Global Precipitation Climatology Centre Data Research Articles

Biases Beyond the Mean in CMIP6 Extreme Precipitation: A Global Investigation

AbstractClimate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L‐moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L‐variation, and of L‐skewness and L‐kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value () distribution coupled with a novel application of the Anderson‐Darling Goodness‐of‐fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within 10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3‐GC31‐MM model performing well in all three scales when compared to the ground‐based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi‐arid regions.

Insight into the precipitation behavior of gridded precipitation data in the Sina basin.

This paper examines the performance of three gridded precipitation data sets, namely, Global Precipitation Climatology Centre (GPCC), Tropical Precipitation Measuring Mission (TRMM), and the Modern-Era Retrospective Analysis for Research and Applications (MERRA), for the duration of 25years using 9 rain gauge data sets of the Sina basin, India. Statistical measures were employed to measure the performance in reproducing the rainfall and to assess its ability to detect the rainfall/no rainfall events, its structure, pattern, and spatio-temporal variations in the monthly and annual time scales. Compromise programming (CP) is used to rank the statistical performances of selected gridded precipitation data sets and found that TRMM attained first rank for the 8 stations followed by MERRA. The precipitation concentration index (PCI) checks the pattern and distribution of rainfall and found that observed data shows a uniform distribution in the basin; however, all the three gridded data sets failed to demonstrate uniform distribution. Categorical metrics like Probability of Detection (POD) and False Alarm Ratio (FAR) revealed that TRMM followed by MERRA and GPCC have good capabilities to detect rainfall/no rainfall events at different thresholds. All the trends drawn between observed data set and gridded precipitation data sets revealed that the MERRA data tend to underestimate and the TRMM and GPCC data tend to overestimate the values and intensities of rainfall data sets at most of the stations for both monthly and annual time scales. The data analysis of extreme rainfall points at monthly and annual time scales exhibits better performance of TRMM data sets. Overall, the TRMM data set is capable in replicating different characteristics of the observed data in the study area and could be used for hydro-meteorological and climatic studies when continuous observed data set is not available.

Recent global performance of the Climate Hazards group Infrared Precipitation (CHIRP) with Stations (CHIRPS)

The Climate Hazards group Infrared Precipitation (CHIRP) and with Stations (CHIRPS) datasets are two new quasi-global (50oS–50oN), high-resolution (0.05° × 0.05°), long-term (1981-present) precipitation estimates based on infrared Cold Cloud Duration (CCD) observations. This study investigates, for the first time, the global performance of CHIRP and CHIRPS against the gauge-based GPCC (Global Precipitation Climatology Centre) data at monthly scale using 36 complete years of data record (1981–2016). Global assessment results indicate that both CHIRP and CHIRPS have negative biases (−5.93% for CHIRP and −2.01% for CHIRPS) before 2000, while this systematic underestimation was effectively removed after 2000. Global analyses also show that the gauge-adjusted CHIRPS estimates generally represent a substantial improvement over CHIRP due to gauge-based bias correction. With respect to regional statistics, temporal analysis and intensity distribution, the gauge-adjusted CHIRPS estimates agree well with GPCC and outperforms CHIRP over most regions, such as the United States, Europe, Africa, Australia and South America. However, southeast China is an exception. Over this region, CHIRPS has a systematic overestimation of 5.55% against GPCC during 2000–2016, especially for spring and summer months, while such positive biases were not found for the pure satellite-derived CHIRP. Possible causes for the discrepancy between these two satellite products over the global and regional scales were further discussed and analyzed. The results reported here will both provide the algorithm developers of CHIRP and CHIRPS with some valuable information and offer the hydrometeorological users a better understanding of their error characteristics and potential limits for various hydrological applications from the global perspective.

Assessment of IMERG Precipitation Estimates over Europe

This paper evaluates Integrated Multi-Satellite Retrievals from GPM (IMERG-F) over Europe for the period 2014–2018 in order to evaluate application of the retrievals to hydrology. IMERG-F is compared with a large pan-European precipitation dataset built on rain gauge stations, i.e., the ENSEMBLES OBServation (E-OBS) gridded dataset. Although there is overall agreement in the spatial distribution of mean precipitation (R2 = 0.8), important discrepancies are revealed in mountainous regions, specifically the Alps, Pyrenees, west coast of the British Isles, Scandinavia, the Iberian and Italian peninsulas, and the Adriatic coastline. The results show that the strongest contributors to poor performance are pixels where IMERG-F has no gauges available for adjustment. If rain gauges are available, IMERG-F yields results similar to those of the surface observations, although the performance varies by region. However, even accounting for gauge adjustment, IMERG-F systematically underestimates precipitation in the Alps and Scandinavian mountains. Conversely, IMERG-F overestimates precipitation in the British Isles, Italian Peninsula, Adriatic coastline, and eastern European plains. Additionally, the research shows that gauge adjustment worsens the spatial gradient of precipitation because of the coarse resolution of Global Precipitation Climatology Centre data.

