TRMM) Multisatellite Precipitation Analysis Research Articles

Which Error Components in TRMM-Based Multisatellite Precipitation Estimates Reduce over Chinese Mainland after Official Bias Adjustments: Systematic or Random?

Abstract Climatological calibration algorithm (CCA) and satellite–gauge combination (SG) are two official bias adjustments for satellite precipitation estimates (SPE) in the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA). The CCA is designed for the near-real-time SPEs, while the SG procedure is a final step to merge pure SPEs with gauge observations. This study explored the impacts of CCA and SG on the systematic and random errors of TMPA SPEs. The errors of TMPA version-7 near-real-time products before and after CCA (RT_UC, RT_C), and the research product TMPA 3B42 (V7), were decomposed into systematic and random components, benchmarked by the China Gauge-based Daily Precipitation Analysis (CGDPA). After being calibrated by CCA, RT_C reduced the systematic errors relative to RT_UC over the Chinese mainland, except in the Tibetan Plateau and Tianshan Mountains. However, CCA did not aid in reducing random errors; instead, it even exacerbated the random errors. On the other hand, the SG merging is more effective in reducing systematic errors of SPEs than CCA calibration because of the direct inclusion of simultaneous gauge data from the Global Precipitation Climatology Centre (GPCC). We also found that SG merging reduced the random errors of pure SPEs over regions with relatively higher elevations. Despite lower random errors in V7 over the complex terrain region, the SG unfavorably increased the random errors over southeastern China. The results reported here may offer valuable insights into the effects of CCA and SG techniques drawn from TMPA, with the potential to advance the development of bias-adjusting algorithms for SPEs in the future.

Superiority of Dynamic Weights against Fixed Weights in Merging Multi-Satellite Precipitation Datasets over Pakistan

Satellite precipitation products (SPPs) are undeniably subject to uncertainty due to retrieval algorithms and sampling issues. Many research efforts have concentrated on merging SPPs to create high-quality merged precipitation datasets (MPDs) in order to reduce these uncertainties. This study investigates the efficacy of dynamically weighted MPDs in contrast to those using static weights. The analysis focuses on comparing MPDs generated using the “dynamic clustered Bayesian averaging (DCBA)” approach with those utilizing the “regional principal component analysis (RPCA)” under fixed-weight conditions. These MPDs were merged from SPPs and reanalysis precipitation data, including TRMM (Tropical Rainfall Measurement Mission) Multi-satellite Precipitation Analysis (TMPA) 3B42V7, PERSIANN-CDR, CMORPH, and the ERA-Interim reanalysis precipitation data. The performance of these datasets was evaluated in Pakistan’s diverse climatic zones—glacial, humid, arid, and hyper-arid—employing data from 102 rain gauge stations. The effectiveness of the DCBA model was quantified using Theil’s U statistic, demonstrating its superiority over the RPCA model and other individual merging methods in the study area The comparative performances of DCBA and RPCA in these regions, as measured by Theil’s U, are 0.49 to 0.53, 0.38 to 0.45, 0.37 to 0.42, and 0.36 to 0.43 in glacial, humid, arid, and hyper-arid zones, respectively. The evaluation of DCBA and RPCA compared with SPPs at different elevations showed poorer performance at high altitudes (>4000 m). The comparison of MPDs with the best performance of SPP (i.e., TMPA) showed significant improvement of DCBA even at altitudes above 4000 m. The improvements are reported as 49.83% for mean absolute error (MAE), 42.31% for root-mean-square error (RMSE), 27.94% for correlation coefficient (CC), 40.15% for standard deviation (SD), and 13.21% for Theil’s U. Relatively smaller improvements are observed for RPCA at 13.04%, 1.56%, 10.91%, 1.67%, and 5.66% in the above indices, respectively. Overall, this study demonstrated the superiority of DCBA over RPCA with static weight. Therefore, it is strongly recommended to use dynamic variation of weights in the development of MPDs.

