Quantitative Precipitation Estimation Research Articles

A Quantitative Precipitation Estimation Method Based on 3D Radar Reflectivity Inputs

Quantitative precipitation estimation (QPE) by radar observation data is a crucial aspect of meteorological forecasting operations. Accurate QPE plays a significant role in mitigating the impact of severe convective weather. Traditional QPE methods mainly employ an exponential Z–R relationship to map the radar reflectivity to precipitation intensity on a point-to-point basis. However, this isolated point-to-point transformation lacks an effective representation of convective systems. Deep learning-based methods can learn the evolution patterns of convective systems from rich historical data. However, current models often rely on 2 km-height CAPPI images, which struggle to capture the complex vertical motions within convective systems. To address this, we propose a novel QPE model: combining the classic extrapolation model ConvLSTM with Unet for an encoder-decoder module assembly. Meanwhile, we utilize three-dimensional radar echo images as inputs and introduce the convolutional block attention module (CBAM) to guide the model to focus on individual cells most likely to trigger intense precipitation, which is symmetrically built on both channel and spatial attention modules. We also employ asymmetry in training using weighted mean squared error to make the model concentrate more on heavy precipitation events which are prone to severe disasters. We conduct experiments using radar data from North China and Eastern China. For precipitation above 1 mm, the proposed model achieves 0.6769 and 0.7910 for CSI and HSS, respectively. The results indicate that compared to other methods, our model significantly enhances precipitation prediction accuracy, with a more pronounced improvement in forecasting accuracy for heavy precipitation events.

Vertical structures and microphysical characteristics of summer precipitation in North China detected by GPM-DPR

The vertical structure and microphysical characteristics of stratiform precipitation (SP) and convective precipitation (CP) in North China are revealed based on the GPM-DPR product during boreal summer of 2014–2021 in this study. Additionally, the differences in precipitation features between the mountain and the plain are investigated. Under the combined influence of climatic factors and local topography, the precipitation amount is larger in the plain than in the mountain while precipitation frequency exhibits an opposite pattern. The proportions of the two precipitation types are similar in the mountain and the plain, with CP contributing to approximately a quarter of total precipitation frequency. In terms of mean intensity, both SP and CP are roughly 20 % more intense in the plain than in the mountain. The greater number of light SPs is a major contributor to higher precipitation frequency in the mountain, while more intense CPs result in larger precipitation amount in the plain. Compared to the mountain, the precipitation system is deeper in the plain, where higher storm top altitudes (STAs) and larger freezing level heights contribute to more intense CPs. Besides, it is observed that for the STA, more intense CPs occur in the plain compared to the mountain. In both the mountain and the plain, the coalescence process is dominant in the low-level layers for heavy (8–20 mm/h) to storm-level (>20 mm/h) CPs. Compared to the mountain, the low-level growth of hydrometeor size and radar reflectivity is more significant in the plain. These findings are important to quantitative precipitation estimation and precipitation prediction in the mountainous region, and can help understand the influence of local topography on precipitation.

Application of Radar-Based Precipitation Data Improves the Effectiveness of Urban Inundation Forecasting

Using radar to estimate and forecast precipitation as input for hydrological models has become increasingly popular in recent years because of its superior spatial and temporal simulation compared with using rain gauge data. This study used radar-based quantitative precipitation estimation (QPE) to select the optimal parameter set for the MIKE URBAN hydrological model and radar-based quantitative precipitation forecasting (QPF) to simulate inundation in Nam Dinh city, Vietnam. The results show the following: (1) radar has the potential to improve the modeling and provide the data needed for real-time smart control if proper bias adjustment is obtained and the risk of underestimated flows after heavy rain is minimized, and (2) the MIKE URBAN model used to calculate two simulation scenarios with rain gauge data and QPE data showed effectiveness in combining the application of radar-based precipitation for the forecasting and warning of urban floods in Nam Dinh city. The results in Scenario 2 with rainfall forecast data from radar provide better simulation results. The average relative error in Scenario 2 is 9%, while the average relative error in Scenario 1 is 15%. Using the grid radar-based precipitation forecasting as input data for the MIKE URBAN model significantly reduces the error between the observed water depth and the simulated results compared with the case using an input rain gauge measured at Nam Dinh station (the difference in inundation level of Scenario 2 using radar-based precipitation is 0.005 m, and it is 0.03 m in Scenario 1). The results obtained using the QPE and QPF radar as input for the MIKE URBAN model will be the basis for establishing an operational forecasting system for the Northern Delta and Midland Regional Hydro-Meteorological Center, Viet Nam Meteorological and Hydrological Administration.

