Reanalysis Precipitation Research Articles

A New and Simplified Approach for Estimating the Daily River Discharge of the Tibetan Plateau Using Satellite Precipitation: An Initial Study on the Upper Brahmaputra River

Collecting in situ observations from remote, high mountain rivers presents major challenges, yet real-time, high temporal resolution (e.g., daily) discharge data are critical for flood hazard mitigation and river management. In this study, we propose a method for estimating daily river discharge (RD) based on free, operational remote sensing precipitation data (Tropical Rainfall Measuring Mission (TRMM), since 2001). In this method, an exponential filter was implemented to produce a new precipitation time series from daily basin-averaged precipitation data to model the time lag of precipitation in supplying RD, and a linear-regression relationship was constructed between the filtered precipitation time series and observed discharge records. Because of different time lags in the wet season (rainfall-dominant) and dry season (snowfall-dominant), the precipitation data were processed in a segmented way (from June to October and from November to May). The method was evaluated at two hydrological gauging stations in the Upper Brahmaputra (UB) river basin, where Nash–Sutcliffe Efficiency (NSE) coefficients for Nuxia (>0.85) and Yangcun (>0.80) indicate good performance. By using the degree-day method to estimate the snowmelt and acquire the time series of new active precipitation (rainfall plus snowmelt) in the target basins, the discharge estimations were improved (NSE > 0.9 for Nuxia) compared to the original data. This makes the method applicable for most rivers on the Tibetan Plateau, which are fed mainly by precipitation (including snowfall) and are subject to limited human interference. The method also performs well for reanalysis precipitation data (Chinese Meteorological Forcing Dataset (CMFD), 1980–2000). The real-time or historical discharges can be derived from satellite precipitation data (or reanalysis data for earlier historical years) by using our method.

Potential of rainfall data hybridization in a data-scarce region

Many impact assessment studies have been using reanalysis precipitation data as an alternative to observed rainfall data even where few rainfall records are available. For that reason, this study investigated fit-to-observations and fit-for-purpose of hybrid rainfall data using four strategically located rainfall stations in the Wami-Ruvu basin, Tanzania. Hybrid rainfall data were generated by randomly filling 15% to 70% hypothetical missing data in the observed records at the rainfall stations with reanalysis precipitation data from nearby grids. Fit-to-observations was used to evaluate the performance of the whole time series of hybrid rainfall data in mimicking that of observed rainfall data at the stations. Fit-for-purpose was used to evaluate the performance of trend and seasonal components of the whole time series of hybrid rainfall data in mimicking those of the whole time series of observed rainfall data at the stations. The findings showed that hybrid rainfall data are superior to reanalysis precipitation data in mimicking observed rainfall data. The findings also revealed that in most cases, trend and seasonal components have greater performances than the whole time series at high degrees of hybridization. Therefore, hybridization of rainfall data is highly encouraged especially in data-scarce regions.

Evaluation of bias correction methods for APHRODITE data to improve hydrologic simulation in a large Himalayan basin

Gridded precipitation products have the advantages of wide spatial coverage and high spatiotemporal resolution compared to conventional rain-gauge data, and have been extensively applied in hydrology-related research. As one of the most representative of the gridded precipitation products, APHRODITE performs well in most regions over Asia except around the Tibetan Plateau. This study implemented four bias correction methods including both mean-based (LS, LOCI) and distribution-based approaches (CDF, LS_CDF) for APHRODITE data over the Yarluzangbu-Brahmaputra River Basin based on different climate zones, and the correction methods were assessed according to the performance of both statistical indices and hydrological simulation. First, it was found that the APHRODITE data underestimated the precipitation amount and overestimated the number of raindays for the entire study region; and the bias is relatively small in upstream (Tibetan Plateau) and large in downstream (floodplains). Second, the result showed that all four correction methods could effectively improve the precipitation estimates of the APHRODITE data and the combined LS_CDF method performed the best because of its greater spatial consistency advantages of bias and closer matching of the wet-day event and cumulative frequency. Hydrological performance also supports that the LS_CDF method is the best due to the fine driving simulation result for all three hydrological stations during long periods and the ability to better force simulation of extreme runoff. Third, this study also found that even though distribution-based approaches performed well in precipitation correction, especially for extreme precipitation, their correction effects also depend on the spatial consistency of the bias, which is even more important than frequency matching. These findings have certain reference values for the evaluation, correction, and application of grid precipitation data, not only for APHRODITE data, but also satellite and reanalysis precipitation data.

