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1
Islam, Md Nazrul, Someshwar Das, and Hiroshi Uyeda. "Calibration of TRMM Derived Rainfall Over Nepal During 1998-2007." Open Atmospheric Science Journal 4, no. 1 (January 19, 2010): 12–23. http://dx.doi.org/10.2174/1874282301004010012.
Повний текст джерелаАнотація:
In this study rainfall is calculated from Tropical Rainfall Measuring Mission (TRMM) Version 6 (V6) 3B42 datasets and calibrated with reference to the observed daily rainfall by rain-gauge collected at 15 locations over Nepal during 1998-2007. In monthly, seasonal and annual scales TRMM estimated rainfalls follow the similar distribution of historical patterns obtained from the rain-gauge data. Rainfall is large in the Southern parts of the country, especially in the Central Nepal. Day-to-day rainfall comparison shows that TRMM derived trend is very similar to the observed data but TRMM usually underestimates rainfall on many days with some exceptions of overestimation on some days. The correlation coefficient of rainfalls between TRMM and rain-gauge data is obtained about 0.71. TRMM can measure about 65.39% of surface rainfall in Nepal. After using calibration factors obtained through regression expression the TRMM estimated rainfall over Nepal becomes about 99.91% of observed data. TRMM detection of rainy days is poor over Nepal; it can approximately detect, under-detect and over-detect by 19%, 72% and 9% of stations respectively. False alarm rate, probability of detection, threat score and skill score are calculated as 0.30, 0.68, 0.53 and 0.55 respectively. Finally, TRMM data can be utilized in measuring mountainous rainfall over Nepal but exact amount of rainfall has to be calculated with the help of adjustment factors obtained through calibration procedure. This preliminary work is the preparation of utilization of Global Precipitation Measurement (GPM) data to be commencing in 2013.
2
Krisnayanti, Denik Sri, Davianto Frangky B. Welkis, Fery Moun Hepy, and Djoko Legono. "Evaluasi Kesesuaian Data Tropical Rainfall Measuring Mission (TRMM) dengan Data Pos Hujan Pada Das Temef di Kabupaten Timor Tengah Selatan." JURNAL SUMBER DAYA AIR 16, no. 1 (May 31, 2020): 51–62. http://dx.doi.org/10.32679/jsda.v16i1.646.
Повний текст джерелаАнотація:
The construction of the Temef Dam in Oenino Village, Oenino District, and Konbaki Village, Polen District, South Central Timor Regency requires long and reliable rainfall data. To overcome the minimum data or the unavailability of automatic rainfall (ARR) and discharge data in the past decades, the use of Tropical Rainfall Measuring Mission (TRMM) satellite data is foreseen. The accuracy of TRMM data is obtained when the parameters of suitability and compatibility of TRMM are in a good agreement with the ARR. For the Temef watershed, there are six rainfall stations that were reviewed, namely Fatumnasi, Oeoh, Noelnoni, Polen, Nifukani, and Batinifukoko rainfall stations. Direct comparisons of rainfall data were conducted for 20 years (1998-2018) with temporal resolution on a monthly and daily basis. The results of the study show that the rainfall patterns in the TRMM data product (version 3B42V7) tend to be consistent with 3 rainfall stations in the Temef watershed namely Noelnoni, Fatumnasi, and Batinifukoko. A correlation coefficient of 0.505 – 0.813 was obtained from TRMM data calibration at monthly basis while a correction factor level of 0.0056 - 0.0129 was obtained for daily. The calibration on the annual maximum daily rainfall data resulted in a correction factor of 0.0298 - 0.2516. Monthly and daily TRMM data fit well with the data of 3 rainfall stations. However, the Noelnoni rainfall station showed poor results on the annual maximum daily rainfall.Keywords: TRMM data, ARR data, correction factor, correlation coefficient
3
Nomleni, Aprianto, Ery Suhartanto, and Donny Harisuseno. "Estimation of Flow Discharge Model at Temef Watershed - East Nusa Tenggara Using TRMM Satellite Data." Civil and Environmental Science 004, no. 02 (October 1, 2021): 115–26. http://dx.doi.org/10.21776/ub.civense.2021.00402.2.
Повний текст джерелаАнотація:
Data collection based on satellite TRMM (Tropical Rainfall Measuring Mission) presents one of the good alternatives in estimating rainfall. TRMM technology can minimize manual rainfall recording errors and improve rainfall accuracy for hydrological analysis. The analysis method used in this research is divided into 3 (three) stages, namely Hydrology analysis, Statistical Analysis and Artificial Neural Network Analysis. From the results of TRMM JAXA analysis in the Temef Watershed Area of East Nusa Tenggara Province obtained TRMM JAXA satellite rainfall relationship to observation data shows rainfall patterns between the two data are interconnected but for cases with very high observation rainfall, TRMM rainfall data tends to be low. From statistical method analysis, the relationship between observation rainfall and TRMM JAXA rainfall obtained results with a "Very Strong" interpretation indicated by the results of 9 years calibration and 1 year validation where the selected equation is a polynomial equation (y=-0,0123x2 + 1,5553x + 20,222). Rain data correction results simulated with Debit data to see the relationship between rain and discharge that occurred, this analysis using Artificial Neural Network with Backpropagation method, the results showed a "Strong" interpretation where statistically the value of Nash-Sutcliffe Efficiency (NSE) 0.920, the coefficient value of correlation of field discharge and TRMM rainfall is 0,877 % and the relative error occurred is 2,62%
4
Barlian Soeryamassoeka, Stefanus Barlian Soeryamassoeka, Robertus Wahyudi Triweko, and Doddi Yudianto. "VALIDATION OF TROPICAL RAINFALL MEASURING MISSION (TRMM) DATA IN THE UPPER KAPUAS RIVER BASIN." Journal of Civil Engineering, Science and Technology 11, no. 2 (September 30, 2020): 125–31. http://dx.doi.org/10.33736/jcest.2618.2020.
Повний текст джерелаАнотація:
Rainfall is a difficult parameter to measure, due to large spatial and temporal variations. Lack of data availability, data incompletely, less spreading of station, less observer, and manual data entry are other problems for rainfall predicting. To encourage these problems rainfall satellite can be used, because it has high temporal and spatial resolution, widely coverage, near real-time and fast accessibility. This research was conducted in the upper Kapuas River Basin, West Kalimantan, to determine how TRMM satellite-derived rainfall compares with ground-measured values and the possibility of using it to complement ground-measured rainfall. The statistical analyses and correction factor development for TRMM data are conducted to validate and correct the TRMM data on eleven sub basin in Kapuas River basin. Validation showed high correlation between TRMM and gauge data. Validation shows a high correlation and lowest RMSE between TRMM and gauge data in the sub basin adjacent to the gauge station (r= 0.76-0.8, RMSE 0,84-0,92). The results of the analysis also show that after correction, the corrected TRMM data errors were reduced for the eleven rainfall
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Schiavo Bernardi, E., D. Allasia, R. Basso, P. Freitas Ferreira, and R. Tassi. "TRMM rainfall estimative coupled with Bell (1969) methodology for extreme rainfall characterization." Proceedings of the International Association of Hydrological Sciences 369 (June 11, 2015): 163–68. http://dx.doi.org/10.5194/piahs-369-163-2015.
Повний текст джерелаАнотація:
Abstract. The lack of rainfall data in Brazil, and, in particular, in Rio Grande do Sul State (RS), hinders the understanding of the spatial and temporal distribution of rainfall, especially in the case of the more complex extreme events. In this context, rainfall's estimation from remote sensors is seen as alternative to the scarcity of rainfall gauges. However, as they are indirect measures, such estimates needs validation. This paper aims to verify the applicability of the Tropical Rainfall Measuring Mission (TRMM) satellite information for extreme rainfall determination in RS. The analysis was accomplished at different temporal scales that ranged from 5 min to daily rainfall while spatial distribution of rainfall was investigated by means of regionalization. An initial test verified TRMM rainfall estimative against measured rainfall at gauges for 1998–2013 period considering different durations and return periods (RP). Results indicated that, for the RP of 2, 5, 10 and 15 years, TRMM overestimated on average 24.7% daily rainfall. As TRMM minimum time-steps is 3 h, in order to verify shorter duration rainfall, the TRMM data were adapted to fit Bell's (1969) generalized IDF formula (based on the existence of similarity between the mechanisms of extreme rainfall events as they are associated to convective cells). Bell`s equation error against measured precipitation was around 5–10%, which varied based on location, RP and duration while the coupled BELL+TRMM error was around 10–35%. However, errors were regionally distributed, allowing a correction to be implemented that reduced by half these values. These findings in turn permitted the use of TRMM+Bell estimates to improve the understanding of spatiotemporal distribution of extreme hydrological rainfall events.
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Worqlul, A. W., B. Maathuis, A. A. Adem, S. S. Demissie, S. Langan, and T. S. Steenhuis. "Comparison of TRMM, MPEG and CFSR rainfall estimation with the ground observed data for the Lake Tana Basin, Ethiopia." Hydrology and Earth System Sciences Discussions 11, no. 7 (July 14, 2014): 8013–38. http://dx.doi.org/10.5194/hessd-11-8013-2014.
Повний текст джерелаАнотація:
Abstract. Planning of drought relief and floods in developing countries is greatly hampered by lack of a sufficiently dense network of weather station measuring precipitation. In this paper we test the utility of three satellite products to augment the ground based precipitation measurement to provide improved spatial estimates of rainfall. The three products are: Tropical Rainfall Measuring Mission (TRMM) product (3B42), Multi-Sensor Precipitation Estimate-Geostationary (MPEG) and Climate Forecast System Reanalysis (CFSR). The accuracy of three products is tested in the Lake Tana Basin in Ethiopia where in 2010 38 weather stations were available with a full record of daily precipitation amounts. Daily grid satellite based rainfall estimates were compared to: (1) point observed ground rainfall (2) areal rainfall in the major river sub-basins of Lake Tana. The result shows that, the MPEG and CFSR satellite provided most accurate rainfall estimates. On the average for 38 stations 78 and 86% of the observed rainfall variation is explained by MPEG and CFSR data respectively while TRIMM explained only 17% of the variation. Similarly, the areal comparison indicated a better performance for both MPEG and CFSR data in capturing the pattern and amount of rainfall. MPEG and CFSR have also a lower RMSE compared to the TRMM satellite rainfall. The Bias indicated that, the MPEG is consistent in underestimating the observed rainfall while the TRMM and CFSR were not consistent; they overestimated for some and underestimated for the others.