Analysis and Prediction of Summer Rainfall over Southwestern Utah

Abstract While many studies have examined intense rainfall and flash flooding during the North American Monsoon (NAM) in Arizona, Nevada, and New Mexico, less attention has focused on the NAMS’s extension into southwestern Utah. This study relates flash flood reports and Multi-Radar Multi-Sensor (MRMS) precipitation across southwestern Utah to atmospheric moisture content and instability analyses and forecasts from the High-Resolution Rapid Refresh (HRRR) model during the 2021–23 monsoon seasons. MRMS quantitative precipitation estimates over southwestern Utah during summer depend largely on the areal coverage from the KICX WSR-88D radar near Cedar City, UT. Those estimates are generally consistent with the limited number of precipitation gauge reports in the region except at extended distances from the radar. A strong relationship is evident between days with widespread precipitation and afternoons with above average precipitable water (PWAT) and convective available potential energy (CAPE) estimated from HRRR analyses across the region. Time-lagged ensembles of HRRR forecasts (initialization times from 03–06 UTC) that are 13–18 h prior to the afternoon period when convection is initiating (18–21 UTC) are useful for situational awareness of widespread precipitation events after adjusting for underprediction of afternoon CAPE. Improved skill is possible using random forest classification relying only on PWAT and CAPE to predict days experiencing excessive (upper quartile) precipitation. Such HRRR predictions may be useful for forecasters at the Salt Lake City National Weather Service Forecast Office to assist issuing flash flood potential statements for visitors to national parks and other recreational areas in the region.

Implementation of a UAV-aided calibration method for a mobile dual-polarization weather radar

Weather radar calibration is a crucial factor to be considered for quantitative applications, such as QPE (Quantitative Precipitation Estimation), which is used as input for weather risks management. The present work proposes a novel approach to the end-to-end radar calibration method through the characterization of the radar weighting functions. These are Gaussian functions that model an additional attenuation factor to the radar received power. This approach, based on the inclusion these parameters, allow the obtainment of a calibrated equivalent reflectivity factor expression for a Doppler dual-polarization weather radar that operates in the X band. To calculate these parameters, a UAS (Unmanned Aircraft System) was implemented for suspending the calibration target with a well-defined cross-section and for measuring its inclination due to wind using an IMU (Inertial Measurement Unit). From its measurements, the position of the target can be estimated, which is essential to the characterization of the weighting functions. Their inclusion within the radar equation, alongside the implementation of the angular measurement system highlight the innovation to the traditional radar calibration methodology that does not contemplate them from the explored state-of-the-art. The reflectivity was compared with the measurements from a disdrometer for a moderate rain event. An average reflectivity difference of 0.75 dBZ and a percent bias of 3.3 % were obtained between the expected and estimated measurements when including these functions compared to the 1.51 dBZ and –62.7 % obtained when disregarding them. These experimental results point out that the proposed method can deliver superior accuracy in the reflectivity estimation.

Quantitative estimation of global mean precipitation throughout the Phanerozoic era

Quantitative estimation of global mean precipitation throughout the Phanerozoic era

The Extreme Rainfall Events of the 2020 Typhoon Season in Vietnam as Seen by Seven Different Precipitation Products

A series of typhoons and tropical storms have produced extreme precipitation events in Vietnam during the first part of the 2020 monsoon season: events of this magnitude pose significant challenges to remote sensing Quantitative Precipitation Estimation (QPE) techniques. The weather-monitoring needs of modern human activities require that these challenges be overcome. In order to address this issue, in this work, seven precipitation products were validated with high spatial and temporal detail against over 1200 rain gauges in Vietnam during six case studies tailored around the most intense events of 2020. The data sources included the Vietnamese weather radar network, IMERG Early run and Final run, the South Korean GEO-KOMPSAT-2A and Chinese FengYun-4A geostationary satellites, DPR on board the GPM-Core Observatory, and European ERA5-Land reanalysis. All products were resampled to a standardized 0.02° grid and compared at hourly scale with ground stations measurements. The results indicated that the radars product was the most capable of reproducing the information collected by the rain gauges during the selected extreme events, with a correlation coefficient of 0.70 and a coefficient of variation of 1.38. However, it exhibited some underestimation, approximately 30%, in both occurrence and intensity. Conversely, geostationary products tended to overestimate moderate rain rates (FY-4A) and areas with low precipitation (GK-2A). More complex products such as ERA5-Land and IMERG failed to capture the highest intensities typical of extreme events, while GPM-DPR showed promising results in detecting the highest rain rates, but its capability to observe isolated events was limited by its intermittent coverage.

