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Abstract
During the last few years, the feasibility of deriving surface radiation budget (SRB) components from satellite observations has been demonstrated and a better understanding of the need for SRB information in climate research was formulated. Much attention has been given to the scales at which such information is needed and to the accuracies required at different spatial and temporal scales. Recently, global acts of satellite observations became available, allowing implementation of satellite models for SRB on a global scale, and international frameworks were established for validating such models. To respond to these developments, we modified and expanded an early version of a physical model to derive surface solar irradiance from satellite observations. The model is based on radiative transfer theory, and can produce both direct and diffuse spectral components in the 0.2–4.0-μm interval. Attention is given to the absorption and scattering processes in the atmosphere and the interaction of radiation with the surface. The bidirectional nature of the exiting radiation at the top of the atmosphere is also accounted for. In this paper the emphasis will be on describing the current status of the model and its implementation on a global scale with the International Satellite Cloud Climatology Project (ISCCP) C1 data.
Abstract
During the last few years, the feasibility of deriving surface radiation budget (SRB) components from satellite observations has been demonstrated and a better understanding of the need for SRB information in climate research was formulated. Much attention has been given to the scales at which such information is needed and to the accuracies required at different spatial and temporal scales. Recently, global acts of satellite observations became available, allowing implementation of satellite models for SRB on a global scale, and international frameworks were established for validating such models. To respond to these developments, we modified and expanded an early version of a physical model to derive surface solar irradiance from satellite observations. The model is based on radiative transfer theory, and can produce both direct and diffuse spectral components in the 0.2–4.0-μm interval. Attention is given to the absorption and scattering processes in the atmosphere and the interaction of radiation with the surface. The bidirectional nature of the exiting radiation at the top of the atmosphere is also accounted for. In this paper the emphasis will be on describing the current status of the model and its implementation on a global scale with the International Satellite Cloud Climatology Project (ISCCP) C1 data.
Abstract
The usefulness of satellites in climate research is primarily due to the ability to produce global, uniformly distributed, long term records of observations. To achieve efficiency in storing, there is a need to compromise on the spatial and temporal resolution of the data. Questions arise about the impact of the reduced resolution on the parameters to be derived. In this study the effect of different spatial sampling of satellite observations on retrieved surface solar irradiance (SW1) was studied. Our results indicate that sampled (8-km resolution) andareally averaged (50-km resolution) visible brightness is highly correlated; the correlation has a regional, seasonal,and diurnal dependence. Using the two different resolutions of satellite observations, SW1 was computed for awhole annual cycle. On the average, the results differed by about 8%-9%. Therefore, to validate satellite methodsagainst ground truth to an accuracy which exceeds 8%-9% of the mean, attention should be given to the typeof satellite data and ground truth used in the validation process. The scales selected for investigation are ofinterest to the International Satellite Goud Climatology Project (ISCCP) B3 sampling.
Abstract
The usefulness of satellites in climate research is primarily due to the ability to produce global, uniformly distributed, long term records of observations. To achieve efficiency in storing, there is a need to compromise on the spatial and temporal resolution of the data. Questions arise about the impact of the reduced resolution on the parameters to be derived. In this study the effect of different spatial sampling of satellite observations on retrieved surface solar irradiance (SW1) was studied. Our results indicate that sampled (8-km resolution) andareally averaged (50-km resolution) visible brightness is highly correlated; the correlation has a regional, seasonal,and diurnal dependence. Using the two different resolutions of satellite observations, SW1 was computed for awhole annual cycle. On the average, the results differed by about 8%-9%. Therefore, to validate satellite methodsagainst ground truth to an accuracy which exceeds 8%-9% of the mean, attention should be given to the typeof satellite data and ground truth used in the validation process. The scales selected for investigation are ofinterest to the International Satellite Goud Climatology Project (ISCCP) B3 sampling.
