Global Precipitation Climatology Project Version Research Articles

The New Version 3.2 Global Precipitation Climatology Project (GPCP) Monthly and Daily Precipitation Products

Abstract The Global Precipitation Climatology Project (GPCP) Version 3.2 Precipitation Analysis provides globally complete analyses of surface precipitation on a 0.5° × 0.5° latitude–longitude grid at both monthly and daily time scales, covering from 1983 to the present and from June 2000 to the present, respectively. These merged products continue the GPCP heritage of incorporating precipitation estimates from low-orbit satellite microwave data, geosynchronous-orbit satellite infrared data, sounder-based estimates, and surface rain gauge observations emphasizing the strengths of various inputs and striving for time and space homogeneity. Furthermore, these analyses incorporate modern algorithms, refined intercalibrations among sensors, climatologies of recent high-quality satellite precipitation data, and fine-scale multisatellite estimates. New data fields have been introduced to better characterize the precipitation, including the fraction of the precipitation that is liquid (rain) in both the monthly and daily products, and a quality index for the monthly product. Compared to the operational GPCP Version 2.3 Monthly, the Version 3.2 Monthly product provides a more reasonable climatology in the Southern Ocean and increases the estimated global average precipitation by about 4.5%, which is similar to estimates from recent global water budget assessments. Global and regional trends for 1983–2020 with this new Monthly dataset are very similar to those computed from Version 2.3. Compared to the operational One-Degree Daily (Version 1.3) product, the new Version 3.2 Daily is designed to better represent the histogram of precipitation rates, particularly at high values and shifts the start of less-certain high-latitude estimates from 40° to 58° latitude in each hemisphere. Significance Statement Studies of Earth’s climate require long-term global datasets based on observations to show how the climate functions and to validate numerical climate models. This study describes an important upgrade to the monthly and daily precipitation (rain and snow) products computed by the Global Precipitation Climatology Project. We use modern analysis schemes, add new sources of data, and deliver results on a finer-scale 0.5° × 0.5° latitude–longitude grid [roughly 55 km (34 mi) on a side at the equator]. The new data show improved agreement with other studies and depict more reasonable behavior in the Southern Ocean. The daily product shows improved estimates of how often different intensities of precipitation occur around the world, particularly the high amounts that drive floods and landslides.

IMERG Precipitation Improves the SMAP Level-4 Soil Moisture Product

Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level-4 Soil Moisture (L4_SM) product provides global, 9-km resolution, 3-hourly surface and root-zone soil moisture from April 2015 to the present with a mean latency of 2.5 days from the time of observation. The L4_SM algorithm assimilates SMAP L-band (1.4 GHz) brightness temperature (Tb) observations into the NASA Catchment land surface model as the model is driven with observation-based precipitation. This paper describes and evaluates the use of satellite- and gauge-based precipitation from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products in the L4_SM algorithm beginning with L4_SM Version 6. Specifically, IMERG is used in two ways: (i) The L4_SM precipitation reference climatology is primarily based on IMERG-Final (Version 06B) data, replacing the Global Precipitation Climatology Project Version 2.2 data used in previous L4_SM versions, and (ii) the precipitation forcing outside of North America and the high latitudes is corrected to match the daily totals from IMERG, replacing the gauge-only, daily product or uncorrected weather analysis precipitation used there in earlier L4_SM versions. The use of IMERG precipitation improves the anomaly time series correlation coefficient of L4_SM surface soil moisture (versus independent satellite estimates) by 0.03 in the global average and by up to ∼0.3 in parts of South America, Africa, Australia, and East Asia, where the quality of the gauge-only precipitation product used in earlier L4_SM versions is poor. The improvements also reduce the time series standard deviation of the Tb observation-minus-forecast residuals from 5.5 K in L4_SM Version 5 to 5.1 K in Version 6. Significance Statement Soil moisture links the land surface water, energy, and carbon cycles. NASA Soil Moisture Active Passive (SMAP) satellite observations and observation-based precipitation data are merged into a numerical model of land surface water and energy balance processes to generate the global, 9-km resolution, 3-hourly Level-4 Soil Moisture (L4_SM) data product. The product is available with ∼2.5-day latency to support Earth science research and applications, such as flood prediction and drought monitoring. We show that a recent L4_SM algorithm update using satellite- and gauge-based precipitation inputs from the NASA Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) products improves the temporal variations in the estimated soil moisture, particularly in otherwise poorly instrumented regions in South America, Africa, Australia, and East Asia.

