Controlling instabilities along a 3DVar analysis cycle by assimilating in the unstable subspace: a comparison with the EnKF

A hybrid scheme obtained by combining 3DVar with the Assimilation in the Unstable Subspace (3DVar-AUS) is tested in a QG model, under perfect model conditions, with a fixed observational network, with and without observational noise. The AUS scheme, originally formulated to assimilate adaptive observations, is used here to assimilate the fixed observations that are found in the region of local maxima of BDAS vectors (Bred vectors subject to assimilation), while the remaining observations are assimilated by 3DVar. The performance of the hybrid scheme is compared with that of 3DVar and of an EnKF. The improvement gained by 3DVar-AUS and the EnKF with respect to 3DVar alone is similar in the present model and observational configuration, while 3DVar-AUS outperforms the EnKF during the forecast stage. The 3DVar-AUS algorithm is easy to implement and the results obtained in the idealized conditions of this study encourage further investigation toward an implementation in more realistic contexts

An update on THORPEX-related research in data assimilation and observing strategies

International audienceThe international programme "THORPEX: a World Weather Research Programme" provides a framework in which to tackle the challenge of improving the forecast skill of high-impact weather through international collaboration between academic institutions, operational forecast centres, and users of forecast products. The objectives of the THORPEX Data Assimilation and Observation Strategy Working Group (DAOS-WG) are two-fold. The primary goal is to assess the impact of observations and various targeting methods to provide guidance for observation campaigns and for the configuration of the Global Observing System. The secondary goal is to setup an optimal framework for data assimilation, including aspects such as targeted observations, satellite data, background error covariances and quality control. The Atlantic THORPEX Regional campaign, ATReC, in 2003, has been very successful technically and has provided valuable datasets to test targeting issues. Various data impact experiments have been performed, showing a small but very slightly positive impact of targeted observations. Projects of the DAOS-WG include working on the AMMA field experiment, in the context of IPY and to prepare the future THORPEX-PARC field campaign in the Pacific by comparing sensitivity of the forecasts to observations between several groups

Variational bias correction of GNSS ZTD in the HARMONIE modeling system

To fill the gap in the observation system for humidity, the HIRLAM–ALADIN Research on Mesoscale Operational NWP in Euromed(HARMONIE) limited-area high-resolution kilometer-scale model has been prepared for assimilation of Global Navigation Satellite System (GNSS) zenith total delay (ZTD) observations. The observation-processing system includes data selection, bias correction, quality control, and a GNSS observation operator for data assimilation. A large part of the bias between observations and model equivalents comes from the relatively low model top used in the HARMONIE experiments. The functionality of the different observation-processing components was investigated in detail as was the overall performance of the GNSS ZTD data assimilation. This paper contains an extensive description of the GNSS ZTD observation-processing system and a comparison of a newly introduced variational bias correction for GNSS ZTD data with an alternative static bias correction, as well as a detailed analysis of the impact of GNSS ZTD data, both in terms of statistical evaluations over a longer period and in terms of individual case studies. Assimilation of the GNSS ZTD observations with a variational bias correction has improved the quality of short-range weather forecasts for the moisture-related parameters in particular, both in a statistical sense and in individual case studies. The paper also discusses further improvements in the HARMONIE variational data-assimilation system that are needed to fully utilize the potential of high-resolution GNSS ZTD observations

Impact of targeted observations on HIRLAM forecasts during HyMeX-SOP1

An adaptive observing system with terrestrial and space-based components has been explored, with the aim of improving numerical weather prediction skill in the Mediterranean. Four Observing System Experiments based on the HIRLAM system have been conducted to test the influence of targeted observations on short-term forecasts of high-impact weather events over the first Special Observation Period of the HyMeX international project. Extra radiosoundings and enhanced Advanced TIROS Operational Vertical Sounder (ATOVS) satellite observations are assimilated as targeted observations, and have been added to the baseline first separately and then jointly. In general, targeted observations have a positive but small impact on the short-term forecasts, noticeably at 700 to 500 hPa in all parameters and precipitation. Targeted radiosoundings produce a clear overall improvement of HIRLAM forecasts. Data targeting based only on satellite observations has a generally positive impact on precipitation, and in short-term forecasts of the rest of the parameters. The assimilation of both types of extra observations produces the highest and most statistically significant improvements. The magnitude of the impact on the forecasts depends on the weather regime that determines the location of sensitive areas. According to the diagnostics obtained from the data assimilation cycle, the targeted observations had a still larger positive influence on the subsequent analyses. Extra radiosoundings and additional satellite radiances clearly improve the first-guess quality over land and sea sensitive areas respectively.This work has been partially supported by PREDIMED (CLI-CGL2011-24458) Project

Improvements in forecasting intense rainfall: results from the FRANC (forecasting rainfall exploiting new data assimilation techniques and novel observations of convection) project

