scholarly journals Systematic and random error components in satellite precipitation data sets

2012 ◽  
Vol 39 (9) ◽  
pp. n/a-n/a ◽  
Author(s):  
Amir AghaKouchak ◽  
Ali Mehran ◽  
Hamidreza Norouzi ◽  
Ali Behrangi
Keyword(s):  
Random Error ◽  
Data Sets ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Error Components ◽  
Systematic And Random Error

scholarly journals Estimating Uncertainties in High-Resolution Satellite Precipitation Products: Systematic or Random Error?

2016 ◽  
Vol 17 (4) ◽  
pp. 1119-1129 ◽  
Author(s):  
Viviana Maggioni ◽  
Mathew R. P. Sapiano ◽  
Robert F. Adler
Keyword(s):  
Real Time ◽  
Random Error ◽  
Hydrological Modeling ◽  
Strong Dependence ◽  
Management Policy ◽  
Satellite Precipitation ◽  
Bias Adjustment ◽  
Error Components ◽  
Random Components ◽  
Rain Rates

Abstract This study proposes a method to quantify systematic and random components of the error associated with satellite precipitation products. Specifically, the Precipitation Uncertainties for Satellite Hydrology (PUSH) model is expanded to provide an estimate of those components of the root-mean-square error. The framework is tested on the TRMM Multisatellite Precipitation Analysis (TMPA) 3B42, real time (3B42RT), and 3B42, version 7 (3B42V7), products over the contiguous United States, using the NOAA Climate Prediction Center (CPC) Unified gauge product as reference. Results show that 3B42V7 exhibits much smaller errors than the real-time product and that the major component of the error associated with both TMPA 3B42 products is random, as the systematic error is almost completely removed by the bias adjustment applied to the two products. A strong dependence of both systematic and random error components on satellite rain rates—with larger error components at larger rain rates—is observed for both satellite products, which suggests that future satellite bias adjustment procedures should account for this dependence. The resulting error estimates and their random and systematic components allow inferences about the accuracy of these datasets and will enhance their deployment in numerous applications, from hydrological modeling and hazard mitigation to climate change studies and water management policy.

scholarly journals Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau

2013 ◽  
Vol 17 (2) ◽  
pp. 837-849 ◽  
Author(s):  
Y. C. Gao ◽  
M. F. Liu
Keyword(s):  
High Resolution ◽  
Tibetan Plateau ◽  
Correlation Coefficients ◽  
Global Precipitation Climatology Project ◽  
Rain Gauge ◽  
Data Sets ◽  
The Tibetan Plateau ◽  
Precipitation Data ◽  
Satellite Precipitation ◽  
Mean Square Errors

Abstract. High-resolution satellite precipitation products are very attractive for studying the hydrologic processes in mountainous areas where rain gauges are generally sparse. Four high-resolution satellite precipitation products are evaluated using gauge measurements over different climate zones of the Tibetan Plateau (TP) within a 6 yr period from 2004 to 2009. The four satellite-based precipitation data sets are: Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis 3B42 version 6 (TMPA) and its Real Time version (TMPART), Climate Prediction Center Morphing Technique (CMOPRH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network (PERSIANN). TMPA and CMORPH, with higher correlation coefficients and lower root mean square errors (RMSEs), show overall better performance than PERSIANN and TMPART. TMPA has the lowest biases among the four precipitation data sets, which is likely due to the correction process against the monthly gauge observations from global precipitation climatology project (GPCP). TMPA also shows large improvement over TMPART, indicating the importance of gauge-based correction on accuracy of rainfall. The four products show better agreement with gauge measurements over humid regions than that over arid regions where correlation coefficients are less than 0.5. Moreover, the four precipitation products generally tend to overestimate light rainfall (0–10 mm) and underestimate moderate and heavy rainfall (>10 mm). Moreover, this study extracts 24 topographic variables from a DEM (digital elevation model) and uses a linear regression model to explore the bias–topography relationship. Results show that biases of TMPA and CMORPH present weak dependence on topography. However, biases of TMPART and PERSIANN present dependence on topography and variability of elevation and surface roughness plays important roles in explaining their biases.

scholarly journals Accelerating assimilation development for new observing systems using EFSO

2018 ◽  
Vol 25 (1) ◽  
pp. 129-143 ◽  
Author(s):  
Guo-Yuan Lien ◽  
Daisuke Hotta ◽  
Eugenia Kalnay ◽  
Takemasa Miyoshi ◽  
Tse-Chun Chen
Keyword(s):  
Selection Criteria ◽  
Ensemble Forecast ◽  
Data Selection ◽  
Development Work ◽  
Observation Data ◽  
Precipitation Data ◽  
Trial And Error ◽  
Satellite Precipitation ◽  
Selection Strategies ◽  
Observing Systems

