Rain Gauge Observations Research Articles

Satellite rainfall products outperform ground observations for landslide prediction in India

Abstract. Landslides are among the most dangerous natural hazards, particularly in developing countries, where ground observations for operative early warning systems are lacking. In these areas, remote sensing can represent an important detection and monitoring process to predict landslide occurrence in space and time, particularly satellite rainfall products that have improved in terms of accuracy and resolution in recent times. Surprisingly, only a few studies have investigated the capability and effectiveness of these products in landslide prediction in reducing the impact of this hazard on the population. We have performed a comparative study of ground- and satellite-based rainfall products for landslide prediction in India by using empirical rainfall thresholds derived from the analysis of historical landslide events. Specifically, we have tested Global Precipitation Measurement (GPM) and SM2RAIN-ASCAT satellite rainfall products, and their merging, at daily and hourly temporal resolution, and Indian Meteorological Department (IMD) daily rain gauge observations. A catalogue of 197 rainfall-induced landslides that occurred throughout India in the 13-year period between April 2007 and October 2019 has been used. Results indicate that satellite rainfall products outperform ground observations thanks to their better spatial (0.1∘ vs. 0.25∘) and temporal (hourly vs. daily) resolutions. The better performance is obtained through the merged GPM and SM2RAIN-ASCAT products, even though improvements in reproducing the daily rainfall (e.g. overestimation of the number of rainy days) are likely needed. These findings open a new avenue for using such satellite products in landslide early warning systems, particularly in poorly gauged areas.

Effect of temporal sampling mismatches between satellite rainfall estimates and rain gauge observations on modelling extreme rainfall in the Upper Awash Basin, Ethiopia

Matching the temporal sampling of Satellite Rainfall Estimates (SREs) with rain gauge data is a prerequisite for evaluating the accuracy of SREs. However, some of the previous studies have evaluated the performance of SREs without considering the timing of the daily rain gauge observations. Such uncertainties may affect the application of SREs for studying the statistics of rainfall extremes. Therefore, this study aims to assess the effect of temporal sampling mismatch on the frequency analysis of daily rainfall extremes over a mountainous region in Ethiopia, the Upper Awash basin. For this purpose, we consider the 0-hr, 1-hr, 3-hr, 5-hr, and 6-hr temporal mismatches between SREs and gauges, which emerge from the use of different definitions of daily precipitation in these data sources. To represent those mismatches, the sub-daily TRMM 3B42v7 and sub-hourly IMERG v06B SREs are aggregated for different time windows, and their impacts on the estimation of the Metastastical Extreme Value Distribution (MEVD) parameters, and high-quantile extremes are then quantified using the relative error metrics. Results show that the 1-hr and 3-hr temporal discrepancies between SREs and gauges produce a comparable result in reproducing the observed MEVD parameters and high quantiles of daily rainfall. However, we found different results when the temporal mismatch between SREs and gauges increases from 3-hr to 6-hr. For all temporal mismatches, IMERG v06B outperforms TRMM 3B42v7 for estimating the MEVD parameters and high quantiles of daily rainfall. Both SREs provide the best estimation of high-quantile extremes for shorter return periods than the longer ones. Additionally, the uncertainty of the MEVD parameters is substantial in the lowland (Elevation < 2000 m) than in the highland (Elevation > 2000 m) regions of the basin. Overall, the temporal discrepancy between SREs and gauges leads to discrepancies in rainfall statistics. Therefore, more caution is necessary when applying the daily rainfall estimates of the remotely sensed datasets that have different temporal sampling compared to the daily rain gauge observations.

Performance of Multi-Radar Multi-Sensor (MRMS) product in monitoring precipitation under extreme events in Harris County, Texas

