The process of transitioning new environmental satellite products and algorithms into operations is a challenging task. Within NOAA's Office of Research and Applications, this transition process is evolving to satisfy the more stringent algorithm requirements for the next generation of satellite users. Taking algorithm research into operations means modifying old processes to suit users' increased need for more accurate, reliable and timely remotely sensed products. At the same time, the entire end-to-end process must support the processing of more complex data, higher data volumes due to increased spatial resolution and improved monitoring latencies, increased satellite coverage, increased spectral bands, and multi-disciplinary products. The products and data also need meta-data about instrument status, shifts in product accuracy, and other information that affect product quality for today's increasingly sophisticated user. These needs are stretching traditional scientific research skills into multi-disciplinary tasks that require an understanding of processes not usually equated with scientific algorithm development and include instrument operation, systems engineering, information technology approaches, data fusion, data assimilation, and related capabilities. These added capabilities require new approaches, and skills, for dealing with this multifaceted problem. Steps for improving algorithm research to operations processes will be identified, discussed and compared to past methods.

In this paper we review the concept of network centricity, and relate it to the evolution that is anticipated in weather and environmental operational remote sensing in the decade ahead. We provide a practical roadmap for implementing concepts that recognize the value of legacy systems, accounting for a spectrum of different interoperability potentials of legacy and future systems, from the perspective of three communities and five levels of integration.

To quantify the radiative forcing of ice clouds, we need to fully understand the optical and microphysical properties of these clouds. This paper reports on some preliminary results associated with the optical properties of ice crystals within ice clouds and the effect of ice crystal habit on the retrieval of ice cloud properties from use of the infrared spectrum. Furthermore, various cloud parameters retrieved from the atmospheric infrared sounder (AIRS) data are also reported.

The High spectral resolution infrared radiances from the Atmospheric InfraRed Sounder (AIRS) onboard the NASA's Earth Observing System (EOS) Aqua satellite are used to retrieve atmospheric temperature and water vapor profiles, etc. Evaluation of these retrieved parameters is performed by a comparison with the operational products from current Geostationary Operational Environmental Satellite (GOES) sounder, radiosonde observations (RAOBs) at the Atmospheric Radiation Measurement (ARM) Program Cloud and Radiation Test Bed (CART) site in Oklahoma, United States. Comparisons show that the physical-based AIRS retrievals agree with the RAOBs from the ARM CART site with a Root Mean Square Error (RMSE) of 1 K on average for temperature profiles above 850hPa, and approximately 10% on average for relative humidity profiles. With its improved spectral resolution, AIRS depicts more detailed structure than the current Geostationary Operational Environmental Satellite (GOES) sounder when comparing AIRS sounding retrievals with the operational GOES sounding products. The advancement of hyperspectral IR sounder in deriving atmospheric parameters over the current broadband IR sounder is demonstrated and analyzed

The direct assimilation of satellite-measured infrared radiances into numerical weather prediction and cloud sounding applications is currently prohibited when these measurements include cloud radiative effects. The difficulty arises from the microphysical complexity of clouds and their radiative responses that are only now being adequately modeled for current and next generation satellite sensors. The parameterization of cloud properties to deliver much needed im-provements in speed and accuracy of forward radiative transfer models is still under development. Indirect use of cloud-contaminated radiances by way of cloud-cleared radiances has thus become the initial focus of efforts to improve the spatial density of useful satellite radiance measurements. This is particularly important for satellite sensors with relatively wide fields of view as the probability of entirely cloud-free observations can be surprisingly low. Two classes of cloud-cleared radiance retrieval approaches developed so far comprise the synergistic use of 1) collo-cated infrared and microwave measurements, and 2) collocated infrared imaging and sounding measurements. For example, AIRS/AMSU and AIRS/MODIS cloud-cleared algorithms are being demonstrated by NASA Earth Observing System and are to be adopted by NPP/NPOESS that have similar measurements available from the instrument suites CrIS/ATMS and CrIS/VIIRS. In this paper, the characteristics of these cloud-cleared radiances and their potential for numerical weather prediction and cloudy sounding applications are evaluated. Preliminary results have shown that these two approaches, though quite different in character, are both effective and complementary. Where microwave measurements are unavailable, the synergistic imaging/sounding approach to cloud-clearing is the only reliable indirect use of cloud-contaminated infrared measurements, as is the case for geostationary platforms due to the antenna requirements for a meteorologically useful microwave radiometer at 35,000 km.

Passive remote sensing of the Earth surface and of its atmosphere must be strongly protected from commercial and military man made active microwave emissions. That need an urgent effort from the international community of passive microwave users and scientists to detail clearly their needs and very carefully justify their choices. This paper describes the current situation and proposes an action to progress urgently in the way of creating a reference document for justify the Earth remote sensing needs using passive microwaves.

