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Abstract
Atmospheric ice nuclei concentrations were measured at Abidjan for 27 months, with a mixing chamber operated at −20C. Seasonal variations appear to be linked to the position of the Intertropical Front. Air masses arriving from continental Africa correspond to high ice nuclei concentrations. The washing out of the atmosphere due to rain gives rise to low concentrations. The distribution of daily concentration averages may be represented by a log-normal distribution with high probability of convergence.
Abstract
Atmospheric ice nuclei concentrations were measured at Abidjan for 27 months, with a mixing chamber operated at −20C. Seasonal variations appear to be linked to the position of the Intertropical Front. Air masses arriving from continental Africa correspond to high ice nuclei concentrations. The washing out of the atmosphere due to rain gives rise to low concentrations. The distribution of daily concentration averages may be represented by a log-normal distribution with high probability of convergence.
Abstract
The vertical variability of reflectivity in the radar beam is an important source of error that interferes with a reliable estimation of the rainfall rate by radar. This source of error can be corrected if the vertical profile of reflectivity (VPR) has been previously determined. This paper presents a method for determining local VPRs from volume scan radar data, that is, from radar data recorded at multiple elevation angles. It is shown that the VPR directly provided by volume scan radar data differs from the true one, which can make it inappropriate to the correction of radar data for the VPR influence. The VPR identification method, based on the analysis of ratios of radar measurements at multiple elevations angles, is then described. The application conditions of the method are defined through sensitivity tests applied to a synthetic case. A “real world” case study allows performing a first evaluation of the proposed method. This analysis demonstrates that the identification of local VPRs and the correction for their influence at a scale of about 100 km2 contributes to improving the reliability of rainfall measurement by radar. Moreover, it is shown that a correction of radar data based on identified VPRs performs better than a correction based on the VPRs directly deduced from volume scan radar data. This last point confirms the importance of the VPR identification stage in the correction of radar data for this source of error.
Abstract
The vertical variability of reflectivity in the radar beam is an important source of error that interferes with a reliable estimation of the rainfall rate by radar. This source of error can be corrected if the vertical profile of reflectivity (VPR) has been previously determined. This paper presents a method for determining local VPRs from volume scan radar data, that is, from radar data recorded at multiple elevation angles. It is shown that the VPR directly provided by volume scan radar data differs from the true one, which can make it inappropriate to the correction of radar data for the VPR influence. The VPR identification method, based on the analysis of ratios of radar measurements at multiple elevations angles, is then described. The application conditions of the method are defined through sensitivity tests applied to a synthetic case. A “real world” case study allows performing a first evaluation of the proposed method. This analysis demonstrates that the identification of local VPRs and the correction for their influence at a scale of about 100 km2 contributes to improving the reliability of rainfall measurement by radar. Moreover, it is shown that a correction of radar data based on identified VPRs performs better than a correction based on the VPRs directly deduced from volume scan radar data. This last point confirms the importance of the VPR identification stage in the correction of radar data for this source of error.
Abstract
This article presents a study of the diurnal and seasonal cycles of dust over North Africa, using surface visibility as an indicator of dust. The diurnal cycle shows a reduction of visibility during the daytime hours in the areas where dust is generated, a consequence of the elimination of the nocturnal inversion. The annual cycle reveals that, at latitudes from 5° to 16°N, there is a latitudinal increase in the duration of the presence of aerosols over the course of the year. The presence of aerosols dimishes in the latitudes from 20° to 35°N, indicating that the aerosol content of the Saharan air is lower than that over the semiarid sub-Saharan zones, such as the Sahel. A comparison of three periods, 1957–61, 1970–74, and 1983–87, shows a continually increasing presence of dust, particularly in the western Sahel. The interannual variability of the dust and its annual cycles in these three periods throughout North Africa bear a strong relationship to rainfall fluctuations in the Sahel. Overall, the results indicate that over the last few decades the Sahel region has replaced the central Sahara as the source of atmospheric aerosols over most of North Africa.
