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Global Cloud Data Assimilation at GMAO

This document outlines key discussions from the NASA/Goddard Space Flight Center symposium celebrating 50 years of Numerical Weather Prediction (NWP) on June 16, 2004. The focus is on the motivation for improved cloud data assimilation, parameter estimation techniques for bias correction, and algorithmic advancements. Validation against CERES data reveals the importance of accurately estimating cloud liquid water and ice. The document also details plans for extending algorithms for new prognostic parameterizations in future models, including insights on merging with precipitation assimilation efforts.

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Global Cloud Data Assimilation at GMAO

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  1. Global Cloud Data Assimilation at GMAO Arlindo da Silva and Peter Norris Global Modeling and Assimilation Office NASA/Goddard Space Flight Center Symposium on the 50th Anniversary of NWP 16 June 2004

  2. Outline • Motivation • Parameter estimation as bias correction • Algorithm overview • Results: • TOA validation against CERES • Surface radiation budget • Summary, Plans

  3. Clouds at GMAO’s fvDAS

  4. Cloud Data Assimilation • Assimilation of cloudy radiances • Radiative transfer model explicitly accounts for clouds • Cloud liquid water and cloud ice included as control variables • UKMO approach: • Cloud observations used to generate pseudo-RH data consistent with model’s diagnostic parameterization, or • Cloud observations used to correct co-located RH observations, consistent with model’s diagnostic parameterization • Cloud fraction parameterization is never modified • Our approach: • Cloud observations used to modify model’s diagnostic cloud parameterization • RH analysis not directly affected by cloud observations

  5. Cloud Fraction Parameterization • CCM3 diagnostic cloud fraction parameterization: • Convective: function of convective mass flux; adjusts RH • Non-convective: based largely on RH, with corrections for vertical velocity, stability, land/ocean, low level stratus

  6. Cloud Parameter Estimation • Revised diagnostic parameterization: • Quadratic f(RH) is generalized to a smoothly asymptoting S-shaped polynomial, depending on 3 parameters: • RH* - critical RH below which f=0 • RH’ – upper threshold above which f=1 • b – asymmetry parameter

  7. damped persistence parameter analysis Adaptive Parameter Estimation • Sequential algorithm: • Increment da determined by minimizing the cost function:

  8. Cloud Data Sources • Cloud top pressure/mask • ISCCP or MODIS • Cloud optical depth • ISCCP or MODIS • Cloud water • SSM/I (liquid) or MODIS (liquid/ice)

  9. LOW Cloud Assim. ISCCP Control MID-HIGH TOTAL

  10. CERES TOA: Cloud Data Only Cloud Assim. CERES Control

  11. Cloud Optical Depth/Water Cloud Data Only ISCCP Control Cloud Data Only SSM/I Control

  12. CERES TOA: Cloud+CLW Data Cloud Assim. CERES Control

  13. CERES TOA: Cloud+CLW+COD Cloud Assim. CERES Control

  14. Cloud Fraction

  15. Cloud Forcing: CERES

  16. Cloud Fraction: Forecast control Cloud assim.

  17. Land Surface Radiation Budget

  18. Skin Temperature Response

  19. CERES: Skin Temperature

  20. Summary • Adaptive parameter estimation scheme is able to reduce mean bias in cloud cover • Cloud forcing validated against independent CERES top-of-atmosphere fluxes: • Need for concurrent tuning of cloud optical depth and cloud liquid water path • Correction of cloud forcing has significant impact on the land surface state • Assimilation of MODIS clouds in progress, preliminary results encouraging

  21. Cloud Assimilation: Plans • Extend algorithm for new prognostic parameterization in GEOS-5 • Explore other MODIS observables • Convective clouds: merge with precipitation assimilation effort • Prepare for “A-Train”

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