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ANDRÉ J. ROBERT

Abstract

This project used a series of 500-mb. charts prepared originally for the study of planetary waves. These charts, covering both hemispheres, provided the initial conditions for a spectral barotropic model. In this model, the calculations proceeded from functions equivalent to spherical harmonics with the stream field represented by 153 degrees of freedom. A set of five integrations carried to 72 hr. produced reasonably good forecasts that did not appear to be affected seriously by the deficiencies of the observational network.

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MICHAEL KWIZAK and ANDRÉ J. ROBERT

Abstract

A semi-implicit time integration scheme tested earlier with a spectral model is now adapted to a grid point model of the primitive equations. Predictions prepared by the implicit method compare quite favorably with the forecasts produced by an explicit technique. The implicit model runs about four times faster; and after 5 days of integration, the forecasts differ by less than 20 m.

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ANDRÉ J. ROBERT, FREDERICK G. SHUMAN, and JOSEPH P. GERRITY JR.

Abstract

A rather general theory of nonlinear computational stability is reported. Instability is caused by both spatial and temporal high frequencies that, if not present initially, will appear from nonlinear interactions. It appears that through simple remedies relative stability, if not perfect stability, can be achieved.

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Else J. M. Van Den Besselaar, Albert M. G. Klein Tank, Gerard Van Der Schrier, Mariama S. Abass, Omar Baddour, Aryan F.V. Van Engelen, Andrea Freire, Peer Hechler, Bayu Imbang Laksono, Iqbal, Rudmer Jilderda, Andre Kamga Foamouhoue, Arie Kattenberg, Robert Leander, Rodney Martínez Güingla, Albert S. Mhanda, Juan José Nieto, Sunaryo, Aris Suwondo, Yunus S. Swarinoto, and Gé Verver

Abstract

The International Climate Assessment & Dataset (ICA&D) concept provides climate services on a regional scale for users in participating countries and the broader scientific community. It builds on the expertise gained in Europe, where national meteorological services collaborate by sharing climate data in order to produce regional climate assessments. Universities and data-rescue initiatives have joined this collaboration. The result is a web-based information system that combines quality-controlled daily station data with derived climate indices. Indices are provided for mean and extreme climate conditions including droughts, heat waves, and heavy rainfall events. ICA&D systems currently exist in Europe and in three regions of the world vulnerable to climate change: Southeast Asia, Latin America, and West Africa. Historical perspectives on climate variability and change are integrated with the monitoring of current climate evolution through regular updates of the data series obtained from meteorological observing stations. Web users have access to plots and maps of climate indices, showing time series, trends, or deviations from climatology. All information can be downloaded for noncommercial research and educational purposes, except for a part of the daily data that the data provider does not want to share.

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David K. Adams, Rui M. S. Fernandes, Kirk L. Holub, Seth I. Gutman, Henrique M. J. Barbosa, Luiz A. T. Machado, Alan J. P. Calheiros, Richard A. Bennett, E. Robert Kursinski, Luiz F. Sapucci, Charles DeMets, Glayson F. B. Chagas, Ave Arellano, Naziano Filizola, Alciélio A. Amorim Rocha, Rosimeire Araújo Silva, Lilia M. F. Assunção, Glauber G. Cirino, Theotonio Pauliquevis, Bruno T. T. Portela, André Sá, Jeanne M. de Sousa, and Ludmila M. S. Tanaka

Abstract

The complex interactions between water vapor fields and deep atmospheric convection remain one of the outstanding problems in tropical meteorology. The lack of high spatial–temporal resolution, all-weather observations in the tropics has hampered progress. Numerical models have difficulties, for example, in representing the shallow-to-deep convective transition and the diurnal cycle of precipitation. Global Navigation Satellite System (GNSS) meteorology, which provides all-weather, high-frequency (5 min), precipitable water vapor estimates, can help. The Amazon Dense GNSS Meteorological Network experiment, the first of its kind in the tropics, was created with the aim of examining water vapor and deep convection relationships at the mesoscale. This innovative, Brazilian-led international experiment consisted of two mesoscale (100 km × 100 km) networks: 1) a 1-yr (April 2011–April 2012) campaign (20 GNSS meteorological sites) in and around Manaus and 2) a 6-week (June 2011) intensive campaign (15 GNSS meteorological sites) in and around Belem, the latter in collaboration with the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud-Resolving Modeling and to the Global Precipitation Measurement (CHUVA) Project in Brazil. Results presented here from both networks focus on the diurnal cycle of precipitable water vapor associated with sea-breeze convection in Belem and seasonal and topographic influences in and around Manaus. Ultimately, these unique observations may serve to initialize, constrain, or validate precipitable water vapor in high-resolution models. These experiments also demonstrate that GNSS meteorology can expand into logistically difficult regions such as the Amazon. Other GNSS meteorology networks presently being constructed in the tropics are summarized.

