Satellite-based Precipitation Estimates Research Articles

Difference Between Cloud Top Height and Storm Height for Heavy Rainfall Using TRMM Measurements

This study compares the regional characteristics of heavy rain clouds in terms of Cloud Top Height (CTH) and Storm Height (SH) from long-term Tropical Rainfall Measuring Mission (TRMM) observations. The SH is derived from Precipitation Radar reflectivity, and the CTH is estimated, using visible and infrared scanner brightness temperature (10.8 µm) and reanalysis temperature profiles. As the rain rate increases, the average CTH and average SH increase, but by different degrees in different regions. Heavy rainfall in continental rainfall regimes, such as Central Africa and the United States, is characterized by high SH, in contrast to oceanic rainfall regions, such as the northwestern Pacific, Korea, and Japan; the increased atmospheric instability in dry environments is interpreted as a continental flood mechanism. Conversely, heavy rain events in Korea and Japan occur in a thermodynamically near-neutral environment with large amounts of water vapor, which are characterized by the lowest CTH, SH, and ice water content. The northwestern Pacific exhibits the lowest SH in humid environments, similar to Korea and Japan; however, this region also characteristically exhibits the highest convective instability condition, as well as high CTH and CTH–SH values, in contrast to Korea and Japan. The observed CTH and SH characteristics of heavy rain clouds should be useful for evaluating and improving satellite-based precipitation estimates and numerical model cloud parameterization.

Quantitative Evaluations and Error Source Analysis of Fengyun-2-Based and GPM-Based Precipitation Products over Mainland China in Summer, 2018

Satellite-based quantitative precipitation estimates (QPE) with a fine quality are of great importance to global water cycle and matter and energy exchange research. In this study, we firstly apply various statistical indicators to evaluate and compare the main current satellite-based precipitation products from Chinese Fengyun (FY)-2 and the Global Precipitation Measurement (GPM), respectively, over mainland China in summer, 2018. We find that (1) FY-2G QPE and Integrated Multi-satellitE Retrievals for GPM (IMERG) perform significantly better than FY-2E QPE, using rain gauge data, with correlation coefficients (CC) varying from 0.65 to 0.90, 0.80 to 0.90, and 0.40 to 0.53, respectively; (2) IMERG agrees well with rain gauge data at monthly scale, while it performs worse than FY-2G QPE at hourly and daily scales, which may be caused by its algorithms; (3) FY-2G QPE underestimates the precipitation in summer, while FY-2E QPE and IMERG generally overestimate the precipitation; (4) there is an interesting error phenomenon in that both FY-based and GPM-based precipitation products perform more poorly during the period from 06:00 to 10:00 UTC than other periods at diurnal scale; and (5) FY-2G QPE agrees well with IMERG in terms of spatial patterns and consistency (CC of ~0.81). These findings can provide valuable preliminary references for improving next generation satellite-based QPE retrieval algorithms and instructions for applying these data in various practical fields.

Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment

Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global Satellite Mapping of Precipitation (GSMaP) constellation over an arid environment like the Sultanate of Oman that is characterized by a complex topography and extremely variable rainfall patterns. Global Satellite-based Precipitation Estimates (GSPEs) can provide wide coverage and high spatial and temporal resolutions, but evaluating their accuracy is a mandatory step before involving them in different hydrological applications. In this paper, the reliability of the Integrated Multi-satellitE Retrievals for the GPM (IMERG) V04 and GSMaP V06 products were evaluated using the reference in-situ rain gauges at sub-daily (e.g., 6, 12, and 18 h) and daily time scales during the period of March 2014–December 2016. A set of continuous difference statistical indices (e.g., mean absolute difference, root mean square error, mean difference, and unconditional bias), and categorical metrics (e.g., probability of detection, critical success index, false alarm ratio, and frequency bias index) were used to evaluate recorded precipitation occurrences. The results showed that the five GSPEs could generally delineate the spatial and temporal patterns of rainfall while they might have over- and under-estimations of in-situ gauge measurements. The overall quality of the GSMaP runs was superior to the IMERG products; however, it also encountered an exaggeration in case of light rain and an underestimation for heavy rain. The effects of the gauge calibration algorithm (GCA) used in the final IMERG (IMERG-F) were investigated by comparison with early and late runs. The IMERG-F V04 product did not show a significant improvement over the early (i.e., after 4 h of rainfall observations) and late (i.e., after 12 h of rainfall observations) products. The results indicated that GCA could not reduce the missed precipitation records considerably.

