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Yun Fan and Huug van den Dool

Abstract

A simple bias correction method was used to correct daily operational ensemble week-1 and week-2 precipitation and 2-m surface air temperature forecasts from the NCEP Global Forecast System (GFS). The study shows some unexpected and striking features of the forecast errors or biases of both precipitation and 2-m surface air temperature from the GFS. They are dominated by relatively large-scale spatial patterns and low-frequency variations that resemble the annual cycle. A large portion of these forecast errors is removable, but the effectiveness is time and space dependent. The bias-corrected week-1 and week-2 ensemble precipitation and 2-m surface air temperature forecasts indicate some improvements over their raw counterparts. However, the overall levels of week-1 and week-2 forecast skill in terms of spatial anomaly correlation and root-mean-square error are still only modest. The dynamical soil moisture forecasts (i.e., land surface hydrological model forced with bias-corrected precipitation and 2-m surface air temperature integrated forward for up to 2 weeks) have very high skill, but hardly beat persistence over the United States. The inability to outperform persistence mainly relates to the skill of the current GFS week-1 and week-2 precipitation forecasts not being above a threshold (i.e., anomaly correlation > 0.5 is required).

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Ben P. Kirtman, Yun Fan, and Edwin K. Schneider

Abstract

The Center for Ocean–Land–Atmosphere Studies (COLA) global coupled and anomaly coupled ocean–atmosphere GCM models are described. The ocean and atmosphere components of these coupled models are identical. The only difference between them is in the coupling strategy. The anomaly coupling strategy guarantees that the climatology of the coupled model is close to the observed climatology, whereas the global coupled model suffers from serious climate drift. This climate drift is largest in the eastern tropical Pacific where the coupled model is too warm by as much as 5°C. The climate drift in the coupled model can also be seen by the predominance of an erroneous double intertopical convergence zone (ITCZ) in the eastern tropical Pacific. Despite the climate drift, both models exhibit robust interannual variability in the tropical Pacific. Composite analysis, however, reveals that the characteristics of interannual variability in the coupled and the anomaly coupled models are markedly different. For example, the coupled model exhibits a distinct eastward migration of the ENSO events, whereas the anomaly coupled model is dominated by a standing mode, which is too strongly phase-locked to the annual cycle. Based on diagnostic ocean model simulations, it is shown that an erroneous eastward migration of the annual cycle of thermocline depth and upwelling is responsible for the eastward migration of the ENSO events in the coupled model. The anomaly coupled model has a comparatively weak annual cycle in the thermocline depth and upwelling. These calculations emphasize the importance of correctly simulating the mean state in order to capture realistic ENSO variability.

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Yun Fan, Huug M. van den Dool, and Wanru Wu

Abstract

Several land surface datasets, such as the observed Illinois soil moisture dataset; three retrospective offline run datasets from the Noah land surface model (LSM), Variable Infiltration Capacity (VIC) LSM, and Climate Prediction Center leaky bucket soil model; and three reanalysis datasets (North American Regional Reanalysis, NCEP/Department of Energy Global Reanalysis, and 40-yr ECMWF Re-Analysis), are used to study the spatial and temporal variability of soil moisture and its response to the major components of land surface hydrologic cycles: precipitation, evaporation, and runoff. Detailed analysis was performed on the evolution of the soil moisture vertical profile. Over Illinois, model simulations are compared to observations, but for the United States as a whole some impressions can be gained by comparing the multiple soil moisture–precipitation–evaporation–runoff datasets to one another. The magnitudes and partitioning of major land surface water balance components on seasonal–interannual time scales have been explored. It appears that evaporation has the most prominent annual cycle but its interannual variability is relatively small. For other water balance components, such as precipitation, runoff, and surface water storage change, the amplitudes of their annual cycles and interannual variations are comparable. This study indicates that all models have a certain capability to reproduce observed soil moisture variability on seasonal–interannual time scales, but offline runs are decidedly better than reanalyses (in terms of validation against observations) and more highly correlated to one another (in terms of intercomparison) in general. However, noticeable differences are also observed, such as the degree of simulated drought severity and the locations affected—this is due to the uncertainty in model physics, input forcing, and mode of running (interactive or offline), which continue to be major issues for land surface modeling.