Evaluation of five gridded rainfall datasets in simulating streamflow in the upper Dong Nai river basin, Vietnam

ABSTRACTRainfall data with an appropriate spatial resolution is a key input to hydrological models. However, networks of rain gauges are often sparsely and unevenly distributed in large catchments, especially in developing countries. High-resolution rainfall datasets, such as the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE), the Climate Forecast System Reanalysis (CFSR), the Climatic Research Unit Time Series (CRU-TS), the Global Precipitation Climatology Centre (GPCC) and the Tropical Rainfall Measuring Mission (TRMM), have become available to overcome such limitations. The objective of this study was to evaluate the impacts of four land-based rainfall products (APHRODITE, CFSR, CRU-TS, and GPCC) and a satellite-based rainfall product (TRMM) on streamflow of the upper catchment of Tri An reservoir in Vietnam using the Hydrological Modeling System (HEC-HMS). In addition, the available rain gauges data were used for comparison purpose. Result indicates that the TRMM and GPCC data show their best match to rain gauges data in simulating the streamflow in the period 1999–2007. Generally, the results indicate that the TRMM and GPCC data could be alternative solutions.

Spatial distribution of secular trends in annual and seasonal precipitation over Pakistan

CR Climate Research Contact the journal Facebook Twitter RSS Mailing List Subscribe to our mailing list via Mailchimp HomeLatest VolumeAbout the JournalEditorsSpecials CR 74:95-107 (2017) - DOI: https://doi.org/10.3354/cr01489 Spatial distribution of secular trends in annual and seasonal precipitation over Pakistan Kamal Ahmed1,*, Shamsuddin Shahid2, Eun-Sung Chung3, Tarmizi Ismail2, Xiao-Jun Wang4,5 1Faculty of Water Resource Management, Lasbela University of Agriculture, Water and Marine Sciences, Balochistan, Pakistan 2Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM), 81310 Johor Bahru, Malaysia 3Department of Civil Engineering, Seoul National University of Science and Technology, Seoul 139-743, South Korea 4State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China 5Research Center for Climate Change, Ministry of Water Resources, Nanjing 210029, China *Corresponding author: kamal_brc@hotmail.com ABSTRACT: The presence of autocorrelation and long-term persistence (LTP) can lead to considerable change in the significance of trends in hydro-climatic time series. This therefore casts doubt on past findings of climatic trend studies that did not consider LTP. We assessed the trends in spatiotemporal patterns of annual and seasonal precipitation of Pakistan in recent years (1961-2010) using precipitation data from the Global Precipitation Climatology Centre (GPCC) using (1) the ordinary Mann-Kendall (MK) test and (2) the modified Mann-Kendall (MMK) test, which can discriminate LTP from unidirectional trends. The results indicate that significance in trends obtained using the MK test is reduced when LTP is taken into consideration. The annual precipitation in Pakistan is increasing in the northern highlands and a few places in the sub-Himalayan ranges in northeast Pakistan, where monsoon precipitation is also increasing. There is no indication of significant change in winter precipitation. Post-monsoon precipitation is increasing at a few locations in the monsoon-dominated southeast region and decreasing in the southwestern arid region. KEY WORDS: Long-term persistence · Precipitation trend analysis · Modified Mann-Kendall · GPCC data · Pakistan Full text in pdf format NextCite this article as: Ahmed K, Shahid S, Chung ES, Ismail T, Wang XJ (2017) Spatial distribution of secular trends in annual and seasonal precipitation over Pakistan. Clim Res 74:95-107. https://doi.org/10.3354/cr01489 Export citation RSS - Facebook - Tweet - linkedIn Cited by Published in CR Vol. 74, No. 2. Online publication date: December 27, 2017 Print ISSN: 0936-577X; Online ISSN: 1616-1572 Copyright © 2017 Inter-Research.