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.

An updated moving window algorithm for hourly-scale satellite precipitation downscaling: A case study in the Southeast Coast of China

Accurate gridded precipitation products with both finer tempo-spatial resolutions are critical for various scientific communities (e.g., hydrology, meteorology, climatology, and agriculture). Downscaling on coarse satellite-based rainfall estimates is an optimal approach to obtain such datasets. The Integrated Multi-satellitE Retrivals for Global Precipitation Measurement (GPM) (IMERG) data provides the “best” satellite-based precipitation estimates at half-hourly/0.1° scales, while its spatial resolution is still coarse for certain hydrometeorology research. To acquire hourly downscaled precipitation estimates based on IMERG, there are two great challenges: (1) limited rainfall-related environmental variables (0.01°×0.01°, hourly) used to downscale the IMERG data; and (2) far few rainfall pixels used for regressing traditional relationships between precipitation and environmental variables. In this case, most traditional or commonly-used regression/empirical models and the state-of-art machine learning algorithms are not suitable to cater these requirements. Therefore, we proposed a new strategy to obtain hourly downscaled precipitation estimates based on IMERG called Geographically Moving Window Weight Disaggregation Analysis (GMWWDA). Additionally, we explored multiple cloud properties, including cloud effective radius (CER), cloud top height (CTH), cloud top temperature (CTT) and cloud optical thickness (COT), as covariates to downscale IMERG using GMWWDA method, and concluded as follows: (1) the downscaled results (0.01° × 0.01°, hourly) based on the above mentioned cloud properties outperformed the original IMERG data; (2) the downscaled results based on CER performed better than those based on CTH, CTT and COT, respectively; (3) the accuracy of satellite-based precipitation products pose significant effects on those of the downscaled results. This study provides a great potential solution for generating satellite-based precipitation dataset with finer spatio-temporal resolutions.

WHU-SGCC: a novel approach for blending daily satellite (CHIRP) and precipitation observations over the Jinsha River basin

Abstract. Accurate and consistent satellite-based precipitation estimates blended with rain gauge data are important for regional precipitation monitoring and hydrological applications, especially in regions with limited rain gauges. However, the existing fusion precipitation estimates often have large uncertainties over mountainous areas with complex topography and sparse rain gauges, and most of the existing data blending algorithms are not good at removing the day-by-day errors. Therefore, the development of effective methods for high-accuracy precipitation estimates over complex terrain and at a daily scale is of vital importance for mountainous hydrological applications. This study aims to offer a novel approach for blending daily precipitation gauge data and the Climate Hazards Group Infrared Precipitation (CHIRP; daily, 0.05∘) satellite-derived precipitation developed by UC Santa Barbara over the Jinsha River basin from 1994 to 2014. This method is called the Wuhan University Satellite and Gauge precipitation Collaborated Correction (WHU-SGCC). The results show that the WHU-SGCC method is effective for liquid precipitation bias adjustments from points to surfaces as evaluated by multiple error statistics and from different perspectives. Compared with CHIRP and CHIRP with station data (CHIRPS), the precipitation adjusted by the WHU-SGCC method has greater accuracy, with overall average improvements of the Pearson correlation coefficient (PCC) by 0.0082–0.2232 and 0.0612–0.3243, respectively, and decreases in the root mean square error (RMSE) by 0.0922–0.65 and 0.2249–2.9525 mm, respectively. In addition, the Nash–Sutcliffe efficiency coefficient (NSE) of the WHU-SGCC provides more substantial improvements than CHIRP and CHIRPS, which reached 0.2836, 0.2944, and 0.1853 in the spring, autumn, and winter. Daily accuracy evaluations indicate that the WHU-SGCC method has the best ability to reduce precipitation bias, with average reductions of 21.68 % and 31.44 % compared to CHIRP and CHIRPS, respectively. Moreover, the accuracy of the spatial distribution of the precipitation estimates derived from the WHU-SGCC method is related to the complexity of the topography. The validation also verifies that the proposed approach is effective at detecting major precipitation events within the Jinsha River basin. In spite of the correction, the uncertainties in the seasonal precipitation forecasts in the summer and winter are still large, which might be due to the homogenization attenuating the extreme rain event estimates. However, the WHU-SGCC approach may serve as a promising tool to monitor daily precipitation over the Jinsha River basin, which contains complicated mountainous terrain with sparse rain gauge data, based on the spatial correlation and the historical precipitation characteristics. The daily precipitation estimations at the 0.05∘ resolution over the Jinsha River basin during all four seasons from 1990 to 2014, derived from WHU-SGCC, are available at the PANGAEA Data Publisher for Earth & Environmental Science portal (https://doi.org/10.1594/PANGAEA.905376, Shen et al., 2019).