Performance Assessment of Chirps, Persiann – CDR, IMERG, and TMPA Precipitation Products across Nepal

The observation of hydrological as well as meteorological data is very essential for any kind of hydraulic and hydrological study. In Nepal, due to the significant variation of topography and climatic characteristics as well it is necessary to establish the meteorological stations densely, here only 231 meteorological stations are available and are handled by government organizations Department of Hydrology and Meteorology (DHM). Even though, due to many limitations, the satellite data is very useful for the water resources study. There are so many satellite products available but the performance of these products varies from place to place. In this study, the performance of four satellite products i.e., Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Integrated Multi-Satellite Retrievals for GPM (IMERG), and TRMM Multi-satellite Precipitation Analysis (TMPA) all over Nepal are evaluated with different elevation bands. The performance of each product is evaluated by Probability of Detection (POD), Critical Success Index (CSI), Frequency Bias Index (FBI), False Alarm Ratio (FAR), Root Mean Square Error (RMSE), and Percentage Bias (PBIAS). After analysis of each product, the PERSIANN-CDR data set gives a reasonable performance for all elevation bands after bias correction.

A comparative analysis of merging strategies for satellite precipitation estimates and ground observations over Chinese mainland

Satellite precipitation estimates are attractive for providing spatially continuous precipitation measurement on the global scale, but improvements in accuracy are still needed relative to the point-scale gauge observations. In this study, two integration frameworks named Geographical Differential Analysis (GDA) and Geographical Ratio Analysis (GRA) combined with two interpolation techniques named Ordinary Kriging (OK) and Inverse Distance Weighting (IDW) were adopted to merge the satellite precipitation with ground-based observations. The Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) research product was merged with the ground rain gauges derived from CGDPA (China Gauge-based Daily Precipitation Analysis) at a monthly scale over the Chinese mainland from 2011 to 2015, and the merged precipitation products were evaluated by 10-fold cross-validation. Results showed that the merged precipitation products based on the GDA integration framework had significant improvements relative to the original TMPA. Moreover, the method based on GDA combined with Ordinary Kriging (GDA-OK) delivered better performances compared to the GDA-IDW (method based on GDA combined with Inverse Distance Weighting) since the OK considers the global spatial correlation. However, the GRA-based merged precipitation products had systematic underestimations over Chinese mainland during all seasons and were not recommended for satellite-gauge precipitation merging over the large spatial scale of Chinese mainland. In addition, we found that the OK is quite sensitive to the density gauge network, the shrinkage of the number of rain gauges may result in great uncertainties and errors in the OK interpolation technique.

Investigating Drought and Flood Evolution Based on Remote Sensing Data Products over the Punjab Region in Pakistan

Over the last five decades, Pakistan experienced its worst drought from 1998 to 2002 and its worst flood in 2010. This study determined the record-breaking impacts of the droughts (1998–2002) and the flood (2010) and analyzed the given 12-year period, especially the follow-on period when the winter wheat crop was grown. We identified the drought, flood, and warm and cold edges over the plain of Punjab Pakistan based on a 12-year time series (2003–2014), using the vegetation temperature condition index (VTCI) approach based on Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data products. During the year 2010, the Global Flood Monitoring System (GFMS) model applied to the real-time Tropical Rainfall Measuring Mission (TRMM) rainfall incorporated data products into the TRMM Multi-Satellite Precipitation Analysis (TMPA) for the flood detection/intensity, stream flow, and daily accumulative precipitation, and presented the plain provisions to wetlands. This study exhibits drought severity, warm and cold edges, and flood levels using the VTCI drought-monitoring approach, which utilizes a combination of the normalized difference vegetation index (NDVI) with land surface temperature (LST) data products. It was found that during the years 2003–2014, the VTCI had a positive correlation coefficient (r) with the cumulative precipitation (r = 0.60) on the day of the year (D-073) in the winter. In the year 2010, at D-201, there was no proportionality (nonlinear), and at D-217, a negative correlation was established. This revealed the time, duration, and intensity of the flood at D-201 and D-217, and described the heavy rainfall, stream flow, and flood events. At D-233 and D-281 during 2010, a significant positive correlation was noticed in normal conditions (r = 0.95 in D-233 and r = 0.97 in D-281 during the fall of 2010), which showed the flood events and normality. Notably, our results suggest that VTCI can be used for drought and wet conditions in both rain-fed and irrigated regions. The results are consistent with anomalies in the GFMS model using the spatial and temporal observations of the MODIS, TRMM, and TMPA satellites, which describe the dry and wet conditions, as well as flood runoff stream flow and flood detection/intensity, in the region of Punjab during 2010. It should be noted that the flood (2010) affected the area, and the production of the winter wheat crop has consistently declined from 19.041 to 17.7389 million tons.

