scholarly journals Quantitative Characteristics of the Current Multi-Source Precipitation Products over Zhejiang Province, in Summer, 2019

Water ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 334
Author(s):  
Chao Qiu ◽  
Leiding Ding ◽  
Lan Zhang ◽  
Jintao Xu ◽  
Ziqiang Ma

Precipitation data with fine quality plays vital roles in hydrological-related applications. In this study, we choose the high-quality China Merged Precipitation Analysis data (CMPA) as the benchmark for evaluating four satellite-based precipitation products (PERSIANN-CCS, FY4A QPE, GSMap_Gauge, IMERG-Final) and one model-based precipitation product (ERA5-Land), respectively, at 0.1°, hourly scales over the Zhejiang province, China, in summer, from June to August 2019. The main conclusions were as follows—(1) all other products demonstrate similar patterns with CMPA (~325.60 mm/h, std ~0.07 mm/h), except FY4A QPE (~281.79 mm/h, std ~0.18 mm/h), while, overall, the PERSIANN-CCS underestimates the precipitation against CMPA with a mean value around 236.29 mm/h (std ~0.06 mm/h), and the ERA5-Land, GSMap_Guage, and IMERG-Final generally overestimate the precipitation with a mean value around 370.00 mm/h (std ~0.06 mm/h). (2) The GSMap_Gauge outperforms IMERG-Final against CMPA with CC ~0.50 and RMSE ~1.51 mm/h, and CC ~0.48 and RMSE ~1.64 mm/h, respectively. (3) The PERSIANN-CCS significantly underestimates the precipitation (CC ~0.26, bias ~−35.03%, RMSE ~1.81 mm/h, probability of detection, POD, ~0.33, false alarm ratio, FAR, ~0.47), potentially due to its weak abilities to capture precipitation events and estimate the precipitation. (4) Though ERA5-Land has the best ability to capture precipitation events (POD ~0.78), the largest misjudgments (FAR ~0.54) result in its great uncertainties with CC ~ 0.39, which performs worse than those of GSMap_Gauge and IMERG-Final. (5) The ranking of precipitation products, in terms of the general evaluation metrics, over Zhejiang province is GSMap_Gauge, IMERG-Final, ERA5-Land, PERSIANN-CCS, and FY4A QPE, which provides valuable recommendations for applying these products in various related application fields.

2010 ◽  
Vol 11 (4) ◽  
pp. 966-978 ◽  
Author(s):  
Kenneth J. Tobin ◽  
Marvin E. Bennett

Abstract Significant concern has been expressed regarding the ability of satellite-based precipitation products such as the National Aeronautics and Space Administration (NASA) Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 products (version 6) and the U.S. National Oceanic and Atmospheric Administration (NOAA) Climate Prediction Center’s (CPC) morphing technique (CMORPH) to accurately capture rainfall values over land. Problems exist in terms of bias, false-alarm rate (FAR), and probability of detection (POD), which vary greatly worldwide and over the conterminous United States (CONUS). This paper directly addresses these concerns by developing a methodology that adjusts existing TMPA products utilizing ground-based precipitation data. The approach is not a simple bias adjustment but a three-step process that transforms a satellite precipitation product. Ground-based precipitation is used to develop a filter eliminating FAR in the authors’ adjusted product. The probability distribution function (PDF) of the satellite-based product is adjusted to the PDF of the ground-based product, minimizing bias. Failure of precipitation detection (POD) is addressed by utilizing a ground-based product during these periods in their adjusted product. This methodology has been successfully applied in the hydrological modeling of the San Pedro basin in Arizona for a 3-yr time series, yielding excellent streamflow simulations at a daily time scale. The approach can be applied to any satellite precipitation product (i.e., TRMM 3B42 version 7) and will provide a useful approach to quantifying precipitation in regions with limited ground-based precipitation monitoring.