Using CMAP, daily observatory rainfall data and NCEP/NCAR reanalysis data, climatic features of moisture transport and its differences between flood years and drought years during the first rainy season of South China (SC) are discussed. Results show that moisture transport influencing South China exhibit distinct difference before South China Sea Summer Monsoon (SCSSM) onset and after SCSSM onset. Therefore the first rainy season of SC should be divided into two stages: from April to SCSSM onset and from SCSSM to June. Variations of moisture transport from the West Pacific and North China have important effects on flood or drought of SC. But that from Arabian Sea-Bay of Bangle mainly influences local rainfall instead of abnormal rainfall over SC.

The Atmospheric Infrared Sounder (AIRS) has been operating since Sept. 2002 and AIRS radiance data is being used operationally by several weather centers. A significant fraction of AIRS observations are contaminated by dust blowoff from arid areas. The almost continous spectral coverage of AIRS in both the 10-12 and 3.7 micron atmospheric windows allows excellent detection of the presence of dust. Dust signals can often survive the cloud-clearing process used in the retrieval system for AIRS, thereby contaminating the low-altitude temperature and water vapor retrieval products. We present techniques being developed to both operationally detect and mitigate the effects of dust on AIRS retrieval products.

Circular polarization of scattered solar radiation is essentially zero for almost all aerosol and cloud cases. Required conditions for non-zero circular polarization include multiple-scattering and large scatterer size relative to wavelength. The single-scattering of incident solar radiation can produce linearly polarized light but not circularly polarized light. A second scattering event can transform some of the linearly polarized light into circularly polarized light. Additional scattering events can both create and destroy circular polarization via the transformation process with linear polarization. The peak in circular polarization ratio magnitude occurs at the optical depth for which the multiplescattering processes have maximized its creation-to-destruction rate. Provided multiple-scattering has occurred, circular polarization can only exist for scatterers of large size relative to the wavelength. For aerosols, this implies desert dust or oceanic aerosols and short wavelength observations (i.e., less than 0.5μm). All cloud particles are considered large as they are roughly an order of magnitude larger than aerosols.

Satellite-based measurements of aerosol optical depth (AOD) over land are obtained from an inversion procedure applied to dense dark vegetation pixels of remotely sensed images. The limited number of pixels over which the inversion procedure can be applied leaves many areas with little or no AOD data. Moreover, satellite coverage by sensors such as MODIS yields only daily images of a given region with four sequential overpasses required to straddle mid-latitude North America. Ground based AOD data from AERONET sun photometers are available on a more continuous basis but only at approximately fifty locations throughout North America. The object of this work is to produce a complete and coherent mapping of AOD over North America with a spatial resolution of 0.1 degree and a frequency of three hours by interpolating MODIS satellite-based data together with available AERONET ground based measurements. Before being interpolated, the MODIS AOD data extracted from different passes are synchronized to the mapping time using analyzed wind fields from the Global Multiscale Model (Meteorological Service of Canada). This approach amounts to a trajectory type of simplified atmospheric dynamics correction method. The spatial interpolation is performed using a weighted least squares method applied to bicubic B-spline functions defined on a rectangular grid. The least squares method enables one to weight the data accordingly to the measurement errors while the B-splines properties of local support and C² continuity offer a good approximation of AOD behaviour viewed as a function of time and space.

For the past ten years, much of the research in hyperspectral image data exploitation techniques has been focused on detection of ground targets. As a passive remote sensing technique, hyperspectral imagers have performed reasonably well in detecting the presence of a variety of objects; from crop species to land mines to mineral deposits to vehicles under camouflage. These often promising results have prompted new studies of hyperspectral remote sensing for other applications - including atmospheric monitoring. Should technologies like hyperspectral imaging prove effective in emission source monitoring, organizations interested in environmental assessment could transition from inspection using hand-held analytical instruments to a truly standoff technique. In this paper, we evaluate the utility of a set of hyperspectral exploitation techniques applied to the task of gas detection. This set of techniques is a sampling of approaches that have appeared in the literature, and all of the methods discussed have demonstrated utility in the reflective regime. Specifically, we look at signature-based detection, anomaly detection, transformations (i.e. rotations) of the spectral space, and even dedicated band combinations and scatter plots. Using real LWIR hyperspectral data recently collected on behalf of the US Environmental Protection Agency, we compare performance in detecting three different industrial gases.

