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23 pages, 7910 KiB

Open AccessArticle

Quantitative Precipitation Estimates Using Machine Learning Approaches with Operational Dual-Polarization Radar Data

by Kyuhee Shin, Joon Jin Song, Wonbae Bang and GyuWon Lee

Remote Sens. 2021, 13(4), 694; https://doi.org/10.3390/rs13040694 - 14 Feb 2021

Cited by 28 | Viewed by 5353

Abstract

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Z_h, R–(Z_h, Z_DR), etc. One of the biggest advantages of machine learning algorithms is the applicability [...] Read more.

Traditional radar-based rainfall estimation is typically done by known functional relationships between the rainfall intensity (R) and radar measurables, such as R–Z_h, R–(Z_h, Z_DR), etc. One of the biggest advantages of machine learning algorithms is the applicability to a non-linear relationship between a dependent variable and independent variables without any predefined relationships. We explored the potential use of two supervised machine learning methods (regression tree and random forest) in rainfall estimation using dual-polarization radar variables. The regression tree does not require normalization and scaling of data; however, this method is quite unstable since each split depends on the parent split. Since the random forest is an ensemble method of regression trees, it has less variability in prediction compared with regression trees, but consumes more computer resources. We considered several different configurations for machine learning algorithms with different sets of dependent and independent variables. The random forest model was appropriately tuned. In the test of variable importance, the specific differential phase (differential reflectivity) was the most important variable to predict the rainfall rate (residual that is the difference between the true rainfall rate and the one estimated from the R–Z relationship). The models were evaluated by 10-fold cross-validation. The best model was the random forest model using a residual with the non-classified training set. The results indicated that the machine learning algorithms outperformed the traditional R–Z relationship. Then, we applied the best machine learning model to an S-band dual-polarization radar (Mt. Myeonbong) and validated the result with ground rain gauges. The results of the application to radar data showed that the estimates of the residuals had spatial variability. The stratiform and weak rain areas had positive residuals while convective areas had negative residuals, indicating that the spatial error structure driven by the R–Z relationship was well captured by the model. The rainfall rates of all pixels over the study area were adjusted with the estimated residuals. The rainfall rates adjusted by residual showed excellent agreement with the rain gauge, especially at high rainfall rates. Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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19 pages, 5278 KiB

Open AccessArticle

A Synthetic Quantitative Precipitation Estimation by Integrating S- and C-Band Dual-Polarization Radars over Northern Taiwan

by Ju-Yu Chen, Wei-Yu Chang and Pao-Liang Chang

Remote Sens. 2021, 13(1), 154; https://doi.org/10.3390/rs13010154 - 5 Jan 2021

Cited by 9 | Viewed by 3751

Abstract

The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band) and NCU C-POL (C-band) dual-polarization radars in northern Taiwan. The radar [...] Read more.

The key factors, namely, the radar data quality, raindrop size distribution (RSD) variability, and the data integration method, which significantly affect radar-based quantitative precipitation estimation (QPE) are investigated using the RCWF (S-band) and NCU C-POL (C-band) dual-polarization radars in northern Taiwan. The radar data quality control (QC) procedures, including the corrections of attenuation, the systematic bias, and the wet-radome effect, have large impact on the QPE accuracy. With the proper QC procedures, the values of normalized root mean square error (NRMSE) decrease about 10~40% for R(Z_HH) and about 5~15% for R(K_DP). The QPE error from the RSD variability is mitigated by applying seasonal coefficients derived from eight-year disdrometer data. Instead of using discrete QPEs (D-QPE) from one radar, the synthetic QPEs are derived via discretely combined QPEs (DC-QPE) from S- and C-band radars. The improvements in DC-QPE compared to D-QPE are about 1.5–7.0% and 3.5–8.5% in R(K_DP) and R(K_DP, Z_DR), respectively. A novel algorithm, Lagrangian-evolution adjustment (LEA), is proposed to compensate D-QPE from a single radar. The LEA-QPE shows 1–4% improvements in R(K_DP, Z_DR) at the C-band radar, which has a larger scanning temporal gap (up to 10 min). The synthetic LEA-QPEs by combining two radars have outperformed both D-QPEs and DC-QPEs. Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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17 pages, 3013 KiB

Open AccessArticle

Impact of Topography and Rainfall Intensity on the Accuracy of IMERG Precipitation Estimates in an Arid Region

by Mohammed T. Mahmoud, Safa A. Mohammed, Mohamed A. Hamouda and Mohamed M. Mohamed

Remote Sens. 2021, 13(1), 13; https://doi.org/10.3390/rs13010013 - 22 Dec 2020

Cited by 46 | Viewed by 4414

Abstract

The influence of topographical characteristics and rainfall intensity on the accuracy of satellite precipitation estimates is of importance to the adoption of satellite data for hydrological applications. This study evaluates the three GPM IMERG V05B products over the arid country of Saudi Arabia. [...] Read more.