Assessing Impacts of Flood and Drought over the Punjab Region of Pakistan Using Multi-Satellite Data Products

The Punjab region of Pakistan faced significant losses from flash flooding in 2010 and experienced a multiyear drought during 1998–2002. The current study illustrates the drought and flood conditions using the multi-satellite data products derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and Tropical Rainfall Measuring Mission (TRMM) as well as the TRMM Multi-satellite Precipitation Analysis (TMPA) satellites with high-quality resolution in the region of Punjab during 2010–2014. To determine the drought and flood events, we used the Vegetation Temperature Condition Index (VTCI) drought monitoring approach combined with the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) to identify the warm and cold edges (WACE) in the provision of soil moisture as well as the VTCI imagery using the MODIS-Aqua data products. We assessed the 2010 flood effect on the four years (2011–2014) of drought conditions during winter wheat crop seasons. The obtained VTCI imagery and precipitation data were utilized to validate the drought and flood conditions in the year 2010 and the drought conditions in the years 2011–2014 during the winter-wheat-crop season. It is worth mentioning that over the four years (2011–2014) of the Julian day~D-041 year, the VTCI shows a stronger link with the accumulative precipitation anomaly (r = 0.77). It was found that for D-201 during the 2010 flood was the relationship was nonlinear, and in D-217, there was a negative relationship which revealed the flood timing, duration, and intensity. For D-281, a correlation (r = 0.97) was noted during fall 2010, which showed the drought and flood extreme conditions for the winter-wheat-crop season in the year 2010–2014. In regard to 2010, the Global Flood Monitoring System (GFMS) model employs the TRMM and TMPA data products to display the study region during the 2010 flood events and validate the VTCI results. This study’s spatial and temporal observations based on the observed results of the MODIS, TRMM, and TMPA satellites are in good agreement with dry and wet conditions as well as the flood runoff stream flow and flood intensity. It demonstrates the flood events with high intensity compared with the normality of flood with the complete establishment of flood events and weather extremes during the year of 2011–2014, thereby highlighting the natural hazards impacts. Our findings show that the winter wheat harvest was affected by the 2010 monsoon’s summer high rain and floods in the plain of Punjab (Pakistan).

Non-parametric severity-duration-frequency analysis of drought based on satellite-based product and model fusion techniques.

Climate change has increased the severity and frequency of droughts over the last decades. To alleviate the adverse impacts of droughts, an effective planning and management framework requires high-resolution spatiotemporal data. TRMM multi-satellite precipitation analysis (TMPA) dataset provides sufficient accuracy with fine spatio-temporal resolution. However, it only covers a short temporal span, which limits its applicability for drought studies. This paper presents a methodology for efficient and accurate temporal extension of TMPA using four artificial intelligence (AI)-based models. To improve AI-based model precipitation estimations, fusion techniques including Orness, Orlike, and genetic algorithm (GA)-based weighting methods were employed. Results show that fusion approaches provide more accurate estimates of precipitation. Different timescales of n-SPI time series and drought spatial maps were prepared to visually evaluate the performance of long-term TMPA (LT-TMPA) alongside statistical error indices. The results confirm that this dataset is effective for meteorological drought monitoring over southern Iran. Finally, drought risk assessment was carried out to determine the spatiotemporal characteristics of droughts through severity-duration-frequency (SDF) contour maps. In contrast to the traditional SDF curves, SDF contour maps provide a superior understanding of drought for policymakers since they preserve spatial information.

