Global Gridded Precipitation Research Articles

A dataset of tracked mesoscale precipitation systems in the tropics

AbstractMesoscale Convective Systems (MCSs) are often quantified via surface‐based radar network, geostationary satellite, or low earth orbit satellite observations. However, each of these has drawbacks for detecting cloud systems such as a lack of global coverage, a lack of variables to quantify deep convective cloud and precipitation properties, and a lack of continuous observations of individual MCSs, respectively. To generate a dataset of tropical Tracked IMERG Mesoscale Precipitation Systems (TIMPS), we use the Forward in Time tracking algorithm to track precipitation systems in the Integrated Multi‐satellitE Retrievals for the Global Precipitation Mission (IMERG). IMERG is a global gridded precipitation product that incorporates observations from a constellation of satellites with passive microwave sensors and other sources, allowing the TIMPS dataset to have continuous temporal precipitation information for MCSs in a global tropical strip with data every 30 min in time and 0.1° in space. TIMPS are provided in a publicly available data base with a variety of variables including MCS size, motion, and precipitation properties, estimations of MCS life cycle stages, and their proximity to the nearest tropical cyclone. By combining the TIMPS dataset with the University of Washington Convective Features database, we also provide snapshots of information from more spatially detailed space‐borne radar coverage. The TIMPS dataset provides the means for detailed long‐term and large‐scale study of MCSs in all regions of the tropics with applications such as composite studies of MCS life cycles and the evaluation of model performance.

Analysis of hydrological responses using semi-distributed conceptual models in a mountainous catchment in the Hindu Kush Himalayan region

ABSTRACT The Hindu Kush Karakoram Himalaya region, an important global water unit, is the origin of 10 major river basins. The Upper Indus basin, a part of this region, is a primary water source for the downstream population and is primarily fed by snow and glacier melt. Four semi-distributed conceptual hydrological models were used to understand this basin’s hydrological responses. As the observed data was unavailable, global gridded precipitation and discharge data from the Global Flood Awareness System (GloFAS) were used to set up the model at the catchment outlet. Degree-day factor (DDF)-based snow and glacier modules are incorporated into the model structure to measure melt contribution. The results showed very good performance of all four models with Nash-Sutcliffe efficiency > 0.75, with the GR4J model exhibiting superior performance. The dominance of snow and glacier melt during summer monsoon affects the peak discharge and offers insights for investigations at the sub-basin scale.

Enhanced performance of CMIP6 climate models in simulating historical precipitation in the Florida Peninsula

AbstractAssessing the performance of General Circulation Models (GCMs) in simulating historical regional precipitation is essential for climate assessments. This study analysed 18 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and 27 GCMs from Phase 6 (CMIP6) for the Florida Peninsula. Naïve combinations formed the CMIP5‐Ensemble and CMIP6‐Ensemble. The reference dataset consisted of global gridded precipitation data from National Oceanic and Atmospheric Administration. GCM‐simulated precipitation data were compared to the reference dataset across multiple time scales between 1951 and 2005. Most CMIP5 and CMIP6 GCMs exhibited a negative bias at the daily time scale, with an absolute value ranging between 0 and 2 mm day−1 at the median level, except for INM‐CM4 and MRI‐CGCM3 (CMIP5) and CNRM‐ESM2‐1 (CMIP6). Two performance metrics, that is, Pearson correlation and mean absolute error (MAE), were used to evaluate the GCMs. A correlation value above 0.3 is statistically significant at the significance level (α = 0.05). For the Pearson correlation between simulated and observed monthly precipitation, only 15% of CMIP5 GCMs (3 out of 19) had a median value above 0.3, whilst this increased to approximately 39% (11 out of 28) for CMIP6 GCMs. For monthly climatological precipitation, only 21% (4 out of 21) of GCMs in CMIP5 showed a correlation with a median value of 0.8 or higher. This percentage rose to 50% (14 out of 28) for CMIP6. A notable difference was observed in the MAE between CMIP5 and CMIP6 climate models. Lastly, GCM raw outputs were evaluated based on rainy season characteristics (onset, demise and duration). Significant improvements from the CMIP5 to the CMIP6 models were observed in capturing the rainy season in the Florida Peninsula. This study thoroughly compares CMIP5 and CMIP6 climate models in simulating historical precipitation at various time scales for the study region. Although the results are specific to the study area, the methods can be applied more broadly.

