scholarly journals Multidimensional evaluation of the TRMM 3B43V7 satellite-based precipitation product in mainland China from 1998–2016

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8615 ◽  
Author(s):  
Ziteng Zhou ◽  
Bin Guo ◽  
Youzhe Su ◽  
Zhongsheng Chen ◽  
Juan Wang
Keyword(s):  
Spatial Scales ◽  
Mainland China ◽  
Rain Gauge ◽  
Seasonal Patterns ◽  
Rain Gauges ◽  
Multidimensional Evaluation ◽  
Precipitation Characteristics ◽  
Temporal And Spatial ◽  
Rain Measurement ◽  
Statistical Metrics

This study evaluates the applicability of the Tropical Rain Measurement Mission (TRMM) 3B43V7 product for use throughout mainland China. Four statistical metrics were used based on the observations made by rain gauges; these metrics were the correlation coefficient (R), the relative bias (RB), the root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE), and they were chosen to evaluate the performance of the 3B43V7 product at temporal and spatial scales. The results revealed that 3B43V7 performed satisfactorily on all timescales (R > 0.9 and NSE > 0.86); however, it overestimated the results when compared with the rain gauge observations in certain circumstances (RB = 9.7%). Monthly estimates from 3B43V7 were in agreement with rain gauge observations. 3B43V7 can effectively capture the seasonal patterns of precipitation characteristics over mainland China. However, 3B43V7 tends to register a greater overestimation of precipitation in the winter (RB = 14%) than in other seasons while showing greater consistency with the observations made by rain gauges during dry periods. The 3B43V7 product performs well in the eastern part of mainland China, while its performance is poor in the western part of mainland China. In terms of altitude, 3B43V7 performs satisfactorily in areas with moderate to low altitudes (when altitude < 3,500 m, R > 0.9, NSE > 0.8 and RB < 10.2%) but RB values increase with altitude. Overall, 3B43V7 had a favorable performance throughout mainland China.

scholarly journals Quality-Based Combination of Multi-Source Precipitation Data

2020 ◽  
Vol 12 (11) ◽  
pp. 1709 ◽  
Author(s):  
Anna Jurczyk ◽  
Jan Szturc ◽  
Irena Otop ◽  
Katarzyna Ośródka ◽  
Piotr Struzik
Keyword(s):  
Spatial Resolution ◽  
Flash Flood ◽  
Radar Data ◽  
Rain Gauge ◽  
Quality Information ◽  
Precipitation Data ◽  
Spatial Error ◽  
Rain Gauges ◽  
Precipitation Measurement ◽  
Temporal And Spatial

A quantitative precipitation estimate (QPE) provides basic information for the modelling of many kinds of hydro-meteorological processes, e.g., as input to rainfall-runoff models for flash flood forecasting. Weather radar observations are crucial in order to meet the requirements, because of their very high temporal and spatial resolution. Other sources of precipitation data, such as telemetric rain gauges and satellite observations, are also included in the QPE. All of the used data are characterized by different temporal and spatial error structures. Therefore, a combination of the data should be based on quality information quantitatively determined for each input to take advantage of a particular source of precipitation measurement. The presented work on multi-source QPE, being implemented as the RainGRS system, has been carried out in the Polish national meteorological and hydrological service for new nowcasting and hydrological platforms in Poland. For each of the three data sources, different quality algorithms have been designed: (i) rain gauge data is quality controlled and, on this basis, spatial interpolation and estimation of quality field is performed, (ii) radar data are quality controlled by RADVOL-QC software that corrects errors identified in the data and characterizes its final quality, (iii) NWC SAF (Satellite Application Facility on support to Nowcasting and Very Short Range Forecasting) products for both visible and infrared channels are combined and the relevant quality field is determined from empirical relationships that are based on analyses of the product performance. Subsequently, the quality-based QPE is generated with a 1-km spatial resolution every 10 minutes (corresponding to radar data). The basis for the combination is a conditional merging technique that is enhanced by involving detailed quality information that is assigned to individual input data. The validation of the RainGRS estimates was performed taking account of season and kind of precipitation.