Global Intercomparison of Atmospheric Rivers Precipitation in Remote Sensing and Reanalysis Products

AbstractAtmospheric rivers (ARs) play an important role in the total annual precipitation regionally and globally, delivering precious freshwater to many arid/semiarid regions. On the other hand, they may cause intense precipitation and floods with huge socioeconomic effects worldwide. In this study, we investigate AR‐related precipitation using 18 years (2001–2018) of globally gridded AR locations derived from Modern‐Era Retrospective Analysis for Research and Applications, Version 2 (MERRA‐2). AR precipitation features are explored regionally and seasonally using remote sensing (Integrated Multi‐satellitE Retrievals for GPM version 6 [IMERG V6], daily Global Precipitation Climatology Project version 1.3 [GPCP V1.3], bias‐adjusted CPC Morphing Technique version 1 [CMORPH V1], and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks [PERSIANN‐CDR]) and reanalysis (MERRA‐2 and ECMWF Reanalysis 5th Generation [ERA5]) precipitation products. The results show that most of the world (except the tropics) experience more intense precipitation from AR‐related events compared to non‐AR events. Over the oceans (especially the Southern Ocean), the contribution of ARs to the total precipitation and extreme events is larger than over land. However, some coastal areas over land are highly affected by ARs (e.g., the western and eastern United States and Canada, Western Europe, North Africa, and part of the Middle East, East Asia, and eastern South America and part of Australia). Although spatial correlations for pairs of IMERG/CMORPH and GPCP/PERSIANN‐CDR are fairly high, considerable discrepancies are shown in their estimation of AR‐related events (i.e., overall IMERG and CMORPH show a higher fraction of AR‐related precipitation). It was found that the degree of consistency between reanalysis and satellite‐based products is highly regionally dependent, partly due to the uneven distribution of in situ measurements.

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.

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.

Regionality of monsoon onset in South America: a three‐stage conceptual model

AbstractThe evolution of monsoon onset across South America has complex temporal and regional variability that are controlled by local and remote land–ocean–atmosphere processes. In this study, a three‐stage conceptual model for the onset of the South American monsoon season is proposed based on a rain threshold analysis and a rotated empirical orthogonal function (REOF) analysis of the Global Precipitation Climatology Project version 2 (GPCP‐v2) dataset. This two‐pronged approach allowed the identification of regions of South America that share a common seasonal cycle of rainfall variability and likely have a common mechanism for monsoon onset.According to this model, the first stage of onset starts around pentad 59 (October 18–22) when precipitation begins in the northwestern part of the continent and gradually progresses towards the south and southeast. The second stage is marked by the abrupt establishment of the South Atlantic Convergence Zone (SACZ). This stage occurs on average around pentad 61 (October 28–November 1). The third stage of monsoon onset involves the late arrival of the monsoon to the mouth of the Amazon River, associated with the slow migration of the Atlantic Intertropical Convergence Zone (ITCZ). This final stage of onset occurs on average by pentad 73 (December 27–31). This three‐stage model of onset provides a useful framework for the study of regional differences in monsoon onset mechanisms, a subject that is further explored in two companion studies. Copyright © 2010 Royal Meteorological Society

ENSO induced monthly oscillations of precipitation: the unique case of the south tropical Indian Ocean in austral summer

Seasonal (three-month average) climate forecasts have advanced due in large part to improved modeling of the ENSO phenomenon. Long-range monthly forecasts are more problematic because of internal atmospheric variability. Further, it is often assumed that monthly precipitation anomalies are representative of the overall seasonal anomaly. This is not always the case as, according to the Global Precipitation Climatology Project Version 2.1 data set, up to 20% of areas demonstrating some significant teleconnection to ENSO show El Nino minus La Nina differences of one sign in the middle month and the opposite sign in the adjacent months. Most interestingly, this maximum percentage occurs in December–January–February (DJF), a time when the ENSO boundary forcing is strongest. These oscillatory DJF seasons also cluster in space—with significant positive–negative-positive differences in the western South Tropical Indian Ocean (STIO) and negative–positive–negative differences in the far eastern STIO. Representative gauges confirm that these precipitation patterns have been associated with ENSO events since 1951, and pentad precipitation data confirm that they are confined to DJF and evolve at the monthly scale. The abrupt end of the Indian Ocean Dipole mode in January, an increase in the importance of local SST anomalies in February, and an ENSO-induced mid-latitude Rossby wave during austral summer combine to generate the cross-basin precipitation gradient around 15°S.

