Special Issue "Precipitation and Water Cycle Measurements Using Remote Sensing"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing of the Water Cycle".

Deadline for manuscript submissions: 31 December 2020.

Special Issue Editor

Prof. Dr. Francisco J. Tapiador
Website
Guest Editor
Head of the Earth and Space Sciences (ess) Research Group, Dean of Enviromental Sciences and Biochemistry (2012-2019), University of Castilla-La Mancha (UCLM), Avda. Carlos III s/n, E-45071 Toledo, Spain
Interests: precipitation; remote sensing; climate; social sciences
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue aims to publish remote sensing research on precipitation and the water cycle from a broad perspective, from tropical to polar research and from solid precipitation to humidity and microphysics. Local/regional studies, negative results (such as retrievals performing poorly when compared with observations), short papers and discussion/position papers are welcomed. Case studies and the analysis of single events/observations are also suitable for this Special Issue.

We invite papers on the following topics:

  • GPM studies.
  • Megha-Tropiques studies.
  • CloudSat studies.
  • FY studies.
  • TRMM studies.
  • Grace studies.
  • Passive microwave retrievals (SSMI/S, AMSU, AMSR, etc.)
  • ATBDs. This Special Issue is an opportunity to disseminate your algorithm theoretical basis description through an international journal.
  • IPWG activities.
  • Precipitation estimation using infrared and visible wavelengths.
  • Hydrological applications.
  • Precipitation estimation from microwave links.
  • Precipitation estimation from GPS measurements.
  • Satellite algorithms: description, case-studies, full validations.
  • Validation/verification of precipitation estimates from NWP models, RCMs, GCMs and ESMs.
  • Precipitation microphysics, including description, verification, comparison, and case studies.
  • Database descriptions.
  • Particle and drop size distribution (PSD, DSD) research.
  • Computing approaches (HPC, cloud, etc.) to improve the remote sensing of precipitation and the water cycle.
  • Uncertainties in the remote measurement of precipitation at ground (disdrometers, radars, etc.)
  • Instrumental biases affecting remote sensing measurements.
  • Spatial variability of precipitation, at any scale.
  • Beam filling issues.
  • Assimilation of satellite precipitation in numerical models.
  • Latent heat studies.
  • Precipitation in hurricanes.
  • Monsoons.
  • Validation of campaign results.
  • Precipitation from sounders.
  • New observational concepts (including geostationary sounders)
  • Projects results or preliminary advances (CMIP5/6, HyMEX, CORDEX, CLIVAR, etc.)
  • Satellite precipitation climatologies, from local to global.
  • Applications of precipitation (hydropower, insurance, agriculture, hazards, etc.)
  • Case studies focused on precipitation processes and/or uncertainties.
  • Precipitation estimates for biogeography.
  • Coupling of precipitation from observations and models with hydrological models.
  • Data fusion techniques (neural networks, etc.)
  • Precipitation trends and analysis of series.
  • Temporal variability of precipitation from satellites, including climate variability.
  • Precipitation in future climates as featured in models.

Prof. Francisco J. Tapiador
Guest Editor

Manuscript Submission Information

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Keywords

  • Precipitation
  • Rainfall
  • Humidity
  • Ice
  • Hail
  • Hydrology

Published Papers (13 papers)

