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Remote Sensing of Precipitation: Part III

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

Deadline for manuscript submissions: closed (31 January 2023) | Viewed by 21745

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Dr. Silas Michaelides

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Eratosthenes Centre of Excellence, Saripolou 2-6, 3036, Achilleos 2 Building, Lemesos, Cyprus
Interests: meteorology; atmospheric remote sensing
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Dear Colleagues,

Precipitation is fundamental in global water and energy balances. The accurate and timely understanding of its characteristics at global, regional, and local scales is indispensable for a clearer insight into the mechanisms underlying the Earth’s complex atmosphere–ocean system. Precipitation is one element that is documented to be greatly affected by climate change.

In its various forms, precipitation comprises the primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively in applications ranging from irrigation to industrial and household usage.

The remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation. This includes ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne sensors.

This Special Issue will host papers on all aspects of the remote sensing of precipitation, including applications that embrace the use of remote sensing techniques of precipitation in tackling issues such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modeling, including validation, instrument comparison, and calibration; understanding cloud microphysical properties; precipitation downscaling; precipitation droplet size distribution; the assimilation of remotely sensed precipitation into numerical weather prediction models; the measurement of precipitable water vapor, etc. Also, papers on new technological advances, as well as campaigns and missions on precipitation remote sensing (e.g., TRMM, GPM), are welcome.

Dr. Silas Michaelides
Guest Editor

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Keywords

precipitation
weather radar
quantitative precipitation estimation (QPE)
underwater precipitation remote sensing
cloud microphysical properties
TRMM and GPM

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Published Papers (11 papers)

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Editorial

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3 pages, 188 KiB

Open AccessEditorial

Editorial for Special Issue “Remote Sensing of Precipitation: Part III”

by Silas Michaelides

Remote Sens. 2023, 15(12), 2964; https://doi.org/10.3390/rs15122964 - 7 Jun 2023

Viewed by 743

Abstract

This Special Issue of Remote Sensing, which is the third in a series entitled “Remote Sensing of Precipitation”, comprises a collection of ten papers devoted to remote sensing applications for measuring precipitation; these include new satellite technologies for the remote sensing of precipitation, the validation of satellite-based precipitation estimates using rain gauge measurements and surface radar estimates, and comparisons between gridded precipitation data [...] Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

Research

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31 pages, 6329 KiB

Open AccessArticle

A Preliminary Assessment of the GSMaP Version 08 Products over Indonesian Maritime Continent against Gauge Data

by Ravidho Ramadhan, Marzuki Marzuki, Helmi Yusnaini, Robi Muharsyah, Fredolin Tangang, Mutya Vonnisa and Harmadi Harmadi

Remote Sens. 2023, 15(4), 1115; https://doi.org/10.3390/rs15041115 - 18 Feb 2023

Cited by 11 | Viewed by 2204

Abstract

This study is a preliminary assessment of the latest version of the Global Satellite Measurement of Precipitation (GSMaP version 08) data, which were released in December 2021, for the Indonesian Maritime Continent (IMC), using rain gauge (RG) observations from December 2021 to June 2022. Assessments were carried out with 586 rain gauge (RG) stations using a point-to-pixel approach through continuous statistical and contingency table metrics. It was found that the coefficient correlation (CC) of GSMaP version 08 products against RG observations varied between low (CC = 0.14–0.29), moderate (CC = 0.33–0.45), and good correlation (CC = 0.72–0.75), for the hourly, daily, and monthly scales with a tendency to overestimate, indicated by a positive relative bias (RB). Even though the correlation of hourly data is still low, GSMaP can still capture diurnal patterns in the IMC, as indicated by the compatibility of the estimated peak times for the precipitation amount and frequency. GSMaP data also manage to observe heavy rainfall, as indicated by the good of detection (POD) values for daily data ranging from probability 0.71 to 0.81. Such a good POD value of daily data is followed by a relatively low false alarm ratio (FAR) (FAR < 0.5). However, the GSMaP overestimates light rainfall (R < 1 mm/day); as a consequence, it overestimates the consecutive wet days (CWD) and number of days with rainfall ≥ 1 mm (R1mm) indices, and underestimates the consecutive dry days (CDD) extreme rain index. GSMaP daily data accuracy depends on IMC’s topographic conditions, especially for GSMaP real-time data. Of all GSMaP version 08 products evaluated, outperformed post-real-time non-gauge-calibrated (GSMaP_MVK), and followed by post-real-time gauge-calibrated (GSMaP_Gauge), near-real-time gauge-calibrated (GSMaP_NRT_G), near-real-time non-gauge-calibrated (GSMaP_NRT), real-time gauge-calibrated (GSMaP_Now_G), and real-time non-gauge-calibrated (GSMaP_Now). Thus, GSMaP near-real-time data have the potential for observing rainfall in IMC with faster latency. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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18 pages, 3652 KiB

