This comprehensive text and reference work on numerical weather prediction, first published in 2002, covers not only methods for numerical modeling, but also the important related areas of data assimilation and predictability. It incorporates all aspects of environmental computer modeling including an historical overview of the subject, equations of motion and their approximations, a modern and clear description of numerical methods, and the determination of initial conditions using weather observations (an important science known as data assimilation). Finally, this book provides a clear discussion of the problems of predictability and chaos in dynamical systems and how they can be applied to atmospheric and oceanic systems. Professors and students in meteorology, atmospheric science, oceanography, hydrology and environmental science will find much to interest them in this book, which can also form the basis of one or more graduate-level courses.

/pdf/atmospheric-modeling-data-assimilation-and-predictability-1qxqadvqdq.pdf

Atmospheric Modeling, Data Assimilation and Predictability

(2005). Atmospheric Modeling, Data Assimilation, and Predictability. Technometrics: Vol. 47, No. 4, pp. 521-521.

Atmospheric Modeling, Data Assimilation, and Predictability

A series of automated tests is developed for tower and aircraft time series to identify instrumentation problems, flux sampling problems, and physically plausible but unusual situations. The automated procedures serve as a safety net for quality controlling data. A number of special flags are developed representing a variety of potential problems such as inconsistencies between different tower levels and the flux error due to fluctuations of aircraft height. The tests are implemented by specifying critical values for parameters representing each specific error. The critical values are developed empirically from experience of applying the tests to real turbulent time series. When these values are exceeded, the record is flagged for further inspection and comparison with the rest of the concurrent data. The inspection step is necessary to either verify an instrumentation problem or identify physically plausible behavior. The set of tests is applied to tower data from the Riso Air Sea Experiment and...

/pdf/quality-control-and-flux-sampling-problems-for-tower-and-315a11jmnz.pdf

Quality Control and Flux Sampling Problems for Tower and Aircraft Data

Publisher Summary This chapter provides a review of current operational practice and of advanced data assimilation techniques in meteorology. Numerical models can be used to assimilate meteorological and oceanographic data, creating a dynamically consistent, complete and accurate “movie” of the two geofluids, atmosphere, and ocean in motion. The ocean's strong stratification helps determine the most energetic scales and processes for the global ocean circulation. Active research on data assimilation is burgeoning rapidly in both meteorology and oceanography. Operational NWP requirements have produced a mature data-assimilation technology in meteorology, from which climatic research has benefitted as well. Ocean is characterized by transient, energetic motions with a broad spectrum in frequency and wave number. A steady component of the circulation may not even exist, and be only a model resulting from the analysis of data sets sparse in space and time, like hydrographic data sets, for which steadiness is assumed a priori. Thus, in oceanic data-assimilation problems, the choice of a model and related data assimilation scheme and the definition of success of the assimilation process itself depend crucially on the scientific issue of interest as the starting point.

Data assimilation in meteorology and oceanography

This Chapter summarizes the steps of quality assurance and quality control of flux measurements with the eddy covariance method An important part is the di erent steps of the control for electronic, meteorological and statistical problems The fulfillment of the theoretical assumptions of the measuring method and thenon-steady state test and the integral turbulence test are extensively discussed as well as an overall flagging for data quality and a site specific quality analysis using footprint models Finally, problems are discussed which are not included yet in the control program, mainly connected with the complicated turbulence structure at a forest site

Post-Field Data Quality Control

This work attempts to provide a theoretical framework for the quality control of data from a large variety of types of observations, with different accuracies and reliabilities. Bayes' theorem is introduced, and is used in a simple example with Gaussian error distributions to derive the well-known formula for the combination of data with errors. A simple model is proposed whereby the error in each datum is either from a known Gaussian distribution, or a gross error, in which case the observation gives no useful information. Bayes' theorem is applied to this, and it is shown that usual operational practice, which is to reject outlying data and to treat the rest as if their errors are Gaussian, is a reasonable approximation to the correct Bayesian analysis. Appropriate rejection criteria are derived in terms of the observational error and the prior probability of a gross error.

These ideas have been implemented in a computer program to check pressure, wind, temperature and position data from ships, weather ships, buoys and coastal synoptic reports. Historical information on the accuracies and reliabilities of various classifications of observation is used to provide prior estimates of observational errors and the prior probabilities of gross error. The latter are then updated in the light of information from a current forecast, and from nearby observations (allowing for the inaccuracies and possible gross errors in these) to give new estimates. The final probabilities can be used to reject or accept the data in an objective analysis.

Results from trials of this system are given. It is shown to be possible using an archive generated by the system to update the prior error statistics necessary to make the method truly objective. Some practical case studies are shown, and compared with careful human quality control.

Objective quality control of observations using Bayesian methods. Theory, and a practical implementation

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Objective quality control of observations using Bayesian methods. Theory, and a practical implementation