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ABSTRACT Geostationary Operational Environmental Satellite-16 (GOES-16) was launched into geostationary orbit in late 2016 and began providing unprecedented spatial and temporal resolution imagery early in 2017. Its Advanced Baseline Imager [...]

This paper summarizes the successful use of Geostationary Operational Environmental Satellite-10 (GOES-10) and -12 (GOES-12), mainly beyond their retirement as operational satellites in the United States, in support of meteorological [...]

NOAA's National Environmental Satellite, Data, and Information Service (NESDIS) has generated sea surface temperature (SST) products from Geostationary Operational Environmental Satellite (GOES)-East (E) and GOES-West (W) on an operational basis since December 2000. Since that time, a process of continual development has produced steady improvements in product accuracy. Recent improvements extended the capability to permit generation of operational SST retrievals from the Japanese Multifunction Transport Satellite (MTSAT)-1R and the European Meteosat Second Generation (MSG) satellite, thereby extending spatial coverage. The four geostationary satellites (at longitudes of 75[degrees]W, 135[degrees]W, 140[degrees]E, and 0[degree]) provide high temporal SST retrievals for most of the tropics and midlatitudes, with the exception of a region between ~60[degrees] and ~80[degrees]. Because of ongoing development, the quality of these retrievals now approaches that of SST products from the polar-orbiting Advanced Very High Resolution Radiometer (AVHRR). These products from GOES provide hourly regional imagery, 3-hourly hemispheric imagery, 24-h merged composites, a GOES SST level 2 preprocessed product every 1/2 h for each hemisphere, and a match-up data file for each product. The MTSAT and the MSG products include hourly, 3-hourly, and 24-h merged composites. These products provide the user community with a reliable source of SST observations, with improved accuracy and increased coverage in important oceanographic, meteorological, and climatic regions.

Satellite retrieval of cirrus cloud microphysical properties is an important but difficult problem because of uncertainties in ice-scattering characteristics. Most methods have been developed for instruments aboard polar-orbiting satellites, which have better spatial and spectral resolution than geostationary sensors. The Geostationary Operational Environmental Satellite (GOES) series has the advantage of excellent temporal resolution, so that the evolution of thunderstorm-cloud-top properties can be monitored. In this paper, the authors discuss the development of a simple ice cloud effective radius retrieval for thick ice clouds using three bands from the GOES imager: one each in the visible, shortwave infrared, and window infrared portion of the spectrum. It is shown that this retrieval compares favorably to the MODIS effective radius algorithm. In addition, a comparison of the retrieval for clouds viewed simultaneously from GOES-East and GOES-West reveals that the assumed ice-scattering properties perform very well. The algorithm is then used to produce maps of mean ice cloud effective radius over the continental United States. A real-time version of this retrieval is currently running and may be used to study the evolution of thunderstorm-top ice crystal size in rapidly evolving convection.

The global convective diagnostic (GCD) is a bispectral (infrared and water vapor), day-night scheme for operationally mapping deep convection by means of geostationary satellite images. This article describes a test of GCD performance over tropical and subtropical waters near North America. The test consists of six cases, each involving a convective cloud complex. A seventh case treats convection over land. For each case, a map of deep convection was constructed from image pairs from Geostationary Operational Environmental Satellite-12 (GOES-12). Case by case and for all maritime cases together, the GCD map was compared with a convective parameter derived from the radar on the Tropical Rainfall Measuring Mission (TRMM), a polar-orbiting satellite. In general, each GCD map showed a bloblike feature. In each case, the radar convective pixels typically fell within the GCD blob. However, (except for the land case) the GCD predicted far too many convective pixels. In the maritime cases overprediction was reduced (without correspondingly impairing other measures of performance) by lowering the nominal GCD threshold. With this adjustment in place, for the six maritime cases taken individually, the GCD tended to yield more consistent results than did a monospectral (infrared) convective scheme. With the cases combined, at the lower threshold the GCD performed somewhat better than one of the more stable versions of the infrared scheme. Comparison with lightning events (also observed by TRMM) suggests the possibility of future improvement to the GCD through the incorporation of geostationary satellite observations of lightning.

