Comparative properties of arginases

Arginase is a primordial enzyme, widely distributed in the biosphere and represented in all primary kingdoms. It plays a critical role in the hepatic metabolism of most higher organisms as a cardinal component of the urea cycle. Additionally, it occurs in numerous organisms and tissues where there is no functioning urea cycle. Many extrahepatic tissues have been shown to contain a second form of arginase, closely related to the hepatic enzyme but encoded by a distinct gene or genes and involved in a host of physiological roles. A variety of functions has been proposed for the "extrahepatic" arginases over the last three decades. In recent years, interest in arginase has been stimulated by a demonstrated involvement in the metabolism of the ubiquitous and multifaceted molecule nitric oxide. Molecular biology has begun to furnish new clues to the disparate functions of arginases in different environments and organisms. Comparative studies of arginase sequences are also beginning to elucidate the comparative evolution of arginases, their molecular structures and the nature of their catalytic mechanism. Further studies have sought to clarify the involvement of arginase in human disease. This review presents an outline of the current state of arginase research by giving a comparative overview of arginases and their associated properties.     

The forward problem and corrections for the SSM/T satellitemicrowave temperature sounder

The relationship between atmospheric temperatures and the brightness temperatures measured by the Special Sensor Microwave/Temperature (SSM/T) radiometer is addressed. Physical algorithms for retrieving temperature profiles explicitly employ the physics of radiative transfer. Consequently, it is necessary to do the forward problem accurately before attempting the retrieval problem. The various problems that arise in doing forward calculations over oceans are discussed. These problems include determining when the measurements are cloud contaminated, the amount of liquid water in the fields of view, the surface emissivity under both clear and cloudy conditions, the corrections for liquid water contamination of the measurements, the adjustments for deficiencies in the radiative transfer model, and the compensation for measurement discrepancies by using shrinkage estimation. These procedures are developed for SSM/T data, and their validity is checked by comparing the forward calculations with the corresponding satellite measurements. Application of the procedures to an independent data set confirms their veracity     

Microwave Emission and Scattering From Deserts: Theory Compared With Satellite Measurements

The emission and scattering from desert surfaces are analyzed using simulations and measurements from the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Sounding Unit (AMSU) microwave satellite instruments. Deserts are virtually free of vegetation, so the satellite radiometers are able to observe the emissivities of different minerals, such as limestone and quartz. Moreover, since deserts contain little moisture, the thermal emission originates below the surface at a depth of many wavelengths. At high frequencies, where the penetration depth of radiation is smallest, the radiometric measurements display the large diurnal variation in surface temperature, which reaches its maximum at around 1 P.M. Conversely, at low frequencies, where the penetration depth is largest, the radiation measurements display the small diurnal variation of subsurface temperature, which reaches a minimum at around 6 A.M. In addition to these emission signals, sand particles also scatter microwave radiation. Volume scattering causes the measurements to decrease as the frequency increases; although compared to other scattering media (snow cover and precipitation), the larger absorption and fractional volume (i.e., solidity) of sand reduce the scattering. Although the scattering effect is small, SSM/I measurements between 19 and 85 GHz show that deserts scatter the upwelling microwave radiation in a manner similar to light precipitation, which makes it difficult to uniquely identify precipitation over arid regions. Interestingly, the higher frequency AMSU measurement at 150 GHz is nearly the same as at 89 GHz for deserts, whereas the 150-GHz measurement is much lower than at 89 GHz for precipitation. These different spectral features at high frequencies can provide a means of separating the scattering from desert surfaces from that of precipitation.     

Remote sensing of atmospheric water content from satellites using microwave radiometry

Analysis is presented which substantiates the high correlation achieved in relating integrated water vapor and liquid water to brightness temperatures at frequencies near the 22.235 GHz water vapor line. The influence of atmospheric and surface variability is shown to be minimal over low emissivity sea surfaces. Determination of atmospheric water content using regression techniques is shown to follow directly from radiation transfer theory. Satellite data from the Nimbus-E Microwave Spectrometer (NEMS) aboard Nimbus-5 are compared with radiosonde water vapor measurements and cloud images recorded by the Temperature Humidity Infrared Radiometer aboard Nimbus 5.     