The Advanced Baseline Imager (ABI) and the Hyperspectral Environmental Suite (HES) on GOES-R and beyond will enable improved monitoring of the distribution and evolution of atmospheric thermodynamics and clouds. The HES will be able to provide hourly atmospheric soundings with spatial resolution of 4 ~ 10 km with high accuracy using its high spectral resolution measurements. However, presence of clouds affects the sounding retrieval and needs to be dealt with properly. The ABI is able to provide at high spatial resolution (0.5 ~ 2km) a cloud mask, surface and cloud types, cloud phase mask, cloud top pressure (CTP), cloud particle size (CPS), and cloud optical thickness (COT), etc. The combined ABI/HES system offers the opportunity for atmospheric and cloud products improved over those possible from either system alone. The key step for synergistic use of ABI/HES radiance measurements is the collocation in space and time. Collocated ABI can (1) provide HES sub-pixel cloud characterization (mask, amount, phase, layer information, etc.) within the HES footprint; (2) be used for HES cloudclearing for partly cloudy HES footprints; (3) provide background information in variational retrieval of cloud properties with HES cloudy radiances. The Moderate-Resolution Imaging Spectroradiometer (MODIS) and the Atmospheric Infrared Sounder (AIRS) measurements from the Earth Observing System's (EOS) Aqua satellite provide the opportunity to study the synergistic use of advanced imager and sounder measurements. The combined MODIS and AIRS data for various scenes are analyzed to study the utility of synergistic use of ABI products and HES radiances for better retrieving atmospheric soundings and cloud properties. In order to derive sounding from combined ABI and HES radiances under HES partly cloudy footprint where no microwave sounding unit is available, an optimal cloud-removal or cloud-clearing algorithm is developed. MODIS and AIRS are used to verify the algorithm. AIRS clear column radiances are retrieved from the combined MODIS IR clear radiances and the AIRS cloudy radiances on a single footprint basis. The AIRS cloud-removed or cloudcleared radiance spectrum is convoluted to all the MODIS IR spectral bands with spectral response functions (SRFs), and the convoluted brightness temperatures (BTs) are compared with MODIS clear BT observations within all successful cloud-cleared footprints. The bias and the standard deviation between the convoluted BTs and MODIS clear BT observations is less than 0.25 K and 0.5 K, respectively, over both water and land for most MODIS IR spectral bands. The AIRS cloud-cleared BT spectrum is also compared with its nearby clear BT spectrum, the difference, accounting the effects due to scene non- uniformity, is reasonable according the analysis. It is found that more than 30% of the AIRS cloudy (partly and overcast) footprints in this study have been successfully cloud-cleared using the optimal cloud-clearing method, revealing the potential application of this method on the operational processing of hyperspectral IR sounder cloudy radiance measurements when the collocated imager IR data is available.

The National Oceanographic and Atmospheric Administration (NOAA) is now considering a microwave radiometer for the new series of Geostationary Operational Environmental Satellites (GOES) to be launched starting in 2012. GOES-R is expected to begin operations around 2014 and will provide significant advances in Earth coverage, environmental data, and prediction capabilities. GOES' unique vantage point in fixed geostationary orbit provides continuous, near-real-time updates (observations) of weather and environmental conditions for the Americas and large portions of the Atlantic and Pacific Oceans. In general, GOES-R sensor improvements arise from more frequent updates, finer spatial/spectral resolution, and an expanded field of view. Infrared (IR) atmospheric sounders are designed to provide excellent observations in clear conditions. Critical information within clouds and under cloud cover, however, is not available in the IR spectrum. Microwave sounders can provide synergistic coverage by their ability to observe energy through clouds. NASA's Earth Observing System (EOS) AQUA with the Advanced Microwave Sounder Unit (AMSU) and Atmospheric IR Sounder (AIRS) has illustrated the benefits of combining infrared and microwave sounder data. The benefits provided by polar microwave sounders can be extended to geostationary satellites. The combination of the Hyperspectral Environmental Suite (HES) IR sounder and Geostationary Microwave Sounder (GMS) can likewise provide complete geostationary sounder coverage and precipitation measurements.through our hemisphere. Three different sounders designs have been proposed for the GOES-R Geostationary Microwave Sounder (GMS); these designs would all use similar frequency bands to those of the AMSU A and B and therefore benefit from existing retrieval algorithms. Two designs use mechanically steered solid dish antennae, while a third design utilizes a sparse aperture antenna technology. All three GMS designs take advantage of the latest developments in sensor technology, algorithms, and antenna design. The joint NOAA/NASA GOES-R Program Office (GPO) is evaluating the various GMS designs for GOES-R. This paper will address the design, status, and advantages and limitations of these GMS approaches in reference to unmet meteorological requirements as part of Pre-Planned Product Improvement (P³I) on the GOES-R series of satellites.

With the launch of GOES-R, the next generation of geostationary environmental monitoring satellites will have gone from the drawing board to on-orbit operations. New products will flow through a processing system that took years to develop and perfect. Around 2012, users will finally get their first taste of the latest remote environmental monitoring products from geostationary orbit. To achieve this capability and make the new environmental monitoring system a success, algorithmic products will have to be developed, tested and then transitioned into an operational environment. This article addresses planned changes in the algorithm development process for GOES-R that will lead to a successful implementation of this new satellite and processing system. Notional concepts will be used to describe potential GOESR algorithm development processes, their benefits and the efficiencies to be gained from those changes. While the concepts described indicate a strategy, the final science into operations concepts will continue to evolve until the contract for building the system is awarded to a commercial vendor.