Evaluating satellite-based and reanalysis precipitation datasets with gauge-observed data and hydrological modeling in the Xihe River Basin, China

Abstract The wide application of satellite-based and reanalysis-based precipitation data has greatly promoted hydrometeorological research in areas where precipitation observations are scarce. However, the suitability of such precipitation products needs to be carefully evaluated before applications in certain basins because their inherited errors vary with different climate zones, seasonal cycles and land surface conditions; in addition, precipitation products have not been evaluated in the Xihe River basin, China. In this paper, two representative satellite-based precipitation products (Tropical Rainfall Measuring Mission (TRMM) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (CDR)) and two reanalysis-based precipitation products (China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) and National Centers for Environmental Prediction - Climate Forecast System Reanalysis (CFSR)) were selected for evaluation and corrected against gauge-observed data (OBS). Furthermore, the performances of precipitation products in hydrological simulations were also assessed using the SWAT model calibrated with OBS forcing and not with individual precipitation products. The results show that satellite-based precipitation has a higher quality than reanalysis-based precipitation. The CFSR and CDR overestimate precipitation (the overestimation of CDR precipitation is mainly concentrated in the precipitation intensity range of 1 mm/d to 5 mm/d), while the TRMM and CMADS underestimate precipitation in the Xihe River basin. The TRMM precipitation performs best during the wet season, while the CDR precipitation performed best during the dry season. After bias correction, the quality of TRMM precipitation improves significantly. The Nash-Sutcliffe coefficient (NS) (the percent bias (|PBIAS|)) increases (decreases) by 0.61 (77.27%) and 0.7 (39.15%) under the two different correction scenarios during 2009–2015. Overall, the above original precipitation products cannot be used as supplements to the OBS in the Xihe River basin unless they are corrected by the OBS.

Evaluating the performances of satellite-based rainfall data for global rainfall-induced landslide warnings

Satellite-based precipitation estimates (SPEs) show great promise for promoting landslide warning and mitigating landslide disaster risk with quasi-global coverage, near real-time monitoring, increasing spatial-temporal resolution, and accuracy. In this study, we evaluated the performances of four SPE products in detecting the initiation of rainfall-induced landslides globally using Hanssen-Kuiper (HK) skill score based on rainfall frequentist thresholds. The results show that SPEs can distinguish rainfall events responsible for landslides from those not related to landslides, suggesting that SPEs can capture rainfall conditions corresponding to landslide occurrence well and are of great use for landslide detecting. Further investigation indicates that performances at the global scale vary with products. CMORPH-3h V1 (HK = 0.43) and TMPA-3B42RT V7 (HK = 0.42) are superior to two other rainfall products with high HK values. Rainfall threshold establishment and evaluation for specific landslide types can improve SPEs’ performances in landslide modeling with higher HK values compared to results based on all landslide records. Performances also vary spatially with HK values ranging from 0.1 to 0.9 at a spatial grid of 5° × 5°. Linear relationship analysis reveals the variation in mean annual precipitation can partially explain the heterogeneous spatial distribution of rainfall threshold parameters. These findings serve to promote the application of satellite-based rainfall data in landslide warnings.

Evaluation of satellite-based precipitation estimates over Algeria during 1998–2016

This study concentrated on the assessment of two operational high-resolution satellite-based rainfall products, Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) and NOAA CPC Morphing Technique (CMORPH) over Algeria. The assessment was carried out for a duration of 19-years ranging between 1998 and 2016 using 20 rain gauge datasets. The study area can be divided into five regions (zones) according to Köppen climate classification (CSA, BSK, BWK, BSH, and BWH). Both satellite derived rainfall estimates (SRE); (CHIRPS and CMORPH) were evaluated over these regions. Continuous statistics was employed to measure their performance in evaluating and reproducing rainfall while categorical statistics employed on daily time scale to assess the ability of SRE in detecting rain/no rain events. Correlation Coefficient (R), Bias and Root Mean Square Error (RMSE) are employed on daily, monthly as well as on annual timescales. At daily scale, most SRE showed unsatisfactorily performance over all the five regions with exception for CMORPH products exhibiting better performance. However, as the time step augmented, the performance of SRE enhanced. At monthly time scale a higher agreement was observed. The CHIRPS performed the best (R = 0.9), whereas CMORPH-CRTv1.0 showed relatively weaker but good correlation (R = 0.83). At annually time scale CHIRPS has a better performance in CSA, BSK regions, on the other hand CMORPH demonstrate a better results BSH, BWK, BWH regions. The study demonstrate that reliable evaluation of rainfall in Algeria with different climate zones employing satellite-based precipitation estimation products remains a challenge.