Evaluation of precipitation from the ERA‐40, NCEP‐1, and NCEP‐2 Reanalyses and CMAP‐1, CMAP‐2, and GPCP‐2 with ground‐based measurements in China

We assess the correspondence between precipitation products from atmospheric reanalyses (ERA‐40, NCEP‐1, and NCEP‐2), the Climate Prediction Center (CPC) Merged Analyses of Precipitation (CMAP‐1 and CMAP‐2), and the Global Precipitation Climatology Project Version 2 (GPCP‐2) with adjusted observational precipitation (AOP) from China for 1979–2001 and also for ERA‐40 and NCEP‐1 over 1958–1978. In general, we conclude that CMAP‐1 and GPCP‐2 agree more closely with AOP than the reanalysis products do, although ERA‐40 data agree more closely with AOP than NCEP data. The percentages of precipitation differences (PPDs) across China between annual ERA‐40, NCEP‐1, NCEP‐2, CMAP‐1, CMAP‐2, and GPCP‐2 data and AOP are −12, 22, 14, −8, −7, and −15%, respectively, for 1979–2001. Although relatively small biases are evident for China as a whole, maximum PPDs, usually occurring around the Qinghai‐Tibetan Plateau, can exceed 1000%, indicating a strong terrain dependence of gridded precipitation data. GPCP‐2, although characterized by greater underestimation for most of China compared with CMAP‐1, exhibits a smaller biases range and hence may be better than CMAP‐1. Compared with the NCEP‐1 system, NCEP‐2 represents an improvement as NCEP‐2 precipitation agrees more closely with AOP than NCEP‐1 data. However, the coherence of NCEP‐2 precipitation needs further improvement. In addition, we find worse consistency and accuracy and larger positive biases in some parts of China for CMAP‐2 versus CMAP‐1, illustrating an advantage of including reanalysis data in CMAP, as CMAP‐1 does. CMAP‐1 could be further improved if they used the more skillful ERA‐40 precipitation instead of the NCEP/NCAR data.

Seasonal variation in precipitation patterns to the global ocean: An analysis of the GPCP version 2 data set

Precipitation affects the thermohaline circulation, chemical mass balance, and primary productivity of surface ocean waters. An analysis of temporal and spatial variation of oceanic precipitation was conducted on the Global Precipitation Climatology Project (GPCP) version 2 data set, a monthly 2.5° × 2.5° latitude‐longitude gridded data set for the period 1979 to 1999. While the precipitation patterns observed are generally similar to those reported in previous climatologies, new features and greater detail of global precipitation were revealed from our analysis of the GPCP data set. High precipitation was observed in the intertropical convergence zone (ITCZ), the South Pacific convergence zone (SPCZ), and the storm tracks in the North Pacific and Atlantic Oceans. Low precipitation was observed in the Polar regions and in the subtropics of the East Pacific, East Atlantic, and the Southeast and Northwest Indian Ocean. The spatial coverage of these high and low precipitation regions changed through the year. A strong seasonal cycle of precipitation was observed for the Northern and the Southern Hemispheres. The area‐weighted mean precipitation increased from a minimum of ∼2 mm d−1 in February to a maximum of ∼5 mm d−1 in August in the Northern Hemisphere and from a minimum of ∼2.5 mm d−1 in September to a maximum of ∼3.5 mm d−1 in March in the Southern Hemisphere. Similar seasonal cycles of precipitation were observed for each ocean basin. Global precipitation also varied significantly with both latitude and longitude, with a latitudinal maximum at 56°S, 39°S, 4°S, 6°N, 39°N, and 56°N, and a longitudinal maximum over each ocean. The seasonal varying precipitation patterns are a foundation for evaluating the effect of wet deposition on ocean circulation, flux of chemical species, and its effect on marine ecosystems.

The Global Precipitation Climatology Project (GPCP) Combined Precipitation Dataset

The Global Precipitation Climatology Project (GPCP) has released the GPCP Version 1 Combined Precipitation Data Set, a global, monthly precipitation dataset covering the period July 1987 through December 1995. The primary product in the dataset is a merged analysis incorporating precipitation estimates from low-orbit-satellite microwave data, geosynchronous-orbit-satellite infrared data, and rain gauge observations. The dataset also contains the individual input fields, a combination of the microwave and infrared satellite estimates, and error estimates for each field. The data are provided on 2.5° × 2.5° latitude-longitude global grids. Preliminary analyses show general agreement with prior studies of global precipitation and extends prior studies of El Nino-Southern Oscillation precipitation patterns. At the regional scale there are systematic differences with standard climatologies.