The indulgence in willful persecution of sandstorm had made great attention of many countries around the world. Chinese government and the Chinese academy of science going with some other countries have devoted a large amount of vigor to study the crucial environment problem. Due to the main source areas of sandstorm all located in the arid and semi-arid regions where there have great area, hard natural condition and bad traffic condition, it's very difficult to accomplish source area and the reason of sandstorm. For this destination, a international cooperation organization has been established to clarify the occur mechanism, transfer process and the following environment impact of sandstorm. The organization includes many researchers come form USA, Japan, Korea, and so on. Beijing surrounding area is one of the main sandstorm sources in recent years. In order to understand fully of the sandstorm form and development, we analyzed the land use degradation of Beijing surrounding area during the last ten years. 71 scenes Landsat TM/ETM, 611 scenes DRG and DEM data had been processed in our study. This paper made a detail describe of using Landsat image data and high resolution DEM data to construe the soil erosion and vegetation degenerate. The result shows that the irrational human activities and land use style are the main factors of land use degradation. In case of Beijing surrounding area, the land degradation directly impacted the frequency and intensity of sand & dust storm in Northern China. The case study region of Beijing surrounding area includes 51 counties that belong to three provinces and autonomous regions.

Northwestern China is a semi-arid or arid area in China. Ningnan (or South Ningxia) district is located south of Ningxia Province belong to Northwestern part of China. Climate in this region is more dry and lack of precipitation. Because global climate have been changing, temperature has been increasing and rainfall has been decreasing in South Ningxia. The ecology has been deteriorating, such as vegetation cover destroying, water losing and soil erosion. Therefore, the people who live in South Ningxia have been poor. Recently, Chinese government put into effect on strategy of "great development of Chinese northwest", aiming to improve environmental and ecological conditions and rise people's living standard. South Ningxia district was defined as area of emigration where the measurements of returning land for farming to forestry were taken into account . How to evaluate the plans and measurements is very important to continue to improving local environmental and ecological conditions further. The basic index of evaluation is soil water profit and loss statement while evapotranspiration (ET) is an important component in statement income and outcome of soil water. It is a very complicated problem to estimate evapotranspiration (ET) over large area of natural surface. In this paper, the natural surface was classified as 5 categories based on information from remote sensing, each categories being dealt with special way. Using data of remote sensing and weather stations, the result of regional evapotranspiration over Ningnan(South Ningxia) was given out, and verified and discussing are also made out. The work helps to assess whether or not improve environmental and ecological conditions.

Land surface emissivity, which varies widely with surface type, is important for the assimilation of microwave satellite data. The fast radiative transfer model RTTOV-7 developed by ECMWF had been introduced into the Global/regional assimilation and prediction system (Grapes)-3Dvar, a new three dimensional data assimilation system developed by the Research Center for Numerical Meteorological Prediction, Chinese Academy of Meteorological Sciences, to assimilate ATOVS microwave satellite radiance directly. To improve the accuracy of land surface emissivity, the NOAA/NESDIS microwave land surface emissivity model developed by F. Weng is merged into RTTOV-7 and an adjusted parameter scheme is designed to provide the surface parameters for the microwave land surface emissivity model. These surface parameters are produced from a global data assimilation system (GDAS) including a boundary layer model in NOAA/NESDIS. The result shows that the accuracy of land surface emissivity for a variety of land types is improved. It results in the improvement of the accuracy of simulated satellite radiance. Following the satellite microwave radiance operating near the window regions, which are affected strongly by land surface emissivity, could be utilized in the assimilation system to investigate their impact on numerical weather forecast.

The National Aeronautics and Space Administration (NASA), National Oceanic and Atmospheric Administration (NOAA), and Department of Defense (DoD), Joint Center for Satellite Data Assimilation (JCSDA) was established in 2000/2001. The goal of the JCSDA is to accelerate the use of observations from earth-orbiting satellites into operational numerical environmental analysis and prediction systems for the purpose of improving weather and oceanic forecasts, seasonal climate forecasts and the accuracy of climate data sets. As a result, a series of data assimilation experiments were undertaken at the JCSDA as part of the preparations for the operational assimilation of AIRS data by its partner organizations^1,2. Here, for the first time full spatial resolution radiance data, available in real-time from the AIRS instrument, were used at the JCSDA in data assimilation studies over the globe utilizing the operational NCEP Global Forecast System (GFS). The radiance data from each channel of the instrument were carefully screened for cloud effects and those radiances which were deemed to be clear of cloud effects were used by the GFS forecast system. The result of these assimilation trials has been a first demonstration of significant improvements in forecast skill over both the Northern and Southern Hemisphere compared to the operational system without AIRS data. The experimental system was designed in a way that rendered it feasible for operational application, and that constraint involved using the subset of AIRS channels chosen for operational distribution and an analysis methodology close to the current analysis practice, with particular consideration given to time limitations. As a result, operational application of these AIRS data was enabled by the recent NCEP operational upgrade. In addition, because of the improved impact resulting from use of this enhanced data set compared to that used operationally to date, provision of a realtime "warmest field" of view data set has been established for use by international NWP Centers.