Performance Evaluation of Three Satellites-Based Precipitation Data Sets Over Iran

The present study aims to evaluate the performance of daily and monthly precipitation data relative to GPM-IMERG, TRMM_3B42 and PERSIANN satellite-based precipitation estimations against historical data for the period 2014–2017 as observed at 70 synoptic stations distributed over Iran. The coefficient of determination (R-squared), root mean square error and the Nash–Sutcliffe model efficiency coefficient were used to evaluate the performance of the used data sets against observed precipitation records at the considered stations. The statistics showed that the considered data sets are generally less successful in estimating daily precipitation at nationwide as the estimation errors were found high at almost all the studied stations. The errors of daily precipitation estimation of GPM-IMERG, TRMM_3B42 and PERSIANN-CDR data sets showed that although there is a considerable similarity between the estimated precipitation by the three data sets, especially between the TRMM_3B42 and GPM-IMERG, the accuracy of GPM-IMERG daily precipitation over Iran is higher than that of TRMM_3B42 and PERSIANN-CDR. The highest R2 value for GPM-IMERG, TRMM_3B42 and PERSIANN-CDR remotely sensed daily precipitation is equal to 0.6, 0.46, and 0.37, respectively. Similarly, on the monthly time scale, the GPM-IMERG, with an average R2 value of 0.83 over the country, performs better than the other two data sets. The TRMM_3B43 with mean nationwide R2 = 0.80 also showed comparative performance with GPM-IMERG, but the PERSIANN-CDR data set with an average R2 value of 0.4 over the stations is not as accurate as the GPM-IMERG and TRMM_3B43.

Evaluating the Drought-Monitoring Utility of Four Satellite-Based Quantitative Precipitation Estimation Products at Global Scale

This study simultaneously analyzed and evaluated the meteorological drought-monitoring utility of the following four satellite-based, quantitative precipitation estimation (QPE) products: the Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis 3B43V7 (TRMM-3B43), the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS), the Climate Prediction Center Morphing Technique gauge-satellite blended product (CMORPH-BLD), and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR). Data from 2000 to 2016 was used at global scale. The global Climate Research Unit (CRU) Version 4.02 was used as reference data to assess QPE products. The Standardized Precipitation Evapotranspiration Index (SPEI) drought index was chosen as an example to evaluate the drought utility of four QPE products. The results indicate that CHIRPS has the best performance in Europe, Oceania, and Africa; the PERSIANN-CDR has the best performance in North America, South America, and Asia; the CMORPH-BLD has the worst statistical indices in all continents. Although four QPE products showed satisfactory performance for most of the world according to SPEI statistics, poor drought monitoring ability occurred in Southeast Asia, Central Africa, the Tibetan plateau, the Himalayas, and Amazonia. The PERSIANN-CDR achieves the best performance of the four QPE products in most regions except for Africa; CHIRPS and TRMM-3B43 have comparable performances. According to the spatial probability of detection (POD) and false alarm ratio (FAR) of the SPEI, more than 50% of all drought events cannot be accurately identified by QPE products in regions with sparse gauge distribution. In other regions, such as the southeastern USA, southeastern China, and South Africa, QPE products capture more than 75% of drought events. Temporally, all datasets (except for CMORPH-BLD) can detect all typical drought events, namely, in the southeastern US in 2007, western Europe in 2003, Kenya in 2006, and Central Asia in 2008. The study concludes that CHIRPS and TRMM-3B43 can be used as near-real-time drought monitoring techniques whereas PERSIANN-CDR might be more suitable for long-term historical drought analysis.

