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Depiction of the Automated Weather Station (AWS) operational rain gauge network, operated by the Korean Meteorological Agency. The density is nearly homogeneous across the southern Korean peninsula with 40 gauges per 1-degree box, each reporting at a 1-minute time update resolution.
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In order to properly utilize meteorological satellite-derived precipitation estimates in operational settings such as numerical weather prediction (NWP) applications, knowledge of the observation error statistics are needed as well as information how they are correlated in space and time. However, very few raingauge networks operate with the necess...
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... During this time, the NOAA-15/16/17 satellites were not yet incorporated into the blended technique (the three SSMIs and the TMI formed the underlying LEO constellation), giving a LEO revisit over Korea of about 4 hours on average and 10 hours worst-case. Considering that an individual satellite observation represents an approximate 0.1-degree area average, whereas the gauge measurement represents a small area less than 1 m 2 , South Korea is divided into smaller boxes, ranging from 0.1 degrees to 3 degrees on a side, where relatively homogeneous gauge distributions are found ( Figure 3 depicts the 1-degree box size). Due to the inhomogeneity of the rain within the spatial averaging box and very small areas represented by individual gauges, a direct comparison of instantaneous (ie, pixel-based) satellite-based retrievals and gauges is inherently limited. ...
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Citations
... deviations, and it has been extensively applied in the error corrections of non-independent satellite data with deviations (Turk et al. 2003;Huffman et al. 2003;Wang et al. 2007;Xie et al. 2011;Shen et al. 2014). ...
Precipitation, a basic component of the water cycle, is significantly important for
meteorological, climatological and hydrological research. However, accurate estimation on the precipitation remains considerably challenging because of the sparsity of gauge networks and the large spatial variability of precipitation over
mountainous regions. Moreover, meteorological stations in mountainous areas are often dispersed and have difficulty in accurately reflecting the intensity and evolution of precipitation events. In this study, we proposed a novel method to produce high-quality, high-resolution precipitation estimates in the Tianshan Mountains, China, based on area-to-point kriging (ATPK) downscaling and a two-step correction, i.e., probability density function matching -optimum interpolation (PDF-OI). We obtained 1-km
hourly precipitation data in the Tianshan Mountains by merging estimates from the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) product with observations from 1065 meteorological stations in the warm season
(May to September) during 2016–2018. The spatial resolution and accuracy of the merged precipitation data greatly increased compared to IMERG. According to a cross-validation with gauged observations, the correlation coefficient (CC), probability of detection (POD) and critical success index (CSI) increased from 0.30, 0.50 and 0.24 for IMERG to 0.63, 0.65 and 0.38, respectively, for the merged estimates, and the root mean squared error (RMSE), mean error (ME) and false alarm ratio (FAR)
decreased from 0.46 to 0.38 mm/h, 0.06 to 0.05 mm/h and 0.69 to 0.52, espectively. The proposed method will be useful for developing high-resolution precipitation estimates in mountainous areas such as central Asia and the Belt and Road Initiative regions.
... Passive microwave observations (PMW), on the other hand, can observe hydrometeors, supporting the retrieval of rainfall intensities using techniques like the Goddard profiling algorithm(s) (GPROF) (Kummerow et al. 1996(Kummerow et al. , 2001(Kummerow et al. , 2015. Seminal combined estimation approaches merge PMW and GEO-IR estimates using weights determined by standard errors (Huffman et al. 1997;Xie and Arkin 1997), use sophisticated artificial neural nets to translate GEO-IR observations into precipitation (Hsu et al. 1997), or use collocated PMW and GEO-IR observations to tune local GEO-IR estimates based upon PMW observations (Huffman et al. 2007;Turk et al. 2003Turk et al. , 2010. The neural net approach can use cloud classifications, as in the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks Cloud Classification System (PERSIANN-CCS) (Hong et al. 2004). ...