The Atmospheric Infrared Sounder (AIRS) was launched on May 4, 2002 on the NASA Aqua Satellite. AIRS measures the infrared spectrum in 2378 channels with a very high spectral resolution of approximately 1200. In this paper, the spatial properties of the infrared channels are presented in their flight configuration. The spatial response for any single channel is slightly irregular and rotates off nadir due to image rotation in the scan mirror. AIRS has several channels with the same spectral frequencies but different spatial responses. These channels are used to demonstrate the efficacy of resampling using standard techniques to improve the co-registration

The Earth Science and Meteorological communities are taking great interest in a new instrument released by NASA. The Atmospheric Infrared Sounder (AIRS), launched on the EOS Aqua Spacecraft on May 4, 2002, is a high spectral resolution infrared imaging spectrometer with over 2300 distinct infrared wavelengths ranging from 3.7 μm to 15.4 μm. AIRS is unique in that it provides the highest infrared spectral resolution to date while also providing coverage of over 95% of the Earth's surface every day at 15 km spatial resolution. The AIRS project is currently managed by NASA's Jet Propulsion Laboratory in Pasadena, California¹. The AIRS is providing a wealth of scientific data to the Earth Science community including upper atmospheric water vapor and atmospheric composition on key greenhouse gases. It is also improving weather forecasting and the studies of processes affecting climate and weather.

Traditional cloud clearing methods utilize a clear estimate of the atmosphere inferred from a microwave sounder to extrapolate cloud cleared radiances (CCR's) from a spatial interpolation of multiple cloudy infrared footprints. Unfortunately, sounders have low information content in the lower atmosphere due to broad weighting functions, interference from surface radiance and the microwave radiances can also suffer from uncorrected side-lobe contamination. Therefore, scenes with low altitude clouds can produce errant CCR's that, in-turn, produce errant sounding products. Radiances computed from the corrupted products can agree with the measurements within the error budget making detection and removal of the errant scenes impractical; typically, a large volume of high quality retrievals are rejected in order to remove a few errant scenes. In this paper we compare and contrast the yield and accuracy of the traditional approach with alternative methods of obtaining CCR's. The goal of this research is three-fold: (1) to have a viable approach if the microwave instruments fail on the EOS-AQUA platform; (2) to improve the accuracy and reliability of infrared products derived from CCR's; and (3) to investigate infrared approaches for geosynchronous platforms where microwave sounding is difficult. The methods discussed are (a) use of assimilation products, (b) use of a statistical regression trained on cloudy radiances, (c) an infrared multi-spectral approach exploiting the non-linearity of the Planck function, and (d) use of clear MODIS measurements in the AIRS sub-pixel space. These approaches can be used independently of the microwave measurements; however, they also enhance the traditional approach in the context of quality control, increased spatial resolution, and increased information content.

Differential spectral imaging made possible by the new generation of hyperspectral sounders, such as AIRS, has applications for process studies and may also have applications for short-term weather forecasting. We use AIRS data to evaluate one such application: measurements of water vapor at 600 and 300 mb. The opacity of the 4.3 micron R-branch channels is almost entirely due to carbon dioxide, with weighting function peaks in the lower troposphere. Differences between these channels and selected pure water channels between 1300 and 1600 cm-1 cancel the local variability of the temperature to first order and measure the water column. Images of bt2388-bt1392, which measure the total water column above 600 mb from AIRS show large-scale patterns with sharp transitions between dry and wet upper tropospheric water vapor zones along frontal boundaries. The wealth of spatial features suggest applications to weather forecasting similar to the use of cloud images from satellite data, but unlike the visible light data, these images are available day and night. Data from tropical storm Arlene on 10 June 2005 are used to illustrate the potential of using high spectral resolution difference images. The motion of feature in these images correlate with the wind at 600 mb from upper airs measurements. The method does not require microwave channels or knowledge of the temperature profiles from a GCM and retains the full spatial resolution of the data. While the 15 km spatial resolution and 12 hour temporal repeat cycle of the EOS Aqua limits the practical application of AIRS data to mesoscale imaging and process studies, extension of the AIRS design concept can be used from hyper-spectral sounding with 1km spatial resolution from Low Earth Orbit (LEO) with AIRS-like spectral resolution, signal-to-noise ratio and calibration quality. Similar performance available with 4 km spatial resolution from geostationary satellites with one hour repeat coverage will allow future hyper-spectral sounders in geostationary orbits to measure the hourly advance of frontal zones in support of short term regional forecasting. The Atmospheric Infrared Sounder, AIRS, was launched in May 2002 on the EOS Aqua satellite. The 4 million spectra gathered globally each day since September 2002 are being assimilated in the global forecast by the major NWP centers in near real time since May 2003. Details about AIRS and data availability can be found at http://jpl.nasa.gov/airs.

For over three decades the Defense Meteorological Satellite Program's Operational Linescan System (OLS) has demonstrated a unique nighttime imaging capability using a high gain visible channel. Designed primarily to detect clouds through relative moonlight reflection contrasts, quantitative applications based on the OLS nighttime visible data are limited due to low radiometric (6-bit, or 64 count levels) resolution, lack of calibration, and not being accompanied by a large suite of other spectral bands (only a single thermal infrared window channel). Despite these limitations, the fundamental capabilities enabled by the nighttime visible band are truly unique, and worthy of closer inspection by the terrestrial, atmospheric, and space science communities alike-particularly in light of the inclusion of a comparable "Day/Night visible Band" (DNB) upon the Visible/Infrared Imager/Radiometer Suite (VIIRS) scheduled to fly upon the National Polar-orbiting Operation Environmental Satellite System (NPOESS) constellation (and a risk-reduction preview upon the NPOESS Preparatory Project Satellite). This paper anticipates some of the capabilities of the VIIRS-DNB in the context of nighttime dust storm and snow cover mapping from lunar reflection, based on heritage sensors from the contemporary environmental satellite constellation.