Blending long-term satellite-based precipitation data with gauge observations for drought monitoring: Considering effects of different gauge densities

Blending satellite-based precipitation estimation (SPE) data and in-situ gauge observation data can generate effective spatially-continuous-precipitation estimates with improved accuracy. This study assessed the improvement of the long-term SPE when blending with in-situ gauge observations for drought monitoring, using a simple but effective blending method named the geographical difference analysis (GDA) method and with the Precipitation Estimation from Remote Sensed Information by using Artificial Neural Networks-Climate Data Records (PERSIANN-CDR) as case study. In-situ precipitation observations from three meteorological station sets with different densities—the sparse (50), medium (200), dense (727) station set—were adopted to evaluate the effect of gauge density on the performance of SPE-gauge data blending. Two widely-used indices—standardized precipitation index (SPI) and self-calibrating Palmer drought severity index (SC_PDSI)—were used as case studies. Except the case of sparse 50-station subset, the SPE-gauge blending shows apparent improvement to the raw PERSIANN-CDR data, for both the accuracy of precipitation input and many aspects of drought monitoring, e.g. reproducing drought magnitude and revealing spatial pattern of drought, in which SC_PDSI shows more significant improvement than SPI. The dense 727-station set shows the largest improvement in the blending data, but the corresponding station-only interpolations also exhibit comparable performance to the blending data, indicating lower utilization value of the SPE data for these cases. Only the blending results of the medium-density 200-station set shows satisfactory drought monitoring performance as well as significant improvements relative to the station-only interpolations. According to the quantitative analyses, the medium density (about 50–75 gauges per 106km2 in our cases) might be the most economic gauge density for SPE-gauge blending, as it has satisfactory improvement in blending results, can make fullest use of the advantages of SPE data and requires relatively fewer gauges. Our results can help to understand how the SPE-gauge blending could improve the SPE-based drought monitoring and serves as a reference for applying drought monitoring under the data-limited conditions. Subsequent studies or applications should also carefully consider the effect of gauge density.

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.

Evaluating the hydrological utility of latest IMERG products over the Upper Huaihe River Basin, China

With increasing spatiotemporal resolution and accuracy, satellite-based precipitation estimations (SPEs) have become critical data sources for hydrological applications. Their global scale coverage and short latency perfectly fit the requirements of flood forecasting. However, the comprehensive evaluation of the latest generation of SPEs is an essential step before the confident use of these indirect precipitation estimators. Therefore, the latest releases of the Integrated MultisatellitE Retrievals for Global Precipitation Measurement (IMERG) Version 5 products, including the near-real-time “Early” run and “Later” run products (IMERG-E and IMERG-L, respectively), and the post-real-time “Final” run IMERG product (IMERG-F), are employed in this paper. Through the hydrometeorological assessment of these three IMERG products against dense gauge observations during the period from April 2014 to December 2015 over the Upper Huaihe River Basin (UHRB), the usability of IMERG products in hydrological application is explored via the Variable Infiltration Capacity model. The results indicate that, irrespective of a slight positive bias, the post-real-time IMERG-F product presents the best performance among the three IMERG products at both the grid scale and basin scale. Although all three IMERG products show remarkably high probability of detection (POD, approximately 0.82), the IMERG-E presents a relatively higher false alarm ratio (FAR = 0.37) in comparison with IMERG-L (FAR = 0.32) and IMERG-F (FAR = 0.31), which is primarily due to the significant overestimation in light rain events. In terms of hydrological simulations of streamflow, the dense gauge observations exhibit very good performance for both entire simulation period and flood events, as expected. IMERG-F demonstrates acceptable performance in entire simulation, while both IMERG-E and IMERG-L demonstrate poor performance. Given the clear overestimation in short-terms flooding and the notable underestimation in long-terms flooding, all three IMERG products should be used with caution in flood simulation over UHRB. Overall, this is a systematic assessment of IMERG products. We hope this study can provide useful guidelines for potential hydrologists of IMERG products and further IMERG algorithm development. Also, given the scope of on-going efforts to improve the IMERG products, we believe the subsequent versions of IMERG can be widely used in global or regional hydrological application, particularly in the field of flood forecasting.

