Gridded Precipitation Products Research Articles

Impact Assessment of Gridded Precipitation Products on Streamflow Simulations over a Poorly Gauged Basin in El Salvador

In this study, five open access gridded precipitation (GP) products (CFSR, MSWEPv1.1, PERSIANN-CDR, CMORPH, and CHIRPSv2.0) and local climate data were evaluated over the Grande de San Miguel (GSM) River Basin in El Salvador. The main purpose was to identify optional data sources of precipitation for hydrological modelling given that ground-based precipitation gauges in El Salvador are scarce and their data includes important temporal and spatial gaps. Firstly, a direct comparison was made between the precipitation data from the five GP products and from the rain gauges. Secondly, the SWAT model was used to simulate the streamflow regimen based on the precipitation datasets. The analysis of results showed that the models produced correct predictions, and the accuracy increased as models were calibrated to each specific precipitation product. Overall, PERSIANN-CDR produced the best simulation results, including streamflow predictions in the GSM basin, and outperformed other GP products and also the results obtained from data precipitation gauges. The findings of this research support the hydrological modelling based on open-access GP products, particularly when the data from precipitation gauges are scarce and poor.

Regional and Seasonal Precipitation and Drought Trends in Ganga–Brahmaputra Basin

Satellite-based precipitation products can be a better alternative of rain gauges for hydro-meteorological studies in data-poor regions. This study aimed to evaluate how regional and seasonal precipitation and drought patterns had changed in the Ganga–Brahmaputra Basin between 1983 and 2020 with PERSIANN-CDR precipitation data. The spatial pattern of winter drought, monsoon drought, and Standardized Precipitation Index (SPI) calculated for different time scales were evaluated using principal component analysis. Ganga–Brahmaputra is one of the most populated river basins that flows through different geographical regions. Rain gauges are heterogeneously distributed in the basin due to its complex orography, highlighting the significance of gridded precipitation products over gauge observations for climate studies. Annual and monthly precipitation trends between 1983 and 2020 were evaluated using the original and modified Mann–Kendall trend test, and annual precipitation in the basin was found to be declining at a rate of 5.8 mm/year. An increasing trend was observed in pre-monsoon rainfall, whereas precipitation exhibited a decreasing trend for other months. Results of the Pettitt test showed precipitation time series was inhomogeneous and changepoint occurred around 2000. Decreasing trends of SPI indicated increasing frequency and intensity of drought events. Winter drought showed a clear spatial pattern in the basin; however, SPIs calculated for different time scales and monsoon drought had complex spatial patterns. This study demonstrates the applicability of satellite-based PERSIANN-CDR precipitation data in climate research in the Ganga–Brahmaputra Basin.

Comprehensive Evaluations on the Error Characteristics of the State‐of‐the‐Art Gridded Precipitation Products Over Jiangxi Province in 2019

AbstractAccurate knowledge of the precipitation estimates with high quality and fine spatiotemporal resolutions is crucial to the precipitation science communities, especially over poorly gauged regions. In this study, we apply various validation metrics to comprehensively evaluate three latest model‐based and satellite‐based precipitation products, namely the fifth‐generation global atmospheric analysis data set of the European Center for Medium‐Range Weather Forecasts (ECMWF) (ERA5), ERA5‐Land and Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (IMERG) Final Run (IMERG‐Final), against hourly rain gauge observations over Jiangxi province, south central China, in 2019. The main conclusions of this study include but not limited to: (a) considering the precipitation amount, both reanalysis precipitation data and satellite‐based precipitation products have similar spatial patterns and show overestimations; (b) IMERG‐Final generally outperforms ERA5 and ERA5‐Land at multiple temporal scales, especially in terms of CC, FAR and MFI; (c) ERA5 and ERA5‐Land precipitation products have similar spatiotemporal error characteristics, and ERA5‐Land performs better than ERA5; (d) in the respect of the capabilities of capturing precipitation events, the spatial characteristics of IMERG‐Final are the closest to those of rain gauges, while ERA5 and ERA5‐Land significantly overestimate the event duration and underestimate the mean event precipitation rate. These findings could help the state‐of‐the‐art model‐based and satellite‐based precipitation products improve the data quality in the future generations.

Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events

AbstractThe performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979–2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation‐conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio‐temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi‐model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra‐annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well‐simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario.

Associated atmospheric mechanisms for the increased cold season precipitation over the Three-River Headwaters region from the late 1980s

AbstractPrecipitation in the Three-River Headwater (TRH) region has undergone significant changes as a result of global warming, which can affect water resources in downstream regions of Asia. However, the underlying mechanisms of the precipitation variability during the cold season (October to April), are still not fully understood. In this study, the daily China gridded precipitation product of CN05.1 as well as the NCEP-NCAR reanalysis are used to investigate the characteristics of the cold season precipitation variability over the TRH region and associated atmospheric mechanisms. The cold season precipitation shows an increasing trend (5.5 mm decade-1) from 1961 to 2014, with a dry-to-wet shift in around the late 1980s. The results indicate that the increased precipitation is associated with the enhanced easterly anomalies over the Tibetan Plateau (TP) and enhanced southeasterly water vapor transport. The enhanced Walker circulations, caused by the gradients of sea surface temperature between the equatorial central-eastern Pacific and Indo-western Pacific in tropical oceans, resulted in strengthened easterly anomalies over the TP and the westward expansion of the anticyclone in the western North Pacific. Meanwhile, the changed Walker circulation is accompanied by a strengthened local Hadley circulation which leads to enhanced meridional water vapor transport from tropical oceans and the South China Sea toward the TRH region. Furthermore, the strengthened East Asia Subtropical Westerly jet may contribute to the enhanced divergence at upper level and anomalous ascending motion above the TRH region leading to more precipitation.

Integrated meteorological drought monitoring framework using multi-sensor and multi-temporal earth observation datasets and machine learning algorithms: A case study of central India

Devising strategies for drought planning and risk mitigation in predominantly small holder agriculture regions like South Asia requires improved understanding of spatio-temporal characteristics of droughts at finer spatial scales. In this study, an integrated framework is developed to generate high-resolution gridded precipitation products from publicly available coarse scale data, and use the downscaled products for analysis of meteorological droughts. Data from a region in Central India is used to develop and test the framework. The Standard Precipitation Index (SPI) is used to characterize meteorological drought. The proposed framework incorporates: (i) a Random Forest machine learning algorithm to downscale CHIRPS (5 km) gridded long term rainfall data to a higher resolution gridded product (1 km) using high resolution multi-sensor and multi-temporal earth observation data of land surface characteristics (vegetation, temperature, and topography) as cofactors; (ii) Computation of 3-month SPI (SPI-3) for the downscaled gridded rainfall; (iii) Run theory applied to downscaled SPI grid to determine drought characteristics - duration, severity, intensity, frequency, and onset and cessation of droughts, and (iv) Principal Components Analysis (PCA) to combine effects of multiple drought characteristics into a single composite Effective Meteorological Drought Index (EMDI), to identify sub-regional drought patterns. The downscaled gridded precipitation product (1 km) was validated using: (i) original CHIRPS (5 km) data, and (ii) measured rainfall data from 65 rain gauge stations in the study area, with respect to conservation of both statistical properties and spatial structure. Comparisons were also made with drought characteristics obtained from Standardized Evapotranspiration Index (SPEI) derived from MODIS evapotranspiration data. The downscaled product captures spatio-temporal variability of droughts at finer village and sub-village scales, compared to original CHIRPS (5 km) data for which each pixel encompassed multiple villages. Spatial distribution of composite index EMDI delineates the study area into two characteristic drought risk regions. In one region, droughts are of longer duration, but are less frequent, with lower severity and intensity. In the second region droughts are of shorter duration but of higher severity, intensity and frequency. The integrated framework developed in this study for high resolution spatio-temporal analysis of droughts, starting from coarse scale precipitation data, is practical and sufficiently general to adopt in other regions to support local drought risk planning and targeting mitigation decisions and actions.