Abstract. To successfully assimilate data from a new observing system, it is necessary to develop appropriate data selection strategies, assimilating only the generally useful data. This development work is usually done by trial and error using observing system experiments (OSEs), which are very time and resource consuming. This study proposes a new, efficient methodology to accelerate the development using ensemble forecast sensitivity to observations (EFSO). First, non-cycled assimilation of the new observation data is conducted to compute EFSO diagnostics for each observation within a large sample. Second, the average EFSO conditionally sampled in terms of various factors is computed. Third, potential data selection criteria are designed based on the non-cycled EFSO statistics, and tested in cycled OSEs to verify the actual assimilation impact. The usefulness of this method is demonstrated with the assimilation of satellite precipitation data. It is shown that the EFSO-based method can efficiently suggest data selection criteria that significantly improve the assimilation results.

scholarly journals Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China

2017 ◽  
Vol 21 (11) ◽  
pp. 5805-5821 ◽  
Author(s):  
Fan Yang ◽  
Hui Lu ◽  
Kun Yang ◽  
Jie He ◽  
Wei Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Temporal Variations ◽  
Spatiotemporal Variability ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Precipitation Data ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System

Abstract. Precipitation and shortwave radiation play important roles in climatic, hydrological and biogeochemical cycles. Several global and regional forcing data sets currently provide historical estimates of these two variables over China, including the Global Land Data Assimilation System (GLDAS), the China Meteorological Administration (CMA) Land Data Assimilation System (CLDAS) and the China Meteorological Forcing Dataset (CMFD). The CN05.1 precipitation data set, a gridded analysis based on CMA gauge observations, also provides high-resolution historical precipitation data for China. In this study, we present an intercomparison of precipitation and shortwave radiation data from CN05.1, CMFD, CLDAS and GLDAS during 2008–2014. We also validate all four data sets against independent ground station observations. All four forcing data sets capture the spatial distribution of precipitation over major land areas of China, although CLDAS indicates smaller annual-mean precipitation amounts than CN05.1, CMFD or GLDAS. Time series of precipitation anomalies are largely consistent among the data sets, except for a sudden decrease in CMFD after August 2014. All forcing data indicate greater temporal variations relative to the mean in dry regions than in wet regions. Validation against independent precipitation observations provided by the Ministry of Water Resources (MWR) in the middle and lower reaches of the Yangtze River indicates that CLDAS provides the most realistic estimates of spatiotemporal variability in precipitation in this region. CMFD also performs well with respect to annual mean precipitation, while GLDAS fails to accurately capture much of the spatiotemporal variability and CN05.1 contains significant high biases relative to the MWR observations. Estimates of shortwave radiation from CMFD are largely consistent with station observations, while CLDAS and GLDAS greatly overestimate shortwave radiation. All three forcing data sets capture the key features of the spatial distribution, but estimates from CLDAS and GLDAS are systematically higher than those from CMFD over most of mainland China. Based on our evaluation metrics, CLDAS slightly outperforms GLDAS. CLDAS is also closer than GLDAS to CMFD with respect to temporal variations in shortwave radiation anomalies, with substantial differences among the time series. Differences in temporal variations are especially pronounced south of 34° N. Our findings provide valuable guidance for a variety of stakeholders, including land-surface modelers and data providers.

scholarly journals Application of TRMM Precipitation Data to Evaluate Drought and Its Effects on Water Resources Instability

2019 ◽  
Vol 9 (24) ◽  
pp. 5377
Author(s):  
Ata Amini ◽  
Abdolnabi Abdeh Kolahchi ◽  
Nadhir Al-Ansari ◽  
Mehdi Karami Moghadam ◽  
Thamer Mohammad
Keyword(s):  
Water Resources ◽  
Standardized Precipitation Index ◽  
Remote Sensing Data ◽  
Tropical Rainfall Measuring Mission ◽  
Monthly Precipitation ◽  
Precipitation Data ◽  
Discharge Data ◽  
Satellite Precipitation ◽  
Discharge Rates ◽  
Trmm Precipitation

The present research was carried out to study drought and its effects upon water resources using remote sensing data. To this end, the tropical rainfall measuring mission (TRMM) satellite precipitation, the synoptic stations, and fountain discharge data were employed. For monitoring of drought in the study area, in Kermanshah province, Iran, the monthly precipitation data of the synoptic stations along with TRMM satellite precipitation datasets were collected and processed in the geographic information system (GIS) environment. Statistical indicators were applied to evaluate the accuracy of TRMM precipitation against the meteorological stations’ data. Standardized precipitation index, SPI, and normalized fountain discharge were used in the monitoring of drought conditions, and fountains discharge, respectively. The fountains were selected so that in addition to enjoying the most discharge rates, they spread along the study area. The evaluation of precipitation data showed that the TRMM precipitation data were of high accuracy. Studies in temporal scale are indicative of the strike of drought in this region to the effect that for most months of the year, frequency and duration in dry periods are much more than in wet periods. As for seasonal scales, apart from winter, the frequency and duration of drought in spring and autumn have been longer than in wet years. Moreover, the duration of these periods was different. A comparison between the results of changes in fountain discharges and drought index in the region has verified that the drought has caused a remarkable decline in the fountain discharges.