Abstract
Concern about possible effects of a steady increase in CO2 on the earth's climate, and the fact that current estimates of sources and sinks of CO2 do not balance, generated interest to improve knowledge of rates at which carbon is cycled between the oceans, land, and atmosphere. The net primary productivity (NPP)—namely, the rate at which inorganic carbon is transformed into organic matter—is strongly controlled by the availability and intensity of photosynthetically active radiation (PAR); the distribution of photoactive pigments; the efficiency with which the light is absorbed; and the efficiency of its conversion into organic matter. In this study the feasibility to derive one of the above parameters is demonstrated—namely, PAR on a global scale. In the past, information on PAR was obtained from local ground measurements in the 0.4−0.7-µm spectral interval. In the absence of such measurements, PAR was estimated from measured total solar irradiance, using empirical “conversion factors.” It is demonstrated that this important bigeophysical parameter can now be derived from satellite observations. The inference model is implemented with global satellite data that are available ftom the International Satellite Cloud Climatology Project (ISCCP) to produce for the fire time global fields of PAR and corresponding “conversion factors.”
Abstract
Concern about possible effects of a steady increase in CO2 on the earth's climate, and the fact that current estimates of sources and sinks of CO2 do not balance, generated interest to improve knowledge of rates at which carbon is cycled between the oceans, land, and atmosphere. The net primary productivity (NPP)—namely, the rate at which inorganic carbon is transformed into organic matter—is strongly controlled by the availability and intensity of photosynthetically active radiation (PAR); the distribution of photoactive pigments; the efficiency with which the light is absorbed; and the efficiency of its conversion into organic matter. In this study the feasibility to derive one of the above parameters is demonstrated—namely, PAR on a global scale. In the past, information on PAR was obtained from local ground measurements in the 0.4−0.7-µm spectral interval. In the absence of such measurements, PAR was estimated from measured total solar irradiance, using empirical “conversion factors.” It is demonstrated that this important bigeophysical parameter can now be derived from satellite observations. The inference model is implemented with global satellite data that are available ftom the International Satellite Cloud Climatology Project (ISCCP) to produce for the fire time global fields of PAR and corresponding “conversion factors.”
Abstract
Surface solar irradiance (SW↓) was derived over the extended Amazon Basin using AVHRR observations from polar-orbiting satellites during four July months (1983–1986). Observations from the geostationary satellite GOES for July 1983 were also used to assess diurnal effects. Both satellite datasets are part of the Satellite Cloud Climatology Project (ISCCP) B3 product. It was demonstrated that it is now possible to derive long-term surface SW↓, which can be useful in climate studies, and that the accuracy of the derived fields is sufficient to detect interannual differences that can exceed at times 70 W m−2. The variability of the daily totals of SW↓ from the monthly means was similar during three of the four years investigated, yet, during the El Niño year of 1982–83, north of 10°N such variability increased drastically. This increase could be attributed to a changed pattern of convective activity as a result of higher SST off the coast of Peru. For the first time, the El Niño influence on the large-scale variability of the SW↓ was demonstrated.
Abstract
Surface solar irradiance (SW↓) was derived over the extended Amazon Basin using AVHRR observations from polar-orbiting satellites during four July months (1983–1986). Observations from the geostationary satellite GOES for July 1983 were also used to assess diurnal effects. Both satellite datasets are part of the Satellite Cloud Climatology Project (ISCCP) B3 product. It was demonstrated that it is now possible to derive long-term surface SW↓, which can be useful in climate studies, and that the accuracy of the derived fields is sufficient to detect interannual differences that can exceed at times 70 W m−2. The variability of the daily totals of SW↓ from the monthly means was similar during three of the four years investigated, yet, during the El Niño year of 1982–83, north of 10°N such variability increased drastically. This increase could be attributed to a changed pattern of convective activity as a result of higher SST off the coast of Peru. For the first time, the El Niño influence on the large-scale variability of the SW↓ was demonstrated.
