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- Author or Editor: NORMAN THOMAS x
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Abstract
Detailed knowledge of the advection of sensible heat is necessary to understand the energy balance of the evaporating surface in many parts of the world. Sensible heat advection can result from regional and/or local sources. The local and regional components of sensible heat advection (A loc and A reg, respectively) are identified and their magnitudes in a semi-arid to sub-humid zone are established in the work reported here. Measurements of dry- and wet-bulb air temperature, wind speed and net radiation were made above an irrigated alfalfa field with relatively dry surroundings upwind at Mead, NE. A modified Bowen ratio-energy balance method which incorporates horizontal gradients of air temperature and vapor pressure was used to compute evapotranspiration (ET) rates.
Sensible heat advection at the furthest upwind location in the irrigated field contributed from 15 to 50% of the energy consumed in ET on a daily basis. A reg was greatest on days with strong winds; A loc was independent of wind speed. The dryer the air, the greater the advection of sensible heat.
Abstract
Detailed knowledge of the advection of sensible heat is necessary to understand the energy balance of the evaporating surface in many parts of the world. Sensible heat advection can result from regional and/or local sources. The local and regional components of sensible heat advection (A loc and A reg, respectively) are identified and their magnitudes in a semi-arid to sub-humid zone are established in the work reported here. Measurements of dry- and wet-bulb air temperature, wind speed and net radiation were made above an irrigated alfalfa field with relatively dry surroundings upwind at Mead, NE. A modified Bowen ratio-energy balance method which incorporates horizontal gradients of air temperature and vapor pressure was used to compute evapotranspiration (ET) rates.
Sensible heat advection at the furthest upwind location in the irrigated field contributed from 15 to 50% of the energy consumed in ET on a daily basis. A reg was greatest on days with strong winds; A loc was independent of wind speed. The dryer the air, the greater the advection of sensible heat.
The current network of internationally exchanged in situ station data is not distributed evenly nor densely around the globe. Consequently, the in situ data contain insufficient information to identify fine spatial structure and variations over many areas of the world. Therefore, satellite observations need to be blended with in situ data to obtain higher resolution over the global land surface. Toward this end, the authors calibrated and independently verified an algorithm that derives land surface temperatures from the Special Sensor Microwave/Imager (SSM/I). This study explains the technique used to refine a set of equations that identify various surface types and to make corresponding dynamic emissivity adjustments. This allowed estimation of the shelter height temperatures from the seven channel measurements flown on the SSM/I instrument. Data from first-order in situ stations over the eastern half of the United States were used for calibration and intersatellite adjustment. The results show that the observational difference between the in situ point measurements and the SSM/I-derived areal values is about 2°C with statistical characteristics largely independent of surface type. High-resolution monthly mean anomalies generated from the U.S. cooperative network served as independent verification over the same study area. This verification work determined that the standard deviation of the monthly mean anomalies is 0.76°C at each 1° × 1° grid box. This level of accuracy is adequate to blend the SSM/I-derived temperature anomaly data with in situ data for monitoring global temperature anomalies in finer detail.
The current network of internationally exchanged in situ station data is not distributed evenly nor densely around the globe. Consequently, the in situ data contain insufficient information to identify fine spatial structure and variations over many areas of the world. Therefore, satellite observations need to be blended with in situ data to obtain higher resolution over the global land surface. Toward this end, the authors calibrated and independently verified an algorithm that derives land surface temperatures from the Special Sensor Microwave/Imager (SSM/I). This study explains the technique used to refine a set of equations that identify various surface types and to make corresponding dynamic emissivity adjustments. This allowed estimation of the shelter height temperatures from the seven channel measurements flown on the SSM/I instrument. Data from first-order in situ stations over the eastern half of the United States were used for calibration and intersatellite adjustment. The results show that the observational difference between the in situ point measurements and the SSM/I-derived areal values is about 2°C with statistical characteristics largely independent of surface type. High-resolution monthly mean anomalies generated from the U.S. cooperative network served as independent verification over the same study area. This verification work determined that the standard deviation of the monthly mean anomalies is 0.76°C at each 1° × 1° grid box. This level of accuracy is adequate to blend the SSM/I-derived temperature anomaly data with in situ data for monitoring global temperature anomalies in finer detail.
