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-likelihood values in all cases compared to models lacking an autoregressive term. Models first were run at monthly intervals to explore seasonal differences (not shown), which revealed two distinct seasons for model relationships roughly corresponding to the warmest and coldest parts of the year. We defined the cold season ( n = 98 days; from 15 December 2012 to 22 March 2013) as periods with widespread snow cover. We defined the warm season ( n = 294 days; from 27 April to 1 October 2012 and from 18 May to
-likelihood values in all cases compared to models lacking an autoregressive term. Models first were run at monthly intervals to explore seasonal differences (not shown), which revealed two distinct seasons for model relationships roughly corresponding to the warmest and coldest parts of the year. We defined the cold season ( n = 98 days; from 15 December 2012 to 22 March 2013) as periods with widespread snow cover. We defined the warm season ( n = 294 days; from 27 April to 1 October 2012 and from 18 May to
surface air temperature (Ts) exceeds 10°C for 10 consecutive days. Therefore, seasonal prediction of the Ts associated with the GSSWC is practically an issue of predicting when a period of 10 consecutive days with mean Ts reaching 10°C emerges in a year. It has long been recognized that the physical basis of seasonal prediction of climate events lies in coupled mechanisms between atmosphere and low boundary forcing anomalies such as snow cover and sea surface temperature anomalies (SSTAs) (e
surface air temperature (Ts) exceeds 10°C for 10 consecutive days. Therefore, seasonal prediction of the Ts associated with the GSSWC is practically an issue of predicting when a period of 10 consecutive days with mean Ts reaching 10°C emerges in a year. It has long been recognized that the physical basis of seasonal prediction of climate events lies in coupled mechanisms between atmosphere and low boundary forcing anomalies such as snow cover and sea surface temperature anomalies (SSTAs) (e
, such as lakes or canals, depends primarily on meteorological parameten like temperatureand humidity of the air, windspced and radiation balance. The morn complicated ice formation in rapidlyflowing riven is not considered in this study. A model is described that simulates ice growth and meltin~ utilizingobserved or forecast wcathcr data. The model includes situations with a snow cover. Special attention is givento the optimal estimation of the net redlation and to the role of the stability of the
, such as lakes or canals, depends primarily on meteorological parameten like temperatureand humidity of the air, windspced and radiation balance. The morn complicated ice formation in rapidlyflowing riven is not considered in this study. A model is described that simulates ice growth and meltin~ utilizingobserved or forecast wcathcr data. The model includes situations with a snow cover. Special attention is givento the optimal estimation of the net redlation and to the role of the stability of the
regions during the daytime simpler than with current satellite data since these satellites have only a few discrete channels. The experimental data The ARMCAS field campaign was deployed in the Arctic tundra region surrounding Prudhoe Bay, Alaska, and the snow- and ice-covered Beaufort Sea during 1–15 June 1995. The main goal of this experiment was to improve our understanding of the mechanisms in cloud microphysics, radiation, and remote sensing in the Arctic. The experiment includes satellite remote
regions during the daytime simpler than with current satellite data since these satellites have only a few discrete channels. The experimental data The ARMCAS field campaign was deployed in the Arctic tundra region surrounding Prudhoe Bay, Alaska, and the snow- and ice-covered Beaufort Sea during 1–15 June 1995. The main goal of this experiment was to improve our understanding of the mechanisms in cloud microphysics, radiation, and remote sensing in the Arctic. The experiment includes satellite remote
the 8-13 micron water-vapor "window" made byTIROS II are studied in relation to conventionally observed information on pressure systems, cloudinessand temperature. These cases demonstrate further the synoptic capabilities, as well as some of the limitations, of these data for cloud detection; determination of cloud-top height; and observation of spatial gradients and temporal changes in the temperature of water-, land-, and snow-covered surfaces.1. Introduction Radiation measurements from
the 8-13 micron water-vapor "window" made byTIROS II are studied in relation to conventionally observed information on pressure systems, cloudinessand temperature. These cases demonstrate further the synoptic capabilities, as well as some of the limitations, of these data for cloud detection; determination of cloud-top height; and observation of spatial gradients and temporal changes in the temperature of water-, land-, and snow-covered surfaces.1. Introduction Radiation measurements from