Evaluation of Gridded Precipitation Datasets in Malaysia

This study compares five readily available gridded precipitation satellite products namely: Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) at 0.05° and 0.25° resolution, Tropical Rainfall Measuring Mission Multi-Satellite Precipitation Analysis (TMPA 3B42v7) and Princeton Global Forcings (PGFv3), both at 0.25°, and Global Satellite Mapping of Precipitation Reanalysis (GSMaP_RNL) at 0.1°, and evaluates their quality and reliability against 41 rain gauge stations in Malaysia. The evaluation was based on three numerical statistical scores (r, Root Mean Squared Error (RMSE) and Bias) and three categorical scores (Probability of Detection (POD), False Alarm Ratio (FAR) and Critical Success Index (CSI)) at temporal resolutions of daily, monthly and seasonal. The results showed that TMPA 3B42v7, PGFv3, CHIRPS25 and CHIRPS05 slightly overestimated the rain gauge data, while the GSMaP_RNL underestimated the value with the largest bias for monthly data. The CHIRPS25 showed the best POD score, while TMPA 3B42v7 scored highest for FAR and CSI. Overall, TMPA 3B42v7 was found to be the best-performing dataset, while PGFv3 registered the worst performance for both for numerical (monthly) and categorical (daily) scores. All products captured the intensity of heavy rainfall (20–50 mm/day) rather well, but tended to underestimate the intensity for categories of no or little rain (rain <1 mm/day) and extremely heavy rain (rain >50 mm/day). In addition, overestimation occurred for low moderate (2–5 mm/day) to low heavy rain and (10–20 mm/day). In the case study of the extreme flooding event of 2006/2007 in the southern area of Peninsular Malaysia, TMPA 3B42v7 and GSMaP_RNL performed well in capturing most heavy rainfall events but tended to overestimate light rainfalls, consistent with their performance for the occurrence intensity of rainfall at different intensity level.

Precipitation Regime Classification Based on Cloud-Top Temperature Time Series for Spatially-Varied Parameterization of Precipitation Models

Satellite and reanalysis precipitation products perform poorly over regions with low-density ground observation networks. In order to improve space-dependent parameterization of precipitation estimation models in data-scarce environments, the delineation boundaries of precipitation regimes should be accurately identified. Existing approaches to characterize precipitation regimes by seasonal or other climatological properties do not account for small scale spatial-temporal variability. Precipitation time series can be used to account for this small-scale variability in regime classification. Unfortunately, precipitation products with global coverage perform poorly at small time scales over data scarce regions. A methodology of using satellite-based cloud-top temperature (CTT) time series as a proxy of precipitation time series for precipitation regime classification was developed, and its potential and uncertainty were analyzed. A precipitation regime in this study was defined on the basis of characteristic small-scale temporal distribution and variability of precipitation at a given place. Dynamic time warping was used to calculate the distance between two time series. Criteria to select the optimal temporal scale of time series for clustering and the number of clusters were also developed. The method was validated over Germany and applied to Tanzania, characterized by complex climatology and low density ground observations. This approach was evaluated against precipitation regime classification based on a satellite precipitation product. Results show that CTT outcompetes satellite-based precipitation for classification of precipitation regime classification. The CTT-based classification can be used as precursor to spatially adapted precipitation estimation algorithms where parameters are calibrated by gauge data or other ground-based precipitation observations, and parameterization can be used for satellite-precipitation estimates, precipitation forecasts in numerical or stochastic weather models, etc.

