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Xiaoli Li
,
Martin Charron
,
Lubos Spacek
, and
Guillem Candille

Abstract

A regional ensemble prediction system (REPS) with the limited-area version of the Canadian Global Environmental Multiscale (GEM) model at 15-km horizontal resolution is developed and tested. The total energy norm singular vectors (SVs) targeted over northeastern North America are used for initial and boundary perturbations. Two SV perturbation strategies are tested: dry SVs with dry simplified physics and moist SVs with simplified physics, including stratiform condensation and convective precipitation as well as dry processes. Model physics uncertainties are partly accounted for by stochastically perturbing two parameters: the threshold vertical velocity in the trigger function of the Kain–Fritsch deep convection scheme, and the threshold humidity in the Sundqvist explicit scheme. The perturbations are obtained from first-order Markov processes. Short-range ensemble forecasts in summer with 16 members are performed for five different experiments. The experiments employ different perturbation and piloting strategies, and two different surface schemes. Verification focuses on quantitative precipitation forecasts and is done using a range of probabilistic measures. Results indicate that using moist SVs instead of dry SVs has a stronger impact on precipitation than on dynamical fields. Forecast skill for precipitation is greatly influenced by the dominant synoptic weather systems. For stratiform precipitation caused by strong baroclinic systems, the forecast skill is improved in the moist SV experiments relative to the dry SV experiments. For convective precipitation rates in the range 15–50 mm (24 h)−1 produced by weak synoptic baroclinic systems, all experiments exhibit noticeably poorer forecast skills. Skill improvements due to the Interactions between Soil, Biosphere, and Atmosphere (ISBA) surface scheme and stochastic perturbations are also observed.

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Yu Xia
,
Jing Chen
,
Jun Du
,
Xiefei Zhi
,
Jingzhuo Wang
, and
Xiaoli Li

Abstract

This study experimented with a unified scheme of stochastic physics and bias correction within a regional ensemble model [Global and Regional Assimilation and Prediction System–Regional Ensemble Prediction System (GRAPES-REPS)]. It is intended to improve ensemble prediction skill by reducing both random and systematic errors at the same time. Three experiments were performed on top of GRAPES-REPS. The first experiment adds only the stochastic physics. The second experiment adds only the bias correction scheme. The third experiment adds both the stochastic physics and bias correction. The experimental period is one month from 1 to 31 July 2015 over the China domain. Using 850-hPa temperature as an example, the study reveals the following: 1) the stochastic physics can effectively increase the ensemble spread, while the bias correction cannot. Therefore, ensemble averaging of the stochastic physics runs can reduce more random error than the bias correction runs. 2) Bias correction can significantly reduce systematic error, while the stochastic physics cannot. As a result, the bias correction greatly improved the quality of ensemble mean forecasts but the stochastic physics did not. 3) The unified scheme can greatly reduce both random and systematic errors at the same time and performed the best of the three experiments. These results were further confirmed by verification of the ensemble mean, spread, and probabilistic forecasts of many other atmospheric fields for both upper air and the surface, including precipitation. Based on this study, we recommend that operational numerical weather prediction centers adopt this unified scheme approach in ensemble models to achieve the best forecasts.

Open access
Lu Yang
,
Mingxuan Chen
,
Xiaoli Wang
,
Linye Song
,
Meilin Yang
,
Rui Qin
,
Conglan Cheng
, and
Siteng Li

Abstract

The ability to forecast thermodynamic conditions aloft and near the surface is critical to the accurate forecasting of precipitation type at the surface. This paper presents an experimental version of a new scheme for diagnosing precipitation type. The method considers the optimum surface temperature threshold associated with each precipitation type and combines model-based explicit fields of hydrometeors with higher-resolution modified thermodynamic and topographic information to determine precipitation types in North China. Based on over 60 years of precipitation-type samples from North China, this study explores the climatological characteristics of the five precipitation types—snow, rain, ice pellets (IP), rain/snow mix (R/S MIX), and freezing rain (FZ)—as well as the suitable air temperature Ta and wet-bulb temperature Tw thresholds for distinguishing different precipitation types. Direct output from numerical weather prediction (NWP) models, such as temperature and humidity, was modified by downscaling and bias correction, as well as by incorporating the latest surface observational data and high-resolution topographic data. Validation of the precipitation-type forecasts from this scheme was performed against observations from the 2016 to 2019 winter seasons and two case studies were also analyzed. Compared with the similar diagnostic routine in the High-Resolution Rapid Refresh (HRRR) forecasting system used to predict precipitation type over North China, the skill of the method proposed here is similar for rain and better for snow, R/S MIX, and FZ. Furthermore, depiction of the diagnosed boundary between R/S MIX and snow is good in most areas. However, the number of misclassifications for R/S MIX is significantly larger than for rain and snow.

