Global Precipitation Datasets Research Articles

Spatial Downscaling of Satellite-Based Precipitation and Its Impact on Discharge Simulations in the Magdalena River Basin in Colombia

Precipitation is one of the most important components of the water cycle and its accurate spatial and temporal representation is fundamental for hydrological modeling. In the present study, we investigated the impact of spatial resolution of various precipitation datasets on discharge estimates. First, a new precipitation spatial downscaling procedure was developed and applied to four gridded global precipitation datasets based on (i) solely satellite observations: CMORPH and PERSIANN, (ii) satellite and in situ observations: TRMM and (iii) satellite and in situ observations and reanalysis data: MSWEP. The here presented downscaling methodology blended global precipitation datasets with data on vegetation and topography to improve the representation of precipitation spatial variability. Second, interpolated in situ, non-downscaled (25 km) and downscaled (1 km) precipitation data were used to force a grid-distributed version of the HBV-96 rainfall-runoff model for the Magdalena River basin in Colombia. Results showed that MSWEP and TRMM outperformed CMORPH and PERSIANN precipitation datasets. The downscaling procedure resulted in considerable improvements in coefficient of determination, root mean square error and bias in comparison with in situ precipitation observations. Discharge model estimates were also in better agreement with the observations when the model was forced with the downscaled precipitation. Model performance was improved with Kling Gupta efficiency increases in the order of 0.1 to 0.5. Moreover, better discharge simulations were obtained using downscaled precipitation compared to using only in situ precipitation data when using less than 100 stations.

Correction: Koutsouris et al. Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania. Atmosphere, 2017, 8, 246

The authors would like to correct the published article [1], following the detection of editorial mistakes by the main author, as explained below[...]

Evaluation of three global gridded precipitation data sets in central Asia based on rain gauge observations

The accuracies of gridded precipitation data sets are important for regional climate studies and hydrological models. In this study, the performances of Global Precipitation Climatology Centre (GPCC) V7, Climatic Research Unit (CRU) TS 3.22 and Willmott and Matsuura (WM) precipitation data sets were examined over central Asia by comparing them against observed precipitation records (OBS) from 586 meteorological stations during 1901–2010. The results show that all the three gridded data sets underestimated the observed precipitation at annual and monthly scales, especially in mountainous areas. Both GPCC and WM underestimated seasonal precipitation, especially for spring precipitation. Among the three gridded data sets, GPCC had the highest correlation and lowest bias compared with CRU and WM when against the OBS. WM had a higher correlation than that of CRU, and its bias was larger than that of CRU. In terms of the drought and heavy rainfall events, CRU had the best performance in capturing drought events, and GPCC was best at representing heavy rainfall events. These differences in the performances between the three gridded data sets were primarily induced by their different interpolation methods and the numbers of available meteorological stations used in the interpolations of the three gridded data sets. Therefore, compared to the other two data sets, GPCC is more suitable for studies of long‐term precipitation variations over central Asia.

A nonparametric statistical technique for combining global precipitation datasets: development and hydrological evaluation over the Iberian Peninsula

Abstract. This study investigates the use of a nonparametric, tree-based model, quantile regression forests (QRF), for combining multiple global precipitation datasets and characterizing the uncertainty of the combined product. We used the Iberian Peninsula as the study area, with a study period spanning 11 years (2000–2010). Inputs to the QRF model included three satellite precipitation products, CMORPH, PERSIANN, and 3B42 (V7); an atmospheric reanalysis precipitation and air temperature dataset; satellite-derived near-surface daily soil moisture data; and a terrain elevation dataset. We calibrated the QRF model for two seasons and two terrain elevation categories and used it to generate ensemble for these conditions. Evaluation of the combined product was based on a high-resolution, ground-reference precipitation dataset (SAFRAN) available at 5 km 1 h−1 resolution. Furthermore, to evaluate relative improvements and the overall impact of the combined product in hydrological response, we used the generated ensemble to force a distributed hydrological model (the SURFEX land surface model and the RAPID river routing scheme) and compared its streamflow simulation results with the corresponding simulations from the individual global precipitation and reference datasets. We concluded that the proposed technique could generate realizations that successfully encapsulate the reference precipitation and provide significant improvement in streamflow simulations, with reduction in systematic and random error on the order of 20–99 and 44–88 %, respectively, when considering the ensemble mean.

