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At least 163 records · Page 9

Configuration and Intraseasonal Duration of Interannual Anomalies of the Great Plains Low-Level Jet

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours, it is one of the most persistent and stable climatological features of the low-level continental flow during the warm-season months, May through August. We have used significant-level data to validate the skill of the GEOS-1 Data Assimilation System (DAS) in realistically detecting this jet and inferring its structure and evolution. We have then carried out a 15-year reanalysis with the GEOS-1 DAS to determine its climatology and mean diurnal cycle and to study its interannual variability. Interannual anomalies of the meridional flow associated with the GPLLJ are much smaller than the mean diurnal fluctuations, than random intraseasonal anomalies, and than the mean wind itself. There are three maxima of low-level meridional flow variance over the Great Plains and the Gulf of Mexico: a 1.2 m2 s-2 peak over the southeast Texas, to the east and south of the mean velocity peak, a 1.0 m2 s-2 peak over the western Gulf of Mexico, and a .8 m2 s-2 peak over the upper Great Plains (UGP), near the Nebraska/South Dakota border. Each of the three variance maxima corresponds to a spatially coherent, jet-like pattern of low-level flow interannual variability. There are also three dominant modes of interannual variability corresponding to the three variance maxima, but not in a simple one-to-one relationship. Cross-sectional profiles of mean southerly wind over Texas remain relatively stable and recognizable from year to year with only its eastward flank showing significant variability. This variability, however, exhibits a distinct, biennial oscillation during the first six to seven years of the reanalysis period and only then. This intermittent biennial oscillation (IBO, one of the three modes discussed in the previous paragraph) in the lowlevel flow is restricted to the region surrounding eastern Texas and is also evident in the NCEP/NCAR reanalysis data set from about 1978 to 1985 or 1986 and again from 1995 to 2000. It is evident as well in surface pressure in both the GEOS-1 and NCEP/NCAR sets. The interannual anomalies do not necessarily persist uniformly throughout an entire season, but can fluctuate from one part of the season to the next. To estimate the characteristic sub-seasonal time scales for coherence of these fluctuations, we have taken the weekly anomaly of low-level wind at each point of the domain from the climatological average for that given point and that given week of the season and computed the covariance of its fluctuations over all weeks and over all years with the weekly climatological anomaly of the meridional wind at each of the three reference points discussed above. The typical duration of a coherent interannual anomaly within a given warm season increases with decreasing latitude from 2 to 3 weeks over the UGP, to 6 to 7 weeks over eastern Texas. Coherence over the western Gulf of Mexico is intermediate between the two with a typical duration of 4 to 5 weeks. There appears to be evidence that the interannual anomalies over Texas the Gulf propagate to the UGP after a week and those over the Gulf propagate there after 2 to 3 weeks. There also appears to be some reverse propagation of interannual anomalies over the UGP to Texas and to the Gulf after a period of about one week. The interannual anomalies in southerly flow over eastern Texas seem to correlate well with interannual anomalies of surface temperature and (negative) ground wetness and over western Texas.

Helfand, H. M.↗

Lifting Condensation Level Height (LCL Height) Value-Added Product Report

The lifting condensation level height (LCL, m) is determined from continuous surface-air observations of relative humidity and temperature as the altitude where the surface-air moisture equals saturation following a dry-adiabatic ascent. Values are computed from surface meteorological observations for 16 facilities belonging to the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility Southern Great Plains (SGP) atmospheric observatory and for 133 Oklahoma Mesonet (OKM) stations. The LCL Height Value-Added Product (VAP) was developed for use by the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) project (Gustafson et al. 2016, 2017, 2018, 2020).