A new technique called the local region of influence (LROI) scheme for supervised cloud classification of the Moderate Resolution Imaging Spectroradiometer (MODIS) is proposed. The classification of each observation is performed within the LROI, where the center of each class is calculated as a weighted average of its training class members with respect to each new observation. The probability of each class is assigned to each observation. The proposed LROI scheme is applied to the MODIS radiances observed from the scenes of clear skies, ice clouds, or water clouds. The classification results are compared with those from the maximum likelihood (ML) classification method, the multicategory support vector machine (MSVM) and the operational MODIS cloud mask algorithm. The lowest misclassification error rates show the advantage of the LROI scheme.

The NPOESS Ozone Mapping and Profiler Suite (OMPS) will collect data to permit the retrieval of the vertical and horizontal distribution of ozone in the Earth's atmosphere. Algorithm development efforts in connection with these measurements include the preparation of Algorithm Theoretical Basis Documents (ATBDs), Operational Algorithm Documents (OADs), and science grade code with associated test sets and documentation. These items are provided to the Interface Data Processing Segment (IDPS) for conversion and integration into the operational system. This paper addresses elements of the process being used to convert, test, and maintain the maturing and changing science grade code to the initial operational source code for the OMPS. The operational OMPS SDRs and EDRs will be generated by the IDPS, that is, ozone output items will include the sensor and environmental data records, associated metadata and product validation; e.g. ancillary data, calibration information and quality flags.

Preparing for the next generation polar orbiting environmental satellite system, the NPOESS system is currently undergoing the design phase of the Program. The NPOESS Direct Readout Mission will service users in the field providing regional environmental coverage with two direct broadcast links, high rate data (HRD) at 20 Mbps and low rate data (LRD) at 3.88 Mbps. Contained within these two links are the suite of sensor data in order to generate 50 HRD and 29 LRD weather products via the NPOESS Field Terminal Segment software being developed for distribution to the weather community or by value added resellers.

The mission of NOAA's Geostationary Operational Environmental Satellite System (GOES) R series satellites, in the 2012 time frame, is to provide continuous, near real-time meteorological, oceanographic, solar, and space environment data that supports NOAA's strategic mission goals. It presents an exciting opportunity to explore new instruments, satellite designs, and system architectures utilizing new communication and instrument technologies in order to meet the ever-increasing demands made of Earth observation systems by national agencies and end users alike. The GOES-R sensor suite includes a 16 spectral band Advanced Baseline Imager (ABI), an approximately 1500 high spectral resolution band Hyperspectral Environmental Suite (HES), plus other sensors designed to detect lightning and to explore the ocean, solar and space environment. The Cooperative Institute for Meteorological Satellite Studies (CIMSS) as part of the Space Science and Engineering Center (SSEC) of the University of Wisconsin-Madison, the long time partner of NOAA, has developed the first operational end-to-end processing system for GOES. Based on this heritage, and with recent support from the NASA/NOAA Geosynchrous Imaging FTS (GIFTS) project, the Navy's Multiple University Research Initiative (MURI), and NOAA's GOES-R Risk Reduction program, SSEC has built a near-complete end-to-end system that is capable of simulating sensor measurements from top of atmosphere radiances, raw sensor data (level 0) through calibrated and navigated sensor physical measurements (level 1) to the processed products (level 2). In this paper, the SSEC Hyperspectral Imaging and Sounding Simulator and Processor (HISSP) will be presented in detail. HISSP is capable of demonstrating most of the processing functions such as data compression/decompression, sensor calibration, data processing, algorithm development, and product generation. In summary, HISSP is an end-to-end system designed to support both government and industry for the GOES-R system and their related broad scope of acquisition activities.

Tracking the Aerosol Optical Depth (AOD) is of particular importance in monitoring aerosol contributions to global radiative forcing. Until now, the two standard techniques used for retrieving AOD were; (i) sun photometry, and (ii) satellite-based approaches, such as based DDV (Dense Dark Vegetation) inversion algorithms. These methods are only available for use during daylight time since they are based on direct or indirect observation of sunlight. Few attempts have been made to measure AOD behaviour at night. One such method uses spectrally-calibrated stars as reference targets but the number of available stars is limited. This is especially true for urban sites where artificial lighting hides most of these stars. In this research, we attempt to provide an alternate method, one which exploits artificial sky glow or light pollution. This methodology links a 3D light pollution model with in situ light pollution measurements. The basic idea is to adjust an AOD value into the model in order to fit measured light pollution. This method requires an accurate model that includes spatial heterogeneity in lighting angular geometry, in lighting spectral dependence, in ground spectral reflectance and in topography. This model, named ILLUMINA, computes 1^st and 2^nd order molecular and aerosol scattering, as well as aerosol absorption. These model features represent major improvements to previous light pollution models. Therefore, new possibilities for light pollution studies will arise, many of which are of particular interest to the astronomical community. In this paper we will present a first sensitive study applied to the ILLUMINA model.

