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Ping Zhu, Bruce Albrecht, and Jon Gottschalck

Abstract

The formation and evolution of nocturnal boundary layer clouds over land are studied using a simple well-mixed boundary layer theory. By analyzing the deepening rate of the mixed layer depth based on the turbulent kinetic energy budget of the whole boundary layer, the authors studied how the formation of idealized nocturnal boundary layer clouds is related to the physical processes associated with the land surface and the boundary layer. Preliminary analysis indicates that for a range of surface moisture and heat fluxes, wind shear can be an important factor in triggering the formation of nocturnal stratus. The relative importance of different physical processes responsible for cloud formation can be evaluated by the ratio between the lifting condensation level and a critical level, which is proportional to the Monin-Obukhov length scale. In this study, data collected from the Atmospheric Radiation Measurement site in the southern Great Plains are used to examine the results of the theoretical analysis. The analyses of the two nocturnal stratus cloud cases observed on 25 October 1996 and 6 November 1997 indicate that the turbulent mixing induced by the wind shear plays a pivotal role in the cloud formation during these two cases.

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Jon Gottschalck, Jesse Meng, Matt Rodell, and Paul Houser

Abstract

Precipitation is arguably the most important meteorological forcing variable in land surface modeling. Many types of precipitation datasets exist (with various pros and cons) and include those from atmospheric data assimilation systems, satellites, rain gauges, ground radar, and merged products. These datasets are being evaluated in order to choose the most suitable precipitation forcing for real-time and retrospective simulations of the Global Land Data Assimilation System (GLDAS). This paper first presents results of a comparison for the period from March 2002 to February 2003. Later, GLDAS simulations 14 months in duration are analyzed to diagnose impacts on GLDAS land surface states when using the Mosaic land surface model (LSM).

A comparison of seasonal total precipitation for the continental United States (CONUS) illustrates that the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) has the closest agreement with a CPC rain gauge dataset for all seasons except winter. The European Centre for Medium-Range Weather Forecasts (ECMWF) model performs the best of the modeling systems. The satellite-only products [the Tropical Rainfall Measuring Mission (TRMM) Real-time Multi-satellite Precipitation Analysis and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN)] suffer from a few deficiencies—most notably an overestimation of summertime precipitation in the central United States (200–400 mm). CMAP is the most closely correlated with daily rain gauge data for the spring, fall, and winter seasons, while the satellite-only estimates perform best in summer. GLDAS land surface states are sensitive to different precipitation forcing where percent differences in volumetric soil water content (SWC) between simulations ranged from −75% to +100%. The percent differences in SWC are generally 25%–75% less than the percent precipitation differences, indicating that GLDAS and specifically the Mosaic LSM act to generally “damp” precipitation differences. Areas where the percent changes are equivalent to the percent precipitation changes, however, are evident. Soil temperature spread between GLDAS runs was considerable and ranged up to ±3.0 K with the largest impact in the western United States.

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Charles Jones, Leila M. V. Carvalho, Jon Gottschalck, and Wayne Higgins

Abstract

The Madden–Julian oscillation (MJO) is the most prominent form of tropical intraseasonal variability that impacts weather and climate. Forecast skill of extreme precipitation in the contiguous United States (CONUS) during winter is higher when the MJO is active and has enhanced convection over the Western Hemisphere, Africa, and/or the western Indian Ocean. This study applies a simple decision model to examine the relationships between the MJO and the relative value of deterministic forecasts of extreme precipitation. Value in the forecasts is significantly higher and extends to longer leads (2 weeks) during active MJO.

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Charles Jones, Jon Gottschalck, Leila M. V. Carvalho, and Wayne Higgins

Abstract

Extreme precipitation events are among the most devastating weather phenomena since they are frequently accompanied by loss of life and property. This study uses reforecasts of the NCEP Climate Forecast System (CFS.v1) to evaluate the skill of nonprobabilistic and probabilistic forecasts of extreme precipitation in the contiguous United States (CONUS) during boreal winter for lead times up to two weeks.

The CFS model realistically simulates the spatial patterns of extreme precipitation events over the CONUS, although the magnitudes of the extremes in the model are much larger than in the observations. Heidke skill scores (HSS) for forecasts of extreme precipitation at the 75th and 90th percentiles showed that the CFS model has good skill at week 1 and modest skill at week 2. Forecast skill is usually higher when the Madden–Julian oscillation (MJO) is active and has enhanced convection occurring over the Western Hemisphere, Africa, and/or the western Indian Ocean than in quiescent periods. HSS greater than 0.1 extends to lead times of up to two weeks in these situations. Approximately 10%–30% of the CONUS has HSS greater than 0.1 at lead times of 1–14 days when the MJO is active.

