scholarly journals Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

2021 ◽  
Vol 13 (4) ◽  
pp. 622
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Ya-Hui Chang ◽  
Cheng-An Lee
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Advantages And Disadvantages ◽  
Level 3 ◽  
Spatio Temporal ◽  
Typhoon Season ◽  
Early Late ◽  
Better Than

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

scholarly journals Comparison of Entropy Methods for an Optimal Rain Gauge Network: A Case Study of Daegu and Gyeongbuk Area in South Korea

2020 ◽  
Vol 10 (16) ◽  
pp. 5620
Author(s):  
Taeyong Kwon ◽  
Junghyun Lim ◽  
Seongsim Yoon ◽  
Sanghoo Yoon
Keyword(s):  
Conditional Entropy ◽  
Rain Gauge ◽  
Optimal Number ◽  
Severe Drought ◽  
Prediction Errors ◽  
Joint Entropy ◽  
Spatio Temporal ◽  
Rain Gauge Network ◽  
Better Than

To reduce hydrological disasters, it is necessary to operate rain gauge stations at locations where the spatio-temporal characteristics of rainfall can be reflected. Entropy has been widely used to evaluate the designs and uncertainties associated with rain gauge networks. In this study, the optimal rain gauge network in the Daegu and Gyeongbuk area, which requires the efficient use of water resources due to low annual precipitation and severe drought damage, was determined using conditional and joint entropy, and the selected network was quantitatively evaluated using the root mean square error (RMSE). To consider spatial distribution, prediction errors were generated using kriging. Four estimators used in entropy calculations were compared, and weighted entropy was calculated by weighting the precipitation. The optimal number of rain gauge stations was determined by calculating the RMSE reduction and the reduction ratio according to the number of selected rain gauge stations. Our findings show that the results of conditional entropy were better than those of joint entropy. The optimal rain gauge stations showed a tendency wherein peripheral rain gauge stations were selected first, with central stations being added afterward.

scholarly journals Evaluation of GPM IMERG Early, Late, and Final rainfall estimates using WegenerNet gauge data in southeastern Austria

2017 ◽  
Vol 21 (12) ◽  
pp. 6559-6572 ◽  
Author(s):  
Sungmin O ◽  
Ulrich Foelsche ◽  
Gottfried Kirchengast ◽  
Juergen Fuchsberger ◽  
Jackson Tan ◽  
...  
Keyword(s):  
Real Time ◽  
Rain Gauge ◽  
Time Data ◽  
Hourly Rainfall ◽  
Precipitation Estimates ◽  
Climate Station ◽  
Using Data ◽  
Global Precipitation ◽  
Early Late ◽  
Rainfall Estimates

Abstract. The Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products provide quasi-global (60° N–60° S) precipitation estimates, beginning March 2014, from the combined use of passive microwave (PMW) and infrared (IR) satellites comprising the GPM constellation. The IMERG products are available in the form of near-real-time data, i.e., IMERG Early and Late, and in the form of post-real-time research data, i.e., IMERG Final, after monthly rain gauge analysis is received and taken into account. In this study, IMERG version 3 Early, Late, and Final (IMERG-E,IMERG-L, and IMERG-F) half-hourly rainfall estimates are compared with gauge-based gridded rainfall data from the WegenerNet Feldbach region (WEGN) high-density climate station network in southeastern Austria. The comparison is conducted over two IMERG 0.1°  ×  0.1° grid cells, entirely covered by 40 and 39 WEGN stations each, using data from the extended summer season (April–October) for the first two years of the GPM mission. The entire data are divided into two rainfall intensity ranges (low and high) and two seasons (warm and hot), and we evaluate the performance of IMERG, using both statistical and graphical methods. Results show that IMERG-F rainfall estimates are in the best overall agreement with the WEGN data, followed by IMERG-L and IMERG-E estimates, particularly for the hot season. We also illustrate, through rainfall event cases, how insufficient PMW sources and errors in motion vectors can lead to wide discrepancies in the IMERG estimates. Finally, by applying the method of Villarini and Krajewski (2007), we find that IMERG-F half-hourly rainfall estimates can be regarded as a 25 min gauge accumulation, with an offset of +40 min relative to its nominal time.

scholarly journals Wet and dry spells in Senegal: comparison of detection based on satellite products, reanalysis, and in situ estimates

