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Abstract
Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap-filling and reconstruction techniques have proven to be effective in producing serially complete station datasets (SCDs) that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). To our knowledge, all SCDs are developed at regional scales. In this study, we developed the serially complete Earth (SC-Earth) dataset, which provides daily precipitation, mean temperature, temperature range, dewpoint temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network–Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). A unified station repository is generated based on GHCN-D and GSOD after station merging and strict quality control. ERA5 is optimally matched with station data considering the time shift issue and then used to assist the global gap filling. SC-Earth is generated by merging estimates from 15 strategies based on quantile mapping, spatial interpolation, machine learning, and multistrategy merging. The final estimates are bias corrected using a combination of quantile mapping and quantile delta mapping. Comprehensive validation demonstrates that SC-Earth has high accuracy around the globe, with degraded quality in the tropics and oceanic islands due to sparse station networks, strong spatial precipitation gradients, and degraded ERA5 estimates. Meanwhile, SC-Earth inherits potential limitations such as inhomogeneity and precipitation undercatch from raw station data, which may affect its application in some cases. Overall, the high-quality and high-density SC-Earth dataset will benefit research in fields of hydrology, ecology, meteorology, and climate. The dataset is available at https://zenodo.org/record/4762586.
Abstract
Meteorological data from ground stations suffer from temporal discontinuities caused by missing values and short measurement periods. Gap-filling and reconstruction techniques have proven to be effective in producing serially complete station datasets (SCDs) that are used for a myriad of meteorological applications (e.g., developing gridded meteorological datasets and validating models). To our knowledge, all SCDs are developed at regional scales. In this study, we developed the serially complete Earth (SC-Earth) dataset, which provides daily precipitation, mean temperature, temperature range, dewpoint temperature, and wind speed data from 1950 to 2019. SC-Earth utilizes raw station data from the Global Historical Climatology Network–Daily (GHCN-D) and the Global Surface Summary of the Day (GSOD). A unified station repository is generated based on GHCN-D and GSOD after station merging and strict quality control. ERA5 is optimally matched with station data considering the time shift issue and then used to assist the global gap filling. SC-Earth is generated by merging estimates from 15 strategies based on quantile mapping, spatial interpolation, machine learning, and multistrategy merging. The final estimates are bias corrected using a combination of quantile mapping and quantile delta mapping. Comprehensive validation demonstrates that SC-Earth has high accuracy around the globe, with degraded quality in the tropics and oceanic islands due to sparse station networks, strong spatial precipitation gradients, and degraded ERA5 estimates. Meanwhile, SC-Earth inherits potential limitations such as inhomogeneity and precipitation undercatch from raw station data, which may affect its application in some cases. Overall, the high-quality and high-density SC-Earth dataset will benefit research in fields of hydrology, ecology, meteorology, and climate. The dataset is available at https://zenodo.org/record/4762586.
Abstract
Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature T a downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim T a is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of T a . The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature T w , for rain–snow classification, using T a and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate T w compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of T w with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based T w using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality T a and T w with high resolution and improving rain–snow partitioning, particularly in mountainous regions.
Abstract
Precipitation phase has an important influence on hydrological processes. The Integrated Multisatellite Retrievals for Global Precipitation Measurement (IMERG) uses temperature data from reanalysis products to implement rain–snow classification. However, the coarse resolution of reanalysis data may not reveal the spatiotemporal variabilities of temperature, necessitating appropriate downscaling methods. This study compares the performance of eight air temperature T a downscaling methods in the contiguous United States and six mountain ranges using temperature from the Parameter-Elevation Regressions on Independent Slopes Model (PRISM) as the benchmark. ERA-Interim T a is downscaled from the original 0.75° to 0.1°. The results suggest that the two purely statistical downscaling methods [nearest neighbor (NN) and bilinear interpolation (BI)] show similar performance with each other. The five downscaling methods based on the free-air temperature lapse rate (TLR), which is calculated using temperature and geopotential heights at different pressure levels, notably improves the accuracy of T a . The improvement is particularly obvious in mountainous regions. We further calculated wet-bulb temperature T w , for rain–snow classification, using T a and dewpoint temperature from ERA-Interim and PRISM. TLR-based downscaling methods result in more accurate T w compared to NN and BI in the western United States, whereas the improvement is limited in the eastern United States. Rain–snow partitioning is conducted using a critical threshold of T w with Snow Data Assimilation System (SNODAS) snowfall data serving as the benchmark. ERA-Interim-based T w using TLR downscaling methods is better than that using NN/BI and IMERG precipitation phase. In conclusion, TLR-based downscaling methods show promising prospects in acquiring high-quality T a and T w with high resolution and improving rain–snow partitioning, particularly in mountainous regions.
