Journal articles on the topic 'Satellite rainfall data'
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Iryani, Sakura Yulia, Febrinasti Alia, Muhammad Abiyyi Tauhid, Ahmad Muhtarom, and Arie Putra Usman. "Utilization of GPM Satellite and PERSIANN Satellite Data for Estimated Monthly Rainfall in South Sumatera." UKaRsT 6, no. 2 (November 29, 2022): 174. http://dx.doi.org/10.30737/ukarst.v6i2.3482.
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Rainfall data are widely used to predict regional rainfall. Limited rainfall data is a problem that has an impact on decreasing accuracy, one of which is in the area of South Sumatra. This can be overcome by using satellites. However, to utilize satellitebased rainfall data, it is necessary to carry out an analysis to determine the accuracy of rainfall data. This research aims to evaluate rainfall data from the GPM satellite and PERSIANN satellite with validation and calibration analysis so that the value of rainfall data from the Satellite is close to the measurement result and can be used to estimate monthly rainfall. In this study, the data used were measured monthly rainfall in the field, GPM, and PERSIANN obtained from 9 South Sumatra districts for 2019 until 2021. The research method was validated using correlation coefficient, Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). Calibration is done using a combination method, a solver algorithm in Microsoft Excel, and manually. The estimated monthly rainfall analysis is carried out using the isohyet method with the IDW interpolation method. The research results were obtained based on the validation and calibration of monthly rainfall data showing that data from the GPM showing it is closer to the results of field rainfall measurements than the data obtained from PERSIANN satellite. Based on the results of research on satellite data that has been calibrated, it can be used to estimate monthly rainfall in the South Sumatra Region
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Kuntoro, A. A., R. K. Hapsari, M. B. Adityawan, M. Farid, Widyaningtias, and Radhika. "Estimation of Extreme Rainfall over Kalimantan Island based on GPM IMERG Daily Data." IOP Conference Series: Earth and Environmental Science 1065, no. 1 (July 1, 2022): 012036. http://dx.doi.org/10.1088/1755-1315/1065/1/012036.
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Abstract Rainfall is one of the critical data for water resources infrastructure planning. In many cases in developing countries such as Indonesia, rainfall stations are not evenly distributed. In many cases, regional development occurs much faster than the improvement of hydrological measurement instruments. The plan to move the capital city of Indonesia to Kalimantan is one example. Satellites rainfall products can be utilized, especially for areas with a limited number of rainfall stations. This study examines the potential use of Global Precipitation Measurement (GPM) satellite products to estimate the spatial distribution of rainfall in the Kalimantan region. Twenty years data of daily maximum rainfall from GPM satellite rainfall products in 2001-2020 were compared to twenty years data of daily maximum rainfall from 16 rainfall stations under the Meteorology, Climatology, and Geophysical Agency (BMKG), with data time spanning from the 1970s to 2020. The analysis results show a significant difference between extreme rainfall analysis computed by using station data and the satellite. The use of the correction function can increase the accuracy of the GPM rainfall product. It can be used as an alternative data source for a region with limited rainfall stations.
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Zeri, Sarah Jabbar, Mohammed Magdy Hamed, Xiaojun Wang, and Shamsuddin Shahid. "Utilizing Satellite Data to Establish Rainfall Intensity-Duration-Frequency Curves for Major Cities in Iraq." Water 15, no. 5 (February 22, 2023): 852. http://dx.doi.org/10.3390/w15050852.
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This study generates intensity-duration-frequency curves for three important cities in Iraq using Global Precipitation Measurement Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), Global Satellite Mapping of Precipitation near real-time (GSMaP NRT), and gauge corrected (GSMaP GC) satellite precipitation datasets. Many probability distribution functions were used to fit the maximum yearly rainfall data. The Sherman equation was used to create intensity-duration-frequency (IDF) curves for rainfall intensities with 2-, 5-, 10-, 25-, 50-, and 100-year return periods, with the estimated coefficients of the best-fit distribution serving as the fitting parameters. The discrepancy between the IDF curves produced from the satellites and the observed data was used to bias correct the satellite IDF curves. The Generalized Extreme Value Distribution model best describes the hourly rainfall distribution of satellite data. GSMaP GC was the best option for creating IDF curves with higher correlations with observed data at Baghdad, Basra, and Mosul. The study indicates the necessity of gauge correction of satellite rainfall data to reduce under- and over-estimating observed rainfall. GSMaP GC can reasonably estimate rainfall in a predominantly arid climate region like Iraq. The generated IDF curves may be an important step toward achieving sustainable urban stormwater management in the country.
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Tama, Danny Riyadi, Lily Montarcih Limantara, Ery Suhartanto, and Yatnanta Padma Devia. "THE USAGE OF GPM DATA IN THE UNGAUGED WONOGIRI CATCHMENT." Journal of Southwest Jiaotong University 57, no. 6 (December 30, 2022): 1004–10. http://dx.doi.org/10.35741/issn.0258-2724.57.6.86.
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This paper intends to establish the usage of Global Precipitation Measurement (GPM) data in the ungauged Wonogiri catchment. Along with technological developments in the field of remote sensing, such as satellite data, the rainfall measurements can be perfgormed by monitoring rainfall in the large areas, even in places that cannot be reached by the conventional equipment. This advantage can be used to obtain the amount of rain in an area du to the benefit of water resource management. The methodology consists of collecting the observed data (rainfall station) and rainfall satellite data, then calibrating the satellite data, and by the end is to validate the result of calibration. The use of satellite rain data needs to be corrected so the rain characteristics produced by the satellite approach the rainfall conditions produced by the observations. The corrections are made by multiplying each data interval against a certain coefficient so that the value generated by the satellite data approaches the characteristic value generated by the observed rain that is used as the Global Precipitation Measurement (GPM). The Global Precipitation Mesaurement (GPM) is required in the ungauged catchment that there is no observed rainfall data. Based on the results of trial and error, the results show that a correction coefficient value of 0 is produced for the interval 0 – 2 mm, 0.75 for the interval 2 – 20 mm, 0.8 for the interval 20 – 30 mm, 0.85 for the interval 30 – 50 mm and 0.95 for the value rain satellites larger than 50 mm.
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González, MH, and I. Velasco. "Rainfall area identification using satellite data." Climate Research 5 (1995): 259–67. http://dx.doi.org/10.3354/cr005259.
