Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

Pattanaik, D. R.; Mukhopadhyay, Biswajit; Kumar, Arun

doi:10.4236/acs.2012.24042

Cited by 10 publications

(5 citation statements)

References 36 publications

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“…It is also highly skilful in capturing the air–sea interaction processes associated with the precipitation features, as demonstrated in sea surface temperature and wind pattern s. For operational purposes, an obvious requirement is the lead time up to which the practical seasonal forecasts could be delivered to the user community. Most of the previous studies, however, concentrated on the analysis of forecast skill from May initial conditions, although a few studies have shown how the prediction skill varies with lead times (Pattanaik et al , ; Singh et al , ; Pattanaik and Kumar, ). However, the previous studies have not explicitly addressed how the variation of the teleconnection pattern could be linked to the variation of prediction skill of the ISMR at different forecast lead times in the context of CFSv2 retrospective forecasts.…”

Section: Introductionmentioning

confidence: 99%

Large‐scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs

Chattopadhyay

Rao

Sabeerali

et al. 2015

Intl Journal of Climatology

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ABSTRACT:The seasonal prediction skill [defined as the linear correlation (cc) between the observed and forecasted rainfall] of the Indian Summer Monsoon Rainfall (ISMR) is evaluated in the Climate Forecast System version 2 (CFSv2) model, at different lead times on the basis of its representation of large scale tropical teleconnection. Surprisingly, the model exhibits reasonable skill at a longer lead time (e.g. forecasts initialized with February initial conditions, Feb IC run, cc > 0.5) that is reasonably better when compared with that with forecast initialized at shorter lead time [April/May IC (Apr/May IC) runs, cc < 0.5]. Although the prediction skill of ISMR improves as lead time increases, the ENSO forecast skill improves as lead time decreases. Probable reasons for these counter-intuitive findings are investigated in this study.The model shows unrealistic teleconnection of ISMR with Indian Ocean Dipole (IOD) and is unable to represent the large scale rainfall pattern over the Indian land region. The Equatorial Indian Ocean Oscillation (EQUINOO) shows unrealistic teleconnection with ISMR as well as equatorial Pacific from all the initial condition runs. Unrealistic EQUINOO and the IOD teleconnection suggest that the air-sea interaction in the Indian Ocean requires to be improved in the model. The relationship between El Nino-Southern Oscillation (ENSO) and monsoon in CFSv2 is realistic in terms of spatial pattern though it is somewhat stronger than that in observations.The equatorial central Pacific sea surface temperature and rainfall show very strong cold and dry biases respectively, and these biases enhance from the Feb IC run to the May IC run. It is found that these biases are due to strong unrealistic coupled feedback in this region. As a result, the associated ENSO-monsoon teleconnection pattern shifts westward with decreasing lead time, resulting in unrealistic patterns as compared with observations, and causing a loss of prediction skill at shorter lead times.

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Section: Introductionmentioning

confidence: 99%

Large‐scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs

Chattopadhyay

Rao

Sabeerali

et al. 2015

Intl Journal of Climatology

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“…Heidke skill score (HSS) is a measure of skill in forecast. It is defined as = (score value -score for the standard forecast) / (perfect score -score for the standard forecast) (Pattanaik et al 2012).…”

Section: Heidke Skill Scorementioning

confidence: 99%

Urban modification of heavy rainfall: a model case study for Bhubaneswar urban region

et al. 2023

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An increase in urbanization has been witnessed from 1980 to 2019 in Bhubaneswar, Odisha. The impact of this increase in urban areas on rainfall pattern and intensity has been assessed in this study. To evaluate these changes, four heavy rainfall events, such as 06th March 2017, 23rd May 2018, 20 – 22 July 2018, and 04 – 08 August 2018, have been simulated with 1980, 2000, and 2019 land use land cover (LULC) obtained from United States Geological Survey imageries. With these two LULC sensitivities, urban canopy model (UCM) experiments have also been carried out. These experiments suggest that incorporating corrected LULC is necessary for simulating heavy rainfall events using the Weather Research and Forecasting (WRF) model. Urbanization increases the rainfall intensity, and the spatial shift was more pronounced along the peripheral region of the city. The vertically integrated moisture flux analysis suggests that more moisture present over the area received intense rainfall. An increase in urbanization increases the temperature at the lower level of the atmosphere, which increases [planetary boundary layer height, local convection, and rainfall over the region. Contiguous Rain Area method analysis suggests that the 2019 LULC with single layer UCM predicts a better spatial representation of rainfall. This combination works well for all the four cases simulated.

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“…This procedure allows comparing the quality of results from different methodologies in order to characterize skill improvements from new science and technology. Verification should drive estimation system development and help advance the knowledge of predictability and also be made available to all users, especially civil defense, to guide their use for better decision-making (Demargne et al 2009;Pattanaik et al 2012;Sukovich et al 2014).…”

Section: Hydrometeorological Empirical Relationshipsmentioning

confidence: 99%

Real-time river level estimation based on variations of radar reflectivity—a case study of the Quitandinha River watershed, Petrópolis, Rio de Janeiro (Brazil)

Silva

Filho

Silva

et al. 2021

Bull. of Atmos. Sci.& Technol.

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The real-time warning for flash floods represents one of the major challenges for monitoring centers and civil defenses due to the short time frame in which such phenomena occur. That situation often takes place particularly in mountainous regions where high slope rate favors an increase in the velocity of the water flow and it might generate conditions to impact the water level due to potential for provoking erosion, landslides, and debris flow. An initial one new and unique prototype of hydrometeorological relationship for real-time, site-tunable, water level for flash flood prediction is presented. Time variations of the reflectivity measured by horizontal polarization radar are evaluated and correlated to generate the local estimate of the elevation of the Quitandinha River water level for the next hour. The proposal of this relationship is not intended to act as a simplified hydraulic model but to characterize if a critical elevation of the water level could be obtained from the radar reflectivity data and consequently whether the flash flooding event should be expected. We verified that the proposed relationship that tended to underestimate the water level peaks, however, qualitatively was initially able to indicate if a flood event could occur or not in the next hour. The expectation related to this initial relationship is to improve the obtained results in order to be used as a tool for decisionmaking in relation to the issuance of warnings delivered by environmental monitoring centers.

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Monthly Forecast of Indian Southwest Monsoon Rainfall Based on NCEP’s Coupled Forecast System

Cited by 10 publications

References 36 publications

Large‐scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs

Large‐scale teleconnection patterns of Indian summer monsoon as revealed by CFSv2 retrospective seasonal forecast runs

Urban modification of heavy rainfall: a model case study for Bhubaneswar urban region

Real-time river level estimation based on variations of radar reflectivity—a case study of the Quitandinha River watershed, Petrópolis, Rio de Janeiro (Brazil)

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