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Journal articles on the topic 'Rainfall estimates'
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Shige, Shoichi, Satoshi Kida, Hiroki Ashiwake, Takuji Kubota, and Kazumasa Aonashi. "Improvement of TMI Rain Retrievals in Mountainous Areas." Journal of Applied Meteorology and Climatology 52, no. 1 (2013): 242–54. http://dx.doi.org/10.1175/jamc-d-12-074.1.
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AbstractHeavy rainfall associated with shallow orographic rainfall systems has been underestimated by passive microwave radiometer algorithms owing to weak ice scattering signatures. The authors improve the performance of estimates made using a passive microwave radiometer algorithm, the Global Satellite Mapping of Precipitation (GSMaP) algorithm, from data obtained by the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) for orographic heavy rainfall. An orographic/nonorographic rainfall classification scheme is developed on the basis of orographically forced upward vertical motion and the convergence of surface moisture flux estimated from ancillary data. Lookup tables derived from orographic precipitation profiles are used to estimate rainfall for an orographic rainfall pixel, whereas those derived from original precipitation profiles are used to estimate rainfall for a nonorographic rainfall pixel. Rainfall estimates made using the revised GSMaP algorithm are in better agreement with estimates from data obtained by the radar on the TRMM satellite and by gauge-calibrated ground radars than are estimates made using the original GSMaP algorithm.
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Kumah, Kingsley K., Joost C. B. Hoedjes, Noam David, Ben H. P. Maathuis, H. Oliver Gao, and Bob Z. Su. "The MSG Technique: Improving Commercial Microwave Link Rainfall Intensity by Using Rain Area Detection from Meteosat Second Generation." Remote Sensing 13, no. 16 (2021): 3274. http://dx.doi.org/10.3390/rs13163274.
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Commercial microwave link (MWL) used by mobile telecom operators for data transmission can provide hydro-meteorologically valid rainfall estimates according to studies in the past decade. For the first time, this study investigated a new method, the MSG technique, that uses Meteosat Second Generation (MSG) satellite data to improve MWL rainfall estimates. The investigation, conducted during daytime, used MSG optical (VIS0.6) and near IR (NIR1.6) data to estimate rain areas along a 15 GHz, 9.88 km MWL for classifying the MWL signal into wet–dry periods and estimate the baseline level. Additionally, the MSG technique estimated a new parameter, wet path length, representing the length of the MWL that was wet during wet periods. Finally, MWL rainfall intensity estimates from this new MSG and conventional techniques were compared to rain gauge estimates. The results show that the MSG technique is robust and can estimate gauge comparable rainfall estimates. The evaluation scores every three hours of RMSD, relative bias, and r2 based on the entire evaluation period results of the MSG technique were 2.61 mm h−1, 0.47, and 0.81, compared to 2.09 mm h−1, 0.04, and 0.84 of the conventional technique, respectively. For convective rain events with high intensity spatially varying rainfall, the results show that the MSG technique may approximate the actual mean rainfall estimates better than the conventional technique.
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MALEKINEZHAD, HOSSEIN, and ARASH ZARE-GARIZI. "Regional frequency analysis of daily rainfall extremes using L-moments approach." Atmósfera 27, no. 4 (2015): 411–27. http://dx.doi.org/10.20937/atm.2014.27.04.07.
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Daily extreme precipitation values are among environmental events with the most disastrous consequences for human society. Information on the magnitudes and frequencies of extreme precipitations is essential for sustainable water resources management, planning for weather-related emergencies, and design of hydraulic structures. In the present study, regional frequency analysis of maximum daily rainfalls was investigated for Golestan province located in the northeastern Iran. This study aimed to find appropriate regional frequency distributions for maximum daily rainfalls and predict the return values of extreme rainfall events (design rainfall depths) for the future. L-moment regionalization procedures coupled with an index rainfall methodwere applied to maximum rainfall records of 47 stations across the study area. Due to complex geographicand hydro-climatological characteristics of the region, an important research issue focused on breaking downthe large area into homogeneous and coherent sub-regions. The study area was divided into five homogeneousregions, based on the cluster analysis of site characteristics and tests for the regional homogeneity.The goodness-of-fit results indicated that the best fitting distribution is different for individual homogeneousregions. The difference may be a result of the distinctive climatic and geographic conditions. The estimatedregional quantiles and their accuracy measures produced by Monte Carlo simulations demonstrate that theestimation uncertainty as measured by the RMSE values and 90% error bounds is relatively low when returnperiods are less than 100 years. But, for higher return periods, rainfall estimates should be treated withcaution. More station years, either from longer records or more stations in the regions, would be required forrainfall estimates above T=100 years. It was found from the analyses that, the index rainfall (at-site averagemaximum rainfall) can be estimated reasonably well as a function of mean annual precipitation in Golestanprovince. Index rainfalls combined with the regional growth curves, can be used to estimate design rainfallsat ungauged sites. Overall, it was found that cluster analysis together with the L-moments based regional frequencyanalysis technique could be applied successfully in deriving design rainfall estimates for northeasternIran. The approach utilized in this study and the findings are of great scientific and practical merit, particularlyfor the purpose of planning for weather-related emergencies and design of hydraulic engineering structures
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Hromadka II, T. V., M. Phillips, P. Rao, B. Espinosa, and T. Hromadka III. "Rainfall Infiltration Return Frequency Estimates." Atmospheric and Climate Sciences 03, no. 04 (2013): 595–609. http://dx.doi.org/10.4236/acs.2013.34062.
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Van Nguyen, Van-Thanh, and Ganesh Raj Pandey. "Estimation of Short-Duration Rainfall Distribution Using Data Measured at Longer Time Scales." Water Science and Technology 29, no. 1-2 (1994): 39–45. http://dx.doi.org/10.2166/wst.1994.0649.
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An investigation on how to estimate the distribution of short-duration (hours or shorter) rainfalls based on available daily rainfall measurements was undertaken. On the basis of the theory of multifractal multiplicative cascades, a scale-independent mathematical model was proposed to represent the probability distribution of rainfalls at various time scales. Using rainfall records from a network of seven recording gauges in the Montreal region in Quebec (Canada), it was found that the proposed model could provide adequate estimates of the distribution of hourly rainfalls at locations where these short-duration rainfall data are not available. Further, it has been observed that one single regional model can be developed to describe the scaling nature of rainfall distributions within the whole study area.
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Molina-Aguilar, Juan Pablo, Bruno Paz-Aviña, Josué Elizondo-Gómez, and Miguel Ángel Sánchez Quijano. "Acoplamiento de estimaciones de precipitación basadas en imágenes satelitales, con registros pluviométricos." Aqua-LAC 11, no. 1 (2019): 77–92. http://dx.doi.org/10.29104/phi-aqualac/2019-v11-1-06.