Application of a Dynamic Clustered Bayesian Model Averaging (DCBA) Algorithm for Merging Multisatellite Precipitation Products over Pakistan

AbstractMerged multisatellite precipitation datasets (MMPDs) combine the advantages of individual satellite precipitation products (SPPs), have a tendency to reduce uncertainties, and provide higher potentials to hydrological applications. This study applied a dynamic clustered Bayesian model averaging (DCBA) algorithm to merge four SPPs across Pakistan. The DCBA algorithm produced dynamic weights to different SPPs varying both spatially and temporally to accommodate the spatiotemporal differences of SPP performances. The MMPD is developed at daily temporal scale from 2000 to 2015 with spatial resolution of 0.25° using extensively evaluated SPPs and a global atmospheric reanalysis–precipitation dataset: Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42V7, Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), Climate Prediction Center morphing technique (CMORPH), and ERA-Interim. The DCBA algorithm is evaluated across four distinct climate regions of Pakistan over 102 ground precipitation gauges (GPGs). DCBA forecasting outperformed all four SPPs with average Theil’s U of 0.49, 0.38, 0.37, and 0.36 in glacial, humid, arid, and hyperarid regions, respectively. The average mean bias error (MBE), mean error (MAE), root-mean-square error (RMSE), correlation coefficient (CC), and standard deviation (SD) of DCBA over all of Pakistan are 0.54, 1.40, 4.94, 0.77, and 5.17 mm day−1, respectively. Seasonal evaluation revealed a dependency of DCBA performance on precipitation magnitude/intensity and elevation. Relatively poor DCBA performance is observed in premonsoon/monsoon seasons and at high/mild elevated regions. Average improvements of DCBA in comparison with TMPA are 59.56% (MBE), 49.37% (MAE), 45.89% (RMSE), 19.48% (CC), 46.7% (SD), and 18.66% (Theil’s U). Furthermore, DCBA efficiently captured extreme precipitation trends (premonsoon/monsoon seasons).

Evaluation of Satellite and Reanalysis Precipitation Products Using GIS for All Basins in Turkey

Use of the satellite and reanalysis precipitation products, as supplementary data sources, are steadily rising for hydrometeorological applications, especially in data-sparse areas. However, the accuracy of these data sets is often lacking, especially in Turkey. This study evaluates the accuracy of satellite precipitation product (TRMM 3B42V7) and reanalysis precipitation product (NCEP-CFSR) against rain gauge observations for the 1998–2010 periods. Average annual precipitation for the 25 basins in Turkey was calculated using rain gauge precipitation data from 225 stations. The inverse distance weighting (IDW) method was used to calculate areal precipitation for each basin using GIS. According to the results of statistical analysis, the coefficient of determination for the TRMM product gave satisfactory results (R2 > 0.88). However, R2 for the CFSR data set ranges from 0.35 for the Eastern Black Sea basin to 0.93 for the West Mediterranean basin. RMSE was calculated to be 95.679 mm and 128.097 mm for the TRMM and CFSR data, respectively. The NSE results of TRMM data showed very good performance for 6 basins, while the PBias value showed very good performance for 7 basins. The NSE results of CFSR data showed very good performance for 3 basins, while the PBias value showed very good performance for 6 basins.

Linking Global Changes of Snowfall and Wet-Bulb Temperature

AbstractSnowfall is one of the primary drivers of the global cryosphere and is declining in many regions of the world with widespread hydrological and ecological consequences. Previous studies have shown that the probability of snowfall occurrence is well described by wet-bulb temperatures below 1°C (1.1°C) over land (ocean). Using this relationship, wet-bulb temperatures from three reanalysis products as well as multisatellite and reanalysis precipitation data are analyzed from 1979 to 2017 to study changes in potential snowfall areas, snowfall-to-rainfall transition latitude, snowfall amount, and snowfall-to-precipitation ratio (SPR). Results are presented at hemispheric scales, as well as for three Köppen–Geiger climate classes and four major mountainous regions including the Alps, the western United States, High Mountain Asia (HMA), and the Andes. In all reanalysis products, while changes in the wet-bulb temperature over the Southern Hemisphere are mostly insignificant, significant positive trends are observed over the Northern Hemisphere (NH). Significant reductions are observed in annual-mean potential snowfall areas over NH land (ocean) by 0.52 (0.34) million km2 decade−1 due to an increase of 0.34°C (0.35°C) decade−1 in wet-bulb temperature. The fastest retreat in NH transition latitudes is observed over Europe and central Asia at 0.7° and 0.45° decade−1. Among mountainous regions, the largest decline in potential snowfall areas is observed over the Alps at 3.64% decade−1 followed by the western United States at 2.81% and HMA at 1.85% decade−1. This maximum decrease over the Alps is associated with significant reductions in annual snowfall of 20 mm decade−1 and SPR of 2% decade−1.