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Open AccessArticle
A Case Study on Microphysical Characteristics of Mesoscale Convective System Using Generalized DSD Parameters Retrieved from Dual-Polarimetric Radar Observations
Remote Sens. 2020, 12(11), 1812; https://doi.org/10.3390/rs12111812 - 03 Jun 2020
Abstract
The microphysical characteristics of a mesoscale convective system (MCS) during a summer monsoon of South Korea are investigated using the generalized drop size distributions (DSD) that are derived from S-band dual-polarization radar data. The characteristics parameters of generalized DSDs (generalized number concentration, N [...] Read more.
The microphysical characteristics of a mesoscale convective system (MCS) during a summer monsoon of South Korea are investigated using the generalized drop size distributions (DSD) that are derived from S-band dual-polarization radar data. The characteristics parameters of generalized DSDs (generalized number concentration, N0′ and generalized mean diameter, Dm) are directly calculated from DSD’s two moments without any assumption on the DSD model. Relationships between ZDR and generalized DSD parameters normalized by ZH are derived in the form of the polynomial equation. Verification of the retrieved DSD parameters is conducted with the 2-D video disdrometer (2DVD) located about 23 km from the radar. The standard deviations (SD) of retrieved DSD parameters are about 0.26 for log N0′, and about 0.11 for Dm because of the variability of DSDs. The SD of the retrieved log N0′ from the dual-polarimetric measurement reaches to about 0.46 (almost double) for 11 rain events while the accuracy of retrieved Dm is quite higher (~0.19). This higher error in retrieved log N0′ is likely attributed to the larger discrepancy in radar-observed and DSD-calculated ZDR when ZH is low. This retrieval technique is applied to a mesoscale convective system (MCS) case to investigate the Lagrangian characteristics of the microphysical process. The MCS is classified into the leading edge and trailing stratiform region by using the storm classification algorithm. The leading edge dominated by strong updraft showed the broad DSD spectra with a steady temporal increase of Dm throughout the event, likely because of the dominant drop growth by the collision-coalescence process. On the other hand, the drop growth is less significant in the trailing stratiform region as shown by the nearly constant Dm for the entire period. The DSD variation is also controlled by the new generation of drops in the leading edge and less extent in the trailing stratiform during the early period when precipitation systems grow. When the system weakens, the characteristic number concentration decreases with time, indicating the new generation of drops becomes less significant in both regions. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
Cross-Examination of Similarity, Difference and Deficiency of Gauge, Radar and Satellite Precipitation Measuring Uncertainties for Extreme Events Using Conventional Metrics and Multiplicative Triple Collocation
Remote Sens. 2020, 12(8), 1258; https://doi.org/10.3390/rs12081258 - 16 Apr 2020
Abstract
Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and [...] Read more.
Quantifying uncertainties of precipitation estimation, especially in extreme events, could benefit early warning of water-related hazards like flash floods and landslides. Rain gauges, weather radars, and satellites are three mainstream data sources used in measuring precipitation but have their own inherent advantages and deficiencies. With a focus on extremes, the overarching goal of this study is to cross-examine the similarities and differences of three state-of-the-art independent products (Muti-Radar Muti-Sensor Quantitative Precipitation Estimates, MRMS; National Center for Environmental Prediction gridded gauge-only hourly precipitation product, NCEP; Integrated Multi-satellitE Retrievals for GPM, IMERG), with both traditional metrics and the Multiplicative Triple Collection (MTC) method during Hurricane Harvey and multiple Tropical Cyclones. The results reveal that: (a) the consistency of cross-examination results against traditional metrics approves the applicability of MTC in extreme events; (b) the consistency of cross-events of MTC evaluation results also suggests its robustness across individual storms; (c) all products demonstrate their capacity of capturing the spatial and temporal variability of the storm structures while also magnifying respective inherent deficiencies; (d) NCEP and IMERG likely underestimate while MRMS overestimates the storm total accumulation, especially for the 500-year return Hurricane Harvey; (e) both NCEP and IMERG underestimate extreme rainrates (>= 90 mm/h) likely due to device insensitivity or saturation while MRMS maintains robust across the rainrate range; (g) all three show inherent deficiencies in capturing the storm core of Harvey possibly due to device malfunctions with the NCEP gauges, relative low spatiotemporal resolution of IMERG, and the unusual “hot” MRMS radar signals. Given the unknown ground reference assumption of MTC, this study suggests that MRMS has the best overall performance. The similarities, differences, advantages, and deficiencies revealed in this study could guide the users for emergency response and motivate the community not only to improve the respective sensor/algorithm but also innovate multidata merging methods for one best possible product, specifically suitable for extreme storm events. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
