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. 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 Pressure and Temperature Heights

Grid-Stat, MODE, Stat-Analysis, UserScript, Gen-Vx-Mask: GFS Cloud Statistics by Type

GridStat: Cloud Fractions Using MPAS and MERRA2 Data

GridStat: Cloud Fractions Using MPAS and SatCORPS Data

GridStat: Cloud Fractions Using GFS and MERRA2 Data

PointStat: Python embedding for ASOS/METAR cloud obs to verify GFS

GridStat: Cloud Height with Neighborhood and Probabilities

GridStat: GFS Cloud Pressure and Temperature Heights vs GOES

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. Fire

Verification of fire weather-related atmospheric parameters and fire spread models

GridStat: WRF and MMA Fire Perimeter

7.2.6. Land Surface

Land Model diagnostics and verification against observations

PointStat: Verify UFS Soil Moisture and Temperature with ISMN Observations

PointStat: CESM and FLUXNET2015 Terrestrial Coupling Index (TCI)

PointStat: Use Python embedding and METcalcpy to calculate and verify CTP/HI

7.2.7. Marine and Cryosphere

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

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

PlotDataPlane: Python Embedding of tripolar coordinate file

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

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, 1 day composite obs

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

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

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

UserScript: Python Script to compute cable transport

GridStat: Python Embedding to read and process sea surface heights

GridStat: Python Embedding to read and process SST

7.2.8. Medium Range

Lower resolution model configuration (>4km) usually producing forecasts out to 5-14 days (also referred to as global models)

UserScript: Calculate the Difficulty Index

GridStat: Use binary observation field to verify percentile forecast

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

Point-Stat: Standard Verification for CONUS Surface

Multi_Tool: Feature Relative by Lead (with lead groupings)

Point-Stat: Standard Verification of Global Upper Air

Point-Stat, Stat-Analysis, UserScript, DataIngest: CREDIT and GFS point statistics, Data Download, Plots

Grid-Stat: Using Python Embedding for Total Column Ozone

Multi_Tool: Feature Relative by Init

Grid-Stat, Stat-Analysis, Data-Ingest: CREDIT and GFS statistics and Data Download

Grid-Stat: Compute Anomaly Correlation using Climatology

Grid-Stat: Standard Verification of Surface Fields

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

7.2.9. Planetary Boundary Layer

Planetary Boundary Layer (PBL) applications

Point-Stat: Computing PBLH from AMDAR data using two methods: Theta-increase, Critical Bulk Richardson Number

7.2.10. Precipitation

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

Grid-Stat: 24-hour QPF Use Case

Grid-Stat: 6hr QPF in GEMPAK format

MTD: 6hr QPF Use Case

Grid-Stat: 6hr QPF in NetCDF format

Point-Stat: Investigating Preciptitation Types

MTD: Build Revision Series to Evaluate Forecast Consistency

Gen-Ens-Prod: Basic Post-Processing only

PointStat: Compare community observed precipitation to model forecasts

Grid-Stat: 6hr PQPF Probability Verification

Ensemble-Stat: WoFS

7.2.11. 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

GridStat: Determine dominant ensemble members terciles and calculate categorical outputs

SeriesAnalysis: Standardize ensemble members and calculate probabilistic outputs

UserScript: Make a Hovmoeller plot

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

GridStat: Apply separate climatologies for forecast and observations

7.2.12. Subseasonal to Seasonal: Mid-Latitude

Subseasonal-to-Seasonal model configurations relating to middle latitudes

UserScript: Calculate Blocking for the ERA

UserScript and StatAnalysis: Calculate and evaluate Blocking for the GFS and ERA

UserScript: Calculate Weather Regimes for ERA

UserScript and StatAnalysis: Calculate and Evaluate Weather Regimes for GFS and ERA

7.2.13. Subseasonal to Seasonal: Madden-Julian Oscillation

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

UserScript: Make ERA RMM plots from calculated MJO indices

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

UserScript: Make GFS and ERA OMI plot from calculated MJO indices

UserScript: Make ERA OMI plot from calculated MJO indices

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

7.2.14. Subseasonal to Seasonal: Soil Moisture

Subseasonal-to-Seasonal model configurations for Soil Moisture evaluation

GridStat: Verifying Soil moisture of SFS-GSL output against ERA5-Land, continuous and categorical statistics

PCP-Combine: Compute 1m Soil Moisture and 30 year Climatology

7.2.15. Subseasonal to Seasonal: Stratosphere

Subseasonal-to-Seasonal model configurations for Stratosphere evaluation

UserScript and StatAnalysis: Compute Polar Cap Temperature and Polar Vortex U and Create Plots

UserScript and SeriesAnalysis: Compute Zonal Mean Bias and Create Plots for Temperature and Wind

UserScript and StatAnalysis: Compute QBO Phase Plots and QBO Index

7.2.16. 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