Evaluation of multiple bias correction methods with different satellite rainfall products in the Main Beles Watershed, Upper Blue Nile (Abbay) Basin, Ethiopia

Abstract This study investigates the utility of satellite-based rainfall products and the performance of bias correction methods in one of the sub-basins of the Upper Blue Nile Basin (Main Beles basin). Four satellite rainfall products are used: Climate Prediction Center (CPC) MORPHing technique (CMORPH), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA) 3B42V7 (TMPA 3B42V7), and Climate Forecast System Reanalysis (CFSR). The performance of the satellite rainfall products (SRPs) was compared using three bias correction methods (Delta, Empirical Quantile Mapping (EQM), and Quantile Mapping (QM)) on five metrological stations. Six statistical criteria were used to evaluate these methods on the period 2003–2016 at daily and monthly scales. The results showed that SRPs and bias correction methods of CMORPH_QM (r = 0.538) and TMPA_3B42V7_EQM (r = 0.95) data showed good performance, while PERSIANN_EQM (r = 0.348) and PERSIANN_Delta (r = 0.83) performed worst at daily and monthly time scales, respectively. This study assessed the importance of SRPs and bias correction methods to use in data scarce regions for water resources planning and other related sectors.

Synergy between satellite observations of soil moisture and water storage anomalies for runoff estimation

Abstract. This paper presents an innovative approach, STREAM – SaTellite-based Runoff Evaluation And Mapping – to derive daily river discharge and runoff estimates from satellite observations of soil moisture, precipitation, and total water storage anomalies (TWSAs). Within a very simple model structure, precipitation and soil moisture data are used to estimate the quick-flow river discharge component while TWSAs are used for obtaining its complementary part, i.e., the slow-flow river discharge component. The two are then added together to obtain river discharge estimates. The method is tested over the Mississippi River basin for the period 2003–2016 by using precipitation data from the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), soil moisture data from the European Space Agency's Climate Change Initiative (ESA CCI), and total water storage data from the Gravity Recovery and Climate Experiment (GRACE). Despite the model simplicity, relatively high-performance scores are obtained in river discharge estimates, with a Kling–Gupta efficiency (KGE) index greater than 0.64 both at the basin outlet and over several inner stations used for model calibration, highlighting the high information content of satellite observations on surface processes. Potentially useful for multiple operational and scientific applications, from flood warning systems to the understanding of water cycle, the added value of the STREAM approach is twofold: (1) a simple modeling framework, potentially suitable for global runoff monitoring, at daily timescale when forced with satellite observations only, and (2) increased knowledge of natural processes and human activities as well as their interactions on the land.

Assessing the Potential of IMERG and TMPA Satellite Precipitation Products for Flood Simulations and Frequency Analyses over a Typical Humid Basin in South China

The availability of the new generation Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06 products facilitates the utility of long-term higher spatial and temporal resolution precipitation data (0.1° × 0.1° and half-hourly) for monitoring and modeling extreme hydrological events in data-sparse watersheds. This study aims to evaluate the utility of IMERG Final run (IMERG-F), Late run (IMERG-L) and Early run (IMERG-E) products, in flood simulations and frequency analyses over the Mishui basin in Southern China during 2000–2017, in comparison with their predecessors, the Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA) products (3B42RT and 3B42V7). First, the accuracy of the five satellite precipitation products (SPPs) for daily precipitation and extreme precipitation events estimation was systematically compared by using high-density gauge station observations. Once completed, the modeling capability of the SPPs in daily streamflow simulations and flood event simulations, using a grid-based Xinanjiang model, was assessed. Finally, the flood frequency analysis utility of the SPPs was evaluated. The assessment of the daily precipitation accuracy shows that IMERG-F has the optimum statistical performance, with the highest CC (0.71) and the lowest RMSE (8.7 mm), respectively. In evaluating extreme precipitation events, among the IMERG series, IMERG-E exhibits the most noticeable variation while IMERG-L and IMERG-F display a relatively low variation. The 3B42RT exhibits a severe inaccuracy and the improvement of 3B42V7 over 3B42RT is comparatively limited. Concerning the daily streamflow simulations, IMERG-F demonstrates a superior performance while 3B42V7 tends to seriously underestimate the streamflow. With regards to the simulations of flood events, IMERG-F has performed optimally, with an average DC of 0.83. Among the near-real-time SPPs, IMERG-L outperforms IMERG-E and 3B42RT over most floods, attaining a mean DC of 0.81. Furthermore, IMERG-L performs the best in the flood frequency analyses, where bias is within 15% for return periods ranging from 2–100 years. This study is expected to contribute practical guidance to the new generation of SPPs for extreme precipitation monitoring and flood simulations as well as promoting the hydro-meteorological applications.