Global evaluation of gridded satellite precipitation products from the NOAA Climate Data Record program

AbstractThree satellite gridded daily precipitation datasets: PERSIANN-CDR, GPCP, and CMORPH, that are part of the NOAA/Climate Data Record (CDR) program are evaluated in this work. The three satellite precipitation products (SPPs) are analyzed over their entire period of record, ranging from over 20-year to over 35-year. The products inter-comparisons are performed at various temporal (daily to annual) and for different spatial domains in order to provide a detailed assessment of each SPP strengths and weaknesses. This evaluation includes comparison with in-situ data sets from the Global Historical Climatology Network (GHCN-Daily) and the US Climate Reference Network (USCRN). While the three SPPs exhibited comparable annual average precipitation, significant differences were found with respect to the occurrence and the distribution of daily rainfall events, particularly in the low and high rainfall rate ranges. Using USCRN stations over CONUS, results indicated that CMORPH performed consistently better than GPCP and PERSIANN-CDR for the usual metrics used for SPP evaluation (bias, correlation, accuracy, probability of detection, and false alarm ratio among others). All SPPs were found to underestimate extreme rainfall (i.e. above the 90th percentile) from about -20% for CMORPH to -50% for PERSIANN-CDR. Those differences in performance indicate that the use of each SPP has to be considered with respect to the application envisioned; from the long-term qualitative analysis of hydro-climatological properties to the quantification of daily extreme events for example. In that regard, the three satellite precipitation CDRs constitute a unique portfolio that can be used for various long-term climatological and hydrological applications.

An IMERG-Based Optimal Extended Probabilistic Climatology (EPC) as a Benchmark Ensemble Forecast for Precipitation in the Tropics and Subtropics

AbstractCurrent numerical weather prediction models show limited skill in predicting low-latitude precipitation. To aid future improvements, be it with better dynamical or statistical models, we propose a well-defined benchmark forecast. We use the arguably best currently high-resolution, gauge-calibrated, gridded precipitation product, the Integrated Multi-Satellite Retrievals for GPM (Global Precipitation Measurement) (IMERG) “final run” in a ± 15-day window around the date of interest to build an empirical climatological ensemble forecast. This window size is an optimal compromise between statistical robustness and flexibility to represent seasonal changes. We refer to this benchmark as Extended Probabilistic Climatology (EPC) and compute it on a 0.1°×0.1° grid for 40°S–40°N and the period 2001–2019. In order to reduce and standardize information, a mixed Bernoulli-Gamma distribution is fitted to the empirical EPC, which hardly affects predictive performance. The EPC is then compared to 1-day ensemble predictions from the European Centre for Medium-Range Weather Forecasts (ECMWF) using standard verification scores. With respect to rainfall amount, ECMWF performs only slightly better than EPS over most of the low latitudes and worse over high-mountain and dry oceanic areas as well as over tropical Africa, where the lack of skill is also evident in independent station data. For rainfall occurrence, EPC is superior over most oceanic, coastal, and mountain regions, although the better potential predictive ability of ECMWF indicates that this is mostly due to calibration problems. To encourage the use of the new benchmark, we provide the data, scripts, and an interactive webtool to the scientific community.

Evaluation of ground-based, daily, gridded precipitation products for Upper Benue River basin, Nigeria

Evaluation of ground-based, daily, gridded precipitation products for Upper Benue River basin, Nigeria

Evaluation of different gridded precipitation products in trend analysis of precipitation features over Iran

Evaluation of different gridded precipitation products in trend analysis of precipitation features over Iran

Measuring precipitation in Eastern Himalaya: Ground validation of eleven satellite, model and gauge interpolated gridded products