Probabilistic forecasts for extreme precipitation events at the 75th percentile show improvements over climatology of 0%–40% at 1-day lead and 0%–5% at 7-day leads. The CFS has better skill in forecasting severe extremes (i.e., events exceeding the 90th percentile) at longer leads than moderate extremes (75th percentile). Improvements over climatology between 10% and 30% at leads of 3 days are observed over several areas across the CONUS—especially in California and in the Midwest.

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Stephen Baxter, Scott Weaver, Jon Gottschalck, and Yan Xue

Abstract

Lagged pentad composites of surface air temperature and precipitation are analyzed for the winter season (December–February) to assess the influence of the Madden–Julian oscillation (MJO) on the climate of the contiguous United States. Composites are based on the Wheeler and Hendon MJO index as well as an index developed and maintained at NOAA’s Climate Prediction Center (CPC), which is based on extended empirical orthogonal function analysis of upper-level velocity potential. Significant positive temperature anomalies develop in the eastern United States 5–20 days following Wheeler and Hendon MJO index phase 3, which corresponds to enhanced convection centered over the eastern Indian Ocean. At the same lag, positive precipitation anomalies are observed from the southern Plains to the Great Lakes region. Negative temperature anomalies appear in the central and eastern United States 10–20 days following Wheeler and Hendon MJO phase 7. These impacts are supported by an analysis of the evolution of 200-hPa geopotential height and zonal wind anomalies. Composites based on the CPC velocity potential MJO index generally yield similar results; however, they capture more cases since the index contains both interannual and subseasonal variability. There are some cases where the CPC index differs from that of WH in both MJO phase identification and its North American impacts, especially near the West Coast. This analysis suggests that MJO-related velocity potential anomalies can be used without the Wheeler and Hendon MJO index to predict MJO impacts.

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Hui Wang, Arun Kumar, Alima Diawara, David DeWitt, and Jon Gottschalck

Abstract

A dynamical–statistical model is developed for forecasting week-2 severe weather (hail, tornadoes, and damaging winds) over the United States. The supercell composite parameter (SCP) is used as a predictor, which is derived from the 16-day dynamical forecasts of the National Centers for Environmental Prediction (NCEP) Global Ensemble Forecast System (GEFS) model and represents the large-scale convective environments influencing severe weather. The hybrid model forecast is based on the empirical relationship between GEFS hindcast SCP and observed weekly severe weather frequency during 1996–2012, the GEFS hindcast period. Cross validations suggest that the hybrid model has a low skill for week-2 severe weather when applying simple linear regression method at 0.5° × 0.5° (latitude × longitude) grid data. However, the forecast can be improved by using the 5° × 5° area-averaged data. The forecast skill can be further improved by using the empirical relationship depicted by the singular value decomposition method, which takes into account the spatial covariations of weekly severe weather. The hybrid model was tested operationally in spring 2019 and demonstrated skillful forecasts of week-2 severe weather frequency over the United States.

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Jon Gottschalck, Paul E. Roundy, Carl J. Schreck III, Augustin Vintzileos, and Chidong Zhang

Abstract

An international field campaign, Dynamics of the Madden Julian Oscillation (DYNAMO), took place in the Indian Ocean during October 2011–March 2012 to collect observations for the Madden–Julian oscillation (MJO), especially its convective initiation processes. The large-scale atmospheric and oceanic conditions during the campaign are documented here. The ENSO and the Indian Ocean dipole (IOD) states, the monthly mean monsoon circulation and its associated precipitation, humidity, vertical and meridional/zonal overturning cells, and ocean surface currents are discussed. The evolution of MJO events is described using various fields and indices that have been used to subdivide the campaign into three periods. These periods were 1) 17 September–8 December 2011 (period 1), which featured two robust MJO events that circumnavigated the global tropics with a period of less than 45 days; 2) 9 December 2011–31 January 2012, which contained less coherent activity (period 2); and 3) 1 February–12 April 2012, a period that featured the strongest and most slowly propagating MJO event of the campaign (period 3). Activities of convectively coupled atmospheric Kelvin and equatorial Rossby (ER) waves and their interaction with the MJO are discussed. The overview of the atmospheric and oceanic variability during the field campaign raises several scientific issues pertaining to our understanding of the MJO, or lack thereof. Among others, roles of Kelvin and ER waves in MJO convective initiation, convection-circulation decoupling on the MJO scale, applications of MJO filtering methods and indices, and ocean–atmosphere coupling need further research attention.

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John E. Janowiak, Peter Bauer, Wanqiu Wang, Phillip A. Arkin, and Jon Gottschalck

Abstract

In this paper, the results of an examination of precipitation forecasts for 1–30-day leads from global models run at the European Centre for Medium-Range Weather Forecasts (ECMWF) and the National Centers for Environmental Prediction (NCEP) during November 2007–February 2008 are presented. The performance of the model precipitation forecasts are examined in global and regional contexts, and results of a case study of precipitation variations that are associated with a moderate to strong Madden–Julian oscillation (MJO) event are presented.

