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- 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. |
- 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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