In order to get more accurate air pollution distribution effected by complex terrain such as hilly or mountain areas two models, one is PBL model other is random walk transportation model (Monte Carlo), are developed to simulate air pollutant's concentration distribution. The PBL model is a fine-mesh, higher resolution, three-dimension non-hydrostatic model; higher order turbulence closure scheme is adapted. A wind tunnel experiment for verifying the model's ability is showed that the results of the model accord with the wind tunnel experiment. The outputs of PBL model are inputs of random walk model as the initial meteorological conditions. The case study of modeling a power station air pollution in hilly area in Henan, China expressed in this paper also shows that complex terrain can significantly increase pollution concentration at ground level.

We propose a retrieval method of Asian dust (Yellow sand or Kosa aerosol) columnar amount around source regions using a near ultraviolet radiometry observation from space. The method simultaneously retrieves an optical thickness and mode radius of Kosa aerosol, and then derives its columnar amount. The method was applied to ADEOS-II / GLI data in the spring of 2003 around Taklimakan desert source region, inland China. The retrieved optical thickness and mode radius were about 0.34 and 1.75 μm, respectively, at a validation site. They are comparable to the in situ observations conducted within the framework of ADEC project. The estimated columnar amount around a validation site is about 2.77 g m^-2, which seems reasonable under a relatively calm situation. The method should be further validated with a regional model simulation study, and then it is useful to monitor Asian dust around source regions from space in the future.

Determination of aerosol optical depth from satellite remote sensing measurements is extremely complex due to the large variability of aerosol optical properties. Significant simplification occurs when measurements are taken over water since the ocean reflection signal can be taken as negligible in the NIR.. Unfortunately, over land, most of the signal can be attributed to ground reflectance. While conventional approaches look for "dark" pixels in an image to isolate aerosols, these pixels are subjected to increased noise. In this paper, we focus on the feasibility of the MODIS sensor to determine aerosol optical depth. In particular, an intercomparison between MODIS and CIMEL shows a significant trend for MODIS to overestimate optical depth. We show that this may be explained through an inaccurate assumption on the correlation between the VIS and NIR surface albedos. In particular, we show through an analysis of hyperspectral high resolution Hyperion data that the correlation coefficient assumption underestimates ground albedo resulting in an overestimate of the VIS optical depth. Preliminary radiative transfer calculations show that this mechanism can help explain the observed overestimation.

Plants metabolism changes as function of parasitic attacks, nutrients of the ground, environmental condition. This characteristic has been exploited to diagnose possible stresses, for a better control of the production processes to increase the perceived quality in agricultural products, to evaluate the environmental quality and to monitor the effects of human activities on the environment, using plants as the primary sensor. These aims have been pursued through the development of a knowledge based instrument for the early identification of a plant stress state, a hard task to be performed even by a human expert. It is known that information on the photosynthetic apparatus can be obtained from the analysis of the chlorophyll fluorescence. The chosen procedure, known as OJIP test, is based on measurements of the fluorescence dynamic behaviour at 730 nm as it is related with the chlorophyll. The stimulus is generated with an amplitude modulated laser diode, the fluorescence detection is done by means of a photomultiplier to reach a good sensibility. The proposed instrument integrates the knowledge of human experts and laboratory diagnostic techniques for an on-site, non-intrusive and remote analysis.

The goal of this study is to assess the impact of the downscale linkage of global model output on the simulated regional scale O3 vertical/horizontal structure. In this research, we incorporated the analysis and forecasting of a global model with the regional model utilizing a specially designed satellite ozone data assimilation process. Two different methods can be applied to incorporate the satellite measurements in the regional ozone modeling. One is a method that utilizes the best-optimized analysis field directly as the initial conditions for the simulation, and the other is the downscale linkage of the output of a global scale model into which the satellite observations have been already assimilated. In this work, we focus on the second indirect method with the global RAQMS (NASA Langley Research Center and University of Wisconsin Regional Air Quality Modeling System), which adopts a simple optimal interpolation approach (SDF; Statistical Digital Filter), to conduct the TOMS column and solar occultation assimilation procedure. To do this, a linking tool to ingest the global RAQMS simulations into CMAQ (Community Multiscale Air Quality) model has been developed. We performed two sets of CMAQ simulations; one with the predefined lateral boundary condition (BC) profile and the other with the lateral BC generated from RAQMS outputs for the 1999 Southern Oxidant Study (SOS-99) period (Jun. 15-Jul. 14, 1999). The two simulation results were compared and evaluated with several ozone soundings and national wide surface observations (AIRS/EPA) in U.S. and Canada. In the upper troposphere and lower stratosphere, CMAQ simulations with the predefined BC profile failed to simulate the so-called "chemical tropopause" above which O3 concentrations rapidly increase with height. On the other hand, the simulation with BC provided with the global RAQMS outputs showed good agreement with the ozone soundings. However, there were no significant differences between the two in the spatial distributions of surface O3. These results indicate that the usage of BC generated from global simulations with a satellite data assimilation procedure certainly improves the performance of regional models above the middle troposphere in the northern part of the U.S. continent and Canada. In future work, we will apply both SDF and 3DVAR methods for CMAQ modeling with GOES ozone column and make a comparison with current results.

