APHRODITE: Constructing a Long-Term Daily Gridded Precipitation Dataset for Asia Based on a Dense Network of Rain Gauges

A daily gridded precipitation dataset for 1961-2004 was created by collecting rain gauge observation data across Asia through the activities of the Asian Precipitation—Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) project. Our number of valid stations was between 5000 and 12,000, representing 2.3 to 4.5 times the data available through the Global Telecommunication System network, which were used for most daily grid precipitation products. APHRODITE’s daily gridded precipitation (APHRO_V0902) is the only long-term (1961 onward) continental-scale daily product that contains a dense network of daily rain gauge data for Asia including the Himalayas and mountainous areas in the Middle East. The product contributes to studies such as the evaluation of Asian water resources, diagnosis of climate change, statistical downscaling, and verification of numerical model simulation and high-resolution precipitation estimates using satellites. We released APHRO_V0902 datasets for Monsoon Asia, Russia and the Middle East (on 0.5° × 0.5° and 0.25° × 0.25° grids) at http://www.chikyu.ac.jp/precip/. Herein, we show the algorithm and input data of APHRO_V0902.

Precipitation measurements in the Mekong River Basin (MRB) are full of variability due to this domain's varied weather systems, climate conditions, elevation, and specific land–atmosphere interactions. This study provides an in-depth evaluation of the differences between four gridded precipitation products [i.e. Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Tropical Rainfall Measuring Mission (TRMM), CPC MORPHing technique (CMORPH), and Climatic Research Unit (CRU)] over the Greater Mekong Subregion. Precipitation data from a total of 242 stations in this domain are used to investigate the quality of the gridded products. Our analysis verifies that gauge-based APHRODITE exhibits the highest correlations with the station data as well as the highest probability of detection of daily precipitation. The false alarm ratio, on the other hand, is slightly in favour of TRMM and CMORPH. Subtracting APHRODITE (as baseline) from TRMM and CMORPH reveals the spatial and frequency distribution of potential biases. The results indicate that TRMM appears to have a wet bias in most areas, while CMORPH shows no similar or consistent bias over APHRODITE. To utilize the higher accuracy of APHRODITE and the finer spatial and temporal footprints of CMORPH, a new restructuring algorithm is introduced in this study. The algorithm is capable of eliminating biases and possible artefacts associated with CMORPH while resolving the resolution discrepancies between the two data sets.

Evaluation of six gauge-based gridded climate products for analyzing long-term historical precipitation patterns across the Lancang-Mekong River Basin

Accurate estimates of monsoonal rainfall at daily time scales are essential inputs for various water-related sectors such as drought and flood forecasting, crop and water management for agriculture. To serve this purpose, a variety of rainfall products, especially the gauge based products which serve as the ground-truth for other derived rainfall products, are available over India. In this study, three different daily gauge based gridded rainfall datasets, namely Indian Meteorological Department (IMD), Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of water resources (APHRODITE) and Climate Prediction Center (CPC) unified rain gauge data are compared over India for the monsoon season of 1979–2007. The comparison among the datasets is based on the duration, frequency and intensity of three different spell characteristics, namely dry, wet and extreme wet spells, and their associated trends. Wet (dry) spells are defined as the consecutive period of wet (dry) days, where a wet (dry) day is defined using rainfall threshold of 1mm. Extreme wet spells are defined using the 90th percentile of rainfall above the depth of wet day. All datasets capture the spatial distribution of precipitation characteristics, albeit with pronounced differences at heavy rainfall regions. CPC and IMD show a close match in spell characteristics while APHRODITE significantly deviates. APHRODITE shows increased intensity of rainfall during dry periods, leading to over-estimation of wet days and under-estimation of dry days. Northern extreme of India (Jammu and Kashmir) show major differences in replicating the spell characteristics. Trend patterns are also not consistent between the three datasets. The present study will provide information on the spatio-temporal pattern of dry, wet and extreme wet spell characteristics over India and aid in selecting appropriate datasets for studying the Indian monsoon rainfall depending on their scope and application of research.