Comparison of gridded precipitation estimates for regional hydrological modeling in West and Central Africa

Study regionData-scarce basins located in West Africa and northern Central Africa. Study focusMultiple studies have shown that global gridded precipitation datasets could provide an alternative to the lack of observed data in Sub-Saharan Africa. This work evaluated 15 precipitation datasets based on satellite rainfall (ARC v.2, CHIRP v.2, CHIRPS v.2, PERSIANN-CDR, MSWEP v2.2 and TAMSAT v3), reanalysis (ERA5, JRA-55 Adj, MERRA-2 PRECTOT, MERRA-2 PRECTOTCORR, WFDEI-CRU and WFDEI-GPCC) and ground measurements (CPC v.1, CRU TS v.4.00 and GPCC v.7), as well as a regional estimation method, based on spatial proximity, for the parameters of a simple monthly water balance model, GR2M. The regional simulations of the GR2M model were evaluated based on a Kling-Gupta Efficiency score in a split-sample spatiotemporal validation scheme. New hydrological insights for the regionThe results show that among all the precipitation products, CHIRPS is the most effective for hydrological modeling in West and Central Africa at a monthly timestep. Also, among the top five products are WFDEI-CRU, CRU, WFDEI-GPCC and GPCC. Overall, regional hydrological modeling is more effective for basins smaller than 80,000 km2. The method of regionalization by spatial proximity causes an overall drop in the ability of the various precipitation products to reproduce discharge, most notably with WFDEI-GPCC and GPCC. CHIRPS remains the best product in terms of KGE2 values in regionalization.

Representativeness of Two Global Gridded Precipitation Data Sets in the Intensity of Surface Short-Term Precipitation over China

This study evaluates the representativeness of two widely used next-generation global satellite precipitation estimates data for short-term precipitation over China, namely the satellite data from the Climate Prediction Center morphing (CMORPH) and the satellite data from the Global Precipitation Measurement (GPM) mission. These two satellite precipitation data sets were compared with the hourly liquid in-situ precipitation from China national surface stations from 2016 to 2020. The results showed that the GPM precipitation data has better representativeness of surface short-term precipitation than that of the CMORPH data, and these two quantitative precipitation estimate (QPE) data sets underestimated extreme precipitation. Moreover, we analyzed the influence of the error between two QPE data sets and the in-situ precipitation on the classification of short-term precipitation intensity. China uses 8.1–16 mm/h as the definition of heavy precipitation, but the accuracy of the satellite QPE product was different due to the different lowest threshold of heavy rain (more than 8.1 mm/h or more than 16 mm/h). Increasing the threshold value of the QPE data for short-term strong precipitation resulted in lower accuracy for detecting such events, but higher accuracy for detecting moderate intensity rainfall. When studying short-term strong precipitation over China using precipitation grade, selecting an appropriate threshold was important to ensure accurate judgments. Additionally, it is important to account for errors caused by QPE data, which can significantly affect the accuracy of precipitation grading.

