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Weihua Yuan
,
Rucong Yu
,
Haoming Chen
,
Jian Li
, and
Minghua Zhang

Abstract

Subseasonal characteristics of the diurnal variation of the summer monsoon rainfall over central eastern China (25°–40°N, 110°–120°E) are analyzed using hourly station rain gauge data. Results show that the rainfall in the monsoon rain belt is dominated by the long-duration rainfall events (≥7 h) with early-morning peaks. The long-duration rainfall events and early-morning diurnal peaks experience subseasonal movement that is similar to that of the monsoon rain belt. When the monsoon rainfall is separated into the active and break periods, the long-duration early-morning precipitation dominates the active period, which is in sharp contrast to the short-duration (≤6 h) rainfall with leading late-afternoon diurnal peaks during the break period. The combination of different diurnal features of monsoon rainfall in the active and break monsoon periods also explains the less coherent diurnal phases of summer mean rainfall over central eastern China. The cause of the early-morning peak of rainfall during the active monsoon period is discussed.

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Xue Long Zhou
,
Marvin A. Geller
, and
Minghua Zhang

Abstract

There has been increasing recognition of the role of the tropical tropopause layer (TTL) in determining stratospheric water vapor concentrations—the TTL being a layer of transition between air showing tropospheric properties below and stratospheric properties above. This study investigates the spatial structure of temperatures in the TTL. A dehydration index based on the atmospheric region with temperatures colder than a specific reference temperature was defined to examine the TTL temperature structure and possible influences on stratospheric water vapor. The results indicate that dehydration regions with cold temperatures (e.g., <190 K) occur mainly over the western Pacific and are about 1.5–2.0 km in depth during Northern Hemisphere winter. The dehydration index is mainly dependent on the annual cycle of the TTL temperatures, but is strongly affected by interannual variations associated with the quasi-biennial oscillation (QBO) and El Niño–Southern Oscillation (ENSO). Dehydration regions with extremely cold temperatures and large sizes occur when cold temperature anomalies associated with the QBO arrive at the TTL in wintertime while the TTL is at the coldest phase of the annual cycle and under La Niña conditions. La Niña events have a more dramatic influence on the dehydration index than El Niño events.

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Hye-Mi Kim
,
Edmund K. M. Chang
, and
Minghua Zhang

Abstract

This study attempts, for the first time, to predict the annual number of tropical cyclones (TCs) affecting New York State (NYS), as part of the effort of the New York State Resiliency Institute for Storms and Emergencies (RISE). A pure statistical prediction model and a statistical–dynamical hybrid prediction model have been developed based on the understanding of the physical mechanism between NYS TCs and associated large-scale climate variability. During the cold phase of El Niño–Southern Oscillation, significant circulation anomalies in the Atlantic Ocean provide favorable conditions for more recurving TCs into NYS. The pure statistical prediction model uses the sea surface temperature (SST) over the equatorial Pacific Ocean from the previous months. Cross validation shows that the correlation between the observed and predicted numbers of NYS TCs is 0.56 for the June 1979–2013 forecasts. Forecasts of the probability of one or more TCs impacting NYS have a Brier skill score of 0.35 compared to climatology. The statistical–dynamical hybrid prediction model uses Climate Forecast System, version 2, SST predictions, which are statistically downscaled to forecast the number of NYS TCs based on a stepwise regression model. Results indicate that the initial seasonal prediction for NYS TCs can be issued in February using the hybrid model, with an update in June using the pure statistical prediction model. Based on the statistical model, for 2014, the predicted number of TCs passing through NYS is 0.33 and the probability of one or more tropical cyclones crossing NYS is 30%, which are both below average and in agreement with the actual activity (0 NYS TCs).

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Brian A. Colle
,
Zhenhai Zhang
,
Kelly A. Lombardo
,
Edmund Chang
,
Ping Liu
, and
Minghua Zhang

Abstract

Extratropical cyclone track density, genesis frequency, deepening rate, and maximum intensity distributions over eastern North America and the western North Atlantic were analyzed for 15 models from phase 5 of the Coupled Model Intercomparison Project (CMIP5) for the historical period (1979–2004) and three future periods (2009–38, 2039–68, and 2069–98). The cyclones were identified using an automated tracking algorithm applied to sea level pressure every 6 h. The CMIP5 results for the historical period were evaluated using the Climate Forecast System Reanalysis (CFSR). The CMIP5 models were ranked given their track density, intensity, and overall performance for the historical period. It was found that six of the top seven CMIP5 models with the highest spatial resolution were ranked the best overall. These models had less underprediction of cyclone track density, more realistic distribution of intense cyclones along the U.S. East Coast, and more realistic cyclogenesis and deepening rates. The best seven models were used to determine projected future changes in cyclones, which included a 10%–30% decrease in cyclone track density and weakening of cyclones over the western Atlantic storm track, while in contrast there is a 10%–20% increase in cyclone track density over the eastern United States, including 10%–40% more intense (<980 hPa) cyclones and 20%–40% more rapid deepening rates just inland of the U.S. East Coast. Some of the reasons for these CMIP5 model differences were explored for the selected models based on model generated Eady growth rate, upper-level jet, surface baroclinicity, and precipitation.