Open AccessArticle

Is the Gridded Data Accurate? Evaluation of Precipitation and Historical Wet and Dry Periods from ERA5 Data for Canadian Prairies

by Thiago Frank, Carlos Antonio da Silva Junior, Krystopher J. Chutko, Paulo Eduardo Teodoro, José Francisco de Oliveira-Júnior and Xulin Guo

Remote Sens. 2022, 14(24), 6347; https://doi.org/10.3390/rs14246347 - 15 Dec 2022

Cited by 2 | Viewed by 1944

Abstract

Precipitation is crucial for the hydrological cycle and is directly related to many ecological processes. Historically, measurements of precipitation totals were made at weather stations, but spatial and temporal coverage suffered due to the lack of a robust network of weather stations and temporal gaps in observations. Several products have been proposed to identify the location of the occurrence of precipitation and measure its intensity from different types of estimates, based on alternative data sources, that have global (or quasi-global) coverage with long historical time series. However, there are concerns about the accuracy of these estimates. The objective of this study is to evaluate the accuracy of the ERA5 product for two ecoregions of the Canadian Prairies through comparison with monthly means measured from 1981–2019 at ten weather stations (in-situ), as well as to assess the intraseasonal variability of precipitation and identify dry and wet periods based on the annual Standardized Precipitation Index (SPI) derived from ERA5. A significant relationship between in-situ data and ERA5 data (with the R² varying between 0.42 and 0.76) (p < 0.01)) was observed in nine of the ten weather stations analyzed, with lower RMSE in the Mixed Ecoregion. The Mean Absolute Percentage Error (MAPE) results showed greater agreement between the datasets in May (average R value of 0.84 and an average MAPE value of 32.33%), while greater divergences were observed in February (average R value of 0.57 and an average MAPE value of 50.40%). The analysis of wet and dry periods, based on the SPI derived from ERA5, and the comparison with events associated with the El Niño-Southern Oscillation (ENSO), showed that from the ERA5 data and the derivation of the SPI it is possible to identify anomalies in temporal series with consistent patterns that can be associated with historical events that have been highlighted in the literature. Therefore, our results show that ERA5 data has potential to be an alternative for estimating precipitation in regions with few in-situ stations or with gaps in the time series in the Canadian Prairies, especially at the beginning of the growing season. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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20 pages, 3692 KiB

Open AccessArticle

Evaluation of Six Satellite Precipitation Products over the Chinese Mainland

by Zhenwei Liu, Zhenhua Di, Peihua Qin, Shenglei Zhang and Qian Ma

Remote Sens. 2022, 14(24), 6277; https://doi.org/10.3390/rs14246277 - 11 Dec 2022

Cited by 9 | Viewed by 1841

Abstract

Satellite precipitation products have been applied to many research fields due to their high spatial and temporal resolution. However, satellite inversion of precipitation is indirect, and different inversion algorithms limit the accuracy of the measurement results, which leads to great uncertainty. Therefore, it is of great significance to quantify and record the error characteristics of different satellite precipitation products for their better application in hydrology and other research fields. In this study, based on CN05.1, which is a set of site–based interpolation data, we evaluated the accuracies of the six satellite precipitation datasets (IMERG–E, IMERG–L, IMERG–F, GSMaP, CMORPH, and PERSIANN–CDR) at different temporal scales (daily, monthly, and yearly) in mainland China for the period from 2001 to 2015. The results were as follows: (1) In terms of mean precipitation, IMERG–F was superior to other data in all areas. IMERG products and PERANN–CDR performed better than other products at all scales and were more suitable for precipitation research in mainland China. Site correction can effectively improve the accuracy of product inversion, so IMERG–F was significantly better than IMERG–E and IMERG–L. (2) Except PERSIANN–CDR, all precipitation products underestimated precipitation in the range of 1–4 mm/day and had a high coincidence with CN05.1 in the range of 4–128 mm/day. (3) The performance of six types of satellite precipitation products in summer was better than that in winter. However, the error was larger in seasons with more precipitation. (4) In the Qinghai–Tibet Plateau, where there are few stations, the inversion of precipitation by satellite products is closer to the actual situation, which is noteworthy. These results help users understand the characteristics of these products and improve algorithms for future algorithm developers. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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16 pages, 6501 KiB

