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- Author or Editor: Rachel Pinker x
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Abstract
Conventional observations of climate parameters are sparse in space and/or time, and the representativeness of such information needs to be optimized. Observations from satellites provide improved spatial coverage over point observations; however, they pose new challenges for obtaining homogeneous coverage. Surface radiative fluxes, the forcing functions of the hydrologic cycle and biogeophysical processes, are now becoming available from global-scale satellite observations. They are derived from independent satellite platforms and sensors that differ in temporal and spatial resolution and in the size of the footprint from which information is derived. Data gaps, degraded spatial resolution near boundaries of geostationary satellites, and different viewing geometries in areas of satellite overlap could result in biased estimates of radiative fluxes. In this study will be discussed issues related to the sources of inhomogeneity in surface radiative fluxes as derived from satellites, development of an approach to obtain homogeneous datasets, and application of the method to the widely used International Satellite Cloud Climatology Project data that currently serve as a source of information for deriving estimates of surface and top-of-the-atmosphere radiative fluxes. Introduced is an empirical orthogonal function (EOF) iteration scheme for homogenizing the fluxes. The scheme is evaluated in several ways, including comparison of the inferred radiative fluxes with ground observations, both before and after the EOF approach is applied. On the average, the latter reduces the RMS error by about 2–3 W m−2.
Abstract
Conventional observations of climate parameters are sparse in space and/or time, and the representativeness of such information needs to be optimized. Observations from satellites provide improved spatial coverage over point observations; however, they pose new challenges for obtaining homogeneous coverage. Surface radiative fluxes, the forcing functions of the hydrologic cycle and biogeophysical processes, are now becoming available from global-scale satellite observations. They are derived from independent satellite platforms and sensors that differ in temporal and spatial resolution and in the size of the footprint from which information is derived. Data gaps, degraded spatial resolution near boundaries of geostationary satellites, and different viewing geometries in areas of satellite overlap could result in biased estimates of radiative fluxes. In this study will be discussed issues related to the sources of inhomogeneity in surface radiative fluxes as derived from satellites, development of an approach to obtain homogeneous datasets, and application of the method to the widely used International Satellite Cloud Climatology Project data that currently serve as a source of information for deriving estimates of surface and top-of-the-atmosphere radiative fluxes. Introduced is an empirical orthogonal function (EOF) iteration scheme for homogenizing the fluxes. The scheme is evaluated in several ways, including comparison of the inferred radiative fluxes with ground observations, both before and after the EOF approach is applied. On the average, the latter reduces the RMS error by about 2–3 W m−2.
Abstract
The next generation of Geostationary Operational Environmental Satellites (GOES M–Q) will have only one thermal window channel instead of the current two split-window thermal channels. There is a need to evaluate the usefulness of this new configuration to retrieve parameters that presently are derived by utilizing the split-window characteristics. Two algorithms for deriving land surface temperatures (LSTs) from the GOES M–Q series have been developed and will be presented here. Both algorithms are based on radiative transfer theory; one uses ancillary total precipitable water (TPW) data, and the other is a two-channel (3.9 and 11.0 μm) algorithm that aims to improve atmospheric correction by utilizing the middle infrared (MIR) channel. The proposed algorithms are compared with a well-known generalized split-window algorithm. It is found that by adding TPW to the 11.0-μm channel, similar results to those from the generalized split-window algorithm are attained, and the combination of 3.9 and 11.0 μm yields further improvement. GOES M–Q retrievals (simulated with GOES-8 observations), when evaluated against skin temperature observations from the Oklahoma Mesonet, show that with the proposed two-channel algorithm, LST can be determined at an rms accuracy of about 2 K. The proposed algorithms are also applicable for the derivation of sea surface temperatures (SSTs) for which less restrictive assumptions on surface emissivity apply.
Abstract
The next generation of Geostationary Operational Environmental Satellites (GOES M–Q) will have only one thermal window channel instead of the current two split-window thermal channels. There is a need to evaluate the usefulness of this new configuration to retrieve parameters that presently are derived by utilizing the split-window characteristics. Two algorithms for deriving land surface temperatures (LSTs) from the GOES M–Q series have been developed and will be presented here. Both algorithms are based on radiative transfer theory; one uses ancillary total precipitable water (TPW) data, and the other is a two-channel (3.9 and 11.0 μm) algorithm that aims to improve atmospheric correction by utilizing the middle infrared (MIR) channel. The proposed algorithms are compared with a well-known generalized split-window algorithm. It is found that by adding TPW to the 11.0-μm channel, similar results to those from the generalized split-window algorithm are attained, and the combination of 3.9 and 11.0 μm yields further improvement. GOES M–Q retrievals (simulated with GOES-8 observations), when evaluated against skin temperature observations from the Oklahoma Mesonet, show that with the proposed two-channel algorithm, LST can be determined at an rms accuracy of about 2 K. The proposed algorithms are also applicable for the derivation of sea surface temperatures (SSTs) for which less restrictive assumptions on surface emissivity apply.