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La estimación de precipitación en tiempo real a partir de imágenes satelitales digitales (ISD) es una metodología ampliamente utilizada por meteorólogos e hidrólogos, su aplicación sobre una región superficial es indirecta, en la cual las resoluciones temporal y espacial de la información definen la precisión, los resultados obtenidos deben validarse empleando registros de redes pluviométricas. La finalidad del presente trabajo es presentar una metodología de acoplamiento temporal y espacial (ATE), para información con resolución de 15 minutos. La información empleada corresponde a valores del nivel digital (ND) en los pixeles de las ISD captadas por el satélite GOES-13 durante el desarrollo del ciclón tropical (CT) Paul. Fueron utilizados los registros de las estaciones meteorológicas automáticas (EMA) localizadas en la región hidrológica 10 Sinaloa. La lectura y el tratamiento digital de las ISD se realizaron empleando el código Fast Infrared Satellite Image Reader GOES 13 (FISIR-G13) desarrollado en lenguaje R. Se obtuvieron lecturas en vecindades de 9 pixeles geográficamente referenciados, generando series temporales del ND, a partir de los cuales se estimó la precipitación empleando el Hidroestimador (HE). La estandarización permitió contrastar ambas fuentes de información, como resultado se identificaron combinaciones de pixeles para el ATE. La evaluación estadística empleando el coeficiente de correlación de la intensidad estimada respecto de la intensidad observada muestra un mejor desempeño de la metodología desarrollada respecto del HE. La metodología establece el acoplamiento temporal de los valores estimados de precipitación empleando ISD respecto de los valores registrados en la EMA, con valores del coeficiente de correlación cercanos a 1.
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Chiew, FHS, and TA Mcmahon. "Assessing the adequacy of catchment streamflow yield estimates." Soil Research 31, no. 5 (1993): 665. http://dx.doi.org/10.1071/sr9930665.
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Rainfall-runoff models are frequently used by hydrologists to estimate runoff from rainfall and climate data, with the model adequacy assessed by comparing the level of agreement between flows simulated by the model and the recorded flows. This paper describes simple methods (visual plots, statistical parameters and dimensionless coefficients) which are commonly used to compare estimated and recorded streamflow time series and discusses their advantages and limitations. Results of a survey conducted to ascertain the required quality of flow estimates before they are considered to be satisfactory, as well as to identify preferred methods used by hydrologists in Australia to determine the adequacy of streamflow estimates, are also discussed in this paper. Information from the survey is also used to suggest objective criteria based on dimensionless coefficients that can be used as guides in assessing the adequacy of flows estimated by rainfall-runoff models. In particular, the coefficient of efficiency is a very useful indicator in assessing model adequacy.
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FUJIOKA, Susumu, Takahiro SAYAMA, Yuuji MIURA, Tomoki KOSHIDA, and Kazuhiko FUKAMI. "STOCHASTIC RAINFALL FIELD GENERATION REPRESENTING UNCERTAINTY IN RADAR RAINFALL ESTIMATES." Journal of Japan Society of Civil Engineers, Ser. B1 (Hydraulic Engineering) 69, no. 4 (2013): I_319—I_324. http://dx.doi.org/10.2208/jscejhe.69.i_319.
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Pereira Fo., Augusto J., Kenneth C. Crawford, and Curtis L. Hartzell. "Improving WSR-88D Hourly Rainfall Estimates." Weather and Forecasting 13, no. 4 (1998): 1016–28. http://dx.doi.org/10.1175/1520-0434(1998)013<1016:iwhre>2.0.co;2.
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Harsa, Hastuadi, Agus Buono, Rahmat Hidayat, et al. "Fine-tuning satellite-based rainfall estimates." IOP Conference Series: Earth and Environmental Science 149 (May 2018): 012047. http://dx.doi.org/10.1088/1755-1315/149/1/012047.
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Ryzhkov, Alexander, Dusan Zrnić, and Richard Fulton. "Areal Rainfall Estimates Using Differential Phase." Journal of Applied Meteorology 39, no. 2 (2000): 263–68. http://dx.doi.org/10.1175/1520-0450(2000)039<0263:areudp>2.0.co;2.
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Zlate, Ionel. "Rainfall field estimates for hydrological models." Atmospheric Research 42, no. 1-4 (1996): 229–36. http://dx.doi.org/10.1016/0169-8095(95)00065-8.
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Ryzhkov, Alexander V., Scott E. Giangrande, and Terry J. Schuur. "Rainfall Estimation with a Polarimetric Prototype of WSR-88D." Journal of Applied Meteorology 44, no. 4 (2005): 502–15. http://dx.doi.org/10.1175/jam2213.1.
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Abstract As part of the Joint Polarization Experiment (JPOLE), the National Severe Storms Laboratory conducted an operational demonstration of the polarimetric utility of the Norman, Oklahoma (KOUN), Weather Surveillance Radar-1988 Doppler (WSR-88D). The capability of the KOUN radar to estimate rainfall is tested on a large dataset representing different seasons and different types of rain. A dense gauge network—the Agricultural Research Service (ARS) Micronet—is used to validate different polarimetric algorithms for rainfall estimation. One-hour rain totals are estimated from the KOUN radar using conventional and polarimetric algorithms and are compared with hourly accumulations measured by the gauges. Both point and areal rain estimates are examined. A new “synthetic” rainfall algorithm has been developed for rainfall estimation. The use of the synthetic polarimetric algorithm results in significant reduction in the rms errors of hourly rain estimates when compared with the conventional nonpolarimetric relation: 1.7 times for point measurements and 3.7 times for areal rainfall measurements.
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Stewart, E. J., D. W. Reed, D. S. Faulkner, and N. S. Reynard. "The FORGEX method of rainfall growth estimation I: Review of requirement." Hydrology and Earth System Sciences 3, no. 2 (1999): 187–95. http://dx.doi.org/10.5194/hess-3-187-1999.
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Abstract. A growth factor is the ratio of the T-year extreme value to an index extreme value such as the mean of annual maxima. Whereas a record length of ten or more years may suffice to estimate the index variable, it is generally necessary to blend data from several sites if estimates of exceptional extreme values are to be obtained. Methods of rainfall growth estimation are reviewed, including traditional methods which extend frequency curves to long return period by a distributional assumption, and methods which study spatial dependence in extreme rainfalls. It is desirable that estimates at neighbouring sites, and across different durations and return periods, are internally consistent. The review concludes that rather special techniques may be required if this goal of estimation extreme rainfall depth consistently is to be met. The motivation of the Focused Rainfall Growth Extension (FORGEX) method is presented.
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Westcott, Nancy E., Steven E. Hollinger, and Kenneth E. Kunkel. "Use of Real-Time Multisensor Data to Assess the Relationship of Normalized Corn Yield with Monthly Rainfall and Heat Stress across the Central United States." Journal of Applied Meteorology 44, no. 11 (2005): 1667–76. http://dx.doi.org/10.1175/jam2303.1.
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Abstract This study evaluated the suitability of rain estimates based on the National Weather Service (NWS) Weather Surveillance Radar-1988 Doppler (WSR-88D) network to estimate yield response to rainfall on a county scale and to provide real-time information related to crop stress resulting from deficient or excessive precipitation throughout the summer. The relationship between normalized corn yield and rainfall was examined for nine states in the central United States for 1997–99 and 2001–02. Monthly rainfall estimates were computed employing multisensor precipitation estimate (MPE) data from the National Centers for Environmental Prediction and quality-controlled (QC_Coop) and real-time (RT_Coop) NWS cooperative gauge data. In-season MPE rain estimates were found to be of comparable quality to the postseason QC_Coop estimates for predicting county corn yields. Both MPE and QC_Coop estimates were better related to corn yield than were RT_Coop estimates, presumably because of the lower density of RT_Coop gauges. Large corn yields typically resulted when May rain was less than 125 mm and July rain was greater than 50 mm. Low yields often occurred when July rainfall was less than 100 mm. For moderate July rains (50–100 mm), positive and negative normalized yields resulted. Parameterization of heat stress (number of July days &gt; 32.2°C) improved the correlation between rainfall and normalized corn yield, particularly for years with the poorest yield-vs-rain relationship (1998 and 1999). For the combined analysis years, the multiple regression correlation coefficient was 0.56, incorporating May and July rainfall and July heat stress and explaining 31% of the variance of normalized corn yield. Results show that MPE rainfall estimates provide timely yield projections within the growing season.
