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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2008

Forecasting Cool Season Daily Peak Winds at Kennedy Space Center and Cape Canaveral Air Force Station

The expected peak wind speed for the day is an important element in the daily 24-Hour and Weekly Planning Forecasts issued by the 45th Weather Squadron (45 WS) for planning operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The morning outlook for peak speeds also begins the warning decision process for gusts ^ 35 kt, ^ 50 kt, and ^ 60 kt from the surface to 300 ft. The 45 WS forecasters have indicated that peak wind speeds are a challenging parameter to forecast during the cool season (October-April). The 45 WS requested that the Applied Meteorology Unit (AMU) develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. The tool must only use data available by 1200 UTC to support the issue time of the Planning Forecasts. Based on observations from the KSC/CCAFS wind tower network, surface observations from the Shuttle Landing Facility (SLF), and CCAFS upper-air soundings from the cool season months of October 2002 to February 2007, the AMU created multiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence, the temperature inversion depth, strength, and wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft. Six synoptic patterns were identified: 1) surface high near or over FL, 2) surface high north or east of FL, 3) surface high south or west of FL, 4) surface front approaching FL, 5) surface front across central FL, and 6) surface front across south FL. The following six predictors were selected: 1) inversion depth, 2) inversion strength, 3) wind gust factor, 4) synoptic weather pattern, 5) occurrence of precipitation at the SLF, and 6) strongest wind in the lowest 3000 ft. The forecast tool was developed as a graphical user interface with Microsoft Excel to help the forecaster enter the variables, and run the appropriate regression equations. Based on the forecaster's input and regression equations, a forecast of the day's peak and average wind is generated and displayed. The application also outputs the probability that the peak wind speed will be ^ 35 kt, 50 kt, and 60 kt.

Barrett, Joe, III↗
NASA NTRS1982

The useful potential of using existing data to uniquely identify predictable wind events and regimes, part 2

Wind data from four sites were stratified and found to naturally fit into a few unique groups. These were compared with synoptic weather patterns using the Booz-Allen classification system. Strong relationships became evident between a particular synoptic type and wind events for each site. Statistics indicate certain patterns which result in strong winds and some that result in weak winds. For each site there is a preferred wind direction associated with the strongest speed. Important relationships were also found comparing 850-mb and surface wind. Additionally, comparisons between pressure gradient and wind speed for a given gradient direction show some significant relationships. It can be stated that the overall results show what by using existing data for any site, the winds can be characterized and correlated with synoptic weather patterns. As a result, reliable wind forecasts can be made for utility companies for the purpose of power generation.

Notis, C.↗
NASA NTRS1986

Empirical and modeled synoptic cloud climatology of the Arctic Ocean

A set of cloud cover data were developed for the Arctic during the climatically important spring/early summer transition months. Parallel with the determination of mean monthly cloud conditions, data for different synoptic pressure patterns were also composited as a means of evaluating the role of synoptic variability on Arctic cloud regimes. In order to carry out this analysis, a synoptic classification scheme was developed for the Arctic using an objective typing procedure. A second major objective was to analyze model output of pressure fields and cloud parameters from a control run of the Goddard Institue for Space Studies climate model for the same area and to intercompare the synoptic climatatology of the model with that based on the observational data.

Barry, R. G.↗
NASA NTRS2010

Investigation of the 2006 Drought and 2007 Flood Extremes at the Southern Great Plains Through an Integrative Analysis of Observations

