[1] The Atmospheric Infrared Sounder (AIRS) is the first of a new generation of advanced satellite-based atmospheric sounders with the capability of obtaining high-vertical resolution profiles of temperature and water vapor. The high-accuracy retrieval goals of AIRS (e.g., 1 K RMS in 1 km layers below 100 mbar for air temperature, 10% RMS in 2 km layers below 100 mbar for water vapor concentration), combined with the large temporal and spatial variability of the atmosphere and difficulties in making accurate measurements of the atmospheric state, necessitate careful and detailed validation using well-characterized ground-based sites. As part of ongoing AIRS Science Team efforts and a collaborative effort between the NASA Earth Observing System (EOS) project and the Department of Energy Atmospheric Radiation Measurement (ARM) program, data from various ARM and other observations are used to create best estimates of the atmospheric state at the Aqua overpass times. The resulting validation data set is an ensemble of temperature and water vapor profiles created from radiosondes launched at the approximate Aqua overpass times, interpolated to the exact overpass time using time continuous ground-based profiles, adjusted to account for spatial gradients within the Advanced Microwave Sounding Unit (AMSU) footprints, and supplemented with limited cloud observations. Estimates of the spectral surface infrared emissivity and local skin temperatures are also constructed. Relying on the developed ARM infrastructure and previous and ongoing characterization studies of the ARM measurements, the data set provides a good combination of statistics and accuracy which is essential for assessment of the advanced sounder products. Combined with the collocated AIRS observations, the products are being used to study observed minus calculated AIRS spectra, aimed at evaluation of the AIRS forward radiative transfer model, AIRS observed radiances, and temperature and water vapor profile retrievals. This paper provides an introduction to the ARM site best estimate validation products and characterizes the accuracy of the AIRS team version 4 atmospheric temperature and water vapor retrievals using the ARM products. The AIRS retrievals over tropical ocean are found to have very good accuracy for both temperature and water vapor, with RMS errors approaching the theoretical expectation for clear sky conditions, while retrievals over a midlatitude land site have poorer performance. The results demonstrate the importance of using specialized ''truth'' sites for accurate assessment of the advanced sounder performance and motivate the continued refinement of the AIRS science team retrieval algorithm, particularly for retrievals over land.

The International H2O Project (IHOP_2002) is one of the largest North American meteorological field experiments in history. From 13 May to 25 June 2002, over 250 researchers and technical staff from the United States, Germany, France, and Canada converged on the Southern Great Plains to measure water vapor and other atmospheric variables. The principal objective of IHOP_2002 is to obtain an improved characterization of the time-varying three-dimensional water vapor field and evaluate its utility in improving the understanding and prediction of convective processes. The motivation for this objective is the combination of extremely low forecast skill for warm-season rainfall and the relatively large loss of life and property from flash floods and other warm-season weather hazards. Many prior studies on convective storm forecasting have shown that water vapor is a key atmospheric variable that is insufficiently measured. Toward this goal, IHOP_2002 brought together many of the existing operational and new state-of-the-art research water vapor sensors and numerical models. The IHOP_2002 experiment comprised numerous unique aspects. These included several instruments fielded for the first time (e.g., reference radiosonde); numerous upgraded instruments (e.g., Wyoming Cloud Radar); the first ever horizontal-pointing water vapor differential absorption lidar (DIAL; i.e., Leandre II on the Naval Research Laboratory P-3), which required the first onboard aircraft avoidance radar; several unique combinations of sensors (e.g., multiple profiling instruments at one field site and the German water vapor DIAL and NOAA/Environmental Technology Laboratory Doppler lidar on board the German Falcon aircraft); and many logistical challenges. This article presents a summary of the motivation, goals, and experimental design of the project, illustrates some preliminary data collected, and includes discussion on some potential operational and research implications of the experiment.

Deep convective storms with overshooting tops (OTs) are capable of producing hazardous weather conditions such as aviation turbulence, frequent lightning, heavy rainfall, large hail, damaging wind, and tornadoes. This paper presents a new objective infrared-only satellite OT detection method called infrared window (IRW)-texture. This method uses a combination of 1) infrared window channel brightness temperature (BT) gradients, 2) an NWP tropopause temperature forecast, and 3) OT size and BT criteria defined through analysis of 450 thunderstorm events within 1-km Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR) imagery. Qualitative validation of the IRW-texture and the well-documented water vapor (WV) minus IRW BT difference (BTD) technique is performed using visible channel imagery, CloudSat Cloud Profiling Radar, and/or Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) cloud-top height for selected cases. Quantitative validation of these two techniques is obtained though comparison with OT detections from synthetic satellite imagery derived from a cloud-resolving NWP simulation. The results show that the IRW-texture method false-alarm rate ranges from 4.2% to 38.8%, depending upon the magnitude of the overshooting and algorithm quality control settings. The results also show that this method offers a significant improvement over the WV-IRW BTD technique. A 5-yr Geosynchronous Operational Environmental Satellite (GOES)-12 OT climatology shows that OTs occur frequently over the Gulf Stream and Great Plains during the nighttime hours, which underscores the importance of using a day/night infrared-only detection algorithm. GOES-12 OT detections are compared with objective Eddy Dissipation Rate Turbulence and National Lightning Detection Network observations to show the strong relationship among OTs, aviation turbulence, and cloud-to-ground lightning activity.

