Comparing and contrasting the performance of high-resolution precipitation products via error decomposition and triple collocation: An application to different climate classes of the central Iran

Ghomlaghi, Arash; Nasseri, Mohsen; Bayat, Bardia

doi:10.1016/j.jhydrol.2022.128298

Cited by 12 publications

(4 citation statements)

References 64 publications

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“…Increase of POD, CSI and FBI over orographic MM and HM region in IMERGV7 against IMERGV6 can be attributed to above facts. Compared to all MPEs, PERSIANN showed the worst performance with severe underestimation, retaining the characteristics of its parent datasets PERSIANN‐CCS and PERSIANN‐CDR as in past studies (Dangol et al., 2022; Derin et al., 2016; Du et al., 2022; Ghomlaghi et al., 2022; Xiao et al., 2020). It is attributed to the fact that, cloud classification scheme of PERSIANN mainly relies on manually defined features to extract cloud information.…”

Section: Discussionsupporting

confidence: 69%

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Nepal,

Bao,

et al. 2024

JGR Atmospheres

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Sub‐seasonal to seasonal (S2S) prediction provides an extended range of lead time for decision‐makers across multiple sectors. S2S forecast is crucial for a country that are susceptible to hydro‐meteorological disasters like Nepal. However, S2S forecast requires assessment with reliable datasets before its application. Since, Nepal lacks a dense station network, multi‐source precipitation estimates (MPEs) are the obvious alternatives. Therefore, using classical evaluation metrics and extreme precipitation indices, this study assessed eleven high‐resolution datasets against 159‐gauge stations over Nepal for the 2001–2020 period. These datasets are classified as gauge‐interpolated (APHRODITE), merged (TPMFD, MSWEP, CLDAS, and CHIRPS), satellite‐based (IMERGV7, IMERGV6, CMORPH, and PERSIANN), and reanalysis (ERA5‐L and HAR). Satellite datasets (except IMERGV7) failed to capture the spatial pattern of mean annual precipitation, while others broadly captured it. Most MPEs struggled to accurately estimate wet season precipitation compared to dry season. Furthermore, increasing estimation error from light to extreme precipitation and decreasing skill metrics from flat to complex terrain, demonstrate the intensity and terrain‐specific limitations of MPEs. In terms of precipitation extremes, APHRODITE exhibits the highest skill, followed by MSWEP, TPMFD, HAR, ERA5‐L, IMERGV7, CLDAS, IMERGV6, CHIRPS, CMORPH, and PERSIANN. IMERGV7 exhibits increased skill in detecting extreme precipitation and precipitation over orography against IMERGV6. APHRODITE and TPMFD datasets performed consistently well in all scales, including weekly anomaly, with an average anomaly correlation of 0.84 and 0.66 respectively. However, since APHRODITE is not widely available, TPMFD can serve as a benchmark dataset for evaluating high‐resolution S2S forecasts within the study region.

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Section: Discussionsupporting

confidence: 69%

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Nepal,

Bao,

et al. 2024

JGR Atmospheres

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“…The TerraClimate precipitation datasets, commonly referred to as TERRA, include monthly rainfall data from 1958 to the present. These datasets give information on precipitation with a geographical resolution of 0.04°(4 km) (Ghomlaghi et al 2022). TerraClimate is available online at https://app.climateengine.org/climateEngine.…”

Section: Gridded Precipitation Productsmentioning

confidence: 99%

Evaluation of five gridded precipitation products for estimating precipitation and drought over Yobe, Nigeria

Dawa,

Tan,

Samat

et al. 2024

Water Supply

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Ground observations are often considered as the most reliable and precise source of precipitation data. However, long-term precipitation data from ground observations are lacking in many parts of the world. Gridded precipitation products (GPPs) therefore have emerged as crucial alternatives to ground observations, but it is essential to assess their capability to accurately replicate precipitation patterns. This study aims to evaluate the performance of five GPPs, NASA POWER, TerraClimate, Climate Hazards Group Infrared Precipitation with Climate Data (CHIRPS), GPCC, and Climate Research Unit (CRU), in capturing precipitation and drought patterns from 1981 to 2021 in Yobe, Nigeria. The results indicate that GPCC had good performance at both monthly and annual scales, with high correlation coefficients and low error values. However, it tends to underestimate precipitation amounts in certain areas. Other products also exhibit satisfactory performance with moderate correlations with ground observations. Drought analysis indicates that GPCC outperforms other products in standardised precipitation index-6 calculations, while NASA POWER demonstrates inconsistencies with ground observations, particularly during the early 1980s and mid-2000s. In conclusion, GPCC is the most preferable GPP for precipitation and drought analysis in the Yobe State in Nigeria.

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“…The performance of IMERG under different atmospheric aerosol concentrations was evaluated by comparing them against ground-based observations with commonly-used statics indicators. Normalized Root Mean Squared Error (NRMSE) [38,39] was used to evaluate the precision of IMERG under different atmospheric aerosol concentrations. The probability of detection (POD) and false alarm ratio (FAR) were selected to understand the precipitation capture ability of IMERG.…”

Section: B Evaluation Metricsmentioning

confidence: 99%

Impact of Atmospheric Aerosols on the Accuracy of IMERG Precipitation Estimates Over Northern China

Li,

Wang

et al. 2024

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

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Accurate satellite precipitation estimates are vital for understanding global and largescale regional water cycles. Among the many factors influencing satellite precipitation data quality, the detection and accuracy of precipitation products at different atmospheric aerosol concentrations are not well studied. In this study, we investigated the impact of atmospheric aerosols on the accuracy of satellite precipitation products (IMERG) over North China by comparing performance metrics such as Bias, Normalized Root Mean Squared Error, probability of detection and false alarm ratio under different atmospheric aerosol conditions. The results revealed that: (1) IMERG generally exhibits poorer detectability and quantification under pollution condition. (2) Based on the error decomposition, the estimated errors in autumn and winter were dominated by false biases, which are mainly affected by atmospheric aerosols. (3) At the sensor level, the false alarm ratios (FARs) of both infrared (IR) and passive microwave (PMW) sensors show escalating trends as pollutant concentrations increase. The probability of detection (POD) of IR sensors is affected by pollution. Pollution has a significant impact on IR detection capability. Our findings suggest that atmospheric aerosols may impact the accuracy of IMERG precipitation estimates over Northern China and need to be taken into consideration in the IMERG retrieval process and data utilization.

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Comparing and contrasting the performance of high-resolution precipitation products via error decomposition and triple collocation: An application to different climate classes of the central Iran

Cited by 12 publications

References 64 publications

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Assessing Multi‐Source Precipitation Estimates in Nepal: A Benchmark for Sub‐Seasonal Model Assessment

Evaluation of five gridded precipitation products for estimating precipitation and drought over Yobe, Nigeria

Impact of Atmospheric Aerosols on the Accuracy of IMERG Precipitation Estimates Over Northern China

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