High-resolution Aircraft Research Articles

LRF-SRNet: Large-Scale Super-Resolution Network for Estimating Aircraft Pose on the Airport Surface.

The introduction of various deep neural network architectures has greatly advanced aircraft pose estimation using high-resolution images. However, realistic airport surface monitors typically take low-resolution (LR) images, and the results of the aircraft pose estimation are far from being accurate enough to be considered acceptable because of long-range capture. To fill this gap, we propose a brand-new, end-to-end low-resolution aircraft pose estimate network (LRF-SRNet) to address the problem of estimating the pose of poor-quality airport surface surveillance aircraft images. The method successfully combines the pose estimation method with the super-resolution (SR) technique. Specifically, to reconstruct high-resolution aircraft images, a super-resolution network (SRNet) is created. In addition, an essential component termed the large receptive field block (LRF block) helps estimate the aircraft's pose. By broadening the neural network's receptive field, it enables the perception of the aircraft's structure. Experimental results demonstrate that, on the airport surface surveillance dataset, our method performs significantly better than the most widely used baseline methods, with AP exceeding Baseline and HRNet by 3.1% and 4.5%.

UCL: Unsupervised Curriculum Learning for water body classification from remote sensing imagery

This paper presents a Convolutional Neural Networks (CNN) based Unsupervised Curriculum Learning approach for the recognition of water bodies to overcome the stated challenges for remote sensing based RGB imagery. The unsupervised nature of the presented algorithm eliminates the need for labelled training data. The problem is cast as a two class clustering problem (water and non-water), while clustering is done on deep features obtained by a pre-trained CNN. After initial clusters have been identified, representative samples from each cluster are chosen by the unsupervised curriculum learning algorithm for fine-tuning the feature extractor. The stated process is repeated iteratively until convergence. Three datasets have been used to evaluate the approach and show its effectiveness on varying scales: (i) SAT-6 dataset comprising high resolution aircraft images, (ii) Sentinel-2 of EuroSAT, comprising remote sensing images with low resolution, and (iii) PakSAT, a new dataset we created for this study. PakSAT is the first Pakistani Sentinel-2 dataset designed to classify water bodies of Pakistan. Extensive experiments on these datasets demonstrate the progressive learning behaviour of UCL and reported promising results of water classification on all three datasets. The obtained accuracies outperform the supervised methods in domain adaptation, demonstrating the effectiveness of the proposed algorithm.

Comprehensive evaluations of diurnal NO2 measurements during DISCOVER-AQ 2011: effects of resolution-dependent representation of NO x emissions.

Nitrogen oxides (NO x =NO+NO2) play a crucial role in the formation of ozone and secondary inorganic and organic aerosols, thus affecting human health, global radiation budget, and climate. The diurnal and spatial variations in NO2 are functions of emissions, advection, deposition, vertical mixing, and chemistry. Their observations, therefore, provide useful constraints in our understanding of these factors. We employ a Regional chEmical and trAnsport model (REAM) to analyze the observed temporal (diurnal cycles) and spatial distributions of NO2 concentrations and tropospheric vertical column densities (TVCDs) using aircraft in situ measurements and surface EPA Air Quality System (AQS) observations as well as the measurements of TVCDs by satellite instruments (OMI: the Ozone Monitoring Instrument; GOME-2A: Global Ozone Monitoring Experiment - 2A), ground-based Pandora, and the Airborne Compact Atmospheric Mapper (ACAM) instrument in July 2011 during the DISCOVER-AQ campaign over the Baltimore-Washington region. The model simulations at 36 and 4 km resolutions are in reasonably good agreement with the regional mean temporospatial NO2 observations in the daytime. However, we find significant overestimations (underestimations) of model-simulated NO2 (O3) surface concentrations during night-time, which can be mitigated by enhancing nocturnal vertical mixing in the model. Another discrepancy is that Pandora-measured NO2 TVCDs show much less variation in the late afternoon than simulated in the model. The higher-resolution 4 km simulations tend to show larger biases compared to the observations due largely to the larger spatial variations in NO x emissions in the model when the model spatial resolution is increased from 36 to 4 km. OMI, GOME-2A, and the high-resolution aircraft ACAM observations show a more dispersed distribution of NO2 vertical column densities (VCDs) and lower VCDs in urban regions than corresponding 36 and 4 km model simulations, likely reflecting the spatial distribution bias of NO x emissions in the National Emissions Inventory (NEI) 2011.

