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Guo Lin
,
Coltin Grasmick
,
Bart Geerts
,
Zhien Wang
, and
Min Deng

Abstract

This observational study documents the consequences of a collision between two converging shallow atmospheric boundaries over the central Great Plains on the evening of 7 June 2015. This study uses data from a profiling airborne Raman lidar [the compact Raman lidar (CRL)] and other airborne and ground-based data collected during the Plains Elevated Convection at Night (PECAN) field campaign to investigate the collision between a weak cold front and the outflow from an MCS. The collision between these boundaries led to the lofting of high-CAPE, low-CIN air, resulting in deep convection, as well as an undular bore. Both boundaries behaved as density currents prior to collision. Because the MCS outflow boundary was denser and less deep than the cold-frontal air mass, the bore propagated over the latter. This bore was tracked by the CRL for about 3 h as it traveled north over the shallow cold-frontal surface and evolved into a soliton. This case study is unique by using the high temporal and spatial resolution of airborne Raman lidar measurements to describe the thermodynamic structure of interacting boundaries and a resulting bore.

Free access
Guo Deng
,
Jun Du
,
Yushu Zhou
,
Ling Yan
,
Jing Chen
,
Fajing Chen
,
Hongqi Li
, and
Jingzhou Wang

Abstract

Using a 3-km regional ensemble prediction system (EPS), this study tested a three-dimensional (3D) rescaling mask for initial condition (IC) perturbation. Whether the 3D mask-based EPS improves ensemble forecasts over current two-dimensional (2D) mask-based EPS has been evaluated in three aspects: ensemble mean, spread, and probability. The forecasts of wind, temperature, geopotential height, sea level pressure, and precipitation were examined for a summer month (1–28 July 2018) and a winter month (1–27 February 2019) over a region in North China. The EPS was run twice per day (initiated at 0000 and 1200 UTC) to 36 h in forecast length, providing 56 warm-season forecast cases and 54 cold-season cases for verification. The warm and cold seasons are verified separately for comparison. The study found the following: 1) The vertical profile of IC perturbation becomes closer to that of analysis uncertainty with the 3D rescaling mask. 2) Ensemble performance is significantly improved in all three aspects. The biggest improvement is in the ensemble spread, followed by the probabilistic forecast, and the least improvement is in the ensemble mean forecast. Larger improvements are seen in the warm season than in the cold season. 3) More improvement is in the shorter time range (<24 h) than in the longer range. 4) Surface and lower-level variables are improved more than upper-level ones. 5) The underlying mechanism for the improvement has been investigated. Convective instability is found to be responsible for the spread increment and, thus, overall ensemble forecast improvement. Therefore, using a 3D rescaling mask is recommended for an EPS to increase its utility especially for shorter time range and surface weather elements.

Significant Statement

A weather prediction model is a complex system that consists of nonlinear differential equations. Small errors in either its inputs or model itself will grow with time during model integration, which will contaminate a forecast. To quantify such contamination (“uncertainty”) of a forecast, the ensemble forecasting technique is used. An ensemble of forecasts is a multiple of model runs at the same time but with slightly “perturbed” inputs or model versions. These small perturbations are supposed to represent true “uncertainty” in inputs or model representation. This study proposed a technique that makes a perturbation’s vertical structure more resemble real uncertainty (intrinsic error) in input data and confirmed that it can significantly improve ensemble forecast quality especially for a shorter time range and lower-level weather elements. It is found that convective instability is responsible for the improvement.

Open access
Jingzhuo Wang
,
Jing Chen
,
Jun Du
,
Yutao Zhang
,
Yu Xia
, and
Guo Deng

Abstract

This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent “strong” and “weak” bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread’s spatial structure is much less; the spread–skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.

Open access
Binbin Guo
,
Yeqiang Shu
,
Weiqiang Wang
,
Gaowen He
,
Qianyong Liang
,
Dongsheng Zhang
,
Lusha Yu
,
Jun Wang
,
Xiguang Deng
,
Yong Yang
,
Qiang Xie
,
Yinan Deng
, and
Danyi Su

Abstract

Observations of currents and temperatures from four moorings deployed around the deep slope (∼2500 m) of Caiwei Guyot in the Pacific Prime Crust Zone were utilized to investigate topographically trapped waves at low-latitude seamounts. Contrasting with commonly reported persistent diurnal seamount-trapped wave cases at middle and high latitudes, the subinertial variability in deep currents and temperatures at the slope of Caiwei Guyot was primarily characterized by two distinct lower-frequency bands (i.e., 13–24 and 3.3–4.7 days). These subinertial variabilities are interpreted as intermittent seamount-trapped waves and topographic Rossby waves (TRWs). During certain time periods, the observations include key signatures of seamount-trapped waves, such as near-opposite phases of azimuthal velocity (and temperature) on opposite flanks of the seamount, and patterns of temporal current rotation consistent with counterrotating cells of horizontal current propagating counterclockwise around the seamount. After comparing these observations to idealized seamount-trapped wave solutions, we conclude that the 13–24-day (3.3–4.7-day) energy is mainly due to radial–vertical mode 5 (3) for azimuthal wavenumber 1 (3). Sometimes the subinertial energy remained pronounced at only one flank of the seamount, primarily explained as TRWs with 192–379-m vertical trapping scale and 14–28-km wavelength. Upper-layer mesoscale perturbations might provide energy for deep seamount-trapped waves and TRWs. This study highlights the role of topographically trapped waves in modulating the deep circulation at low-latitude seamounts.

