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  • Author or Editor: Gary Jedlovec x
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Gary J. Jedlovec

Abstract

A technique that uses the spatial variance of image brightness temperature to derive total column Precipitable water is applied to high-resolution multispectral aircraft scanner data for the 19 June 1986 COHMEX day. The technique has several advantages over other approaches in that it requires only relative calibration accuracy, is less susceptible to instrument error, and does not directly use a priori information. Results indicate significant horizontal variability of precipitable water at the mesoscale. Precipitable water gradients of 6 mm per 10 km are not uncommon. The results verify well against special rawinsonde measurements and the ensuing cloud field development. While only applied to this specialized aircraft data, the applicability of the technique to operational AVHRR and VAS data is discussed.

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Anthony R. Guillory, Gary J. Jedlovec, and Henry E. Fuelberg

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.

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Gary J. Jedlovec, Jeffrey A. Lerner, and Robert J. Atkinson

Abstract

A new approach is presented to quantify upper-level moisture transport from geostationary satellite data. Daily time sequences of Geostationary Operational Environmental Satellite GOES-7 water vapor imagery were used to produce estimates of winds and water vapor mixing ratio in the cloud-free region of the upper troposphere sensed by the 6.7-μm water vapor channel. The winds and mixing ratio values were gridded and then combined to produce a parameter called the water vapor transport index (WVTI), which represents the magnitude of the two-dimensional transport of water vapor in the upper troposphere. Daily grids of WVTI, meridional moisture transport, mixing ratio, pressure, and other associated parameters were averaged to produce monthly fields for June, July, and August (JJA) of 1987 and 1988 over the Americas and surrounding oceanic regions. The WVTI was used to compare upper-tropospheric moisture transport between the summers of 1987 and 1988, contrasting the latter part of the 1986/87 El Niño event and the La Niña period of 1988. A similar product derived from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) 40-Year Reanalysis Project was used to help to validate the index. Although the goal of this research was to describe the formulation and utility of the WVTI, considerable insight was obtained into the interannual variability of upper-level water vapor transport.

Both datasets showed large upper-level water vapor transport associated with synoptic features over the Americas and with outflow from tropical convective systems. Minimal transport occurred over tropical and subtropical high pressure regions where winds were light. Index values from NCEP–NCAR were 2–3 times larger than that determined from GOES. This difference resulted from large zonal wind differences and an apparent overestimate of upper-tropospheric moisture in the reanalysis model.

A comparison of the satellite-derived monthly values between the summers of 1987 and 1988 provided some insight into the impact of the ENSO event on upper-level moisture and its transport during the period. During July 1987, a large portion of the Tropics in the eastern Pacific Ocean and Caribbean Sea was dominated by strong vapor transport in excess of 4.0 g kg−1 m s−1, with relatively small amounts in the other months. JJA 1988 transport values reached similar magnitude and showed similar patterns for all three months. The meridional transport of upper-level water vapor indicated large poleward transport from the Tropics to the higher latitudes. This transport favored the Southern Hemisphere, with large transport occurring south of the ITCZ, which extended across the eastern Pacific and northern South America. Zonally averaged monthly transport values were shown to provide a simple way to quantify the monthly and interannual changes in water vapor transport. Zonally averaged WVTI values peaked in the Southern Hemisphere subtropics during both austral winters. In the Tropics, a single, more-pronounced peak located over the equator and south latitudes occurred in 1988 as opposed to a dual peak in 1987. The second peak around 20°N latitude is consistent with findings of others in which upper-tropospheric winds were noted to be stronger in this region during warm ENSO events. Zonally averaged meridional transport was southward for all summer months and was stronger in 1988. The asymmetric nature of the zonally averaged meridional transport (more southerly water vapor transport) was enhanced during JJA 1988, thus indicating a stronger upper-level branch of the Hadley circulation during this notably strong La Niña period.

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Ronnie J. Suggs, Gary J. Jedlovec, and Anthony R. Guillory

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.

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Richard T. McNider, William M. Lapenta, Arastoo P. Biazar, Gary J. Jedlovec, Ronnie J. Suggs, and Jonathan Pleim

Abstract

In weather forecast and general circulation models the behavior of the atmospheric boundary layer, especially the nocturnal boundary layer, can be critically dependent on the magnitude of the effective model grid-scale bulk heat capacity. Yet, this model parameter is uncertain both in its value and in its conceptual meaning for a model grid in heterogeneous conditions. Current methods for estimating the grid-scale heat capacity involve the areal/volume weighting of heat capacity (resistance) of various, often ill-defined, components. This can lead to errors in model performance in certain parameter spaces. Here, a technique is proposed and tested for recovering bulk heat capacity using time tendencies in satellite-retrieved surface skin temperature (SST). The technique builds upon sensitivity studies that show that surface temperature is most sensitive to thermal inertia in the early evening hours. The retrievals are made within the context of a surface energy budget in a regional-scale model [the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5)]. The retrieved heat capacities are used in the forecast model, and it is shown that the model predictions of temperature are improved in the nighttime during the forecast periods.

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