The influence of topographical characteristics and rainfall intensity on the accuracy of satellite precipitation estimates is of importance to the adoption of satellite data for hydrological applications. This study evaluates the three GPM IMERG V05B products over the arid country of Saudi Arabia. Statistical indices quantifying the performance of IMERG products were calculated under three evaluation techniques: seasonal-based, topographical, and rainfall intensity-based. Results indicated that IMERG products have the capability to detect seasons with the highest precipitation values (spring) and seasons with the lowest precipitation (summer). Moreover, results showed that IMERG products performed well under various rainfall intensities, particularly under light rain, which is the most common rainfall in arid regions. Furthermore, IMERG products exhibited high detection accuracy over moderate elevations, whereas it had poor performance over coastal and mountainous regions. Overall, the results confirmed that the performance of the final-run product surpassed the near-real-time products in terms of consistency and errors. IMERG products can improve temporal resolution and play a significant role in filling data gaps in poorly gauged regions. However, due to the errors in IMERG products, it is recommended to use sub-daily rain gauge data in satellite calibration for better rainfall estimation over arid and semiarid regions. Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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23 pages, 12265 KiB

Open AccessArticle

Sensitivities of Quantitative Precipitation Forecasts for Typhoon Soudelor (2015) near Landfall to Polarimetric Radar Data Assimilation

by Chih-Chien Tsai and Kao-Shen Chung

Remote Sens. 2020, 12(22), 3711; https://doi.org/10.3390/rs12223711 - 12 Nov 2020

Cited by 5 | Viewed by 2200

Abstract

Based on the preciousness and uniqueness of polarimetric radar observations collected near the landfall of Typhoon Soudelor (2015), this study investigates the sensitivities of very short-range quantitative precipitation forecasts (QPFs) for this typhoon to polarimetric radar data assimilation. A series of experiments assimilating [...] Read more.

Based on the preciousness and uniqueness of polarimetric radar observations collected near the landfall of Typhoon Soudelor (2015), this study investigates the sensitivities of very short-range quantitative precipitation forecasts (QPFs) for this typhoon to polarimetric radar data assimilation. A series of experiments assimilating various combinations of radar variables are carried out for the purpose of improving a 6 h deterministic forecast for the most intense period. The results of the control simulation expose three sources of the observation operator errors, including the raindrop shape-size relation, the limitations for ice-phase hydrometeors, and the melting ice model. Nevertheless, polarimetric radar data assimilation with the unadjusted observation operator can still improve the analyses, especially rainwater, and consequent QPFs for this typhoon case. The different impacts of assimilating reflectivity, differential reflectivity, and specific differential phase are only distinguishable at the lower levels of convective precipitation areas where specific differential phase is found most helpful. The positive effect of radar data assimilation on QPFs can last three hours in this study, and further improvement can be expected by optimizing the observation operator in the future Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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25 pages, 4375 KiB

Open AccessArticle

A Comparative Evaluation of the Performance of CHIRPS and CFSR Data for Different Climate Zones Using the SWAT Model

by Yeganantham Dhanesh, V. M. Bindhu, Javier Senent-Aparicio, Tássia Mattos Brighenti, Essayas Ayana, P. S. Smitha, Chengcheng Fei and Raghavan Srinivasan

Remote Sens. 2020, 12(18), 3088; https://doi.org/10.3390/rs12183088 - 21 Sep 2020

Cited by 24 | Viewed by 5781

Abstract

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. [...] Read more.

The spatial and temporal scale of rainfall datasets is crucial in modeling hydrological processes. Recently, open-access satellite precipitation products with improved resolution have evolved as a potential alternative to sparsely distributed ground-based observations, which sometimes fail to capture the spatial variability of rainfall. However, the reliability and accuracy of the satellite precipitation products in simulating streamflow need to be verified. In this context, the objective of the current study is to assess the performance of three rainfall datasets in the prediction of daily and monthly streamflow using Soil and Water Assessment Tool (SWAT). We used rainfall data from three different sources: Climate Hazards Group InfraRed Rainfall with Station data (CHIRPS), Climate Forecast System Reanalysis (CFSR) and observed rain gauge data. Daily and monthly rainfall measurements from CHIRPS and CFSR were validated using widely accepted statistical measures, namely, correlation coefficient (CC), root mean squared error (RMSE), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). The results showed that CHIRPS was in better agreement with ground-based rainfall at daily and monthly scale, with high rainfall detection ability, in comparison with the CFSR product. Streamflow prediction across multiple watersheds was also evaluated using Kling-Gupta Efficiency (KGE), Nash-Sutcliffe Efficiency (NSE) and Percent BIAS (PBIAS). Irrespective of the climatic characteristics, the hydrologic simulations of CHIRPS showed better agreement with the observed at the monthly scale with the majority of the NSE values ranging between 0.40 and 0.78, and KGE values ranging between 0.62 and 0.82. Overall, CHIRPS outperformed the CFSR rainfall product in driving SWAT for streamflow simulations across the multiple watersheds selected for the study. The results from the current study demonstrate the potential of CHIRPS as an alternate open access rainfall input to the hydrologic model. Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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27 pages, 9916 KiB

