Extreme Daily Rainfall Events and Their Impact on Ensemble Forecasts of the Indian Monsoon

Stephenson, David B.; Kumar, Krishan; Doblas‐Reyes, F. J.; Royer, Jean‐François; Chauvin, Fabrice; Pezzulli, S.

doi:10.1175/1520-0493(1999)127<1954:edreat>2.0.co;2

Cited by 105 publications

(61 citation statements)

References 50 publications

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“…These encouraging results are in agreement with the relatively strong relationship between total Indian rainfall and ENSO (e.g. Shukla and Paolino, 1983;Yasunari, 1990;Shukla, 1995) although its stationarity is questioned by the apparent waning of Indian monsoon precipitation-ENSO relationships during the last few decades Webster, 1998, 1999;Kumar et al, 1999;Stephenson et al, 1999;Clark et al, 2000Clark et al, , 2003. However, other studies using different proxies of the Indian monsoon suggest the relationship with ENSO is still significant (Gershunov et al, 2001;Goswami and Xavier, 2005;van Oldenborgh and Burgers, 2005).…”

Section: Introductionsupporting

confidence: 72%

Large‐scale controls on Ganges and Brahmaputra river discharge on intraseasonal and seasonal time‐scales

Jian

Webster

Hoyos

2009

Quart J Royal Meteoro Soc

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Reliable water supply from the Ganges and Brahmaputra is of critical importance to the sustainability of the agricultural societies of India and Bangladesh. But, the flow in both basins is highly variable on time-scales ranging from days to years, creating challenges for the optimization of agricultural practices, water resource management and disaster mitigation. The following questions are addressed. Is intraseasonal monsoon variability related to the subseasonal variability of river flow? Do variations in the large-scale tropical sea-surface temperature (SST) located both regionally and remotely promote seasonal and interannual variations of river discharge? And, if these relationships do exist, are they determinable with sufficient lead-times to allow useful predictions for user communities in South Asia? We examine these questions using 50 years of daily river discharge data for both rivers calculated at the points where they enter Bangladesh, and with SST data in the Indo-Pacific region. We also examine the question of determining the impact of man-made dams, diversions and barrages on the data record, especially that of the Ganges. A comparison of discharge prior to 1974 (the time of the construction of the largest barrage) shows no statistical difference that cannot be explained by basin-wide rainfall distributions. Changes that do occur are restricted to the dry-season months.Subseasonal river discharge is found to be strongly tied to the monsoon intraseasonal cycle resulting in a near-inphase timing of Ganges and Brahmaputra discharge. A basin isochrone analysis is used to couple stream-flow variability and intraseasonal precipitation during the different phases of the intraseasonal cycle. On longer time-scales, statistically significant correlations are found between mean monthly equatorial Pacific SST and the boreal summer Ganges discharge with lead times of 2-3 months. These relationships are tied to El Niño-Southern Oscillation (ENSO) oscillations in addition to SST variability in the southwest and northwest Pacific that also seems to be related to ENSO. The Brahmaputra discharge, on the other hand, shows somewhat weaker relationships with tropical SST. Strong lagged correlation relationships are found with SST in the Bay of Bengal but these are the result of very warm SSTs and exceptional Brahmaputra discharge during the summer of 1998. When this year is removed from the time series, relationships with SST anomalies weaken everywhere except in the northwest Pacific for the June discharge and in areas of the central Pacific straddling the Equator for the July discharge. In addition, the northwest Pacific relationship changes polarity for June and July discharges. Although the relationships are weaker than those found for the Ganges, they are persistent from month to month and suggest that two different and sequential factors influence Brahmaputra river flow.

