The importance of climate data: knowing to make decisions

Climate data have become a valuable resource. But in many countries there are insufficient records. And if there are any, they are often unreliable. This shortcoming limits the capacity to make projections and define strategies to adapt to climate change. As Petteri Taalas, Secretary General of the World Meteorological Organization (WMO) states, “if you put garbage in the prediction models, you will get garbage”.

How to remedy this situation?

From meteorological data to climatic data

The answer to the above question is relatively simple: by increasing investment in weather observation systems. The more the better.

Why do we mention meteorology when the purpose of our article is to highlight climate data? Because meteorology is an auxiliary science of climatology. Thus, by collecting variables such as temperature, precipitation, etc., over a long period of time (minimum 30 years), the climate sciences can characterize the climate of an area.

The Vielha series, for example, includes data on average maximum and minimum temperatures since 1950. Its graphical representation shows a certain upward trend, especially since the 1980s, in accordance with the progressive global warming.

Similarly, if back in 1992 we had placed one of our Smarty Meteo automatic weather stations on the slopes of the Aneto peak, the data would reflect part of the progressive increase in temperature that the Pyrenees mountain range is experiencing.

Transforming weather data into climate data

Once we have seen that meteorological observations over time are fundamental to obtain climate products, we will briefly explain the validation process that avoids making climate projections with “garbage”.

The steps described below are those carried out by the National Oceanic and Atmospheric Administration (NOAA) in the USA. However, these procedures are more or less standardized throughout the world. In the case of Spain, for example, one of the applicable standards is UNE 500540:2004, which sets 7 levels of validation (1).

Data, especially those collected through citizen science projects such as the Cooperative Observer Program (Coop), driven by the U.S. National Weather Service (NWS), can be used as a source of information for the U.S. National Weather Service (NWS) and the U.S. National Weather Service (NWS). and which began in 1890, go through the following phases:

  • Quality control immediately after capture to visualize possible deviations that require checking the measuring instruments.
  • Raw data are sent to NOAA’s National Center for Environmental Information (NCEI), where they are further checked for spikes, outliers, flat values, etc.
  • Checking the consistency of meteorological data in a region. If inconsistencies are found, data are labeled as erroneous or excluded, but never changed or edited unless they are the result of transcription errors.
  • Data processing by calculating the mean values or the sum of the measurements.

What does it mean to have no reliable climate data or information?

The lack of climate data, unreliable information and the lack of digitization of many records make it difficult to create climate models and scenario projections. And, consequently, making decisions on what to do in areas that, from a probability point of view, are most at risk from heat waves, floods, droughts, etc.

This is a situation that takes on worrying overtones in areas such as the Andean highlands, where frequent droughts sow uncertainty in local communities.

But this is a situation common to most middle- and low-income countries. Much of Africa, for example, with a density of weather stations far below the recommended level, also suffers from the consequences of the lack of weather stations and technological infrastructures.

The result is the impossibility of forecasting the impact of meteorological phenomena, a problem that leaves the population at the mercy of cyclones, hurricanes, etc.


We began this article by referring to the lack of investment. But alleviating the lack of climate data also means strengthening technology transfer, establishing public-private partnerships, exchanging information and providing access to existing databases.

Projects such as the one announced by the World Meteorological Organization to extend early warning systems to the entire planet within 5 years may help mitigate the situation.

But these types of initiatives, like the 1,000-mile trips, also start with a first step. For example, the deployment of monitoring systems such as those offered by Arantec. Consider it from this point of view: the solutions you implement today will save lives in about 30 years.

Sources consulted:

  • (1) Estévez, J. and Gavilán, P. (2008). Automatic weather station data validation procedures. Application to the Andalusian Agroclimatic Information Network. II Conference on Agrometeorology 2008. Available at

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