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Liao, Yifan, Bingzhang Lin, Xiaoyang Chen, and Hui Ding. "A New Look at Storm Separation Technique in Estimation of Probable Maximum Precipitation in Mountainous Areas." Water 12, no. 4 (2020): 1177. http://dx.doi.org/10.3390/w12041177.
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Storm separation is a key step when carrying out storm transposition analysis for Probable Maximum Precipitation (PMP) estimation in mountainous areas. The World Meteorological Organization (WMO) has recommended the step-duration-orographic-intensification-factor (SDOIF) method since 2009 as an effective storm separation technique to identify the amounts of precipitation caused by topography from those caused by atmospheric dynamics. The orographic intensification factors (OIFs) are usually developed based on annual maximum rainfall series under such assumption that the mechanism of annual maximum rainfalls is close to that of the PMP-level rainfall. In this paper, an alternative storm separation technique using rainfall quantiles, instead of annual maximum rainfalls, with rare return periods estimated via Regional L-moments Analysis (RLMA) to calculate the OIFs is proposed. Based on Taiwan’s historical 4- and 24-h precipitation data, comparisons of the OIFs obtained from annual maximum rainfalls with that from extreme rainfall quantiles at different return periods, as well as the PMP estimates of Hong Kong from transposing the different corresponding separated nonorographic rainfalls, were conducted. The results show that the OIFs obtained from rainfall quantiles with certain rare probabilities are more stable and reasonable in terms of stability and spatial distribution pattern.
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Wu, Shiang-Jen, Ho-Cheng Lien, Chih-Tsung Hsu, Che-Hao Chang, and Jhih-Cyuan Shen. "Modeling probabilistic radar rainfall estimation at ungauged locations based on spatiotemporal errors which correspond to gauged data." Hydrology Research 46, no. 1 (2013): 39–59. http://dx.doi.org/10.2166/nh.2013.197.
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This study presents a probabilistic radar rainfall estimation (PRRE) model to quantify the reliability and accuracy of the resulting radar rainfall estimates at ungauged locations from a radar-based quantitative precipitation estimation (QPE) model. This model primarily estimates the quantiles of the radar rainfall errors at ungauged locations by incorporating seven spatiotemporal variogram models with a nonparametric sample quantile estimate method based on the radar rainfall errors at rain gauges. Then, by adding the resulting error quantiles to the radar rainfall estimates, the corresponding radar rainfall quantiles can be obtained. The QPE system Quantitative Precipitation Estimation Using Multiple Sensors (QPESUMS) provides hourly observed and radar precipitation for three typhoons in the Shinmen reservoir watershed in Northern Taiwan, which are used in the model development and validation. The results indicate that the proposed PRRE model can quantify the spatial and temporal variations of radar rainfall estimates at ungauged locations provided by the QPESUMS system. Also, its reliability and accuracy could be evaluated based on a 95% confidence interval and occurrence probability resulting from the cumulative probability distribution established by the proposed PRRE model.
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Hambali, Roby, Djoko Legono, and Rachmad Jayadi. "Correcting Radar Rainfall Estimates Based on Ground Elevation Function." Journal of the Civil Engineering Forum 5, no. 3 (2019): 300. http://dx.doi.org/10.22146/jcef.49395.
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X-band radar gives several advantages for quantitative rainfall estimation, involving higher spatial and temporal resolution, also the ability to reduce attenuation effects and hardware calibration errors. However, the estimates error due to attenuation in heavy rainfall condition cannot be avoided. In the mountainous region, the impact of topography is considered to contribute to radar rainfall estimates error. To have more reliable estimated radar rainfall to be used in various applications, a rainfall estimates correction needs to be applied. This paper discusses evaluation and correction techniques for radar rainfall estimates based on ground elevation function. The G/R ratio is used as a primary method in the correction process. The novel approach proposed in this study is the use of correction factor derived from the relationship between Log (G/R) parameter and elevation difference between radar and rain gauge stations. A total of 4590 pairs of rainfall data from X-band MP radar and 15 rain gauge stations in the Mt. Merapi region were used in evaluation and correction process. The results show the correction method based on the elevation function is relatively good in correcting radar rainfall depth with values of Log (G/R) decreased up to 81.1%, particularly for light rainfall (≤ 20 mm/hour) condition. Also, the method is simple to apply in a real-time system.
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Young, Matthew P., Charles J. R. Williams, J. Christine Chiu, Ross I. Maidment, and Shu-Hua Chen. "Investigation of Discrepancies in Satellite Rainfall Estimates over Ethiopia." Journal of Hydrometeorology 15, no. 6 (2014): 2347–69. http://dx.doi.org/10.1175/jhm-d-13-0111.1.
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Abstract Tropical Applications of Meteorology Using Satellite and Ground-Based Observations (TAMSAT) rainfall estimates are used extensively across Africa for operational rainfall monitoring and food security applications; thus, regional evaluations of TAMSAT are essential to ensure its reliability. This study assesses the performance of TAMSAT rainfall estimates, along with the African Rainfall Climatology (ARC), version 2; the Tropical Rainfall Measuring Mission (TRMM) 3B42 product; and the Climate Prediction Center morphing technique (CMORPH), against a dense rain gauge network over a mountainous region of Ethiopia. Overall, TAMSAT exhibits good skill in detecting rainy events but underestimates rainfall amount, while ARC underestimates both rainfall amount and rainy event frequency. Meanwhile, TRMM consistently performs best in detecting rainy events and capturing the mean rainfall and seasonal variability, while CMORPH tends to overdetect rainy events. Moreover, the mean difference in daily rainfall between the products and rain gauges shows increasing underestimation with increasing elevation. However, the distribution in satellite–gauge differences demonstrates that although 75% of retrievals underestimate rainfall, up to 25% overestimate rainfall over all elevations. Case studies using high-resolution simulations suggest underestimation in the satellite algorithms is likely due to shallow convection with warm cloud-top temperatures in addition to beam-filling effects in microwave-based retrievals from localized convective cells. The overestimation by IR-based algorithms is attributed to nonraining cirrus with cold cloud-top temperatures. These results stress the importance of understanding regional precipitation systems causing uncertainties in satellite rainfall estimates with a view toward using this knowledge to improve rainfall algorithms.
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Cook, Werner E., and J. Scott Greene. "Gridded Monthly Rainfall Estimates Derived from Historical Atoll Observations." Journal of Atmospheric and Oceanic Technology 36, no. 4 (2019): 671–87. http://dx.doi.org/10.1175/jtech-d-18-0140.1.