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

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

Machine Learning–Based Blending of Satellite and Reanalysis Precipitation Datasets: A Multiregional Tropical Complex Terrain Evaluation

Abstract This study evaluates a machine learning–based precipitation ensemble technique (MLPET) over three mountainous tropical regions. The technique, based on quantile regression forests, integrates global satellite precipitation datasets from CMORPH, PERSIANN, GSMaP (V6), and 3B42 (V7) and an atmospheric reanalysis precipitation product (EI_GPCC) with daily soil moisture, specific humidity, and terrain elevation datasets. The complex terrain study areas include the Peruvian and Colombian Andes in South America and the Blue Nile in East Africa. Evaluation is performed at a daily time scale and 0.25° spatial resolution based on 13 years (2000–12) of reference rainfall data derived from dense in situ rain gauge networks. The technique is evaluated using K-fold, separately in each region, and leave-one-region-out validation experiments. Comparison of MLPET with the individual satellite and reanalysis precipitation datasets used for the blending and the recent Multi-Source Weighted-Ensemble Precipitation (MSWEP) global precipitation product exhibited improved systematic and random error statistics for all regions. In addition, it is shown that observations are encapsulated well within the ensemble envelope generated by the blending technique.

Regional modeling of daily precipitation fields across the Great Lakes region (Canada) using the CFSR reanalysis

High densities of local-scale daily precipitation series across relatively large domains are of special interest for a wide range of applications (e.g., hydrological modeling, agriculture). The focus of the present study is to post-process gridded precipitation from a single reanalysis to correct bias and scale mismatch with observations, and to extend the same post-processing at sites without historical data. A Stochastic Model Output Statistical approach combined with meta-Gaussian spatiotemporal random fields, calibrated at sites, is employed to post-process the Climate Forecast System Reanalysis (CFSR) precipitation. The post-processed data, characterized by local parameters, is then mapped across the Great Lakes region (Canada) using two different approaches: (1) kriging, and (2) Vector Generalized Additive Model (VGAM) with spatial covariates. The kriging enables the interpolation of these parameters, while the spatial VGAM helps to spatially post-process CFSR precipitation using a single model. The k-fold cross-validation procedure is employed to assess the ability of the two approaches to predict selected characteristics and climate indices. The kriging and spatial VGAM approaches modeled effectively the distribution of the precipitation process to similar extents (e.g., mean daily precipitation, variability and the number of wet days). The kriging approach produces slightly better estimates of climate indices than the spatial VGAM models. Both approaches demonstrate significant improvement of the metric estimation compared to those of CFSR without post-processing.

Performance of Two Long-Term Satellite-Based and GPCC 8.0 Precipitation Products for Drought Monitoring over the Yellow River Basin in China

