1 00/XXXX © Crown copyright The User-Requirement Document Damian Wilson, Met Office Jean-Marcel Piriou, Meteo-France

2 00/XXXX © Crown copyright The User Requirenment Document Which variables? How to compare models and observations? Errors Data Formats Recommendations The document outlines the requirements of the modelling community

3 00/XXXX © Crown copyright Variables of interest These tend to be the bulk variables which are carried by each model, although sometimes in different forms. Ice water content / kg kg -1, LWC, q. All ice in the atmosphere at the end of the timestep. Cloud fraction. Volume fraction of a gridbox which contains condensate(?). Also mixed-phase fraction. PDF information on the moisture distribution: variance and skewness, …?

4 00/XXXX © Crown copyright Rainfall/snowfall fluxes at the surface or other specified level / kg m -2 s -1. Ice particle size distributions / m -4, shapes, densities, fall-speeds. Optical depths. Air density. Variables of Interest The correlations of these quantities in vertical and horizontal is also important.

5 00/XXXX © Crown copyright How to compare models and obs IWC LWC CF Z  A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites How do we transform between fields? Processing to combine ice categories etc. Processing to remove clutter etc.

6 00/XXXX © Crown copyright Observations to models IWC LWC CF Z  A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites We could transform obs-like fields into model- like fields Algorithms Assumptions

7 00/XXXX © Crown copyright How accurate would we like the processed observations? Condensate: to 10% Cloud fractions: 0.05 Variance of moisture PDF: to 10% of width Condensate flux: to 10% Particle sizes: Mean size to 20% Particle concentrations: to 20% Optical depth: pick up 0.05, to 20%

8 00/XXXX © Crown copyright Models to observations IWC LWC CF Z  A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites Or from models to observations. The algorithms might not be reversible. A -1 A  I New algorithms, assumptions

9 00/XXXX © Crown copyright Direct comparison IWC LWC CF Z  A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites It might be possible to transform directly from the model, but not for all models and obs fields. No new assumptions

10 00/XXXX © Crown copyright Unavailable information IWC LWC CF Z  A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites If data is absent then different transforms are required. New algorithms, assumptions.

11 00/XXXX © Crown copyright Comparison strategies Climatologies: reduces random errors Regime: identifies in what weather situations particular errors occur Case study: potentially more direct attribution of model error Variability: subgrid-scale time and space variations

12 00/XXXX © Crown copyright Sources of error IWC LWC CF Z  A B C Models Well defined Model-like fields Well defined Obs-like fields Observation sites Error in transforms and representivity Initial conditions and forward model can produce errors Measurement and estimation error CloudNet wishes to assess forward model errors.

13 00/XXXX © Crown copyright Sources of Error Comparison at different lead times will look at the balance between data-assimilation errors and forward-model errors. Thresholding can introduce errors. Characterization of errors is extremely important.

14 00/XXXX © Crown copyright Data Formats Data should be saved in the same format. This doesn’t necessarily mean the same space and time sampling strategy.

15 00/XXXX © Crown copyright Recommendations Sets of well defined quantities should be identified, which correspond closely with variables available in models and measurements available from observing sites. Models and observations should store data in their processed state. Algorithms should be developed to transform in either direction. These algorithms are not necessarily reversible.

16 00/XXXX © Crown copyright Recommendations Algorithms should transform between variables which are readily available from different types of models and observation sites, so transforms are not site or model specific. This will help future comparisons with other sites and models. If a piece information is not available from a model or site then a different algorithm must be developed.

17 00/XXXX © Crown copyright Recommendations Comparisons can be carried out in both model and observation space by using the transforms. Each would provide different sorts of information. It may be possible in specific, limited circumstances to transform directly from a model to observations with the addition of no new assumptions. Such comparison is also of value and a model should supply information to do this if this is possible.

18 00/XXXX © Crown copyright Recommendations Errors should be assessed for each part of the comparison.