As human exposure to hydro-climatic extremes and the number of in situ precipitation observations declines, precipitation estimates, such as the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) mission (IMERG), provide a critical source of information. Here, we present a new gauge-enhanced data set (CHIMES) designed to support global crop and hydrologic modeling and monitoring. CHIMES enhances the IMERG Late Run product using an updated Climate Hazards Center’s (CHC) high-resolution climatology (CHP clim ) and low-latency rain-gauge observations. CHP clim differs from other products because it incorporates long-term averages of satellite precipitation, which increases CHP clim ’s fidelity in data-sparse areas with complex terrain. This fidelity translates into performance increases in unbiased IMERG late data, which we refer to as CHIME. This is augmented with gauge observations to produce CHIMES.
The CHC’s curated rain-gauge archive contains valuable contributions from many countries. There are two versions of CHIMES: preliminary and final. The final product has more copious and better-curated station data. Every pentad and month, bias-adjusted IMERG late fields are combined with gauge observations to create pentadal and monthly CHIMES prelim and CHIMES final . Comparisons with pentadal, high-quality gridded station data show that IMERG late performs well (r=0.75), but has some systematic biases which can be reduced. Monthly cross-validation results indicate that unbiasing increases the variance explained from 50 to 63 percent and decreases the mean absolute error from 48 to 39 mm month ⁻¹ . Gauge enhancement then increases the variance explained to 75 percent, reducing the mean absolute error to 27 mm month ⁻¹ .
... Miller et al. [29] developed another technique in which the estimation derived from the microwave sensors is regressed with IR brightness temperature locally to estimate precipitation when microwave data were unavailable. Furthermore, Turk et al. [30] developed a scheme to determine the brightness temperature limit for precipitation by comparing the brightness temperature distribution with estimates of microwave precipitation at the same point. is relationship was used to estimate rainfall from IR data where no microwave data were available. ...
Accurate daily rainfall estimation is required in several applications such as in hydrology, hydrometeorology, water resources management, geomorphology, civil protection, and agriculture, among others. CMORPH daily rainfall estimations were integrated with rain gauge measurements in Brazil between 2000 and 2015, in order to reduce daily rainfall estimation errors by means of the statistical objective analysis scheme (SOAS). Early comparisons indicated high discrepancies between daily rain gauge rainfall measurements and respective CMORPH areal rainfall accumulation estimates that tended to be reduced with accumulation time span (e.g., yearly accumulation). Current results show CMORPH systematically underestimates daily rainfall accumulation along the coastal areas. The normalized error variance (NEXERVA) is higher in sparsely gauged areas at Brazilian North and Central-West regions. Monthly areal rainfall averages and standard deviation were obtained for eleven Brazilian watersheds. While an overall negative tendency (3 mm·h ⁻¹ ) was estimated, the Amazon watershed presented a long-term positive tendency. Monthly areal mean precipitation and respective spatial standard deviation closely follow a power-law relationship for data-rich watersheds, i.e., with denser rain gauge networks. Daily SOAS rainfall accumulation was also used to calculate the spatial distribution of frequencies of 3-day rainfall episodes greater than 100 mm. Frequencies greater than 3% were identified downwind of the Peruvian Andes, the Bolivian Amazon Basin, and the La Plata Basin, as well as along the Brazilian coast, where landslides are recurrently triggered by precipitation.
... IR-based satellite QPE algorithms generally use deterministic cloud top temperature and rainfall (Tb-R) relationships to estimate surface precipitation ( Kuligowski 2002;Kidd et al. 2003;Turk et al. 2003;Huffman et al. 2007). The Geostationary Operational Environmental Satellite Precipitation Index (GPI; Arkin and Meisner, 1987) and subsequent versions (Adjusted GPI; Adler and Negri, 1988;Xu et al., 1999) applied a constant precipitation rate to pixels with a Tb lower than a certain threshold. ...