Impact of climate, vegetation, soil and crop management variables on multi-year ISBA-A-gs simulations of evapotranspiration over a Mediterranean crop site

Abstract. Generic land surface models are generally driven by large-scale data sets to describe the climate, the soil properties, the vegetation dynamic and the cropland management (irrigation). This paper investigates the uncertainties in these drivers and their impacts on the evapotranspiration (ET) simulated from the Interactions between Soil, Biosphere, and Atmosphere (ISBA-A-gs) land surface model over a 12-year Mediterranean crop succession. We evaluate the forcing data sets used in the standard implementation of ISBA over France where the model is driven by the SAFRAN (Système d'Analyse Fournissant des Renseignements Adaptés à la Nivologie) high spatial resolution atmospheric reanalysis, the leaf area index (LAI) time courses derived from the ECOCLIMAP-II land surface parameter database and the soil texture derived from the French soil database. For climate, we focus on the radiations and rainfall variables and we test additional data sets which include the ERA-Interim (ERA-I) low spatial resolution reanalysis, the Global Precipitation Climatology Centre data set (GPCC) and the MeteoSat Second Generation (MSG) satellite estimate of downwelling shortwave radiations. The evaluation of the drivers indicates very low bias in daily downwelling shortwave radiation for ERA-I (2.5 W m−2) compared to the negative biases found for SAFRAN (−10 W m−2) and the MSG satellite (−12 W m−2). Both SAFRAN and ERA-I underestimate downwelling longwave radiations by −12 and −16 W m−2, respectively. The SAFRAN and ERA-I/GPCC rainfall are slightly biased at daily and longer timescales (1 and 0.5 % of the mean rainfall measurement). The SAFRAN rainfall is more precise than the ERA-I/GPCC estimate which shows larger inter-annual variability in yearly rainfall error (up to 100 mm). The ECOCLIMAP-II LAI climatology does not properly resolve Mediterranean crop phenology and underestimates the bare soil period which leads to an overall overestimation of LAI over the crop succession. The simulation of irrigation by the model provides an accurate irrigation amount over the crop cycle but the timing of irrigation occurrences is frequently unrealistic. Errors in the soil hydrodynamic parameters and the lack of irrigation in the simulation have the largest influence on ET compared to uncertainties in the large-scale climate reanalysis and the LAI climatology. Among climate variables, the errors in yearly ET are mainly related to the errors in yearly rainfall. The underestimation of the available water capacity and the soil hydraulic diffusivity induce a large underestimation of ET over 12 years. The underestimation of radiations by the reanalyses and the absence of irrigation in the simulation lead to the underestimation of ET while the overall overestimation of LAI by the ECOCLIMAP-II climatology induces an overestimation of ET over 12 years. This work shows that the key challenges to monitor the water balance of cropland at regional scale concern the representation of the spatial distribution of the soil hydrodynamic parameters, the variability of the irrigation practices, the seasonal and inter-annual dynamics of vegetation and the spatiotemporal heterogeneity of rainfall.

Soil as a natural rain gauge: Estimating global rainfall from satellite soil moisture data

AbstractMeasuring precipitation intensity is not straightforward; and over many areas, ground observations are lacking and satellite observations are used to fill this gap. The most common way of retrieving rainfall is by addressing the problem “top‐down” by inverting the atmospheric signals reflected or radiated by atmospheric hydrometeors. However, most applications are interested in how much water reaches the ground, a problem that is notoriously difficult to solve from a top‐down perspective. In this study, a novel “bottom‐up” approach is proposed that, by doing “hydrology backward,” uses variations in soil moisture (SM) sensed by microwave satellite sensors to infer preceding rainfall amounts. In other words, the soil is used as a natural rain gauge. Three different satellite SM data sets from the Advanced SCATterometer (ASCAT), the Advanced Microwave Scanning Radiometer (AMSR‐E), and the Microwave Imaging Radiometer with Aperture Synthesis are used to obtain three new daily global rainfall products. The “First Guess Daily” product of the Global Precipitation Climatology Centre (GPCC) is employed as main benchmark in the validation period 2010–2011 for determining the continuous and categorical performance of the SM‐derived rainfall products by considering the 5 day accumulated values. The real‐time version of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis product, i.e., the TRMM‐3B42RT, is adopted as a state‐of‐the‐art satellite rainfall product. The SM‐derived rainfall products show good Pearson correlation values (R) with the GPCC data set, mainly in areas where SM retrievals are found to be accurate. The global median R values (in the latitude band ±50°) are equal to 0.54, 0.28, and 0.31 for ASCAT‐, AMSR‐E‐, and SMOS‐derived products, respectively. For comparison, the median R for the TRMM‐3B42RT product is equal to 0.53. Interestingly, the SM‐derived products are found to outperform TRMM‐3B42RT in terms of average global root‐mean‐square error statistics and in terms of detection of rainfall events. The regions for which the SM‐derived products perform very well are Australia, Spain, South and North Africa, India, China, the Eastern part of South America, and the central part of the United States. The SM‐derived products are found to estimate accurately the rainfall accumulated over a 5 day period, an aspect particularly important for their use for hydrological applications, and that address the difficulties of estimating light rainfall from TRMM‐3B42RT.

