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Weiwei Li, Zhuo Wang, and Melinda S. Peng

Abstract

Tropical cyclone (TC) forecasts from the NCEP Global Ensemble Forecasting System (GEFS) Reforecast version 2 (1985–2012) were evaluated from the climate perspective, with a focus on tropical cyclogenesis. Although the GEFS captures the climatological seasonality of tropical cyclogenesis over different ocean basins reasonably well, large errors exist on the regional scale. As different genesis pathways are dominant over different ocean basins, genesis biases are related to biases in different aspects of the large-scale or synoptic-scale circulations over different basins. The negative genesis biases over the western North Pacific are associated with a weaker-than-observed monsoon trough in the GEFS, the erroneous genesis pattern over the eastern North Pacific is related to a southward displacement of the ITCZ, and the positive genesis biases near the Cape Verde islands and negative biases farther downstream over the Atlantic can be attributed to the hyperactive Africa easterly waves in the GEFS. The interannual and subseasonal variability of TC activity in the reforecasts was also examined to evaluate the potential skill of the GEFS in providing subseasonal and seasonal predictions. The GEFS skillfully captures the interannual variability of TC activity over the North Pacific and the North Atlantic, which can be attributed to the modulation of TCs by the El Niño–Southern Oscillation (ENSO) and the Atlantic meridional mode (AMM). The GEFS shows promising skill in predicting the active and inactive periods of TC activity over the Atlantic. The skill, however, has large fluctuations from year to year. The analysis presented herein suggests possible impacts of ENSO, the Madden–Julian oscillation (MJO), and the AMM on the TC subseasonal predictability.

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Weiwei Li, Zhuo Wang, Melinda S. Peng, and James A. Ridout

Abstract

Navy Operational Global Atmospheric Prediction System (NOGAPS) analysis and operational forecasts are evaluated against the Interim ECMWF Re-Analysis (ERA-Interim; ERAI) and satellite data, and compared with the Global Forecast System (GFS) analysis and forecasts, using both performance- and physics-based metrics. The NOGAPS analysis captures realistic Madden–Julian oscillation (MJO) signals in the dynamic fields and the low-level premoistening leading to active convection, but the MJO signals in the relative humidity (RH) and diabatic heating rate (Q1) fields are weaker than those in the ERAI or the GFS analysis. The NOGAPS forecasts, similar to the GFS forecasts, have relatively low prediction skill for the MJO when the MJO initiates over the Indian Ocean and when active convection is over the Maritime Continent. The NOGAPS short-term precipitation forecasts are broadly consistent with the Climate Prediction Center (CPC) morphing technique (CMORPH) precipitation results with regionally quantitative differences. Further evaluation of the precipitation and column water vapor (CWV) indicates that heavy precipitation develops too early in the NOGAPS forecasts in terms of the CWV, and the NOGAPS forecasts show a dry bias in the CWV increasing with forecast lead time. The NOGAPS underpredicts light and moderate-to-heavy precipitation but overpredicts extremely heavy rainfall. The vertical profiles of RH and Q1 reveal a dry bias within the marine boundary layer and a moist bias above. The shallow heating mode is found to be missing for CWV < 50 mm in the NOGAPS forecasts. The diabatic heating biases are associated with weaker trade winds, weaker Hadley and Walker circulations over the Pacific, and weaker cross-equatorial flow over the Indian Ocean in the NOGAPS forecasts.

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Shih-Yu Wang, Tsing-Chang Chen, and S. Elwynn Taylor

Abstract

In the U.S. northern plains, summer progressive convective storms that occur in weakly forced environments are often coupled with short-wave perturbations that are embedded in the midlevel northwesterly flow. These midtropospheric perturbations (MPs) are capable of inducing propagating convection that contributes to a majority of the rainfall over the northern plains during July and August. There is a possibility that the difficulties of numerical weather prediction models in forecasting summer convective rainfall over the northern plains are partly attributed to their deficiency in forecasting MPs. The present study tests this possibility through examining operational forecasts by the North American Mesoscale (NAM) model during the summers of 2005 and 2006.

Forecasted MPs exhibit slower propagation speeds and weaker relative vorticity than the observations leading to systematic position errors. Underpredicted vorticity magnitudes weaken horizontal vorticity advection that influences the vorticity tendency throughout the MP life cycle and, in turn, slows the propagation speed of MPs. Moreover, biases of weak ambient flow speed and vortex stretching contribute to the magnitude and propagation speed errors of MPs. Skill scores of precipitation forecasts associated with MPs are low, but can be considerably improved after removing the MP position error that displaces the rainfall pattern. The NAM also tends to underpredict precipitation amounts. A modified water vapor budget analysis reveals that the NAM insufficiently generates atmospheric humidity over the central United States. The shortage of moisture in the forecast reduces the water vapor flux convergence that is part of the precipitation process. The precipitation bias may feed back to affect the MP growth through the bias in heating, thus further slowing the perturbation.

