Weather forecasting

Weather forecasting

1. WE A THER FORECASTING

2. The weather elements which influence the agricultural operations and crop production can be forecast upto different spans of time. Weather forecast is defined as “prediction of weather for the next few days to follow”. Weather forecasting is foretelling the coming weather in advance. It may be defined as advance information about the probable weather conditions for few days to follow.

3. Weather is a dominant factor determine the success or failure of agriculture enterprises. This is because farmer has no control over over this natural force. Weather manifests its influence on agriculture operations and farm production through its effect on soil and plant growth. Out of total annual crop losses, a sustainable portion is because of aberrant weather condition. But losses could be minimize by making the adjustment with coming weather through timely and accurate weather forecasting Weather forecasting also provides guidelines for long range seasonal planning and selection of crops most suited to anticipated climatic condition.

4. Types of weather forecasting W eather foreca s tin g for agricult u re m ay be di v ide in t o four types: Nowcasting (few hours to one day) Short range forecasting (24 hours to < 3 days) Medium range forecasting (3-10 days) Long range forecasting ( for >10 days, a month and for a season)

5. Nowcasting A weather forecast in which the details about the current weather and forecasts up to a few hours ahead (but less than 24 hours) are given. It is a powerful tool in warning the public of hazardous, high-impact weather including tropical cyclones, thunderstorms and tornados which cause flash floods, lightning strikes and destructive winds.

6. Short range forecasting (SRF) I t i s th e fore c ast and warning of weather elements hazardous to agr i c ul t ure valid for 36 h o urs a n d an outlook for subsequent 3 days. The SRF is issued twice a day based on synoptic conditions. Though SRF is use full in weather based agr i c u l t ural operations , th e react i on time available to formers is too short for preventive measures against adverse weather. The error in forecast ranges from 20-30 per cent. The SRF includes cloud spread, rainfall distribution, heavy rainfall waning, m aximum and m i nimum temperature heat and cold waves, low pressure area, cyclone warning, hail storm and dust storm, snow, frost and

7. Medium range forecasting (MRF) It is the forecast and warning of weather elements hazardous to agriculture valid for 3- 10 days. The MRF is an objective and challenging one to weather scientists as it involves enormous numerical computations with expertise in weather sciences. A national center for medium range weather forecasting (NCMRWF) was established in 1988 in new Delhi to develop atmospheric model for medium range weather forecasting. This forecast is issued twice in a week i.e. on Tuesday and Friday. Forecast error ranges from 30 to 40 per cent. Forecast includes cloud amount, rainfall, maximum and minimum temperature

8. L ong range forecasting (LRF) It is the forecast for more than 10 days, a month, a season. IMD started issuing the long range forecasting since 1988 onward on total monsoon rainfall of the country by 25th may. The predicted and actual long period average of monsoon (June –September) rainfall of the country were in agreement except in1994. This forecast is issued region wise i.e. country is divided in four zones: North eastern region Central region Nort h W estern R egion Peninsular region.

9. Significance of weather forecasting in agriculture Agriculture is mainly dependent of weather. If the weather is favourable, then crop production will be higher. But if the weather is not under optimum/favourable range, then it will cause losses to crop production depending upon its intensity of abnormality. Such type of weather is termed as aberrant weather or abnormal weather. However the losses due to aberrant weather can be minimized if it is forecasted accurately. Rather it is impracticable to avoid crop losses due to aberrant weather but it is possible to minimize crop losses to some extent, if weather forecast is accurate and in time. The input cost can be minimized by avoiding wastage of inputs through short term adjustment of input applications with coming weather. The applications of forecasting also depend upon the lead time of forecasting. So the applications can be grouped with the type of forecasting:

10. Short range applications Adjustment of day to day field operation Scheduling of irrigation and application of agro-chemicals Protection of field crops & livestock from frost/cold wave & heat wave Efficient use of labour

11. Medium range applications Sowing and planting of crops Management of labour, irrigation water and agro-chemicals Protection measures against frost/cold wave and heat wave Management of inputs and products of livestock Transportation of farm products

12. Long range applications Selection of crops, varieties and breeds Management of water resources Management of farm inputs such as labour, machinery, seeds

13. Climatic / agro climatic forecasting It requires past meteorological data for a good numbers of years (say 30-50 years) the trends in rainfall and its variability, probability on the distribution of rainfall over a season can be determined weekly using the past data on the rainfall for a given location. This information is useful to crop planners and farmer as crop growth periods can be adjusted under rainfed conditions depending upon rainfall probabilities. the climatic trends also helps in understanding the impact of climatic variability on agricultural production over a period of time.

14. Different tools used in weather fore c asting Pilot balloons Radiosnodes Radar Satellites Surface data

15. Methods of weather forecasting Synaptic chart Statistical methods Numerical weather prediction

16. Synaptic method The atmosphere at an area is known at an instant through a set of meteorological variables, viz. rainfall, maximum and minimum temperature, wind and pressure system measured simultaneously at various locations. These observation are called synoptic. Using the observation recorded simultaneously, surface and upper air chart are prepared which give the present state of atmosphere. The inference on expected movement of weather system is drawn using previous and present charts. In addition to synoptic charts, satellites picture also supply considerable information evolved on the lines of the past analogous one. Often, selection of past analogous situation is based on experience and memory of the person involved, but with the advent of computer picking up of analogies is quite easy and became faster and more objective.

17. Statistical method The statistical method are mostly used in long range and climate forecasting. Techniques based on multiple regression and auto regressive integrated moving average (ARIMA) are used for predicting Indian monsoon rainfall based on 16 global- land-ocean-atmospheric variables. Using above technique, the total rainfall during south-west monsoon is predicted well in advance during the last week of may. The technique is working well for prediction of total monsoonal rainfall of the country. However the ARIMA model fail on metrological sub division wise since the model are not area specific. Also, model needs testing, verification and validation regularly.

18. Numerical weather prediction Thinkers frequently advance ideas long before the technology exists to implement them. Few better examples exist than that of numerical weather forecasting. Instead of mental estimates or rules of thumb about the movement of storms, numerical forecasts are objective calculations of changes to the weather map based on sets of physics-based equations called models.

19. Reliability of weather forecasting depends on A wareness o f th e o b se r ver o n th e importance of weather forecasting. Networ k o f o b servatorie s - s u rface and upper air stations . Dissemination role of telecommunication networ k li k e AI R D D and new s m ed ia. Type of weather forecasting (SRF, MRF and LRF). Efficiency of space tool. metrological data and past analogies. Skill and experience of weatherman Present day technology knowledge gain in the field of weather forecasting W eather a n a l ysis a n d generati o n of computer output- how fast it does Communication of data from collection to weather forecasting center Frequency of data collection T im e ly and correctnes s o f the observation