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Abstract
Comparisons are made between analyses and forecasts which incorporate VAS geopotential data as either scalar or horizontal gradient information for a mw study on the AVE/VAS day of 6 March 1982. On this day, incorporating the VAS information in analysis as a variational constraint on horizontal geopotential gradients significantly mitigated the effects of large data biases which made VAS assimilation by standard scalar methods very difficult. A subsequent forecast made from the gradient assimilation was superior to one made from the standard analysis and of comparable quality in geopotentials to a control forecast from synoptic data. Most impact was noted in the forecasts of vertical motion and precipitation in the gradient versus this control simulation.
Abstract
Comparisons are made between analyses and forecasts which incorporate VAS geopotential data as either scalar or horizontal gradient information for a mw study on the AVE/VAS day of 6 March 1982. On this day, incorporating the VAS information in analysis as a variational constraint on horizontal geopotential gradients significantly mitigated the effects of large data biases which made VAS assimilation by standard scalar methods very difficult. A subsequent forecast made from the gradient assimilation was superior to one made from the standard analysis and of comparable quality in geopotentials to a control forecast from synoptic data. Most impact was noted in the forecasts of vertical motion and precipitation in the gradient versus this control simulation.
Abstract
The effective cloud fraction evaluated using a minimum residual method and microwave data synthesized for the Advanced Microwave Sounding Unit (AMSU) has been extended by a simple statistical procedure to make estimates of the column cloud water amount for nonprecipitating clouds. In this simulation study of the channel pairs investigated, the highest overall skill in the estimation of column cloud water amounts is shown by the 52–54-GHz oxygen channel pair 3 and 4 of the AMSU over a water surface, where the low surface emissivity produces a very strong cloud water signature (warming of the clear scene). As the surface emissivity rises to representative values for land surfaces, the skill of estimation of cloud water amounts is reduced. At a surface emissivity value representative of a dry land surface, estimation of cloud liquid water from the oxygen channels and these techniques is not possible. In contrast to the oxygen channels, the quality of cloud water estimates made using the 183-GHz water vapor channel pair 19 and 20 of the AMSU is poorest for a water surface and higher for land surfaces with higher emissivity. The quality of the estimates made using thew water vapor channels is shown to strongly depend on the quality of the atmospheric guess used in the procedures.
Abstract
The effective cloud fraction evaluated using a minimum residual method and microwave data synthesized for the Advanced Microwave Sounding Unit (AMSU) has been extended by a simple statistical procedure to make estimates of the column cloud water amount for nonprecipitating clouds. In this simulation study of the channel pairs investigated, the highest overall skill in the estimation of column cloud water amounts is shown by the 52–54-GHz oxygen channel pair 3 and 4 of the AMSU over a water surface, where the low surface emissivity produces a very strong cloud water signature (warming of the clear scene). As the surface emissivity rises to representative values for land surfaces, the skill of estimation of cloud water amounts is reduced. At a surface emissivity value representative of a dry land surface, estimation of cloud liquid water from the oxygen channels and these techniques is not possible. In contrast to the oxygen channels, the quality of cloud water estimates made using the 183-GHz water vapor channel pair 19 and 20 of the AMSU is poorest for a water surface and higher for land surfaces with higher emissivity. The quality of the estimates made using thew water vapor channels is shown to strongly depend on the quality of the atmospheric guess used in the procedures.
Abstract
The simple physical model to estimate surface insolation from GOES data (described in Gautier et al., 1980) has been improved through some modifications to existing physics (Rayleigh scattering and water vapor absorption), and also the inclusion of ozone absorption, previously neglected. An empirical correction for clouds smaller than the GOES sensor field-of-view has also been introduced. The resulting model is more physically realistic than the old one and requires no additional computer time. Tests indicate that with minimal tuning, the error in daily insulation estimates has been reduced by ∼1% compared to the old model. Removal of systematic error resulted in an additional 0.2% improvement.
Abstract
The simple physical model to estimate surface insolation from GOES data (described in Gautier et al., 1980) has been improved through some modifications to existing physics (Rayleigh scattering and water vapor absorption), and also the inclusion of ozone absorption, previously neglected. An empirical correction for clouds smaller than the GOES sensor field-of-view has also been introduced. The resulting model is more physically realistic than the old one and requires no additional computer time. Tests indicate that with minimal tuning, the error in daily insulation estimates has been reduced by ∼1% compared to the old model. Removal of systematic error resulted in an additional 0.2% improvement.