Hydrological years 2006 (HY06, 10/2005-09/2006) and 2007 (HY07, 10/2006-09/2007) provide a unique opportunity to examine hydrological extremes in the central US because there are no other examples of two such highly contrasting precipitation extremes occurring in consecutive years at the Southern Great Plains (SGP) in recorded history. The HY06 annual precipitation in the state of Oklahoma, as observed by the Oklahoma Mesonet, is around 61% of the normal (92.84 cm, based on the 1921-2008 climatology), which results in HY06 the second-driest year in the record. In particular, the total precipitation during the winter of 2005-06 is only 27% of the normal, and this winter ranks as the driest season. On the other hand, the HY07 annual precipitation amount is 121% of the normal and HY07 ranks as the seventh-wettest year for the entire state and the wettest year for the central region of the state. Summer 2007 is the second-wettest season for the state. Large-scale dynamics play a key role in these extreme events. During the extreme dry period (10/2005-02/2006), a dipole pattern in the 500-hPa GH anomaly existed where an anomalous high was over the southwestern U.S. region and an anomalous low was over the Great Lakes. This pattern is associated with inhibited moisture transport from the Gulf of Mexico and strong sinking motion over the SGP, both contributing to the extreme dryness. The precipitation deficit over the SGP during the extreme dry period is clearly linked to significantly suppressed cyclonic activity over the southwestern U.S., which shows robust relationship with the Western Pacific (WP) teleconnection pattern. The precipitation events during the extreme wet period (May-July 2007) were initially generated by active synoptic weather patterns, linked with moisture transport from the Gulf of Mexico by the northward low level jet, and enhanced by the mesoscale convective systems. Although the drought and pluvial conditions are dominated by large-scale dynamic patterns, we have demonstrated that the two positive feedback processes during the extreme dry and wet periods found in this study play a key role to maintain and reinforce the length and severity of existing drought and flood events. For example, during the extreme dry period, with less clouds, LWP, PWV, precipitation, and thinner Cu cloud thickness, more net radiation was absorbed and used to evaporate water from the ground. The evaporated moisture, however, was removed by low-level divergence. Thus, with less precipitation and removed atmospheric moisture, more absorbed incoming solar radiation was used to increase surface temperature and to make the ground drier.

Dong, Xiquan↗
NASA NTRS1981

Optimum employment of satellite indirect soundings as numerical model input

The characteristics of satellite-derived temperature soundings that would significantly affect their use as input for numerical weather prediction models were examined. Independent evaluations of satellite soundings were emphasized to better define error characteristics. Results of a Nimbus-6 sounding study reveal an underestimation of the strength of synoptic scale troughs and ridges, and associated gradients in isobaric height and temperature fields. The most significant errors occurred near the Earth's surface and the tropopause. Soundings from the TIROS-N and NOAA-6 satellites were also evaluated. Results again showed an underestimation of upper level trough amplitudes leading to weaker thermal gradient depictions in satellite-only fields. These errors show a definite correlation to the synoptic flow patterns. In a satellite-only analysis used to initialize a numerical model forecast, it was found that these synoptically correlated errors were retained in the forecast sequence.

Horn, L. H.↗
NASA NTRS2002

The Effects of Amazon Deforestation on Rainfall

This study begins with the hypothesis that heavily deforested regions will experience increased surface heating, leading to local circulations that will ultimately enhance the rainfall, or at least, change the pattern of diurnal evolution of rainfall. This would be an important finding because several modeling studies have concluded that widespread deforestation would lead to decreased rainfall. Towards that end rain estimates from a combined GOES infrared/TRMM microwave technique were analyzed with respect to percent forest cover from Landsat data (courtesy of TRFIC at Michigan State University) and GOES visible channel data over a deforested area in Rondonia (southwest Brazil). Five 1" x 1" areas of varying forest cover were examined during the onset of the wet season in Amazonia (Aug-Sept), when the effects of the surface would not be dominated by large-scale synoptic weather patterns. Preliminary results revealed that: maximum rainfall fell in most deforested area; heavily forested areas received the least rainfall; cumulus cloud development initiated at borders; the amplitude of the diurnal cycle of precipitation was a function of th surface cover. Further work will be presented detailing effects of land surface cover on the GOES infrared-measured surface heating, GOES visible observed cumulus development, thunderstorm initiation based on the location of temperature minima in the infrared data, and estimated rainfall and its diurnal cycle from a combined GOES/TRMM technique. Rainfall estimates derived from non-geosynchronous microwave observations (i.e. Goddard Profiling Algorithm, GPROF) will also be examined.