2021 ◽  
Vol 21 (3) ◽  
pp. 1051-1069
Author(s):  
Cheikh Modou Noreyni Fall ◽  
Christophe Lavaysse ◽  
Mamadou Simina Drame ◽  
Geremy Panthou ◽  
Amadou Thierno Gaye
Keyword(s):  
Measurement Techniques ◽  
West African Monsoon ◽  
Tropical Rainfall Measuring Mission ◽  
Early Warning Systems ◽  
Rain Gauge ◽  
Major Influence ◽  
Dry Spells ◽  
Dry Spell ◽  
Spatio Temporal ◽  
Wet Spells

Abstract. In this study, the detection and characteristics of dry/wet spells (defined as episodes when precipitation is abnormally low or high compared to usual climatology) drawn from several datasets are compared for Senegal. Here, four datasets are based on satellite data (TRMM-3B42 V7, CMORPH V1.0, TAMSAT V3, and CHIRPS V2. 0), two on reanalysis products (NCEP-CFSR and ERA5), and three on rain gauge observations (CPC Unified V1.0/RT and a 65-rain-gauge network regridded by using two kriging methods, namely ordinary kriging, OK, and block kriging, BK). All datasets were converted to the same spatio-temporal resolution: daily cumulative rainfall on a regular 0.25∘ grid. The BK dataset was used as a reference. Despite strong agreement between the datasets on the spatial variability in cumulative seasonal rainfall (correlations ranging from 0.94 to 0.99), there were significant disparities in dry/wet spells. The occurrence of dry spells is less in products using infrared measurement techniques than in products coupling infrared and microwave, pointing to more frequent dry spell events. All datasets show that dry spells appear to be more frequent at the start and end of rainy seasons. Thus, dry spell occurrences have a major influence on the duration of the rainy season, in particular through the “false onset” or “early cessation” of seasons. The amplitude of wet spells shows the greatest variation between datasets. Indeed, these major wet spells appear more intense in the OK and Tropical Rainfall Measuring Mission (TRMM) datasets than in the others. Lastly, the products indicate a similar wet spell frequency occurring at the height of the West African monsoon. Our findings provide guidance in choosing the most suitable datasets for implementing early warning systems (EWSs) using a multi-risk approach and integrating effective dry/wet spell indicators for monitoring and detecting extreme events.

Hydrometeorological Analysis and Remote Sensing of Extremes: Was the July 2012 Beijing Flood Event Detectable and Predictable by Global Satellite Observing and Global Weather Modeling Systems?

2015 ◽  
Vol 16 (1) ◽  
pp. 381-395 ◽  
Author(s):  
Yu Zhang ◽  
Yang Hong ◽  
Xuguang Wang ◽  
Jonathan J. Gourley ◽  
Xianwu Xue ◽  
...  
Keyword(s):  
Real Time ◽  
Extreme Event ◽  
Tropical Rainfall Measuring Mission ◽  
Extreme Rainfall ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Economic Losses ◽  
Lead Times ◽  
Additional Information ◽  
Streamflow Forecasts

Abstract Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.

Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach

2008 ◽  
Vol 12 (3) ◽  
pp. 243-248 ◽  
Author(s):  
Shan-Tai Chen ◽  
◽  
Chien-Chen Wu ◽  
Wann-Jin Chen ◽  
Jen-Chi Hu ◽  
...  
Keyword(s):  
Data Mining ◽  
Tropical Rainfall Measuring Mission ◽  
Identification Problem ◽  
Rain Gauge ◽  
Rain Area ◽  
Data Mining Approach ◽  
Microwave Imager ◽  
Typhoon Season ◽  
Scattering Index ◽  
Trmm Microwave Imager

Rain-area identification distinguishes between rainy and non-rainy areas, which is the first step in some critical real-world problems, such as rain intensity identification and rain-rate estimation. We develop a data mining approach for oceanic rain-area identification during typhoon season, using microwave data from the Tropical Rainfall Measuring Mission (TRMM) satellite. Three schemes tailored for the problem are developed, namely (1) association rule analysis for uncovering the set of potential attributes relevant to the problem, (2) three-phase outlier removal for cleaning data and (3) the neural committee classifier (NCC) for achieving more accurate results. We created classification models from 1998-2004 TRMM Microwave Imager (TRMM-TMI) satellite data and used Automatic Rainfall and Meteorological Telemetry System (ARMTS) rain gauge data measurements to evaluate the model. Experimental results show that our approach achieves high accuracy for the rain-area identification problem. The classification accuracy of our approach, 96%, outperforms the 78.6%, 77.3%, 83.3% obtained by the scattering index, threshold check, and rain flag methods, respectively.

