The Taklimakan Desert (TD) is located in the Eurasian continent in the mid-latitude region of the Northern Hemisphere (Rittner et al., 2016). As the second-largest desert in the world, it occupies the central part of the Tarim Basin, with a total area of ∼337,600 km 2 , and the average elevation is about 1.1 km. TD is flanked by high mountain ranges, the Pamirs to the west, Tianshan Mountain to the north, and Kunlun Mountain to the south. Due to the special topography, the easterly wind is dominant in the lower troposphere (Ge et al., 2016). As shown in Figure 1, TD is covered by moving sand that can spread hundreds of kilometers, causing inconvenient transportation (Sun et al., 2001). Besides, due to the dry climate, the frequency of sand storms have been increased in this region in the last decades (
In this article, precipitation in a new regional reanalysis dataset, namely, the East Asia regional reanalysis system (EARS‐CMA) with a 12‐km resolution based on the Weather Research and Forecast (WRF) model, ERA‐Interim, ERA5, TRMM 3B42V7, Climate Prediction Center Morphing Technique (CMORPH) and CN05.1, is compared in 2008–2017. The results show that EARS‐CMA can capture the spatial features and temporal variation in precipitation over East Asia well. Focusing on mainland China, CMORPH performs worse in regard to winter precipitation and dryer areas than TRMM against CN05.1. Although ERA5 behaves better with integrated properties compared with ERA‐Interim and EARS‐CMA over East Asia, EARS‐CMA produces more reliable annual, summer and winter mean precipitation pattern than ERA5 over the subregions dominated by large scales in mainland China, including the Tibetan Plateau and northwestern China, similar to its driving fields, ERA‐Interim. In addition, the improvement from ERA‐Interim to EARS‐CMA can be obtained in reasonably reproducing precipitation seasonality in South China, Indo‐China and southern Japan. Thus, the compatibility of EARS is outstanding in maintaining the advantages of its corresponding driving fields, ERA‐Interim, as well as developing finer small scales. Although slightly behind ERA5 in the subregions affected by tropical systems and monsoons, EARS‐CMA is competitive in acting as reference data for daily, seasonal, annual and interannual precipitation estimations over mainland China.
Abstract. Reanalysis data plays a vital role in weather and climate study, as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre of Medium-Range Weather Forecasts (ECMWF) global reanalysis EAR-Interim data at 6-h intervals; and hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in a three-dimensional variational data assimilation (3D-VAR) mode at analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12-km horizontal resolution, 74 sigma levels, and 3-hour intervals. Finally, an evaluation of EARS has been performed with the respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of the EAR-Interim, the regional reanalysis data of the EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of China Meteorological Administration (CMA) at http://data.cma.cn.
Both dynamic and cloud microphysical processes play significant roles in the intensity of severe rainfall within a convective storm. In this study, a quantitative analysis has been performed to investigate dynamic and cloud microphysical contributions to extreme hourly rainfall (EHR) with the peak value of 201.9 mm in Zhengzhou City, China, on 20 July 2021. It is found that the EHR is generated by the overlay of rainwater provided by both dynamic delivery and cloud microphysical production within a meso‐γ‐scale convective storm over Zhengzhou. Specifically, part of the rainwater is directly produced by cloud microphysical processes over the EHR region. More importantly, considerable rainwater, which is produced in the front of the storm associated with strong updraughts, is delivered into the EHR region. The dynamically delivered rainwater overlays the rainwater produced by cloud microphysical processes, forming a deep layer with a large amount of rainwater over the EHR region. As the massive rainwater pours down within a short time, EHR is formed. It should be highlighted that the dynamic delivery plays a decisive role in EHR formation, although sometimes EHR can be generated mainly through cloud microphysical production in the case of weak dynamic delivery. Concerning the cloud microphysical processes, the collision of cloud droplets by raindrops produces the largest amount of rainwater, followed by graupel melting. Linking the EHR with dynamic and cloud microphysical processes within a convective storm, a new light on further understanding and forecasting of short‐duration extreme rainfall would be shed.
Nearly 70% of the world’s maritime crude oil transportation relies on the Maritime Silk Road (MSR). In order to deeply explore the impact of slumping oil price on the shipping situation of tanker along the MSR, this paper establishes the relationship between monthly ship and oil price through Autoregressive Distributed Lag model. Distributions of cargo flow before and after the oil price slumped are compared to explore the changing law of tanker shipping situation. The study finds: (1) The correlation between the cargo flow situation of the tanker seaborne export and oil price, where the export cargo flow correlation is stronger than that of the import cargo flow. (2) The MSR tanker shipping situation is lagging (3 months) behind the impact of oil price. The lag effect in Europe, North Asia and East Asia is strong while that in Southeast Asia and South Asia is weak. (3) After the oil price slumped, the tanker shipping cargo flow increased less during the crude oil export stage, and the increase in the crude oil shipping trade after the transfer period was larger. The research results can provide a scientific basis for improving the decision-making ability of the crude oil shipping market and formulating maritime operations management measures.
Abstract. Reanalysis data play a vital role in weather and climate study as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis ERA-Interim data at 6 h intervals. Hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in three-dimensional variational data assimilation (3D-VAR) mode at the analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12 km horizontal resolution, 74 sigma levels, and 3 h intervals. Finally, an evaluation of EARS has been performed with respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of ERA-Interim, the regional reanalysis data of EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of the China Meteorological Administration (CMA) at http://data.cma.cn (last access: 19 May 2023).
Dealiasing is a common procedure in radar radial velocity quality control. Generally, there are two dealiasing steps: a continuity check and a reference check. In this paper, a modified version that uses azimuthal variance of radial velocity is introduced based on the integrating velocity–azimuth process (IVAP) method, referred to as the V-IVAP method. The new method can retrieve the averaged winds within a local area instead of averaged wind within a full range circle by the velocity–azimuth display (VAD) or the modified VAD method. The V-IVAP method is insensitive to the alias of the velocity, and provides a better way to produce reference velocities for a reference check. Instead of a continuity check, we use the IVAP method for a fine reference check because of its high-frequency filtering function. Then a dealiasing procedure with two steps of reference check is developed. The performance of the automatic dealiasing procedure is demonstrated by retrieving the wind field of a tornado. Using the dealiased radar velocities, the retrieved winds reveal a clear mesoscale vortex. A test based on radar network observations also has shown that the two-step dealiasing procedure based on V-IVAP and IVAP methods is reliable.
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