Simulation of Large Scale Resolution IAP DCP Model for Pre-Monsoon and Southwest Monsoon Events over Indo China Peninsular

Abstract. Water risk management is a ubiquitous challenge faced by stakeholders in the water or agricultural sector. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest ranked probability skill score and lowest root-mean-squared error in a leave-one-out cross-validation mode. Adaptive forecasts were made in the years 2001 to 2013 using the identified predictors and a non-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct 9 out of 13 times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.

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Large-scale variations of the Somali current during the southwest monsoon, 1970

Deep Sea Research and Oceanographic Abstracts ◽

10.1016/0011-7471(73)90005-3 ◽

1973 ◽

Vol 20 (9) ◽

pp. 837-846

Author(s):

J.G. Bruce

Keyword(s):

Large Scale ◽

Southwest Monsoon ◽

Somali Current

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Role of Narrow Mountains in Large-Scale Organization of Asian Monsoon Convection*

Journal of Climate ◽

10.1175/jcli3777.1 ◽

2006 ◽

Vol 19 (14) ◽

pp. 3420-3429 ◽

Cited By ~ 201

Author(s):

Shang-Ping Xie ◽

Haiming Xu ◽

N. H. Saji ◽

Yuqing Wang ◽

W. Timothy Liu

Keyword(s):

Large Scale ◽

Asian Monsoon ◽

Climate Models ◽

Asian Summer Monsoon ◽

Global Climate ◽

Southwest Monsoon ◽

Global Climate Models ◽

Eastern Coast ◽

Windward Side ◽

Continental Scale

Abstract The Asian summer monsoon is organized into distinct convection centers, but the mechanism for this organization is not well understood. Analysis of new satellite observations reveals that narrow mountain ranges are an important organizing agent anchoring monsoon convection centers on the windward side. The Bay of Bengal convection, in particular, features the heaviest precipitation on its eastern coast because of orographic lifting as the southwest monsoon impinges on the coastal mountains of Myanmar (also known as Burma). This is in contrast to the widely held view that this convection is centered over the open ocean as implied by coarse-resolution datasets, a view that would require an entirely different explanation for its formation. Narrow in width and modest in height (≤1 km), these mountains are hardly mentioned in conceptual depictions of the large-scale monsoon and poorly represented in global climate models. The numerical simulations of this study show that orographic rainbands are not a local phenomenon but exert far-reaching effects on the continental-scale monsoon. The realization that these overlooked geographical features are an important element of the Asian monsoon has important implications for studying the monsoon in the past, present, and future.

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Variability of Air–Sea Interactions over the Indian Ocean Derived from Satellite Observations

Journal of Climate ◽

10.1175/1520-0442-11.8.1859 ◽

1998 ◽

Vol 11 (8) ◽

pp. 1859-1873 ◽

Cited By ~ 5

Author(s):

Catherine Gautier ◽

Peter Peterson ◽

Charles Jones

Keyword(s):

Heat Flux ◽

Indian Ocean ◽

Interannual Variability ◽

Satellite Data ◽

Large Scale ◽

Southwest Monsoon ◽

Seasonal And Interannual Variability ◽

The Indian Ocean ◽

The Difference

Abstract Novel ways of monitoring the large-scale variability of the southwest monsoon in the Indian Ocean are presented using multispectral satellite datasets. The fields of sea surface temperature (SST), surface latent heat flux (LHF), net surface solar radiation (SW), precipitation (P), and SW − LHF over the Indian Ocean are analyzed to characterize the seasonal and interannual variability with special emphasis on the period 1988–90. It is shown that satellite data are able to make a significant contribution to the multiplatform strategy necessary to describe the large-scale spatial and temporal variability of air–sea interactions associated with the Indian Ocean Monsoon. The satellite data analyzed here has shown for the first time characteristics of the interannual variability of air–sea interactions over the entire Indian Ocean. Using monthly means of SST, LHF, SW, P, and the difference SW − LHF, the main features of the seasonal and interannual variability of air–sea interactions over the Indian Ocean are characterized. It is shown that the southwest monsoon strongly affects these interactions, inducing dramatic exchanges of heat between air and sea and large temporal variations of these exchanges over relatively small timescale (with regards to typical oceanic timescales). The analyses indicate an overall good agreement between satellite and in situ (ship) estimates, except in the southern Indian Ocean, where ship sampling is minimal, the disagreement can be large. In the latitudinal band of 10°N–15°S, differences in climatological in situ estimates of surface sensible heat flux and net longwave radiation has a larger influence on the net surface heat flux than the difference between satellite and in situ estimates of SW and LHF.

