scholarly journals Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAIN data

2013 ◽  
Vol 118 (5) ◽  
pp. 2184-2196 ◽  
Author(s):  
Yingjun Chen ◽  
Elizabeth E. Ebert ◽  
Kevin J.E. Walsh ◽  
Noel E. Davidson
Keyword(s):  
Tropical Cyclone ◽  
Precipitation Estimates ◽  
Trmm 3B42

scholarly journals Mean Features of Tropical Cyclone Precipitation from TRMM/3B42

SOLA ◽  
2012 ◽  
Vol 8 ◽  
pp. 17-20 ◽  
Author(s):  
Hirotaka Kamahori
Keyword(s):  
Tropical Cyclone ◽  
Trmm 3B42

scholarly journals A comparison between high-resolution satellite precipitation estimates and gauge measured data: case study of Gorganrood basin, Iran

2018 ◽  
Vol 67 (3) ◽  
pp. 236-251 ◽  
Author(s):  
Donya Dezfooli ◽  
Banafsheh Abdollahi ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Kumars Ebrahimi
Keyword(s):  
Pearson Correlation ◽  
Absolute Error ◽  
Probability Of Detection ◽  
Critical Success ◽  
Precipitation Estimates ◽  
Seasonal Basis ◽  
Precipitation Gauge ◽  
Critical Success Index ◽  
Trmm 3B42

Abstract The aim of this paper is to evaluate the accuracy of the precipitation data gathered from satellites including PERSIANN, TRMM-3B42V7, TRMM-3B42RTV7, and CMORPH, over Gorganrood basin, Iran. The data collected from these satellites (2003–2007) were then compared with precipitation gauge observations at six stations, namely, Tamar, Ramiyan, Bahlakeh-Dashli, Sadegorgan, Fazel-Abad, and Ghaffar-Haji. To compare these two groups, mean absolute error (MAE), bias, root mean square error (RMSE), and Pearson correlation coefficient criteria were calculated on daily, monthly, and seasonal basis. Furthermore, probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were calculated for these datasets. Results indicate that, on a monthly scale, the highest correlation between observed and satellite-gathered data calculated is 0.404 for TRMM-3B42 at Bahlakeh-Dashli station. At a seasonal scale, the highest correlation is calculated for winter data and using PERSIANN data, while for the other seasons, TRMM-3B42 data showed the best correlation with observed data. The high values of RMSE and MAE for winter data showed that the satellites provided poor estimations at this season. The best and the worst values of RMSE for studied satellites belonged to Sadegorgan and Ramiyan stations, respectively. Furthermore, the PERSIANN gains a better CSI and POD while TRMM-3B42V7 showed a better FAR.

Analysis of Tropical Cyclone Precipitation Using an Object-Based Algorithm

2013 ◽  
Vol 26 (8) ◽  
pp. 2563-2579 ◽  
Author(s):  
Gregor Skok ◽  
Julio Bacmeister ◽  
Joseph Tribbia
Keyword(s):  
Tropical Cyclone ◽  
Tropical Rainfall Measuring Mission ◽  
Object Identification ◽  
Northwest Pacific ◽  
Identification Algorithm ◽  
Tropical Ocean ◽  
The North ◽  
Linear Relationships ◽  
Significant Downward Trend ◽  
Precipitation Estimates

Abstract A recently developed object identification algorithm is applied to multisensor precipitation estimates from the Tropical Rainfall Measuring Mission (TRMM 3B42) to detect and quantify the contribution of tropical cyclone precipitation (TCP) to total precipitation between 1998 and 2008. The study period includes 1144 storms. Estimates of TCP derived here are similar in pattern and seasonal variation to earlier estimates but are somewhat higher in magnitude. Annual-mean TCP fractions of over 20% are diagnosed over large swaths of tropical ocean, with seasonal means in some regions of more than 50%. Interannual variability of TCP is examined, and a small but significant downward trend in global TCP from 1998 to 2008 is found, consistent with results from independent studies examining accumulated cyclone energy (ACE). Relationships between annual-mean ACE and TCP in each major tropical cyclone basin are examined. High correlations are found in almost every basin, although different linear relationships exist in each. The highest ACE/TCP ratios are obtained in the North Atlantic and northeast Pacific basins, with lower ratios present in the northwest Pacific and South Pacific basins.

scholarly journals Evaluation of Multi-Precipitation Products for Multi-Time Scales and Spatial Distribution During 2007-2015

