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  • 1
    Online Resource
    Online Resource
    Observations and Forecasts of Urban Transportation Meteorology in China: A Review
    MDPI AG ; 2022
    In:  Atmosphere Vol. 13, No. 11 ( 2022-11-02), p. 1823-
    Details
    In: Atmosphere, MDPI AG, Vol. 13, No. 11 ( 2022-11-02), p. 1823-
    Abstract: Against the backdrop of intensified global warming, extreme weather events such as dense fog, low visibility, heavy precipitation, and extreme temperatures have been increased and enhanced to a great extent. They are likely to pose severe threats to the operation of urban transportation and associated services, which has drawn much attention in recent decades. However, there are still plenty of issues to be resolved in improving the emergency meteorological services and developing targeted urban transportation meteorological services in modern cities. The present review briefly illustrates the current cutting-edge developments and trends in the field of urban transportation meteorology in China, including the establishment of observation networks and experiments and the development of early warning and prediction technologies, as well as the related meteorological commercial services. Meanwhile, reflections and discussions are provided in terms of the state-of-the-art observation channels and methods and the application of numerical model forecasts and artificial intelligence. With the advantages of various advanced technologies from multiple aspects, researchers could further expand explorations on urban transportation meteorological observations, forecasts, early warnings, and services. Associated theoretical studies and practical investigations are also to be carried out to provide solid scientific foundations for urban transportation disaster prevention and mitigation, for implementing the action of meteorological guarantees, and for the construction of a high-quality smart society.
    Type of Medium: Online Resource
    ISSN: 2073-4433
    URL: Article
    DOI: 10.3390/atmos13111823
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2605928-9
    SSG: 23
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  • 2
    Online Resource
    Online Resource
    Pavement Temperature Forecasts Based on Model Output Statistics: Experiments for Highways in Jiangsu, China
    MDPI AG ; 2023
    In:  Remote Sensing Vol. 15, No. 16 ( 2023-08-10), p. 3956-
    Details
    In: Remote Sensing, MDPI AG, Vol. 15, No. 16 ( 2023-08-10), p. 3956-
    Abstract: Forecasts on transportation meteorology, such as pavement temperature, are becoming increasingly important in the face of global warming and frequent disruptions from extreme weather and climate events. In this study, we propose a pavement temperature forecast model based on stepwise regression—model output statistics (SRMOS) at the short-term timescale, using highways in Jiangsu, China, as examples. Experiments demonstrate that the SRMOS model effectively calibrates against the benchmark of the linear regression model based on surface air temperature (LRT). The SRMOS model shows a reduction in mean absolute errors by 0.7–1.6 °C, with larger magnitudes observed for larger biases in the LRT forecasts. Both forecasts exhibit higher accuracy in predicting minimum nighttime temperatures compared to maximum daytime temperatures. Additionally, it overall shows increasing biases from the north to the south, and the SRMOS superiority is greater over the south with larger initial LRT biases. Predictor importance analysis indicates that temperature, moisture, and larger-scale background are basically the key predictors in the SRMOS model for pavement temperature forecasts, of which the air temperature is the most crucial factor in the model’s construction. Although larger-scale circulation backgrounds are generally characterized by relatively low importance, their significance increases with longer lead times. The presented results demonstrate the considerable skill of the SRMOS model in predicting pavement temperatures, highlighting its potential in disaster prevention for extreme transportation meteorology events.
    Type of Medium: Online Resource
    ISSN: 2072-4292
    URL: Article
    DOI: 10.3390/rs15163956
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2513863-7
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  • 3
    Online Resource
    Online Resource
    Impacts of Indian Ocean Dipole–Like SST on Rice Yield Anomalies in Jiangsu Province
    Frontiers Media SA ; 2021
    In:  Frontiers in Earth Science Vol. 8 ( 2021-3-10)
    Details
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 8 ( 2021-3-10)
    Abstract: One of the main water vapor sources of eastern China is the Bay of Bengal, over which the circulation is influenced by the Tropical Indian Ocean Dipole (TIOD). The TIOD has a long-lasting effect on weather patterns, which in turn influence the rice yield and quality in eastern China, such as in Jiangsu Province. To identify the main mechanism involved, we perform a detailed investigation of the connections between the TIOD-like sea surface temperature (SST) and the climatic suitability for growing rice, and the subsequent rice yield anomalies, in Jiangsu Province. In particular, we compare the relationships, and identify the underlying mechanisms, of TIOD SST with suitable sunshine duration, temperature and precipitation during the period of rice culture in the province. Singular Value Decomposition (SVD) results show that the TIOD-like SST has a close correlation with the rice yield anomalies, with a temporal correlation coefficient of 0.43 for 53 years, passing the 99% significance level. Furthermore, in the negative TIOD-like SST years, the background circulation weakens the transport effect of the atmospheric river through which water vapor is transported from the Bay of Bengal to eastern China. This decreased amount of transported water vapor decreases the precipitation and total cloud cover in the province. In turn, this significantly increases the sunshine duration, which plays a key role in rice yield anomalies. The increased sunshine duration and higher temperatures lead to positive rice yield anomalies, and vice versa. Our findings highlight that climatic factors, such as TIOD-like SST, have a far-reaching influence on agricultural production (in this case, rice yield), and thus special attention should be paid to interdisciplinary research in the fields of climate and agriculture.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    URL: Article
