Search Results
You are looking at 1 - 10 of 68 items for
- Author or Editor: Patrick Minnis x
- Refine by Access: All Content x
Abstract
Land surface microwave emissivities are important geophysical parameters for atmospheric, hydrological, and biospheric studies. This study estimates land surface microwave emissivity using an atmospheric microwave radiative transfer model and a combination of the Special Sensor Microwave Imager (SSM/I) satellite observations and data from the Atmospheric Radiation Measurement Program southern Great Plains (SGP) site during October of 1995. Emissivities are retrieved for both clear and cloudy conditions. Emissivity standard deviations of ∼0.035 were found at the SGP site. Much of the variability is produced by a distinct diurnal cycle. The emissivity variability at each SSM/I overpass time (0630, 1100, 1730, and 1000 local time) is about half that for all four times combined. Early morning emissivities are ∼0.06 less than those at other times, and the polarization differences at the four times are similar. This behavior is likely the result of dew and surface rewetting effects. Ground observations of dewpoint and temperature difference between air and skin support this theory. The surface emissivities have a significant negative correlation with soil moisture, which can explain about 60%–80% of the emissivity variance when pentad running means are used. Strong correlations among all seven SSM/I channels indicate that the emissivities need to be determined directly for only two or three channels.
Abstract
Land surface microwave emissivities are important geophysical parameters for atmospheric, hydrological, and biospheric studies. This study estimates land surface microwave emissivity using an atmospheric microwave radiative transfer model and a combination of the Special Sensor Microwave Imager (SSM/I) satellite observations and data from the Atmospheric Radiation Measurement Program southern Great Plains (SGP) site during October of 1995. Emissivities are retrieved for both clear and cloudy conditions. Emissivity standard deviations of ∼0.035 were found at the SGP site. Much of the variability is produced by a distinct diurnal cycle. The emissivity variability at each SSM/I overpass time (0630, 1100, 1730, and 1000 local time) is about half that for all four times combined. Early morning emissivities are ∼0.06 less than those at other times, and the polarization differences at the four times are similar. This behavior is likely the result of dew and surface rewetting effects. Ground observations of dewpoint and temperature difference between air and skin support this theory. The surface emissivities have a significant negative correlation with soil moisture, which can explain about 60%–80% of the emissivity variance when pentad running means are used. Strong correlations among all seven SSM/I channels indicate that the emissivities need to be determined directly for only two or three channels.
Abstract
Computer simulations of satellite-derived Earth radiation parameters are examined to determine the source and size of errors arising from averaging parameters over 1 month on a 2.5°×2.5° longitude-latitude grid. November 1978 data from the Geostationary Operational Environmental Satellite (GOES) have been used as a source of radiation parameter fields within each region. The regions are sampled according to various combinations of satellite orbits which have been chosen on the basis of their applicability to the Earth Radiation Budget Experiment. A mathematical model is given for the data-processing algorithms that are used to produce daily, monthly and monthly hourly estimates of shortwave, longwave and net radiant exitance. Because satellite sampling of each region is sparse during any day, and because the meteorological behavior between measurements is unknown, the retrieved diurnal cycle in shortwave radiant exitance is especially sensitive to the temporal distribution of measurements. The resulting retrieval errors are seen to be due to insufficient knowledge of the temporal distribution of both cloud fraction and albedo. These errors, in combination with similar sampling errors resulting from diurnal variations in longwave radiant exitance (especially over land), produce biases in monthly net radiant exitance which are complex, regionally-dependent functions of the local time of the measurements. The regions studied have shown standard errors of estimate for monthly net radiant exitance ranging from about 20 W m−2 for the worst single-satellite sample to ∼2 W m−2 for the three-satellite sampling assumed to be available.