for adjacent snow-freeland and ice-covered sea surfaces along the Hudson Bay coastline indicate that multiple reflection enhancementcan be large over high-aibedo surfaces in the presence of cloud. The type and thickness of cloud is the majorfactor determining the magnitude of multiple reflection, and there is a wide range in cloud base albedos. When,under cloudy skies, the global solar radiation equals the mngnitude for clear sides, it is due to the fact that theclouds are thin and largely
for adjacent snow-freeland and ice-covered sea surfaces along the Hudson Bay coastline indicate that multiple reflection enhancementcan be large over high-aibedo surfaces in the presence of cloud. The type and thickness of cloud is the majorfactor determining the magnitude of multiple reflection, and there is a wide range in cloud base albedos. When,under cloudy skies, the global solar radiation equals the mngnitude for clear sides, it is due to the fact that theclouds are thin and largely
,alfalfa, soybeans, and green peas) for a combined total of 5778 days between 2 i November 1969-31 December1985. Statistical summaries of the calculated mean daily albedos of all surfaces are shown for months, seasonsand years. There are, in effect, three albedo seasons:, the high albedo season with snow cover (DecemberFebruary), the low albedo scasnn (April-October), and transitions between the two that occur in March andNovember. The least variation was associated with the low albedo season., increasing from a
,alfalfa, soybeans, and green peas) for a combined total of 5778 days between 2 i November 1969-31 December1985. Statistical summaries of the calculated mean daily albedos of all surfaces are shown for months, seasonsand years. There are, in effect, three albedo seasons:, the high albedo season with snow cover (DecemberFebruary), the low albedo scasnn (April-October), and transitions between the two that occur in March andNovember. The least variation was associated with the low albedo season., increasing from a
Diffusion Laboratory, NOAA Oak Ridge, TN 37830. A. D. OZMENT AND G. J. GOTTFRIEDRocky Mountain Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, Tempe, AZ 85281.(Manuscript received 28 May 1982, in final form 2 September 1982) ABSTRACT The integrated albedo for solar radiation in the 0.4-0.7 #m wavelength range was measured near noonover a wet snow cover before and after a new snowfall. Observed values were compared with those
Diffusion Laboratory, NOAA Oak Ridge, TN 37830. A. D. OZMENT AND G. J. GOTTFRIEDRocky Mountain Forest and Range Experiment Station, Forest Service, U.S. Department of Agriculture, Tempe, AZ 85281.(Manuscript received 28 May 1982, in final form 2 September 1982) ABSTRACT The integrated albedo for solar radiation in the 0.4-0.7 #m wavelength range was measured near noonover a wet snow cover before and after a new snowfall. Observed values were compared with those
MARCH 1981 ISIDORE HALBERSTAM AND JOHN P. SCHIELDGE 255Anomalous Behavior of the Atmospheric Surface Layer over a Melting Snowpack ISIDORE HALBERSTAM1 AND JOHN P. SCHIELDGEJet Propulsion Laboratory, Pasadena, CA 91103(Manuscript received 16 June 1980, in final form 13 October 1980)ABSTRACT During March, 1978 on a snow-covered field near Lee Vining, California, measurements were made thatincluded: 1) variations
MARCH 1981 ISIDORE HALBERSTAM AND JOHN P. SCHIELDGE 255Anomalous Behavior of the Atmospheric Surface Layer over a Melting Snowpack ISIDORE HALBERSTAM1 AND JOHN P. SCHIELDGEJet Propulsion Laboratory, Pasadena, CA 91103(Manuscript received 16 June 1980, in final form 13 October 1980)ABSTRACT During March, 1978 on a snow-covered field near Lee Vining, California, measurements were made thatincluded: 1) variations
percent coverage of ground surface with live vegetation up to 35% cover,with little further change in albedo with cover, up to the maximum oberved value of 70%. The ratio at midday of albedo in visible wavelengths (400-700 nm) to total shortwave albedo decreasedfrom 0.49 in mid-May 1982 to a minimum of 0.22 in mid-July and then increased to 0.45 in mid-October,after leaf-fall. Midday shortwave albedo during winter varied from 91% over fresh snow to 76% over old, compacted snow.1. Introduction
percent coverage of ground surface with live vegetation up to 35% cover,with little further change in albedo with cover, up to the maximum oberved value of 70%. The ratio at midday of albedo in visible wavelengths (400-700 nm) to total shortwave albedo decreasedfrom 0.49 in mid-May 1982 to a minimum of 0.22 in mid-July and then increased to 0.45 in mid-October,after leaf-fall. Midday shortwave albedo during winter varied from 91% over fresh snow to 76% over old, compacted snow.1. Introduction