We present an analysis of the charging interactivity between surrounding surface materials aboard a spacecraft at geosynchronous altitudes. In particular, bootstrap charging of a small surface may occur if is surrounded by a large negatively charged surface. Here, a negative potential barrier forms above the small surface, resulting in suppression of photo- and secondary electron emission from that surface. Additionally, the small surface experiences an enhancement of the collection of the photo- and secondary electrons emitted from the surrounding surface. This mechanism results in the charging of the small surface to higher levels than that of the patch in isolation, and in many cases the final potential will reach that of the potential of the larger surrounding surface. With this study we examine bootstrap charging behavior with model data and with data collected on orbit. We have modeled the DSCS-III B7 geosynchronous satellite with realistic geometry and spacecraft materials. Additionally, a previous study has shown that bootstrap charging has been observed on the DSCS-III B7 geosynchronous spacecraft. Both Astroquartz and Kapton cloth patches charged up to the frame potential of the satellite during periods of severe frame charging. The results of modeling bootstrap charging of a small Kapton patch floating relative to the DSCS-III frame fixed at a potential of -1,000 V show that the patch will indeed charge up negatively to match the frame potential, with the temporal increase in negative potential following an exponential time characteristic. Index Terms--Bootstrap charging, Defense Satellite Communication System (DSCS-III), differential charging, GEO satellite charging, spacecraft surface charging.

The salient features of an earth-observing sensorweb developed by the Jet Propulsion Laboratory and Goddard Space Flight Center are described. The sensorweb collects and provides important data from volcanic eruptions anywhere in the world to the volcanologists within hours of such eruption. For the purpose, the sensorweb uses satellites.

NIST ended its Geostationary Operational Environmental Satellites (GOES) time code service at 0 hours, 0 minutes Coordinated Universal Time (UTC) on January 1, 2005. To commemorate the end of this […]

The spatial and temporal continuity of the infrared measurements from the Geostationary Operational Environmental Satellite (GOES)-8 sounder data are investigated, and an experimental processing approach is presented. Spatial filtering and cloud detection are performed in a joint algorithm: the preparation of the data for sounding analysis starts with spatial smoothing, followed by cloud detection, followed by averaging the clear-sky (cloud free) subsamples. Analysis of the sounder images reveals the presence of coherent noise on large spatial scales in some of the spectral bands. Analysis of a temporal sequence of spatially smoothed sounder images reveals regions of unphysical hourly change likely induced by instrument noise. A nonlinear temporal-spatial filtering algorithm is presented and tested that improves the noise filtering for the sounder spectral measurements and the thermodynamical spatial and temporal consistency of the sounding retrievals in the troposphere.

This paper describes a new semi-Markov propagation channel model for land mobile satellite systems using geostationary satellites. The multistate model switches between propagation states representing line-of-sight, shadowing, or blockage of the signal. The duration of times spent in each state follow probability distributions recommended by the radiocommunication sector of the International Telecommunication Union (ITU-R). The actual parameters to be used with the ITU-R distributions were modified and fitted to observed data. The open-area state durations follow a power-law distribution, while the state durations for both the shadowed and the blocked states follow a lognormal distribution. Parameters for both two- and three-state models are extracted from an L-band measurement campaign performed by Inmarsat in the United Kingdom. Propagation channel models characterizing the fading within the open, shadowed, or blocked propagation states are described as well. The semi-Markov models represent an improvement over the more commonly used Markov models where the duration in each state follows an exponential distribution. The new model enables more accurate prediction and simulation of system performance and availability. Index Terms--Land mobile satellite (LMS) communications, propagation channel modeling, state duration statistics.

