Relationships between the Southwest Monsoon Surge and the Heavy Rainfall Associated with Landfalling Super Typhoon Rammasun

Based on the China Meteorological Administration (CMA) best-track data, the ERA5 reanalysis, and the Global Precipitation Measurement (GPM) precipitation data, this paper analyzes the reasons for the heavy rainfall event of Super Typhoon Rammasun in 2014, and the results are as follows: (1) Rammasun was blocked by the western Pacific subtropical high (WPSH), the continental high, and the mid-latitude westerly trough. Such a stable circulation pattern maintained the vortex circulation of Rammasun. (2) During the period of landfall, the southwest summer monsoon surge was reinforced due to the dramatic increase of the zonal wind and the cross-equatorial flow near 108° E. The results of the dynamic monsoon surge index (DMSI) and boundary water vapor budget (BWVB) show that the monsoon surge kept providing abundant water vapor for Rammasun, which led to the enhanced rainfall. (3) The East Asian monsoon manifested an obvious low-frequency oscillation with a main period of 20–40 days in the summer of 2014, which propagated northward significantly. When the low-frequency oscillation reached the extremely active phase, the monsoon surge hit the maximum and influenced the circulation of Rammasun. Meanwhile, the convergence and water vapor flux associated with the low-frequency oscillation significantly contributed to the heavy rainfall.

Factors Controlling Tropical Cyclone Intensification Over the Marginal Seas of China

Tropical cyclone (TC) intensification over marginal seas, especially rapid intensification (RI), often poses great threat to lives and properties in coastal regions and is subject to large forecast errors. It is thus important to understand the characteristics of TC intensification and the involved key factors affecting TC intensification over marginal seas. In this study, the 6-hourly TC best-track data from Shanghai Typhoon Institute of China Meteorological Administration, ERA-Interim reanalysis data, and TRMM satellite rainfall products are used to analyze and compare the climatological characteristics and key factors of different intensification stratifications over the marginal seas of China (MSC) and the western North Pacific (WNP) during 1980–2018. The statistical results show that TC intensification over the MSC is more likely to occur when TCs experience relatively large intensities, weak vertical wind shear, small translation perpendicular to the coastline, relatively high fullness, strong upper-level divergence, low-level relative vorticity, and high inner-core precipitation rate. The box difference index method is used to quantify the relative contributions of these factors to TC RI. Results show that the initial (relative) intensity contributes the most to TC RI over both the MSC and the WNP. The inner-core precipitation rate and translation perpendicular to the coastline are of second importance to TC RI over the MSC, while both vertical wind shear and TC fullness are crucial to TC RI over the WNP. These findings may help understand TC activity over the MSC and provide a basis for improving intensity prediction of TCs in the MSC.

Evaluation of Forecast Performance for Four Meteorological Models in Summer Over Northwestern China

At present, numerical models, which have been used for forecasting services in northwestern China, have not been extensively evaluated. We used national automatic ground station data from summer 2016 to test and assess the forecast performance of the high-resolution global European Centre for Medium-Range Weather Forecast (ECMWF) model, the mesoscale Northwestern Mesoscale Numerical Prediction System (NW-MNPS), the global China Meteorological Administration T639 model, and the mesoscale Global/Regional Assimilation and Prediction System (GRAPES) model over northwestern China. The root mean square error (RMSE) of the 2-m temperature forecast by ECMWF was the lowest, while that by T639 was the highest. The distribution of RMSE for each model forecast was similar to that of the difference between the modeled and observed terrain. The RMSE of the 10-m wind speed forecast was lower for the global ECMWF and T639 models and higher for the regional NW-MNPS and GRAPES models. The 24-h precipitation forecast was generally higher than observed for each model, with NW-MNPS having the highest score for light rain and heavy storm rain, ECMWF for medium and heavy rain, and T639 for storm rain. None of the models could forecast small-scale and high-intensity precipitation, but they could forecast large-scale precipitation. Overall, ECMWF had the best stability and smallest prediction errors, followed by NW-MNPS, T639, and GRAPES.

