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12 pages, 3663 KiB

Open AccessArticle

Using Satellite Error Modeling to Improve GPM-Level 3 Rainfall Estimates over the Central Amazon Region

by Rômulo Oliveira, Viviana Maggioni, Daniel Vila and Leonardo Porcacchia

Remote Sens. 2018, 10(2), 336; https://doi.org/10.3390/rs10020336 - 23 Feb 2018

Cited by 21 | Viewed by 4834

Abstract

This study aims to assess the characteristics and uncertainty of Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 rainfall estimates and to improve those estimates using an error model over the central Amazon region. The S-band Amazon Protection National System [...] Read more.

This study aims to assess the characteristics and uncertainty of Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 rainfall estimates and to improve those estimates using an error model over the central Amazon region. The S-band Amazon Protection National System (SIPAM) radar is used as reference and the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework is adopted to characterize uncertainties associated with the satellite precipitation product. PUSH is calibrated and validated for the study region and takes into account factors like seasonality and surface type (i.e., land and river). Results demonstrated that the PUSH model is suitable for characterizing errors in the IMERG algorithm when compared with S-band SIPAM radar estimates. PUSH could efficiently predict the satellite rainfall error distribution in terms of spatial and intensity distribution. However, an underestimation (overestimation) of light satellite rain rates was observed during the dry (wet) period, mainly over rivers. Although the estimated error showed a lower standard deviation than the observed error, the correlation between satellite and radar rainfall was high and the systematic error was well captured along the Negro, Solimões, and Amazon rivers, especially during the wet season. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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27 pages, 12895 KiB

Open AccessArticle

Fine-Resolution Precipitation Mapping in a Mountainous Watershed: Geostatistical Downscaling of TRMM Products Based on Environmental Variables

by Yueyuan Zhang, Yungang Li, Xuan Ji, Xian Luo and Xue Li

Remote Sens. 2018, 10(1), 119; https://doi.org/10.3390/rs10010119 - 17 Jan 2018

Cited by 54 | Viewed by 7859

Abstract

Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon [...] Read more.

Accurate precipitation data at a high spatial resolution are essential for hydrological, meteorological, and ecological research at regional scales. This study presented a geostatistical downscaling-calibration procedure to derive the high spatial resolution maps of precipitation over a mountainous watershed affected by a monsoon climate. Based on the relationships between precipitation and other environmental variables, such as the Normalized Difference Vegetation Index (NDVI) and digital elevation model (DEM), a regression model with a residual correction method was applied to downscale the Tropical Rainfall Measuring Mission (TRMM) 3B43 product from coarse resolution (25 km) to fine resolution (1 km). Two methods, geographical difference analysis (GDA) and geographical ratio analysis (GRA), were used to calibrate the downscaled TRMM precipitation data. Monthly 1 km precipitation data were obtained by disaggregating 1 km annual downscaled and calibrated precipitation data using monthly fractions derived from original TRMM data. The downscaled precipitation datasets were validated against ground observations measured by rain gauges. According to the comparison of different regression models and residual interpolation methods, a geographically-weighted regression kriging (GWRK) method was accepted to conduct the downscaling of TRMM data. The downscaled TRMM precipitation data obtained using GWRK described the spatial patterns of precipitation reasonably well at a spatial resolution of 1 km with more detailed information when compared with the original TRMM precipitation. The results of validation indicated that the GRA method provided results with higher accuracy than that of the GDA method. The final annual and monthly downscaled precipitation not only had significant improvement in spatial resolution, but also agreed well with data from the validation rain gauge stations (i.e., R² = 0.72, RMSE = 161.0 mm, MAE = 127.5 mm, and Bias = 0.050 for annual downscaled precipitation during 2001 to 2015; and R² = 0.91, RMSE = 22.2 mm, MAE = 13.5 mm, and Bias = 0.048 for monthly downscaled precipitation during 2001 to 2015). In general, the downscaling-calibration procedure is useful for complex mountainous areas with insufficient ground gauges. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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20 pages, 9090 KiB

Open AccessArticle

Incorporating Satellite Precipitation Estimates into a Radar-Gauge Multi-Sensor Precipitation Estimation Algorithm

by Yuxiang He, Yu Zhang, Robert Kuligowski, Robert Cifelli and David Kitzmiller

Remote Sens. 2018, 10(1), 106; https://doi.org/10.3390/rs10010106 - 13 Jan 2018

Cited by 10 | Viewed by 6820

Abstract

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and [...] Read more.