Mapping rainfall erosivity over India using multiple precipitation datasets

Rainfall erosivity is a measure of the erosive force of rainfall which represents the potential of rain to cause soil erosion. A large proportion of the total eroded soil in India is due to erosion by water, and rainfall erosivity is one of the major components. The current assessments of rainfall erosivity in India are however largely based on rain-gauge recordings and surveys which hinders its estimation and understanding over large areas. Growing availability of remotely-sensed gridded precipitation datasets presents an unprecedented opportunity to study long-term rainfall erosivity over varied terrains and address some of the limitations of point data-based estimations. In this study, multiple national and global gridded precipitation datasets were utilized to develop a high-resolution rainfall erosivity factor (R-factor) map to highlight areas prone to rainfall-induced erosion. Further, a large selection of empirical equations from literature were employed for estimating rainfall erosivity to provide a comparative analysis of these commonly adopted methods. The calculated rainfall erosivity is also compared with alternative methods to estimate R-factor such as Fournier Index (FI) and Modified Fournier Index (MFI). It was observed that MFI is highly correlated with rainfall erosivity, and an equation was finally proposed to estimate R-factor using MFI. This is the first such national-scale assessment of rainfall erosivity over India using gridded precipitation datasets, which will aid in understanding and mitigating rainfall-induced erosion.

Evaluating the performance of eight global gridded precipitation datasets across Iran

Precipitation is one of the crucial variables in hydrological and water resources studies. Runoff, soil moisture, groundwater recharge, and other hydrological variables change with the amount of precipitation in a region. Thus, accurate precipitation estimation is a major issue in the field of water resources and hydrological modeling. Most hydrological studies and water management decisions in Iran are established using in situ observation of precipitation. However, due to limited spatial distribution and lack of long-term high-quality data in many stations, this information usually does not fully meet the information requirements of a given study. In this study, the accuracies of eight global gridded precipitation datasets including Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), Climate Prediction Center (CPC), Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), PERSIANN-Climate Data Record (PERSIANN-CDR), Global Precipitation Climatology Project (GPCP), Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2), and ECMWF Reanalysis version 5 (ERA5) were evaluated to identify the strengths and weaknesses of each dataset in different regions and main catchments of Iran. The precipitation of 96 synoptic stations during the years 1987–2016 was used as the basis for the evaluations. The evaluations were made at monthly, seasonal, and annual scales using grid-based evaluation and areal average evaluation at the catchments scale. In addition to the amount of precipitation, the datasets were evaluated using three statistical performance indices (i.e., correlation coefficient, RMSE, and relative bias) and other metrics such as probability distribution similarity with observation data (using Anderson-darling test), empirical cumulative distribution function (ECDF), and Taylor diagrams. The results indicated that dataset performance varied by catchment. Except for central parts, GPCC was the best dataset in 44% of the evaluated grids in terms of time series pattern recognition. It showed correlation coefficients of 0.71, 0.80, and 0.85, in monthly, seasonal, and annual scales, respectively. In central areas, MERRA2 estimated the average annual precipitation in this catchment with + 2 mm difference, and CRU had a correlation coefficient greater than 0.90. Moreover, CHIRPS was the best dataset in terms of relative bias in the south and southwestern catchments, so that the relative bias was negligible in these catchments in most months. This study provides rankings and recommendations for selecting an appropriate alternative precipitation dataset, which, in turn, provides the knowledge required in monitoring systems and hydrological and water resources modeling that need high-resolution input data.

Triple collocation-based error estimation and data fusion of global gridded precipitation products over the Yangtze River basin

Error estimation and data fusion are critical to improving the accuracy of global model- and satellite-based precipitation products for practical applications. However, they face challenges over vast areas of the world due to limited ground observations. Triple collocation (TC) method can overcome this limitation and provide an efficient way for error estimation without the “ground truth” and thus also for data fusion, by leveraging multi-source observations and model outputs, which have been increasingly available in recent years. In this work, we conducted a comprehensive study on error estimation and data fusion of a number of global gridded precipitation products over the Yangtze River basin from 2015 to 2018 using TC and multiplicative TC (MTC) methods. We use three satellite-based precipitation products such as the IMERG Final (IMERG-F), PERSIANN-CDR (PCDR) and SM2RAIN-ASCAT (SM2R), and one reanalysis dataset ERA5 which contains precipitation estimates. They were grouped into two TC triplets based on different combinations: IMERG-F + SM2R + ERA5 and PCDR + SM2R + ERA5. For performance evaluation, the TC-based error estimation methods were compared to the traditional method using rain gauge data, and the TC-based data fusion methods were compared with two widely-used data fusion methods Bayesian Model Averaging (BMA) and Random Forest based MErging Procedure (RF-MEP). Results showed that ERA5 had the best performance with the largest correlation coefficient (CC, 0.435), while PCDR had the worst accuracy with the smallest CC (0.304) and the largest absolute relative bias (RB, 0.365). TC tended to underestimate the root mean square error (RMSE) with respect to the traditional gauged-based method, but MTC showed a consistent result owing to the employment of a multiplicative error model. The performance of TC-based data fusion methods had no significant difference from BMA and RF-MEP. All data fusion results were better than the original triplets, as the mean CC value increased from 0.38 to 0.47 and the mean RMSE decreased from 15.0 to 13.5 mm/day. In addition, we found that the zero value replacement in MTC had great influence on error estimation, while had limited impacts on data fusion.