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Dong-Peng Guo, Peng Zhao, Ren-Tai Yao, Yun-Peng Li, Ji-Min Hu, and Dan Fan

Abstract

In this paper, the kε renormalization group (RNG) turbulence model is used to simulate the flow and dispersion of pollutants emitted from a source at the top of a cubic building under neutral and stable atmospheric stratifications, the results of which were compared with corresponding wind tunnel experiment results. When atmosphere stratification is stable, the separation zones on the sides and at the top of a building are relatively smaller than those under neutral conditions, and the effect of the building in the horizontal direction is stronger than that in the vertical direction. The variation in turbulent kinetic energy under stable conditions is significantly lower than that under neutral conditions. The effect of atmospheric stratification on the turbulent kinetic energy becomes gradually more prominent with increased distance. When atmosphere conditions are stable, the vertical distribution of the plume is smaller than that of neutral conditions, but the lateral spread and near-ground concentration are slightly larger than those of neutral conditions, mainly because stable atmospheric stratification suppresses the vertical motions of airflow and increases the horizontal spread of the plume.

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Yun Fan, M. R. Allen, D. L. T. Anderson, and M. A. Balmaseda

Abstract

The predictability of any complex, inhomogeneous system depends critically on the definition of analysis and forecast errors. A simple and efficient singular vector analysis is used to study the predictability of a coupled model of El Niño–Southern Oscillation (ENSO). Error growth is found to depend critically on the desired properties of the forecast errors (“where and what one wants to predict”), as well as on the properties of the analysis error (“what information is available for that prediction”) and choice of optimization time. The time evolution of singular values and singular vectors shows that the predictability of the coupled model is clearly related to the seasonal cycle and to the phase of ENSO. It is found that the use of an approximation to the analysis error covariance to define the relative importance of errors in different variables gives very different results to the more frequently used “energy norm,” and indicates a much larger role for sea surface temperature information in seasonal (3–6-month timescale) predictability. Seasonal variations in the predictability of the coupled model are also investigated, addressing in particular the question of whether seasonal variations in the dominant singular values (the “spring predictability barrier”) may be largely due to the seasonality in the variance of SST anomalies.

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Yun Lin, Jiwen Fan, Jong-Hoon Jeong, Yuwei Zhang, Cameron R. Homeyer, and Jingyu Wang

Abstract

Changes in land surface and aerosol characteristics from urbanization can affect dynamic and microphysical properties of severe storms, thus affecting hazardous weather events resulting from such storms such as hail and tornados. We examine the joint and individual effects of urban land and anthropogenic aerosols of Kansas City on a severe convective storm observed during the 2015 Plains Elevated Convection At Night (PECAN) field campaign, focusing on storm evolution, convective intensity, and hail characteristics. The simulations are carried out at the cloud-resolving scale (1 km) using a version of WRF-Chem in which the spectral-bin microphysics (SBM) is coupled with the Model for Simulating Aerosol Interactions and Chemistry (MOSAIC). It is found that the urban land effect of Kansas City initiated a much stronger convective cell and the storm got further intensified when interacting with stronger turbulence induced by the urban land. The urban land effect also changed the storm path by diverting the storm toward the city mainly resulting from enhanced urban land-induced convergence in the urban area and around the urban-rural boundaries. The joint effect of urban land and anthropogenic aerosols enhances occurrences of both severe hail and significant severe hail by ~ 20% by enhancing hail formation and growth from riming. Overall the urban land effect on convective intensity and hail is relatively larger than the anthropogenic aerosol effect, but the joint effect is much notable than either of the individual effects, emphasizing the importance of considering both effects in evaluating urbanization effects.

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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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Song Yang, Zuqiang Zhang, Vernon E. Kousky, R. Wayne Higgins, Soo-Hyun Yoo, Jianyin Liang, and Yun Fan

Abstract

Analysis of the retrospective ensemble predictions (hindcasts) of the NCEP Climate Forecast System (CFS) indicates that the model successfully simulates many major features of the Asian summer monsoon including the climatology and interannual variability of major precipitation centers and atmospheric circulation systems. The model captures the onset of the monsoon better than the retreat of the monsoon, and it simulates the seasonal march of monsoon rainfall over Southeast Asia more realistically than that over South Asia. The CFS predicts the major dynamical monsoon indices and monsoon precipitation patterns several months in advance. It also depicts the interactive oceanic–atmospheric processes associated with the precipitation anomalies reasonably well at different time leads. Overall, the skill of monsoon prediction by the CFS mainly comes from the impact of El Niño–Southern Oscillation (ENSO).

The CFS produces weaker-than-observed large-scale monsoon circulation, due partially to the cold bias over the Asian continent. It tends to overemphasize the relationship between ENSO and the Asian monsoon, as well as the impact of ENSO on the Asian and Indo-Pacific climate. A higher-resolution version of the CFS (T126) captures the climatology and variability of the Asian monsoon more realistically than does the current resolution version (T62). The largest improvement occurs in the simulations of precipitation near the Tibetan Plateau and over the tropical Indian Ocean associated with the zonal dipole mode structure. The analysis suggests that NCEP’s next operational model may perform better in simulating and predicting the monsoon climate over Asia and the Indo-Pacific Oceans.