A reel-down instrument system for profile measurements of water vapor, temperature, clouds, and aerosol beneath constant-altitude scientific balloons

Abstract. The tropical tropopause layer (TTL; 14–18.5 km) is the gateway for most air entering the stratosphere, and therefore processes within this layer have an outsized influence in determining global stratospheric ozone and water vapor concentrations. Despite the importance of this layer there are few in situ measurements with the necessary detail to resolve the fine-scale processes within this region. Here, we introduce a novel platform for high-resolution in situ profiling that lowers and retracts a suspended instrument package beneath drifting long-duration balloons in the tropics. During a 100 d circumtropical flight, the instrument collected over a hundred 2 km profiles of temperature, water vapor, and aerosol at 1 m resolution, yielding unprecedented geographic sampling and vertical resolution. The instrument system integrates proven sensors for water vapor, temperature, pressure, and cloud and aerosol particles with an innovative mechanical reeling and control system. A technical evaluation of the system performance demonstrated the feasibility of this new measurement platform for future missions with minor modifications. Six instruments planned for two upcoming field campaigns are expected to provide over 4000 profiles through the TTL, quadrupling the number of high-resolution aircraft and balloon profiles collected to date. These and future measurements will provide the necessary resolution to diagnose the importance of competing mechanisms for the transport of water vapor across the TTL.

SRARNet: A Unified Framework for Joint Superresolution and Aircraft Recognition

Aircraft recognition in high-resolution remote sensing images has rapidly progressed with the advance of convolutional neural networks (CNNs). However, the previous CNN-based methods may not work well for recognizing aircraft in low-resolution remote sensing images because the blurred aircraft in these images offer insufficient details to distinguish them from similar types of targets. An intuitive solution is to introduce superresolution preprocessing. However, conventional superresolution methods mainly focus on reconstructing natural images with detailed texture rather than constructing a high-resolution object with strong discriminative information for the recognition task. To address these problems, we propose a unified framework for joint superresolution and aircraft recognition (Joint-SRARNet) that tries to improve the recognition performance by generating discriminative, high-resolution aircraft from low-resolution remote sensing images. Technically, this network integrates superresolution and recognition tasks into the generative adversarial network (GAN) framework through a joint loss function. The generator is constructed as a joint superresolution and refining subnetwork that can upsample small blurred images into high-resolution ones and restore high-frequency information. In the discriminator, we introduce a new classification loss function that forces the discriminator to distinguish between real and fake images while recognizing the type of aircraft. In addition, the classification loss function is back-propagated to the generator to obtain high-resolution images with discriminative information for easier recognition. Extensive experiments on the challenging multitype aircraft of remote sensing images (MTARSI) dataset demonstrate the effectiveness of the proposed method in restoring a clear super-resolved image from a small blurred image and significant improvement in the recognition performance. To our knowledge, this is the first work on joint superresolution and aircraft recognition tasks.

The Impact of Stochastic Physics-Based Hybrid GSI/EnKF Data Assimilation on Hurricane Forecasts Using EMC Operational Hurricane Modeling System

The National Oceanic and Atmospheric Administration’s (NOAA) cloud-permitting high-resolution operational Hurricane Weather and Research Forecasting (HWRF) model includes the sophisticated hybrid grid-point statistical interpolation (GSI) and Ensemble Kalman Filter (EnKF) data assimilation (DA) system, which allows assimilating high-resolution aircraft observations in tropical cyclone (TC) inner core regions. In the operational HWRF DA system, the flow-dependent background error covariance matrix is calculated from the HWRF self-cycled 40-member ensemble. This DA system has proved to provide improved initial TC structure and therefore improved TC track and intensity forecasts. However, the uncertainties from the model physics are not taken into account in the FY2017 version of the HWRF DA system. In order to further improve the HWRF DA system, the stochastic physics perturbations are introduced in the HWRF DA, including the cumulus convection scheme, the planetary boundary layer (PBL) scheme, and model surface physics (drag coefficient), for HWRF-based ensembles. This study shows that both TC initial conditions and TC track and intensity forecast skills are improved by adding stochastic model physics in the HWRF self-cycled DA system. It was found that the improvements in the TC initial conditions and forecasts are the results of ensemble spread increases which realistically represent the model background error covariance matrix in HWRF DA. For all 2016 Atlantic storms, the TC track and intensity forecast skills are improved by about ~3% and 6%, respectively, compared to the control experiment. The case study shows that the stochastic physics in HWRF DA is especially helpful for those TCs that have inner-core high-resolution aircraft observations, such as tail Doppler radar (TDR) data.

