Abstract

Statistical procedures are used to compare vertical profiles of temperature and moisture derived from VAS with three different algorithms to those of corresponding rawinsonde measurements for a clear-cold environment. To account for time and space discrepancies between the data, rawinsonde values were adjusted to the satellite sounding times. Both rawinsonde and satellite soundings were objectively analyzed onto a mesoscale grid. These grid point values were compared at 50 mb pressure increments from the surface up to 100 mb. The data were analyzed for horizontal and vertical structure, representativeness of derived parameter, and significant departure (improvement) from the a priori (first guess) information.

Results indicate strong temperature and moisture biases in the satellite soundings. Temperature biases of 1–4°C and dew-point biases of 2–6°C generally occur in layers where strong inversions are present. Magnitudes vary with time as the atmospheric features evolve. The biases change as a function retrieval scheme, suggesting limitations and restrictions on the applications of the various techniques. Standard deviations of temperature range from 1–2°C for each retrieval scheme with maxima near 800 and 400 mb. Derived parameters (precipitable water and thickness) suffer from similar biases, though to a somewhat lesser extent. Although satellite-derived gradients of basic and derived parameters are generally weaker than those from rawinsondes, they have good horizontal structure where magnitudes of the parameters are relatively strong. Integrated thermal (thickness) and moisture (precipitable water) parameters show mixed results. Although biases are small in the precipitable water values from the regression scheme, horizontal structure is poor.

Analysis of first guess information shows similar biases when compared to the ground truth measurements. This information, however, seems to provide the majority of the vertical structure present in the VAS retrievals.

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.

Abstract

The kinetic energy balance during the Red River Valley tornado outbreak (10–11 April 1979) is examined using mesa a-scale rawinsonde data from the first regional-scale day of AVE-SESAME 1979. Computational procedures account for non-simultaneous sonde releases, sonde drift downwind, and the substitution for some missing data. When the entire area is considered for the composite 2A h period, horizontal flux convergence due to jet intrusion is the primary kinetic energy source to the region. Cross-contour destruction of kinetic energy is the primary sink. Using the 3 h data, the energy balance is seen to change considerably throughout the experiment. Horizontal maps and limited area budgets are used to examine the energetics of a low-level jet stream and an upper-level wind maximum. Both features are maintained primarily by cross-contour kinetic energy generation that may be related to feedback mechanisms from the severe storm outbreak. Random error analyses are used to quantify confidence limits in the energy budget parameters.

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.

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

Red–green–blue (RGB) composite imagery combines information from several spectral channels into one image to aid in the operational analysis of atmospheric processes. However, infrared channels are adversely affected by the limb effect, the result of an increase in optical pathlength of the absorbing atmosphere between the satellite and the earth as viewing zenith angle increases. This study develops a technique to quickly correct for limb effects in both clear and cloudy regions using latitudinally and seasonally varying limb correction coefficients for real-time applications. These limb correction coefficients account for the increase in optical pathlength in order to produce limb-corrected RGB composites. The improved functionality of limb-corrected RGB composites is demonstrated by multiple case studies of Air Mass and Dust RGB composites using Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) and Suomi–National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) imagery. However, the limb correction can be applied to any polar-orbiting sensor infrared channels, provided the proper limb correction coefficients are calculated. Corrected RGB composites provide multiple advantages over uncorrected RGB composites, including increased confidence in the interpretation of RGB features, improved situational awareness for operational forecasters, and the ability to use RGB composites from multiple sensors jointly to increase the temporal frequency of observations.

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

Red–green–blue (RGB) composites are increasingly used by operational forecasters to interpret vast amounts of satellite imagery by reducing several bands into a single, easily understood product which identifies important atmospheric features with unique colors. Limb effects, a result of an increase in optical pathlength of the absorbing atmosphere between the satellite and Earth as viewing zenith angle increases, adversely affects RGB composite interpretation by causing anomalous reductions in brightness temperature, thus changing the color interpretation of the RGB composites. In a previous paper, Elmer et al. demonstrated a limb correction technique that effectively removes limb effects from polar-orbiting infrared channels in both clear and cloudy regions using latitudinally and seasonally varying correction coefficients. This study applies the Elmer et al. limb correction to Air Mass RGB composites derived from geostationary sensors and compares the limb-corrected geostationary imagery to limb-corrected polar-orbiter imagery and satellite-derived atmospheric profiles. A statistical comparison in overlapping regions shows that the limb correction reduces the absolute mean brightness temperature difference from 4–12 K to 0–2 K for all infrared bands, demonstrating that the Elmer et al. limb correction algorithm is a robust method of removing limb effects from infrared imagery derived from both geostationary and polar-orbiting sensors. The limb-corrected RGB composites derived from geostationary sensors present several advantages, including the improved depiction of atmospheric features and enabling wider use of imagery from overlapping geostationary coverage regions where viewing zenith angles are large for both geostationary sensors.