Abstract
An investigation of the effects of spatially averaging brightness measurements from a geostationary satellite on the calculation of insolation is presented. The calculation of insolation from calibrated hourly visible GOES satellite data is based on the model described by Gautier and others.
A series of experiments are reported in which averaging over scales varying by a factor of 8 to 64 from the initial resolution as well as averaging within the model itself have been performed. Results from these experiments have been intercompared and compared with surface measurements. They indicate that spatially averaged daily insolation can be estimated from mean hourly brightness measurements at an eight-times reduced resolution. Furthermore, the mean insolation is not sensitive to averaging of the physical processes up to the largest spatial averaging of 64.
This result is important, since it may allow a sizable reduction in the data processing necessary to obtain accurate estimates of insolation for various climate studies.
Abstract
An investigation of the effects of spatially averaging brightness measurements from a geostationary satellite on the calculation of insolation is presented. The calculation of insolation from calibrated hourly visible GOES satellite data is based on the model described by Gautier and others.
A series of experiments are reported in which averaging over scales varying by a factor of 8 to 64 from the initial resolution as well as averaging within the model itself have been performed. Results from these experiments have been intercompared and compared with surface measurements. They indicate that spatially averaged daily insolation can be estimated from mean hourly brightness measurements at an eight-times reduced resolution. Furthermore, the mean insolation is not sensitive to averaging of the physical processes up to the largest spatial averaging of 64.
This result is important, since it may allow a sizable reduction in the data processing necessary to obtain accurate estimates of insolation for various climate studies.
Abstract
A new microwave algorithm, analogous to the infrared “radiance-ratioing” method (Eyre and Menzel 1989) is developed to retrieve the height and “effective” fraction (defined as the product of the emissivity times the actual physical fractional coverage) of nonprecipitating water clouds using various pairs of the 20 microwave channels planned for the Advanced Microwave Sounding Unit (AMSU), an instrument slated to fly on polar-orbiting satellites beginning in 1994. The results of a simulation study are presented to provide some insights into the potentials of this technique using different AMSU channel combinations. This study suggests that the use of the oxygen channels 3 and 5 and water vapor channels 19 and 20 will produce the most accurate retrievals of liquid water cloud parameters and the highest percentage of good-quality retrievals over a range of meteorological and cloud conditions. The use of channels 1, 2, 16, and 17, which all may have a strong surface component in their measured brightness temperature, does not give optimal results chiefly because the large uncertainties in the microwave surface temperature and emissivity obscure the brightness–temperature signatures of cloud liquid water. As with the infrared radiance ratioing method (and similar C02 slicing techniques), the best retrieval of cloud parameters is for high cloud, with poorer results for those at middle and low levels.
Abstract
A new microwave algorithm, analogous to the infrared “radiance-ratioing” method (Eyre and Menzel 1989) is developed to retrieve the height and “effective” fraction (defined as the product of the emissivity times the actual physical fractional coverage) of nonprecipitating water clouds using various pairs of the 20 microwave channels planned for the Advanced Microwave Sounding Unit (AMSU), an instrument slated to fly on polar-orbiting satellites beginning in 1994. The results of a simulation study are presented to provide some insights into the potentials of this technique using different AMSU channel combinations. This study suggests that the use of the oxygen channels 3 and 5 and water vapor channels 19 and 20 will produce the most accurate retrievals of liquid water cloud parameters and the highest percentage of good-quality retrievals over a range of meteorological and cloud conditions. The use of channels 1, 2, 16, and 17, which all may have a strong surface component in their measured brightness temperature, does not give optimal results chiefly because the large uncertainties in the microwave surface temperature and emissivity obscure the brightness–temperature signatures of cloud liquid water. As with the infrared radiance ratioing method (and similar C02 slicing techniques), the best retrieval of cloud parameters is for high cloud, with poorer results for those at middle and low levels.
Abstract
We present a model designed to estimate the incident solar radiation at the suface from GOES satellite brightness measurements in clear and cloudy conditions. In this simple physical model, the effect of Rayleigh scattering is taken into account. Water vapor absorption is also introduced by means of its climatological effects on shortwave radiation in southern Canada, but the main emphasis is on cloud effects. Cloud albedo and absorption are derived from brightness measurements on the assumption that they both are linearly related to the brightness. This simple treatment, however, applied to individual picture elements represents quite accurately the bulk effect of clouds, as illustrated by our results. Comparisons with daily insolation measurements from three pyranometers located in Toronto, Montreal and Ottawa in spring and summer 1978 showed that the satellite estimates were, on the average, within 9% of the ground measurements for a large variety of cloud conditions. The hourly variations monitored by the satellite also followed very closely the variations measured on the ground. This study has shown that a simple model is sufficient for the determination of the incident solar radiation when the high spatial and temporal coverage of a geostationary satellite is used.
