Global Precipitation Climatology Project Research Articles

Global partitioning of runoff generation mechanisms using remote sensing data

Abstract. A set of complex processes contribute to generate river runoff, which in the hydrological sciences are typically divided into two major categories: surface runoff, sometimes called Hortonian flow, and baseflow-driven runoff or Dunne flow. In this study, we examine the covariance of global satellite-based surface water inundation (SWI) observations with two remotely sensed hydrological variables, precipitation, and terrestrial water storage, to better understand how apparent runoff generation responds to these two dominant forcing mechanisms in different regions of the world. Terrestrial water storage observations come from NASA’s Gravity Recovery and Climate Experiment (GRACE) mission, while precipitation comes from the Global Precipitation Climatology Project (GPCP) combined product, and surface inundation levels from the NASA Surface WAter Microwave Product Series (SWAMPS) product. We evaluate the statistical relationship between surface water inundation, total water storage anomalies (TWS; TWSAs), and precipitation values under different time lag and quality control adjustments between the data products. We find that the global estimation of surface inundation improves when considering a quality control threshold of 50 % reliability for the SWAMPS data and after applying time lags ranging from 1 to 5 months. Precipitation and total water storage equally control the majority of surface inundation developments across the globe. The model tends to underestimate and overestimate at locations with high interannual variability and with low inundation measurements, respectively.

Inter-comparison of ERA-5, ERA-interim and GPCP rainfall over the last 40 years: Process-based analysis of systematic and random differences

This study presents a comprehensive inter-comparison between two widely used rainfall datasets – the Global Precipitation Climatology Project (GPCP) and the ERA-Interim reanalysis – and the recently released ERA-5, which will replace ERA-Interim as the main European Centre for Medium-Range Weather and Forecasting (ECMWF) reanalysis by 2020. Systematic and random error components were computed for the reanalysis datasets over the 1979–2018 period using GPCP as reference. The analysis was taken at monthly timescale at 2.5° spatial resolution, limited by GPCP. Despite its resolution limitations and complex algorithms, GPCP has been extensively used for model evaluation due to its global and long-term (over 40 years) coverage. Over most of the tropics, and over localized mid-latitude regions, ERA-5 showed lower bias and unbiased root-mean squared error (ubRMSE), as well as higher correlations, compared to ERA-Interim. Throughout the rest of the globe, these error metrics displayed similar values between both reanalysis, except over localized regions of the eastern tropical Pacific, the Andes and the Himalayas, where ERA-Interim outperformed ERA-5. A process-based analysis revealed that ERA-Interim tended to overestimate deep convection and moisture flux convergence over the tropical oceans and land, leading to excessive rainfall. Similarly, ERA-Interim showed significant rainfall underestimation over the mid-latitude oceans due to underestimation of deep convection and moisture flux convergence. Both cases were significantly improved in ERA-5, likely due to its improved parameterizations and higher resolution. Indeed, the differences in monthly rainfall between the two reanalysis were mainly due to improved dynamical (circulation) rather than thermodynamical (Clausius-Clapeyron) processes or surface evaporation changes. Nonetheless, the results also revealed improved representation of the moisture sink/source patterns over the tropical oceans in ERA-5. Finally, there were significant differences in the long-term rainfall trend patterns amongst the three datasets (with differences reaching ~30%/decade), that can extend even to the sign of the trend, with the most notorious differences occurring over tropical Africa. Furthermore, ERA-5 didn’t show improved representation of these trends. In fact, the trend of global-mean rainfall in ERA-Interim was closer to GPCP than ERA-5.

Inversion of Evaporation and Water Vapor Transport Using HY-2 Multi-Sensor Data

HY-2 satellite is the first marine dynamic environment satellite of China. In this study, global evaporation and water vapor transport of the global sea surface are calculated on the basis of HY-2 multi-sensor data from April 1 to 30, 2014. The algorithm of evaporation and water vapor transport is discussed in detail, and results are compared with other reanalysis data. The sea surface temperature of HY-2 is in good agreement with the ARGO buoy data. Two clusters are shown in the scatter plot of HY-2 and OAFlux evaporation due to the uneven global distribution of evaporation. To improve the calculation accuracy, we compared the different parameterization schemes and adopted the method of calibrating HY-2 precipitation data by SSM/I and Global Precipitation Climatology Project (GPCP) data. In calculating the water vapor transport, the adjustment scheme is proposed to match the balance of the water cycle for data in the low latitudes.