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AbstractTo provide an analysis tool for areal rainfall estimates, 1° gridded monthly sea level rainfall estimates have been derived from historical atoll rainfall observations contained in the Pacific Rainfall (PACRAIN) database. The PACRAIN database is a searchable repository of in situ rainfall observations initiated and maintained by the University of Oklahoma and supported by a research grant from the National Oceanic and Atmospheric Administration (NOAA)/Climate Program Office/Ocean Observing and Monitoring. The gridding algorithm employs ordinary kriging, a standard geostatistical technique, and selects for nonnegative estimates and for local estimation neighborhoods yielding minimum kriging variance. This methodology facilitates the selection of fixed-size neighborhoods from available stations beyond simply choosing the closest stations, as it accounts for dependence between estimator stations. The number of stations used for estimation is based on bias and standard error exhibited under cross estimation. A cross validation is conducted, comparing estimated and observed rains, as well as theoretical and observed standard errors for the ordinary kriging estimator. The conditional bias of the kriging estimator and the predictive value of kriging standard errors, with respect to observed standard errors, are discussed. Plots of the gridded rainfall estimates are given for sample El Niño and La Niña cases and standardized differences between the estimates produced here and the merged monthly rainfall estimates published by the Global Precipitation Climatology Project (GPCP) are shown and discussed.
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Prakash, Satya, Ashwin Seshadri, J. Srinivasan, and D. S. Pai. "A New Parameter to Assess Impact of Rain Gauge Density on Uncertainty in the Estimate of Monthly Rainfall over India." Journal of Hydrometeorology 20, no. 5 (2019): 821–32. http://dx.doi.org/10.1175/jhm-d-18-0161.1.
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Abstract Rain gauges are considered the most accurate method to estimate rainfall and are used as the “ground truth” for a wide variety of applications. The spatial density of rain gauges varies substantially and hence influences the accuracy of gridded gauge-based rainfall products. The temporal changes in rain gauge density over a region introduce considerable biases in the historical trends in mean rainfall and its extremes. An estimate of uncertainty in gauge-based rainfall estimates associated with the nonuniform layout and placement pattern of the rain gauge network is vital for national decisions and policy planning in India, which considers a rather tight threshold of rainfall anomaly. This study examines uncertainty in the estimation of monthly mean monsoon rainfall due to variations in gauge density across India. Since not all rain gauges provide measurements perpetually, we consider the ensemble uncertainty in spatial average estimation owing to randomly leaving out rain gauges from the estimate. A recently developed theoretical model shows that the uncertainty in the spatially averaged rainfall is directly proportional to the spatial standard deviation and inversely proportional to the square root of the total number of available gauges. On this basis, a new parameter called the “averaging error factor” has been proposed that identifies the regions with large ensemble uncertainties. Comparison of the theoretical model with Monte Carlo simulations at a monthly time scale using rain gauge observations shows good agreement with each other at all-India and subregional scales. The uncertainty in monthly mean rainfall estimates due to omission of rain gauges is largest for northeast India (~4% uncertainty for omission of 10% gauges) and smallest for central India. Estimates of spatial average rainfall should always be accompanied by a measure of uncertainty, and this paper provides such a measure for gauge-based monthly rainfall estimates. This study can be further extended to determine the minimum number of rain gauges necessary for any given region to estimate rainfall at a certain level of uncertainty.
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Ozturk, U., H. Saito, Y. Matsushi, I. Crisologo, and W. Schwanghart. "Can global rainfall estimates (satellite and reanalysis) aid landslide hindcasting?" Landslides 18, no. 9 (2021): 3119–33. http://dx.doi.org/10.1007/s10346-021-01689-3.
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AbstractPredicting rainfall-induced landslides hinges on the quality of the rainfall product. Satellite rainfall estimates or rainfall reanalyses aid in studying landslide occurrences especially in ungauged areas, or in the absence of ground-based rainfall radars. Quality of these rainfall estimates is critical; hence, they are commonly crosschecked with their ground-based counterparts. Beyond their temporal precision compared to ground-based observations, we investigate whether these rainfall estimates are adequate for hindcasting landslides, which particularly requires accurate representation of spatial variability of rainfall. We developed a logistic regression model to hindcast rainfall-induced landslides in two sites in Japan. The model contains only a few topographic and geologic predictors to leave room for different rainfall products to improve the model as additional predictors. By changing the input rainfall product, we compared GPM IMERG and ERA5 rainfall estimates with ground radar–based rainfall data. Our findings emphasize that there is a lot of room for improvement of spatiotemporal prediction of landslides, as shown by a strong performance increase of the models with the benchmark radar data attaining 95% diagnostic performance accuracy. Yet, this improvement is not met by global rainfall products which still face challenges in reliably capturing spatiotemporal patterns of precipitation events.
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Pandey, Ashish, S. K. Mishra, and Amar Kant Gautam. "Soil Erosion Modeling Using Satellite Rainfall Estimates." Journal of Water Resource and Hydraulic Engineering 4, no. 4 (2015): 318–25. http://dx.doi.org/10.5963/jwrhe0404002.
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Cash, Benjamin A., Xavier Rodó, James L. Kinter, Michael J. Fennessy, and Brian Doty. "Differing Estimates of Observed Bangladesh Summer Rainfall." Journal of Hydrometeorology 9, no. 5 (2008): 1106–14. http://dx.doi.org/10.1175/2008jhm928.1.
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Abstract The differences in boreal summer (June–August) monthly-mean rainfall estimates over the Indian Ocean region in five research-quality products are examined for the period 1979–2003. Two products derived from the merged satellite and surface observations are considered: the Climate Prediction Center (CPC) Merged Analysis of Precipitation (CMAP) and the Global Precipitation Climatology Project (GPCP). In addition, three products derived solely from rain gauge observations are considered: the Chen et al. product; the Indian Meteorological Department (IMD) product; and a new, objectively analyzed product based on the Climate Anomaly Monitoring System (CAMS) dataset. Significant discrepancies have been found between the different products across the entire Indian Ocean region, with the greatest disagreement over Burma and neighboring Bangladesh. These differences appear to be primarily due to the absence of reported rain gauge data for Burma and differences in the algorithms used to merge the satellite microwave emission and scattering data in coastal regions. Representations of rainfall across much of the eastern Indian Ocean region would likely be improved by the identification and inclusion of reporting stations from Burma and a refinement of the techniques used for merging microwave data. The differences among the five products are sufficient to affect both quantitative and qualitative conclusions drawn about rainfall, particularly over Bangladesh and Burma. Consequently, the results of precipitation studies in this region will depend, in some cases, on the choice of the data product, including such basic questions as to whether a given summer was wet or dry. Of particular note is that the apparent relationship between rainfall and ENSO can depend on the choice of the data product.
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Mganu Manyatsi, Absalom. "Evaluation of Satellite Rainfall Estimates for Swaziland." American Journal of Agriculture and Forestry 3, no. 3 (2015): 93. http://dx.doi.org/10.11648/j.ajaf.20150303.15.
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Diro, G. T., D. I. F. Grimes, E. Black, A. O'Neill, and E. Pardo-Iguzquiza. "Evaluation of reanalysis rainfall estimates over Ethiopia." International Journal of Climatology 29, no. 1 (2009): 67–78. http://dx.doi.org/10.1002/joc.1699.
