Liu, Y., Tan, Z. - M., & Wu, Z. (2019). Noninstantaneous Wave-CISK for the Interaction between Convective Heating and Low-Level Moisture Convergence in the Tropics. J. Atmos. Sci., 76(7), 2083–2101.
Abstract: The interaction between tropical convective heating and thermally forced circulation is investigated using a global dry primitive-equation model with the parameterization of wave-conditional instability of the second kind (CISK). It is demonstrated that deep convective heating can hardly sustain itself through the moisture convergence at low levels regardless of the fraction of immediate consumption of converged moisture. In contrast, when the fraction is large, shallow convective heating and its forced circulation exhibit preferred growth of small scales. As the “CISK catastrophe” mainly comes from the instantaneous characters of moisture-convection feedback in the conventional wave-CISK, a noninstantaneous wave-CISK is proposed, which highlights the accumulation-consumption (AC) time scale for the convective heating accumulation and/or the converged moisture consumption. In the new wave-CISK, once moisture is converged, the release of latent heat takes place gradually within an AC time scale. In this sense, convective heating is not only related to the instantaneous moisture convergence at the current time, but also to that which occurred in the past period of the AC time scale. The noninstantaneous wave-CISK could guarantee the occurrence of convective heating and/or moisture convergence at larger scales, and then favor the growth of long waves, and thus solve the problem of CISK catastrophe. With the new wave-CISK and AC time scale of 2 days, the simulated convective heating-driven system bears a large similarity to that of the observed convectively coupled Kelvin wave.
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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.
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Hoffman, R. N., Privé, N., & Bourassa, M. (2017). Comments on “Reanalyses and Observations: What's the Difference?”. Bull. Amer. Meteor. Soc., 98(11), 2455–2459.
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Hoffman, R. N., Privé, N., & Bourassa, M. (2017). Comments on “Reanalyses and Observations: What's the Difference?”. Bull. Amer. Meteor. Soc., 98(11), 2455–2459.
Abstract: Are there important differences between reanalysis data and familiar observations and measurements? If so, what are they? This essay evaluates four possible answers that relate to: the role of inference, reliance on forecasts, the need to solve an ill-posed inverse problem, and understanding of errors and uncertainties. The last of these is argued to be most significant. The importance of characterizing uncertainties associated with results—whether those results are observations or measurements, analyses or reanalyses, or forecasts—is emphasized.
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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.
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Shin, D. W., Cocke, S., & Larow, T. E. (2003). Ensemble Configurations for Typhoon Precipitation Forecasts. Journal of the Meteorological Society of Japan, 81(4), 679–696.
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Shin, D. W., & Ahn, J. B. (2002). Adaptive Use of TRMM Observations for Tropical Precipitation Forecasts. Jmsj, 80(1), 85–97.
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Dukhovskoy, D., & Bourassa, M. (2011). Comparison of ocean surface wind products in the perspective of ocean modeling of the Nordic Seas. In OCEANS 2011.
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Bourassa, M. A., & Weissman, D. E. (2003). The development and application of a sea surface stress model function for the QuikSCAT and ADEOS-II SeaWinds scatterometers. In IEEE International Symposium on Geoscience and Remote Sensing (IGARSS) (pp. 239–241).
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Nyadjro, E. S., Jensen, T. G., Richman, J. G., & Shriver, J. F. (2017). On the Relationship Between Wind, SST, and the Thermocline in the Seychelles-Chagos Thermocline Ridge. IEEE Geosci. Remote Sensing Lett., 14(12), 2315–2319.
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