Asked 23rd Apr, 2023
  • Agricultural Ministry of Ieran. Agricultural Organization of Khuzestan. Agricultural Management of Shush.

How can we predict precipitation distribution in practice?

Knowing this distribution provides a good chance to regulate crop planting date to be shorter than usual state to consume less water in order to be more adaptive in climate changes circumstances.

All Answers (3)

Prem Baboo
The Institution of Engineers (India)
Rainfall Prediction is the application area of data science and machine learning to predict the state of the atmosphere. It is important to predict the rainfall intensity for effective use of water resources and crop production to reduce mortality due to flood and any disease caused by rain. Precipitable water is measured using information from weather balloons and water vapour imagery from weather satellites. The equation of the line is given as Y=wx+b. It provides an estimate of rainfall using various atmospheric variables like cloud cover, humidity, wind, and average temperature to predict is accepted that the main variables for predicting precipitation are temperature and humidity . Nevertheless, there are other four meteorological parameters—air, dewpoint temperature (or relative humidity), wind speed, and cloud cover—which are strongly correlated with rainfall.To predict rainfall, several types of research have been conducted using data mining and machine learning techniques of different countries’ environmental datasets.To choose the better machine learning algorithms to study the daily rainfall amount prediction, various papers can be reviewed concerning rainfall prediction. To predict the daily rainfall intensity using the real-time environmental data, three algorithms such as MLP, RF, and XGBoost gradient descent can chosen for the experiment. Hence, the three machine learning algorithms experiment with and compared to report the better algorithms to predict the daily rainfall amount.
Fayaz Hussain
University of Agriculture Faisalabad
Predicting precipitation distribution involves a combination of both theoretical and observational methods. Here are some common approaches used in practice:
  1. Meteorological Models: These are computer models that simulate the behavior of the atmosphere and predict future weather conditions based on mathematical equations. Precipitation can be predicted using meteorological models that take into account atmospheric conditions such as temperature, humidity, pressure, wind speed and direction.
  2. Satellite Imagery: Satellites equipped with sensors can measure cloud cover, water vapor, and temperature of the Earth's surface. This information can be used to determine the likelihood of precipitation in a given area. The data collected by satellites is analyzed to create maps of precipitation distribution.
  3. Radar: Doppler radar can detect precipitation in the atmosphere by bouncing radio waves off precipitation particles. The data collected by radar can be used to create maps of precipitation intensity and movement.
  4. Historical Data: Past precipitation data can be used to create a model of future precipitation distribution. This can be done by analyzing past weather patterns and trends, as well as using statistical methods to identify correlations between different variables.
  5. Ensemble Forecasting: This involves running multiple simulations with slightly different starting conditions to create a range of possible outcomes. This method helps to identify the most likely scenarios and potential outliers.
It's important to note that predicting precipitation distribution is complex and not always accurate. The use of multiple methods and data sources can help improve accuracy, but it's important to also consider the limitations of each method and the uncertainty associated with any prediction.
Ahmad Khanifarzade
Agricultural Ministry of Ieran. Agricultural Organization of Khuzestan. Agricultural Management of Shush.
Thanks a lot for all excellent answers 👌

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