Starr, David OC.↗
NASA NTRS1982

Use of remote sensing for land use policy formulation

Research projects described include: (1) identifying coniferous forest types in Michigan using LANDSAT imagery; (2) investigating synoptic temperature patterns in Michigan as determined via GOES and HCMM thermal imagery; (3) land surface change detection using satellite data and a geographic data base; (4) determining soil map unit composition by electronic scanning densitometry; and (5) delimiting areas of virus infection in vineyards and blueberry fields in southwestern and western Michigan. Contractual activities involve important farmlands inventory, changes in aquatic vegetation in Saginaw Bay, digitized soil association map of Michigan, and aerial photography for hybrid-poplar research. On-going projects are also being conducted in Jamaica, Honduras, the Dominican Republic and Kenya.

Source record↗
NASA NTRS1985

A First Evaluation of LANDSAT TM Data to Monitor Suspended Sediments in Lakes

The use of LANDSAT to monitor and track changes in the water quality of Lake Chicot, Arizona was assessed using MSS and TM digital data from nine water sites. Results show that: (1) TM Bands 1, 2, 3, and 4 appear to be providing information on concentrations of particulate matter suspended in surface waters. These bands are also highly interrelated for water samples; (2) preliminary evaluation indicates that TM Band 3 showed the best relationship to surface suspended solids; (3) TM Bands 5 and 7 are useful for separating water from nonwater areas; (4) the MSS Bands 2 and 3 can be related to suspended solids in surface water, as has already been shown from previous LANDSAT research; and (5) analysis of TM Band 6 indicates that while synoptic temperature patterns may be discerned, the digital sensitivity to a two degree temperature difference is low.

Schiebe, F. R.↗
NASA NTRS1984

Analysis of tropical synoptic disturbances using satellite-derived soundings and radiance data from selected channels

Some of the results of comparing and combining conventional meteorological data with data retrieved from satellite radiance measurements in determining the atmospheric structure associated with a portion of the life cycle of an eastern Pacific moisture burst are presented. The satellite-derived data, consisting of microwave and water vapor channel brightness temperature fields and cloud-tracked winds, detect synoptic scale patterns of temperature, moisture, and wind. The microwave channel temperatures provide 'all weather' coverage but require special interpretation in rain areas. The need for further investigations to improve the accuracy of inferring structure from satellite-derived information is emphasized.

Thompson, A. H.↗
NASA NTRS1988

Cloud cover determination in polar regions from satellite imagery

The principal objectives of this project are: to develop suitable validation data sets to evaluate the effectiveness of the ISCCP operational algorithm for cloud retrieval in polar regions and to validate model simulations of polar cloud cover; to identify limitations of current procedures for varying atmospheric surface conditions, and to explore potential means to remedy them using textural classifiers: and to compare synoptic cloud data from a control run experiment of the Goddard Institute for Space Studies (GISS) climate model 2 with typical observed synoptic cloud patterns. Current investigations underway are listed and the progress made to date is summarized.

Barry, R. G.↗
NASA NTRS1988

Cloud cover determination in polar regions from satellite imagery

The principal objectives of this project are: (1) to develop suitable validation data sets to evaluate the effectiveness of the International Satellite Cloud Climatology Project (ISCCP) operational algorithm for cloud retrieval in polar regions and to validate model simulations of polar cloud cover; (2) to identify limitations of current procedures for varying atmospheric surface conditions, and to explore potential means to remedy them using textural classifiers; and (3) to compare synoptic cloud data from a control run experiment of the GISS climate model II with typical observed synoptic cloud patterns.

Barry, R. G.↗
NASA NTRS1997

The Remote Sensing of Mineral Aerosols and their Impact on Phytoplankton Productivity

The overall objective of this experiment was to test the iron hypothesis does the addition of iron to nutrient rich surface waters enhance productivity? Our specific objectives in this experiment included sampling and studying the marine aerosol size and type (which are related to chemical reactivity) during the PlumEx cruise to determine the importance of local (Galapagos Islands) versus long-range sources of atmospheric material. Detailed results of single particle analysis of our samples are being prepared for publication in two papers. We collect aerosol samples and they have been analyzed for trace metals and other elements. We are mapped aerosol distribution and the desert source areas around the Arabian Sea region. We did record a clear relationship between the aerosol radiance and synoptic weather patterns with distinct signals over the ocean northwest and southwest of Australia. While the interpretation was limited an aerosol climatology pattern was presented.