The uncertainty structure of satellite-based passive infrared quantitative precipitation estimation (QPE) is largely unknown at fine spatiotemporal scales, and requires more than just one deterministic “best estimate” to adequately cope with the intermittent, highly skewed distribution that characterizes precipitation. An investigation of this subject has been carried out within the framework of the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). A new method, PIRSO (Probabilistic QPE using Infrared Satellite Observations), is proposed to advance the use of uncertainty as an integral part of QPE. Probability distributions of precipitation rates are computed instead of deterministic values using a model quantifying the relation between satellite infrared brightness temperatures and the corresponding “true” precipitation rate. Ensembles of brightness temperatures-to-precipitation rate relationships are derived at a 30-min/0.04° scale. This approach conditions probabilistic quantitative precipitation estimates (PQPE) on the precipitation rate and typology. PIRSO's components were estimated based on a data sample covering two warm seasons over the Conterminous US. Precipitation probability maps outperform the deterministic PERSIANN-CCS QPE. PIRSO is shown to mitigate systematic biases from deterministic retrievals, quantify uncertainty, and advance the monitoring of precipitation extremes. It also provides the basis for precipitation probability maps and satellite precipitation ensembles needed for satellite multisensor merging of precipitation, early warning and mitigation of hydrometeorological hazards, and hydrological modeling.
... But, because of its complex terrain and harsh natural environment, very few gauges and meteorological radars are installed in the southeast of the TP, and there are no gauges or radar in the vast areas of the southern and western TP. Fortunately, with the development of remote-sensing technology , several satellite-based precipitation retrieval algorithms have been developed, and their related precipitation products are available now (Joyce et al. 2004; Hong, Hsu, and Sorooshian 2004; Hsu et al. 1997 Hsu et al. , 1999 Sorooshian et al. 2000; Turk et al. 2003; Huffman and Adler 2007;). However, satellite-based retrieval is an indirect way to obtain precipitation estimates, and different retrieval algorithms tend to have their own merits and demerits, quantified by uncertainty measures as a function of space, time, and rainfall intensity (Ebert, Janowiak, and Kidd 2007; Tian et al. 2007Tian et al. , 2009 Shen, Xiong, et al. 2010; Yong et al. 2012; Chen et al. 2013). ...
... (2) Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Network (PERSIANN) (Hsu et al. 1997Hsu et al. , 1999); (3) the Naval Research Laboratory (NRL) blended satellite precipitation estimates (Turk et al. 2003); (4) Tropical Rainfall Measuring Mission (TRMM) precipitation products 3B42 version 7; and (5) its realtime version 3B42RT (Huffman and Adler 2007). The main differences in the five products arise from two factors: one is the input satellite sources and the other is the IR-PMW merging algorithms. ...
... As such, accurate quantification of the spatiotemporal variability of precipitation is essential for applications involving environmental, atmospheric, water resource, and related science and engineering disciplines. The increased availability of data products from microwave (passive and active) remote sensing has contributed toward our understanding of the spatiotemporal distribution of precipitation by providing near-real-time spatially continuous precipitation estimates at smaller temporal sampling intervals [Petty, 1994;Ferraro, 1997;Bauer, 2001;Grecu and Anagnostou, 2001;Kummerow et al., 2001;Turk et al., 2002;McCollum and Ferraro, 2003;Wilheit et al., 2003;Ferraro et al., 2005;Levizzani and Gruber, 2007]. These include data products from the Special Sensor Microwave Imager (SSM/I) on Defense Meteorological Satellite Program satellites [Ferraro, 1997], Advanced Microwave Sounding Unit (AMSU) on National Oceanic and Atmospheric Agency (NOAA) Polar Orbiting environmental satellites , Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and precipitation radar (PR) Wang et al., 2009], Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) [Wilheit et al., 2003] on National Aeronautics and Space Adminis tration (NASA) and Japan Aerospace and Exploration Agency (JAXA) joint satellites, etc. ...