7
Zakaria, Ahmad. "Correlation of BMKG with TRMM for daily and monthly rainfall series in Banten region." Journal of Engineering and Scientific Research 4, no. 1 (August 23, 2022): 1–7. http://dx.doi.org/10.23960/jesr.v4i1.78.
Повний текст джерелаАнотація:
Daily and monthly rainfall data series are necessary data for planning purposes in Civil Engineering and other fields. Incomplete rainfall data often occurs, so that rainfall data must be estimated based on rainfall data from several other nearby locations. The addition of rainfall data can lead to inaccurate planning.
Rainfall data used for planning in the civil engineering sector is usually taken from the BMKG station. This data is taken from the rainfall station above the ground. Besides, that can also produce rainfall data from TRMM. Can take rainfall data from TRMM at all locations according to a coordinate of location. This rainfall data denotes an average rainfall taken from the satellite approximately 250 meters above the ground surface.
An equation will be obtained by comparing the daily and monthly rainfall data from the two data sources. Based on TRMM rainfall, we can use the equation to estimate ground rainfall in a location.
In this study, daily rainfall, monthly rainfall, the spectrum of daily and monthly rainfall data from BMKG are compared with rainfall and the spectrum of daily and monthly rainfall data from TRMM. The analysis results show that the monthly rainfall data from TRMM and BMKG correlate better than daily rainfall data.
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Kuswanto, H., D. Setiawan, and A. Sopaheluwakan. "Clustering of Precipitation Pattern in Indonesia Using TRMM Satellite Data." Engineering, Technology & Applied Science Research 9, no. 4 (August 10, 2019): 4484–89. http://dx.doi.org/10.48084/etasr.2950.
Повний текст джерелаАнотація:
This paper identifies the climatic regions in Indonesia based on the rainfall pattern similarity using TRMM data. Indonesia is a tropical climate region with three main climate clusters, i.e. monsoonal, anti-monsoonal and semi-monsoonal. The clusters were formed by examining rainfall observation datasets recorded at a number of stations over Indonesia with coarse spatial resolution. Clustering based on higher resolution datasets is needed to characterize the rainfall pattern over remote areas with no stations. TRMM provides a high resolution gridded dataset. A statistical test has been applied to evaluate the significance of TRMM bias, and it indicated that the TRMM based satellite precipitation product is a reasonable choice to be used as an input to cluster regions in Indonesia based on the similarity of rainfall patterns. The clustering by Euclidean distance revealed that Indonesia can be grouped into three significantly different rainfall patterns. Compared to the existing references, there have been regions where the rainfall pattern has been shifted. The results in this research thus update the previously defined climate regions in Indonesia.
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David Raj, Anu, K. R. Sooryamol, and Aju David Raj. "Exploring Temporal Rainfall Variability and Trends Over a Tropical Region Using Tropical Rainfall Measurement Mission (TRMM) and Observatory Data." Hydrospatial Analysis 5, no. 2 (September 26, 2021): 56–71. http://dx.doi.org/10.21523/gcj3.2021050202.
Повний текст джерелаАнотація:
Kerala is the gateway of the Indian southwest monsoon. The Tropical Rainfall Measurement Mission (TRMM) rainfall data is an efficient approach to rainfall measurement. This study explores the temporal variability in rainfall and trends over Kerala from 1998-2019 using TRMM data and observatory data procured from India Meteorological Department (IMD). Direct comparison with observatory data at various time scales proved the reliability of the TRMM data (monthly, seasonal and annual). The temporal rainfall converted by averaging the data on an annual, monthly and seasonal time scale, and the results have confirmed that the rainfall estimated based on satellite data is dependable. The station wise comparison of rainfall in monsoon season provides satisfactory results. However, estimation of rainfall in mountainous areas is challenging task using the TRMM. In the basins of humid tropical regions, TRMM data can be a valuable source of rainfall data for water resource management and monitoring with some vigilance. In Kerala, the study found an insignificant increase in the southwest monsoon and winter season rainfall during last two decades. The rainfall over Kerala showed uncertainty in the distribution of monthly, seasonal and yearly time scales. This study provides a preview of recent weather patterns that would enable us to make better decisions and improve public policy against climate change.
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Li, D., X. Ding, and J. Wu. "Simulating the regional water balance through hydrological model based on TRMM satellite rainfall data." Hydrology and Earth System Sciences Discussions 12, no. 2 (February 27, 2015): 2497–525. http://dx.doi.org/10.5194/hessd-12-2497-2015.
Повний текст джерелаАнотація:
Abstract. Spatial rainfall is a key input to Distributed Hydrological Models, which is the most important limitation for the accuracy of hydrological models. Model performance and uncertainty could increase when rain gauge is sparse. Satellite-based precipitation products would be an alternative to ground-based rainfall estimates in present and the foreseeable future, however, it is necessary to evaluate the products before further implication. The objective of this paper is to provide assessments of: (a) the Tropical Rainfall Measuring Mission (TRMM) rainfall product using gauge data, (b) the TRMM rainfall as forcing data for hydrological simulation, and (c) the role of satellite data in calculating water balance and water management. TRMM rainfall data show reasonable performances at monthly and annual scales, fitting well with surface observation-based histogram of precipitation. Satisfactory performances in monthly runoff simulation (NS = 0.50 ~ 0.70, R2 = 0.73 ~ 0.85) observed in our study indicate that the TRMM rainfall data have potential applications in driving hydrological model, water balance analysis, and basin water resource management in developing countries or remote locations, where precipitation gauges are scare.
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Vernimmen, R. R. E., A. Hooijer, and E. Aldrian. "Evaluation and bias correction of satellite rainfall data for drought monitoring in Indonesia." Hydrology and Earth System Sciences Discussions 8, no. 3 (June 22, 2011): 5969–97. http://dx.doi.org/10.5194/hessd-8-5969-2011.
Повний текст джерелаАнотація:
Abstract. The accuracy of satellite rainfall data from different sources, TRMM 3B42RT, CMORPH and PERSIANN, was investigated through comparison with reliable ground station rainfall data in Indonesia, with a focus on their ability to detect patterns of low rainfall that may lead to drought conditions. It was found that all sources underestimated rainfall in dry season months. The CMORPH and PERSIANN data differed most from ground station data and are also very different from the TRMM data. However, it proved possible to improve TRMM data to yield sufficiently accurate estimates, both for dry periods (R2 0.65–0.92) and annually (R2 0.84–0.96), applying a single parameterized bias correction equation that is constant in space and time. It is proposed that these bias corrected TRMM data be used in real-time drought monitoring, in Indonesia and probably in other countries where similar conditions exist. This will yield major advantages, in terms of accuracy, spatial coverage, timely availability and cost efficiency, over drought monitoring with only ground stations.
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Chen, Shan-Tai, Shung-Lin Dou, and Wann-Jin Chen. "A Data Mining Approach to Rainfall Intensity Classification Using TRMM/TMI Data." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 6 (November 20, 2008): 516–22. http://dx.doi.org/10.20965/jaciii.2008.p0516.
Повний текст джерелаАнотація:
The systematic approach we propose for classifying oceanic rainfall intensity during the typhoon season consists of two major steps – 1) identifying the rain areas and 2) classifying rainfall intensity intonormalandheavyfor these areas. The heterogeneous hierarchical classifier (HHC), an ensemble model we developed for accurately identifying heavy rainfall events, consists of a set of base classifiers. The base classifiers are independently constructed through heterogeneous data mining approaches such as artificial neural networks, decision trees, and self-organizing maps. The meteorological satellite Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) data from 2000 to 2005 are used to create the classification models. TRMM precipitation radar (PR) data and rain gauge data from Automatic Rainfall and Meteorological Telemetry System (ARMTS) measurement are used as ground truth data to evaluate models. Two thirds of the dataset is used for model training and one third for testing. Experimental results show that the proposed model classifies rainfall intensity highly accurately and outperforms previously published methods.
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Nobert, Joel, and Patric Kibasa. "Application of Remotely Sensed Rainfall Data in Rainfall-Runoff Modelling. A Case of Pangani River Basin, Tanzania." Tanzania Journal of Engineering and Technology 35, no. 1 (June 30, 2014): 1–14. http://dx.doi.org/10.52339/tjet.v35i1.465.
Повний текст джерелаАнотація:
Rainfall runoff modelling in a river basin is vital for number of hydrologic applicationincluding water resources assessment. However, rainfall data from sparse gauging stationsare usually inadequate for modelling which is a major concern in Tanzania. This studypresents the results of comparison of Tropical Rainfall Measuring Mission (TRMM)satellite rainfall products at daily and monthly time-steps with ground stations rainfalldata; and explores the possibility of using satellite rainfall data for rainfall runoffmodelling in Pangani River Basin, Tanzania. Statistical analysis was carried out to find thecorrelation between the ground stations data and TRMM estimates. It was found thatTRMM estimates at monthly scale compare reasonably well with ground stations data.Time series comparison was also done at daily and annual time scales. Monthly and annualtime series compared well with coefficient of determination of 0.68 and 0.70, respectively.It was also found that areal rainfall comparison in the northern parts of the study area hadpoor results compared to the rest of areas. On the other hand, rainfall runoff modellingwith ground stations data alone and TRMM data set alone was carried out using five Real-Time River Flow Forecasting System models and then outputs combined by Models OutputsCombination Techniques. The results showed that ground stations data performed betterduring calibration period with coefficient of efficiency of 76.7%, 81.7% and 89.1% forSimple Average Method, Weight Average Method and Neural Network Method respectively.Simulation results using TRMM data were 59.8%, 73.5% and 76.8%. It can therefore beconcluded that TRMM data are adequate and promising in hydrological modelling.