This study investigated the accuracy and drought monitoring application of two newly-released long-term satellite precipitation products (i.e., the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR and the Climate Hazards Group Infrared Precipitation with Station data version 2.0 CHIRPS) and the latest reanalysis precipitation product (i.e., the Global Precipitation Climatology Centre full data monthly version 2018, GPCC 8.0). Satellite- and reanalysis-based precipitation sequences and standardized precipitation indices (SPIs) were compared comprehensively with background estimates of the China Gauge-based Daily Precipitation Analysis (CGDPA) dataset at spatial and multiple temporal scales over the Yellow River Basin (YRB) in China during 1983–2016. Results indicated the PERSIANN-CDR, CHIRPS and GPCC 8.0 precipitation products generally had good consistency with CGDPA (correlation coefficient, CC > 0.78). At spatial, monthly and seasonal scales, the consistency between GPCC 8.0 and CGDPA precipitation was found to be better than that of the two satellite products. Due to their good performance at the spatiotemporal scale, the satellite with long-time record and GPCC 8.0 products were evaluated and compared with CGDPA to derive SPI-1 (1-month SPI), SPI-3 (3-month SPI), and SPI-12 (12-month SPI) for drought monitoring in the YRB. The results showed that they had good application in monitoring droughts (CC > 0.65 at spatial scale, CC > 0.84 at temporal scale). The historical drought years (i.e., 1997, 1999, and 2006) and the spatial distribution of drought area in August 1997 were captured successfully, but the performance of GPCC 8.0 was found to be the best. Overall, GPCC 8.0 is considered best suited to complement precipitation datasets for long-term hydrometeorological research in the YRB.

Evaluation of satellite precipitation products over Central Vietnam

A comprehensive validation of three satellite precipitation datasets (SPDs), including (1) the Climate Prediction Center Morphing algorithm (CMORPH), (2) Global Satellite Mapping of Precipitation (GSMaP) Reanalysis, and (3) Tropical Rainfall Measuring Mission multi-satellite precipitation analysis (TRMM) 3B42, was conducted using the rain gauge-based Vietnam Gridded Precipitation dataset (VnGP) and rain gauge station data for Central Vietnam. The three SPDs were compared and evaluated for two contrasting topographic regions, i.e., the Vietnam Central Highlands (VCH) and the Vietnam Central Coast (VCC), during the rainy seasons from 2001 to 2010 at different spatial (grid and regional) and temporal (daily and monthly) scales. Widespread heavy rainfall (WHR) days caused by the Northeast Winter Monsoon (NM), the Inter-tropical Convergence Zone (ITCZ), and tropical cyclones (TCs) were also identified, and the accuracies of the SPDs in detecting heavy rainfall during the WHR days were evaluated. TRMM was shown to exhibit advantages over the other SPDs, regardless of the spatial and temporal scales. Although the CMORPH and GSMaP datasets appeared to correlate moderately well with the VnGP dataset and proved able to capture the spatial distribution of rainfall characteristics in the VCC, they significantly underestimated rainfall in the VCH. Regarding the capability to reproduce WHR events, the three SPDs exhibited better performance for TCs and the ITCZ than for the NM. TRMM exhibited the best performance among the three datasets, especially for rainfall thresholds ranging from 25 to 80 mm day−1. The GSMaP and CMORPH biases showed a clear dependence on elevation and zonal wind speed, indicating the need to improve correction methods.

Modeling nitrogen and phosphorus export with InVEST model in Bosten Lake basin of Northwest China.

Bosten Lake is an important region of Northwest China that has transformed from a freshwater lake to a saltwater lake since the 1970s. The water quality in the Bosten Lake basin is important for social and economic development, and nitrogen (N) and phosphorus (P) are the key indicators of water quality. The land use data, precipitation data and Digital Elevation Model (DEM) data with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were used to simulate the N and P exports of the Bosten Lake basin. The spatial and temporal dynamics of nitrogen and phosphorus exports, and the response of nitrogen and phosphorus exports to land use change and precipitation change were analyzed between 2000 and 2015. The results show that the amount of N and P exports increased during 2000–2015, and the N and P exports are mainly distributed around Bosten Lake. The N and P exports are greatly affected by cultivated land, built-up areas and grassland, while they are less affected by other land use types. The high precipitation areas with small exports of N and P are mainly distributed in mountain areas, while small precipitation areas with large exports of N and P are distributed in plains where the cultivated land and built-up areas are concentrated. The InVEST model can be used in Northwest China, and the statistical downscaling of reanalysis precipitation data can be used in the InVEST model to improve the simulation accuracy in the data scarce regions of Northwest China.