Disentangling Satellite Precipitation Estimate Errors of Heavy Rainfall at the Daily and Sub-Daily Scales in the Western Mediterranean

In the last decade, substantial improvements have been achieved in quantitative satellite precipitation estimates, which are essential for a wide range of applications. In this study, we evaluated the performance of Integrated Multi-satellitE Retrievals for GPM (IMERG V06B) at the sub-daily and daily scales. Ten years of half-hourly precipitation records aggregated at different sub-daily periods were evaluated over a region in the Western Mediterranean. The analysis at the half-hourly scale examined the contribution of passive microwave (PMW) and infrared (IR) sources in IMERG estimates, as well as the relationship between various microphysical cloud properties using Cloud Microphysics (CMIC–NWC SAF) data. The results show the following: (1) a marked tendency to underestimate precipitation compared to rain gauges which increases with rainfall intensity and temporal resolution, (2) a weaker negative bias for retrievals with PMW data, (3) an increased bias when filling PMW gaps by including IR information, and (4) an improved performance in the presence of precipitating ice clouds compared to warm and mixed-phase clouds. This work contributes to the understanding of the factors affecting satellite estimates of extreme precipitation. Their relationship with the microphysical characteristics of clouds generates added value for further downstream applications and users’ decision making.

Statistical characteristics of convective and stratiform precipitation during the rainy season over South China based on GPM-DPR observations

South China is one of the wettest regions in the world, and is prone to precipitation-related disasters. The GPM-DPR product is utilized in this study to investigate the vertical structure and drop size distribution (DSD) characteristics of different precipitation types, i.e., stratiform precipitation (SP) and convective precipitation (CP) in South China. The precipitation frequency and accumulation in South China are dominated by SP and deep CP, respectively. Shallow CP has comparable occurrence ratio to deep CP, but the mean intensity is much lower. As such, it contributes <5% to the total rainfall. Vertical features differ among precipitation types and intensities. Higher storm top altitude (STA) is roughly related to more intense precipitation, especially for CPs. As surface PR increases in deep CP, the contribution of increase of PR (ΔPR) in liquid-phase region to total PR also increases, from approximately 10% for 4 mm/h to 55% for 100 mm/h. The intensity of shallow CP is generally low, but ΔPR in the liquid-phase region is significant. For SP and shallow CP, higher precipitation intensity is often caused by higher hydrometer concentration. Differently, for deep CP with PR >15 mm/h, both the increases in hydrometer size and concentration contribute to the increase in PR. The collision-coalescence process is dominant in the low-level layers in SP without bright band and CP, and approximately four fifth of shallow CP samples are influenced by collision-coalescence process. Compared with inland mountainous region, collision-coalescence process plays more important role in intense precipitation in coastal region. The results of this study indicate that it is important to consider the vertical evolution and DSD characteristics of different precipitation types for improving the accuracy of near surface quantitative precipitation estimate.

On collisional drop breakup in orographic rain

Collisional drop breakup is one of the important processes during evolution of the rain drops and leads to modified shape of the drop size distribution (DSD), which is a primary parameter in rain retrieval in remote sensing techniques. Though several experimental and observational studies have been performed in the recent past on this aspect, the impact of the same in natural rain, particularly in orographic rain, is still poorly represented. The present study aims to examine and identify the rain conditions in which collisional drop breakup is prominent and is capable of modifying the shape of DSD in orographic rain over north-eastern India. The 2-min averaged DSDs from four hilly locations are studied in both stratiform and convective rain types separately using ground-based DSD measurements. Even though drop breakup in intense to moderate rain seemed to be in notable percentage (0.2% - 11.2%), the low intensity rain also showed a considerable break-up signature (2.5% - 9%), depending upon the locations. The overall results showed that collisional breakup was more prominent in convective rain (1%–11%) than in stratiform rain (4%–5%), however, breakup is also seen primarily during stratiform rain in two of the locations. The Z-R coefficients are found to be significantly different for the break-up and non-break up cases. For breakup cases, both convective and stratiform rainfall shows an increase in intercept parameter for all locations. For stratiform rain types, the coefficient a and b ranged from 307.6 to 321.4 (167.1–178.7) and 1.27–1.37 (1.31–1.34) for rain with break-up (without break-up), respectively. In the case of convective rain, the coefficients were found to be in the range of 377.5–654.7 (270.4–641.3) and 1.28–1.42 (1.14–1.31) with break-up (without break-up), respectively. The results suggest that collisional breakup is an important process in orographic rain over North-Eastern India and it has to be taken care of, while studying rainfall over these regions, particularly in conventional radar derived quantitative precipitation estimates (QPE).