Byline: Naoto Ebuchi (1), Shoichi Kizu (2) Keywords: Wind waves; surface slope; sun glitter; scatterometer; Geostationary Meteorological Satellite; visible radiometer Abstract: The probability distribution of the sea surface slope has been estimated using sun glitter images derived from the visible wavelength radiometer on the Geostationary Meteorological Satellite (GMS) and surface vector winds observed by spaceborne scatterometers. The brightness of the visible images is converted to the probability of wave surfaces which reflect the sunlight toward GMS in grids of 0.25deg x 0.25deg (latitude x longitude). The slope and azimuth angle required for the reflection of the sun's rays toward GMS are calculated for each grid from the geometry of GMS observation and location of the sun. The GMS images are then collocated with surface wind data observed by three scatterometers. Using the collocated data set of about 30 million points obtained in a period of 4 years from 1995 to 1999, the probability distribution function of the surface slope is estimated as a function of wind speed and azimuth angle relative to the wind direction. The results are compared with those of Cox and Munk (1954a). The surface slope estimated by the present method shows a narrower distribution and much less directivity relative to the wind direction than that reported by Cox and Munk. It is expected that their data were obtained under conditions of growing wind waves. In general, wind waves are not always developing, and the slope distribution might differ from the results of Cox and Munk. Most of our data are obtained in the subtropical seas under clear-sky conditions. This difference in the conditions may be the reason for the difference of slope distribution. Author Affiliation: (1) Center for Atmospheric and Oceanic Studies, Graduate School of Science, Tohoku University, Aoba, Sendai, 980-8578, Japan (2) Department of Geophysics, Graduate School of Science, Tohoku University, Aoba, Sendai, 980-8578, Japan Article History: Registration Date: 12/10/2004

A methodology is proposed to design a regional navigation system using geosynchronons satellites and stratospheric airships. One important factor in designing a navigation system is dilution of precision (DOP). We design a regional navigation system based on the simulations of the system's DOP. The system would consist of geosynchronons orbit satellites (GSO) and stratospheric airships for the urban areas. In the beginning stage, the system would augment the existing GPS constellation, and in the later stage with sufficient satellites and airships, we could achieve an independent alternative navigation system.

A new approach is presented to quantify upper-level moisture transport from geostationary satellite data. Daily time sequences of Geostationary Operational Environmental Satellite GOES-7 water vapor imagery were used to produce estimates of winds and water vapor mixing ratio in the cloud-free region of the upper troposphere sensed by the 6.7-(mu)m water vapor channel. The winds and mixing ratio values were gridded and then combined to produce a parameter called the water vapor transport index (WVTI), which represents the magnitude of the two-dimensional transport of water vapor in the upper troposphere. Daily grids of WVTI, meridional moisture transport, mixing ratio, pressure, and other associated parameters were averaged to produce monthly fields for June, July, and August (JJA) of 1987 and 1988 over the Americas and surrounding oceanic regions. The WVTI was used to compare upper-tropospheric moisture transport between the summers of 1987 and 1988, contrasting the latter part of the 1986/87 El Nino event and the La Nina period of 1988. A similar product derived from the National Centers for Environmental Prediction (NCEP)-National Center for Atmospheric Research (NCAR) 40-Year Reanalysis Project was used to help to validate the index. Although the goal of this research was to describe the formulation and utility of the WVTI, considerable insight was obtained into the interannual variability of upper-level water vapor transport. Both datasets showed large upper-level water vapor transport associated with synoptic features over the Americas and with outflow from tropical convective systems. Minimal transport occurred over tropical and subtropical high pressure regions where winds were light. Index values from NCEP-NCAR were 2-3 times larger than that determined from GOES. This difference resulted from large zonal wind differences and an apparent overestimate of upper-tropospheric moisture in the reanalysis model. A comparison of the satellite-derived monthly values between the summers of 1987 and 1988 provided some insight into the impact of the ENSO event on upper-level moisture and its transport during the period. During July 1987, a large portion of the Tropics in the eastern Pacific Ocean and Caribbean Sea was dominated by strong vapor transport in excess of 4.0 g kg(super -1) m s(super -1), with relatively small amounts in the other months. JJA 1988 transport values reached similar magnitude and showed similar patterns for all three months. The meridional transport of upper-level water vapor indicated large poleward transport from the Tropics to the higher latitudes. This transport favored the Southern Hemisphere, with large transport occurring south of the ITCZ, which extended across the eastern Pacific and northern South America. Zonally averaged monthly transport values were shown to provide a simple way to quantify the monthly and interannual changes in water vapor transport. Zonally averaged WVTI values peaked in the Southern Hemisphere subtropics during both austral winters. In the Tropics, a single, more pronounced peak located over the equator and south latitudes occurred in 1988 as opposed to a dual peak in 1987. The second peak around 20 degrees N latitude is consistent with findings of others in which upper-tropospheric winds were noted to be stronger in this region during warm ENSO events. Zonally averaged meridional transport was southward for all summer months and was stronger in 1988. The asymmetric nature of the zonally averaged meridional transport (more southerly water vapor transport) was enhanced during JJA 1988, thus indicating a stronger upper-level branch of the Hadley circulation during this notably strong La Nina period.