On the use of satellite, gauge, and reanalysis precipitation products for drought studies

Precipitation is a critical variable to monitor and predict meteorological drought. The WMO recommended standardized precipitation index (SPI) is calculated from gauge (i.e. GPCC), satellite-gauge (GPCP, CHIRPS), reanalysis (i.e. ERA-Interim, and MERRA-2), and satellite-gauge-reanalysis (i.e. MSWEP) over the global domain. Measured differences among the precipitation datasets include metrics such as percent area under drought, number of drought events, spread and correlation in the number of drought events, and critical success index in capturing moderate and severe-exceptional droughts. As precipitation products are available at different lengths and spatial resolutions, sensitivity of drought metrics to record-length and spatial resolution were explored. The results suggest that precipitation-based drought metrics can vary significantly with the choice of precipitation product, its record-lengths, and spatial resolution. These relationships also vary with the severity of drought events with more severe drought events being more sensitive to the differences in resolution and record length. The quantified variation among the products has to be recognized in the interpretation of drought events when a single or a subset of products used.

Role of satellite and reanalysis precipitation products in streamflow and sediment modeling over a typical alpine and gorge region in Southwest China

Satellite and reanalysis precipitation products, as new and complementary data sources, are attractive for hydro-meteorological applications, especially in data-sparse areas. This study evaluates the accuracy of two satellite precipitation products (TMPA 3B42V7 and PERSIANN-CDR) and one reanalysis precipitation product (NCEP-CFSR) against gauge precipitation observations with four statistical indices over the upstream of the Lancang River Basin (ULRB), Southwest China. The reliability and applicability of these precipitation products as inputs to a hydrological model (Soil and Water Assessment Tool, SWAT) for streamflow and sediment simulations are also assessed. Furthermore, we compare the spatial plots of extreme water yield (99 percentiles) and suspended sediment yield (99 percentiles) driven by the four precipitation sources, and investigate the spatial and temporal variability of water yield and suspended sediment yield over the ULRB. Results show that for direct comparisons with gauge precipitation observations, monthly TMPA 3B42V7 precipitation product performs the best at the basin scale with the smallest error and bias, and the highest correlation, followed by NCEP-CFSR, and PERSIANN-CDR. For modeling-based indirect inference, TMPA 3B42V7 presents great capability for streamflow and sediment simulations in the SWAT model on a monthly time step at the basin outlet, and PERSIANN-CDR also performs well. NCEP-CFSR shows acceptable skills in modeling sediment but unacceptable skills in modeling streamflow. Extreme water yield presents moderate spatial variability over the ULRB while extreme suspended sediment yield presents strong spatial variability. Water yield of this basin shows a decreasing trend during 1998–2008 while there is no obvious trend in suspended sediment yield in this period.

Representation of Precipitation Characteristics and Extremes in Regional Reanalyses and Satellite- and Gauge-Based Estimates over Western and Central Europe

Abstract This paper evaluates several daily precipitation products over western and central Europe, identifies and documents their respective strengths and shortcomings, and relates these to uncertainties associated with each of the products. We analyze one gauge-based, three satellite-based, and two reanalysis-based products using high-density rain gauge observations as reference. First, we assess spatial patterns and frequency distributions using aggregated statistics. Then, we determine the skill of precipitation event detection from these products with a focus on extremes, using temporally and spatially matched pairs of precipitation estimates. The results show that the quality of the datasets largely depends on the region, season, and precipitation characteristic addressed. The satellite and the reanalysis precipitation products are found to have difficulties in accurately representing precipitation frequency with local overestimations of more than 40%, which occur mostly in dry regions (all products) as well as along coastlines and over cold/frozen surfaces (satellite-based products). The frequency distributions of wet-day intensities are generally well reproduced by all products. Concerning the frequency distributions of wet-spell durations, the satellite-based products are found to have clear deficiencies for maritime-influenced precipitation regimes. Moreover, the analysis of the detection of extreme precipitation events reveals that none of the non-station-based datasets shows skill at the shortest temporal and spatial scales (1 day, 0.25°), but at and above the 3-day and 1.25° scale the products start to exhibit skill over large parts of the domain. Added value compared to coarser-resolution global benchmark products is found both for reanalysis and satellite-based products.