Open AccessArticle

Comprehensive Analysis of PERSIANN Products in Studying the Precipitation Variations over Luzon

by Jie Hsu, Wan-Ru Huang and Pin-Yi Liu

Remote Sens. 2022, 14(22), 5900; https://doi.org/10.3390/rs14225900 - 21 Nov 2022

Cited by 7 | Viewed by 1802

Abstract

This study evaluated the capability of satellite precipitation estimates from five products derived from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (including PERSIANN, PERSIANN-CCS, PERSIANN-CDR, PERSIANN-CCS-CDR, and PDIR-Now) to represent precipitation characteristics over Luzon. The analyses focused on monthly and daily timescales from 2003–2015 and adopted surface observations from the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) platform as the evaluation base. Among the five satellite precipitation products (SPPs), PERSIANN-CDR was observed to possess a better ability to qualitatively and quantitatively estimate spatiotemporal variations of precipitation over Luzon for the majority of the examined features with the exception of the extreme precipitation events, for which PERSIANN-CCS-CDR is superior to the other SPPs. These results highlight the usefulness of the addition of the cloud patch approach to PERSIANN-CDR to produce PERSIANN-CCS-CDR to depict the characteristics of extreme precipitation events over Luzon. A similar advantage of adopting the cloud patch approach in producing extreme precipitation estimates was also revealed from the comparison of PERSIANN, PERSIANN-CCS, and PDIR-Now. Our analyses also highlighted that all PERSIANN-series exhibit improved skills in regard to detecting precipitation characteristics over west Luzon compared to that over east Luzon. To overcome this weakness, we suggest that an adjustment in the cloud patch approach (e.g., using different cloud temperature thresholds or different brightness temperature and precipitation rate relationships) over east Luzon may be helpful. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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22 pages, 9500 KiB

Open AccessArticle

Performance Assessment of GPM IMERG Products at Different Time Resolutions, Climatic Areas and Topographic Conditions in Catalonia

by Eric Peinó, Joan Bech and Mireia Udina

Remote Sens. 2022, 14(20), 5085; https://doi.org/10.3390/rs14205085 - 12 Oct 2022

Cited by 9 | Viewed by 2047

Abstract

Quantitative Precipitation Estimates (QPEs) from the Integrated Multisatellite Retrievals for GPM (IMERG) provide crucial information about the spatio-temporal distribution of precipitation in semiarid regions with complex orography, such as Catalonia (NE Spain). The network of automatic weather stations of the Meteorological Service of Catalonia is used to assess the performance of three IMERG products (Early, Late and Final) at different time scales, ranging from yearly to sub-daily periods. The analysis at a half-hourly scale also considered three different orographic features (valley, flat and ridgetop), diverse climatic conditions (BSk, Csa, Cf and Df) and five categories related to rainfall intensity (light, moderate, intense, very intense and torrential). While IMERG_E and IMERG_L overestimate precipitation, IMERG_F reduces the error at all temporal scales. However, the calibration to which a Final run is subjected causes underestimation regardless in some areas, such as the Pyrenees mountains. The proportion of false alarms is a problem for IMERG, especially during the summer, mainly associated with the detection of false precipitation in the form of light rainfall. At sub-daily scales, IMERG showed high bias and very low correlation values, indicating the remaining challenge for satellite sensors to estimate precipitation at high temporal resolution. This behaviour was more evident in flat areas and cold semi-arid climates, wherein overestimates of more than 30% were found. In contrast, rainfall classified as very heavy and torrential showed significant underestimates, higher than 80%, reflecting the inability of IMERG to detect extreme sub-daily precipitation events. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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27 pages, 10400 KiB

