Meteorological data

Meteorological data


Quantitative data on surface weather variables including forecasts, local observations and historic archives.

Key datasets

  • Short-term weather forecast
  • Seasonal weather forecasts (3-6 months ahead)
  • Real-time observations
  • Historic archives of observations
  • Historical simulated weather from re-analysis (e.g. ERA-INTERIM)
  • Climatological observations
  • Climatological reference data
  • Climate zones
  • Climate change predictions


Plant growth is driven by weather variables and therefore agricultural production is directly dependent on weather conditions. Many agricultural activities (e.g. sowing, harvesting, fertilizer application) are dependent on weather conditions for planning and effectiveness. Given this, all agricultural stakeholders are interested in some form of meteorological data. Most governments have a specific department or agency dealing with weather information. By making meteorological information available as open data it not only becomes easier to share, but also allows the development of specialised information services by infomediaries targeting specific user needs. Examples of such specialized services are early warning systems for weather-related crop diseases, encouraging farmers to take preventive measures, and the prediction of suitable conditions for farm activities.

Expected impact:

Farmer use

  • By having timely and accurate weather information, a farmer can plan farming activities better

Use by other actors

  • By having access to weather archives, (local) climatic conditions can be objectively determined, allowing more accurate:
    • farm management advice for the farmer; and
    • investment risk investigation for financial institutions, resulting in better access to finance for farmers.
  • By having access to archives of local weather variables, the weather forecast for that particular area can be improved (downscaling).
  • By having access to near real-time weather observations, insurance companies are enabled to build index insurance products, reducing food security risks for the farmer and increasing the access to finance.


Nearly all countries have a network of weather stations for collecting local weather data. However, some countries have a high-density network of automated weather stations, archives of measurements with long time series and local weather models, and radar equipment. Others have limited numbers of weather stations, often managed manually, with data being stored irregularly. Sharing of local weather data is strongly dependent on local policies for data sharing and access. In many countries, local weather observations are considered to be a commercial or strategic asset, which makes data sharing difficult. Institutional barriers may thus obstruct the sharing of available information.

Globally, the World Meteorological Organisation (WMO) is facilitating the exchange and distribution of observations and processed information via the Global Telecommunications System (GTS) However, the number of stations available on the GTS is small compared with the total number of operational stations worldwide.

Sharing of products from numerical weather prediction models (forecasts and re-analyses) is also dependent on the organisational data-sharing policy. Two main sources of such products are the US National Oceanic and Atmospheric Administration (NOAA) with its Global Forecasting System (GFS), and European Centre for Medium-Range Weather Forecasts (ECMWF). The GFS forecasts can be obtained without restriction as open data while the ECMWF forecasts and products are often restricted to ECMWF member states or only available at commercial rates. Nevertheless, some of the ECMWF products are available on GeoNETCast to a wider user community.

In addition, sharing of local (high-resolution) weather forecasts made by individual weather services for specific regions are often difficult to acquire due to restrictions in data sharing policies or technological difficulties in providing access.

Examples of Implementation

Initiatives that support interoperability

Global standards for meteorological and climate data are given by:

Box 4: Harnessing weather data for better decision-making at farm level

UNMA staff installing a meteostation in Uganda

Despite its obvious importance, many farmers lack a reliable source of weather information – even when a national weather service exists in the country. Different initiatives are trying to fill this gap by using a Short Message Service (SMS) or Interactive Voice Response (IVR) service to provide weather information or a crop advice service based on (open) globally available weather forecasting resources. Examples are Farmerline Ltd, Human Network International (HNI), Esoko, aWhere Inc, Weather Impact, among others. However, a local weather forecast based on a global model is less accurate without local information. Especially when the topography is rough or when applied to a convective rain system (many rain showers), as is often the case in tropical regions, providing an accurate forecast for the farmer’s field is challenging. When open data (historical) weather observations from government sources are available, the global weather forecast can be downscaled to better match local conditions to the benefit of farmers or a well calibrated and validated regional model can be built. In many parts of the world (especially Africa, Asia and Latin America) there are in inadequate weather observations from meteorological stations. To address this gap, initiatives such as the Trans-African Hydro-Meteorological Observatory (TAHMO) aims to install 20,000 automatic weather stations near schools across Sub-Saharan Africa through a Public Private Partnership (PPP) arrangement with the National Meteorological Agencies, and provide this data freely to government agencies, schools and researchers to improve their capacity to forecast and make informed decisions based on weather information.

Government in Action 7: AGROASEMEX, state-supported micro insurance in Mexico

Satellite image of Mexico. Photo Credit to NASA/NOAA GOES Project.

In Mexico, smallholder farmers can access weather insurance, underwritten by the state insurance company AGROASEMEX, along with other private providers. By bringing together weather data, crop data and insurance payout data, and working with researchers at Harvard University, the Coordination of National Digital Strategy and the Ministry of Agriculture (CEDN-Harvard) were able to identify better thresholds for triggering payouts, as well as visualising the data to communicate the research results and the basis for policy change.

Policy area: Supporting agrifinance
Key data category: Weather data, crop data
Region: North America