| 1. |
- Fu, Yunfei, et al.
(författare)
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Fundamental Characteristics of Tropical Rain Cell Structures as Measured by TRMM PR
- 2020
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Ingår i: Journal of Meteorological Research. - : Springer Science and Business Media LLC. - 2095-6037 .- 2198-0934. ; 34:6, s. 1129-1150
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Tidskriftsartikel (refereegranskat)abstract
- Rain cells are the most elementary unit of precipitation system in nature. In this study, fundamental geometric and physical characteristics of rain cells over tropical land and ocean areas are investigated by using 15-yr measurements of the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). The rain cells are identified with a minimum bounding rectangle (MBR) method. The results indicate that about 50% of rain cells occur at length of about 20 km and width of 15 km. The proportion of rain cells with length > 200 km and width > 100 km is less than 1%. There is a a log-linear relationship between the mean length and width of rain cells. Usually, for the same horizontal geometric parameters, rain cells tend to be square horizontally and lanky vertically over land, while vertically squatty over ocean. The rainfall intensity of rain cells varies from 0.4 to 10 mm h(-1) over land to 0.4-8 mm h(-1) over ocean. Statistical results indicate that the occurrence frequency of rain cells decreases as the areal fraction of convective precipitation in rain cells increases, while such frequency remains almost invariant when the areal fraction of stratiform precipitation varies from 10% to 80%. The relationship between physical and geometric parameters of rain cells shows that the mean rain rate of rain cells is more frequently associated with the increase of their area, with the increasing rate over land greater than that over ocean. The results also illustrate that heavy convective rain rate prefers to occur in larger rain cells over land while heavy stratiform rain rate tends to appear in larger rain cells over ocean. For the same size of rain cells, the areal fraction and the contribution of convective precipitation are about 10%-15% higher over land than over ocean.
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| 2. |
- Gao, K. L., et al.
(författare)
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Synoptic Climate Settings and Moisture Supply for the Extreme Heavy Snowfall in Northern China during 6-8 November 2021
- 2023
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Ingår i: Journal of Meteorological Research. - : Springer Science and Business Media LLC. - 2095-6037 .- 2198-0934. ; 37:1, s. 75-89
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Tidskriftsartikel (refereegranskat)abstract
- A record-breaking extreme heavy snowfall (EHS) event hit northern China during 6-8 November 2021, with two maximum snowfall centers in North China (NC) and Northeast China (NEC), which inflicted severe socioeconomic impacts. This paper compares the differences in the synoptic processes and moisture supply associated with the EHS event in NC and NEC, as well as the atmospheric circulation anomalies before the event, to provide a reference for better prediction and forecasting of EHS in northern China. Synoptic analyses show that a positively tilted, inverted 500-hPa trough channeled cold-air outbreaks into NC, while dynamic updrafts along the front below the trough promoted moisture convergence over this region. In NEC, the dynamic updraft south of the frontogenesis region firstly triggered a low-level Yellow-Bohai Sea cyclone, which then converged with the 500-hPa trough to ultimately form an NEC cold vortex. Calculation of the vorticity tendency indicates that absolute vorticity advection was a better indicator than absolute vorticity divergence for the movement of the trough/ridge at the synoptic scale. Moreover, NOAA's HYSPLIT (Hybrid Single-Particle Lagrangian Integrated Trajectory) model results reveal that the moisture for the EHS over NC mainly originated from the mid-to-low levels over the Asian-African region and the Eurasian mid-to-high latitudes, accounting for 32% and 31%, respectively. In contrast, the source of water vapor for the EHS over NEC was mainly the Eurasian mid-to-high latitudes and East Asia, with contributions of 38% and 28%, respectively. The findings of this study shed some fresh light on the distinctive contributions of different moisture sources to local precipitation. Further analyses of the atmospheric circulation anomalies in October reveal that a phase shift in the Arctic Oscillation related to the weakening of the polar vortex could have served as a useful indicator for the cold-air outbreaks in this EHS event.
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| 3. |
- Jiang, Shanhu, et al.
(författare)
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Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China
- 2020
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Ingår i: Journal of Meteorological Research. - : Springer Science and Business Media LLC. - 2095-6037 .- 2198-0934. ; 34:5, s. 1096-1113
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Tidskriftsartikel (refereegranskat)abstract
- Satellite- and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs; i.e., Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 Version 7 (TMPA 3B42V7), Climate Prediction Center (CPC) morphing technique satellite-gauge blended product (CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang (XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that: (1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient (CC) and root-mean-square error (RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias (BIAS; −22.72%); (2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation; (3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash-Sutcliffe efficiency (NSE) values (0.85 and 0.75 for calibration period and validation period, respectively); and (4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in data-scarce regions with similar climates and topography in the Global Precipitation Measurement (GPM) era.
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| 4. |
- Kazemzadeh, Majid, et al.
(författare)
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Four Decades of Air Temperature Data over Iran Reveal Linear and Nonlinear Warming
- 2022
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Ingår i: Journal of Meteorological Research. - : Springer Science and Business Media LLC. - 2095-6037 .- 2198-0934. ; 36:3, s. 462-477
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Tidskriftsartikel (refereegranskat)abstract
- Spatiotemporal analysis of long-term changes in air temperature is of prime importance for climate change research and the development of effective mitigation and adaptation strategies. Although there is considerable research on air temperature change across the globe, most of it has been on linear trends and time series analysis of nonlinear trends has not received enough attention. Here, we analyze spatiotemporal patterns of monthly and annual mean (Tmean), maximum (Tmax) and minimum (Tmin) air temperature at 47 synoptic stations across climate zones in Iran for a 40-year time period (1978–2017). We applied a polynomial fitting scheme (Polytrend) to both monthly and annual air temperature data to detect trends and classify them into linear and nonlinear (quadratic and cubic) categories. The highest magnitude of increasing trends were observed in the annual Tmin (0.47 °C per decade) and the lowest magnitude was for the annual Tmax (0.4°C per decade). Across the country, increasing trends (x̄ = 37.2%) had higher spatial coverage than the decreasing trends (x̄ = 3.2%). Warming trends in Tmean (65.3%) and Tmin (73.1%) were mainly observed in humid climate zone while warming trends in Tmax were in semi-arid (43.9%) and arid (34.1%) climates. Linear change with a positive trend was predominant in all Tmean (56.7%), Tmax (67.8%) and Tmin (71.2%) and for both monthly and annual datasets. Further, the linear trends had the highest warming rate in annual Tmin (0.83°C per decade) and Tmean (0.46°C per decade) whereas the nonlinear trends had the highest warming rate in annual Tmax (0.52°C per decade). The linear trend type was predominant in humid climate zones whereas the nonlinear trends (quadratic and cubic) were mainly observed in the arid climate zones.
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