Recent deployments of the National Oceanic and Atmospheric Administration (NOAA) Geostationary Operational Environmental Satellites (GOES-8 and -9) include full-time 3.9-[[micro]meter] imaging capabilities. This shortwave (near infrared) channel has been available at 3.7 [[micro]meter] on the Advanced Very High Resolution Radiometer (AVHRR) instrument aboard the NOAA polar-orbiting satellite systems. In this spectral region, daytime satellite-observed radiances include contributions from both the reflected solar radiation and the emitted thermal emission. In particular, typical stratus and fog clouds posess near-infrared emissivities less than unity, which requires special processing to account for the angular dependence of the solar reflection. In this paper, a side-by-side comparison of time-coincident GOES- and AVHRR-derived near-infrared cloud reflectance is carried out in order to demonstrate the capability of GOES-8 and -9 in both identifying and characterizing the microphysics of stratus and fog clouds during the daytime. The authors first present the mathematical formalism and then apply the technique to extract the near-infrared reflectances from GOES-8 and -9 data. The technique is applicable for operational usage and requires a lookup table to account for the continuously changing sun-satellite viewing geometry. Near-infrared cloud reflectances are extracted from coincident GOES-9 and AVHRR data from both NOAA-14 and -12 for different times of day and are verified against theoretical reflectances derived from radiative transfer theory and previously published results. A retrieval of the cloud drop size distribution effective radius is demonstrated on satellite data along coastal California during the summer of 1996.

The introduction of the 10-bit, five-band, multispectral visible and thermal infrared scanner on the National Oceanic and Atmospheric Administration's GOES-8 satellite in 1994 offers an opportunity to estimate sea surface temperatures from a geostationary satellite. The advantage of the Geostationary Operational Environmental Satellite (GOES) over the traditional Advanced Very High Resolution Radiometer is the 30-min interval between images, which can increase the daily quantity of cloud-free ocean observations. Linear regression coefficients are estimated for GOES-8 by using the sea surface temperatures derived from the NOAA-14 polar-orbiting satellite as the dependent variable and the GOES infrared split window channels and the satellite zenith angle as independent variables. The standard error between the polar and geostationary sea surface temperature is 0.35 [degree] C. Since the polar satellite sea surface temperature is estimated within 0.5 [degree] C relative to drifting buoy near-surface measurements, this implies that the GOES-8 infrared scanner can be used to estimate sea surface temperatures to better than 1.0 [degree] C relative to buoys. Daily composites of hourly GOES-8 sea surface temperatures are used to illustrate the capability of the GOES to produce improved cloud-free images of the ocean. Hourly time series reveal a 2 [degrees] C diurnal surface temperature cycle in the eastern subtropical Pacific with a peak near 1200 LT. The rapid onset of coastal upwelling along the southern coast of Mexico during December of 1996 was resolved at hourly intervals.