This chapter describes the foundations of rain/no-rain classification (RNC) based on passive microwave brightness temperatures, outstanding issues, areas of future research, and a comprehensive review of the existing RNC algorithms. It discusses the fundamental principle of passive microwave data and radiative transfer model. The chapter also discusses the complex interactions of atmospheric hydrometeors (like water vapor, ice, precipitation) with different microwave frequencies. It describes the fundamentals of the RNC classification technique and highlights prominent RNC algorithms that are embedded in the Goddard profiling (GPROF), the global satellite mapping (GSMaP) of precipitation, and the Goddard scattering (GSCAT) algorithms. A summary of the various indices used for performance evaluation of a typical RNC classification is presented. Compared to RNC classification over oceans, overland classification offers a myriad of complications as land presents itself as a radiometrically warm background with highly varying surface emissivities.
... But, because of its complex terrain and harsh natural environment, very few gauges and meteorological radars are installed in the southeast of the TP, and there are no gauges or radar in the vast areas of the southern and western TP. Fortunately, with the development of remote-sensing technology, several satellite-based precipitation retrieval algorithms have been developed, and their related precipitation products are available now (Joyce et al. 2004;Hong, Hsu, and Sorooshian 2004;Hsu et al. 1997Hsu et al. , 1999Sorooshian et al. 2000;Turk et al. 2003;Huffman and Adler 2007;). However, satellite-based retrieval is an indirect way to obtain precipitation estimates, and different retrieval algorithms tend to have their own merits and demerits, quantified by uncertainty measures as a function of space, time, and rainfall intensity (Ebert, Janowiak, and Kidd 2007;Tian et al. 2007Tian et al. , 2009Shen, Xiong, et al. 2010;Yong et al. 2012;Chen et al. 2013). ...
... (2) Precipitation Estimation From Remotely Sensed Information Using Artificial Neural Network (PERSIANN) (Hsu et al. 1997(Hsu et al. , 1999; (3) the Naval Research Laboratory (NRL) blended satellite precipitation estimates (Turk et al. 2003); (4) Tropical Rainfall Measuring Mission (TRMM) precipitation products 3B42 version 7; and (5) its realtime version 3B42RT (Huffman and Adler 2007). The main differences in the five products arise from two factors: one is the input satellite sources and the other is the IR-PMW merging algorithms. ...
This study is the first comprehensive examination of uncertainty with respect to region, season, rain rate, topography, and snow cover of five mainstream satellite-based precipitation products over the Tibetan Plateau (TP) for the period 2005–2007. It further investigates three merging approaches in order to provide the best possible products for climate and hydrology research studies. Spatial distribution of uncertainty varies from higher uncertainty in the eastern and southern TP and relatively smaller uncertainty in the western and northern TP. The uncertainty is highly seasonal, temporally varying with a decreasing trend from January to April and then remaining relatively low and increasing after October, with an obvious winter peak and summer valley. Overall, the uncertainty also shows an exponentially decreasing trend with higher rainfall rates. The effect of topography on the uncertainty tends to rapidly increase when elevation exceeds 4000 m, while the impact slowly decreases in areas lower than that topography. The influence of the elevation on the uncertainty is significant for all seasons except for the summer. Further cross-investigation found that the uncertainty trend is highly correlated with the MODIS-derived snow cover fraction (SCF) time series over the TP (e.g. correlation coefficient ≥0.75). Finally, to reduce the still relatively large and complex uncertainty over the TP, three data merging methods are examined to provide the best possible satellite precipitation data by optimally combining the five products. The three merging methods – arithmetic mean, inverse-error-square weight, and one-outlier-removed arithmetic mean – show insignificant yet subtle differences. The Bias and RMSE of the three merging methods is dependent on the seasons, but the one-outlier-removed method is more robust and its result outperforms the five individual products in all the seasons except for the winter. The correlation coefficient of the three merging methods is consistently higher than any of five individual satellite estimates, indicating the superiority of the method. This optimally merging multi-algorithm method is a cost-effective way to provide satellite precipitation data of better quality with less uncertainty over the TP in the present era prior to the Global Precipitaton Measurement Mission.