Observation Preprocessing System for RC LACE (OPLACE)

Abstract. Meteorological observations are indispensable for the initialization of numerical weather prediction (NWP) forecast. To enable the application of observations in NWP models a technical preprocessing is necessary. Within the framework of RC LACE (Regional Cooperation for Limited Area modelling in Central Europe) consortium, a common observation preprocessing system (OPLACE) has been built up to deliver meteorological observations in an appropriate format for data assimilation in the NWP system ALADIN (Air Limiteée Adaptation Dynamique Développment International) The purpose of this paper is to document the OPLACE data sources, preprocessing steps and means to make preprocessed observations available. Furthermore, it describes an exchange of dense national surface synoptic measurements and high-resolution aircraft data in real-time among RC LACE national meteorological services (NMS) of Austria, Croatia, the Czech Republic, Hungary, Romania, Slovakia, and Slovenia.

Axisymmetric Potential Vorticity Evolution of Hurricane Patricia (2015)

Abstract Operational numerical models failed to predict the record-setting rapid intensification and rapid overwater weakening of Hurricane Patricia (2015) in the eastern North Pacific basin, resulting in large intensity forecast errors. In an effort to better understand the mesoscale processes contributing to Patricia’s rapid intensity changes, we analyze high-resolution aircraft observations collected on 22–23 October. Spline-based variational analyses are created from observations collected via in situ measurements, Doppler radar, and full-tropospheric dropsonde profiles as part of the Office of Naval Research Tropical Cyclone Intensity (TCI) experiment and the National Oceanic and Atmospheric Administration Intensity Forecasting Experiment (IFEX). We present the first full-tropospheric calculation of the dry, axisymmetric Ertel’s potential vorticity (PV) in a tropical cyclone without relying on balance assumptions. Detailed analyses reveal the formation of a “hollow tower” PV structure as Patricia rapidly approached its maximum intensity, and a subsequent breakdown of this structure during Patricia’s rapid overwater weakening phase. Transforming the axisymmetric PV analyses from radius–height to potential radius–isentropic coordinates reveals that Patricia’s rapid intensification was closely related to the distribution of diabatic heating and eddy mixing. During Patricia’s rapid overwater weakening phase, eddy mixing processes are hypothesized to be the primary factor rearranging the PV distribution near the eye–eyewall region, diluting the PV previously confined to the hollow tower while approximately conserving the absolute circulation.

Remote Sensing Data to Detect Hessian Fly Infestation in Commercial Wheat Fields

Remote sensing data that are efficiently used in ecological research and management are seldom used to study insect pest infestations in agricultural ecosystems. Here, we used multispectral satellite and aircraft data to evaluate the relationship between normalized difference vegetation index (NDVI) and Hessian fly (Mayetiola destructor) infestation in commercial winter wheat (Triticum aestivum) fields in Kansas, USA. We used visible and near-infrared data from each aerial platform to develop a series of NDVI maps for multiple fields for most of the winter wheat growing season. Hessian fly infestation in each field was surveyed in a uniform grid of multiple sampling points. For both satellite and aircraft data, NDVI decreased with increasing pest infestation. Despite the coarse resolution, NDVI from satellite data performed substantially better in explaining pest infestation in the fields than NDVI from high-resolution aircraft data. These results indicate that remote sensing data can be used to assess the areas of poor growth and health of wheat plants due to Hessian fly infestation. Our study suggests that remotely sensed data, including those from satellites orbiting >700 km from the surface of Earth, can offer valuable information on the occurrence and severity of pest infestations in agricultural areas.