Saturation pressure differences, a measure of parcel saturation, are calculated from upper-air soundings and compared to manual surface observations of cloudiness. The saturation pressure level [p.sup.*] (more commonly referred to as the lifted condensation level, LCL), can be calculated for each level in a sounding using the temperature and dewpoint temperatures. Thus, [p.sup.*] of an unsaturated air parcel is found by dry-adiabatic ascent to the pressure level where the parcel is just saturated. The difference between air parcel pressure and saturation pressure level defines the parcel saturation pressure difference. The mean saturation pressure difference between 1000 and 700, 700 and 400, and 400 and 300 mb is calculated and compared to the observed composite cloudiness for those layers. Results indicate that as the absolute value of saturation pressure difference decreases toward zero, the resulting ground observed composite cloud amount increases. However, the mean saturation pressure difference for high clouds ranges from 64 mb under clear skies to 16 mb for overcast conditions. This corresponds to relative humidities between 25% and 76%. Most previous studies do not indicate such large cloud amounts at these humidities. Three empirical relationships that define low, middle, and high clouds are developed based on one year of comparisons. These relationships are then tested on an independent dataset that include a wide variety of cloud cover conditions. Qualitative comparisons are made to manual observations of cloudiness and indicate that the relationships overall slightly overestimate the frequency of cloudiness. Cloudiness derived from the Visible-Infrared Spin Scan Radiometer (VISSR) Atmospheric Sounder (VAS) onboard the Geostationary Environmental Operational Satellite (GOES) 7 using the C[O.sub.2] slicing technique is also compared to surface observations. Results indicate that the satellite-derived cloudiness overestimates cloudiness compared with surface observations but is also very similar to the saturation pressure difference estimates.

A new communication and radio determination system has been developed and experiments to evaluate the positioning accuracy of the system have been made. Theoretical estimates of the positioning accuracy and the results of experimental data analysis are presented. The relationship between the positioning error and the satellite ephemeris error while considering the dynamics of orbital motions are the main topics of discussion. Estimated improvements in positioning accuracy using benchmark stations are also described.

Initial results of upper-tropospheric water vapor obtained from the Microwave Limb Sounder (MLS) on the Upper Atmosphere Research Satellite (UARS) are presented. MLS is less affected by clouds than infrared or visible techniques, and the UARS orbit provides daily humidity monitoring for approximately two-thirds of the earth. Best results are currently obtained when water vapor abundances are approximately 100-300 ppmv, corresponding to approximately 12-km height in the Tropics and 7 km at high latitudes. The observed latitude variation of water vapor at 215 hPa is in good agreement with the U.K. Universities's Global Atmospheric Modelling Project model. The ability to observe synoptic-scale features associated with tropopause height variations is clearly illustrated by comparison with the National Aeronautics and Space Administration Goddard Space Flight Center assimilation model. Humidity detrainment streams extending from tropical convective regions are also observed.

A one-dimensional variational analysis technique is used to assimilate the total precipitable water (TPW) retrieved from Special Sensor Microwave/Imager (SSM/I) brightness temperature and specific humidity retrieved from Geostationary Operational Environmental Satellite (GOES) radiances. It is seen that the error reduction of specific humidity in the upper levels of the troposphere is due to the GOES retrievals, while in the lower levels it is due to SSM/I TPW.

The remote sensing of water vapor from geostationary satellites, also known as 'reflectance technique,' was used to analyze three near-IR channels, one in the 0.94 micrometer vapor band absorption domain and the rest in neighboring atmospheric windows. Water vapor in the atmosphere reduces the intensity of solar radiation. Surface reflectance and the average atmospheric water vapor transmittance of the absorption were measured using the proposed near-IR reflectance technique.

The use of satellite data to estimate solar irradiance at ground level represents a valid alternative to ground measurements of solar radiation. This paper continues the analysis and evaluation, started in a previous paper, of the best known methods for calculating solar irradiance at the earth's surface using geostationary satellite data. In the previous paper, we examined and compared the so-called statistical models. Now we will consider the physical models and point out the differences between them. Finally, a summary will be made of the assessments and comparisons carried out on the methods described.