Comprehensive evaluation of precipitation datasets over Iran

Global gridded precipitation datasets have been developed using rain gauges, satellite observations, and data assimilation techniques to fulfill the need in regions with a limited contribution of ground observations like Iran. This study presents a comprehensive evaluation of currently available precipitation datasets over Iran at monthly (44 datasets) and daily (34 datasets) time scales. To include the maximum number of datasets and in situ data, we consider two periods for the evaluations, namely 2003–2010 for the daily and monthly assessment and 2014–2018 for the daily. For the assessment, a network of more than 1500 rain gauges is utilized within 2003–2010 and 370 rain gauges within 2014–2018. Moreover, we compare the pixel-to-pixel (interpolated in situ data v.s. gridded datasets), and point-to-pixel (in situ data as a point v.s. gridded datasets) approaches in assessing datasets performances. In terms of the Kling-Gupta efficiency (KGE) parameters, the datasets perform worse in bias at monthly time scales and correlation at daily time scales. However, considering in situ precipitation above 5 mm/day, all datasets perform poorly in estimating precipitation variability. We find that, in general, reanalysis products have a higher KGE, ranging between 0.41 (0.21) and 0.91 (0.71), than satellite-based products with a KGE ranging from 0.14 (-0.57) to 0.92 (0.57) over Iran at monthly (daily) scale. Moreover, GPCC overall matches the validation dataset better than other products over Iran’s basins, whereas CPC, ERA5, and IMERG-Final are more suitable for near-real-time studies. Also, if latency is a top criterion, PERSIANN-PDIR will be the first option. Indeed, PERSIANN-PDIR with a KGE value of 0.69 (0.33) at monthly (daily) time scale within 2003–2010 performs remarkably well, as a non-adjusted real-time satellite-based product. The comparison between the point-to-pixel and the pixel-to-pixel approaches shows that the point-to-pixel approach underestimates the quality of the datasets but does not change the ranking of the datasets.

Evaluation of three gauge-based global gridded precipitation datasets for drought monitoring over Iran

ABSTRACT Gridded precipitation products (GPPs) can provide precipitation data at high spatial coverage and resolution; however, no assessment has been conducted on the performance of GPPs in drought monitoring for Iran’s major basins. In this study, the accuracy of three GPPs, i.e. the Global Precipitation Climatology Centre (GPCC), Climatic Research Unit (CRU), and Climate Prediction Centre (CPC), was evaluated to derive the nonparametric Standardized Precipitation Index (SPI) for the period 1987–2016. Six common statistical metrics and typical drought event characteristic criteria, including drought area and drought class identification, were used to measure the performance of the GPPs. The results indicate that GPCC was the best dataset, with high correlation, small errors, and high detectability. The results also revealed a slight difference between the GPPs regarding the correct diagnosis of the drought classes. The findings of this study can help decision makers to effectively use GPPs in drought monitoring for Iran.