Study of the Boundary Layer Structure of a Landfalling Typhoon Based on the Observation from Multiple Ground-Based Doppler Wind Lidars

The boundary layer structure is crucial to the formation and intensification of typhoons, but there is still a lack of high-precision turbulence observations in the typhoon boundary layer due to limitations of the observing instruments under typhoon conditions. Using joint observations from multiple ground-based Doppler wind lidars (DWL) collected by the Shanghai Typhoon Institute of China Meteorological Administration (CMA) during the transit of Typhoon Lekima (8–11 August 2019), the characteristics of the wind field and physical quantities (including turbulent kinetic energy (TKE) and typhoon boundary layer height (TBLH)) of the boundary layer of typhoon Lekima were analyzed. The magnitude of TKE was shown to be related not only to the horizontal wind speed but also to the presence of a strong downdraft, which leads to a rapid increase of TKE. The magnitudes of TKE in different quadrants of Typhoon Lekima were also found to differ. The TKE in the front right quadrant of the typhoon was 2.5–6.0 times that in the rear left quadrant and ~1.7 times that in the rear right quadrant. The TKE over the island was larger than that over the urban area. Before Typhoon Lekima made landfall, the TKE increased with decreasing distance to the typhoon center. After typhoon landfall, the TKE changes were different on the left and right sides of the typhoon center, with the TKE on the left decreasing rapidly, while that on the right changed little. The typhoon boundary layer height calculated by five methods was compared and was found to decrease gradually before typhoon landfall and increased gradually afterward. The trends of the TBLH calculated using helicity and TKE were consistent, and both determine the TBLH well, while the maximum tangential wind speed height (humax) was larger than the height calculated by other methods. This study confirms that DWL has a strong detecting capability for the finescale structure of the typhoon boundary layer and provides a powerful tool for the validation of numerical simulations of typhoon structure.

Development and validation of a new MODIS snow-cover-extent product over China

Based on the MOD09GA/MYD09GA 500-m surface reflectance, a new MODIS snow-cover-extent (SCE) product over China has been produced by the Northwest Institute of Eco-Environment and Resources (NIEER), Chinese Academy of Sciences. The NIEER MODIS SCE product contains two preliminary clear-sky SCE datasets — Terra-MODIS and Aqua-MODIS SCE datasets, and a final daily cloud-gap-filled (CGF) SCE dataset. The formers are generated mainly through optimizing snow-cover discriminating rules over different land-cover types, and the latter is produced after a series of gap-filling processes such as aggregating the two preliminary datasets, reducing cloud gaps with adjacent information in space and time, and eliminating all gaps with auxiliary data. Validation against 362 China Meteorological Administration (CMA) stations shows during snow seasons the overall accuracies (OA) of the three datasets are all larger than 93 %, the omission errors (OE) are all constrained within 9 %, and the commission errors (CE) are all constrained within 10 %. Biases ranging from the lowest 0.98 to the medium 1.02, to the largest 1.03 demonstrate on a whole the SCEs given by the new product are neither overestimated nor underestimated significantly. Based on the same ground reference data, we found the new product’s accuracies are clearly higher than those of standard MODIS snow products, especially for Aqua-MODIS and CGF SCE. For examples, compared with the CE of 23.78 % that the standard MYD10A1 product shows, the CE of the new Aqua-MODIS SCE dataset is 6.78 %; the OA of the new CGF SCE dataset is up to 93.15 %, versus 89.54 % of the standard MOD10A1F product and 84.36 % of the standard MYD10A1F product. Besides, as expected snow discrimination in forest areas is also improved significantly. An isolated validation at four forest CMA stations demonstrates the OA has increased by 3–10 percentage points, the OE has dropped by 1–8 percentage points, and the CE has dropped by 4–21 percentage points. Therefore, our product has virtually provided more reliable snow knowledge over China, and thereby can better serve for hydrological, climatic, environmental, and other related studies there.