scholarly journals An Early Performance Evaluation of the NEXRAD Dual-Polarization Radar Rainfall Estimates for Urban Flood Applications

2013 ◽  
Vol 28 (6) ◽  
pp. 1478-1497 ◽  
Author(s):  
Luciana K. Cunha ◽  
James A. Smith ◽  
Mary Lynn Baeck ◽  
Witold F. Krajewski
Keyword(s):  
Kansas City ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Dual Polarization ◽  
Radar Rainfall ◽  
Urban Flood ◽  
Temporal And Spatial ◽  
Single Polarization ◽  
Early Performance ◽  
Rainfall Estimates

Abstract Dual-polarization radars are expected to provide better rainfall estimates than single-polarization radars because of their ability to characterize hydrometeor type. The goal of this study is to evaluate single- and dual-polarization radar rainfall fields based on two overlapping radars (Kansas City, Missouri, and Topeka, Kansas) and a dense rain gauge network in Kansas City. The study area is located at different distances from the two radars (23–72 km for Kansas City and 104–157 km for Topeka), allowing for the investigation of radar range effects. The temporal and spatial scales of radar rainfall uncertainty based on three significant rainfall events are also examined. It is concluded that the improvements in rainfall estimation achieved by polarimetric radars are not consistent for all events or radars. The nature of the improvement depends fundamentally on range-dependent sampling of the vertical structure of the storms and hydrometeor types. While polarimetric algorithms reduce range effects, they are not able to completely resolve issues associated with range-dependent sampling. Radar rainfall error is demonstrated to decrease as temporal and spatial scales increase. However, errors in the estimation of total storm accumulations based on polarimetric radars remain significant (up to 25%) for scales of approximately 650 km2.

scholarly journals Relationships between lightning and rainfall intensities during rainy events in Cyprus

2010 ◽  
Vol 23 ◽  
pp. 87-92 ◽  
Author(s):  
S. Michaelides ◽  
K. Savvidou ◽  
K. Nicolaides
Keyword(s):  
Rain Gauge ◽  
Rainfall Data ◽  
Time Lags ◽  
Spatial Relationships ◽  
Time And Space ◽  
Data Set ◽  
Rain Gauges ◽  
Hourly Rainfall ◽  
Temporal And Spatial ◽  
The Relationship

Abstract. The objective of this work is to study the relationship between the number of lightning recorded by a network of lightning detectors and the amount of rainfall recorded by the network of automatic rain gauges, during rainy events in Cyprus. This study aims at revealing possible temporal and spatial "relationships" between rainfall and lightning intensities. The data used are based on the available records of hourly rainfall data and the "associated" lightning data, with respect to both time and space. The search for temporal and spatial relationships between lightning and rainfall is made by considering various time-lags between lightning and rainfall, and by varying the area around the rain gauge which the associated lightning data set refers to. The methodology adopted in this paper is a statistical one and rainy events registered under the European Project "FLASH" are examined herein.

scholarly journals Evaluating the Drought-Monitoring Utility of GPM and TRMM Precipitation Products over Mainland China

2021 ◽  
Vol 13 (20) ◽  
pp. 4153
Author(s):  
Shuai Cheng ◽  
Weiguang Wang ◽  
Zhongbo Yu
Keyword(s):  
Real Time ◽  
Spatial Scales ◽  
Eastern China ◽  
Mainland China ◽  
Western China ◽  
Drought Monitoring ◽  
Ground Observation ◽  
Trmm Precipitation ◽  
Temporal And Spatial

