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C. Prabhakara, H. D. Chang, and A. T. C. Chang

Abstract

Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperature measurements in the 21 and 18 GHz channels are used to sense the precipitable water in the atmosphere over oceans. The difference in the brightness temperature (T 21T 18), both in the horizontal and vertical polarization, is found to be essentially a function of the precipitable water in the atmosphere. An equation, based on the physical considerations of the radiative transfer in the microwave region, is developed to relate the precipitable water to (T 21T 18). It is shown from theoretical calculations that the signal (T 21T 18) does not suffer severely from the noise introduced by variations in sea surface temperature, surface winds and liquid water content in non-raining clouds. The rms deviation between the estimated precipitable water from SMMR data and that given by the closely coincident ship radiosondes is about 0.25 g cm−2.

Global maps of precipitable water over oceans derived from SMMR data reveal several salient features associated with ocean currents and the large-scale general circulation in the atmosphere.

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C. Prabhakara, I. Wang, A. T. C. Chang, and P. Gloersen

Abstract

The Nimbus 7 Scanning Multichannel Microwave Radiometer (SMMR) brightness temperature measurements over the global oceans have been examined with the help of statistical and empirical techniques. Such analyses show that zonal averages of brightness temperature measured by SMMR, over the oceans, on a large scale are primarily influenced by the water vapor in the atmosphere. Liquid water in the clouds and rain, which has a much smaller spatial and temporal scale, contributes substantially to the variability of the SMMR measurements within the latitudinal zones. The surface wind not only increase the surface emissivity but through its interactions with the atmosphere produces correlations, in the SMMR brightness temperature data, that have significant meteorological implications. It is found that a simple meteorological model can explain the general characteristics of these data. With the help of this model, methods are developed for investigation of surface temperature, liquid water content in the atmosphere, and surface wind speed over the global oceans. Monthly mean estimates of the sea surface temperature and surface winds are compared with ship measurements. Estimates of liquid water content in the atmosphere are consistent with earlier satellite measurements.

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A. T. C. Chang, A. Barnes, M. Glass, R. Kakar, and T. T. Wilheit

Abstract

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 non-precipitating clouds were studied. Model assumptions seem to be supported by these observations.

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W. Viezee, H. Shigeishi, and A. T. C. Chang

Abstract

We describe a research study in which we explored the application to rainfall prediction of cloud liquid water data obtained from the SCAMS experiment of Nimbus 6. The study area is the Pacific Northwest coast of the United States, where rainfall is produced by extratropical storms that approach from across the Pacific Ocean.

SCAMS data related to cloud liquid water over the ocean, and coastal rainfall data, are analyzed for 20 different storm systems in the northeastern Pacific Ocean; these produced significant rainfall from Washington to central California during the period October 1975-March 1976. Results show that the distribution of storm-cloud water analyzed from the SCAMS data over the ocean foreshadows the distribution of coastal rainfall accumulated from the storm at a later time.

We conclude that passive microwave sensor measurements of cloud water over the ocean, when used in conjunction with numerical and other objective guidance, can be used to enhance the accuracy of predictions of coastal rainfall distribution.

Limitations in the SCAMS measurements and in the data analysis and interpretation are noted.

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Edward Rodgers, Honnappa Siddalingaiah, A. T. C. Chang, and Thomas Wilheit

Abstract

At 37 GHz, the frequency at which the Nimbus 6 Electrically Scanning Microwave Radiometer (ESMR 6) measures upwelling radiance, it has been shown theoretically that the atmospheric scattering and the relative independence on electromagnetic polarization of the radiances emerging from hydrometeors make it possible to monitor remotely active rainfall over land. In order to verify experimentally these theoretical findings and to develop an algorithm to monitor rainfall over land, the digitized ESMR 6 measurements were examined statistically.

Horizontally and vertically polarized brightness temperature pairs (TH,TV) from ESMR 6 were sampled for areas of rainfall over land as determined from the rain recording stations and the WSR 57 radar, and areas of wet and dry ground (whose thermodynamic temperatures were greater than 5°C) over the southeastern United States. These three categories of brightness temperatures were found to be significantly different in the sense that the chances that the mean vectors of any two populations coincided were less than 1 in 100. Since these categories were significantly different, classification algorithms were then developed. Three decision rules were examined: the Fisher linear classifier, the Bayesian quadratic classifier, and a non-parametric linear classifier. The Bayesian algorithm was found to perform best, particularly at a higher confidence level. An independent test case analysis showed that a rainfall area delineated by the Bayesian classifier coincided well with the synoptic-scale rainfall area mapped by ground recording rain data and radar echoes.

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A. T. C. Chang, L. S. Chiu, and G. Yang

Abstract

Four and a half years of the global monthly oceanic rain rates derived from the DMSP (Defense Meteorological Satellite Program) F-8 SSM/I (Special Sensor Microwave/Imager) data are used to study the diurnal cycles. Annual mean rainfall maps based on the SSM/I morning and evening observations are presented, and their differences are examined using a paired t test. The morning estimates are larger than the afternoon estimates by about 20% over the oceanic region between 50°S and 50°N, with significant differences located mainly along the intertropical convergence zone region. Using the measurements from two satellites, either DMSP F-8 and F-10 or DMSP F-10 and F-11, amplitudes and phases of the 24-h harmonic are estimated. The diurnal cycle shows a nocturnal or early morning maximum in 35%–40% of the oceanic regions. Monte Carlo simulations show that the rms errors associated with the estimated amplitude and phase are about 100% and 2 h, respectively, mainly due to the large random errors (50%) associated with the present rainfall estimates and the nonoptimal separation times of the DMSP satellite sampling.