Open AccessArticle

Performance Evaluation of Six Gridded Precipitation Products throughout Iran Using Ground Observations over the Last Two Decades (2000–2020)

by Arsalan Ghorbanian, Ali Mohammadzadeh, Sadegh Jamali and Zheng Duan

Remote Sens. 2022, 14(15), 3783; https://doi.org/10.3390/rs14153783 - 6 Aug 2022

Cited by 8 | Viewed by 2287

Abstract

Precipitation, as an important component of the Earth’s water cycle, plays a determinant role in various socio-economic practices. Consequently, having access to high-quality and reliable precipitation datasets is highly demanded. Although Gridded Precipitation Products (GPPs) have been widely employed in different applications, the lack of quantitative assessment of GPPs is a critical concern that should be addressed. This is because the inherent errors in GPPs would propagate into any models in which precipitation values are incorporated, introducing uncertainties into the final results. This paper aims to quantify the capability of six well-known GPPs (TMPA, CHIRPS, PERSIANN, GSMaP, IMERG, and ERA5) at multiple time scales (daily, monthly, and yearly) using in situ observations (over 1.7 million) throughout Iran over the past two decades (2000–2020). Both continuous and categorical metrics were implemented for precipitation intensity and occurrence assessment based on the point-to-pixel comparison approach. Although all metrics did not support the superior performance of any specific GPP, taking all investigations into account, the findings suggested the better performance of the Global Satellite Mapping of Precipitation (GSMaP) in estimating daily precipitation (CC = 0.599, RMSE = 3.48 mm/day, and CSI = 0.454). Based on the obtained continuous metrics, all the GPPs had better performances in dry months, while this did not hold for the categorical metrics. The validation at the station level was also carried out to present the spatial characteristics of errors throughout Iran, indicating higher overestimation/underestimation in regions with higher precipitation rates. The validation analysis over the last two decades illustrated that the GPPs had stable performances, and no improvement was seen, except for the GSMaP, in which its bias error was significantly reduced. The comparisons on monthly and yearly time scales suggested the higher accuracy of monthly and yearly averaged precipitation values than accumulated values. Our study provides valuable guidance to the selection and application of GPPs in Iran and also offers beneficial feedback for further improving these products. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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19 pages, 5450 KiB

Open AccessArticle

Validation of IMERG Oceanic Precipitation over Kwajalein

by Jianxin Wang, David B. Wolff, Jackson Tan, David A. Marks, Jason L. Pippitt and George J. Huffman

Remote Sens. 2022, 14(15), 3753; https://doi.org/10.3390/rs14153753 - 5 Aug 2022

Cited by 8 | Viewed by 1909

Abstract

The integrated Multi-satellitE Retrievals for GPM (IMERG) Version V05B and V06B precipitation products from the Global Precipitation Measurement (GPM) mission are validated against ground-based observations from the Kwajalein Polarimetric S-band Weather Radar (KPOL) deployed at Kwajalein Atoll in the central Pacific Ocean. Such a validation is particularly important as comprehensive surface measurements over the oceans are practically infeasible, which hampers the identification of possible errors, and improvement of future versions of IMERG and other satellite-based retrieval algorithms. The V05B and V06B IMERG products are validated at their native 0.1°, 30 min resolution from 2014 to 2018 based on both volumetric and categorical metrics. This validation study indicates that precipitation rates from both IMERG V05B and V06B are underestimated with respect to radar surface estimates, but the underestimation is much reduced from V05B to V06B. IMERG V06B outperforms V05B with reduced systematic bias and improved precipitation detectability. The IMERG performance is further traced back to its individual sensors and morphing-based algorithms. The overall underestimation in V05B is mainly driven by the negative relative biases from morphing-based algorithms which are largely corrected in V06B. Imagers perform generally better than sounders because of the usage of low-frequency channels in imagers which can better detect emission signals by the hydrometeors. Among imagers, the GPM Microwave Imager (GMI) and Advanced Microwave Scanning Radiometer Version 2 (AMSR2) are the best, followed by Special Sensor Microwave Imager/Sounder (SSMIS). Among sounders, the Microwave Humidity Sounder (MHS) is the best, followed by Advanced Technology Microwave Sounder (ATMS) and the Sounder for Atmospheric Profiling of Humidity in the Intertropics by Radiometry (SAPHIR) for V06B. Among all categories, morph-only and IR + morph only perform better than SAPHIR. SAPHIR shows the worst performance among all categories, likely due to its limited channel selection. It is envisaged that these results will improve our understanding of IMERG performance over oceans and aid in the improvement of future versions of IMERG. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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19 pages, 4254 KiB