Tindale, Neil W.↗
NASA NTRS2008

Peak Wind Tool for General Forecasting

This report describes work done by the Applied Meteorology Unit (AMU) in predicting peak winds at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron requested the AMU develop a tool to help them forecast the speed and timing of the daily peak and average wind, from the surface to 300 ft on KSC/CCAFS during the cool season. Based on observations from the KSC/CCAFS wind tower network , Shuttle Landing Facility (SLF) surface observations, and CCAFS sounding s from the cool season months of October 2002 to February 2007, the AMU created mul tiple linear regression equations to predict the timing and speed of the daily peak wind speed, as well as the background average wind speed. Several possible predictors were evaluated, including persistence , the temperature inversion depth and strength, wind speed at the top of the inversion, wind gust factor (ratio of peak wind speed to average wind speed), synoptic weather pattern, occurrence of precipitation at the SLF, and strongest wind in the lowest 3000 ft, 4000 ft, or 5000 ft.

Barrett, Joe H., III↗
NASA NTRS2010

Peak Wind Tool for General Forecasting

The expected peak wind speed of the day is an important forecast element in the 45th Weather Squadron's (45 WS) daily 24-Hour and Weekly Planning Forecasts. The forecasts are used for ground and space launch operations at the Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45 WS also issues wind advisories for KSC/CCAFS when they expect wind gusts to meet or exceed 25 kt, 35 kt and 50 kt thresholds at any level from the surface to 300 ft. The 45 WS forecasters have indicated peak wind speeds are challenging to forecast, particularly in the cool season months of October - April. In Phase I of this task, the Applied Meteorology Unit (AMU) developed a tool to help the 45 WS forecast non-convective winds at KSC/CCAFS for the 24-hour period of 0800 to 0800 local time. The tool was delivered as a Microsoft Excel graphical user interface (GUI). The GUI displayed the forecast of peak wind speed, 5-minute average wind speed at the time of the peak wind, timing of the peak wind and probability the peak speed would meet or exceed 25 kt, 35 kt and 50 kt. For the current task (Phase II ), the 45 WS requested additional observations be used for the creation of the forecast equations by expanding the period of record (POR). Additional parameters were evaluated as predictors, including wind speeds between 500 ft and 3000 ft, static stability classification, Bulk Richardson Number, mixing depth, vertical wind shear, temperature inversion strength and depth and wind direction. Using a verification data set, the AMU compared the performance of the Phase I and II prediction methods. Just as in Phase I, the tool was delivered as a Microsoft Excel GUI. The 45 WS requested the tool also be available in the Meteorological Interactive Data Display System (MIDDS). The AMU first expanded the POR by two years by adding tower observations, surface observations and CCAFS (XMR) soundings for the cool season months of March 2007 to April 2009. The POR was expanded again by six years, from October 1996 to April 2002, by interpolating 1000-ft sounding data to 100-ft increments. The Phase II developmental data set included observations for the cool season months of October 1996 to February 2007. The AMU calculated 68 candidate predictors from the XMR soundings, to include 19 stability parameters, 48 wind speed parameters and one wind shear parameter. Each day in the data set was stratified by synoptic weather pattern, low-level wind direction, precipitation and Richardson Number, for a total of 60 stratification methods. Linear regression equations, using the 68 predictors and 60 stratification methods, were created for the tool's three forecast parameters: the highest peak wind speed of the day (PWSD), 5-minute average speed at the same time (A WSD), and timing of the PWSD. For PWSD and A WSD, 30 Phase II methods were selected for evaluation in the verification data set. For timing of the PWSD, 12 Phase\I methods were selected for evaluation. The verification data set contained observations for the cool season months of March 2007 to April 2009. The data set was used to compare the Phase I and II forecast methods to climatology, model forecast winds and wind advisories issued by the 45 WS. The model forecast winds were derived from the 0000 and 1200 UTC runs of the 12-km North American Mesoscale (MesoNAM) model. The forecast methods that performed the best in the verification data set were selected for the Phase II version of the tool. For PWSD and A WSD, linear regression equations based on MesoNAM forecasts performed significantly better than the Phase I and II methods. For timing of the PWSD, none of the methods performed significantly bener than climatology. The AMU then developed the Microsoft Excel and MIDDS GUls. The GUIs display the forecasts for PWSD, AWSD and the probability the PWSD will meet or exceed 25 kt, 35 kt and 50 kt. Since none of the prediction methods for timing of the PWSD performed significantly better thanlimatology, the tool no longer displays this predictand. The Excel and MIDDS GUIs display forecasts for Day-I to Day-3 and Day-I to Day-5, respectively. The Excel GUI uses MesoNAM forecasts as input, while the MIDDS GUI uses input from the MesoNAM and Global Forecast System model. Based on feedback from the 45 WS, the AMU added the daily average wind speed from 30 ft to 60 ft to the tool, which is one of the parameters in the 24-Hour and Weekly Planning Forecasts issued by the 45 WS. In addition, the AMU expanded the MIDDS GUI to include forecasts out to Day-7.

