Andrew C. Lorenc

Lidar-Measured Winds from Space: A Key Component for Weather and Climate Prediction

Abstract The deployment of a space-based Doppler lidar would provide information that is fundamental to advancing the understanding and prediction of weather and climate. This paper reviews the concepts of wind measurement by Doppler lidar, highlights the results of some observing system simulation experiments with lidar winds, and discusses the important advances in earth system science anticipated with lidar winds. Observing system simulation experiments, conducted using two different general circulation models, have shown 1) that there is a significant improvement in the forecast accuracy over the Southern Hemisphere and tropical oceans resulting from the assimilation of simulated satellite wind data, and 2) that wind data are significantly more effective than temperature or moisture data in controlling analysis error. Because accurate wind observations are currently almost entirely unavailable for the vast majority of tropical cyclones worldwide, lidar winds have the potential to substantially improve...

Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction.

The World Weather Research Programme (WWRP) and the World Climate Research Programme (WCRP) have identified collaborations and scientific priorities to accelerate advances in analysis and prediction at subseasonalto-seasonal time scales, which include i) advancing knowledge of mesoscale–planetary-scale interactions and their prediction; ii) developing high-resolution global–regional climate simulations, with advanced representation of physical processes, to improve the predictive skill of subseasonal and seasonal variability of high-impact events, such as seasonal droughts and floods, blocking, and tropical and extratropical cyclones; iii) contributing to the improvement of data assimilation methods for monitoring and predicting used in coupled ocean–atmosphere–land and Earth system models; and iv) developing and transferring diagnostic and prognostic information tailored to socioeconomic decision making. The document puts forward specific underpinning research, linkage, and requirements necessary to achi...

Comparison of Hybrid-4DEnVar and Hybrid-4DVar Data Assimilation Methods for Global NWP

AbstractThe Met Office has developed an ensemble-variational data assimilation method (hybrid-4DEnVar) as a potential replacement for the hybrid four-dimensional variational data assimilation (hybrid-4DVar), which is the current operational method for global NWP. Both are four-dimensional variational methods, using a hybrid combination of a fixed climatological model of background error covariances with localized covariances from an ensemble of current forecasts designed to describe the structure of “errors of the day.” The fundamental difference between the methods is their modeling of the time evolution of errors within each data assimilation window: 4DVar uses a linear model and its adjoint and 4DEnVar uses a localized linear combination of nonlinear forecasts. Both hybrid-4DVar and hybrid-4DEnVar beat their three-dimensional versions, which are equivalent, in NWP trials. With settings based on the current operational system, hybrid-4DVar performs better than hybrid-4DEnVar. Idealized experiments desig...

On the use of radiosonde humidity observations in mid-latitude NWP

SummaryWe compare radiosonde observations of relative humidity with NWP versions of the Meteorological Office Unified Model, and attempt to understand the causes of the systematic differences seen. The differences are found to have a different structure in cyclonic and anticyclonic situations over the UK. In cyclonic situations the mid-tropospheric temperature and humidity differences could be due to model biases, consistent with the conservation of energy; the latent heating from precipitation of the models excess moisture would remove the models cold bias. There is also some evidence for observational bias. Wetting of the sonde sensor in cloud can cause a moist bias at higher levels. The Väisala RS80 sonde also appears to have a dry bias near saturation.The Unified Model has a parameterisation for stratiform cloud which calculates the fractional cloud cover in a gridbox from the box-average relative humidity, allowing for sub-grid-scale variability within the box. This scheme has been tuned to give reasonable cloud amounts with the models relative humidities. The cloud amounts implied (by the scheme) for radiosonde relative humidities are systematically less than the observed cloud. So assimilation of the observed humidities can significantly degrade analyses and predictions of cloud. Bias corrections for the radiosonde humidities have been calculated to compensate for this.Experiments have been performed to test the effect of the bias correction on the assimilation and prediction of cloud and precipation. With the control system, cloud cover and precipitation spins-up during the forecast period; the bias correction improves this. A large improvement was also found when the relationship between the temperature and humidity assimilation was changed; it is better to assume that temperature and relative humidity errors are uncorrelated, rather than temperature and specific humidity.

The use of ERS-1 products in operational meteorology

Abstract The Meteorological Office processes ERS-1 fast-delivery products from the scatterometer, the altimeter, and the Along-Track Scanning Radiometer. Wind vectors, wind speed, wave height, and sea-surface temperature data are all validated against the operational atmospheric, wave, and sea-surface temperature analyses. Summary statistics from these validations are presented. The ERS-1 data appear to have smaller errors than the ship data currently used. Surface winds from the scatterometer should both improve the atmospheric analyses and forecasts, and improve the fluxes from the atmospheric analyses used to drive wave and ocean models. The wave data have made it worthwhile for the first time to develop a wave assimilation, rather than deducing the wave field solely from the history of atmospheric forcing. Results from preliminary parallel tests measuring the impact of these data sources are presented. Further work developing the model assimilation schemes is needed, before they give the full improvement hoped for.

Assimilation of satellite data for global numerical weather prediction

Abstract The object of assimilation for NWP is to find an internally consistent model state which best fits all available information. This comes from remote sensing and in situ observations for the present time and the past few days; the NWP model is essential for the efficient use of earlier data. The determination of “best fit” must take account of the error characteristics of observations and model; Gaussian errors and a linearizable model give idealized equation with convenient properties, but gross errors and biases in observations are important, and spoil these. The growing diversity and volume of satellite data makes some pre-processing, before incorporation in NWP assimilations, essential. However it is important that the processing and assimilation is conceived as a single integrated process. The paper gives a brief summary of the theory, and approximate implementation, of the ideal equations, and how non-Gaussian errors affect this. Examples are considered from satellite temperature soundings, wind lidar, and radar scatterometer winds.

Atmospheric Data Assimilation and Quality Control

In this paper we discuss the basic physics of the atmospheric data assimilation problem, in order to understand the important factors to be considered in its mathematical solution. The key mathematical technique, the optimal combination of information, is also approached from its Bayesian basics. Much of this is based on earlier papers (e.g. Lorenc, 1986). The novelty of this paper is its bringing together of these in a simple didactic form, following the agreed notation of Ide et al. (1997), with very simple examples to aid in the physical interpretation of the analysis equations.

Retrieval and Assimilation: System Considerations

The information content of a piece of information can only be defined with regard to what is already known. An expression, following Shannon, for this is given in section 3. But for practical applications, this is not a sufficient measure. We need also to consider the usefulness of the information.