Abstract
Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: 1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? 2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates.
Abstract
Stations are an important source of meteorological data, but often suffer from missing values and short observation periods. Gap filling is widely used to generate serially complete datasets (SCDs), which are subsequently used to produce gridded meteorological estimates. However, the value of SCDs in spatial interpolation is scarcely studied. Based on our recent efforts to develop a SCD over North America (SCDNA), we explore the extent to which gap filling improves gridded precipitation and temperature estimates. We address two specific questions: 1) Can SCDNA improve the statistical accuracy of gridded estimates in North America? 2) Can SCDNA improve estimates of trends on gridded data? In addressing these questions, we also evaluate the extent to which results depend on the spatial density of the station network and the spatial interpolation methods used. Results show that the improvement in statistical interpolation due to gap filling is more obvious for precipitation, followed by minimum temperature and maximum temperature. The improvement is larger when the station network is sparse and when simpler interpolation methods are used. SCDs can also notably reduce the uncertainties in spatial interpolation. Our evaluation across North America from 1979 to 2018 demonstrates that SCDs improve the accuracy of interpolated estimates for most stations and days. SCDNA-based interpolation also obtains better trend estimation than observation-based interpolation. This occurs because stations used for interpolation could change during a specific period, causing changepoints in interpolated temperature estimates and affect the long-term trends of observation-based interpolation, which can be avoided using SCDNA. Overall, SCDs improve the performance of gridded precipitation and temperature estimates.
Abstract
The goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and Excess Storage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically, even with its limited data availability to date, in this well-gauged midlatitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed.
Abstract
The goal of this study is to quantitatively intercompare the standard products of the Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) and its successor, the Global Precipitation Measurement (GPM) mission Integrated Multisatellite Retrievals for GPM (IMERG), with a dense gauge network over the midlatitude Ganjiang River basin in southeast China. In general, direct comparisons of the TMPA 3B42V7, 3B42RT, and GPM Day-1 IMERG estimates with gauge observations over an extended period of the rainy season (from May through September 2014) at 0.25° and daily resolutions show that all three products demonstrate similarly acceptable (~0.63) and high (0.87) correlation at grid and basin scales, respectively, although 3B42RT shows much higher overestimation. Both of the post-real-time corrections effectively reduce the bias of Day-1 IMERG and 3B42V7 to single digits of underestimation from 20+% overestimation of 3B42RT. The Taylor diagram shows that Day-1 IMERG and 3B42V7 are comparable at grid and basin scales. Hydrologic assessment with the Coupled Routing and Excess Storage (CREST) hydrologic model indicates that the Day-1 IMERG product performs comparably to gauge reference data. In many cases, the IMERG product outperforms TMPA standard products, suggesting a promising prospect of hydrologic utility and a desirable hydrologic continuity from TRMM-era product heritages to GPM-era IMERG products. Overall, this early study highlights that the Day-1 IMERG product can adequately substitute TMPA products both statistically and hydrologically, even with its limited data availability to date, in this well-gauged midlatitude basin. As more IMERG data are released, more studies to explore the potential of GPM-era IMERG in water, weather, and climate research are urgently needed.
Abstract
This study presents a statistical analysis of the variability of the vertical structure of precipitation in the eastern downstream region of the Tibetan Plateau as measured by the Precipitation Radar (PR) on the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission (TRMM) satellite. Data were analyzed over an 11-yr time span (January 2004–December 2014). The results show the seasonal and spatial variability of the storm height, freezing level, and bright band for different types of precipitation as well as the characteristics of intensity-related and type-related vertical profiles of reflectivity (VPR). Major findings were as follows: About 90% of the brightband peak reflectivity of stratiform precipitation was less than 32 dBZ, and 40% of the maximum reflectivity of convective precipitation exceeded 35 dBZ. The intensity of surface rainfall rates also depended on the shapes of VPRs. For stratiform precipitation, ice–snow aggregation was faster during moderate and heavy rainfall than it was in light rainfall. Since both the moisture and temperature are lower in winter, the transformation efficiency of hydrometeors becomes slower. Typical Ku-band representative climatological VPRs (CPRs) for stratiform precipitation have been created on the basis of the integration of normalized VPR shape for the given area and the rainfall intensity. All of the findings indicate that the developed CPRs can be used to improve surface precipitation estimates in regions with complex terrain where the ground-based radar net has limited visibility at low levels.