The purpose of this study was to evaluate the applicability of medium and long-term satellite rainfall estimation (SRE) precipitation products for drought monitoring over mainland China. Four medium and long-term (19 a) SREs, i.e., the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7, the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement V06 post-real time Final Run precipitation products (IMF6), Global Rainfall Map in Near-real-time Gauge-calibrated Rainfall Product (GSMaP_Gauge_NRT) for product version 6 (GNRT6) and gauge-adjusted Global Satellite Mapping of Precipitation V6 (GGA6) were considered. The accuracy of the four SREs was first evaluated against ground observation precipitation data. The Standardized Precipitation Evapotranspiration Index (SPEI) based on four SREs was then compared at multiple temporal and spatial scales. Finally, four typical drought-influenced regions, i.e., the Northeast China Plain (NEC), Huang-Huai-Hai Plain (3HP), Yunnan–Guizhou Plateau (YGP) and South China (SC) were chosen as examples to analyze the ability of four SREs to capture the temporal and spatial changes of typical drought events. The results show that compared with GNRT6, the precipitation estimated by GGA6, IMF6 and 3B42V7 are in better agreement with the ground observation results. In the evaluation using SPEI, the four SREs performed well in eastern China but have large uncertainty in western China. GGA6 and IMF6 perform superior to GNRT6 and 3B42V7 in estimating SPEI and identifying typical drought events and behave almost the same. In general, GPM precipitation products have great potential to substitute TRMM precipitation products for drought monitoring. Both GGA6 and IMF6 are suitable for historical drought analysis. Due to the shorter time latency of data release and good performance in the eastern part of mainland China, GNRT6 and GGA6 might play a role for near real-time drought monitoring in the area. The results of this research will provide reference for the application of the SREs for drought monitoring in the GPM era.

scholarly journals Temporal and spatial scale and positional effects on rain erosivity derived from contiguous rain data

2018 ◽  
Author(s):  
Franziska K. Fischer ◽  
Tanja Winterrath ◽  
Karl Auerswald
Keyword(s):  
Spatial Scale ◽  
Temporal Resolution ◽  
Rain Gauge ◽  
Point Scale ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Intensity Threshold ◽  
Gauge Data ◽  
Temporal And Spatial ◽  
Rain Data

Abstract. Up until now, erosivity required for soil loss predictions has been mainly estimated from rain gauge data at point scale and then spatially interpolated to erosivity maps. Contiguous radar rain data are now available but they differ in temporal and spatial scale from the point scale. We determined how the intensity threshold has to be modified and which temporal and spatial scaling factors have to be applied to account for the differences in scale. Furthermore, a positional effect quantifies heterogeneity of erosivity within 1 km2, which presently is the highest resolution of freely available gauge-adjusted radar rain data. A method effect accounts for differences in measuring peculiarities between rain gauges and weather radars. These effects were analysed using several large data sets with a total of approximately 2 x 106 erosive events (e.g., records of 115 rain gauges for 16 years distributed across Germany and radar rain data for the same locations and events). With decreasing temporal resolution, peak intensities decreased and the intensity threshold of erosive rains was met less often. This became especially pronounced, when time increments became larger than 30 min. With decreasing spatial resolution, intensity peaks were also reduced but additionally large areas without erosive rain were included within one pixel. This was due to the steep spatial gradients in erosivity. Erosivity of single events could be zero or more than twice the mean annual sum within a distance of less than 1 km. We conclude that the resulting large positional effect requires use of contiguous rain data, even over distances of less than 1 km, but at the same time contiguously measured radar data cannot be resolved to point scale. The temporal scale is easier to consider but time increments larger than 30 min should be avoided because the loss of information increases considerably. We provide functions to account for temporal scale (from 1 min to 120 min) and spatial scale (from rain gauge to pixels of 18 km width) that can be applied to rain gauge data of low temporal resolution and to contiguous radar rain data.

scholarly journals Hydrological evaluation of open-access precipitation data using SWAT at multiple temporal and spatial scales