scholarly journals Evaluation and Application of Satellite Precipitation Products in Studying the Summer Precipitation Variations over Taiwan

2020 ◽  
Vol 12 (3) ◽  
pp. 347 ◽  
Author(s):  
Wan-Ru Huang ◽  
Pin-Yi Liu ◽  
Ya-Hui Chang ◽  
Chian-Yi Liu
Keyword(s):  
Summer Precipitation ◽  
Tropical Rainfall Measuring Mission ◽  
Temporal Variations ◽  
Rain Gauge ◽  
Satellite Precipitation ◽  
Reference Base ◽  
Precipitation Measurement ◽  
June July ◽  
Global Precipitation Measurement ◽  
Global Precipitation

In March 2019, Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG)-Final v6 (hereafter IMERG6) was released, with data concerning precipitation dating back to June 2000. The National Aeronautics and Space Administration (NASA) has suggested that researchers use IMERG6 to replace the frequently used Tropical Rainfall Measuring Mission (TRMM)-3B42 v7 (hereafter TRMM7), which is expected to cease operation in December 2019. This study aims to evaluate the performance of IMERG6 and TRMM7 in depicting the variations of summer (June, July, and August) precipitation over Taiwan during the period 2000–2017. Data used for the comparison also includes IMERG-Final v5 (hereafter IMERG5) and Global Satellite Mapping of Precipitation for Global Precipitation Measurement (GSMaP)-Gauge v7 (hereafter GSMaP7) during the summers of 2014–2017. Capabilities to apply the four satellite precipitation products (SPPs) in studying summer connective afternoon rainfall (CAR) events, which are the most frequently observed weather patterns in Taiwan, are also examined. Our analyses show that when using more than 400 local rain-gauge observations as a reference base for comparison, IMERG6 outperforms TRMM7 quantitatively and qualitatively, more accurately depicting the variations of the summer precipitation over Taiwan at multiple timescales (including mean status, daily, interannual, and diurnal). IMERG6 also performs better than TRMM7 in capturing the characteristics of CAR activities in Taiwan. These findings highlight that using IMERG6 to replace TRMM7 adds value in studying the spatial-temporal variations of summer precipitation over Taiwan. Furthermore, the analyses also indicated that IMERG6 outperforms IMERG5 and GSMaP7 in the examination of most of the features of summer precipitation over Taiwan during 2014–2017.

scholarly journals Spatial Variations in the Diurnal Pattern of Precipitation over Nepal Himalayas

2015 ◽  
Vol 15 (2) ◽  
pp. 57-64 ◽  
Author(s):  
Dibas Shrestha ◽  
Rashila Deshar
Keyword(s):  
Summer Monsoon ◽  
Diurnal Cycle ◽  
Monsoon Season ◽  
Tropical Rainfall Measuring Mission ◽  
Early Morning ◽  
Temporal Variations ◽  
Diurnal Cycles ◽  
Diurnal Cycle Of Precipitation ◽  
Spatio Temporal ◽  
The Impact

The Central Himalayan Region (Nepal Himalayas), comprised of two clear sub-parallel mountain ranges, is atypical location for studying the impact of rugged topography on spatio temporal variations of precipitation. The relationship between topography and diurnal cycles of rainfall have been investigated utilizing 13-year (1998–2010) high resolution (0.05° × 0.05°) Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) data. An investigation of diurnal cycle of precipitation revealed an afternoon maximum during the pre-monsoon season (March–May) and midnight–early morning maximum during the summer monsoon season (June–August)over the southern slopes of the Himalayas. The summer monsoon exhibited a robust spatial variation of diurnal cycle of precipitation, during afternoon-evening time, primary rainfall peak appeared along the Lesser Himalayas (~2,000–2,200 m above mean sea level), while early-morning rain in contrast showed maximum concentration along the southern margin of the Himalayas (~500–700 m above MSL). An afternoon-evening rainfall peak was attributed to higher rain frequency, whereas early-morning rainfall peak was attributed to fewer but rather intense rainfall. It is suggested that, confluence between down slope and moist south easterly monsoon flow triggers convection near the foothills of the Himalayas during early morning period. The results further suggested the morning precipitation moves southward in the mature monsoon season.DOI: http://dx.doi.org/njst.v15i2.12116Nepal Journal of Science and Technology Vol. 15, No.2 (2014), 57-64

scholarly journals Precipitation downscaling using a probability-matching approach and geostationary infrared data: an evaluation over six climate regions

