The atmospheric limb sounding technique based on the Global Positioning System (GPS) has been shown to be accurate and of very high vertical resolution. The fact that the GPS radio occultation (RO) technique is not affected by clouds or precipitation, has no instrument drift, and requires no calibration makes it ideally suited for climate monitoring and global weather prediction. The Constellation Observing System for Meteorology, Ionosphere, and Climate (ROCSAT-3/COSMIC) mission will be launched in late 2005, and will provide ~2,500 GPS radio occultation soundings per day to support operational weather prediction, climate analysis, and ionospheric research. Other radio occultation missions are expected to coincide with COSMIC promising additional data. In this paper, we provide status overview of the COSMIC mission, describe its science goals, and review selected GPS RO studies that are relevant to weather prediction and climate analysis.

Atmospheric soundings using signals received in low Earth orbit from Global Positioning System (GPS) satellite transmissions are widely recognized as important data for establishing a precise climate record of upper-air temperatures, due to their self-calibrating nature and all-weather acquisition. More recently, advances in retrieval methods using the same GPS data have opened the possibility of new scientific studies related to atmospheric processes and climate change. We will present recent innovations in extracting scientifically useful information from the phase and amplitude of received GPS transmissions, and discuss the technical challenges that need to be overcome to achieve new scientific results. Promising areas being pursued include: remote sensing of the planetary boundary layer from space, important for understanding ocean-atmosphere coupling; retrieving tropopause temperature structure at high vertical resolution, important for understanding troposphere-stratosphere exchange mechanisms and the role of convection; high accuracy and precision of upper altitude (25+ km) retrievals in the stratosphere. Using an end-to-end simulator recently developed at JPL, we will investigate in realistic detail the relationship between the atmospheric state and retrieved scientific parameters, and discuss retrieval research needed to address new scientific applications.

A profile of temperature and relative humidity retrieved from Mt. Fuji observed GPS “Downward Looking (DL)” data was assimilated into mesoscale weather prediction model by using four-dimensional variational data assimilation (4D-var) procedure for typhoon case of September 9, 2001. The DL observation offered the profile of the atmosphere over the ocean where typhoon approached. Because the retrieved case was few, the observation error was expediently decided as 1 centigrade for temperature and 4 percent for relative humidity without a statistical investigation. The assimilation results show a small but positive impact for precipitation forecast. But the position of the typhoon center in the initial field slightly shifted to the opposite direction from the best track analysis by the Japan Meteorological Agency (JMA). To decide observation error of DL retrieved refractive index profile, error estimation using a three-dimensional (3D) ray-tracing model which uses mesoscale weather model outputs was executed. The 3D ray-tracing model simulated propagation of GPS signal in the model atmosphere every one-second. Then, Doppler shift, bending angle, partial bending angle (PBA), and finally refractive index profile were retrieved. It was proven that PBAs are able to reproduce from Doppler shift in high accuracy. Error of retrieved refractive index showed high correlation with horizontal variation of refractive index. The results suggest that we should assimilate bending angle or excess phase delay rather than profile of retrieved refractive index, temperature and humidity.

We simultaneously calculate atmospheric parameters (zenith wet delay and a gradient vector) and positioning errors estimated from tropospheric slant delays using ray tracing technique through a one epoch data set of the mesoscale non-hydrostatic numerical model. In this numerical calculation both isotropic and anistropic mapping functions are evaluated. We find that the positioning errors are not reduced by an anisotropic mapping function, with the exception of the north-south component. Elevation weighting is more important than mapping function type for reducing horizontal errors with the present specific data set. Large horizontal and vertical positioning errors associated with topography and mountain lee wave effect are indicated.

A cloud resolving 4-dimensional variational data assimilation system (4DVAR) based on the Japan Meteorological Agency nonhydrostatic model (JMA-NHM) is under development. One of the targets of this system is the analysis of mesoscale convective systems. Features of background error statistics for the model with a horizontal resolution of 2km (hereinafter abbreviated as 2km model) are much different from those with a 5km resolution (5km model). Thus, forecast error estimated by the scale-down method from that forecast error obtained from the 5km model was not applicable. To develop the cloud resolving system, background error statistics for the system with 2km horizontal intervals were calculated and a suitable set of control variables was designed. Using the new background error statistics and the new control variable set, a preliminary data assimilation experiment of the Global Positioning System (GPS)-derived precipitable water vapor (PWV) and radial wind observed by Doppler radars (RW) was performed. By assimilating GPS-PWV and RW, the convergence of horizontal wind was strengthened, and observed features of horizontal winds and PWV were reproduced in the analyzed field.