With an increase in intensity and frequency of extreme precipitations as a result of climate change, it is necessary to develop effective strategies for emergency flood management plan. It is critical to integrate the use of high-resolution hydro-meteorological data (e.g., precipitation) as input for hydrologic modeling to accurately predict and reduce the negative impact of floods. The Multi-Radar Multi-Sensor (MRMS) system has been developed by the National Severe Storms Laboratory (NSSL)to produce high-resolution spatio-temporal precipitation data. While the MRMS data are available at relatively high spatial (1 km) and temporal (2 min) resolutions across the continental United States (CONUS), MRMS’s accuracy in measuring actual precipitation needs to be investigated across some urban areas such as Harris County, TX. Therefore, the objectives of this study are to evaluate i) the performance of the MRMS system compared to other precipitation products (rain gauge network, Multisensor Precipitation Estimator (MPE)) at different spatial (5, 10, 15, 30 km) and temporal aggregations (5, 10,15, 30, 60 min) during four major flooding events of May 2015 (Memorial Day flood), April 2016 (Tax Day Flood), August 2017 (Hurricane Harvey), and September 2019 (Tropical Storm Imelda) in Harris County, Texas; and ii) the effects of temporal and spatial aggregation scales on the performance of the MRMS system using a suite of statistical parameters. Point-to-grid comparisons were conducted between 142 rain gauges and MRMS system data during four extreme flood events. Overall, the MRMS system captured precipitation reasonably well with a coefficient of determination (R2) of 0.78, correlation coefficient (CC) of 0.88, root mean square error (RMSE) of 1.21 mm, critical success index (CSI) of 0.65, probability of detection (POD) of 0.98, and false alarm ratio (FAR) of 0.34 over Harris County at 15 min and 15 km temporal and spatial resolutions. The results indicate that MRMS product tends to underestimate higher precipitation rates and overestimate light precipitation. Coarser temporal resolutions from 5 min to 1 h resolved some of the overestimation issues. Temporal aggregation increased R2, CC, CSI, and error variances and decreased FAR. However, increasing spatial resolution from 1 to 30 km increased R2, CC, and CSI and reduced RMSE and FAR. A comparison of MPE QPE and MRMS products at hourly temporal resolution with gauge observations showed that both products estimate rainfall accurately for the four events. Still, on average, MRMS product has a slightly better agreement with rain gauge observations at 1-hr temporal resolution.

Benefits from high-density rain gauge observations for hydrological response analysis in a small alpine catchment

Abstract. Spatial rainfall patterns exert a key control on the catchment-scale hydrologic response. Despite recent advances in radar-based rainfall sensing, rainfall observation remains a challenge, particularly in mountain environments. This paper analyzes the importance of high-density rainfall observations for a 13.4 km2 catchment located in the Swiss Alps, where rainfall events were monitored during 3 summer months using a network of 12 low-cost, drop-counting rain gauges. We developed a data-based analysis framework to assess the importance of high-density rainfall observations to help predict the hydrological response. The framework involves the definition of spatial rainfall distribution metrics based on hydrological and geomorphological considerations and a regression analysis of how these metrics explain the hydrologic response in terms of runoff coefficient and lag time. The gained insights on dominant predictors are then used to investigate the optimal rain gauge network density for predicting the streamflow response metrics, including an extensive test of the effect of down-sampled rain gauge networks and an event-based rainfall–runoff model to evaluate the resulting optimal rain gauge network configuration. The analysis unravels that, besides rainfall amount and intensity, the rainfall distance from the outlet along the stream network is a key spatial rainfall metric. This result calls for more detailed observations of stream network expansions and the parameterization of along-stream processes in rainfall–runoff models. In addition, despite the small spatial scale of this case study, the results show that an accurate representation of the rainfall field (with at least three rain gauges) is of prime importance for capturing the key characteristics of the hydrologic response in terms of generated runoff volumes and delay for the studied catchment (0.22 rain gauges per square kilometer). The potential of the developed rainfall monitoring and analysis framework for rainfall–runoff analysis in small catchments remains to be fully unraveled in future studies, potentially also including urban catchments.

Performance evaluation of IMERG precipitation products during typhoon Lekima (2019)

It is of great significance to evaluate satellite precipitation products performance for short-duration heavy precipitation in typhoon events. In this study, the performance of GPM (Global Precipitation Measurement) IMERG (Integrated Multi-satellitE Retrievals for GPM) with different run products (early run (IMERG-ER), late run (IMERG-LR), and final run (IMERG-FR)) were evaluated when typhoon Lekima landed in the eastern coastal area of China in 2019. Taking the rain gauge data as the reference, we carried out the study by comparing precipitation characteristics, verifying evaluation indices, and analyzing the heavy precipitation process. The differences between IMERG products in different stages and affected areas of the typhoon Lekima were compared. On the whole, the correlation coefficient (CC) of IMERG products was 0.51–0.56, and the IMERG-FR had the best probability of detection (POD, 0.88), followed by IMERG-LR (POD, 0.84) and IMERG-ER (POD, 0.81). With an increase in release latency, the precipitation retrieved by IMERG products increased and gradually approached the rain gauge observations. The evaluation indices result showed that IMERG products tend to underestimate precipitation and failed to show good performance for high-intensity precipitation. The error was positively related to the precipitation intensity. The performance of IMERG-LR was very close to that of IMERG-FR during the typhoon short-duration heavy precipitation, although IMERG-FR had higher latency and more processing.