Abstract
The worldwide network of in situ land surface temperatures archived in near-real time at the National Climatic Data Center (NCDC) has limited applications, since many areas are poorly represented or provide no observations. Satellite measurements offer a possible way to fill in the data voids and obtain a complete map of surface temperature over the entire globe. A method has been developed to calculate near-surface temperature using measurements from the Special Sensor Microwave/Imager (SSM/I). To accomplish this, the authors identify numerous surface types and make dynamic adjustments for variations in emissivity. Training datasets were used to define the relationship between the seven SSM/I channels and the near-surface temperature. For instance, liquid water on the surface reduces emissivity; therefore, the authors developed an adjustment to correct for this reduction. Other surface types (e.g., snow, ice, and deserts) as well as precipitation are identified, and numerous adjustments and/or filters were developed for these features. The article presents the results obtained from training datasets, as well as an independent case study, containing extreme conditions for deriving temperature from the SSM/I. The U.S. networks of first-order and cooperative stations, quality controlled by NCDC, serve as validation data. The correlation between satellite-derived and in situ temperatures during the independent case (“Blizzard of 1996”) was greater than 0.95, and the standard error was 2°C. The authors also present SSM/I-derived snow cover and wetness maps from this 2-week period of the blizzard. A prototype for blending the satellite and in situ measurements into a single land surface temperature product is also presented.
Abstract
The worldwide network of in situ land surface temperatures archived in near-real time at the National Climatic Data Center (NCDC) has limited applications, since many areas are poorly represented or provide no observations. Satellite measurements offer a possible way to fill in the data voids and obtain a complete map of surface temperature over the entire globe. A method has been developed to calculate near-surface temperature using measurements from the Special Sensor Microwave/Imager (SSM/I). To accomplish this, the authors identify numerous surface types and make dynamic adjustments for variations in emissivity. Training datasets were used to define the relationship between the seven SSM/I channels and the near-surface temperature. For instance, liquid water on the surface reduces emissivity; therefore, the authors developed an adjustment to correct for this reduction. Other surface types (e.g., snow, ice, and deserts) as well as precipitation are identified, and numerous adjustments and/or filters were developed for these features. The article presents the results obtained from training datasets, as well as an independent case study, containing extreme conditions for deriving temperature from the SSM/I. The U.S. networks of first-order and cooperative stations, quality controlled by NCDC, serve as validation data. The correlation between satellite-derived and in situ temperatures during the independent case (“Blizzard of 1996”) was greater than 0.95, and the standard error was 2°C. The authors also present SSM/I-derived snow cover and wetness maps from this 2-week period of the blizzard. A prototype for blending the satellite and in situ measurements into a single land surface temperature product is also presented.
A near-global surface temperature dataset was produced by blending several sources of information. For the oceans, these include in situ and infrared satellite-derived sea surface temperatures that were already processed into a monthly product. Land data analysis uses two sources of data. The first is high quality monthly in situ reports from the Global Historical Climatologic Network with more than 1000 stations from around the world. The second source of information is the recently developed passive microwave satellite-derived land surface temperature derivation methodology described in Williams et al. These data are blended on a 1° × 1° grid that excludes only ice- and snow-covered regions lacking in situ observations. Available starting in January 1992 and updated 10 days after the end of the calendar month, this product is useful for monitoring regional climate anomalies and provides insights into climate variations.
A near-global surface temperature dataset was produced by blending several sources of information. For the oceans, these include in situ and infrared satellite-derived sea surface temperatures that were already processed into a monthly product. Land data analysis uses two sources of data. The first is high quality monthly in situ reports from the Global Historical Climatologic Network with more than 1000 stations from around the world. The second source of information is the recently developed passive microwave satellite-derived land surface temperature derivation methodology described in Williams et al. These data are blended on a 1° × 1° grid that excludes only ice- and snow-covered regions lacking in situ observations. Available starting in January 1992 and updated 10 days after the end of the calendar month, this product is useful for monitoring regional climate anomalies and provides insights into climate variations.