A Review of Global Precipitation Data Sets: Data Sources, Estimation, and Intercomparisons

AbstractIn this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge‐based, satellite‐related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high‐latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation.

On the evolution of hydrological modelling for water resources in Eastern Africa.

Abstract This review provides an assessment of the evolution of hydrological modelling for Eastern Africa. We outline the historical development and perspectives considered as this region, like many regions around the world, sees increasing attention on how water resources can be sustainably developed. We emphasize the spatial scales and modelling approaches that typify the region and how these have changed with time. The review is done in two complementary approaches. The first approach is to explore a practical, real-world example providing context for the Eastern Africa region and the water resource development issues presently faced. We use Tanzania's 34 000 km 2 Kilombero Valley (KV) river basin to explore implications of significant gaps in data and modelling scales. We hypothesize that these gaps limit the application of the current state-of-the-science to inform water management policy and practice under current and estimated future conditions. In our second approach, we investigate possible solutions to bridge these gaps through a review of case studies from other Eastern Africa's basins across a range of sizes. Our result highlight that some applications of the models considered under this review anticipated more recent international developments as indicated in Predictions in Ungauged Basins and Panta Rhei initiatives. Through this review, it is clear that there is a possibility to improve understanding of the hydrological processes relevant at scales such as the KV river basin through the use of (1) global precipitation datasets (e.g. satellite and/or homogenized observed data) as input data; (2) remote sensing datasets as model evaluation variable; (3) regionalization around the transferability of model parameters; (4) modification of model codes/structures to suit local conditions; and (5) understanding and application of uncertainty principles in hydrological modelling. Given that many regions of the world face similar water resource management challenges as Eastern Africa; it is likely that the findings of this review could help guide how we develop the next generation of modelling approaches to leverage information from various scales.

Assessing the ability of three global reanalysis products to reproduce South American monsoon precipitation

The South American monsoon system (SAMS) refers to the strong seasonal variability in precipitation observed over South America, with high precipitation amounts during the summer while winter is the driest season. The aim of this work is to understand how different global reanalysis precipitation datasets (NCEP-2, ERA-Interim and CFSR-1) represent rainfall patterns and intensity associated with SAMS in comparison to gauge-based analysis (CPC). The results show that reanalysis products have some difficulties in simulating both spatial distribution and intensity of the observed precipitation. However, ERA-Interim seems to better correlate with the observed variability in precipitation during the austral warm season. The intensity and positioning of the 850-hPa moisture fluxes and the associated divergence patterns might explain differences found in precipitation from the three global reanalysis products in the SAMS core region.

Application of global precipitation dataset for drought monitoring and forecasting over the Lake Urmia basin with the GA-SVR model

Application of global precipitation dataset for drought monitoring and forecasting over the Lake Urmia basin with the GA-SVR model

Application of global precipitation dataset for drought monitoring and forecasting over the Lake Urmia basin with the GA-SVR model

In the present study, the accuracy of the climate research unit (CRU) precipitation data was assessed as an alternative source instead of in situ data for monitoring the drought in the Lake Urmia Basin area during the period from 1984 to 2013. Later, a genetic algorithm-support vector regression (GA-SVR) model was utilised in order to forecast drought conditions up to four months ahead. The results demonstrated that the CRU data had acceptable accuracy in drought monitoring so that in at least 75% of the cases, there was no difference between the monitored drought classed through observed data and CRU data. In the forecasting section, the results showed two general patterns. The first pattern indicated a descending trend of forecast accuracy with an increase in the lead-times ahead of forecasts; the second pattern revealed the ascending trend of forecast accuracy, with an increase in the SPI scale.

Utilization of Global Precipitation Datasets in Data Limited Regions: A Case Study of Kilombero Valley, Tanzania

This study explored the potential for bias correction of global precipitation datasets (GPD) to support streamflow simulation for water resource management in data limited regions. Two catchments, 580 km2 and 2530 km2, in the Kilombero Valley of central Tanzania were considered as case studies to explore three GPD bias correction methods: quantile mapping (QM), daily percentages (DP) and a model based (ModB) bias correction. The GPDs considered included two satellite rainfall products, three reanalysis products and three interpolated observed data products. The rainfall-runoff model HBV was used to simulate streamflow in the two catchments using (1) observed rain gauge data; (2) the original GPDs and (3) the bias-corrected GPDs as input. Results showed that applying QM to bias correction based on limited observed data tends to aggravate streamflow simulations relative to not bias correcting GPDs. This is likely due to a potential lack of representativeness of a single rain gauge observation at the scale of a hydrological catchment for these catchments. The results also indicate that there may be potential benefits in combining streamflow and rain gauge data to bias correct GPDs during the model calibration process within a hydrological modeling framework.