Spatial and temporal variabilities of rainstorms over China under climate change

Rainstorms are one of the extreme rainfall events that cause serious disasters, such as urban flooding and mountain torrents. Traditional studies have used rain gauge observations to analyze rainstorm events, but relevant information is usually missing in gauge-sparse areas. Satellite-derived precipitation datasets serve as excellent supplements or substitutes for the gauge observations. By developing a grid-based rainstorm-identification tool, we used the Tropical Rainfall Measurement Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) time series product to reveal the spatial and temporal variabilities of rainstorms over China during 1998–2017. Significant patterns of both increasing and decreasing rainstorm occurrences were detected, with no spatially uniform trend being observed across the whole country. There was an increase in the area being affected by rainstorms during the 20-year period, with rainstorm centers shifting along the southwest-northeast direction. Rainstorm occurrence was found to be correlated with local total precipitation. By comparing rainstorm occurrence with climate variables such as the El Nino-Southern Oscillation and Pacific Decadal Oscillation, we also found that climate change was likely to be the primary reason for rainstorm occurrence in China. This study complements previous studies that used gauge observations by providing a better understanding of the spatiotemporal dynamics of China’s rainstorms.

New approach for local C-band weather radar precipitation calibration

Weather radar calibration is a topic of great current interest because it is useful for various hydrological applications. Several methods have been developed for adjusting the relation between reflectivity data Z and rainfall intensity R (Z/R) because droplet size distributions in different storm events are unknown and highly variable in time and space. The present study developed and tested a new space and time window-based procedure for optimal local calibration of weather radar using Z/R relations and applying it to convective and stratiform storms in the lower Grijalva river basin in Mexico. Improving rain estimates from the Sabancuy, Campeche radar is essential because it monitors this basin, which is prone to floods. The resulting estimates of the optimal power-law (Z = ARb) window-based procedure (OP) are compared with those of the default Marshall and Palmer (MP) relation using the observed rain gauge records. The appropriate window was selected using a criterion that considers factors affecting the free fall of raindrops. For most of the storms tested, metrics for the OP models showed better values than those calculated for the MP ones. The best MP performance is when using smooth calibration data, achieving similar metric results to that of the OP. The proposed observed calibration method could be useful to improve the default MP model estimates at any weather radar with similar characteristics to the ones analyzed in this work. The resulting Z/R relations could improve precipitation radar estimates for hydrologic model inputs.

Assessment on IMERG V06 Precipitation Products Using Rain Gauge Data in Jinan City, Shandong Province, China

In this study, a comprehensive assessment on precipitation estimation from the latest Version 06 release of the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) algorithm is conducted by using 24 rain gauge observations at daily scale from 2001 to 2016. The IMERG V06 dataset fuses Tropical Rainfall Measuring Mission (TRMM) satellite data (2000–2015) and Global Precipitation Measurement (GPM) satellite data (2014–present), enabling the use of IMERG data for long-term study. Correlation coefficient (CC), root mean square error (RMSE), relative bias (RB), probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) were used to assess the accuracy of satellite-derived precipitation estimation and measure the correspondence between satellite-derived and observed occurrence of precipitation events. The probability density distributions of precipitation intensity and influence of elevation on precipitation estimation were also examined. Results showed that, with high CC and low RMSE and RB, the IMERG Final Run product (IMERG-F) performs better than two other IMERG products at daily, monthly, and yearly scales. At daily scale, the ability of satellite products to detect general precipitation is clearly superior to the ability to detect heavy and extreme precipitation. In addition, CC and RMSE of IMERG products are high in Southeastern Jinan City, while RMSE is relatively low in Southwestern Jinan City. Considering the fact that the IMERG estimation of extreme precipitation indices showed an acceptable level of accuracy, IMERG products can be used to derive extreme precipitation indices in areas without gauged data. At all elevations, IMERG-F exhibits a better performance than the other two IMERG products. However, POD and FAR decrease and CSI increase with the increase of elevation, indicating the need for improvement. This study will provide valuable information for the application of IMERG products at the scale of a large city.