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Shih, Sun F. "GOES Satellite Data in Rainfall Estimation." Journal of Irrigation and Drainage Engineering 115, no. 5 (October 1989): 839–52. http://dx.doi.org/10.1061/(asce)0733-9437(1989)115:5(839).
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Wu, Qiaoyan, and Yilei Wang. "Comparison of Oceanic Multisatellite Precipitation Data from Tropical Rainfall Measurement Mission and Global Precipitation Measurement Mission Datasets with Rain Gauge Data from Ocean Buoys." Journal of Atmospheric and Oceanic Technology 36, no. 5 (May 2019): 903–20. http://dx.doi.org/10.1175/jtech-d-18-0152.1.
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AbstractThree satellite-derived precipitation datasets [the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) dataset, the NOAA Climate Prediction Center morphing technique (CMORPH) dataset, and the newly available Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) dataset] are compared with data obtained from 55 rain gauges mounted on floating buoys in the tropics for the period 1 April 2014–30 April 2017. All three satellite datasets underestimate low rainfall and overestimate high rainfall in the tropical Pacific Ocean, but the TMPA dataset does this the most. In the high-rainfall (higher than 4 mm day−1) Atlantic region, all three satellite datasets overestimate low rainfall and underestimate high rainfall, but the IMERG dataset does this the most. For the Indian Ocean, all three rainfall satellite datasets overestimate rainfall at some gauges and underestimate it at others. Of these three satellite products, IMERG is the most accurate in estimating mean precipitation over the tropical Pacific and Indian Oceans, but it is less accurate over the tropical Atlantic Ocean for regions of high rainfall. The differences between the three satellite datasets vary by region and there is a need to consider uncertainties in the data before using them for research.
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Zhou, Yuanyuan, Nianxiu Qin, Qiuhong Tang, Huabin Shi, and Liang Gao. "Assimilation of Multi-Source Precipitation Data over Southeast China Using a Nonparametric Framework." Remote Sensing 13, no. 6 (March 11, 2021): 1057. http://dx.doi.org/10.3390/rs13061057.
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The accuracy of the rain distribution could be enhanced by assimilating the remotely sensed and gauge-based precipitation data. In this study, a new nonparametric general regression (NGR) framework was proposed to assimilate satellite- and gauge-based rainfall data over southeast China (SEC). The assimilated rainfall data in Meiyu and Typhoon seasons, in different months, as well as during rainfall events with various rainfall intensities were evaluated to assess the performance of this proposed framework. In rainy season (Meiyu and Typhoon seasons), the proposed method obtained the estimates with smaller total absolute deviations than those of the other satellite products (i.e., 3B42RT and 3B42V7). In general, the NGR framework outperformed the original satellites generally on root-mean-square error (RMSE) and mean absolute error (MAE), especially on Nash-Sutcliffe coefficient of efficiency (NSE). At monthly scale, the performance of assimilated data by NGR was better than those of satellite-based products in most months, by exhibiting larger correlation coefficients (CC) in 6 months, smaller RMSE and MAE in at least 9 months and larger NSE in 9 months, respectively. Moreover, the estimates from NGR have been proven to perform better than the two satellite-based products with respect to the simulation of the gauge observations under different rainfall scenarios (i.e., light rain, moderate rain and heavy rain).
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Cheng, Ke S., and Sun F. Shih. "Rainfall Area Identification Using GOES Satellite Data." Journal of Irrigation and Drainage Engineering 118, no. 1 (January 1992): 179–90. http://dx.doi.org/10.1061/(asce)0733-9437(1992)118:1(179).
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Teo, Chee-Kiat, and David I. F. Grimes. "Stochastic modelling of rainfall from satellite data." Journal of Hydrology 346, no. 1-2 (November 2007): 33–50. http://dx.doi.org/10.1016/j.jhydrol.2007.08.014.
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Hossain, F., and E. N. Anagnostou. "Assessment of a Multidimensional Satellite Rainfall Error Model for Ensemble Generation of Satellite Rainfall Data." IEEE Geoscience and Remote Sensing Letters 3, no. 3 (July 2006): 419–23. http://dx.doi.org/10.1109/lgrs.2006.873686.
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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.
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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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Parisouj, Peiman, Taesam Lee, Hamid Mohebzadeh, and Hadi Mohammadzadeh Khani. "Rainfall-runoff simulation using satellite rainfall in a scarce data catchment." Journal of Applied Water Engineering and Research 9, no. 2 (March 1, 2021): 161–74. http://dx.doi.org/10.1080/23249676.2021.1884617.
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Balakrishnan Manikiam, Kamsali Nagaraja. "Climate Change Analysis using Satellite Data." Mapana - Journal of Sciences 14, no. 1 (July 28, 2017): 25–39. http://dx.doi.org/10.12723/mjs.32.4.
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Recent times have witnessed increasing impact of industrialization and urban growth on environment. In addition, the potential climate changes and possible adverse impacts on the economy and society at large are causing concern. In India, one of the major concerns is the variability of monsoon rainfall and effects on agriculture and water management. The various parameters associated with environment and climate change need to be monitored and analyzed. The effects of global warming on the Indian subcontinent vary from the submergence of low-lying islands, frequent flooding, coastal degradation and melting of glaciers in the Indian Himalayas. Indian satellites INSAT and IRS launched in early 1980s heralded the era of Space observations. The IRS satellites are providing observations of parameters such as land use/cover, forest, water bodies, crops etc. while INSAT provides quantitative products such as Cloud Motion Vectors (CMVs), Quantitative Precipitation Estimates (QPEs), Outgoing Long-wave Radiation (OLR), Vertical Temperature Profiles (VTPRs), Sea Surface Temperature. The satellite data is operationally used for generating long term database on vegetation, soil condition, rainfall, groundwater etc.. Some of the unique studies are Biosphere Reserve Monitoring, Mapping of
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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.
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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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Le Coz, Camille, Arnold Heemink, Martin Verlaan, Marie-claire ten Veldhuis, and Nick van de Giesen. "Correcting Position Error in Precipitation Data Using Image Morphing." Remote Sensing 11, no. 21 (October 31, 2019): 2557. http://dx.doi.org/10.3390/rs11212557.
