Angela Benedetti

THE CLOUDSAT MISSION AND THE A-TRAIN A New Dimension of Space-Based Observations of Clouds and Precipitation

CloudSat is a satellite experiment designed to measure the vertical structure of clouds from space. The expected launch of CloudSat is planned for 2004, and once launched, CloudSat will orbit in formation as part of a constellation of satellites (the A-Train) that includes NASAs Aqua and Aura satellites, a NASA–CNES lidar satellite (CALIPSO), and a CNES satellite carrying a polarimeter (PARASOL). A unique feature that CloudSat brings to this constellation is the ability to fly a precise orbit enabling the fields of view of the CloudSat radar to be overlapped with the CALIPSO lidar footprint and the other measurements of the constellation. The precision and near simultaneity of this overlap creates a unique multisatellite observing system for studying the atmospheric processes essential to the hydrological cycle. The vertical profiles of cloud properties provided by CloudSat on the global scale fill a critical gap in the investigation of feedback mechanisms linking clouds to climate. Measuring these profi...

Aerosol analysis and forecast in the European Centre for Medium‐Range Weather Forecasts Integrated Forecast System: 2. Data assimilation

[1] 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 modeling and analysis system is fully integrated in the operational four-dimensional assimilation apparatus. Its purpose is to produce aerosol forecasts and reanalyses of aerosol fields using optical depth data from satellite sensors. This paper is the second of a series which describes the GEMS aerosol effort. It 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 2-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 data sets 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.

Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling

[1] This paper presents the aerosol modeling now part of the ECMWF Integrated Forecasting System (IFS). It includes new prognostic variables for the mass of sea salt, dust, organic matter and black carbon, and sulphate aerosols, interactive with both the dynamics and the physics of the model. It details the various parameterizations used in the IFS to account for the presence of tropospheric aerosols. Details are given of the various formulations and data sets for the sources of the different aerosols and of the parameterizations describing their sinks. Comparisons of monthly mean and daily aerosol quantities like optical depths against satellite and surface observations are presented. The capability of the forecast model to simulate aerosol events is illustrated through comparisons of dust plume events. The ECMWF IFS provides a good description of the horizontal distribution and temporal variability of the main aerosol types. The forecastonly model described here generally gives the total aerosol optical depth within 0.12 of the relevant observations and can therefore provide the background trajectory information for the aerosol assimilation system described in part 2 of this paper.

Toward a Monitoring and Forecasting System For Atmospheric Composition: The GEMS Project

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.

Assimilation of MODIS Cloud Optical Depths in the ECMWF Model

At the European Centre for Medium-Range Weather Forecasts (ECMWF), a large effort has recently been devoted to define and implement moist physics schemes for variational assimilation of rain- and cloud-affected brightness temperatures. This study expands on the current application of the new linearized moist physics schemes to assimilate cloud optical depths retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the Aqua platform for the first time in the ECMWF operational four-dimensional assimilation system. Model optical depths are functions of ice water and liquid water contents through established parameterizations. Linearized cloud schemes in turn link these cloud variables with temperature and humidity. A bias correction is applied to the optical depths to minimize the differences between model and observations. The control variables in the assimilation are temperature, humidity, winds, and surface pressure. One-month assimilation experiments for April 2006 demonstrated an impact of the assimilated MODIS cloud optical depths on the model fields, particularly temperature and humidity. Comparison with independent observations indicates a positive effect of the cloud information assimilated into the model, especially on the amount and distribution of the ice water content. The impact of the cloud assimilation on the medium-range forecast is neutral to slightly positive. Most importantly, this study demonstrates that global assimilation of cloud observations in ECMWF four-dimensional variational assimilation system (4DVAR) is technically doable but a continued research effort is necessary to achieve clear positive impacts with such data.

AEROSOLS FOR CONCENTRATING SOLAR ELECTRICITY PRODUCTION FORECASTS Requirement Quantification and ECMWF/MACC Aerosol Forecast Assessment

The potential for transferring a larger share of our energy supply toward renewable energy is a widely discussed goal in society, economics, environment, and climate-related programs. For a larger share of electricity to come from fluctuating solar and wind energy-based electricity, production forecasts are required to ensure successful grid integration. Concentrating solar power holds the potential to make the fluctuating solar electricity a dispatchable resource by using both heat storage systems and solar production forecasts based on a reliable weather prediction. These solar technologies exploit the direct irradiance at the surface, which is a quantity very dependent on the aerosol extinction with values up to 100%. Results from present-day numerical weather forecasts are inadequate, as they generally use climatologies for dealing with aerosol extinction. Therefore, meteorological forecasts have to be extended by chemical weather forecasts. The paper aims at quantifying on a global scale the question...