Precipitation plays a key role in shaping land surface processes in Himalaya. It is also the most challenging meteorological variable to model in climate change studies due to inadequate ground data. Gridded Precipitation Products (GPPs) are useful alternatives to ground data but require validation, especially in topographically complex and wet Eastern Himalaya. This study presents a fine-scaled ground validation of eleven GPPs, including five satellite-based (GPM-IMERGV06, TRMM-3B42V7, TRMM-3B42V7RT, CHIRPS-2.0 and PERSIANN-CCS), four reanalysis model-based (ERA5, ERA5-Land, AgERA5, and WRF) and two gauge-interpolated (IMD-0.25° and APHRODITE-2V18) GPPs in Eastern Himalaya. Hourly precipitation data from 27 rain gauges (Gauges) from Sikkim, representing the Eastern Himalayan climatology, is used to statistically validate the GPPs and assess their ability to capture diurnal and seasonal patterns, and extreme events.Overall, GPM-IMERG, WRF, and IMD-0.25° outperformed the other satellite, model, and interpolated GPPs, respectively, in comparison to Gauges. Near real-time TRMM-3B42RTV7 performed better than TRMM-3B42V7 for high-intensity precipitation and large storms, but not in overall performance. CHIRPS-2.0 and PERSIANN-CCS showed the least detectability and highest errors; however, PERSIANN-CCS was better at reproducing known spatial patterns in ground precipitation. The GPPs underestimated (overestimated) precipitation frequencies and volumes below (above) 3000 masl, and high (low) intensities. The highest detectability and lowest errors were observed at mid-elevation (1000-2000 masl), and in monsoon (JJASO) and summer (MAM), whereas winter (NDJF) precipitation was overestimated with high false hits. Strong diurnal cycles in precipitation were observed in Gauges with peaks around late-night-early morning (2300–0300 h) in monsoon and afternoon-evening (1600–1900 h) in summer. Satellite GPPs captured the diurnal cycle in Gauges, albeit with subdued amplitudes, whereas model GPPs failed. Owing to their superior performance and fine spatiotemporal resolutions, GPM-IMERG and WRF are recommended for hydrological studies in Eastern Himalaya. The three ERA5-based products (ERA5, ERA5-Land, AGERA5), along with TRMM-3B42RTV7 and IMD-0.25°, showed the lowest bias in tracking large storms (longer than 5 days) and are advocated for understanding extreme events and in geohazard applications. IMD-0.25° is recommended for precipitation trend and variability analysis, albeit with suitable bias-correction to address the negative biase at high elevations and the positive bias in trans-Himalayan regions. The study improves our understanding of remotely-sensed and spatially-gridded precipitation in the world's highest mountain range, where ground observations are likely to be inadequate for years to come.

Uncertainty in different precipitation products in the case of two atmospheric river events

One of the World Climate Research Programme Grand Challenges is to evaluate whether existing observations are enough to underpin the assessment of weather and climate extremes. In this study, we focus on extreme associated with atmospheric rivers (ARs). ARs are characterized by intense moisture transport usually from the tropics to the extra-tropics. They can either be beneficial, providing critical water supply, or hazardous, when excessive precipitation accumulation leads to floods. Here, we examine the uncertainty in gridded precipitation products included in the Frequent Rainfall Observations on GridS (FROGS) database during two atmospheric river events in distinct Mediterranean climates: one in California, USA, and another in Portugal. FROGS is composed of gridded daily-precipitation products on a common 1° × 1° grid to facilitate intercomparison and assessment exercises. The database includes satellite, ground-based and reanalysis (RE) products. Results show that the precipitation products based on satellite data, individually or combined with other products, perform least well in capturing daily precipitation totals over land during both cases studied here. The RE and the gauge-based products show the best agreement with local ground stations. As expected, there is an overall underestimation of precipitation by the different products. For the Portuguese AR, the multi-product ensembles reveal mean absolute percentage errors between −25% and −60%. For the western US case, the range is from −60% to −100%.

Spatio‐temporal evaluation of gridded precipitation products for the high‐altitude Indus basin

AbstractThe high‐altitude Indus basin is one of the most complex and inadequately explored mountain terrains in the World, where reliable observations of precipitation are highly lacking. Therefore, spatially distributed precipitation products developed at global/regional scale are often used in several scientific disciplines. However, large uncertainties in precipitation estimates of such precipitation data sets often lead to suboptimal outcomes. In this study, performance of 27 widely used gridded precipitation products belonging to three different categories of gauge‐based, reanalysis and merged products is evaluated with respect to high‐quality reference climatologies of mean monthly precipitation. Widely used statistical measures and quantitative analysis techniques are used to analyse the spatial patterns and quantitative distribution of mean monthly, seasonal and annual precipitation at sub‐regional scale. Mean annual precipitation estimates of the gridded data sets are cross validated with the corresponding adjusted streamflows using Turc‐Budyko non‐dimensional analysis. Results reveal poor to moderately good performance of the gridded data sets. Marked differences in spatiotemporal and quantitative distribution of precipitation are found among the data sets. All data sets are consistent in their patterns showing negative or dry bias in wet areas and positive or wet bias in dry areas, although considerable differences in the magnitudes of the biases are noticed at sub‐regional scale. None of the data sets is equally good for all sub‐regions due to very high spatiotemporal variability in their performance at sub‐regional scale. Gauge‐based and merged products performed better in dry regions and during monsoon season, while reanalysis products provided better estimates in wet areas and during winter months. GPCC V8, ERA5 and MSWEP2.2 are found better than their counter‐grouped data sets. Overall, ERA5 is found most acceptable for all sub‐regions, particularly at higher‐altitudes, in wet areas and during winter months.