Waves on Louisiana Continental Shelf Influenced by Atmospheric Fronts

Ocean-atmospheric dynamical processes influence the wave characteristics, and sea surface temperature (SST). The processes that affect SST in the ocean area included surface heat fluxes, wind, and precipitation. In this study, we analyzed the wave data in response to the cold front passages over Louisiana continental shelf. The data examined in this research is mainly from WAVCIS (Wave-Current-surge Information System), Coastal Ocean Estuarine Dynamics Lab at Louisiana State University and Global Precipitation Climatology Project (GPCP). With respect to the wave response to the atmospheric forcing, here we consider: (1) the connection between wave variability and atmospheric frontal passages, and (2) the influence on the SST variations during the processes. The occurrences of wind wave, precipitation, and weather processes have distinct regularities. Atmospheric cold fronts have an important influence on wave formation and associated processes over Louisiana continental shelf.

Validation of Three Satellite Precipitation Products in Two South-Western African Watersheds: Bandama (Ivory Coast) and Mono (Togo)

Satellite precipitation products are widely used in different domain, in area where there is a lack in observation. These have different spatio-temporal resolutions consequently resulting in different precipitation amounts depending on the product. The present study validates three satellite products, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), the Climate Research Unit (CRU) and the Global Precipitation Climatology Project (GPCP) over Bandama and Mono river basins for 1981-2005 and 1981-2016 respectively by comparing them to the observation precipitation of the basin. The available studies are focused on the regional scale but not on a watershed scale for hydrological studies. The analysis reveals that all the products are strongly correlated to each other as well as to the observed data at basin level. The Lamb coefficient test shows that most all the chosen basin namely Bandama and Mono presents the same climatic indices. All the products present the same variability and trend as the observation at basins scale. By comparing those products to observation, CHIRPS product following by GPCP give the lowest mean absolute error (MAE) at annual and seasonal time scales while CHIRPS is followed by CRU at monthly scale. Overall, all products overestimate the precipitation at Bandama basin while they underestimate it over Mono river basin. The comparison over 1981-2017 period of the total annual precipitation increasing southern ward (from Sahel to the coastal zone) for all the three studied products which varies from 300 mm to 2400 mm/year. All the three products are not significantly different from one another and they all highlight the same areas of hotspot rainfall in the region. The same conclusion is made at monthly and seasonal scales. Therefore, any of these products especially CHIRPS can be used for study in this region due to its lowest bias and MAE.

Interannual Variability of Rainfall over the West Africa Sahel

Interannual variability of the precipitation over West Africa Sahel is analyzed based on 32 years (1979-2010) from monthly and daily database of the Global Precipitation Climatology Project (GPCP). In this region, we found that there is a link between the West Africa Monsoon (WAM) and the daily means of the precipitation in the summer, unseasonal rains can occur in the transition seasons and even in the heart of the dry season. Rainfall is the most important element for agro-pastoral activities in this region. The 850-hPa wind and wind divergence structure show a maximum convection over Mountain region (Fouta-Djalon and Mont-Cameroon) which corresponds to the high precipitation and OLR observed in these regions. The trend and empirical orthogonal function (EOF) of the precipitation are presented, including the mid-July variability of the precipitation. The dominant EOF of GPCP precipitation accounts for around 25.3% of the variance with slightly large amplitude in the north while relatively small in the equatorial band respectively. The second and third EOF which accounts for 20.5% and 14%, describes a longitudinal contrast with a zonal gradient.