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Saeidizand, Rosa, Samaneh Sabetghadam, Elena Tarnavsky, and Arnaldo Pierleoni. "Evaluation of CHIRPS rainfall estimates over Iran." Quarterly Journal of the Royal Meteorological Society 144, S1 (2018): 282–91. http://dx.doi.org/10.1002/qj.3342.
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Chandrasekar, V., Eugenio Gorgucci, and Gianfranco Scarchilli. "Optimization of Multiparameter Radar Estimates of Rainfall." Journal of Applied Meteorology 32, no. 7 (1993): 1288–93. http://dx.doi.org/10.1175/1520-0450(1993)032<1288:oomreo>2.0.co;2.
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Jordan, Phillip, Alan Seed, and Geoff Austin. "Sampling errors in radar estimates of rainfall." Journal of Geophysical Research: Atmospheres 105, no. D2 (2000): 2247–57. http://dx.doi.org/10.1029/1999jd900130.
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Ya’acob, Norsuzila, Noraisyah Tajudin, and Aziean Mohd Azize. "Rainfall–landslide early warning system (RLEWS) using TRMM precipitation estimates." Indonesian Journal of Electrical Engineering and Computer Science 13, no. 3 (2019): 1259. http://dx.doi.org/10.11591/ijeecs.v13.i3.pp1259-1266.
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<span lang="EN-MY">This paper presents Rainfall–Landslide Early Warning System (RLEWS) using Tropical Rainfall Measuring Mission (TRMM) precipitation estimates to notify the warning level for the possibility of landslide occurrences in Ulu Kelang, Selangor. In this study, RLEWS is developed to monitor the possibility of rainfall-induced landslide occurrences by comparing real time TRMM rainfall data with a landslide rainfall threshold. The landslide rainfall threshold is constructed by using the accumulated rainfall-accumulated rainfall (E-E) diagram method. The warning levels of rainfall threshold are classified into three levels; high, moderate and low. The analysis and notification are updating every 24 hours to provide the initial potential landslide information signal. The rainfall threshold analysis was able to detect the early signal of initial potential landslide occurrences. The aims of this study are to develop a low-cost, sustainable early warning system and web base application to send notification and awareness for residential areas in Ulu Kelang, Selangor.</span>
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Shi, Tingting, Xiaomei Yang, George Christakos, Jinfeng Wang, and Li Liu. "Spatiotemporal Interpolation of Rainfall by Combining BME Theory and Satellite Rainfall Estimates." Atmosphere 6, no. 9 (2015): 1307–26. http://dx.doi.org/10.3390/atmos6091307.
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Park, Taewoong, Taesam Lee, Dasang Ko, Juyoung Shin, and Dongryul Lee. "Assessing spatially dependent errors in radar rainfall estimates for rainfall-runoff simulation." Stochastic Environmental Research and Risk Assessment 31, no. 7 (2016): 1823–38. http://dx.doi.org/10.1007/s00477-016-1325-4.
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Chua, Zhi-Weng, Yuriy Kuleshov, and Andrew Watkins. "Evaluation of Satellite Precipitation Estimates over Australia." Remote Sensing 12, no. 4 (2020): 678. http://dx.doi.org/10.3390/rs12040678.
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This study evaluates the U.S. National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH) and the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) satellite precipitation estimates over Australia across an 18 year period from 2001 to 2018. The evaluation was performed on a monthly time scale and used both point and gridded rain gauge data as the reference dataset. Overall statistics demonstrated that satellite precipitation estimates did exhibit skill over Australia and that gauge-blending yielded a notable increase in performance. Dependencies of performance on geography, season, and rainfall intensity were also investigated. The skill of satellite precipitation detection was reduced in areas of elevated topography and where cold frontal rainfall was the main precipitation source. Areas where rain gauge coverage was sparse also exhibited reduced skill. In terms of seasons, the performance was relatively similar across the year, with austral summer (DJF) exhibiting slightly better performance. The skill of the satellite precipitation estimates was highly dependent on rainfall intensity. The highest skill was obtained for moderate rainfall amounts (2–4 mm/day). There was an overestimation of low-end rainfall amounts and an underestimation in both the frequency and amount for high-end rainfall. Overall, CMORPH and GSMaP datasets were evaluated as useful sources of satellite precipitation estimates over Australia.
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Nguyen, V.-T.-V., T. D. Nguyen, and F. Ashkar. "Regional frequency analysis of extreme rainfalls." Water Science and Technology 45, no. 2 (2002): 75–81. http://dx.doi.org/10.2166/wst.2002.0030.
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This study proposes two alternative methods for estimating the distribution of extreme rainfalls for sites where rainfall data are available (gaged sites) and for locations without data (ungaged sites). The first method deals with the estimation of short-duration rainfall extremes from available rainfall data for longer durations using the “scale-invariance” concept to account for the relationship between statistical properties of extreme rainfall processes for different time scales. The second method is concerned with the estimation of extreme rainfalls for ungaged sites. This method relies on a new definition of homogeneous sites. Results of the numerical application using data from a network of 10 recording rain gauges in Quebec (Canada) indicate that the proposed methods are able to provide extreme rainfall estimates that are comparable with those based on observed at-site rainfall data.
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Crosbie, R. S., I. D. Jolly, F. W. Leaney, and C. Petheram. "Can the dataset of field based recharge estimates in Australia be used to predict recharge in data-poor areas?" Hydrology and Earth System Sciences 14, no. 10 (2010): 2023–38. http://dx.doi.org/10.5194/hess-14-2023-2010.
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Abstract. Effective management of water resources requires that all elements of the water balance be estimated. Groundwater recharge measurements are difficult, time consuming and expensive. In some cases a field study cannot be justified and simple empirical relationships are used to estimate recharge, and often the value chosen is simply a percentage of rainfall. This paper aims to use a database of 4386 field based estimates of recharge from 172 studies in Australia to produce simple empirical relationships that relate recharge to nationally available datasets and hence can be used to estimate recharge in data-poor areas in a scientifically defensible way. It was found that the vegetation and soil type were critical determinants in forming relationships between average annual rainfall and average annual recharge. Climate zones and surface geology (lithology) were not found to be significant determinants in the relationship between rainfall and recharge. The method used to estimate recharge had an impact upon the magnitude of the recharge estimates due to the spatial and temporal scales over which the different methods estimate recharge. Relationships have been developed here between average annual rainfall and average annual recharge for combinations of soil and vegetation type that can be used with only nationally available datasets to provide a recharge estimate. These relationships can explain 60% of the variance in recharge measurements across Australia. The uncertainty in the recharge estimated using these relationships is generally greater than an order of magnitude. This means that if these relationships are used to help determine water allocations, then the precautionary principle should limit allocations to less than about 5% of the estimated recharge. If allocations are greater than this, a more detailed site specific study is warranted.
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Cooperman, Alicia Dailey. "Randomization Inference with Rainfall Data: Using Historical Weather Patterns for Variance Estimation." Political Analysis 25, no. 3 (2017): 277–88. http://dx.doi.org/10.1017/pan.2017.17.