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Section: Introductionsupporting

confidence: 72%

Large‐scale controls on Ganges and Brahmaputra river discharge on intraseasonal and seasonal time‐scales

Jian

Webster

Hoyos

2009

Quart J Royal Meteoro Soc

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“…If the precipitation amounts vector, P, follows a gamma distribution, the distribution of transformed vector by the cube-root, called Anscombe residuals, is approximately normal, as has been described by Terrell (2003) and Yang et al (2005). The gamma distribution has been fitted to rainfall amounts in many studies (Stephenson et al 1999;Giorgi et al 2001;Yang et al 2005).…”

Section: Model Applicationmentioning

confidence: 99%

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada

Jeong

St‐Hilaire

Ouarda

et al. 2011

Stoch Environ Res Risk Assess

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This study compares three linear models and one non-linear model, specifically multiple linear regression (MLR) with ordinary least squares (OLS) estimates, robust regression, ridge regression, and artificial neural networks (ANNs), to identify an appropriate transfer function in statistical downscaling (SD) models for the daily maximum and minimum temperatures (T max and T min ) and daily precipitation occurrence and amounts (P occ and P amount ). This comparison was made over twenty-five observation sites located in five different Canadian provinces (British Columbia, Saskatchewan, Manitoba, Ontario, and Québec). Reanalysis data were employed as atmospheric predictor variables of SD models. Predictors of linear transfer functions and ANN were selected by linear correlations coefficient and mutual information, respectively. For each downscaled case, annual and monthly models were developed and analysed. The monthly MLR, annual ANN, annual ANN, and annual MLR yielded the best performance for T max , T min , P occ and P amont according to the modified Akaike information criterion (AICu). A monthly MLR is recommended for the transfer functions of the four predictands because it can provide a better performance for the T max and as good performance as the annual MLR for the T min , P occ , and P amount . Furthermore, a monthly MLR can provide a slightly better performance than an annual MLR for extreme events. An annual MLR approach is also equivalently recommended for the transfer functions of the four predictands because it showed as good a performance as monthly MLR in spite of its mathematical simplicity. Robust and ridge regressions are not recommended because the data used in this study are not greatly affected by outlier data and multicollinearity problems. An annual ANN is recommended only for the T min , based on the best performance among the models in terms of both the RMSE and AICu.

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“…During its active phase monsoon could evolve on large spatial scales within a short period of time and hence large parts of the land mass could receive rainfall simultaneously or within short time delay of a few days. Active periods of monsoon seem to be formed as a cumulative effect of several events scattered over large spatial scales (100-1000 km), which may also include massive rain events resulting in 400 mm of rainfall at a single day (May, 2004;Stephenson et al, 1999). Such massive events take place due to the formation of monsoon depressions (Sikka, 1977) or midtroposheric cyclones (Keshavamurthy, 1973).…”

Section: Introductionmentioning

confidence: 99%

Spatial structures and directionalities in Monsoonal precipitation over South Asia

Malik

Marwan

Kurths

2010

Nonlin. Processes Geophys.

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Abstract. Precipitation during the monsoon season over the Indian subcontinent occurs in form of enormously complex spatiotemporal patterns due to the underlying dynamics of atmospheric circulation and varying topography. Employing methods from nonlinear time series analysis, we study spatial structures of the rainfall field during the summer monsoon and identify principle regions where the dynamics of monsoonal rainfall is more coherent or homogenous. Moreover, we estimate the time delay patterns of rain events. Here we present an analysis of two separate high resolution gridded data sets of daily rainfall covering the Indian subcontinent. Using the method of event synchronization (ES), we estimate regions where heavy rain events during monsoon happen in some lag synchronised form. Further using the delay behaviour of rainfall events, we estimate the directionalities related to the progress of such type of rainfall events. The Active (break) phase of a monsoon is characterised by an increase(decrease) of rainfall over certain regions of the Indian subcontinent. We show that our method is able to identify regions of such coherent rainfall activity.

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Extreme Daily Rainfall Events and Their Impact on Ensemble Forecasts of the Indian Monsoon

Cited by 105 publications

References 50 publications

Large‐scale controls on Ganges and Brahmaputra river discharge on intraseasonal and seasonal time‐scales

Large‐scale controls on Ganges and Brahmaputra river discharge on intraseasonal and seasonal time‐scales

Comparison of transfer functions in statistical downscaling models for daily temperature and precipitation over Canada

Spatial structures and directionalities in Monsoonal precipitation over South Asia

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