Open AccessArticle

Statistical Characteristics of Cloud Occurrence and Vertical Structure Observed by a Ground-Based Ka-Band Cloud Radar in South Korea

by Bo-Young Ye, Eunsil Jung, Seungsook Shin and GyuWon Lee

Remote Sens. 2020, 12(14), 2242; https://doi.org/10.3390/rs12142242 - 13 Jul 2020

Cited by 11 | Viewed by 3735

Abstract

The cloud measurements for two years from the vertical pointing Ka-band cloud radar at Boseong in Korea are used to analyze detailed cloud properties. The reflectivity of the cloud radar is calibrated with other vertical pointing radars compared with the two disdrometers. A [...] Read more.

The cloud measurements for two years from the vertical pointing Ka-band cloud radar at Boseong in Korea are used to analyze detailed cloud properties. The reflectivity of the cloud radar is calibrated with other vertical pointing radars compared with the two disdrometers. A simple threshold-based quality control method is applied to eliminate non-meteorological echoes (insects and noise) in conjunction with despeckling along the radial direction. Clouds are classified into five types: high (HC), middle (MC), low (LC) for non-precipitating clouds, and deep (RainDP) and shallow (RainSH) for precipitating clouds. The average cloud frequency was about 35.9% with the maximum frequency of 50% in June for the total two-year sampling period. The RainDP occurred most frequently (11.8%), followed by HC (9.3%), MC (7.4%), RainSH (4.4%), and LC (2.9%) out of the average occurrence of the total 35.9%. HC and RainDP were frequently observed in summer and autumn, while RainSH, LC, and MC were dominant in the winter due to the dominant cloud development by the air-sea interaction during the cold air outbreak. The HC showed a significant seasonal variation of the maximum height and the rapid growth in the layer above 7 km (about −15 °C) in summer and autumn. This rapid growth appears in HC, MC, LC, and RainDP and is linked with rapid increases in Doppler velocity and mass flux. Thus, this growth is originated from the dominant riming processes in addition to depositional growth and is supported by an updraft in the layer between 6 and 8 km. MC showed a single frequency peak around 6 km with rapid growth above and strong evaporation below. The Doppler velocity of MC rapidly increases above 8 km and is nearly constant below this height due to strong evaporation except in the summer. LC had a similar trend of reflectivity (rapid growth in the HC region and strong evaporation in the lower region) lacking high frequency in the MC region. Unlike LC, the RainDP had continuous growth toward the ground in the entire layer with rapid growth in the HC and MC regions. In addition, two modes (cloud and precipitation) appear on the ground in spring and fall with the vertical continuity of the high frequency in the precipitation mode. The precipitation growth was most efficient in RainSH in summer with a reflectivity gradient of about 20 dBZ km⁻¹ and frequent updrafts larger than 1 m s⁻¹ and was smaller in the MC and HC regions. Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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20 pages, 5910 KiB

Open AccessArticle

Extreme Events of Precipitation over Complex Terrain Derived from Satellite Data for Climate Applications: An Evaluation of the Southern Slopes of the Pyrenees

by Andrés Navarro, Eduardo García-Ortega, Andrés Merino and José Luis Sánchez

Remote Sens. 2020, 12(13), 2171; https://doi.org/10.3390/rs12132171 - 7 Jul 2020

Cited by 18 | Viewed by 4023

Abstract

Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new [...] Read more.

