Dick Dee

ERA-20C: An Atmospheric Reanalysis of the Twentieth Century

AbstractThe ECMWF twentieth century reanalysis (ERA-20C; 1900–2010) assimilates surface pressure and marine wind observations. The reanalysis is single-member, and the background errors are spatiotemporally varying, derived from an ensemble. The atmospheric general circulation model uses the same configuration as the control member of the ERA-20CM ensemble, forced by observationally based analyses of sea surface temperature, sea ice cover, atmospheric composition changes, and solar forcing. The resulting climate trend estimations resemble ERA-20CM for temperature and the water cycle. The ERA-20C water cycle features stable precipitation minus evaporation global averages and no spurious jumps or trends. The assimilation of observations adds realism on synoptic time scales as compared to ERA-20CM in regions that are sufficiently well observed. Comparing to nighttime ship observations, ERA-20C air temperatures are 1 K colder. Generally, the synoptic quality of the product and the agreement in terms of climat...

Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part I: Methodology

Abstract The maximum-likelihood method for estimating observation and forecast error covariance parameters is described. The method is presented in general terms but with particular emphasis on practical aspects of implementation. Issues such as bias estimation and correction, parameter identifiability, estimation accuracy, and robustness of the method, are discussed in detail. The relationship between the maximum-likelihood method and generalized cross-validation is briefly addressed. The method can be regarded as a generalization of the traditional procedure for estimating covariance parameters from station data. It does not involve any restrictions on the covariance models and can be used with data from moving observers, provided the parameters to be estimated are identifiable. Any available a priori information about the observation and forecast error distributions can be incorporated into the estimation procedure. Estimates of parameter accuracy due to sampling error are obtained as a by-product.

Data Assimilation in the Presence of Forecast Bias: the GEOS Moisture Analysis

The authors describe the application of the unbiased sequential analysis algorithm developed by Dee and da Silva to the Goddard Earth Observing System moisture analysis. The algorithm estimates the slowly varying, systematic component of model error from rawinsonde observations and adjusts the first-guess moisture field accordingly. Results of two seasonal data assimilation cycles show that moisture analysis bias is almost completely eliminated in all observed regions. The improved analyses cause a sizable reduction in the 6-h forecast bias and a marginal improvement in the error standard deviations.

Toward a Consistent Reanalysis of the Climate System

This article reviews past and current reanalysis activities at the European Centre for Medium-Range Weather Forecasts (ECMWF) and describes plans for developing future reanalyses of the coupled climate system. Global reanalyses of the atmosphere, ocean, land surface, and atmospheric composition have played an important role in improving and extending the capabilities of ECMWFs operational forecasting systems. The potential role of reanalysis in support of climate change services in Europe is driving several interesting new developments. These include the production of reanalyses that span a century or more and the implementation of a coupled data assimilation capability suitable for climate reanalysis. Although based largely on ECMWFs achievements, capabilities, and plans, the article serves more generally to provide a review of pertinent issues affecting past and current reanalyses and a discussion of the major challenges in moving to more fully coupled systems.

The Choice of Variable for Atmospheric Moisture Analysis

Abstract The implications of using different control variables for the analysis of moisture observations in a global atmospheric data assimilation system are investigated. A moisture analysis based on either mixing ratio or specific humidity is prone to large extrapolation errors, due to the high variability in space and time of these parameters and to the difficulties in modeling their error covariances. Using the logarithm of specific humidity does not alleviate these problems, and has the further disadvantage that very dry background estimates cannot be effectively corrected by observations. Relative humidity is a better choice from a statistical point of view, because this field is spatially and temporally more coherent and error statistics are therefore easier to obtain. If, however, the analysis is designed to preserve relative humidity in the absence of moisture observations, then the analyzed specific humidity field depends entirely on analyzed temperature changes. If the model has a cool bias in ...