Using Very High Resolution Thermal Infrared Imagery for More Accurate Determination of the Impact of Land Cover Differences on Evapotranspiration in an Irrigated Agricultural Area

Land cover has a strong effect on the evapotranspiration (ET) and the hydrologic cycle. Urbanization alters the land cover affecting the surface energy balance and ET by, for example, urban encroachment in agricultural areas. This study investigates the potential utility of high resolution ET in determining more accurately the impact of land cover on water use for an agricultural area. The approach was to apply the physically based two-source energy balance (TSEB) model to very high resolution (~8 m) aircraft thermal data and compare the ET pattern and distribution to TSEB output using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data acquired on 2 August 2012. Modeled flux components were validated using measurements collected from a network of 16 eddy covariance (EC) towers at the study site. The modeled ET using the aircraft data agreed satisfactorily with the flux tower measurements and had better performance than the TSEB model applied to the ASTER data. The percent errors between ET closed by the Bowen ratio (BR) and residual (RE) approaches were 3 and 1%, respectively. It is shown that the high resolution aircraft ET can more accurately determine the change in ET magnitude by having pure pixels of the main land cover types, namely urban, agriculture, and natural vegetation. As a result, the ET histogram exhibits a significant bi-modal distribution which can be used to accurately distinguish the impact on ET from urban versus agricultural land cover areas and potentially monitor the effect on ET over a landscape due to small changes in land cover. At the coarser 90 m resolution of ASTER, the TSEB ET estimates are more often a combination of urban and agricultural land cover ET near the urban-agriculture land cover boundaries. As a result, the bi-modal distribution in ET is almost nonexistent. This study demonstrates the potential utility of high resolution ET mapping for more accurately determining the magnitude of the ET differences between cropland and urban land cover. It also suggests that, with high resolution thermal imagery, TSEB is a potential tool for monitoring the impact on ET due to relatively small changes in land cover as a result of urban expansion. Such a tool would be useful for watershed management.

Intra-pixel variability in satellite tropospheric NO<sub>2</sub> column densities derived from simultaneous space-borne and airborne observations over the South African Highveld

Abstract. Aircraft measurements of NO2 using an imaging differential optical absorption spectrometer (iDOAS) instrument over the South African Highveld region in August 2007 are presented and compared to satellite measurements from OMI and SCIAMACHY. In situ aerosol and trace-gas vertical profile measurements, along with aerosol optical thickness and single-scattering albedo measurements from the Aerosol Robotic Network (AERONET), are used to devise scenarios for a radiative transfer modelling sensitivity study. Uncertainty in the air-mass factor due to variations in the aerosol and NO2 profile shape is constrained and used to calculate vertical column densities (VCDs), which are compared to co-located satellite measurements. The lower spatial resolution of the satellites cannot resolve the detailed plume structures revealed in the aircraft measurements. The airborne DOAS in general measured steeper horizontal gradients and higher peak NO2 vertical column density. Aircraft measurements close to major sources, spatially averaged to the satellite resolution, indicate NO2 column densities more than twice those measured by the satellite. The agreement between the high-resolution aircraft instrument and the satellite instrument improves with distance from the source, this is attributed to horizontal and vertical dispersion of NO2 in the boundary layer. Despite the low spatial resolution, satellite images reveal point sources and plumes that retain their structure for several hundred kilometres downwind.

Mapping evapotranspiration with high-resolution aircraft imagery over vineyards using one- and two-source modeling schemes

Abstract. Thermal and multispectral remote sensing data from low-altitude aircraft can provide high spatial resolution necessary for sub-field (≤ 10 m) and plant canopy (≤ 1 m) scale evapotranspiration (ET) monitoring. In this study, high-resolution (sub-meter-scale) thermal infrared and multispectral shortwave data from aircraft are used to map ET over vineyards in central California with the two-source energy balance (TSEB) model and with a simple model having operational immediate capabilities called DATTUTDUT (Deriving Atmosphere Turbulent Transport Useful To Dummies Using Temperature). The latter uses contextual information within the image to scale between radiometric land surface temperature (TR) values representing hydrologic limits of potential ET and a non-evaporative surface. Imagery from 5 days throughout the growing season is used for mapping ET at the sub-field scale. The performance of the two models is evaluated using tower-based measurements of sensible (H) and latent heat (LE) flux or ET. The comparison indicates that TSEB was able to derive reasonable ET estimates under varying conditions, likely due to the physically based treatment of the energy and the surface temperature partitioning between the soil/cover crop inter-row and vine canopy elements. On the other hand, DATTUTDUT performance was somewhat degraded presumably because the simple scaling scheme does not consider differences in the two sources (vine and inter-row) of heat and temperature contributions or the effect of surface roughness on the efficiency of heat exchange. Maps of the evaporative fraction (EF = LE/(H + LE)) from the two models had similar spatial patterns but different magnitudes in some areas within the fields on certain days. Large EF discrepancies between the models were found on 2 of the 5 days (DOY 162 and 219) when there were significant differences with the tower-based ET measurements, particularly using the DATTUTDUT model. These differences in EF between the models translate to significant variations in daily water use estimates for these 2 days for the vineyards. Model sensitivity analysis demonstrated the high degree of sensitivity of the TSEB model to the accuracy of the TR data, while the DATTUTDUT model was insensitive to systematic errors in TR as is the case with contextual-based models. However, it is shown that the study domain and spatial resolution will significantly influence the ET estimation from the DATTUTDUT model. Future work is planned for developing a hybrid approach that leverages the strengths of both modeling schemes and is simple enough to be used operationally with high-resolution imagery.