Abstract
Computer simulations of satellite-derived Earth radiation parameters are examined to determine the source and size of errors arising from averaging parameters over 1 month on a 2.5°×2.5° longitude-latitude grid. November 1978 data from the Geostationary Operational Environmental Satellite (GOES) have been used as a source of radiation parameter fields within each region. The regions are sampled according to various combinations of satellite orbits which have been chosen on the basis of their applicability to the Earth Radiation Budget Experiment. A mathematical model is given for the data-processing algorithms that are used to produce daily, monthly and monthly hourly estimates of shortwave, longwave and net radiant exitance. Because satellite sampling of each region is sparse during any day, and because the meteorological behavior between measurements is unknown, the retrieved diurnal cycle in shortwave radiant exitance is especially sensitive to the temporal distribution of measurements. The resulting retrieval errors are seen to be due to insufficient knowledge of the temporal distribution of both cloud fraction and albedo. These errors, in combination with similar sampling errors resulting from diurnal variations in longwave radiant exitance (especially over land), produce biases in monthly net radiant exitance which are complex, regionally-dependent functions of the local time of the measurements. The regions studied have shown standard errors of estimate for monthly net radiant exitance ranging from about 20 W m−2 for the worst single-satellite sample to ∼2 W m−2 for the three-satellite sampling assumed to be available.
Abstract
Two cases of aircraft dissipation trails (distrails) with associated fall streak clouds were analyzed with multispectral geostationary satellite data. One dissipation trail was observed in a single cloud layer on 23 July 2000 over southeastern Virginia and the Chesapeake Bay. Another set of trails developed at the top of multilayer cloudiness off the coasts of Georgia and South Carolina on 6 January 2000. The distrails on both days formed in optically thin, midlevel stratified clouds with cloud-top heights between 7.6 and 9.1 km. The distrail features remained intact and easily visible from satellite images over a period of 1–2 h despite winds near 50 kt at cloud level. The width of the distrails became as large as 20 km within a period of 90 min or less. Differences between the optical properties of the fall streak particles inside the distrails and those of the clouds surrounding the trails allowed for the easy identification of the fall streak clouds in either the 3.9-μm brightness temperature imagery, or the 10.7-μm minus 12.0-μm brightness temperature difference imagery. Two independent remote sensing retrievals of both distrail cases showed that the fall streaks had larger particle sizes than the clouds outside of the trails, although the three-channel infrared retrieval was better at retrieving cloud properties in the multilayer cloud case.
Abstract
Two cases of aircraft dissipation trails (distrails) with associated fall streak clouds were analyzed with multispectral geostationary satellite data. One dissipation trail was observed in a single cloud layer on 23 July 2000 over southeastern Virginia and the Chesapeake Bay. Another set of trails developed at the top of multilayer cloudiness off the coasts of Georgia and South Carolina on 6 January 2000. The distrails on both days formed in optically thin, midlevel stratified clouds with cloud-top heights between 7.6 and 9.1 km. The distrail features remained intact and easily visible from satellite images over a period of 1–2 h despite winds near 50 kt at cloud level. The width of the distrails became as large as 20 km within a period of 90 min or less. Differences between the optical properties of the fall streak particles inside the distrails and those of the clouds surrounding the trails allowed for the easy identification of the fall streak clouds in either the 3.9-μm brightness temperature imagery, or the 10.7-μm minus 12.0-μm brightness temperature difference imagery. Two independent remote sensing retrievals of both distrail cases showed that the fall streaks had larger particle sizes than the clouds outside of the trails, although the three-channel infrared retrieval was better at retrieving cloud properties in the multilayer cloud case.
Abstract
Simulations of the Earth Radiation Budget Experiment with several satellite sampling schemes have been used to compare three different approaches to modeling longwave diurnal behavior observed over certain kinds of land regions. November 1978 data from the GOES satellite have been used to produce a reference set of radiation parameters over the regions of interest. The monthly average longwave radiant exitance has been estimated first with linear interpolation between satellite measurements, then with a method that replaces linear interpolations across day-night boundaries with piecewise constant extrapolations to the boundaries, and finally with a trigonometric model which replaces some of the linear interpolations that go through daytime measurements over land. This third model consists of constant extrapolation of nighttime measurements to sunrise or sunset, with a half-sine curve fitted through existing daytime measurements and constrained at sunrise and sunset to an average of the surrounding nighttime measurements. It applies only when the daytime and surrounding nighttime measurements meet certain restrictive criteria, including tests that tend to limit the trigonometric model to cloud-free regions. For all satellite sampling strategies considered, the trigonometric model gave the best overall monthly estimate of longwave radiant exitance. For non-land regions, the linear interpolation model generally gave better results than the piecewise constant model.