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Ming Cai, Chul-Su Shin, H. M. van den Dool, Wanqiu Wang, S. Saha, and A. Kumar

Abstract

This paper analyzes long-term surface air temperature trends in a 25-yr (1982–2006) dataset of retrospective seasonal climate predictions made by the NCEP Climate Forecast System (CFS), a model that has its atmospheric greenhouse gases fixed at the 1988 concentration level. Although the CFS seasonal forecasts tend to follow the observed interannual variability very closely, there exists a noticeable time-dependent discrepancy between the CFS forecasts and observations, with a warm model bias before 1988 and a cold bias afterward except for a short-lived warm bias during 1992–94. The trend from warm to cold biases is likely caused by not including the observed increase in the anthropogenic greenhouse gases in the CFS, whereas the warm bias in 1992–94 reflects the absence of the anomalous aerosols released by the 1991 Mount Pinatubo eruption. Skill analysis of the CFS seasonal climate predictions with and without the warming trend suggests that the 1997–98 El Niño event contributes significantly to the record-breaking global warmth in 1998 whereas the record-breaking warm decade since 2000 is mainly due to the effects of the increased greenhouse gases. Implications for operational seasonal prediction will be discussed.

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Yunheng Wang, Jidong Gao, Patrick S. Skinner, Kent Knopfmeier, Thomas Jones, Gerry Creager, Pamela L. Heiselman, and Louis J. Wicker

Abstract

A real-time, weather adaptive, dual-resolution, hybrid Warn-on-Forecast (WoF) analysis and forecast system using the WRF-ARW forecast model has been developed and implemented. The system includes two components, an ensemble analysis and forecast component, and a deterministic hybrid three-dimensional ensemble–variational (3DEnVAR) analysis and forecast component. The goal of the system is to provide on-demand, ensemble-based, and physically consistent gridded analysis and forecast products to forecasters for making warning decisions. Both components, the WRF-DART system with 36 ensemble members and the hybrid 3DEnVAR system, assimilate radar data, satellite-retrieved cloud water path, and surface observations at 15-min intervals with dual-resolution capability. In the current hybrid configuration, one-way coupling of the two analysis systems is performed: ensemble covariances derived from the WRF-DART system are incorporated into the hybrid 3DEnVAR system with each data assimilation (DA) cycle. This study examines deterministic, 3-h forecasts launched from the hybrid 3DEnVAR analyses every 30 min for three severe weather events in 2017. The performance of the deterministic component is evaluated for four configurations: dual-resolution coupling, single-resolution coupling, forecasts initialized using a cloud analysis for reflectivity assimilation, and forecasts initialized from the WRF-DART ensemble mean. Quantitative and subjective evaluation of composite reflectivity and updraft helicity (UH) swath forecasts for the three events indicate that the dual-resolution strategy without the cloud analysis performs best among the four configurations and provides the most realistic prediction of reflectivity patterns and UH tracks.

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Corey K. Potvin, Jacob R. Carley, Adam J. Clark, Louis J. Wicker, Patrick S. Skinner, Anthony E. Reinhart, Burkely T. Gallo, John S. Kain, Glen S. Romine, Eric A. Aligo, Keith A. Brewster, David C. Dowell, Lucas M. Harris, Israel L. Jirak, Fanyou Kong, Timothy A. Supinie, Kevin W. Thomas, Xuguang Wang, Yongming Wang, and Ming Xue

Abstract

The 2016–18 NOAA Hazardous Weather Testbed (HWT) Spring Forecasting Experiments (SFE) featured the Community Leveraged Unified Ensemble (CLUE), a coordinated convection-allowing model (CAM) ensemble framework designed to provide empirical guidance for development of operational CAM systems. The 2017 CLUE included 81 members that all used 3-km horizontal grid spacing over the CONUS, enabling direct comparison of forecasts generated using different dynamical cores, physics schemes, and initialization procedures. This study uses forecasts from several of the 2017 CLUE members and one operational model to evaluate and compare CAM representation and next-day prediction of thunderstorms. The analysis utilizes existing techniques and novel, object-based techniques that distill important information about modeled and observed storms from many cases. The National Severe Storms Laboratory Multi-Radar Multi-Sensor product suite is used to verify model forecasts and climatologies of observed variables. Unobserved model fields are also examined to further illuminate important intermodel differences in storms and near-storm environments. No single model performed better than the others in all respects. However, there were many systematic intermodel and intercore differences in specific forecast metrics and model fields. Some of these differences can be confidently attributed to particular differences in model design. Model intercomparison studies similar to the one presented here are important to better understand the impacts of model and ensemble configurations on storm forecasts and to help optimize future operational CAM systems.