Evaluation of multiple gridded precipitation datasets for the arid region of northwestern China

Abstract Gridded precipitation datasets for the arid region of northwestern China (ARNC) are thought to have large discrepancies. This study systematically evaluated the performance of multiple datasets for the ARNC over the period 1980–2014 using the observed gridded precipitation dataset (OBS) from the China Meteorological Administration (CMA). The various datasets include two National Center for Environmental Prediction (NCEP) reanalyses (NCEP-1 and NCEP-2), two Climate Prediction Center (CPC) Merged Analyses of Precipitation (CMAP-1 and CMAP-2), the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis (ERA-Interim), the Global Precipitation Climatology Project (GPCP), the Climatic Research Unit, University of East Anglia, UK (CRU), and the second Modern-Era Retrospective Analysis for Research and Application system (MERRA-2). The results show that all the multiple datasets reasonably reproduce the climatology, seasonality, interannual variability, and spatiotemporal patterns of ARNC precipitation, but there are discrepancies in the long-term trends. MERRA-2 and CMAP-2 best capture the spatial distributions and inter-annual variability of mean annual precipitation (MAP). CRU best reproduced the seasonality and temporal variability of MAP. However, ERA-Interim and NCEP overestimated the precipitation amount in mountainous regions, and MERRA-2 underestimated precipitation in almost all seasons. Empirical orthogonal function (EOF) analysis indicated that GPCP and CMAP-2 better described both the spatial modes and principal components of the first two EOF patterns. The systematic evaluation in this study indicated that, overall, most of the satellite-merged datasets (GPCP, MERRA-2, and CMAP-2) perform better than gauge-based and reanalysis datasets alone. This study demonstrates that a systematic evaluation of the differences between multiple datasets is critical for reducing discrepancies.

Evaluation of PERSIANN-CDR Constructed Using GPCP V2.2 and V2.3 and A Comparison with TRMM 3B42 V7 and CPC Unified Gauge-Based Analysis in Global Scale

Providing reliable long-term global precipitation records at high spatial and temporal resolutions is crucial for climatological studies. Satellite-based precipitation estimations are a promising alternative to rain gauges for providing homogeneous precipitation information. Most satellite-based precipitation products suffer from short-term data records, which make them unsuitable for various climatological and hydrological applications. However, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) provides more than 35 years of precipitation records at 0.25° × 0.25° spatial and daily temporal resolutions. The PERSIANN-CDR algorithm uses monthly Global Precipitation Climatology Project (GPCP) data, which has been recently updated to version 2.3, for reducing the biases in the output of the PERSIANN model. In this study, we constructed PERSIANN-CDR using the newest version of GPCP (V2.3). We compared the PERSIANN-CDR dataset that is constructed using GPCP V2.3 (from here on referred to as PERSIANN-CDR V2.3) with the PERSIANN-CDR constructed using GPCP V2.2 (from here on PERSIANN-CDR V2.2), at monthly and daily scales for the period from 2009 to 2013. First, we discuss the changes between PERSIANN-CDR V2.3 and V2.2 over the land and ocean. Second, we evaluate the improvements in PERSIANN-CDR V2.3 with respect to the Climate Prediction Center (CPC) unified gauge-based analysis, a gauged-based reference, and Tropical Rainfall Measuring Mission (TRMM 3B42 V7), a commonly used satellite reference, at monthly and daily scales. The results show noticeable differences between PERSIANN-CDR V2.3 and V2.2 over oceans between 40° and 60° latitude in both the northern and southern hemispheres. Monthly and daily scale comparisons of the two bias-adjusted versions of PERSIANN-CDR with the above-mentioned references emphasize that PERSIANN-CDR V2.3 has improved mostly over the global land area, especially over the CONUS and Australia. The updated PERSIANN-CDR V2.3 data has replaced V2.2 data for the 2009–2013 period on CHRS data portal and NOAA National Centers for Environmental Information (NCEI) Program.