Evaluation of satellite-based precipitation products from GPM IMERG and GSMaP over the three-river headwaters region, China

The three-river headwaters region (TRHR) is the birthplace of the Yangtze River, the Yellow River and the Lantsang River in China. Based on the grid surface precipitation data released by China Meteorological Administration (CMA), this paper evaluated the accuracy and error components of four near-real-time satellite precipitation products (GSMaP-NRT, GSMaP-MVK, IMERG-Early and IMERG-Late) in the era of a GPM (Global Precipitation Measurement) in TRHR. The conclusions are as follows: (1) The precipitation in TRHR is concentrated in the east and south, and the precipitation in the west is very low. IMERG (Early and Late) has a good spatial distribution of precipitation, while GSMaP has an obvious spatial smoothing of precipitation distribution, and does not better highlight the local precipitation characteristics. (2) The inversion accuracy of the satellite products is the best in the source region of the Lantsang River, followed by the source region of the Yellow River. The satellite products all show the lower correlation coefficient and serious underestimation of precipitation in the west of the TRHR. In addition, the closer to the west of the TRHR, the lower hit rate and the higher false alarm rate of the satellite products, especially the NRT and MVK products. In the eastern margin of the Yellow River headwater region and the Lantsang River headwater Region, RMSE and overestimated precipitation were higher in NRT and MVK, and FAR was higher in spite of higher POD and CSI. (3) The errors of GSMaP in the source region of the Yellow River and the Lantsang River are mainly caused by misreporting precipitation and overestimating the precipitation level, while the errors of GSMaP in the west of the TRHR are mainly caused by missing measurements of precipitation events. The underestimated precipitation of IMERG mainly comes from the missed measurement of precipitation and the underestimate of precipitation level, and there is no large false precipitation. (4) In addition, we found that the satellite products in the lake distribution area of the TRHR have serious missed precipitation errors, indicating that the GPM satellite products have the poor detection ability of precipitation near plateau lakes. On the whole, the precipitation inversion accuracy of IMERG (Early and Late) products is higher, which can better detect the occurrence of precipitation events, but the estimation of precipitation level is still not accurate. The precision of precipitation of satellite products near inland lakes on the plateau is poor, so the algorithm improvement of new products needs to be further solved in the future.

Variations of the Precipitation Extremes over the Guangdong-Hong Kong-Macao Greater Bay Area in China

By using the gridded 0.25°×0.25° observation dataset of CN05.1 provided by the China Meteorological Administration, this study investigates the variations of the nine precipitation extreme indices over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China in the period from 1961 to 2018. Based on trends and inter-annual variations, the nine kinds of extreme precipitation are classified into four categories: the category 1 is the very wet days (R95P), the extremely wet days (R99P), the maximum 1-day precipitation amount (RX1day) and the maximum 5-day precipitation amount (RX5day). The category 2 is the number of heavy precipitation days (R10day), the number of very heavy precipitation days (R20day) and the simple daily intensity index (SDII). The category 3 and 4 is the consecutive wet days (CWDday) and the consecutive dry days (CDDday), respectively. For the extreme precipitations in the category 1, the abrupt change point from less to more values occurs in 1991 in summer. Three abrupt change points, from less to more in 1972 and 2009, and from more to less in 1994 occur in spring. For the extreme precipitations in the category 2, the abrupt change point from less to more values occurs in 1993 in summer. Three abrupt change points, from less to more in 1965 and 2010, and from more to less in 1990 occur in spring. Annually and seasonally, the abrupt changes occur in early 2010s for CWDday which has clearly been more and for CDDday which has clearly been less. In addition, CWDday occurs abrupt change points from less to more in 1966 and from more to less in1983 in spring. The variations of these extreme precipitations have significant periodic oscillations of 3–5 years, quasi-8 years or 8–14 years. During 1961–1994, 1995–2009 and 2010–2018 three stages, the changes of the annual and most seasonal R95P, R99P, R10day, R20day and SDII are consistent with those of precipitation. The values in the latter stage are increasing compared with those in the former stage. The changes of RX1day, RX5day, CWDday and CDDday have their own characteristics.

Validation of FY-3D MERSI-2 Precipitable Water Vapor (PWV) Datasets Using Ground-Based PWV Data from AERONET

