TRMM) Multisatellite Precipitation Analysis Research Articles

Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the Sefidrood River Basin, Iran

ABSTRACTHydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).

Evaluation of Satellite-Based Rainfall Estimates in the Lower Mekong River Basin (Southeast Asia)

Satellite-based precipitation is an essential tool for regional water resource applications that requires frequent observations of meteorological forcing, particularly in areas that have sparse rain gauge networks. To fully realize the utility of remotely sensed precipitation products in watershed modeling and decision-making, a thorough evaluation of the accuracy of satellite-based rainfall and regional gauge network estimates is needed. In this study, Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42 v.7 and Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) daily rainfall estimates were compared with daily rain gauge observations from 2000 to 2014 in the Lower Mekong River Basin (LMRB) in Southeast Asia. Monthly, seasonal, and annual comparisons were performed, which included the calculations of correlation coefficient, coefficient of determination, bias, root mean square error (RMSE), and mean absolute error (MAE). Our validation test showed TMPA to correctly detect precipitation or no-precipitation 64.9% of all days and CHIRPS 66.8% of all days, compared to daily in-situ rainfall measurements. The accuracy of the satellite-based products varied greatly between the wet and dry seasons. Both TMPA and CHIRPS showed higher correlation with in-situ data during the wet season (June–September) as compared to the dry season (November–January). Additionally, both performed better on a monthly than an annual time-scale when compared to in-situ data. The satellite-based products showed wet biases during months that received higher cumulative precipitation. Based on a spatial correlation analysis, the average r-value of CHIRPS was much higher than TMPA across the basin. CHIRPS correlated better than TMPA at lower elevations and for monthly rainfall accumulation less than 500 mm. While both satellite-based products performed well, as compared to rain gauge measurements, the present research shows that CHIRPS might be better at representing precipitation over the LMRB than TMPA.

Long‐Term Precipitation Estimates Generated by a Downscaling‐Calibration Procedure Over the Tibetan Plateau From 1983 to 2015

AbstractThe World Meteorological Organization stipulates a minimum of 30 years of historical data is needed to obtain meaningful results in climatological research. However, large numbers of studies have explored downscaling approaches based on the TRMM Multi‐Satellite Precipitation Analysis (TMPA) data, which span only from 1998 to the present, to obtain the precipitation estimates (~1‐km resolution). The main aim of the present study was to develop a new method for obtaining long‐term (>30 years) precipitation estimates at ~1‐km resolution and to apply that method to a region with complex topography, the Tibetan Plateau. First, PERSIANN‐CDR (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks—Climate Data Record) data were used for downscaling. Considering the characteristics of the PERSIANN‐CDR data, a new downscaling‐calibration procedure utilizing a combination of a spatial data mining downscaling algorithm (Cubist) and a geographical ratio analysis calibration method was proposed. We found that (1) both the original PERSIANN‐CDR data (Bias ~40.79%) and the downscaled results before calibration (Bias ~26.78%) overestimated the precipitation compared with ground observations; (2) the final downscaled results based on the PERSIANN‐CDR data after calibration were close to the ground observations (Bias ~5%); (3) compared to the results interpolated based on the PERSIANN‐CDR data (E <−1.0), both the downscaling procedure and calibration procedure contributed significantly to the accuracy of the final downscaled results (E ~0.83). These findings suggest that the proposed downscaling‐calibration procedure has great potential as an approach for retrieving long‐term precipitation estimates (~1‐km resolution) over the Tibetan Plateau.

Role of precipitation forcing on the uncertainty of land surface model simulated soil moisture estimates

