OROLOGfCAL SOCIETY : With the advance of satellite remote sensing of the Earth's atmosphere, a clearer picture is starting to emerge of how natural and anthropogenic emissions are influencing the composition of the atmosphere on a global scale. While climate and pollution problems, such as the Antarctic ozone hole (Farman et al. 1985), increase in atmospheric C02 (Keeling et al. 1976) and pollution around cities, have often been first detected by ground-based measurements, satellite observations have the capability of showing the large-scale patterns. Good examples are the geostationary images of desert dust plumes stretching all the way from the Sahara to South America (see information online at http://oiswww.eumetsat.org/WEBOPS/iotm/ iotm/20040306 dust/20040306_dust.html), observations of the Antarctic ozone hole by various satellite sensors (e.g., www.nasa.gov/vision/earth/environment/ozone_resource__ ), and the recent global views of tropospheric NO, pollution as measured by the Global Ozone Monitoring Experiment (GOME), the Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCI AM ACHY), and the Ozone Monitoring Instrument (OMI; information at www.knmi.nl/omi/publ-n/metingen/no2/metingen_no2__nrt. html). These examples clearly show •

The Global and Regional Earth System Monitoring Using Satellite and In Situ Data (GEMS) project is combining the manifold expertise in atmospheric composition research and numerical weather prediction of 32 European institutes to build a comprehensive monitoring and forecasting System for greenhouse gases, reactive gases, aerosol, and regional air quality. The project is funded by the European Commission as part of the Global Monitoring of Environment and Security (GMES) framework. GEMS has extended the data assimilation system of the European Centre for Medium-Range Weather Forecasts (ECMWF) to include various tracers for which satellite observations exist. A chemical transport model has been coupled to this system to account for the atmospheric chemistry. The GEMS system provides lateral boundary conditions for a set of 10 regional air quality forecast models and global atmospheric fields for use in surface flux inversions for the greenhouse gases. Observations from both in situ and satellite sources are used as input, and the output products will serve users such as policy makers, environmental agencies, the science community, and providers of end-user services for air quality and health. This article provides an overview of GEMS and uses some recent results to illustrate the current status of the project. It is expected that GEMS will grow into a full operational service for the atmospheric component of GMES in the next decade. Part of this transition will be the merge with the Protocol Monitoring for the GMES Service Element: Atmosphere (PROMOTE) GMES project into the Monitoring of Atmospheric Composition and Climate (MACC) project.

This study presents the new aerosol assimilation system developed at the European Centre for Medium–Range Weather Forecasts for the Global and regional Earth-system Monitoring using Satellite and in-situ data (GEMS) project. The aerosol modelling and analysis system is fully integrated in the operational four–dimensional assimilation apparatus. Its purpose is to produce aerosol forecasts and reanalysis of aerosol fields using optical depth data from satellite sensors. This paper is the second of a series which describes the GEMS aerosol effort and focuses on the theoretical architecture and practical implementation of the aerosol assimilation system. It also provides a discussion of the background errors and observations errors for the aerosol fields, and presents a subset of results from the two–year reanalysis which has been run for 2003 and 2004 using data from the Moderate Resolution Imaging Spectroradiometer on the Aqua and Terra satellites. Independent datasets are used to show that, despite some compromises that have been made for feasibility reasons in regards to the choice of control variable and error characteristics, the analysis is very skillful in drawing to the observations and in improving the forecasts of aerosol optical depth.

