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- Author or Editor: Curtis D. Mobley x
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Abstract
The problem of determining confidence intervals for climatic signals using data sets with spatial and temporal sampling inhomogeneities is solved by a four-step process. First, the actual data set is analysed to determine autoregressive models which are consistent with the actual data at daily, monthly and annual time scales. Second, these models are used to generate artificial, but realistic, data sets which reproduce selected statistical properties of the actual data. Third, these artificial data sets are sampled by Monte Carlo techniques to determine certain confidence interval coefficients appropriate to different fields, geographical regions, and averaging periods. Fourth, these confidence interval coefficients are used to place error bars on climatic signals derived from the actual data set. The technique is illustrated by the analysis of historical sea surface temperature and sea level pressure data in the eastern tropical Pacific Ocean.
Abstract
The problem of determining confidence intervals for climatic signals using data sets with spatial and temporal sampling inhomogeneities is solved by a four-step process. First, the actual data set is analysed to determine autoregressive models which are consistent with the actual data at daily, monthly and annual time scales. Second, these models are used to generate artificial, but realistic, data sets which reproduce selected statistical properties of the actual data. Third, these artificial data sets are sampled by Monte Carlo techniques to determine certain confidence interval coefficients appropriate to different fields, geographical regions, and averaging periods. Fourth, these confidence interval coefficients are used to place error bars on climatic signals derived from the actual data set. The technique is illustrated by the analysis of historical sea surface temperature and sea level pressure data in the eastern tropical Pacific Ocean.
Abstract
A nine year record (December 1973 to February 1983) of seasonal temperature and precipitation anomaly forecasts is examined. The four independent forecasters were J. Namias, the National Weather Service (D. Gilman and colleagues), the Analoger (T. Barnett and R. Preisendorfer), and A. Douglas. The skills of these human forecasters are compared to three benchmark forecasters, climatology, persistence and random chance, and to several simple objective forecasters.
It was found that the human forecasters are all of comparable skill, and that they are generally better than climatology or random chance. However, the humans are often no better than persistence or some of the objective forecasters. In general, temperature was predicted better than precipitation. For both temperature and precipitation, winters were most-well predicted and falls were least-well predicted. Temperature was best predicted in the Southwest Desert, Pacific Coast and Northern Plains, and worst predicted on the Gulf Coast, Atlantic Coast and Southern Plains. Precipitation was best predicted in the Southwest Desert, Great Lakes, and Northern Great Basin and worst predicted along the Gulf, Atlantic and Pacific Coasts.
Abstract
A nine year record (December 1973 to February 1983) of seasonal temperature and precipitation anomaly forecasts is examined. The four independent forecasters were J. Namias, the National Weather Service (D. Gilman and colleagues), the Analoger (T. Barnett and R. Preisendorfer), and A. Douglas. The skills of these human forecasters are compared to three benchmark forecasters, climatology, persistence and random chance, and to several simple objective forecasters.
It was found that the human forecasters are all of comparable skill, and that they are generally better than climatology or random chance. However, the humans are often no better than persistence or some of the objective forecasters. In general, temperature was predicted better than precipitation. For both temperature and precipitation, winters were most-well predicted and falls were least-well predicted. Temperature was best predicted in the Southwest Desert, Pacific Coast and Northern Plains, and worst predicted on the Gulf Coast, Atlantic Coast and Southern Plains. Precipitation was best predicted in the Southwest Desert, Great Lakes, and Northern Great Basin and worst predicted along the Gulf, Atlantic and Pacific Coasts.
Abstract
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Abstract
The downward albedo (irradiance reflectance) r − and the upward albedo r + of a random air–water surface, formed by capillary waves, are computed as a function of lighting conditions and wind speed by Monte Carlo means for incident unpolarized radiant flux. The possibility of multiple scattering of light rays and of ray-shielding of waves by other waves is included in the calculations. The effects on r ± of multiple scattering and wave shielding are found to be important for higher speeds (≳10 m s−1) and nearly horizontal light ray angles of incidence (≳70°). The Monte Carlo procedure is used to generate reflected and transmitted glitter patterns as functions of wind speed and sun position. These results are used to check the procedure's patterns against observed patterns. A simple analytic first-order model of glitter patterns and irradiance reflectance, which assumes a binormal distribution of water facet slopes, is tested against the relatively exact Monte Carlo results. Regions are defined in wind-speed and incident-angle space over which the first-order model is acceptable. Plots of the Monte Carlo r ± are drawn as functions of wind speed and angle of incidence of light rays. The albedos r ± are also found for various continuous radiance distribution simulating overcast skies and upwelling submarine light fields just below the air–water surface. Good agreement is found, were comparison can be made, between the computed albedos and albedos measured over the ocean surface.