Land surface processes considerably influence the global weather and climate patterns which makes its quantification significant to scientists, hydrologists as well as policymakers alike. Considering the lack of available in-situ measurement, retrieval of the land surface fluxes mostly relies on remotely sensed satellite retrieval and through simulations from land surface models (LSMs). Hence, it is essential to quantify the uncertainties present in the output of these land surface models which are mainly due to errors in forcing data, model parameters and model structure. Precipitation is one of the key input forcing data used in LSMs. With the advancement of remote sensing techniques, multiple sources of precipitation products are made available to the user community. This study examines the effect of precipitation uncertainty in LSM simulated soil moisture. For this study, four precipitation products are used namely, Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RT v7, Global Data Assimilation System (GDAS), Climate Hazards Infrared Precipitation with Stations (CHIRPS) and Multi-Source Weighted-Ensemble Precipitation (MSWEP). These data products are used as meteorological forcing in Noah 3.6 LSM for the simulation of soil moisture. The uncertainty inherent in the precipitation products are examined using two approaches a) By evaluating the precipitation products against the gridded India Meteorological Department (IMD) precipitation dataset and b) By using the precipitation products for simulating soil moisture outputs. These were validated over the Indian subcontinent using validation data from the European Space Agency-Climate Change Initiative (ESA-CCI) soil moisture and Central Tibetan Plateau Soil Moisture and Temperature Monitoring Network (CTP-SMTMN) dataset for the years 2010 to 2012. The study utilizes various graphical as well as quantitative evaluation methods to determine the best performing precipitation product. Our study indicates that the simulated soil moisture forced with GDAS and MSWEP precipitation product performed consistently superior among all the other simulation outputs over India.

Precipitation Trends over the Indus Basin

A large population relies on water input to the Indus basin, yet basinwide precipitation amounts and trends are not well quantified. Gridded precipitation data sets covering different time periods and based on either station observations, satellite remote sensing, or reanalysis were compared with available station observations and analyzed for basinwide precipitation trends. Compared to observations, some data sets tended to greatly underestimate precipitation, while others overestimate it. Additionally, the discrepancies between data set and station precipitation showed significant time trends in such cases, suggesting that the precipitation trends of those data sets were not consistent with station data. Among the data sets considered, the station-based Global Precipitation Climatology Centre (GPCC) gridded data set showed good agreement with observations in terms of mean amount, trend, and spatial and temporal pattern. GPCC had average precipitation of about 500 mm per year over the basin and an increase in mean precipitation of about 15% between 1891 and 2016. For the more recent past, since 1958 or 1979, no significant precipitation trend was seen. Among the remote sensing based data sets, the Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) compared best to station observations and, though available for a shorter time period than station-based data sets such as GPCC, may be especially valuable for parts of the basin without station data. The reanalyses tended to have substantial biases in precipitation mean amount or trend relative to the station data. This assessment of precipitation data set quality and precipitation trends over the Indus basin may be helpful for water planning and management.

A Machine Learning Approach for Improving Near-Real-Time Satellite-Based Rainfall Estimates by Integrating Soil Moisture

Near-real-time (NRT) satellite-based rainfall estimates (SREs) are a viable option for flood/drought monitoring. However, SREs have often been associated with complex and nonlinear errors. One way to enhance the quality of SREs is to use soil moisture information. Few studies have indicated that soil moisture information can be used to improve the quality of SREs. Nowadays, satellite-based soil moisture products are becoming available at desired spatial and temporal resolutions on an NRT basis. Hence, this study proposes an integrated approach to improve NRT SRE accuracy by combining it with NRT soil moisture through a nonlinear support vector machine-based regression (SVR) model. To test this novel approach, Ashti catchment, a sub-basin of Godavari river basin, India, is chosen. Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-based NRT SRE 3B42RT and Advanced Scatterometer-derived NRT soil moisture are considered in the present study. The performance of the 3B42RT and the corrected product are assessed using different statistical measures such as correlation coefficient (CC), bias, and root mean square error (RMSE), for the monsoon seasons of 2012–2015. A detailed spatial analysis of these measures and their variability across different rainfall intensity classes are also presented. Overall, the results revealed significant improvement in the corrected product compared to 3B42RT (except CC) across the catchment. Particularly, for light and moderate rainfall classes, the corrected product showed the highest improvement (except CC). On the other hand, the corrected product showed limited performance for the heavy rainfall class. These results demonstrate that the proposed approach has potential to enhance the quality of NRT SRE through the use of NRT satellite-based soil moisture estimates.

