Instrument : Model Output Location Time Series (MOLTS)

Instrument Categories
Derived Quantities and Models

General Overview

The Model Output Location Time Series (MOLTS) data are provided by the National Centers for Environmental Prediction (NCEP). Currently there are over 500 MOLTS stations across the United States (including Alaska and Hawaii) and southern Canada. The locations of the MOLTS stations are based on requests from the scientific community.

The measurements provided here are products of NCEP's mesoscale numerical weather prediction (NWP) model known as the Early Eta Model and its associated 4-D data assimilation system, known as the EDAS (for Eta Data Assimilation System). The name "Eta" derives from the model's vertical coordinate known as the "eta" or "step-mountain" coordinate. Whereas the Eta forecast model generates forecast fields out to 36 hours from initial states at 00Z and 12Z, the EDAS generates eight 3-hourly initial states or analyses during each 24-hour period, utilizing a vast set of observed data.

These data are the hourly output at the selected locations that contain values for various surface parameters and "sounding" output at model levels.

There are two possible classes assigned to each station, Class 0 (basic) and Class 1 (enhanced). The classes differ in the number of surface parameters and sounding parameters reported. After November 1999 only Class 1 data are provided but with many more stations than previously.

Output Datastreams

  • moltsedassfcclass0 : Model Output Loc. Time Ser. (MOLTS): EDAS meteor. analy., basic surface, params, stations
  • moltsedassfcclass1 : Model Output Loc. Time Ser. (MOLTS): EDAS meteor. analy., enhanced surface, params, stations
  • moltsedassndclass0 : Model Output Loc. Time Ser. (MOLTS): EDAS meteor. analy., basic soundings, params, stations
  • moltsedassndclass1 : Model Output Loc. Time Ser. (MOLTS): EDAS meteor. analy., enhances soundings, params, stations

Primary Measurements

The following measurements are those considered scientifically relevant.

Locations