Estimating extreme precipitation events over complex terrain is challenging but crucial for evaluating the performance of climate models for the present climate and expected changes of the climate in the future. New satellites operating in the microwave wavelengths have started to open new opportunities for performing such estimation at adequate temporal and spatial scales and within sensible error limits. This paper illustrates the feasibility and limits of estimating precipitation extremes from satellite data for climatological applications. Using a high-resolution gauge database as ground truth, it was found that global precipitation measurement (GPM) constellation data can provide valuable estimates of extreme precipitation over the southern slopes of the Pyrenees, a region comprising several climates and a very diverse terrain (a challenge for satellite precipitation algorithms). Validation using an object-based quality measure showed reasonable performance, suggesting that GPM estimates can be advantageous reference data for climate model evaluation. Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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19 pages, 10795 KiB

Open AccessArticle

A Case Study on Microphysical Characteristics of Mesoscale Convective System Using Generalized DSD Parameters Retrieved from Dual-Polarimetric Radar Observations

by Soohyun Kwon, Sung-Hwa Jung and GyuWon Lee

Remote Sens. 2020, 12(11), 1812; https://doi.org/10.3390/rs12111812 - 3 Jun 2020

Cited by 6 | Viewed by 3118

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, N_0′ and generalized mean diameter, D_m) are directly calculated from DSD’s two moments without any assumption on the DSD model. Relationships between Z_DR and generalized DSD parameters normalized by Z_H 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 N_0′, and about 0.11 for D_m because of the variability of DSDs. The SD of the retrieved log N_0′ from the dual-polarimetric measurement reaches to about 0.46 (almost double) for 11 rain events while the accuracy of retrieved D_m is quite higher (~0.19). This higher error in retrieved log N_0′ is likely attributed to the larger discrepancy in radar-observed and DSD-calculated Z_DR when Z_H 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 D_m 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 D_m 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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18 pages, 27225 KiB

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

by Zhi Li, Mengye Chen, Shang Gao, Zhen Hong, Guoqiang Tang, Yixin Wen, Jonathan J. Gourley and Yang Hong

Remote Sens. 2020, 12(8), 1258; https://doi.org/10.3390/rs12081258 - 16 Apr 2020

Cited by 42 | Viewed by 4924

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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23 pages, 4996 KiB

Open AccessArticle

Uncertainty in Measured Raindrop Size Distributions from Four Types of Collocated Instruments

by Wei-Yu Chang, GyuWon Lee, Ben Jong-Dao Jou, Wen-Chau Lee, Pay-Liam Lin and Cheng-Ku Yu

Remote Sens. 2020, 12(7), 1167; https://doi.org/10.3390/rs12071167 - 5 Apr 2020

Cited by 25 | Viewed by 4200

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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24 pages, 8239 KiB

Open AccessArticle

X-Net-Based Radar Data Assimilation Study over the Seoul Metropolitan Area

by Ji-Won Lee, Ki-Hong Min, Young-Hee Lee and GyuWon Lee

Remote Sens. 2020, 12(5), 893; https://doi.org/10.3390/rs12050893 - 10 Mar 2020

Cited by 17 | Viewed by 5444

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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19 pages, 4160 KiB

Open AccessArticle

Spatiotemporal Analysis of Precipitation in the Sparsely Gauged Zambezi River Basin Using Remote Sensing and Google Earth Engine

by Hongwei Zeng, Bingfang Wu, Ning Zhang, Fuyou Tian, Elijah Phiri, Walter Musakwa, Miao Zhang, Liang Zhu and Emmanuel Mashonjowa

Remote Sens. 2019, 11(24), 2977; https://doi.org/10.3390/rs11242977 - 11 Dec 2019

Cited by 21 | Viewed by 5524

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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19 pages, 7560 KiB

Open AccessArticle

A Temporal Disaggregation Approach for TRMM Monthly Precipitation Products Using AMSR2 Soil Moisture Data

by Dong Fan, Hua Wu, Guotao Dong, Xiaoguang Jiang and Huazhu Xue

Remote Sens. 2019, 11(24), 2962; https://doi.org/10.3390/rs11242962 - 11 Dec 2019

Cited by 7 | Viewed by 3765

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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24 pages, 3953 KiB

Open AccessEditor’s ChoiceArticle

Evaluation of GPM-era Global Satellite Precipitation Products over Multiple Complex Terrain Regions

by Yagmur Derin, Emmanouil Anagnostou, Alexis Berne, Marco Borga, Brice Boudevillain, Wouter Buytaert, Che-Hao Chang, Haonan Chen, Guy Delrieu, Yung Chia Hsu, Waldo Lavado-Casimiro, Bastian Manz, Semu Moges, Efthymios I. Nikolopoulos, Dejene Sahlu, Franco Salerno, Juan-Pablo Rodríguez-Sánchez, Humberto J. Vergara and Koray K. Yilmaz

Remote Sens. 2019, 11(24), 2936; https://doi.org/10.3390/rs11242936 - 7 Dec 2019

Cited by 86 | Viewed by 7249

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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18 pages, 6948 KiB