The use of satellite data to estimate solar irradiance at ground level represents a valid alternative to ground measurements of solar radiation. The best known methods of estimating the solar irradiance at the earth's surface using geostationary satellite data are reviewed. The models may be classified into statistical and physical models, depending on the approach used to treat the interaction between solar radiation and atmosphere. The main hypotheses and algorithms used in four statistical models are extensively presented and discussed. The differences between these methods are pointed out. Physical models will be examined in a second paper. This second paper will include various assessments regarding the performances of the described methods and summaries of comparisons between the different models.

The globalization of air traffic management systems should facilitate the development of an integrated air traffic management system which would enable pilots to fly between any two points, either on or off a well-defined route at a fuel-efficient altitude to cut down on travel time and fuel usage. The use of communication satellites have made it possible to facilitate high-frequency exchanges. Inmarsat satellites have also been usedas part of the Aeronautical Mobile Satellite System. In addition, the Russian gloabal orbiting navigation satellite system presents an accurate and viable alternative to air traffic tracking.

Geostationary operational environmental satellite (GOES) infrared (IR) images and rain gauge measurements are used to study five convective storm events in Florida. The study region is divided into a training area and a test area. An approach is initiated in this study to identify rainfall areas within GOES images using the coldest cloud-top temperature (CCTT) and the standard deviation of cloud-top temperatures (STD-DEV) within a group of pixels. A threshold value of satellite-derived cloud-top temperature is used to define the cloud-covered areas. GOES IR images are then divided into two categories: expanding cloud images and contracting cloud images. The Bayesian optimal classifier using CCTT and STD-DEV is implemented to further classify the cloud pixels in each category into precipitation-free cloud pixels and precipitating cloud pixels. Cutoff rain rates from the gauge measurements are used to define the actual precipitation-free and precipitating pixels. The results show that the classification accuracies are about 73% in the training area, and 63% in test area., Hourly rainfall intensity measured by rain gages and geostationary operational environmental satellite (GOES) infrared (IR) data imagery of five convective storm events in Florida were studied. The GOES IR data was used to classify cloud pixels into expanding and contracting cloud images using the Bayesian optimal classifier based on the coldest cloud-top temperature and the standard deviation of cloud-top temparatures. This classification accurately identifies rainfall areas with cloud life history taken into consideration.

The US Soil Conservation Service (SCS) formula for infiltration families, which are defined as water intake rates of soil in inches per hour at terminal time, is widely used in irrigation research. An alternative formula is the two-parameter Kostiakov equation whose simplicity makes it popular among modelers. Equivalent Kostiakov equation parameters are established for the different SCS infiltration families by minimizing the deviations between the representation k and (alpha) in the Kostiakov formula F = kt(raised to the power alpha). F represents the cumulative infiltration amount in inches or centimeters and t represents time in minutes.

With data sets gained from Defense Meteorological Satellite Program (DMSP) Special Sensor Microwave/Imager (SSM/I) microwave channels, Geostationary Meteorological Satellite (GMS-5) infrared channels, and ship-measured data, the statistical algorithms to estimate sea surface temperature and near-sea surface air humidity around Taiwan and the South China Sea areas are developed. Then a new, improved method to estimate near-sea surface air temperature based on the algorithm proposed by Konda et al. [1] is established in this study. The results estimated with SSM/I data show that the root mean square error (RMSE) of SST, near-sea surface air humidity and air temperature over the oceans around Taiwan and the South China Sea are 1.2 K, 1.43 g/kg, and 1.6 K, respectively. The results with GMS data are 1.7 K, 1.71 g/kg and 1.7 K, respectively. The results also show that the improvements in the algorithm of Konda et al. [1] simplify the computation scheme, improve the accuracy, and match the regional ocean-atmosphere properties in retrieving near-sea surface air temperature. The estimate produced using SSM/I and GMS data also show good consistency between them, both in temporal and spatial variations. Basically, the accuracy of this result implies strong potential for application of satellite data to relative studies and operational work in the ocean-atmosphere interaction. Index Terms--Geostationary Meteorological Satellite (GMS), near-sea surface air humidity, near-sea surface air temperature, Special Sensor Microwave/Imager (SSM/I).

