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- Author or Editor: Anthony R. Guillory x
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Abstract
An algorithm is examined that uses Visible–Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) 11- and 12-µm (split-window) data to derive column-integrated water content (IWC) at mesoscale resolution. The algorithm is physically based and derives its first-guess information from radiosonde data. The procedure is applied first to a test case dataset and then to the 19 June 1986 study day from the Cooperative Huntsville Meteorological Experiment (COHMEX). Ground truth data for verifying results from the technique include IWC from National Weather Service and COHMEX radiosondes, the Multispectral Atmospheric Mapping Sensor (MAMS), and a special set of VAS soundings (12 channel) using an independent retrieval method. Results from the test case show reasonable accuracy with the root-mean-square errors as low as ±3.8 mm. On the 19 June case study day IWC analyses depict reasonable gradients and exhibit good spatial and temporal continuity. Furthermore, they provide insight into preferred regions for cumulus cloud and thunderstorm formation. On the average, a mean absolute retrieval error of 2.4 mm (an 8.1% error) and a root-mean-square error of ±2.9 mm are obtained on the case study day. These results compare favorably with those from existing VAS IWC techniques. Overall, the findings indicate that the technique has excellent potential to depict mesoscale moisture variations.
Abstract
An algorithm is examined that uses Visible–Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) 11- and 12-µm (split-window) data to derive column-integrated water content (IWC) at mesoscale resolution. The algorithm is physically based and derives its first-guess information from radiosonde data. The procedure is applied first to a test case dataset and then to the 19 June 1986 study day from the Cooperative Huntsville Meteorological Experiment (COHMEX). Ground truth data for verifying results from the technique include IWC from National Weather Service and COHMEX radiosondes, the Multispectral Atmospheric Mapping Sensor (MAMS), and a special set of VAS soundings (12 channel) using an independent retrieval method. Results from the test case show reasonable accuracy with the root-mean-square errors as low as ±3.8 mm. On the 19 June case study day IWC analyses depict reasonable gradients and exhibit good spatial and temporal continuity. Furthermore, they provide insight into preferred regions for cumulus cloud and thunderstorm formation. On the average, a mean absolute retrieval error of 2.4 mm (an 8.1% error) and a root-mean-square error of ±2.9 mm are obtained on the case study day. These results compare favorably with those from existing VAS IWC techniques. Overall, the findings indicate that the technique has excellent potential to depict mesoscale moisture variations.
Abstract
Mesoscale surface data and special satellite-derived soundings from the Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) are used to investigate a case of sudden mesoscale drying at the surface on 17 June 1986, a day during the Cooperative Huntsville Meteorological Experiment (COHMEX). Dewpoints fall as much as −6.3°C in less than 1 h over a small portion of central Tennessee. The drying occurs prior to the onset of convective activity. The satellite retrievals detect a narrow tongue of midtropospheric dry air that overlays moist air near the surface. The analyses indicate that heating-induced surface-based mixing penetrates the midlevels, bringing drier air to the surface and resulting in the sudden decreases in surface dewpoints.
Abstract
Mesoscale surface data and special satellite-derived soundings from the Visible Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) are used to investigate a case of sudden mesoscale drying at the surface on 17 June 1986, a day during the Cooperative Huntsville Meteorological Experiment (COHMEX). Dewpoints fall as much as −6.3°C in less than 1 h over a small portion of central Tennessee. The drying occurs prior to the onset of convective activity. The satellite retrievals detect a narrow tongue of midtropospheric dry air that overlays moist air near the surface. The analyses indicate that heating-induced surface-based mixing penetrates the midlevels, bringing drier air to the surface and resulting in the sudden decreases in surface dewpoints.