2020 ◽  
Vol 33 (8) ◽  
pp. 3289-3305 ◽  
Author(s):  
Yan Yan ◽  
Huan Wu ◽  
Guojun Gu ◽  
Zhijun Huang ◽  
Lorenzo Alfieri ◽  
...  

AbstractSpatial and temporal variations of global floods during the TRMM period (1998–2013) are explored by means of the outputs of the Dominant River Routing Integrated with VIC Environment model (DRIVE) driven by the precipitation rates from the TRMM Multisatellite Precipitation Analysis (TMPA). Climatological and seasonal mean features of floods including frequency (FF), duration (FD), and mean and total intensity (FI and FTI) are examined and further compared to those for a variety of precipitation indices derived from the daily TMPA rain rates. In general, floods and precipitation manifest similar spatial distributions, confirming that more precipitation (both amount and frequency) often indicates higher probability of floods. However, different flood indices can be associated with different precipitation characteristics with a highly region-dependent distribution. FF and FD tend to be more related to daily precipitation frequency globally, especially the mid- to high-end precipitation frequencies (F10, F25, F50). However, FI and FTI tend to be more associated with the mean volume/magnitude of those (extreme) daily precipitation events (Pr10 and Pr25). Nonetheless, daily precipitation intensity except the very high end one (R50) generally has a relatively weak effect on floods. The precipitation–flood relations at the 10 large regions are further examined, providing an improved understanding of precipitation-related flood-generating mechanisms in different locations. On the interannual time scale, El Niño–Southern Oscillation (ENSO) can significantly affect floods in many flood-prone zones. However, it is noted that even though the ENSO effect on floods is mostly through modulating various aspects of precipitation events, significant ENSO signals in precipitation cannot always translate to an effective, simultaneous impact on floods.


2012 ◽  
Vol 140 (7) ◽  
pp. 2232-2252 ◽  
Author(s):  
Thomas M. Hamill

Abstract Probabilistic quantitative precipitation forecasts (PQPFs) were generated from The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) database from July to October 2010 using data from Europe (ECMWF), the United Kingdom [Met Office (UKMO)], the United States (NCEP), and Canada [Canadian Meteorological Centre (CMC)]. Forecasts of 24-h accumulated precipitation were evaluated at 1° grid spacing within the contiguous United States against analysis data based on gauges and bias-corrected radar data. PQPFs from ECMWF’s ensembles generally had the highest skill of the raw ensemble forecasts, followed by CMC. Those of UKMO and NCEP were less skillful. PQPFs from CMC forecasts were the most reliable but the least sharp, and PQPFs from NCEP and UKMO ensembles were the least reliable but sharper. Multimodel PQPFs were more reliable and skillful than individual ensemble prediction system forecasts. The improvement was larger for heavier precipitation events [e.g., >10 mm (24 h)−1] than for smaller events [e.g., >1 mm (24 h)−1]. ECMWF ensembles were statistically postprocessed using extended logistic regression and the five-member weekly reforecasts for the June–November period of 2002–09, the period where precipitation analyses were also available. Multimodel ensembles were also postprocessed using logistic regression and the last 30 days of prior forecasts and analyses. The reforecast-calibrated ECMWF PQPFs were much more skillful and reliable for the heavier precipitation events than ECMWF raw forecasts but much less sharp. Raw multimodel PQPFs were generally more skillful than reforecast-calibrated ECMWF PQPFs for the light precipitation events but had about the same skill for the higher-precipitation events; also, they were sharper but somewhat less reliable than ECMWF reforecast-based PQPFs. Postprocessed multimodel PQPFs did not provide as much improvement to the raw multimodel PQPF as the reforecast-based processing did to the ECMWF forecast. The evidence presented here suggests that all operational centers, even ECMWF, would benefit from the open, real-time sharing of precipitation forecast data and the use of reforecasts.