Multi-sensor analysis of monthly gridded snow precipitation on alpine glaciers

Abstract. Accurate and reliable solid precipitation estimates for high mountain regions are crucial for many research applications. Yet, measuring snowfall at high elevation remains a major challenge. In consequence, observational coverage is typically sparse, and the validation of spatially distributed precipitation products is complicated. This study presents a novel approach using reliable daily snow water equivalent (SWE) estimates by a cosmic ray sensor on two Swiss glacier sites to assess the performance of various gridded precipitation products. The ground observations are available during two and four winter seasons. The performance of three readily-available precipitation data products based on different data sources (gauge-based, remotely-sensed, and re-analysed) is assessed in terms of their accuracy compared to the ground reference. Furthermore, we include a data set, which corresponds to the remotely-sensed product with a local adjustment to independent SWE measurements. We find a large bias of all precipitation products at a monthly and seasonal resolution, which also shows a seasonal trend. Moreover, the performance of the precipitation products largely depends on in situ wind direction during snowfall events. The varying performance of the three precipitation products can be partly explained with their compilation background and underlying data basis.

Assessment of Remote Sensing and Re-Analysis Estimates of Regional Precipitation over Mato Grosso, Brazil

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

Error Correction of Multi-Source Weighted-Ensemble Precipitation (MSWEP) over the Lancang-Mekong River Basin

The demand for accurate long-term precipitation data is increasing, especially in the Lancang-Mekong River Basin (LMRB), where ground-based data are mostly unavailable and inaccessible in a timely manner. Remote sensing and reanalysis quantitative precipitation products provide unprecedented observations to support water-related research, but these products are inevitably subject to errors. In this study, we propose a novel error correction framework that combines products from various institutions. The NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA), the Asian Precipitation Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Hazards group InfraRed Precipitation with Stations (CHIRPS), the Multi-Source Weighted-Ensemble Precipitation Version 1.0 (MSWEP), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Records (PERSIANN) were used. Ground-based precipitation data from 1998 to 2007 were used to select precipitation products for correction, and the remaining 1979–1997 and 2008–2014 observe data were used for validation. The resulting precipitation products MSWEP-QM derived from quantile mapping (QM) and MSWEP-LS derived from linear scaling (LS) are evaluated by statistical indicators and hydrological simulation across the LMRB. Results show that the MSWEP-QM and MSWEP-LS can better capture major annual precipitation centers, have excellent simulation results, and reduce the mean BIAS and mean absolute BIAS at most gauges across the LMRB. The two corrected products presented in this study constitute improved climatological precipitation data sources, both time and space, outperforming the five raw gridded precipitation products. Among the two corrected products, in terms of mean BIAS, MSWEP-LS was slightly better than MSWEP-QM at grid-scale, point scale, and regional scale, and it also had better simulation results at all stations except Strung Treng. During the validation period, the average absolute value BIAS of MSWEP-LS and MSWEP-QM decreased by 3.51% and 3.4%, respectively. Therefore, we recommend that MSWEP-LS be used for water-related scientific research in the LMRB.

The potential and uncertainty of triple collocation in assessing satellite precipitation products in Central Asia