This study conducted data assimilation experiments using the operational mesoscale four-dimensional variational data assimilation (4D-Var DA) system for Mesoscale Model (MSM) and three-dimensional variational data assimilation (3D-Var DA) system for Non-hydrostatic Model (NHM) of the Japan Meteorological Agency (JMA). Experiments investigated the impacts of GPS-derived water vapor and Doppler radar-derived radial wind (RW) on precipitation prediction for a heavy rain event on 21 July 1999. Mesoscale model (MSM) is a hydrostatic model with the horizontal grid interval of 10 km. If the only conventional meteorological data was assimilated into MSM, precipitation regions were generated over a mountainous area far from Tokyo. If GPS-derived water vapor data, RW data, and conventional data were all simultaneously assimilated, the precipitation position was modeled correctly, and precipitation onset occurred as observed. However, the intensity of the precipitation was much weaker than observed one. The fields obtained by MSM-4DVar DA system were used as the initial condition of NHM, which was expected to improve intensity of precipitation. However, the convections over the southern Kanto were not reproduced. To strengthen updraft, RW data was further assimilated by NHM-3DVar DA system. The convective cells were also considered by saturating water vapor at intense updraft grids within the precipitation region. Evolution of the precipitation system was considered by introducing rain water, snow estimated from observed reflectivity fields, and relative humidity (RH) at the grids of downdraft within the precipitation region. From this modified condition, intense convective system was well reproduced by NHM.

Precipitable water is a powerful constraint in both synoptic analysis and numerical weather prediction, because precipitable water provides an estimate of the available moisture that fuels convection and thunderstorms. As it has been shown that the vertically integrated water vapor can be determined from the GPS signal delay due to atmospheric water vapor, the GPS water vapor sensing technique has been developed and applied for study of the impact of GPS water vapor observations on short-range forecast of convective weather system. In this study, the data assimilation experiment of GPS-derived precipitable water was carried out using the MM5 four-dimensional variational data assimilation (4DVAR) system in order to investigate the impact of GPS-derived precipitable water on simulating a mesoscale convective system over the Korean Peninsula. During 3-h assimilation window, we assimilated the precipitable water derived from the zenith wet delay of GPS signals for five GPS stations into the model. The MM5 4DVAR system successfully assimilated GPS-derived precipitable water observations for a strong mesoscale convective system. We confirmed that the moisture fields and wind fields dynamically consistent with a given GPS-derived precipitable water information in assimilation window. The adjustments of GPS-derived precipitable water to the model initial condition by the 4DVAR procedure were all within reasonable ranges and the optimal initial condition was created. Although we have used just five points GPS-derived precipitable water, the moisture contents provided by a given GPS-derived precipitable water information through 4DVAR procedure increased total rainfall amount significantly. The assimilation of GPS-derived precipitable water observations may have a potential ability to improve rainfall forecast over the Korean Peninsula.

University of Florida (UF) researchers are developing an airborne laser swath mapping (ALSM) unit based on a paradigm referred to as photon counting ALSM, or PC-ALSM. In the PC-ALSM approach relatively low energy (few micro-joule) laser pulses are used to illuminate a surface 'patch' of terrain a few meters in extent. Reflected photons are imaged onto a multi-channel photomultiplier tube, to achieve high resolution (few decimeter) contiguous coverage of the terrain. A multi-channel multi-stop timing unit records both noise and signal events within a range gated window, and the noise events are filtered out of the data during post flight processing. A first generation PC-ALSM sensor, the Coastal Area Tactical-mapping System (CATS), is being developed with funding from the Office of Naval Research (ONR). The CATS sensor will be tested and operated from the UF Cessna 337 aircraft, which is equipped with a commercial ALSM unit. However, the ultimate goal of the UF research is to verify that PC-ALSM offers the possibility of developing a high resolution airborne laser mapping unit small and light enough, and with sufficient energy efficiency, to operate from a UAV.

