Engineering topics
Publications and source records attributed to Chang, A. T. C..
Remote sensing of snow depth has been used to infer snow depth for many years. Passive microwave remote sensing of snow depth is compared with the snow gauge data.
Seasonal snow cover in South America was examined in this study using passive microwave satellite data from the Special Sensor Microwave Imagers (SSM/I) on board Defense Meteorological Satellite Program (DMSP) satellites. For the period from 1992-1998, both snow cover extent and snow depth (snow mass) were investigated during the winter months (May-August) in the Patagonia region of Argentina. Since above normal temperatures in this region are typically above freezing, the coldest winter month was found to be not only the month having the most extensive snow cover but also the month having the deepest snows. For the seven-year period of this study, the average snow cover extent (May-August) was about 0.46 million sq km and the average monthly snow mass was about 1.18 x 10(exp 13) kg. July 1992 was the month having the greatest snow extent (nearly 0.8 million sq km) and snow mass (approximately 2.6 x 10(exp 13) kg).
The Tropical Rainfall Measuring Mission (TRMM) satellite was launched on November 27, 1997, and data from all the instruments first became available approximately 30 days after launch. Since then, much progress has been made in the calibration of the sensors, the improvement of the rainfall algorithms, in related modeling applications and in new datasets tailored specifically for these applications. This paper reports the latest results regarding the calibration of the TRMM Microwave Imager, (TMI), Precipitation Radar (PR) and Visible and Infrared Sensor (VIRS). For the TMI, a new product is in place that corrects for a still unknown source of radiation leaking in to the TMI receiver. The PR calibration has been adjusted upward slightly (by 0.6 dBZ) to better match ground reference targets, while the VIRS calibration remains largely unchanged. In addition to the instrument calibration, great strides have been made with the rainfall algorithms as well, with the new rainfall products agreeing with each other to within less than 20% over monthly zonally averaged statistics. The TRMM Science Data and Information System (TSDIS) has responded equally well by making a number of new products, including real-time and fine resolution gridded rainfall fields available to the modeling community. The TRMM Ground Validation (GV) program is also responding with improved radar calibration techniques and rainfall algorithms to provide more accurate GV products which will be further enhanced with the new multiparameter 10 cm radar being developed for TRMM validation and precipitation studies. Progress in these various areas has, in turn, led to exciting new developments in the modeling area where Data Assimilation, and Weather Forecast models are showing dramatic improvements after the assimilation of observed rainfall fields.
A technique for estimating monthly oceanic rainfall rate using multi-channel microwave measurements has been developed. There are three prominent features of this algorithm. First, the knowledge of the form of the rainfall intensity probability density function used to augment the measurements. Second, utilizing a linear combination of the 19.35 and 22.235 GHz channels to de-emphasize the effect of water vapor. Third, an objective technique has been developed to estimate the rain layer thickness from the 19.35 and 22.235 GHz brightness temperature histograms. This technique is applied to the SSM/I data since 1987 to infer monthly rainfall for the Global Precipitation Climatology Project (GPCP). A modified version of this algorithm is now being applied to the TRMM Microwave Imager (TMI) data. TMI data with better spatial resolution and 24 hour sampling (vs. sun-synchronized sampling, which is limited to two narrow intervals of local solar time for DMSP satellites) prompt us to study the similarity and difference between these two rainfall estimates. Six months of rainfall data (January to June 1998) are used in this study. Means and standard deviations are calculated. Paired student t-tests are administrated to evaluate the differences between rainfall estimates from SSM/I and TMI data. Their differences are discussed in the context of global satellite rainfall estimation.
For this study, consideration is given to the role crystal orientation plays in scattering and absorbing microwave radiation. A discrete dipole scattering model is used to measure the passive microwave radiation, at two polarizations (horizontal and vertical), scattered by snow crystals oriented in random and non random positions, having various sizes (ranging between 1 micrometers to 10,000 micrometers in radius), and shapes (including spheroids, cylinders, hexagons). The model results demonstrate that for the crystal sizes typically found in a snowpack, crystal orientation is insignificant compared to crystal size in terms of scattering microwave energy in the 8,100 gm (37 GHz) region of the spectrum. Therefore, the assumption used in radiative transfer approaches, where snow crystals are modeled as randomly oriented spheres, is adequate to account for the transfer of microwave energy emanating from the ground and passing through a snowpack.