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Senjaya, Theo, Doddi Yudianto, Xie Yuebo, and Wanny K. Adidarma. "Application of TRMM in the Hydrological Analysis of Upper Bengawan Solo River Basin." Journal of the Civil Engineering Forum 6, no. 3 (September 16, 2020): 309. http://dx.doi.org/10.22146/jcef.57125.
Повний текст джерелаАнотація:
Rainfall is a major water resource with a significant role in terms of growth, environment concerns, and sustainability. Several human activities demand adequate water supply for drinking, agriculture, domestic, and commercial consumption. The accuracy of any hydrologic study depends heavily on the availability of good-quality precipitation estimates. Most countries are unable to provide sufficient climatic data, including rainfall and observed discharge statistics. This scarcity is a huge obstacle in conducting thorough hydrologic studies over a certain period. For instance, Indonesia, as an archipelagic country, has long been faced with data availability problems. For this reason, Tropical Rainfall Measuring Mission (TRMM), which was developed by NASA, became an alternative solution to rainfall data limitations. However, to be applied in hydrologic investigations, TRMM data require proper estimation and adjustment. The aim of this study was to evaluate the quality of TRMM rainfall data and its application in determining design flood and water availability. Dividing the data into several groups based on its magnitude and multiplying each unit with a correction coefficient are parts of the modification process. Subsequently, objective functions, including false alarm ratio (FAR), probability of detection (POD), and root mean square error (RMSE) were also applied. Rainfall-runoff modeling and design storm analysis at Delingan dam were used to study the TRMM correction performance. Based on the analysis, corrected TRMM showed considerable findings compared to ground station data. Model calibration and verification using corrected TRMM data provide satisfactory model parameters compared to ground station derivatives. The results also disclosed a closer fit of the corrected TRMM to catchment response translated from derived rainfall-runoff model parameters to ground station compared to control. Furthermore, design storm calculated from corrected TRMM reflects an improvement compared to uncorrected TRMM data.
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Osei-Kwarteng, Josephine, Qiong Fang Li, and Kwaku Amaning Adjei. "Comparison of TRMM Data with Rain Gauge Observations in the Upper Huaihe River Basin of China." Advanced Materials Research 726-731 (August 2013): 3385–90. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.3385.
Повний текст джерелаАнотація:
In this study, the Tropical Rainfall Measuring Mission (TRMM) version 7 satellite rainfall product, TRMM 3B42 (V7), was validated using rain gauge measurements in the Upper Huaihe Basin, China. This validation was carried out at monthly and annual temporal scales for an 11-year period using four selected grids with six, four, two and one rain gauge station (s) located within the TRMM grid respectively; the rain gage measurements for grids with more than one rain gauge were averaged. This study found that the validation of the TRMM dataset in grids where there were adequate rain gauge were present to capture the distributed and stochastic nature of rainfall with very good correlation (0.87-0.94) and with very little relative bias when the rain gage accumulations were compared with the TRMM estimates. From the study we found that the TRMM dataset can be used as precipitation input for hydrological modeling at monthly and annual scales for sustainable water resources management in the Upper Huaihe River and even in un-gaged or sparsely gaged basins in other parts of the world.
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Willy, Willy, Bambang Adi Riyanto, Doddi Yudianto, and Albert Wicaksono. "Application of TRMM Data to the Analysis of Water Availability and Flood Discharge in Duriangkang Dam." Journal of the Civil Engineering Forum 6, no. 1 (January 31, 2020): 79. http://dx.doi.org/10.22146/jcef.51521.
Повний текст джерелаАнотація:
One of the challenges in hydrologic modelling in Indonesia is data limitation. Rainfall data quality is rarely evaluated, and in some cases, the data are unavailable. The Tropical Rainfall Measuring Mission (TRMM), satellite rainfall data provided by NASA, is an alternative method to solve such problems. This study aims to promote the use of TRMM data to analyze water availability and flood discharge in Duriangkang Dam, Batam City, Indonesia, in comparison with the use of available ground station data. Results show that the ground station data contain some errors; however, overall, the data show similar patterns and acceptable differences compared with the TRMM data. The NRECA and HEC-HMS models are used to analyze water availability, and both models are calibrated using the available reservoir water level data. The NRECA model generally shows a good fit of monthly discharge, although the use of TRMM results in slightly overestimated values in dry years. Similar results are obtained for daily discharge computation using the HEC-HMS model. Water availability analysis using the TRMM data shows an acceptable margin of error. When flood discharge is computed using an uncalibrated HEC-HMS model, the TRMM data somehow yield a lower maximum daily rainfall value than the ground station data. As a result, the obtained 10,000-year flood calculated using the Hang Nadim Station and TRMM data are 1,086 and 624 m3/s, respectively. Therefore, the use of corrected TRMM data in flood discharge computation is essential but increases the value up to 897 m3/s.
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Gu, Jiyang, Xizhong Cui, and Hanping Hong. "A Statistical-Based Model for Typhoon Rain Hazard Assessment." Atmosphere 13, no. 8 (July 24, 2022): 1172. http://dx.doi.org/10.3390/atmos13081172.
Повний текст джерелаАнотація:
Extreme typhoon rainfall can lead to damaging floods near the coastal region in mainland China. In the present study, we calibrate the parameters for a parametric hurricane rain model by using the precipitation radar (PR) data from the Tropical Rainfall Measuring Mission (TRMM) (i.e., PR-TRMM) and the TRMM microwave imager (TMI) data (i.e., TMI-TRMM). To show the applicability of the model for the tropical cyclone (TC) rain hazard assessment, we combine the developed rainfall intensity model with historical and synthetic TC tracks to estimate the T-year return period value of the accumulated rainfall in 24 h, QA24-T. We map QA24-100 for part of the coastal region in mainland China, showing that the spatial variation of QA24-100 is relatively smooth. It was found that the estimated QA24-100 using the model developed, based on the snapshots from PR-TRMM, is about 60% of that obtained using the model developed based on the snapshots from TMI-TRMM. This reflects the differences in the rainfall intensities reported in TMI-TRMM and PR-TRMM. As part of verification, we compare the estimated return period value to that obtained by using the record from surface meteorological stations at a few locations. The comparison indicates that, on average, QA24-100 based on gauge data is about 1.4 and 2.3 times that obtained using the model developed based on the snapshots from PR-TRMM and TRM-TRMM, respectively. This suggests that, for TC rain hazard estimation, one may consider the empirical scaling factor of 1.4 and 2.4 for the rainfall intensity models developed based on snapshots from PR-TRMM and TMI-TRMM, respectively.
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Nugroho, Yosevina Nugrahenny, Ferdy ., and Wandayantolis . "Verifikasi Data Estimasi Curah Hujan dari Satelit TRMM dan Pos Pengamatan Hujan BMKG di Sulawesi Utara." Jurnal MIPA 3, no. 1 (March 2, 2014): 35. http://dx.doi.org/10.35799/jm.3.1.2014.3904.
Повний текст джерелаАнотація:
Telah dilakukan penelitian untuk mengetahui keakuratan data estimasi curah hujan TRMM dan data curah hujan dari pos pengamatan hujan BMKG untuk wilayah Sulawesi Utara. Data yang diolah merupakan data bulanan dari tahun 2008 hingga 2012. Data awal TRMM (bentuk netCDF) sebelumnya diakses dengan menggunakan software GrADS dengan menggunakan script tertentu. Kemudian pada tahap selanjutnya digunakan software Microsoft Excel 2007 dalam pengolahan data serta dalam menghitung uji t. Hasilnya menunjukkan bahwa data estimasi curah hujan bulanan TRMM secara umum dapat digunakan pada wilayah Sulawesi Utara, khususnya pada daerah yang belum terdapat penakar hujan. Adapun kualitas data pos pengamatan hujan BMKG, terdapat 13 % pos hujan dengan data yang tidak valid.A research aimed to calculated the accuration of TRMM rainfall estimation data and rainfall conventional observation data of Meteorology Climatology and Geophysics Agency for North Sulawesi had been done. This research used monthly data from 2008 to 2012. TRMM data is in netCDF form that was accesed and processed by GrADS software using certain script. Microsoft Excel software was used to calculate the data and compute t test. The result showed that the TRMM monthly data can be used in North Sulawesi generally. The data could be used also at the place with no rainfall observation. There are 13 % of total rainfall conventional observation data with poor data quality.
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Min, Xinyi, Chuanguo Yang, and Ningpeng Dong. "Merging Satellite and Gauge Rainfalls for Flood Forecasting of Two Catchments Under Different Climate Conditions." Water 12, no. 3 (March 13, 2020): 802. http://dx.doi.org/10.3390/w12030802.
Повний текст джерелаАнотація:
As satellite rainfall data has the advantages of wide spatial coverage and high spatial and temporal resolution, it is an important means to solve the problem of flood forecasting in ungauged basins (PUB). In this paper, two catchments under different conditions, Xin’an River Basin and Wuding River Basin, were selected as the representatives of humid and arid regions, respectively, and four kinds of satellite rainfall data of TRMM 3B42RT, TRMM 3B42V7, GPM IMERG Early, and GPM IMERG Late were selected to evaluate the monitoring accuracy of rainfall processes in the two catchments on hourly scale. Then, these satellite rainfall data were respectively integrated with the gauged data. HEC-HMS (The Hydrologic Engineering Center's-Hydrologic Modeling System) model was calibrated and validated to simulate flood events in the two catchments. Then, improvement effect of the rainfall merging on flood forecasting was evaluated. According to the research results, in most cases, the Nash–Sutcliffe efficiency coefficients of the simulated streamflow from initial TRMM (Tropical Rainfall Measuring Mission) and GPM (Global Precipitation Measurement) satellite rainfall data were negative at the two catchments. By merging gauge and TRMM rainfall, the Nash–Sutcliffe efficiency coefficient is mostly around 0.7, and the correlation coefficient is as high as 0.9 for streamflow simulation in the Xin'an River basin. For the streamflow simulated by merging gauge and GPM rainfall in Wuding River basin, the Nash–Sutcliffe efficiency coefficient is about 0.8, and the correlation coefficient is more than 0.9, which indicate good flood forecasting accuracy. Generally, higher performance statistics were obtained in the Xin'an River Basin than the Wuding River Basin. Compared with the streamflow simulated by the initial satellite rainfalls, significant improvement was obtained by the merged rainfall data, which indicates a good prospect for application of satellite rainfall in hydrological forecasting. In the future, it is necessary to further improve the monitoring accuracy of satellite rainfall products and to develop the method of merging multi-source rainfall data, so as to better applications in PUB and other hydrological researches.