The contribution of tropical cyclones to the atmospheric branch of Middle America\u2019s hydrological cycle using observed and reanalysis tracks

Middle America is affected by tropical cyclones (TCs) from the Eastern Pacific and the North Atlantic Oceans. We characterize the regional climatology (1998–2016) of the TC contributions to the atmospheric branch of the hydrological cycle, from May to December. TC contributions to rainfall are quantified using Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) product 3B42 and TC tracks derived from three sources: the International Best Track Archive for Climate Stewardship (IBTrACS), and an objective feature tracking method applied to the Japanese 55-year and ERA-Interim reanalyses. From July to October, TCs contribute 10–30% of rainfall over the west and east coast of Mexico and central Mexico, with the largest monthly contribution during September over the Baja California Peninsula (up to 90%). TCs are associated with 40–60% of daily extreme rainfall (above the 95th percentile) over the coasts of Mexico. IBTrACS and reanalyses agree on TC contributions over the Atlantic Ocean but disagree over the Eastern Pacific Ocean and continent; differences over the continent are mainly attributed to discrepancies in TC tracks in proximity to the coast and TC lifetime. Reanalysis estimates of TC moisture transports show that TCs are an important moisture source for the regional water budget. TC vertically integrated moisture flux (VIMF) convergence can turn regions of weak VIMF divergence by the mean circulation into regions of weak VIMF convergence. We discuss deficiencies in the observed and reanalysis TC tracks, which limit our ability to quantify robustly the contribution of TCs to the regional hydrological cycle.

Comparison of TRMM multi-satellite precipitation analysis (TMPA) estimation with ground-based precipitation data over Maharashtra, India

The Tropical Rainfall Measuring Mission (TRMM) is a multi-satellite precipitation product which is important for various hydro-meteorological applications. It is important to assess the correlation between precipitation records obtained by satellite and a ground observation station. The current study focuses to understand if satellite estimates can be used over ground station. The study has used bivariate correlation analysis to assess daily and 8-day composite precipitation records of TRMM satellite and Indian Meteorological Department (IMD) over Maharashtra, India, for monsoon months during 2004–2013. The Pearson’s correlation coefficient (r) varies significantly for daily and 8-day composite analysis. It was found that the 8-day composite TRMM showed strong correlation when compared to daily rainfall amounts. From the values recorded and scrutiny carried out, it was seen that the TRMM estimates were very well in synchronization with the IMD data for western region of study area. Precipitation in this region is heavily influenced by the Western Ghats which helps in exact determination of trend. Geographical influence on rainfall is a crucial thing to be addressed while conducting such studies. Research concludes that TRMM satellite estimates when considered over weekly analysis yielded better results against IMD’s rainfall making a suitable replacement for hydro-metrological studies.

A novel bias correction framework of TMPA 3B42 daily precipitation data using similarity matrix/homogeneous conditions

Reduction of bias in remotely sensed precipitation products is a major challenge in environment modeling, hydrology, and managing the water resources. Various bias correction techniques are applied to reduce the bias from pixel to gauge data. However, a successful methodology to improve bias correction on the daily scale is often challenging and limited. We present a methodology that can be used to correct the daily bias in remote sensing rainfall data, and to demonstrate the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42 data was used. The proposed bias correction method is based on the concept of similarity (homogeneous) conditions developed based on the periodicity and different percentile-based precipitation amount, and by identifying the best donor pixel to transfer bias correction factor to a specific ungauged pixel (the receptor pixel) based on the similarity (elevation, latitude, and longitude). Bias correction factors were obtained using the mean bias-removal (MBR) and multiplicative ratio (MR) techniques in the cells of the similarity matrix. The proposed methodology demonstrates a significant removal of bias associated with TMPA 3B42 data sets and it is capable of removing the bias in daily precipitation data on an average by 57% (51%) in the gauged pixels, and 25% (22%) in the ungauged pixels for MBR (MR) method.

