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

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.

Download Full-text

Identification of possible dynamical drivers for long-term changes in temperature and rainfall patterns over Europe

Theoretical and Applied Climatology ◽

10.1007/s00704-020-03373-3 ◽

2020 ◽

Vol 143 (1-2) ◽

pp. 177-191

Author(s):

Peter Hoffmann ◽

Arne Spekat

Keyword(s):

Decadal Variability ◽

Meteorological Data ◽

Weather Type ◽

Seasonal Rainfall ◽

Weather Types ◽

Temperature And Precipitation ◽

Long Term Changes ◽

Rainfall Patterns ◽

Climate Station

AbstractThis study looks into the question to what extent long-term change patterns of observed temperature and rainfall over Europe can be attributed to dynamical causes, in other words: Are the observed changes due to a change in frequency of the patterns or have the patterns’ dynamical properties changed? By using a combination of daily meteorological data and a European weather-type classification, the long-term monthly mean temperature and precipitation were calculated for each weather-type. Subsequently, the observed weather-type sequences were used to construct analogue time series for temperature and precipitation which only include the dynamical component of the long-term variability since 1961. The results show that only a fraction of about 20% of the past temperature rise since 1990, which for example amounted to 1 °C at the Potsdam Climate Station can be explained by dynamical changes, i.e. most of the weather-types have become warmer. Concerning long-term changes of seasonal rainfall patterns, a fraction of more than 60% is considerably higher. Moreover, the results indicate that for rainfall compared with temperature, the decadal variability and trends of the dynamical component follow the observed ones much stronger. Consequently, most of the explained seasonal rainfall variances can be linked to changes in weather-type sequences in Potsdam and over Europe. The dynamical contribution to long-term changes in annual and seasonal rainfall patterns dominates due to the fact that the alternation of wet and dry weather-types (e.g. the types Trough or High pressure over Central Europe), their frequencies and duration has significantly changed in the last decades.

Download Full-text

Spatial Coherence of Tropical Rainfall at the Regional Scale

Journal of Climate ◽

10.1175/2007jcli1623.1 ◽

2007 ◽

Vol 20 (21) ◽

pp. 5244-5263 ◽

Cited By ~ 80

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.

Download Full-text

Temporal rainfall trend analysis in different agro-ecological regions of southern Africa

Water SA ◽

10.17159/wsa/2021.v47.i4.3844 ◽

2021 ◽

Vol 47 (4 October) ◽

Author(s):

W Mupangwa ◽

R Makanza ◽

L Chipindu ◽

M Moeletsi ◽

S Mkuhlani ◽

...

Keyword(s):

Southern Africa ◽

Linear Trend ◽

Rainfall Variability ◽

Daily Rainfall ◽

Farming Systems ◽

Seasonal Rainfall ◽

Smallholder Farming ◽

Significant Linear Trend ◽

Semi Arid ◽

Linear Trends

Rainfall is a major driver of food production in rainfed smallholder farming systems. This study was conducted to assess linear trends in (i) different daily rainfall amounts (<5, 5–10, 11–20, 21–40 and >40 mm∙day-1), and (ii) monthly and seasonal rainfall amounts. Drought was determined using the rainfall variability index. Daily rainfall data were derived from 18 meteorological stations in southern Africa. Daily rainfall was dominated by <5 mm∙day-1 followed by 5–10 mm∙day-1. Three locations experienced increasing linear trends of <5 mm∙day-1 amounts and two others in sub-humid region had increases in the >40 mm day-1 category. Semi-arid location experienced increasing trends in <5 and 5–10 mm∙day-1 events. A significant linear trend in seasonal rainfall occurred at two locations with decreasing rainfall (1.24 and 3 mm∙season-1). A 3 mm∙season-1 decrease in seasonal rainfall was experienced under semi-arid conditions. There were no apparent linear trends in monthly and seasonal rainfall at 15 of the 18 locations studied. Drought frequencies varied with location and were 50% or higher during the November–March growing season. Rainfall trends were location and agro-ecology specific, but most of the locations studied did not experience significant changes between the 1900s and 2000s.

Download Full-text

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

Monthly Weather Review ◽

10.1175/mwr3252.1 ◽

2006 ◽

Vol 134 (11) ◽

pp. 3248-3262 ◽

Cited By ~ 59

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.