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Season-Ahead Forecasting of Water Storage and Irrigation Requirements – An Application to the Southwest Monsoon in India

10.5194/hess-2018-183 ◽

2018 ◽

Author(s):

Arun Ravindranath ◽

Naresh Devineni ◽

Upmanu Lall ◽

Paulina Concha Larrauri

Keyword(s):

Water Stress ◽

Large Scale ◽

Agricultural Sector ◽

Mean Squared Error ◽

Monsoon Season ◽

Water Storage ◽

Skill Score ◽

Southwest Monsoon ◽

False Alarms ◽

K Nearest Neighbors

Abstract. Water risk management is perhaps the most ubiquitous challenge a stakeholder in the water or agricultural sector faces. We present a methodological framework for forecasting water storage requirements and present an application of this methodology to risk assessment in India. The application focused on forecasting crop water stress for potatoes grown during the monsoon season in the Satara district of Maharashtra. Pre-season large-scale climate predictors used to forecast water stress were selected based on an exhaustive search method that evaluates for highest Rank Probability Skill Score and lowest Mean Squared Error in a leave-one-out cross validation mode. Adaptive forecasts were made over the years 2001 through 2013 using the identified predictors and a semi-parametric k-nearest neighbors approach. The accuracy of the adaptive forecasts (2001–2013) was judged based on directional concordance and contingency metrics such as hit/miss rate and false alarms. Based on these criteria, our forecasts were correct nine out of thirteen times, with two misses and two false alarms. The results of these drought forecasts were compared with precipitation forecasts from the Indian Meteorological Department (IMD). We assert that it is necessary to couple informative water stress/risk indices with an effective forecasting methodology to maximize the utility of such indices, thereby optimizing water management decisions.

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The Arabian Sea as a high-nutrient, low-chlorophyll region during the late Southwest Monsoon

Biogeosciences ◽

10.5194/bg-7-2091-2010 ◽

2010 ◽

Vol 7 (7) ◽

pp. 2091-2100 ◽

Cited By ~ 48

Author(s):

S. W. A. Naqvi ◽

J. W. Moffett ◽

M. U. Gauns ◽

P. V. Narvekar ◽

A. K. Pratihary ◽

...

Keyword(s):

Arabian Sea ◽

Large Scale ◽

Southwest Monsoon ◽

Simple Relationship ◽

Iron Supply ◽

The Us ◽

High Nutrient ◽

Bottom Waters ◽

Long Term Trends ◽

Upwelling Intensity

Abstract. Extensive observations were made during the late Southwest Monsoon of 2004 over the Indian and Omani shelves, and along a transect that extended from the southern coast of Oman to the central west coast of India, tracking the southern leg of the US JGOFS expedition (1994–1995) in the west. The data are used, in conjunction with satellite-derived data, to investigate long-term trends in chlorophyll and sea surface temperature, indicators of upwelling intensity, and to understand factors that control primary production (PP) in the Arabian Sea, focussing on the role of iron. Our results do not support an intensification of upwelling in the western Arabian Sea, reported to have been caused by the decline in the winter/spring Eurasian snow cover since 1997. We also noticed, for the first time, an unexpected development of high-nutrient, low-chlorophyll condition off the southern Omani coast. This feature, coupled with other characteristics of the system, such as a narrow shelf and relatively low iron concentrations in surface waters, suggest a close similarity between the Omani upwelling system and the Peruvian and California upwelling systems, where PP is limited by iron. Iron limitation of PP may complicate simple relationship between upwelling and PP assumed by previous workers, and contribute to the anomalous offshore occurrence of the most severe oxygen (O2) depletion in the region. Over the much wider Indian shelf, which experiences large-scale bottom water O2-depletion in summer, adequate iron supply from reducing bottom-waters and sediments seems to support moderately high PP; however, such production is restricted to the thin, oxygenated surface layer, probably because of the unsuitability of the O2-depleted environment for the growth of oxygenic photosynthesizers.