2021 ◽  
Author(s):  
Myriam Benkirane ◽  
Nour-Eddine Laftouhi ◽  
Said Khabba ◽  
Bouabid El Mansouri

Abstract. The performance of Tropical Precipitation Measurement Mission (TRMM) and its successor, Global Precipitation Measurement (GPM), has provided hydrologists with a source of critical precipitation data for hydrological applications in basins where ground-based observations of precipitation are sparse, or spatially undistributed. The very high temporal and spatial resolution satellite precipitation products have therefore become a reliable alternative that researchers are increasingly using in various hydro-meteorological and hydro-climatological applications. This study aims to evaluate statistically and hydrologically the TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitations products (SPPs), at multiple temporal scales from 2010 to 2017, in a mountainous watershed characterized by the Mediterranean climate. The results show that TRMM (3B42 V7) and GPM (IMERG V5) satellite precipitation products have a significant capacity for detecting precipitation at different time steps. However, the statistical analysis of SPPs against ground observation shows good results for both statistical metrics and contingency statistics with notable values (CC > 0.8), and representative values relatively close to 0 for the probability of detection (POD), critical success index (CSI), and false alarm ratio (FAR). Moreover, the sorting of the events implemented on the hydrological model was performed seasonally, at daily time steps. The calibrated episodes showed very good results with Nash values ranging from 53.2 % to 95.5 %. Nevertheless, the (IMERG V5) product detects more efficiently precipitation events at short time steps (daily), while (3B42 V7) has a strong ability to detect precipitation events at large time steps (monthly and yearly). Furthermore, the modeling results illustrate that both satellite precipitation products tend to underestimate precipitation during wet seasons and overestimate them during dry seasons, while they have a better spatial distribution of precipitation measurements performance, which shows the importance of their use for basin modeling and potentially for flood forecasting in Mediterranean catchment areas.


Author(s):  
Zahra Ghoncheh ◽  
Behrang Moghaddamzadeh ◽  
Hanieh Kaviani ◽  
Golshan Jamali ◽  
Maral Feizi

Objectives: This study aimed to measure the buccal cortical plate thickness in the mandible of dentate adults in an Iranian population using cone-beam computed tomography (CBCT). Materials and Methods: Eighty CBCT images were evaluated in this study using NNT Viewer 6.0 software. Images had high-resolution and had been taken by NewTom CBCT scanner with 11 x 8cm field of view. Measurements were made using the digital ruler of the software with 0.1mm accuracy. All analyses were performed by two observers: an oral and maxillofacial radiologist and a general dentist. In case of disagreement between the observers, measurements were repeated and the mean value was used for analysis. Data were analyzed by using linear regression. Results: The results showed that the thickness of buccal cortical plate increased from the canine towards the second molar site. The second molar site had the greatest density and thickness. Gender had a significant effect on the thickness of buccal cortical plate (P<0.05) but the effect of right/ left quadrant was not significant (P>0.05). The effect of age on this thickness was insignificant in some (P>0.05) and significant (P<0.05) in some other areas such that by an increase in age of patients, this thickness decreased (i.e. at the apex of canine, second premolar and second molar teeth). Conclusion: The buccal cortical plate thickness of the mandible increases from the anterior towards the posterior region, and the second molar area has the greatest thickness and density suitable for placement of orthodontic mini-implants or harvesting autogenous grafts.


2015 ◽  
Vol 10 (3) ◽  
pp. 436-447 ◽  
Author(s):  
Yuji Sugihara ◽  
◽  
Sho Imagama ◽  
Nobuhiro Matsunaga ◽  
Yukiko Hisada ◽  
...  

It is difficult to forecast hourly rainfall locally even using the latest meteorological models, although hourly rainfall averaged spatially to some extent can be used for calculating practical rainfall. This study conducts numerical experiments with triple nesting on the 2012 heavy rainfall event in northern Kyushu using the weather research and forecasting (WRF) model and examines the features of hourly rainfall averaged spatially. The dependence of rainfall is averaged spatially on a spatial averaging scale and clarified by comparing rainfall calculated by simulation using the WRF model with radar/AMeDAS precipitation analysis data. This study’s findings indicate the effective spatial averaging scale making relative error of calculated values to the observed ones minimum.