Abstract
We present a model designed to estimate the incident solar radiation at the suface from GOES satellite brightness measurements in clear and cloudy conditions. In this simple physical model, the effect of Rayleigh scattering is taken into account. Water vapor absorption is also introduced by means of its climatological effects on shortwave radiation in southern Canada, but the main emphasis is on cloud effects. Cloud albedo and absorption are derived from brightness measurements on the assumption that they both are linearly related to the brightness. This simple treatment, however, applied to individual picture elements represents quite accurately the bulk effect of clouds, as illustrated by our results. Comparisons with daily insolation measurements from three pyranometers located in Toronto, Montreal and Ottawa in spring and summer 1978 showed that the satellite estimates were, on the average, within 9% of the ground measurements for a large variety of cloud conditions. The hourly variations monitored by the satellite also followed very closely the variations measured on the ground. This study has shown that a simple model is sufficient for the determination of the incident solar radiation when the high spatial and temporal coverage of a geostationary satellite is used.
Abstract
The effect of variations in surface parameters on 24-hour limited area forecasts has been examined on a day in July 1981. The vehicle for the study is a ten-level primitive equation model covering most of the continental United States. Variations in moisture availability, surface roughness and soil flux treatment generally do not produce large differences in 24-hour forecasts of primary variables, except in the extreme cases. Precipitation totals, however, are surprisingly sensitive to the surface treatment in several areas where significant amounts of precipitation are evidenced in a control forecast. In these areas, modulations of 10–30% of the control amount due to surface changes are common. In the areas exhibiting principally large-scale precipitation, evidence points to modulations in evaporation as the cause for the precipitation differences. Areas of principally convective precipitation exhibit differences which can be attributed to model-calculated changes in low-level moisture convergence patterns between forecasts.
Diurnal surface temperature range measured by the VAS instrument on GOUES-3 is compared to those generated in model experiments over the model grid. Good agreement is found in arm with moderate to large moisture availability and roughness heights. Poor agreement is evidenced in the western United States where the daytime surface temperature and flux balance is shown to be critically sensitive to small errors in the moisture availability.
Abstract
The effect of variations in surface parameters on 24-hour limited area forecasts has been examined on a day in July 1981. The vehicle for the study is a ten-level primitive equation model covering most of the continental United States. Variations in moisture availability, surface roughness and soil flux treatment generally do not produce large differences in 24-hour forecasts of primary variables, except in the extreme cases. Precipitation totals, however, are surprisingly sensitive to the surface treatment in several areas where significant amounts of precipitation are evidenced in a control forecast. In these areas, modulations of 10–30% of the control amount due to surface changes are common. In the areas exhibiting principally large-scale precipitation, evidence points to modulations in evaporation as the cause for the precipitation differences. Areas of principally convective precipitation exhibit differences which can be attributed to model-calculated changes in low-level moisture convergence patterns between forecasts.
Diurnal surface temperature range measured by the VAS instrument on GOUES-3 is compared to those generated in model experiments over the model grid. Good agreement is found in arm with moderate to large moisture availability and roughness heights. Poor agreement is evidenced in the western United States where the daytime surface temperature and flux balance is shown to be critically sensitive to small errors in the moisture availability.
The Advanced Microwave Sounding Unit (AMSU) is a 20-channel microwave satellite remote-sensing system comprising two separate radiometers, which is slated to be launched on NOAA polar-orbiting satellites beginning in 1994. With the AMSU will come improvements in remote sounding of the atmosphere and estimation of atmospheric and surface physical characteristics (rain and cloud water, snow and ice cover, etc.) gained through application of the dramatic improvements in microwave technology that have taken place in the past 20 years. In this work we describe prelaunch work designed to evaluate the remote-sensing information that will be available with the launch of the AMSU.
The Advanced Microwave Sounding Unit (AMSU) is a 20-channel microwave satellite remote-sensing system comprising two separate radiometers, which is slated to be launched on NOAA polar-orbiting satellites beginning in 1994. With the AMSU will come improvements in remote sounding of the atmosphere and estimation of atmospheric and surface physical characteristics (rain and cloud water, snow and ice cover, etc.) gained through application of the dramatic improvements in microwave technology that have taken place in the past 20 years. In this work we describe prelaunch work designed to evaluate the remote-sensing information that will be available with the launch of the AMSU.