Using Ancillary Information from Radar-based Observations and Rain Gauges to Identify Error and Bias

AbstractAncillary information that exists within rain gauge and radar-based data sets provides opportunities to better identify error and bias between the two observing platforms as compared to error and bias statistics without ancillary information. These variables include precipitation type identification, air temperature, and radar quality. There are two NEXRAD based data sets used for reference; the National Centers for Environmental Prediction (NCEP) stage IV and the NOAA NEXRAD Reanalysis (NNR) gridded data sets. The NCEP stage IV data set is available at 4km hourly and includes radar-gauge bias adjusted precipitation estimates. The NNR data set is available at 1km at 5-minute and hourly time intervals and includes several different variables such as reflectivity, radar-only estimates, precipitation flag, radar quality indicator, and radar-gauge bias adjusted precipitation estimates. The NNR data product provides additional information to apply quality control such as identification of precipitation type, identification of storm type and Z-R relation. Other measures of quality control are a part of the NNR data product development. In addition, some of the variables are available at 5-minute scale. We compare the radar-based estimates with the rain gauge observations from the U.S. Climate Reference Network (USCRN). The USCRN network is available at the 5-minute scale and includes observations of air temperature, wind, and soil moisture among others. We present statistical comparisons of rain gauge observations with radar-based estimates by segmenting information based on precipitation type, air temperature, and radar quality indicator.

Wet and dry spells in Senegal: comparison of detection based on satellite products, reanalysis, and in situ estimates

Abstract. In this study, the detection and characteristics of dry/wet spells (defined as episodes when precipitation is abnormally low or high compared to usual climatology) drawn from several datasets are compared for Senegal. Here, four datasets are based on satellite data (TRMM-3B42 V7, CMORPH V1.0, TAMSAT V3, and CHIRPS V2. 0), two on reanalysis products (NCEP-CFSR and ERA5), and three on rain gauge observations (CPC Unified V1.0/RT and a 65-rain-gauge network regridded by using two kriging methods, namely ordinary kriging, OK, and block kriging, BK). All datasets were converted to the same spatio-temporal resolution: daily cumulative rainfall on a regular 0.25∘ grid. The BK dataset was used as a reference. Despite strong agreement between the datasets on the spatial variability in cumulative seasonal rainfall (correlations ranging from 0.94 to 0.99), there were significant disparities in dry/wet spells. The occurrence of dry spells is less in products using infrared measurement techniques than in products coupling infrared and microwave, pointing to more frequent dry spell events. All datasets show that dry spells appear to be more frequent at the start and end of rainy seasons. Thus, dry spell occurrences have a major influence on the duration of the rainy season, in particular through the “false onset” or “early cessation” of seasons. The amplitude of wet spells shows the greatest variation between datasets. Indeed, these major wet spells appear more intense in the OK and Tropical Rainfall Measuring Mission (TRMM) datasets than in the others. Lastly, the products indicate a similar wet spell frequency occurring at the height of the West African monsoon. Our findings provide guidance in choosing the most suitable datasets for implementing early warning systems (EWSs) using a multi-risk approach and integrating effective dry/wet spell indicators for monitoring and detecting extreme events.

Diurnal variation of precipitation from the perspectives of precipitation amount, intensity and duration over Sumatra from rain gauge observations

AbstractThe characteristics of diurnal variation of precipitation in terms of amount (PA), frequency (PF), and intensity (PI) over Sumatra have been investigated using rain‐gauge data from 186 stations. Mean PA and PF show spatial variations significantly affected by the terrain elevation and stations' distance to the west coastline of Sumatra. A slightly larger PA and PF appear over the middle and western sections of the Barisan mountains, in which the mean PI is smaller in these regions. Most stations with large rainfall amount also have large rainfall frequencies, indicated by a strong correlation between PA and PF. The timing of the diurnal peak of PA, PF, and PI also has prominent regional characteristics. The cluster analysis reveals different grouping of stations with distinctive diurnal peaks. The prevailing afternoon and early‐evening peaks, that is, 1500–2000 LST, appear mostly over mountain ranges where amplitude of PA and PF tended to increase with elevation. Moreover, the amplitude of diurnal cycles tended to decrease as distance from the Sumatra's west coastline increases. On the western side of Barisan's mountain range, high amplitude of diurnal cycle prevails compared to the eastern side. We also found a strong relationship between rain event duration with diurnal cycle pattern. Generally, rain event with a long duration tended to have a peak occurring at a later time compared with those events of shorter duration. The diurnal variation of precipitation corresponds to the temporal evolution of the low‐level convergences and wind vectors, and cloud migration modulated by land‐sea breeze system.