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Akbari, A., F. Othman, and A. Abu Samah. "Probing on suitability of TRMM data to explain spatio-temporal pattern of severe storms in tropic region." Hydrology and Earth System Sciences Discussions 8, no. 5 (October 24, 2011): 9435–68. http://dx.doi.org/10.5194/hessd-8-9435-2011.
Повний текст джерелаАнотація:
Abstract. Spatial and temporal pattern of rainfall play an important role in runoff generation. Raingauge density influences the accuracy of spatial pattern and time interval influence the accuracy of temporal pattern of storms. Usually due to practical and financial limitation the perfect distribution is not achievable. Several sources of data are used to define the behavior of rainfall over a watershed. Raingauges station, radar operation and satellite sensor are the main source of rainfall estimation over the space and time. Recording raingauges are the most common source of rainfall data in many countries. However raingauge network has not adequate coverage in many watersheds spatially in developing countries. Therefore other global source of rainfall data may be useful for hydrological analysis such as flood modeling. This research assessed the ability of TRMM rainfall estimates for explain the Spatio-temporal pattern of severe storm over Klang watershed which is a hydrologically well instrumented watershed. It was experienced that TRMM rainfall estimates are 35% less than actual data for the investigated events. Due to coarse temporal resolution of TRMM (3 h) compare to gauge rainfall (15 min), significant uncertainty influences identifying the start and end of storm event and consequently their resultant time to peak of flood hydrograph which is extremely important in flood forecasting systems. Due to coarse pixel size of TRMM data, watershed scale is important issue.
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Li, Xianghu, Zhen Li, and Yaling Lin. "Suitability of TRMM Products with Different Temporal Resolution (3-Hourly, Daily, and Monthly) for Rainfall Erosivity Estimation." Remote Sensing 12, no. 23 (November 30, 2020): 3924. http://dx.doi.org/10.3390/rs12233924.
Повний текст джерелаАнотація:
Rainfall erosivity (RE) is a significant indicator of erosion capacity. The application of Tropical Rainfall Measuring Mission (TRMM) rainfall products to deal with RE estimation has not received much attention. It is not clear which temporal resolution of TRMM data is most suitable. This study quantified the RE in the Poyang Lake basin, China, based on TRMM 3B42 3-hourly, daily, and 3B43 monthly rainfall data, and investigated their suitability for estimating RE. The results showed that TRMM 3-hourly product had a significant systematic underestimation of monthly RE, especially during the period of April–June for the large values. The TRMM 3B42 daily product seems to have better performance with the relative bias of 3.0% in summer. At the annual scale, TRMM 3B42 daily and 3B43 monthly data had acceptable accuracy, with mean error of 1858 and −85 MJ∙mm/ha∙h and relative bias of 18.3% and −0.85%, respectively. A spatial performance analysis showed that all three TRMM products generally captured the overall spatial patterns of RE, while the TRMM 3B43 product was more suitable in depicting the spatial characteristics of annual RE. This study provides valuable information for the application of TRMM products in mapping RE and risk assessment of soil erosion.
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Li, Xianghu, Qi Zhang, and Xuchun Ye. "Capabilities of Satellite-Based Precipitation to Estimate the Spatiotemporal Variation of Flood/Drought Class in Poyang Lake Basin." Advances in Meteorology 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/901240.
Повний текст джерелаАнотація:
Poyang Lake basin is one of the most frequently affected areas by a variety of flood or drought events in China. Satellite-based precipitation data have greatly improved their temporal and spatial resolution in recent years, but the short length of records limited their applications in some fields. This paper compared and evaluated the creditability of using a short period data series to estimate the statistics characteristics of long period data series and investigated the usefulness of TRMM rainfall data for monitoring the temporal and spatial distribution of flood/drought classes by theZindex method in Poyang Lake basin. The results show that (1) the 1998–2010 data series are sufficiently robust to depict the statistics characteristics of long period data; (2) the intra-annual distribution and interannual variability of flood/drought classes based on TRMM rainfall data matched well with the results from rain gauges data; (3) the spatial agreement between TRMM and interpolated gauges rainfall varied with the precipitation characteristics; and (4) TRMM rainfall data described the similar spatial pattern of flood/drought classes with the interpolated gauges rainfall. In conclusion, it is suitable and credible for flood/drought classes evaluation based on the TRMM rainfall data in Poyang Lake basin.
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Jie, Gao. "An Assessment of Rainfall Measurement Based on TRMM Products." Advanced Materials Research 864-867 (December 2013): 2193–99. http://dx.doi.org/10.4028/www.scientific.net/amr.864-867.2193.
Повний текст джерелаАнотація:
Contrasts between TRMM 3B43 monthly data and rainfall observations of 720 stations in China are conducted based on a linear regression model. During January 1999 and December 2007, there is a significant correlation between TRMM data and the observed ones with an average r2 0.834. TRMM data performs better in the South and North, especially for flat regions. Limited by radar signal degradation due to heavy rain and low resolution of monitoring, TRMM data have better results in low-flow season than that in flood season. TRMM data cover all the places in middle and low latitudes. It is useful for long-term water resources planning, drought analysis in ungauged basins (PUB), and will be helpful for flood warning. Spatiotemporal data with higher resolution will greatly promote the development of hydrology in the future.
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Ishak, Asnor Nadirah, Noor Hidayah Tauhid Ahmad, and Mandeep Singh Jit Singh. "The Diurnal Variation of Rain Intensity in Malaysia for Monsoon Region using TRMM Satelit Data." Jurnal Kejuruteraan 33, no. 3 (August 30, 2021): 719–31. http://dx.doi.org/10.17576/jkukm-2021-33(3)-30.
Повний текст джерелаАнотація:
Malaysia is located in an equatorial region with high and stable temperatures, high humidity, and rainy throughout of the year. The rains are caused by the monsoon regime and influenced by local topography. However, Malaysia is near the Equator and surrounded by the sea, there is no real dry season. The weather in Malaysia is mainly affected by two monsoon regimes which is the Southwest Monsoon (MBD) from May to September, while the Northeast Monsoon (MTL) from November to March for every year. The sea in Malaysia are causing the effects of sea breeze and land breeze that have a huge impact on wind patterns. The abundant rainfall in Malaysia which is suitable in this study is to analyze the 3B42 rainfall data from Tropical Rainfall Measuring Mission (TRMM) and 14 study areas including of 8 areas from Peninsular of Malaysia (SM) and 6 areas from East of Malaysia (MT) for the period of 11 years of data from 2009 to 2019. This paper is to study the intensity of diurnal rainfall according the temporal and spatial data using Segal models which is conversion the 1-minute rainfall interval in Malaysia. The Segal model is the best model for conversion of rainfall data for Malaysia. This paper also analyses the rainfall data in Malaysia. The conversion of 1-minute rainfall interval data can give the statistical stability of rainfall distribution that influenced by the diversity of landforms, the movement of monsoon winds, and the latitude of surface areas. The result of this study show that Kuching, Sarawak received 1-minute rainfall data at 175.25mm/hour with the highest annual rainfall of 4641.34mm in the entire study area within Malaysia. The lowest 1-minute rainfall data for SM is Cameron Highlands, Pahang which came up to 103.09mm/hour while the highest 1-minute rainfall data is Kota Bahru, Kelantan at 171.13mm/hour. Kota Bahru study area also received the average of highest annual rainfall data for SM which is at 3013.33mm. Cameron Highlands is located near Titiwangsa Range, where the backbone of SM is located, and the area is protected by strong MTL winds. The results found that the lowest average daily, monthly, and annual rainfall is Mersing, Johor at 1992.98mm for the entire study area. The pattern of rainfall distribution as overall show the east coast area of SM and the entire state of Sarawak received heavy rainfall affected by MTL winds and have the same average diurnal rainfall pattern which has two maximum rainfall points per day. The amount of rainfall distributions is different from one area to another and changes from time to time. The El Nino and La Nina phenomenon known as the El Nino Southern Oscillation (ENSO) is also affecting the rainfall distribution in Malaysia because of the sea surface temperature that keeps changing in the equatorial Pacific Ocean. Almost the entire study area were affected by the ENSO phenomenon in 2015 and 2016. This rainfall distribution study in Malaysia is very useful and helps government and private sectors to make prepararations for the seasonal rainfall, flood, and drought.
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Nucifera, F., W. Riasasi, and A. L. Permatasari. "Identification of Rainfall Variability Using TRMM Data Analysis." IOP Conference Series: Earth and Environmental Science 313 (August 27, 2019): 012043. http://dx.doi.org/10.1088/1755-1315/313/1/012043.
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Setti, Sridhara, Rathinasamy Maheswaran, Venkataramana Sridhar, Kamal Kumar Barik, Bruno Merz, and Ankit Agarwal. "Inter-Comparison of Gauge-Based Gridded Data, Reanalysis and Satellite Precipitation Product with an Emphasis on Hydrological Modeling." Atmosphere 11, no. 11 (November 20, 2020): 1252. http://dx.doi.org/10.3390/atmos11111252.