This paper presents a new and enhanced fusion module for the Multi-Sensor Precipitation Estimator (MPE) that would objectively blend real-time satellite quantitative precipitation estimates (SQPE) with radar and gauge estimates. This module consists of a preprocessor that mitigates systematic bias in SQPE, and a two-way blending routine that statistically fuses adjusted SQPE with radar estimates. The preprocessor not only corrects systematic bias in SQPE, but also improves the spatial distribution of precipitation based on SQPE and makes it closely resemble that of radar-based observations. It uses a more sophisticated radar-satellite merging technique to blend preprocessed datasets, and provides a better overall QPE product. The performance of the new satellite-radar-gauge blending module is assessed using independent rain gauge data over a five-year period between 2003–2007, and the assessment evaluates the accuracy of newly developed satellite-radar-gauge (SRG) blended products versus that of radar-gauge products (which represents MPE algorithm currently used in the NWS (National Weather Service) operations) over two regions: (I) Inside radar effective coverage and (II) immediately outside radar coverage. The outcomes of the evaluation indicate (a) ingest of SQPE over areas within effective radar coverage improve the quality of QPE by mitigating the errors in radar estimates in region I; and (b) blending of radar, gauge, and satellite estimates over region II leads to reduction of errors relative to bias-corrected SQPE. In addition, the new module alleviates the discontinuities along the boundaries of radar effective coverage otherwise seen when SQPE is used directly to fill the areas outside of effective radar coverage. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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13907 KiB

Open AccessArticle

Can Satellite Precipitation Products Estimate Probable Maximum Precipitation: A Comparative Investigation with Gauge Data in the Dadu River Basin

by Yuan Yang, Guoqiang Tang, Xiaohui Lei, Yang Hong and Na Yang

Remote Sens. 2018, 10(1), 41; https://doi.org/10.3390/rs10010041 - 27 Dec 2017

Cited by 24 | Viewed by 6226

Abstract

Probable Maximum Precipitation (PMP) is an essential prerequisite in designing dams, spillways, and reservoirs in order to minimize the risk of overtopping infrastructure collapse, especially under today’s changing climate. This study investigates conventional PMP estimation approach by using both scarce in-situ observations and [...] Read more.

Probable Maximum Precipitation (PMP) is an essential prerequisite in designing dams, spillways, and reservoirs in order to minimize the risk of overtopping infrastructure collapse, especially under today’s changing climate. This study investigates conventional PMP estimation approach by using both scarce in-situ observations and mainstream satellite precipitation products in the Dadu River basin, where plenty of reservoirs and dams are being built. The satellite data include Climate Prediction Center (CPC) MORPHing algorithm (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and Tropic Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42V7. The evaluation of satellite products shows that CMORPH and 3B42V7 agree well with gauge-based dataset for the period of 1998–2013 at both the grid and basin scales, also capturing the extreme precipitation events, with high Correlation Coefficients (CC) in terms of 0.68 and 0.71, respectively. Also, CMORPH and 3B42V7 show better performance for the magnitude and spatial distribution of 24-h PMP in such complex terrains. PERSIANN-CDR shows an overestimation in the upstream and an underestimation in the downstream. As among the first studies of satellite precipitation-based PMP estimation, this work sheds lights on the suitability of satellite precipitation in PMP estimation and could provide a reference for future extended spatially-distributed PMP estimation in vast ungauged regions. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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6241 KiB

Open AccessArticle

Evaluation of Satellite-Based Precipitation Products from IMERG V04A and V03D, CMORPH and TMPA with Gauged Rainfall in Three Climatologic Zones in China

by Guanghua Wei, Haishen Lü, Wade T. Crow, Yonghua Zhu, Jianqun Wang and Jianbin Su

Remote Sens. 2018, 10(1), 30; https://doi.org/10.3390/rs10010030 - 25 Dec 2017

Cited by 59 | Viewed by 6192

Abstract

A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates [...] Read more.