Abstract
A simple model of energy exchange between the land surface and the atmospheric boundary layer, driven by input that can be derived primarily through remote sensing, is described and applied over continental scales at a horizontal resolution of 10 km. Surface flux partitioning into sensible and latent heating is guided by time changes in land surface brightness temperatures, which can be measured from a geostationary satellite platform such as the Geostationary Operational Environmental Satellite. Other important inputs, including vegetation cover and type, can be derived using the Normalized Difference Vegetation Index in combination with vegetation and land use information. Previous studies have shown that this model performs well on small spatial scales, in comparison with surface flux measurements acquired during several field experiments. However, because the model requires only a modicum of surface-based measurements and is designed to be computationally efficient, it is particularly well suited for regional- or continental-scale applications. The input data assembly process for regional-scale applications is outlined. Model flux estimates for the central United States are compared with climatological moisture and vegetation patterns, as well as with surface-based flux measurements acquired during the Southern Great Plains (SGP-97) Hydrology Experiment. These comparisons are quite promising.
Abstract
A simple model of energy exchange between the land surface and the atmospheric boundary layer, driven by input that can be derived primarily through remote sensing, is described and applied over continental scales at a horizontal resolution of 10 km. Surface flux partitioning into sensible and latent heating is guided by time changes in land surface brightness temperatures, which can be measured from a geostationary satellite platform such as the Geostationary Operational Environmental Satellite. Other important inputs, including vegetation cover and type, can be derived using the Normalized Difference Vegetation Index in combination with vegetation and land use information. Previous studies have shown that this model performs well on small spatial scales, in comparison with surface flux measurements acquired during several field experiments. However, because the model requires only a modicum of surface-based measurements and is designed to be computationally efficient, it is particularly well suited for regional- or continental-scale applications. The input data assembly process for regional-scale applications is outlined. Model flux estimates for the central United States are compared with climatological moisture and vegetation patterns, as well as with surface-based flux measurements acquired during the Southern Great Plains (SGP-97) Hydrology Experiment. These comparisons are quite promising.
Abstract
Reliable procedures that accurately map surface insolation over large domains at high spatial and temporal resolution are a great benefit for making the predictions of potential and actual evapotranspiration that are required by a variety of hydrological and agricultural applications. Here, estimates of hourly and daily integrated insolation at 20-km resolution, based on Geostationary Operational Environmental Satellite (GOES) visible imagery are compared to pyranometer measurements made at 11 sites in the U.S. Climate Reference Network (USCRN) over a continuous 15-month period. Such a comprehensive survey is necessary in order to examine the accuracy of the satellite insolation estimates over a diverse range of seasons and land surface types. The relatively simple physical model of insolation that is tested here yields good results, with seasonally averaged model errors of 62 (19%) and 15 (10%) W m−2 for hourly and daily-averaged insolation, respectively, including both clear- and cloudy-sky conditions. This level of accuracy is comparable, or superior, to results that have been obtained with more complex models of atmospheric radiative transfer. Model performance can be improved in the future by addressing a small elevation-related bias in the physical model, which is likely the result of inaccurate model precipitable water inputs or cloud-height assessments.
Abstract
Reliable procedures that accurately map surface insolation over large domains at high spatial and temporal resolution are a great benefit for making the predictions of potential and actual evapotranspiration that are required by a variety of hydrological and agricultural applications. Here, estimates of hourly and daily integrated insolation at 20-km resolution, based on Geostationary Operational Environmental Satellite (GOES) visible imagery are compared to pyranometer measurements made at 11 sites in the U.S. Climate Reference Network (USCRN) over a continuous 15-month period. Such a comprehensive survey is necessary in order to examine the accuracy of the satellite insolation estimates over a diverse range of seasons and land surface types. The relatively simple physical model of insolation that is tested here yields good results, with seasonally averaged model errors of 62 (19%) and 15 (10%) W m−2 for hourly and daily-averaged insolation, respectively, including both clear- and cloudy-sky conditions. This level of accuracy is comparable, or superior, to results that have been obtained with more complex models of atmospheric radiative transfer. Model performance can be improved in the future by addressing a small elevation-related bias in the physical model, which is likely the result of inaccurate model precipitable water inputs or cloud-height assessments.