Spatial and temporal assessment of the extreme and daily precipitation of the Tropical Rainfall Measuring Mission satellite in Northeast Brazil

ABSTRACT This study assesses the ability of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42 Version 7 (3B42 V7) product to estimate the occurrence and intensity of daily precipitation events, emphasizing extreme events, over a 16 year period (2000 to 2015) in the Northeast region of Brazil (NEB), which is characterized by different climatic conditions and high seasonal and spatial variability of rainfall. Statistical techniques such as the paired Student t-test, receiver operating characteristic curve (ROC), sensitivity analysis, cluster analysis and density graphing, along with descriptive statistics such as bias, standard deviation, mean square error and root mean square error were used. The results indicate that the quality of the satellite estimates depends on the seasonal period and the location and time scale in which the precipitation events occur. In general, the daily precipitation estimates of the satellite are statistically equal (p-value > 0.05) to those observed by rain gauges, except in the NEB east coast, where the observed precipitation values are associated with the occurrence of warm clouds. Regarding the extreme events, the satellite did not provide good estimates of extreme daily precipitation. However, rain accumulated in two days is enough to obtain better results for the quality of the extreme precipitation estimates coming from the satellite. Thus, the study provides users of the TMPA 3B42 V7 with prior knowledge regarding the quality of the estimates regarding the occurrence and intensity of precipitation events, especially extreme ones, by seasonal period in different areas of NEB.

Spatial Error Distribution and Error Cause Analysis of TMPA-3B42V7 Satellite-Based Precipitation Products over Mainland China

With a high spatial resolution and wide coverage, satellite-based precipitation products have compensated for the shortcomings of traditional measuring methods based on rain gauge stations, such as the sparse and uneven distribution of rain gauge stations. However, the accuracy of satellite precipitation products is not high enough in some areas, and the causes of their errors are complicated. In order to better calibrate and apply the product’s data, relevant research on this kind of product is required. Accordingly, this study investigated the spatial error distribution and spatial influence factors of the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) post-process 3B42V7 (hereafter abbreviated as 3B42V7) data over mainland China. This study calculated accuracy indicators based on the 3B42V7 data and daily precipitation data from 797 rain gauge stations across mainland China over the time range of 1998–2012. Then, a clustering analysis was conducted based on the accuracy indicators. Moreover, the geographical detector (GD) was used to perform the error cause analysis of the 3B42V7. The main findings of this study are the following. (1) Within mainland China, the 3B42V7 data accuracy decreased gradually from the southeast coast to the northwest inland, and shows a similar distribution for precipitation. High values of systematic error (>1.0) is mainly concentrated in the southwest Tibetan Plateau, while high values of random error (>1.0) are mainly concentrated around the Tarim Basin. (2) Mainland China can be divided into three areas by the spectral clustering method. It is recommended that the 3B42V7 can be effectively used in Area I, while in Area III the product should be calibrated before use, and the product in Area II can be used after an applicability study. (3) The GD result shows that precipitation is the most important spatial factor among the seven factors influencing the spatial error distribution of the 3B42V7 data. The relationships between spatial factors are synergistic rather than individual when influencing the product’s accuracy.

An Extreme Precipitation Categorization Scheme and its Observational Uncertainty over the Continental United States

Abstract An extreme precipitation categorization scheme, used to temporally and spatially visualize and track the multiscale variability of extreme precipitation climatology, is applied over the continental United States. The scheme groups 3-day precipitation totals exceeding 100 mm into one of five precipitation categories, or “P-Cats.” To demonstrate the categorization scheme and assess its observational uncertainty across a range of precipitation measurement approaches, we compare the climatology of P-Cats defined using in situ station data from the Global Historical Climatology Network-Daily (GHCN-D); satellite-derived data from the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA); gridded station data from the Parameter-Elevation Regression on Independent Slopes Model (PRISM); global reanalysis from the Modern-Era Retrospective Analysis for Research and Applications, version 2; and regional reanalysis from the North American Regional Reanalysis. While all datasets capture the principal spatial patterns of P-Cat climatology, results show considerable variability across the suite in frequency, spatial extent, and magnitude. Higher-resolution datasets, PRISM and TMPA, most closely resemble GHCN-D and capture a greater frequency of high-end P-Cats relative to the lower-resolution products. When all datasets are rescaled to a common coarser grid, differences persist with datasets originally constructed at a high resolution maintaining a higher frequency and magnitude of P-Cats. Results imply that dataset choice matters when applying the P-Cat scheme to track extreme precipitation over space and time. Potential future applications of the P-Cat scheme include providing a target for climate model evaluation and a basis for characterizing future change in extreme precipitation as projected by climate model simulations.

