Lightning Tracking Tool for Assessment of Total Cloud Lightning within AWIPS II
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Multispectral data from two ERTS-1 scenes of the same central Pennsylvania area were brought into registration by translation and then merged. The two scenes were viewed on different dates, but from adjacent ground tracks, as frequent cloud cover in Pennsylvania made it impossible to choose two scenes from the same track. Targets selected to be mapped included river water, railroad yards, creeks, urban areas, industrial areas, and vegetation. Equivalent training areas were chosen from each of the original scenes and from the merged data. Classification maps were produced for each, and a comparison was made. Scene brightness was found to have the most important effect on classification differences.
Ultraviolet (UV) remote sensing of volcanic ash and other absorbing aerosols from space began with the launch of the first Total Ozone Mapping Spectrometer (TOMS) instrument in 1978. Subsequent UV satellite missions (TOMS, GOME, SCIAMACHY, OMI, GOME-2, OMPS) have extended UV ash measurements to the present, generating a unique multidecadal record. A UV Aerosol Index (UVAI) based on two near-UV wavelengths, equally applicable to multispectral (TOMS, DSCOVR) or hyperspectral (GOME, SCIAMACHY, OMI, GOME-2, OMPS) instruments, has been used to derive a unique absorbing aerosol climatology across multiple UV satellite missions. Advantages of UV ash measurements relative to infrared (IR) techniques include the ability to detect ash at any altitude (assuming no clouds), above clouds, and over bright surfaces, where visible and IR techniques may fail. Disadvantages include the daytime-only restriction and nonspecificity to silicate ash, since UV measurements are sensitive to any UV-absorbing aerosol, including smoke, desert dust, and pollution. However, simultaneous retrieval of sulfur dioxide (SO2) abundance and UVAI provides robust discrimination of volcanic clouds. Although the UVAI is only semiquantitative, it has proved successful at detecting and tracking volcanic ash clouds from many volcanic eruptions since 1978. NASA A-Train measurements since 2006 (eg, CALIOP) have provided much improved constraints on volcanic ash altitude, and also permit identification of aerosol type through sensor synergy. Quantitative UV retrievals of ash optical depth, effective particle size, and ash column mass are possible and require assumptions of ash refractive index, particle size distribution, and ash layer altitude. The lack of extensive ash refractive index data in the UV-visible and the effects of ash particle shape on retrievals introduce significant uncertainty in the retrieved parameters, although limited validation against IR ash retrievals has been successful. In this contribution, we review UV ash detection and retrieval techniques and provide examples of volcanic eruptions detected in the approx. 37 year data record.
Under certain conditions ships can affect the structure of shallow overlying layer clouds. It has been suggested that this is due to particles emitted from the ships, which increase the concentration of cloud condensation nuclei in the air, which, in turn, increases the concentrations of cloud droplets and reduces the average size of the droplets. Two ship track signatures in stratus clouds were observed simultaneously from a satellite and from an aircraft. The airborne measurements showed that in the ship tracks the droplet sizes and concentrations, as well as the total concentrations of particles, were substantially different from those in adjacent clouds. Furthermore, in-situ solar radiation measurements showed a significant enhancement in the upwelling radiance within the ship tracks at visible wavelengths and a significant reduction in radiance at 2.2 micrometers. Remote sensing measurements from the NOAA-10 satellite showed the effects of these differences in cloud structure as an enhancement in cloud reflectivity at 0.63 and 3.7 micrometers. These observations support the contention that ship track signatures in clouds are produced primarily by particles generated by ships.
