Accuracy Of Precipitation Estimates Research Articles

Improving daily spatial precipitation estimates by merging gauge observation with multiple satellite-based precipitation products based on the geographically weighted ridge regression method

Merging gauge observation with a single original satellite-based precipitation product (SPP) is a common approach to generate spatial precipitation estimates. For the generation of high-quality precipitation maps, however, this common method has two drawbacks: (1) the spatial resolutions of original SPPs are still too coarse; and (2) a single SPP can’t capture the spatial pattern of precipitation well. To overcome these drawbacks, a two-step scheme consisting of downscaling and fusion was proposed to merge gauge observation with multiple SPPs. In both downscaling and fusion steps, the geographically weighted ridge regression (GWRR) method, which is a combination of the geographically weighted regression (GWR) method and the ridge regression method, is proposed and implemented to generate improved spatial precipitation estimates by overcoming the collinearity problem of the pure GWR method. The proposed two-step merging scheme was applied to Xijiang Basin of China for deriving daily precipitation estimates from the data of both gauge observation and four near real-time SPPs (i.e., TMPA-3B42RT, CMORPH, PERSIANN and GSMaP_NRT) during the period of 2010–2017. The results showed that: (1) the collinearity problem caused by GWR was not serious in downscaling but serious enough to prevent GWR from being directly used in the fusion; and (2) the proposed two-step merging scheme significantly improved the spatial resolution and accuracy of precipitation estimates over the original SPPs. Comparisons also showed that, in the second step (fusion) of the merging scheme, the use of multiple SPPs provided more reliable spatial precipitation estimates than using a single SPP.

An improved post-processing technique for automatic precipitation gauge time series

Abstract. The unconditioned data retrieved from accumulating automated weighing precipitation gauges are inherently noisy due to the sensitivity of the instruments to mechanical and electrical interference. This noise, combined with diurnal oscillations and signal drift from evaporation of the bucket contents, can make accurate precipitation estimates challenging. Relative to rainfall, errors in the measurement of solid precipitation are exacerbated because the lower accumulation rates are more impacted by measurement noise. Precipitation gauge measurement post-processing techniques are used by Environment and Climate Change Canada in research and operational monitoring to filter cumulative precipitation time series derived from high-frequency, bucket-weight measurements. Four techniques are described and tested here: (1) the operational 15 min filter (O15), (2) the neutral aggregating filter (NAF), (3) the supervised neutral aggregating filter (NAF-S), and (4) the segmented neutral aggregating filter (NAF-SEG). Inherent biases and errors in the first two post-processing techniques have revealed the need for a robust automated method to derive an accurate noise-free precipitation time series from the raw bucket-weight measurements. The method must be capable of removing random noise, diurnal oscillations, and evaporative (negative) drift from the raw data. This evaluation primarily focuses on cold-season (October to April) accumulating automated weighing precipitation gauge data at 1 min resolution from two sources: a control (pre-processed time series) with added synthetic noise and drift and raw (minimally processed) data from several WMO Solid Precipitation Intercomparison Experiment (SPICE) sites. Evaluation against the control with synthetic noise shows the effectiveness of the NAF-SEG technique, recovering 99 %, 100 %, and 102 % of the control total precipitation for low-, medium-, and high-noise scenarios respectively for the cold-season (October–April) and 97 % of the control total precipitation for all noise scenarios in the warm season (May–September). Among the filters, the fully automated NAF-SEG produced the highest correlation coefficients and lowest root-mean-square error (RMSE) for all synthetic noise levels, with comparable performance to the supervised and manually intensive NAF-S method. Compared to the O15 method in cold-season testing, NAF-SEG shows a lower bias in 37 of 44 real-world test cases, a similar bias in 5 cases, and a higher bias in 2 cases. In warm-season testing, the NAF-SEG bias was lower or similar in 7 of 11 cases. The results indicate that the NAF-SEG post-processing technique provides substantial improvement over current automated techniques, reducing both uncertainty and bias in accumulating-gauge measurements of precipitation, with a 24 h latency. Because it cannot be implemented in real time, we recommend that NAF-SEG be used in combination with a simple real-time filter, such as the O15 or similar filter.