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

by Mojtaba Sadeghi, Ata Akbari Asanjan, Mohammad Faridzad, Vesta Afzali Gorooh, Phu Nguyen, Kuolin Hsu, Soroosh Sorooshian and Dan Braithwaite

Remote Sens. 2019, 11(23), 2755; https://doi.org/10.3390/rs11232755 - 23 Nov 2019

Cited by 22 | Viewed by 5255

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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23 pages, 6361 KiB

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

by Yabin Gou, Haonan Chen and Jiafeng Zheng

Remote Sens. 2019, 11(20), 2335; https://doi.org/10.3390/rs11202335 - 9 Oct 2019

Cited by 9 | Viewed by 3800

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 (Z_H), specific attenuation (A_H), 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 (Z_H), specific attenuation (A_H), specific differential phase (K_DP), and double parameters that further integrate the differential reflectivity (Z_DR), namely, R(Z_H, Z_DR), R(K_DP, Z_DR), and R(A_H, Z_DR), 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(A_H, Z_DR) 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 A_H and K_DP can make R(A_H), R(A_H, Z_DR), R(K_DP), and R(K_DP, Z_DR) less effective than R(Z_H) and R(Z_H,Z_DR). Instead, adjustments of the α coefficient can partly reduce such impact and hence render a superior A_H–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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29 pages, 7685 KiB

Open AccessArticle

Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China

by Shanlei Sun, Shujia Zhou, Huayu Shen, Rongfan Chai, Haishan Chen, Yibo Liu, Wanrong Shi, Jia Wang, Guojie Wang and Yang Zhou

Remote Sens. 2019, 11(15), 1805; https://doi.org/10.3390/rs11151805 - 1 Aug 2019

Cited by 19 | Viewed by 4468

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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17 pages, 14042 KiB

Open AccessArticle

The Summertime Diurnal Cycle of Precipitation Derived from IMERG

by Daniel Watters and Alessandro Battaglia

Remote Sens. 2019, 11(15), 1781; https://doi.org/10.3390/rs11151781 - 30 Jul 2019

Cited by 24 | Viewed by 4470

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}^{°} \times {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}^{°} \times {0.1}^{°}

and half-hour resolution. The diurnal cycles of occurrence, intensity and accumulation are determined using four years of data at

2^{°} \times 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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28 pages, 14797 KiB

Open AccessArticle

Evaluation of the Performance of SM2RAIN-Derived Rainfall Products over Brazil

by Franklin Paredes-Trejo, Humberto Barbosa and Carlos A. C. dos Santos

Remote Sens. 2019, 11(9), 1113; https://doi.org/10.3390/rs11091113 - 9 May 2019

Cited by 37 | Viewed by 5690

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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Review

Jump to: Research, Other

41 pages, 6009 KiB

Open AccessFeature PaperEditor’s ChoiceReview

Satellite Remote Sensing of Precipitation and the Terrestrial Water Cycle in a Changing Climate

by Vincenzo Levizzani and Elsa Cattani

Remote Sens. 2019, 11(19), 2301; https://doi.org/10.3390/rs11192301 - 2 Oct 2019

Cited by 114 | Viewed by 20697

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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Other

Jump to: Research, Review

10 pages, 765 KiB

Open AccessPerspective

Future Directions in Precipitation Science

by Francisco J. Tapiador, Anahí Villalba-Pradas, Andrés Navarro, Eduardo García-Ortega, Kyo-Sun Sunny Lim, Kwonil Kim, Kwang Deuk Ahn and Gyuwon Lee

Remote Sens. 2021, 13(6), 1074; https://doi.org/10.3390/rs13061074 - 12 Mar 2021

Cited by 6 | Viewed by 3132

Abstract

Precipitation science is a growing research field. It is concerned with the study of the water cycle from a broad perspective, from tropical to polar research and from solid precipitation to humidity and microphysics. It includes both modeling and observations. Drawing on the [...] Read more.

Precipitation science is a growing research field. It is concerned with the study of the water cycle from a broad perspective, from tropical to polar research and from solid precipitation to humidity and microphysics. It includes both modeling and observations. Drawing on the results of several meetings within the International Collaborative Experiments for the PyeongChang 2018 Olympics and Paralympic Winter Games (ICE-POP 2018), and on two Special Issues hosted by Remote Sensing starting with “Winter weather research in complex terrain during ICE-POP 2018”, this paper completes the “Precipitation and Water Cycle” Special Issue by providing a perspective on the future research directions in the field. Full article

(This article belongs to the Special Issue Precipitation and Water Cycle Measurements Using Remote Sensing)

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