Abstract
This article presents a study of the diurnal and seasonal cycles of dust over North Africa, using surface visibility as an indicator of dust. The diurnal cycle shows a reduction of visibility during the daytime hours in the areas where dust is generated, a consequence of the elimination of the nocturnal inversion. The annual cycle reveals that, at latitudes from 5° to 16°N, there is a latitudinal increase in the duration of the presence of aerosols over the course of the year. The presence of aerosols dimishes in the latitudes from 20° to 35°N, indicating that the aerosol content of the Saharan air is lower than that over the semiarid sub-Saharan zones, such as the Sahel. A comparison of three periods, 1957–61, 1970–74, and 1983–87, shows a continually increasing presence of dust, particularly in the western Sahel. The interannual variability of the dust and its annual cycles in these three periods throughout North Africa bear a strong relationship to rainfall fluctuations in the Sahel. Overall, the results indicate that over the last few decades the Sahel region has replaced the central Sahara as the source of atmospheric aerosols over most of North Africa.
Abstract
Attenuation in rainfall is recognized as one of the most significant limitations in rain-rate estimation from weather radar returns at X- or C-band wavelengths. This paper introduces a radar measurement correction as an inverse problem that accounts for attenuation effects in rainfall. First, a direct theoretical model, relating radar returns at attenuating wavelengths to the rainfall rates between the radar and the point of measurement, is presented. Second, the inverse algorithm used to identify rain-rate estimates from radar returns is described and its application to the attenuation correction is discussed, with the well-known characteristics of the attenuation model (i.e., instability, underdetermination, and nonlinearity) receiving particular attention. Third, a sensitivity analysis is then performed to test the influence of the raindrop size distribution, radar measurement features, and statistical parameters involved in the inverse method. The sensitivity analysis allows for establishing the application conditions of the method. Last, a preliminary evaluation of the method is provided, through simulated radar rainfall measurements and through a limited case study. Various attenuation correction methods are compared with the inverse algorithm. These methods include the standard radar reflectivity–rainfall rate algorithm and two versions of the Hitschfeld–Bordan algorithm. In the simulation exercise, various examples of rainfall field, with different characteristics, are tested. The case study confirms the utility of the proposed method and its ability to provide a robust and stable solution. The method consistently provides better results than the well-known Hitschfeld–Bordan algorithm.
Abstract
Attenuation in rainfall is recognized as one of the most significant limitations in rain-rate estimation from weather radar returns at X- or C-band wavelengths. This paper introduces a radar measurement correction as an inverse problem that accounts for attenuation effects in rainfall. First, a direct theoretical model, relating radar returns at attenuating wavelengths to the rainfall rates between the radar and the point of measurement, is presented. Second, the inverse algorithm used to identify rain-rate estimates from radar returns is described and its application to the attenuation correction is discussed, with the well-known characteristics of the attenuation model (i.e., instability, underdetermination, and nonlinearity) receiving particular attention. Third, a sensitivity analysis is then performed to test the influence of the raindrop size distribution, radar measurement features, and statistical parameters involved in the inverse method. The sensitivity analysis allows for establishing the application conditions of the method. Last, a preliminary evaluation of the method is provided, through simulated radar rainfall measurements and through a limited case study. Various attenuation correction methods are compared with the inverse algorithm. These methods include the standard radar reflectivity–rainfall rate algorithm and two versions of the Hitschfeld–Bordan algorithm. In the simulation exercise, various examples of rainfall field, with different characteristics, are tested. The case study confirms the utility of the proposed method and its ability to provide a robust and stable solution. The method consistently provides better results than the well-known Hitschfeld–Bordan algorithm.
Abstract
Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.