The medium resolution spectral imager-2 (MERSI-2) is one of the most important sensors onboard China’s latest polar-orbiting meteorological satellite, Fengyun-3D (FY-3D). The National Satellite Meteorological Center of China Meteorological Administration has developed four precipitable water vapor (PWV) datasets using five near-infrared bands of MERSI-2, including the P905 dataset, P936 dataset, P940 dataset and the fusion dataset of the above three datasets. For the convenience of users, we comprehensively evaluate the quality of these PWV datasets with the ground-based PWV data derived from Aerosol Robotic Network. The validation results show that the P905, P936 and fused PWV datasets have relatively large systematic errors (−0.10, −0.11 and −0.07 g/cm2), whereas the systematic error of the P940 dataset (−0.02 g/cm2) is very small. According to the overall accuracy of these four PWV datasets by our assessments, they can be ranked in descending order as P940 dataset, fused dataset, P936 dataset and P905 dataset. The root mean square error (RMSE), relative error (RE) and percentage of retrieval results with error within ±(0.05+0.10∗PWVAERONET) (PER10) of the P940 PWV dataset are 0.24 g/cm2, 0.10 and 76.36%, respectively. The RMSE, RE and PER10 of the P905 PWV dataset are 0.38 g/cm2, 0.15 and 57.72%, respectively. In order to obtain a clearer understanding of the accuracy of these four MERSI-2 PWV datasets, we compare the accuracy of these four MERSI-2 PWV datasets with that of the widely used MODIS PWV dataset and AIRS PWV dataset. The results of the comparison show that the accuracy of the MODIS PWV dataset is not as good as that of all four MERSI-2 PWV datasets, due to the serious overestimation of the MODIS PWV dataset (0.40 g/cm2), and the accuracy of the AIRS PWV dataset is worse than that of the P940 and fused MERSI-2 PWV datasets. In addition, we analyze the error distribution of the four PWV datasets in different locations, seasons and water vapor content. Finally, the reason why the fused PWV dataset is not the one with the highest accuracy among the four PWV datasets is discussed.

The UK-China Climate Science to Service Partnership

We present results from the first 6 years of this major UK government funded project to accelerate and enhance collaborative research and development in climate science, forge a strong strategic partnership between UK and Chinese climate scientists and demonstrate new climate services developed in partnership. The development of novel climate services is described in the context of new modelling and prediction capability, enhanced understanding of climate variability and change, and improved observational datasets. Selected highlights are presented from over three hundred peer reviewed studies generated jointly by UK and Chinese scientists within this project. We illustrate new observational datasets for Asia and enhanced capability through training workshops on the attribution of climate extremes to anthropogenic forcing. Joint studies on the dynamics and predictability of climate have identified new opportunities for skilful predictions of important aspects of Chinese climate such as East Asian Summer Monsoon rainfall. In addition, the development of improved modelling capability has led to profound changes in model computer codes and climate model configurations, with demonstrable increases in performance. We also describe the successes and difficulties in bridging the gap between fundamental climate research and the development of novel real time climate services. Participation of dozens of institutes through sub-projects in this programme, which is governed by the Met Office Hadley Centre, the China Meteorological Administration and the Institute of Atmospheric Physics, is creating an important legacy for future collaboration in climate science and services.

Characteristics and Development Mechanisms of Northeast Cold Vortices

The northeast cold vortices (NECVs) in May-September during 1989–2018 are classified, based on the 6 h NCEP/NCAR reanalysis data (2.5° × 2.5°) and observational data from the Meteorological Information Comprehensive Analysis and Process System (MICAPS) provided by China Meteorological Administration. Meanwhile, characteristics and development mechanisms for NECVs of different types are also analyzed. In the recent 30 years, the occurrences of NECV processes have been increasing year by year, with an average of 7.4 times per year in Northeast China and a duration of 3–5 days on average for each process. NECVs mostly occur in late spring and early summer, and the longest time influenced by NECVs exceeds 19 days, with annual means of 9.9 days, 8.8 days, and 7.0 days in May, June, and July, respectively. The frequency of weak NECVs is about 1.2 times that of strong NECVs. Strong NCVs in late spring and early autumn as well as weak MCVs in summer are with high-frequency occurrences. It is found that when NCVs occur in late spring and early autumn, the upper-level westerly jets are relatively stronger, thus strengthening the divergence in the upper troposphere and the vortex circulation. The circulation fields in upper and lower levels cooperate with the strong jets, promoting the continuous development and maintenance of the cold vortices. Apart from the jets and circulation, the lower central potential height combined with the obvious cold-core and stronger ascending motions favor the NCV’s development. In addition, the dry intrusion has a strong promotion due to the stronger lower-level cold advection and downward intrusion of high potential vorticity. However, when MCVs occur in summer, things are just the opposite.