1. Overview

1. 1. Overview • The aim of FASTER (FAst-physics System TEstbed and Research) is to evaluate and improve the parameterizations of fast physics (involving clouds, precipitation, aerosol) in numerical models using ARM measurements. • One objective within FASTER is to evaluate model representations of fast physics with long-term continuous cloud observations using an NWP testbed. This approach was successful in the European Cloudnet project. • NWP model data (NCEP, ECMWF, etc.) is routinely output at ARM sites, and model evaluation can potentially be achieved in quasi-real time. • In this poster we outline various ways of evaluating the parametrization of cloud fraction in NWP models. Ewan O’Connor1,2 (e.j.oconnor@reading.ac.uk) Robin Hogan1 Yangang Liu3 1 Department of Meteorology, University of Reading, UK 2 Finnish Meteorological Institute, Helsinki, Finland 3 Brookhaven National Laboratory Establishment of an NWP testbed using ARM Data 4. Cloud fraction skill scores • How well do NWP models forecast cloud fraction? • Over the SGP site, models tend to show more skill in the mid-troposphere than in the boundary-layer. • Met Office global model has much lower skill for high cloud fraction amounts. It is known that this model has difficulty in filling the grid-box completely with cloud. • NB. Not all models are shown with the same forecast leadtime! 2. Observed and NWP model cloud fraction • The observed cloud fraction is determined from Doppler radar and lidar data and is calculated on each model grid. Model cloud fraction can be prognostic or diagnostic. • Below is an example of one month of data for June 2004 over ARM SGP. Observations 5. Forecast degradation NCEP GFS model ECMWF model • How does the model forecast degrade over time? • Is mid-level cloud more susceptible? • Does the model have issues with spin-up? ERA Interim Analyses Met Office Global Model 6. Variability 3. Mean cloud fraction Seasonal • All models underpredict cloud fraction throughout the year. Diurnal cycle • At SGP, models tend to underestimate the mean cloud fraction, especially in the mid-levels. Note: Météo France adjusted their cloud fraction parametrization in 2006. Their mean model cloud fraction is now similar to the other models shown here. • In addition, boundary layer cloud during the day is not forecast often enough Cloud fraction vs. omega at 500 mb • Rather than ‘tune’ the model to give the correct mean, we want to know why the mean model cloud fraction is low. • Split the mean cloud fraction into two components: • How often is cloud present (above a given threshold)? • When cloud is present, how much cloud is there? • Investigate the PDF of cloud fraction in the mid-level. • ECMWF has non-radiative ‘snow’ and does not produce enough grid-boxes which are full of cloud. • Not enough cloud forecast in anti-cylconic conditions, especially low-level boundary-layer cloud. 7. Cloud fraction forecasts: Has there been any improvement in the skill scores? • At SGP, cloud fraction skill scores drop substantially in summer, even for ERA Interim Re-Analyses. In some years, at the height of summer, forecasts are often no better than persistence. • There appears to be no improvement in the cloud fraction forecast over time. At SGP, any small improvement is likely to dominated by the variability from year to year.