54 ENVIRONMENTAL SCIENCES↗

Sub-Cloud Turbulence Explains Cloud-Base Updrafts for Shallow Cumulus Ensembles: First Observational Evidence

Sub-cloud turbulent kinetic energy has been used to parameterize the cloud-base updraft velocity (w b ) in cumulus parameterizations. The validity of this idea has never been proved in observations. Instead, it was challenged by recent Doppler Lidar observations showing a poor correlation between the two. We argue that the low correlation is likely caused by the difficulty of a fixed-point Lidar to measure ensemble properties of cumulus fields. Taking advantage of the stationarity and ergodicity of early afternoon convection, we developed a Lidar sampling methodology to measure w b of a shallow cumulus (ShCu) ensemble (not a single ShCu). By analyzing 128 ShCu ensembles over the Southern Great Plains, we show that the ensemble properties of sub-cloud turbulence explain nearly half of the variability in ensemble-mean wb, demonstrating the ability of sub-cloud turbulence to dictate w b . Lastly, the derived empirical formulas will be useful for developing cumulus parameterizations and satellite inference of w b .

54 ENVIRONMENTAL SCIENCES↗

Aerosol Optical Depth Best Estimate Value-Added Product Report

Four aerosol optical depth (AOD) products are offered by four collocated ground-based instruments deployed at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility’s Southern Great Plains (SGP) observatory (SGP Central Facility 1 [C1] and Extended Facility 13 [E13]) for more than two decades. Two of these instruments, the multifilter rotating shadowband radiometers (MFRSRs C1 and E13), are sensors with horizontal hemispherical receivers shaded by rotating shadowbands.

47 OTHER INSTRUMENTATION↗

AWAKEN Dual-Doppler Lidar (ADDLidar) Field Campaign Report

The AWAKEN Dual-Doppler Lidar (ADDLidar) experiment was conducted as part of the larger AWAKEN field campaign (https://www.nrel.gov/wind/awaken.html). The American Wake Experiment (AWAKEN) is an international, multi-institutional wind energy field campaign that was conducted from May 2022 to 2024, in the vicinity of the King Plains wind farm in north central Oklahoma. The goal of AWAKEN was to provide observations to better understand interactions between wind turbines in a wind farm and the interactions between the wind farm as a whole and the atmosphere. The focus of the ADDLidar campaign was to provide height-resolved measurements of wind speed and direction at key locations upwind of the wind farm to characterize the inflow and possible blockage effects upwind of the farm. Specifically, dual-Doppler scanning methods were employed to create a number of so-called virtual towers (Calhoun et al 2006, Debnath et al. 2017, Fernando et al. 2019, Hill et al. 2010, Newman et al. 2016, Newsom et al. 2008, 2015) upwind of the farm. The ADDLidar campaign involved the deployment of two U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility scanning Doppler lidars (S/N 236 and 237) to AWAKEN sites A4 (36.361894°, -97.356352°) and A7 (36.347259°, -97.389968°). Both sites are located approximately 29 km south of the ARM Southern Great Plains (SGP) observatory C1 site, as shown in Figure 1. These sites were chosen for their close proximity to the most southerly row of turbines in the King Plains wind farm.

54 ENVIRONMENTAL SCIENCES↗

Surface Energy Balance System (SEBS) Instrument Handbook

A Surface Energy Balance System (SEBS) has been installed collocated with each deployed ECOR system at the Southern Great Plains (SGP), North Slope of Alaska (NSA), Tropical Western Pacific (TWP), ARM Mobile Facility 1 (AMF1), and ARM Mobile Facility 2 (AMF2). The surface energy balance system consists of upwelling and downwelling solar and infrared radiometers within one net radiometer, a wetness sensor, and soil measurements. The SEBS measurements allow the comparison of ECOR sensible and latent heat fluxes with the energy balance determined from the SEBS and provide information on wetting of the sensors for data quality purposes.

54 ENVIRONMENTAL SCIENCES↗

Carbon Dioxide Flux Measurement System (CO2FLX) Instrument Handbook

The Southern Great Plains (SGP) carbon dioxide flux (CO2 flux) measurement systems provide half-hour average fluxes of CO2, H2O (latent heat), and sensible heat. The fluxes are obtained by the eddy covariance technique, which computes the flux as the mean product of the vertical wind component with CO2 and H2O densities, or estimated virtual temperature. A three-dimensional sonic anemometer is used to obtain the orthogonal wind components and the virtual (sonic) temperature. An infrared gas analyzer is used to obtain the CO2 and H2O densities. A separate sub-system also collects half-hour average measures of meteorological and soil variables from separate 4-m towers.