Hourly rain-gauge observations at 56 stations over Hong Kong

Hourly rain-gauge observations at 56 stations over Hong Kong

Improving quantitative precipitation estimates by radar-rain gauge merging and an integration algorithm in the Yishu River catchment, China

High-precision areal rainfall is crucial for hydrometeorological coupled forecasts. The accuracy of quantitative precipitation estimates (QPE) is improved by merging radar-rain gauge data with an integration approach based on a statistical weight matrix in the Yishu River catchment, China. First, a local Z-R relationship (Z = 85R1.82) is reconstructed using a genetic optimization algorithm to minimize the error from different precipitation patterns and climate zones. Next, based on the local Z-R relationship, six methods of merging radar-rain gauge data are respectively adapted to improve the accuracy of QPE, as follows: mean field bias (MFB), Kalman filter (KLM), optimum interpolation (OPT), variation method (VAR), two-step calibration of KLM and OPT (KOP), and two-step calibration of KLM and VAR (KVR). The results indicate that QPE accuracy is clearly improved, and is in good agreement with rain gauge observations, after the six merging methods are applied. Among these methods, KOP performs the best, reducing the mean relative error from 55.2 to 15.1%. An innovative aspect of this work is the inclusion of an integrated ideology based on a statistical weight matrix, which further improves the accuracy of QPE by incorporating the advantages of each estimation mode. The results further show that the accuracy of QPE derived from the integration approach is higher than that obtained by any individual method; QPE values are similar to those obtained the automatic rain gauge network in both the spatial distribution and location of the intense precipitation centers, and better reflects the precipitation status over the ground surface. This approach could serve as a promising conventional method for QPE in the study region.

Impact of Lightning Data Assimilation on the Short-Term Precipitation Forecast over the Central Mediterranean Sea

Lightning data assimilation (LDA) is a powerful tool to improve the weather forecast of convective events and has been widely applied with this purpose in the past two decades. Most of these applications refer to events hitting coastal and land areas, where people live. However, a weather forecast over the sea has many important practical applications, and this paper focuses on the impact of LDA on the precipitation forecast over the central Mediterranean Sea around Italy. The 3 h rapid update cycle (RUC) configuration of the weather research and forecasting (WRF) model) has been used to simulate the whole month of November 2019. Two sets of forecasts have been considered: CTRL, without lightning data assimilation, and LIGHT, which assimilates data from the LIghtning detection NETwork (LINET). The 3 h precipitation forecast has been compared with observations of the Integrated Multi-satellitE Retrievals for Global Precipitation Mission (GPM) (IMERG) dataset and with rain gauge observations recorded in six small Italian islands. The comparison of CTRL and LIGHT precipitation forecasts with the IMERG dataset shows a positive impact of LDA. The correlation between predicted and observed precipitation improves over wide areas of the Ionian and Adriatic Seas when LDA is applied. Specifically, the correlation coefficient for the whole domain increases from 0.59 to 0.67, and the anomaly correlation (AC) improves by 5% over land and by 8% over the sea when lightning is assimilated. The impact of LDA on the 3 h precipitation forecast over six small islands is also positive. LDA improves the forecast by both decreasing the false alarms and increasing the hits of the precipitation forecast, although with variability among the islands. The case study of 12 November 2019 (time interval 00–03 UTC) has been used to show how important the impact of LDA can be in practice. In particular, the shifting of the main precipitation pattern from land to the sea caused by LDA gives a much better representation of the precipitation field observed by the IMERG precipitation product.