A critical evaluation of the newly released precipitation data set is very important for both the end users and data developers. Meanwhile, the evaluation may provide a benchmark for the product’s continued development and future improvement. To these ends, the four precipitation estimates including IMERG (the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement) V04A, IMERG V03D, CMORPH (the Climate Prediction Center Morphing technique)-CRT and TRMM (the Tropical Rainfall Measuring Mission) 3B42 are systematically evaluated against the gauge precipitation estimates at multiple spatiotemporal scales from 1 June 2014 to 30 November 2015 over three different topographic and climatic watersheds in China. Meanwhile, the statistical methods are utilized to quantize the performance of the four satellite-based precipitation estimates. The results show that: (1) over the Tibetan Plateau cold region, among all products, IMERG V04A underestimates precipitation with the largest RB (−46.98%) during the study period and the similar results are seen at the seasonal scale. However, IMERG V03D demonstrates the best performance according to RB (7.46%), RMSE (0.44 mm/day) and RRMSE (28.37%). Except for in summer, TRMM 3B42 perform better than CMORPH according to RMSEs, RRMSEs and Rs; (2) within the semi-humid Huaihe River Basin, IMERG V04A has a slight advantage over the other three satellite-based precipitation products with the lowest RMSE (0.32 mm/day) during the evaluation period and followed by IMERG V03D, TRMM 3B42 and CMORPH orderly; (3) over the arid/semi-arid Weihe River Basin, in comparison with the other three products, TRMM 3B42 demonstrates the best performance with the lowest RMSE (0.1 mm/day), RRMSE (8.44%) and highest R (0.92) during the study period. Meanwhile, IMERG V03D perform better than IMERG V04A according all the statistical indicators; (4) in winter, IMERG V04A and IMERG V03D tend to underestimate the total precipitation with RBs (−70.62% vs. −6.47% over the Tibetan Plateau, −46.92% vs. −0.66% over the Weihe River Basin, respectively); and (5) overall, except for IMERG V04A in Tibetan Plateau, all satellite-based precipitation captured the gauge-based precipitation well over the three regions according to RRMSEs, Rs and Rbs during the study period. IMERG V03D performs better than its predecessors-TRMM 3B42 and CMORPH over the Tibetan Plateau region and the Huaihe River Basin, while IMERG V04A only does so over the latter. Between the two IMERG products, IMERG V04A does not show an advantage over IMERG V03D over the Tibetan Plateau region and the Weihe River Basin. In particular, over the former, IMERG V04A performs far worse than IMERG V03D. These findings provide valuable feedback for both IMERG algorithm developers and data users. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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6343 KiB

Open AccessArticle

Validation of Satellite Rainfall Products over a Mountainous Watershed in a Humid Subtropical Climate Region of Brazil

by João Bosco Coura Dos Reis, Camilo Daleles Rennó and Eymar Silva Sampaio Lopes

Remote Sens. 2017, 9(12), 1240; https://doi.org/10.3390/rs9121240 - 1 Dec 2017

Cited by 33 | Viewed by 6913

Abstract

Remote sensing allows for the continuous and repetitive measurement of rainfall values. Satellite rainfall products such as Tropical Rainfall Measurement Mission (TRMM) 3B42 and the Hydroestimator (Hydroe) can be potential sources of data for hydrologic applications, mainly in areas with irregular and sparse [...] Read more.

Remote sensing allows for the continuous and repetitive measurement of rainfall values. Satellite rainfall products such as Tropical Rainfall Measurement Mission (TRMM) 3B42 and the Hydroestimator (Hydroe) can be potential sources of data for hydrologic applications, mainly in areas with irregular and sparse spatial distributions of traditional rain gauge stations. However, the accuracy of these satellite rainfall products over different spatial and temporal scales is unknown. In this study, we examined the potential of the TRMM 3B42 and Hydroe rainfall products to provide reliable rainfall estimates for a mountainous watershed in a humid subtropical climate region of Brazil. The purpose was to develop useful guidelines for future hydrologic studies on the potential and uncertainties of the rainfall products at different spatial and temporal resolutions. We compared the satellite products to reference rainfall data collected at 11 rain gauge stations irregularly distributed in the area. The results showed different levels of accuracy for each temporal scale evaluated. TRMM 3B42 performed better at the daily, monthly, and seasonal scales than Hydroe, while Hydroe presented a better correlation at the annual scale. In general, TRMM 3B42 overestimated the rainfall over the watershed at all evaluated temporal scales, whereas Hydroe underestimated it except for June–August at the seasonal scale. An evaluation based on contingency tables indicated that TRM 3B42 was better able to represent the local rainfall than Hydroe. The findings of this study indicate that satellite rainfall products are better suited for applications at the monthly and annual scales rather than the daily scale. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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6486 KiB

Open AccessArticle

by Jinyu Gao, Guoqiang Tang and Yang Hong

Remote Sens. 2017, 9(11), 1142; https://doi.org/10.3390/rs9111142 - 7 Nov 2017

Cited by 44 | Viewed by 8865

Abstract

Spaceborne precipitation radars are powerful tools used to acquire adequate and high-quality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM) mission, which deployed the first spaceborne Ka- and Ku-dual frequency radar (DPR), [...] Read more.