2020 ◽  
Vol 231 ◽  
pp. 104671
Author(s):  
Yanmin Lv ◽  
Jianping Guo ◽  
Steve Hung-Lam Yim ◽  
Yuxing Yun ◽  
Jinfang Yin ◽  
...  

2019 ◽  
Vol 11 (8) ◽  
pp. 973 ◽  
Author(s):  
Yuanbing Wang ◽  
Yaodeng Chen ◽  
Jinzhong Min

In this study, the China Hourly Merged Precipitation Analysis (CHMPA) data which combines the satellite-retrieved Climate Prediction Center Morphing (CMORPH) with the automatic weather station precipitation observations is firstly assimilated into the Weather Research and Forecasting (WRF) model using the Four-Dimensional Variational (4DVar) method. The analyses and subsequent forecasts of heavy rainfall during Meiyu season occurred in July 2013 over eastern China is evaluated. Besides, the sensitivity of rainfall forecast skill of assimilating the CHMPA data to the rainfall error, the rainfall thinning distance, and the rainfall accumulation time within assimilation window are investigated in this study. Then, the impact of 4DVar data assimilation with and without CHMPA rainfall data is evaluated to show how the assimilation of CHMPA impacts the precipitation simulations. It is found that assimilation of the CHMPA data helps to produce a better short-range precipitation forecast in this study. The rainfall fields after assimilation of CHMPA is closer to observations in terms of quantity and pattern. However, the leading time of improved forecast is limited to about 18 hours. It is also found that CHMPA data assimilation produces stronger realistic moisture divergence, precipitabale water field and the vertical wind field in the forecasting fields, which eventually contributes to the improved forecast of heavy rainfall. This study can provide references for the assimilation of CHMPA data into the WRF model using 4DVar, which is valuable for limited-area numerical weather prediction and hydrological applications.


2019 ◽  
Vol 4 (33) ◽  
pp. 223-237
Author(s):  
Yong Wu ◽  
Fonny Dameaty Hutagalung ◽  
Mohd Rashid Saad

The purpose of this research mainly investigates the level of psychological capital in Chinese university EFL lecturers. And the related level of aspects of psychological capital – optimism, hope, self-efficacy, and resilience. 556 Chinese university EFL teachers in Zhejiang province have involved in this study and there is a high level of psychological capital. The mean value for the four dimensions, hope, and resilience, belong to the moderator level, while; efficacy and optimism are high levels. And then some discussions are proposed.


2013 ◽  
Vol 26 (3) ◽  
pp. 1047-1062 ◽  
Author(s):  
Olivier P. Prat ◽  
Brian R. Nelson

Abstract The objective of this paper is to characterize the precipitation amounts originating from tropical cyclones (TCs) in the southeastern United States during the tropical storm season from June to November. Using 12 years of precipitation data from the Tropical Rainfall Measurement Mission (TRMM), the authors estimate the TC contribution on the seasonal, interannual, and monthly precipitation budget using TC information derived from the International Best Track Archive for Climate Stewardship (IBTrACS). Results derived from the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42 showed that TCs accounted for about 7% of the seasonal precipitation total from 1998 to 2009. Rainfall attributable to TCs was found to contribute as much as 8%–12% for inland areas located between 150 and 300 km from the coast and up to 15%–20% for coastal areas from Louisiana to the Florida Panhandle, southern Florida, and coastal Carolinas. The interannual contribution varied from 1.3% to 13.8% for the period 1998–2009 and depended on the TC seasonal activity, TC intensity, and TC paths as they traveled inland. For TCs making landfall, the rainfall contribution could be locally above 40% and, on a monthly basis, TCs contributed as much as 20% of September rainfall. The probability density functions of rainfall attributable to tropical cyclones showed that the percentage of rainfall associated with TC over land increased with increasing rain intensity and represent about 20% of heavy rainfall (&gt;20 mm h−1), while TCs account for less than 5% of all seasonal precipitation events.


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