The FRANC project (Forecasting Rainfall exploiting new data Assimilation techniques and Novel observations of Convection) has researched improvements in numerical weather prediction of convective rainfall via the reduction of initial condition uncertainty. This article provides an overview of the project’s achievements. We highlight new radar techniques: correcting for attenuation of the radar return; correction for beams that are over 90% blocked by trees or towers close to the radar; and direct assimilation of radar reflectivity and refractivity. We discuss the treatment of uncertainty in data assimilation: new methods for estimation of observation uncertainties with novel applications to Doppler radar winds, Atmospheric Motion Vectors, and satellite radiances; a new algorithm for implementation of spatially-correlated observation error statistics in operational data assimilation; and innovative treatment of moist processes in the background error covariance model. We present results indicating a link between the spatial predictability of convection and convective regimes, with potential to allow improved forecast interpretation. The research was carried out as a partnership between University researchers and the Met Office (UK). We discuss the benefits of this approach and the impact of our research, which has helped to improve operational forecasts for convective rainfall event

On the Predictability of Hub Height Winds

Wind energy is a major source of power in over 70 countries across the world, and the worldwide share of wind energy in electricity consumption is growing. The introduction of signicant amounts of wind energy into power systems makes accurate wind forecasting a crucial element of modern electrical grids. These systems require forecasts with temporal scales of tens of minutes to a few days in advance at wind farm locations. Traditionally these forecasts predict the wind at turbine hub heights; this information is then converted by transmission system operators and energy companies into predictions of power output at wind farms. Since the power available in the wind is proportional to the wind speed cubed, even small wind forecast errors result in large power prediction errors. Accurate wind forecasts are worth billions of dollars annually; forecast improvements will result in reduced costs to consumers due to better integration of wind power into the power grid and more effcient trading of wind power on energy markets.This thesis is a scientic contribution to the advancement of wind energy forecasting with mesoscale numerical weather prediction models. After an economic and theoretical overview of the importance of wind energy forecasts, this thesis continues with an analysis of wind speed predictions at hub height using the Weather Research and Forecasting (WRF) model. This analysis demonstrates the need for more detailed analyses of wind speeds and it is shown that wind energy forecasting cannot be reduced solely to forecasting winds at hub height. Calculating only the power output from hub height winds can result in erroneous estimates due to the vertical wind shear in the atmospheric boundary layer (PBL). Results show that the accuracy of modeled wind conditions and wind proles in the PBL depends on the PBL scheme adopted and is different under varying atmospheric stability conditions, among other modeling factors. This has important implications for wind energy applications: shallow stable boundary layers can result in excessive wind shear, which is detrimental for wind energy applications. This is particularly relevant with offshore facilities, which represent a significant portion of new wind farms being constructed. Furthermore, a novel aspect to this study is the presentation of a verification methodology that takes into account wind at different heights where turbines operate.The increasing number of wind farm deployments represents a novel and unique data source for improving mesoscale wind forecasts for wind energy applications. These new measurements include nacelle wind speeds and the turbines' angle of rotation into the wind (yaw angles). This thesis continues with an extensive description of this new data set and its challenges in data assimilation, focusing on data from the Horns Rev I wind farm. Since wind farm data are such a dense data set there is need to derive representative information from the measurements, i.e., thin the data. Different thinning strategies and their impact on improving wind forecasts for wind power predictions are investigated with the WRF Four-Dimensional Data Assimilation system. The median of the whole wind farm was found to be the most successful thinning strategy. Nacelle winds and yaw angles are a promising data set to improve wind predictions downstream of a wind farm as well as at the wind farm itself: Their impact lasted up to 5 hours and depends on time of the day, forecast lead time and weather situation

Estimating interchannel observation-error correlations for IASI radiance data in the Met Office system

The optimal utilisation of hyper-spectral satellite observations in numerical weather prediction is often inhibited by incorrectly assuming independent interchannel observation errors. However, in order to represent these observation-error covariance structures, an accurate knowledge of the true variances and correlations is needed. This structure is likely to vary with observation type and assimilation system. The work in this article presents the initial results for the estimation of IASI interchannel observation-error correlations when the data are processed in the Met Office one-dimensional (1D-Var) and four-dimensional (4D-Var) variational assimilation systems. The method used to calculate the observation errors is a post-analysis diagnostic which utilises the background and analysis departures from the two systems. The results show significant differences in the source and structure of the observation errors when processed in the two different assimilation systems, but also highlight some common features. When the observations are processed in 1D-Var, the diagnosed error variances are approximately half the size of the error variances used in the current operational system and are very close in size to the instrument noise, suggesting that this is the main source of error. The errors contain no consistent correlations, with the exception of a handful of spectrally close channels. When the observations are processed in 4D-Var, we again find that the observation errors are being overestimated operationally, but the overestimation is significantly larger for many channels. In contrast to 1D-Var, the diagnosed error variances are often larger than the instrument noise in 4D-Var. It is postulated that horizontal errors of representation, not seen in 1D-Var, are a significant contributor to the overall error here. Finally, observation errors diagnosed from 4D-Var are found to contain strong, consistent correlation structures for channels sensitive to water vapour and surface properties