A ground-based Fourier transform spectrometer has been developed to measure the atmospheric downwelling infrared radiance spectrum at the earth's surface with high absolute accuracy. The Atmospheric Emitted Radiance Interferometer (AERI) instrument was designed and fabricated by the University of Wisconsin Space Science and Engineering Center (UW-SSEC) for the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program. This paper emphasizes the key features of the UW-SSEC instrument design that contribute to meeting the AERI instrument requirements for the ARM Program. These features include a highly accurate radiometric calibration system, an instrument controller that provides continuous and autonomous operation, an extensive data acquisition system for monitoring calibration temperatures and instrument health, and a real-time data processing system. In particular, focus is placed on design issues crucial to meeting the ARM requirements for radiometric calibration, spectral calibration, noise performance, and operational reliability. The detailed performance characteristics of the AERI instruments built for the ARM Program are described in a companion paper.

The Atmospheric Emitted Radiance Interferometer (AERI) instrument was developed for the Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Program by the University of Wisconsin Space Science and Engineering Center (UW-SSEC). The infrared emission spectra measured by the instrument have the sensitivity and absolute accuracy needed for atmospheric remote sensing and climate studies. The instrument design is described in a companion paper. This paper describes in detail the measured performance characteristics of the AERI instruments built for the ARM Program. In particular, the AERI systems achieve an absolute radiometric calibration of better than 1% (3) of ambient radiance, with a reproducibility of better than 0.2%. The knowledge of the AERI spectral calibration is better than 1.5 ppm (1) in the wavenumber range 400-3000 cm Ϫ1 .

An automated volcanic cloud detection algorithm that utilizes four spectral channels (0.65, 3.75, 11, and 12 μm) that are common among several satellite-based instruments is presented. The new algorithm is physically based and globally applicable and can provide quick information on the horizontal location of volcanic clouds that can be used to improve real-time ash hazard assessments. It can also provide needed input into volcanic cloud optical depth and particle size retrieval algorithms, the products of which can help improve ash dispersion forecasts. The results of this new four-channel algorithm for several scenes were compared to a threshold-based reverse absorption algorithm, where the reverse absorption algorithm is used to identify measurements with a negative 11–12-μm brightness temperature difference. The results indicate that the new four-channel algorithm is not only more sensitive to the presence of volcanic clouds but also generally less prone to false alarms than the standard reverse absorption algorithm. The greatest impact on detection sensitivity is seen in the Tropics, where water vapor can often mask the reverse absorption signal. The four-channel algorithm was able to detect volcanic clouds even when the 11–12-μm brightness temperature difference was greater than +2 K. In the higher latitudes, the greatest impact seen was the significant reduction in false alarms compared to the reverse absorption algorithm and the improved ability to detect optically thick volcanic clouds. Cloud water can also mask the reverse absorption signal. The four-channel algorithm was shown to be more sensitive to volcanic clouds that have a water (ice or liquid water) component than the reverse absorption algorithm.

This review paper summarizes current knowledge available for aviation operations related to meteorology and provides suggestions for necessary improvements in the measurement and prediction of weather-related parameters, new physical methods for numerical weather predictions (NWP), and next-generation integrated systems. Severe weather can disrupt aviation operations on the ground or in-flight. The most important parameters related to aviation meteorology are wind and turbulence, fog visibility (Vis) and ceiling, rain and snow amount and rates, icing, ice microphysical parameters, convection and precipitation intensity, microbursts, hail, and lightning. Measurements of these parameters are also functions of sensor response times and measurement thresholds in extreme weather conditions. In addition to these, airport environments can play an important role leading to intensification of extreme weather conditions or high impact weather events, e.g., anthropogenic ice fog. To observe meteorological parameters, new remote sensing platforms, namely wind LIDAR, sodars, radars, and geostationary satellites, and in-situ observations at the surface and in the cloud, as well as aircraft and Unmanned Aerial Vehicles (UAV) mounted sensors, are becoming more common. Because of prediction issues at smaller time and space scales (e.g., <1 km), meteorological forecasts from NWP models need to be continuously improved. Aviation weather forecasts also need to be developed to provide information that represents both deterministic and statistical approaches. In this review, we present available resources and issues for aviation meteorology and evaluate them for required improvements related to measurements, nowcasting, forecasting, and climate change, and emphasize future challenges.

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