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Fuqing Zhang, Rebecca E. Morss, J. A. Sippel, T. K. Beckman, N. C. Clements, N. L. Hampshire, J. N. Harvey, J. M. Hernandez, Z. C. Morgan, R. M. Mosier, S. Wang, and S. D. Winkley

Abstract

Hurricane Rita made landfall near the Texas–Louisiana border in September 2005, causing major damage and disruption. As Rita approached the Gulf Coast, uncertainties in the storm’s track and intensity forecasts, combined with the aftermath of Hurricane Katrina, led to major evacuations along the Texas coast and significant traffic jams in the broader Houston area. This study investigates the societal impacts of Hurricane Rita and its forecasts through a face-to-face survey with 120 Texas Gulf Coast residents. The survey explored respondents’ evacuation decisions prior to Hurricane Rita, their perceptions of hurricane risk, and their use of and opinions on Hurricane Rita forecasts. The vast majority of respondents evacuated from Hurricane Rita, and more than half stated that Hurricane Katrina affected their evacuation decision. Although some respondents said that their primary reason for evacuating was local officials’ evacuation order, many reported using information about the hurricane to evaluate the risk it posed to them and their families. Despite the major traffic jams and the minor damage in many evacuated regions, most evacuees interviewed do not regret their decision to evacuate. The majority of respondents stated that they intend to evacuate for a future category 3 hurricane, but the majority would stay for a category 2 hurricane. Most respondents obtained forecasts from multiple sources and reported checking forecasts frequently. Despite the forecast uncertainties, the respondents had high confidence in and satisfaction with the forecasts of Rita provided by the National Hurricane Center.

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John S. Kain, Ming Xue, Michael C. Coniglio, Steven J. Weiss, Fanyou Kong, Tara L. Jensen, Barbara G. Brown, Jidong Gao, Keith Brewster, Kevin W. Thomas, Yunheng Wang, Craig S. Schwartz, and Jason J. Levit

Abstract

The impacts of assimilating radar data and other mesoscale observations in real-time, convection-allowing model forecasts were evaluated during the spring seasons of 2008 and 2009 as part of the Hazardous Weather Test Bed Spring Experiment activities. In tests of a prototype continental U.S.-scale forecast system, focusing primarily on regions with active deep convection at the initial time, assimilation of these observations had a positive impact. Daily interrogation of output by teams of modelers, forecasters, and verification experts provided additional insights into the value-added characteristics of the unique assimilation forecasts. This evaluation revealed that the positive effects of the assimilation were greatest during the first 3–6 h of each forecast, appeared to be most pronounced with larger convective systems, and may have been related to a phase lag that sometimes developed when the convective-scale information was not assimilated. These preliminary results are currently being evaluated further using advanced objective verification techniques.

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Michelle L. L’Heureux, Michael K. Tippett, Ken Takahashi, Anthony G. Barnston, Emily J. Becker, Gerald D. Bell, Tom E. Di Liberto, Jon Gottschalck, Michael S. Halpert, Zeng-Zhen Hu, Nathaniel C. Johnson, Yan Xue, and Wanqiu Wang

Abstract

Three strategies for creating probabilistic forecast outlooks for El Niño–Southern Oscillation (ENSO) are compared. One is subjective and is currently used by the NOAA/Climate Prediction Center (CPC) to produce official ENSO outlooks. A second is purely objective and is based on the North American Multimodel Ensemble (NMME). A new third strategy is proposed in which the forecaster only provides the expected value of the Niño-3.4 index, and then categorical probabilities are objectively determined based on past skill. The new strategy results in more confident probabilities compared to the subjective approach and higher verification scores, while avoiding the significant forecast busts that sometimes afflict the NMME-based objective approach. The higher verification scores of the new strategy appear to result from the added value that forecasters provide in predicting the mean, combined with more reliable representations of uncertainty, which is difficult to represent because forecasters often assume less confidence than is justified. Moreover, the new approach can produce higher-resolution probabilistic forecasts that include ENSO strength information and that are difficult, if not impossible, for forecasters to produce. To illustrate, a nine-category ENSO outlook based on the new strategy is assessed and found to be skillful. The new approach can be applied to other outlooks where users desire higher-resolution probabilistic forecasts, including the extremes.

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