Improvement in rainfall estimates using bias correction with the IAP-DCP Global Model

This study uses a bias correction method to modify the estimates of the Institute of Atmospheric Physics Dynamical Seasonal Prediction (IAP-DCP) model over the Indo-China Peninsula (ICP). Bias correction is a way of improving the results from post processing model simulation to nearly observation data and increase accuracy. This study used the result from the global IAP-DCP model of Humphries et al. (2018) and improved it by using bias correction. Observation data from the Global Precipitation Climatology Project Version 2.3 (GPCP) were compared with the results of Humphries et al. (2018) and bias correction. Experiments with two bias correction weighting factors w of 5% (0.05) and 8% (0.08) were used. The results are shown in terms of spatial patterns and statistical analysis. In the statistical analysis, during the southern monsoon (JJA), bias correction at w = 0.05 decreased RMSE member mean from 3.598 to 3.022 (decrease = 0.576) and MAE member mean from 2.804 to 2.441 (decrease = 0.363). Bias correction of w = 0.05 decreased RMSE member mean from 3.598 to 2.927 (decrease = 0.671) and decreased MAE member mean from 2.804 to 2.364 (decrease = 0.440). In the spatial pattern case, bias correction improved on the results of Humphries et al. (2018). Furthermore, the results using bias correction of w = 0.05 were better than those with w = 0.05. However, from the mean spatial pattern and statistical analyses, that is demonstrated that bias correction can help to decrease the error and increase the accuracy of model results.

Assessing Gauge Undercatch Correction in Arctic Basins in Light of GRACE Observations

AbstractPrecipitation measurements at gauges are often considered as reference truth for evaluation of satellite precipitation products. However, gauges may contain large errors. A major source of gauge‐measurement error is snowfall undercatch in high latitudes. We show that the two popular correction factors (CFs) used in the Global Precipitation Climatology Centre monitoring and the Global Precipitation Climatology Project products are different by more than 50%. The CFs can be as large as 3; thus, the choice of CF introduces large uncertainties. Here, in light of observation of storage change from the Gravity Recovery and Climate Experiment (GRACE) and by using the mass conservation principle, we assess the two popular CFs over six Arctic basins. By investigating monthly time series and multiyear precipitation rates over the studied basins using GRACE‐based analysis, the CF based on Fuchs dynamic correction model used in Global Precipitation Climatology Centre monitoring is preferred.

Convectively Coupled Equatorial Waves Simulated by CAMS-CSM

The Chinese Academy of Meteorological Sciences developed a Climate System Model (CAMS-CSM) to participate in the upcoming Coupled Model Intercomparison Project phase 6 (CMIP6). In this study, we assessed the model performance in simulating the convectively coupled equatorial waves (CCEWs) by comparing the daily output of precipitation from a 23-yr coupled run with the observational precipitation data from Global Precipitation Climatology Project (GPCP). Four dominant modes of CCEWs including the Kelvin, equatorial Rossby (ER), mixed Rossby–gravity (MRG), tropical depression-type (TD-type) waves, and their annual mean and seasonal cycle characteristics are investigated respectively. It is found that the space–time spectrum characteristics of each wave mode represented by tropical averaged precipitation could be very well simulated by CAMS-CSM, including the magnitudes and the equivalent depths. The zonal distribution of wave associated precipitation is also well simulated, with the maximum centers over the Indian Ocean and the Pacific Ocean. However, the meridional distribution of the wave activities is poorly simulated, with the maximum centers shifted from the Northern Hemisphere to the Southern Hemisphere, especially the Kelvin, MRG, and TD waves. The seasonal cycle of each wave mode is generally captured by the model, but their amplitudes over the Southern Hemisphere during boreal winter are grossly overestimated. The reason for the excessive wave activity over the southern Pacific Ocean in the simulation is discussed.

Recent Ogallala Aquifer Region Drought Conditions as Observed by Terrestrial Water Storage Anomalies from GRACE