Restricted access
Haijun Deng
,
N. C. Pepin
,
Yaning Chen
,
Bin Guo
,
Shuhua Zhang
,
Yuqing Zhang
,
Xingwei Chen
,
Lu Gao
,
Liu Meibing
, and
Chen Ying

Abstract

Systematic analyses of the daytime and nocturnal precipitation changes provide a better understand of the impact of global warming on the environment. In this study, the daytime and nocturnal precipitation across China from 1990 to 2019 was analyzed using observational data from 698 meteorological stations. Both daytime and nocturnal precipitation have increased in the western parts of China (including the Continental basin, headwaters of the Yangtze River basin, and Yellow River basin), whereas the trends in the eastern part are more complex. Climatological differences between daytime and nocturnal precipitation in summer were more significant than in other seasons. We developed a Z index to quantify the diurnal differences of precipitation. The annual mean Z index of China is about −2%, and its long-term change on an annual basis increased at a rate of 0.06% yr−1 (p < 0.1). The mean Z-index values during the year and seasons (except for summer) are negative and show an increasing trend. The intensity of the diurnal differences of precipitation has been decreasing in China since 1990. Topographic exposure and distance from the coast also influence the daytime and nocturnal precipitation changes. The Z index of the first-category stations (distance from the coast ≤ 100 km) was positively correlated with the distance from the coast (r = 0.39; p < 0.001) in summer, which may result from the superposition of the summer monsoon and sea-breeze effects.

Significance Statement

The diurnal cycle of precipitation is an important indicator for diagnosing the impact of global warming on the environment. There is a slight annual difference between daytime and nocturnal precipitation in China. The nocturnal precipitation maximum is in winter, spring, and autumn and the opposite occurs in summer. We define a precipitation index to quantifying the intensity of the diurnal differences of precipitation. The mean precipitation index is negative annually and seasonally (except for summer), with an increasing trend indicating that the intensity of the diurnal differences of precipitation has decreased in China from 1990 to 2019. These results are valuable for understanding the impact of recent warming on the diurnal differences of precipitation in China.

Free access
Yihong Duan
,
Jiandong Gong
,
Jun Du
,
Martin Charron
,
Jing Chen
,
Guo Deng
,
Geoff DiMego
,
Masahiro Hara
,
Masaru Kunii
,
Xiaoli Li
,
Yinglin Li
,
Kazuo Saito
,
Hiromu Seko
,
Yong Wang
, and
Christoph Wittmann

The Beijing 2008 Olympics Research and Development Project (B08RDP), initiated in 2004 under the World Meteorological Organization (WMO) World Weather Research Programme (WWRP), undertook the research and development of mesoscale ensemble prediction systems (MEPSs) and their application to weather forecast support during the Beijing Olympic Games. Six MEPSs from six countries, representing the state-of-the-art regional EPSs with near-real-time capabilities and emphasizing on the 6–36-h forecast lead times, participated in the project.

The background, objectives, and implementation of B08RDP, as well as the six MEPSs, are reviewed. The accomplishments are summarized, which include 1) providing value-added service to the Olympic Games, 2) advancing MEPS-related research, 3) accelerating the transition from research to operations, and 4) training forecasters in utilizing forecast uncertainty products. The B08RDP has fulfilled its research (MEPS development) and demonstration (value-added service) purposes. The research conducted covers the areas of verification, examining the value of MEPS relative to other numerical weather prediction (NWP) systems, combining multimodel or multicenter ensembles, bias correction, ensemble perturbations [initial condition (IC), lateral boundary condition (LBC), land surface IC, and model physics], downscaling, forecast applications, data assimilation, and storm-scale ensemble modeling. Seven scientific issues important to MEPS have been identified. It is recognized that the daily use of forecast uncertainty information by forecasters remains a challenge. Development of forecaster-friendly products and training activities should be a long-term effort and needs to be continuously enhanced.

The B08RDP dataset is also a valuable asset to the research community. The experience gained in international collaboration, organization, and implementation of a multination regional EPS for a common goal and to address common scientific issues can be shared by the ongoing projects The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble—Limited Area Models (TIGGE-LAM) and North American Ensemble Forecast System—Limited Area Models (NAEFS-LAM).

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