When performing remote sensing, one often uses vehicular mounted sensors. This provides the flexibility of moving around and searching over a large area and can be done via airborne vehicles, ground vehicles or marine vehicles. For this type of sensing, one needs to know the position and orientation of the sensor platform in order to ground register the location of detected objects. The research reported herein is concerned with the use of a ground vehicle as a sensor platform. Digital camera-type sensors such as infra-red are considered. The focus is on requirements for accurate ground registration of detected objects of interest. A four-antenna GPS array has been chosen for vehicle attitude measurement. Relationships between positions of the array elements and vehicle attitude are derived. It is seen that the attitude computations depend on differences in the various measurements. Thus common-mode errors in the measured position of the array elements cancel, enabling quite accurate attitude measurement even when utilizing somewhat imprecise units in the GPS array. A more precise single differential GPS (DGPS) has been chosen for vehicle position measurements. Relationships between pixel coordinates in the image frame and the angle of the corresponding ray from the camera to the object of interest are derived. A series of transformations are used to convert this ray to ground coordinates. Finally the intersection of the ray with the ground is computed based on the assumption that the ground in the field-of-view is flat and at a known elevation. In this manner an object of interest in the image frame may be ground registered. Sensitivity of the ground registration with respect to vehicle attitude measurement errors is developed. It is seen that small errors in pitch, roll or yaw can cause quite large errors in the computed ground coordinates. In the case of multiple looks at the same object of interest, the geo-registration process involves target tracking and data association. This process is facilitated by combining the single-look measurements in an optimal fashion via a Kalman Filter. In fact the accuracy obtained via multiple looks can be significantly greater than for a single look. The results obtained indicate that accurate geo-registration of remotely sensed objects is possible when using vehicular mounted sensors in conjunction with DGPS and that such a scheme is feasible with commercially available GPS and IR cameras. Geo-registration accuracy within a fraction of a meter is attainable for near objects.

Merging of point data acquired from ground-based and airborne scanning laser rangers has been demonstrated for cases in which a common set of targets can be readily located in both data sets. However, direct merging of point data was not generally possible if the two data sets did not share common targets. This is often the case for ranging measurements acquired in forest canopies, where airborne systems image the canopy crowns well, but receive a relatively sparse set of points from the ground and understory. Conversely, ground-based scans of the understory do not generally sample the upper canopy. An experiment was conducted to establish a viable procedure for acquiring and georeferencing laser ranging data underneath a forest canopy. Once georeferenced, the ground-based data points can be merged with airborne points even in cases where no natural targets are common to both data sets. Two ground-based laser scans are merged and georeferenced with a final absolute error in the target locations of less than 10cm. This is comparable to the accuracy of the georeferenced airborne data. Thus, merging of the georeferenced ground-based and airborne data should be feasible. The motivation for this investigation is to facilitate a thorough characterization of airborne laser ranging phenomenology over forested terrain as a function of vertical location in the canopy.

The environmental and health effects of wildfires are discussed. The monitoring of wildfires from aircraft using remote sensing techniques is reviewed. A future autonomous aerial observing system for fire monitoring is described.

The National Aeronautics and Space Administration (NASA), Aeronautics Research Mission Directorate, is developing Intelligent Mission Management (IMM) technology for Uninhabited Aerial Vehicles (UAV’s) under the Vehicle Systems Program’s Autonomous Robust Avionics Project. The objective of the project is to develop air vehicle and associated ground element technology to enhance mission success by increasing mission return and reducing mission risk. Unanticipated science targets, uncertain conditions and changing mission requirements can all influence a flight plan and may require human intervention during the flight; however, time delays and communications bandwidth limit opportunities for operator intervention. To meet these challenges, we will develop UAV-specific technologies enabling goal-directed autonomy, i.e. the ability to redirect the flight in response to current conditions and the current goals of the flight. Our approach divides goal-directed autonomy into two components, an on-board Intelligent Agent Architecture (IAA) and a ground based Collaborative Decision Environment (CDE). These technologies cut across all aspects of a UAV system, including the payload, inner- and outer-loop onboard control, and the operator’s ground station.

Better knowledge of the atmosphere, ocean and land are needed by a wide range of users spanning the scientific, civil and defense communities. Observations to provide this knowledge will require aerial systems with greater operational flexibility and lower life-cycle costs than are currently available. Persistent monitoring of severe storms, sampling and measurements of the Earth’s carbon cycle, wildfire monitoring/management, crop assessments, ozone and polar ice changes, and natural disaster response (communications and surveillance) are but a few applications where autonomous aerial observations can effectively augment existing measurement systems. User driven capabilities include high altitude, long range, long-loiter (days/weeks), smaller deployable sensor-ships for in-situ sampling, and sensors providing data with spectral bandwidth and high temporal and three-dimensional spatial resolution. Starting with user needs and considering all elements and activities required to acquire the needed observations leads to the definition of autonomous aerial observation systems (AAOS) that can significantly complement and extend the current Earth observation capability. In this approach, UAVs are viewed as only one, albeit important, element in a mission system and overall cost and performance for the user are the critical success factors. To better understand and meet the challenges of developing such AAOSs, a systems oriented multi-dimensional analysis has been performed that illuminates the enabling and high payoff investments that best address the needs of scientific, civil, and defense users of Earth observations. The analysis further identifies technology gaps and serves to illustrate how investments in a range of mission subsystems together can enable a new class of Earth observations.