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Many recent papers in political science and economics use rainfall as a strategy to facilitate causal inference. Rainfall shocks are as-if randomly assigned, but the assignment of rainfall by county is highly correlated across space. Since clustered assignment does not occur within well-defined boundaries, it is challenging to estimate the variance of the effect of rainfall on political outcomes. I propose using randomization inference with historical weather patterns from 73 years as potential randomizations. I replicate the influential work on rainfall and voter turnout in presidential elections in the United States by Gomez, Hansford, and Krause (2007) and compare the estimated average treatment effect (ATE) to a sampling distribution of estimates under the sharp null hypothesis of no effect. The alternate randomizations are random draws from national rainfall patterns on election and would-be election days, which preserve the clustering in treatment assignment and eliminate the need to simulate weather patterns or make assumptions about unit boundaries for clustering. I find that the effect of rainfall on turnout is subject to greater sampling variability than previously estimated using conventional standard errors.
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Curtis, David C. "Use of Weather Surveillance Radars—88 Doppler Data in Hydrologic Modeling." Transportation Research Record: Journal of the Transportation Research Board 1647, no. 1 (1998): 61–66. http://dx.doi.org/10.3141/1647-08.
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Successful hydrologic modeling depends heavily on high-quality rainfall data sets. If hydrologists cannot determine what is coming into a watershed, there is little chance that any hydrologic model will accurately estimate what is coming out on a consistent basis. Hydrologists are frequently forced to use rainfall data sets derived from sparse rain gauge networks that poorly resolve critical rainfall features, leading to inadequate model results. Over the past several years, the modernizing National Weather Service, the Federal Aviation Administration, and the Department of Defense have installed a new nationwide network of weather radars, providing a rich suite of real-time meteorological observations. Radar rainfall estimates from the new radars cover vast areas at a spatial and temporal resolution that would be impossibly expensive to match with a conventional rain gauge network. Hydrologists can now literally see between the gauges and view truer representations of the spatial distribution of rainfall than ever before. Results from the analysis of the January 9-10, 1995, storms in Sacramento, California, show that gauge-adjusted radar rainfall estimates help resolve rainfall features that could not have been inferred from rain gauge analysis alone. Accurate estimates of the volume, timing, and distribution of rainfall helped create excellent modeling results. In Waco, Texas, radar rainfall estimates were used to improve the analysis of excess inflow and infiltration into city storm sewers. The radar rainfall analyses enabled modelers to account for inflow/infiltration variations down to the neighborhood level.
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Sahlu, Dejene, Semu A. Moges, Efthymios I. Nikolopoulos, Emmanouil N. Anagnostou, and Dereje Hailu. "Evaluation of High-Resolution Multisatellite and Reanalysis Rainfall Products over East Africa." Advances in Meteorology 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/4957960.
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The performance of six satellite-based and three newly released reanalysis rainfall estimates are evaluated at daily time scale and spatial grid size of 0.25 degrees during the period of 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, with the view of improving the reliability of precipitation estimates of the wet (June to September) and secondary rainy (March to May) seasons. The study evaluated both adjusted and unadjusted satellite-based products of TMPA, CMORPH, PERSIANN, and ECMWF ERA-Interim reanalysis as well as Multi-Source Weighted-Ensemble Precipitation (MSWEP) estimates. Among the six satellite-based rainfall products, adjusted CMORPH exhibits the best accuracy of the wet season rainfall estimate. In the secondary rainy season, unadjusted CMORPH and 3B42V7 are nearly equivalent in terms of bias, POD, and CSI error metrics. All error metric statistics show that MSWEP outperform both unadjusted and gauge adjusted ERA-Interim estimates. The magnitude of error metrics is linearly increasing with increasing percentile threshold values of gauge rainfall categories. Overall, all precipitation datasets need further improvement in terms of detection during the occurrence of high rainfall intensity. MSWEP detects higher percentiles values better than satellite estimate in the wet and poor in the secondary rainy seasons.
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Crosbie, R. S., I. D. Jolly, F. W. Leaney, and C. Petheram. "Can the dataset of field based recharge estimates in Australia be used to predict recharge in data-poor areas?" Hydrology and Earth System Sciences Discussions 7, no. 4 (2010): 5647–84. http://dx.doi.org/10.5194/hessd-7-5647-2010.
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Abstract. Effective management of water resources requires that all elements of the water balance be estimated. Groundwater recharge measurements are difficult, time consuming and expensive. In some cases a field study cannot be justified and simple empirical relationships are used to estimate recharge, and often the value chosen is simply a percentage of rainfall. This paper aims to use a data-base of 4386 field based estimates of recharge from 172 studies in Australia to produce simple empirical relationships that relate recharge to nationally available datasets and hence can be used to estimate recharge in data-poor areas in a scientifically defensible way. It was found that the vegetation and soil type were critical determinants in forming relationships between average annual rainfall and average annual recharge. Climate zones and surface geology were not found to be significant determinants in the relationship between rainfall and recharge. The method used to estimate recharge had an impact upon the magnitude of the recharge estimates due to the spatial and temporal scales over which the different methods estimate recharge. Relationships have been developed here between average annual rainfall and average annual recharge for combinations of soil and vegetation type that can be used with only nationally available datasets to provide a recharge estimate. The 95 percent confidence limits about the recharge predicted using these relationships is generally greater than an order of magnitude either side of the relationship developed. This means that if these relationships are used to help determine water allocations then the precautionary principle should limit allocations to less than about 5% of the estimated recharge, if allocations are greater than this a more detailed site specific study is warranted.
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Nguyen, V. T. V., T. D. Nguyen, and H. Wang. "Regional estimation of short duration rainfall extremes." Water Science and Technology 37, no. 11 (1998): 15–19. http://dx.doi.org/10.2166/wst.1998.0425.
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The present study proposes a method for estimating the distribution of short-duration (e.g., 1 hour) extreme rainfalls at sites where data for the time interval of interest do not exist, but rainfall data for longer-duration (e.g., 1 day) are available (partially-gaged sites). The proposed method is based on the recently developed “scale-invariance” (or “scaling”) theory. In this study, the scaling concept implies that statistical properties of the extreme rainfall processes for different temporal scales are related to each other by a scale-changing operator involving only the scale ratio. Further, it is assumed that these hydrologic series possess a simple scaling behaviour. The suggested methodology has been applied to extreme rainfall data from a network of 14 recording raingages in Quebec (Canada). The Generalised Extreme Value (GEV) distribution was used to estimate the rainfall quantiles. Results of the numerical application have indicated that for partially-gaged sites the proposed scaling method is able to provide extreme rainfall estimates which are comparable with those based on available at-site rainfall data.
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Meyer, Hanna, Johannes Drönner, and Thomas Nauss. "Satellite-based high-resolution mapping of rainfall over southern Africa." Atmospheric Measurement Techniques 10, no. 6 (2017): 2009–19. http://dx.doi.org/10.5194/amt-10-2009-2017.