An assessment of the performance of global rainfall estimates without ground-based observations

Abstract. Satellite-based rainfall estimates over land have great potential for a wide range of applications, but their validation is challenging due to the scarcity of ground-based observations of rainfall in many areas of the planet. Recent studies have suggested the use of triple collocation (TC) to characterize uncertainties associated with rainfall estimates by using three collocated rainfall products. However, TC requires the simultaneous availability of three products with mutually uncorrelated errors, a requirement which is difficult to satisfy with current global precipitation data sets. In this study, a recently developed method for rainfall estimation from soil moisture observations, SM2RAIN, is demonstrated to facilitate the accurate application of TC within triplets containing two state-of-the-art satellite rainfall estimates and a reanalysis product. The validity of different TC assumptions are indirectly tested via a high-quality ground rainfall product over the contiguous United States (CONUS), showing that SM2RAIN can provide a truly independent source of rainfall accumulation information which uniquely satisfies the assumptions underlying TC. On this basis, TC is applied with SM2RAIN on a global scale in an optimal configuration to calculate, for the first time, reliable global correlations (vs. an unknown truth) of the aforementioned products without using a ground benchmark data set. The analysis is carried out during the period 2007–2012 using daily rainfall accumulation products obtained at 1° × 1° spatial resolution. Results convey the relatively high performance of the satellite rainfall estimates in eastern North and South America, southern Africa, southern and eastern Asia, eastern Australia, and southern Europe, as well as complementary performances between the reanalysis product and SM2RAIN, with the first performing reasonably well in the Northern Hemisphere and the second providing very good performance in the Southern Hemisphere. The methodology presented in this study can be used to identify the best rainfall product for hydrologic models with sparsely gauged areas and provide the basis for an optimal integration among different rainfall products.

A Global ETCCDI-Based Precipitation Climatology from Satellite and Rain Gauge Measurements

Precipitation is still one of the most complex climate variables to observe, to understand, and to handle within climate monitoring and climate analysis as well as to simulate in numerical weather prediction and climate models. Especially over ocean, less is known about precipitation than over land due to the sparsity of in situ observations. Here, we introduce and discuss a global Expert Team on Climate Change and Indices (ETCCDI)-based precipitation climatology. The basis for computation of this climatology is the global precipitation dataset Daily Precipitation Analysis for Climate Prediction (DAPACLIP) which combines in situ observation data over land and satellite-based remote sensing data over ocean in daily temporal resolution, namely data from the Global Precipitation Climatology Centre (GPCC) and the Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data (HOAPS) dataset. The DAPACLIP dataset spans the period 1988–2008 and thus the global ETCCDI-based precipitation climatology covers 21 years in total. Regional aspects of the climatology are also discussed with focus on Europe and the monsoon region of south-east Asia. To our knowledge, this is the first presentation and discussion of an ETCCDI-based precipitation climatology on a global scale.

MSWEP: 3-hourly 0.25° global gridded precipitation (1979–2015) by merging gauge, satellite, and reanalysis data