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Large Scale Processes for Understanding Extreme Rain Storm Over Rangamati on 12 June 2017- A Case Study

Journal of Engineering Science ◽

10.3329/jes.v11i2.50900 ◽

2020 ◽

Vol 11 (2) ◽

pp. 87-97

Author(s):

Samarendra Karmakar ◽

Mohan Kumar Das ◽

Haripada Sarker

Keyword(s):

Bay Of Bengal ◽

Heavy Rainfall ◽

Large Scale ◽

Early Period ◽

Analysis Data ◽

Southwest Monsoon ◽

Monsoon Depression ◽

Heavy Rainfall Event ◽

Strong Wind ◽

Synoptic Conditions

Attempts have been made to study the large-scale surface and upper air synoptic processes associated with a monsoon depression during 11-12 June 2017. In this study, Grid Analysis and Display System (GrADS) software has been used to prepare the large-scale sea level pressure and upper flow patterns by analyzing the FNL re-analysis data. In this analysis, FNL dataset is used to characterize the rainstorms, with key hydrometeorological variables describing the prior conditions of the very heavy rainfall event presented the study. National Center for Environmental Prediction (NCEP) Final (FNL) analysis data of 1o by 1o grids for every 6 hours are used for large scale synoptic analysis. The disastrous event was a strong monsoon depression in the early period of southwest monsoon 2017. Due to this depression, very heavy rainfall occurred in the southeastern Bangladesh. Rangamati recorded 343 mm of rainfall in the 24 hours on 12 June 2017. Massive landslides occurred in three districts such as Rangamati, Bandarban and Chittagong. The analysis of surface and upper air synoptic conditions has revealed that a well-marked low was formed over the northwest Bay of Bengal within the low-pressure belt passing from Somalia coast extending through southern Pakistan, and India up to east central Bay of Bengal and adjoining Bangladesh. The wind speed is calculated from the pressure distribution and is found to be 24.23ms-1, which is at par with the observed one. The well-marked low was subsequently intensified into a depression and moved northeastwards over Bangladesh. Strong southsouthwesterly winds were associated in the eastern side of the depression, especially over Chittagong Hill Tracts. The depression was found to extend up to 500 hPa level as seen from the distribution of geopotential and strong circulation around the centre. Winds were advecting from large continental and Ocean areas over the South Asia. Strong winds and moisture influx, strong narrow coma-like trough from a micro low at the surface to 500 hPa level as well as strong wind shear were responsible for the heavy rainfall, disastrous effects and massive landslides over Rangamati and adjoining areas. Journal of Engineering Science 11(2), 2020, 87-97

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Impacts of the Madden-Julian Oscillation (MJO) on rainfall in Sri Lanka

MAUSAM ◽

10.54302/mausam.v71i3.39 ◽

2021 ◽

Vol 71 (3) ◽

pp. 405-422

Author(s):

JAYAWARDENA I M SHIROMANI PRIYANTHIKA ◽

WHEELER MATTHEW C ◽

SUMATHIPALA W L ◽

BASNAYAKE B R S B

Keyword(s):