Atmospheric winds are a key physical phenomenon impacting natural hazards, energy transport, ocean currents, large-scale circulation, and ecosystem fluxes. Observing winds is a complex process and presents a large gap in NASA’s Earth Observation System. Atmospheric motion vectors (AMVs) aim to fill this gap by making numerical estimates of cloud movement between sequences of multi-spectral satellite images, tracking clouds and water vapor. Recent imaging hardware and software advancements have enabled the use of numerical optical flow techniques to produce accurate and dense vector fields outperforming traditional methods. This work presents WindFlow as the first machine learning based system for feature tracking atmospheric motion using optical flow. Due to the lack of large-scale satellite-based observations, we leverage high-resolution numerical simulations from NASA's GEOS-5 Nature Run to perform supervised learning and transfer to satellite images. We demonstrate that our approach using deep learning based optical flow scales to ultra-high-resolution images of size 2881x5760 with less than 1 m/s bias and 2.5 m/s average error. Four network and learning architectures are compared and it is found that recurrent all-pairs field transforms (RAFT) produces the lowest errors on all metrics for wind speed and direction. Results on held out numerical outputs shows RAFT's good performance in each of the spatial, temporal, and physical dimensions. A comparison between WindFlow and an operational AMV product against rawinsonde observations show that RAFT transfers across simulations and thermal infrared satellite observations. This work shows that machine learning based optical flow is an efficient approach to generating robust feature tracking for AMVs consistently over large regions.
The use of a tracking radar to measure the flight paths of migrating birds on nights with opaque clouds is discussed. The effects of wind and lack of visual references are examined. The limitations of the radar observations are described, and samples of tracks obtained during radar observations are included. It is concluded that nonvisual mechanisms of orientation make it possible for birds to migrate in opaque clouds, but the exact nature of the sensory information cannot be determined by radar observations.
Recent satellite observations indicate a significant decrease of cloud water in ship tracks, in contrast to an ensemble of in situ ship-track measurements that show no average change in cloud water relative to the surrounding clouds. We find through large-eddy simulations of stratocumulus that the trend in the satellite data is likely an artifact of sampling only overcast clouds. The simulations instead show cloud cover increasing with droplet concentrations. Our simulations also show that increases in cloud water from drizzle suppression (by increasing droplet concentrations) are favored at night or at extremely low droplet concentrations.
The present engineering prediction method addresses the aerodynamic characteristics and associated flowfields of circular and noncircular bodies at large incidence angles and arbitrary roll angles. 3D attached-flow models are used to represent the body, and 2D, incompressible, separated flow models are used to represent the separated vortex wake. The predicted pressure distribution of the body, under the influence of both the freestream and the separation vortex wake, is used to calculate aerodynamic loads. The separation vortex wake is represented by clouds of discrete vortices in crossflow planes normal to the body axis.
Water vapor imagery from geostationary satellites has been available for over a decade. These data are used extensively by operational analysts and forecasters, mainly in a qualitative mode (Weldon and Holmes 1991). In addition to qualitative applications, motions deduced in animated water vapor imagery can be used to infer wind fields in cloudless regimes, thereby augmenting the information provided by cloud-drift wind vectors. Early attempts at quantifying the data by tracking features in water vapor imagery met with modest success (Stewart et al. 1985; Hayden and Stewart 1987). More recently, automated techniques have been developed and refined, and have resulted in upper-level wind observations comparable in quality to current operational cloud-tracked winds (Laurent 1993). In a recent study by Velden et al. (1993) it was demonstrated that wind sets derived from Meteosat-3 (M-3) water vapor imagery can provide important environmental wind information in data void areas surrounding tropical cyclones, and can positively impact objective track forecasts. M-3 was repositioned to 75W by the European Space Agency in 1992 in order to provide complete coverage of the Atlantic Ocean. Data from this satellite are being transmitted to the U.S. for operational use. Compared with the current GOES-7 (G-7) satellite (positioned near 112W), the M-3 water vapor channel contains a superior horizontal resolution (5 km vs. 16 km ). In this paper, we examine wind sets derived using automated procedures from both GOES-7 and Meteosat-3 full disk water vapor imagery in order to assess this data as a potentially important source of large-scale wind information. As part of a product demonstration wind sets were produced twice a day at CIMSS during a six-week period in March and April (1994). These data sets are assessed in terms of geographic coverage, statistical accuracy, and meteorological impact through preliminary results of numerical model forecast studies.