AbstractRecent severe drought events have occurred over the Ogallala Aquifer region (OAR) during the period 2011–2015, creating significant impacts on water resources and their use in regional environmental and economic systems. The changes in terrestrial water storage (TWS), as indicated by the Gravity Recovery and Climate Experiment (GRACE), reveals a detailed picture of the temporal and spatial evolution of drought events. The observations by GRACE indicate the worst drought conditions occurred in September 2012, with an average TWS deficit of ~8 cm in the northern OAR and ~11 cm in the southern OAR, consistent with precipitation data from the Global Precipitation Climatology Project. Comparing changes in TWS with precipitation shows the TWS changes can be predominantly attributable to variations in precipitation. Power spectrum and squared wavelet coherence analysis indicate a significant correlation between TWS change and the El Nino‐Southern Oscillation, and the influence of equatorial Pacific sea surface temperatures on TWS change is much stronger in the southern OAR than the northern OAR. The results of this study illustrate the value of GRACE in not just the diagnosis of significant drought events, but also in possibly improving the predictive power of remote signals that are impacted by nonregional climatic events (El Nino), ultimately leading to improved water resource management applications on a regional scale. Editor’s note: This paper is part of the featured series on Optimizing Ogallala Aquifer Water Use to Sustain Food Systems. See the February 2019 issue for the introduction and background to the series.

Decreasing southwest monsoon rainfall over Myanmar in the prevailing global warming era

AbstractThe characteristics of the southwest monsoon (SWM) rainfall over Myanmar were studied using APHRODITE gridded daily rainfall datasets for the period 1951–2010. Myanmar, being an agrarian economy, is predominantly dependent on the SWM rainfall. The spatial distribution of rainfall (rainy days) has shown higher rainfall of 2,000 mm (>140 rainy days) over southwest parts, rainfall of ~1,000 mm (100–120 days) over north and eastern parts and an arid zone with rainfall of less than approximately 500 mm (<80 days) over west central parts, all indicating earlier onset over the southwest and late onset over the northeast. Analysis of the rainfall and rainy days between the two 30 year periods 1951–1980 and 1981–2010 representative of the pre‐global warming and global warming periods showed a reduction of rainfall by 100 mm and 5–10 rainy days during the later period of global warming. The Climate Research Unit and Global Precipitation Climatology Project rainfall datasets and the Geophysical Fluid Dynamics Laboratory Earth System global model simulated rainfall data for the current climate period exhibited a similar decline of SWM rainfall over major parts of Myanmar. The results indicated a 20% reduction in the mean westerly strength over south Myanmar in the global warming period, which was related to a decrease of the meridional gradient of sea surface temperatures over the Bay of Bengal. This study establishes two distinct rainfall zones in the southwest and northeast over Myanmar and a reduction of rainfall in the recent global warming period that will lead to a reduction in agriculture production.

Simulation of wet and dry West African monsoon rainfall seasons using the Weather Research and Forecasting model

This paper presents an evaluation of the Weather Research and Forecasting (WRF) model in simulating wet and dry West African monsoon (WAM) rainfall seasons. Three model experiments with varying selected microphysics (MP), cumulus convection (CU), and planetary boundary layer (PBL) schemes based on previous study were performed. Each of the model combinations is used to run four WAM seasons that consist of two wet (2008 and 2010) and two dry years (2001 and 2011). To investigate the behavior of WAM in the context of wet and dry years, the four seasons were used to compute composites of wet and dry WAM seasons in terms of rainfall amount. The analyses majorly focus on the rainfall composites relative to rainfall from Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measurement Mission (TRMM) as well as temperature, moisture, and atmospheric circulation fields with respect to NCEP reanalyses. This study documents significant sensitivity in simulation of the West African monsoon to the choices of the MP, CU, and PBL schemes. The simulation with the combination of WRF single moment 5 (WSM5) MP, Yonsei University (YSU) PBL, and new Simplified Arakawa-Schubert CU (WSM5-YSU-nSAS) shows good spatial distribution pattern of rainfall and the dynamics associated with the monsoon. Quantitatively, the combination shows less agreement in distinguishing the selected WAM seasons compared with the Goddard MP, Mellor-Yamada-Janjic PBL, and Betts-Miller-Janjic CU (GD-MYJ-BMJ) and the WSM5, Mellor-Yamada-Nakanishi-Niino 2.5 level and new Tiedtke CU (WSM5-MYNN-nTDK). Also, the dynamical structures of the wet and dry WAM circulation composites are reasonably reproduced in GD-MYJ-BMJ and WSM5-YSU-nSAS. The GD-MYJ-BMJ was able to distinguish between wet and dry years and thus underscores its potential to reproduce climate change signals in future work.