Classification of Localized Heavy Rainfall Events in South Korea

Characteristics of localized heavy rainfall events (LHREs) in South Korea are analyzed using high-resolution precipitation reanalysis data from the Korea Meteorological Administration (KMA). The LHREs, defined as summertime heavy rainfall events with 30 mm/h or more, are detected using in-situ measurements, and their spatio-temporal structures are classified into the three groups using precipitation reanalysis data. The first group is mainly composed of band-type precipitation around the central regions of South Korea (CHREs). The second group predominantly consists of LHREs in the southern regions (SHREs). The third group contains diverse and isolated rainfall events (IHREs). Each group exhibits a different rainfall persistency, diurnal cycle, and occurrence frequency. In general, CHREs peak mostly in July and have shorter time scales compared to SHREs. SHREs are more nocturnal in nature with maximum frequencies in both July and August. IHREs, which consist of 47% of total LHREs, are composed of both nocturnal rainfall events in the southern coastal regions and daytime rainfall events in the inland regions. These results suggest that LHREs in South Korea have multiple characteristics and cannot be simply explained by one or two representative mechanisms.

Merging multi-source precipitation products or merging their simulated hydrological flows to improve streamflow simulation

ABSTRACTSatellite and reanalysis precipitation products are widely utilized for streamflow simulation, which is one critical hydrological application, especially for ungauged regions. Possible ways to improve streamflow simulation are investigated in this study by merging multi-source precipitation products, or directly merging streamflow simulated with different precipitation products. Two satellite-based precipitation products, Tropical Rainfall Measuring Mission (3B42 Version 7) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and one reanalysis precipitation product, National Centers for Environment Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) are selected. Bayesian model averaging (BMA) is used to merge multi-source precipitation estimates and streamflow simulations. The results show that merging multi-source precipitation products made little difference to improve streamflow simulation. Merging multi-source streamflow simulations using the BMA generally achieved better performance on streamflow simulation, indicating that this approach is more efficient than merging multi-source precipitation products.

Improving Monsoon Precipitation Prediction Using Combined Convolutional and Long Short Term Memory Neural Network

Precipitation downscaling is widely employed for enhancing the resolution and accuracy of precipitation products from general circulation models (GCMs). In this study, we propose a novel statistical downscaling method to foster GCMs’ precipitation prediction resolution and accuracy for the monsoon region. We develop a deep neural network composed of a convolution and Long Short Term Memory (LSTM) recurrent module to estimate precipitation based on well-resolved atmospheric dynamical fields. The proposed model is compared against the GCM precipitation product and classical downscaling methods in the Xiangjiang River Basin in South China. Results show considerable improvement compared to the European Centre for Medium-Range Weather Forecasts (ECMWF)-Interim reanalysis precipitation. Also, the model outperforms benchmark downscaling approaches, including (1) quantile mapping, (2) the support vector machine, and (3) the convolutional neural network. To test the robustness of the model and its applicability in practical forecasting, we apply the trained network for precipitation prediction forced by retrospective forecasts from the ECMWF model. Compared to the ECMWF precipitation forecast, our model makes better use of the resolved dynamical field for more accurate precipitation prediction at lead times from 1 day up to 2 weeks. This superiority decreases with the forecast lead time, as the GCM’s skill in predicting atmospheric dynamics is diminished by the chaotic effect. Finally, we build a distributed hydrological model and force it with different sources of precipitation inputs. Hydrological simulation forced with the neural network precipitation estimation shows significant advantage over simulation forced with the original ERA-Interim precipitation (with NSE value increases from 0.06 to 0.64), and the performance is only slightly worse than the observed precipitation forced simulation (NSE = 0.82). This further proves the value of the proposed downscaling method, and suggests its potential for hydrological forecasts.