Multiscale interactions between monsoon intra-seasonal oscillations and low pressure systems that produce heavy rainfall events of different spatial extents
Akshaya C Nikumbh, A. Chakraborty, G. S. Bhat, Dargan M. W. Frierson
备注：
36 pages, 10 figures, 1 table. This work has been submitted to Journal of climate. Copyright in this work may be transferred without further notice
The sub-seasonal and synoptic-scale variability of the Indian summer monsoon rainfall are controlled primarily by monsoon intra-seasonal oscillations (MISO) and low pressure systems (LPS), respectively. The positive and negative phases of MISO lead to alternate epochs of above-normal (active) and below-normal (break) spells of rainfall. LPSs are embedded within the different phases of MISO and are known to produce heavy precipitation events over central India. Whether the interaction with the MISO phases modulates the precipitation response of LPSs, and thereby the characteristics of extreme rainfall events (EREs) remains unaddressed in the available literature. In this study, we analyze the LPSs that produce EREs of various spatial extents viz., Small, Medium, and Large over central India from 1979 to 2012. We also compare them with the LPSs that pass through central India and do not give any ERE (LPS-noex). We find that thermodynamic characteristics of LPSs that trigger different spatial extents of EREs are similar. However, they show differences in their dynamic characteristics. The ERE producing LPSs are slower, moister and more intense than LPS-noex. The LPSs that lead to Medium and Large EREs tend to occur during the positive phase of MISO when an active monsoon trough is present over central India. On the other hand, LPS-noex and the LPSs that trigger Small EREs occur mainly during the neutral or negative phases of the MISO. The large-scale dynamic forcing, intensification of LPSs, and diabatic generation of low-level potential vorticity due to the presence of active monsoon trough help in the organization of convection and lead to Medium and Large EREs. On the other hand, the LPSs that form during the negative or neutral phases of MISO do not intensify much during their lifetime and trigger scattered convection, leading to EREs of small size.
Summer monsoon rainfall over India depends on synoptic-scale phenomena like lows and depressions. India's summer monsoon season 2020 saw above-average rainfall. North Indian Ocean had 12 LPAs in summer 2020 in JJAS season. This monsoon season has the highest all-India rainfall since 1976 in August 2020. 50% of seasonal LPAs emerged in August 2020, but none intensified into a monsoon depression (MDs). This study explores the potential factors behind LPAs not intensifying/concentrating as MDs. Warming over the Northern Parts of the Arabian Sea (NPAS) boosted convection in August 2020, resulting …
Four case studies of cyclonic vortices over the Indian peninsular region during northeast monsoon season are presented. It was observed that the propagation of vortices over the peninsular orography was controlled by the vortex Froude number, Frv = Vmax/(NH), where, Vmax is the maximum tangential velocity of winds in the vortex, H is the height of the orography, and N is the Brunt-Vaisala frequency of atmosphere along the orography. Strong vortices with Frv > 1 overcame the orographic barrier and crossed the peninsula. While, those with Frv < 1 were blocked by the orography. The blocked vortic…
Sub-seasonal Modulation and Predictability of Indian monsoon hourly Rainfall Extremes
Gauhati University（古哈提大学）；Indian Institute of Technology Madras（马德拉斯印度理工学院）；Indian Institute of Tropical Meteorology（印度热带气象研究所）；Cotton University（科顿大学）
During 2015, the southwest monsoon (SWM) rainfall over the country remained deficient with seasonal rainfall of about 86% of the long period average (Table 1.1). Last year, the seasonal rainfall deficiency over the country as a whole was 12% (www.imd.gov.in). Thus, this is a fourth episode of two consecutive years, with deficient monsoon, similar to 1904-05, 1965-66 and 1986-87 (www.imd.gov.in).