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Svetla M. Hristova-Veleva, P. Peggy Li, Brian Knosp, Quoc Vu, F. Joseph Turk, William L. Poulsen, Ziad Haddad, Bjorn Lambrigtsen, Bryan W. Stiles, Tsae-Pyng Shen, Noppasin Niamsuwan, Simone Tanelli, Ousmane Sy, Eun-Kyoung Seo, Hui Su, Deborah G. Vane, Yi Chao, Philip S. Callahan, R. Scott Dunbar, Michael Montgomery, Mark Boothe, Vijay Tallapragada, Samuel Trahan, Anthony J. Wimmers, Robert Holz, Jeffrey S. Reid, Frank Marks, Tomislava Vukicevic, Saiprasanth Bhalachandran, Hua Leighton, Sundararaman Gopalakrishnan, Andres Navarro, and Francisco J. Tapiador

Abstract

Tropical cyclones (TCs) are among the most destructive natural phenomena with huge societal and economic impact. They form and evolve as the result of complex multiscale processes and nonlinear interactions. Even today the understanding and modeling of these processes is still lacking. A major goal of NASA is to bring the wealth of satellite and airborne observations to bear on addressing the unresolved scientific questions and improving our forecast models. Despite their significant amount, these observations are still underutilized in hurricane research and operations due to the complexity associated with finding and bringing together semicoincident and semicontemporaneous multiparameter data that are needed to describe the multiscale TC processes. Such data are traditionally archived in different formats, with different spatiotemporal resolution, across multiple databases, and hosted by various agencies. To address this shortcoming, NASA supported the development of the Jet Propulsion Laboratory (JPL) Tropical Cyclone Information System (TCIS)—a data analytic framework that integrates model forecasts with multiparameter satellite and airborne observations, providing interactive visualization and online analysis tools. TCIS supports interrogation of a large number of atmospheric and ocean variables, allowing for quick investigation of the structure of the tropical storms and their environments. This paper provides an overview of the TCIS’s components and features. It also summarizes recent pilot studies, providing examples of how the TCIS has inspired new research, helping to increase our understanding of TCs. The goal is to encourage more users to take full advantage of the novel capabilities. TCIS allows atmospheric scientists to focus on new ideas and concepts rather than painstakingly gathering data scattered over several agencies.

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Douglas J Parker, Alan M Blyth, Steven J. Woolnough, Andrew J. Dougill, Caroline L. Bain, Estelle de Coning, Mariane Diop-Kane, Andre Kamga Foamouhoue, Benjamin Lamptey, Ousmane Ndiaye, Paolo Ruti, Elijah A. Adefisan, Leonard K Amekudzi, Philip Antwi-Agyei, Cathryn E. Birch, Carlo Cafaro, Hamish Carr, Benard Chanzu, Samantha J. Clarke, Helen Coskeran, Sylvester K. Danuor, Felipe M. de Andrade, Kone Diakaria, Cheikh Dione, Cheikh Abdoulahat Diop, Jennifer K. Fletcher, Amadou T Gaye, James L. Groves, Masilin Gudoshava, Andrew J. Hartley, Linda C. Hirons, Ishiyaku Ibrahim, Tamora D. James, Kamoru A. Lawal, John H Marsham, J N Mutemi, Emmanuel Chilekwu Okogbue, Eniola Olaniyan, J. B. Omotosho, Joseph Portuphy, Alexander J. Roberts, Juliane Schwendike, Zewdu T. Segele, Thorwald H.M. Stein, Andrea L Taylor, Christopher M Taylor, Tanya A. Warnaars, Stuart Webster, Beth J. Woodhams, and Lorraine Youds

Abstract

Africa is poised for a revolution in the quality and relevance of weather predictions, with potential for great benefits in terms of human and economic security. This revolution will be driven by recent international progress in nowcasting, numerical weather prediction, theoretical tropical dynamics and forecast communication, but will depend on suitable scientific investment being made. The commercial sector has recognized this opportunity and new forecast products are being made available to African stakeholders. At this time, it is vital that robust scientific methods are used to develop and evaluate the new generation of forecasts. The GCRF African SWIFT project represents an international effort to advance scientific solutions across the fields of nowcasting, synoptic and short-range severe weather prediction, subseasonal-to-seasonal (S2S) prediction, user engagement and forecast evaluation. This paper describes the opportunities facing African meteorology and the ways in which SWIFT is meeting those opportunities and identifying priority next steps.

Delivery and maintenance of weather forecasting systems exploiting these new solutions requires a trained body of scientists with skills in research and training; modelling and operational prediction; communications and leadership. By supporting partnerships between academia and operational agencies in four African partner countries, the SWIFT project is helping to build capacity and capability in African forecasting science. A highlight of SWIFT is the coordination of three weather-forecasting “Testbeds” – the first of their kind in Africa – which have been used to bring new evaluation tools, research insights, user perspectives and communications pathways into a semi-operational forecasting environment.

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