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Rainfall estimates based on satellite data are subject to errors in the position of the rainfall events in addition to errors in their intensity. This is especially true for localized rainfall events such as the convective rainstorms that occur during the monsoon season in sub-Saharan Africa. Many satellite-based estimates use gauge information for bias correction. However, bias adjustment methods do not correct the position errors explicitly. We propose to gauge-adjust satellite-based estimates with respect to the position using a morphing method. Image morphing transforms an image, in our case a rainfall field, into another one, by applying a spatial transformation. A benefit of this approach is that it can take both the position and the intensity of a rain event into account. Its potential is investigated with two case studies. In the first case, the rain events are synthetic, represented by elliptic shapes, while the second case uses real data from a rainfall event occurring during the monsoon season in southern Ghana. In the second case, the satellite-based estimate IMERG-Late (Integrated Multi-Satellite Retrievals for GPM ) is adjusted to gauge data from the Trans-African Hydro-Meteorological Observatory (TAHMO) network. The results show that the position errors can be corrected, while preserving the higher spatial variability of the satellite-based estimate.
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Abbas, Mohamed Mustafa, Abdin M. A. Salih, Adil M. Elkhider, and Salih H. Hamid. "Using satellite rainfall data to estimate direct flow." DESALINATION AND WATER TREATMENT 176 (2020): 139–47. http://dx.doi.org/10.5004/dwt.2020.25510.
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Motell, Craig E., and Bryan C. Weare. "Estimating Tropical Pacific Rainfall Using Digital Satellite Data." Journal of Climate and Applied Meteorology 26, no. 10 (October 1987): 1436–46. http://dx.doi.org/10.1175/1520-0450(1987)026<1436:etprud>2.0.co;2.
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Tsonis, A. A., G. N. Triantafyllou, and K. P. Georgakakos. "Hydrological applications of satellite data: 1. Rainfall estimation." Journal of Geophysical Research: Atmospheres 101, no. D21 (November 1, 1996): 26517–25. http://dx.doi.org/10.1029/96jd01654.
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Abdurahman, Abdurahman, Nazli Ismail, Faisal Abdullah, and I. Dewa Gede Arya Putra. "EVALUATION OF HOTSPOTS BASED ON CLIMATE DATA IN THE NAGAN RAYA REGENCY, ACEH." JURNAL GEOGRAFI 14, no. 2 (July 13, 2022): 145. http://dx.doi.org/10.24114/jg.v14i2.31358.
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Identifying hotspots as a potential for wildfires due to weather/climate factors needs to be studied in more detail to consider the policies taken by relevant agencies in the Nagan Raya Regency, mainly consisting of peatlands. Rainfall observation data in some areas are incomplete, so alternative data are needed for rainfall estimation for those areas, such as satellite data. However, the satellite data does not necessarily match the conditions in the field, so validation is needed. In this study, satellite data were validated with available observational data in the area, so the results can be used as a reference when field data is unavailable. The data used are GSMaP_GNRT6 and observation data from 5 rainfall observation posts: Beutong, Cut Nyak Dien Meteorological Station, Darul Makmur, PT. Socfindo and Pulo Ie for the period 2010-2019. The satellite and the observation data were correlated with the Pearson method to see the relationship between the two data. The difference between each satellite data and observations at the same time and place is calculated using the formulas Mean Error (ME), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). Furthermore, a case study of fire incidents and satellite hotspot data at several locations was also observed simultaneously. In addition, the validated rainfall data were also used to calculate the Standardized Precipitation Index (SPI) value. The result shows the validation of rainfall data with GSMaP_GNRT6 satellite data has a moderate correlation with the MAE value ranging from 101.3 to 195.12 from the five rainfall observation posts. The results also show that a 10-day base of rainfall before the occurrence of the wildfires was in a low category (86%). The number of hotspot occurrences was also supported by the negative monthly SPI value, high air temperature, and the type of land in the study area. Keywords: Hotspot, Rainfall, Air Temperature, Wildfires, Peat Lands
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Xie, Yanhui, Jiancheng Shi, Shuiyong Fan, Min Chen, Youjun Dou, and Dabin Ji. "Impact of Radiance Data Assimilation on the Prediction of Heavy Rainfall in RMAPS: A Case Study." Remote Sensing 10, no. 9 (August 30, 2018): 1380. http://dx.doi.org/10.3390/rs10091380.
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Herein, a case study on the impact of assimilating satellite radiance observation data into the rapid-refresh multi-scale analysis and prediction system (RMAPS) is presented. This case study targeted the 48 h period from 19–20 July 2016, which was characterized by the passage of a low pressure system that produced heavy rainfall over North China. Two experiments were performed and 24 h forecasts were produced every 3 h. The results indicated that the forecast prior to the satellite radiance data assimilation could not accurately predict heavy rainfall events over Beijing and the surrounding area. The assimilation of satellite radiance data from the advanced microwave sounding unit-A (AMSU-A) and microwave humidity sounding (MHS) improved the skills of the quantitative precipitation forecast to a certain extent. In comparison with the control experiment that only assimilated conventional observations, the experiment with the integrated satellite radiance data improved the rainfall forecast accuracy for 6 h accumulated precipitation after about 6 h, especially for rainfall amounts that were greater than 25 mm. The average rainfall score was improved by 14.2% for the 25 mm threshold and by 35.8% for 50 mm of rainfall. The results also indicated a positive impact of assimilating satellite radiances, which was primarily reflected by the improved performance of quantitative precipitation forecasting and higher spatial correlation in the forecast range of 6–12 h. Satellite radiance observations provided certain valuable information that was related to the temperature profile, which increased the scope of the prediction of heavy rainfall and led to an improvement in the rainfall scoring in the RMAPS. The inclusion of satellite radiance observations was found to have a small but beneficial impact on the prediction of heavy rainfall events as it relates to our case study conditions. These findings suggest that the assimilation of satellite radiance data in the RMAPS can provide an overall improvement in heavy rainfall forecasting.
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Liu, Gin-Rong, Chun-Chieh Chao, and Czu-Yi Ho. "Applying Satellite-Estimated Storm Rotation Speed to Improve Typhoon Rainfall Potential Technique." Weather and Forecasting 23, no. 2 (April 1, 2008): 259–69. http://dx.doi.org/10.1175/2007waf2006101.1.