Download Full-text

Validation of Satellite Rainfall Products for Western Uganda

Journal of Hydrometeorology ◽

10.1175/jhm-d-13-0193.1 ◽

2014 ◽

Vol 15 (5) ◽

pp. 2030-2038 ◽

Cited By ~ 42

Author(s):

Jeremy E. Diem ◽

Joel Hartter ◽

Sadie J. Ryan ◽

Michael W. Palace

Keyword(s):

Daily Rainfall ◽

Summer Rainfall ◽

Seasonal Rainfall ◽

Annual Rainfall ◽

Boreal Summer ◽

Time Step ◽

African Rainfall ◽

Rainfall Total ◽

Mean Annual Rainfall ◽

Western Uganda

Abstract Central equatorial Africa is deficient in long-term, ground-based measurements of rainfall; therefore, the aim of this study is to assess the accuracy of three high-resolution, satellite-based rainfall products in western Uganda for the 2001–10 period. The three products are African Rainfall Climatology, version 2 (ARC2); African Rainfall Estimation Algorithm, version 2 (RFE2); and 3B42 from the Tropical Rainfall Measuring Mission, version 7 (i.e., 3B42v7). Daily rainfall totals from six gauges were used to assess the accuracy of satellite-based rainfall estimates of rainfall days, daily rainfall totals, 10-day rainfall totals, monthly rainfall totals, and seasonal rainfall totals. The northern stations had a mean annual rainfall total of 1390 mm, while the southern stations had a mean annual rainfall total of 900 mm. 3B42v7 was the only product that did not underestimate boreal-summer rainfall at the northern stations, which had ~3 times as much rainfall during boreal summer than did the southern stations. The three products tended to overestimate rainfall days at all stations and were borderline satisfactory at identifying rainfall days at the northern stations; the products did not perform satisfactorily at the southern stations. At the northern stations, 3B42v7 performed satisfactorily at estimating monthly and seasonal rainfall totals, ARC2 was only satisfactory at estimating seasonal rainfall totals, and RFE2 did not perform satisfactorily at any time step. The satellite products performed worst at the two stations located in rain shadows, and 3B42v7 had substantial overestimates at those stations.

Download Full-text

Analysis of Intra and Interseasonal Rainfall Variability and Its Effects on Pearl Millet Yield in a Semiarid Agroclimate: Significance of Scattered Fields and Tied Ridges

Water ◽

10.3390/w11030578 ◽

2019 ◽

Vol 11 (3) ◽

pp. 578 ◽

Cited By ~ 3

Author(s):

Festo Silungwe ◽

Frieder Graef ◽

Sonoko Bellingrath-Kimura ◽

Siza Tumbo ◽

Frederick Kahimba ◽

...

Keyword(s):

Pearl Millet ◽

Crop Production ◽

Rainfall Variability ◽

Daily Rainfall ◽

Rain Gauge ◽

Seasonal Rainfall ◽

African Countries ◽

Climate Conditions ◽

Semiarid Areas ◽

Millet Yield

Establishing food security in sub-Saharan African countries requires a comprehensive and high resolution understanding of the driving factors of crop production. Poor soil and adverse climate conditions are among the major drivers of poor regional crop production. Drought and rainfall variability challenges are not fully being addressed by rainfed producers in semiarid areas. In this study, we analysed the spatiotemporal rainfall variability (STRV) and its effects on pearl millet yield using two seasons of data collected from 38 rain gauge stations scattered randomly in farm plots within a 1500 ha area of semiarid central Tanzania. The STRV effects on pearl millet yield under flat and tied ridge management were analysed. Our results show that seasonal rainfall can vary significantly for neighboring fields at distances of less than 200 m, which impacts yield. The STRV for daily rainfall was found to be more critical than for total seasonal rainfall amounts. Scattering fields can help farmers avoid total harvest loss by obtaining at least some yield from the areas that received adequate rain. The use of tied ridges is recommended to conserve soil moisture and improve yields more than flat cultivation in semiarid areas.

Download Full-text

Defining Intraseasonal Rainfall Variability within the North American Monsoon

Journal of Climate ◽

10.1175/jcli3852.1 ◽

2006 ◽

Vol 19 (17) ◽

pp. 4243-4253 ◽

Cited By ~ 23

Author(s):

Phil J. Englehart ◽

Arthur V. Douglas

Keyword(s):

North American ◽

Linear Trend ◽

Monsoon Season ◽

Rainfall Variability ◽

Daily Rainfall ◽

Principal Component ◽

Seasonal Rainfall ◽

North American Monsoon ◽

The North ◽

The Pacific

Abstract This study provides an empirical description of intraseasonal rainfall variability within the North American monsoon (NAM) region. Applying particular definitions to historical daily rainfall observations, it demonstrates that distinct intraseasonal rainfall modes exist and that these modes differ considerably from the monsoon core region in northwest Sonora (SON), California, to its northward extension in southeast Arizona (AZ). To characterize intraseasonal rainfall variability (ISV), separate P-mode principal component (PC) analyses were performed for SON and AZ. The results indicate that in each area, much of the ISV in rainfall can be described by three orthogonal modes. The correlations between ISV modes and total seasonal rainfall reinforce the notion of differing behaviors between the monsoon’s core and extension. For SON all three ISV modes exhibit significant correlation with seasonal rainfall, with the strongest relationship in evidence for the ISV mode, which is related to rainfall intensity. For AZ, total rainfall exhibits the strongest correlation with the ISV mode, which emphasizes season length and rainfall consistency. Examination of longer-period behavior in the ISV modes indicates that, for SON, there is a positive linear trend in intensity, but a countervailing trend toward a shorter monsoon season along with less consistent rainfall in the form of shorter wet spells. For AZ, the evidence for trend in the ISV modes is not nearly as compelling, though one of the modes appears to exhibit distinct multidecadal variability. This study also evaluates teleconnectivity between ENSO, the Pacific decadal oscillation (PDO), and the NAM’s intraseasonal rainfall variability. Results indicate that part of the intraseasonal rainfall variability in both SON and AZ is connected to ENSO while only SON exhibits a teleconnection with the long-period fluctuations of the PDO.