Barrett, Joe H., III↗
NASA NTRS2010

Tool for Forecasting Cool-Season Peak Winds Across Kennedy Space Center and Cape Canaveral Air Force Station

The expected peak wind speed for the day is an important element in the daily morning forecast for ground and space launch operations at Kennedy Space Center (KSC) and Cape Canaveral Air Force Station (CCAFS). The 45th Weather Squadron (45 WS) must issue forecast advisories for KSC/CCAFS when they expect peak gusts for >= 25, >= 35, and >= 50 kt thresholds at any level from the surface to 300 ft. In Phase I of this task, the 45 WS tasked the Applied Meteorology Unit (AMU) to develop a cool-season (October - April) tool to help forecast the non-convective peak wind from the surface to 300 ft at KSC/CCAFS. During the warm season, these wind speeds are rarely exceeded except during convective winds or under the influence of tropical cyclones, for which other techniques are already in use. The tool used single and multiple linear regression equations to predict the peak wind from the morning sounding. The forecaster manually entered several observed sounding parameters into a Microsoft Excel graphical user interface (GUI), and then the tool displayed the forecast peak wind speed, average wind speed at the time of the peak wind, the timing of the peak wind and the probability the peak wind will meet or exceed 35, 50 and 60 kt. The 45 WS customers later dropped the requirement for >= 60 kt wind warnings. During Phase II of this task, the AMU expanded the period of record (POR) by six years to increase the number of observations used to create the forecast equations. A large number of possible predictors were evaluated from archived soundings, including inversion depth and strength, low-level wind shear, mixing height, temperature lapse rate and winds from the surface to 3000 ft. Each day in the POR was stratified in a number of ways, such as by low-level wind direction, synoptic weather pattern, precipitation and Bulk Richardson number. The most accurate Phase II equations were then selected for an independent verification. The Phase I and II forecast methods were compared using an independent verification data set. The two methods were compared to climatology, wind warnings and advisories issued by the 45 WS, and North American Mesoscale (NAM) model (MesoNAM) forecast winds. The performance of the Phase I and II methods were similar with respect to mean absolute error. Since the Phase I data were not stratified by precipitation, this method's peak wind forecasts had a large negative bias on days with precipitation and a small positive bias on days with no precipitation. Overall, the climatology methods performed the worst while the MesoNAM performed the best. Since the MesoNAM winds were the most accurate in the comparison, the final version of the tool was based on the MesoNAM winds. The probability the peak wind will meet or exceed the warning thresholds were based on the one standard deviation error bars from the linear regression. For example, the linear regression might forecast the most likely peak speed to be 35 kt and the error bars used to calculate that the probability of >= 25 kt = 76%, the probability of >= 35 kt = 50%, and the probability of >= 50 kt = 19%. The authors have not seen this application of linear regression error bars in any other meteorological applications. Although probability forecast tools should usually be developed with logistic regression, this technique could be easily generalized to any linear regression forecast tool to estimate the probability of exceeding any desired threshold . This could be useful for previously developed linear regression forecast tools or new forecast applications where statistical analysis software to perform logistic regression is not available. The tool was delivered in two formats - a Microsoft Excel GUI and a Tool Command Language/Tool Kit (Tcl/Tk) GUI in the Meteorological Interactive Data Display System (MIDDS). The Microsoft Excel GUI reads a MesoNAM text file containing hourly forecasts from 0 to 84 hours, from one model run (00 or 12 UTC). The GUI then displays e peak wind speed, average wind speed, and the probability the peak wind will meet or exceed the 25-, 35- and 50-kt thresholds. The user can display the Day-1 through Day-3 peak wind forecasts, and separate forecasts are made for precipitation and non-precipitation days. The MIDDS GUI uses data from the NAM and Global Forecast System (GFS), instead of the MesoNAM. It can display Day-1 and Day-2 forecasts using NAM data, and Day-1 through Day-5 forecasts using GFS data. The timing of the peak wind is not displayed, since the independent verification showed that none of the forecast methods performed significantly better than climatology. The forecaster should use the climatological timing of the peak wind (2248 UTC) as a first guess and then adjust it based on the movement of weather features.