54 ENVIRONMENTAL SCIENCES↗

Raman Lidar (RL) Instrument Handbook

The Raman lidar at the ARM Climate Research Facility (ACRF) Southern Great Plains (SGP) Central Facility (SGPRL) is an active, ground-based laser remote sensing instrument that measures height and time resolved profiles of water vapor mixing ratio and several cloud- and aerosol-related quantities. The system is a non-commercial custom-built instrument developed by Sandia National Laboratories specifically for the ARM Program. It is fully computer automated, and will run unattended for many days following a brief (~5-minute) startup period. The self-contained system (requiring only external electrical power) is housed in a climate-controlled 8’x8’x20’ standard shipping container.

54 ENVIRONMENTAL SCIENCES↗

Retrieving Point Cloud of Cloud Points (PCCP) Value-Added Product from Stereo Cameras

In this report, we refer to a pair of cameras that capture synchronized pictures with overlapping fields of view (FOV) as a stereo pair. Each stereo pair independently performs a stereo reconstruction of cloud points. Currently, there are three U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility stereo pairs (six cameras in total) positioned around the Southern Great Plains (SGP) observatory’s Central Facility (CF; Romps and Oktem 2017). Time-synchronized pictures from the two cameras in a stereo pair can be paired together to obtain a three-dimensional (3D) reconstruction of feature points by triangulation. This document explains how we use ARM stereo cameras and stereophotogrammetric principles to generate the Point Cloud of Cloud Points (PCCP) Value-Added Product (VAP). The PCCP VAP is essentially a set of 3D positions representing the locations of cloud features in the sky. Thousands of cloud features can be reconstructed instantly in each stereo pair's FOV, which covers an area of tens of square kilometers. Cloud base and cloud top heights can be extracted from the PCCP product.

42 ENGINEERING↗

Influences of Cloud Microphysics on the Components of Solar Irradiance in the WRF-Solar Model

An accurate forecast of Global Horizontal solar Irradiance (GHI) and Direct Normal Irradiance (DNI) in cloudy conditions remains a major challenge in the solar energy industry. This study focuses on the impact of cloud microphysics on GHI and its partition into DNI and Diffuse Horizontal Irradiance (DHI) using the Weather Research and Forecasting model specifically designed for solar radiation applications (WRF-Solar) and seven microphysical schemes. Three stratocumulus (Sc) and five shallow cumulus (Cu) cases are simulated and evaluated against measurements at the US Department of Energy’s Atmospheric Radiation Measurement (ARM) user facility, Southern Great Plains (SGP) site. Results show that different microphysical schemes lead to spreads in simulated solar irradiance components up to 75% and 350% from their ensemble means in the Cu and Sc cases, respectively. The Cu cases have smaller microphysical sensitivity due to a limited cloud fraction and smaller domain-averaged cloud water mixing ratio compared to Sc cases. Cloud properties also influence the partition of GHI into DNI and DHI, and the model simulates better GHI than DNI and DHI due to a non-physical error compensation between DNI and DHI. The microphysical schemes that produce more accurate liquid water paths and effective radii of cloud droplets have a better overall performance.

54 ENVIRONMENTAL SCIENCES↗

Aerosol-Ice Formation Closure Pilot Study (AEROICESTUDY) Field Campaign Report

The aerosol-ice formation closure pilot study (AEROICESTUDY) was a field campaign conducted at the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) user facility at the Southern Great Plains (SGP) observatory from October 7 to 28, 2019. The purpose of this campaign was to test a field observational approach for conducting an aerosol-ice formation closure study. In other words, the goal was to predict the number of ice nucleating particles (INPs) from the measurements of physical and chemical aerosol properties and compare those to measured INP number concentrations. This project was motivated by the fact that the aerosol community has widely conducted aerosol radiative closure, aerosol-cloud condensation nuclei (CCN), and CCN-droplet closure studies to test the physical models and parameterizations that cloud-resolving and climate models rely on to perform reliable simulations of the Earth system and energy budget. However, very few closure studies related to INPs have been conducted, and to our knowledge, none using robust, size-resolved, ambient aerosol composition measurements as model inputs