    DOI: 10.3389/feart.2020.568365
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2021
    detail.hit.zdb_id: 2741235-0
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  • 4
    Online Resource
    Online Resource
    Subseasonal forecasts of precipitation over maritime continent in boreal summer and the sources of predictability
    Frontiers Media SA ; 2022
    In:  Frontiers in Earth Science Vol. 10 ( 2022-8-30)
    Details
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 10 ( 2022-8-30)
    Abstract: In this study, subseasonal precipitation forecast skills over Maritime Continent in boreal summer are investigated for the ECMWF and CMA models involved in the S2S Project. Results indicate that the ECMWF model shows generally superior forecast performances than CMA, which is characterized by lower errors and higher correlations compared with the observations. Meanwhile, ECMWF tends to produce wet biases with increasing lead times, while the mean errors of CMA are revealed to be approximately constant throughout lead times of 2–4 weeks over most areas. Besides, the temporal correlations between model outputs and observations obviously decrease with growing lead times, with a high-low distribution presented from north to south. In addition, the roles of large-scale drivers like ENSO and BSISO in modulating subseasonal precipitation forecast skills are also assessed in the models. Both ECMWF and CMA can reasonably capture the ENSO related precipitation anomalies for all lead times, while their capabilities of capturing BSISO related precipitation anomalies decrease with growing lead times, which is more obvious in CMA. The enhanced subseasonal precipitation forecast skills mainly respond to the BSISO associated precipitation variability. For most MC areas such as southern Indochina, western Indonesia, Philippines and the eastern ocean, the forecast skills of both ECMWF and CMA can be improved to a great extent by enhancing the capture of BSISO related precipitation anomalies, with the temporal correlations for both ECMWF and CMA increased by about 0.15 for lead times of 3–4 weeks. It provides an opportunity window for the models to improve precipitation forecasts on the subseasonal timescale.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    URL: Article
    DOI: 10.3389/feart.2022.970791
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2741235-0
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  • 5
    Online Resource
    Online Resource
    Precipitation over Indochina during the monsoon transition: modulation by Indian Ocean and ENSO regimes
    Springer Science and Business Media LLC ; 2021
    In:  Climate Dynamics Vol. 57, No. 9-10 ( 2021-11), p. 2491-2504
    Details
    In: Climate Dynamics, Springer Science and Business Media LLC, Vol. 57, No. 9-10 ( 2021-11), p. 2491-2504
    Type of Medium: Online Resource
    ISSN: 0930-7575 , 1432-0894
    URL: Article
    DOI: 10.1007/s00382-021-05817-6
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2021
    detail.hit.zdb_id: 382992-3
    detail.hit.zdb_id: 1471747-5
    SSG: 16,13
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  • 6
    Online Resource
    Online Resource
    Forecast calibrations of surface air temperature over Xinjiang based on U-net neural network
    Frontiers Media SA ; 2022
    In:  Frontiers in Environmental Science Vol. 10 ( 2022-9-20)
    Details
    In: Frontiers in Environmental Science, Frontiers Media SA, Vol. 10 ( 2022-9-20)
    Abstract: In this study, a deep learning method named U-net neural network is utilized to calibrate the gridded forecast of surface air temperature from the Global Ensemble Forecasting System (GEFS), with forecast lead times of 1–7 days in Xinjiang. The calibration performance of U-net is compared with three conventional postprocessing methods: unary linear regression (ULR), the decaying averaging method (DAM) and Quantile Mapping (QM). Results show that biases of the raw GEFS forecasts are mainly distributed in the Altai Mountains, the Junggar Basin, the Tarim Basin and the Kunlun Mountains. The four postprocessing methods effectively improve the forecast skills for all lead times, whereas U-net shows the best correction performance with the lowest mean absolute error (MAE) and the highest hit rate of 2°C (HR2) and pattern correlation coefficient (PCC). The U-net model considerably reduces the warm biases of the raw forecasts. The skill improvement magnitudes are greater in southern than northern Xinjiang, showing a higher mean absolute error skill score (MAESS). Furthermore, in order to distinguish the error sources of each forecasting scheme and to reveal their capabilities of calibrating errors of different sources, the error decomposition analysis is carried out based on the mean square errors. It shows that the bias term is the leading source of error in the raw forecasts, and barely changes as the lead time increases, which is mainly distributed in Tarim Basin and Kunlun Mountains. All four forecast calibrations effectively reduce the bias and distribution error of the raw forecasts, but only the U-net significantly reduces the sequence error.