Although satellite-based precipitation estimation has extensive application potential, validation of its reliability is challenging for areas lacking ground-based data which is particularly true for many arid and semiarid regions. The triple collocation (TC) method can be used to evaluate three independent inputs with unknown true values, and thus provides an appealing alternative for assessment of satellite precipitation products in lack of observation regions. This study is the first to utilize TC to comprehensively assess the uncertainties of various satellite precipitation products in Central Asia (CA) with a distinctive continental arid and semi-arid climate. TC requires the errors of inputs to be independent with each other, while many multi-satellite precipitation products use overlapped data sources. To address this problem, this study uses a soil moisture-based product (SM2RAIN) and a reanalysis model-based product (ERA5) as two inputs of a triplet with the last input coming from each one of the six satellite precipitation products (3B42, CHIRPS, CMORPH, GSMaP, IMERG, PERSIANN). Six independent triplets are obtained in this way. The temporal/spatial resolution is daily/0.1° and the period is 2007–2019. The results show the overall performance of GSMaP is best among eight gridded precipitation products over CA, followed by IMERG, CMORPH and PERSIANN. All precipitation products show degraded performance with increasing altitude. Moreover, the accuracy estimates are subjected to uncertainties caused by the TC method and data inputs. Overall, the study concludes the TC method can provide a new perspective for the assessment of precipitation products over data-absent arid and semiarid regions, while careful check and explanation of evaluation results are always necessary to defend the rationality of TC in specific cases.

Spatio-temporal variations of extreme precipitation events over North Central of Vietnam

The study provides a qualitative source of gridded daily precipitation at high resolution of 5 km based on bias correction factors for Quang Binh, a province in North Central of Vietnam. The reproduced daily precipitation product, namely QBv1 is then evaluated with the station, station-based, satellite, reanalysis precipitation data. More specifically, understanding the spatial distribution of extreme precipitation events is fully interpreted. With this, it is firstly vial to analyse the accuracy of QBv1 product using the statistical indices of continuous and dichotomous. The results showed an excellent reproduction for daily precipitation of QBv1 product at high resolution. The extreme precipitation values are then calculated on the gridded precipitation product. It is illustrated extremely large amounts of precipitation defined by 99th percentile with 81.9 mm, 994.9 mm and 2,357.8 mm in a day, month and season, respectively. The maximum consecutive five-day precipitation can rise to over 1,600 mm.

Spatio-temporal variations of extreme precipitation events over North Central of Vietnam

The study provides a qualitative source of gridded daily precipitation at high resolution of 5 km based on bias correction factors for Quang Binh, a province in North Central of Vietnam. The reproduced daily precipitation product, namely QBv1 is then evaluated with the station, station-based, satellite, reanalysis precipitation data. More specifically, understanding the spatial distribution of extreme precipitation events is fully interpreted. With this, it is firstly vial to analyse the accuracy of QBv1 product using the statistical indices of continuous and dichotomous. The results showed an excellent reproduction for daily precipitation of QBv1 product at high resolution. The extreme precipitation values are then calculated on the gridded precipitation product. It is illustrated extremely large amounts of precipitation defined by 99th percentile with 81.9 mm, 994.9 mm and 2,357.8 mm in a day, month and season, respectively. The maximum consecutive five-day precipitation can rise to over 1,600 mm.

Evaluation of Sixteen Gridded Precipitation Datasets over the Caribbean Region Using Gauge Observations

The existence of several gridded precipitation products (GPP) has facilitated studies related to climate change, climate modeling, as well as a better understanding of the physical processes underpinning this key variable. Due to complexities in estimating rainfall, gridded datasets exhibit different levels of accuracy across regions, even when they are developed at relatively high resolution or using sophisticated procedures. The performance of 16 GPP are evaluated over the Caribbean region, which includes the Caribbean Islands, and portions of Central South America. Monthly data for sixty weather stations are used as a reference for the period 1983–2010. The 16 GPP include six products based on station data only, two that combine ground station and satellite information, two merging station and reanalysis information, four based on reanalysis, and two using multisource information. The temporal resolution of the GPP ranged between daily and monthly and spatial resolution from 0.033° to 0.5°. The methodological approach employed combined a comparison of regional and sub-regional precipitation annual cycles, the Kling–Gupta efficiency (KGE) index, as well as several metrics derived from the standardized precipitation index (SPI). Overall, the best performances were obtained from GPCC025 and MSWEP2, likely reflecting the positive impact of the large number of station data utilized in their development. It is also demonstrated that a higher spatial resolution does not always mean better accuracy. There is a need for this kind of assessment when undertaking climate studies in regions like the Caribbean where resolution is a significant consideration. ERA5 performed best among the reanalyses analyzed and has the potential to be used to develop regionally based GPP by applying bias correction or downscaling techniques. The methodological approach employed provides a comprehensive and robust evaluation of the relative strengths and weaknesses of GPP in the Caribbean region.