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Randolph H. Ware, David W. Fulker, Seth A. Stein, David N. Anderson, Susan K. Avery, Richard D. Clark, Kelvin K. Droegemeier, Joachim P. Kuettner, J. Bernard Minster, and Soroosh Sorooshian

“SuomiNet,” a university-based, real-time, national Global Positioning System (GPS) network, is being developed for atmospheric research and education with funding from the National Science Foundation and with cost share from collaborating universities. The network, named to honor meteorological satellite pioneer Verner Suomi, will exploit the recently shown ability of ground-based GPS receivers to make thousands of accurate upper- and lower-atmospheric measurements per day. Phase delays induced in GPS signals by the ionosphere and neutral atmosphere can be measured with high precision simultaneously along a dozen or so GPS ray paths in the field of view. These delays can be converted into integrated water vapor (if surface pressure data or estimates are available) and total electron content (TEC), along each GPS ray path. The resulting continuous, accurate, all-weather, real-time GPS moisture data will help advance university research in mesoscale modeling and data assimilation, severe weather, precipitation, cloud dynamics, regional climate, and hydrology. Similarly, continuous, accurate, all-weather, real-time TEC data have applications in modeling and prediction of severe terrestrial and space weather, detection and forecasting of low-altitude ionospheric scintillation activity and geomagnetic storm effects at ionospheric midlatitudes, and detection of ionospheric effects induced by a variety of geophysical events. SuomiNet data also have potential applications in coastal meteorology, providing ground truth for satellite radiometry, and detection of scintillation associated with atmospheric turbulence in the lower troposphere. The goal of SuomiNet is to make large amounts of spatially and temporally dense GPS-sensed atmospheric data widely available in real time, for academic research and education. Information on participation in SuomiNet is available via

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E. J. Zipser, Daniel J. Cecil, Chuntao Liu, Stephen W. Nesbitt, and David P. Yorty

The instruments on the Tropical Rainfall Measuring Mission (TRMM) satellite have been observing storms as well as rainfall since December 1997. This paper shows the results of a systematic search through seven full years of the TRMM database to find indicators of uncommonly intense storms. These include strong (> 40 dBZ) radar echoes extending to great heights, high lightning flash rates, and very low brightness temperatures at 37 and 85 GHz. These are used as proxy variables, indicating powerful convective updrafts. The main physical principles supporting this assertion involve the effects of such updrafts in producing and lofting large ice particles high into the storm, where TRMM's radar easily detects them near storm top. TRMM's passive microwave radiometer detects the large integrated ice water path as very low brightness temperatures, while high lightning flash rates are a physically related but instrumentally independent indicator. The geographical locations of these very intense convective storms demonstrate strong regional preferences for certain land areas while they are extremely rare over tropical oceans. Favored locations include the south-central United States, southeast South America, and equatorial Africa. Other regions have extreme storms mainly in specific seasons, such as the Sahel, the Indian subcontinent, and northern Australia. Because intense storms are distributed quite differently from rainfall, these maps provide some new metrics for global models, if they are to simulate the type of convection as a component of our climate system.

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John W. Winchester

Air pollution monitoring for trace elements in aerosols should seek to determine which elements are pollutants, where they go, and whether they may cause undesirable effects. Several sampling and analysis schemes and auxiliary studies, carried out within and remote from the Chicago area, are reviewed here and are evaluated for their possible inclusion into larger national air monitoring programs. Initially, an approximate elemental emissions inventory for the urban region was calculated from available published information and was used to account for contributions from major pollution sources. A multi-station study of urban and non-urban near surface air led to discovery of certain elemental correlations useful in distinguishing pollution from regional background and in tracing long distance transport of pollutants. Particle size distributions gave insights concerning types of processes at pollution sources and identification of unsuspected sources. Diurnal variations suggested meteorological factors which may regulate concentrations in air. Auxiliary laboratory studies can confirm mechanisms for alteration of aerosol composition by chemical reactions in the atmosphere. For certain elements, e.g., some non-metals and semi-metals, anomalous natural sources at the sea or land surface must be further documented before long distance transport from localized pollution sources can be quantified.

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Mark Russo, David Changnon, Mike Podolak, Hugh Freestrom, and Jon B. Davis

The El Niño-Southern Oscillation (ENSO) phenomenon explains some of the interannual climate variability in many tropical and midlatitude regions. It is important in developing more accurate seasonal climate forecasts and thus in aiding long-range weather-sensitive decision making in various sectors.