Uncertainty in Measured Raindrop Size Distributions from Four Types of Collocated Instruments
Remote Sens. 2020, 12(7), 1167; https://doi.org/10.3390/rs12071167 - 05 Apr 2020
Cited by 1
Abstract
Four types (2D-video disdrometer: 2DVD; precipitation occurrence sensor system: POSS; micro-rain radar: MRR; and Joss–Waldvogel disdrometer: JWD) of sixteen instruments were collocated within a square area of 400 m2 from 16 April to 8 May 2008 for intercomparison of drop size distribution [...] Read more.
Four types (2D-video disdrometer: 2DVD; precipitation occurrence sensor system: POSS; micro-rain radar: MRR; and Joss–Waldvogel disdrometer: JWD) of sixteen instruments were collocated within a square area of 400 m2 from 16 April to 8 May 2008 for intercomparison of drop size distribution (DSD) of rain. This unique dataset was used to study the inherent measurement uncertainty due to the diversity of the measuring principles and sampling sizes of the four types of instruments. The DSD intercomparison shows generally good agreement among them, except that the POSS and MRR had higher concentrations of small raindrops (<1.0 mm) and offered a better chance to observe big raindrops (>5.2 mm). The measurement uncertainty ( σ ) was obtained quantitatively after considering the zero or non-zero measurement error covariance between two instruments of the same type. The results indicate the measurement uncertainties were found to be neither independent nor identical among the same type of instruments. The MRR is relatively accurate (lower σ ) due to large sampling volumes and accurate measurement of the Doppler power spectrum. The JWD is the least accurate due to the small sampling volumes. The σ decreases rapidly with increasing time-averaging window. The 2DVD shows the best accuracy of R in longer averaging time, but this is not true for Z due to the small sampling volume. The MRR outperformed other instruments for Z for entire averaging time due to its measuring principle. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
X-Net-Based Radar Data Assimilation Study over the Seoul Metropolitan Area
Remote Sens. 2020, 12(5), 893; https://doi.org/10.3390/rs12050893 - 10 Mar 2020
Abstract
This study investigates the ability of the high-resolution Weather Research and Forecasting (WRF) model to simulate summer precipitation with assimilation of X-band radar network data (X-Net) over the Seoul metropolitan area. Numerical data assimilation (DA) experiments with X-Net (S- and X-band Doppler radar) [...] Read more.
This study investigates the ability of the high-resolution Weather Research and Forecasting (WRF) model to simulate summer precipitation with assimilation of X-band radar network data (X-Net) over the Seoul metropolitan area. Numerical data assimilation (DA) experiments with X-Net (S- and X-band Doppler radar) radial velocity and reflectivity data for three events of convective systems along the Changma front are conducted. In addition to the conventional assimilation of radar data, which focuses on assimilating the radial velocity and reflectivity of precipitation echoes, this study assimilates null-echoes and analyzes the effect of null-echo data assimilation on short-term quantitative precipitation forecasting (QPF). A null-echo is defined as a region with non-precipitation echoes within the radar observation range. The model removes excessive humidity and four types of hydrometeors (wet and dry snow, graupel, and rain) based on the radar reflectivity by using a three-dimensional variational (3D-Var) data assimilation technique within the WRFDA system. Some procedures for preprocessing radar reflectivity data and using null-echoes in this assimilation are discussed. Numerical experiments with conventional radar DA over-predicted the precipitation. However, experiments with additional null-echo information removed excessive water vapor and hydrometeors and suppressed erroneous model precipitation. The results of statistical model verification showed improvements in the analysis and objective forecast scores, reducing the amount of over-predicted precipitation. An analysis of a contoured frequency by altitude diagram (CFAD) and time–height cross-sections showed that increased hydrometeors throughout the data assimilation period enhanced precipitation formation, and reflectivity under the melting layer was simulated similarly to the observations during the peak precipitation times. In addition, overestimated hydrometeors were reduced through null-echo data assimilation. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine
Remote Sens. 2019, 11(24), 2977; https://doi.org/10.3390/rs11242977 - 11 Dec 2019
Abstract
Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged [...] Read more.
