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Abstract
The silicon photovoltaic solar cell has made possible the construction of simple pyranometers of reasonable accuracy. Cell response is linear, temperature sensitivity is low, and spectral response does not cause serious error, provided the cell is used in open sunlight.
The solar cell has been mounted beneath a special diffusing unit to obtain a rugged pyranometer with excellent cosine response. This pyranometer has been coupled with a solid state integrator developed for this purpose. The integral is recorded with either visual or printing counters.
Tests were made during September 1965 through February 1966 when low solar altitude and severe operating conditions would cause greatest error; and again during March 1966 through July 1966 when solar radiation intensities were high. For the first period the standard error of estimate and the solar radiation means were, respectively, 84 and 2200 Wh m−2 day−1. For the second period the corresponding values were 158 and 5630 Wh m−2 day−1.
Abstract
The silicon photovoltaic solar cell has made possible the construction of simple pyranometers of reasonable accuracy. Cell response is linear, temperature sensitivity is low, and spectral response does not cause serious error, provided the cell is used in open sunlight.
The solar cell has been mounted beneath a special diffusing unit to obtain a rugged pyranometer with excellent cosine response. This pyranometer has been coupled with a solid state integrator developed for this purpose. The integral is recorded with either visual or printing counters.
Tests were made during September 1965 through February 1966 when low solar altitude and severe operating conditions would cause greatest error; and again during March 1966 through July 1966 when solar radiation intensities were high. For the first period the standard error of estimate and the solar radiation means were, respectively, 84 and 2200 Wh m−2 day−1. For the second period the corresponding values were 158 and 5630 Wh m−2 day−1.
Abstract
The time and space variability of global radiation have been studied using data collected from a mesoscale network of integrating pyranometers established in Wisconsin, for the period December 1966 through June 1967. The data have been normalized so that they are expressed as a percent of the clear day global radiation. The atmospheric transmission coefficient over the State changes from about 0.75 in winter to 0.60 in summer. For a typical month, the standard deviations of the State daily mean varied from a few percent up to 50 percent of the State mean. Mean day-to-day changes of approximately ± 18 percent-radiation were recorded. From use of records for any one site in the State, the global radiation elsewhere in the State can be estimated with an approximate standard error of ±25 percent or less of the clear day radiation on a daily basis, ± 15 percent or less on a 5-day basis, and ± 10 percent or less on a monthly basis. Alternatively, if the network data from the sites surrounding the unknown point can be used for interpolation, the global radiation anywhere in the State can be estimated with an approximate standard error of ± 20 percent or less of the clear day radiation on a daily basis, ± 10 percent or less on a 5-day basis, and ± 6 percent or less on a monthly basis.
Abstract
The time and space variability of global radiation have been studied using data collected from a mesoscale network of integrating pyranometers established in Wisconsin, for the period December 1966 through June 1967. The data have been normalized so that they are expressed as a percent of the clear day global radiation. The atmospheric transmission coefficient over the State changes from about 0.75 in winter to 0.60 in summer. For a typical month, the standard deviations of the State daily mean varied from a few percent up to 50 percent of the State mean. Mean day-to-day changes of approximately ± 18 percent-radiation were recorded. From use of records for any one site in the State, the global radiation elsewhere in the State can be estimated with an approximate standard error of ±25 percent or less of the clear day radiation on a daily basis, ± 15 percent or less on a 5-day basis, and ± 10 percent or less on a monthly basis. Alternatively, if the network data from the sites surrounding the unknown point can be used for interpolation, the global radiation anywhere in the State can be estimated with an approximate standard error of ± 20 percent or less of the clear day radiation on a daily basis, ± 10 percent or less on a 5-day basis, and ± 6 percent or less on a monthly basis.
Abstract
Successful prediction of possible climate change depends on realistic parameterization of land surface processes in climate models. Such parameterizations must take appropriate account of the heterogeneities that are found in most earth surfaces. In this study, different average strategies for aggregating patch-scale heterogeneities to scales that are appropriate for mesoscale and climate model gods have been explored. A simple model for estimating area-average “effective” surface flux parameters is evaluated. The model satisfies the energy balance equation and leads to a set of relationships between local and effective parameters in the governing equations for the surface energy balance. One outcome is that the resulting effective surface temperature is not a simple area-weighted average of component temperatures, but is a function of a specific combination of different resistance of the individual surface elements. A set of heterogeneous surfaces has been simulated to study the effective fluxes obtained using the described model. A comparison with results obtained by other investigators using different averaging methods is also performed.
Abstract
Successful prediction of possible climate change depends on realistic parameterization of land surface processes in climate models. Such parameterizations must take appropriate account of the heterogeneities that are found in most earth surfaces. In this study, different average strategies for aggregating patch-scale heterogeneities to scales that are appropriate for mesoscale and climate model gods have been explored. A simple model for estimating area-average “effective” surface flux parameters is evaluated. The model satisfies the energy balance equation and leads to a set of relationships between local and effective parameters in the governing equations for the surface energy balance. One outcome is that the resulting effective surface temperature is not a simple area-weighted average of component temperatures, but is a function of a specific combination of different resistance of the individual surface elements. A set of heterogeneous surfaces has been simulated to study the effective fluxes obtained using the described model. A comparison with results obtained by other investigators using different averaging methods is also performed.
A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset.
In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models. The International Satellite Land Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs.