Spaceborne precipitation radars are powerful tools used to acquire adequate and high-quality precipitation estimates with high spatial resolution for a variety of applications in hydrological research. The Global Precipitation Measurement (GPM) mission, which deployed the first spaceborne Ka- and Ku-dual frequency radar (DPR), was launched in February 2014 as the upgraded successor of the Tropical Rainfall Measuring Mission (TRMM). This study matches the swath data of TRMM PR and GPM DPR Level 2 products during their overlapping periods at the global scale to investigate their similarities and DPR’s improvements concerning precipitation amount estimation and type classification of GPM DPR over TRMM PR. Results show that PR and DPR agree very well with each other in the global distribution of precipitation, while DPR improves the detectability of precipitation events significantly, particularly for light precipitation. The occurrences of total precipitation and the light precipitation (rain rates < 1 mm/h) detected by GPM DPR are ~1.7 and ~2.53 times more than that of PR. With regard to type classification, the dual-frequency (Ka/Ku) and single frequency (Ku) methods performed similarly. In both inner (the central 25 beams) and outer swaths (1–12 beams and 38–49 beams) of DPR, the results are consistent. GPM DPR improves precipitation type classification remarkably, reducing the misclassification of clouds and noise signals as precipitation type “other” from 10.14% of TRMM PR to 0.5%. Generally, GPM DPR exhibits the same type division for around 82.89% (71.02%) of stratiform (convective) precipitation events recognized by TRMM PR. With regard to the freezing level height and bright band (BB) height, both radars correspond with each other very well, contributing to the consistency in stratiform precipitation classification. Both heights show clear latitudinal dependence. Results in this study shall contribute to future development of spaceborne radar precipitation retrievals and benefit hydrological and meteorological research. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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6289 KiB

Open AccessArticle

Improving Rainfall Erosivity Estimates Using Merged TRMM and Gauge Data

by Hongfen Teng, Ziqiang Ma, Adrian Chappell, Zhou Shi, Zongzheng Liang and Wu Yu

Remote Sens. 2017, 9(11), 1134; https://doi.org/10.3390/rs9111134 - 6 Nov 2017

Cited by 33 | Viewed by 5376

Abstract

Soil erosion is a global issue that threatens food security and causes environmental degradation. Management of water erosion requires accurate estimates of the spatial and temporal variations in the erosive power of rainfall (erosivity). Rainfall erosivity can be estimated from rain gauge stations [...] Read more.

Soil erosion is a global issue that threatens food security and causes environmental degradation. Management of water erosion requires accurate estimates of the spatial and temporal variations in the erosive power of rainfall (erosivity). Rainfall erosivity can be estimated from rain gauge stations and satellites. However, the time series rainfall data that has a high temporal resolution are often unavailable in many areas of the world. Satellite remote sensing allows provision of the continuous gridded estimates of rainfall, yet it is generally characterized by significant bias. Here we present a methodology that merges daily rain gauge measurements and the Tropical Rainfall Measuring Mission (TRMM) 3B42 data using collocated cokriging (ColCOK) to quantify the spatial distribution of rainfall and thereby to estimate rainfall erosivity across China. This study also used block kriging (BK) and TRMM to estimate rainfall and rainfall erosivity. The methodologies are evaluated based on the individual rain gauge stations. The results from the present study generally indicate that the ColCOK technique, in combination with TRMM and gauge data, provides merged rainfall fields with good agreement with rain gauges and with the best accuracy with rainfall erosivity estimates, when compared with BK gauges and TRMM alone. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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4334 KiB

Open AccessFeature PaperCommunication

On the Spatial and Temporal Sampling Errors of Remotely Sensed Precipitation Products

by Ali Behrangi and Yixin Wen

Remote Sens. 2017, 9(11), 1127; https://doi.org/10.3390/rs9111127 - 5 Nov 2017

Cited by 31 | Viewed by 4881

Abstract

Observation with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and duration of precipitation events. In this study, the errors resulting from temporal and spatial sampling of precipitation events were quantified and examined using the latest [...] Read more.

Observation with coarse spatial and temporal sampling can cause large errors in quantification of the amount, intensity, and duration of precipitation events. In this study, the errors resulting from temporal and spatial sampling of precipitation events were quantified and examined using the latest version (V4) of the Global Precipitation Measurement (GPM) mission integrated multi-satellite retrievals for GPM (IMERG), which is available since spring of 2014. Relative mean square error was calculated at 0.1° × 0.1° every 0.5 h between the degraded (temporally and spatially) and original IMERG products. The temporal and spatial degradation was performed by producing three-hour (T3), six-hour (T6), 0.5° × 0.5° (S5), and 1.0° × 1.0° (S10) maps. The results show generally larger errors over land than ocean, especially over mountainous regions. The relative error of T6 is almost 20% larger than T3 over tropical land, but is smaller in higher latitudes. Over land relative error of T6 is larger than S5 across all latitudes, while T6 has larger relative error than S10 poleward of 20°S–20°N. Similarly, the relative error of T3 exceeds S5 poleward of 20°S–20°N, but does not exceed S10, except in very high latitudes. Similar results are also seen over ocean, but the error ratios are generally less sensitive to seasonal changes. The results also show that the spatial and temporal relative errors are not highly correlated. Overall, lower correlations between the spatial and temporal relative errors are observed over ocean than over land. Quantification of such spatiotemporal effects provides additional insights into evaluation studies, especially when different products are cross-compared at a range of spatiotemporal scales. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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1376 KiB