Precipitation plays an important role in the food production of Southern Africa. Understanding the spatial and temporal variations of precipitation is helpful for improving agricultural management and flood and drought risk assessment. However, a comprehensive precipitation pattern analysis is challenging in sparsely gauged and underdeveloped regions. To solve this problem, Version 7 Tropical Rainfall Measuring Mission (TRMM) precipitation products and Google Earth Engine (GEE) were adopted in this study for the analysis of spatiotemporal patterns of precipitation in the Zambezi River Basin. The Kendall’s correlation and sen’s Slop reducers in GEE were used to examine precipitation trends and magnitude, respectively, at annual, seasonal and monthly scales from 1998 to 2017. The results reveal that 10% of the Zambezi River basin showed a significant decreasing trend of annual precipitation, while only 1% showed a significant increasing trend. The rainy-season precipitation appeared to have a dominant impact on the annual precipitation pattern. The rainy-season precipitation was found to have larger spatial, temporal and magnitude variation than the dry-season precipitation. In terms of monthly precipitation, June to September during the dry season were dominated by a significant decreasing trend. However, areas presenting a significant decreasing trend were rare (<12% of study area) and scattered during the rainy-season months (November to April of the subsequent year). Spatially, the highest and lowest rainfall regions were shifted by year, with extreme precipitation events (highest and lowest rainfall) occurring preferentially over the northwest side rather than the northeast area of the Zambezi River Basin. A “dry gets dryer, wet gets wetter” (DGDWGW) pattern was also observed over the study area, and a suggestion on agriculture management according to precipitation patterns is provided in this study for the region. This is the first study to use long-term remote sensing data and GEE for precipitation analysis at various temporal scales in the Zambezi River Basin. The methodology proposed in this study is helpful for the spatiotemporal analysis of precipitation in developing countries with scarce gauge stations, limited analytic skills and insufficient computation resources. The approaches of this study can also be operationally applied to the analysis of other climate variables, such as temperature and solar radiation. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data
Remote Sens. 2019, 11(24), 2962; https://doi.org/10.3390/rs11242962 - 11 Dec 2019
Abstract
Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the [...] Read more.
Accurate and spatially-distributed precipitation information is vital to the study of the regional hydrological cycle and water resources, as well as for environmental management. To provide high spatio-temporal resolution precipitation estimates over insufficient rain-gauge areas, great efforts have been taken in using the Normalized Difference Vegetation Index (NDVI) and other land surface variables to improve the spatial resolution of satellite precipitation datasets. However, the strong spatio-temporal heterogeneity of precipitation and the “hysteresis phenomenon” of the relationship between precipitation and vegetation has limited the application of these downscaling methods to high temporal resolutions. To overcome this limitation, a new temporal downscaling method was proposed in this study by introducing daily soil moisture data to explore the relationship between precipitation and the soil moisture increment index. The performance of this proposed temporal downscaling was assessed by downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from a monthly scale to a daily scale over the Hekouzhen to Tongguan of the Yellow River in 2013, and the downscaled daily precipitation datasets were validated with in-situ measurement from 23 rainfall observation stations. The validation results indicate that the downscaled daily precipitation agrees with the rain gauge observations, with a correlation coefficient of 0.59, a mean error range of 1.70 mm, and a root mean square error of 5.93 mm. In general, the monthly precipitation decomposition method proposed in this paper has combined the advantage of both microwave remote sensing products. It has acceptable precision and can generate precipitation on a diurnal scale. It is an important development in the field of using auxiliary data to perform temporal downscaling. Furthermore, this method also provides a reference example for the temporal downscaling of other low temporal resolution datasets. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessEditor’s ChoiceArticle
Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions
Remote Sens. 2019, 11(24), 2936; https://doi.org/10.3390/rs11242936 - 07 Dec 2019
Cited by 5
Abstract
The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in [...] Read more.
