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George R. Diak

Abstract

The effective cloud fraction evaluated using a minimum residual method and microwave data synthesized for the Advanced Microwave Sounding Unit (AMSU) has been extended by a simple statistical procedure to make estimates of the column cloud water amount for nonprecipitating clouds. In this simulation study of the channel pairs investigated, the highest overall skill in the estimation of column cloud water amounts is shown by the 52–54-GHz oxygen channel pair 3 and 4 of the AMSU over a water surface, where the low surface emissivity produces a very strong cloud water signature (warming of the clear scene). As the surface emissivity rises to representative values for land surfaces, the skill of estimation of cloud water amounts is reduced. At a surface emissivity value representative of a dry land surface, estimation of cloud liquid water from the oxygen channels and these techniques is not possible. In contrast to the oxygen channels, the quality of cloud water estimates made using the 183-GHz water vapor channel pair 19 and 20 of the AMSU is poorest for a water surface and higher for land surfaces with higher emissivity. The quality of the estimates made using thew water vapor channels is shown to strongly depend on the quality of the atmospheric guess used in the procedures.

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George R. Diak and Catherine Gautier

Abstract

The simple physical model to estimate surface insolation from GOES data (described in Gautier et al., 1980) has been improved through some modifications to existing physics (Rayleigh scattering and water vapor absorption), and also the inclusion of ozone absorption, previously neglected. An empirical correction for clouds smaller than the GOES sensor field-of-view has also been introduced. The resulting model is more physically realistic than the old one and requires no additional computer time. Tests indicate that with minimal tuning, the error in daily insulation estimates has been reduced by ∼1% compared to the old model. Removal of systematic error resulted in an additional 0.2% improvement.

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Hung-Lung Huang and George R. Diak

Abstract

A new microwave algorithm, analogous to the infrared “radiance-ratioing” method (Eyre and Menzel 1989) is developed to retrieve the height and “effective” fraction (defined as the product of the emissivity times the actual physical fractional coverage) of nonprecipitating water clouds using various pairs of the 20 microwave channels planned for the Advanced Microwave Sounding Unit (AMSU), an instrument slated to fly on polar-orbiting satellites beginning in 1994. The results of a simulation study are presented to provide some insights into the potentials of this technique using different AMSU channel combinations. This study suggests that the use of the oxygen channels 3 and 5 and water vapor channels 19 and 20 will produce the most accurate retrievals of liquid water cloud parameters and the highest percentage of good-quality retrievals over a range of meteorological and cloud conditions. The use of channels 1, 2, 16, and 17, which all may have a strong surface component in their measured brightness temperature, does not give optimal results chiefly because the large uncertainties in the microwave surface temperature and emissivity obscure the brightness–temperature signatures of cloud liquid water. As with the infrared radiance ratioing method (and similar C02 slicing techniques), the best retrieval of cloud parameters is for high cloud, with poorer results for those at middle and low levels.

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George R. Diak, Dongsoo Kim, Mark S. Whipple, and Xiaohua Wu

The Advanced Microwave Sounding Unit (AMSU) is a 20-channel microwave satellite remote-sensing system comprising two separate radiometers, which is slated to be launched on NOAA polar-orbiting satellites beginning in 1994. With the AMSU will come improvements in remote sounding of the atmosphere and estimation of atmospheric and surface physical characteristics (rain and cloud water, snow and ice cover, etc.) gained through application of the dramatic improvements in microwave technology that have taken place in the past 20 years. In this work we describe prelaunch work designed to evaluate the remote-sensing information that will be available with the launch of the AMSU.

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Jason A. Otkin, Martha C. Anderson, John R. Mecikalski, and George R. Diak

Abstract

Reliable procedures that accurately map surface insolation over large domains at high spatial and temporal resolution are a great benefit for making the predictions of potential and actual evapotranspiration that are required by a variety of hydrological and agricultural applications. Here, estimates of hourly and daily integrated insolation at 20-km resolution, based on Geostationary Operational Environmental Satellite (GOES) visible imagery are compared to pyranometer measurements made at 11 sites in the U.S. Climate Reference Network (USCRN) over a continuous 15-month period. Such a comprehensive survey is necessary in order to examine the accuracy of the satellite insolation estimates over a diverse range of seasons and land surface types. The relatively simple physical model of insolation that is tested here yields good results, with seasonally averaged model errors of 62 (19%) and 15 (10%) W m−2 for hourly and daily-averaged insolation, respectively, including both clear- and cloudy-sky conditions. This level of accuracy is comparable, or superior, to results that have been obtained with more complex models of atmospheric radiative transfer. Model performance can be improved in the future by addressing a small elevation-related bias in the physical model, which is likely the result of inaccurate model precipitable water inputs or cloud-height assessments.