Abstract
Dynamical forecasting systems are being used to skillfully predict deterministic ice-free and freeze-up date events in the Arctic. This paper extends such forecasts to a probabilistic framework and tests two calibration models to correct systematic biases and improve the statistical reliability of the event dates: trend-adjusted quantile mapping (TAQM) and nonhomogeneous censored Gaussian regression (NCGR). TAQM is a probability distribution mapping method that corrects the forecast for climatological biases, whereas NCGR relates the calibrated parametric forecast distribution to the raw ensemble forecast through a regression model framework. For NCGR, the observed event trend and ensemble-mean event date are used to predict the central tendency of the predictive distribution. For modeling forecast uncertainty, we find that the ensemble-mean event date, which is related to forecast lead time, performs better than the ensemble variance itself. Using a multidecadal hindcast record from the Canadian Seasonal to Interannual Prediction System (CanSIPS), TAQM and NCGR are applied to produce categorical forecasts quantifying the probabilities for early, normal, and late ice retreat and advance. While TAQM performs better than adjusting the raw forecast for mean and linear trend bias, NCGR is shown to outperform TAQM in terms of reliability, skill, and an improved tendency for forecast probabilities to be no worse than climatology. Testing various cross-validation setups, we find that NCGR remains useful when shorter hindcast records (~20 years) are available. By applying NCGR to operational forecasts, stakeholders can be more confident in using seasonal forecasts of sea ice event timing for planning purposes.
Abstract
The east coast of Australia is a region of the world where a particular type of extratropical cyclone, known locally as an east coast low, frequently occurs with severe consequences such as extreme rainfall, winds, and waves. The likelihood of formation of these storms is examined using an upper-tropospheric diagnostic applied to three reanalyses and three global climate models (GCMs). Strong similarities exist among the results derived from the individual reanalyses in terms of their seasonal variability (e.g., winter maxima and summer minima) and interannual variability. Results from reanalyses indicate that the threshold value used in the diagnostic method is dependent on the spatial resolution. Results obtained when applying the diagnostic to two of the three GCMs are similar to expectations given their spatial resolutions, and produce seasonal cycles similar to those from the reanalyses. Applying the methodology to simulations from these two GCMs for both current and future climate in response to increases in greenhouse gases indicates a reduction in extratropical cyclone occurrence of about 30% from the late twentieth century to the late twenty-first century for eastern Australia. In addition to the absolute risk of formation of these extratropical cyclones, spatial climatologies of occurrence are examined for the broader region surrounding eastern Australia. The influence of large-scale modes of atmospheric and oceanic variability on the occurrence of these storms in this region is also discussed.
Abstract
The east coast of Australia is a region of the world where a particular type of extratropical cyclone, known locally as an east coast low, frequently occurs with severe consequences such as extreme rainfall, winds, and waves. The likelihood of formation of these storms is examined using an upper-tropospheric diagnostic applied to three reanalyses and three global climate models (GCMs). Strong similarities exist among the results derived from the individual reanalyses in terms of their seasonal variability (e.g., winter maxima and summer minima) and interannual variability. Results from reanalyses indicate that the threshold value used in the diagnostic method is dependent on the spatial resolution. Results obtained when applying the diagnostic to two of the three GCMs are similar to expectations given their spatial resolutions, and produce seasonal cycles similar to those from the reanalyses. Applying the methodology to simulations from these two GCMs for both current and future climate in response to increases in greenhouse gases indicates a reduction in extratropical cyclone occurrence of about 30% from the late twentieth century to the late twenty-first century for eastern Australia. In addition to the absolute risk of formation of these extratropical cyclones, spatial climatologies of occurrence are examined for the broader region surrounding eastern Australia. The influence of large-scale modes of atmospheric and oceanic variability on the occurrence of these storms in this region is also discussed.
Abstract
Optical depth of Saharan dust derived from photometric measurements made during the dry season at a Sahelian site (Niamey, Republic of Niger) is compared with METEOSAT-2 radiance in the 10.5–12.5 μm channel for different times of the daily cycle. The ability of retrieving dual optical depth using the outgoing radiance of infrared atmospheric window is clearly demonstrated for the middle of the day. Results obtained with nighttime data through a relation between dust optical depth and visibility are also discussed. The major causes of error are identified and quantitatively estimated.
Abstract
Optical depth of Saharan dust derived from photometric measurements made during the dry season at a Sahelian site (Niamey, Republic of Niger) is compared with METEOSAT-2 radiance in the 10.5–12.5 μm channel for different times of the daily cycle. The ability of retrieving dual optical depth using the outgoing radiance of infrared atmospheric window is clearly demonstrated for the middle of the day. Results obtained with nighttime data through a relation between dust optical depth and visibility are also discussed. The major causes of error are identified and quantitatively estimated.