Evaluation of IMERG Level-3 Products in Depicting the July to October Rainfall over Taiwan: Typhoon Versus Non-Typhoon

This study assesses the performance of satellite precipitation products (SPPs) from the latest version, V06B, Integrated Multi-satellitE Retrievals for Global Precipitation Mission (IMERG) Level-3 (including early, late, and final runs), in depicting the characteristics of typhoon season (July to October) rainfall over Taiwan within the period of 2000–2018. The early and late runs are near-real-time SPPs, while final run is post-real-time SPP adjusted by monthly rain gauge data. The latency of early, late, and final runs is approximately 4 h, 14 h, and 3.5 months, respectively, after the observation. Analyses focus on the seasonal mean, daily variation, and interannual variation of typhoon-related (TC) and non-typhoon-related (non-TC) rainfall. Using local rain-gauge observations as a reference for evaluation, our results show that all IMERG products capture the spatio-temporal variations of TC rainfall better than those of non-TC rainfall. Among SPPs, the final run performs better than the late run, which is slightly better than the early run for most of the features assessed for both TC and non-TC rainfall. Despite these differences, all IMERG products outperform the frequently used Tropical Rainfall Measuring Mission 3B42 v7 (TRMM7) for the illustration of the spatio-temporal characteristics of TC rainfall in Taiwan. In contrast, for the non-TC rainfall, the final run performs notably better relative to TRMM7, while the early and late runs showed only slight improvement. These findings highlight the advantages and disadvantages of using IMERG products for studying or monitoring typhoon season rainfall in Taiwan.

Radar and Rain Gauge Data Fusion Based on Disaggregation of Radar Imagery

AbstractRemotely sensed radar data and in situ rain gauge observations provide complementary information about rainfall. Specifically, radar data inform rainfall location and extent, while rain gauges provide accurate observations of the local rain intensity and its temporal variability. Drawing on the respective strengths of these two data sources, radar and rain gauge data fusion is becoming more and more common to derive both accurate and comprehensive rain estimates. However, combined precipitation estimates are often restricted to a resolution of 1 km in space and 1 h in time. In this paper, I propose to use radar data disaggregation to merge radar and rain gauge observations at a high space‐time resolution. To this end, a stochastic rainfall model is first trained on rain gauge observations and then combined with area‐to‐point geostatistical simulations to simultaneously: (i) disaggregate radar data, (ii) honor rain gauge observations, and (iii) reproduce local rain statistics. The proposed approach is applied to summer rain data collected in a small Alpine catchment, and data fusion provides rainfall reanalysis results at a resolution of 200 m in space and 2 min in time, including remote locations where in‐situ observation was not feasible.

Enhanced Large-Scale Validation of Satellite-Based Land Rainfall Products

AbstractSatellite-based precipitation estimates (SPEs) are generally validated using ground-based rain gauge or radar observations. However, in poorly instrumented regions, uncertainty in these references can lead to biased assessments of SPE accuracy. As a result, at regional or continental scales, an objective basis to evaluate SPEs is currently lacking. Here, we evaluate the potential for large-scale, spatially continuous evaluation of SPEs over land via the application of collocation-based techniques [i.e., triple collocation (TC) and quadruple collocation (QC) analyses]. Our collocation approach leverages the Soil Moisture to Rain (SM2RAIN) rainfall product, derived from the time series analysis of satellite-based soil moisture retrievals, in combination with independent rainfall datasets acquired from ground observations and climate reanalysis to validate four years of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Support to Operational Hydrology and Water Management (H-SAF) H23 daily rainfall product. Large-scale maps of the H23 correlation metric are generated using both TC and QC analyses. Results demonstrate that the SM2RAIN product is a uniquely valuable independent product for collocation analyses, because other available large-scale rainfall datasets are often based on overlapping data sources and algorithms. In particular, the availability of SM2RAIN facilitates the large-scale evaluation of SPE products like H23—even in areas that lack adequate ground-based observations to apply traditional validation approaches.