Use of a Lidar Forward Model for Global Comparisons of Cloud Fraction between the ICESat Lidar and the ECMWF Model

The performance of the European Centre for Medium-Range Weather Forecasts (ECMWF) model in simulating clouds is evaluated using observations by the Geoscience Laser Altimeter System lidar on the Ice, Cloud, and Land Elevation Satellite (ICESat). To account for lidar attenuation in the comparison, model variables are used to simulate the attenuated backscatter using a lidar forward model. This generates a new model cloud fraction that can then be fairly compared with the ICESat lidar. The lidar forward model and ICESat comparison is performed over 15 days (equivalent to 226 orbits of Earth, or roughly 9 million km) of data. The model is assessed by cloud fraction statistics, skill scores, and its ability to simulate lidar backscatter. The results show that the model generally simulates the occurrence and location of clouds well but overestimates the mean amount when present of the ice cloud by around 10%, particularly in the tropics. The skill of the model is slightly better over the land than over the sea. The model also has some problems representing the amount when present in tropical boundary layer cloud, particularly over land, where there is an underestimate by as much as 15%. Calculations of backscatter reveal that the ECMWF model predicts the lidar backscatter to within 5% on average, for a lidar ratio of 20 sr, apart from in thick ice clouds. Sensitivity tests show that realistic variations in extinction-to-backscatter ratio and effective radius affect the forward modeled mean cloud fraction by no more than 10%.

International Operational Aerosol Observability Workshop

WHAt: approximately 15 developers for many of the world’s operational numerical weather prediction centers with aerosol forecasting mandates met with an equal number of representatives for the satellite data providers to discuss the aerosol observability issues facing the next generation of aerosol forecast systems. WHen: 27–29 april 2010 WHere: Monterey, California W hile the last three years have seen rapid operational implementation of aerosol and pollution models around the world, the key to further development of aerosol forecasting systems is aerosol observational data from satellites for model evaluation and data assimilation. However, although the dynamical meteorology community has a well-developed, near-real-time observing system to support forecasting, the aerosol community is only beginning to address its needs. This meeting was the first ever to combine the lead aerosol model developers and remote sensing data providers from around the globe in discussing state-of-the-art technologies and operational requirements for aerosol forecasting. Participants included representatives from the operational centers of ECMWF, FNMOC, JMA, NCEP, and the Met Office; remote sensing data providers from EUMETSAT, ESA, JAXA, NASA, and NOAA NESDIS; and additional developers from NASA GMAO, NGST, NOAA, NRL, and several universities.1 Overviews were given by operational participants as to their centers’ current forecasting status and projected data needs. Remote sensing agencies described current and planned relevant space missions. Last, developers provided an overview of future directions in aerosol data assimilation. Much of the development of operational aerosol systems has relied on climate satellite datasets, predominantly from the MODIS instrument on the NASA Terra and Aqua spacecraft. With near-real time data available from the joint NASA–NOAA NRTPE (aka “bent pipe”) beginning in 2002 and the recent implementation of the NASA LANCE data server, operational centers have developed a neartotal reliance on MODIS aerosol, fire, and albedo products for model initialization and assimilation.

International Cooperative for Aerosol Prediction Workshop on Aerosol Forecast Verification

The purpose of this workshop was to reinforce the working partnership between centers who are actively involved in global aerosol forecasting, and to discuss issues related to forecast verification. Participants included representatives from operational centers with global aerosol forecasting requirements, a panel of experts on Numerical Weather Prediction and Air Quality forecast verification, data providers, and several observers from the research community. The presentations centered on a review of current NWP and AQ practices with subsequent discussion focused on the challenges in defining appropriate verification measures for the next generation of aerosol forecast systems.

MPLNET lidar data assimilation in the ECMWF MACC-II Aerosol system: evaluation of model performances at NCU lidar station

Atmospheric profiles of the optical aerosol properties through the retrieved backscattering or extinction coefficients by lidar measurements can improve drastically the MACC-II aerosol model performances on vertical dimension. Currently the MODIS Aerosol Optical Depth data (both from Terra and Aqua) are assimilated into the model. Being a columnintegrated quantity, these data do not modify the model aerosol vertical profile, especially if the aerosols are not interactive with the meteorology. Since 1999, the MPLNET lidar network provides continuously lidar data measurements from worldwide permanent stations (currently 21), deployed from the Arctic to the Antarctic regions and in tropical and equatorial zones. The purpose of this study is to show the first preliminary results of the intercomparison of MPLNET lidar data against the ECWMF MACC-II aerosol model, for a selected MPLNET permanent observational site at National Central University of Taiwan. Assessing the model performances it is the first step for future near-real time lidar data assimilation into MACC-II aerosol model forecast.