 and here students and farmers in this class we like to discuss on simple methods of verification of weather forecast with real event comparing the weather forecast and the real event by R through science tools. This is very very important. You may ask a question why the verification is necessary. Just we get the forecast, we prepare agro advisories and communicate with the farmers. But even then verification is necessary for two thing. To improve the methodology being adopted presently, synoptic scale or planetary scale or numerical weather prediction model or regional climatic model are they suitable to our condition. So for improving the methodology the validation or verification of the weather forecast is totally required. Furthermore to develop confidence of the farmers on the weather forecast to be issued by us. So considering this the validation and verification are totally required. As I told you earlier the weather forecast is not being developed by us. It is by people of different organization using so many different higher resolution models by using better computers. Similarly the validation also can be done by us or by some institution to indicate the worthiness of the weather forecast being given to our farmers. This we will be discussing today. Now there are different tools are available, statistical tools, simple tools are available to compare or verify the forecasted value and also the actual events occurred. This is very very important. For example tomorrow I predict or foretell or forecast rainfall of 10 millimetre coin quantity wise. So after the arrival of the tomorrow and the day consent whether the rainfall is received or not. Suppose the predicted value is 10, the actual value is 0, like that we enumerate so many document data and verify through so many tools and one among them is your ratio score or hit score. This is a simple tool, very very simple tool. Here in the forecast accuracy is being indicated by ACC, this is there ACC there. This is the your ratio score, hit score. So here I have given a pair of alpabets, pair of alpabets. First pair letter in the pair is forecasted one, rainfall is 10 millimetre. And second one letter is the events occurred here. Suppose 10 mm y, this is y 0. So plus here n means I have given no, y is equal to s, that is predicted 10 millimetre, events occurred at 10 millimetre, predicted no prediction, no event. So like that the combination, different permutation combination occurs. This equation or tool can be utilized properly whether to find out the accuracy of the weather forecast being given. See normally the agro advisory is being prepared by the agricultural specialist. They must know whether they prepare the agro advisory for a particular precise weather forecast or better weather forecast for that this tool can be employed. And another tool is HHS score, skill score. This is another skill code, HDK skill score. This is tool number 2. This seems to be a cumbersome. It may get confused to you but don't worry. But a simple tool, I have given the legend here. Z is equal to number of correct predictions of the no rain, neither predicted nor observed. Here F is equal to the false alarms, that is wrong predictions predicted but not observed wrong predictions. So everything is given clear. By using this score you can able to assess the worthiness of the forecast to be used for preparing your agro advisories. Then the third one is your correlation and correlation coefficient. This is very simple. In every software you can find out this type of analysis between predicted and observed. So you make a correlation. Correlation means it proves the relationship between the two things that is your predicted and observed. When you take the intensity of the relationship then we go for the regression analysis. But here simple correlation means the association between the two data. One is predicted, another one is observed. But here the condition is a minimum of that the pairs are required to get analyzed. Otherwise you may get a wrong result or a yes result. The correlation value goes from 0.1 to 1. Whenever the value is 0.9 means you are having better accuracy of the weather forecast. When the value is 0.2 it is less valued forecast like that you have to interpret. And the fourth one also can be used by different people. Root means square, error RMSC. This is a simple tool being used. Anybody can use it. So far anything that means RMSC means here I have given the equation. This is summation of your forecast and occurred and the square value and square and everything I was given. Very simple. If you do it by a manual it can be done. By statistical also it can be analyzed. By computer software also it can be analyzed. But for everything this analysis must be done. At least for a season which is very important for a particular cropping system or for a particular crop management activities. Otherwise it is very difficult to use the weather forecast to be obtained from the weather development generating institutions. So with this I like to say that the validation of the weather forecast received is very very important. It must be verified. Otherwise in the long run you may end with a wrong result or no outcome will come. No output will come. So weather forecast validation is most important to be done by us. Thank you very much.