Abstract
Simulations of the Earth Radiation Budget Experiment with several satellite sampling schemes have been used to compare three different approaches to modeling longwave diurnal behavior observed over certain kinds of land regions. November 1978 data from the GOES satellite have been used to produce a reference set of radiation parameters over the regions of interest. The monthly average longwave radiant exitance has been estimated first with linear interpolation between satellite measurements, then with a method that replaces linear interpolations across day-night boundaries with piecewise constant extrapolations to the boundaries, and finally with a trigonometric model which replaces some of the linear interpolations that go through daytime measurements over land. This third model consists of constant extrapolation of nighttime measurements to sunrise or sunset, with a half-sine curve fitted through existing daytime measurements and constrained at sunrise and sunset to an average of the surrounding nighttime measurements. It applies only when the daytime and surrounding nighttime measurements meet certain restrictive criteria, including tests that tend to limit the trigonometric model to cloud-free regions. For all satellite sampling strategies considered, the trigonometric model gave the best overall monthly estimate of longwave radiant exitance. For non-land regions, the linear interpolation model generally gave better results than the piecewise constant model.
Abstract
A hybrid bispectral threshold method (HBTM) is developed for hourly regional cloud and radiative parameters from geostationary satellite visible and infrared radiance data. The quantities derived with the HBTM include equivalent blackbody temperatures for clear skies, for the total cloud cover and for the cloud cover at three levels in the atmosphere; the total fractional cloud cover and the fractional cloud amounts at three altitudes; and the clear-sky and total cloud reflectance characteristics. Geostationary satellite data taken during November 1978 are analyzed. A minimum reflectance technique is used to determine clear-sky brightness. A visible bidirectional reflectance model is derived for clear ocean areas. Clear-sky radiative temperature is found with a bispectral clear radiance technique during daylight hours. An empirical model is derived to predict clear-sky temperature at night. A combination of previously published infrared threshold and bispectral techniques is used to determine the remaining parameters. Sources of uncertainty are discussed and means to minimize them are proposed. Monthly mean, regional fractional cloudiness determined with this method agrees well with more conventional subjective techniques. On the average, the present results are approximately 0.05 less than corresponding surface observations; this is consistent with previous comparisons of satellite- and surface-based nephanalyses. Comparisons between subjective analyses of satellite photographs and the HBTM yielded average differences in mean regional cloudiness, mean hourly cloudiness and instantaneous cloud amounts of 0.04, 0.05 and 0.11 respectively. Root-mean-square differences in these same quantities derived by two satellite data analysts were 0.03, 0.04 and 0.08 respectively.
Abstract
A hybrid bispectral threshold method (HBTM) is developed for hourly regional cloud and radiative parameters from geostationary satellite visible and infrared radiance data. The quantities derived with the HBTM include equivalent blackbody temperatures for clear skies, for the total cloud cover and for the cloud cover at three levels in the atmosphere; the total fractional cloud cover and the fractional cloud amounts at three altitudes; and the clear-sky and total cloud reflectance characteristics. Geostationary satellite data taken during November 1978 are analyzed. A minimum reflectance technique is used to determine clear-sky brightness. A visible bidirectional reflectance model is derived for clear ocean areas. Clear-sky radiative temperature is found with a bispectral clear radiance technique during daylight hours. An empirical model is derived to predict clear-sky temperature at night. A combination of previously published infrared threshold and bispectral techniques is used to determine the remaining parameters. Sources of uncertainty are discussed and means to minimize them are proposed. Monthly mean, regional fractional cloudiness determined with this method agrees well with more conventional subjective techniques. On the average, the present results are approximately 0.05 less than corresponding surface observations; this is consistent with previous comparisons of satellite- and surface-based nephanalyses. Comparisons between subjective analyses of satellite photographs and the HBTM yielded average differences in mean regional cloudiness, mean hourly cloudiness and instantaneous cloud amounts of 0.04, 0.05 and 0.11 respectively. Root-mean-square differences in these same quantities derived by two satellite data analysts were 0.03, 0.04 and 0.08 respectively.