Examining spectral reflectance features related to foliar nitrogen in forests: Implications for broad-scale nitrogen mapping

Abstract The concentration of nitrogen (N) in foliage often limits photosynthesis and can influence a number of important biogeochemical processes. For this reason, methods for estimating foliar %N over a range of scales are needed to enhance understanding of terrestrial carbon and nitrogen cycles. High spectral resolution aircraft remote sensing has become an increasingly common tool for landscape-scale estimates of canopy %N because reflectance in some portions of the spectrum has been shown to correlate strongly with field-measured %N. These patterns have been observed repeatedly over a wide range of biomes, opening new possibilities for planned Earth observation satellites. Nevertheless, the effects of spectral resolution and other sensor characteristics on %N estimates have not been fully examined, and may have implications for future analyses at landscape, regional and global scales. In this study, we explored the effects of spectral resolution, spatial resolution and sensor fidelity on relationships between forest canopy %N and reflectance measurements from airborne and satellite platforms. We conducted an exercise whereby PLS, simple and multiple regression calibrations to field-measured canopy %N for a series of forested sites were iteratively performed using (1) high resolution data from AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) that were degraded spectrally from 10 nm to 30 nm, 50 nm, 70 nm, and 90 nm bandwidths, and spatially from 18 m to 30 m and 60 m pixels; (2) data representing Landsat and MODIS (Moderate Resolution Imaging Spectroradiometer) spectral bands simulated with data from AVIRIS; and (3) actual data from Landsat and MODIS. We observed virtually no reduction in the strength of relationships between %N and reflectance when using coarser bandwidths from AVIRIS, but instead saw declines with increasing spatial resolution and loss of sensor fidelity. This suggests that past efforts to examine foliar %N using broad-band sensors may have been limited as much by the latter two properties as by their coarser spectral bandwidths. We also found that regression models were driven primarily by reflectance over broad portions of the near infrared (NIR) region, with little contribution from the visible or mid infrared regions. These results suggest that much of the variability in canopy %N is related to broad reflectance properties in the NIR region, indicating promise for broad scale canopy N estimation from a variety of sensors.

Convection and Rapid Filamentation in Typhoon Sinlaku during TCS-08/T-PARC

Abstract This study examines the convection and rapid filamentation in Typhoon Sinlaku (2008) using the Naval Research Laboratory (NRL) P-3 aircraft data collected during the Tropical Cyclone Structure 2008 (TCS-08) and The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) field experiments. The high-resolution aircraft radar and wind data are used to directly compute the filamentation time, to allow an investigation into the effect of filamentation on convection. During the reintensification stage, some regions of deep convection near the eyewall are found in the vorticity-dominated area where there is little filamentation. In some other parts of the eyewall and the outer spiral rainband region, including areas of upward motion, the filamentation process appears to suppress deep convection. However, the magnitude of the suppression differs greatly in the two regions. In the outer spiral band region, which is about 200 km from the center, the suppression is much more effective, such that the ratio of the deep convective regime occurrence over the stratiform regime varies from around 50% (200%) for filamentation time shorter (longer) than 24 min. In the eyewall cloud region where the conditions are conducive to deep convection, the filamentation effect may be quite limited. While effect of filamentation suppression is only about 10%, it is still systematic and conspicuous for filamentation times shorter than 19 min. The results suggest the possible importance of vortex-scale filamentation dynamics in suppressing deep convection and organizing spiral bands, which may affect the development and evolution of tropical cyclones.

