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William L. Woodley, John A. Flueck, Ronald Biondini, Robert I. Sax, Joanne Simpson, and Abe Gagin

The Florida Area Cumulus Experiment (FACE) is a long-term program to determine the potential of dynamic seeding for increasing convective rainfall over a fixed target area. The first phase of FACE (FACE-1) provided strong indications for increased, seeding induced rainfall. The second phase, FACE-2 (beginning in June 1978 and ending in August 1980), was conducted in an attempt to confirm these indications of a positive seeding effect. The criteria for confirmation in FACE-2 were published in a NOAA Technical Report prior to program commencement. A clarification and sharpening of these confirmatory criteria are discussed in this paper. In addition, a minority position of what is to constitute confirmation in FACE-2 involving the use of linear predictor models also is discussed. This paper was written and accepted for publication before the treatment decisions of FACE-2 were known.

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Xiping Zeng, Wei-Kuo Tao, Minghua Zhang, Christa Peters-Lidard, Stephen Lang, Joanne Simpson, Sujay Kumar, Shaocheng Xie, Joseph L. Eastman, Chung-Lin Shie, and James V. Geiger

Abstract

Two 20-day, continental midlatitude cases are simulated with a three-dimensional (3D) cloud-resolving model (CRM) and are compared to Atmospheric Radiation Measurement Program (ARM) data. Surface fluxes from ARM ground stations and a land data assimilation system are used to drive the CRM. This modeling evaluation shows that the model simulates precipitation well but overpredicts clouds, especially in the upper troposphere. The evaluation also shows that the ARM surface fluxes can have noticeable errors in summertime.

Theoretical analysis reveals that buoyancy damping is sensitive to spatial smoothers in two-dimensional (2D) CRMs, but not in 3D ones. With this theoretical analysis and the ARM cloud observations as background, 2D and 3D simulations are compared, showing that the 2D CRM has not only rapid fluctuations in surface precipitation but also spurious dehumidification (or a decrease in cloud amount). The present study suggests that the rapid precipitation fluctuation and spurious dehumidification be attributed to the sensitivity of buoyancy damping to dimensionality.

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Xiping Zeng, Wei-Kuo Tao, Minghua Zhang, Arthur Y. Hou, Shaocheng Xie, Stephen Lang, Xiaowen Li, David O’C. Starr, Xiaofan Li, and Joanne Simpson

Abstract

A three-dimensional cloud-resolving model (CRM) with observed large-scale forcing is used to study how ice nuclei (IN) affect the net radiative flux at the top of the atmosphere (TOA). In all the numerical experiments carried out, the cloud ice content in the upper troposphere increases with IN number concentration via the Bergeron process. As a result, the upward solar flux at the TOA increases whereas the infrared one decreases. Because of the opposite response of the two fluxes to IN concentration, the sensitivity of the net radiative flux at the TOA to IN concentration varies from one case to another.

Six tropical and three midlatitudinal field campaigns provide data to model the effect of IN on radiation in different latitudes. Classifying the CRM simulations into tropical and midlatitudinal and then comparing the two types reveals that the indirect effect of IN on radiation is greater in the middle latitudes than in the tropics. Furthermore, comparisons between model results and observations suggest that observational IN data are necessary to evaluate long-term CRM simulations.

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EXECUTIVE COMMITTEE, James R. Mahoney, William D. Bonner, Joanne Simpson, Roscoe R. Braham Jr., Robert J. Serafin, Paul D. Try, Richard E. Hallgren, and Kenneth C. Spengler
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EXECUTIVE COMMITTEE, R. Braham Jr., Joanne Simpson, Albert J. Kaehn Jr., J. Smagorinsky, Douglas H. Sargeant, Ian D. Rutherford, Richard E. Hallgren, and Kenneth C. Spengler
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EXECUTIVE COMMITTEE, William D. Bonner, Donald R. Johnson, James R. Mahoney, Joanne Simpson, Kristina B. Katsaros, Robert J. Serafin, Richard E. Hallgren, and Kenneth C. Spengler
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EXECUTIVE COMMITTEE, Joanne Simpson, James R. Mahoney, Roscoe R. Braham Jr., Albert J. Kaehn Jr., Ian D. Rutherford, William D. Bonner, Richard E. Hallgren, and Kenneth C. Spengler
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T. N. Krishnamurti, Sajani Surendran, D. W. Shin, Ricardo J. Correa-Torres, T. S. V. Vijaya Kumar, Eric Williford, Chris Kummerow, Robert F. Adler, Joanne Simpson, Ramesh Kakar, William S. Olson, and F. Joseph Turk