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Abstract Heavy rainfall from typhoons or tropical cyclones often causes inland flooding and mudslides that threaten lives and property. In this study, Special Sensor Microwave Imager (SSM/I) satellite data observed from 2000 to 2004 were used to calculate the rainfall rates of different typhoons in the northwestern Pacific. Geostationary weather satellite infrared images were also applied to estimate the typhoon rotation speed via the maximum cross-correlation technique. By including such information in the tropical rainfall potential (TRaP) technique, an improved typhoon rainfall potential technique can be constructed. Considering the fact that a typhoon’s spiral rainbands move constantly, half-hourly or hourly infrared data observed from geostationary weather satellites were used to calculate the revolving speed, which was subsequently used to predict the rainband movement over the next hour. After comparing the predicted rainfall potential with the rain gauge data of Taiwan’s small offshore islands, it was found that this new method can improve the typhoon’s accumulated rainfall by approximately 40% over the original TRaP method. Therefore, to produce a more accurate short-term typhoon rainfall forecast, it is very important to factor in the satellite-estimated storm rotation speed.
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Samosir, Desy Yunita, I. Made Yuliara, and Rakhmat Prasetia. "Comparison and Analysis of Rainfall Spatial Patterns IMERG (Integrated Multi-Satellite Retrievals for GPM) Data and Observation Data on Bali Province." BULETIN FISIKA 22, no. 2 (November 28, 2020): 67. http://dx.doi.org/10.24843/bf.2021.v22.i02.p03.
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Limitations of observational data such as insufficient data length, incomplete, and uneven station distribution make it difficult to analyze and predict rain, so it requires supporting instruments such as satellites to provide a better and broader picture of rainfall distribution. However, it is necessary to test the accuracy of satellite data because the resolution and conditions of each region are different. This research aims to validate IMERG rain data against observation data in the 2015 El Nino period using observation rainfall data from BMKG Negara and IMERG data from GPM satellite at 12 rain points in Bali Province. The analytical method used is quantitative statistics, the calculation of errors and correlations and the comparison of the spatial pattern of the two data. The results of the analysis of the spatial pattern of the IMERG data show that, there was a decrease in rainfall from May to July, but the rainfall increased into August, and again experienced a decline entering the months of September to December where the same pattern was also shown from the results of the spatial pattern analysis on the Observation data. The decrease in rainfall in the May-December 2015 period was a strong El Nino effect as evidenced by the results of the correlation analysis of the SOI index on rainfall which showed a fairly strong correlation value, namely 0.55.The validation of IMERG data on monthly observation data showed that the average correlation was sufficient strong is 0.42 and analysis per rain post shows a weak correlation namely 0.31, which means that data IMERG is not yet accurate as an alternative to the observation rainfall data in Bali Province.
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Verma, P., and S. K. Ghosh. "STUDY OF GPM-IMERG RAINFALL DATA PRODUCT FOR GANGOTRI GLACIER." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 383–88. http://dx.doi.org/10.5194/isprs-archives-xlii-5-383-2018.
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<p><strong>Abstract.</strong> This study presents a comparison of new generation weather observatory satellites Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) rainfall products with field data collected for Gangotri glacier in India. The meteorological analysis of rainfall estimates has been performed on GPM IMERG Final, Late and Early precipitation products available at daily scale with a spatial resolution of 0.1&deg;<span class="thinspace"></span>&times;<span class="thinspace"></span>0.1&deg; for melting season from May to September for the year 2014 and 2015 respectively. The comparison of satellite products with field data was done using correlation coefficient and standard anomaly. The Late run curve showed a high degree of similarity with final run curve while early run showed variation from them. The satellite meteorological data correctly identified non-rainy days with an average of &sim;86.7%, &sim;67.5% and &sim;95% for pre-monsoon, monsoon and post-monsoon season respectively. The rmse for final run data product for 2014 and 2015 are 4.5, 1.23, 1.55, 1.24, 0.8 and 1.14, 7.1, 1.82, 1.15, 1.52 from May to September respectively. Overall, it has been observed that for medium to heavy rainfall final run estimates are close to field data and for light to medium rainfall late run estimates are close. Similar results have been obtained from both datasets for non-rainy days in the study area.</p>
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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.
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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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Santos, Luiz Octavio Fabricio dos, Carlos Alexandre Santos Querino, Juliane Kayse Albuquerque da Silva Querino, Altemar Lopes Pedreira Junior, Aryanne Resende de Melo Moura, Nadja Gomes Machado, and Marcelo Sacardi Biudes. "Validation of rainfall data estimated by GPM satellite on Southern Amazon region." Ambiente e Agua - An Interdisciplinary Journal of Applied Science 14, no. 1 (January 2, 2019): 1. http://dx.doi.org/10.4136/ambi-agua.2249.
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Rainfall is a meteorological variable of great importance for hydric balance and for weather studies. Rainfall estimation, carried out by satellites, has increased the climatological dataset related to precipitation. However, the accuracy of these data is questionable. This paper aimed to validate the estimates done by the Global Precipitation Measurement (GPM) satellite for the mesoregion of Southern Amazonas State, Brazil. The surface data were collected by the National Water Agency – ANA and National Institute of Meteorology – INMET, and is available at both institutions’ websites. The satellite precipitation data were accessed directly from the NASA webpage. Statistical analysis of Pearson correlation was used, as well as the Willmott’s “d” index and errors from the MAE (Mean Absolute Error) and RMSE (Root Mean Square Error). The GPM satellite satisfactorily estimated the precipitation, once it had correlations above 73% and high Willmott coefficients (between 0.86 and 0.97). The MAE and RMSE showed values that varied from 36.50 mm to 72.49 mm and 13.81 mm to 71.76 mm, respectively. Seasonal rain variations are represented accordingly. In some cases, either an underestimation or an overestimation of the rain data was observed. In the yearly totals, a high rate of similarity between the estimated and measured values was observed. We concluded that the GPM-based multi-satellite precipitation estimates can be used, even though they are not 100% reliable. However, adjustments in calibration for the region are necessary and recommended.
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Rahman, Sayma, Amvrossios C. Bagtzoglou, Faisal Hossain, Ling Tang, Lance D. Yarbrough, and Greg Easson. "Investigating Spatial Downscaling of Satellite Rainfall Data for Streamflow Simulation in a Medium-Sized Basin." Journal of Hydrometeorology 10, no. 4 (August 1, 2009): 1063–79. http://dx.doi.org/10.1175/2009jhm1072.1.
