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Abstract
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.
Abstract
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.
Abstract
The fact that aerosols are important players in Earth’s radiation balance is well accepted by the scientific community. Several studies have shown the importance of characterizing aerosols in order to constrain surface radiative fluxes and temperature in climate runs. In numerical weather prediction, however, there has not been definite proof that interactive aerosol schemes are needed to improve the forecast. Climatologies are instead used that allow for computational efficiency and reasonable accuracy. At the monthly to subseasonal range, it is still worth investigating whether aerosol variability could afford some predictability, considering that it is likely that persisting aerosol biases might manifest themselves more over time scales of weeks to months and create a nonnegligible forcing. This paper explores this hypothesis using the ECMWF’s Ensemble Prediction System for subseasonal prediction with interactive prognostic aerosols. Four experiments are conducted with the aim of comparing the monthly prediction by the default system, which uses aerosol climatologies, with the prediction using radiatively interactive aerosols. Only the direct aerosol effect is considered. Twelve years of reforecasts with 50 ensemble members are analyzed on the monthly scale. Results indicate that the interactive aerosols have the capability of improving the subseasonal prediction at the monthly scales for the spring/summer season. It is hypothesized that this is due to the aerosol variability connected to the different phases of the Madden–Julian oscillation, particularly that of dust and carbonaceous aerosols. The degree of improvement depends crucially on the aerosol initialization. More work is required to fully assess the potential of interactive aerosols to increase predictability at the subseasonal scales.
Abstract
The fact that aerosols are important players in Earth’s radiation balance is well accepted by the scientific community. Several studies have shown the importance of characterizing aerosols in order to constrain surface radiative fluxes and temperature in climate runs. In numerical weather prediction, however, there has not been definite proof that interactive aerosol schemes are needed to improve the forecast. Climatologies are instead used that allow for computational efficiency and reasonable accuracy. At the monthly to subseasonal range, it is still worth investigating whether aerosol variability could afford some predictability, considering that it is likely that persisting aerosol biases might manifest themselves more over time scales of weeks to months and create a nonnegligible forcing. This paper explores this hypothesis using the ECMWF’s Ensemble Prediction System for subseasonal prediction with interactive prognostic aerosols. Four experiments are conducted with the aim of comparing the monthly prediction by the default system, which uses aerosol climatologies, with the prediction using radiatively interactive aerosols. Only the direct aerosol effect is considered. Twelve years of reforecasts with 50 ensemble members are analyzed on the monthly scale. Results indicate that the interactive aerosols have the capability of improving the subseasonal prediction at the monthly scales for the spring/summer season. It is hypothesized that this is due to the aerosol variability connected to the different phases of the Madden–Julian oscillation, particularly that of dust and carbonaceous aerosols. The degree of improvement depends crucially on the aerosol initialization. More work is required to fully assess the potential of interactive aerosols to increase predictability at the subseasonal scales.
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 of whether and where daily mean or hourly forecasts are required, or if persistence is sufficient in some regions. It assesses the performance of recently introduced NWP aerosol schemes by using the ECMWF/Monitoring Atmospheric Composition and Climate (MACC) forecast, which is a preparatory activity for the upcoming European Global Monitoring for Environment and Security (GMES) Atmosphere Service.
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 of whether and where daily mean or hourly forecasts are required, or if persistence is sufficient in some regions. It assesses the performance of recently introduced NWP aerosol schemes by using the ECMWF/Monitoring Atmospheric Composition and Climate (MACC) forecast, which is a preparatory activity for the upcoming European Global Monitoring for Environment and Security (GMES) Atmosphere Service.
No abstract available.
No abstract available.
Abstract
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%.
Abstract
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%.
No Abstract available.
No Abstract available.
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 NASA's 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 profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy.
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 NASA's 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 profiles requires a combination of active and passive instruments, and this will be achieved by combining the radar data of CloudSat with data from other active and passive sensors of the constellation. This paper describes the underpinning science and general overview of the mission, provides some idea of the expected products and anticipated application of these products, and the potential capability of the A-Train for cloud observations. Notably, the CloudSat mission is expected to stimulate new areas of research on clouds. The mission also provides an important opportunity to demonstrate active sensor technology for future scientific and tactical applications. The CloudSat mission is a partnership between NASA's JPL, the Canadian Space Agency, Colorado State University, the U.S. Air Force, and the U.S. Department of Energy.
Abstract
The Aeolus mission objectives are to improve numerical weather prediction (NWP) and enhance the understanding and modeling of atmospheric dynamics on global and regional scale. Given the first successes of Aeolus in NWP, it is time to look forward to future vertical wind profiling capability to fulfill the rolling requirements in operational meteorology. Requirements for wind profiles and information on vertical wind shear are constantly evolving. The need for high-quality wind and profile information to capture and initialize small-amplitude, fast-evolving, and mesoscale dynamical structures increases, as the resolution of global NWP improved well into the 3D turbulence regime on horizontal scales smaller than 500 km. In addition, advanced requirements to describe the transport and dispersion of atmospheric constituents and better depict the circulation on climate scales are well recognized. Direct wind profile observations over the oceans, tropics, and Southern Hemisphere are not provided by the current global observing system. Looking to the future, most other wind observation techniques rely on cloud or regions of water vapor and are necessarily restricted in coverage. Therefore, after its full demonstration, an operational Aeolus-like follow-on mission obtaining globally distributed wind profiles in clear air by exploiting molecular scattering remains unique.
Abstract
The Aeolus mission objectives are to improve numerical weather prediction (NWP) and enhance the understanding and modeling of atmospheric dynamics on global and regional scale. Given the first successes of Aeolus in NWP, it is time to look forward to future vertical wind profiling capability to fulfill the rolling requirements in operational meteorology. Requirements for wind profiles and information on vertical wind shear are constantly evolving. The need for high-quality wind and profile information to capture and initialize small-amplitude, fast-evolving, and mesoscale dynamical structures increases, as the resolution of global NWP improved well into the 3D turbulence regime on horizontal scales smaller than 500 km. In addition, advanced requirements to describe the transport and dispersion of atmospheric constituents and better depict the circulation on climate scales are well recognized. Direct wind profile observations over the oceans, tropics, and Southern Hemisphere are not provided by the current global observing system. Looking to the future, most other wind observation techniques rely on cloud or regions of water vapor and are necessarily restricted in coverage. Therefore, after its full demonstration, an operational Aeolus-like follow-on mission obtaining globally distributed wind profiles in clear air by exploiting molecular scattering remains unique.