Quantitative Precipitation Estimation using X-Band Radar for Orographic Rainfall in the San Francisco Bay Area

In the San Francisco Bay Area, precipitation occurs in the wintertime, mostly as rain. Wintertime rainfall can be further classified into cold or stratiform rain with a typical radar bright band signature and warm orographic rain with absence of a radar bright band. Vertical Pointing S-Band profiler radar and disdrometer measurements from two of NOAA’s Hydrometeorology Testbed (HMT) sites in California are used to study the differences in microphysical properties between these two types of rain and their implications in radar rainfall estimation. A methodology has been developed to discriminate non bright band (NBB) rainfall from bright band (BB) rainfall using reflectivity (Z) and differential reflectivity (Z <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DR</sub> ) computed from disdrometer data. Delineating the two rainfall types in this way allowed for an algorithm to be applied to the radar scans to identify rainfall types and apply appropriate reflectivity based and specific differential phase (K <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DP</sub> ) based rainfall estimators. Recently, a gap-filling X-Band weather radar with dual-polarization capabilities was deployed in the San Francisco Bay Area in Santa Rosa to aid in weather monitoring and provide high resolution Quantitative Precipitation Estimation (QPE) products. When applied to real radar observations, this method shows great potential for improving the QPE compared to traditional operational products which more often tend to underestimate rainfall in the California coastal region.

Understanding the Influence of Urban Form on the Spatial Pattern of Precipitation

AbstractUrban areas are known to modify the spatial pattern of precipitation climatology. Existing observational evidence suggests that precipitation can be enhanced downwind of a city. Among the proposed mechanisms, the thermodynamic and aerodynamic processes in the urban lower atmosphere interact with the meteorological conditions and can play a key role in determining the resulting precipitation patterns. In addition, these processes are influenced by urban form, such as the impervious surface extent. This study aims to unravel how different urban forms impact the spatial patterns of precipitation climatology under different meteorological conditions. We use the Multi‐Radar Multi‐Sensor quantitative precipitation estimation data products and analyze the hourly precipitation maps for 27 selected cities across the continental United States from the years 2015–2021 summer months. Results show that about 80% of the studied cities exhibit a statistically significant downwind enhancement of precipitation. Additionally, we find that the precipitation pattern tends to be more spatially clustered in intensity under higher wind speed; the location of radial precipitation maxima is located closer to the city center under low background winds but shifts downwind under high wind conditions. The magnitude of downwind precipitation enhancement is highly dependent on wind directions and is positively correlated with the city size for the south, southwest, and west directions. This study presents observational evidence through a cross‐city analysis that the urban precipitation pattern can be influenced by the urban modification of atmospheric processes, providing insight into the mechanistic link between future urban land‐use change and hydroclimates.

Multi-Scale and Multi-Level Feature Fusion Network for Quantitative Precipitation Estimation with Passive Microwave

Multi-Scale and Multi-Level Feature Fusion Network for Quantitative Precipitation Estimation with Passive Microwave

Error Structure Analysis for Multiple Dual-polarization Radar using Ensemble Learning

Accurate quantitative precipitation estimation (QPE) was essential for the prediction and prevention of natural disasters. Recently, radar has been attracting attention as a technique for performing QPE with high spatiotemporal resolution. In particular, the QPE was improved by introducing a dual-polarization technique that observed several hydrometeorological variables at various scales compared to the single polarization radar that utilized only the existing reflectivity-precipitation relationship. This study aimed to analyze the error structure of dual-polarization radar by predicting gauged rainfall using ensemble models. The location of the radar was Gwanaksan, Garisan, and Gwangdeoksan which belonged to the Bukhan river basin. The Pearson correlation coefficients between reflectance-precipitation and gauged rainfall were examined initially. After that, each gauged rainfall was predicted using ensemble learning, which included random forest (RF), gradient boost, and XGboost. Mean absolute error, root mean squared error, and R squared were evaluated as the predictive performance. Hyper-parameters were optimized by 5-fold cross-validation, and the reliability of the research results was obtained by 10 iterations. The result showed that RF had better predictive performance than other models. Gauged stations that operated at high altitudes were not good enough for the QPE because of the mountain effect. Due to the mountain range effect, the importance of the Gwanaksan radar at an altitude of 3 km was very high.