Within the EU-funded GEMS project (Global Earth-system Modelling using Satellite and in-situ data) the European Centre for Medium-range Weather Forecasts (ECMWF) has been building a 4dimensional variational data assimilation system that will be capable of assimilating data from various satellite sensors as well as in-situ observations to model and constrain global fields of greenhouse gases, reactive gases, and aerosol. These fields will then be used to monitor the environmental aspect of the atmosphere, to infer surface fluxes for several species, and to provide boundary conditions for regional air quality models. This paper presents the background and aims of the project as well as the current status illustrated with some initial results. THE GEMS PROJECT The GEMS project (Global Earth-system Modelling using Space and in-situ data) is an Integrated Project funded under the EU’s initiative for Global Monitoring for Environment and Security (GMES). The aim of the project is to extend the modelling, forecasting and data assimilation capabilities used in numerical prediction to problems of atmospheric composition. This will deliver improved services and products in near-real time (e.g. global air quality forecasts to provide boundary conditions for more detailed regional air-quality forecasts). In addition the operational analyses and retrospective reanalyses will support treaty assessments (e.g. the Kyoto protocol on greenhouse gases and the Montreal protocol on the ozone layer) while the joint use of satellite and in-situ data will enable sources, sinks and transports of atmospheric constituents to be estimated. The project involves about thirty institutes in fourteen European countries. It will run for four years from spring 2005 to spring 2009 with coordination carried out by ECMWF. The GEMS forecast capabilities will require sophisticated operational models. In addition global and regional data assimilation systems will be needed to exploit satellite and in-situ data so as to provide initial data (‘status assessments’) for the forecasts. These operational ‘status assessments’ are also invaluable for documenting sources, sinks and transports of atmospheric trace constituents. The specific objectives of the GEMS Project are to: • Develop and implement at ECMWF a validated, comprehensive, and operational global data assimilation/forecast system for atmospheric composition and dynamics, which combines all available remotely sensed and in-situ data. Operational deliverables will include current and forecast three-dimensional global distributions (four times daily with a horizontal resolution of 50–100 km, and vertical resolution of 60 levels between the surface and 65 km) of key atmospheric trace constituents including greenhouse gases, reactive gases and aerosols. • Provide initial and boundary conditions for operational regional air-quality and ‘chemical weather’ forecast systems across Europe. This will provide a methodology for assessing the impact of global climate changes on regional air quality. It will also provide improved operational real-time air-quality forecasts.

ERA‐40 is a re‐analysis of meteorological observations from September 1957 to August 2002 produced by the European Centre for Medium‐Range Weather Forecasts (ECMWF) in collaboration with many institutions. The observing system changed considerably over this re‐analysis period, with assimilable data provided by a succession of satellite‐borne instruments from the 1970s onwards, supplemented by increasing numbers of observations from aircraft, ocean‐buoys and other surface platforms, but with a declining number of radiosonde ascents since the late 1980s. The observations used in ERA‐40 were accumulated from many sources. The first part of this paper describes the data acquisition and the principal changes in data type and coverage over the period. It also describes the data assimilation system used for ERA‐40. This benefited from many of the changes introduced into operational forecasting since the mid‐1990s, when the systems used for the 15‐year ECMWF re‐analysis (ERA‐15) and the National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) re‐analysis were implemented. Several of the improvements are discussed. General aspects of the production of the analyses are also summarized.

The Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) onboard the Envisat satellite provides temperature and various gas profiles from limb‐viewing midinfrared emission measurements. The stratospheric temperatures retrieved at the Institut für Meteorologie und Klimaforschung (IMK) for September/October 2002 and October/November 2003 are compared with a number of reference data sets, including global radiosonde (RS) observations, radio occultation (RO) measurements of Global Positioning System (GPS) on German Challenging Minisatellite Payload (CHAMP) and Argentinean Satelite de Aplicaciones Cientificas‐C (SAC‐C) satellite, Halogen Occultation Experiment (HALOE) on the Upper Atmosphere Research Satellite (UARS), and the analyses of European Centre for Medium‐Range Weather Forecasts (ECMWF) and Met Office (METO), United Kingdom. The data sets show a good general agreement. Between 10 and 30 km altitude the mean differences are within ±0.5 K for the averages over the height interval and within ±(1–1.5) K at individual levels for comparisons with RS, GPS‐RO/CHAMP, and SAC‐C, ECMWF, and METO. Between 30 and 45 km the MIPAS mean temperatures, averaged over the height region, are higher than ECMWF but lower than METO by ∼1.5 K, while they differ by ±0.5 K with respect to HALOE, with maximum discrepancies of ∼2.5 K peaking around 35 km. Between 45 and 50 km, MIPAS temperatures show a low bias compared to HALOE, ECMWF, and METO with mean differences of −1 to −3 K and with a better agreement with HALOE. The large discrepancies between MIPAS and the analyses above 30 km likely suggest deficiency in the underlying general circulation models. The standard deviations vary between 2.5 and 3.5 K for individual data sets, with more than 70% being contributed from the expected variability of the atmosphere. Retrieved temperatures with accuracy of ∼0.5–1 K after removing the atmospheric variability provide highly accurate knowledge to characterize our environment.