The precipitation forecasts from the ECMWF and NCEP operational prediction models have nearly identical temporal correlation with observed precipitation at forecast leads from 2 to 9 days over the Northern Hemisphere during the cool season, despite the higher resolution of the ECMWF operational model, while the ECMWF operational model forecasts are slightly better in the tropics and the Southern Hemisphere during the warm season. The ECMWF Re-Analysis Interim (ERA-Interim) precipitation forecasts perform only slightly worse than the NCEP operational model, while NCEP’s Climate Forecast System low-resolution coupled model forecasts perform the worst among the four models. In terms of bias, the ECMWF operational model performs the best among the four model forecasts that were examined, particularly with respect to the ITCZ regions in both the Atlantic and Pacific. Local temporal correlations that were computed on daily precipitation totals for day-2 forecasts against observations indicate that the operational models at ECMWF and NCEP perform the best during the 4-month study period, and that all of the models have low to insignificant correlations over land and over much of the tropics. They perform the best in subtropical and extratropical oceanic regions.

Also presented are results that show that striking improvements have been made over the past two decades in the ability of the models to represent precipitation variations that are associated with MJO. The model precipitation forecasts exhibit the ability to characterize the evolution of precipitation variations during a moderate–strong period of MJO conditions for forecast leads as long as 10 days.

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Yun Fan, Vladimir Krasnopolsky, Huug van den Dool, Chung-Yu Wu, and Jon Gottschalck

Abstract

Forecast skill from dynamical forecast models decreases quickly with projection time due to various errors. Therefore, post-processing methods, from simple bias correction methods to more complicated multiple linear regression-based Model Output Statistics, are used to improve raw model forecasts. Usually, these methods show clear forecast improvement over the raw model forecasts, especially for short-range weather forecasts. However, linear approaches have limitations because the relationship between predictands and predictors may be nonlinear. This is even truer for extended range forecasts, such as Week 3-4 forecasts.

In this study, neural network techniques are used to seek or model the relationships between a set of predictors and predictands, and eventually to improve Week 3-4 precipitation and 2-meter temperature forecasts made by the NOAA NCEP Climate Forecast System. Benefitting from advances in machine learning techniques in recent years, more flexible and capable machine learning algorithms and availability of big datasets enable us not only to explore nonlinear features or relationships within a given large dataset, but also to extract more sophisticated pattern relationships and co-variabilities hidden within the multi-dimensional predictors and predictands. Then these more sophisticated relationships and high-level statistical information are used to correct the model Week 3-4 precipitation and 2-meter temperature forecasts. The results show that to some extent neural network techniques can significantly improve the Week 3-4 forecast accuracy and greatly increase the efficiency over the traditional multiple linear regression methods.

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Kyong-Hwan Seo, Wanqiu Wang, Jon Gottschalck, Qin Zhang, Jae-Kyung E. Schemm, Wayne R. Higgins, and Arun Kumar

Abstract

This work examines the performance of Madden–Julian oscillation (MJO) forecasts from NCEP’s coupled and uncoupled general circulation models (GCMs) and statistical models. The forecast skill from these methods is evaluated in near–real time. Using a projection of El Niño–Southern Oscillation (ENSO)-removed variables onto the principal patterns of MJO convection and upper- and lower-level circulations, MJO-related signals in the dynamical model forecasts are extracted. The operational NCEP atmosphere–ocean fully coupled Climate Forecast System (CFS) model has useful skill (>0.5 correlation) out to ∼15 days when the initial MJO convection is located over the Indian Ocean. The skill of the CFS hindcast dataset for the period from 1995 to 2004 is nearly comparable to that from a lagged multiple linear regression model, which uses information from the previous five pentads of the leading two principal components (PCs). In contrast, the real-time analysis for the MJO forecast skill for the period from January 2005 to February 2006 using the lagged multiple linear regression model is reduced to ∼10–12 days. However, the operational CFS forecast for this period is skillful out to ∼17 days for the winter season, implying that the coupled dynamical forecast has some usefulness in predicting the MJO compared to the statistical model.

It is shown that the coupled CFS model consistently, but only slightly, outperforms the uncoupled atmospheric model (by one to two days), indicating that only limited improvement is gained from the inclusion of the coupled air–sea interaction in the MJO forecast in this model. This slight improvement may be the result of the existence of a propagation barrier around the Maritime Continent and the far western Pacific in the NCEP Global Forecast System (GFS) and CFS models, as shown in several previous studies. This work also suggests that the higher horizontal resolution and finer initial data might contribute to improving the forecast skill, presumably as a result of an enhanced representation of the Maritime Continent region.

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