Abstract

The damage surveys conducted by the NWS in the aftermath of a reported tornadic event are used to document the location of the tornado ground damage track (pathlength and width) and an estimation of the tornado intensity. This study explores the possibility of using near-real-time medium and high spatial resolution satellite imagery from the NASA Earth Observing System satellites to provide additional information for the surveys. Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) data were used to study the damage tracks from three tornadic storms: the La Plata, Maryland, storm of 28 April 2002 and the Ellsinore and Marquand, Missouri, storms of 24 April 2002. These storms varied in intensity and occurred over regions with significantly different land cover. It was found that, depending on the nature of the land cover, tornado damage tracks from intense storms (F1 or greater) and hail storms may be evident in ASTER, Landsat, and MODIS satellite imagery. In areas where the land cover is dominated by forests, the scar patterns can show up very clearly, while in areas of grassland and regions with few trees, scar patterns are not as obvious or cannot be seen at all in the satellite imagery. The detection of previously unidentified segments of a damage track caused by the 24 April 2002 Marquand, Missouri, tornado demonstrates the utility of satellite imagery for damage surveys. However, the capability to detect tornado tracks in satellite imagery depends on the ability to observe the ground without obstruction from space and appears to be as much dependent on the nature of the underlying surface and land cover as on the severity of the tornadic storm.

Abstract

It is hypothesized that high-resolution, accurate representations of surface properties such as soil moisture and sea surface temperature are necessary to improve simulations of summertime pulse-type convective precipitation in high-resolution models. This paper presents model verification results of a case study period from June to August 2008 over the southeastern United States using the Weather Research and Forecasting numerical weather prediction model. Experimental simulations initialized with high-resolution land surface fields from the National Aeronautics and Space Administration’s (NASA) Land Information System (LIS) and sea surface temperatures (SSTs) derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) are compared to a set of control simulations initialized with interpolated fields from the National Centers for Environmental Prediction’s (NCEP) 12-km North American Mesoscale model. The LIS land surface and MODIS SSTs provide a more detailed surface initialization at a resolution comparable to the 4-km model grid spacing. Soil moisture from the LIS spinup run is shown to respond better to the extreme rainfall of Tropical Storm Fay in August 2008 over the Florida peninsula. The LIS has slightly lower errors and higher anomaly correlations in the top soil layer but exhibits a stronger dry bias in the root zone. The model sensitivity to the alternative surface initial conditions is examined for a sample case, showing that the LIS–MODIS data substantially impact surface and boundary layer properties. The Developmental Testbed Center’s Meteorological Evaluation Tools package is employed to produce verification statistics, including traditional gridded precipitation verification and output statistics from the Method for Object-Based Diagnostic Evaluation (MODE) tool. The LIS–MODIS initialization is found to produce small improvements in the skill scores of 1-h accumulated precipitation during the forecast hours of the peak diurnal convective cycle. Because there is very little union in time and space between the forecast and observed precipitation systems, results from the MODE object verification are examined to relax the stringency of traditional gridpoint precipitation verification. The MODE results indicate that the LIS–MODIS-initialized model runs increase the 10 mm h⁻¹ matched object areas (“hits”) while simultaneously decreasing the unmatched object areas (“misses” plus “false alarms”) during most of the peak convective forecast hours, with statistically significant improvements of up to 5%. Simulated 1-h precipitation objects in the LIS–MODIS runs more closely resemble the observed objects, particularly at higher accumulation thresholds. Despite the small improvements, however, the overall low verification scores indicate that much uncertainty still exists in simulating the processes responsible for airmass-type convective precipitation systems in convection-allowing models.