Abstract
This study presents a statistical analysis of the variability of the vertical structure of precipitation in the eastern downstream region of the Tibetan Plateau as measured by the Precipitation Radar (PR) on the National Aeronautics and Space Administration Tropical Rainfall Measuring Mission (TRMM) satellite. Data were analyzed over an 11-yr time span (January 2004–December 2014). The results show the seasonal and spatial variability of the storm height, freezing level, and bright band for different types of precipitation as well as the characteristics of intensity-related and type-related vertical profiles of reflectivity (VPR). Major findings were as follows: About 90% of the brightband peak reflectivity of stratiform precipitation was less than 32 dBZ, and 40% of the maximum reflectivity of convective precipitation exceeded 35 dBZ. The intensity of surface rainfall rates also depended on the shapes of VPRs. For stratiform precipitation, ice–snow aggregation was faster during moderate and heavy rainfall than it was in light rainfall. Since both the moisture and temperature are lower in winter, the transformation efficiency of hydrometeors becomes slower. Typical Ku-band representative climatological VPRs (CPRs) for stratiform precipitation have been created on the basis of the integration of normalized VPR shape for the given area and the rainfall intensity. All of the findings indicate that the developed CPRs can be used to improve surface precipitation estimates in regions with complex terrain where the ground-based radar net has limited visibility at low levels.
Abstract
Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.
Abstract
Global gridded precipitation products have proven essential for many applications ranging from hydrological modeling and climate model validation to natural hazard risk assessment. They provide a global picture of how precipitation varies across time and space, specifically in regions where ground-based observations are scarce. While the application of global precipitation products has become widespread, there is limited knowledge on how well these products represent the magnitude and frequency of extreme precipitation—the key features in triggering flood hazards. Here, five global precipitation datasets (MSWEP, CFSR, CPC, PERSIANN-CDR, and WFDEI) are compared to each other and to surface observations. The spatial variability of relatively high precipitation events (tail heaviness) and the resulting discrepancy among datasets in the predicted precipitation return levels were evaluated for the time period 1979–2017. The analysis shows that 1) these products do not provide a consistent representation of the behavior of extremes as quantified by the tail heaviness, 2) there is strong spatial variability in the tail index, 3) the spatial patterns of the tail heaviness generally match the Köppen–Geiger climate classification, and 4) the predicted return levels for 100 and 1000 years differ significantly among the gridded products. More generally, our findings reveal shortcomings of global precipitation products in representing extremes and highlight that there is no single global product that performs best for all regions and climates.
Abstract
Precipitation over the Tibetan Plateau (TP), known as Asia’s water tower, plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP, which features complex terrain and a harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large, poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) dataset was generated at a daily time scale and a spatial resolution of 0.1° across the TP for the 1998–2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root-mean-square error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow Rivers in the TP. The MSP achieved the best Nash–Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004–14. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high-quality precipitation data for hydrological research.
Abstract
Precipitation over the Tibetan Plateau (TP), known as Asia’s water tower, plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP, which features complex terrain and a harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large, poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) dataset was generated at a daily time scale and a spatial resolution of 0.1° across the TP for the 1998–2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root-mean-square error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow Rivers in the TP. The MSP achieved the best Nash–Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004–14. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high-quality precipitation data for hydrological research.
Abstract
Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region.
Significance Statement
The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.
Abstract
Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region.
Significance Statement
The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations.
Abstract
Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.
Abstract
Prediction, and thus preparedness, in advance of flood events is crucial for proactively reducing their impacts. In the summer of 2012, Beijing, China, experienced extreme rainfall and flooding that caused 79 fatalities and economic losses of $1.6 billion. Using rain gauge networks as a benchmark, this study investigated the detectability and predictability of the 2012 Beijing event via the Global Hydrological Prediction System (GHPS), forced by the NASA Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis at near–real time and by the deterministic and ensemble precipitation forecast products from the NOAA Global Forecast System (GFS) at several lead times. The results indicate that the disastrous flooding event was detectable by the satellite-based global precipitation observing system and predictable by the GHPS forced by the GFS 4 days in advance. However, the GFS demonstrated inconsistencies from run to run, limiting the confidence in predicting the extreme event. The GFS ensemble precipitation forecast products from NOAA for streamflow forecasts provided additional information useful for estimating the probability of the extreme event. Given the global availability of satellite-based precipitation in near–real time and GFS precipitation forecast products at varying lead times, this study demonstrates the opportunities and challenges that exist for an integrated application of GHPS. This system is particularly useful for the vast ungauged regions of the globe.