Abstract

This paper addresses real-time precipitation forecasts from a multianalysis–multimodel superensemble. The methodology for the construction of the superensemble forecasts follows previous recent publications on this topic. This study includes forecasts from multimodels of a number of global operational centers. A multianalysis component based on the Florida State University (FSU) global spectral model that utilizes TRMM and SSM/I datasets and a number of rain-rate algorithms is also included. The difference in the analysis arises from the use of these rain rates within physical initialization that produces distinct differences among these components in the divergence, heating, moisture, and rain-rate descriptions. A total of 11 models, of which 5 represent global operational models and 6 represent multianalysis forecasts from the FSU model initialized by different rain-rate algorithms, are included in the multianalysis–multimodel system studied here. In this paper, “multimodel” refers to different models whose forecasts are being assimilated for the construction of the superensemble. “Multianalysis” refers to different initial analysis contributing to forecasts from the same model. The term superensemble is being used here to denote the bias-corrected forecasts based on the products derived from the multimodel and the multianalysis. The training period is covered by nearly 120 forecast experiments prior to 1 January 2000 for each of the multimodels. These are all 3-day forecasts. The statistical bias of the models is determined from multiple linear regression of these forecasts against a “best” rainfall analysis field that is based on TRMM and SSM/I datasets and using the rain-rate algorithms recently developed at NASA Goddard Space Flight Center. This paper discusses the results of real-time rainfall forecasts based on this system. The main results of this study are that the multianalysis–multimodel superensemble has a much higher skill than the participating member models. The skill of this system is higher than those of the ensemble mean that assigns a weight of 1.0 to all including the poorer models and the ensemble mean of bias-removed individual models. The selective weights for the entire multianalysis–multimodel superensemble forecast system make it superior to individual models and the above mean representations. The skill of precipitation forecasts is addressed in several ways. The skill of the superensemble-based rain rates is shown to be higher than the following: (a) individual model's skills with and without physical initialization, (b) skill of the ensemble mean, and (c) skill of the ensemble mean of individually bias-removed models.

The equitable-threat scores at many thresholds of rain are also examined for the various models and noted that for days 1–3 of forecasts, the superensemble-based forecasts do have the highest skills. The training phase is a major component of the superensemble. Issues on optimizing the number of training days is addressed by examining training with days of high forecast skill versus training with low forecast skill, and training with the best available rain-rate datasets versus those from poor representations of rain. Finally the usefulness of superensemble forecasts of rain for providing possible guidance for flood events such as the one over Mozambique during February 2000 is shown.

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Wei-Kuo Tao, Jiun-Dar Chern, Robert Atlas, David Randall, Marat Khairoutdinov, Jui-Lin Li, Duane E. Waliser, Arthur Hou, Xin Lin, Christa Peters-Lidard, William Lau, Jonathan Jiang, and Joanne Simpson

A multiscale modeling framework (MMF), which replaces the conventional cloud parameterizations with a cloud-resolving model (CRM) in each grid column of a GCM, constitutes a new and promising approach for climate modeling. The MMF can provide for global coverage and two-way interactions between the CRMs and their parent GCM. The CRM allows for explicit simulation of cloud processes and their interactions with radiation and surface processes, and the GCM allows for global coverage.

A new MMF has been developed that is based on the NASA Goddard Space Flight Center (GSFC) finite-volume GCM (fvGCM) and the Goddard Cumulus Ensemble (GCE) model. This Goddard MMF produces many features that are similar to another MMF that was developed at Colorado State University (CSU), such as an improved surface precipitation pattern, better cloudiness, improved diurnal variability over both oceans and continents, and a stronger propagating Madden-Julian oscillation (MJO) compared to their parent GCMs using traditional cloud parameterizations. Both MMFs also produce a large and positive precipitation bias in the Indian Ocean and western Pacific during the Northern Hemisphere summer. However, there are also notable differences between the two MMFs. For example, the CSU MMF simulates less rainfall over land than its parent GCM. This is why the CSU MMF simulated less overall global rainfall than its parent GCM. The Goddard MMF simulates more global rainfall than its parent GCM because of the high contribution from the oceanic component. A number of critical issues (i.e., the CRM's physical processes and its configuration) involving the Goddard MMF are discussed in this paper.

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