Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

Aerodynamic Methods for Estimating Turbulent Fluxes

The exchange of energy and mass between a surface and the lowest region of the troposphere is a complex process that governs many hydrological, agricultural, and atmospheric processes. The layer of air directly affected by surface– atmosphere exchanges is strongly influenced by turbulent processes at the surface–atmosphere boundary and extends upward into the atmosphere to a height of approximately 1 km. This region is commonly referred to as the atmospheric boundary layer (ABL) that is uniquely characterized by turbulence resulting from mechanical (wind shear) and buoyancy (thermal) forces at or near the surface. Methods have been developed to evaluate energy/mass (heat, water vapor, trace gases, and pollutants) exchanges between the ABL and the underlying surface. In this chapter, we describe the flux gradient approach for estimating mass and energy fluxes under the rubric of aerodynamic methods. We provide some historical perspective, present fundamental equations in the context of Monin-Obukhov similarity theory and introduce recent developments of an alternative method to compute heat and water vapor fluxes using turbulence variance statistics

Temperature extremes: Effect on plant growth and development

AbstractTemperature is a primary factor affecting the rate of plant development. Warmer temperatures expected with climate change and the potential for more extreme temperature events will impact plant productivity. Pollination is one of the most sensitive phenological stages to temperature extremes across all species and during this developmental stage temperature extremes would greatly affect production. Few adaptation strategies are available to cope with temperature extremes at this developmental stage other than to select for plants which shed pollen during the cooler periods of the day or are indeterminate so flowering occurs over a longer period of the growing season. In controlled environment studies, warm temperatures increased the rate of phenological development; however, there was no effect on leaf area or vegetative biomass compared to normal temperatures. The major impact of warmer temperatures was during the reproductive stage of development and in all cases grain yield in maize was significantly reduced by as much as 80−90% from a normal temperature regime. Temperature effects are increased by water deficits and excess soil water demonstrating that understanding the interaction of temperature and water will be needed to develop more effective adaptation strategies to offset the impacts of greater temperature extreme events associated with a changing climate

Evaluation of SMAP Freeze/Thaw Retrieval Accuracy at Core Validation Sites in the Contiguous United States

Seasonal freeze-thaw (FT) impacts much of the northern hemisphere and is an important control on its water, energy, and carbon cycle. Although FT in natural environments extends south of 45°N, FT studies using the L-band have so far been restricted to boreal or greater latitudes. This study addresses this gap by applying a seasonal threshold algorithm to Soil Moisture Active Passive (SMAP) data (L3_SM_P) to obtain a FT product south of 45°N (‘SMAP FT’), which is then evaluated at SMAP core validation sites (CVS) located in the contiguous United States (CONUS). SMAP landscape FT retrievals are usually in good agreement with 0–5 cm soil temperature at SMAP grids containing CVS stations (\u3e70%). The accuracy could be further improved by taking into account specific overpass time (PM), the grid-specific seasonal scaling factor, the data aggregation method, and the sampling error. Annual SMAP FT extent maps compared to modeled soil temperatures derived from the Goddard Earth Observing System Model Version 5 (GEOS-5) show that seasonal FT in CONUS extends to latitudes of about 35–40°N, and that FT varies substantially in space and by year. In general, spatial and temporal trends between SMAP and modeled FT were similar

Aglite: A 3-Wavelength Lidar System for Quantitative Assessment of Agricultural Air Quality and Whole Facility Emissions

Ground based remote sensing technologies such as scanning lidar systems (light detection and ranging) are increasingly being used to characterize ambient aerosols due to key advantages (i.e., wide area of regard (10 km2), fast response time (s-1), high spatial resolution (\u3c10 \u3em) and high sensitivity). Scanning lidar allows for 3D imaging of atmospheric motion and aerosol variability, which can be used to quantitatively evaluate particulate matter (PM) concentrations and emissions. Space Dynamics Laboratory, in conjunction with USDA ARS, has developed and successfully deployed a lidar system called Aglite to characterize PM in diverse settings. Aglite is a portable scanning elastic lidar system with three wavelengths (355, 532, and 1064 nm), 6 m long range bins, and an effective range from 0.5 to 15 km. Filter-based PM samplers, optical particle counters, and various meteorological instruments were deployed to provide environmental and PM conditions for use in the lidar retrieval method. The developed retrieval algorithm extracts aerosol optical parameters, which were constrained by the point measurements, and converts return signals to PM concentrations. Once calibrated, the Aglite system can map the spatial distribution and temporal variation of the PM concentrations. Whole facility or operation-based emission rates were calculated from the lidar PM data with a mass balance approach. Concentration comparisons with upwind and downwind point sensors were made to verify data quality; lidar-derived PM levels were usually in good agreement with point sensor measurements. Comparisons of lidar-based emissions with emissions estimated through other methods using point sensor data generally show good agreement

Emissions Calculated from Particulate Matter and Gaseous Ammonia Measurements from Commercial Dairy in California, USA

Emission rates and factors for particulate matter (PM) and gaseous ammonia (NH3) were estimated from measurements taken at a dairy in June 2008. Concentration measurements were made using both point and remote sensors. Filter-based PM samplers and optical particle counters (OPCs) characterized aerodynamic and optical properties, while a scanning elastic lidar measured particles around the facility. The lidar was calibrated to PM concentration using the point measurements. NH3 concentrations were measured using 23 passive samplers and 2 open-path Fourier transform infrared spectrometers (FTS). Emission rates and factors were estimated through both an inverse modeling technique using AERMOD coupled with measurements and a mass-balance approach applied to lidar PM data. Mean PM emission factors ± 95% confidence interval were 3.8 ± 3.2, 24.8 ± 14.5, and 75.9 ± 33.2 g/d/AU for PM2.5, PM10, and TSP, respectively, from inverse modeling and 1.3 ± 0.2, 15.1 ± 2.2, and 46.4 ± 7.0 g/d/AU for PM2.5, PM10, and TSP, respectively, from lidar data. Average daily NH3 emissions from the pens, liquid manure ponds, and the whole facility were 143.4 ± 162.0, 29.0 ± 74.7, and 172.4 ± 121.4 g/d/AU, respectively, based on the passive sampler data and 190.6 ± 55.8, 16.4 ± 8.4, and 207.1 ± 54.7 g/d/AU, respectively, based on FTS measurements. Liquid manure pond emissions averaged 5.4 ± 13.9 and 3.1 ± 1.6 g/m2/d based on passive sampler and FTS measurements, respectively. The calculated PM10 and NH3 emissions were of similar magnitude as those found in literature. Diurnal emission patterns were observed