Lars Isaksen

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...

The atmospheric dynamics mission for global wind field measurement

The prime aim of the Atmospheric Dynamics Mission is to demonstrate measurements of vertical wind profiles from space. Extensive studies conducted by the European Space Agency over the past 15 years have culminated in the selection of a high-performance Doppler wind lidar based on direct-detection interferometric techniques. Such a system, with a pulsed laser operating at 355-nm wavelength, would utilize both Rayleigh scattering from molecules and Mie scattering from thin cloud and aerosol particles; measurement of the residual Doppler shift from successive levels in the atmosphere provides the vertical wind profiles. The lidar would be accommodated on a satellite flying in a sun-synchronous orbit, at an altitude of ~400 km, providing near-global coverage; target date for launch is in 2007. Processing of the backscatter signals will provide about 3000 globally distributed wind profiles per day, above thick clouds or down to the surface in clear air, at typically 200-km separation along the satellite track...

Soil Moisture Analyses at ECMWF: Evaluation Using Global Ground-Based In Situ Observations

AbstractIn situ soil moisture from 117 stations across the world and under different biome and climate conditions are used to evaluate two soil moisture products from the European Centre for Medium-Range Weather Forecasts (ECMWF)—namely, the operational analysis and the interim reanalysis [ECMWF Re-Analysis Interim (ERA-Interim)]. ECMWF’s operational Integrated Forecasting System (IFS) is based on a continuous effort to improve the analysis and modeling systems, resulting in frequent updates (a few times a year). The ERA-Interim reanalysis is produced by a fixed IFS version (for the main component of the atmospheric model and data assimilation). It has the advantage of being consistent over the whole period from 1979 onward and by design, reanalysis products are more suitable than their operational counterparts for use in climate studies. Although the two analyses show good skills in capturing surface soil moisture variability, they tend to overestimate soil moisture, particularly for dry land. Over the 2...

Characteristics of Occasional Poor Medium-Range Weather Forecasts for Europe

Medium-range weather prediction has become more skillful over recent decades, but forecast centers still suffer from occasional very poor forecasts, which are often referred to as “dropouts” or “busts.” This study focuses on European Centre for Medium-Range Weather Forecasts (ECMWF) day-6 forecasts for Europe. Although busts are defined by gross scores, bust composites reveal a coherent “Rex type” blocking situation, with a high over northern Europe and a low over the Mediterranean. Initial conditions for these busts also reveal a coherent flow, but this is located over North America and involves a trough over the Rockies, with high convective available potential energy (CAPE) to its east. This flow type occurs in spring and is often associated with a Rossby wave train that has crossed the Pacific. A composite on this initial flow type displays enhanced day-6 random forecast errors and some-what enhanced ensemble forecast spread, indicating reduced inherent predictability. Mesoscale convective systems, as...

Initialisation of Land Surface Variables for Numerical Weather Prediction

Land surface processes and their initialisation are of crucial importance for Numerical Weather Prediction (NWP). Current land data assimilation systems used to initialise NWP models include snow depth analysis, soil moisture analysis, soil temperature and snow temperature analysis. This paper gives a review of different approaches used in NWP to initialise land surface variables. It discusses the observation availability and quality, and it addresses the combined use of conventional observations and satellite data. Based on results from the European Centre for Medium-Range Weather Forecasts (ECMWF), results from different soil moisture and snow depth data assimilation schemes are shown. Both surface fields and low-level atmospheric variables are highly sensitive to the soil moisture and snow initialisation methods. Recent developments of ECMWF in soil moisture and snow data assimilation improved surface and atmospheric forecast performance.