Global reanalysis precipitation products could provide valuable meteorological information for flow forecasting in poorly gauged areas, helping to overcome a long-standing challenge in the field. But not all data sources are suitable for all regions or perform the same way in hydrological modeling, so it is essential to test the suitability of precipitation products before applying them. In this study, five widely used global high-resolution precipitation products—Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), National Centers for Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR), Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS), China Gauge-based Daily Precipitation Analysis developed by China Meteorological Administration (CMA) and Agricultural Model Intercomparison and Improvement Project based on the NASA Modern-Era Retrospective Analysis for Research and Applications (AgMERRA)—were evaluated using statistical methods and a hydrological approach for their suitability for the Lancang River Basin. The results indicated that APHRODITE, CMA, AgMERRA and CHIRPS were more accurate precipitation indicators than NCEP-CFSR in terms of the multiyear average and seasonal spatial distribution pattern, all of the CHIRPS, AgMERRA and APHRODITE perform better than CMA and NCEP-CFSR at the small, medium and high precipitation intensities ranges in subbasin11 and sunbabsin46. All five products performed better in subbasin46 (a low-altitude region) than in subbasin11 (a high-altitude region) on the daily and monthly scales. In addition to NCEP-CFSR, the other four products all presented encouraging potential for streamflow simulation at daily (Yunjinghong) and monthly (Yunjinghong, Jiuzhou and Gajiu) scale. Hydrological simulations forced with APHRODITE were the best of the five for the Yunjinghong station in capturing daily and monthly measured streamflow. Except for NCEP-CFSR, all products were very good for hydrological simulations for the Gajiu and Jiuzhou stations.

ABSTRACTSeveral global and regional high‐resolution precipitation products have been released over the past decade by combining precipitation estimates from various sources including satellite‐based measurements and gauge‐based observations. With relatively few validation studies over the Eastern Himalayan region, this study examined seasonal and interannual skills of four gridded precipitation products including the regional gauge‐based APHRODITE (Asian Precipitation‐Highly Resolved Observational Data Integration Towards Evaluation of Water Resources) and three near‐global satellite‐based products: Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Centre (CPC) MORPHing (CMORPH), and Climate Hazards Group InfraRed Precipitation (CHIRP) using in‐situ rain gauge data from Bhutan for the period 1998–2012. Principal component analysis (PCA) was used to assess the dominant rainfall patterns over Bhutan. An attempt has also been made to correct precipitation biases in the satellite‐only products using a gamma (γ)‐based distribution approach. Results indicated that APHRODITE and satellite‐based precipitation products were able to adequately capture the spatio‐temporal patterns of rainfall variability over Bhutan. Extreme precipitation events and extreme drought periods were well captured with very good correlations (>0.5). APHRODITE and TRMM 3B43v7 were remarkably similar, whereas satellite‐only products (CMORPH and CHIRP) highly underestimated (20–60% or 200–450 mm month−1) monsoon rainfall over Bhutan. While TRMM 3B43v7 still underestimated monsoon rainfall (by ∼25%), it has significantly improved the seasonal bias (by 20–40%) from its previous version (TRMM 3B43v6). CHIRP performed relatively better in the high rainfall regions but indicated very low correlations over mountainous regions and during the pre‐ and post‐monsoon season. The bias correction approach indicated best results for TRMM 3B43v6 (up to 33%) in the Southern Foothills, whereas satellite‐only products improved only moderately (5–20%).

Precipitation is one of the most important inputs for hydrological simulation because it controls water balance, and accurate representation of rainfall distribution is essential for hydrological simulation. In the Yarlung Tsangpo–Brahmaputra River Basin (YBRB), gauge‐based gridded datasets have difficulties in representing complex orographic precipitation as the rainfall stations are too sparse to capture accurate precipitation patterns. This study used Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) estimates to drive the Soil and Water Assessment Tool (SWAT) model in the YBRB. Bias correction on APHRODITE estimates was achieved through linear scaling based on a denser network of gauges. In addition, the elevation bands method was implemented in SWAT to reproduce spatial precipitation by considering orographic effects. The results showed that original APHRODITE data tended to systematically underestimate rainy season precipitation with considerable underestimation on windward slopes of the Himalayas. Corrected APHRODITE data increased precipitation estimates and were able to better represent the spatial patterns of precipitation. During the calibration and validation periods, the mean monthly Nash–Sutcliffe efficiency (NSE) of the corrected APHRODITE‐simulation were 0.86 and 0.82, respectively, which were much higher than those of the original APHRODITE‐simulation (0.79 and 0.72), indicating the reliability of corrected APHRODITE estimates in driving hydrological model. In addition, although the elevation bands method has some limitations induced by the constant precipitation lapse rate, it was also found that this method could improve the accuracy of hydrological simulation. In large transboundary sparsely gauged river basins, implementation of bias correction and elevation bands can improve the representation of spatial precipitation of gridded gauge‐based rainfall products, which may be beneficial for hydrological simulation and water resources management.