A new approach for obtaining precipitation estimates with a finer spatial resolution on a daily scale based on TMPA V7 data over the Tibetan Plateau

ABSTRACTPrecipitation plays vital roles in the worldwide hydrological cycles. Gridded precipitation estimates with finer spatio-temporal resolutions are critical in various application fields. In this study, we focused on obtaining downscaled precipitation estimates (approximately1 km) at daily scale over the Tibetan Plateau (TP), which was considered as a great challenge in previous downscaling studies. To meet this challenge, a new approach, incorporating geographically ratio analysis (GRA) and spatially weighted moving window technique, was proposed. The performances of the downscaled results and those of TRMM Multisatellite Precipitation Analysis (TMPA) data were evaluated against point-based ground observations. The results indicated that: (1) the monthly downscaled results (R2 around 0.70, bias around 10%) outperformed TMPA 3B43 data (R2 around 0.55, bias around 25%) against ground observations; (2) the performances of the daily downscaled results (R2 around 0.65, bias around 10%) were better than those of the TMPA 3B42 data (R2 around 0.50, bias around 28%); and (3) the anomalies in the TMPA data did not exist in the downscaled results based on the proposed approach. Therefore, the proposed approach was suitable for obtaining both monthly and daily downscaled results based on TMPA data over the TP.

Monitoring the Indian Summer Monsoon Evolution at the Granularity of the Indian Meteorological Sub-divisions using Remotely Sensed Rainfall Products

We make use of satellite-based rainfall products from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) to objectively define local onset and demise of the Indian Summer Monsoon (ISM) at the spatial resolution of the meteorological subdivisions defined by the Indian Meteorological Department (IMD). These meteorological sub-divisions are the operational spatial scales for official forecasts issued by the IMD. Therefore, there is a direct practical utility to target these spatial scales for monitoring the evolution of the ISM. We find that the diagnosis of the climatological onset and demise dates and its variations from the TMPA product is quite similar to the rain gauge based analysis of the IMD, despite the differences in the duration of the two datasets. This study shows that the onset date variations of the ISM have a significant impact on the variations of the seasonal length and seasonal rainfall anomalies in many of the meteorological sub-divisions: for example, the early or later onset of the ISM is associated with longer and wetter or shorter and drier ISM seasons, respectively. It is shown that TMPA dataset (and therefore its follow up Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG)) could be usefully adopted for monitoring the onset of the ISM and therefore extend its use to anticipate the potential anomalies of the seasonal length and seasonal rainfall anomalies of the ISM in many of the Indian meteorological sub-divisions.

Comparison of Oceanic Multisatellite Precipitation Data from Tropical Rainfall Measurement Mission and Global Precipitation Measurement Mission Datasets with Rain Gauge Data from Ocean Buoys

AbstractThree satellite-derived precipitation datasets [the Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) dataset, the NOAA Climate Prediction Center morphing technique (CMORPH) dataset, and the newly available Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) dataset] are compared with data obtained from 55 rain gauges mounted on floating buoys in the tropics for the period 1 April 2014–30 April 2017. All three satellite datasets underestimate low rainfall and overestimate high rainfall in the tropical Pacific Ocean, but the TMPA dataset does this the most. In the high-rainfall (higher than 4 mm day−1) Atlantic region, all three satellite datasets overestimate low rainfall and underestimate high rainfall, but the IMERG dataset does this the most. For the Indian Ocean, all three rainfall satellite datasets overestimate rainfall at some gauges and underestimate it at others. Of these three satellite products, IMERG is the most accurate in estimating mean precipitation over the tropical Pacific and Indian Oceans, but it is less accurate over the tropical Atlantic Ocean for regions of high rainfall. The differences between the three satellite datasets vary by region and there is a need to consider uncertainties in the data before using them for research.