Exploring the potential of satellite precipitation after bias correction in streamflow simulation in a semi-arid watershed in northeastern China

Study RegionThe Upper Taoer River Watershed (UTRW), Northeastern China Study FocusThis study presents a comprehensive hydrometeorological evaluation of six satellite precipitation products (SPPs) over a sparsely gauged semi-arid watershed, including the adjusted and unadjusted versions of Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA), Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM products (IMERG), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERISIANN) series. The effectiveness of monthly bias correction in improving SPPs estimation accuracy and in enhancing their feasibility in hydrological applications was also analyzed. New hydrological insights for the regionThe SPPs, especially bias-corrected SPPs, adequately provide complementary precipitation information for UTRW where ground measurement is insufficient. The adjusted SPPs after bias correction obtained a higher Nash-Sutcliffe Efficiency (NSE) than gauge data when driving streamflow simulation. The unadjusted SPPs hydrological applicability was significantly enhanced after bias correction, with NSE< 0 (except for IMERG product) and NSE> 0.34 before and after correction, respectively. Performance of the TMPA series improved the most through bias correction, making it the preferred choice for daily and monthly simulations. Additionally, SPPs were more applicable in wet and normal years and require improvement in dry years. This study provides valuable references for identifying better alternative precipitation sources for local water resource management and exploring an effective SPP utilization approach in practical applications.

Performance evaluation of IMERG and TMPA daily precipitation products over CONUS (2000–2019)

Ground validation of satellite-based precipitation products is necessary to evaluate the performance of satellite observation at local, regional, and global scales. The evaluation of the rainfall data accuracy will reveal both its strengths and weaknesses, thereby determining its suitability for hydrometeorological applications. To reach this goal, this study comprehensively evaluated and characterized the systematic bias in the Global Precipitation Measurement (GPM) Integrated Multi-SatellitE Retrievals for GPM (IMERG) precipitation product, to be compared with its predecessor Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), based on the wide-spread ground-based observation data, PRISM (Parameter-Elevation Regression on Independent Slopes Model) over the Conterminous United States. The evaluation was conducted for daily datasets from June 2000 to the end of December 2019, in which IMERG and TMPA overlap using statistical error metrics both in space and time. While some statistical measures do not show significant improvement in daily IMERG compared to TMPA, systematic bias is substantially diminished. We detected systematic bias related to the vertical relief for both IMERG and TMPA but with a substantial decrease in the IMERG. The systematic bias in both satellite products distinctively differs in the summer and winter seasons, but, both products showed high performance in summer. According to our findings, the spatiotemporal performance of the IMERG precipitation product varies in different seasons, regions, time scales, and topographic conditions but with superiority over the TMPA daily product.

Climatology of cyclones in western and northwestern Iran using reanalysis and remote sensing data