For daytime water phase clouds, an iterative physical retrieval algorithm is proposed to determine the cloud top pressure, temperature, and height that best matches the window-IR channel radiance to that predicted from the atmospheric/surface state as specified by numerical weather prediction model inputs and other data. The iterative retrieval uses a fast radiative transfer (or forward) model and includes a parameterization of cloud multiple scattering. The optical thickness and effective particle size are used as explicit inputs and so it can account for both optically thin and thick clouds.

Top-of-atmosphere radiances and adjoint sensitivities for ice clouds at 600-2300 cm^-1 are studied using a new fast radiative transfer system (forward, tangent linear, and adjoint) developed for the NASA/NOAA/DOD Joint Center for Satellite Data Assimilation. The radiative transfer model is based on a hybrid solution method for computing thermal radiances that fully accounts for multiple scattering and that allows clouds to be placed at any number of arbitrary layers. Called the successive order of interaction model, it has been shown to be faster in most cases and more accurate than the popular delta-Eddington model. Ice particle scattering properties are obtained from rigorous scattering theory for various particle shapes and sizes. Gas optical depths are derived from line-by-line calculations. Results indicate that top-of-atmosphere brightness temperatures are sensitive to ice water path occurring in multiple cloud layers, which suggests major challenges for retrieving cloud properties under conditions other than single-layered clouds.

Understanding upper troposphere humidity is important in the context of radiative forcing and climate. We present a detailed statistic comparison of upper troposphere water vapor retrieval profiles derived from the Atmospheric Infrared Sounder (AIRS) and in-situ measurements. The in-situ measurements are based on a recently compiled database of "best estimate" atmospheric state profiles, obtained from a careful selection of RS-90 radiosondes at Department of Energy Atmospheric Radiation Measurement (ARM) sites, during AIRS overpasses. The aim of this research is to improve the skill and accuracy of the retrieval algorithms in order to understand and quantify the biases between AIRS and RS-90 radiosondes.

The helicity of a severe sandstorm happened in Northwest China was analyzed by using global reanalysis grid data of NCEP/NCAR for 4 times a day. As an important physical parameter in analyzing and predicting severe convective weather, the helicity also has good indication in the forecasting of sandstorm. The distribution of helicity over the sandstorm area was negative at higher levels and positive at lower levels. There was definite relationship between the evolvement of helicity's negative value at higher levels and the occurrence of sandstorm.

Two classes of cloud-cleared radiances retrieval approaches developed so far comprise the synergistic use of 1) collocated infrared and microwave measurements, and 2) collocated infrared imaging and sounding measurements that is discussed in detail in the companion paper entitle "Evaluation of Cloud-Cleared Radiances for Numerical Weather Prediction and Cloud Contaminated Sounding Applications" [1]. In that paper AIRS/AMSU and AIRS/MODIS cloud-cleared algorithms are discussed and their performance evaluated. The focus of this paper is to present additional examples and statistics for not only cloud-cleared radiances but also cloud-cleared retrieval of temperature and water vapor.

Drought is a principal agrometeorological disaster to winter wheat zones in North China. From the correlation of wheat yield to rainfall the drought indices are determined that correspond to varying levels of severities on an agricultural basis. In wheat growing season, when rainfall displays its negative anomalies of <15, 15-35, 36-55 and <55%, there occurs a slight, moderate, heavy and extreme drought, leading to yield drop by <10, 10-20, 21-30 and <30%, respectively. The economic loss consists of yield reduction and drought-fighting input like irrigation. A drought-caused loss model is presented from historical meteorological and wheat yield datasets, with which to make the distribution of economic losses in the last 30 years over the province of Henan. Evidence suggests that in years of heavy droughts the loss was between 450 to 675 (<250) RMB yuans per hectare in the NE (west − SW) segment of the province.

Drought is one of the major meteorological disasters to agriculture in north China so that the development of methods for effectively monitoring droughts is of great significance to dry land crops. This paper makes analysis of products of energy and water balances retrieved from LAS (Large Aperture Scintillometer) measurements, indicating that the structural parameter of LAS refractive index shows regular difference in daily variation between different weather backgrounds and remarkable difference in sensible heat flux on a seasonal basis, with higher negative correlation between such flux and soil humidity at 0 ~ 50 cm depth.

The data on spatial aerosol distribution over Western Siberia are obtained by airborne laboratory during 1983-1989 and 1997-2003. Analysis of the samples allows us to reveal time variability of the aerosol content in the lower and middle troposphere. The concentrations of Si, Al, Fe, Mg, Ca, Ti, Cu, Mn, Cr, Pb, and Ni elements, K⁺, Na⁺, Cl^-, SO₄^2-, NO₃^-, and NH₄⁺ions, and their vertical profiles are calculated. The seasonal variability of characteristics of atmospheric aerosol concentration is also estimated.