Download Full-text

Space–Time Evolution of the Low- and High-Frequency Intraseasonal Modes of the Indian Summer Monsoon

Monthly Weather Review ◽

10.1175/mwr-d-16-0075.1 ◽

2017 ◽

Vol 145 (2) ◽

pp. 413-435 ◽

Cited By ~ 13

Author(s):

Nirupam Karmakar ◽

Arindam Chakraborty ◽

Ravi S. Nanjundiah

Keyword(s):

Summer Monsoon ◽

Indian Summer Monsoon ◽

Time Scale ◽

Rainfall Variability ◽

Daily Rainfall ◽

Equatorial Indian Ocean ◽

Space Time ◽

Vertical Shear ◽

Boreal Summer ◽

Comparable Amount

In this study, rainfall estimates by the Tropical Rainfall Measuring Mission are used to understand the spatiotemporal structures of convection in the intraseasonal time scale and their intensity during the boreal summer over South Asia. A quantitative analysis on how these intraseasonal modes modulate the central Indian rainfall is also provided. Two dominant modes of variability with periodicities of 10–20 and 20–60 days are found, with the latter strongly modulated by sea surface temperature. The 20–60-day mode shows northward propagation from the equatorial Indian Ocean linked with eastward-propagating modes of convective systems over the tropics. The 10–20-day mode shows a complex space–time structure with a northwestward-propagating anomalous pattern emanating from the Indonesian coast. This pattern is found to be interacting with a structure emerging from higher latitudes propagating southeastward, the development of which is attributed to the vertical shear of the zonal wind. The two modes exhibit profound variability in their intensity on the interannual time scale and they contribute a comparable amount to the daily rainfall variability in a season. The intensity of the 20–60 and 10–20-day modes shows a significantly strong inverse and direct relationship with the all-India June–September rainfall, respectively. This study establishes that the probability of the occurrence of substantial rainfall over central India increases significantly if the two intraseasonal modes simultaneously exhibit positive anomalies over the region. The results presented in this paper will provide a pathway to understand, using observations and numerical model simulations, intraseasonal variability and its relative contribution to the Indian summer monsoon. It can also be used for model evaluation.

Download Full-text

Modeling and forecasting of rainfall reoccurrence changes using Markov Switching in Iran

SN Applied Sciences ◽

10.1007/s42452-021-04728-9 ◽

2021 ◽

Vol 3 (8) ◽

Author(s):

Majid Javari

Keyword(s):

Environmental Planning ◽

Rainfall Variability ◽

Markov Switching ◽

Daily Rainfall ◽

First Order ◽

Markov Switching Models ◽

Markov Transition Matrix ◽

Switching Models ◽

Markov Transition ◽

Modeling And Forecasting

AbstractThis paper represents the recurrence (reoccurrence) changes in the rainfall series using Markov Switching models (MSM). The switching employs a dynamic pattern that allows a linear model to be combined with nonlinearity models a discrete structure. The result is the Markov Switching models (MSM) reoccurrence predicting technique. Markov Switching models (MSM) were employed to analyze rainfall reoccurrence with spatiotemporal regime probabilities. In this study, Markov Switching models (MSM) were used based on the simple exogenous probability frame by identifying a first-order Markov process for the regime probabilities. The Markov transition matrix and regime probabilities were used to analyze the rainfall reoccurrence in 167 synoptic and climatology stations. The analysis results show a low distribution from 0.0 to 0.2 (0–20%) per day spatially from selecting stations, probability mean of daily rainfall recurrence is 0.84, and a different distribution based on the second regime was found to be more remarkable to the rainfall variability. The rainfall reoccurrence in daily rainfall was estimated with relatively low variability and strong reoccurrence daily with ranged from 0.851 to 0.995 (85.1–99.5%) per day based on the spatial distribution. The variability analysis of rainfall in the intermediate and long variability and irregular variability patterns would be helpful for the rainfall variability for environmental planning.

Download Full-text

Application of SIGMA model for landslide forecasting in Darjeeling Himalayas

10.5194/egusphere-egu21-669 ◽

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

SIGMA (Sistema Integrato Gestione Monitoraggio Allerta &#8211; 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.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.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 &#8211; 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).

Download Full-text