Abstract
During the winter, lake-effect snowstorms that form over Lake Ontario represent a significant weather hazard for the populace around the lake. These storms, which typically are only 2 km deep, frequently can produce narrow swaths (20–50 km wide) of heavy snowfall (2–5 cm h−1 or more) that extend 50–75 km inland over populated areas. Subtle changes in the low-altitude flow direction can mean the difference between accumulations that last for 1–2 h and accumulations that last 24 h or more at a given location. Therefore, it is vital that radars surrounding the lake are able to detect the presence and strength of these shallow storms. Starting in 2002, the Canadian operational radars on the northern side of the lake at King City, Ontario, and Franktown, Ontario, began using elevation angles of as low as −0.1° and 0.0°, respectively, during the winter to more accurately estimate snowfall rates at the surface. Meanwhile, Weather Surveillance Radars-1988 Doppler in New York State on the southern and eastern sides of the lake—Buffalo (KBUF), Binghamton (KBGM), and Montague (KTYX)—all operate at 0.5° and above. KTYX is located on a plateau that overlooks the lake from the east at a height of 0.5 km. With its upward-pointing radar beams, KTYX’s detection of shallow lake-effect snowstorms is limited to the eastern quarter of the lake and surrounding terrain. The purpose of this paper is to show—through simulations—the dramatic increase in snowstorm coverage that would be possible if KTYX were able to scan downward toward the lake’s surface. Furthermore, if KBUF and KBGM were to scan as low as 0.2°, detection of at least the upper portions of lake-effect storms over Lake Ontario and all of the surrounding land area by the five radars would be complete. Overlake coverage in the lower half (0–1 km) of the typical lake-effect snowstorm would increase from about 40% to about 85%, resulting in better estimates of snowfall rates in landfalling snowbands over a much broader area.
Abstract
During the winter, lake-effect snowstorms that form over Lake Ontario represent a significant weather hazard for the populace around the lake. These storms, which typically are only 2 km deep, frequently can produce narrow swaths (20–50 km wide) of heavy snowfall (2–5 cm h−1 or more) that extend 50–75 km inland over populated areas. Subtle changes in the low-altitude flow direction can mean the difference between accumulations that last for 1–2 h and accumulations that last 24 h or more at a given location. Therefore, it is vital that radars surrounding the lake are able to detect the presence and strength of these shallow storms. Starting in 2002, the Canadian operational radars on the northern side of the lake at King City, Ontario, and Franktown, Ontario, began using elevation angles of as low as −0.1° and 0.0°, respectively, during the winter to more accurately estimate snowfall rates at the surface. Meanwhile, Weather Surveillance Radars-1988 Doppler in New York State on the southern and eastern sides of the lake—Buffalo (KBUF), Binghamton (KBGM), and Montague (KTYX)—all operate at 0.5° and above. KTYX is located on a plateau that overlooks the lake from the east at a height of 0.5 km. With its upward-pointing radar beams, KTYX’s detection of shallow lake-effect snowstorms is limited to the eastern quarter of the lake and surrounding terrain. The purpose of this paper is to show—through simulations—the dramatic increase in snowstorm coverage that would be possible if KTYX were able to scan downward toward the lake’s surface. Furthermore, if KBUF and KBGM were to scan as low as 0.2°, detection of at least the upper portions of lake-effect storms over Lake Ontario and all of the surrounding land area by the five radars would be complete. Overlake coverage in the lower half (0–1 km) of the typical lake-effect snowstorm would increase from about 40% to about 85%, resulting in better estimates of snowfall rates in landfalling snowbands over a much broader area.
Abstract
The frequencies flown on the Special Sensor Microwave Imager (SSM/I) are sensitive to liquid water near the earth's surface. These frequencies are primarily atmospheric window channels, which receive the majority of their radiation from the surface. Liquid water near the surface depresses the emissivity as a function of wavelength. The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation at 1/3° resolution. These data are averaged at 1° resolution throughout the globe for each month during the period of 1992–97, and the 6-yr monthly means and the monthly anomalies of the wetness index are computed from this base period. To quantify the relationship between precipitation and surface wetness, these anomalies are compared with precipitation anomalies derived from the Global Precipitation Climate Program. The analysis was performed for six agricultural regions across six continents. There is generally a good correspondence between the two variables. The correlation generally increases when the wetness index is compared with precipitation anomalies accumulated over a 2-month period. These results indicate that the wetness index has a strong correspondence to the upper layer of the soil moisture in many cultivated areas of the world. The region in southeastern Australia had the best relationship, with a correlation coefficient of 0.76. The Sahel, France, and Argentina showed that the wetness index had memory of precipitation anomalies from the previous months. The memory is shorter for southeastern Australia and central China. The weakest correlations occurred over the southeastern United States, where the surface is covered by dense vegetation. The unique signal, strengths, and weaknesses of the wetness index in each of the six study regions are discussed.
