that the region-wide increasing trend in extreme rainfalls is highly significant. The change in extreme value distribution reflects an increase in both the mean and variability, producing a 5%/decade increase in extreme rainfall intensity whatever the return period. The statistical framework provides operational elements for revising the design methods of hydraulic structures which most often assume a stationary climate. Finally, the study shows that the increase in extreme rainfall is more attributable to an increase in the intensity of storms (80%) than to their occurrence (20%), reflecting a major disruption from the decadal variability of the rainfall regime documented in the region since 1950.

Stochastic multi-site generation of daily rainfall occurrence in south Florida

2007 ◽  
Vol 22 (6) ◽  
pp. 705-717 ◽  
Author(s):  
Tae-woong Kim ◽  
Hosung Ahn ◽  
Gunhui Chung ◽  
Chulsang Yoo
Keyword(s):  
Daily Rainfall ◽  
South Florida ◽  
Rainfall Occurrence

Analysis of Selected Earth-Space Rain Attenuation Models for a Tropical Station

2016 ◽  
Vol 3 (2) ◽  
pp. 383 ◽  
Author(s):  
A. I. O. Yussuff
Keyword(s):  
Prediction Models ◽  
Measurement Data ◽  
Daily Rainfall ◽  
Rain Attenuation ◽  
Propagation Path ◽  
Tropical Regions ◽  
Earth Space ◽  
Budget Analysis ◽  
Link Budget ◽  
Attenuation Models

The restrained use of millimeter bands is due to severe rain attenuation. Attenuation is caused when rain cells intersects radio wave’s propagation path; resulting in deep fades. The effect of rainfall is more severe in tropical regions characterized by heavy rainfall intensity and large raindrops; hence, rain attenuation analyses are essential to study rain fade characteristics for use in earth-space link budget analysis, for outage prediction resulting from rain attenuation. Tropical regions are particularly challenged with signal outage, necessitating the formulation and development of suitable prediction model(s) for the region. Therefore, extensive knowledge of the propagation phenomena mitigating system availability and signal quality in these bands are required. Daily rainfall data were collected from the Nigerian Meteorological Services for Lagos for spanning January to December 2010. Results showed that although, the ITU-R model out-performed the other prediction models under consideration, none of prediction models matched the measurement data.

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