AbstractAgriculture and hydrological management in western and northwestern Iran is highly sensitive to precipitation associated with Mediterranean and Sudanese cyclones particularly during spring and winter. Although many studies have focused on the theoretical basis of these cyclones, statistical analyses, especially with satellite observations, have not been undertaken. In this study, 40 cyclones during all months of 2015–2021 are characterized using data collected by Global Precipitation Measurement (GPM) dual‐frequency precipitation radar (DPR), Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and ERA5 reanalysis within each cyclone quadrant. The results show that about 45% of cyclones have a lifetime between 1 and 2 days, and approximately 25% occur during April with a secondary peak in March (22.5%). Accordingly, cyclonic rainy days and rainfall amounts peak in April with values of 76.1% and 64.1%, respectively. The lowest (most negative) low‐ and mid‐tropospheric vertical velocity in the southeast and northeast quadrants indicates upward motion, which leads to the most intense latent heat release within the cyclone. Accordingly, the highest rainfall rates occur in the southeast and northeast quadrants, located within and ahead of the highest specific humidity quadrant (southeast) where the warm conveyor belt plays a significant role for rainfall formation. Maximum stratiform and convective rainfall rates are observed in the southwest and southeast quadrants, respectively. The distribution of rainfall rate is consistent with that of the storm‐top height pattern in all quadrants. The maximum percentage of warm rain to the total rain (34.6% on average) for both stratiform and convective precipitation falls in the southeast quadrant. The structure of these analysed cyclones is largely similar to that shown previously in the literature. According to the analysed rainfall in different life cycles, it is expected that the cyclones have the greatest impact (i.e., extreme rainfall) during the developing stage in the northeast and southeast quadrants, and during the mature stage in the southwest quadrant. The lowest influence is during the dissipating stage. The results from this study can help decision makers within the hydrological and risk management industries.

Evaluation of IMERG, TMPA, ERA5, and CPC precipitation products over mainland China: Spatiotemporal patterns and extremes

A comprehensive assessment of representative satellite-retrieved (Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) and Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA)), reanalysis-based (fifth generation of atmospheric reanalysis by the European Centre for Medium Range Weather Forecasts (ERA5)), and gauge-estimated (Climate Prediction Center (CPC)) precipitation products was conducted using the data from 807 meteorological stations across mainland China from 2001 to 2017. Error statistical metrics, precipitation distribution functions, and extreme precipitation indices were used to evaluate the quality of the four precipitation products in terms of multi-timescale accuracy and extreme precipitation estimation. When the timescale increased from daily to seasonal scales, the accuracy of the four precipitation products first increased and then decreased, and all products performed best on the monthly timescale. Their accuracy ranking in descending order was CPC, IMERG, TMPA, and ERA5 on the daily timescale and IMERG, CPC, TMPA, and ERA5 on the monthly and seasonal timescales. IMERG was generally superior to its predecessor TMPA on the three timescales. ERA5 exhibited large statistical errors. CPC provided stable estimated values. For extreme precipitation estimation, the quality of IMERG was relatively consistent with that of TMPA in terms of precipitation distribution and extreme metrics, and IMERG exhibited a significant advantage in estimating moderate and heavy precipitation. In contrast, ERA5 and CPC exhibited poor performance with large systematic underestimation biases. The findings of this study provide insight into the performance of the latest IMERG product compared with the widely used TMPA, ERA5, and CPC datasets, and points to possible directions for improvement of multi-source precipitation data fusion algorithms in order to better serve hydrological applications.

Hydrological modeling using remote sensing precipitation data in a Brazilian savanna basin

Precipitation is the main input for hydrological models. However, due to limitations of rain gauge stations, satellite precipitation estimates have become a good alternative to precipitation information. In this context, this study aimed to validate the precipitation data with Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis (TMPA) and Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) data, in addition to assessing the uncertainty and performance of the Soil and Water Assessment Tool (SWAT) using observed precipitation (OP), TMPA, and IMERG data. Statistical coefficients were used to validate TMPA and IMERG precipitation data. P-factor and r-factor were considered for the uncertainty analysis, while the Nash-Sutcliffe efficiency (NSE), its logarithmic version (LNSE), and the percent bias (PBIAS) were analyzed to characterize the model performance analysis in monthly time steps. There was an overestimation by TMPA and IMERG in the precipitation estimation, especially in the dry period. OP, TMPA, and IMERG setups presented satisfactory results for uncertainty and performance analysis in hydrological modeling. The IMERG setup generally showed better results than the TMPA setup, being a good alternative for hydrological modeling, especially in regions with scarce precipitation datasets.