Regional Characteristics of Interannual Variability of Summer Rainfall in the Maritime Continent and Their Related Anomalous Circulation Patterns

AbstractUsing the NCEP–NCAR reanalysis and Global Precipitation Climatology Project monthly rainfall, we have investigated the regional features of interannual variations of rainfall in the Maritime Continent (MC) and their related anomalous atmospheric circulation patterns during boreal summer by employing the rotated empirical orthogonal function (REOF) analysis. Our results demonstrate that the rainfall variabilities in the MC are of very striking regional characteristics. The MC is divided into four independent subregions on the basis of the leading REOF modes; these subregions are located in central-eastern Indonesia (subregion I), the oceanic area to the west of Indonesia (subregion II+V), the part of the warm pool in the equatorial western Pacific Ocean (subregion III), and Guam (subregion IV+VI).The anomalous precipitation in different subregions exhibits different variation periodicities, which are associated with different circulation patterns as a result of atmospheric response to different sea surface temperature anomaly (SSTA) patterns in the tropical Indo-Pacific sector. It is found that rainfall anomalies in subregion I are induced by the Pacific ENSO, whereas those in subregion II+V are dominated by a triple SSTA pattern with positive correlations in the MC and negative correlation centers in the tropical Pacific and tropical Indian Ocean. Rainfall anomalies in subregion III mainly resulted from an SSTA pattern with negative correlations in the eastern MC and positive correlations in the western equatorial Pacific east of the MC. A horseshoe SSTA pattern in the central Pacific is found to affect the precipitation anomalies in subregion IV+VI. All of the results of this study are helpful for us to better understand both the climate variations in the MC and monsoon variations in East Asia.

Summer monsoon climate simulations over BIMSTEC countries using RegCM4 regional climate model

Regional climate models are useful by downscaling from global climate model simulations for climate studies and climate applications at a regional scale. The South Asian monsoon region is one of the most challenging regions towards understanding the monsoon variability by implementing various climate simulations. A model version of the RegCM4 regional climate model provided by the International Centre for Theoretical Physics (ICTP) is used in this study to simulate the climate of Bay of Bengal Initiative for Multi-Sectoral Technical and Economic Cooperation (BIMSTEC) countries (India, Bangladesh, Bhutan, Nepal, Myanmar, Thailand and Sri Lanka) during the periods 1985–2000, which shows the present-day climate simulations. The simulation is carried out with RegCM4.3 with the lateral boundary forcing provided by the European Center for Medium Range Weather Forecast Reanalysis (Era-Interim) at 25 km horizontal resolution. The convective scheme Grell with closures Arakawa–Schubert is investigated during the south-west monsoon seasons (June–September, JJAS). The results indicate that the JJAS surface mean temperature has a large cold bias of 2–\(6{^{\circ }}\hbox {C}\) over the coastal regions near India, Myanmar, Thailand, Sri Lanka and Bangladesh when compared with the Climate Research Unit (CRU) observed dataset. The model is able to produce the spatial rainfall distribution pattern of the observed CRU and Global Precipitation Climatology Project (GPCP) averaged seasonal rainfall in most of the land regions over the BIMSTEC countries during this period, but with underestimation. Further, the south-westerly prevailing wind pattern at 850 hPa pressure level is also well captured by the model but with a higher intensity over Sri Lanka, Western India, Nepal and Thailand. The westerly is stronger over the Bay of Bengal in the model simulations than the observed Era-Reanalysis data sets obtained from ECMWF. Also, the model is able to capture the mean monsoon circulation features. This model may be used for a few of the BIMSTEC regions for climate study applications.

A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

AbstractCharacterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle.