2020 ◽  
Author(s):  
Jianzhuang Pang ◽  
Huilan Zhang ◽  
Quanxi Xu ◽  
Yujie Wang ◽  
Yunqi Wang ◽  
...  
Keyword(s):  
Open Access ◽  
Water Balance ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Surface Flow ◽  
Base Flow ◽  
Detection Methods ◽  
Hydrological Process ◽  
Water Balance Components ◽  
Temporal And Spatial

Abstract. Temporal and spatial precipitation information is key to conducting effective hydrological process simulation and forecasting. Herein, we implemented a comprehensive evaluation of three selected precipitation products in the Jiang River Watershed (JRW) located in southwest China. A number of indices were used to statistically analyze the differences between two open-access precipitation products (OPPs), i.e. Climate Hazards Group Infra-Red Precipitation with Station (CHIRPS) and CPC-Global (CPC), and the rain gauge (Gauge). The three products were then categorized into sub-basins to drive SWAT simulations. The results show: (1) the three products are highly consistent in temporal variation on a monthly scale, yet distinct on a daily scale. CHIRPS is characterized by overestimation of light rain, underestimation of heavy rain, and a high probability of false alarm. CPC generally underestimates rainfall of all magnitudes; (2) All three products satisfactorily reproduce the stream discharges at the JRW outlet with better performance than the Gauge model. On a temporal scale, the OPPs are inferior with respect to capturing flood peak, yet superior at describing other hydrograph features, e.g. rising and falling processes and base flow. On a spatial scale, CHIRPS offers the advantage of deriving smooth, distributed precipitation and runoff due to its high resolution; (3) The water balance components derived from SWAT models with equal simulated streamflow discharges are remarkably different between the three precipitation inputs. The precipitation spatial pattern results in an increasing surface flow trend from upstream to downstream. The results of this study demonstrate that evaluating precipitation products using only streamflow simulation accuracy will conceal the dissimilarities between these products. Hydrological models alter hydrologic mechanisms by adjusting calibrated parameters. Specifically, different precipitation detection methods lead to temporal and spatial variation of water balance components, demonstrating the complexity in describing natural hydrologic processes.

scholarly journals Hydrological evaluation of open-access precipitation data using SWAT at multiple temporal and spatial scales

2020 ◽  
Vol 24 (7) ◽  
pp. 3603-3626 ◽  
Author(s):  
Jianzhuang Pang ◽  
Huilan Zhang ◽  
Quanxi Xu ◽  
Yujie Wang ◽  
Yunqi Wang ◽  
...  
Keyword(s):  
Open Access ◽  
Model Simulation ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Surface Flow ◽  
Hydrological Process ◽  
Water Balance Components ◽  
Light Rain ◽  
Extreme Flood ◽  
Temporal And Spatial

Abstract. Temporal and spatial precipitation information is key to conducting effective hydrological-process simulation and forecasting. Herein, we implemented a comprehensive evaluation of three selected precipitation products in the Jialing River watershed (JRW) located in southwestern China. A number of indices were used to statistically analyze the differences between two open-access precipitation products (OPPs), i.e., Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and Climate Prediction Center Gauge-Based Analysis of Global Daily Precipitation (CPC), and the rain gauge (Gauge). The three products were then categorized into subbasins to drive SWAT simulations. The results show the following. (1) The three products are highly consistent in temporal variation on a monthly scale yet distinct on a daily scale. CHIRPS is characterized by an overestimation of light rain, underestimation of heavy rain, and high probability of false alarm. CPC generally underestimates rainfall of all magnitudes. (2) Both OPPs satisfactorily reproduce the stream discharges at the JRW outlet with slightly worse performance than the Gauge model. Model with CHIRPS as inputs performed slightly better in both model simulation and fairly better in uncertainty analysis than that of CPC. On a temporal scale, the OPPs are inferior with respect to capturing flood peak yet superior at describing other hydrograph features, e.g., rising and falling processes and baseflow. On a spatial scale, CHIRPS offers the advantage of deriving smooth, distributed precipitation and runoff due to its high resolution. (3) The water balance components derived from SWAT models with equal simulated streamflow discharges are remarkably different between the three precipitation inputs. The precipitation spatial pattern results in an increasing surface flow trend from upstream to downstream. The results of this study demonstrate that with similar performance in simulating watershed runoff, the three precipitation datasets tend to conceal the identified dissimilarities through hydrological-model parameter calibration, which leads to different directions of hydrologic processes. As such, multiple-objective calibration is recommended for large and spatially resolved watersheds in future work. The main findings of this research suggest that the features of OPPs facilitate the widespread use of CHIRPS in extreme flood events and CPC in extreme drought analyses in future climate.

