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

MODE: CESM and GPCP Asian Monsoon Precipitation

Grid-Stat: CESM and GFS Analysis CONUS Temp

7.2.3. Clouds

A category for use cases interested in the composition of, or characteristics associated with, clouds

GridStat: Cloud Fractions Using GFS and ERA5 Data

GridStat: Cloud Height with Neighborhood and Probabilities

GridStat: Cloud Pressure and Temperature Heights

GridStat: Cloud Fractions Using GFS and MERRA2 Data

GridStat: Cloud Fractions Using MPAS and SatCORPS Data

GridStat: Cloud Fractions Using MPAS and MERRA2 Data

7.2.4. Data Assimilation

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

StatAnalysis: IODAv1

StatAnalysis: IODAv2

7.2.5. Land Surface

Land Model diagnostics and verification against observations

PointStat: CESM and FLUXNET2015 Terrestrial Coupling Index (TCI)

7.2.6. Marine and Cryosphere

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

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

Grid-Stat and MODE: Sea Ice Validation

GridStat: Python Embedding to read and process SST

GridStat: Python Embedding to read and process sea surface heights

PointStat: read in directory of ASCAT files over user-specified time

UserScript: Python Script to compute cable transport

PlotDataPlane: Python Embedding of tripolar coordinate file

GridStat: Python Embedding to read and process ice cover

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

PointStat: read in satellite data and verify wind speeds or wave heights

PointStat: Python embedding to read Argo netCDF files to verify ocean temperature forecast at 50 m depth

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

7.2.7. Medium Range

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

Grid-Stat: Using Python Embedding for Total Column Ozone

Multi_Tool: Feature Relative by Lead (with lead groupings)

Grid-Stat: Standard Verification of Surface Fields

GridStat: Use binary observation field to verify percentile forecast

Multi_Tool: Feature Relative by Init

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

Point-Stat: Standard Verification of Global Upper Air

UserScript: Calculate the Difficulty Index

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

Point-Stat: Standard Verification for CONUS Surface

Grid-Stat: Compute Anomaly Correlation using Climatology

7.2.8. Planetary Boundary Layer

Planetary Boundary Layer (PBL) applications

GenVxMask and Point-Stat: Computing PBLH from AMDAR data using “Theta-increase” method

GenVxMask and Point-Stat: Computing PBLH from AMDAR data using "Theta-increase" method

7.2.9. Precipitation

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

Gen-Ens-Prod: Basic Post-Processing only

Grid-Stat: 6hr QPF in NetCDF format

Ensemble-Stat: WoFS

Grid-Stat: 6hr PQPF Probability Verification

Point-Stat: Investigating Preciptitation Types

Grid-Stat: 6hr QPF in GEMPAK format

Grid-Stat: 24-hour QPF Use Case

MTD: Build Revision Series to Evaluate Forecast Consistency

MTD: 6hr QPF Use Case

PointStat: Compare community observed precipitation to model forecasts

7.2.10. Subseasonal to Seasonal

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

UserScript: Compute Cross Spectra and Make a Plot

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

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

UserScript: Make a Hovmoeller plot

GridStat: Determine dominant ensemble members terciles and calculate categorical outputs

SeriesAnalysis: Standardize ensemble members and calculate probabilistic outputs

7.2.11. 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

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

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

7.2.12. Subseasonal to Seasonal: Madden-Julian Oscillation

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

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

UserScript: Make OMI plot from calculated MJO indices

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

UserScript: Make OMI plot from calculated MJO indices

UserScript: Make RMM plots from calculated MJO indices

7.2.13. Subseasonal to Seasonal: Stratosphere

Subseasonal-to-Seasonal model configurations for Stratosphere evaluation

Bias Plot on Polar Cap Temperature and Polar Vortex U: UserScript, Stat-Analysis

QBO Phase plots and QBO Index: UserScript, Stat-Analysis

Bias Plot on Zonal Mean Wind and Temperature: UserScript, Series-Analysis

7.2.14. 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