... Sin embargo, datos de pluviómetros se encuentran escasamente y se limitan a mediciones en un único punto, mientras que en datos de radar su cobertura es limitada por la topografía, además de una baja disponibilidad al público. Existen muchos algoritmos basados en satélites para producir estimaciones de lluvias por períodos que van desde media hora a un mes y disponibles a los dos días de las observaciones por satélite; a menudo, también, en algunas horas (Aonashi et al., 2009;Huffman et al., 2002;Huffman et al., 2001;Joyce et al., 2004;Kidd et al., 2003;Sorooshian et al., 2000;Tapiador, 2002;Turk et al., 2002. Estas nuevas tecnologías están permitiendo importantes y decisivos avances, en el contexto de la hidrología aplicada, al llevarse a la práctica la modelación hidrológica distribuida con variables hidrológicas, obtenidas a través de sensores de satélite, sobre todo en zonas con carencia de datos observados. ...
... Examinations of these products can provide vital information on their performance upon which further improvements may be made, and strategies to combine them with other observations (such as gaugebased analyses) may be designed (Sorooshian et al., 2000;McCollum et al., 2002;Ebert et al., 2007). Although considerable progresses has been achieved during the last two decades in estimating precipitation from radar and satellite observations and from climate data reanalysis, there is still an increasing demand for accurate high-resolution T. ZHAO AND A. YATAGAI gauge-based precipitation analyses over land areas for evaluation of numerical models and satellite-based highresolution precipitation products (Turk et al., 2004). Many efforts have been made to develop and construct global and regional gridded precipitation analyses based on gauge observations during recent decades (e.g. ...
In this study, a new gauge-based analysis of daily precipitation (regard as observations) developed by the `Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of the Water Resources (APHRODITE)' project will be used to validate the precipitation products from NCEP-NCAR, NCEP-DOE, ERA-40 and JRA-25 reanalysis over China, a typical monsoon region in East Asia for a 25-year period from 1979 to 2003. The applicability represented by reanalyzed precipitation in climate research will be analyzed through multi-statistical diagnostic analysis methods on different spatio-temporal scales, especially in seasonal and interannual variation over China. At same time, we developed the 0.25-degree daily precipitation data from 756 Chinese stations using the APHRODITE data analysis system from 1960 to 2008. And such products were used to evaluate the Tropical Rainfall Measuring Mission (TRMM) merged high quality (HQ)/infrared (IR) precipitation over south China, especially in the Yangtze-Huai River Valley of China during the periods 1998-2008. The main results about this study reveal that the ERA-40 and JRA-25 are better than NCRP-NCAR and NCEP-DOE to describe the spatial distribution and temporal variation represented by observations in most regions of China, but the NCEP-DOE has a better ability to represent the variation of Meiyu belt in the Yangtze-Huai River Valley of China. With regard to the magnitude of the precipitation difference between the reanalyes and the observations, the JRA-25 is closer to the observed precipitation than others over most domains. The comparison indicates that the TRMM product describes well the observed precipitation and its seasonal and annual variations for most of China, but has some limitations in estimating extreme precipitation. Therefore, the TRMM precipitation product should be used with caution as reference data to detect errors in gauge observations in the absence other data.
... The 3-hourly averaged NRL satellite rainfall-rate estimates used in this study are processed with the blended technique (Turk et al., 2002;Turk and Miller, 2005), which was initially developed in 1998 at the NRL in an effort to improve upon 2-3 day hurricane track forecasting (Turk et al., 2000). In the past two decades there was a rapid evolution of the low Earthorbiting (LEO) passive microwave (PMW) imaging sensor from a research setting into routine operational settings. ...
... Analysis of one year (April 2001-April 2002) worth of daily comparisons (24-hour totals) between the BOM continental Australian raingauge analysis system and the NRL rainfall estimates demonstrated the performance of the blended technique at both tropical and mid-latitude regions, summer and winter seasons, and surface regions ranging from desert to steep terrain responsible for orographically enhanced precipitation (Turk et al., 2002). ...