Evaluation of Global Gridded Precipitation and Temperature Datasets against Gauged Observations over the Upper Tekeze River Basin, Ethiopia

Evaluation of Global Gridded Precipitation and Temperature Datasets against Gauged Observations over the Upper Tekeze River Basin, Ethiopia

Impacts of the seesaw mode between the Indian and East Asian summer monsoons on interannual variations in surface solar radiation across the low‐latitude highlands of China

AbstractThe relationship between the seesaw mode that connects the Indian summer monsoon (ISM) and East Asian summer monsoon (EASM), and variations in summer surface solar radiation (SSR) across the low‐latitude highlands of China (CLLH), was investigated using atmospheric reanalysis and NOAA global gridded precipitation and outgoing longwave radiation datasets. Singular value decomposition, correlation analysis, and composite analysis were employed. The results indicate that the interannual variations in summer SSR in the CLLH are significantly modulated by the seesaw mode that connects the ISM with the EASM. During the boreal summer, the CLLH SSR variability is positively correlated with ISM intensity, but negatively correlated with EASM intensity. When a strong ISM and a weak EASM occur together, significant anomalous northeasterly winds appear over the CLLH, which reduces the moisture supply to the region from the Bay of Bengal. In addition, significant anomalous descending motion dominates the CLLH, leading to significant negative relative humidity anomalies over the CLLH. Consequently, water vapour, total liquid water form column clouds and high cloud cover decreased significantly over the CLLH, resulting in a higher CLLH SSR than average. Conversely, a lower‐than‐normal CLLH SSR seems to result from the approximately opposite physical process during the years where the ISM is weak and EASM is strong over the CLLH. Given the fact that the correlation between the aerosol optical depth (AOD) and the CLLH SSR is relatively weak, the AOD variability may not be the main control on interannual variations of summer CLLH SSR.

The NASA‐JAXA Global Precipitation Measurement mission – part II: New frontiers in precipitation science

The <scp>NASA‐JAXA</scp> Global Precipitation Measurement mission – part <scp>II</scp>: New frontiers in precipitation science

Large-Scale Analysis of Global Gridded Precipitation and Temperature Datasets for Climate Change Impact Studies

AbstractCurrently, there are a large number of diverse climate datasets in existence, which differ, sometimes greatly, in terms of their data sources, quality control schemes, estimation procedures, and spatial and temporal resolutions. Choosing an appropriate dataset for a given application is therefore not a simple task. This study compares nine global/near-global precipitation datasets and three global temperature datasets over 3138 North American catchments. The chosen datasets all meet the minimum requirement of having at least 30 years of available data, so they could all potentially be used as reference datasets for climate change impact studies. The precipitation datasets include two gauged-only products (GPCC and CPC-Unified), two satellite products corrected using ground-based observations (CHIRPS V2.0 and PERSIANN-CDR V1R1), four reanalysis products (NCEP CFSR, JRA55, ERA-Interim, and ERA5), and one merged product (MSWEP V1.2). The temperature datasets include one gauge-based (CPC-Unified) and two reanalysis (ERA-Interim and ERA5) products. High-resolution gauge-based gridded precipitation and temperature datasets were combined as the reference dataset for this intercomparison study. To assess dataset performance, all combinations were used as inputs to a lumped hydrological model. The results showed that all temperature datasets performed similarly, albeit with the CPC performance being systematically inferior to that of the other three. Significant differences in performance were, however, observed between the precipitation datasets. The MSWEP dataset performed best, followed by the gauge-based, reanalysis, and satellite datasets categories. Results also showed that gauge-based datasets should be preferred in regions with good weather network density, but CHIRPS and ERA5 would be good alternatives in data-sparse regions.