Evaluating the accuracy of MSWEP V2.1 and its performance for drought monitoring over mainland China

Long-term, reliable quantitative precipitation estimate products provide an ideal data source for large-scale drought monitoring and other related hydrological and meteorological applications. The foremost purpose of this study was to evaluate the suitability of Multi-Source Weighted-Ensemble Precipitation Version 2.1 (MSWEP V2.1) for drought monitoring over mainland China. The accuracy of MSWEP V2.1 was first evaluated against observed precipitation data derived from 586 meteorological stations. The Standardized Precipitation Index (SPI) based on MSWEP V2.1 was then validated and analyzed at multiple spatial (regional and grid) and temporal (1, 3, 6, and 12 months) scales. Finally, we considered three typical drought events as case studies and analyzed the potential of MSWEP V2.1 in capturing the temporal and spatial variations of drought conditions. The results indicated that there was good agreement between MSWEP V2.1 and the rain gauge observations, even though MSWEP V2.1 generally underestimated the precipitation over most parts of mainland China. MSWEP V2.1 exhibited the highest correlation coefficient (CC) with in situ observations at monthly timescales, followed by those at yearly timescales and daily timescales with an average CC of 0.93, 0.86, and 0.77, respectively. The MSWEP V2.1-based SPI matched well with that calculated from the precipitation data derived from the meteorological stations at multiple spatial and temporal scales. Generally, the performance of MSWEP V2.1 over eastern China was significantly superior to its performance in western China. Despite this, MSWEP V2.1 still provided a greater potential for drought monitoring over western China than two satellite-based precipitation estimates, namely the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 and the Climate Prediction Center morphing technique (CMORPH) satellite-gauge merged product (CMORPH BLD). Through the analysis of three typical drought events, we found that the MSWEP V2.1-based SPI could not only precisely describe the occurrence and development of drought events, but also reasonably reflect important characteristics of typical drought events, such as intensity and areal extent. In general, MSWEP V2.1 could be used as an alternative data source for drought monitoring over mainland China, particularly in eastern China. However, the performance of MSWEP V2.1 in the arid and semi-arid areas of western China still requires improvement.

Seasonal Effect on Spatial and Temporal Consistency of the New GPM-Based IMERG-v5 and GSMaP-v7 Satellite Precipitation Estimates in Brazil’s Central Plateau Region

This study assesses the performance of the new Global Precipitation Measurement (GPM)-based satellite precipitation estimates (SPEs) datasets in the Brazilian Central Plateau and compares it with the previous Tropical Rainfall Measurement Mission (TRMM)-era datasets. To do so, the Integrated Multi-satellitE Retrievals for GPM (IMERG)-v5 and the Global Satellite Mapping of Precipitation (GSMaP)-v7 were evaluated at their original 0.1° spatial resolution and for a 0.25° grid for comparison with TRMM Multi-satellite Precipitation Analysis (TMPA). The assessment was made on an annual, monthly, and daily basis for both wet and dry seasons. Overall, IMERG presents the best annual and monthly results. In both time steps, IMERG’s precipitation estimations present bias with lower magnitudes and smaller root-mean-square error. However, GSMaP performs slightly better for the daily time step based on categorical and quantitative statistical analysis. Both IMERG and GSMaP estimates are seasonally influenced, with the highest difficulty in estimating precipitation occurring during the dry season. Additionally, the study indicates that GPM-based SPEs products are capable of continuing TRMM-based precipitation monitoring with similar or even better accuracy than obtained previously with the widely used TMPA product.

Complementing near-real time satellite rainfall products with satellite soil moisture-derived rainfall through a Bayesian Inversion approach

