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• NWP models & variational assimilation . Fast RT model – RTTOV.• Satellite radiance observations• The accuracy of forecast water vapour fields ?• Issues for MW observations of WV
• The Assimilation of SSMI / SSMIS Radiances
• Fundamental limitations of TCWV / 22GHz observations – no profile information• RT Model issues (22GHz line parameters)
• Assimilation of 183 GHz radiances (AMSU-B)
• Forward modelling in the presence of ice cloud
• Ground based MWR (nowcasting)
• 1DVar• Instrument• Retrieval performance ( accuracy and resolution )• Pros and Cons
Determine most probable state vector, xa, given Observation, y Background state xb (prior knowledge of atmospheric state) Error characteristics of each (assumed Gaussian)
1DVar ( QC & intelligent thinning of obs) : Analyse skin temperature Check convergence Detect cloud and select channels for 4D Var No of obs per 6 hour window :
Parameterisations and approximations are required to constrain the unknowns
A forecast model can give temperature and ice (and liquid) water profiles which can be input to the RTM. Few forecast models give ice microphysics as diagnostic output.
Many parameterisations for density and size distribution exist in the literature. Relating these to other known quantities (such as T and IWC) is a promising way forward.
For speed, spherical ice particles are usually assumed in NWP. Errors from this assumption are small (~15%) compared to possible errors from size distribution uncertainties (~40%)
Determine most probable state vector, xa, given Observation, y Background state xb (prior knowledge of atmospheric state) Error characteristics of each (assumed Gaussian)
Vertical resolution of analysis temperature and humidity (lnqt) profiles calculated as the inverse of the trace of the averaging kernel matrix [Purser and Huang, 1993] (US Std Atm)
Pros Optimal method to integrate observations with background Provides estimate of error in retrieval Shows impact from MWR below ~4km – most <1km
Cons Fundamentally poor vertical resolution of passive profilers Convergence problems for very non-linear problems Difficult when background is wrong (shifting patterns)
Future Work Add ceilometer cloud base/cloud radar tops/GPS IWV to y Integrate with Wind Profiler SNR – e.g. Boundary Layer top How to exploit high time resolution? 4D-VAR? Variability?
MW radiances are an important component of operational NWP systems, it is now normal to assimilate these directly as radiances, rather than using retrievals
Variational assimilation (1d, 3d or 4d) is an optimal way of combining background and observational information to define an atmospheric state
Fast RT models are important in achieving this
Challenges presented by MW radiance measurements include : dealing with cloud and precipitation, limited vertical resolution, biases (eg RT biases)
Ground based MWR is being assessed for nowcasting and assimilation applications. Column water estimates are accurate , but vertical resolution is poor (~1km for q)