Detection and classification of rainfall in South America using satellite images and machine learning techniques

The study of precipitation is one of the most intriguing areas in atmospheric sciences, with significant implications for our daily lives and climate change projections. This paper explores the estimation of rainfall trends in South American regions using convolutional neural networks (CNNs). The study focuses on the application of Cloud-Net, a CNN-based model with a format similar to an autoencoder, to obtain qualitative estimates of precipitation patterns. The employed loss functions, Categorical Cross Entropy and Categorical Focal Loss, address the challenges of classifying minority categories in unbalanced data. Regional analysis was conducted, identifying days with high rainfall intensity and the predominant intensities in 25 regions. The CNN model’s performance was compared with the XGBoost algorithm, showing excellent results for extreme rainfall categories and challenging intermediate categories. Furthermore, a comparison was made with Quantitative Precipitation Estimation (QPE) data and ground measurements from rain gauges. While the CNN model provided a valuable qualitative estimate of precipitation trends, achieving precise quantitative estimation would require an extensive data set of in-situ measurements. Overall, this research demonstrates the potential of CNNs for estimating rainfall trends and understanding precipitation patterns in South American regions. The findings offer valuable insights for further applications in meteorology and environmental studies.

Impacts of precipitation uncertainty on hydrological ensemble simulations over the Ganjiang River basin

Study regionThe Ganjiang River basin (113°−117°E, 24°−30°N; 83,374 km2) is a large watershed with complex topography in the Poyang Lake basin in Jiangxi province, China. Study focusThis study evaluates three quantitative precipitation estimates (QPEs) over the Ganjiang River basin, namely the China Gauged-Based Daily Precipitation Analysis (CGDPA) data, the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) data, and the Asian Precipitation - Highly-Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE). The study also investigates the impacts of precipitation uncertainty on hydrological ensemble streamflow simulations using the three QPEs as precipitation inputs of the Variable Infiltration Capacity (VIC) hydrological model. New hydrological insights for the regionAPHRODITE underestimates precipitation compared to CGDPA, while PERSIANN-CDR shows greater spatiotemporal variability. The ensemble mean streamflow demonstrates greater improvement compared to the results obtained from a single parameter set. Among the three QPEs, the simulations forced by CGDPA show the best deterministic and probabilistic verification scores, followed by APHRODITE. PERSIANN-CDR tends to underestimate evaporation and leads to the lowest score of ensemble streamflow simulations, but shows advantages in simulating extremely low streamflow. The study highlights that high-density gauge-based QPEs remain the most accurate source of precipitation inputs for reliable hydrological simulations, while satellite-gauge merged QPEs can provide valuable inputs for hydrological simulations over the basins where meteorological stations are scarce.

An attention-mechanism-based deep fusion model for improving quantitative precipitation estimation in a sparsely-gauged basin

Improving quantitative precipitation estimation (QPE) in sparsely-gauged basins via merging remote sensing precipitation data and rain gauge data still remains a challenge since most existing merging models degrade when the rain gauge data become limited. To address the challenge, we propose an attention-mechanism-based deep learning model, Multi-Level Transformer Fusion (MLTF) model, which allows to capture the inner interactions among the multi-source input data (TRMM 3B42 V7 data, GridSat-B1 data, and DEM) in the sparsely-gauged basin. Taking the source region of the Yellow River basin (SRYRB) as a representative case study, we demonstrate the performance of the proposed model and compare it with conventional methods (e.g., Multiplicative Bias Correction, Additive Bias Removal, linear regression, and Kriging) and deep-learning-based models (CNN, CNN-LSTM). Results indicate that the merged precipitation in SRYRB produced by the MLTF model exhibits a RMSE reduction of 27.1 %, MAE decrease of 11.2 %, and CC increase of 19.2 % in comparison to the original TRMM data, outperforming all the selected comparative methods. Finally, an improved daily precipitation dataset during 1999–2019 with a spatial resolution of 0.05° is produced for the study area. This study proposes a new method for QPE improvement in a sparsely-gauged basin, which would provide valuable data support for regional hydrological study and water resources management.