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Abstract The objective of this study was to investigate spatial downscaling of satellite rainfall data for streamflow prediction in a medium-sized (970 km2) river basin prone to flooding. The spatial downscaling scheme used in the study was based on the principle of scale invariance. It reproduced the rainfall variability at finer scales while being conditioned on the large-scale rainfall. Two Tropical Rainfall Measuring Mission (TRMM)-based real-time global satellite rainfall products were analyzed: 1) the infrared (IR)-based 3B41RT product available at 1 hourly and 0.25° scales and 2) the combined passive microwave (PMW) and IR-based 3B42RT product available at 3 hourly and 0.25° scales. The conceptual Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) was used for the simulation of streamflow. It was found that propagation of spatially downscaled satellite rainfall in the hydrologic model increased simulation uncertainty in streamflow as rainfall grid scales became smaller than 0.25°. The streamflow simulation uncertainty for satellite downscaling was found to be very similar to that for ground validation Next Generation Weather Radar (NEXRAD) downscaling at any given scale, indicating that the effectiveness of the spatial downscaling scheme is not influenced by rainfall data type. Closer inspection at the subbasin level revealed that the limitation of the selected spatial downscaling scheme to preserve the mean rainfall intensity for irregularly sized drainage units was responsible for the increase in simulation uncertainty as scales became smaller. Although the findings should not be construed as a generalization for spatial downscaling schemes, there is a need for more rigorous hydrometeorological assessment of downscaled satellite rainfall data prior to institutionalizing its use for real-time streamflow simulation over ungauged basins.
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Hossain, Faisal, and George J. Huffman. "Investigating Error Metrics for Satellite Rainfall Data at Hydrologically Relevant Scales." Journal of Hydrometeorology 9, no. 3 (June 1, 2008): 563–75. http://dx.doi.org/10.1175/2007jhm925.1.
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Abstract This paper addresses the following open question: What set of error metrics for satellite rainfall data can advance the hydrologic application of new-generation, high-resolution rainfall products over land? The authors’ primary aim is to initiate a framework for building metrics that are mutually interpretable by hydrologists (users) and algorithm developers (data producers) and to provide more insightful information on the quality of the satellite estimates. In addition, hydrologists can use the framework to develop a space–time error model for simulating stochastic realizations of satellite estimates for quantification of the implication on hydrologic simulation uncertainty. First, the authors conceptualize the error metrics in three general dimensions: 1) spatial (how does the error vary in space?); 2) retrieval (how “off” is each rainfall estimate from the true value over rainy areas?); and 3) temporal (how does the error vary in time?). They suggest formulations for error metrics specific to each dimension, in addition to ones that are already widely used by the community. They then investigate the behavior of these metrics as a function of spatial scale ranging from 0.04° to 1.0° for the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) geostationary infrared-based algorithm. It is observed that moving to finer space–time scales for satellite rainfall estimation requires explicitly probabilistic measures that are mathematically amenable to space–time stochastic simulation of satellite rainfall data. The probability of detection of rain as a function of ground validation rainfall magnitude is found to be most sensitive to scale followed by the correlation length for detection of rain. Conventional metrics such as the correlation coefficient, frequency bias, false alarm ratio, and equitable threat score are found to be modestly sensitive to scales smaller than 0.24° latitude/longitude. Error metrics that account for an algorithm’s ability to capture rainfall intermittency as a function of space appear useful in identifying the useful spatial scales of application for the hydrologist. It is shown that metrics evolving from the proposed conceptual framework can identify seasonal and regional differences in reliability of four global satellite rainfall products over the United States more clearly than conventional metrics. The proposed framework for building such error metrics can lay a foundation for better interaction between the data-producing community and hydrologists in shaping the new generation of satellite-based, high-resolution rainfall products, including those being developed for the planned Global Precipitation Measurement (GPM) mission.
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Megantara, Anggit, Sri Wahyuni, and Lily Limantara. "Rationalization of Rainfall Station Density in the Jatiroto Sub-Watershed Using Ground and Satellite Rainfall Data." Civil and Environmental Science 005, no. 02 (October 3, 2022): 129–43. http://dx.doi.org/10.21776/ub.civense.2022.00502.3.
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This study uses ground and CHIRPS data to rationalize the density of rainfall stations in the Jatiroto Sub-watershed, Lumajang Regency. This study aimed to determine the suitability of the CHIRPS satellite rainfall data to the measurement data. In addition, it determines the density of rainfall stations based on WMO standards. Also, the Kagan-Rodda method uses measurement and satellite data to determine rainfall station recommendations' results. The method used for the suitability test uses the value of RMSE, NSE, Correlation Coefficient, and Relative Error. And the WMO standard for analyzing the number of rainfall station. Knowing the rationalization and recommendations for placing rainfall stations using the Kagan-Rodda method by considering WMO standards, root mean square error, and interpolation errors. The results obtained include the appropriateness of satellite data, the number of rainfall stations at the research location according to WMO standards, and recommendations for rainfall stations based on Kagan-Rodda
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Hayashi, Yoshiaki, Taichi Tebakari, and Akihiro Hashimoto. "A Comparison Between Global Satellite Mapping of Precipitation Data and High-Resolution Radar Data – A Case Study of Localized Torrential Rainfall over Japan." Journal of Disaster Research 16, no. 4 (June 1, 2021): 786–93. http://dx.doi.org/10.20965/jdr.2021.p0786.
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This paper presents a case study comparing the latest algorithm version of Global Satellite Mapping of Precipitation (GSMaP) data with C-band and X-band Multi-Parameter (MP) radar as high-resolution rainfall data in terms of localized heavy rainfall events. The study also obliged us to clarify the spatial and temporal resolution of GSMaP data using high-accuracy ground-based radar, and evaluate the performance and reporting frequency of GSMaP satellites. The GSMaP_Gauge_RNL data with less than 70 mm/day of daily rainfall was similar to the data of both radars, but the GSMaP_Gauge_RNL data with over 70 mm/day of daily rainfall was not, and the calibration by rain-gauge data was poor. Furthermore, both direct/indirect observations by the Global Precipitation Measurement/Microwave Imager (GPM/GMI) and the frequency thereof (once or twice) significantly affected the difference between GPM/GMI data and C-band radar data when the daily rainfall was less than 70 mm/day and the hourly rainfall was less than 20 mm/h. Therefore, it is difficult for GSMaP_Gauge to accurately estimate localized heavy rainfall with high-density particle precipitation.