AEROICESTUDY↗

Use of Satellite, Surface Observations and Numerical Weather Prediction Model Data to Improve Cloud Base Height and Cloud Base Vertical Velocity Estimation

Cloud base height (CBH) and cloud base vertical velocity (CBVV) are important variables that impact the overall climate in a region as they influence the formulation, longevity, and evolution of clouds. Retrieval of both parameters have long used ground instrumentation (e.g., Doppler lidar (DL), ground base radar); however, retrieving CBH from satellites is particularly challenging given that space-based instruments only observe cloud tops. In this manuscript, CBH is retrieved using a multi-linear regression equation, while CBVV used a random forests model. Both retrievals combine satellite and numerical weather prediction data. The satellite data used are the Visible Infrared Imaging Radiometer Suite imagery, while measurements of CBH and CBVV include DL and radiosonde data at the Southern Great Plains (SGP) Atmospheric Radiation Measurement observatory. Data from 83 summer days (May-August) in 2018–2021 featuring cumulus clouds forced by solar heating were examined and used to train the models, with years 2022–2023 used for validation. Various spatial domains were defined with one large (2.4° longitude by 2.0° latitude) SGP domain being split into smaller sections (smallest being 0.99° and 0.61° longitude and latitude respectably). CBH and CBVV values obtained from the DL as compared to the models show root mean square errors between 150 and 200 m, with CBVV values between 0.45 and 1 ms -1 . Finally, it was found that the CBH formulation performs well over all domains, while the CBVV retrievals become less accurate due to more turbulence being introduced into the observations as the number of DL stations decreases in the smaller domains.

54 ENVIRONMENTAL SCIENCES↗

ARM Data-Oriented Metrics and Diagnostics Package for Climate Model Evaluation

A Python-based metrics and diagnostics package is currently being developed by the U.S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Infrastructure Team at Lawrence Livermore National Laboratory (LLNL) to facilitate the use of long-term, high-frequency measurements from the ARM Facility in evaluating the regional climate simulation of clouds, radiation, and precipitation. This metrics and diagnostics package computes climatological means of targeted climate model simulation and generates tables and plots for comparing the model simulation with ARM observational data. The Coupled Model Intercomparison Project (CMIP) model data sets are also included in the package to enable model intercomparison as demonstrated in Zhang et al. (2017). The mean of the CMIP model can serve as a reference for individual models. Basic performance metrics are computed to measure the accuracy of mean state and variability of climate models. The evaluated physical quantities include cloud fraction, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, and radiative fluxes, with plan to extend to more fields, such as aerosol and microphysics properties. Process-oriented diagnostics focusing on individual cloud- and precipitation-related phenomena are also being developed for the evaluation and development of specific model physical parameterizations. The version 1.0 package is designed based on data collected at ARM’s Southern Great Plains (SGP) Research Facility, with the plan to extend to other ARM sites. The metrics and diagnostics package is currently built upon standard Python libraries and additional Python packages developed by DOE (such as CDMS and CDAT). The ARM metrics and diagnostic package is available publicly with the hope that it can serve as an easy entry point for climate modelers to compare their models with ARM data. In this report, we first present the input data, which constitutes the core content of the metrics and diagnostics package in section 2, and a user's guide documenting the workflow/structure of the version 1.0 codes, and including step-by-step instruction for running the package in section 3.

54 ENVIRONMENTAL SCIENCES↗

Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land-Atmosphere Coupling

The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from ‘off-the-shelf’ products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high resolution land surface model and in-situ soil moisture estimates. Case studies are analyzed using the local land-atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land vs. atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