The potential of combining SSM/I and SSM/T2 measurements to improvethe identification of snowcover and precipitation

The potential of passive microwave radiometry for classifying snowcover and precipitation using measurements from the Special Sensor Microwave/Imager (SSM/I) and the Special Sensor Microwave Water Vapor Profiler (SSM/T2) is investigated by modelling the radiative transfer for different surface types and atmospheric conditions. The model accounts for various land surfaces and vegetation covers, different snow types as well as wind roughened ocean water. The atmospheric part includes multiple scattering and depolarization by cloud droplets and precipitating water as well as ice spheres. It was found, that the combination of a window channel (91 GHz) and an atmospheric sounding channel (183±7 GHz) can improve the separation of snowcover and precipitation which is difficult by using only SSM/I channels. The 183±7 GHz channel is strongly influenced by the water vapor distribution which makes its use difficult for warm rain cases and low cloud tops. Then, the signature at this frequency is not unique and the above relation gives no further improvement of the classification. However, the identification of rainfall over cold land backgrounds can be significantly improved, which is illustrated by the application of a combined SSM/I-SSM/T2 algorithm to two satellite datasets when compared to the SSM/I algorithm and to operational surface weather maps     

Global identification of snowcover using SSM/I measurements

Visible satellite sensors have monitored snowcover throughout the Northern Hemisphere for almost thirty years. These sensors can detect snowcover during daylight, cloud-free conditions. The operational procedure developed by NOAA/NESDIS requires an analyst to manually view the images in order to subjectively distinguish between clouds and snowcover. Because this procedure is manually intensive, it is only performed weekly. Since microwave sensors see through nonprecipitating clouds, snowcover can be determined objectively without the intervention of an analyst. Furthermore, microwave sensors can provide daily analysis of snowcover in real-time, which is essential for operational forecast models and regional hydrologic monitoring. Snowcover measurements are obtained from the Special Sensor Microwave Imager (SSM/I), flown aboard the DMSP satellites. A decision tree, containing various filters, is used to separate the scattering signature of snowcover from other scattering signatures. Problem areas are discussed and when possible, a filter is developed to eliminate biases. The finalized decision tree is an objective algorithm to monitor the global distribution of snowcover. Comparisons are made between the SSM/I snowcover product and the NOAA/NESDIS subjectively analyzed weekly product     

Interpretation of SSM/I measurements over Greenland

Multispectral brightness temperature (T_B) measurements over Greenland are obtained from the Special Sensor Microwave Imager (SSM/I), which are flown aboard the DMSP satellites. This paper examines the different spectral characteristics over Greenland throughout the year. Although snow covers the vast majority of Greenland, the southern regions rarely exhibit the spectral characteristics associated with snowcover (i.e., T_B decreases at higher frequencies). In fact, the SSM/I polarization and frequency measurements over southern Greenland are more indicative of water than a snow-covered surface (i.e., T_B increases at higher frequencies). A simplified physical model is developed to help explain the anomalous measurements over southern Greenland. Model results indicate that high frequency radiation is mainly scattered by snow grains residing above the subsurface ice layers, whereas low frequency radiation is scattered throughout a much greater depth. Since low frequencies are scattered throughout a greater volume, they are depressed relative to high frequencies, and the typical snowcover signature is absent     

Surface identification using satellite microwave radiometers

The use of satellite microwave radiometers for identifying natural surfaces is analyzed. A retrieval technique is developed by considering the related mixed-pixel problem where two or more surfaces are contained within the viewing area. At a given frequency &ogr;, the emissivity measurement ϵ(&ogr;) depends on the fractional amounts f _n and a priori emissivities ϵ_n(&ogr;) where ϵ(&ogr;)=Σϵ_n(&ogr;)f_n. In applications involving surface identification the fractional amounts act as discriminants to identify the most likely surface among the a priori candidates. In principle, the fractional amounts can be obtained using multispectral measurements of emissivity. However, due to the limited spectral characteristics of emissivity the maximum number of distinguishable surfaces is reduced to three. The fractional amounts are derived using dual-frequency emissivity measurements and the effects of errors in measurement and a priori values are analyzed