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Atiah, Winifred Ayinpogbilla, Leonard Kofitse Amekudzi, Jeffrey Nii Armah Aryee, Kwasi Preko, and Sylvester Kojo Danuor. "Validation of Satellite and Merged Rainfall Data over Ghana, West Africa." Atmosphere 11, no. 8 (August 14, 2020): 859. http://dx.doi.org/10.3390/atmos11080859.
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In regions of sparse gauge networks, satellite rainfall products are mostly used as surrogate measurements for various rainfall impact studies. Their potential to complement rain gauge measurements is influenced by the uncertainties associated with them. This study evaluates the performance of satellites and merged rainfall products over Ghana in order to provide information on the consistency and reliability of such products. Satellite products were validated with gridded rain gauge data from the Ghana Meteorological Agency (GMet) on various time scales. It was observed that the performance of the products in the country are mostly scale and location dependent. In addition, most of the products showed relatively good skills on the seasonal scale (r > 0.90) rather than the annual, and, after removal of seasonality from the datasets, except ARC2 that had larger biases in most cases. Again, all products captured the onsets, cessations, and spells countrywide and in the four agro-ecological zones. However, CHIRPS particularly revealed a better skill on both seasonal and annual scales countrywide. The products were not affected by the number of gauge stations within a grid cell in the Forest and Transition zones. This study, therefore, recommends all products except ARC2 for climate impact studies over the region.
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Hu, Tengfei, Jingqiao Mao, Peipei Zhang, Diandian Xu, Weiyu Chen, and Huichao Dai. "Hydrological utilization of satellite precipitation estimates in a data-scarce lake region." Water Supply 18, no. 5 (November 13, 2017): 1581–89. http://dx.doi.org/10.2166/ws.2017.223.
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Abstract In developing regions, accurate rain gauge measurements and satellite precipitation estimates that effectively capture rainfall spatial variability are promising sources of rainfall information. In this study, the latest Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) research product, 3B42V7, was validated against ground measurements in the region surrounding the Dongting Lake in China. In the subsequent model-based evaluation and comparison, the two precipitation datasets were separately included as the inputs for data-driven predictive models of the daily Dongting Lake level. The results show that (i) the daily 3B42V7 agrees well with the gauge measurements (correlation coefficient: 0.64–0.73); (ii) 3B42V7 underestimates the frequency of low-intensity (0–30 mm/day) rainfall and the contribution of low-intensity rainfall to the total rainfall volume, but slightly overestimates those of more intense rainfall; (iii) the lake level models driven by rainfall data from the two sources have similar performance, highlighting the potential of using 3B42V7 in data-driven modeling and prediction of hydrological variables in data-scarce regions; and (iv) the inclusion of rainfall as the model input helps achieve a balance between underestimation and overestimation of the lake levels in terms of both magnitude and quantity.
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Karbalaee, Negar, Kuolin Hsu, Soroosh Sorooshian, and Dan Braithwaite. "Bias adjustment of infrared-based rainfall estimation using Passive Microwave satellite rainfall data." Journal of Geophysical Research: Atmospheres 122, no. 7 (April 12, 2017): 3859–76. http://dx.doi.org/10.1002/2016jd026037.
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KELKAR, R. R., and A. V. R. K. RAO. "Interannual variability of monsoon rainfall as Estimated from INSAT -1B data." MAUSAM 41, no. 2 (February 22, 2022): 42–47. http://dx.doi.org/10.54302/mausam.v41i2.2522.
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Satellite estimated rainfall using INSA T -lB data is utilised to study the annual variations of monsoon rainfall during the years 1986, 1987 and 1988. Patterns of mean monthly rainfall for the monsoon months and the deviations from the mean of rainfall for each monsoon month are presented.
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Brocca, Luca, Paolo Filippucci, Sebastian Hahn, Luca Ciabatta, Christian Massari, Stefania Camici, Lothar Schüller, Bojan Bojkov, and Wolfgang Wagner. "SM2RAIN–ASCAT (2007–2018): global daily satellite rainfall data from ASCAT soil moisture observations." Earth System Science Data 11, no. 4 (October 22, 2019): 1583–601. http://dx.doi.org/10.5194/essd-11-1583-2019.
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Abstract. Long-term gridded precipitation products are crucial for several applications in hydrology, agriculture and climate sciences. Currently available precipitation products suffer from space and time inconsistency due to the non-uniform density of ground networks and the difficulties in merging multiple satellite sensors. The recent “bottom-up” approach that exploits satellite soil moisture observations for estimating rainfall through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Polar System programme. The continuity of the scatterometer sensor is ensured until the mid-2040s through the MetOp Second Generation Programme. Therefore, by applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The paper describes the recent improvements in data pre-processing, SM2RAIN algorithm formulation, and data post-processing for obtaining the SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a 12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record is assessed on a regional scale through comparison with high-quality ground networks in Europe, the United States, India, and Australia. Moreover, an assessment on a global scale is provided by using the triple-collocation (TC) technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG), and the gauge-based Global Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively well at both a regional and global scale, mainly in terms of root mean square error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data record provides performance better than IMERG and GPCC in data-scarce regions of the world, such as Africa and South America. In these areas, we expect larger benefits in using SM2RAIN–ASCAT for hydrological and agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist of the underestimation of peak rainfall events and the presence of spurious rainfall events due to high-frequency soil moisture fluctuations that might be corrected in the future with more advanced bias correction techniques. The SM2RAIN–ASCAT data record is freely available at https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of August 2019).
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Pan, Ming, and Eric F. Wood. "A Multiscale Ensemble Filtering System for Hydrologic Data Assimilation. Part II: Application to Land Surface Modeling with Satellite Rainfall Forcing." Journal of Hydrometeorology 10, no. 6 (December 1, 2009): 1493–506. http://dx.doi.org/10.1175/2009jhm1155.1.