Open AccessArticle

Enhanced Resolution of Microwave Sounder Imagery through Fusion with Infrared Sensor Data

by Igor Yanovsky, Ali Behrangi, Yixin Wen, Mathias Schreier, Van Dang and Bjorn Lambrigtsen

Remote Sens. 2017, 9(11), 1097; https://doi.org/10.3390/rs9111097 - 27 Oct 2017

Cited by 1 | Viewed by 4491

Abstract

The images acquired by microwave sensors are blurry and have low resolution. On the other hand, the images obtained using infrared/visible sensors are often of higher resolution. In this paper, we develop a data fusion methodology and apply it to enhance the resolution [...] Read more.

The images acquired by microwave sensors are blurry and have low resolution. On the other hand, the images obtained using infrared/visible sensors are often of higher resolution. In this paper, we develop a data fusion methodology and apply it to enhance the resolution of a microwave image using the data from a collocated infrared/visible sensor. Such an approach takes advantage of the spatial resolution of the infrared instrument and the sensing accuracy of the microwave instrument. The model leverages sparsity in signals and is based on current research in sparse optimization and compressed sensing. We tested our method using a precipitation scene captured with the Advanced Microwave Sounding Unit (AMSU-B) microwave instrument and the Advanced Very High Resolution Radiometer (AVHRR) infrared instrument and compared the results to simultaneous radar observations. We show that the data fusion product is better than the original AMSU-B and AVHRR observations across all statistical indicators. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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11232 KiB

Open AccessArticle

Spatial and Temporal Variability in Winter Precipitation across the Western United States during the Satellite Era

by Deanna Nash, Hengchun Ye and Eric Fetzer

Remote Sens. 2017, 9(9), 928; https://doi.org/10.3390/rs9090928 - 7 Sep 2017

Cited by 1 | Viewed by 4820

Abstract

The western United States is known for its water shortages due to large seasonal and inter-annual variability of precipitation as well as increasing demand. Climate change will impact the availability of water in the western United States through the modification of precipitation characteristics. [...] Read more.

The western United States is known for its water shortages due to large seasonal and inter-annual variability of precipitation as well as increasing demand. Climate change will impact the availability of water in the western United States through the modification of precipitation characteristics. Satellite data presents the opportunity to study these changes at a fine, continuous spatial resolution—particularly in places where no traditional ground observations exist. Utilizing the Tropical Rainfall Measuring Mission (TRMM) 3B42 version 7 precipitation data, this study examines the spatio-temporal changes in precipitation characteristics over the western United States between 1998 and 2015, and their connections with the atmospheric total column water vapor and the El Niño Southern Oscillation during the winter season. The results show that precipitation frequency in the western United States has been decreasing in general, precipitation totals and mean daily intensity are increasing in northwestern United States, but decreasing in the southwest United States during the 18 years of the study time period. Additionally, results show a strong relationship between total column water vapor and the precipitation characteristics, specifically in the southwestern United States. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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5305 KiB

Open AccessArticle

Monitoring Rainfall Patterns in the Southern Amazon with PERSIANN-CDR Data: Long-Term Characteristics and Trends

by Damien Arvor, Beatriz M. Funatsu, Véronique Michot and Vincent Dubreuil

Remote Sens. 2017, 9(9), 889; https://doi.org/10.3390/rs9090889 - 27 Aug 2017

Cited by 54 | Viewed by 10846

Abstract

Satellite-derived estimates of precipitation are essential to compensate for missing rainfall measurements in regions where the homogeneous and continuous monitoring of rainfall remains challenging due to low density rain gauge networks. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data [...] Read more.