The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987–88, and all but a few are spatially continuous over the earth's land surface. All have been mapped to a common 1° × 1° equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means.
A comprehensive series of global datasets for land-atmosphere models has been collected, formatted to a common grid, and released on a set of CD-ROMs. This paper describes the motivation for and the contents of the dataset.
In June of 1992, an interdisciplinary earth science workshop was convened in Columbia, Maryland, to assess progress in land-atmosphere research, specifically in the areas of models, satellite data algorithms, and field experiments. At the workshop, representatives of the land-atmosphere modeling community defined a need for global datasets to prescribe boundary conditions, initialize state variables, and provide near-surface meteorological and radiative forcings for their models. The International Satellite Land Surface Climatology Project (ISLSCP), a part of the Global Energy and Water Cycle Experiment, worked with the Distributed Active Archive Center of the National Aeronautics and Space Administration Goddard Space Flight Center to bring the required datasets together in a usable format. The data have since been released on a collection of CD-ROMs.
The datasets on the CD-ROMs are grouped under the following headings: vegetation; hydrology and soils; snow, ice, and oceans; radiation and clouds; and near-surface meteorology. All datasets cover the period 1987–88, and all but a few are spatially continuous over the earth's land surface. All have been mapped to a common 1° × 1° equal-angle grid. The temporal frequency for most of the datasets is monthly. A few of the near-surface meteorological parameters are available both as six-hourly values and as monthly means.
Seasonal and Local Solar Time Variation of the Meridional Wind at 95 km from Observations of the 11.072-GHz Ozone Line and the 557.7-nm Oxygen LineAbstract
Ground-based spectrometers have been deployed to measure the concentration, velocity, and temperature of ozone in the mesosphere and lower thermosphere (MLT), using low-cost satellite television electronics to observe the 11.072-GHz line of ozone. The ozone line was observed at an altitude near 95 km at 38°N, 71°W using three spectrometers located at the Massachusetts Institute of Technology’s Haystack Observatory (Westford, Massachusetts), Chelmsford High School (Chelmsford, Massachusetts), and Union College (Schenectady, New York), each pointed south at 8° elevation. Observations from 2009 through 2014 were used to derive the nightly averaged seasonal variation of the 95-km altitude meridional wind velocity, as well as the seasonally averaged variation of the meridional wind with local solar time. The results indicate a seasonal trend in which the winds at 95 km are directed southward at about 10 m s−1 in the summer of the Northern Hemisphere and northward at about 10 m s−1 in the winter. Nighttime data from −5 to +5 local solar time show a gradual transition of the meridional wind velocity from about −20 to 20 m s−1. These variations correlate well with nighttime wind measurements using 557.7-nm optical airglow observations from the Millstone Hill high-resolution Fábry–Perot interferometer (FPI) in Westford.
Abstract
Ground-based spectrometers have been deployed to measure the concentration, velocity, and temperature of ozone in the mesosphere and lower thermosphere (MLT), using low-cost satellite television electronics to observe the 11.072-GHz line of ozone. The ozone line was observed at an altitude near 95 km at 38°N, 71°W using three spectrometers located at the Massachusetts Institute of Technology’s Haystack Observatory (Westford, Massachusetts), Chelmsford High School (Chelmsford, Massachusetts), and Union College (Schenectady, New York), each pointed south at 8° elevation. Observations from 2009 through 2014 were used to derive the nightly averaged seasonal variation of the 95-km altitude meridional wind velocity, as well as the seasonally averaged variation of the meridional wind with local solar time. The results indicate a seasonal trend in which the winds at 95 km are directed southward at about 10 m s−1 in the summer of the Northern Hemisphere and northward at about 10 m s−1 in the winter. Nighttime data from −5 to +5 local solar time show a gradual transition of the meridional wind velocity from about −20 to 20 m s−1. These variations correlate well with nighttime wind measurements using 557.7-nm optical airglow observations from the Millstone Hill high-resolution Fábry–Perot interferometer (FPI) in Westford.
Abstract
The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.
Abstract
The Soil Moisture Ocean Salinity (SMOS) satellite mission routinely provides global multiangular observations of brightness temperature TB at both horizontal and vertical polarization with a 3-day repeat period. The assimilation of such data into a land surface model (LSM) may improve the skill of operational flood forecasts through an improved estimation of soil moisture SM. To accommodate for the direct assimilation of the SMOS TB data, the LSM needs to be coupled with a radiative transfer model (RTM), serving as a forward operator for the simulation of multiangular and multipolarization top of the atmosphere TBs. This study investigates the use of the Variable Infiltration Capacity model coupled with the Community Microwave Emission Modelling Platform for simulating SMOS TB observations over the upper Mississippi basin, United States. For a period of 2 years (2010–11), a comparison between SMOS TBs and simulations with literature-based RTM parameters reveals a basin-averaged bias of 30 K. Therefore, time series of SMOS TB observations are used to investigate ways for mitigating these large biases. Specifically, the study demonstrates the impact of the LSM soil moisture climatology in the magnitude of TB biases. After cumulative distribution function matching the SM climatology of the LSM to SMOS retrievals, the average bias decreases from 30 K to less than 5 K. Further improvements can be made through calibration of RTM parameters related to the modeling of surface roughness and vegetation. Consequently, it can be concluded that SM rescaling and RTM optimization are efficient means for mitigating biases and form a necessary preparatory step for data assimilation.
