Observation Diagnostics Overview

The observation diagnostics subsystem processes IODA files and generates a comprehensive suite of observation‑space diagnostics, including statistics, histograms, QC summaries, and ATMS‑specific channel and scan‑position diagnostics. These tools quantify how well the background and analysis fields fit the observations and complement the increment and spectral diagnostics. For the mathematical formulation and example figures, see Diagnostics Overview.

Subsystem Components

The observation diagnostics subsystem consists of the following components:

  • ObsDiagnostic — core engine for computing O–B and O–A departures, bias, RMS, normalized RMS, and QC‑filtered statistics

  • IODA loaders — readers for satellite and conventional observation files

  • Scalar histograms — e.g., temperature, humidity, pressure

  • Vector histograms — e.g., wind components (u, v)

  • ATMS diagnostics — channel‑wise extended RMS statistics and scan‑position bias checks

  • QC summaries — counts and statistics for QC‑passed and QC‑failed observations

  • Plotting utilities — generation of extended RMS, histogram, and scan‑position figures

Innovation‑Space Error Diagnostics

In addition to standard O–B/O–A statistics, the diagnostics subsystem includes an innovation‑space error diagnostic based on the Desroziers method. This tool computes innovation variance, estimated observation‑error variance, and recommended R‑scaling factors using only OMB and OMA. It is implemented in ufs_da_diagnostics/obs/innovation_br_check.py and is intended for routine monitoring of observation‑error consistency and tuning.

The diagnostic reports:

  • Sd = E[OMB²] — innovation variance

  • R_est = E[OMA × OMB] — Desroziers estimate of true observation‑error variance

  • Sd/R — innovation chi‑square proxy

  • R_est/R — variance‑scaling factor

  • HBHᵀ = Sd − R_est — background‑error contribution

  • scale_R = R_est / R — recommended R multiplier

  • infl_chi — standard‑deviation inflation needed to reach a target chi‑square

These quantities provide a lightweight, observation‑space method for evaluating the consistency of assumed observation‑error variances.

Workflow

A typical observation‑diagnostic workflow consists of:

  1. Load IODA file Read observation values, metadata, and QC flags.

  2. Load H(x) files Load background and analysis model equivalents for computing O–B and O–A departures.

  3. Compute statistics Compute bias, RMS, normalized RMS, bias‑corrected RMS, and QC‑filtered statistics for each variable and channel.

  4. Generate plots Produce histograms, extended RMS figures, scan‑position diagnostics, and QC summaries.

  5. Save outputs Write statistics tables and figures to the output directory.