A two-stage blending approach for merging multiple satellite precipitation estimates and rain gauge observations: an experiment in the northeastern Tibetan Plateau

Abstract. Substantial biases exist in satellite precipitation estimates (SPEs) over complex terrain regions, and it has always been a challenge to quantify and correct such biases. The combination of multiple SPEs and rain gauge observations would be beneficial to improve the gridded precipitation estimates. In this study, a two-stage blending (TSB) approach is proposed, which firstly reduces the systematic errors of the original SPEs based on a Bayesian correction model and then merges the bias-corrected SPEs with a Bayesian weighting model. In the first stage, the gauge-based observations are assumed to be a generalized regression function of the SPEs and terrain feature. In the second stage, the relative weights of the bias-corrected SPEs are calculated based on the associated performances with ground references. The proposed TSB method has the ability to extract benefits from the bias-corrected SPEs in terms of higher performance and mitigate negative impacts from the ones with lower quality. In addition, Bayesian analysis is applied in the two phases by specifying the prior distributions on model parameters, which enables the posterior ensembles associated with their predictive uncertainties to be produced. The performance of the proposed TSB method is evaluated with independent validation data in the warm season of 2010–2014 in the northeastern Tibetan Plateau. Results show that the blended SPE is greatly improved compared to the original SPEs, even in heavy rainfall events. This study can be expanded as a data fusion framework in the development of high-quality precipitation products in any region of interest.

High Space-Time Resolution Observation of Extreme Orographic Rain Gradients in a Pacific Island Catchment

In the vicinity of orographic barriers, interactions between mountains and prevailing winds can enhance rainfall and generate strong spatial gradients of precipitation. Orographic rainfall is still poorly quantified despite being an important driver of headwater catchment hydrology, in particular when considered at high space-time resolution. In this paper, we propose a complete framework for the observation and quantification of orographic rainfall gradients at the local scale. This framework, based on the stochastic interpolation of drop-counting rain gauge observations, provides reconstructions of local rain fields at high space-time resolution. It allows us to capture the life-cycle of individual rain cells, which typically occurs at a spatial scale of approximately 1–5 km and a temporal scale of approximately 5–15 min over our study area. In addition, the resulting rain estimates can be used to investigate how rainfall gradients develop during rain storms, and to provide better input data to drive hydrological models. The proposed framework is presented in the form of a proof-of-concept case study aimed at exploring orographic rain gradients in Mānoa Valley, on the leeward side of the Island of Oʻahu, Hawaiʻi, USA. Results show that our network of eight rain gauges captured rainfall variations over the 6 × 5 km2 study area, and that stochastic interpolation successfully leverages these in-situ data to produce rainfall maps at 200 m × 1 min resolution. Benchmarking against Kriging shows better performance of stochastic interpolation in reproducing key statistics of high-resolution rain fields, in particular rain intermittency and low intensities. This leads to an overall enhancement of rain prediction at ungauged locations.

Multiscale Comparative Evaluation of the GPM and TRMM Precipitation Products Against Ground Precipitation Observations Over Chinese Tibetan Plateau

This study aims to evaluate the performances of two latest released global precipitation measurement (GPM)-era satellite precipitation final run products (integrated merged multisatellite retrievals IMERG-V06B and Global Satellite Mapping of Precipitation GSMaP-V07) and one tropical rainfall measuring mission (TRMM)-era product (TMPA-3B42-V07) at hourly, daily, and monthly scale over the Tibetan plateau (TP), with special focus on the performances at different rain intensities, subbasins, and elevations at daily scales. Besides, hourly scale evaluations were carried out for one grid box value of IMERG-V06B and GSMaP-V07 versus 15 rain gauges on the rainy season, 2019. Results indicated that: First, GSMaP-V07 outperformed TMPA-3B42-V07 and IMERG-V06B with higher correlation coefficient and lower relative bias, mean absolute error, and root-mean-squared error values at daily and monthly scale. Second, satellite products showed overestimation during light rain and underestimation throughout moderate rain, heavy rain, and rainstorm; Third, satellite products have performed relatively better in lower elevation ( 4000 m). Fourth, satellite precipitation products performed better in the source region of the Yellow River, Yangtze River, Lancang River, and Nujiang River basins, whereas the products greatly overestimated the precipitation in the source region of the Yarlung zangbo River basin. In arid climate regions (Qiangtang Basin and Qaidam Basin), GSMaP-V07 had better performances than other products; and finally, although GSMaP-V07 showed higher overestimation than IMERG-V06B product at hourly scale, it has better consistency with rain-gauge observations. For satellite precipitation data application within the TP, we recommend the GSMaP-V07 over the IMERG-V06B. Furthermore, improvement on the multisatellite rainfall retrieval algorithm is required.