Abstract
This study focuses on documenting the seasonal progression of the Asian monsoon by analyzing clouds and convection in the pre-, peak-, and postmonsoon seasons. This effort was possible as a result of the movement of Meteosat-5 over the Indian continent during the Indian Ocean Experiment (INDOEX) starting in 1998. The Meteosat-5 observations provide a unique opportunity to study in detail the daytime diurnal variability of clouds and components of the radiation budget. Hourly Meteosat-5 observations are utilized to characterize the Indian monsoon daytime cloud variability on seasonal and diurnal time scales. Distinct patterns of variability can be identified during the various stages of the monsoon cycle. The daytime (0800–1500 LST) diurnal cycle of total cloud amounts is generally flat during the premonsoon season, U shaped during peak-monsoon season, and ascending toward an afternoon peak in the postmonsoon season. Low clouds dominate the Tibetan Plateau and northern Arabian Sea while high clouds are more frequent in the southern Bay of Bengal and Arabian Sea. An afternoon peak in high clouds is most prominent in central India and the Bay of Bengal. Afternoon convection peaks earlier over water than land. Preliminary comparison of cloud amounts from Meteosat-5, International Satellite Cloud Climatology Project (ISCCP) D1, and model output from the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis indicates a large disparity among cloud amounts from the various sources, primarily during the peak-monsoon period. The availability of the high spatial and temporal resolution of Meteosat-5 data is important for characterizing cloud variability in regions where clouds vary strongly in time and space and for the evaluation of numerical models known to have difficulties in predicting clouds correctly in this monsoon region. This study also has implications for findings on cloud variability from polar-orbiting satellites that might not correctly represent the daily average situation.
Abstract
This study focuses on documenting the seasonal progression of the Asian monsoon by analyzing clouds and convection in the pre-, peak-, and postmonsoon seasons. This effort was possible as a result of the movement of Meteosat-5 over the Indian continent during the Indian Ocean Experiment (INDOEX) starting in 1998. The Meteosat-5 observations provide a unique opportunity to study in detail the daytime diurnal variability of clouds and components of the radiation budget. Hourly Meteosat-5 observations are utilized to characterize the Indian monsoon daytime cloud variability on seasonal and diurnal time scales. Distinct patterns of variability can be identified during the various stages of the monsoon cycle. The daytime (0800–1500 LST) diurnal cycle of total cloud amounts is generally flat during the premonsoon season, U shaped during peak-monsoon season, and ascending toward an afternoon peak in the postmonsoon season. Low clouds dominate the Tibetan Plateau and northern Arabian Sea while high clouds are more frequent in the southern Bay of Bengal and Arabian Sea. An afternoon peak in high clouds is most prominent in central India and the Bay of Bengal. Afternoon convection peaks earlier over water than land. Preliminary comparison of cloud amounts from Meteosat-5, International Satellite Cloud Climatology Project (ISCCP) D1, and model output from the 40-yr ECMWF Re-Analysis (ERA-40) and the NCEP–NCAR reanalysis indicates a large disparity among cloud amounts from the various sources, primarily during the peak-monsoon period. The availability of the high spatial and temporal resolution of Meteosat-5 data is important for characterizing cloud variability in regions where clouds vary strongly in time and space and for the evaluation of numerical models known to have difficulties in predicting clouds correctly in this monsoon region. This study also has implications for findings on cloud variability from polar-orbiting satellites that might not correctly represent the daily average situation.
Abstract
Snow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.
Abstract
Snow-covered mountain ranges are a major source of water supply for runoff and groundwater recharge. Snowmelt supplies as much as 75% of the surface water in basins of the western United States. Net radiative fluxes make up about 80% of the energy balance over snow-covered surfaces. Because of the large extent of snow cover and the scarcity of ground observations, use of remotely sensed data is an attractive option for estimating radiative fluxes. Most of the available methods have been applied to low-spatial-resolution satellite observations that do not capture the spatial variability of snow cover, clouds, or aerosols, all of which need to be accounted for to achieve accurate estimates of surface radiative fluxes. The objective of this study is to use high-spatial-resolution observations that are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to derive surface shortwave (0.2–4.0 μm) downward radiative fluxes in complex terrain, with attention on the effect of topography (e.g., shadowing or limited sky view) on the amount of radiation received. The developed method has been applied to several typical melt seasons (January–July during 2003, 2004, 2005, and 2009) over the western part of the United States, and the available information was used to derive metrics on spatial and temporal variability of shortwave fluxes. Issues of scale in both the satellite and ground observations are also addressed to illuminate difficulties in the validation process of satellite-derived quantities. It is planned to apply the findings from this study to test improvements in estimation of snow water equivalent.
Abstract
A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime.
Abstract
A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime.