This work investigates the potential of using the Bayesian-based Model Conditional Processor (MCP) for complementing satellite precipitation products with a rainfall dataset derived from satellite soil moisture observations. MCP – which is a Bayesian Inversion approach – was originally developed for predictive uncertainty estimates of water level and discharge to support real-time flood forecasting. It is applied here for the first time to precipitation to provide its probability distribution conditional on multiple satellite precipitation estimates derived from TRMM Multi-Satellite Precipitation Analysis real-time product v.7.0 (3B42RT) and the soil moisture-based rainfall product SM2RAIN-CCI. In MCP, 3B42RT and SM2RAIN-CCI represent a priori information (predictors) about the “true” precipitation (predictand) and are used to provide its real-time a posteriori probabilistic estimate by means of the Bayes theorem. MCP is tested across Italy during a 6-year period (2010–2015) at daily/0.25 deg temporal/spatial scale. Results demonstrate that the proposed methodology provides rainfall estimates that are superior to both 3B42RT (as well as its successor IMERG-early run) and SM2RAIN-CCI in terms of both median bias, random errors and categorical scores. The study confirms that satellite soil moisture-derived rainfall can provide valuable information for improving state-of-the-art satellite precipitation products, thus making them more attractive for water resource management and large scale flood forecasting applications.

Comparing Multiple Precipitation Products against In-Situ Observations over Different Climate Regions of Pakistan

Various state-of-the-art gridded satellite precipitation products (GPPs) have been derived from remote sensing and reanalysis data and are widely used in hydrological studies. An assessment of these GPPs against in-situ observations is necessary to determine their respective strengths and uncertainties. GPPs developed from satellite observations as a primary source were compared to in-situ observations, namely the Climate Hazard group Infrared Precipitation with Stations (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission (TRMM) multi-satellite precipitation analysis (TMPA). These products were compared to in-situ data from 51 stations, spanning 1998–2016, across Pakistan on daily, monthly, annual and interannual time scales. Spatiotemporal climatology was well captured by all products, with more precipitation in the north eastern parts during the monsoon months and vice-versa. Daily precipitation with amount larger than 10 mm showed significant (95%, Kolmogorov-Smirnov test) agreement with the in-situ data, especially TMPA, followed by CHIRPS and MSWEP. At monthly scales, there were significant correlations (R) between the GPPs and in-situ records, suggesting similar dynamics; however, statistical metrics suggested that the performance of these products varies from north towards south. Temporal agreement on an interannual scale was higher in the central and southern parts which followed precipitation seasonality. TMPA performed the best, followed in order by CHIRPS, MSWEP and PERSIANN-CDR.

MSWEP V2 Global 3-Hourly 0.1° Precipitation: Methodology and Quantitative Assessment

AbstractWe present Multi-Source Weighted-Ensemble Precipitation, version 2 (MSWEP V2), a gridded precipitation P dataset spanning 1979–2017. MSWEP V2 is unique in several aspects: i) full global coverage (all land and oceans); ii) high spatial (0.1°) and temporal (3 hourly) resolution; iii) optimal merging of P estimates based on gauges [WorldClim, Global Historical Climatology Network-Daily (GHCN-D), Global Summary of the Day (GSOD), Global Precipitation Climatology Centre (GPCC), and others], satellites [Climate Prediction Center morphing technique (CMORPH), Gridded Satellite (GridSat), Global Satellite Mapping of Precipitation (GSMaP), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT)], and reanalyses [European Centre for Medium-Range Weather Forecasts (ECMWF) interim reanalysis (ERA-Interim) and Japanese 55-year Reanalysis (JRA-55)]; iv) distributional bias corrections, mainly to improve the P frequency; v) correction of systematic terrestrial P biases using river discharge Q observations from 13,762 stations across the globe; vi) incorporation of daily observations from 76,747 gauges worldwide; and vii) correction for regional differences in gauge reporting times. MSWEP V2 compares substantially better with Stage IV gauge–radar P data than other state-of-the-art P datasets for the United States, demonstrating the effectiveness of the MSWEP V2 methodology. Global comparisons suggest that MSWEP V2 exhibits more realistic spatial patterns in mean, magnitude, and frequency. Long-term mean P estimates for the global, land, and ocean domains based on MSWEP V2 are 955, 781, and 1,025 mm yr−1, respectively. Other P datasets consistently underestimate P amounts in mountainous regions. Using MSWEP V2, P was estimated to occur 15.5%, 12.3%, and 16.9% of the time on average for the global, land, and ocean domains, respectively. MSWEP V2 provides unique opportunities to explore spatiotemporal variations in P, improve our understanding of hydrological processes and their parameterization, and enhance hydrological model performance.