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Abstract Part I of this series of studies developed procedures to implement the multiscale filtering algorithm for land surface hydrology and performed assimilation experiments with rainfall ensembles from a climate model. However, a most important application of the multiscale technique is to assimilate satellite-based remote sensing observations into a land surface model—and this has not been realized. This paper focuses on enabling the multiscale assimilation system to use remotely sensed precipitation data. The major challenge is the generation of a rainfall ensemble given one satellite rainfall map. An acceptable rainfall ensemble must contain a proper multiscale spatial correlation structure, and each ensemble member presents a realistic rainfall process in both space and time. A pattern-based sampling approach is proposed, in which random samples are drawn from a historical rainfall database according to the pattern of the satellite rainfall and then a cumulative distribution function matching procedure is applied to ensure the proper statistics for the pixel-level rainfall intensity. The assimilation system is applied using Tropical Rainfall Measuring Mission real-time satellite rainfall over the Red–Arkansas River basin. Results show that the ensembles so generated satisfy the requirements for spatial correlation and realism and the multiscale assimilation works reasonably well. A number of limitations also exist in applying this generation method, mainly stemming from the high dimensionality of the problem and the lack of historical records.
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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.
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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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Shih, Sun F. "Satellite Data and Geographic Information System for Rainfall Estimation." Journal of Irrigation and Drainage Engineering 116, no. 3 (May 1990): 319–31. http://dx.doi.org/10.1061/(asce)0733-9437(1990)116:3(319).
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Grimes, D. I. F., E. Pardo-Igúzquiza, and R. Bonifacio. "Optimal areal rainfall estimation using raingauges and satellite data." Journal of Hydrology 222, no. 1-4 (September 1999): 93–108. http://dx.doi.org/10.1016/s0022-1694(99)00092-x.
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Hur, Jina, Srivatsan V. Raghavan, Ngoc Son Nguyen, and Shie-Yui Liong. "Evaluation of High-resolution Satellite Rainfall Data over Singapore." Procedia Engineering 154 (2016): 158–67. http://dx.doi.org/10.1016/j.proeng.2016.07.437.
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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.
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Bastin, GN, G. Pickup, J. Stanes, and A. Stanes. "Estimating Landscape Resilience From Satellite Data and Its Application to Pastoral Land Management." Rangeland Journal 18, no. 1 (1996): 118. http://dx.doi.org/10.1071/rj9960118.
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Episodic rainfall provides infrequent opportunities for vegetation recovery from grazing in the arid rangelands. The magnitude of resultant vegetation growth, or resilience, varies widely in space and is not simply controlled by rainfall amount or past grazing practices. The non predictable nature of vegetation response fits within the model of nonequilibrium rangeland behaviour and we propose use of the Resilience Method as a means of spatially assessing vegetation resilience, or the ability of herbage to respond to infrequent rainfall. Information required to calculate an index of resilience is obtained from remotely-sensed satellite data, is plant cover-based and is processed to produce landscape-scale maps of vegetation resilience. In this paper, we demonstrate and evaluate products developed to transfer the Resilience Method to a pastoralist family to assist in their future property management. These products include maps of scaled herbage response to rainfall, herbage response in conjunction with dry period vegetation cover and herbage biomass derived from vegetation cover. The cover-based products effectively showed herbage response following a large rainfall. Much of the variation in response was natural and was related to timber density and soil factors. Some areas with below average herbage response were attributable to damage caused by high rabbit populations. Herbage response on much of the productive grazing country was average to above average indicating good resilience and potential for continued beef production. The resilience approach to landscape assessment provided a useful pictorial representation of herbage response across the whole station following one rainfall. The participating pastoralists consider that the Resilience Method will have greater validity when repeated following further significant rainfalls. Some insights were gained into future property development. However, the technology was difficult to understand and requires a close liaison between the technician and client. Confusion arose where below average herbage response could occur in areas of both high and low initial cover when the two areas appear vastly different and require separate management for beef production and rehabilitation. Both the nonequilibrium approach to understanding vegetation dynamics and the Resilience Method need further explanation and demonstration before they are accepted as being useful for pastoral land management.
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Mahmud, Mohd Rizaludin, Aina Afifah Mohd Yusof, Mohd Nadzri Mohd Reba, and Mazlan Hashim. "Mapping the Daily Rainfall over an Ungauged Tropical Micro-Watershed: A Downscaling Algorithm Using GPM Data." Water 12, no. 6 (June 10, 2020): 1661. http://dx.doi.org/10.3390/w12061661.
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In this study, half-hourly Global Precipitation Mission (GPM) satellite precipitation data were downscaled to produce high-resolution daily rainfall data for tropical coastal micro-watersheds (100–1000 ha) without gauges or with rainfall data conflicts. Currently, daily-scale satellite rainfall downscaling techniques rely on rain gauge data as corrective and controlling factors, making them impractical for ungauged watersheds or watersheds with rainfall data conflicts. Therefore, we used high-resolution local orographic and vertical velocity data as proxies to downscale half-hourly GPM precipitation data (0.1°) to high-resolution daily rainfall data (0.02°). The overall quality of the downscaled product was similar to or better than the quality of the raw GPM data. The downscaled rainfall dataset improved the accuracy of rainfall estimates on the ground, with lower error relative to measured rain gauge data. The average error was reduced from 41 to 27 mm/d and from 16 to 12 mm/d during the wet and dry seasons, respectively. Estimates of localized rainfall patterns were improved from 38% to 73%. The results of this study will be useful for production of high-resolution satellite precipitation data in ungauged tropical micro-watersheds.
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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.
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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.
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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.
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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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Mahmud, Mohd Rizaludin. "How Has Malaysia Benefited from the High-Resolution Satellite Rainfall? Trends, Gaps and Further Research Opportunities." ASM Science Journal 12 (November 5, 2019): 1–11. http://dx.doi.org/10.32802/asmscj.2019.325.