Open AccessArticle

A Simple Statistical Model of the Uncertainty Distribution for Daily Gridded Precipitation Multi-Platform Satellite Products

by Rômulo A. J. Oliveira and Rémy Roca

Remote Sens. 2022, 14(15), 3726; https://doi.org/10.3390/rs14153726 - 3 Aug 2022

Cited by 3 | Viewed by 2008

Abstract

Multi-platform satellite-based precipitation gridded estimates are becoming widely available in support of climate monitoring and climate science. The characterization of the performances of these emerging Level-4 products is an active field of research. This study introduced a simple Gaussian mixture model (GMM) to characterize the distribution of uncertainty in these satellite products. The following three types of uncertainty were analyzed: constellation changes-induced uncertainties, sampling uncertainties and comparison with rain-gauges. The GMM was systematically compared with a single Gaussian approach and shown to perform well for the variety of uncertainties under consideration regardless of the precipitation levels. Additionally, GMM has also been demonstrated to be effective in evaluating the impact of Level-2 PMW rain estimates’ detection threshold definition on the constellation changes-induced uncertainty characteristics at Level-4. This simple additive perspective opens future avenues for better understanding error propagation from Level-2 to Level-4. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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12 pages, 8712 KiB

Open AccessArticle

Precipitation Estimation from the NASA TROPICS Mission: Initial Retrievals and Validation

by Chris Kidd, Toshi Matsui, William Blackwell, Scott Braun, Robert Leslie and Zach Griffith

Remote Sens. 2022, 14(13), 2992; https://doi.org/10.3390/rs14132992 - 22 Jun 2022

Cited by 9 | Viewed by 1836

Abstract

This paper describes the initial results of precipitation estimates from the Time-Resolved Observations of Precipitation structure and storm Intensity with a Constellation of Smallsats (TROPICS) Millimeter-wave Sounder (TMS) using the Precipitation Retrieval and Profiling Scheme (PRPS). The TROPICS mission consists of a Pathfinder CubeSat and a constellation of six CubeSats, providing a low-cost solution to the frequent sampling of precipitation systems across the Tropics. The TMS instrument is a 12-channel cross-track scanning radiometer operating at frequencies of 91.655 to 204.8 GHz, providing similar resolutions to current passive microwave sounding instruments. These retrievals showcase the potential of the TMS instrument for precipitation retrievals. The PRPS has been modified for use with the TMS using a database based upon observations from current sounding sensors. The results shown here represent the initial postlaunch version of the retrieval scheme, as analyzed for the Pathfinder CubeSat launched on 30 June 2021. In terms of monthly precipitation estimates, the results fall within the mission specifications and are similar in performance to retrievals from other sounding instruments. At the instantaneous scale, the results are very promising. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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20 pages, 3845 KiB

Open AccessTechnical Note

Where Can IMERG Provide a Better Precipitation Estimate than Interpolated Gauge Data?

by Samantha H. Hartke and Daniel B. Wright

Remote Sens. 2022, 14(21), 5563; https://doi.org/10.3390/rs14215563 - 4 Nov 2022

Cited by 7 | Viewed by 1672

Abstract

Although rain gauges provide valuable point-based precipitation observations, gauge data is globally sparse, necessitating interpolation between often-distant measurement locations. Interpolated gauge data is subject to uncertainty just as other precipitation data sources. Previous studies have focused either on the effect of decreasing gauge density on interpolated gauge estimate performance or on the ability of gauge data to accurately assess satellite multi-sensor precipitation data as a function of gauge density. No previous work has directly compared the performance of interpolated gauge estimates and satellite precipitation data as a function of gauge density to identify the gauge density at which satellite precipitation data and interpolated estimates have similar accuracy. This study seeks to provide insight into interpolated gauge product accuracy at low gage densities using a Monte Carlo interpolation scheme at locations across the continental U.S. and Brazil. We hypothesize that the error in interpolated precipitation estimates increases drastically at low rain gauge densities and at high distances to the nearest gauge. Results show that the multisatellite precipitation product, IMERG, has comparable performance in precipitation detection to interpolated gauge data at very low gauge densities (i.e., less than 2 gauges/10,000 km²) and that IMERG often outperforms interpolated data when the distance to the nearest gauge used during interpolation is greater than 80–100 km. However, there does not appear to be a consistent relationship between this performance ‘break point’ and the geographical variables of elevation, distance to coast, and annual precipitation. Full article

(This article belongs to the Special Issue Remote Sensing of Precipitation: Part III)

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