Sri Lanka ◽

Large Scale ◽

Daily Rainfall ◽

Extreme Rainfall ◽

Southwest Monsoon ◽

Boreal Winter ◽

Northeast Monsoon ◽

Madden Julian Oscillation ◽

Extreme Rainfall Events ◽

Rainfall Events

The influence of the Madden Julian Oscillation (MJO) on rainfall in Sri Lanka (SL) is examined based on 30 years of daily station data from 1981-2010. Composites are constructed for each of the eight phases of the MJO defined with the Real-time Multivariate MJO (RMM) index, using daily rainfall data from 44 stations over SL for four climatic seasons and comparing to similar results from a satellite-based rainfall product. Composites of lower tropospheric wind and convective anomaly are also investigated in order to examine how the local rainfall anomalies are associated with large-scale circulations. The greatest impact of the MJO on rainfall over SL occurs in the Second Inter-Monsoon (SIM) and Southwest Monsoon (SWM) seasons. Enhanced rainfall generally occurs over SL during RMM phases 2 and 3 when the MJO convective envelop is located in the Indian Ocean and conversely suppressed rainfall in phases 6 and 7. This rainfall impact is due to the direct influence of the MJO’s tropical convective anomalies and associated low-level circulations in the vicinity of SL. In contrast, the MJO influence during the Northeast Monsoon (NEM) season is slightly less than during the SWM and SIM seasons as a result of the southward shift of the MJO convective envelop during boreal winter. Occurrence of extreme rainfall events is most frequent during phase 2 in First Inter-Monsoon (FIM) phases 2 and 3 in SWM, phases 1, 2 and 3 in SIM and phases 2 and 3 in NEM seasons. The analysis of this study provides a useful reference of when and where the MJO has significant impacts on rainfall as well as extreme rainfall events during four climatic seasons in SL. This information can be used along with accurately predicted MJO phase by dynamical or statistical models, to improve extended range forecasting in SL.

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Analyzing Future Rainfall Variations Over Southern Malay Peninsula Based On CORDEX-SEA Dataset

10.21203/rs.3.rs-681108/v1 ◽

2021 ◽

Author(s):

Xiaosheng Qin ◽

Chao Dai ◽

Lilingjun Liu

Keyword(s):

Large Scale ◽

Southwest Monsoon ◽

Mapping Method ◽

Extreme Condition ◽

Annual Rainfall ◽

Study Region ◽

Rainfall Characteristics ◽

Long Run ◽

Far Future

Abstract Gridded rainfall datasets based on various data sources and techniques have emerged to help describe the spatiotemporal features of rainfall patterns over large areas and have gained popularity in many regional/global climatic analyses. This study explored future variations of rainfall characteristics over peninsula Malaysia and Singapore region based on rainfall indices of PRCPTOT, Rx1day, Rx5day, R95pTOT, R1mm, and R20mm, under 9 CORDEX-SEA RCM datasets with RCP8.5 emission scenario. A monthly quantile delta mapping method (MQDM) was adopted for bias-correction of the RCM modelled data. It was indicated that all the studied rainfall indices have long-term variations both temporally and spatially. Generally, the further the future, the higher the variability and uncertainty of indices. For the study region, the relative increments of the medians from RCM models averaged over all climatic zones in the far future are 40.3%, 25.9%, and 4.7% for Rx1day, Rx5day and R95pTOT, respectively. The annual rainfall amount (PRCPTOT) in the long run would likely increase mainly in the northeast coastal zone and drop in most of other areas over the peninsula, with the median being -5.9% averaged over all zones. The frequency of wet days (R1mm) would generally drop over the whole peninsula, with the median averaged over all zones being -6.8% in the far future. The frequency of heavy rains (R20mm) would overall decrease (by -3.4% in average in the far future) but might still notably increase in the northeast zone (NE) at both annual and southwest monsoon. The extreme condition implied from various RCM models would be more alarming. The study result would be useful in revealing the essential spatiotemporal variations of rainfall over the peninsula from short- to long-term futures and supporting large-scale flood risk assessment and adaptation planning.

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