The degree to which ENSO information could forecast one of three classes of seasonal cooling degree days (CDD) and heating degree days (HDD) was examined using 1) the magnitude of the ENSO event during a given season, 2) the preseason rate of change of sea surface temperature (SSTs) (December–May for summers and June–October for winters), and 3) the effects of strong winter ENSO events on future seasons. All three ENSO-related indices were based on monthly equatorial Pacific SST anomalies in the Niño-3.4 region. Regional probabilities of each HDD/CDD category (above, average, and below) were determined for each ENSO predictive index. The highest probability of experiencing an HDD/CDD anomaly occurs with strong preseason SST trends. When presummer SST cooling occurs, the northeast and midcontinent experience above-average CDD (80% and 75%, respectively). Other interesting relationships were found between strong winter ENSO events and ensuing HDD/CDD anomalies. These results suggest that utility-based decision makers who can utilize enhanced climate information may reap benefits during particular years by integrating the ENSO information into their models. This study was part of a special student training experiment conducted at Northern Illinois University.

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Steven J. Ghan and Stephen E. Schwartz

Aerosol particles in the lower atmosphere exert a substantial influence on climate and climate change through a variety of complex mechanisms. Consequently, there is a need to represent these influences in global climate models, and models have begun to include representations of these influences. However, the present treatment of aerosols in global climate models is highly simplified, omitting many processes and feedbacks that are thought to be climatically important. Thus, there is need for substantial improvement. Here we describe the strategy of the U.S. Department of Energy for improving representation of the properties, processes, and effects of tropospheric aerosols in global climate models. The strategy begins with a foundation of field and laboratory measurements that provide the basis for modules describing specific aerosol properties and processes. These modules are then integrated into regional aerosol models, which are evaluated by comparison with field measurements. Issues of scale are then addressed so that the modules can be applied to global aerosol models, which are evaluated by comparison with satellite retrievals and other observations. Finally, the validated set of modules is applied in global climate models for multicentury simulations. This strategy is expected to be applied to successive generations of global climate models.

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Peter J. Webster and Carlos Hoyos

Most attempts at predicting south Asian monsoon variability have concentrated on seasonally averaged rainfall over the Indian subcontinent some months in advance using regional and remote boundary effects as predictors. Overall, about 30% of the variance of mean seasonal monsoon rainfall can be explained, but the statistics appear to be nonstationary and correlations vary strongly on interdecadal time scales. Model intercomparisons show that climate models have difficulty in simulating even gross-scale features of the monsoon such as mean summer rainfall, and there is little demonstrated skill when the models are used in predictive mode. Even if the statistics were stable and model predictions were skillful it is argued that the information is not readily downscalable because the mean rainfall does not define the timing or number of intraseasonal variations or even the spatial distributions of the seasonal mean rainfall. Based on these concerns, it is argued that skillful and timely forecasts of intraseasonal variability possess a greater potential utility for agriculture and water resource management and should be the highest priority for prediction within the monsoon regions.

A physically based empirical Bayesian prediction scheme is developed for forecasting regional intraseasonal variability of the monsoon. Ten predictors are chosen that depict the morphology of the monsoon intraseasonal mode. The scheme employs a wavelet-banding technique and linear regression to forecast 5-day average rainfall variability over regions of south Asia 15–30 days (i.e., six 5-day lags) in the future. Hindcasts conducted for the central Indian region for the period 1992–2002 show considerable skill out to 30 days in both the timing and amplitude of the intraseasonal oscillations. Skill, albeit reduced, is also found in smaller regions such as the Indian states of Rajasthan and Orissa. The use of wavelet analysis to sort time series and isolate each band from the noise generated in other bands, together with the careful choice of predictors, are the defining elements of the scheme. Anomaly correlations of rainfall in the 28–80-day band in central India are 0.88, 0.76, 0.73, 0.66, and 0.58 for 10,15, 20, 25, and 30 days, respectively. Similar skill is found for forecasting the discharge of the Ganges and Brahmaputra into Bangladesh. The potential utility of these forecasts for applications in agriculture and water resource management is discussed together with the possible use of the empirical scheme as a diagnostic tool and as a guide for the development of a new type of numerical model.