    Type of Medium: Online Resource
    ISSN: 2296-665X
    URL: Article
    DOI: 10.3389/fenvs.2022.1011321
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2741535-1
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  • 7
    Online Resource
    Online Resource
    Statistical calibrations to improve the 2–5-year prediction skill for SST over the North Atlantic
    Springer Science and Business Media LLC ; 2022
    In:  Meteorology and Atmospheric Physics Vol. 134, No. 3 ( 2022-06)
    Details
    In: Meteorology and Atmospheric Physics, Springer Science and Business Media LLC, Vol. 134, No. 3 ( 2022-06)
    Type of Medium: Online Resource
    ISSN: 0177-7971 , 1436-5065
    URL: Article
    DOI: 10.1007/s00703-022-00888-4
    RVK:
    RA 1000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 232907-4
    detail.hit.zdb_id: 863-1
    detail.hit.zdb_id: 1462145-9
    SSG: 16,13
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  • 8
    Online Resource
    Online Resource
    Attribution and Causality Analyses of Regional Climate Variability
    MDPI AG ; 2023
    In:  Land Vol. 12, No. 4 ( 2023-04-03), p. 817-
    Details
    In: Land, MDPI AG, Vol. 12, No. 4 ( 2023-04-03), p. 817-
    Abstract: A two-step attribution and causality diagnostic is designed by employing singular spectrum analysis to unfold the attributed climate time series into a trajectory matrix and then subjected to an empirical orthogonal function analysis to identify the evolving driving forces, which can finally be related to major climate modes through their independent frequencies by wavelet analysis. Application results from the arid and drought-prone southern Intermountain region of North America are compared with the climate or larger scale forcing diagnosed from slow feature analysis using the sources of the water and energy flux balance. The following results are noted: (i) The changes between the subsequent four 20-year periods from 1930 to 2010 suggest predominantly climate-induced forcing by the Pacific Decadal Oscillation and the Atlantic Multidecadal Oscillation. (ii) Land cover influences on the changing land cover are of considerably smaller magnitude (in terms of area percentage cover) whose time evolution is well documented from forestation documents. (iii) The drivers of the climate-induced forcings within the last 20 years are identified as the quasi-biennial oscillation and the El Niño–Southern Oscillation by both the inter-annual two-step attribution and the causality diagnostics with monthly scale-based slow feature analysis.
    Type of Medium: Online Resource
    ISSN: 2073-445X
    URL: Article
    DOI: 10.3390/land12040817
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2682955-1
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  • 9
    Online Resource
    Online Resource
    Forecasts of the Warm-Sector Heavy Rainfall With a Warm Shear Pattern Over Coastal Areas of the Yangtze–Huaihe River in a Regional Business Forecast Model
    Frontiers Media SA ; 2022
    In:  Frontiers in Earth Science Vol. 10 ( 2022-7-22)
    Details
    In: Frontiers in Earth Science, Frontiers Media SA, Vol. 10 ( 2022-7-22)
    Abstract: Based on the observational hourly precipitation data and the ERA5 reanalysis datasets, the short-term forecasts of the warm-sector heavy rainfall with warm-type shear line (WRWS) events over the coastal areas of the Yangtze–Huaihe River (YHR) are investigated in the regional business model Precision Weather Analysis and Forecasting System (PWAFS). Evaluations and diagnoses are carried out via objective estimations and composition analyses for the rainy season of 2017. Results show that the short-term forecasts of PWAFS are characterized by considerable skills for WRWS events in the coastal areas of YHR in view of the object-based diagnostic evaluation, which, however, tend to generate the rain belts with northeast shift phases and weaker intensities. Meantime, the threat score results for WRWS-associated processes show that the model forecasting skill declines sharply as the precipitation intensity increases. Moreover, composition differences of the synoptic-scale thermodynamic characteristics between observations and forecast results are diagnosed to reveal the possible mechanisms of the short-term forecast biases toward WRWS. The zonal westerlies are overestimated in the model, while the southerlies are underestimated in the lower troposphere over coastal areas of YHR, leading to the northeastward shifted shear line and the absent moisture channel associated with the East China Sea at the boundary layer. Attributed to these atmospheric circulation biases, the accumulated warm and moist energy is weaker at the boundary layer, and hence, the short-term forecasts of the rain-belt location for WRWS over the YHR coastal areas have northeast shifting phases with weaker intensities of precipitation in forecasts of the regional business model PWAFS.
    Type of Medium: Online Resource
    ISSN: 2296-6463
    URL: Article
    DOI: 10.3389/feart.2022.938336
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2022
    detail.hit.zdb_id: 2741235-0
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  • 10
    Online Resource
    Online Resource
    Trends and variabilities of precipitation and temperature extremes over Southeast Asia during 1981–2017
    Springer Science and Business Media LLC ; 2022
    In:  Meteorology and Atmospheric Physics Vol. 134, No. 4 ( 2022-08)
    Details
    In: Meteorology and Atmospheric Physics, Springer Science and Business Media LLC, Vol. 134, No. 4 ( 2022-08)
    Type of Medium: Online Resource
    ISSN: 0177-7971 , 1436-5065
    URL: Article
    DOI: 10.1007/s00703-022-00913-6
    RVK:
    RA 1000
    Language: English
    Publisher: Springer Science and Business Media LLC
    Publication Date: 2022
    detail.hit.zdb_id: 232907-4
    detail.hit.zdb_id: 863-1
    detail.hit.zdb_id: 1462145-9
    SSG: 16,13
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