scholarly journals Precipitation Trends over the Indus Basin

Climate ◽  
2019 ◽  
Vol 7 (10) ◽  
pp. 116 ◽  
Author(s):  
Nir Y. Krakauer ◽  
Tarendra Lakhankar ◽  
Ghulam H. Dars
Keyword(s):  
Remote Sensing ◽  
Large Population ◽  
Tropical Rainfall Measuring Mission ◽  
Indus Basin ◽  
Data Sets ◽  
Precipitation Trend ◽  
Precipitation Data ◽  
Data Set ◽  
Station Data ◽  
Precipitation Trends

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

scholarly journals Dynamic spatio-temporal generation of large-scale synthetic gridded precipitation: with improved spatial coherence of extremes

2019 ◽  
Vol 34 (9) ◽  
pp. 1369-1383 ◽  
Author(s):  
Dirk Diederen ◽  
Ye Liu
Keyword(s):  
Large Scale ◽  
Spatial Coherence ◽  
Original Data ◽  
Return Level ◽  
Data Sets ◽  
Large Set ◽  
Precipitation Data ◽  
Data Set ◽  
Spatio Temporal ◽  
Synthetic Precipitation

Abstract With the ongoing development of distributed hydrological models, flood risk analysis calls for synthetic, gridded precipitation data sets. The availability of large, coherent, gridded re-analysis data sets in combination with the increase in computational power, accommodates the development of new methodology to generate such synthetic data. We tracked moving precipitation fields and classified them using self-organising maps. For each class, we fitted a multivariate mixture model and generated a large set of synthetic, coherent descriptors, which we used to reconstruct moving synthetic precipitation fields. We introduced randomness in the original data set by replacing the observed precipitation fields in the original data set with the synthetic precipitation fields. The output is a continuous, gridded, hourly precipitation data set of a much longer duration, containing physically plausible and spatio-temporally coherent precipitation events. The proposed methodology implicitly provides an important improvement in the spatial coherence of precipitation extremes. We investigate the issue of unrealistic, sudden changes on the grid and demonstrate how a dynamic spatio-temporal generator can provide spatial smoothness in the probability distribution parameters and hence in the return level estimates.

scholarly journals Difference in Bias Approach for Commutability Assessment: Application to Frozen Pools of Human Serum Measured by 8 Direct Methods for HDL and LDL Cholesterol

2015 ◽  
Vol 61 (8) ◽  
pp. 1107-1113 ◽  
Author(s):  
William J Korzun ◽  
Göran Nilsson ◽  
Lorin M Bachmann ◽  
Gary L Myers ◽  
Ikunosuke Sakurabayashi ◽  
...  
Keyword(s):  
Random Error ◽  
Ldl Cholesterol ◽  
Direct Method ◽  
Direct Methods ◽  
Measurement Procedure ◽  
Critical Value ◽  
Reference Measurement ◽  
Reference Procedure ◽  
Error Components ◽  
The Difference

Abstract BACKGROUND We used a difference in bias approach to evaluate the commutability of 4 frozen serum pools for 8 direct methods for measurement of HDL and LDL cholesterol (HDLC and LDLC). METHODS Freshly collected nonfrozen sera from 138 diseased and 37 nondiseased patients and 4 frozen pools from the CDC Lipid Standardization Program were measured by direct methods and by the beta-quantification reference measurement procedure of the CDC. We used an error components model to estimate the difference in the bias component of error plus its uncertainty for frozen pools vs patient samples between the direct method and the reference procedure. Frozen pools with bias differences less than a critical value determined by either medical requirements for bias or the random error components of the measurement procedures were considered commutable. RESULTS On the basis of medical requirement criteria, 1 of the 4 frozen pools was commutable for most of the HDLC methods for both diseased and nondiseased patients, and none was commutable for LDLC methods. On the basis of random error criteria, all of the frozen pools were generally commutable for all of the HDLC methods for both diseased and nondiseased patients, and 1 of the 4 frozen pools was generally commutable for most of the LDLC methods for both diseased and nondiseased patients. CONCLUSIONS Commutability was assessed as the closeness of agreement of the difference in bias between a reference material and a set of patient samples. Criteria for commutability could be based on fixed medical requirements for bias or on random error components.

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