Atmospheric Soundings in Near-Real Time from Combined Satellite and Ground-Based Remotely Sensed Data

Abstract

A mobile profiling system has been developed that is capable of probing the atmosphere from the surface to over 30 km. The Mobile Profiling System (MPS) combines ground-based instruments, including a five-beam 924-MHz radar wind profiler, a radio acoustic sounding system, and two passive microwave sounders, with a receiver and processor for meteorological satellite data. Software in the MPS produces profiles from the surface to the highest satellite sounding level by combining surface data and profiles generated from the suite of groundbased sensors with those from a meteorological satellite. The algorithms generate soundings of temperature, humidity, wind velocity, and other meteorological variables. The method for combining data from the separate sources is not site specific and requires no a priori information. The MPS has the potential for a variety of applications, including detailed analysis of meteorological variables for research and operations over mesoscale areas, such as regional pollution studies and severe storm forecasting. This paper describes the method for merging data from satellite and ground-based remote sensing systems, and presents results from a series of field tests of both individual sensors and combined soundings. Accuracy of the combined soundings appears comparable to that from rawinsonde with the exception of wind velocity at satellite sounding altitudes. The MPS has operated successfully in several different climates: in the

Shipboard multisensor merged wind profiles from the New England Air Quality Study 2004

“…[17] As discussed earlier, a unique opportunity provided by these data is the ability to create a single or merged wind profile using both the RWP and HRDL data. Techniques for creating merged profiles from different profiling instruments are discussed by Wolfe et al [1995] and Cogan et al [1997]. The main factors controlling how data can be merged are their spatial and temporal differences and their accuracies.…”

“…The main factors controlling how data can be merged are their spatial and temporal differences and their accuracies. Cogan et al [1997] uses an equation incorporating temporal and spatial weighting factors times the accuracy ratio for the two instruments. This equation is best used for data that are nonoverlapping in height and have different temporal resolution.…”

Neural network retrieval of wind velocity profiles from satellite data

“…Recently completed research on the error structure of temperature retrievals indicates that neural network methods may retrieve individual soundings with lower errors for all standard levels than conventional retrieval methods, that is, more soundings with no large errors (not greater than 1 standard deviation) at any standard level (Bustamante & Pearson, 1998, private communication). Eventually, we plan to use output from the wind retrieval in conjunction with wind profiles from ground-based sensors, perhaps in a manner similar to that in Cogan et al (1997). A network that uses information from both the satellite and ground-based sensors may produce better profiles, from the surface to the highest satellite data level.…”

<title>Neural networks for sensor fusion of meteorological measurements</title>

“…In particular Bustamante et al (1999) and Bustamante et al (1994) developed a neural network that input data from National Oceanic and Atmospheric Administration (NOAA) satellites, Butler et al (1996) obtained data from Defense Meteorological Satellite Program (DMSP) satellites, and Gutman et al (1998) used radiances from Geostationary Operational Environmental Satellites (GOES). Satellite sounder data potentially provide a means to routinely and accurately obtain meteorological profile data of interest for military and weather forecasting applications (Cogan, et al, 1997).…”