Abstract. Current global precipitation (P) datasets do not take full advantage of the complementary nature of satellite and reanalysis data. Here, we present Multi-Source Weighted-Ensemble Precipitation (MSWEP) version 1.1, a global P dataset for the period 1979–2015 with a 3-hourly temporal and 0.25° spatial resolution, specifically designed for hydrological modeling. The design philosophy of MSWEP was to optimally merge the highest quality P data sources available as a function of timescale and location. The long-term mean of MSWEP was based on the CHPclim dataset but replaced with more accurate regional datasets where available. A correction for gauge under-catch and orographic effects was introduced by inferring catchment-average P from streamflow (Q) observations at 13 762 stations across the globe. The temporal variability of MSWEP was determined by weighted averaging of P anomalies from seven datasets; two based solely on interpolation of gauge observations (CPC Unified and GPCC), three on satellite remote sensing (CMORPH, GSMaP-MVK, and TMPA 3B42RT), and two on atmospheric model reanalysis (ERA-Interim and JRA-55). For each grid cell, the weight assigned to the gauge-based estimates was calculated from the gauge network density, while the weights assigned to the satellite- and reanalysis-based estimates were calculated from their comparative performance at the surrounding gauges. The quality of MSWEP was compared against four state-of-the-art gauge-adjusted P datasets (WFDEI-CRU, GPCP-1DD, TMPA 3B42, and CPC Unified) using independent P data from 125 FLUXNET tower stations around the globe. MSWEP obtained the highest daily correlation coefficient (R) among the five P datasets for 60.0 % of the stations and a median R of 0.67 vs. 0.44–0.59 for the other datasets. We further evaluated the performance of MSWEP using hydrological modeling for 9011 catchments (< 50 000 km2) across the globe. Specifically, we calibrated the simple conceptual hydrological model HBV (Hydrologiska Byråns Vattenbalansavdelning) against daily Q observations with P from each of the different datasets. For the 1058 sparsely gauged catchments, representative of 83.9 % of the global land surface (excluding Antarctica), MSWEP obtained a median calibration NSE of 0.52 vs. 0.29–0.39 for the other P datasets. MSWEP is available via http://www.gloh2o.org.

Performance of IMERG as a Function of Spatiotemporal Scale.

The Integrated Multi-satellitE Retrievals for GPM (IMERG), a global high-resolution gridded precipitation data set, will enable a wide range of applications, ranging from studies on precipitation characteristics to applications in hydrology to evaluation of weather and climate models. These applications focus on different spatial and temporal scale and thus average the precipitation estimates to coarser resolutions. Such a modification of scale will impact the reliability of IMERG. In this study, the performance of the Final run of MERG is evaluated against ground-based measurements as a function of increasing spatial resolution (from 0.1° to 2.5 ) and accumulation periods (from 0.5 h to 24 h) over a region in the southeastern US. For ground reference, a product derived from the Multi-Radar/Multi-Sensor suite, a radar- and gauge-based operational precipitation dataset, is used. The TRMM Multi satellite Precipitation Analysis (TMPA) is also included as a benchmark. In general, both IMERG and TMPA improve when scaled up to larger areas and longer time periods, with better identification of rain occurrences and consistent improvements in systematic and random errors of rain rates. Between the two satellite estimates, IMERG is slightly better than TMPA most of the time. These results will inform users on the reliability of IMERG over the scales relevant to their studies.

Evaluating global reanalysis precipitation datasets with rain gauge measurements in the Sudano‐Sahel region: case study of the Logone catchment, Lake Chad Basin

ABSTRACTAfrica has a paucity of long term reliable meteorological ground station data and reanalysis products are used to provide the climate estimations that are important for climate change projections. In this paper monthly observed precipitation records in the Logone catchment of the Lake Chad Basin are used to evaluate the performance of two global reanalysis products: the Climate Forecasting System Reanalysis (CFSR) and ERA Interim datasets.The two reanalysis products reproduced the monthly, annual and decadal cycle of precipitation and variability relatively accurately albeit with some discrepancies. The catchment rainfall gradient was also well captured by the two products. There are good correlations between the reanalysis and rain gauge datasets although significant deviations exist, especially for CFSR. Both reanalysis products overestimated rainfall in 68% of the rain gauge stations. ERA Interim produced the lowest bias and mean absolute error with average values of 2% and 6.5 mm month−1 respectively compared to 15% and 34 mm month−1 for the CFSR. However, both reanalysis products systematically underestimated annual rainfall in the catchment during the period 1997–2002 for ERA Interim and 1998–2000 for CFSR. This research demonstrates that evaluating reanalysis products in remote areas like the Logone catchment enables users to identify artefacts inherent in reanalysis datasets. This will facilitate improvements in certain aspects of the reanalysis forecast model physics and parameterization to improve reanalysis dataset quality.This study concludes that the application of each reanalysis product in the catchment will depend on the purpose for which it is to be used and the spatial scale required.

