咨询：是否存在NOAA过往天气预报（预报归档）API？寻求美国城市历史预报数据源

Hey there! Great questions—tracking historical forecast data can be surprisingly tricky, since it’s not as widely accessible as real-time observations or future forecasts. But there are solid options to explore, including NOAA-related resources and other specialized platforms.

Yes, NOAA does maintain archives of past forecasts, though they’re not served through a simple, public-facing API like their current weather data. Here’s how to access them:

• Start with the NOAA National Centers for Environmental Information (NCDC). Their archives store past runs of NOAA’s forecast models (like GFS, NAM, or HRRR), which include the exact predictions issued at specific times for future periods. For example, a GFS run from 30 days ago would include the forecast it issued for the subsequent 7 days—this is exactly the "historical forecast" data you’re after.
• Note that these archives are typically stored in specialized formats like GRIB2 or NetCDF. To work with them, you’ll need tools like wgrib2 (for GRIB2 parsing) or Python libraries such as xarray and pygrib to extract data for specific U.S. cities.

If you’re looking for more user-friendly, ready-to-use data (without dealing with meteorological file formats), these options are worth checking:

• Meteostat: Offers both free and paid tiers. The free tier includes access to historical forecast data for major U.S. cities, with date ranges going back several years. Data can be exported as CSV or accessed via their simple API, making it easy to integrate into projects.
• AccuWeather: Provides paid historical forecast datasets with comprehensive coverage of U.S. cities. Their data comes in clean formats like JSON or CSV, with detailed forecast metrics (temperature ranges, precipitation probability, wind speed, etc.) for past days, months, or years.
• Weather Underground: While their public API has restricted access now, their historical forecast archives still contain data for U.S. cities. You can manually export data from their historical pages for non-commercial use, or explore their enterprise access options if you need larger volumes.

Quick tip: Free sources often require more data cleaning or format parsing, while paid services save you time by delivering pre-processed, ready-to-use data. Choose based on your project scale and technical comfort level.

内容的提问来源于stack exchange，提问作者ttulinsky