We present a series of gasdynamical simulations of the interaction of a dense, cool interstellar cloud with a high-speed, supersonic wind that confines and accelerates the embedded cloud. Our goal is to attempt to determine if such clouds can survive various potentially disruptive instabilities, that occur at their peripheries, long enough to be accelerated to speeds which are comparable to the wind velocity. These simulations are performed using two-dimensional, Eulerian gas dynamics on both an axisymmetric (about the cloud axis) and 'slab' geometric grid. The spatial and temporal resolutions of the simulations are varied over a wide range to investigate the effects of small-scale instabilities on the overall acceleration of clouds and the development of large-scale, disruptive instabilities. Also, we study the effects of wind/cloud Mach number variations by changing the wind speed constant at about 12 km/s (which corresponds to a cloud temperature of 10,000 K). The current simulations track the evolution of clouds as they are accelerated to speeds approximately 4-5 times greater than their internal sound speeds. Furthermore, the models with the highest resolution were extended far beyond quasi-linear Rayleigh-Taylor growth times reaching 6-7 Rayleigh-Taylor growth times for the largest scale instabilities before being terminated because of the accumulation of errors at the rear grid boundary.
This paper describes an integrated system for providing ground-based cockpit display, transmitting to an aircraft, upon request via VHF radio, important ground-based thunderstorm data such as radar precipitation reflectivity contours, aircraft ground track, and cloud-to-ground lightning locations. Examples of the airborne X-band weather radar display and the ground-based display are presented for two different missions during the NASA Storm Hazards Program. In spite of some limitation, the system was found to be helpful in the selection of the route of flight, the general ground track to be used, and, occasionally, in clarifying the location of a specific cell of interest.
Water vapor imagery from the geostationary satellites such as GOES, Meteosat, and GMS provides synoptic views of dynamical events on a continual basis. Because the imagery represents a non-linear combination of mid- and upper-tropospheric thermodynamic parameters (three-dimensional variations in temperature and humidity), video loops of these image products provide enlightening views of regional flow fields, the movement of tropical and extratropical storm systems, the transfer of moisture between hemispheres and from the tropics to the mid- latitudes, and the dominance of high pressure systems over particular regions of the Earth. Despite the obvious larger scale features, the water vapor imagery contains significant image variability down to the single 8 km GOES pixel. These features can be quantitatively identified and tracked from one time to the next using various image processing techniques. Merrill et al. (1991), Hayden and Schmidt (1992), and Laurent (1993) have documented the operational procedures and capabilities of NOAA and ESOC to produce cloud and water vapor winds. These techniques employ standard correlation and template matching approaches to wind tracking and use qualitative and quantitative procedures to eliminate bad wind vectors from the wind data set. Techniques have also been developed to improve the quality of the operational winds though robust editing procedures (Hayden and Veldon 1991). These quality and control approaches have limitations, are often subjective, and constrain wind variability to be consistent with model derived wind fields. This paper describes research focused on the refinement of objective quality and control parameters for water vapor wind vector data sets. New quality and control measures are developed and employed to provide a more robust wind data set for climate analysis, data assimilation studies, as well as operational weather forecasting. The parameters are applicable to cloud-tracked winds as well with minor modifications. The improvement in winds through use of these new quality and control parameters is measured without the use of rawinsonde or modeled wind field data and compared with other approaches.
The Teton-Yellowstone Tornado, rated F4, crossed the Continental Divide at 3070 m, leaving behind a damage swath 39.2-km long and 2.5-km wide. A detailed damage analysis by using stereo-pair and color photos revealed the existence of four spinup swirl marks and 72 microburst outflows inside the damage area. The tornado was spawned by a mesocyclone that formed at the intersection of a mesohigh boundary and a warm front. The parent cloud of the tornado, tracked on eight infrared-temperature maps from GOES East and West, moved at 25 m s-1 and the number of cold temperature pixels below -60 C reached a distinct peak during the tornado time. Identified and tracked also are two warm spots enclosed inside the cold anvil cloud. On the basis of their identity and movement, an attempt was made to explain the cause of these spots as being the stratospheric cirrus clouds.