Hydrological evaluation of global gridded precipitation datasets in a heterogeneous and data-scarce basin in Iran

Accurate estimation of the precipitation characteristics, including the value, temporal pattern, and spatial distribution, plays a significant role in the input uncertainty reduction for rainfall-runoff models. In many basins, the improper spatial distribution of rain gauge stations or their limited historical recorded data causes many challenges, especially in heterogeneous catchments which due to the impact of the drastic geographical alterations on the rainfall distribution pattern, the cover of the ground stations cannot estimate the actual precipitation rate. This challenge can be potentially solved by adopting rainfall products as alternative or complementary data sources. In this research, three rainfall products (PERSIANN-CCS, CMORPH and ERA-Interim), were compared against rain gauge stations for calibration of a daily conceptual lumped rainfall-runoff model (CRFM) in a data-scarce and heterogeneous basin located in southwestern Iran. The results indicated that ERA-Interim has the best performance among other datasets. Better performance of this dataset compared to the in-situ data also suggests a better estimation of the basin average as well as the temporal pattern of precipitation. The KGE value was obtained as 0.8 and 0.74, respectively, for a rainfall-runoff model that utilized the ERA-Interim as input in the calibration and validation periods. The results showed that the performance of satellite-based data of CMORPH and PERSIANN-CCS is not acceptable in simulating the daily flow. Also, the seasonal assessment showed that ERA-Interim has a better performance compared to other datasets, during fall and winter. However, in the spring, the performance of all datasets significantly reduces, and the range of BIAS variation increases. Generally, all datasets were shown to perform better in simulating the flow in terms of the transition from dry to wet periods, rather than wet to dry periods.

Evaluating Precipitation Datasets Using Surface Water and Energy Budget Closure

AbstractEvaluation of global gridded precipitation datasets typically entails using the in situ or satellite-based data used to derive them, so that out-of-sample testing is usually not possible. Here we detail a methodology that incorporates the physical balance constraints of the surface water and energy budgets to evaluate gridded precipitation estimates, providing the capacity for out-of-sample testing. Performance conclusions are determined by the ability of precipitation products to achieve closure of the linked budgets using adjustments that are within their prescribed uncertainty bounds. We evaluate and compare five global gridded precipitation datasets: IMERG, GPCP, GPCC, REGEN, and MERRA-2. At the spatial level, we show that precipitation is best estimated by GPCC over the high latitudes, by GPCP over the tropics, and by REGEN over North Africa and the Middle East. IMERG and REGEN appear best over Australia and South Asia. Furthermore, our results give insight into the adequacy of prescribed uncertainties of these products and shows that MERRA-2, while being less competent than the other four products in estimating precipitation, has the best representation of uncertainties in its precipitation estimates. The spatial extent of our results is not only limited to grid cells with in situ observations. Therefore, the approach enables a robust evaluation of precipitation estimates and goes some way to addressing the challenge of validation over observation scarce regions.

Potential hydropower estimation for the Mindanao River Basin in the Philippines based on watershed modelling using the soil and water assessment tool

This study aims to identify potential hydropower sites and calculate the theoretical potential hydropower capacity based on watershed modelling of the Mindanao River Basin (MRB) in the Philippines for the sustainable development of a previously unstudied region. The Soil and Water Assessment Tool (SWAT) was applied to delineate the watershed of the MRB and simulate the river discharges with inputs from observed precipitation and global gridded precipitation datasets. Observed weather data, such as temperature, humidity, and solar radiation, from four meteorological stations in the Philippines were also used as inputs for SWAT modelling. Simulated discharges were calibrated at three river gauges on the Nituan, Libungan and Pulangi Rivers. However, due to limited river discharge records, model validations were conducted in proxy basins: the calibrated model parameters in river A were used in the watershed modelling of proxy river B. Of the delineated 107 sub-basins in the MRB watershed, only 33 were identified as having potential sites for hydropower development. These potential sub-basins hosted a total of 154 potential sites with an estimated monthly average power capacity of 5,551.35 MW for all 33 sub-basins. The estimated theoretical power capacity of 15,266.22 MW for all sites in the MRB is approximately equivalent to the Philippines’ total available power capacity in 2017 of 15,393 MW. These sites were classified into 16 mini-scale hydropower sites, 114 small-scale hydropower sites and 24 medium-scale hydropower sites based on the simulated river discharges and potential power capacities. Based on these results, hydropower development could be an alternative to strengthen the exploration of renewable energy resources and improve the energy situation in Mindanao; hydropower development could also have mitigation effects on frequent floods in flat, low-lying downstream areas.