Abstract
Regional (250 Ă— 250 km2) diurnal cloud variability is examined using mean hourly cloud amounts derived from November 1978 GOES-East visible and infrared data with a hybrid bispectral threshold technique. A wide variety of diurnal variations in cloud cover is presented. A morning maximum in low cloudiness is found over much of the eastern Pacific. Many regions in the western Atlantic have peak low-cloud cover near noon. Low clouds reach a maximum most often near noon over most of South America and in the morning over North America. Midlevel clouds are most frequent in the evening over oceans and in the early morning over land. High-cloud maxima are found mainly in the late afternoon over land and in the midafternoon over the oceans. An early morning minimum in high-cloud-top temperature is observed over marine areas. The amplitude of the semidiurnal component of cloudiness is generally much less than that of the diurnal component.
The largest diurnal cloud variations occur over the southeastern Pacific where low clouds are dominant. On the average, mean cloud fraction varied by about 0.35 in this area with a maximum near sunrise. Over the Amazon Basin, the vertical distribution of cloud cover follows a pronounced diurnal cycle which shows maximum high-cloud cover occurring in the late afternoon. A large-scale diurnally modulated circulation feature between the Amazon and the adjacent oceans is suggested. High clouds occur most frequently over the southern Andes during the afternoon and are most common over the adjacent lowlands during the night, indicating the existence of a diurnally-dependent mountain-plains circulation.
Abstract
Regional (250 Ă— 250 km2) diurnal cloud variability is examined using mean hourly cloud amounts derived from November 1978 GOES-East visible and infrared data with a hybrid bispectral threshold technique. A wide variety of diurnal variations in cloud cover is presented. A morning maximum in low cloudiness is found over much of the eastern Pacific. Many regions in the western Atlantic have peak low-cloud cover near noon. Low clouds reach a maximum most often near noon over most of South America and in the morning over North America. Midlevel clouds are most frequent in the evening over oceans and in the early morning over land. High-cloud maxima are found mainly in the late afternoon over land and in the midafternoon over the oceans. An early morning minimum in high-cloud-top temperature is observed over marine areas. The amplitude of the semidiurnal component of cloudiness is generally much less than that of the diurnal component.
The largest diurnal cloud variations occur over the southeastern Pacific where low clouds are dominant. On the average, mean cloud fraction varied by about 0.35 in this area with a maximum near sunrise. Over the Amazon Basin, the vertical distribution of cloud cover follows a pronounced diurnal cycle which shows maximum high-cloud cover occurring in the late afternoon. A large-scale diurnally modulated circulation feature between the Amazon and the adjacent oceans is suggested. High clouds occur most frequently over the southern Andes during the afternoon and are most common over the adjacent lowlands during the night, indicating the existence of a diurnally-dependent mountain-plains circulation.
Abstract
Straightforward application of the Schmidt–Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper-tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy: the percent correct (PC) and the Hanssen–Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher (i.e., the forecasts are more skillful) when the climatological frequency of contrail occurrence is used as the critical threshold, whereas the PC scores are higher (i.e., the forecasts are more accurate) when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85% for the prediction of both contrail occurrence and nonoccurrence, although, in practice, larger errors would be anticipated.