Recent satellite observations of ship tracks surprisingly indicate that cloud water decreases with increasing droplet concentrations. However, we find by analyzing detailed simulations of stratocumulus that the reported trend is likely an artifact of sampling, only overcast clouds. The simulations instead show cloud coverage increasing with droplet concentrations, accounting for 25% of cloud albedo increase at moderate droplet concentrations. Our simulations also show that increases in cloud water from drizzle suppression (by increasing droplet concentrations) are favored only at night or at extremely low droplet concentrations, suggesting that the indirect aerosol forcing is overestimated in climate change projections by many general circulation models.
Basic problems in the interpretation of satellite-tracked low-cloud velocities are reviewed. The METRACOM system of cloud velocity computation is outlined, and caution is urged in converting cloud velocities into wind velocities. The motion of various cumulus cells over Springfield, Mo., Barbados, and Tampa, Fla., is analyzed. It is shown that multiturret cells do not always move with the wind, that addition and deletion of turrets belonging to a specific cell may cause erratic motion in a tracer cell, and that cumulus turrets between 0.3 and 2 miles in size are the best targets for inferring the mean wind velocity within the subcloud layers. It is concluded that the accuracy of wind velocity estimates will be no better than 2 meters/sec unless the physical and dynamic characteristics of cumulus motion are further investigated.
A review of meteorological satellites, the instrumentation mounted on them, and uses to which they were put. The history of the development of the system of meteorological satellites is reviewed, followed by detailed coverage of the sensors (imagers, scanners, sounders) mounted on the satellites. Global cloud cover, storm tracking, tropospheric water vapor, stratospheric temperature and air flow, and the radiation budget are among the objects of study in this decade-and-a-half program. Weather analysis and prediction have been advanced appreciably.
During the 1975, 1976, and 1977, NOAA's National Environmental Satellite Service and NASA's Goddard Space Flight Center conducted a cooperative program to determine the optimum resolution and frequency of satellite images for deriving winds to study and forecast tropical cyclones. Rapid scan images were obtained at 7.5 minute interval from SMS-2 for hurricane Eloise and cyclone Caroline, and at 3 minute intervals from GOES-1 for tropical storms Belle, Holly, and Anita. Cloud motions were derived from these images using the Atmospheric and Oceanographic Information Processing System. Winds that were derived from the movement of upper and lower tropospheric level clouds using rapid scan data were compared with the 15 and 30 minute interval data. Greater than 10 (5) times as many clouds could be tracked to obtain winds using 3 and 7.5 minute rapid scan images as when using 15 or 30 minute interval images. A few bright areas within the central dense overcast which appeared to be moving with the winds at low levels were tracked.
Five-minute interval 1-km resolution SMS visible channel data were used to derive low-level wind fields by tracking small cumulus clouds on NASA's Atmospheric and Oceanographic Information Processing System. The satellite-derived wind fields were combined with surface mixing ratios to derive horizontal moisture convergence in the prestorm environment of April 24, 1975. Storms began developing in an area extending from southwest Oklahoma to eastern Tennessee 2 h subsequent to the time of the derived fields. The maximum moisture convergence was computed to be 0.0022 g/kg per sec and areas of low-level convergence of moisture were in general indicative of regions of severe storm genesis. The resultant moisture convergence fields derived from two wind sets 20 min apart were spatially consistent and reflected the mesoscale forcing of ensuing storm development. Results are discussed with regard to possible limitations in quantifying the relationship between low-level flow and between low-level flow and satellite-derived cumulus motion in an antecedent storm environment.