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Xiaoduo Pan
,
Xuejun Guo
,
Xin Li
,
Xiaolei Niu
,
Xiaojuan Yang
,
Min Feng
,
Tao Che
,
Rui Jin
,
Youhua Ran
,
Jianwen Guo
,
Xiaoli Hu
, and
Adan Wu

Abstract

The Tibetan Plateau, as the world's third pole due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the third pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript.

The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect third pole-related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in upcoming “big data” era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the third pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use.

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Xiaoduo Pan
,
Xuejun Guo
,
Xin Li
,
Xiaolei Niu
,
Xiaojuan Yang
,
Min Feng
,
Tao Che
,
Rui Jin
,
Youhua Ran
,
Jianwen Guo
,
Xiaoli Hu
, and
Adan Wu

Abstract

The Tibetan Plateau, known as the world’s “Third Pole” due to its high altitude, is experiencing rapid, intense climate change, similar to and even far more than that occurring in the Arctic and Antarctic. Scientific data sharing is very important to address the challenges of better understanding the unprecedented changes in the Third Pole and their impacts on the global environment and humans. The National Tibetan Plateau Data Center (TPDC, http://data.tpdc.ac.cn) is one of the first 20 national data centers endorsed by the Ministry of Science and Technology of China in 2019 and features the most complete scientific data for the Tibetan Plateau and surrounding regions, hosting more than 3,500 datasets in diverse disciplines. Fifty datasets featuring high-mountain observations, land surface parameters, near-surface atmospheric forcing, cryospheric variables, and high-profile article-associated data over the Tibetan Plateau, frequently being used to quantify the hydrological cycle and water security, early warning assessments of glacier avalanche disasters, and other geoscience studies on the Tibetan Plateau, are highlighted in this manuscript. The TPDC provides a cloud-based platform with integrated online data acquisition, quality control, analysis, and visualization capability to maximize the efficiency of data sharing. The TPDC shifts from the traditional centralized architecture to a decentralized deployment to effectively connect Third Pole–related data from other domestic and international data sources. As an embryo of data sharing and management over extreme environment in the upcoming “big data” era, the TPDC is dedicated to filling the gaps in data collection, discovery, and consumption in the Third Pole, facilitating scientific activities, particularly those featuring extensive interdisciplinary data use.

Full 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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Xin Li
,
Guodong Cheng
,
Shaomin Liu
,
Qing Xiao
,
Mingguo Ma
,
Rui Jin
,
Tao Che
,
Qinhuo Liu
,
Weizhen Wang
,
Yuan Qi
,
Jianguang Wen
,
Hongyi Li
,
Gaofeng Zhu
,
Jianwen Guo
,
Youhua Ran
,
Shuoguo Wang
,
Zhongli Zhu
,
Jian Zhou
,
Xiaoli Hu
, and
Ziwei Xu

A major research plan entitled “Integrated research on the ecohydrological process of the Heihe River Basin” was launched by the National Natural Science Foundation of China in 2010. One of the key aims of this research plan is to establish a research platform that integrates observation, data management, and model simulation to foster twenty-first-century watershed science in China. Based on the diverse needs of interdisciplinary studies within this research plan, a program called the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) was implemented. The overall objective of HiWATER is to improve the observability of hydrological and ecological processes, to build a world-class watershed observing system, and to enhance the applicability of remote sensing in integrated ecohydrological studies and water resource management at the basin scale. This paper introduces the background, scientific objectives, and experimental design of HiWATER. The instrumental setting and airborne mission plans are also outlined. The highlights are the use of a flux observing matrix and an eco-hydrological wireless sensor network to capture multiscale heterogeneities and to address complex problems, such as heterogeneity, scaling, uncertainty, and closing water cycle at the watershed scale. HiWATER was formally initialized in May 2012 and will last four years until 2015. Data will be made available to the scientific community via the Environmental and Ecological Science Data Center for West China. International scientists are welcome to participate in the field campaign and use the data in their analyses.

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