Повний текст джерелаАнотація:
Precipitation is essential for modeling the hydrologic behavior of watersheds. There exist multiple precipitation products of different sources and precision. We evaluate the influence of different precipitation product on model parameters and streamflow predictive uncertainty using a soil water assessment tool (SWAT) model for a forest dominated catchment in India. We used IMD (gridded rainfall dataset), TRMM (satellite product), bias-corrected TRMM (corrected satellite product) and NCEP-CFSR (reanalysis dataset) over a period from 1998–2012 for simulating streamflow. The precipitation analysis using statistical measures revealed that the TRMM and CFSR data slightly overestimate rainfall compared to the ground-based IMD data. However, the TRMM estimates improved, applying a bias correction. The Nash–Sutcliffe (and R2) values for TRMM, TRMMbias and CFSR, are 0.58 (0.62), 0.62 (0.63) and 0.52 (0.54), respectively at model calibrated with IMD data (Scenario A). The models of each precipitation product (Scenario B) yielded Nash–Sutcliffe (and R2) values 0.71 (0.76), 0.74 (0.78) and 0.76 (0.77) for TRMM, TRMMbias and CFSR datasets, respectively. Thus, the hydrological model-based evaluation revealed that the model calibration with individual rainfall data as input showed increased accuracy in the streamflow simulation. IMD and TRMM forced models to perform better in capturing the streamflow simulations than the CFSR reanalysis-driven model. Overall, our results showed that TRMM data after proper correction could be a good alternative for ground observations for driving hydrological models.
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Worqlul, A. W., B. Maathuis, A. A. Adem, S. S. Demissie, S. Langan, and T. S. Steenhuis. "Comparison of rainfall estimations by TRMM 3B42, MPEG and CFSR with ground-observed data for the Lake Tana basin in Ethiopia." Hydrology and Earth System Sciences 18, no. 12 (December 5, 2014): 4871–81. http://dx.doi.org/10.5194/hess-18-4871-2014.
Повний текст джерелаАнотація:
Abstract. Planning for drought relief and floods in developing countries is greatly hampered by the lack of a sufficiently dense network of weather stations measuring precipitation. In this paper, we test the utility of three satellite products to augment the ground-based precipitation measurement to provide improved spatial estimates of rainfall. The three products are the Tropical Rainfall Measuring Mission (TRMM) product (3B42), Multi-Sensor Precipitation Estimate–Geostationary (MPEG) and the Climate Forecast System Reanalysis (CFSR). The accuracy of the three products is tested in the Lake Tana basin in Ethiopia, where 38 weather stations were available in 2010 with a full record of daily precipitation amounts. Daily gridded satellite-based rainfall estimates were compared to (1) point-observed ground rainfall and (2) areal rainfall in the major river sub-basins of Lake Tana. The result shows that the MPEG and CFSR satellites provided the most accurate rainfall estimates. On average, for 38 stations, 78 and 86% of the observed rainfall variation is explained by MPEG and CFSR data, respectively, while TRMM explained only 17% of the variation. Similarly, the areal comparison indicated a better performance for both MPEG and CFSR data in capturing the pattern and amount of rainfall. MPEG and CFSR also have a lower root mean square error (RMSE) compared to the TRMM 3B42 satellite rainfall. The bias indicated that TRMM 3B42 was, on average, unbiased, whereas MPEG consistently underestimated the observed rainfall. CFSR often produced large overestimates.
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Munzimi, Yolande A., Matthew C. Hansen, Bernard Adusei, and Gabriel B. Senay. "Characterizing Congo Basin Rainfall and Climate Using Tropical Rainfall Measuring Mission (TRMM) Satellite Data and Limited Rain Gauge Ground Observations." Journal of Applied Meteorology and Climatology 54, no. 3 (March 2015): 541–55. http://dx.doi.org/10.1175/jamc-d-14-0052.1.
Повний текст джерелаАнотація:
AbstractQuantitative understanding of Congo River basin hydrological behavior is poor because of the basin’s limited hydrometeorological observation network. In cases such as the Congo basin where ground data are scarce, satellite-based estimates of rainfall, such as those from the joint NASA/JAXA Tropical Rainfall Measuring Mission (TRMM), can be used to quantify rainfall patterns. This study tests and reports the use of limited rainfall gauge data within the Democratic Republic of Congo (DRC) to recalibrate a TRMM science product (TRMM 3B42, version 6) in characterizing precipitation and climate in the Congo basin. Rainfall estimates from TRMM 3B42, version 6, are compared and adjusted using ground precipitation data from 12 DRC meteorological stations from 1998 to 2007. Adjustment is achieved on a monthly scale by using a regression-tree algorithm. The output is a new, basin-specific estimate of monthly and annual rainfall and climate types across the Congo basin. This new product and the latest version-7 TRMM 3B43 science product are validated by using an independent long-term dataset of historical isohyets. Standard errors of the estimate, root-mean-square errors, and regression coefficients r were slightly and uniformly better with the recalibration from this study when compared with the 3B43 product (mean monthly standard errors of 31 and 40 mm of precipitation and mean r2 of 0.85 and 0.82, respectively), but the 3B43 product was slightly better in terms of bias estimation (1.02 and 1.00). Despite reasonable doubts that have been expressed in studies of other tropical regions, within the Congo basin the TRMM science product (3B43) performed in a manner that is comparable to the performance of the recalibrated product that is described in this study.
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Dinis, Pedro, Vasco Mantas, Pedro Santarém Andrade, Júlio Tonecas, Elvira Kapula, Alcides Pereira, and Francisco S. Carvalho. "Contribution of TRMM rainfall data to the study of natural systems and risk assessment. Cases of application in SW Angola." Estudos do Quaternário / Quaternary Studies, no. 9 (June 22, 2013): 33–43. http://dx.doi.org/10.30893/eq.v0i9.154.
Повний текст джерелаАнотація:
Researchers studying exogenous processes in developing countries have to deal with the problem of scarcity of rainfall data. With satellite measurements, such as those provided by the Tropical Rainfall Measuring Mission (TRMM), it may be possible to overcome this limitation. In this work we link the TRMM rainfall measurements with two examples of exogenous processes from southwest Angola: landslide processes, focused on the events at the Leba road in early 2011, and flow conditions, based on the water level determined in gauging stations from rivers Cavaco and Catumbela. It is demonstrated that major mass flow movements occur when specific TRMM rainfall thresholds are reached. Regarding the flow conditions, the water level in two gauging stations is strongly conditioned by other factors beside the atmospheric precipitation in their watersheds (retention and release of water from reservoirs, channel obstruction, etc.), but short term oscillations are closely linked with the rainfall in the proximity. TRMM data is found to be very useful for the analysis of specific extreme events or the patterns of behavior of natural systems and, consequently, constitute a valuable tool in natural risks assessment.
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Chen, Shan-Tai, Chien-Chen Wu, Wann-Jin Chen, and Jen-Chi Hu. "Rain-Area Identification Using TRMM/TMI Data by Data Mining Approach." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 3 (May 20, 2008): 243–48. http://dx.doi.org/10.20965/jaciii.2008.p0243.
Повний текст джерелаАнотація:
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.
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Abdulrazzaq, Zaidoon, Nadia Aziz, and Abdulkareem Mohammed. "Flood modelling using satellite-based precipitation estimates and digital elevation model in eastern Iraq." International Journal of Advanced Geosciences 6, no. 1 (January 28, 2018): 72. http://dx.doi.org/10.14419/ijag.v6i1.8946.
Повний текст джерелаАнотація:
Increasingly available and a virtually uninterrupted supply of satellite-estimated rainfall data is gradually becoming a cost-effective source of input for flood prediction under a variety of circumstances. The study conducted in Wasit province/Eastern Iraq when a flood occurs due to heavy rainfall in May 2013. In this study the capability of Tropical Rainfall Measuring Mission (TRMM) rainfall daily data have been used to estimate the relationship between measured precipitation and the Digital Elevation Model (DEM), also to study the relationship between rainfall intensity and flood waters areas. Rainfall estimation by remote sensing using satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) is a possible means of supplementing rain gauge data, having the better spatial cover of rainfall fields. The approach used throughout this paper has integrated recently compiled data derived from satellite imagery (rainfall, and digital elevation model) into a GIS geodatabase to study the relationship between rainfall intensity and floodwater's areas then the results' comparison with the Normalized Difference Water Index (NDWI) after the flood. ArcGIS software has been used to process, analyze the archived Tropical Rainfall Measuring Mission (TRMM) precipitation data, and calculate NDWI from Landsat 8 images. In conclusions, the study explains the flood-area clearly captured by the TRMM measurements; and the region’s water increased. Also, good correlation between measured precipitation and the Digital Elevation Model (DEM) has been detected.
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Cho, Hye-Kyung, Kenneth P. Bowman, and Gerald R. North. "Equatorial Waves Including the Madden–Julian Oscillation in TRMM Rainfall and OLR Data." Journal of Climate 17, no. 22 (November 15, 2004): 4387–406. http://dx.doi.org/10.1175/3215.1.
Повний текст джерелаАнотація:
Abstract Four years of outgoing longwave radiation (OLR) and rainfall data from the Tropical Rainfall Measuring Mission (TRMM) are investigated to find the dominant large-scale wave modes in the Tropics. By using space– time cross-section analysis and spectral analysis, the longitudinal and latitudinal behaviors of the overall waves and the dominant waves are observed. Despite the noisy nature of precipitation data and the limited sampling by the TRMM satellite, pronounced peaks are found for Kelvin waves, n = 1 equatorial Rossby waves (ER), and mixed Rossby–gravity waves (MRG). Madden–Julian oscillation (MJO) and tropical depression (TD)-type disturbances are also detected. The seasonal evolution of these waves is investigated. An appendix includes a study of sampling and aliasing errors due to the peculiar space–time sampling pattern of TRMM. It is shown that the waves detected in this study are not artifacts of these sampling features. The results presented here are compared with previous studies. Consistency with their results gives confidence in the TRMM data for wave studies. The results from this study can be utilized for modeling and testing theories. Also, it may be useful for the future users of the TRMM data to understand the nature of the TRMM satellite sampling.
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Moreira, Luana Lavagnoli, Rafael Rezende Novais, Dimaghi Schwamback, and Salomão Martins de Carvalho Júnior. "Spatial–temporal dynamics of rainfall erosivity in the state of Espírito Santo (Brazil) from remote sensing data." World Journal of Science, Technology and Sustainable Development 17, no. 3 (May 29, 2020): 297–309. http://dx.doi.org/10.1108/wjstsd-08-2019-0059.
