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Cloudnet level 3 products

This Cloudnet Level 3 product provides high-resolution meteorological model comparisons, statistics, and skill scores for cloud fraction and ice water content. It includes observations and model data from various models such as Met Office Mesoscale Model, ECMWF Global Model, Meteo-France ARPEGE Model, KNMI RACMO Model, Swedish RCA model. The product also addresses the challenge of high cloud detection and offers insights into potential model biases.

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Cloudnet level 3 products

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  1. Cloudnetlevel 3 products Robin Hogan Ewan O’Connor

  2. Cloudnet data levels • Level 2a daily files • High-resolution meteorological products on the radar grid • Level 2b daily files • Meteorological products averaged on to the grid of each particular model: separate dataset for each model and product • So far we have done cloud fraction and ice water content • Includes equivalent model values, both “raw” and “modified” to make necessary adjustments to allow unbiased comparison, e.g. • Remove high cirrus from model cloud fraction if not detectable • Flag model ice clouds above rain; would not be used in obs. • Level 3 files by month and year (& model version?) • Statistics of a comparison between model and the observations • Observed, and raw & modified model means on same vert. grid • PDFs, skill scores, correlations, anything that might be useful!

  3. Cloud fraction Observations Met Office Mesoscale Model ECMWF Global Model Meteo-France ARPEGE Model KNMI RACMO Model Swedish RCA model Swedish RCA model

  4. Monthly statistics • On model height grid • Mean obs & model fraction • Frequency of occurrence and amount when present (thresholds 0.05-0.95) • On regular 1km grid for fair comparison between models • Contingency table, ETS, Q • Mean cloud fraction • In four height ranges (0-3, 3-7, 7-12, 12-18 km) • PDFs of obs & model fraction • Height-independent • Contingency table, ETS, Q

  5. Yearly statistics • Concatenation of monthly statistics to produce yearly file with exactly the same format • Skill scores etc. all much smoother • If modellers prefer, we could group together periods with forecasts from the same version of the model

  6. Intercomparison • Parameters on universal 1 km grid can easily be compared between models

  7. What can we do about high cloud? • All models see more cirrus than observed, and modification of model does not usually remove enough cloud to bring them into agreement • Are all models are wrong? • Does radar miss more IWC than it thinks due to small particles? • Just a symptom of models having plane-parallel clouds?

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