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David J. Diner, Thomas P. Ackerman, Theodore L. Anderson, Jens Bösenberg, Amy J. Braverman, Robert J. Charlson, William D. Collins, Roger Davies, Brent N. Holben, Chris A . Hostetler, Ralph A. Kahn, John V. Martonchik, Robert T. Menzies, Mark A. Miller, John A. Ogren, Joyce E. Penner, Philip J. Rasch, Stephen E. Schwartz, John H. Seinfeld, Graeme L. Stephens, Omar Torres, Larry D. Travis, Bruce A . Wielicki, and Bin Yu

Aerosols exert myriad influences on the earth's environment and climate, and on human health. The complexity of aerosol-related processes requires that information gathered to improve our understanding of climate change must originate from multiple sources, and that effective strategies for data integration need to be established. While a vast array of observed and modeled data are becoming available, the aerosol research community currently lacks the necessary tools and infrastructure to reap maximum scientific benefit from these data. Spatial and temporal sampling differences among a diverse set of sensors, nonuniform data qualities, aerosol mesoscale variabilities, and difficulties in separating cloud effects are some of the challenges that need to be addressed. Maximizing the longterm benefit from these data also requires maintaining consistently well-understood accuracies as measurement approaches evolve and improve. Achieving a comprehensive understanding of how aerosol physical, chemical, and radiative processes impact the earth system can be achieved only through a multidisciplinary, interagency, and international initiative capable of dealing with these issues. A systematic approach, capitalizing on modern measurement and modeling techniques, geospatial statistics methodologies, and high-performance information technologies, can provide the necessary machinery to support this objective. We outline a framework for integrating and interpreting observations and models, and establishing an accurate, consistent, and cohesive long-term record, following a strategy whereby information and tools of progressively greater sophistication are incorporated as problems of increasing complexity are tackled. This concept is named the Progressive Aerosol Retrieval and Assimilation Global Observing Network (PARAGON). To encompass the breadth of the effort required, we present a set of recommendations dealing with data interoperability; measurement and model integration; multisensor synergy; data summarization and mining; model evaluation; calibration and validation; augmentation of surface and in situ measurements; advances in passive and active remote sensing; and design of satellite missions. Without an initiative of this nature, the scientific and policy communities will continue to struggle with understanding the quantitative impact of complex aerosol processes on regional and global climate change and air quality.

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W. P. Kustas, D.C. Goodrich, M.S. Moran, S. A. Amer, L. B. Bach, J. H. Blanford, A. Chehbouni, H. Claassen, W. E. Clements, P. C. Doraiswamy, P. Dubois, T. R. Clarke, C. S. T. Daughtry, D. I. Gellman, T. A. Grant, L. E. Hipps, A. R. Huete, K. S. Humes, T. J. Jackson, T. O. Keefer, W. D. Nichols, R. Parry, E. M. Perry, R. T. Pinker, P. J. Pinter Jr., J. Qi, A. C. Riggs, T. J. Schmugge, A. M. Shutko, D. I. Stannard, E. Swiatek, J. D. van Leeuwen, J. van Zyl, A. Vidal, J. Washburne, and M. A. Weltz

Arid and semiarid rangelands comprise a significant portion of the earth's land surface. Yet little is known about the effects of temporal and spatial changes in surface soil moisture on the hydrologic cycle, energy balance, and the feedbacks to the atmosphere via thermal forcing over such environments. Understanding this interrelationship is crucial for evaluating the role of the hydrologic cycle in surface–atmosphere interactions.

This study focuses on the utility of remote sensing to provide measurements of surface soil moisture, surface albedo, vegetation biomass, and temperature at different spatial and temporal scales. Remote-sensing measurements may provide the only practical means of estimating some of the more important factors controlling land surface processes over large areas. Consequently, the use of remotely sensed information in biophysical and geophysical models greatly enhances their ability to compute fluxes at catchment and regional scales on a routine basis. However, model calculations for different climates and ecosystems need verification. This requires that the remotely sensed data and model computations be evaluated with ground-truth data collected at the same areal scales.

The present study (MONSOON 90) attempts to address this issue for semiarid rangelands. The experimental plan included remotely sensed data in the visible, near-infrared, thermal, and microwave wavelengths from ground and aircraft platforms and, when available, from satellites. Collected concurrently were ground measurements of soil moisture and temperature, energy and water fluxes, and profile data in the atmospheric boundary layer in a hydrologically instrumented semiarid rangeland watershed. Field experiments were conducted in 1990 during the dry and wet or “monsoon season” for the southwestern United States. A detailed description of the field campaigns, including measurements and some preliminary results are given.