Southern Great Plains↗

Description of the LASSO Data Bundles Product

The U. S. Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) user facility began a pilot project in May 2015 to design a routine, high-resolution modeling capability to complement ARM’s extensive suite of measurements. This modeling capability has evolved into the Large-Eddy Simulation (LES) ARM Symbiotic Simulation and Observation (LASSO) datastream. The datastream, broadly termed data bundles, contains high-resolution model output, input files, observations for evaluation, and skill scores for the simulations. The initial focus of LASSO is on shallow convection at the ARM Southern Great Plains (SGP) atmospheric observatory. The availability of LES simulations with concurrent observations serves many purposes. LES helps bridge the scale gap between DOE ARM observations and models, and the use of routine LES adds value to observations. It provides a self-consistent representation of the atmosphere and a dynamical context for the observations. Further, it elucidates unobservable processes and properties. LASSO generates a simulation library for researchers that enables statistical approaches beyond a single-case mentality. It also provides tools necessary for modelers to reproduce the LES and conduct their own sensitivity experiments. The LASSO library of data bundles is designed to facilitate a wide range of research. For an observationalist, LASSO can help inform instrument remote-sensing retrievals, conduct observation system simulation experiments (OSSEs), and test implications of radar scan strategies or flight paths. For a theoretician, LASSO can help calculate estimates of fluxes and co-variability of values, and test relationships without having to run the model personally. For a modeler, LASSO can help one know ahead of time which days have good forcing, have co-registered observations at high-resolution scales, and have simulation inputs and corresponding outputs to test parameterizations. Further details on the overall LASSO project are available at https://www.arm.gov/capabilities/modeling/lasso.

54 ENVIRONMENTAL SCIENCES↗

Macro-physical Properties of Shallow Cumulus from Integrated ARM Observations (Final Report)

Fair-weather shallow cumuli (ShCu) play an important role in many climate-related processes. Irregular geometry of ShCu and their strong temporal and spatial variability make it challenging to observe ShCu holistically and to represent them correctly in climate models. To improve ShCu parameterizations, information on both vertically and horizontally resolved cloud properties is required. Commonly, the vertically resolved cloud properties are provided by zenith pointing lidar-radar observations with a very narrow field of view (FOV). Thus, these “pencil-beam” properties may not be representative of a larger surrounding area. Limited number of areal-averaged cloud properties, such as fractional sky cover (FSC), are offered typically by wide-FOV observations. The main goal of our project was to integrate advantages of the narrow-FOV (vertical structure of clouds) and wide-FOV (spatial arrangement of clouds) observations for an improved characterization of single-layer ShCu observed at the Atmospheric Radiation Measurement (ARM) Southern Great Plains (SGP) site for an 18-yr period (2000-2017). There are four major accomplishments of our project, First, an updated operational cloud classification for days with ShCu has been suggested and evaluated through a detailed comparison with the manually curated records. Our classification extends successfully the latest ARM cloud type Value Added Product (VAP) based on the Active Remote Sensing of Clouds (ARSCL) cloud product by incorporating both cloud fraction (CF) provided by narrow-FOV ceilometer data and FSC from wide-FOV images offered by a Total Sky Imager (TSI). Moreover, our classification allows one to identify impact of instrumentation changes at the SGP site, namely the transition to KAZRARSCL with the updated cloud radar, on the identification of periods with single-layer ShCu. Second, a new approach that resolves cloud area distributions for a given region (up to 4x4 km 2 ) has been suggested and cloud equivalent diameters (CEDs) have been estimated for the first time. These estimations have been performed over a wide range of cloud sizes (about 0.01–3.5 km) with high temporal resolution (30s) using wide-FOV TSI images and cloud base height (CBH) provided by complementary narrow-FOV lidar measurements. Our simple and computationally inexpensive approach offers a previously unavailable dataset for process studies in the convective boundary layer and evaluation of ShCu parameterizations in cloud-resolving models. Third, a long-term integrated record of ShCu macrophysical properties has been developed. The developed record represents the longest available compilation of events with ShCu and includes (i) a novel visualization of the spatial variability in cloud cover both along- and across-wind directions, (ii) updated estimates of narrow-FOV CF and wide-FOV FSC, (iii) updated narrow-FOV CBH, and (iv) complementary data, such as wind speed and direction from the 915-MHz Radar Wind Profiler (RWP) data. The developed record has been used successfully to assess conventional observational estimates of cloud cover and their sensitivity to the following two factors: (i) instrument-dependent cloud detection and data merging criteria and (ii) FOV configuration. Fourth, co-variability of the ShCu macrophysical properties and environmental parameters has been analyzed for a 3-yr period (2016-2018). Our initial analysis includes diurnal changes of FSCs obtained for clouds with small, moderate and large CEDs and several environmental parameters, such as lifted condensation level (LCL) and mixed layer height (zi). Preliminary results of our analysis suggest that the horizontal extent of ShCu is controlled substantially by the sign and magnitude of difference between these two parameters (zi-LCL): the CED tends to grow with increase of this difference (zi exceeds LCL). We have initiated relationships between the ShCu and key atmospheric parameters that control both the development and evolution of ShCu using our new data product, which combines effectively the advantages of narrow-FOV data offered by zenith pointing cloud radars and lidars and wide-FOV TSI images. While the latest instrumentation at the ARM sites may address these challenging relationships in the future, we believe that the historical ARM data at the SGP site has not yet been fully utilized. Overall, our data product can be used by researchers working on a wide range of climate-related projects. These projects may include (i) a comprehensive evaluation of outputs from the Large-Eddy Simulation (LES) and single-column models for their future improvement, (ii) the representativeness of “short-period” results obtained from the previous model and observational studies and (iii) the planning of future field campaigns with focus on improved understanding of the diurnal cycle of cumulus convection.