Correcting for Mobile X-Band Weather Radar Tilt Using Solar Interference

Precise knowledge of the antenna pointing direction is a key facet to ensure the accuracy of observations from scanning weather radars. The sun is an often-used reference point to aid accurate alignment of weather radar systems and is particularly useful when observed as interference during normal scanning operations. In this study, we combine two online solar interference approaches to determine the pointing accuracy of an X-band mobile weather radar system deployed for 26 months in northern England (54.517°N, 3.615°W). During the deployment, several shifts in the tilt of the radar system are diagnosed between site visits. One extended period of time (&gt;11 months) is shown to have a changing tilt that is independent of human intervention. To verify the corrections derived from this combined approach, quantitative precipitation estimates (QPEs) from the radar system are compared to surface observations: an approach that takes advantage of the variations in the magnitude of partial beam blockage corrections required due to tilting of the radar system close to mountainous terrain. The observed improvements in QPE performance after correction support the use of the derived tilt corrections for further applications using the corrected dataset. Finally, recommendations for future deployments are made, with particular focus on higher latitudes where solar interference spikes show more seasonality than those at mid-latitudes.

Development of quantitative precipitation estimation (QPE) relations for dual-polarization radars based on raindrop size distribution measurements in Metro Manila, Philippines

Quantitative precipitation estimates (QPE) can be further improved using estimation algorithms derived from localized raindrop size distribution (DSD) observations. In this study, DSD measurements from two disdrometer stations within Metro Manila during the Southwest monsoon (SWM) period were used to investigate the microphysical properties of rainfall and develop localized dual-polarimetric relations for different radar bands and rainfall types. Observations show that the DSD in Metro Manila is more distributed to larger diameters compared to Southern Luzon and neighboring countries and regions in the Western Pacific. This is reflected by the relatively higher mass-weighted mean diameter (Dm) and smaller shape (μ) and slope (Λ) parameters measured in the region. The average values of Dm and normalized intercept parameter (Nw) in convective rain samples also suggest that convective rains in Metro Manila are highly influenced by both continental and oceanic convective processes. Dual-polarimetric variables simulated using the T-matrix scattering method showed good agreement with disdrometer-derived reflectivity (ZH) values. The 0.5 dB and 0.3° km−1 thresholds for the differential reflectivity (ZDR) and specific differential phase (KDP) based on the blended algorithm of Cifelli (J Atmos Ocean Technol 28:352-364, 2011) and Thompson et al. (2017) are proven to be useful since the utility of the dual-polarimetric variables as rainfall estimators are shown to have dependencies on the radar band and rainfall type. Evaluation of the QPE products with respect to the C-band shows that R (KDP, ZDR) has the best performance among the dual-pol relations and statistically outperformed the conventional Marshall & Palmer relation [R(ZMP)]. The results show that dual-polarimetric variables such as ZDR and KDP can better represent the DSD properties compared to one-dimensional Z, hence providing more accurate QPE products than the conventional R(Z) relations.

Paired Satellite and NWP Precipitation for Global Flood Forecasting

Abstract Precipitation data are known to be the key driver of hydrological simulations. Hence, reliable quantitative precipitation estimates and forecasts are vital for accurate hydrological forecasting. Satellite-based precipitation estimates from Integrated Multi-satellitE Retrievals for GPM Early Run (IMERG-E) and forecasted precipitation from NASA’s Goddard Earth Observing System Forward Processing (GEOS-FP) have shown values in global flood nowcasting and forecasting. However, few studies have comprehensively evaluated their hydrological performance let alone explored the potential value of combining them. Therefore, this study undertakes a quasi-global evaluation of their utility in real-time hydrological monitoring and 1–5-day forecasting with the Dominant River Tracing-Routing Integrated with Variable Infiltration Capacity (VIC) Environment (DRIVE) model. The gauge-corrected IMERG Final Run precipitation estimates and corresponding hydrological simulation are used as the references. Results showed that the hit bias is the dominant error source of IMERG-E, while the false precipitation is more noticeable in GEOS-FP. In terms of hydrological performance, the GEOS-FP-driven model (DRIVE-FP) performance is close to the IMERG-E-driven model (DRIVE-E) performance on day 1, indicating that GEOS-FP could nicely fill the gap of nowcasting caused by the IMERG-E time latency. For longer lead-time forecasts, the bias tends to diminish in most regions, likely because the under- or overestimation in IMERG-E is generally offset by the distinct types of misestimation in GEOS-FP. The skillful initial hydrological conditions present outperformed forecasts in most regions, except for tropical areas where the accuracy of GEOS-FP prevails. Overall, this study provides a valuable view of the combined use of IMERG-E and GEOS-FP precipitation in the context of hydrological nowcasts and forecasts.