Abstract
The method used to estimate the unfiltered longwave broadband radiance from the filtered radiances measured by the Geostationary Earth Radiation Budget (GERB) instrument is presented. This unfiltering method is used to generate the first released edition of the GERB-2 dataset. This method involves a set of regressions between the unfiltering factor (i.e., the ratio of the unfiltered and filtered broadband radiances) and the narrowband observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The regressions are theoretically derived from a large database of simulated spectral radiance curves obtained by radiative transfer computations. The generation of this database is fully described.
Different sources of error that may affect the GERB unfiltering have been identified and the associated error magnitudes are assessed on the database. For most of the earth–atmosphere conditions, the error introduced during the unfiltering processes is well under 0.5% (RMS error of about 0.1%). For more confidence, the unfiltered radiances of GERB-2 are validated by cross comparison with collocated and coangular Clouds and the Earth’s Radiant Energy System (CERES) observations. The agreement between the unfiltered radiances is within the science goals (1% accuracy for GERB and 0.5% for CERES) for the Flight Model 2 (FM2). For the CERES Flight Model 3 (FM3) instrument, an overall difference of 1.8% is observed. The intercomparisons indicate some scene-type dependency, which is due to the unfiltering for the cloudy scenes. This should be corrected for subsequent editions of the database.
Abstract
The method used to estimate the unfiltered longwave broadband radiance from the filtered radiances measured by the Geostationary Earth Radiation Budget (GERB) instrument is presented. This unfiltering method is used to generate the first released edition of the GERB-2 dataset. This method involves a set of regressions between the unfiltering factor (i.e., the ratio of the unfiltered and filtered broadband radiances) and the narrowband observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The regressions are theoretically derived from a large database of simulated spectral radiance curves obtained by radiative transfer computations. The generation of this database is fully described.
Different sources of error that may affect the GERB unfiltering have been identified and the associated error magnitudes are assessed on the database. For most of the earth–atmosphere conditions, the error introduced during the unfiltering processes is well under 0.5% (RMS error of about 0.1%). For more confidence, the unfiltered radiances of GERB-2 are validated by cross comparison with collocated and coangular Clouds and the Earth’s Radiant Energy System (CERES) observations. The agreement between the unfiltered radiances is within the science goals (1% accuracy for GERB and 0.5% for CERES) for the Flight Model 2 (FM2). For the CERES Flight Model 3 (FM3) instrument, an overall difference of 1.8% is observed. The intercomparisons indicate some scene-type dependency, which is due to the unfiltering for the cloudy scenes. This should be corrected for subsequent editions of the database.
Abstract
The method used to estimate the unfiltered shortwave broadband radiance from the filtered radiances measured by the Geostationary Earth Radiation Budget (GERB) instrument is presented. This unfiltering method is used to generate the first released edition of the GERB-2 dataset. The method involves a set of regressions between the unfiltering factor (i.e., the ratio of the unfiltered and filtered broadband radiances) and the narrowband observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The regressions are theoretically derived from a large database of simulated spectral radiance curves obtained by radiative transfer computations. The generation of the database is fully described.
Different sources of error that may affect the GERB unfiltering have been identified and the associated error magnitudes are assessed on this database. For most of the earth–atmosphere conditions, the error introduced during the unfiltering process is below 1%. In some conditions (e.g., low sun elevation above the horizon) the error can present a higher relative value, but the absolute error value remains well under the accuracy goal of 1% of the full instrument scale (2.4 W m−2 sr−1).
To increase the confidence level, the edition 1 unfiltered radiances of GERB-2 are validated by cross comparison with collocated and coangular Clouds and the Earth’s Radiant Energy System (CERES) observations for different scene types. In addition to an overall offset between the two instruments, the intercomparisons indicate a scene-type dependency up to 4% in unfiltered radiance. Further studies are required to confirm the cause, but an insufficiently accurate characterization of the shortwave spectral response of the GERB instrument in the visible part of the spectrum is one area under further investigation.