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John R. Mecikalski, George R. Diak, Martha C. Anderson, and John M. Norman

Abstract

A simple model of energy exchange between the land surface and the atmospheric boundary layer, driven by input that can be derived primarily through remote sensing, is described and applied over continental scales at a horizontal resolution of 10 km. Surface flux partitioning into sensible and latent heating is guided by time changes in land surface brightness temperatures, which can be measured from a geostationary satellite platform such as the Geostationary Operational Environmental Satellite. Other important inputs, including vegetation cover and type, can be derived using the Normalized Difference Vegetation Index in combination with vegetation and land use information. Previous studies have shown that this model performs well on small spatial scales, in comparison with surface flux measurements acquired during several field experiments. However, because the model requires only a modicum of surface-based measurements and is designed to be computationally efficient, it is particularly well suited for regional- or continental-scale applications. The input data assembly process for regional-scale applications is outlined. Model flux estimates for the central United States are compared with climatological moisture and vegetation patterns, as well as with surface-based flux measurements acquired during the Southern Great Plains (SGP-97) Hydrology Experiment. These comparisons are quite promising.

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Xiaohua Wu, George R. Diak, Christopher M. Hayden, and John A. Young

Abstract

These observing system simulation experiments investigate the assimilation of satellite-observed water vapor and cloud liquid water data in the initialization of a limited-area primitive equations model with the goal of improving short-range precipitation forecasts. The assimilation procedure presented includes two aspects: specification of an initial cloud liquid water vertical distribution and diabatic initialization. The satellite data is simulated for the next generation of polar-orbiting satellite instruments, the Advanced Microwave Sounding Unit (AMSU) and the High-Resolution Infrared Sounder (HIRS), which are scheduled to be launched on the NOAA-K satellite in the mid-1990s.

Based on cloud-top height and total column cloud liquid water amounts simulated for satellite data, a diagnostic method is used to specify an initial cloud water vertical distribution and to modify the initial moisture distribution in cloudy areas. Using a diabatic initialization procedure, the associated latent heating profiles are directly assimilated into the numerical model. The initial heating is estimated by time averaging the latent heat release from convective and large-scale condensation during the early forecast stage after insertion of satellite-observed temperature, water vapor, and cloud water information.

The assimilation of satellite-observed moisture and cloud water, together with three-mode diabatic initialization, significantly alleviates the model precipitation spinup problem, especially in the first 3 h of the forecast. Experimental forecasts indicate that the impact of satellite-observed temperature and water vapor profiles and cloud water alone in the initialization procedure shortens the spinup time for precipitation rates by 1–2 h and for regeneration of the areal coverage by 3 h. The diabatic initialization further reduces the precipitation spinup time (compared to adiabatic initialization) by 1 h.

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George R. Diak, Martha C. Anderson, William L. Bland, John M. Norman, John M. Mecikalski, and Robert M. Aune

In a NASA-sponsored program entitled “Use of Earth and Space Science Data Over the Internet,” scientists at the University of Wisconsin—Madison have developed a suite of products for agriculture that are based in satellite and conventional observations, as well as state-of-the-art forecast models of the atmosphere and soil–canopy environments. These products include an irrigation scheduling product based in satellite estimates of daily solar energy, a frost protection product that relies on prediction models and satellite estimates of clouds, and a product for the prediction of foliar disease that is based in satellite net radiation, rainfall measured by NEXRAD, and a detailed model of the soil–canopy environment. During the growing season, the first two products are available in near-real time on the Internet. The last product involving foliar disease depends on a decision support system named WISDOM developed by the University of Wisconsin—Extension, which resides locally on growers' home computers. Growers interface WISDOM with a server to obtain the rainfall, meteorological data, surface radiation inputs, and canopy model output required by WISDOM for the blight models.

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George R. Diak, John R. Mecikalski, Martha C. Anderson, John M. Norman, William P. Kustas, Ryan D. Torn, and Rebecca L. DeWolf

Since the advent of the meteorological satellite, a large research effort within the community of earth scientists has been directed at assessing the components of the land surface energy balance from space. The development of these techniques from first efforts to the present time are reviewed, and the integrated system used to estimate the radiative and turbulent land surface fluxes is described. This system is now running in real time over the continental United States at a resolution of 10 km, producing daily and time-integrated flux components.

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