Knapp, K. R., Ansari, S., Bain, C. L., Bourassa, M. A., Dickinson, M. J., Funk, C., et al. (2011). Globally Gridded Satellite Observations for Climate Studies.
Bull. Amer. Meteor. Soc., 92(7), 893–907.
Kunkel, K. E., Karl, T. R., Brooks, H., Kossin, J., Lawrimore, J. H., Arndt, D., et al. (2013). Monitoring and Understanding Trends in Extreme Storms: State of Knowledge.
Bull. Amer. Meteor. Soc., 94(4), 499–514.
Legg, S., Briegleb, B., Chang, Y., Chassignet, E. P., Danabasoglu, G., Ezer, T., et al. (2009). Improving Oceanic Overflow Representation in Climate Models: The Gravity Current Entrainment Climate Process Team.
Bull. Amer. Meteor. Soc., 90(5), 657–670.
MacKinnon, J. A., Alford, M. H., Ansong, J. K., Arbic, B. K., Barna, A., Briegleb, B. P., et al. (2017). Climate Process Team on Internal-Wave Driven Ocean Mixing.
Bull. Amer. Meteor. Soc., 98(11), 2429–2454.
Maloney, E. D., Gettelman, A., Ming, Y., Neelin, J. D., Barrie, D., Mariotti, A., et al. (2019). Process-Oriented Evaluation of Climate and Weather Forecasting Models.
Bull. Amer. Meteor. Soc., 100(9), 1665–1686.
Abstract: Realistic climate and weather prediction models are necessary to produce confidence in projections of future climate over many decades and predictions for days to seasons. These models must be physically justified and validated for multiple weather and climate processes. A key opportunity to accelerate model improvement is greater incorporation of process-oriented diagnostics (PODs) into standard packages that can be applied during the model development process, allowing the application of diagnostics to be repeatable across multiple model versions and used as a benchmark for model improvement. A POD characterizes a specific physical process or emergent behavior that is related to the ability to simulate an observed phenomenon. This paper describes the outcomes of activities by the Model Diagnostics Task Force (MDTF) under the NOAA Climate Program Office (CPO) Modeling, Analysis, Predictions and Projections (MAPP) program to promote development of PODs and their application to climate and weather prediction models. MDTF and modeling center perspectives on the need for expanded process-oriented diagnosis of models are presented. Multiple PODs developed by the MDTF are summarized, and an open-source software framework developed by the MDTF to aid application of PODs to centers' model development is presented in the context of other relevant community activities. The paper closes by discussing paths forward for the MDTF effort and for community process-oriented diagnosis.
Sharp, R. J., Bourassa, M. A., & O'Brien, J. J. (2003). Comments on “Early detection of tropical cyclones using SeaWinds-derived vorticity” – Reply.
Bull. Amer. Meteor. Soc., 84(10), 1417.
Sharp, R. J., Bourassa, M. A., & O'Brien, J. J. (2002). Early Detection of Tropical Cyclones Using Seawinds-Derived Vorticity.
Bull. Amer. Meteor. Soc., 83(6), 879–889.
Smith, S. R., & O'Brien, J. J. (2001). Regional Snowfall Distributions Associated with ENSO: Implications for Seasonal Forecasting.
Bull. Amer. Meteor. Soc., 82(6), 1179–1191.
Smith, S. R., Servain, J., Legler, D. M., Stricherz, J. N., Bourassa, M. A., & O'Brien, J. J. (2004). In Situ-Based Pseudo-Wind Stress Products for the Tropical Oceans.
Bull. Amer. Meteor. Soc., 85(7), 979–994.
Vinayachandran, P. N., Davidson, F., & Chassignet, E. P. (2020). Towards joint assessments, modern capabilities and new links for ocean prediction systems.
Bull. Amer. Meteor. Soc., 101(4).
Abstract: Approximately 260 individuals from forecasting centers, research laboratories, academia, and industry representing 40 countries met to discuss recent developments in operational oceanography and brainstorm about the future directions of ocean prediction services.