ABSTRACTThis article presents a detailed comparison of ten precipitation products over the central southwest Asia (CSWA) region. The spatial characteristics and temporal variations of wintertime precipitation over CSWA region are assessed using four gauge‐only [Asian Precipitation – Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), Climate Prediction Center Unified Rain gauge (CPC‐uni), Global Precipitation Climatology Center Full Data Reanalysis (GPCC) and Climate Research Unit (CRU)]; three satellite‐derived (3B42‐V6, 3B42‐V7 and GPCP‐1DD) and three reanalysis [Climate Forecast System Reanalysis (CFSR), ERA‐Interim and Modern‐Era Retrospective Analysis for Research and Applications (MERRA)] precipitation products. The analysis is performed for two different periods: (1979–2007) for the gauge‐only/reanalyses and (1998–2007) for the satellite‐derived products. Using an ensemble average of four gauge‐only observational data sets as our reference, we carry out comprehensive qualitative assessment of the uncertainties/biases associated with each data set. Additionally, we examine the relationship pattern between CSWA wintertime precipitation and El Niño‐Southern Oscillation (ENSO) phases. In the gauge‐only category, APHRODITE and GPCC perform better than CPC‐uni and CRU data sets in terms of the spatial and temporal variations of skill matrices. Overall, GPCC shows the best performance (including the precipitation sensitivity to ENSO events) and is the preferable observational data set for long‐term climatological analysis over the CSWA region. Among the satellite‐derived precipitation products, 3B42‐V7 displays the most realistic wintertime precipitation distribution pattern when compared to 3B42‐V6 and GPCP‐1DD; however, efforts are needed to further improve the accuracy of satellite‐derived products over the dry arid and semi‐arid areas of CSWA. In the reanalysis category, MERRA's performance is relatively better than CFSR and ERA‐Interim, although significant biases exist in all of the reanalyses due to overestimation of precipitation over the mountainous regions of CSWA.

Accurate precipitation data are the basis for hydro‐climatological studies. As a highly populated river basin, with the biggest inland fishery in Southeast Asia, freshwater dynamics is extremely important for the Mekong River Basin (MB). This study focuses on evaluating the reliability of existing gridded precipitation datasets both from satellite and reanalysis, with a ground observations‐based gridded precipitation dataset as the reference. Two satellite products (Tropical Rainfall Measuring Mission [TRMM] and the Precipitation Estimation from Remote Sensing Information using an Artificial Neural Network—Climate Data Record [PERSIANN‐CDR]), as well as three reanalysis products (Modern‐Era Retrospective analysis for Research and Applications [MERRA2], the European Centre for Medium‐Range Weather Forecasts interim reanalysis [ERA‐Interim], and the Climate Forecast System Reanalysis [CFSR]) were compared with the Asian Precipitation—Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) over the MB. The APHRODITE was chosen as the reference for the comparison because it was developed based on ground observations and has also been selected as reference data in previous studies. Results show that most of the assessed datasets are able to capture the major climatological characteristics of precipitation in the MB for the 10‐year study period (1998–2007). Generally, both satellite data (TRMM and PERSIANN‐CDR) show higher reliability than reanalysis products at both spatial and temporal scales across the MB, with the TRMM outperforming when compared to the PERSIANN‐CDR. For the reanalysis products, MERRA2 is more reliable in terms of temporal variability, but with some underestimation of precipitation. The other two reanalysis products CFSR and ERA‐Interim are relatively unreliable due to large overestimations. CFSR is better positioned to capture the spatial variability of precipitation, while ERA‐Interim shows inconsistent spatial patterns but more realistically resembles the daily precipitation probability. These findings have practical implications for future hydro‐climatological studies.

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

This study examines the influence of Madden–Julian Oscillation (MJO) on Indonesian precipitation during the extended boreal summer (May–September). The MJO is one of the dominant intra-seasonal variabilities that influence the extreme precipitation in the tropics, especially in Indonesia. Here, the episodes of intense precipitation (95th percentiles) during active MJO phases from 1998 to 2015 are evaluated, using the daily precipitation datasets from the gridded Asian Precipitation–Highly Resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE) and several station rain gauges. The boreal summer MJO influences the likelihood of extreme precipitation, especially in the west and north parts of Indonesia. The west part experiences an increase in the probability of extreme precipitation by up to 55 and 80% during phases 2 and 3, respectively. Moreover, the extreme precipitation probability in the north part increases by up to 40–70% during phases 2–4. On the other hand, the influence of MJO is relatively weak in the south and east parts of Indonesia. The contrast of the precipitation response between the north and south parts of Indonesia is consistent with the northward movement of boreal summer MJO.