2019 ◽  
Vol 5 (1) ◽  
pp. 255 ◽  
Author(s):  
Nguyen Tien Thanh
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Water Resources ◽  
Time Scales ◽  
Heavy Rain ◽  
Multiple Time Scales ◽  
Multiple Time ◽  
Precipitation Estimates ◽  
Rain Events ◽  
Trmm 3B42

Recently, several precipitation products are released with the improved algorithm to strengthen the performance of precipitation construction and monitoring. These data play a key role in a wide range of hydrological models, water resources modeling and environmental researches. Especially in developing countries like Vietnam, it is challenging to gather data for long-term time series at scales of daily and sub-daily due to the very coarse density of observation station. In order to overcome the problem of data scarcity, this study aims to evaluate the performance of newest multiple precipitation products including Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Climate Prediction Center (CPC) MORPHing Version 1.0 (CMORPH_V1.0), European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis systems (ERA-Interim), Climate Research Unit Time series Version 4.0.1 (CRU TS 4.0.1) and Asian Precipitation-Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources version 2 (APHRODITE) in comparison with measured precipitation for multiple time scales (daily, monthly, seasonal and annual), taking the VuGia-ThuBon (VG-TB) as a pilot basin where climate regime is complex. Seven continuous and four dichotomous statistics are applied to evaluate the precipitation estimates qualitatively at multiple time scales. In addition, specifically, evaluation of spatial distribution of multiple time scales is implemented. The results show lower precipitation estimates in areas of high elevation and higher precipitation estimates over the areas of plain and coastal in comparison with measured precipitation for all considered precipitation data. More importantly, ERA-Interim well captures rain events of heavy rain (50.0-100 mm/day). CMORHPH_V1.0 better reproduces the rain events with little overestimation of light rain (0.6-6 mm/day) than the others. For zero rain events (0-0.6 mm/day), TRMM 3B42 V7 gives the best performance. Furthermore, the cumulative distribution function of APHRODITE well matches the distribution of measured precipitation. All precipitation products completely fail to capture the rain events of extremely heavy rain. More importantly, a formula is proposed to scale and adjust the merged satellite precipitation at a sub-daily scale.

scholarly journals Evaluation of TRMM 3B42 precipitation estimates and WRF retrospective precipitation simulation over the Pacific–Andean region of Ecuador and Peru

2014 ◽  
Vol 18 (8) ◽  
pp. 3179-3193 ◽  
Author(s):  
A. Ochoa ◽  
L. Pineda ◽  
P. Crespo ◽  
P. Willems
Keyword(s):  
South America ◽  
Weather Prediction ◽  
Probability Of Detection ◽  
Rainfall Data ◽  
Andean Region ◽  
Data Scarcity ◽  
Time Aggregation ◽  
The Pacific ◽  
Precipitation Estimates ◽  
Trmm 3B42

Abstract. The Pacific–Andean region in western South America suffers from rainfall data scarcity, as is the case for many regions in the South. An important research question is whether the latest satellite-based and numerical weather prediction (NWP) model outputs capture well the temporal and spatial patterns of rainfall over the region, and hence have the potential to compensate for the data scarcity. Based on an interpolated gauge-based rainfall data set, the performance of the Tropical Rainfall Measuring Mission (TRMM) 3B42 V7 and its predecessor V6, and the North Western South America Retrospective Simulation (OA-NOSA30) are evaluated over 21 sub-catchments in the Pacific–Andean region of Ecuador and Peru (PAEP). In general, precipitation estimates from TRMM and OA-NOSA30 capture the seasonal features of precipitation in the study area. Quantitatively, only the southern sub-catchments of Ecuador and northern Peru (3.6–6° S) are relatively well estimated by both products. The accuracy is considerably less in the northern and central basins of Ecuador (0–3.6° S). It is shown that the probability of detection (POD) is better for light precipitation (POD decreases from 0.6 for rates less than 5 mm day−1 to 0.2 for rates higher than 20 mm day−1. Compared to its predecessor, 3B42 V7 shows modest region-wide improvements in reducing biases. The improvement is specific to the coastal and open ocean sub-catchments. In view of hydrological applications, the correlation of TRMM and OA-NOSA30 estimates with observations increases with time aggregation. The correlation is higher for the monthly time aggregation in comparison with the daily, weekly, and 15-day time scales. Furthermore, it is found that TRMM performs better than OA-NOSA30 in generating the spatial distribution of mean annual precipitation.