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T. T. Wilheit, A. T. C. Chang, M. S. V. Rao, E. B. Rodgers, and J. S. Theon

Abstract

A theoretical model for calculating microwave radiative transfer in raining atmospheres is developed. These calculations are compared with microwave brightness temperatures at a wavelength of 1.55 cm measured by the Electrically Scanning Microwave Radiometer (ESMR) on the Nimbus 5 satellite and rain rates derived from WSR-57 meteorological radar measurements. A specially designed ground-based verification experiment was also performed, wherein upward viewing microwave brightness temperature measurements at wavelengths of 1.55 and 0.81 cm were compared with directly measured rain rates. It is shown that over ocean areas, brightness temperature measurements from ESMR may be interpreted in terms of rain rate with about an accuracy of a factor of 2 over the range 1–25 mm h−1 rain rate.

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T. T. Wilheit, A. T. C. Chang, J. L. King, E. B. Rodgers, R. A. Nieman, B. M. Krupp, A. S. Milman, J. S. Stratigos, and H. Siddalingaiah

Abstract

Observations of rain cells in the remains of a decaying tropical storm were made by Airborne Microwave Radiometers at 19.35 and 92 GHz and three frequencies near 183 GHz. Extremely low brightness temperatures, as low as 140 K, were noted in the 92 and 183 GHz observations. These can be accounted for by the ice often associated with raindrop formation. Further, the 183 GHz observations can be interpreted in terms of the height of the ice. The brightness temperatures observed suggest the presence of precipitationsized ice as high as 9 km or more.

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A. T. C. Chang, J. L. Foster, R. E. J. Kelly, E. G. Josberger, R. L. Armstrong, and N. M. Mognard

Abstract

Accurate estimation of snow mass is important for the characterization of the hydrological cycle at different space and time scales. For effective water resources management, accurate estimation of snow storage is needed. Conventionally, snow depth is measured at a point, and in order to monitor snow depth in a temporally and spatially comprehensive manner, optimum interpolation of the points is undertaken. Yet the spatial representation of point measurements at a basin or on a larger distance scale is uncertain. Spaceborne scanning sensors, which cover a wide swath and can provide rapid repeat global coverage, are ideally suited to augment the global snow information. Satellite-borne passive microwave sensors have been used to derive snow depth (SD) with some success. The uncertainties in point SD and areal SD of natural snowpacks need to be understood if comparisons are to be made between a point SD measurement and satellite SD. In this paper three issues are addressed relating satellite derivation of SD and ground measurements of SD in the northern Great Plains of the United States from 1988 to 1997. First, it is shown that in comparing samples of ground-measured point SD data with satellite-derived 25 × 25 km2 pixels of SD from the Defense Meteorological Satellite Program Special Sensor Microwave Imager, there are significant differences in yearly SD values even though the accumulated datasets showed similarities. Second, from variogram analysis, the spatial variability of SD from each dataset was comparable. Third, for a sampling grid cell domain of 1° × 1° in the study terrain, 10 distributed snow depth measurements per cell are required to produce a sampling error of 5 cm or better. This study has important implications for validating SD derivations from satellite microwave observations.

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A. C. Haza, E. D’Asaro, H. Chang, S. Chen, M. Curcic, C. Guigand, H. S. Huntley, G. Jacobs, G. Novelli, T. M. Özgökmen, A. C. Poje, E. Ryan, and A. Shcherbina

Abstract

The Lagrangian Submesoscale Experiment (LASER) was designed to study surface flows during winter conditions in the northern Gulf of Mexico. More than 1000 mostly biodegradable drifters were launched. The drifters consisted of a surface floater extending 5 cm below the surface, containing the satellite tracking system, and a drogue extending 60 cm below the surface, hanging beneath the floater on a flexible tether. On some floats, the drogue separated from the floater during storms. This paper describes methods to detect drogue loss based on two properties that distinguish drogued from undrogued drifters. First, undrogued drifters often flip over, pointing their satellite antenna downward and thus intermittently reducing the frequency of GPS fixes. Second, undrogued drifters respond to wind forcing more than drogued drifters. A multistage analysis is used: first, two properties are used to create a preliminary drifter classification; then, the motion of each unclassified drifter is compared to that of its classified neighbors in an iterative process for nearly all of the drifters. The algorithm classified drifters with a known drogue status with an accuracy of virtually 100%. Drogue loss times were estimated with a precision of less than 0.5 and 3 h for 60% and 85% of the drifters, respectively. An estimated 40% of the drifters lost their drogues in the first 7 weeks, with drogue loss coinciding with storm events, particularly those with steep waves. Once the drogued and undrogued drifters are classified, they can be used to quantify the differences in material dispersion at different depths.

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