Evaluation of Satellite Precipitation Estimates over Australia

Chua, Zhi‐Weng; Kuleshov, Yuriy; Watkins, Andrew

doi:10.3390/rs12040678

Cited by 46 publications

(25 citation statements)

References 29 publications

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“…It was consistent with the other "best-performing" datasets-the ERA-Interim (ERA-INT) and Global Precipitation Climatology Centre (GPCC) reanalyses [3]. Studies have indicated that significant degradation of performance occurs over elevated topography, particularly during winter where snow and cold surface contamination can become an issue [9,14]. This is a concern given the significant presence of mountainous terrain on the PNG mainland, though the effect of snow and cold surface contamination is only a minor issue confined to the highest peaks of the country.…”

Section: Introductionsupporting

confidence: 79%

“…The gridded comparison demonstrated that the errors in satellite precipitation estimates across PNG can be large in some areas, especially over the mountainous regions. The poor performance of satellites over topography has been shown in earlier studies [9,14]. Cold surface contamination is unlikely to be the source of these large biases over PNG's topography as snow only falls on the highest peaks in PNG and it would lead an overestimation of rainfall rather than the underestimation observed.…”

Section: Validation Studymentioning

confidence: 81%

“…Cold surface contamination is unlikely to be the source of these large biases over PNG's topography as snow only falls on the highest peaks in PNG and it would lead an overestimation of rainfall rather than the underestimation observed. Instead, this underestimation of rainfall is likely due to the poor ability of PMW satellites in detecting warm orographic clouds that are lacking in the ice particles which PMW detection is heavily reliant on [14]. The high spatial variance of rainfall across topography is likely to be another factor adding to the difficulty of satellite retrieval over such areas.…”

Section: Validation Studymentioning

confidence: 99%

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Drought Detection over Papua New Guinea Using Satellite-Derived Products

2020

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This study evaluates the World Meteorological Organization’s (WMO) Space-based Weather and Climate Extremes Monitoring (SWCEM) Demonstration Project precipitation products over Papua New Guinea (PNG). The products evaluated were based on remotely-sensed precipitation, vegetation health, soil moisture, and outgoing longwave radiation (OLR) data. The satellite precipitation estimates of the Climate Prediction Center/National Oceanic and Atmospheric Administration’s (CPC/NOAA) morphing technique (CMORPH) and Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP) were assessed on a monthly timescale over an 18-year period from 2001 to 2018. Station data along with the ERA5 reanalysis were used as the reference datasets for assessment purposes. In addition, a case study was performed to investigate how well the SWCEM precipitation products characterised drought in PNG associated with the 2015–2016 El Niño. Overall statistics from the validation study suggest that although there remains significant variability between satellite and ERA5 rainfall data in remote areas, this difference is much less at locations where rain gauges exist. The case study illustrated that the Vegetation Health Index (VHI), OLR anomaly and the Standardized Precipitation Index (SPI) were able to reliably capture the spatial and temporal aspects of the severe 2015–2016 El Niño-induced drought in PNG. Of the three, VHI appeared to be the most effective, in part due to its reduced incidence of false alarms. This study is novel as modern-day satellite-derived products have not been evaluated over PNG before. A focus on their value in monitoring drought can bring great value in mitigating the impact of future droughts. It is concluded that these satellite-derived precipitation products could be recommended for operational use for drought detection and monitoring in PNG, and that even a modest increase in ground-based observations will increase the accuracy of satellite-derived observations remotely.