Satellite-derived estimates of precipitation are essential to compensate for missing rainfall measurements in regions where the homogeneous and continuous monitoring of rainfall remains challenging due to low density rain gauge networks. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record (PERSIANN-CDR) is a relatively new product (released in 2013) but that contains data since 1983, thus enabling long-term rainfall analysis. In this work, we used three decades (1983–2014) of PERSIANN-CDR daily rainfall data to characterize precipitation patterns in the southern part of the Amazon basin, which has been drastically impacted in recent decades by anthropogenic activities that exacerbate the spatio-temporal variability of rainfall regimes. We computed metrics for the rainy season (onset date, demise date and duration) on a pixel-to-pixel basis for each year in the time series. We identified significant trends toward a shortening of the rainy season in the southern Amazon, mainly linked to earlier demise dates. This work thus contributes to monitoring possible signs of climate change in the region and to assessing uncertainties in rainfall trends and their potential impacts on human activities and natural ecosystems. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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Open AccessArticle

Reconstructing Satellite-Based Monthly Precipitation over Northeast China Using Machine Learning Algorithms

by Wenlong Jing, Pengyan Zhang, Hao Jiang and Xiaodan Zhao

Remote Sens. 2017, 9(8), 781; https://doi.org/10.3390/rs9080781 - 30 Jul 2017

Cited by 18 | Viewed by 5073

Abstract

Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over [...] Read more.

Attaining accurate precipitation data is critical to understanding land surface processes and global climate change. The development of satellite sensors and remote sensing technology has resulted in multi-source precipitation datasets that provide reliable estimates of precipitation over un-gauged areas. However, gaps exist over high latitude areas due to the limited spatial extent of several satellite-based precipitation products. In this study, we propose an approach for the reconstruction of the Tropical Rainfall Measuring Mission (TRMM) 3B43 monthly precipitation data over Northeast China based on the interaction between precipitation and surface environment. Two machine learning algorithms, support vector machine (SVM) and random forests (RF), are implemented to detect possible relationships between precipitation and normalized difference vegetation index (NDVI), land surface temperature (LST), and digital elevation model (DEM). The relationships between precipitation and geographical location variations based on longitude and latitude are also considered in the reconstruction model. The reconstruction of monthly precipitation in the study area is conducted in two spatial resolutions (25 km and 1 km). The validation is performed using in-situ observations from eight meteorological stations within the study area. The results show that the RF algorithm is robust and not sensitive to the choice of parameters, while the training accuracy of the SVM algorithm has relatively large fluctuations depending on the parameter settings and month. The precipitation data reconstructed with RF show strong correlation with in situ observations at each station and are more accurate than that obtained using the SVM algorithm. In general, the accuracy of the estimated precipitation at 1 km resolution is slightly lower than that of data at 25 km resolution. The estimation errors are positively related to the average precipitation. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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Open AccessArticle

Assessment of GPM and TRMM Precipitation Products over Singapore

by Mou Leong Tan and Zheng Duan

Remote Sens. 2017, 9(7), 720; https://doi.org/10.3390/rs9070720 - 13 Jul 2017

Cited by 199 | Viewed by 11550

Abstract

The evaluation of satellite precipitation products (SPPs) at regional and local scales is essential in improving satellite-based algorithms and sensors, as well as in providing valuable guidance when choosing alternative precipitation data for the local community. The Tropical Rainfall Measuring Mission (TRMM) has [...] Read more.

The evaluation of satellite precipitation products (SPPs) at regional and local scales is essential in improving satellite-based algorithms and sensors, as well as in providing valuable guidance when choosing alternative precipitation data for the local community. The Tropical Rainfall Measuring Mission (TRMM) has made significant contributions to the development of various SPPs since its launch in 1997. The Global Precipitation Measurement (GPM) mission launched in 2014 and is expected to continue the success of TRMM. During the transition from the TRMM era to the GPM era, it is necessary to assess GPM products and make comparisons with TRMM products in different regions to achieve a global view of the performance of GPM products. To this end, this study aims to assess the capability of the latest Integrated Multi-satellite Retrievals for GPM (IMERG) and two TRMM Multisatellite Precipitation Analysis (TMPA) products (TMPA 3B42 and TMPA 3B42RT) in estimating precipitation over Singapore that represents a typical tropical region. The evaluation was conducted at daily, monthly, seasonal and annual scales from 1 April 2014 to 31 January 2016. The capability of SPPs in detecting rainy/non-rainy days and different precipitation classes was also evaluated. The findings showed that: (1) all SPPs correlated well with measurements from gauges at the monthly scale, but moderately at the daily scale; (2) SPPs performed better in the northeast monsoon season (1 December–15 March) than in the inter-monsoon 1 (16 March–31 May), southwest monsoon (1 June–30 September) and inter-monsoon 2 (1 October–30 November) seasons; (3) IMERG had better performance in the characterization of spatial precipitation variability and precipitation detection capability compared to the TMPA products; (4) for the daily precipitation estimates, IMERG had the lowest systematic bias, followed by 3B42 and 3B42RT; and (5) most of the SPPs overestimated moderate precipitation events (1–20 mm/day), while underestimating light (0.1–1 mm/day) and heavy (>20 mm/day) precipitation events. Overall, IMERG is superior but with only slight improvement compared to the TMPA products over Singapore. This study is one of the earliest assessments of IMERG and a comparison of it with TMPA products in Singapore. Our findings were compared with existing studies conducted in other regions, and some limitations of the IMERG and TMPA products in this tropical region were identified and discussed. This study provides an added value to the understanding of the global performance of the IMERG product. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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Open AccessArticle