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Fedor Mesinger, Geoff DiMego, Eugenia Kalnay, Kenneth Mitchell, Perry C. Shafran, Wesley Ebisuzaki, Dušan Jović, Jack Woollen, Eric Rogers, Ernesto H. Berbery, Michael B. Ek, Yun Fan, Robert Grumbine, Wayne Higgins, Hong Li, Ying Lin, Geoff Manikin, David Parrish, and Wei Shi

In 1997, during the late stages of production of NCEP–NCAR Global Reanalysis (GR), exploration of a regional reanalysis project was suggested by the GR project's Advisory Committee, “particularly if the RDAS [Regional Data Assimilation System] is significantly better than the global reanalysis at capturing the regional hydrological cycle, the diurnal cycle and other important features of weather and climate variability.” Following a 6-yr development and production effort, NCEP's North American Regional Reanalysis (NARR) project was completed in 2004, and data are now available to the scientific community. Along with the use of the NCEP Eta model and its Data Assimilation System (at 32-km–45-layer resolution with 3-hourly output), the hallmarks of the NARR are the incorporation of hourly assimilation of precipitation, which leverages a comprehensive precipitation analysis effort, the use of a recent version of the Noah land surface model, and the use of numerous other datasets that are additional or improved compared to the GR. Following the practice applied to NCEP's GR, the 25-yr NARR retrospective production period (1979–2003) is augmented by the construction and daily execution of a system for near-real-time continuation of the NARR, known as the Regional Climate Data Assimilation System (R-CDAS). Highlights of the NARR results are presented: precipitation over the continental United States (CONUS), which is seen to be very near the ingested analyzed precipitation; fits of tropospheric temperatures and winds to rawinsonde observations; and fits of 2-m temperatures and 10-m winds to surface station observations. The aforementioned fits are compared to those of the NCEP–Department of Energy (DOE) Global Reanalysis (GR2). Not only have the expectations cited above been fully met, but very substantial improvements in the accuracy of temperatures and winds compared to that of GR2 are achieved throughout the troposphere. Finally, the numerous datasets produced are outlined and information is provided on the data archiving and present data availability.

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Tandong Yao, Yongkang Xue, Deliang Chen, Fahu Chen, Lonnie Thompson, Peng Cui, Toshio Koike, William K.-M. Lau, Dennis Lettenmaier, Volker Mosbrugger, Renhe Zhang, Baiqing Xu, Jeff Dozier, Thomas Gillespie, Yu Gu, Shichang Kang, Shilong Piao, Shiori Sugimoto, Kenichi Ueno, Lei Wang, Weicai Wang, Fan Zhang, Yongwei Sheng, Weidong Guo, Ailikun, Xiaoxin Yang, Yaoming Ma, Samuel S. P. Shen, Zhongbo Su, Fei Chen, Shunlin Liang, Yimin Liu, Vijay P. Singh, Kun Yang, Daqing Yang, Xinquan Zhao, Yun Qian, Yu Zhang, and Qian Li

Abstract

The Third Pole (TP) is experiencing rapid warming and is currently in its warmest period in the past 2,000 years. This paper reviews the latest development in multidisciplinary TP research associated with this warming. The rapid warming facilitates intense and broad glacier melt over most of the TP, although some glaciers in the northwest are advancing. By heating the atmosphere and reducing snow/ice albedo, aerosols also contribute to the glaciers melting. Glacier melt is accompanied by lake expansion and intensification of the water cycle over the TP. Precipitation has increased over the eastern and northwestern TP. Meanwhile, the TP is greening and most regions are experiencing advancing phenological trends, although over the southwest there is a spring phenological delay mainly in response to the recent decline in spring precipitation. Atmospheric and terrestrial thermal and dynamical processes over the TP affect the Asian monsoon at different scales. Recent evidence indicates substantial roles that mesoscale convective systems play in the TP’s precipitation as well as an association between soil moisture anomalies in the TP and the Indian monsoon. Moreover, an increase in geohazard events has been associated with recent environmental changes, some of which have had catastrophic consequences caused by glacial lake outbursts and landslides. Active debris flows are growing in both frequency of occurrences and spatial scale. Meanwhile, new types of disasters, such as the twin ice avalanches in Ali in 2016, are now appearing in the region. Adaptation and mitigation measures should be taken to help societies’ preparation for future environmental challenges. Some key issues for future TP studies are also discussed.

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