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Section: Introductionsupporting

confidence: 79%

Section: Validation Studymentioning

confidence: 81%

Section: Validation Studymentioning

confidence: 99%

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Drought Detection over Papua New Guinea Using Satellite-Derived Products

2020

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“…In this study, the CMORPH Blended (CMORPH-BLD) product which further incorporates gauge data through optimal interpolation was selected for evaluation. CMORPH-BLD has been shown to be more accurate than the gauge-corrected product CMORPH-CRT, over regions where a dense gauge network exists [27], though performance was similar when gauge density is low such as, for example, over PNG [28].…”

Section: Datasetsmentioning

confidence: 99%

Evaluation of Satellite Precipitation Estimates over the South West Pacific Region

2021

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Rainfall estimation over the Pacific region is difficult due to the large distances between rain gauges and the high convection nature of many rainfall events. This study evaluates space-based rainfall observations over the South West Pacific Region from the Japan Aerospace Exploration Agency’s (JAXA) Global Satellite Mapping of Precipitation (GSMaP), the USA National Oceanographic and Atmospheric Administration’s (NOAA) Climate Prediction Center morphing technique (CMORPH), the Climate Hazards group Infrared Precipitation with Stations (CHIRPS), and the National Aeronautics and Space Administration's (NASA) Integrated Multi-Satellite Retrievals for GPM (IMERG). The technique of collocation analysis (CA) is used to compare the performance of monthly satellite precipitation estimates (SPEs). Multi-Source Weighted-Ensemble Precipitation (MSWEP) was used as a reference dataset to compare with each SPE. European Centre for Medium-range Weather Forecasts' (ECMWF) ERA5 reanalysis was also combined with Soil Moisture-2-Rain–ASCAT (SM2RAIN–ASCAT) to perform triple CA for the six sub-regions of Fiji, New Caledonia, Papua New Guinea (PNG), the Solomon Islands, Timor, and Vanuatu. It was found that GSMaP performed best over low rain gauge density areas, including mountainous areas of PNG (the cross-correlation, CC = 0.64), and the Solomon Islands (CC = 0.74). CHIRPS had the most consistent performance (high correlations and low errors) across all six sub-regions in the study area. Based on the results, recommendations are made for the use of SPEs over the South West Pacific Region.

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“…Further, they showed that the zonal mean of precipitation extremes greatly differed and had biases among datasets. Chua et al [14] validated the U.S. National Oceanographic and Atmospheric Administration (NOAA) Climate Prediction Center morphing technique (CMORPH) ( [15]) and the GSMaP over Australia, and demonstrated that satellite precipitation estimates exhibited good skills over Australia and that the skill of satellite precipitation estimates was highly dependent on rainfall intensity. Therefore, detecting precipitation extremes by using satellite-derived precipitation products requires an understanding of the characteristics of the product being used and an examination of a suitable method of detection.…”

Section: Introductionmentioning

confidence: 99%

Precipitation Extremes Monitoring Using the Near-Real-Time GSMaP Product

Tashima

Kubota

Mega

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A need to monitor precipitation extremes from space is widely recognized, especially for regions where ground-based observations are limited or unavailable. This paper examines the usefulness of precipitation extremes monitoring using the Global Satellite Mapping of Precipitation (GSMaP) near-real-time product in the East Asia and Western Pacific region-one of the world's most disaster-prone regions. With case studies and statistical analysis, heavy rainfall and drought detected using the GSMaP Near-real-time Gauge-adjusted Rainfall Product (GNRT6) were validated. Heavy rainfall for daily and weekly precipitation and short-term drought from one month up to three months were defined by a 90th percentile threshold or more and the Standardized Precipitation Index over periods from April 2000 to March 2019, respectively. The results of analyses suggested that the detectability varied depending on the region, such as good detection in dry areas and poor detection in rainy island nations. While the accuracy of GNRT6 is confirmed as being generally better than that of the satellite-only uncorrected product, low detectability can be caused by coarse resolutions of parameters used in the gauge-adjustment technique of the GNRT6 and is regarded as a future task.

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Evaluation of Satellite Precipitation Estimates over Australia

Cited by 46 publications

References 29 publications

Drought Detection over Papua New Guinea Using Satellite-Derived Products

Drought Detection over Papua New Guinea Using Satellite-Derived Products

Evaluation of Satellite Precipitation Estimates over the South West Pacific Region

Precipitation Extremes Monitoring Using the Near-Real-Time GSMaP Product

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