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Kerry Emanuel, Frauke Hoss, David Keith, Zhiming Kuang, Julie Lundquist, and Lee Miller

standardized modeling intercomparisons to evaluate approaches to representing wind turbine impacts could be beneficial. An important goal should be a quantification of climate impacts per TW power generated for comparison to climate effects of other power sources. Climate effects of wind farms also need to be considered for local- to regional-scale deployments. For example, satellite data and field campaigns suggest slightly higher nocturnal temperatures within and downwind of presently operational wind

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Executive Summary

Important opportunities to decrease the risks to the nation from hazardous weather and global climate change and to create economic advantages through better understanding and prediction will be available to the administration that takes office in January 1989. In contrast to Mark Twain's observation that “Everybody talks about the weather, but nobody does anything about it,” today there is much that is being done, and even more that must be done. The atmospheric sciences community is prepared to help carry out the needed actions.

There are two areas in which action should be taken: protecting life and property, and assessing and preparing for coming dramatic changes in climate. This report recommends specific actions and programs. It has been prepared jointly by the American Meteorological Society (AMS), a nonprofit scientific society of 10,000 members, and the University Corporation for Atmospheric Research (UCAR), a consortium of 57 universities.

Protecting Life and Property. Tornadoes, flash floods, hurricanes, blizzards, and severe thunderstorms accompanied by hail and microbursts are collectively more frequent in, and pose a greater threat to, the United States than any other nation. No part of the country is immune. Significantly improved warnings of these threatening, sometimes catastrophic events are now within reach.

Weather on a regional scale also has significant and pervasive effects on the nation's agriculture, water resources, transportation, industry, and economy. Improved weather prediction, which could provide guidance a week and longer in advance, would provide substantial economic benefits to the nation.

A key to mitigating the effects of severe weather and flood events is an improved warning system that will provide the public and industry with the weather information they need, when they need it. This can be achieved by combining new observational and information processing systems with a research program focused on understanding the development of severe storms. The nation is already moving forward with new weather satellites, advanced surface radars, and new wind profiling systems and with information systems to collect and use the vast amounts of new data. Plans have been developed for acquiring supercomputers to prepare forecasts and carry out complementary research to realize the full benefits from the investment in the new systems and to restore the United States to leadership in weather prediction.

One of the two top-priority actions recommended by the AMS and UCAR is to complete the implementation of the existing national program to improve warnings, and to expand our capabilities in weather prediction by the acquisition of supercomputers and enhancement of a severe storms research program.

The total costs of this national program are small compared to the actual and potential losses of life and property from severe weather and flooding that occur in the United States. They are also small compared to the potential savings to the nation from more accurate and longer-range weather predictions.

Anticipating the Consequences of Climate Change. The world is increasingly aware that the global climate is changing as a result of human activities that are altering concentrations of trace gases in the atmosphere and characteristics of the earth's surface. In the next few decades, we can expect a significant global warming, an increase in sea level, and marked changes in regional and local climate. These can dramatically change agricultural productivity and human habitability in many regions of the world. The release of chlorofluorocarbons has reduced the amount of ozone in the stratosphere, with potentially disastrous effects on life on the planet, if left unchecked. Even with the vigorous effort that we must make to slow the emissions of heat-trapping gases, major climate changes are already unavoidable.

Therefore, the second top-priority action recommended by the AMS and UCAR is that the United States and other nations combine efforts to develop the observational data base, the computer models, and the understanding needed to anticipate the course of climate-related events, to estimate their impacts, and to prepare for future changes.

The United States must play a leadership role in this endeavor that is fundamental to the well-being of all peoples of the world and therefore inherently international. The investment, while substantial, is essential to ensure that the international actions to limit and adapt to the changes in climate are taken on a sound basis.

Setting Firm Priorities. The United States' effort in atmospheric sciences is executed by unique partnerships of federal agencies, universities, and private sector firms. Climate change is global in nature, and therefore coordinated international action is also needed. A top priority of the new Administration must be a responsible fiscal policy that diminishes federal deficits. Hence, the recommendations made here are constrained to the two highest-priority targets—taking essential actions to strengthen programs in climate and severe weather.

As with many investments made to realize the full potential of earlier investments, those described here will be repaid many times over in reduction of damage, in increased economic efficiency and productivity, and in the foresight that may allow us to cope wisely with the impacts of climate change on this nation. Letting Mark Twain's aphorism, that “nobody does anything about it,” be our national policy in the face of the threat of global change could be the origin of a national and international disaster early in the next century.

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