Evaluation of TMPA 3B42 daily precipitation estimates of tropical cyclone rainfall over Australia

2013 ◽  
Vol 118 (21) ◽  
pp. 11,966-11,978 ◽  
Author(s):  
Yingjun Chen ◽  
Elizabeth E. Ebert ◽  
Kevin J. E. Walsh ◽  
Noel E. Davidson
Keyword(s):  
Tropical Cyclone ◽  
Daily Precipitation ◽  
Precipitation Estimates

Comparison of two multi-satellite algorithms for estimation of tropical cyclone precipitation in the United States and Mexico: TMPA and IMERG

2021 ◽  
Author(s):  
Shanshui Yuan ◽  
Laiyin Zhu ◽  
Steven M. Quiring
Keyword(s):  
United States ◽  
Tropical Cyclone ◽  
The United States ◽  
Satellite Retrievals ◽  
Noticeable Improvement ◽  
Precipitation Estimates ◽  
Daily Scale ◽  
Systematic Biases ◽  
Precipitation Analysis ◽  
Annual Scale

AbstractTropical cyclone precipitation (TCP) contributes a significant amount of precipitation each year in the contiguous United States and Mexico and it can cause damaging floods. In this study, we evaluate the ability of two precipitation estimates from the latest Integrated Multi-satellitE Retrievals for GPM (IMERG Final Run V06, hereafter referred to as IMERG-F) and its predecessor, the TRMM Multi-satellite Precipitation Analysis (TMPA research product 3B42V7, hereafter referred to as TMPA), to capture TCP at daily, event and annual scales by comparing the satellite observations with gauge measurements based on data from 2014 to 2018. The results show that both TMPA and IMERG-F are able to accurately capture the general TCP patterns. IMERG-F provides a noticeable improvement in accuracy over TMPA, especially for times and locations with light and heavy TCP. However, both IMERG-F and TMPA still systematically underestimate TCP during extreme events. At the annual scale, both TMPA and IMERG-F slightly underestimate annual TCP, but IMERG-F to a lesser degree. For individual TC events, IMERG-F has lower bias and a higher Nash-Sutcliffe efficiency than TMPA in the majority of the events. The differences between IMERG-F and TMPA are especially pronounced for extreme TCP events, such as Hurricane Harvey in 2017. At the daily scale, both IMERG-F and TMPA underestimate TCP when daily TCP exceeds ~150 mm. However, IMERG-F shows closer agreements with gauge-based measurements than TMPA. This study demonstrates that IMERG-F can more accurately measure TCP than TMPA. However, there are still systematic biases in IMERG-F when it comes to heavy TCP at all of the timescales.

scholarly journals Evaluation of Gridded Multi-Satellite Precipitation (TRMM-3B42-V7) Estimation Performance in the Upper Indus Basin (UIB)

2018 ◽  
Author(s):  
Asim Jahangir Khan ◽  
Manfred Koch ◽  
Karen Milena Chinchilla
Keyword(s):  
Time Scale ◽  
Winter Season ◽  
Indus Basin ◽  
False Alarms ◽  
Temporal Aggregation ◽  
Upper Indus Basin ◽  
Gauge Data ◽  
Precipitation Estimates ◽  
Trmm Precipitation ◽  
Trmm 3B42

The present study aims to evaluate the capability of the TRMM-3B42-(V7) precipitation product to estimate appropriate precipitation rates in the Upper Indus basin (UIB) and the analysis of the dependency of the estimates’ accuracies on the time scale. To that avail statistical analyses and comparison of the TMPA- products with gauge measurements in the UIB are carried out. The dependency of the TMPA estimates’ quality on the time scale is analysed by comparisons of daily, monthly, seasonal and annual sums for the UIB. The results show considerable biases in the TMPA- (TRMM) precipitation estimates for the UIB, as well as high false alarms and miss ratios. The correlation of the TMPA- estimates with ground-based gauge data increases considerably and almost in a linear fashion with increasing temporal aggregation, i.e. time scale. The BIAS is mostly positive for the summer season, while for the winter season it is predominantly negative, thereby showing a slight over-estimation of the precipitation in summer and under-estimation in winter. The results of the study suggest that, in spite of these discrepancies between TMPA- estimates and gauge data, the use of the former in hydrological watershed modelling, endeavoured presently by the authors, may be a valuable alternative in data- scarce regions, like the UIB, but still must be taken with a grain of salt.

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