Evaluation of multisource precipitation input for hydrological modeling in an Alpine basin: a case study from the Yellow River Source Region (China)

Abstract Alpine basins are typically poorly gauged and inaccessible owing to the harsh prevailing environment and complex terrain. In this study, two representative satellite precipitation products (Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) 3B42RTV7 and Integrated Multi-Satellite Retrievals for GPM (IMERG) Final Run Version 06) and two reanalysis precipitation products (China Meteorological Assimilation Driving Datasets for the SWAT model (CMADS) and Climate Forecast System Reanalysis (CFSR)) in the Yellow River Source Region (YRSR) were selected for evaluation and hydrological verification against gauge-observed data (GO). Results show that the accuracy of these precipitation products in the warm season is higher than that in the cold season, and IMERG exhibits the best performance, followed by the CMADS, CFSR, and 3B42RTV7. Models that use the GO as input yielded satisfactory performance during 2008–2013, and precipitation products have poor simulation results. Although the model using the IMERG as input yielded unsatisfactory performance during 2014–2016, this did not affect the use of the IMERG as a potential data source for the YRSR. The model driven by the combination of GO and CMADS precipitation performed the best in all scenarios (R2=0.77, Nash–Sutcliffe efficiency (NSE)=0.72 at the Tangnaihai station; R2=0.53, NSE=0.48 at the Jimai station).

Comparison of Multi-Satellite Precipitation Data from the Global Precipitation Measurement Mission and Tropical Rainfall Measurement Mission Datasets: Seasonal and Diurnal Cycles

A comparison was made between seasonal and diurnal variations in the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) version 06B Final run and TRMM Multi-Satellite Precipitation Analysis (TMPA) 3B42 version 7 products from April 2014 to March 2019. As for earlier IMERG versions, systematic differences between IMERG version 06B precipitation and TMPA precipitation data were larger over the oceans than over land. Systematic annual mean differences between the IMERG and TMPA data over the oceans were smaller for IMERG version 06B than for earlier IMERG versions, possibly because of updated calibration processes. The mean differences between the IMERG version 06B and TMPA data for tropical oceans were relatively smtropical Pacific for all four seasons were not. The diurnal amplitudes of the IMERG were smaller than those of the TMPA over most continents, and the differences increased with mean diurnal amplitudes. The diurnal amplitudes of the IMERG were larger than those of the TMPA data over the oceans. The differences between the phases of the precipitation diurnal harmonics in the IMERG and TMPA datasets varied widely in all four seasons, but the mean phases were almost the same over both the oceans and the land. The sources of the differences in diurnal precipitation amplitudes in the Bay of Bengal and along the west coast of Central America, which showed large diurnal ranges and rather different diurnal amplitudes, were assessed. Differences in seasonal means caused differences in diurnal amplitudes in the Bay of Bengal, but for Central America, differences in diurnal amplitudes were associated with seasonal mean diurnal amplitudes.

From TRMM to GPM, how do improvements of post/near-real-time satellite precipitation estimates manifest?

With the end of the Tropical Rainfall Measuring Mission (TRMM) era, its successor of Global Precipitation Measurement (GPM) aims to provide a new generation of satellite precipitation estimates (SPE) with higher spatiotemporal resolution and wider spatial coverage. As the representative precipitation retrieval algorithms for GPM and TRMM eras, it is necessary to compare the Integrated Multi-satellite Retrievals for GPM (IMERG) with the TRMM Multi-satellite Precipitation Analysis (TMPA) to investigate the improvements of SPEs from TRMM to GPM. This study analyzes the post/near-real-time SPEs from TMPA and IMERG over Mainland China from April 2014 to March 2018 at daily scale, using precipitation observations from 510 meteorological stations as reference. A very comprehensive evaluation system, which contains four indicators of continuous metrics, categorical metrics, error components, and systematic and random errors is adopted in this study. Besides, four patterns with spatial distribution, temporal pattern, precipitation intensity distribution, and overall metrics are applied to illustrate each indicator's performance. The results show that IMERG products have lower RB and RMSE and higher CC than TMPA and greatly reduce the three error components (hit bias, missed precipitation, and false precipitation). Furthermore, IMERG products present a higher capability to capture the precipitation events correctly and generally have lower systematic and random errors than TMPA products. However, there are still rooms for IMERG products to improve, such as higher false alarm ratio (FAR) and more false precipitation of IMERG products at relatively low rain rates, the 25% higher systematic error of IMERG-E (IMERG “Early Run” near-real-time SPE) compared to TMPA-RT (TMPA near-real-time SPE). In addition, we find the gauge adjustment from GPCC is not a very effective way to improve the detecting capability and reduce the systematic errors for near-real-time SPEs over Mainland China. This study provides some valuable information on the transition from TMPA to IMERG, which may advance the algorithm's development and the hydrometeorological applications.

