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Ensemble Empirical Mode Decomposition

Tue, 30 Jan 2024 07:43:47 -0800

EMD working python code can be accessed using following link

https://github.com/rakeshtejak/emd_v1_jupyter.git

Or you can clone at your workspace using following command

gh repo clone rakeshtejak/emd_v1_jupyter

Lorenz 96

Tue, 30 Jan 2024 07:37:43 -0800

Lorenz 96 1-tier dynamical system

Lorenz 96 speaks about a 2 dimensional dynamical system where a variable changes with time.

From the atmospheric point of view we can assume a latitude circle, where surface temperature

changes with time.

Lorenz 96 2-tier dynamical system
Two different scales say large scale and small scales interact with each other. This is slightly complex dynamical system. (Lorenz 2006).

Here,

b is the ratio of amplitude of large to small scale variables. To have better separation in the scales b is set to 10.

c is the ratio of rates of evolution of large to small scale variables. To have better separation in the scales c is set to 10.

h is the amount of influence that large and small scale can have on each other. To have better influence among the scales scales h is set to 1.

After applying above parametes in 2.1 and 2.2,

Diurnal cycle representation in global climate models

Fri, 05 May 2023 09:38:13 -0700

Joint Probability distribution calculated for diurnal magnitude and phase of precipitation from TRMM, WRF, and AGCM CMIP simulations

Main Findings (Rakesh and Takahashi 2020, Extended abstracts of Non-hydrostatic Modelling Workshop, 2018)

Recent AMIP models in the CMIP5 project and parameterized simulations failed to represent the diurnal cycle of Indian summer monsoon precipitation. They were unable to simulate the afternoon/late afternoon precipitation over land.
Explicit simulations better represent the diurnal cycle of precipitation and its characteristics over the land, ocean, and along the land-sea and mountains.
These results outline the better representation of the diurnal cycle of precipitation in Indian summer monsoon that could simulate monsoon-related seasonal mean rainfall and its variability accurately.

Land-atmosphere coupling

Fri, 05 May 2023 08:55:30 -0700

Surface roughness length for heat (m) in the Control (a, b) and MODIFIED (c, d) simulations during the pre-monsoon (a, c) and monsoon (b, d) seasons.

SEASONAL DIFFERENCES IN THE LAND-ATMOSPHERE COUPLING OVER SOUTH ASIA SIMULATED USING A REGIONAL CLIMATE MODEL

Main Findings (Rakesh and Takahashi 2020, Geographical Reports of Tokyo Metropolitan University)

Default WRF coupled with NOAH land surface model configuration overestimated the roughness length for heat over short vegetation and underestimated the same metric over tall vegetation, leading to a larger sensible heat flux over South Asia.
A modified NOAH LSM reduced the over-estimation of roughness length for heat, and thus reduced the sensible heat flux.
A dry precipitation bias during the onset phase of the Indian summer monsoon over central India was found to be effectively reduced in the modified NOAH land surface model.

Nocturnal offshore propagating precipitation systems (NOPS)

Fri, 14 Apr 2023 21:30:50 -0700

Indian Monsoon region

Konduru et al. Meteorological Applications, 2022

The schematic diagram summarizes the influence of monsoon low-level winds and gravity waves on the eastward propagation of the diurnal precipitation peak over southeast India. In this schematic model, we explain the diurnal strengthening of monsoon southwesterly winds over the southeast coast onshore (offshore) regions that support inland (near the coast) afternoon/evening (night/late night) diurnal peaks around 1500–1800 LST (0100–0400 LST). The presence of a strong anomalous westerly in the lower atmosphere (900 hPa) at night strengthens near the land–sea boundary along the southeastern coast. Late at night, an atmospheric gravity wave causes the nocturnal diurnal wave to propagate eastward from the southeast coast offshore, following the gravity wave propagation speed. Precipitating clouds differ according to the development mechanism of precipitation systems; for instance, southeast India experiences mainly stratiform clouds, and southwest India experiences deep convective clouds. Therefore, diurnal variations and propagation to the leeward side of the WGs are determined by the interaction between monsoon low-level southwesterly winds and gravity waves.

NOPS over Asian monsoon regions (In preparation)

This figure shows highest diurnal variability over the asian region along the coasts. As per the concept these high variability precipitation regions are the nocturnal offshore propagations.

2015 Chennai floods: Role of climate variability and urbanization

Thu, 13 Apr 2023 22:04:43 -0700

Atmospheric natural variability is the cause of Chennai extreme rains.
Anthropogenic variability due to urbanization influenced the hydrological cycle to result in extreme rainfall.
Unprecedented urbanization over Chennai in 2015 modified the hydrological cycle.
Convective coupled equatorial waves were found to contribute to natural variability.
Northward propagating ‘tropical disturbance’ triggered extreme rain events along the southeast coast of India.

How to effectively understand urban climate interactions?

Flow chart shown in above figure can give an effective understanding about reasons/factors that cause floods over the metropolitan cities. Above concept in flow chart is applicable over almost all metropolitan cities in the world.

Dirunal rainfall representation in explicit and parameterized convection.

Thu, 13 Apr 2023 21:51:21 -0700

Diurnal precipitation characteristics of the Indian summer monsoon were simulated realistically without convective parameterization
Precipitation characteristics were more dependent on convection representation than model horizontal grid resolution
Explicit (parameterized) convection experiments simulate high-intensity localized (high-frequency widespread) precipitation events

A precipitation system from early morning to noon that travels southeastward from the western coast of the Bay of Bengal to the central Bay of Bengal, which can be associated with the low-level southeastward winds, was simulated in COFF (a snapshot that shows offshore-propagating precipitation systems). The precipitation system simulated in CON over the Bay of Bengal during the midnight or early morning was not likely to travel southeastward, but a stronger precipitation system developed within a shorter number of hours. This type of difference in diurnally propagating precipitation systems and characteristics is an important aspect differentiating COFF and CON.