Evaluation of Satellite-Based Rainfall Estimates and Application to Monitor Meteorological Drought for the Upper Blue Nile Basin, Ethiopia

by Yared Bayissa, Tsegaye Tadesse, Getachew Demisse and Andualem Shiferaw

Remote Sens. 2017, 9(7), 669; https://doi.org/10.3390/rs9070669 - 29 Jun 2017

Cited by 193 | Viewed by 12552

Abstract

Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the [...] Read more.

Drought is a recurring phenomenon in Ethiopia that significantly impacts the socioeconomic sector and various components of the environment. The overarching goal of this study is to assess the spatial and temporal patterns of meteorological drought using a satellite-derived rainfall product for the Upper Blue Nile Basin (UBN). The satellite rainfall product used in this study was selected through evaluation of five high-resolution products (Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) v2.0, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), African Rainfall Climatology and Time-series (TARCAT) v2.0, Tropical Rainfall Measuring Mission (TRMM) and Africa Rainfall Estimate Climatology version 2 [ARC 2.0]). The statistical performance measuring techniques (i.e., Pearson correlation coefficient (r), mean error (ME), root mean square error (RMSE), and Bias) were used to evaluate the satellite rainfall products with the corresponding ground observation data at ten independent weather stations. The evaluation was carried out for 1998–2015 at dekadal, monthly, and seasonal time scales. The evaluation results of these satellite-derived rainfall products show there is a good agreement (r > 0.7) of CHIRPS and TARCAT rainfall products with ground observations in majority of the weather stations for all time steps. TARCAT showed a greater correlation coefficient (r > 0.70) in seven weather stations at a dekadal time scale whereas CHIRPS showed a greater correlation coefficient (r > 0.84) in nine weather stations at a monthly time scale. An excellent score of Bias (close to one) and mean error was observed in CHIRPS at dekadal, monthly and seasonal time scales in a majority of the stations. TARCAT performed well next to CHIRPS whereas PERSSIAN presented a weak performance under all the criteria. Thus, the CHIRPS rainfall product was selected and used to assess the spatial and temporal variability of meteorological drought in this study. The 3-month Z-Score values were calculated for each grid and used to assess the spatial and temporal patterns of drought. The result shows that the known historic drought years (2014–2015, 2009–2010, 1994–1995 and 1983–1984) were successfully indicated. Moreover, severe drought conditions were observed in the drought prone parts of the basin (i.e., central, eastern and southeastern). Hence, the CHIRPS rainfall product can be used as an alternative source of information in developing the grid-based drought monitoring tools for the basin that could help in developing early warning systems. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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846 KiB

Open AccessArticle

Simulation of Ship-Track versus Satellite-Sensor Differences in Oceanic Precipitation Using an Island-Based Radar

by Jörg Burdanowitz, Christian Klepp, Stephan Bakan and Stefan A. Buehler

Remote Sens. 2017, 9(6), 593; https://doi.org/10.3390/rs9060593 - 11 Jun 2017

Cited by 9 | Viewed by 5398

Abstract

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based [...] Read more.

The point-to-area problem strongly complicates the validation of satellite-based precipitation estimates, using surface-based point measurements. We simulate the limited spatial representation of light-to-moderate oceanic precipitation rates along ship tracks with respect to areal passive microwave satellite estimates using data from a subtropical island-based radar. The radar data serves to estimate the discrepancy between point-like and areal precipitation measurements. From the spatial discrepancy, two statistical adjustments are derived so that along-track precipitation ship data better represent areal precipitation estimates from satellite sensors. The first statistical adjustment uses the average duration of a precipitation event as seen along a ship track, and the second adjustment uses the median-normalized along-track precipitation rate. Both statistical adjustments combined reduce the root mean squared error by 0.24 mm h

^{- 1}

(55%) compared to the unadjusted average track of 60 radar pixels in length corresponding to a typical ship speed of 24–34 km h

^{- 1}

depending on track orientation. Beyond along-track averaging, the statistical adjustments represent an important step towards a more accurate validation of precipitation derived from passive microwave satellite sensors using point-like along-track surface precipitation reference data. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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3882 KiB

Open AccessArticle

Assessment of GPM and TRMM Multi-Satellite Precipitation Products in Streamflow Simulations in a Data-Sparse Mountainous Watershed in Myanmar

by Fei Yuan, Limin Zhang, Khin Wah Wah Win, Liliang Ren, Chongxu Zhao, Yonghua Zhu, Shanhu Jiang and Yi Liu

Remote Sens. 2017, 9(3), 302; https://doi.org/10.3390/rs9030302 - 22 Mar 2017

Cited by 130 | Viewed by 9493

Abstract

Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the [...] Read more.