A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sample applications including centennial (trend) analysis from 1901–present

Abstract. The availability of highly accessible and reliable monthly gridded data sets of global land-surface precipitation is a need that was already identified in the mid-1980s when there was a complete lack of globally homogeneous gauge-based precipitation analyses. Since 1989, the Global Precipitation Climatology Centre (GPCC) has built up its unique capacity to assemble, quality assure, and analyse rain gauge data gathered from all over the world. The resulting database has exceeded 200 yr in temporal coverage and has acquired data from more than 85 000 stations worldwide. Based on this database, this paper provides the reference publication for the four globally gridded monthly precipitation products of the GPCC, covering a 111-yr analysis period from 1901–present. As required for a reference publication, the content of the product portfolio, as well as the underlying methodologies to process and interpolate are detailed. Moreover, we provide information on the systematic and statistical errors associated with the data products. Finally, sample applications provide potential users of GPCC data products with suitable advice on capabilities and constraints of the gridded data sets. In doing so, the capabilities to access El Niño–Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO) sensitive precipitation regions and to perform trend analyses across the past 110 yr are demonstrated. The four gridded products, i.e. the Climatology (CLIM) V2011, the Full Data Reanalysis (FD) V6, the Monitoring Product (MP) V4, and the First Guess Product (FG), are publicly available on easily accessible latitude/longitude grids encoded in zipped clear text ASCII files for subsequent visualization and download through the GPCC download gate hosted on ftp://ftp.dwd.de/pub/data/gpcc/html/download_gate.html by the Deutscher Wetterdienst (DWD), Offenbach, Germany. Depending on the product, four (0.25°, 0.5°, 1.0°, 2.5° for CLIM), three (0.5°, 1.0°, 2.5°, for FD), two (1.0°, 2.5° for MP) or one (1.0° for FG) resolution is provided, and for each product a DOI reference is provided allowing for public user access to the products. A preliminary description of the scope of a fifth product – the Homogenized Precipitation Analysis (HOMPRA) – is also provided. Its comprehensive description will be submitted later in an extra paper upon completion of this data product. DOIs of the gridded data sets examined are as follows: doi:10.5676/DWD_GPCC/CLIM_M_V2011_025, doi:10.5676/DWD_GPCC/CLIM_M_V2011_050, doi:10.5676/DWD_GPCC/CLIM_M_V2011_100, doi:10.5676/DWD_GPCC/CLIM_M_V2011_250, doi:10.5676/DWD_GPCC/FD_M_V6_050, doi:10.5676/DWD_GPCC/FD_M_V6_100, doi:10.5676/DWD_GPCC/FD_M_V6_250, doi:10.5676/DWD_GPCC/MP_M_V4_100, doi:10.5676/DWD_GPCC/MP_M_V4_250, doi:10.5676/DWD_GPCC/FG_M_100.

Large-scale precipitation estimation using Kalpana-1 IR measurements and its validation using GPCP and GPCC data

Large-scale precipitation estimation is very important for climate science because precipitation is a major component of the earth's water and energy cycles. In the present study, the GOES precipitation index technique has been applied to the Kalpana-1 satellite infrared (IR) images of every three-hourly, i.e., of 0000, 0300, 0600,…., 2100 hours UTC, for rainfall estimation as a preparatory to the INSAT-3D. After the temperatures of all the pixels in a grid are known, they are distributed to generate a three-hourly 24-class histogram of brightness temperatures of IR (10.5–12.5 μm) images for a 1.0° × 1.0° latitude/longitude box. The daily, monthly, and seasonal rainfall have been estimated using these three-hourly rain estimates for the entire south-west monsoon period of 2009 in the present study. To investigate the potential of these rainfall estimates, the validation of monthly and seasonal rainfall estimates has been carried out using the Global Precipitation Climatology Project and Global Precipitation Climatology Centre data. The validation results show that the present technique works very well for the large-scale precipitation estimation qualitatively as well as quantitatively. The results also suggest that the simple IR-based estimation technique can be used to estimate rainfall for tropical areas at a larger temporal scale for climatological applications.