Scientific basis for long-lead seasonal prediction of Indian summer monsoon rainfall (ISMR) critical for water resource and crop strategy planning is lacking. Using a new predictor discovery method, here we show that the depth of 20 degree isotherm (D20) is least influenced by atmospheric noise and that the 18-month lead forecasts of ISMR have high potential skill (r = 0.86). The high potential predictability is due to smaller initial errors associated with the 18-month lead initial conditions and their slow growth associated with the El Nino and Southern Oscillation (ENSO). The potential skil…
The LNM Institute of Information Technology（LNM信息技术研究所）
Importance of monsoon rainfall cannot be ignored as it affects round the year activities ranging from agriculture to industrial. Accurate rainfall estimation and prediction is very helpful in decision making in the sectors of water resource management and agriculture. Due to dynamic nature of monsoon rainfall, it's accurate prediction becomes very challenging task. In this paper, we analyze and evaluate various deep learning approaches such as one dimensional Convolutional Neutral Network, Multi-layer Perceptron and Wide Deep Neural Networks for the prediction of summer monsoon rainfall in Ind…
Large socio-economic impact of the Indian Summer Monsoon (ISM) extremes motivated numerous attempts at its long range prediction over the past century. However, a rather estimated low potential predictability limit (PPL) of seasonal prediction of the ISM, contributed significantly by 'internal' interannual variability was considered insurmountable. Here we show that the 'internal' variability contributed by the ISM sub-seasonal (synoptic + intra-seasonal) fluctuations, so far considered chaotic, is partly predictable as found to be tied to slowly varying forcing (e.g. El Nino and Southern Osci…
Evaluation of High-Resolution Gridded Precipitation Datasets Against a Dense Rain Gauge Network During the Indian Summer Monsoon
Indian Institute of Technology (IIT) Madras（印度理工学院马德拉斯分校）；CSIR National Institute of Data Science and Artificial Intelligence（印度科学、技术和医学研究理事会数据科学与人工智能国家研究所）；IIT Delhi（印度理工学院德里分校）
We analyse Indian summer monsoon (ISM) seasonal reforecasts by CFSv2 model, initiated from January (4-month lead time, L4) through May (0-month lead time, L0) initial conditions (ICs), to examine the cause for highest all-India ISM rainfall (ISMR) forecast skill with February (L3) ICs. The reported highest L3 skill is based on correlation between observed and predicted interannual variation (IAV) of ISMR. Other scores such as mean error, bias, RMSE, mean, standard deviation and coefficient of variation, indicate higher or comparable skill for April(L1)/May(L0) ICs. Though theory suggests that …
Accurate short range weather forecasting has significant implications for various sectors. Machine learning based approaches, e.g., deep learning, have gained popularity in this domain where the existing numerical weather prediction (NWP) models still have modest skill after a few days. Here we use a ConvLSTM network to develop a deep learning model for precipitation forecasting. The crux of the idea is to develop a forecasting model which involves convolution based feature selection and uses long term memory in the meteorological fields in conjunction with gradient based learning algorithm. P…
Boundary-layer scheme selection outweighs microphysics in WRF simulations of the August 2018 Kerala extreme rainfall event
Indian Institute of Technology Bhubaneswar（印度布巴内斯瓦尔理工学院）；Indian Institute of Technology Bombay（印度孟买理工学院）；Cochin University of Science and Technology（科钦科技大学）
Indian Institute of Tropical Meteorology, Ministry of Earth Sciences（印度热带气象研究所，地球科学部）；Indian Institute of Technology Bombay（印度理工学院孟买分校）；Ramrao Adik Institute of Technology（拉姆劳·阿迪克技术学院）；Defence Institute of Advanced Technology（国防高级技术研究所）
Downscaling is necessary to generate high-resolution observation data to validate the climate model forecast or monitor rainfall at the micro-regional level operationally. Dynamical and statistical downscaling models are often used to get information at high-resolution gridded data over larger domains. As rainfall variability is dependent on the complex Spatio-temporal process leading to non-linear or chaotic Spatio-temporal variations, no single downscaling method can be considered efficient enough. In data with complex topographies, quasi-periodicities, and non-linearities, deep Learning (DL…
The Indian summer monsoon rainfall (ISMR) has a decisive influence on India's agricultural output and economy. Extreme deviations from the normal seasonal amount of rainfall can cause severe droughts or floods, affecting Indian food production and security. Despite the development of sophisticated statistical and dynamical climate models, a long-term and reliable prediction of the ISMR has remained a challenging problem. Towards achieving this goal, here we construct a series of dynamical and physical climate networks based on the global near surface air temperature field. We uncover that some…
Skilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio-economic value. It represents a lifeline for about a sixth of the world's population. The ISMR prediction remained a challenging problem with the sub-critical skills of the dynamical models attributable to limited understanding of the interaction among clouds, convection, and circulation. The variability of cloud hydrometeors (cloud ice and cloud water) in different time scales (3-7 days, 10-20 days and 30-60 days bands) are examined from re-analysis data during Indian …