In order to learn more about the Martian polar caps, it is important to compare and contrast the behavior of both frozen H2O and CO2 in different parts of the electromagnetic spectrum. Relatively little attention has been given, thus far, to observing the thermal microwave part of the spectrum. In this experiment, passive microwave radiation emanating from within a 33 cm snowpack was measured with a 35 GHz hand-held radiometer, and in addition to the natural snow measurements, the radiometer was used to measure the microwave emission and scattering from layers of manufactured CO2 (dry ice). A 1 m x 2 m plate of aluminum sheet metal was positioned beneath the natural snow so that microwave emissions from the underlying soil layers would be minimized. Compared to the natural snow crystals, results for the dry ice layers exhibit lower' microwave brightness temperatures for similar thicknesses, regardless of the incidence angle of the radiometer. For example, at 50 degree H (horizontal polarization) and with a covering of 21 cm of snow and 18 cm of dry ice, the brightness temperatures were 150 K and 76 K, respectively. When the snow depth was 33 cm, the brightness temperature was 144 K, and when the total thickness of the dry ice was 27 cm, the brightness temperature was 86 K. The lower brightness temperatures are due to a combination of the lower physical temperature and the larger crystal sizes of the commercial CO2 Crystals compared to the snow crystals. As the crystal size approaches the size of the microwave wavelength, it scatters microwave radiation more effectively, thus lowering the brightness temperature. The dry ice crystals in this experiment were about an order of magnitude larger than the snow crystals and three orders of magnitude larger than the CO2 Crystals produced in the cold stage of a scanning electron microscope. Spreading soil, approximately 2 mm in thickness, on the dry ice appeared to have no effect on the brightness temperatures.
Taiwan is situated in the prevalent typhoon track in the northwestern Pacific. On average, about one third of the island total annual precipitation is due to typhoons, with the other two third being contributed by the summer monsoon, or Meiyu, and wintertime large scale frontal rainfall. While the typhoons bring the needed moisture for agricultural consumption and industrial utilization, heavy rainfall associated with typhoons often result in large scale flooding and land slide. The prediction of the typhoon track and its severity is therefore a high priority topic both for operation and research. The severity of a typhoon can be defined in terms of the wind strength and the moisture content. The Special Sensor Microwave/Imager (SSM/I) on board of the Defense Meteorological Satellite Program (DMSP) satellites measures microwave radiation in 19.4, 22.2, 37, and 85.5 GHz. These measurements provide an opportunity to estimate parameters such as surface wind speed, water vapor and cloud water contents, and rainfall rate over oceans. In 1994, Taiwan experienced an above normal frequency of typhoon hits, five typhoons hit the island in two months. In this report, estimates of the moisture content of these typhoons are made based on the SSM/I measurements. An assessment of the relative strength of the typhoons are made with analyzed data.
Passive microwave data have been used to infer the snow-covered area and snow water equivalent (SWE) over forested areas, but the accuracy of these retrieved snow parameters cannot be easily validated for heterogeneous vegetated regions. The Boreal Ecosystem-Atmosphere Study Winter Field Campaign provided the opportunity to study the effect of boreal forests on snow parameter retrieval in detail. Microwave radiometers (18, 37, and 92 GHz) were flown on board the Canadian National Aeronautical Establishment's Twin Otter. Flight lines covered both the southern study area near Prince Albert and the northern study area near Thompson, Canada. During the 1994 winter campaign, extensive ground-based snow cover information, including depth, density, and grain size, was collected along most of the flight lines, jointly by U.S. and Canadian investigators. Satellite data collected by the special sensor microwave imager are also used for comparison. Preliminary results reconfirmed the relationship between microwave brightness temperature and SWE. However, the effect of forest cover observed by the aircraft sensors is different from that of the satellite observations. This is probably due to the difference in footprint averaging. There were also several flight lines flown over Candle Lake and Waskesiu Lake to assess lake ice signatures. Preliminary results show the thickness of the lake ice may be inferred from the airborne microwave observations. The microwave signature relationship between lake ice and snow matches the results from radiative transfer calculations.
The radiometric calibration of SeaWiFS data includes a correction for the temperature dependence of the individual detector sensitivities. The detector temperature are measured by temperature sensors mounted on instrument focal planes. Processing of the temperature sensor output by an onboard instrument computer introduces a nonlinear response into the temperature data. This paper describes the calibration of the temperature sensor output and the computation of the temperature corrections for the radiometric calibration of the instrument.
While it is recognized that no single snow algorithm is capable of producing accurate global estimates of snow depth, for research purposes it is useful to test an algorithm's performance in different climatic areas in order to see how it responds to a variety of snow conditions. This study is one of the first to develop separate passive microwave snow algorithms for North America and Eurasia by including parameters that consider the effects of variations in forest cover and crystal size on microwave brightness temperature. A new algorithm (GSFC 1996) is compared to a prototype algorithm (Chang et al., 1987) and to a snow depth climatology (SDC), which for this study is considered to be a standard reference or baseline. It is shown that the GSFC 1996 algorithm compares much more favorably to the SDC than does the Chang et al. (1987) algorithm. For example, in North America in February there is a 15% difference between the GSFC 198-96 Algorithm and the SDC, but with the Chang et al. (1987) algorithm the difference is greater than 50%. In Eurasia, also in February, there is only a 1.3% difference between the GSFC 1996 algorithm and the SDC, whereas with the Chang et al. (1987) algorithm the difference is about 20%. As expected, differences tend to be less when the snow cover extent is greater, particularly for Eurasia. The GSFC 1996 algorithm performs better in North America in each month than dose the Chang et al. (1987) algorithm. This is also the case in Eurasia, except in April and May when the Chang et al.(1987) algorithms is in closer accord to the SDC than is GSFC 1996 algorithm.
