# 2023).

The problem with measuring the truth of the weather is not the technology but the personnel. It is insufficient to simply have the instruments to capture the data. One requires a person to record and report the data. In our setting, where we use LSMS-ISA data that provides tens of thousands of observations across 13 years and six countries, one would need to provide each household with the appropriate instruments and sufficient compensation to record the daily record of the weather. Additionally, one would need to employ individuals to collect the information from around the country and do quality checks on the data. The cost of a rain gauge and a thermometer might be trivial. The cost of converting that data into a geospatially explicit time series spanning across decades and countries is where measuring the truth becomes a problem.

Given the prohibitively high costs of recording and reporting the weather for every household in a nationally representative survey, two alternatives exist. The first alternative is to rely on gauge data from nationally run meteorological stations. This is considered the gold standard of weather data, as the quality control process in capturing, recording, and reporting the weather should be pristine. However, not everyone lives near a weather station. And in low-income countries, weather stations can be sparsely distributed (Tzachor et al., 2023). Figure 3 maps the locations of LSMS-ISA households with locations of Global Historical Climatology Network (GHCN) weather stations.<sup>1</sup> These maps show the limited density of these stations in Africa. Consider the case of Uganda, in which there are no in-country stations currently reporting to the GHCN. This means that the closest weather station for LSMS-ISA households in Uganda are one station in Kenya and one in Tanzania. And so, the second alternative (and the one most widely employed in economic research at this time) is to rely on remotely sensed EO data. Using EO data provides global-scale data on the weather at various spatial and temporal resolutions, overcoming the sparseness of station data.

Spatial datasets of weather variables, like precipitation and temperature, that are produced using remotely sensed EO data, are not direct measurements of the variable of interest. Satellite sensors provide spatially continuous observation of reflectance from the Earth’s surface in different parts of the magnetic spectrum. These values are used to estimate related phenomena, such as cloud presence, cloud top temperature or earth surface temperature. The continuous datasets are then used in combination with directly observed, but often sparsely distributed, gauge data to produce weather variables. Some inputs are common across products, but there are differences in other inputs as well as modeling techniques.

The type of analysis matters in assessing weather datasets for use in economic contexts. Is the goal to understand climate trends; to capture characteristics of a particular agricultural season; or to identify extreme weather events occurring in near real-time? This can help determine the

> 1GHCN is a database which addresses the critical need for historical daily weather records. It is a composite of climate records from numerous sources, merged together and assessed for quality. At its best, the data includes more than 40 meteorological elements, including temperature daily maximum and minimum, temperature at observation time, precipitation, snowfall, snow depth, evaporation, wind movement and maximums, soil temperature, and more.

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