Abstract
The method used to estimate the unfiltered shortwave broadband radiance from the filtered radiances measured by the Geostationary Earth Radiation Budget (GERB) instrument is presented. This unfiltering method is used to generate the first released edition of the GERB-2 dataset. The method involves a set of regressions between the unfiltering factor (i.e., the ratio of the unfiltered and filtered broadband radiances) and the narrowband observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument. The regressions are theoretically derived from a large database of simulated spectral radiance curves obtained by radiative transfer computations. The generation of the database is fully described.
Different sources of error that may affect the GERB unfiltering have been identified and the associated error magnitudes are assessed on this database. For most of the earth–atmosphere conditions, the error introduced during the unfiltering process is below 1%. In some conditions (e.g., low sun elevation above the horizon) the error can present a higher relative value, but the absolute error value remains well under the accuracy goal of 1% of the full instrument scale (2.4 W m−2 sr−1).
To increase the confidence level, the edition 1 unfiltered radiances of GERB-2 are validated by cross comparison with collocated and coangular Clouds and the Earth’s Radiant Energy System (CERES) observations for different scene types. In addition to an overall offset between the two instruments, the intercomparisons indicate a scene-type dependency up to 4% in unfiltered radiance. Further studies are required to confirm the cause, but an insufficiently accurate characterization of the shortwave spectral response of the GERB instrument in the visible part of the spectrum is one area under further investigation.
Climate has been recognized to have direct and indirect impact on society and economy, both in the long term and daily life. The challenge of understanding the climate system, with its variability and changes, is enormous and requires a joint long-term international commitment from research and governmental institutions. An important international body to coordinate worldwide climate monitoring efforts is the World Meteorological Organization (WMO). The Global Climate Observing System (GCOS) has the mission to provide coordination and the requirements for global observations and essential climate variables (ECVs) to monitor climate changes. The WMO-led activity on Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) is responding to these requirements by ensuring a continuous and sustained generation of climate data records (CDRs) from satellite data in compliance with the principles and guidelines of GCOS. SCOPE-CM represents a new partnership between operational space agencies to coordinate the generation of CDRs. To this end, pilot projects for different ECVs, such as surface albedo, cloud properties, water vapor, atmospheric motion winds, and upper-tropospheric humidity, have been initiated. The coordinated activity on land surface albedo involves the operational meteorological satellite agencies in Europe [European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], in Japan [the Japan Meteorological Agency (JMA)], and in the United States [National Oceanic and Atmospheric Administration (NOAA)]. This paper presents the first results toward the generation of a unique land surface albedo CDR, involving five different geostationary satellite positions and approximately three decades of data starting in the 1980s, and combining close to 30 different satellite instruments.
Climate has been recognized to have direct and indirect impact on society and economy, both in the long term and daily life. The challenge of understanding the climate system, with its variability and changes, is enormous and requires a joint long-term international commitment from research and governmental institutions. An important international body to coordinate worldwide climate monitoring efforts is the World Meteorological Organization (WMO). The Global Climate Observing System (GCOS) has the mission to provide coordination and the requirements for global observations and essential climate variables (ECVs) to monitor climate changes. The WMO-led activity on Sustained, Coordinated Processing of Environmental Satellite Data for Climate Monitoring (SCOPE-CM) is responding to these requirements by ensuring a continuous and sustained generation of climate data records (CDRs) from satellite data in compliance with the principles and guidelines of GCOS. SCOPE-CM represents a new partnership between operational space agencies to coordinate the generation of CDRs. To this end, pilot projects for different ECVs, such as surface albedo, cloud properties, water vapor, atmospheric motion winds, and upper-tropospheric humidity, have been initiated. The coordinated activity on land surface albedo involves the operational meteorological satellite agencies in Europe [European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], in Japan [the Japan Meteorological Agency (JMA)], and in the United States [National Oceanic and Atmospheric Administration (NOAA)]. This paper presents the first results toward the generation of a unique land surface albedo CDR, involving five different geostationary satellite positions and approximately three decades of data starting in the 1980s, and combining close to 30 different satellite instruments.
