7.2. Model Applications

7.2.1. Air Quality and Composition

Data related to all areas of atmospheric composition, including ozone, smoke, dust, AOD and PM2.5

EnsembleStat: Using Python Embedding for Aerosol Optical Depth

7.2.2. Climate

Average long range earth system predictions

Grid-Stat: CESM and GFS Analysis CONUS Temp

MODE: CESM and GPCP Asian Monsoon Precipitation

7.2.3. Data Assimilation

Observational data used as part of the initial conditions for numerical weather prediction

StatAnalysis: IODAv1

StatAnalysis: IODAv2

7.2.4. Marine and Cryosphere

Data related to verification involving marine and cryosphere systems, which includes sea-ice

PointStat: read in buoy ASCII files to compare to model wave heights

GridStat: Python Embedding for sea surface salinity using level 3, 1 day composite obs

GridStat: Python Embedding to read and process ice cover

Grid-Stat and MODE: Sea Ice Validation

GridStat: Python Embedding for sea surface salinity using level 3, 8 day mean obs

GridStat: Python Embedding to read and process SST

PlotDataPlane: Python Embedding of tripolar coordinate file

GridStat: Python Embedding to read and process sea surface heights

UserScript: Python Script to compute cable transport

7.2.5. Medium Range

Lower resolution model configuration (>4km) usually producing forecasts out to 7-14 days (also referred to as global models)

Multi_Tool: Feature Relative by Lead using Multiple User-Defined Fields

UserScript: Calculate the Difficulty Index

Multi_Tool (MTD): Feature Relative by Lead (with lead groupings)

GridStat: Use binary observation field to verify percentile forecast

Grid-Stat: Standard Verification of Surface Fields

Point-Stat: Standard Verification for CONUS Surface

Multi_Tool: Feature Relative by Lead (with lead groupings)

Multi_Tool: Feature Relative by Init

Grid-Stat: Compute Anomaly Correlation using Climatology

Point-Stat: Standard Verification of Global Upper Air

7.2.6. Precipitation

Any fields that can be defined as precipitation, including rain, snow, and other precipitation types

Grid-Stat: 6hr QPF in NetCDF format

Ensemble-Stat: WoFS

Grid-Stat: 6hr PQPF Probability Verification

Point-Stat: Investigating Preciptitation Types

MTD: Build Revision Series to Evaluate Forecast Consistency

Grid-Stat: 6hr QPF in GEMPAK format

PointStat: Compare community observed precipitation to model forecasts

Grid-Stat: 24-hour QPF Use Case

MTD: 6hr QPF Use Case

Gen-Ens-Prod: Basic Post-Processing only

7.2.7. Subseasonal to Seasonal

Subseasonal-to-Seasonal model configurations; Lower resolution model configurations (>4km) usually producing forecasts out beyond 14 days and up 1 year

TCGen: Genesis Density Function (GDF) and Track Density Function (TDF)

UserScript: Make a Cross Spectra plot

UserScript: Make a Hovmoeller plot

UserScript: Make zonal and meridonial means

SeriesAnalysis: Standardize ensemble members and calculate probabilistic outputs

GridStat: Determine dominant ensemble members terciles and calculate categorical outputs

Grid-Stat and Series-Analysis: BMKG APIK Seasonal Forecast

7.2.8. Subseasonal to Seasonal: Mid-Latitude

Subseasonal-to-Seasonal model configurations relating to middle latitudes

Blocking Calculation: ERA RegridDataPlane, PcpCombine, and Blocking python code

WeatherRegime Calculation: ERA RegridDataPlane, PcpCombine, and WeatherRegime python code

WeatherRegime Calculation: GFS and ERA RegridDataPlane, PcpCombine, and WeatherRegime python code

Blocking Calculation: GFS and ERA RegridDataPlane, PcpCombine, and Blocking python code

7.2.9. Subseasonal to Seasonal: Madden-Julian Oscillation

Subseasonal-to-Seasonal model configurations relating to the Madden-Julian oscillation

UserScript: Make OMI plot from calculated MJO indices

UserScript: Make a Phase Diagram plot from input RMM or OMI

UserScript: Make RMM plots from calculated MJO indices

UserScript: Make MaKE-MaKI plot from calculated MaKE and MaKI indices

UserScript: Make OMI plot from calculated MJO indices

7.2.10. Short Range

High resolution model configurations (1-4km) usually producing forecasts between 0-3 days (also referred to as limited area models, stand-alone regional, and short range weather applications); Previously named Convection Allowing Models