Abstract
The downward albedo (irradiance reflectance) r − and the upward albedo r + of a random air–water surface, formed by capillary waves, are computed as a function of lighting conditions and wind speed by Monte Carlo means for incident unpolarized radiant flux. The possibility of multiple scattering of light rays and of ray-shielding of waves by other waves is included in the calculations. The effects on r ± of multiple scattering and wave shielding are found to be important for higher speeds (≳10 m s−1) and nearly horizontal light ray angles of incidence (≳70°). The Monte Carlo procedure is used to generate reflected and transmitted glitter patterns as functions of wind speed and sun position. These results are used to check the procedure's patterns against observed patterns. A simple analytic first-order model of glitter patterns and irradiance reflectance, which assumes a binormal distribution of water facet slopes, is tested against the relatively exact Monte Carlo results. Regions are defined in wind-speed and incident-angle space over which the first-order model is acceptable. Plots of the Monte Carlo r ± are drawn as functions of wind speed and angle of incidence of light rays. The albedos r ± are also found for various continuous radiance distribution simulating overcast skies and upwelling submarine light fields just below the air–water surface. Good agreement is found, were comparison can be made, between the computed albedos and albedos measured over the ocean surface.
Abstract
Radiative transfer calculations are used to quantify the effects of physical and biological processes on variations in the transmission of solar radiation through the upper ocean. Results indicate that net irradiance at 10 cm and 5 m can vary by 23 and 34 W m−2, respectively, due to changes in the chlorophyll concentration, cloud amount, and solar zenith angle (when normalized to a climatological surface irradiance of 200 W m−2). Chlorophyll influences solar attenuation in the visible wavebands, and thus has little effect on transmission within the uppermost meter where the quantity of near-infrared energy is substantial. Beneath the top few meters, a chlorophyll increase from 0.03 to 0.3 mg m−3 can result in a solar flux decrease of more than 10 W m−2. Clouds alter the spectral composition of the incident irradiance by preferentially attenuating in the near-infrared region, and serve to increase solar transmission in the upper few meters as a greater portion of the irradiance exists in the deep-penetrating, visible wavebands. A 50% reduction in the incident irradiance by clouds causes a near 60% reduction in the radiant heating rate for the top 10 cm of the ocean. Solar zenith angle influences transmission during clear sky periods through changes in sea-surface albedo. This study provides necessary information for improved physically and biologically based solar transmission parameterizations that will enhance upper ocean modeling efforts and sea-surface temperature prediction.
Abstract
Radiative transfer calculations are used to quantify the effects of physical and biological processes on variations in the transmission of solar radiation through the upper ocean. Results indicate that net irradiance at 10 cm and 5 m can vary by 23 and 34 W m−2, respectively, due to changes in the chlorophyll concentration, cloud amount, and solar zenith angle (when normalized to a climatological surface irradiance of 200 W m−2). Chlorophyll influences solar attenuation in the visible wavebands, and thus has little effect on transmission within the uppermost meter where the quantity of near-infrared energy is substantial. Beneath the top few meters, a chlorophyll increase from 0.03 to 0.3 mg m−3 can result in a solar flux decrease of more than 10 W m−2. Clouds alter the spectral composition of the incident irradiance by preferentially attenuating in the near-infrared region, and serve to increase solar transmission in the upper few meters as a greater portion of the irradiance exists in the deep-penetrating, visible wavebands. A 50% reduction in the incident irradiance by clouds causes a near 60% reduction in the radiant heating rate for the top 10 cm of the ocean. Solar zenith angle influences transmission during clear sky periods through changes in sea-surface albedo. This study provides necessary information for improved physically and biologically based solar transmission parameterizations that will enhance upper ocean modeling efforts and sea-surface temperature prediction.
