Default values for many model parameters in Numerical Weather Prediction models are typically adopted based on theoretical or experimental investigations by scheme designers. Short-range forecasts are substantially affected by the specification of parameters in the Weather Research and Forecasting (WRF) model. The presence of a multitude of parameters and several output variables in the WRF model renders appropriate parameter value identification quite challenging. The objective of the current study is to reduce the uncertainty in the model outcomes through the recognition of parameters that strongly influence the model performance using a Global Sensitivity Analysis (GSA) method. Morris one-step-at-a-time (MOAT), GSA method, is used to identify the sensitivities of 23 chosen tunable parameters corresponding to seven physical parameterization schemes of the WRF model. The sensitivity measures (MOAT mean and standard deviation) are evaluated for eleven output variables, out of which some are surface meteorological variables and the remaining are atmospheric variables, which are simulated by the WRF model with different parameters. Twelve high-intensity four day precipitation events during the Indian summer monsoon (ISM) for the years 2015, 2016, and 2017 over the monsoon core region in India are considered for the study. Six out of 23 parameters have high MOAT mean in the case of almost all the model output variables indicating that these parameters have a considerable effect on the outcome of the simulations. MOAT mean values of a few parameters are noticeably small for all the model output variables, and thus the uncertainty associated with them has a negligible effect on the WRF model performance. The study also presents the physical insights into the trends of the parameter sensitivity.
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 …
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…
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）
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…
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…
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…
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 …
The initiation of the Indian summer monsoon circulation during late May / early June arises through large-scale land-sea thermal contrast and setting up of negative pressure gradient between the Monsoon Trough over the Indo-Gangetic plains and the Mascarene High over the subtropical Indian Ocean. The meridional pressure gradient together with the Earth's rotation (Coriolis force) creates the summer monsoon cross-equatorial flow, while feedbacks between moisture-laden winds and latent heat release from precipitating systems maintain the monsoon circulation during the June-September (JJAS) rainy…
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…
Did recent sea surface temperature warming reinforce the extreme East Asian summer monsoon precipitation in 2020?
Ulsan National Institute of Science and Technology（蔚山科学技术院）；Pohang University of Science and Technology（浦项科技大学）；Yonsei University（延世大学）；Seoul National University（首尔大学）
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 …
$ $From June--September, the summer monsoon current (SMC) flows eastward south of Sri Lanka and bends northeastward to form a swift jet that enters the Bay of Bengal (BoB). As such, it is a crucial part of the water exchange between the Arabian Sea (AS) and BoB. The processes that determine the evolution, intensification and meandering of the SMC are only partly understood. They involve both local and remote forcing by the wind, as well as interactions with westward-propagating Rossby waves and eddies. In this study, we investigate these processes using an Indian-Ocean general circulation mode…
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…
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…
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…
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 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 r…
We study the forced response of the teleconnection between the El Nino-Southern Oscillation (ENSO) and global precipitation in general and the Indian summer monsoon (IM) in particular in the Max Planck Institute Grand Ensemble. The forced response of the teleconnection is defined as the time-dependence of a correlation coefficient evaluated over the ensemble. The ensemble-wise variability is taken either wrt. spatial averages or dominant spatial modes in the sense of Maximal Covariance Analysis or Canonical Correlation Analysis or EOF analysis. We find that the strengthening of the ENSO-IM tel…
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…
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…
Marine Heatwaves in the Arabian Sea: Drivers and Impacts on Atmospheric Circulation and Extreme Precipitation
University of Colorado, Boulder（科罗拉多大学博尔德分校）；Texas A&M University–Corpus Christi（德克萨斯农工大学科珀斯克里斯蒂分校）；Scripps Institution of Oceanography, University of California, San Diego（加州大学圣地亚哥分校斯克里普斯海洋研究所）；Florida State University（佛罗里达州立大学）