Development of a 0.5° global monthly raining day product from 1901 to 2010

AbstractWhile several long‐term global data sets of monthly precipitation amount (P) are widely available, only the Climate Research Unit (CRU) provides long‐term global monthly raining day number (N) data (i.e., daily precipitation frequency in a month), with P/N representing the daily precipitation intensity. However, because CRU N is based on a limited number of gauges, it is found to perform poorly over data sparse regions. By combining the CRU method with a short‐term gauge‐satellite merged global daily precipitation data set (Climate Prediction Center morphing technique) and a global long‐term monthly precipitation data set (Global Precipitation Climatology Centre) with far more gauges than used in CRU, a new 0.5° global N data set from 1901 to 2010 is developed, which differs significantly from CRU N. Compared with three independent regional daily precipitation products over the U.S., China, and South America based on much denser gauge networks than used in CRU, the new product shows significant improvement over CRU N.

Development of a global historic monthly mean precipitation dataset

Global historic precipitation dataset is the base for climate and water cycle research. There have been several global historic land surface precipitation datasets developed by international data c ...

Rainfall variations induced by the lunar gravitational atmospheric tide and their implications for the relationship between tropical rainfall and humidity

AbstractClassical tidal theory predicts that the lunar gravitational semidiurnal tide (L2) should induce perturbations in relative humidity (RH). Adiabatic expansion in divergent flow in advance of the L2 pressure minimum cools the air and reduces its saturation vapor pressure, thereby increasing the rate of condensation in saturated air parcels and causing the relative humidity (RH) of unsaturated parcels to rise. Here we detect a statistically significant L2 signature in precipitation rate (P) in a 15 year, eight times daily, global precipitation data set acquired in the Tropical Rainfall Measuring Mission. Consistent with tidal theory and with the notion that L2 modulates P mainly by perturbing RH, P varies out of phase with pressure, and it increases at a rate of about 10% per 1% increase in RH. These measurements thus provide a measure of the sensitivity of P to planetary‐scale changes in RH. Analysis of solar semidiurnal (S2) tidal statistics yields similar results.

Bias in the variance of gridded data sets leads to misleading conclusions about changes in climate variability

ABSTRACTMany studies addressing climate change and climate variability over large regions rely on gridded data. Grids are preferred to station‐based data sets because they help avoiding bias arising from the irregular spatial distribution of the observations. However, while spatial interpolation techniques used for constructing gridded data are good at preserving the mean of the data, they do not offer an adequate representation of their variance. In fact, the grid's variance depends largely on the spatial density of observations used for constructing it. Most global and regional climate data sets are characterized by large temporal changes in the number of observations available for interpolation, with a strong reduction in the last 30 years. These changes in the sample size result in changes in the variance of gridded data that are merely an effect of the interpolation process, and ignoring this fact may lead to erroneous conclusions about changes in climate variability and extremes. We discuss this problem and we demonstrate its importance with a widely used global dataset of temperature and precipitation. We propose to move from interpolation techniques towards statistical simulation approaches that provide a better representation of climate variability when constructing climatic grids.

Case Study of Moisture and Heat Budgets within Atmospheric Rivers

Abstract This work studies moisture and heat budgets within two atmospheric rivers (ARs) that made landfall on the west coast of North America during January 2009. Three-dimensional kinematic and thermodynamic fields were constructed using ECMWF Year of Tropical Convection data and global gridded precipitation datasets. Differences between the two ARs are observed, even though both had embedded precipitating convective organizations of the same spatial scale. AR1 extended from 20° to 50°N in an almost west–east orientation. It had excessive warm and moist near-surface conditions. Its precipitating systems were mainly distributed on the southwest and northeast sides of the AR, and tended to exhibit stratiform-type vertical heat and moisture transports. In contrast, AR2 spanned latitudes between 20° and 60°N in a north–south orientation. It was narrower and shorter than AR1, and was mostly covered by pronounced precipitating systems, dominated by a deep convection type of heating throughout the troposphere. In association with these distinctions, the atmosphere over the northeastern Pacific on average experienced episodic cooling and drying despite the occurrence of AR1, yet underwent heating and drying during AR2, when latent heating was strong. Downward sensible heat flux and weak upward surface latent heat flux were observed particularly in AR1. In addition, cloud radiative forcing (CRF) was very weak in AR1, whereas it was strongly negative in AR2. In short, it is found that the oceanic convection in ARs both impacts the moisture transport of ARs, as well as modifies the heat balance in the midlatitudes through latent heat release, convective heat transport, surface heat fluxes, and CRF.