Effect of Urban Expansion on Weather Prediction

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North-East has observed huge urban growth in past few decades. Many green areas and agricultural lands have altered into built-up areas, however the default 24-category USGS land use data used in Weather Research and Forecasting (WRF) model for mapping land-use to model domain is ineffectual in terms of current land-use representation. In significance, there is a considerable discrepancy between land-use data that is contributed to the model and actual land use particularly for North-East region. An updated land-use data, thus, provides a scope in enhancement of model performance.

The Indian summer monsoon is a main mechanism having a significant role in the global climate system and also towards the global water cycle (Trenberth et al., 2000). The south-west summer monsoon governs agricultural, energy and water resources sectors. Thus, it is crucial for all applications to precisely estimate and predict the summer monsoon precipitation.

Variations in the pattern of precipitation have the most adverse impacts among all meteorological variables on the humanity. More precisely, the main concern is on the changes in extreme rainfall. As the intense precipitation events often causes disasters like flash floods, which in turn results in large-scale damage to the infrastructure, also on natural ecosystems.

Extremely heavy rainfall on shorter timescales are particularly difficult to predict in mountainous terrains and continue to be a challenge to operational and research communities (Das et al., 2008; Li et al., 2017). Global models have been employed in several studies to understand the large-scale circulation pattern and for quantitative analysis of the monsoon rainfall, but due to their coarse resolution, they are unable to represent the local to regional characteristics of monsoon rainfall. Regional models, however, can explicitly simulate the interactions between the large-scale weather phenomenon and regional topography, making the climate simulations reliable (Gadgil and Sajani, 1998; Ratna et al., 2011; Srinivas et al., 2013).

Furthermore, regional models have a better representation of convection, thus offsetting one of the major sources of errors and uncertainties in the global models. Therefore, regional models become a preferred choice to study seasonal monsoon rainfall. The advanced research version of the Weather Research and Forecasting model (hereafter referred to as the WRF model) is a regional popular community model that is widely used for both studying as well as forecasting a variety of high-impact meteorological events, such as rainfall, tropical and thunderstorms (Madala et al., 2014; Osuri et al., 2017).

Numerical weather prediction is one of the most modern weather prediction technique. It uses current weather information to predict future weather condition using ocean and atmospheric mathematical models. This technique requires very high computation facilities like supercomputers. Even with supercomputers the forecast skill is limited to six days. Atmospheric and ocean models use partial differential equation which cannot be solved exactly and error grows with time. In addition to that model uses parameterizations for solar radiation, moist process (clouds & precipitation), heat exchange, soil, vegetation, surface water and effect of terrain.

The developments in geographical information system (GIS) and remote geographical information system (GIS) and remote sensing techniques provides a new platform for spatial visualization of information sensing techniques provides a new platform for spatial visualization of information about the natural resources. The integration of derived formats provides enormous about the natural resources. The integration of derived formats provides enormous potential for identification, monitoring potential for identification, monitoring and forecast of extreme rainfall event. The expansion of urban construction land in the study area leads to regional meteorology changes, as mentioned before.

In addition, LULC changes involve not only urban expansion, but also transformations from natural LULC in recent decades, which also impact the climate in the study area. Existing global land cover products cannot enough express the real land surface properties, which dominant the energy exchange in the region, thus directly influence the simulation results in weather and climate models. Meanwhile, understanding the effects of LULC changes on precipitation in extreme rainfall event is particularly important to enhance the adaptive capacity of a region.


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Effect of Urban Expansion on Weather Prediction. (2021, May 16). Retrieved from https://samploon.com/effect-of-urban-expansion-on-weather-prediction/



Does urban growth lead to climate change?
Yes, urban growth contributes to climate change as it leads to an increase in greenhouse gas emissions, energy consumption, and deforestation, which in turn exacerbates global warming and other climate-related issues. However, sustainable urban planning and development can help mitigate these negative effects and create a more environmentally friendly and resilient urban environment.
Does urbanization increase rainfall?
No, urbanization does not increase rainfall.
How does urban expansion affect climate?
As cities grow and sprawl outward, they have a large impact on the climate. The expansion of urban areas leads to a decrease in vegetation and an increase in hard surfaces, which results in more heat being absorbed and released into the atmosphere. This can lead to higher temperatures and changes in local weather patterns.
How does Urbanisation affect weather?
In summary, urbanization will affect the rainfall amount in two ways: (1) enhance rainfall by providing greater buoyancy of surface air encouraging local convection and (2) reduce rainfall by less local moisture source due to the elimination of greenery.
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