Abstract
NOAA’s operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service developmental (research mode) particulate matter (PM2.5) predictions tested during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period included long-range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States. Over the 30-day time period for which daytime hourly forecasts were compared with observations, the categorical (exceedance defined as AOD > 0.55) forecast accuracy was between 0% and 20%. Hourly normalized mean bias (forecasts − observations) ranged between −50% and +50% with forecasts being positively biased when observed AODs were small and negatively biased when observed AODs were high. Normalized mean errors are between 50% and 100% with the errors on the lower end during the 18–22 July 2004 time period when a regional-scale sulfate event occurred. Spatially, the errors are small over the regions where sulfate plumes were present. The correlation coefficient also showed similar features (spatially and temporally) with a peak value of ∼0.6 during the 18–22 July 2004 time period. The dominance of long-range transport of smoke into the United States during the summer of 2004, neglected in the model predictions, skewed the model forecast performance. Enhanced accuracy and reduced normalized mean errors during the time period when a sulfate event prevailed show that the forecast system has skill in predicting PM2.5 associated with urban/industrial pollution events.
Abstract
NOAA’s operational geostationary satellite retrievals of aerosol optical depths (AODs) were used to verify National Weather Service developmental (research mode) particulate matter (PM2.5) predictions tested during the summer 2004 International Consortium for Atmospheric Research on Transport and Transformation/New England Air Quality Study (ICARTT/NEAQS) field campaign. The forecast period included long-range transport of smoke from fires burning in Canada and Alaska and a regional-scale sulfate event over the Gulf of Mexico and the eastern United States. Over the 30-day time period for which daytime hourly forecasts were compared with observations, the categorical (exceedance defined as AOD > 0.55) forecast accuracy was between 0% and 20%. Hourly normalized mean bias (forecasts − observations) ranged between −50% and +50% with forecasts being positively biased when observed AODs were small and negatively biased when observed AODs were high. Normalized mean errors are between 50% and 100% with the errors on the lower end during the 18–22 July 2004 time period when a regional-scale sulfate event occurred. Spatially, the errors are small over the regions where sulfate plumes were present. The correlation coefficient also showed similar features (spatially and temporally) with a peak value of ∼0.6 during the 18–22 July 2004 time period. The dominance of long-range transport of smoke into the United States during the summer of 2004, neglected in the model predictions, skewed the model forecast performance. Enhanced accuracy and reduced normalized mean errors during the time period when a sulfate event prevailed show that the forecast system has skill in predicting PM2.5 associated with urban/industrial pollution events.
Shortwave radiative fluxes that reach the earth's surface are key factors that influence atmospheric and oceanic circulations as well as surface climate. Yet, information on these fluxes is meager. Surface site data are generally available from only a limited number of observing stations over land. Much less is known about the large-scale variability of the shortwave radiative fluxes over the oceans, which cover most of the globe. Recognizing the need to produce global-scale fields of such fluxes for use in climate research, the World Climate Research Program has initiated activities that led to the establishment of the Surface Radiation Budget Climatology Project with the ultimate goal to determine various components of the surface radiation budget from satellite data. In this paper, the first global products that resulted from this activity are described. Monthly and daily data on a 280-km grid scale are available. Samples of climate parameters obtainable from the dataset are presented. Emphasis is given to validation and limitations of the results. For most of the globe, satellite estimates have bias values between ±20 W m−2 and rms values are around 25 W m−2. There are specific regions with much larger uncertainties however.
Shortwave radiative fluxes that reach the earth's surface are key factors that influence atmospheric and oceanic circulations as well as surface climate. Yet, information on these fluxes is meager. Surface site data are generally available from only a limited number of observing stations over land. Much less is known about the large-scale variability of the shortwave radiative fluxes over the oceans, which cover most of the globe. Recognizing the need to produce global-scale fields of such fluxes for use in climate research, the World Climate Research Program has initiated activities that led to the establishment of the Surface Radiation Budget Climatology Project with the ultimate goal to determine various components of the surface radiation budget from satellite data. In this paper, the first global products that resulted from this activity are described. Monthly and daily data on a 280-km grid scale are available. Samples of climate parameters obtainable from the dataset are presented. Emphasis is given to validation and limitations of the results. For most of the globe, satellite estimates have bias values between ±20 W m−2 and rms values are around 25 W m−2. There are specific regions with much larger uncertainties however.