Combined rainfall estimates from personal weather station and commercial microwave link data in Germany

2021 ◽  
Author(s):  
Maximilian Graf ◽  
Abbas El Hachem ◽  
Micha Eisele ◽  
Jochen Seidel ◽  
Christian Chwala ◽  
...  
Keyword(s):  
Water Resource Management ◽  
Hydrological Cycle ◽  
Spatial Scales ◽  
Rainfall Variability ◽  
Rain Gauge ◽  
Data Sets ◽  
Rain Gauge Data ◽  
Rain Gauges ◽  
Gauge Data ◽  
Rainfall Estimates

&lt;p&gt;Rain gauges and weather radars are the default sources of rainfall information. Rainfall estimates from these sensors improve our understanding of the hydrological cycle and are vital for water-resource management, agriculture, urban planning, as well as for weather, climate, and hydrological modelling. Still, due to the high spatio-temporal variability of rainfall and the specific drawbacks of the individual rainfall sensors, the rainfall variability cannot be captured completely. In the last decade, the number and availability of opportunistic rainfall sensors increased rapidly. These sensors are initially not meant to measure rainfall for scientific or operational purposes, but, if processed carefully, can be used for these cases . Here we present an analysis of two years of data from two opportunistic rainfall sensors, namely personal weather stations (PWS) and commercial microwave links (CMLs). We evaluate the performance of rainfall maps derived from these sensors on different spatial and temporal scales in Germany.&lt;/p&gt;&lt;p&gt;The data from around 15000 PWS tipping bucket-style rain gauges from the Netatmo network were accessed via Netatmos API. The data from around 4000 CMLs, which can be used to derive rainfall estimates from the rain-induced attenuation of the CMLs&amp;#8217; signal, were obtained from Ericsson. As both, PWS and CML data, can suffer from various error sources e.g. from unfavourable positioning and poor maintenance of PWS and from non-rain induced attenuation of the CMLs signal, we used a strict filtering routine. A total of seven gridded rainfall products were derived from different combinations of PWS, CML, and rain gauge data from the German Weather Service (DWD) with a geostatistical interpolation approach. This approach incorporates the uncertainty of the opportunistic sensors and the path-averaging characteristic of the CML observations.&lt;/p&gt;&lt;p&gt;To evaluate the resulting rainfall maps, we used three rain gauge data sets with different temporal and spatial scales covering the whole of Germany, the state of Rhineland-Palatinate and the city of Reutlingen, respectively. For all three reference data sets, rainfall maps from opportunistic sensors provided good agreement, with best results being derived from the combinations with PWS. Rainfall maps including CML data had the lowest bias. In a comparison with gauge adjusted radar products from the DWD, the radar products yielded better results than the rainfall maps from opportunistic sensors for the country-wide comparison of daily rainfall sums, which was carried out using the DWD&amp;#8217;s independent network of manual rain gauges. But for the hourly references covering Rhineland-Palatinate and Reutlingen, the rainfall maps derived from opportunistic sensors outperformed the radar products. These results highlight the capabilities of opportunistic rainfall sensors which could be used in many hydrometeorological applications.&lt;/p&gt;

scholarly journals Wet and dry spells in Senegal: Evaluation of satellite-based and model re-analysis rainfall estimates