Повний текст джерелаАнотація:
PurposeThe most common methodology to estimate erosivity is using rainfall data obtained from rain monitoring stations. However, the quality of this estimation may be compromised due to low density, operational problems and maintenance cost of rainfall monitoring stations, common problem encountered in developing countries such as Brazil. The objective of this study was to evaluate the applicability of pluviometric data obtained by TRMM satellite images for the spatiotemporal characterization of erosivity in the state of Espírito Santo (Brazil).Design/methodology/approachFor this, rainfall data and annual and monthly erosivities of 71 rainfall stations were statistically compared with those from TRMM images.FindingsFor this, rainfall data and annual and monthly erosivities of 71 rainfall stations were statistically compared with those from TRMM images. The estimate proved that TRMM is efficient since the NSE values were higher than 0.70 and the coefficient of determination was higher than 0.77 for monthly and annual erosivities, but in most months and yearly, erosivity was overestimated.Practical implicationsThe use of satellite images to estimate rainfall allowed the spatial representation over time (months) of the oscillating degree of erosivity in the state of Espírito Santo (Brazil). The spatialization may provide an identification of areas and periods in which are essential for the implementation of land use management in order to minimize environmental problems related to soil loss.Originality/valueThe technique applied may be an alternative to overcome common problems on rainfall monitoring station, such as low density, low data reliability, high manutention and maintenance cost and operational problems.
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Li, Xianghu, Xuchun Ye, and Chengyu Xu. "Assessment of Satellite-Based Precipitation Products for Estimating and Mapping Rainfall Erosivity in a Subtropical Basin, China." Remote Sensing 14, no. 17 (August 31, 2022): 4292. http://dx.doi.org/10.3390/rs14174292.
Повний текст джерелаАнотація:
Rainfall erosivity is an important indicator for quantitatively representing the erosive power of rainfall. This study expanded three satellite-based precipitation products (SPPs) for estimating and mapping rainfall erosivity in a subtropical basin in China and evaluated their performance at different rainfall erosivity intensities, seasons, and spaces. The results showed that the rainfall erosivity data from GPM-IMERG had the smallest errors compared to the estimates from rain gauge data on monthly and seasonal scales, while data from PERSIANN-CDR and TRMM 3B42 significantly underestimated and slightly overestimated rainfall erosivity, respectively. The three SPPs generally presented different strengths and weaknesses in different seasons. TRMM 3B42 performed best in summer, with small biases, but its performance was less satisfactory in winter. The precision of estimates from GPM-IMERG was higher than that from TRMM 3B42; the biases, especially in winter, were significantly reduced. For different intensities, PERSIANN-CDR overestimated light rainfall erosivity but underestimated heavy rainfall erosivity. In terms of space, TRMM 3B42 and GPM-IMERG correctly presented the spatial pattern of rainfall erosivity. However, PERSIANN-CDR tended to be less skillful in describing its spatial maps. Outcomes of the study provide an insight into the suitability of the SPPs for estimating and mapping rainfall erosivity and suggest possible directions for further improving these products.
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Liu, Zhong, Dana Ostrenga, William Teng, and Steven Kempler. "Tropical Rainfall Measuring Mission (TRMM) Precipitation Data and Services for Research and Applications." Bulletin of the American Meteorological Society 93, no. 9 (September 1, 2012): 1317–25. http://dx.doi.org/10.1175/bams-d-11-00152.1.
Повний текст джерелаАнотація:
Precipitation is a critical component of the Earth's hydrological cycle. Launched on 27 November 1997, TRMM is a joint U.S.–Japan satellite mission to provide the first detailed and comprehensive dataset of the four-dimensional distribution of rainfall and latent heating over vastly undersampled tropical and subtropical oceans and continents (40°S–40°N). Over the past 14 years, TRMM has been a major data source for meteorological, hydrological, and other research and application activities around the world. This short article describes how the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) provides TRMM archive and nearreal- time precipitation datasets and services for research and applications. TRMM data consist of orbital data from TRMM instruments at the sensor's resolution, gridded data at a range of spatial and temporal resolutions, subsets, ground-based instrument data, and ancillary data. Data analysis, display, and delivery are facilitated by the following services: (1) Mirador (data search and access); (2) TOVAS (TRMM Online Visualization and Analysis System); (3) OPeNDAP (Opensource Project for a Network Data Access Protocol); (4) GrADS Data Server (GDS); and (5) Open Geospatial Consortium (OGC) Web Map Service (WMS) for the GIS community. Precipitation data application services are available to support a wide variety of applications around the world. Future plans include enhanced and new services to address data-related issues from the user community. Meanwhile, the GES DISC is preparing for the Global Precipitation Measurement (GPM) mission, which is scheduled for launch in 2014.
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Zhang, Tianyu, Yu Yang, Zeyu Dong, and Shu Gui. "A Multiscale Assessment of Three Satellite Precipitation Products (TRMM, CMORPH, and PERSIANN) in the Three Gorges Reservoir Area in China." Advances in Meteorology 2021 (June 2, 2021): 1–27. http://dx.doi.org/10.1155/2021/9979216.
Повний текст джерелаАнотація:
This study evaluated three satellite precipitation products, namely, TRMM, CMORPH, and PERSIANN, over the Three Gorges Reservoir area in China at multiple timescales. The assessment covered the following aspects: the rainfall amount, extreme precipitation, and the rainy-day detection ability. Results indicated that the CMORPH and TRMM estimates of rainfall amount were reasonably good, but the PERSIANN showed a larger bias than the other two satellite products. The data precision of CMORPH was slightly better than TRMM. All three satellite products could reproduce the diurnal cycle of rainfall, i.e., more precipitation in the morning than in the evening. The CMORPH estimates were closest to the gauge observation at 3-hourly and 12-hourly timescales. The data accuracy of CMORPH data was better during the night than in the daytime. At daily timescale, the quality of TRMM data was slightly inferior to the CMORPH, whereas the PERSIANN still differed much from the ground observation. At monthly, seasonally, and yearly timescales, the performance of TRMM was comparable to CMORPH, and both of them were obviously superior to PERSIANN. The rainy-day detection ability of CMORPH and TRMM was much better than PERSIANN. The PERSIANN data tended to overestimate the light rainy days but underestimate the heavy and torrential rainy days. The CMORPH data overestimated mainly the moderate rainy days. The TRMM data overestimated the occurrence frequency of heavy rain during the winter half year (from October to the next March). Both the CMORPH and the TRMM provided good estimates of the regional average rainy days. The data accuracy of CMORPH was slightly better than TRMM, and both were far better than the PERSIANN with respect to the rainfall amount and rainy-day detection. Nevertheless, all satellite estimates showed large biases of extreme precipitation. The CMORPH estimate of the maximum 5-day precipitation was the best of all. Both the CMORPH and TRMM data overestimated the 95th percentile of precipitation, but the PERSIANN data severely underestimated it. The PERSIANN estimates of extreme precipitation amount were the best of all during the daytime, nighttime, and the whole day. The above evaluation results could facilitate the application of satellite rainfall products and provide a reference to precipitation-related studies.
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Abdulrazzaq, Zaidoon T. "The feasibility of using TRMM satellite data for missing terrestrial stations in Iraq for mapping the rainfall contour lines." Civil Engineering Beyond Limits 1, no. 3 (May 9, 2020): 15–19. http://dx.doi.org/10.36937/cebel.2020.003.003.
Повний текст джерелаАнотація:
Rainfall data are considered an important and critical element of many environmental and hydrological studies such as drought, desertification, climate change and other strategic studies. These studies are mainly based on the rainfall data archive for previous years. During the last two decades, a large number of meteorological stations have been destroyed as a result of wars and internal conflicts, reducing the stations to 16 after the number was more than 30 stations, resulting in a significant lack of meteorological data archive. In addition to the spatial distribution of these stations does not adequately cover Iraq. The research aim to evaluate the feasibility of the TRMM satellite data (3B42 V7 product) to complete the rainfall data archive of the missing terrestrial stations. Several rainfall contour maps of the season 2017-2018 were drawn from data of 16 terrestrial stations, 16 and 30 stations derived from TRMM satellite data, and a hybrid map derived from the TRMM satellite data and available terrestrial stations, afterwards there were compared with the general rainfall contour map. The correlation was made between the satellite data and terrestrial stations data, and the results showed a positive correlation with a strong correlation coefficient reach to 0.91. The results showed that TRMM data could be used as a good alternative to terrestrial station data for its accuracy, wide coverage and ease of availability.
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Erna, Erna, Muliddin Muliddin, and Jamal Harimudin. "Analisis Pola dan Intensitas Curah Hujan Berdasarkan Data TRMM di Sulawesi Tenggara." JAGAT (Jurnal Geografi Aplikasi dan Teknologi) 5, no. 2 (October 28, 2021): 105. http://dx.doi.org/10.33772/jagat.v5i2.21465.