The retrieval of rainfall intensity over the oceans from passive microwave observations is based on a radiative transfer model. Direct rainfall observations of oceanic rainfall are virtually nonexistent making validation of the retrievals extremely difficult. Observations of the model assumptions provide an alternative approach for improving and developing confidence in the rainfall retrievals. In the winter of 1983, the NASA CV-990 aircraft was equipped with a payload suitable for examining several of the model assumptions. The payload included microwave and infrared radiometers, mirror hygrometers, temperature probes, and PMS probes. On two occasions the aircraft ascended on a spiral track through stratiform precipitation providing an opportunity to study the atmospheric parameters. The assumptions concerning liquid hydrometeors, water vapor, lapse rate, and nonprecipitating clouds were studied. Model assumptions seem to be supported by these observations.
Simultaneous inversion of the three parameters was performed which included mean-grain size of ice particles in snow, snow density, and snow temperatures from five brightness temperatures. Good results for the inversion of parameters were obtained using the neural network based on the simulated data computed from the dense media radiative transfer equation that takes into account the effects of multiple scattering.
Airborne radiometric measurements at frequencies near 92 GHz and 183 GHz were conducted over two precipitation events near Wallops Island, Virginia during February, 1986. The measured brightness temperatures are compared with those from calculations to estimate the snowfall and rainfall rates for both events. The estimated rates over water surface are within a factor of two of those derived from the concurrent measurements by the SPANDAR radar at the NASA Wallops Flight Facility. These estimated rates, however, suggest that both snowfall events are light and close to the threshold of radiometric detection especially over land surface. Observations of additional snowfall events with lower frequency channels are needed to demonstrate the approach and to account for cloud effects.
An effort is made to measure snow reflectance under different snow and surface conditions, as well as to improve current capabilities for measuring snow reflectance over large regions of space, on the basis of visible and near-IR measurements of clean and dirty snow near Yellowknife, Canada, using both a portable spectrometer and the Landsat TM. Similar measurements were acquired over snow-covered glaciers in Alaska. Visible reflectance of the dirty snow was 30 percent lower than for cleaner snow; the shapes of the reflectance curves were also flatter for the dirty snow. The shape of the reflectance curves and the anisotropic reflectance properties are more useful in differentiating snow type and moisture type than actual reflectances.
The Botswana water and surface energy balance research program was developed to study and evaluate the integrated use of multispectral satellite remote sensing for monitoring the hydrological status of the Earth's surface. The research program consisted of two major, mutually related components: a surface energy balance modeling component, built around an extensive field campaign; and a passive microwave research component which consisted of a retrospective study of large scale moisture conditions and Nimbus scanning multichannel microwave radiometer microwave signatures. The integrated approach of both components are explained in general and activities performed within the passive microwave research component are summarized. The microwave theory is discussed taking into account: soil dielectric constant, emissivity, soil roughness effects, vegetation effects, optical depth, single scattering albedo, and wavelength effects. The study site is described. The soil moisture data and its processing are considered. The relation between observed large scale soil moisture and normalized brightness temperatures is discussed. Vegetation characteristics and inverse modeling of soil emissivity is considered.
The inversion of snow parameters from passive microwave remote sensing measurements is performed with a neural network trained with a dense media multiple scattering model. A constrained iterative inversion scheme is used. Inversion of four parameters is performed from five brightness temperatures. The four parameters are: mean grain size of ice particles in snow, snow density, snow temperature, and snow depth. The five brightness temperatures are that of 19-GHz vertical polarization, 19-GHz horizontal polarization, 22-GHz vertical polarization, 37-GHz vertical polarization, and 37-GHz horizontal polarization. Based on the neural network constrained iterative inversion algorithm, synthetic mapping of the terrain is performed. The retrieval of synthetic mapping has been achieved. The incorporation of ground truth information is considered.
Airborne and satellite passive microwave measurements acquired simultaneously with ground measurements of depth, density, and stratigraphy of the snow in central and northern Alaska between March 11 and 19, 1988, are reported. A good correspondence in brightness temperature (TB) trends between the aircraft and satellite data was found. An expected inverse correlation between depth hoar thickness and TB was not found to be strong. A persistent TB minimum in both the aircraft and the satellite data was detected along the northern foothills of the Brooks Range. In an area located at about 68 deg 60 min N, 149 deg 20 min W, the TB as recorded from the aircraft microwave sensor dropped by 55 K. Satellite microwave measurements showed a TB decrease of up to 45 K at approximately the same location. An examination of microwave satellite data from 1978 to 1987 revealed that similar low late-winter values were found in approximately the same locations as those observed in March 1988.
Satellite microwave data have been used to derive areal snow water equivalent in flat homogeneous areas. Over heterogeneous mountainous areas different algorithms are needed to retrieve the water equivalent of the snow cover. A mixed pixel model based on the percentage of vegetation cover within a pixel has been developed to simulate the microwave brightness temperatures for the Rio Grande basin in southwestern Colorado. A relationship between the difference in microwave-brightness temperature at two different frequencies (37- and 18-GHz horizontal polarization), and the basin-wide average snow water equivalent was obtained. The areal snow-water equivalent values derived from the model were consistent with values generated by a reliable snowmelt run-off model using snow-cover extent data.