The equivalent brightness temperature [T.sub.b] recorded by geosynchronous infrared (geo-IR) 'window' channel (10.7-11.5 [micro]m) satellite sensors is shown to depend on the zenith angle (local angle from the zenith to the satellite for a pixel's ground location) in addition to the mix of clouds and surface that would be observed from a direct overhead viewpoint (nadir view). This zenith-angle dependence is characterized, and two corrections are developed from a collection of half-hourly geo-IR pixel data that have been parallax corrected and averaged to a 0.5 [degrees] X 0.5 [degrees] latitude/longitude grid for each geosynchronous satellite separately. First, composites of collocated [T.sub.b] over tropical regions from the Geostationary Operational Environmental Satellite (GOES)-8/GOES-10 and the Meteosat-5/Meteosat-7 satellite pairs are used to produce robust estimates of isotropic zenith-angle corrections as a function of zenith angle and grid-box-averaged Th. The corrections range from zero for a zenith angle of ~26.5 [degrees] to increases of more than 20 K at [T.sub.b] = 230 K near the limb. Near-limb corrections in clear and very cold thick overcast conditions are smaller but are still positive. This empirical result depends on the surface--tropopause temperature differences, so a second correction was developed. Using collocations from the same two satellite pairs from 60 [degrees] N to 60 [degrees] S, differences in uncorrected [T.sub.b] divided by differences in the corresponding corrected values were accumulated as a function of latitude and season. The resultant ratios smoothly vary from ~1 in the Tropics to ~0.5 at 60 [degrees] N and 60 [degrees] S, with a quicker decrease in the winter hemisphere. In comparison with the uncorrected geo-IR data, there is a 50% reduction in the root-mean-square differences between collocated values from adjacent satellites by applying the latitude/season-adjusted zenith-angle corrections. Histograms of corrected geo-IR [T.sub.b] at large zenith angles closely match histograms from collocated near-nadir-view values. Residual difference maps are smooth and indicate intersatellite differences.

A system has been developed and implemented that merges pixel resolution (~4 km) infrared (IR) satellite data from all available geostationary meteorological satellites into a global (60 [degrees] N-60 [degrees] S) product. The resulting research-quality, nearly seamless global array of information is made possible by recent work by Joyce et al., who developed a technique to correct IR temperatures at targets far from satellite nadir. At such locations, IR temperatures are colder than if identical features were measured at a target near satellite nadir. This correction procedure yields a dataset that is considerably more amenable to quantitative manipulation than if the data from the individual satellites were merely spliced together. Several unique features of this product exist. First, the data from individual geostationary satellites have been merged to form nearly seamless maps after correcting the IR brightness temperatures for viewing angle effects. Second, with the availability of IR data from the Meteosat-5 satellite (currently positioned at a subsatellite longitude of 63 [degrees] E), globally complete (60 [degrees] N-60 [degrees] S) fields can be produced. Third, the data have been transformed from the native satellite projection of each individual geostationary satellite and have been remapped to a uniform latitude/longitude grid. Fourth, globally merged datasets of full resolution IR brightness temperature have been produced routinely every half hour since November 1998. Fifth, seven days of globally merged, half-hourly data are available on a rotating archive that is maintained by the Climate Prediction Center Web page (http://www.cpc.ncep.noaa.gov/products/global_precip/html/ web.html). Unfortunately, international agreement prevents us from distributing Meteosat data within three days of real time, so the data availability is delayed appropriately. Finally, these data are permanently saved at the National Climatic Data Center in Asheville, North Carolina, beginning with data in mid-September of 1999. In this paper, the authors briefly describe the merging methodology and describe key aspects of the merged product. Present and potential applications of this dataset are also discussed. Applications include near-real time global disaster monitoring and mitigation and assimilation of these data into numerical weather prediction models and research, among others.

Over the next few years, a number of space agencies in North America, Europe, and Asia have plans to launch geostationary satellites for the purpose of analyzing atmospheric pollution. These [...]