The great success of the Tropical Rainfall Measuring Mission (TRMM) and its successor Global Precipitation Measurement (GPM) has accelerated the development of global high-resolution satellite-based precipitation products (SPP). However, the quantitative accuracy of SPPs has to be evaluated before using these datasets in water resource applications. This study evaluates the following GPM-era and TRMM-era SPPs based on two years (2014–2015) of reference daily precipitation data from rain gauge networks in ten mountainous regions: Integrated Multi-SatellitE Retrievals for GPM (IMERG, version 05B and version 06B), National Oceanic and Atmospheric Administration (NOAA)/Climate Prediction Center Morphing Method (CMORPH), Global Satellite Mapping of Precipitation (GSMaP), and Multi-Source Weighted-Ensemble Precipitation (MSWEP), which represents a global precipitation data-blending product. The evaluation is performed at daily and annual temporal scales, and at 0.1 deg grid resolution. It is shown that GSMaPV07 surpass the performance of IMERGV06B Final for almost all regions in terms of systematic and random error metrics. The new orographic rainfall classification in the GSMaPV07 algorithm is able to improve the detection of orographic rainfall, the rainfall amounts, and error metrics. Moreover, IMERGV05B showed significantly better performance, capturing the lighter and heavier precipitation values compared to IMERGV06B for almost all regions due to changes conducted to the morphing, where motion vectors are derived using total column water vapor for IMERGV06B. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale
Remote Sens. 2019, 11(23), 2755; https://doi.org/10.3390/rs11232755 - 23 Nov 2019
Cited by 2
Abstract
Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based precipitation estimations are a promising alternative to rain gauges for providing homogeneous precipitation information. Most satellite-based precipitation products suffer from short-term data records, which make [...] Read more.
Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based precipitation estimations are a promising alternative to rain gauges for providing homogeneous precipitation information. Most satellite-based precipitation products suffer from short-term data records, which make them unsuitable for various climatological and hydrological applications. However, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides more than 35 years of precipitation records at 0.25° × 0.25° spatial and daily temporal resolutions. The PERSIANN-CDR algorithm uses monthly Global Precipitation Climatology Project (GPCP) data, which has been recently updated to version 2.3, for reducing the biases in the output of the PERSIANN model. In this study, we constructed PERSIANN-CDR using the newest version of GPCP (V2.3). We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 (from here on referred to as PERSIANN-CDR V2.3) with the PERSIANN-CDR constructed using GPCP V2.2 (from here on PERSIANN-CDR V2.2), at monthly and daily scales for the period from 2009 to 2013. First, we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean. Second, we evaluate the improvements in PERSIANN-CDR V2.3 with respect to the Climate Prediction Center (CPC) unified gauge-based analysis, a gauged-based reference, and Tropical Rainfall Measuring Mission (TRMM 3B42 V7), a commonly used satellite reference, at monthly and daily scales. The results show noticeable differences between PERSIANN-CDR V2.3 and V2.2 over oceans between 40° and 60° latitude in both the northern and southern hemispheres. Monthly and daily scale comparisons of the two bias-adjusted versions of PERSIANN-CDR with the above-mentioned references emphasize that PERSIANN-CDR V2.3 has improved mostly over the global land area, especially over the CONUS and Australia. The updated PERSIANN-CDR V2.3 data has replaced V2.2 data for the 2009–2013 period on CHRS data portal and NOAA National Centers for Environmental Information (NCEI) Program. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
Polarimetric Radar Signatures and Performance of Various Radar Rainfall Estimators during an Extreme Precipitation Event over the Thousand-Island Lake Area in Eastern China
Remote Sens. 2019, 11(20), 2335; https://doi.org/10.3390/rs11202335 - 09 Oct 2019
Abstract
Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific [...] Read more.
Polarimetric radar provides more choices and advantages for quantitative precipitation estimation (QPE) than single-polarization radar. Utilizing the C-band polarimetric radar in Hangzhou, China, six radar QPE estimators based on the horizontal reflectivity (ZH), specific attenuation (AH), specific differential phase (KDP), and double parameters that further integrate the differential reflectivity (ZDR), namely, R(ZH, ZDR), R(KDP, ZDR), and R(AH, ZDR), are investigated for an extreme precipitation event that occurred in Eastern China on 1 June 2016. These radar QPE estimators are respectively evaluated and compared with a local rain gauge network and drop size distribution data observed by two disdrometers. The results show that (i) although R(AH, ZDR) underestimates in the light rain scenario, it performs the best among all radar QPE estimators according to the normalized mean error; (ii) the optimal radar rainfall relationship and consistency between radar measurements aloft and their surface counterparts are both required to obtain accurate rainfall estimates close to the ground. The contamination from melting layer on AH and KDP can make R(AH), R(AH, ZDR), R(KDP), and R(KDP, ZDR) less effective than R(ZH) and R(ZH,ZDR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior AH–based rainfall estimator; (iii) each radar QPE estimator may outperform others during some time intervals featured by particular rainfall characteristics, but they all tend to underestimate rainfall if radar fails to capture the rapid development of rainstorms. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China