Ice formation and development in aged, wintertime cumulus over the UK: observations and modelling

Abstract. In situ high resolution aircraft measurements of cloud microphysical properties were made in coordination with ground based remote sensing observations of a line of small cumulus clouds, using Radar and Lidar, as part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate (APPRAISE) project. A narrow but extensive line (~100 km long) of shallow convective clouds over the southern UK was studied. Cloud top temperatures were observed to be higher than −8 °C, but the clouds were seen to consist of supercooled droplets and varying concentrations of ice particles. No ice particles were observed to be falling into the cloud tops from above. Current parameterisations of ice nuclei (IN) numbers predict too few particles will be active as ice nuclei to account for ice particle concentrations at the observed, near cloud top, temperatures (−7.5 °C). The role of mineral dust particles, consistent with concentrations observed near the surface, acting as high temperature IN is considered important in this case. It was found that very high concentrations of ice particles (up to 100 L−1) could be produced by secondary ice particle production providing the observed small amount of primary ice (about 0.01 L−1) was present to initiate it. This emphasises the need to understand primary ice formation in slightly supercooled clouds. It is shown using simple calculations that the Hallett-Mossop process (HM) is the likely source of the secondary ice. Model simulations of the case study were performed with the Aerosol Cloud and Precipitation Interactions Model (ACPIM). These parcel model investigations confirmed the HM process to be a very important mechanism for producing the observed high ice concentrations. A key step in generating the high concentrations was the process of collision and coalescence of rain drops, which once formed fell rapidly through the cloud, collecting ice particles which caused them to freeze and form instant large riming particles. The broadening of the droplet size-distribution by collision-coalescence was, therefore, a vital step in this process as this was required to generate the large number of ice crystals observed in the time available. Simulations were also performed with the WRF (Weather, Research and Forecasting) model. The results showed that while HM does act to increase the mass and number concentration of ice particles in these model simulations it was not found to be critical for the formation of precipitation. However, the WRF simulations produced a cloud top that was too cold and this, combined with the assumption of continual replenishing of ice nuclei removed by ice crystal formation, resulted in too many ice crystals forming by primary nucleation compared to the observations and parcel modelling.

ET Mapping with High-Resolution Airborne Remote Sensing Data in an Advective Semiarid Environment

AbstractAccurate estimates of spatially distributed evapotranspiration (ET) are essential for managing water in irrigated regions and for hydrologic modeling. METRIC (Mapping ET at high Resolutions with Internal Calibration) energy balance algorithm was applied to derive ET from six high-resolution aircraft images (0.5–2.0 m pixels). Images were acquired over the USDA Agricultural Research Service (USDA-ARS) Conservation and Production Research Laboratory (CPRL) in the semiarid Southern High Plains. The remote sensing (RS) campaign occurred during the 2007 summer cropping season. Daily ET estimations were evaluated using measured ET data from five monolithic weighing lysimeters located in the CPRL. On average, errors in estimating hourly ET were -0.7±11.6%; for daily ET, errors were 2.4±9.3%. Results indicated that METRIC algorithm estimated ET values well when the surface roughness for momentum transfer considered heterogeneous surface conditions and when the grass reference ET fraction was used to extra...

Relationship of Rapid Intensification of Tropical Cyclones to Dynamical/Thermodynamical Parameters

Many avenues of research are currently being tried for understanding and predicting hurricane intensity forecasts. We have listed several such research avenues being pursued by the research community. This article addresses the use of high-resolution aircraft reconnaissance datasets used for daily data assimilation over three years of hurricane histories that cover rapid intensification cases. We show a number of dynamical and thermodynamical parameters, derived from these datasets, which seem to hold promise for future use in short range statistical forecasts on rapid intensification. These include the vertical differential of heating in the complete potential vorticity equation, the conversion of divergent kinetic energy to rotational kinetic energy, the transformation of shear vorticity to curvature vorticity, and the advection of the earth's and the relative angular momentum into the inner core of hurricanes. We find a rapid growth in the time history of each of these parameters as the rapid intensification occurs. These are demonstrated for a number of recent hurricanes.

