Equations are not being displayed properly on some articles. We hope to have this fixed soon. Our apologies.

Bagadi, R. (2017). Methods of Reducing the Error In Weather Forecasts Of Dynamical Nature. PHILICA.COM Article number 949.

ISSN 1751-3030  
Log in  
Register  
  1270 Articles and Observations available | Content last updated 13 December, 05:46  
Philica entries accessed 3 486 341 times  


NEWS: The SOAP Project, in collaboration with CERN, are conducting a survey on open-access publishing. Please take a moment to give them your views

Submit an Article or Observation

We aim to suit all browsers, but recommend Firefox particularly:

Methods of Reducing the Error In Weather Forecasts Of Dynamical Nature

Ramesh Chandra Bagadiunconfirmed user (Physics, Engineering Mechanics, Civil & Environmental Engineering, University of Wisconsin)

Published in engi.philica.com

Abstract
In general Weather Forecast consists of Modelling Weather Parametric Relationships using Complex Navier-Stokes Equations and Computation of the same using Special Set Of Initial Conditions. It has been observed that even a small variation in the Initial Condition can change the Forecast drastically. Therefore, this problem can be addressed by using (the below stated) methods by which such Error Propagation can be Minimized.

Article body

Methods of Reducing the Error In Weather Forecasts Of Dynamical Nature

                                                                                                                            ISSN 1751-3030

 

Author:

Ramesh Chandra Bagadi

Data Scientist

International School Of Engineering (INSOFE)

Postal Address:  Plot No 63/A, 1st Floor,

Road No 13, Film Nagar, Jubilee Hills,

Hyderabad – 500033, Telengana State, India.

 

 

Abstract

In general Weather Forecast consists of Modelling Weather Parametric Relationships using Complex Navier-Stokes Equations and Computation of the same using Special Set Of Initial Conditions. It has been observed that even a small variation in the Initial Condition can change the Forecast drastically. Therefore, this problem can be addressed by using (the below stated) methods by which such Error Propagation can be Minimized.

Theory

Use Of Buckingham’s Pi Theorem

One can use Buckingham’s Pi Theorem to find the Inter-Relations among the Parameters themselves which can greatly help

a.     Reduce the Total  Number of Parameters themselves

b.     Secondly even if we wish to consider all the Parameters we began with, use or/ and knowledge of a. will tell us about the Deviation or Spread of the afore-mentioned Inter-Relations, and such spread can be used as Quantum Uncertainties for such Functions.

Knowing the Order of the Values Taken By The Parameters (especially the Ones To Be Predicated)

Knowing the Order of the Values Taken By The Parameters (especially the Ones To Be Predicted) and constructing a Function of the Same, will help us understand the Order of Deviations From Forecasts The Initial Condition Values Will Cause If We consider Measurements at below a threshold Level of Measurement Precision of the Initial Condition Parameter Values.

Entropy Analysis Of Error Function

The Error Function can be Analysed For Its Entropy and the key aspect Parameters that influence it can be known and focus can be made to Measure them at a Higher Precision or/ and can Hyper-Refine the Dynamical State System Model appropriately.

Using The Bounds Of The Atmospheric Variability

One can use the Bounds of the Atmospheric Variability, i.e., composition wise, which can be Recursively and Reverse Engineering wisely used for Evaluation of the True Error Bounds.

Using Quantum Uncertainty Function Of The Error (Deviations Of Outcome From Predicted Value) Data

A Quantum Uncertainty Function can be built using the Error (Deviations Of Outcome From Predicted Value) Data and can be used to create Prediction Error Bounds. Also, Artificial Neural Network type Learning Aspect can be incorporated in the same for wise Modelling and Parameter Sensing.

Machine Learning

Concepts of Machine Learning can also be used to a Holistic Degree of Logic Refinement of Cause Effect Chain Tree of Interplay among the Parameters of concern and this can be used to distill Error to some degree.

Universal Locally Linear Transformations Based Forecasting Model [1], [2], [3], [4]

Using Locally Linear Transformations, Normalization Of State Vectors, Normalized State Vector Element Wise Inner Product Matching, De-Normalization Of Normalized States Using Magnitude Scaling can also be used for Rough Determination of Predictions.

References

1.     Bagadi, R. (2017). Universal One Step Holistic Convergent Forecasting Model For Dynamical State Systems {Version 0}. ISSN 1751-3030. PHILICA.COM Article number 945.

2.     Bagadi, R. (2017). Universal One Step Holistic Forecasting Model For Dynamical State Systems {Version 2}. ISSN 1751-3030.. PHILICA.COM Article number 944.

3.     Bagadi, R. (2017). Universal One Step Holistic Convergent Hyper-Refined Forecasting Model For Dynamical State Systems {Version 1}. ISSN 1751-3030. PHILICA.COM Article number 940.

4.     Bagadi, R. (2017). Universal One Step Hyper-Refined Forecasting Model For Dynamical State Systems {Version 2}. ISSN 1751-3030. PHILICA.COM Article number 939.





Information about this Article
This Article has not yet been peer-reviewed
This Article was published on 1st February, 2017 at 05:52:44 and has been viewed 1392 times.

Creative Commons License
This work is licensed under a Creative Commons Attribution 2.5 License.
The full citation for this Article is:
Bagadi, R. (2017). Methods of Reducing the Error In Weather Forecasts Of Dynamical Nature. PHILICA.COM Article number 949.


<< Go back Review this ArticlePrinter-friendlyReport this Article



Website copyright © 2006-07 Philica; authors retain the rights to their work under this Creative Commons License and reviews are copyleft under the GNU free documentation license.
Using this site indicates acceptance of our Terms and Conditions.

This page was generated in 0.9631 seconds.