2018 ◽  
Vol 22 (7) ◽  
pp. 3685-3699 ◽  
Author(s):  
Ruifang Guo ◽  
Yuanbo Liu ◽  
Han Zhou ◽  
Yaqiao Zhu
Keyword(s):  
Water Cycle ◽  
Regional Scale ◽  
Rain Gauge ◽  
Cumulative Distribution ◽  
Rainfall Regime ◽  
Precipitation Data ◽  
Main Hypothesis ◽  
Basin Scale ◽  
Spatio Temporal ◽  
Better Than

Abstract. Precipitation is one of the most important components of the global water cycle. Precipitation data at high spatial and temporal resolutions are crucial for basin-scale hydrological and meteorological studies. In this study, we propose a cumulative distribution of frequency (CDF)-based downscaling method (DCDF) to obtain hourly 0.05∘ × 0.05∘ precipitation data. The main hypothesis is that a variable with the same resolution of target data should produce a CDF that is similar to the reference data. The method was demonstrated using the 3-hourly 0.25∘ × 0.25∘ Climate Prediction Center morphing method (CMORPH) dataset and the hourly 0.05∘ × 0.05∘ FY2-E geostationary (GEO) infrared (IR) temperature brightness (Tb) data. Initially, power function relationships were established between the precipitation rate and Tb for each 1∘ × 1∘ region. Then the CMORPH data were downscaled to 0.05∘ × 0.05∘. The downscaled results were validated over diverse rainfall regimes in China. Within each rainfall regime, the fitting functions' coefficients were able to implicitly reflect the characteristics of precipitation. Quantitatively, the downscaled estimates not only improved spatio-temporal resolutions, but also performed better (bias: −7.35–10.35 %; correlation coefficient, CC: 0.48–0.60) than the CMORPH product (bias: 20.82–94.19 %; CC: 0.31–0.59) over convective precipitating regions. The downscaled results performed as well as the CMORPH product over regions dominated with frontal rain systems and performed relatively poorly over mountainous or hilly areas where orographic rain systems dominate. Qualitatively, at the daily scale, DCDF and CMORPH had nearly equivalent performances at the regional scale, and 79 % DCDF may perform better than or nearly equivalently to CMORPH at the point (rain gauge) scale. The downscaled estimates were able to capture more details about rainfall motion and changes under the condition that DCDF performs better than or nearly equivalently to CMORPH.

scholarly journals Evaluation of Multiple Satellite-Based Precipitation Products over Complex Topography

2014 ◽  
Vol 15 (4) ◽  
pp. 1498-1516 ◽  
Author(s):  
Yagmur Derin ◽  
Koray K. Yilmaz
Keyword(s):  
Real Time ◽  
Tropical Rainfall Measuring Mission ◽  
Cold Season ◽  
Rain Gauge ◽  
Research Quality ◽  
Complex Topography ◽  
Precipitation Regime ◽  
Black Sea Region ◽  
Main Challenge ◽  
Precipitation Estimation

Abstract This study evaluates the performance of four satellite-based precipitation (SBP) products over the western Black Sea region of Turkey, a region characterized by complex topography that exerts strong controls on the precipitation regime. The four SBP products include the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis version 7 experimental near-real-time product (TMPA-7RT) and post-real-time research-quality product (TMPA-7A), the Climate Prediction Center morphing technique (CMORPH), and the Multisensor Precipitation Estimate (MPE) of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT). Evaluation is performed at various spatial (point and grid) and temporal (daily, monthly, seasonal, and annual) scales over the period 2007–11. For the grid-scale evaluation, a rain gauge–based gridded precipitation dataset was constructed using a knowledge-based system in which “physiographic descriptors” are incorporated in the precipitation estimation through an optimization framework. The results indicated that evaluated SBP products generally had difficulty in representing the precipitation gradient normal to the orography. TMPA-7RT, TMPA-7A, and MPE products underestimated precipitation along the windward region and overestimated the precipitation on the leeward region, more significantly during the cold season. The CMORPH product underestimated the precipitation on both windward and leeward regions regardless of the season. Further investigation of the datasets used in the development of these SBP products revealed that, although both infrared (IR) and microwave (MW) datasets contain potential problems, the inability of MW sensors to detect precipitation especially in the cold season was the main challenge over this region with complex topography.

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