Remote Sens. 2019, 11(15), 1805; https://doi.org/10.3390/rs11151805 - 01 Aug 2019
Cited by 2
Abstract
Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate [...] Read more.
Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
The Summertime Diurnal Cycle of Precipitation Derived from IMERG
Remote Sens. 2019, 11(15), 1781; https://doi.org/10.3390/rs11151781 - 30 Jul 2019
Cited by 2
Abstract
The Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation product derived from the Global Precipitation Measurement (GPM) constellation offers a unique opportunity of observing the diurnal cycle of precipitation in the latitudinal band 60°N–S at unprecedented 0.1°×0.1° and [...] Read more.
The Integrated Multi-satellitE Retrievals for GPM (IMERG) precipitation product derived from the Global Precipitation Measurement (GPM) constellation offers a unique opportunity of observing the diurnal cycle of precipitation in the latitudinal band 60 ° N–S at unprecedented 0.1 ° × 0.1 ° and half-hour resolution. The diurnal cycles of occurrence, intensity and accumulation are determined using four years of data at 2 ° × 2 ° resolution; this study focusses on summertime months when the diurnal cycle shows stronger features. Harmonics are fitted to the diurnal cycle using a non-linear least squares method weighted by random errors. Results suggest that mean-to-peak amplitudes for the diurnal cycles of occurrence and accumulation are greater over land (generally larger than 25% of the diurnal mean), where the diurnal harmonic dominates and peaks at ~16–24 LST, than over ocean (generally smaller than 25%), where the diurnal and semi-diurnal harmonics contribute comparably. Over ocean, the diurnal harmonic peaks at ~0–10 LST (~8–15 LST) over open waters (coastal waters). For intensity, amplitudes of the diurnal and semi-diurnal harmonics are generally comparable everywhere (~15–35%) with the diurnal harmonic peaking at ~20–4 LST (~3–12 LST) over land (ocean), and the semi-diurnal harmonic maximises at ~5–8 LST and 17–20 LST. The diurnal cycle of accumulation is dictated by occurrence as opposed to intensity. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessArticle
Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil
Remote Sens. 2019, 11(9), 1113; https://doi.org/10.3390/rs11091113 - 09 May 2019
Cited by 5
Abstract
Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion [...] Read more.
Microwave-based satellite soil moisture products enable an innovative way of estimating rainfall using soil moisture observations with a bottom-up approach based on the inversion of the soil water balance Equation (SM2RAIN). In this work, the SM2RAIN-CCI (SM2RAIN-ASCAT) rainfall data obtained from the inversion of the microwave-based satellite soil moisture (SM) observations derived from the European Space Agency (ESA) Climate Change Initiative (CCI) (from the Advanced SCATterometer (ASCAT) soil moisture data) were evaluated against in situ rainfall observations under different bioclimatic conditions in Brazil. The research V7 version of the Tropical Rainfall Measurement Mission Multi-satellite Precipitation Analysis (TRMM TMPA) was also used as a state-of-the-art rainfall product with an up-bottom approach. Comparisons were made at daily and 0.25° scales, during the time-span of 2007–2015. The SM2RAIN-CCI, SM2RAIN-ASCAT, and TRMM TMPA products showed relatively good Pearson correlation values (R) with the gauge-based observations, mainly in the Caatinga (CAAT) and Cerrado (CER) biomes (R median > 0.55). SM2RAIN-ASCAT largely underestimated rainfall across the country, particularly over the CAAT and CER biomes (bias median < −16.05%), while SM2RAIN-CCI is characterized by providing rainfall estimates with only a slight bias (bias median: −0.20%), and TRMM TMPA tended to overestimate the amount of rainfall (bias median: 7.82%). All products exhibited the highest values of unbiased root mean square error (ubRMSE) in winter (DJF) when heavy rainfall events tend to occur more frequently, whereas the lowest values are observed in summer (JJA) with light rainfall events. The SM2RAIN-based products showed larger contribution of systematic error components than random error components, while the opposite was observed for TRMM TMPA. In general, both SM2RAIN-based rainfall products can be effectively used for some operational purposes on a daily scale, such as water resources management and agriculture, whether the bias is previously adjusted. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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Open AccessFeature PaperEditor’s ChoiceReview
Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate
Remote Sens. 2019, 11(19), 2301; https://doi.org/10.3390/rs11192301 - 02 Oct 2019
Cited by 5
Abstract
The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater [...] Read more.
The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle’s acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management. Full article
(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)
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