Abstract
Straightforward application of the Schmidt–Appleman contrail formation criteria to diagnose persistent contrail occurrence from numerical weather prediction data is hindered by significant bias errors in the upper-tropospheric humidity. Logistic models of contrail occurrence have been proposed to overcome this problem, but basic questions remain about how random measurement error may affect their accuracy. A set of 5000 synthetic contrail observations is created to study the effects of random error in these probabilistic models. The simulated observations are based on distributions of temperature, humidity, and vertical velocity derived from Advanced Regional Prediction System (ARPS) weather analyses. The logistic models created from the simulated observations were evaluated using two common statistical measures of model accuracy: the percent correct (PC) and the Hanssen–Kuipers discriminant (HKD). To convert the probabilistic results of the logistic models into a dichotomous yes/no choice suitable for the statistical measures, two critical probability thresholds are considered. The HKD scores are higher (i.e., the forecasts are more skillful) when the climatological frequency of contrail occurrence is used as the critical threshold, whereas the PC scores are higher (i.e., the forecasts are more accurate) when the critical probability threshold is 0.5. For both thresholds, typical random errors in temperature, relative humidity, and vertical velocity are found to be small enough to allow for accurate logistic models of contrail occurrence. The accuracy of the models developed from synthetic data is over 85% for the prediction of both contrail occurrence and nonoccurrence, although, in practice, larger errors would be anticipated.
Abstract
A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.
Abstract
A probabilistic forecast to accurately predict contrail formation over the conterminous United States (CONUS) is created by using meteorological data based on hourly meteorological analyses from the Advanced Regional Prediction System (ARPS) and the Rapid Update Cycle (RUC) combined with surface and satellite observations of contrails. Two groups of logistic models were created. The first group of models (SURFACE models) is based on surface-based contrail observations supplemented with satellite observations of contrail occurrence. The most common predictors selected for the SURFACE models tend to be related to temperature, relative humidity, and wind direction when the models are generated using RUC or ARPS analyses. The second group of models (OUTBREAK models) is derived from a selected subgroup of satellite-based observations of widespread persistent contrails. The most common predictors for the OUTBREAK models tend to be wind direction, atmospheric lapse rate, temperature, relative humidity, and the product of temperature and humidity.
Abstract
The land skin temperature, an important feature for agricultural monitoring, convective processes, and the earth’s radiation budget, is monitored from limited-view satellite imagers. The angular dependence of this parameter is examined using simultaneous views of clear areas from up to three geostationary satellites. Daytime temperatures from different satellites differed by up to 6 K and varied as a function of the time of day. Larger differences are expected to occur but were not measured because of limited viewing angles. These differences suggest that biases may occur in both the magnitude and phase of the diurnal cycle of skin temperature and its mean value whenever geostationary satellite data are used to determine skin temperature. The temperature differences were found over both flat and mountainous regions with some slight dependence on vegetation. The timing and magnitude of the temperature differences provide some initial validation for relatively complex model calculations of skin temperature variability. The temperature differences are strongly correlated with terrain and the anisotropy of reflected solar radiation for typical land surfaces. These strong dependencies suggest the possibility for the development of a simple empirical approach for characterizing the temperature anisotropy. Additional research using a much greater range of viewing angles is required to confirm the potential of the suggested empirical approach.
Abstract
The land skin temperature, an important feature for agricultural monitoring, convective processes, and the earth’s radiation budget, is monitored from limited-view satellite imagers. The angular dependence of this parameter is examined using simultaneous views of clear areas from up to three geostationary satellites. Daytime temperatures from different satellites differed by up to 6 K and varied as a function of the time of day. Larger differences are expected to occur but were not measured because of limited viewing angles. These differences suggest that biases may occur in both the magnitude and phase of the diurnal cycle of skin temperature and its mean value whenever geostationary satellite data are used to determine skin temperature. The temperature differences were found over both flat and mountainous regions with some slight dependence on vegetation. The timing and magnitude of the temperature differences provide some initial validation for relatively complex model calculations of skin temperature variability. The temperature differences are strongly correlated with terrain and the anisotropy of reflected solar radiation for typical land surfaces. These strong dependencies suggest the possibility for the development of a simple empirical approach for characterizing the temperature anisotropy. Additional research using a much greater range of viewing angles is required to confirm the potential of the suggested empirical approach.
