Daily Historical Weather Data - US Zip Codes

AWS Data Exchange/Daily Historical Weather Data - US Zip Codes is a Weather & Agriculture data product listed on AWS Data Exchange and indexed by The Alternative Data Encyclopedia.

AWS Data Exchange publishes Daily Historical Weather Data - US Zip Codes as an AWS Data Exchange offering in the Weather & Agriculture signal family.

The data

Access to historical and forecasted weather data plays a vital role in many industries. This detailed weather file, updated daily with the most up to date information, provides you with a rolling 14 days of daily historical weather information for the most common weather variables that can impact your business each and every day.

It is one of 76 listings in the Weather & Agriculture family; between them the practical differences come down to coverage, history depth and how the raw signal is cleaned and delivered.

Structure, access and licensing

Access is through a dataset that is pulled through an AWS Data Exchange product. The listing does not state an explicit refresh schedule, which is worth confirming before backtesting.

The signal

Meteorological observations, seasonal outlooks and agricultural statistics that connect weather realizations to crop yields, harvest timing and commodity supply. The category pairs official statistical series with gridded reanalysis and station data to quantify growing conditions.

Markets move on scheduled government reports, so independent estimates built from weather and remote sensing that deviate from official numbers carry tradable surprise value around release dates. Season-long monitoring of precipitation and temperature anomalies anticipates yield downgrades weeks before survey-based revisions. Insurance-linked strategies and food-company margin models consume the same signals for pricing weather risk. Government services publish weekly crop progress and condition ratings, planted acreage, production forecasts and livestock inventories on fixed calendars, while environmental archives maintain station records, reanalysis grids and drought indices. Derived analytics include yield-model divergences from official projections, growing-degree-day accumulation, soil-moisture anomalies and heat-stress flags for specific growing regions.

Statistical agencies run producer surveys, objective yield measurements and the five-yearly agriculture census, publishing through searchable databases and APIs. Environmental agencies archive station networks, satellite-derived rainfall and reanalysis products that vendors ingest into agronomic models calibrated on decades of paired weather-yield history. Systematic traders blend these with crop-condition imagery and futures positioning.

Caveats and compliance

Weather effects are nonlinear and localized, so national averages mask county-level stress that drives yields. Official reports revise substantially between initial and final estimates, punishing overreaction to first prints. Model risk is high when transferring yield regressions to new seed technologies or climate regimes.

Most underlying sources are free government data with open licenses, but redistribution terms vary by country and some agencies restrict bulk reuse. Users should cite official revisions rather than silently overwrite historical prints when building research databases.

Who uses this signal

Commodity trading houses and macro funds trade grains, softs and livestock off report surprises; agribusiness and food manufacturers plan sourcing and hedging; insurers price multi-peril crop policies. Sovereign buyers track food-security indicators.

Complementary signals

This kind of signal pairs naturally with adjacent categories of the encyclopedia:

Further reading