This study examines the influence of Madden-Julian Oscillation (MJO) on Indonesian precipitation during the extended boreal summer (May to September). The MJO is one of the dominant intraseasonal variabilities that influence the extreme precipitation in the tropics, especially in Indonesia. The episodes of intense precipitation (95th percentiles) during active MJO phases from 1998-2015 are evaluated using the daily precipitation datasets from the gridded Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE) and several station rain gauges. The boreal summer MJO influences the probability of extreme precipitation, especially in the west and north parts of Indonesia. The west part experiences an increase in the probability of extreme precipitation by up to 55\% and 80\% during phases 2 and 3, respectively. Moreover, the extreme precipitation probability in the north part increases by up to 40 to 70\% during phases 2 to 4. On the other hand, the influence of MJO is relatively weak in the south and east parts of Indonesia. The contrast of the precipitation response between the north and south parts of Indonesia is consistent with the northward movement of boreal summer MJO.

Rainfall intensity and frequency are important parameters in agricultural development and water resource management. The middle of the Indochina peninsula climate is characterized by rainfall variability associated with complex terrains. The present study focuses on spatial seasonal extreme precipitation trends over the middle of the Indochina Peninsula for the 30 year period from 1978-2007. Daily gridded precipitation data obtained at 0.5° horizontal grid resolution from APHRODITE (Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources) was used to detect the spatial trends with the use of the Man-Kendall and Theil-Sen approach. Extreme precipitation indices were selected from the WMO–CCL/CLIVAR list of extreme precipitation indices focusing on intensity and frequency. The study shows a consistently increasing upward trend at 10.04 dfrom the WDAY index. In seasonal analysis, the pre-monsoon trend shows a significant upward trend in the PRCTOT index, while the WDAY index for pre-monsoon season has the highest correlation coefficient in downward trend. Spatial analysis of extreme precipitation indices shows that the PRCTOT index of the pre-monsoon season has the largest percentage change in significant upward trend over the northern Basins that are consistent with MAX and Mean but not for WDAY. In addition, the inter-annual relationship between the Oceanic Nino Index and PRCTOT is shown in relation with the La Niña phase for both April and May.

This study evaluated the performance of 07 gridded datasets viz. Asian Precipitation Highly-resolved Observational Data Integration towards Evaluation of Water Resources (APHRODITE), Climate Research Unit Time-Series (CRU-TS), University of Delaware (UDEL), Tropical rainfall Measurement Mission (TRMM)/ TMPA (TRMM Multi-Satellite Precipitation Analysis), Global Precipitation Climatology Centre (GPCC), Princeton Global Forcings Dataset (PGF), and European Reanalysis Interim (ERA-I) in capturing the amount, seasonality and trend of precipitation over different climatic zones of Northwestern Himalaya (NWH) i.e. Lower Himalaya (LH), Greater Himalaya (GH) and Karakoram Himalaya (KH). A similar comparison was also done for the temperature data but only with 05 datasets, viz. APHRODITE, CRU-TS, PGF, UDEL and ERA-I since TMPA and GPCC are precipitation datasets only. This study is a maiden attempt where in situ observation includes the data from elevations above 5000 m amsl (07 observatories) in NWH (Indian sub-region). Results reveal that for precipitation over NWH; ERA-I, GPCC, and TMPA/TRMM were found to be quite reliable datasets. For temperature, all datasets performed quite well but CRU-TS and ERA-I provided more reliable estimates. The mean absolute error ranged from 13.5 mm/month to 150.7 mm/month for precipitation and 0.75°C/month to 9.9°C/month for temperature. High values of the errors underpin the need for bias correction. On the basis of this analysis, monthly correction factors for wintertime temperature and precipitation have also been suggested for each dataset which when multiplied with corresponding datasets would result in closely approximated values for the area of interest. These results can serve as a guide for bias correction and selection of appropriate gridded datasets for use in studies pertaining to hydrological modeling over NWH.

The Mekong, the longest river in Southeast Asia, has received great attention due to its various water-related issues, including the catastrophic seasonal flooding at the lower part of the basin, extending to the Mekong Delta. A reliable assessment of the flood risk in the Mekong requires high-quality precipitation data. However, precipitation data in this region is subject to considerable uncertainty, both spatially and temporally, which can be observed from an intercomparison of gaugeand satellite-based precipitation products. This study aims to investigate precipitation uncertainty and its effect on runoff predictions in the Mekong. Precipitation uncertainty is characterized by analyzing several gridded precipitation data sets: Tropical Rainfall Measuring Mission (TRMM; 3B42 V7), CPC Morphing technique (CMORPH), and Asian Precipitation—Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). Our results indicate that using APHRODITE can lead to accurate runoff predictions at the zonal outlets of the Mekong River Basin, and CMORPH and TRMM require corrections and further model calibration.