2019 ◽  
Author(s):  
Cheikh Modou Noreyni Fall ◽  
Christophe Lavaysse ◽  
Mamadou Simina Drame ◽  
Geremy Panthou ◽  
Amadou Thierno Gaye
Keyword(s):  
Spatial Scales ◽  
Spatial Coherence ◽  
West African Monsoon ◽  
Rain Gauge ◽  
West African ◽  
Dry Spells ◽  
Dry Spell ◽  
False Start ◽  
Temporal And Spatial ◽  
Rain Gauge Network

Abstract. In this study, wet and dry spells over Senegal provided by four datasets based on satellite data (TRMM-3B42 V7, TAMSAT V3, CMORPH V1.0, CHIRPS V2.0), two fully based on (re)analyses (NCEP-CFSR, ERA5) and one was fully based on gauge observations (CPC Unified V1.0/RT) are compared with respect to observation datasets derived from 65 rain gauge network. All datasets were converted to the same temporal and spatial scales with 0.25 × 0.25 as resolution. Ordinary kriging (OK) and block kriging (BK) were used for the spatial interpolation of the gauge data. Despite a spatial coherence of the seasonal rainfall accumulation between all products, more variability with intra-seasonal features are shown in this paper. The seasonal cycle of dry days shows that TRMM, CPC, ERA5, NCEP and OK record more dry days (from 45 % to 55 % of dry days in August) while TAMSAT, CHIRPS, CMORPH and BK record less dry day (from 40 % to 30 % of dry days in August). All datasets highlighted an agreement that dry spell indicator underscore often false start and early cessation of the rainy Season in Senegal. Although, it can rarely occurs during intensification of West African monsoon (August–September). The most contrast is found on the detection of wet indicators intensity. Wet spell (defined as period with precipitation higher than a certain percentile of historical precipitation) are more severe in OK and TRMM than in other datasets. However, a great similarity is shown on their temporal frequencies.

scholarly journals Performance of the CMORPH and GPM IMERG Products over the United Arab Emirates

2020 ◽  
Vol 12 (9) ◽  
pp. 1426 ◽  
Author(s):  
Tareefa S. Alsumaiti ◽  
Khalid Hussein ◽  
Dawit T. Ghebreyesus ◽  
Hatim O. Sharif
Keyword(s):  
United Arab Emirates ◽  
Spatial Scales ◽  
Rain Gauge ◽  
Light Rain ◽  
Rain Gauges ◽  
Statistical Measures ◽  
Quantitative Accuracy ◽  
Highly Correlated ◽  
Global Precipitation Mission ◽  
Global Precipitation

Satellite-based precipitation products are becoming available at very high temporal and spatial resolutions, which has accelerated their use in various hydro-meteorological and hydro-climatological applications. Because the quantitative accuracy of such products is affected by numerous factors related to atmospheric and terrain properties, validating them over different regions and environments is needed. This study investigated the performance of two high-resolution global satellite-based precipitation products: the climate prediction center MORPHing technique (CMORPH) and the latest version of the Integrated Multi-SatellitE Retrievals for the Global Precipitation Mission (GPM) algorithm (IMERG), V06, over the United Arab Emirates from 2010 through 2018. The estimates of the products and that of 71 in situ rain gauges distributed across the country were compared by employing several common quantitative, categorical, and graphical statistical measures at daily, event-duration, and annual temporal scales, and at the station and study area spatial scales. Both products perform quite well in rainfall detection (above 70%), but report rainfall not observed by the rain gauges at an alarming rate (more than 30%), especially for light rain (lower quartile). However, for moderate and intense (upper quartiles) rainfall rates, performance is much better. Because both products are highly correlated with rain gauge observations (mostly above 0.7), the satellite rainfall estimates can probably be significantly improved by removing the bias. Overall, the CMORPH and IMERG estimates demonstrate great potential for filling spatial gaps in rainfall observations, in addition to improving the temporal resolution. However, further improvement is required, regarding the overestimation and underestimation of small and large rainfall amounts, respectively.

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