Copula-based stochastic uncertainty analysis of satellite precipitation products

Satellite products, like all datasets, are subject to errors and uncertainties. Due to inherent biases embedded in satellite precipitation estimates, we present an error-adjustment approach based on the statistical differences between satellite precipitation products and in-situ observations (observed errors) employing two widely-used error models, namely that additive and the multiplicative error models, in an attempt to assess their suitability for the error correction of satellite-based daily precipitation estimates over northeast Austria. An error-adjustment technique based on the concept of the copula is adopted and applied to correct the supplied precipitation fields. It was found that IMERG precipitation estimates improved after error adjustment when compared to original satellite precipitation estimate (OSPE). The additive error model resulted in a better improvement by fitting the entire range of data when compared with the multiplicative error model. Moreover, the additive error model extracted the error with more accuracy and produced a better estimation of their characteristics, while the method based on the multiplicative error was less robust. However, the overall spatial dependence of the observed errors is reasonably preserved as that of the generated errors by copula. In addition, the validation results implied that the simulated realizations error-adjusted band, encompassed the observed data reasonably. Moreover, the copula-based simulations associated with the additive error model performed much better in comparison to the multiplicative error model. Overall, by using the t-copula model with an emphasis on the additive error model and imposing the simulated error fields on the OSPE, one may generate multiple realizations of precipitation fields.

Hydrological Simulation Using TRMM and CHIRPS Precipitation Estimates in the Lower Lancang-Mekong River Basin

Satellite-based products with high spatial and temporal resolution provide useful precipitation information for data-sparse or ungauged large-scale watersheds. In the Lower Lancang-Mekong River Basin, rainfall stations are sparse and unevenly distributed, and the transboundary characteristic makes the collection of precipitation data more difficult, which has restricted hydrological processes simulation. In this study, daily precipitation data from four datasets (gauge observations, inverse distance weighted (IDW) data, Tropical Rainfall Measuring Mission (TRMM) estimates, and Climate Hazards Group InfraRed Precipitation with Stations (CHIRPS) estimates), were applied to drive the Soil and Water Assessment Tool (SWAT) model, and then their capability for hydrological simulation in the Lower Lancang-Mekong River Basin were examined. TRMM and CHIRPS data showed good performances on precipitation estimation in the Lower Lancang-Mekong River Basin, with the better performance for TRMM product. The Nash-Sutcliffe efficiency (NSE) values of gauge, IDW, TRMM, and CHIRPS simulations during the calibration period were 0.87, 0.86, 0.95, and 0.93 for monthly flow, respectively, and those for daily flow were 0.75, 0.77, 0.86, and 0.84, respectively. TRMM and CHIRPS data were superior to rain gauge and IDW data for driving the hydrological model, and TRMM data produced the best simulation performance. Satellite-based precipitation estimates could be suitable data sources when simulating hydrological processes for large data-poor or ungauged watersheds, especially in international river basins for which precipitation observations are difficult to collect. CHIRPS data provide long precipitation time series from 1981 to near present and thus could be used as an alternative precipitation input for hydrological simulation, especially for the period without TRMM data. For satellite-based precipitation products, the differences in the occurrence frequencies and amounts of precipitation with different intensities would affect simulation results of water balance components, which should be comprehensively considered in water resources estimation and planning.

Comparisons of Spatially Downscaling TMPA and IMERG over the Tibetan Plateau

Accurate precipitation data is crucial in many applications such as hydrology, meteorology, and ecology. Compared with ground observations, satellite-based precipitation estimates can provide much more spatial information to characterize precipitation. In this study, the satellite-based precipitation products of Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) were firstly evaluated over the Tibetan Plateau (TP) in 2015 against ground observations at both annual and monthly scales. Secondly, random forest algorithm was used to obtain the annual downscaled results (~1 km) based on IMERG and TMPA data and the downscaled results were examined against rain gauge data. Thirdly, a disaggregation algorithm was used to obtain the monthly downscaled results based on those at annual scale. The results indicated that (1) IMERG performed better than TMPA at both annual and monthly scales; (2) IMERG had few anomalies while TMPA displayed significant numbers of outliers in central and western parts of the TP; (3) random forest was a promising algorithm in acquiring high resolution precipitation data with improved accuracy; (4) the downscaled results based on IMERG had better performances than those based on TMPA.