Gridded Monthly Rainfall Estimates Derived from Historical Atoll Observations

AbstractTo provide an analysis tool for areal rainfall estimates, 1° gridded monthly sea level rainfall estimates have been derived from historical atoll rainfall observations contained in the Pacific Rainfall (PACRAIN) database. The PACRAIN database is a searchable repository of in situ rainfall observations initiated and maintained by the University of Oklahoma and supported by a research grant from the National Oceanic and Atmospheric Administration (NOAA)/Climate Program Office/Ocean Observing and Monitoring. The gridding algorithm employs ordinary kriging, a standard geostatistical technique, and selects for nonnegative estimates and for local estimation neighborhoods yielding minimum kriging variance. This methodology facilitates the selection of fixed-size neighborhoods from available stations beyond simply choosing the closest stations, as it accounts for dependence between estimator stations. The number of stations used for estimation is based on bias and standard error exhibited under cross estimation. A cross validation is conducted, comparing estimated and observed rains, as well as theoretical and observed standard errors for the ordinary kriging estimator. The conditional bias of the kriging estimator and the predictive value of kriging standard errors, with respect to observed standard errors, are discussed. Plots of the gridded rainfall estimates are given for sample El Niño and La Niña cases and standardized differences between the estimates produced here and the merged monthly rainfall estimates published by the Global Precipitation Climatology Project (GPCP) are shown and discussed.

An objective definition of summer monsoon onset in the northwestern maritime continent

Unlike in other parts of the Asian Monsoon region, the summer monsoon in the Northwestern Maritime Continent (NwMC) is relatively dry. Hence, an objective definition is needed in order to determine the arrival of the dry season in this particular sub‐region. Criteria for the objective definition of summer monsoon onset in the NwMC were defined based on reanalysis data from the National Centers for Environment Prediction (NCEP) and Global Precipitation Climatology Project (GPCP) databases. Considering the Matsuno‐Gill theoretical relationship between tropical precipitation and low‐level winds, the summer monsoon onset date can be determined objectively based on the commencement of the sustained 850‐hPa zonal winds averaged over the area from 5° to 10°N and from 100° to 115°E. The establishment of steady westerly/southwesterly winds over the NwMC region is consistent with the abrupt shift of rainfall from the equatorial region (5°S–5°N) to areas north of the equator. The onset is also associated with the northeastward propagation of anomalous convection from the western equatorial Indian Ocean on an intraseasonal time scale. In view of its representation of large‐scale circulation and objectiveness, this new monsoon onset index will provide a useful metric with which to predict NwMC summer monsoon onset and for studying the interannual variation in monsoon onset. A simple lead–lag correlation shows the monsoon onset in NwMC has a close relationship with ENSO. Generally an early (late) monsoon onset can be expected when cold (warm) ENSO event occurred in the preceding summer.

Evaluating three satellite-based precipitation products of different spatial resolutions in Shanghai based on upscaling of rain gauge

ABSTRACTGridded precipitation products have been widely used in scientific literatures, e.g., weather forecasting, hydrological process modelling and disaster simulating. However, it is necessary to evaluate their accuracies before using them for practical purposes. In this study, three mainstream different-spatial-scale daily gridded precipitation data (GPD), including [Global Satellite Mapping of Precipitation–gauge adjusted (GSMAP_Gauge), TRMM 3B42 version-7 (TRMM 3B42V7) and Global Precipitation Climatology Project version 1.2 (GPCP-V1.2)] were systematically evaluated against local rain gauges for their ability to detect precipitation characteristics of Shanghai in 2008 based on upscaling of rain gauge. In general, the gridded precipitation products overestimate precipitation compared to upscaled ground observations (UGOs). However, all of the products have an obvious underestimation on extreme precipitation (>50 mm day−1). In contrast, the results show that GSMaP_Gauge has the highest correlation coefficient, TRMM 3B42V7 has the lowest RMSE, and Probability of Detection (POD) in GPCP-V1.2 is better than that in TRMM 3B42V7 and GSMaP_Gauge. As the spatial resolution decreases, the frequency and amount of extreme precipitation events decrease regardless of detection from UGOs and GPD. We also find that those heavy precipitation events detected from TRMM 3B42V7 at 0.25° and 1.0° are significantly underestimated. Overall, we believe that it is necessary to evaluate gridded precipitation products at the urban scale based on upscaling of rain gauge.