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Tao Zhang
,
Wuyin Lin
,
Yanluan Lin
,
Minghua Zhang
,
Haiyang Yu
,
Kathy Cao
, and
Wei Xue

Abstract

Tropical cyclone (TC) genesis is a problem of great significance in climate and weather research. Although various environmental conditions necessary for TC genesis have been recognized for a long time, prediction of TC genesis remains a challenge due to complex and stochastic processes involved during TC genesis. Different from traditional statistical and dynamical modeling of TC genesis, in this study, a machine learning framework is developed to determine whether a mesoscale convective system (MCS) would evolve into a tropical cyclone. The machine learning models 1) are built upon a number of essential environmental predictors associated with MCSs/TCs, 2) predict whether MCSs can become TCs at different lead times, and 3) provide information about the relative importance of each predictor, which can be conducive to discovering new aspects of TC genesis. The results indicate that the machine learning classifier, AdaBoost, is able to achieve a 97.2% F1-score accuracy in predicting TC genesis over the entire tropics at a 6-h lead time using a comprehensive set of environmental predictors. A robust performance can still be attained when the lead time is extended to 12, 24, and 48 h, and when this machine learning classifier is separately applied to the North Atlantic Ocean and the western North Pacific Ocean. In contrast, the conventional approach based on the genesis potential index can have no more than an 80% F1-score accuracy. Furthermore, the machine learning classifier suggests that the low-level vorticity and genesis potential index are the most important predictors to TC genesis, which is consistent with previous discoveries.

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Shaocheng Xie
,
Timothy Hume
,
Christian Jakob
,
Stephen A. Klein
,
Renata B. McCoy
, and
Minghua Zhang

Abstract

This study documents the characteristics of the large-scale structures and diabatic heating and drying profiles observed during the Tropical Warm Pool–International Cloud Experiment (TWP-ICE), which was conducted in January–February 2006 in Darwin during the northern Australian monsoon season. The examined profiles exhibit significant variations between four distinct synoptic regimes that were observed during the experiment. The active monsoon period is characterized by strong upward motion and large advective cooling and moistening throughout the entire troposphere, while the suppressed and clear periods are dominated by moderate midlevel subsidence and significant low- to midlevel drying through horizontal advection. The midlevel subsidence and horizontal dry advection are largely responsible for the dry midtroposphere observed during the suppressed period and limit the growth of clouds to low levels. During the break period, upward motion and advective cooling and moistening located primarily at midlevels dominate together with weak advective warming and drying (mainly from horizontal advection) at low levels. The variations of the diabatic heating and drying profiles with the different regimes are closely associated with differences in the large-scale structures, cloud types, and rainfall rates between the regimes. Strong diabatic heating and drying are seen throughout the troposphere during the active monsoon period while they are moderate and only occur above 700 hPa during the break period. The diabatic heating and drying tend to have their maxima at low levels during the suppressed periods. The diurnal variations of these structures between monsoon systems, continental/coastal, and tropical inland-initiated convective systems are also examined.

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Weihua Yuan
,
Rucong Yu
,
Minghua Zhang
,
Wuyin Lin
,
Jian Li
, and
Yunfei Fu

Abstract

The simulations of summertime diurnal cycle of precipitation and low-level winds by the Community Atmosphere Model, version 5, are evaluated over subtropical East Asia. The evaluation reveals the physical cause of the observed diurnal rainfall variation in East Asia and points to the source of model strengths and weaknesses. Two model versions with horizontal resolutions of 2.8° and 0.5° are used.