Component Analysis of Errors in Four GPM-Based Precipitation Estimations over Mainland China

As the Global Precipitation Measurement (GPM) Core Observatory satellite continues its mission, the latest GPM-era satellite-based precipitation estimations, including Global Satellite Mapping of Precipitation (GSMaP) and Integrated Multi-satellitE Retrievals for the GPM (IMERG), have been released. However, few studies have systematically evaluated these products over mainland China, although this is very important for both the end users and data developers. To these ends, the final-run uncalibrated IMERG V05 (V05UC), gauge-calibrated IMERG V05 (V05C) and IMERG V04 (V04C), and latest gauge-calibrated GSMaP V7 (GSMaP) are systematically evaluated and mutually compared against a merged product obtained from the China Meteorological Data Service Center via continuous statistical indices and an error decomposition analysis technology suite over mainland China from April 2014 to December 2016 at a 3 hourly scale and 0.1° × 0.1° resolution. The results show that, irrespective of the slight overestimation in the southeast and underestimation in the northern Tibetan Plateau, all four GSPEs could generally capture the spatial patterns of precipitation over mainland China. Meanwhile, the overall quality of the GSMaP is slightly superior to the IMERG products in east and south China; however, it also suffers from an overestimation of light rain and an underestimation of heavy rain. Such overestimation and underestimation are primarily from a large false precipitation in light rain and a negative hit bias in heavy rain, respectively. The latest IMERG V05 products have not shown significant improvement over the earlier version (V04C) in east and south China, but the calibrated V05C can best reproduce the probability density function in terms of precipitation intensity. Furthermore, V04C shows remarkable underestimation over the Tibetan Plateau, while this shortcoming has been resolved significantly in V05C. Alternately, the effects of the gauge calibration algorithm (GCA) used in IMERG are examined by comparison of V05UC and V05C. The results indicate that GCA cannot reduce the missed precipitation, and even enlarges the false precipitation over some regions. This reveals that GCA cannot effectively alleviate the bias resulting from the rain areas’ delineation and raining or not-raining detection. In addition, all of the products’ performance can be improved, particularly in the dry climate and high-latitude regions. This is a systematic estimation for GSPEs, providing deep insight into the characteristics and sources of error, and it could be valuable as a reference for both algorithm developers and data users, as well as for associated global products and various applications.

Bias adjustment of satellite-based precipitation estimation using artificial neural networks-cloud classification system over Saudi Arabia

Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations SPEs appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this study, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverse-weighted distance for the interpolation of gauge measurements. Seven years (2010–2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimation over Saudi Arabia. The first 6 years (2010–2015) are used for calibration, while 1 year (2016) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the yearly root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations.

TRMM-Based Optical and Microphysical Features of Precipitating Clouds in Summer Over the Yangtze–Huaihe River Valley, China

The optical and microphysical features of precipitating clouds are key information for studying the satellite-based precipitation estimation, cloud radiative effects, aerosol–cloud–precipitation interactions, cloud and precipitation parameterization in weather and climate models. In this study, 15-year synchronous spectral and radar observations from the TRMM satellite were used to statistically explore the optical and microphysical features of precipitating clouds (PCs), including cloud effective radius (CER), cloud optical thickness (COT), cloud water path (CWP), thermal infrared brightness temperature at channel 4 (TB4) of cloud top, and storm top height (STH) and their relationships with surface rain rates in summer over Yangtze–Huaihe River Valley (YHRV). Results show that the optical and microphysical features of PCs/stratiform PCs/convective PCs vary with geographical locations in summer over YHRV, due to the different ambient meteorological and topographical conditions. Higher CER/COT/CWP/STH and lower TB4 mainly locate at areas of bigger rain rates. For PCs, their spatial distribution of CER is mainly dominated by stratiform PCs, while their spatial distribution of COT/CWP is mainly dominated by convective PCs. Moreover, stratiform precipitation is the dominant form in summer over YHRV and, thus, most PCs present vertical structures of optical and microphysical features as stratiform PCs. Stratiform PCs are usually thicker and contain more water vapor with bigger cloud particles than convective PCs (including deep and shallow convective PCs). In addition, existing shallow convective PCs are associated with lower storm heights and warmer cloud tops. Finally, surface rain rates of PCs (convective/stratiform PCs) increase gradually with the increment of CER/COT/CWP/STH, especially under 5 (15/5) mm/h. Similar relationship between surface rain rates and COT/CWP for shallow convective PCs is also found under 0.75 mm/h. Surface rain rate of PCs (convective/stratiform PCs) with cold cloud tops (TB4 264 K), surface rain rate usually increases as CER decreases, which suggests that aerosol indirect effects are dominant in lower PCs, because over pollution regions abundant aerosols enter into lower clouds more easily and then suppress the development of shallow convective PCs.