Comments on Reanalyses suitable for characterizing long-term trends

recent article by Thorne and Vose (2010, hereafter TV) concerns the use of reanalysis data for characterizing long-term climate trends. The article raises legitimate questions about the ability to extract accurate climate information from time-varying observational datasets by means of model-based data assimilation techniques. TV predict that current approaches adopted by producers of reanalysis data are unlikely to result in climate-quality datasets. They make several recommendations for improving reanalysis methodology, and also propose a definition of “climate-quality” that is based on robust accuracy requirements. The purpose of this note is to explain our views on these issues and to address some of TV’s specific recommendations. To derive accurate and complete representations of climate variability and trends from observations is an ambitious goal. The difficulties involved, of course, are not specific to reanalysis. Fundamentally, the climate system is incompletely and inaccurately observed; data coverage, measurement techniques, and associated uncertainties are continually changing. Any such change can generate or modulate systematic errors in estimates of climate parameters. The idea behind reanalysis is to try to combine the observations by making optimal use of all available information. This includes metadata pertaining to data quality, as well as information about the physics of the climate system that can help us interpret and compare different pieces of data. In this way it becomes possible to expose the underlying uncertainties and to reduce their impact on the representation of climate parameters.

ERA-CLIM: Historical Surface and Upper-Air Data for Future Reanalyses

Future reanalyses might profit from assimilating additional historical surface as well as upper-air data. In the framework of the European Reanalysis of Global Climate Observations (ERACLIM; www.era-clim.eu) project, significant amounts of pre-1957 upper-air and surface data have been cataloged (>2.5 million station days), imaged (>450,000 images), and digitized (>1.25 million station days) to prepare new input datasets for upcoming reanalyses. These data cover large parts of the globe, focusing henceforth on less well-covered regions such as the tropics, the polar regions, and the oceans and on very early twentieth-century upper-air data from Europe and the United States. The total numbers of digitized/inventoried records (i.e., time series of meteorological data at fixed stations or from moving observational platforms) are 80/214 (surface), 735/1,783 (upper air), and 61/101 [moving upper-air (i.e., data from ships, etc.)]. Here, the authors give an overview of the data rescue activities, the data, and t...

Maximum-Likelihood Estimation of Forecast and Observation Error Covariance Parameters. Part II: Applications

Abstract Three different applications of maximum-likelihood estimation of error covariance parameters for atmospheric data assimilation are described. Height error standard deviations, vertical correlation coefficients, and isotropic decorrelation length scales are estimated from rawinsonde height observed-minus-forecast residuals. Sea level pressure error standard deviations and decorrelation length scales are obtained from ship reports, and wind observation error standard deviations and forecast error stream function and velocity potential decorrelation length scales are estimated from aircraft data. These applications serve to demonstrate the ability of the method to estimate covariance parameters using multivariate data from moving observers. Estimates of the parameter uncertainty due to sampling error can be obtained as a by-product of the maximum-likelihood estimation. By bounding this source of error, it is found that many statistical parameters that are usually presumed constant in operational dat...

Atmospheric Reanalyses and Climate Variations

The Earth’s climate has traditionally been studied by statistical analysis of observations of particular weather elements such as temperature, wind and rainfall. Climatological information, usually expressed as long-term averages and variability, is then presented over a geographical area or at a single location and time series of these quantities or of the observations themselves are examined for evidence of warming, more-frequent severe storms, and so on.

On the reprocessing and reanalysis of observations for climate

The long observational record is critical to our understanding of the Earth’s climate, but most observing systems were not developed with a climate objective in mind. As a result, tremendous efforts have gone into assessing and reprocessing the data records to improve their usefulness in climate studies. The purpose of this paper is to both review recent progress in reprocessing and reanalyzing observations, and summarize the challenges that must be overcome in order to improve our understanding of climate and variability. Reprocessing improves data quality through more scrutiny and improved retrieval techniques for individual observing systems, while reanalysis merges many disparate observations with models through data assimilation, yet both aim to provide a climatology of Earth processes. Many challenges remain, such as tracking the improvement of processing algorithms and limited spatial coverage. Reanalyses have fostered significant research, yet reliable global trends in many physical fields are not yet attainable, despite significant advances in data assimilation and numerical modeling. Oceanic reanalyses have made significant advances in recent years, but will only be discussed here in terms of progress toward integrated Earth system analyses. Climate data sets are generally adequate for process studies and large-scale climate variability. Communication of the strengths, limitations and uncertainties of reprocessed observations and reanalysis data, not only among the community of developers, but also with the extended research community, including the new generations of researchers and the decision makers is crucial for further advancement of the observational data records. It must be emphasized that careful investigation of the data and processing methods are required to use the observations appropriately.