Comparison of Gridded Multisatellite Rainfall Estimates with Gridded Gauge Rainfall over West Africa

Abstract Two monthly gridded precipitation datasets of the Global Precipitation Climatology Project (GPCP; the multisatellite product) and the Global Precipitation Climatology Centre (GPCC) Variability Analysis of Surface Climate Observations (VASClimO; rain gauge data) are compared for a 22-yr period, from January 1979 to December 2000, over land areas (i.e., latitudes 4°–20°N and longitudes 18°W–15°E). The two datasets are consistent with respect to the spatial distribution of the annual and seasonal rainfall climatology over the domain and along latitudinal bands. However, the satellite generally overestimates rainfall. The inability of the GPCC data to capture the bimodal rainfall pattern along the Guinea coast (i.e., south of latitude 8°N) is an artifact of the interpolation of the rain gauge data. For interannual variability, the gridded multisatellite and gridded gauge datasets agree on the sign of the anomaly 15 out of the 22 yr (68% of the time) for region 1 (between longitude 5° and 18°W and north of latitude 8°N) and 18 out of the 22 yr (82% of the time) for region 2 (between longitude 5°W and 15°E and north of latitude 8°N). The datasets agreed on the sign of the anomaly 14 out of the 22 yr (64% of the time) over the Guinea Coast. The magnitudes of the anomaly are very different in all years. Most of the years during which the two datasets did not agree on the sign of the anomaly were years with El Niño events. The ratio of the seasonal root-mean-square differences to the seasonal mean rainfall range between 0.24 and 2.60. The Kendall’s tau statistic indicated statistically significant trends in both datasets, separately.

Evaluation of Community Climate System Model soil temperatures using observations from Russia

Soil temperatures simulated by the fully coupled Community Climate System Model (CCSM) version 3.0 are evaluated using three gridded climatologies (1951–1980, 1961–1990, 1971–2000) based on data from more than 400 Russian sites. CCSM captures the annual phase of the soil temperature cycle well, but not the amplitude. It provides slightly too high (low) soil temperatures in winter (summer). Root mean square errors, on average, are less than 5 K. Simulated near-surface air temperatures agree well, on average, with near-surface air temperatures from reanalysis data. Errors in simulated atmospheric-temperature forcing correlate statistically significantly (95% or higher confidence level) with soil temperature errors, i.e. contribute to discrepancy in soil temperature simulation. Comparison to International Satellite Cloud Climatology project data shows that errors in simulated cloud fraction explain some soil and near-surface air temperature and precipitation discrepancies. Evaluation by means of Global Precipitation Climatology Centre data identifies inaccurately-simulated precipitation as a contributor to underestimating summer soil temperatures. Comparison to snow-depth observations shows that overestimating snow depth yields winter soil-temperature overestimation. Sensitivity studies show that uncertainty in mineral-soil composition notably, and differences between the vegetation in CCSM and nature marginally contribute to discrepancies between simulated and observed soil-temperature climatology.

Surface data sets used in WetNet's PIP‐1 from the Comprehensive Pacific Rainfall Data Base and the Global Precipitation Climatology Centre

Abstract For intercomparison purposes and validation of different satellite‐based precipitation estimates in the framework of the first WetNet Precipitation Intercomparison Project (PIP‐1), two surface precipitation data sets have been used. The Comprehensive Pacific Rainfall Data Base (CPRDB) has been assimilated specifically for the calibration and verification of satellite rainfall algorithms over parts of the tropical Pacific ocean. The CPRDB consists of daily rain gauge data from more than 250 sites in the tropical Pacific, most of which are atolls and low‐lying islands. The data set evaluated by the Global Precipitation Climatology Centre (GPCC), a central element of the World Climate Research Programme (WCRP) Global Precipitation Climatology Project (GPCP), provides areal mean monthly precipitation totals on a 2.5° grid on a global scale for climate research and the verification of climate models. Although the merged GPCC data set is global in extent, it is based on an objective analysis of rain ga...