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Abstract. A spatially explicit mapping of rainfall is necessary for southern Africa for eco-climatological studies or nowcasting but accurate estimates are still a challenging task. This study presents a method to estimate hourly rainfall based on data from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and Infrared Imager (SEVIRI). Rainfall measurements from about 350 weather stations from 2010–2014 served as ground truth for calibration and validation. SEVIRI and weather station data were used to train neural networks that allowed the estimation of rainfall area and rainfall quantities over all times of the day. The results revealed that 60 % of recorded rainfall events were correctly classified by the model (probability of detection, POD). However, the false alarm ratio (FAR) was high (0.80), leading to a Heidke skill score (HSS) of 0.18. Estimated hourly rainfall quantities were estimated with an average hourly correlation of ρ = 0. 33 and a root mean square error (RMSE) of 0.72. The correlation increased with temporal aggregation to 0.52 (daily), 0.67 (weekly) and 0.71 (monthly). The main weakness was the overestimation of rainfall events. The model results were compared to the Integrated Multi-satellitE Retrievals for GPM (IMERG) of the Global Precipitation Measurement (GPM) mission. Despite being a comparably simple approach, the presented MSG-based rainfall retrieval outperformed GPM IMERG in terms of rainfall area detection: GPM IMERG had a considerably lower POD. The HSS was not significantly different compared to the MSG-based retrieval due to a lower FAR of GPM IMERG. There were no further significant differences between the MSG-based retrieval and GPM IMERG in terms of correlation with the observed rainfall quantities. The MSG-based retrieval, however, provides rainfall in a higher spatial resolution. Though estimating rainfall from satellite data remains challenging, especially at high temporal resolutions, this study showed promising results towards improved spatio-temporal estimates of rainfall over southern Africa.
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Uijlenhoet, R., J. M. Cohard, and M. Gosset. "Path-Average Rainfall Estimation from Optical Extinction Measurements Using a Large-Aperture Scintillometer." Journal of Hydrometeorology 12, no. 5 (2011): 955–72. http://dx.doi.org/10.1175/2011jhm1350.1.
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Abstract The potential of a near-infrared large-aperture boundary layer scintillometer as path-average rain gauge is investigated. The instrument was installed over a 2.4-km path in Benin as part of the African Monsoon Multidisciplinary Analysis (AMMA) Enhanced Observation Period during 2006 and 2007. Measurements of the one-minute-average received signal intensity were collected for 6 rainfall events during the dry season and 16 events during the rainy season. Using estimates of the signal base level just before the onset of the rainfall events, the optical extinction coefficient is estimated from the path-integrated attenuation for each minute. The corresponding path-average rain rates are computed using a power-law relation between the optical extinction coefficient and rain rate obtained from measurements of raindrop size distributions with an optical spectropluviometer and a scaling-law formalism for describing raindrop size distribution variations. Comparisons of five-minute rainfall estimates with measurements from two nearby rain gauges show that the temporal dynamics are generally captured well by the scintillometer. However, the instrument has a tendency to underestimate rain rates and event total rain amounts with respect to the gauges. It is shown that this underestimation can be explained partly by systematic differences between the actual and the employed mean power-law relation between rain rate and specific attenuation, partly by unresolved spatial and temporal rainfall variations along the scintillometer path. Occasionally, the signal may even be lost completely. It is demonstrated that if these effects are properly accounted for by employing appropriate relations between rain rate and specific attenuation and by adapting the pathlength to the local rainfall climatology, scintillometer-based rainfall estimates can be within 20% of those estimated using rain gauges. These results demonstrate the potential of large-aperture scintillometers to estimate path-average rain rates at hydrologically relevant scales.
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Fatkhuroyan, Fatkhuroyan, Trinah Wati, Alfan Sukmana, and Roni Kurniawan. "Validation of Satellite Daily Rainfall Estimates Over Indonesia." Forum Geografi 32, no. 2 (2018): 170–80. http://dx.doi.org/10.23917/forgeo.v32i2.6288.
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Rainfall is the most important factor in the Earth’s water and energy cycles. The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the correlation coefficient (CC) provides better validation in the rainy season while root mean square error (RMSE) is more accurate in the dry season. The highest proportion correct (PC) value is obtained for Bali-NTT, while the highest probability of detection (POD) and false alarm ratio (FAR) values are obtained for Kalimantan. GSMaP-MVK data is over-estimated compared to observations in Indonesia, with the mean accuracy for daily rainfall estimation being 85.47% in 2014, 85.74% in 2015, 82.73 in 2016, and 82.59% in 2017.
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Wesson, S. M., and G. G. S. Pegram. "Improved radar rainfall estimation at ground level." Natural Hazards and Earth System Sciences 6, no. 3 (2006): 323–42. http://dx.doi.org/10.5194/nhess-6-323-2006.
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Abstract. A technique has been developed to provide an estimate of the rainfall reaching the earth's surface by extrapolating radar data contained aloft to ground level, simultaneously estimating unknown data in the radar volume scan. The technique has been developed so as to be computationally fast, to work in real time and comprises the following steps. A rainfall classification algorithm is applied to separate the rainfall into two separate types: convective and stratiform rainfall. Climatological semivariograms based on the rainfall type are then defined and justified by testing, which result in a fast and effective means of determining the semivariogram parameters anywhere in the radar volume scan. Then, extrapolations to ground level are computed by utilising 3-D Universal and Ordinary Cascade Kriging; computational efficiency and stability in Kriging are ensured by using a nearest neighbours approach and a Singular Value Decomposition (SVD) matrix rank reduction technique. To validate the proposed technique, a statistical comparison between the temporally accumulated radar estimates and the Block Kriged raingauge estimates is carried out over matching areas, for selected rainfall events, to determine the quality of the rainfall estimates at ground level.
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Ekström, Marie, Phaedon C. Kyriakidis, Adrian Chappell, and Philip D. Jones. "Spatiotemporal Stochastic Simulation of Monthly Rainfall Patterns in the United Kingdom (1980–87)." Journal of Climate 20, no. 16 (2007): 4194–210. http://dx.doi.org/10.1175/jcli4233.1.
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Abstract With few exceptions, spatial estimation of rainfall typically relies on information in the spatial domain only. In this paper, a method that utilizes information in time and space and provides an assessment of estimate uncertainty is used to create a gridded monthly rainfall dataset for the United Kingdom over the period 1980–87. Observed rainfall profiles within the region were regarded as the sum of a deterministic temporal trend and a stochastic residual component. The parameters of the temporal trend components established at the rain gauges were interpolated in space, accounting for their auto- and cross correlation, and for relationships with ancillary spatial variables. Stochastic Gaussian simulation was then employed to generate alternative realizations of the spatiotemporal residual component, which were added to the estimated trend component to yield realizations of rainfall (after distributional corrections). In total, 40 realizations of rainfall were generated for each month of the 8-yr period. The methodology resulted in reasonably accurate estimates of rainfall but underestimated in northwest and north Scotland and northwest England. The cause for the underestimation was identified as a weak relationship between local rainfall and the spatial area average rainfall, used to estimate the temporal trend model in these regions, and suggestions were made for improvement. The strengths of this method are the utilization of information from the time and space domain, and the assessment of spatial uncertainty in the estimated rainfall values.
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Fisher, Brad L. "Statistical Error Decomposition of Regional-Scale Climatological Precipitation Estimates from the Tropical Rainfall Measuring Mission (TRMM)." Journal of Applied Meteorology and Climatology 46, no. 6 (2007): 791–813. http://dx.doi.org/10.1175/jam2497.1.