Velocity and Temperature Structure Functions in the Upper Troposphere and Lower Stratosphere from High-Resolution Aircraft Measurements

Abstract High-resolution measurements obtained from NOAA “best” atmospheric turbulence (BAT) probes mounted on an EGRETT high-altitude research aircraft were used to characterize turbulence in the upper troposphere and lower stratosphere at scales from 2 m to 20 km, focusing on three-dimensional behavior in the sub-kilometer-scale range. Data were analyzed for 129 separate level flight segments representing 41 h of flight time and 12 600 km of wind-relative flight distances. The majority of flights occurred near the tropopause layer of the winter subtropical jet stream in the Southern Hemisphere. Second-order structure functions for velocity and temperature were analyzed for the separate level-flight segments, individually and in various ensembles. A 3D scaling range was observed at scales less than about 100 m, with power-law exponents for the structure functions of the velocity component in the flight direction varying mostly between 0.4 and 0.75 for the separate flight segments, but close to ⅔ for the ensemble-averaged curves for all levels and for various subensembles. Structure functions in the 3D scaling range were decoupled from those at scales greater than 10 km, with the large-scale structure functions showing less variation than those at smaller scales. Weakly anisotropic behavior was observed in the 3D range, with structure parameters for the lateral and vertical velocities on the same order as those in the flight direction but deviating from the expected isotropic value. Anisotropy was correlated with turbulence intensity, with greater anisotropy associated with weaker turbulence.

Diagnostics of the Tropical Tropopause Layer from in-situ observations and CCM data

Abstract. A suite of diagnostics is applied to in-situ aircraft measurements and one Chemistry-Climate Model (CCM) data to characterize the vertical structure of the Tropical Tropopause Layer (TTL). The diagnostics are based on vertical tracer profiles and relative vertical tracer gradients, using tropopause-referenced coordinates, and tracer-tracer relationships in the tropical Upper Troposphere/Lower Stratosphere (UT/LS). Observations were obtained during four tropical campaigns performed from 1999 to 2006 with the research aircraft Geophysica and have been compared to the output of the ECHAM5/MESSy CCM. The model vertical resolution in the TTL (~500 m) allows for appropriate comparison with high-resolution aircraft observations and the diagnostics used highlight common TTL features between the model and the observational data. The analysis of the vertical profiles of water vapour, ozone, and nitrous oxide, in both the observations and the model, shows that concentration mixing ratios exhibit a strong gradient change across the tropical tropopause, due to the role of this latter as a transport barrier and that transition between the tropospheric and stratospheric regimes occurs within a finite layer. The use of relative vertical ozone and carbon monoxide gradients, in addition to the vertical profiles, helps to highlight the region where this transition occurs and allows to give an estimate of its thickness. The analysis of the CO-O3 and H2O-O3 scatter plots and of the Probability Distribution Function (PDF) of the H2O-O3 pair completes this picture as it allows to better distinguish tropospheric and stratospheric regimes that can be identified by their different chemical composition. The joint analysis and comparison of observed and modelled data allows to state that the model can represent the background TTL structure and its seasonal variability rather accurately. The model estimate of the thickness of the interface region between tropospheric and stratospheric regimes agrees well with average values inferred from observations. On the other hand, the measurements can be influenced by regional scale variability, local transport processes as well as deep convection, that can not be captured by the model.

Estimating hourly crop ET using a two-source energy balance model and multispectral airborne imagery

Efficient water use through improved irrigation scheduling is expected to moderate fast declining groundwater levels and improve sustainability of the Ogallala Aquifer. An accurate estimation of spatial actual evapotranspiration (ET) is needed for this purpose. Therefore, during 2007, the Bushland ET and Agricultural Remote Sensing Experiment (BEAREX07) was conducted at the USDA-ARS, in Bushland, Texas, to evaluate remote sensing (RS)-based surface energy balance models. Very high-resolution aircraft images were acquired using the Utah State University airborne multispectral system. Instantaneous ET was estimated using a two-source energy balance model (TSM). A minor modification was made in the calculation of sensible heat fluxes to improve ET estimation. Furthermore, a sensitivity analysis of selected variables was conducted to evaluate their effect on ET estimation. Data from four weighing lysimeters, planted to sorghum and corn, were used for evaluating ET predictions. Instantaneous ET was predicted with mean bias error and root mean square error of 0.03 and 0.07 mm h−1 (4.3 and 11.7%), respectively. Results indicated that crop height, roughness length for momentum transfer, clumping factor and soil resistance sub-models need to be refined. Nevertheless, the application of the TSM using high-resolution RS imagery in the Southern High Plains is promising.