1. Overview
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1. Overview. The aim of FASTER ( FA st-physics S ystem TE stbed and R esearch) is to evaluate and improve the parameterizations of fast physics (involving clouds, precipitation, aerosol) in numerical models using ARM measurements.

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1. Overview

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1 overview

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 ([email protected])

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.


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