Abstract
The performance of a physical split-window retrieval algorithm used to retrieve skin temperature (ST) and precipitable water (PW) from Geostationary Operational Environmental Satellites’ (GOES) infrared measurements is evaluated. The evaluation assesses the potential of using GOES measurements to provide accurate retrieval products for climate research studies. Several algorithm performance issues are addressed, including the time of retrieval (diurnal effects), sensitivity to the first-guess field, and an evaluation of performance differences associated with the split-window channel characteristics of the GOES-7 VISSR (Visible and Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS) and the GOES-8 imager and sounder. The investigation used a mesoscale model, initialized by radiosonde data, to generate a simulated atmosphere representative of a case study characterized by summertime conditions over the east-central United States. Synthetic GOES channel radiances were developed from the surrogate atmosphere using GOES channel response functions and an appropriate radiative transfer code. The model fields also provided the necessary ground truth and first-guess field for the retrieval algorithm. Retrievals of ST and PW were made from the simulated channel radiances associated with the VAS and GOES-8 imager and sounder split-window channels. Retrieval methodologies were applied to address issues of importance in climate research studies, such as long-term trends and diurnal variability of ST and PW. The performance was measured by comparing the retrieved values with the model values at each of the retrieval locations.
The algorithm performance for both ST and PW was found to be sensitive to the quality of the first-guess field and to the channel characteristics of the GOES sensors. An estimate of the lower bound on ST and PW retrieval errors was determined. The ST retrievals in all cases showed a significant improvement over the first-guess values. The GOES-8 imager ST retrieval errors, which were about half of the VAS values, ranged from about 0.2 to 0.6 K, exhibiting little diurnal effect. The PW retrieval errors ranged from about 2.0 to 7.0 mm with a modest sensitivity to the different sensor channels. However, a significant diurnal trend in the PW retrieval errors that correlated with the presence of surface- and low-level temperature inversions was observed. The algorithm performance results provide insight into the application of GOES split-window retrieval methodologies for climate variability studies and may have implications for operational applications of similar retrieval techniques.
Abstract
The performance of a physical split-window retrieval algorithm used to retrieve skin temperature (ST) and precipitable water (PW) from Geostationary Operational Environmental Satellites’ (GOES) infrared measurements is evaluated. The evaluation assesses the potential of using GOES measurements to provide accurate retrieval products for climate research studies. Several algorithm performance issues are addressed, including the time of retrieval (diurnal effects), sensitivity to the first-guess field, and an evaluation of performance differences associated with the split-window channel characteristics of the GOES-7 VISSR (Visible and Infrared Spin Scan Radiometer) Atmospheric Sounder (VAS) and the GOES-8 imager and sounder. The investigation used a mesoscale model, initialized by radiosonde data, to generate a simulated atmosphere representative of a case study characterized by summertime conditions over the east-central United States. Synthetic GOES channel radiances were developed from the surrogate atmosphere using GOES channel response functions and an appropriate radiative transfer code. The model fields also provided the necessary ground truth and first-guess field for the retrieval algorithm. Retrievals of ST and PW were made from the simulated channel radiances associated with the VAS and GOES-8 imager and sounder split-window channels. Retrieval methodologies were applied to address issues of importance in climate research studies, such as long-term trends and diurnal variability of ST and PW. The performance was measured by comparing the retrieved values with the model values at each of the retrieval locations.
The algorithm performance for both ST and PW was found to be sensitive to the quality of the first-guess field and to the channel characteristics of the GOES sensors. An estimate of the lower bound on ST and PW retrieval errors was determined. The ST retrievals in all cases showed a significant improvement over the first-guess values. The GOES-8 imager ST retrieval errors, which were about half of the VAS values, ranged from about 0.2 to 0.6 K, exhibiting little diurnal effect. The PW retrieval errors ranged from about 2.0 to 7.0 mm with a modest sensitivity to the different sensor channels. However, a significant diurnal trend in the PW retrieval errors that correlated with the presence of surface- and low-level temperature inversions was observed. The algorithm performance results provide insight into the application of GOES split-window retrieval methodologies for climate variability studies and may have implications for operational applications of similar retrieval techniques.