Improving Operational Short- to Medium-Range (SR2MR) Streamflow Forecasts in the Upper Zambezi Basin and Its Sub-Basins Using Variational Ensemble Forecasting

The combination of Hydrological Models and high-resolution Satellite Precipitation Products (SPPs) or regional Climatological Models (RCMs), has provided the means to establish baselines for the quantification, propagation, and reduction in hydrological uncertainty when generating streamflow forecasts. This study aimed to improve operational real-time streamflow forecasts for the Upper Zambezi River Basin (UZRB), in Africa, utilizing the novel Variational Ensemble Forecasting (VEF) approach. In this regard, we describe and discuss the main steps required to implement, calibrate, and validate an operational hydrologic forecasting system (HFS) using VEF and Hydrologic Processing Strategies (HPS). The operational HFS was constructed to monitor daily streamflow and forecast them up to eight days in the future. The forecasting process called short- to medium-range (SR2MR) streamflow forecasting was implemented using real-time rainfall data from three Satellite Precipitation Products or SPPs (The real-time TRMM Multisatellite Precipitation Analysis TMPA-RT, the NOAA CPC Morphing Technique CMORPH, and the Precipitation Estimation from Remotely Sensed data using Artificial Neural Networks, PERSIANN) and rainfall forecasts from the Global Forecasting System (GFS). The hydrologic preprocessing (HPR) strategy considered using all raw and bias corrected rainfall estimates to calibrate three distributed hydrological models (HYMOD_DS, HBV_DS, and VIC 4.2.b). The hydrologic processing (HP) strategy considered using all optimal parameter sets estimated during the calibration process to increase the number of ensembles available for operational forecasting. Finally, inference-based approaches were evaluated during the application of a hydrological postprocessing (HPP) strategy. The final evaluation and reduction in uncertainty from multiple sources, i.e., multiple precipitation products, hydrologic models, and optimal parameter sets, was significantly achieved through a fully operational implementation of VEF combined with several HPS. Finally, the main challenges and opportunities associated with operational SR2MR streamflow forecasting using VEF are evaluated and discussed.

Spatiotemporal Trends and Variations of the Rainfall Amount, Intensity, and Frequency in TRMM Multi-satellite Precipitation Analysis (TMPA) Data

The spatiotemporal mean rain rate (MR) can be characterized by the rain frequency (RF) and the conditional rain rate (CR). We computed these parameters for each season using the TMPA 3-hourly, 0.25° gridded data for the 1998–2017 period at a quasi-global scale, 50°N~50°S. For the global long-term average, MR, RF, and CR are 2.83 mm/d, 10.55%, and 25.05 mm/d, respectively. The seasonal time series of global mean RF and CR show significant decreasing and increasing trends, respectively, while MR depicts only a small but significant trend. The seasonal anomaly of RF decreased by 5.29% and CR increased 13.07 mm/d over the study period, while MR only slightly decreased by −0.029 mm/day. The spatiotemporal patterns in MR, RF, and CR suggest that although there is no prominent trend in the total precipitation amount, the frequency of rainfall events becomes smaller and the average intensity of a single event becomes stronger. Based on the co-variability of RF and CR, the paper optimally classifies the precipitation over land and ocean into four categories using K-means clustering. The terrestrial clusters are consistent with the dry and wet climatology, while categories over the ocean indicate high RF and medium CR in the Inter Tropical Convergence Zone (ITCZ) region; low RF with low CR in oceanic dry zones; and low RF and high CR in storm track areas. Empirical Orthogonal Function (EOF) analysis was then performed, and these results indicated that the major pattern of MR is characterized by an El Niño-Southern Oscillation (ENSO) signal while RF and CR variations are dominated by their trends.