weather forecasts

Patchy Skies: Missing ‘Critical Weather Forecasts’ Holds Back Indian Agriculture

As weather patterns turn more erratic, there is a need to address data gaps by strengthening weather-monitoring networks to build effective early warning systems for Indian agriculture.

Weather Forecasting and Agriculture

India’s hardworking farmers, who form the backbone of the nation’s economy, are confronted with an increasingly formidable adversary – the unpredictable and erratic climate patterns. As a solution, the implementation of effective early warning systems is of paramount importance. Nonetheless, the progress in developing these vital systems is impeded by the absence of essential weather data from micro-level (village) weather stations. The current network of automated weather stations and associated machine learning-based prediction covers a wide geographic area but can only provide a 5-day lead time forecast.

It’s crucial to understand how the uncertainty in weather forecasting changes with lead time, and how such uncertainty affects prediction errors in machine-learning-based outage prediction models. Forecasting errors based on numerical weather prediction are caused by uncertainties in the model’s initial conditions, boundary conditions, physical parameterizations, and model errors. These factors, particularly the initial-condition errors, determine the accuracy of forecasted weather parameters at different lead times (up to five days) used in prediction models.

forcast lead time

Short-term weather predictions of up to 5-7 days are relatively good. Similarly, seasonal forecasts are considerable. However, researchers are endeavouring to enhance the quality of forecasts, which anticipate the weather for a 7 days ahead period, by understanding sub-seasonal weather patterns. This is indeed a complex task because many different factors influence sub-seasonal weather patterns, and some of these factors are hard to predict with a fair amount of accuracy; for instance: Madden-Julian Oscillation (MJO). Therefore, based on the reliability the forecasts can be categorized as under:

  • Short-term (0-3 days): Most accurate, with errors being relatively small.
  • Medium-term (4-7 days): Still reasonably accurate, but errors become more likely.
  • Extended-term (8-14 days): Less accurate, with significant potential for errors.
  • Sub-seasonal forecast (15-45 days): Very poor, not reliable
  • Seasonal forecasts (beyond 45 days): Broad trends can be predicted, but specific details are unreliable.

In agriculture, the medium to extended term is more crucial to make informed agricultural decisions. If the farmers miss critical days within the 5-15 day window, the advisory may be ineffective. Furthermore, accessing the medium-term (4-7 days) extended range forecasts at the block level provided by the Indian Meteorological Department (IMD) is challenging without a good understanding of meteorology.

The 8-14 days forecast bears a significant potential for errors as the stations are spread out over a large spatial extent, approximately the size of an average district boundary. This limits the point data available for bias correction and leaves a lot up to the imagination of meteorologists who rely on their experiences and rainfall patterns from the past 1-2 decades to guess which village is more likely to receive rainfall in the predicted block for the aforementioned period. However, this level of accuracy leaves farmers at the mercy of their fate when making crucial farm decisions.

Imagine trying to predict a game’s outcome without knowing the score. That’s the challenge for climate forecasters in India with sparse weather station networks. These stations collect vital data like rainfall, temperature, and humidity, forming the foundation for accurate forecasts. But with limited stations, especially in rural areas, capturing the full picture of India’s diverse climate becomes difficult. India is actively working on improving its weather forecast network, but there’s still room for growth. The existing network, primarily managed by the India Meteorological Department (IMD), doesn’t cover all areas, especially rural ones. The government aims for complete Doppler Weather Radar (DWR) coverage by 2025. DWRs provide more detailed data compared to traditional stations.

Risks of Inaccurate Weather Forecasts

The current situation of data scarcity causes a significant bottleneck in the process of scaling climate-adaptive methods such as direct seeding of rice (DSR) and cover crops as the third addition to the existing cropping system. The use of dense networks to collect timely data is essential for the development of advanced forecasting models, especially those used for agricultural forecasts. It is without it that forecasts become less precise, which in turn limits their ability to warn farmers of extreme weather occurrences such as prolonged dry spells throughout the cropping season, droughts, irregular rainfalls, floods, and heat and cold waves simultaneously. Any assumptions in the current system of weather forecasting leading to wrong results may have severe repercussions. Inaccurate weather forecasts might cause farmers to sow their crops at the incorrect time, use insufficient irrigation or over irrigation, or be unprepared for sudden changes in the weather leading to washouts of applied insecticides and pesticides. Furthermore, the current weather forecast systems frequently fail to provide accurate forecasts during crucial days of crop production, which has a significant impact on the yields. This not only affects the produce but also has a detrimental effect on the livelihoods of small and marginal farmers.

Making Weather Forecasting Work for Indian Agriculture

  • Expanding the Weather-monitoring Network: Increasing the number of weather stations, particularly in remote areas, is essential. This would provide a more comprehensive picture of India’s climate and improve forecast accuracy. The government is working with ICAR to deploy more than 1 lakh automated weather stations in different blocks of districts across India.
  • Embracing the Innovation: Exploring alternative data sources like radar and remote sensing data can supplement traditional methods. This can improve the accuracy of forecasts with reduced time and space constraints. This is notable that the current scale of the forecast is a 5-day time period with district boundaries as spatial extent.
  • Data Sharing and Collaboration: Facilitating the exchange of data between government agencies and research institutions has the potential to unlock the power of existing data for developing more sophisticated and dependable models. This approach can enhance model validation and bias correction, leading to more precise and reliable predictions with minimized chances of errors.

By addressing the data gaps, India can strengthen its early warning systems. This ensures that Indian skies are predictable to our farmers and empowers them with the knowledge to make informed agriculture decisions, ultimately leading to greater resilience and productivity in the face of a changing climate.

Kumar Abbhishek | Senior Technical Associate – Soil Health

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