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This paper presents a scientific review on how Malaysia has benefited from the high-resolution satellite rainfall since its first launch in 1998. As a tropical country in which the environment is highly characterised by rainfall dynamics, public domain access of high-resolution satellite rainfall data could be very useful to support the hydrologic and related environmental studies. The scope of this paper includes achievements, the trend of studies, as well as gaps and future opportunities for scientific research. Examining this element is crucial in determining the present information on the status quo of the applications of space-based technology to Malaysian hydrologic research. Furthermore, this information is critical to charter the future action for the policymakers and revision of respective disciplines, including climate change, environmental sustainability, disaster resilience, food security, and education. Based on the search throughout the largest scientific databases of Web of Science and Scopus, five major trends have been identified. Those trends were ranked based on the number of research, 1) Satellite rainfall data performance and quality evaluation (40%), 2) Satellite rainfall data as input to environmental modelling (27%), 3) Rain fade & telecommunication (16%), 4) Satellite rainfall data quality improvement (7%), and 5) Rainfall studies. These trends were identified about 11 years after the satellite rainfall project started in 1998. The major achievement till now is validating the accuracy of the satellite rainfall and also downscaling it for local application.
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PURDOM, JAMES F. W. "Local severe storm monitoring and prediction using satellite data." MAUSAM 54, no. 1 (January 18, 2022): 141–54. http://dx.doi.org/10.54302/mausam.v54i1.1498.
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This paper addresses using satellite data for nowcasting severe storms and their intensity in the 0-6 hour time frame. Weather, and weather related phenomena extend across a broad range of scales. In meteorology the link between the synoptic scale and the mesoscale is many times a key factor in controlling the intensity of local weather. The only observing tool capable of monitoring weather across those scales (and those scales interactions) is the geostationary satellite! Just as imagery from polar orbiting satellites helped advance understanding of synoptic scale phenomena, imagery from geostationary satellites is advancing our understanding of the mesoscale. A number of important discoveries using geostationary satellite imagery have had a dramatic impact on mesoscale meteorology and, in turn, our ability to provide short term forecasts and warnings for disaster related weather events, including: areas of incipient squall line development; location of regions with high probability of tornadoes and severe thunderstorms; mesoscale convective complexes; and, areas with heavy convective rainfall. As exciting as current capabilities are, satellite systems that will come into being during the next several years will provide capabilities well beyond the present ones.
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Bitew, Menberu M., Mekonnen Gebremichael, Lula T. Ghebremichael, and Yared A. Bayissa. "Evaluation of High-Resolution Satellite Rainfall Products through Streamflow Simulation in a Hydrological Modeling of a Small Mountainous Watershed in Ethiopia." Journal of Hydrometeorology 13, no. 1 (February 1, 2012): 338–50. http://dx.doi.org/10.1175/2011jhm1292.1.
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Abstract This study focuses on evaluating four widely used global high-resolution satellite rainfall products [the Climate Prediction Center’s morphing technique (CMORPH) product, the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) near-real-time product (3B42RT), the TMPA method post-real-time research version product (3B42), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) product] with a spatial resolution of 0.25° and temporal resolution of 3 h through their streamflow simulations in the Soil and Water Assessment Tool (SWAT) hydrologic model of a 299-km2 mountainous watershed in Ethiopia. Results show significant biases in the satellite rainfall estimates. The 3B42RT and CMORPH products perform better than the 3B42 and PERSIANN. The predictive ability of each of the satellite rainfall was examined using a SWAT model calibrated in two different approaches: with rain gauge rainfall as input, and with each of the satellite rainfall products as input. Significant improvements in model streamflow simulations are obtained when the model is calibrated with input-specific rainfall data than with rain gauge data. Calibrating SWAT with satellite rainfall estimates results in curve number values that are by far higher than the standard tabulated values, and therefore caution must be exercised when using standard tabulated parameter values with satellite rainfall inputs. The study also reveals that bias correction of satellite rainfall estimates significantly improves the model simulations. The best-performing model simulations based on satellite rainfall inputs are obtained after bias correction and model recalibration.
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Radhika, Radhika, Rendy Firmansyah, and Waluyo Hatmoko. "Perhitungan ketersediaan air permukaan di Indonesia berdasarkan data satelit." JURNAL SUMBER DAYA AIR 13, no. 2 (February 6, 2018): 115–30. http://dx.doi.org/10.32679/jsda.v13i2.206.
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Information on water availability is vital in water resources management. Unfortunately, information on the condition of hydrological data, either river flow data, or rainfall data is very limited temporally and spatially. With the availability of satellite technology, rainfall in the tropics can be monitored and recorded for further analysis. This paper discusses the calculation of surface water availability based on rainfall data from TRMM satellite, and then Wflow, a distributed rainfall-runoff model generates monthly time runoff data from 2003 to 2015 for all river basin areas in Indonesia. It is concluded that the average surface water availability in Indonesia is 88.3 thousand m3/s or equivalent to 2.78 trillion m3/ year. This figure is lower than the study of Water Resources Research Center 2010 based on discharge at the post estimated water that produces 3.9 trillion m3/year, but very close to the study of Aquastat FAO of 2.79 trillion m3 / year. The main benefit of this satellite-based calculation is that at any location in Indonesia, potential surface water can be obtained by multiplying the area of the catchment and the runoff height.
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Kak Ahmed, Huner Abdullah. "Using CHIRPS Satellite-Based Data for Spatio-Temporal Variability of Rainfall in Dashti Hawler District – Kurdistan Region of Iraq 1982 – 2019." Twejer 5, no. 1 (June 2022): 1285–324. http://dx.doi.org/10.31918/twejer.2251.29.
Full textAbstract:
Abstract Rainfall is one of the most essential climatic indicators of climate change, The changes in rainfall in space and time would impact runoff, soil moisture, and groundwater reserves. The study of the climate change effects on water resource planning and management necessitates the analysis of precipitation changes (Khayyat, et. al, p. 35, 2019; M. Nawaz, et. al., 2020, p.2). The availability of long-term and temporally consistent rainfall time series is one of the most important criteria for undertaking a detailed study of regional and temporal variation and trends in rainfall in any location. Agriculture activities, flood disaster risk management and reduction, drought, water harvesting, water supply to communities, and other human activities related to the spatiotemporal distribution of rainfall in any region benefit from the analysis of long-term climate time-series data at a high temporal and spatial resolution. Water resource management, agricultural productivity, and climate change mitigation benefit from understanding the spatial distribution of rainfall and its temporal trends (Morales-Acuña, et al., 2021, p. 1). Rainfall data and measurements from traditional ground weather stations have traditionally been the major sources of such climate data; however, due to limited or nonexistent station networks in many regions of the world, historical records from station observations are insufficient. As a result, satellite-based rainfall data are increasingly being utilized to supplement or replace station observations.