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This study assesses a Continuous Data Assimilation (CDA) dynamical-downscaling algorithm for enhancing the simulation of the Indian summer monsoon (ISM) system. CDA is a mathematically rigorous technique that has been recently introduced to constrain the large-scale features of high-resolution atmospheric models with coarse spatial scale data. It is similar to spectral nudging but does not require any spectral decomposition for scales separation. This is expected to be particularly relevant for ISM, which involves various interactions between large-scale circulations and regional physical proc…
The identification of regions of similar climatological behavior can be utilized for the discovery of spatial relationships over long-range scales, including teleconnections. In this regard, the global picture of the interdependence patterns of extreme rainfall events (EREs) still needs to be further explored. To this end, we propose a top-down complex-network-based clustering workflow, with the combination of consensus clustering and mutual correspondences. Consensus clustering provides a reliable community structure under each dataset, while mutual correspondences build a matching relationsh…
The Yangtze River has been subject to heavy flooding throughout history, and in recent times severe floods such as those in 1998 have resulted in heavy loss of life and livelihoods. Dams along the river help to manage flood waters, and are important sources of electricity for the region. Being able to forecast high-impact events at long lead times therefore has enormous potential benefit. Recent improvements in seasonal forecasting mean that dynamical climate models can start to be used directly for operational services. The teleconnection from El Niño to Yangtze River basin rainfall meant tha…
Seasonal cycle of Precipitation over Major River Basins in South and Southeast Asia: A Review of the CMIP5 climate models data for present climate and future climate projections
Rainfall is an important variable to be able to monitor and forecast across Africa, due to its impact on agriculture, food security, climate related diseases and public health. Numerical Weather Models (NWM) are an important component of this work, due to their complete spatial coverage, high resolution, and ability to forecast into the future. In this study, we seek to evaluate the spatiotemporal skill of short-term rainfall forecasts of rainfall across Africa. Specifically, the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction-Gl…
Massive river interlinking projects are proposed to offset observed increasing trends of extremes, such as droughts and floods in India, the second highest populated country.These river interlinking projects involve water transfer from surplus to deficit river basins through reservoirs and canals, but without an in-depth understanding of the hydro-meteorological consequences. Using information theory-based causal delineation techniques, a coupled regional climate model, and multiple reanalysis datasets, we show that causal pathways exist across different basins in India due to strong land-atmo…
For modeling extreme rainfall, the widely used Brown-Resnick max-stable model extends the concept of the variogram to suit bloc maxima, allowing the explicit modeling of the extremal dependence shown by the spatial data. This extremal dependence stems from the geometrical characteristics of the observed rainfall, which is associated with different meteorological processes and is usually considered to be constant when designing the model for a study. However, depending on the region, this dependence can change throughout the year, as the prevailing meteorological conditions that drive the rainf…
Low-latitude rainfall variability on the daily to intraseasonal timescale is often related to tropical waves, including convectively coupled equatorial waves, the Madden-Julian Oscillation (MJO), and tropical disturbances. Despite the importance of rainfall variability for vulnerable societies in tropical Africa, the relative influence of tropical waves for this region is largely unknown. This article presents the first systematic comparison of the impact of six wave types on precipitation over northern tropical Africa during the transition and full monsoon seasons, using two satellite product…
In order to study the relation between the components of atmospheric circulation and summer precipitation over the Iranian plateau, daily rainfall data of synoptic stations of Iran were obtained from IRIMO for the period 1970 to 2003. Composite maps of different components such as pressure levels, vector wind, U and V winds, vertical velocity, Specific humidity, OLR, streamlines, vorticity, and the temperature was drawn and analyzed. The results showed that the summer circulation of the atmosphere over South West Asia after a sudden change from late May to early June was established in its nor…
The effects of El Niño's two distinct flavors, East Pacific (EP) and Central Pacific (CP)/Modoki El Niño, on global climate variability have been studied intensively in recent years. Most of these studies have made use of linear multivariate statistics or composite analysis. Especially the former assumes the same type of linear statistical dependency to apply across different phases of the El Niño--Southern Oscillation (ENSO), which appears not necessarily a justified assumption. Here, we statistically evaluate the likelihood of co-occurrences between very high or very low seasonal precipitati…
We study the relationship between the El Niño--Southern Oscillation (ENSO) and the Indian summer monsoon in ensemble simulations from state-of-the-art climate models, the Max Planck Institute Earth System Model (MPI-ESM) and the Community Earth System Model (CESM). We consider two simple variables: the Tahiti--Darwin sea-level pressure difference and the Northern Indian precipitation. We utilize ensembles converged to the system's snapshot attractor for analyzing possible changes (i) in the teleconnection between the fluctuations of the two variables, and (ii) in their climatic means. (i) With…