The current Earth observing capability depends primarily on spacecraft missions and ground-based networks to provide the critical on-going observations necessary for improved understanding of the Earth system. Aircraft missions play an important role in process studies but are limited to relatively short-duration flights. Suborbital observations have contributed to global environmental knowledge by providing in-depth, high-resolution observations that space-based and in-situ systems are challenged to provide; however, the limitations of aerial platforms - e.g., limited observing envelope, restrictions associated with crew safety and high cost of operations have restricted the suborbital program to a supporting role. For over a decade, it has been recognized that autonomous aerial observations could potentially be important. Advances in several technologies now enable autonomous aerial observation systems (AAOS) that can provide fundamentally new observational capability for Earth science and applications and thus lead scientists and engineers to rethink how suborbital assets can best contribute to Earth system science. Properly developed and integrated, these technologies will enable new Earth science and operational mission scenarios with long term persistence, higher-spatial and higher-temporal resolution at lower cost than space or ground based approaches. This paper presents the results of a science driven, systems oriented study of broad Earth science measurement needs. These needs identify aerial mission scenarios that complement and extend the current Earth Observing System. These aerial missions are analogous to space missions in their complexity and potential for providing significant data sets for Earth scientists. Mission classes are identified and presented based on science driven measurement needs in atmospheric, ocean and land studies. Also presented is a nominal concept of operations for an AAOS: an innovative set of suborbital assets that complements and augments current and planned space-based observing systems.

Improving the understanding of the Cryosphere and its impact on global hydrology is an important element of NASA’s Earth Science Enterprise (ESE). A Cold Land Processes Working Group (CLPWG) was formed by the NASA Terrestrial Hydrology Program to identify important science objectives necessary to address ESE priorities. These measurement objectives included Snow Water Equivalent (SWE), snow wetness, and freeze/thaw status of underlying soil. The spatial resolution requirement identified by the CLPWG was 100 m to 5000 m. Microwave sensors are well suited to measure these and other properties of interests to the study of the terrestrial cryosphere. It is well known that the EM properties of snow and soil at microwave frequencies are a strong function of the phase of water, i.e. ice/water. Further, both active and passive microwave sensors have demonstrated sensitivity to important properties of snowpack including, depth, density, wetness, crystal size, ice crust layer structure, and surface roughness. These sensors are also sensitive to the underlying soil state (frozen or thawed). Multiple microwave measurements including both active and passive sensors will likely be required to invert the effects of various snowpack characteristics, vegetation, and underlying soil properties to provide the desired characterization of the surface and meet the science needs required by the ESE. A major technology driver with respect to fully meeting these measurement needs is the 100 to 5000 m spatial resolution requirement. Meeting the threshold requirement of 5000 m at microwave frequencies from Low Earth Orbit is a technology challenge. The emerging capabilities of unmanned aircraft and particularly the system perspective of the Autonomous Aerial Observation Systems (AAOS) may provide high-fidelity/high-resolution measurements on regional scales or larger that could greatly improve our measurement capability. This paper explores a vehicle/sensor concept that could augment satellite measurements to enhance our understanding of the Cryosphere. The measurement performance and technology issues related to the sensor and aircraft will be assessed. Finally, specific technology needs and research necessary to enable this AAOS concept will be discussed.

The need for better atmospheric predictions is causing the atmospheric science community to look for new ways to obtain longer, higher-resolution measurements over several diurnal cycles. The high resolution, in-situ measurements required to study many atmospheric phenomena can be achieved by an Autonomous Aerial Observation System (AAOS); however, meeting the long on-station time requirements with an aerial platform poses many challenges. Inspired by the half-scale drop test of the deployable Aerial Regional-scale Environmental Survey (ARES) Mars airplane, a study was conducted at the NASA Langley Research Center to examine the possibility of increasing on-station time by launching an airplane directly at the desired altitude. The ARES Mars airplane concept was used as a baseline for Earth atmospheric flight, and parametric analyses of fundamental configuration elements were performed to study their impact on achieving desired on-station time with this class of airplane. The concept involved lifting the aircraft from the ground to the target altitude by means of an air balloon, thereby unburdening the airplane of ascent requirements. The parameters varied in the study were aircraft wingspan, payload, fuel quantity, and propulsion system. The results show promising trends for further research into aircraft-payload design using this unconventional balloon-based launch approach.