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Abstract Monthly rainfall estimates inferred from the NASA Tropical Rainfall Measuring Mission (TRMM) satellite contain errors due to discrete temporal sampling and remote spaceborne rain retrievals. This paper develops a regional-scale error model that uses the rain information in the ground data to disentangle the sampling and retrieval errors in the satellite estimate statistically. The proposed method computes a mean rain rate from monthly rainfall statistics for each TRMM rain sensor by subsampling high-resolution ground-based rain data at satellite overpass times. This additional rain-subsampled parameter plays an essential role in the statistical decomposition of the total error distribution into its sampling and retrieval error components. Using the statistical formalism developed in this study, an error analysis was performed on 5 yr of monthly rain estimates produced by the TRMM Microwave Imager (TMI) and precipitation radar (PR) rain sensors aboard TRMM over a quasi 2° × 2° region of the TRMM ground validation (GV) site at Melbourne, Florida. Annual retrieval and sampling error statistics were computed for the TMI and PR using monthly rainfall estimates derived from two independent ground-based sensors: a regional rain gauge network and the Next-Generation Weather Radar (NEXRAD). Subsampled ground-based rainfall estimates produced for the radar and the gauges were highly correlated with the TMI and PR rainfall estimates, and both GV sensors produced relatively consistent error estimates. The PR-to-TMI sampling error ratio was equal to about 1.3, which was in close agreement with prelaunch predications, and the TMI-to-PR retrieval error ratio was about 2.0. For the TMI, a seasonally alternating rainfall bias was also observed that was negative during winter and positive during summer.
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Lamptey, Benjamin L. "Comparison of Gridded Multisatellite Rainfall Estimates with Gridded Gauge Rainfall over West Africa." Journal of Applied Meteorology and Climatology 47, no. 1 (2008): 185–205. http://dx.doi.org/10.1175/2007jamc1586.1.
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Abstract Two monthly gridded precipitation datasets of the Global Precipitation Climatology Project (GPCP; the multisatellite product) and the Global Precipitation Climatology Centre (GPCC) Variability Analysis of Surface Climate Observations (VASClimO; rain gauge data) are compared for a 22-yr period, from January 1979 to December 2000, over land areas (i.e., latitudes 4°–20°N and longitudes 18°W–15°E). The two datasets are consistent with respect to the spatial distribution of the annual and seasonal rainfall climatology over the domain and along latitudinal bands. However, the satellite generally overestimates rainfall. The inability of the GPCC data to capture the bimodal rainfall pattern along the Guinea coast (i.e., south of latitude 8°N) is an artifact of the interpolation of the rain gauge data. For interannual variability, the gridded multisatellite and gridded gauge datasets agree on the sign of the anomaly 15 out of the 22 yr (68% of the time) for region 1 (between longitude 5° and 18°W and north of latitude 8°N) and 18 out of the 22 yr (82% of the time) for region 2 (between longitude 5°W and 15°E and north of latitude 8°N). The datasets agreed on the sign of the anomaly 14 out of the 22 yr (64% of the time) over the Guinea Coast. The magnitudes of the anomaly are very different in all years. Most of the years during which the two datasets did not agree on the sign of the anomaly were years with El Niño events. The ratio of the seasonal root-mean-square differences to the seasonal mean rainfall range between 0.24 and 2.60. The Kendall’s tau statistic indicated statistically significant trends in both datasets, separately.
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Xu, Weixin, Robert F. Adler, and Nai-Yu Wang. "Improving Geostationary Satellite Rainfall Estimates Using Lightning Observations: Underlying Lightning–Rainfall–Cloud Relationships." Journal of Applied Meteorology and Climatology 52, no. 1 (2013): 213–29. http://dx.doi.org/10.1175/jamc-d-12-040.1.
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AbstractThis study quantifies the relationships among lightning activity, convective rainfall, and associated cloud properties on both convective-system scale (or storm scale) and satellite-pixel scale (~5 km) on the basis of 13 yr of Tropical Rainfall Measuring Mission measurements of rainfall, lightning, and clouds. Results show that lightning frequency is a good proxy to separate storms of different intensity, identify convective cores, and screen out false convective-core signatures in areas of thick anvil debris. Significant correlations are found between storm-scale lightning parameters and convective rainfall for systems over the southern United States, the focus area of the study. Storm-scale convective rainfall or heavy-precipitation area has the best correlation (coefficient r = 0.75–0.85) with lightning-flash area. It also increases linearly with increasing lightning-flash rate, although correlations between convective/heavy rainfall and lightning-flash rate are somewhat weaker (r = 0.55–0.75). Statistics further show that active lightning and intense precipitation are not well collocated on the pixel scale (5 km); for example, only 50% of the lightning flashes are coincident with heavy-rain cores, and more than 20% are distributed in light-rain areas. Simple positive correlations between lightning-flash rate and precipitation intensity are weak on the pixel scale. Lightning frequency and rain intensity have positive probabilistic relationships, however: the probability of heavy precipitation, especially on a coarser pixel scale (~20 km), increases with increasing lightning-flash density. Therefore, discrete thresholds of lightning density could be applied in a rainfall estimation scheme to assign precipitation in specific rate categories.
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Kuligowski, Robert J. "A Self-Calibrating Real-Time GOES Rainfall Algorithm for Short-Term Rainfall Estimates." Journal of Hydrometeorology 3, no. 2 (2002): 112–30. http://dx.doi.org/10.1175/1525-7541(2002)003<0112:ascrtg>2.0.co;2.
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Kong, Fanzhe, Wei Huang, Zhilin Wang, and Xiaomeng Song. "Effectof Unit Hydrographs and Rainfall Hyetographs on Critical Rainfall Estimates of Flash Flood." Advances in Meteorology 2020 (June 10, 2020): 1–15. http://dx.doi.org/10.1155/2020/2801963.
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To obtain critical rainfall (CR) estimates similar to the rainfall value that causes minor basin outlet flooding, and to reduce the flash flood warning missed/false alarm rate, the effect of unit hydrographs (UHs) and rainfall hyetographs on computed threshold rainfall (TR) values was investigated. The Tanjia River basin which is a headwater subbasin of the Greater Huai River basin in China was selected as study basin. Xin’anjiang Model, with subbasins as computation units, was constructed, and time-variant distributed unit hydrographs (TVUHs) were used to route the channel network concentration. Calibrated Xin’anjiang Model was employed to derive the TVUHs and to obtain the maximum critical rainfall duration (Dmax) of the study basin. Initial soil moisture condition was represented by the antecedent precipitation index (Pa). Rainfall hyetographs characterized by linearly increasing, linearly decreasing, and uniform hyetographs were used. Different combinations of the three hyetographs and UHs including TVUHs and time-invariant unit hydrographs (TIVUHs) were utilized as input to the calibrated Xin’anjiang Model to compute the relationships between TR and Pa (TR-Pa curves) by using trial and error methodology. The computed TR-Pa curves reveal that, for given Pa and UH, the TR corresponding to linearly increasing hyetograph is the minimum one. So, the linearly increasing hyetograph is the optimum hyetograph type for estimating CR. In the linearly increasing hyetograph context, a comparison was performed between TR-Pa curves computed from different UHs. The results show that TR values for different TIVUHs are significantly different and the TR-Pa curve gradient of TVUHs is lower than that of TIVUHs. It is observed that CR corresponds to the combination of linearly increasing hyetograph and TVUHs. The relationship between CR and Pa (CR-Pa curves) and that between CR and duration (D) (CR-D curves) were computed. Warnings for 12 historical flood events were performed. Warning results show that the success rate was 91.67% and that the critical success index (CSI) was 0.91. It is concluded that the combination of linearly increasing hyetograph and TVUHs can provide the CR estimate similar to the minimum rainfall value necessary to cause flash flooding.