Повний текст джерелаАнотація:
Abstrak: Cuaca dan iklim merupakan sebuah proses fenomena di atmosfer yang keberadaannya sangat penting dalam berbagai aktivitas kehidupan. Perhatian mengenai informasi cuaca dan iklim semakin meningkat seiring dengan meningkatnya fenomena alam yang tidak lazim terjadi atau biasa disebut dengan cuaca ekstrim yang sulit untuk dikendalikan dan dimodifikasi. Penelitian ini bertujuan untuk mengetahui pola dan intensitas curah hujan berdasarkan data TRMM di Sulawesi Tenggara berdasarkan aspek temporal. Metode analisis data yaitu analisis korelasi dan uji signifikan untuk mengetahui hubungan data TRMM dengan data stasiun curah hujan, serta menggunakan persamaan Mononobe untuk intensitas curah hujan. Hasil penelitian ini didapatkan bahwa pola hujan di Sulawesi Tenggara merupakan pola region A tipe monsunal dengan ciri terjadi puncak musim hujan yang terjadi antara bulan Desember, Januari, Februari dan puncak musim kemarau terjadi antara Bulan Agustus dan September. Kecendrungan intensitas curah hujan mengalami kenaikan dengan kala ulang yang lebih lama. Kata Kunci: Curah Hujan, TRMM, Monsunal Abstract: Weather and climate are a process of phenomena in the atmosphere whose existence is very important in various activities life. Concern about weather and climate information is increasing along with the increase in natural phenomena that are not uncommon or commonly referred to as extreme weather that are difficult to control and modify . This study aims to determine rainfall patterns and intensity based on TRMM data in Southeast Sulawesi based on temporal aspects. Data analysis method is correlation analysis and significant test to determine the relationship of TRMM data with rainfall station data, and using the Mononobe equation for rainfall intensity. The results of this study found that the pattern of rain in Southeast Sulawesi is a type of Region A Monsunal pattern characterized by the peak of the rainy season which occurs between December, January, February and the peak of the dry season occurs between August and September. The intensity of rainfall increases with a longer return period. Keywords: Precipitation, TRMM, Monsoonal
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Tajudin, Noraisyah, Norsuzila Ya’acob, Darmawaty Mohd Ali, and Nor Aizam Adnan. "Estimation of TRMM rainfall for landslide occurrences based on rainfall threshold analysis." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 3 (June 1, 2020): 3208. http://dx.doi.org/10.11591/ijece.v10i3.pp3208-3215.
Повний текст джерелаАнотація:
Landslide can be triggered by intense or prolonged rainfall. Precipitation data obtained from ground-based observation is very accurate and commonly used to do analysis and landslide prediction. However, this approach is costly with its own limitation due to lack of density of ground station, especially in mountain area. As an alternative, satellite derived rainfall techniques have become more favorable to overcome these limitations. Moreover, the satellite derived rainfall estimation needs to be validated on its accuracy and its capability to predict landslide which presumably triggered by rainfall. This paper presents the investigation of using the TRMM-3B42V7 data in comparison to the available rain-gauge data in Ulu Kelang, Selangor. The monthly average rainfall, cumulative rainfall and rainfall threshold analysis from 1998 to 2011 is compared using quantitative statistical criteria (Pearson correlation, bias, root mean square error, mean different and mean). The results from analysis showed that there is a significant and strong positive correlation between the TRMM 3B42V7 and rain gauge data. The threshold derivative from the satellite products is lower than the rain gauge measurement. The findings indicated that the proposed method can be applied using TRMM satellite estimates products to derive rainfall threshold for the possible landslide occurrence.
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Omotosho, T. V., J. S. Mandeep, M. Abdullah, and A. T. Adediji. "Distribution of one-minute rain rate in Malaysia derived from TRMM satellite data." Annales Geophysicae 31, no. 11 (November 19, 2013): 2013–22. http://dx.doi.org/10.5194/angeo-31-2013-2013.
Повний текст джерелаАнотація:
Abstract. Total rainfall accumulation, as well as convective and stratiform rainfall rate data from the Tropical Rainfall Measuring Mission (TRMM) satellite sensors have been used to derive the thunderstorm ratio and one-minute rainfall rates, R0.01, for 57 stations in Malaysia for exceedance probabilities of 0.001–1% for an average year, for the period 1998–2010. The results of the rain accumulations from the TRMM satellite were validated with the data collected from different ground data sources from the National Oceanic and Atmospheric Administration (NOAA) global summary of the day (1949–2010), Global Precipitation Climatology Centre (GPCC) (1986–2010), and NASA (1950–1999). The correlation coefficient and the average bias error between TRMM and GPCC for Malaysia were found to be 0.79–0.89 and ±50 mm, respectively. The deduced one-minute rainfall rates correlated fairly well with those obtained from the previous work carried out in Malaysia, with correlation coefficients of 0.7 in all the 57 locations. The inferred mean annual one-minute rainfall rates were found to be highest in the eastern Malaysia, with values between 84.7 and 153.9 mm h−1 for 0.01% exceedance, and in western Malaysia with values between 81.8 and 143.8 mm h−1. The present results will be useful for satellite rain attenuation modeling in tropical and subtropical stations around the world.
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Safdar, Fasiha, Muhammad Fahim Khokhar, Muhammad Arshad, and Iftikhar Hussain Adil. "Climate Change Indicators and Spatiotemporal Shift in Monsoon Patterns in Pakistan." Advances in Meteorology 2019 (December 29, 2019): 1–14. http://dx.doi.org/10.1155/2019/8281201.
Повний текст джерелаАнотація:
Monsoon rainfall is the principle source of fresh water essential for agricultural practices and human sustenance in the Indian subcontinent during summer. This study is primarily designed to analyse the extent of rainfall and temperature variations in Pakistan over the northern monsoon belt by using satellite and ground-based observations. The satellite gridded data for rainfall are acquired from Tropical Rainfall Measuring Mission (TRMM) along with rainfall and temperature data from 15 ground stations of Pakistan Meteorological Department (PMD). Data were analysed to identify changes in climatic parameters and spatiotemporal shift in monsoon precipitation in Pakistan. Analysis shows that there is significant correlation between TRMM and PMD datasets. Decrease in monsoon rainfall is observed during the last two decades. A more pronounced decrease is observed in monsoon rainfall during the years 2010–2017, i.e., 17.58 mm/year accompanied by 0.18°C increase in temperature. A southward spatial shift in monsoon rainfall occurrence (rainfall ≥2.5 mm/day) is observed while an eastward shift in moderate to heavy monsoon rainfall is identified. This study may be helpful for an agricultural country like Pakistan which is heavily dependent on monsoon rainfalls for assessing the impacts of changing monsoon season and to adapt towards changing climate.
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Noor, Riza Arian, Muhammad Ruslan, Gusti Rusmayadi, and Badaruddin Badaruddin. "PEMANFAATAN DATA SATELIT TROPICAL RAINFALL MEASURING MISSION (TRMM) UNTUK PEMETAAN ZONA AGROKLIMAT OLDEMAN DI KALIMANTAN SELATAN." EnviroScienteae 12, no. 3 (December 10, 2016): 267. http://dx.doi.org/10.20527/es.v12i3.2452.
Повний текст джерелаАнотація:
The irregularity of observation sites distribution and network density, lack data availability and discontinuity are the obstacles to analyzing and producing the information of agroclimate zone in South Kalimantan. TRMM satellite needs to be researched to overcome the limitations of surface observation data. This study intended to validate TRMM 3B43 satellite data with surface rainfall, to produce Oldeman agroclimate zone based on TRMM satellite data and to analyze the agroclimate zone for agricultural resources management. Data validation is done using the statistical method by analyzing the correlation value (r) and RMSE (Root Mean Square Error). The agroclimate zone is classified based on Oldeman climate classification type. The calculation results are mapped spatially using Arc GIS 10.2 software. The validation result of the TRMM satellite and surface rainfall data shows a high correlation value for the monthly average. The value of correlation coefficient is 0,97 and 25 mm for RMSE value. Oldeman agroclimate zone based on TRMM satellite data in south Kalimantan is divided into five climate zones, such as B1, B2, C1, C2, and D1.
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Shukla, A. K., C. S. P. Ojha, and R. D. Garg. "Satellite-based estimation and validation of monthly rainfall distribution over Upper Ganga river basin." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-8 (November 28, 2014): 399–404. http://dx.doi.org/10.5194/isprsarchives-xl-8-399-2014.
Повний текст джерелаАнотація:
Water is one of the most precious natural resources for all living flora and fauna. 97.5% of water on the Earth is sea water, the remaining 2.5 % is fresh water of which slightly over two thirds is frozen in glaciers and polar ice caps. The unfrozen fresh water is mainly found as groundwater, with only a small fraction present above ground or in the air. Since one of the main source of water is rainfall. Therefore, proper information on rainfall and its variability in space and time is required for better watershed planning and management and other applications. In Himalayan basin, the rain gauge network is relatively sparse with uneven distribution. Hence, there is lack of proper information on rainfall patterns of this region. The main advantage of satellite derived rainfall estimation over rain gauge derived rain data is that these provide homogenous spatio-temporal rainfall information over a large area e.g. Upper Ganga river basin region. Therefore, a better understanding of the rainfall patterns of this region is required for better disaster mitigation. The objectives of this study are to evaluate the reliability of Tropical Rainfall Measuring Mission (TRMM) 3B43 V7 derived high resolution satellite product to study the rainfall distribution over the Upper Ganga river basin. TRMM 3B43 V7 derived monthly rainfall data is analyzed and the monthly rainfall product is validated and correlated with IMD (Indian Meteorological Department) gauge station's rainfall data. The monthly rainfall data of 15 years i.e. from 1998 to 2012 is used in the study. Statistical indices can be used to evaluate, compare and validate satellite rainfall data with respect to gauge rainfall data. Statistical indices used in this study are Correlation Coefficient (r), Mean Square Error (MSE), Root Mean Square Error (RMSE), and Average Percentage Error (Avg. % Error). Most of the rainfall in the study area occurs in the months of June, July, August, September and October. The isohyets were prepared using gauge rainfall data and are matched with the spatially distributed rainfall surface prepared from TRMM satellite data for all the months of the rainy season of the study area. Kriging spatial interpolation method was used to generate the spatially distributed rainfall surface. From the results it was observe d that they matched fairly well with each other showing high spatial correlation. The monthly rainfall result showed that TRMM data is underestimated with low accuracy, though TRMM data and rain gauge data have positive correlation.
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Kuligowski, Robert J., Yaping Li, and Yu Zhang. "Impact of TRMM Data on a Low-Latency, High-Resolution Precipitation Algorithm for Flash-Flood Forecasting." Journal of Applied Meteorology and Climatology 52, no. 6 (June 2013): 1379–93. http://dx.doi.org/10.1175/jamc-d-12-0107.1.