Satellite precipitation products from the Global Precipitation Measurement (GPM) mission and its predecessor the Tropical Rainfall Measuring Mission (TRMM) are a critical data source for hydrological applications in ungauged basins. This study conducted an initial and early evaluation of the performance of the Integrated Multi-satellite Retrievals for GPM (IMERG) final run and the TRMM Multi-satellite Precipitation Analysis 3B42V7 precipitation products, and their feasibility in streamflow simulations in the Chindwin River basin, Myanmar, from April 2014 to December 2015 was also assessed. Results show that, although IMERG and 3B42V7 can potentially capture the spatiotemporal patterns of historical precipitation, the two products contain considerable errors. Compared with 3B42V7, no significant improvements were found in IMERG. Moreover, 3B42V7 outperformed IMERG at daily and monthly scales and in heavy rain detections at four out of five gauges. The large errors in IMERG and 3B42V7 distinctly propagated to streamflow simulations via the Xinanjiang hydrological model, with a significant underestimation of total runoff and high flows. The bias correction of the satellite precipitation effectively improved the streamflow simulations. The 3B42V7-based streamflow simulations performed better than the gauge-based simulations. In general, IMERG and 3B42V7 are feasible for use in streamflow simulations in the study area, although 3B42V7 is better suited than IMERG. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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1566 KiB

Open AccessTechnical Note

Measurement of Precipitation in the Alps Using Dual-Polarization C-Band Ground-Based Radars, the GPM Spaceborne Ku-Band Radar, and Rain Gauges

by Marco Gabella, Peter Speirs, Ulrich Hamann, Urs Germann and Alexis Berne

Remote Sens. 2017, 9(11), 1147; https://doi.org/10.3390/rs9111147 - 8 Nov 2017

Cited by 33 | Viewed by 6703

Abstract

The complex problem of quantitative precipitation estimation in the Alpine region is tackled from four different points of view: (1) the modern MeteoSwiss network of automatic telemetered rain gauges (GAUGE); (2) the recently upgraded MeteoSwiss dual-polarization Doppler, ground-based weather radar network (RADAR); (3) [...] Read more.

The complex problem of quantitative precipitation estimation in the Alpine region is tackled from four different points of view: (1) the modern MeteoSwiss network of automatic telemetered rain gauges (GAUGE); (2) the recently upgraded MeteoSwiss dual-polarization Doppler, ground-based weather radar network (RADAR); (3) a real-time merging of GAUGE and RADAR, implemented at MeteoSwiss, in which a technique based on co-kriging with external drift (CombiPrecip) is used; (4) spaceborne observations, acquired by the dual-wavelength precipitation radar on board the Global Precipitation Measuring (GPM) core satellite. There are obviously large differences in these sampling modes, which we have tried to minimize by integrating synchronous observations taken during the first 2 years of the GPM mission. The data comprises 327 “wet” overpasses of Switzerland, taken after the launch of GPM in February 2014. By comparing the GPM radar estimates with the MeteoSwiss products, a similar performance was found in terms of bias. On average (whole country, all days and seasons, both solid and liquid phases), underestimation is as large as −3.0 (−3.4) dB with respect to RADAR (GAUGE). GPM is not suitable for assessing what product is the best in terms of average precipitation over the Alps. GPM can nevertheless be used to evaluate the dispersion of the error around the mean, which is a measure of the geographical distribution of the error inside the country. Using 221 rain-gauge sites, the result is clear both in terms of correlation and in terms of scatter (a robust, weighted measure of the dispersion of the multiplicative error around the mean). The best agreement was observed between GPM and CombiPrecip, and, next, between GPM and RADAR, whereas a larger disagreement was found between GPM and GAUGE. Hence, GPM confirms that, for precipitation mapping in the Alpine region, the best results are obtained by combining ground-based radar with rain-gauge measurements using a geostatistical approach. The GPM mission is adding significant new coverage to mountainous areas, especially in poorly instrumented parts of the world and at latitudes not previously covered by the Tropical Rainfall Measuring Mission (TRMM). According to this study, one could expect an underestimation of the precipitation product by the dual-frequency precipitation radar (DPR) also in other mountainous areas of the world. Full article

(This article belongs to the Special Issue Remote Sensing Precipitation Measurement, Validation, and Applications)

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