Abstract
The frequencies flown on the Special Sensor Microwave Imager (SSM/I) are sensitive to liquid water near the earth's surface. These frequencies are primarily atmospheric window channels, which receive the majority of their radiation from the surface. Liquid water near the surface depresses the emissivity as a function of wavelength. The relationship between brightness temperatures at different frequencies is used to dynamically derive the amount of liquid water in each SSM/I observation at 1/3° resolution. These data are averaged at 1° resolution throughout the globe for each month during the period of 1992–97, and the 6-yr monthly means and the monthly anomalies of the wetness index are computed from this base period. To quantify the relationship between precipitation and surface wetness, these anomalies are compared with precipitation anomalies derived from the Global Precipitation Climate Program. The analysis was performed for six agricultural regions across six continents. There is generally a good correspondence between the two variables. The correlation generally increases when the wetness index is compared with precipitation anomalies accumulated over a 2-month period. These results indicate that the wetness index has a strong correspondence to the upper layer of the soil moisture in many cultivated areas of the world. The region in southeastern Australia had the best relationship, with a correlation coefficient of 0.76. The Sahel, France, and Argentina showed that the wetness index had memory of precipitation anomalies from the previous months. The memory is shorter for southeastern Australia and central China. The weakest correlations occurred over the southeastern United States, where the surface is covered by dense vegetation. The unique signal, strengths, and weaknesses of the wetness index in each of the six study regions are discussed.
Abstract
This note describes changes that have been made to the National Centers for Environmental Prediction (NCEP) operational “early” eta model. The changes are 1) an decrease in horizontal grid spacing from 80 to 48 km, 2) incorporation of a cloud prediction scheme, 3) replacement of the original static analysis system with a 12-h intermittent data assimilation system using the eta model, and 4) the use of satellite-sensed total column water data in the eta optimum interpolation analysis. When tested separately, each of the four changes improved model performance. A quantitative and subjective evaluation of the full upgrade package during March and April 1995 indicated that the 48-km eta model was more skillful than the operational 80-km model in predicting the intensity and movement of large-scale weather systems. In addition, the 48-km eta model was more skillful in predicting severe mesoscale precipitation events than either the 80-km eta model, the nested grid model, or the NCEP global spectral model during the March-April 1995 period. The implementation of this new version of the operational early eta system was performed in October 1995.
Abstract
This note describes changes that have been made to the National Centers for Environmental Prediction (NCEP) operational “early” eta model. The changes are 1) an decrease in horizontal grid spacing from 80 to 48 km, 2) incorporation of a cloud prediction scheme, 3) replacement of the original static analysis system with a 12-h intermittent data assimilation system using the eta model, and 4) the use of satellite-sensed total column water data in the eta optimum interpolation analysis. When tested separately, each of the four changes improved model performance. A quantitative and subjective evaluation of the full upgrade package during March and April 1995 indicated that the 48-km eta model was more skillful than the operational 80-km model in predicting the intensity and movement of large-scale weather systems. In addition, the 48-km eta model was more skillful in predicting severe mesoscale precipitation events than either the 80-km eta model, the nested grid model, or the NCEP global spectral model during the March-April 1995 period. The implementation of this new version of the operational early eta system was performed in October 1995.
Abstract
The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.
Abstract
The estimate of surface irradiance on a global scale is possible through radiative transfer calculations using satellite-retrieved surface, cloud, and aerosol properties as input. Computed top-of-atmosphere (TOA) irradiances, however, do not necessarily agree with observation-based values, for example, from the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents a method to determine surface irradiances using observational constraints of TOA irradiance from CERES. A Lagrange multiplier procedure is used to objectively adjust inputs based on their uncertainties such that the computed TOA irradiance is consistent with CERES-derived irradiance to within the uncertainty. These input adjustments are then used to determine surface irradiance adjustments. Observations by the Atmospheric Infrared Sounder (AIRS), Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), CloudSat, and Moderate Resolution Imaging Spectroradiometer (MODIS) that are a part of the NASA A-Train constellation provide the uncertainty estimates. A comparison with surface observations from a number of sites shows that the bias [root-mean-square (RMS) difference] between computed and observed monthly mean irradiances calculated with 10 years of data is 4.7 (13.3) W m−2 for downward shortwave and −2.5 (7.1) W m−2 for downward longwave irradiances over ocean and −1.7 (7.8) W m−2 for downward shortwave and −1.0 (7.6) W m−2 for downward longwave irradiances over land. The bias and RMS error for the downward longwave and shortwave irradiances over ocean are decreased from those without constraint. Similarly, the bias and RMS error for downward longwave over land improves, although the constraint does not improve downward shortwave over land. This study demonstrates how synergetic use of multiple instruments (CERES, MODIS, CALIPSO, CloudSat, AIRS, and geostationary satellites) improves the accuracy of surface irradiance computations.