Повний текст джерелаАнотація:
AbstractData from the Tropical Rainfall Measuring Mission (TRMM) have made great contributions to hydrometeorology from both a science and an operations standpoint. However, direct application of TRMM data to short-fuse hydrologic forecasting has been challenging because of the data refresh and latency issues inherent in an instrument in low Earth orbit (LEO). To evaluate their potential impact on low-latency satellite rainfall estimates, rain rates from both the TRMM Microwave Imager (TMI) and precipitation radar (PR) were ingested into a multisensor framework that calibrates high-refresh, low-latency IR brightness temperature data from geostationary platforms against the more accurate but low-refresh, higher-latency rainfall rates available from microwave (MW) instruments on board LEO platforms. The TRMM data were used in two ways: to bias adjust the other MW data sources to match the distribution of the TMI rain rates, and directly alongside the MW rain rates in the calibration dataset. The results showed a significant reduction in false alarms and also a significant reduction in bias for those pixels for which rainfall was correctly detected. The MW bias adjustment was found to have much greater impact than the direct use of the TMI and PR rain rates in the calibration data, but this is not surprising since the latter represented perhaps only 10% of the calibration dataset.
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Harsanto, Puji, and Muhammad Sufyan Tsaury. "Analisis Limpasan Langsung Metode SCS Menggunakan Data Hujan TRMM Studi Kasus Subdas Code Hulu." Bulletin of Civil Engineering 1, no. 2 (August 30, 2021): 64–72. http://dx.doi.org/10.18196/bce.v1i2.12417.
Повний текст джерелаАнотація:
Ketersediaan data menjadi hal yang krusial dalam analisis hidrologi. Banyak permasalahan menyangkut ketersediaan data yang seringkali ditemui di lapangan, seperti minimnya data, data yang tidak kontinyu, atau sebaran stasiun yang tidak merata. Seiring berkembangnya teknologi, permasalahan tersebut dapat diselesaikan dengan memanfaatkan data pengamatan satelit yang memiliki resolusi spasial dan temporal tinggi, cakupan luas, akses cepat, dan ekonomis. Akan tetapi, data satelit perlu divalidasi dengan data pengamatan nyata di lapangan. Penelitian ini dilakukan untuk validasi data satelit TRMM terhadap data observasi berbasis darat dengan membandingkan debit limpasan dari data hujan terukur di darat atau ARR (Automatic Rainfall Recorder) dengan data hujan TRMM, lalu dikoreksi dengan debit limpasan terukur di stasiun AWLR (Automatic Water Level Recorder) Gemawang. Debit limpasan dari hujan dihitung dengan menggunakan Metode SCS. Hasil penelitian menunjukan jeda waktu rata-rata pengukuran hujan TRMM dan ARR sekitar 8,5 jam. Ditemukan perbedaan bentuk hidrograf limpasanTRMM. Pada data 18 Januari 2018, terdapat kesalahan bentuk gelombang hidrograf (Ew) sebesar 11.843. Dari analisis indeks kesesuaian dan efisiensi, data satelit TRMM mendapat hasil koefisien korelasi rata-rata debit ARR-AWLR dan TRMM-AWLR tergolong rendah yaitu masing-masing sebesar 0,2416 dan 0,1041, sedangkan koefisien efisiensinya 1,67 yang dikategorikan sebagai data yang efisien. Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data.Availability of sufficient data as input data is important. Data availability tends to have several data problems, such as the lack of data availability, incomplete data, or the number of stations that are less scattered. As the development of the technology problems, those probelms can be solved by replacing ground-based observation data with satellite observations that have high spatial and temporal resolution, wide area coverage, fast access, and economics. This research was conducted to validate and correct TRMM satellite data on observation data at the AWLR Gemawang station with the SCS Method. The results of this study showed a delay in the average measurements of satellite rainfall and surface approximately 8.5 hours based on the data analysis used in this study. The results of the model error analysis can be concluded that TRMM rainfall data can be used in these needs. However, there is still an error in the TRMM data, which is on the data of January 18, 2018 which results in a hydrograph (Ew) waveform error of 11.843. From the conformity index and efficiency analysis, TRMM satellite data gets the correlation coefficient average ARR-AWLR debit of 0,2416 which is categorized as low efficiency data and TRMM-AWLR of 0,1041 which is categorized as quite low coefficient data, while the efficiency coefficient gets an average value 1,67 which is categorized as highly efficient optimization data.
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Xu, Weixin, and Steven A. Rutledge. "Morphology, Intensity, and Rainfall Production of MJO Convection: Observations from DYNAMO Shipborne Radar and TRMM." Journal of the Atmospheric Sciences 72, no. 2 (February 1, 2015): 623–40. http://dx.doi.org/10.1175/jas-d-14-0130.1.
Повний текст джерелаАнотація:
Abstract This study uses Dynamics of the Madden–Julian Oscillation (DYNAMO) shipborne [Research Vessel (R/V) Roger Revelle] radar and Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) datasets to investigate MJO-associated convective systems in specific organizational modes [mesoscale convective system (MCS) versus sub-MCS and linear versus nonlinear]. The Revelle radar sampled many “climatological” aspects of MJO convection as indicated by comparison with the long-term TRMM PR statistics, including areal-mean rainfall (6–7 mm day−1), convective intensity, rainfall contributions from different morphologies, and their variations with MJO phase. Nonlinear sub-MCSs were present 70% of the time but contributed just around 20% of the total rainfall. In contrast, linear and nonlinear MCSs were present 10% of the time but contributed 20% and 50%, respectively. These distributions vary with MJO phase, with the largest sub-MCS rainfall fraction in suppressed phases (phases 5–7) and maximum MCS precipitation in active phases (phases 2 and 3). Similarly, convective–stratiform rainfall fractions also varied significantly with MJO phase, with the highest convective fractions (70%–80%) in suppressed phases and the largest stratiform fraction (40%–50%) in active phases. However, there are also discrepancies between the Revelle radar and TRMM PR. Revelle radar data indicated a mean convective rain fraction of 70% compared to 55% for TRMM PR. This difference is mainly due to the reduced resolution of the TRMM PR compared to the ship radar. There are also notable differences in the rainfall contributions as a function of convective intensity between the Revelle radar and TRMM PR. In addition, TRMM PR composites indicate linear MCS rainfall increases after MJO onset and produce similar rainfall contributions to nonlinear MCSs; however, the Revelle radar statistics show the clear dominance of nonlinear MCS rainfall.
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As-syakur, Abd Rahman, Tasuku Tanaka, Takahiro Osawa, and Made Sudiana Mahendra. "Indonesian rainfall variability observation using TRMM multi-satellite data." International Journal of Remote Sensing 34, no. 21 (September 2, 2013): 7723–38. http://dx.doi.org/10.1080/01431161.2013.826837.
Повний текст джерела
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PAL, P. K., C. M. KISHTAWAL, and NEERAJ AGARWAL. "Multifeature classification based rainfall estimation using visible infrared TRMM data." MAUSAM 54, no. 1 (January 18, 2022): 67–74. http://dx.doi.org/10.54302/mausam.v54i1.1492.
Повний текст джерелаАнотація:
In this study an attempt has been made to estimate the rain potential of clouds using the signatures in visible and infrared spectral channels by multi spectral classification approach. The data for this study was obtained from Visible Infrared Scanner (VIRS) and TRMM Microwave Instrument (TMI) onboard Tropical Rainfall Measuring Mission (TRMM) satellite. Fifteen VIRS derived parameters have been used for classification and the clouds were separated into 24 classes using K-Mean classification algorithm. Six out of these 24 classes were found to have high raining probability as well as high cumulative contribution to the total rainfall (~80%). A regression analysis has been performed to explain the TMI rainfall rate in terms of features derived from VIRS observations for these six classes. The results of classification and verification have been discussed.
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Hirose, M., and K. Okada. "A 0.01° Resolving TRMM PR Precipitation Climatology." Journal of Applied Meteorology and Climatology 57, no. 8 (August 2018): 1645–61. http://dx.doi.org/10.1175/jamc-d-17-0280.1.
Повний текст джерелаАнотація:
AbstractIn this study, rainfall data are prepared at a 0.01° scale using 16-yr spaceborne radar data over the area of 36.13°S–36.13°N as provided by the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). A spatial resolution that is finer than the field of view is obtained by assuming rainfall uniformity within an instantaneous footprint centered on the PR footprint geolocation. These ultra-high-resolution data reveal local rainfall concentrations over slope areas. A new estimate of the maximum rainfall at Cherrapunji, India, was observed on the valley side, approximately 5 km east of the gauge station, and is approximately 50% higher than the value indicated by the 0.1°-scale data. A case study of Yakushima Island, Japan, indicates that several percent of the sampling error arising from the spatial mismatch may be contained in conventional 0.05°-scale datasets generated without footprint areal information. The differences attributable to the enhancement in the resolution are significant in complex terrain such as the Himalayas. The differences in rainfall averaged for the 0.1° and 0.01° scales exceed 10 mm day−1 over specific slope areas. In the case of New Guinea, the mean rainfall on a mountain ridge can be 30 times smaller than that on an adjacent slope at a distance of 0.25°; this is not well represented by other high-resolution datasets based on gauges and infrared radiometers. The substantial nonuniformity of rainfall climatology highlights the need for a better understanding of kilometer-scale geographic constraints on rainfall and retrieval approaches.
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Rhee, Jinyoung, and Gregory J. Carbone. "Estimating Drought Conditions for Regions with Limited Precipitation Data." Journal of Applied Meteorology and Climatology 50, no. 3 (March 1, 2011): 548–59. http://dx.doi.org/10.1175/2010jamc2604.1.
Повний текст джерелаАнотація:
Abstract Three closely related issues that affect drought estimation in regions with limited precipitation data are addressed by investigating methods for filling missing daily precipitation data, handling short-term records, and deriving drought information for unsampled locations. The analysis yields three general conclusions: 1) it is better to conduct spatial interpolation prior to calculating drought index values, 2) using weather stations with moderate lengths of records (usually at least 10 years) improves the spatial–temporal characterization of drought, and 3) alternative precipitation sources of the National Weather Service multisensor precipitation rainfall estimates and the Tropical Rainfall Measuring Mission (TRMM) satellite monthly rainfall product (3B43) do not outperform spatially interpolated daily precipitation data in most regions, except in the western United States where the TRMM-based precipitation data work better than the spatially interpolated values for drought monitoring.