The models can reproduce the diurnal phase of large-scale winds over East Asia, with an enhanced low-level southwesterly in early morning. Correspondingly, models successfully simulated the diurnal variation of stratiform rainfall with a maximum in early morning. However, the simulated convective rainfall occurs at local noontime, earlier than observations and with larger amplitude (normalized by the daily mean). As a result, models simulated a weaker diurnal cycle in total rainfall over the western plain of China due to an out-of-phase cancellation between convective and stratiform rainfalls and a noontime maximum of total rainfall over the eastern plain of China. Over the East China Sea, models simulated the early-morning maximum of convective precipitation and, together with the correct phase of the stratiform rainfall, they captured the diurnal cycle of total precipitation. The superposition of the stratiform and convective rainfalls also explains the observed diurnal cycle in total rainfall in East Asia. Relative to the coarse-resolution model, the high-resolution model simulated slight improvement in diurnal rainfall amplitudes, due to the larger amplitude of stratiform rainfall. The two models, however, suffer from the same major biases in rainfall diurnal cycles due to the convection parameterization.

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Weihua Yuan
,
Rucong Yu
,
Minghua Zhang
,
Wuyin Lin
,
Haoming Chen
, and
Jian Li

Abstract

Using hourly rain gauge records and Tropical Rainfall Measuring Mission 3B42 from 1998 to 2006, the authors present an analysis of the diurnal characteristics of summer rainfall over subtropical East Asia. The study shows that there are four different regimes of distinct diurnal variation of rainfall in both the rain gauge and the satellite data. They are located over the Tibetan Plateau with late-afternoon and midnight peaks, in the western China plain with midnight to early-morning peaks, in the eastern China plain with double peaks in late afternoon and early morning, and over the East China Sea with an early-morning peak. No propagation of diurnal phases is found from the land to the ocean across the coastlines. The different diurnal regimes are highly correlated with the inhomogeneous underlying surface, such as the plateau, plain, and ocean, with physical mechanisms consistent with the large-scale “mountain–valley” and “land–sea” breezes and convective instability. These diurnal characteristics over subtropical East Asia can be used as diagnostic metrics to evaluate the physical parameterization and hydrological cycle of climate models over East Asia.

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Cheng Zheng
,
Edmund Kar-Man Chang
,
Hyemi Kim
,
Minghua Zhang
, and
Wanqiu Wang

Abstract

The prediction of wintertime extratropical cyclone activity (ECA) on subseasonal time scales by models participating in the Subseasonal Experiment (SubX) and the Seasonal to Subseasonal Prediction (S2S) is assessed. Consistent with a previous study that investigated the S2S models, the SubX models have skillful predictions of ECA over regions from central North Pacific across North America to western North Atlantic, as well as East Asia and northern and southern part of eastern North Atlantic at 3–4 weeks lead time. SubX provides daily mean data, while S2S provides instantaneous data at 0000 UTC each day. This leads to different variance of ECA. Different S2S and SubX models have different reforecast initialization times and reforecast time periods. These factors can all lead to differences in prediction skill. To fairly compare the prediction skill between different models, we develop a novel way to evaluate the prediction of individual model across the two ensembles by comparing every model to the Climate Forecast System, version 2 (CFSv2), as CFSv2 has 6-hourly output and forecasts initialized every day. Among the S2S and SubX models, the European Centre for Medium-Range Weather Forecasts model exhibits the best prediction skill, followed by CFSv2. Our results also suggest that while the prediction skill is sensitive to forecast lead time, including forecasts up to 4 days old into the ensemble may still be useful for weeks 3–4 predictions of ECA.

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Cheng Zheng
,
Edmund Kar-Man Chang
,
Hye-Mi Kim
,
Minghua Zhang
, and
Wanqiu Wang

Abstract

In this study, the intraseasonal variations in storm-track activity, surface air temperature, and precipitation over North America associated with the Madden–Julian oscillation (MJO) in boreal winter (November–April) are investigated. A lag composite strategy that considers different MJO phases and different lag days is developed. The results highlight regions over which the MJO has significant impacts on surface weather on intraseasonal time scales. A north–south shift of storm-track activity associated with the MJO is found over North America. The shift is consistent with the MJO-related surface air temperature anomaly over the eastern United States. In many regions over the western, central, and southeastern United States, the MJO-related precipitation signal is also consistent with nearby storm-track activity. An MJO-related north–south shift of precipitation is also found near the west coast of North America, with the precipitation over California being consistent with the MJO-related storm-track activity over the eastern Pacific. MJO-related temperature and storm-track anomalies are also found near Alaska. Further analyses of streamfunction anomalies and wave activity flux show clear signatures of Rossby wave trains excited by convection anomalies related to MJO phases 3 and 8. These wave trains propagate across the Pacific and North America, bringing an anticyclonic (cyclonic) anomaly to the eastern part of North America, shifting the westerly jet to the north (south), thereby modulating the surface air temperature and storm-track activity over the continent. Rossby waves associated with phases 2 and 6 are also found to impact the U.S. West Coast.

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