Use and Impact of Automated Aircraft Data in a Global 4DVAR Data Assimilation System

Abstract The use of automated aircraft data [Aircraft Meteorological Data Relay (AMDAR) and Aircraft Communication Addressing and Reporting System (ACARS)] has recently been extended in ECMWFs operational 4DVAR data assimilation system. Herein, a modified data selection procedure is reported on that allows the use of more aircraft profiling data during the aircrafts ascending and descending phase, and more of the most frequent reports at cruise level. It is shown that the accuracy of analyzed jet streams is improved through these changes, as verified against independent (non–real time) aircraft data that had not been used in the experiments. The modifications are shown to have a clear positive impact on the short- and medium-range forecast performance. The revised aircraft usage was implemented operationally in January 2002. The impact in 4DVAR of profiles from American and European automated aircraft in ascending and descending phase has been tested in a data denial impact study, for January and July 2...

The IFS model: a parallel production weather code

Abstract The integrated Forecasting System (IFS) of the European Centre for Medium-range Weather Forecasts (ECMWF) is a spectral weather forecasting model, which daily produces weather forecasts on up to 16 processors of a CRAY C90. This paper describes the shared-memory implementation of the code and the subsequent development that has been carried out in order to generate a parallel version, suitable for a scalable distributed-memory architecture with many processors. Performance results presented for several vector and parallel systems indicate that the parallelization effort has been successful in achieving good performance and high efficiency.

EnKF and Hybrid Gain Ensemble Data Assimilation. Part I: EnKF Implementation

AbstractThe desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF. A broad description of the ECMWF EnKF is given in this paper, focusing on highlighting differences compared to standard EnKF practice. In particular, a discussion of the novel algorithm used to control imbalances between the mass and wind fields in the EnKF analysis is given. The scalability and computational properties of the EnKF are reviewed and the implementation choices adopted at ECMWF described. The sensitivity of the ECMWF EnKF to ensemble size, horizontal resolution, and representation of model errors is also discussed. A comparison with 4DVar will be found in Part II of this two-part study.

EnKF and Hybrid Gain Ensemble Data Assimilation. Part II: EnKF and Hybrid Gain Results

AbstractThe desire to do detailed comparisons between variational and more scalable ensemble-based data assimilation systems in a semioperational environment has led to the development of a state-of-the-art EnKF system at ECMWF, which has been described in Part I of this two-part study. In this part the performance of the EnKF system is evaluated compared to a 4DVar of similar resolution. It is found that there is not a major difference between the forecast skill of the two systems. However, similarly to the operational hybrid 4DVar–EDA, a hybrid EnKF–variational system [which we refer to as the hybrid gain ensemble data assimilation (HG-EnDA)] is capable of significantly outperforming both component systems. The HG-EnDA has been implemented with relatively little effort following Penny’s recent study. Results of numerical experimentation comparing the HG-EnDA with the hybrid 4DVar–EDA used operationally at ECMWF are presented, together with diagnostic results, which help characterize the behavior of the ...

Soil temperature at ECMWF: An assessment using ground-based observations

Soil temperature is an important variable for the representation of many physical processes in numerical weather prediction (NWP). It is the key driver for all surface emissions of energy, carbon dioxide, and water and forward operator for all satellite sensors sensitive to land. Yet the forecast quality of this variable in NWP is largely unknown. In this study, in situ soil temperature measurements from nearly 700 stations belonging to four networks across the United States and Europe are used to assess the European Centre for Medium-Range Weather Forecasts (ECMWF) forecasts of soil temperature during 2012. Evaluation of the time series shows a good performance of the short-range forecasts (day one) in capturing both soil temperature annual and diurnal cycles with very high level of correlation (0.92 and over), averaged root-mean-square differences ranging from 2.54°C to 3.89°C and averaged biases ranging from −0.52°C to 0.94°C. The orography data set used in the forecast system was found to have a strong impact on the outcomes of the evaluation. The difference between elevation of a station and that of the corresponding grid cell in the ECMWF model may lead to large temperature differences linked to linear processes resulting in a constant bias, as well as nonlinear processes (e.g., to snow melt in spring). This verification study aims to contribute to a better understanding of the near-surface forecasts performance highlighting land-atmosphere processes that need to be better represented in future model development such as snow pack melting and heat diffusion in the soil.