MIPAS ozone data has been compared with that from different satellite instruments, for the period July- December 2002. The MIPAS O 3 profiles have been intercompared systematically with co-located data from HALOE, SAGE-II, POAM-III, ODIN SMR, GOME , URAP climatology, and IMK independent MIPAS retrievals. At pressures less than 50hPa, results showed generally good agreement. The largest discrepancies were found to occur at pressure s greater than 50 hPa, and in tropical and polar latitudes. Estimates of precision and accuracy of MIPAS O3 are reported on the basis of the conducted comparisons.

RESUME At the 2 Workshop of the Atmospheric Chemistry Validation of Envisat (ACVE-2), re-processed temperature data from MIPAS (V 4.61) were evaluated using data assimilation models and independent satellite data. This paper presents results of the validation activities of the following groups: (i) the European Centre for Medium-range Weather Forecasts; (ii) the Data Assimilation Research Centre UK; (iii) The Institut fur Meteorologie und Klimaforschung at the Universitat Karlsruhe; and (iv) Service d’Aeronomie, CNRS, France, who are all members of the Modelling and Assimilation and Satellite Intercomparison (MASI) validation subgroup of the Atmospheric Chemistry Validation Team (ACVT) for Envisat.

In this paper we report the results of three validation studies. First, a comparison of the recently processed SCIAMACHY ”validation reference set” ozone columns (software version 5.01) with assimilated ozone fields based on the new GOME ozone column retrieval of KNMI (TOGOMI). Second, a direct comparison between the SCIAMACHY ”validation reference set” and the scientific retrieval of the KNMI (TOSOMI algorithm). Third, a monitoring of the early 2004 SCIAMACHY total ozone meteo product with the ozone analyses of the ECMWF model. The SCIAMACHY operational ozone column product has improved compared to ACVE-1, but an upgrade of the processor is still urgently needed to include the latest algorithm developments inplemented for the GOME ozone column retrieval. The TOSOMI scientifically retrieved O 3 column of KNMI is a stable product with a small bias < 1% compared to GOME-TOGOMI and an overall bias of 1.5% compared to Brewer and Dobson. The SCIAMACHY operational ozone data showed large problems in March and April 2003. Data assimilation complements validation with independent observations. The differences between the model forecast and new observations that have not been used in the assimilation (the observation minus forecast departures, OMF) provide a wealth of information about the model performance, the quality of the observations and the retrieval approach. These departures form the core of each data assimilation system: all new observation are first compared with a model prediction of this observation. Based on the OMF departures and knowledge of the observation and forecast error statistics, the model state is updated in the assimilation step to account for the information brought by the observations. With the forecast model one can construct a model predicted value for each of the typically millions of satellite observations available, and statements can be made with great statistical confidence. This is an advantage compared to validation studies with ground stations and balloon and aircraft campaigns, where only typically between one and a few hundred collocations are available. The forecast, however, is itself based on earlier observations of the same, or similar, satellite sensors. Therefore assimilation of one data set alone can not fully determine the quality of the satellite observations, for which independent observations are crucial.

Workshop of the Atmospheric Chemistry Validation of Envisat (ACVE-2), re-processed water vapour data from MIPAS (V 4.61) were evaluated using data assimilation models. The following groups participated: (i) the Data Assimilation Research Centre in collaboration with the Met Office, both UK; (ii) the European Centre for Medium-range Weather Forecasts; and (iii) the Belgian Institute for Space Aeronomy. This paper discusses issues concerning the assimilation of water vapour in the troposphere and stratosphere, and presents preliminary results from the evaluation of the re-processed MIPAS water vapour data. This evaluation exercise was done under the auspices of the Modelling and Assimilation (MA) validation subgroup of the Atmospheric Chemistry Validation Team (ACVT) for Envisat. 1. INTRODUCTION The work carried out in this evaluation was as follows:

In this paper we outline the ozone model and analysis system used in the ECMWF 45‐year reanalysis project, and take a first look at the quality of the resulting ozone fields. The analysed ozone, both profiles and total‐column ozone field, generally compares well with independent observations. However, there are certain problematic situations when the profile is not correct even if the analysed total ozone is close to observations. These situations appear when there is a bias between the model and the observations used in the assimilation. Suggestions are made for improvements of the ozone assimilation and modelling, including the introduction of a bias correction scheme for ozone data. Copyright © 2004 Royal Meteorological Society