Barrett, Joe H., III↗
NASA NTRS2010

Understanding the Transport of Patagonian Dust and Its Influence on Marine Biological Activity in the South Atlantic Ocean

Modeling and remote sensing techniques were applied to examine the horizontal and vertical transport pathways of Patagonian dust and quantify the effect of soluble-iron- laden mineral dust deposition on marine primary productivity in the South Atlantic Ocean (SAO) surface waters. The global chemistry transport model GEOS-Chem, implemented with an iron dissolution scheme, was applied to evaluate the atmospheric transport and deposition of mineral dust and bioavailable iron during two dust outbreaks originating in the source regions of Patagonia. In addition to this "rapidly released" iron, offline calculations were also carried out to estimate the amount of bioavailable iron leached during the residence time of dust in the ocean mixed layer. Model simulations showed that the horizontal and vertical transport pathways of Patagonian dust plumes were largely influenced by the synoptic meteorological patterns of high and low pressure systems. Model-predicted horizontal and vertical transport pathways of Patagonian dust over the SAO were in reasonable agreement with remotely-sensed data. Comparison between remotely-sensed and offline calculated ocean surface chlorophyll-a concentrations indicated that, for the two dust outbreaks examined in this study, the deposition of bioavailable iron in the SAO through atmospheric pathways was insignificant. As the two dust transport episodes examined here represent typical outflows of mineral dust from South American sources, our study suggests that the atmospheric deposition of mineral dust is unlikely to induce large scale marine primary productivity and carbon sequestration in the South Atlantic sector of the Southern Ocean.

Johnson, Matthew↗
NASA NTRS2016

Thinking Outside of the Blue Marble: Novel Ocean Applications Using the VIIRS Sensor

While planning for future space-borne sensors will increase the quality, quantity, and duration of ocean observations in the years to come, efforts to extend the limits of sensors currently in orbit can help shed light on future scientific gains as well as associated uncertainties. Here, we present several applications that are unique to the polar orbiting Visual Infrared Imaging Radiometer Suite (VIIRS), each of which challenge the threshold capabilities of the sensor and provide lessons for future missions. For instance, while moderate resolution polar orbiters typically have a one day revisit time, we are able to obtain multiple looks of the same area by focusing on the extreme zenith angles where orbital views overlap, and pair these observations with those from other sensors to create pseudo-geostationary data sets. Or, by exploiting high spatial resolution (imaging) channels and analyzing patterns of synoptic covariance across the visible spectrum, we can obtain higher spatial resolution bio-optical products. Alternatively, non-traditional products can illuminate important biological interactions in the ocean, such as the use of the Day-Night-Band to provide some quantification of phototactic behavior of marine life along light polluted beaches, as well as track the location of marine fishing vessel fleets along ocean fronts. In this talk, we explore ways to take full advantage of the capabilities of existing sensors in order to maximize insights for future missions.

ocean color↗
NASA NTRS1993

Stratiform clouds and their interaction with atmospheric motion

The spatial patterns of stratocumulus cloud frequency for the continental United States and adjacent oceans were routinely obtained from surface cloud observations every six hours. These frequencies were correlated with upper air patterns at 850, 700, and 500 mb. Significant frequency maxima were found near trough axes over marine areas during relatively stationary large-scale wave patterns. These maxima tended to occur to the east of trough axes. Over continental regions, there was little relationship between stratocumulus and synoptic-scale flows patterns, probably because of the short lifetime of cloud over land. A summary of these findings is included.

Clark, John H. E.↗