Watershed Modelling of the Mindanao River Basin in the Philippines Using the SWAT for Water Resource Management

This study aims to simulate the watershed of the Mindanao River Basin (MRB) to enhance water resource management for potential hydropower applications to meet the power demand in Mindanao with an average growth of 3.8% annually. The soil and water assessment tool (SWAT) model was used with inputs for geospatial datasets and weather records at four meteorological stations from DOST-PAGASA. To overcome the lack of precipitation data in the MRB, the precipitation records were investigated by comparing the records with the global gridded precipitation datasets from the NCDC-CPC and the GPCC. Then, the SWAT simulated discharges with the three precipitation data were calibrated with river discharge records at three stations in the Nituan, Libungan and Pulangi rivers. Due to limited records for the river discharges, the model results were, then, validated using the proxy basin principle along the same rivers in the Nituan, Libungan, and Pulangi areas. The R2 values from the validation are 0.61, 0.50 and 0.33, respectively, with the DOST-PAGASA precipitation; 0.64, 0.46 and 0.40, respectively, with the NCDC-CPC precipitation; and 0.57, 0.48 and 0.21, respectively, with the GPCC precipitation. The relatively low model performances in Libungan and Pulangi rivers are mainly due to the lack of datasets on the dam and water withdrawal in the MRB. Therefore, this study also addresses the issue of data quality for precipitation and data scarcity for river discharge, dam, and water withdrawal for water resource management in the MRB and show how to overcome the data quality and scarcity.

Meteorological drought lacunarity around the world and its classification

Abstract. The measure of drought duration strongly depends on the definition considered. In meteorology, dryness is habitually measured by means of fixed thresholds (e.g. 0.1 or 1 mm usually define dry spells) or climatic mean values (as is the case of the standardised precipitation index), but this also depends on the aggregation time interval considered. However, robust measurements of drought duration are required for analysing the statistical significance of possible changes. Herein we climatically classified the drought duration around the world according to its similarity to the voids of the Cantor set. Dryness time structure can be concisely measured by the n index (from the regular or irregular alternation of dry or wet spells), which is closely related to the Gini index and to a Cantor-based exponent. This enables the world’s climates to be classified into six large types based on a new measure of drought duration. To conclude, outcomes provide the ability to determine when droughts start and finish. We performed the dry-spell analysis using the full global gridded daily Multi-Source Weighted-Ensemble Precipitation (MSWEP) dataset. The MSWEP combines gauge-, satellite-, and reanalysis-based data to provide reliable precipitation estimates. The study period comprises the years 1979–2016 (total of 45 165 d), and a spatial resolution of 0.5∘, with a total of 259 197 grid points. The dataset is publicly available at https://doi.org/10.5281/zenodo.3247041 (Monjo et al., 2019).

A Global Gridded Dataset of the Characteristics of the Rainy And Dry Seasons

AbstractThe Rainy and Dry Seasons (RADS) dataset, a new compilation of precipitation statistics available to the public, is described. The dataset contains the dates of onset and demise of the rainy season (one date per year), the duration of the rainy and dry seasons, and the accumulated precipitation during the rainy and dry seasons. The methodology for detecting the characteristics of the rainy season is based solely on precipitation data. RADS was developed from multiple global gridded daily precipitation datasets [Tropical Rainfall Measuring Mission (TRMM), 1998–2015; Climate Prediction Center Unified Gauge-Based Analysis of Global Daily Precipitation (CPC_UNI), 1979–present; and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), 1980–present] and therefore shares the spatial resolution, temporal range, and limitations of the original precipitation datasets. This is the first free public dataset of the characteristics of the rainy and dry seasons created using a consistent methodology across the globe, including all major monsoonal regions. We expect that the RADS dataset will contribute to our understanding of the sources of variability of the timing of rainy seasons (on local to regional scales) and monsoons (on large scales) and their impacts on water resource management and other aspects of geosciences and human activities.