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Evaluating Cloudy Boundary Layer Forecasts with Radar & Lidar

Examining forecasts of stratocumulus evolution is crucial for accurate climate prediction. This study analyzes radar and lidar observations to assess the performance of different models in predicting the diurnal evolution of stratocumulus clouds. The research compares mixing schemes, entrainment rates, and cloud physics to determine the accuracy of forecasted cloud properties. Insights are drawn on the strengths and weaknesses of various models in capturing the complex dynamics of the cloudy boundary layer.

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Evaluating Cloudy Boundary Layer Forecasts with Radar & Lidar

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  1. Evaluating forecasts of the evolution of the cloudy boundary layer using radar and lidar observations Andrew Barrett, Robin Hogan and Ewan O’Connor Submitted to Geophys. Res. Lett.

  2. Introduction • Stratocumulus interacts strongly with radiation • Important for forecasting surface temperature • A key uncertainty in climate prediction • Very difficult to forecast because of many factors: • Surface sensible and latent heat fluxes: to first order, sensible heat flux grows the boundary layer while latent heat flux moistens it • Turbulent mixing, which transports heat, moisture and momentum vertically • Entrainment rate at cloud top • Drizzle rate, which depletes the cloud of liquid water • Use Chilbolton observations to evaluate the diurnal evolution of stratocumulus in six models

  3. Models Used

  4. Different mixing schemes Local mixing scheme (e.g. Meteo France) Longwave cooling • Define Richardson Number: • Eddy diffusivity is a function of Ri and is usually zero for Ri>0.25 Height (z) dqv/dz<0 Virtual potential temp. (qv) Eddy diffusivity (Km) (strength of the mixing) • Local schemes known to produce boundary layers that are too shallow, moist and cold, because they don’t entrain enough dry, warm air from above (Beljaars and Betts 1992)

  5. Different mixing schemes Non-local mixing scheme (e.g. Met Office, ECMWF, RACMO) • Use a “test parcel” to locate the unstable regions of the atmosphere • Eddy diffusivity is positive over this region with a strength determined by the cloud-top cooling rate (Lock 1998) Longwave cooling Height (z) Virtual potential temp. (qv) Eddy diffusivity (Km) (strength of the mixing) • Entrainment velocity we is the rate of conversion of free-troposphere air to boundary-layer air, and is parameterized explicitly

  6. Different mixing schemes Prognostic turbulent kinetic energy (TKE) scheme (e.g. SMHI-RCA) • Model carries an explicit variable for TKE • Eddy diffusivity parameterized as Km~TKE1/2l, where l is a typical eddy size Longwave cooling TKE generated Height (z) TKE transported downwards by turbulence itself dqv/dz<0 dqv/dz>0 TKE destroyed Virtual potential temp. (qv)

  7. Observed Radar And Lidar Figures from cloud-net.org

  8. Cloud values compared Observed Cloud Fraction • Cloud Existence • Cloud Top • Cloud Base • Cloud Thickness • Liquid Water Path ECMWF Model Cloud Fraction

  9. Composite over diurnal cycle

  10. Liquid Water Composite

  11. Biases and random errors • Worst two models in terms of bias and random error • Tendency for all models to place cloud too low

  12. Conclusions • Met Office Mes best at placing clouds at right time • Met Office, ECMWF & RACMO best diurnal cycle • All use non-local mixing with explicit entrainment • Met Office and ECMWF clouds too low by 1 model level • RACMO height good: ECMWF physics but higher res. • Meteo-France clouds too low and thin • Local mixing scheme underestimates growth • SMHI-RCA clouds too thick and evolve little through the day • Only model to use prognostic turbulent kinetic energy

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