54 ENVIRONMENTAL SCIENCES↗

Climatology, Natural Cycles, and Modes of Interannual Variability of the Great Plains Low-Level Jet as Assimilated by the GEOS-1 Data Analysis System

Despite the fact that the low-level jet of the southern Great Plains (the GPLLJ) of the U.S. is primarily a nocturnal phenomenon that virtually vanishes during the daylight hours, it is one of the most persistent and stable features of the low-level continental flow during the warm-season months, May through August. We have first used significant-level data to validate the skill of the GEOS-1 Data Assimilation System (DAS) in realistically detecting this jet and inferring its structure and evolution. We have then carried out a 15-year reanalysis with the GEOS-1 DAS to determine and validate its climatology and mean diurnal cycle and to study its interannual variability. Interannual variability of the GPLLJ is much smaller than mean diurnal and random intraseasonal variability and comparable in magnitude, but not location, to mean seasonal variability. There are three maxima of interannual low-level meridional flow variability of the GPLLJ over the upper Great Plains, southeastern Texas, and the western Gulf of Mexico. Cross-sectional profiles of mean southerly wind through the Texas maximum remain relatively stable and recognizable from year to year with only its eastward flank showing significant variability. This variability, however, exhibits a distinct, biennial oscillation during the first six years of the reanalysis period and only then. Each of the three variability maxima corresponds to a spatially coherent, jet-like pattern of low-level flow interannual variability. There are three prominent modes of interannual. variability. These include the intermittent biennial oscillation (IBO), local to the Texas maximum. Its signal is evident in surface pressure, surface temperature, ground wetness and upper air flow, as well. A larger-scale continental convergence pattern (CCP) of covariance, exhibiting strong anti-correlation between the flow near the Texas and the upper Great Plains variability maxima, is revealed only when the IBO is removed from the interannual time series. A third, subtropical mode of covariance is associated with the Gulf of Mexico variability maximum. Significant interannual anti-correlations of the southeasterly flow over the Arizona/New Mexico region with the CCP and the subtropical mode are enhanced when restricted to the month of July. These anti-correlations may relate to an observed out-of-phase precipitation relationship between the Great Plains and the southwestern U.S.. The typical duration of interannual low-level meridional wind anomalies within a given season increases over the continent with decreasing latitude from two to three weeks over the upper Great Plains to six to seven weeks over eastern Texas.

Helfand, H. M.↗

Climatological relationships of severe duststorms in the Great Plains to synoptic weather patterns: A potential for predictability

A data base provided by 35 severe duststorms that occurred between 1968 and 1977 in the central and southern Great Plains allowed construction of a classification scheme of meteorological causes of duststorms, and a telescopic forecast technique for medium range (6 to 48 hour) prediction of severe cyclogenic duststorms. In addition, areal coverage definitions for duststorms based on characteristics of the storms, and a hierarchy of weather causes of severe duststorms were developed. The man machine mix forecast correctly predicted six of seven duststorms observed during the 1976-77 winter, with one overforecast; the machine-only forecast correctly predicted four of the seven duststorms, with one overforecast. Both techniques had problems correctly predicting the duration of severe duststorms.

Woiceshyn, P. M.↗