Ground radar precipitation estimation with deep learning approaches in meteorological private cloud

Accurate precipitation estimation is significant since it matters to everyone on social and economic activities and is of great importance to monitor and forecast disasters. The traditional method utilizes an exponential relation between radar reflectivity factors and precipitation called Z-R relationship which has a low accuracy in precipitation estimation. With the rapid development of computing power in cloud computing, recent researches show that artificial intelligence is a promising approach, especially deep learning approaches in learning accurate patterns and appear well suited for the task of precipitation estimation, given an ample account of radar data. In this study, we introduce these approaches to the precipitation estimation, proposing two models based on the back propagation neural networks (BPNN) and convolutional neural networks (CNN) respectively, to compare with the traditional method in meteorological service systems. The results of the three approaches show that deep learning algorithms outperform the traditional method with 75.84% and 82.30% lower mean square errors respectively. Meanwhile, the proposed method with CNN achieves a better performance than that with BPNN for its ability to preserve the spatial information by maintaining the interconnection between pixels, which improves 26.75% compared to that with BPNN.

Bias Correction of High‐Resolution Regional Climate Model Precipitation Output Gives the Best Estimates of Precipitation in Himalayan Catchments

AbstractThe need to provide accurate estimates of precipitation over catchments in the Hindu Kush, Karakoram, and Himalaya mountain ranges for hydrological and water resource systems assessments is widely recognized, as is identifying precipitation extremes for assessing hydro‐meteorological hazards. Here, we investigate the ability of bias‐corrected Weather Research and Forecasting model output at 5‐km grid spacing to reproduce the spatiotemporal variability of precipitation for the Beas and Sutlej river basins in the Himalaya, measured by 44 stations spread over the period 1980 to 2012. For the Sutlej basin, we find that the raw (uncorrected) model output generally underestimated annual, monthly, and (particularly low‐intensity) daily precipitation amounts. For the Beas basin, the model performance was better, although biases still existed. It is speculated that the cause of the dry bias over the Sutlej basin is a failure of the model to represent an early‐morning maximum in precipitation during the monsoon period, which is related to excessive precipitation falling upwind. However, applying a nonlinear bias‐correction method to the model output resulted in much better results, which were superior to precipitation estimates from reanalysis and two gridded datasets. These findings highlight the difficulty in using current gridded datasets as input for hydrological modeling in Himalayan catchments, suggesting that bias‐corrected high‐resolution regional climate model output is in fact necessary. Moreover, precipitation extremes over the Beas and Sutlej basins were considerably underrepresented in the gridded datasets, suggesting that bias‐corrected regional climate model output is also necessary for hydro‐meteorological risk assessments in Himalayan catchments.

Ground Validation of GPM IMERG Precipitation Products over Iran

Accurate estimation of precipitation is crucial for fundamental input to various hydrometeorological applications. Ground-based precipitation data suffer limitations associated with spatial resolution and coverage; hence, satellite precipitation products can be used to complement traditional rain gauge systems. However, the satellite precipitation data need to be validated before extensive use in the applications. Hence, we conducted a thorough validation of the Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals (IMERG) product for all of Iran. The study focused on investigating the performance of daily and monthly GPM IMERG (early, late, final, and monthly) products by comparing them with ground-based precipitation data at synoptic stations throughout the country (2014–2017). The spatial and temporal performance of the GPM IMERG was evaluated using eight statistical criteria considering the rainfall index at the country level. The rainfall detection ability index (POD) showed that the best IMERG product’s performance is for the spring season while the false alarm ratio (FAR) index indicated the inferior performance of the IMERG products for the summer season. The performance of the products generally increased from IMERG-Early to –Final according to the relative bias (rBIAS) results while, based on the quantile-quantile (Q-Q) plots, the IMERG-Final could not be suggested for the applications relying on extreme rainfall estimates compared to IMERG-Early and -Late. The results in this paper improve the understanding of IMERG product’s performance and open a door to future studies regarding hydrometeorological applications of these products in Iran.

A Machine Learning System for Precipitation Estimation Using Satellite and Ground Radar Network Observations

Space-based precipitation products are often used for regional and/or global hydrologic modeling and climate studies. A number of precipitation products at multiple space and time scales have been developed based on satellite observations. However, their accuracy is limited due to the restrictions on spatiotemporal sampling of the satellite sensors and the applied parametric retrieval algorithms. Similarly, a ground-based weather radar is widely used for quantitative precipitation estimation (QPE), especially after the implementation of dual-polarization capability and urban scale deployment of high-resolution X-band radar networks. Ground-based radars are often used for the validation of various spaceborne measurements and products. This article introduces a novel machine learning-based data fusion framework to improve the satellite-based precipitation retrievals by incorporating dual-polarization measurements from a ground radar network. The prototype architecture of this fusion system is detailed. In particular, a deep learning multi-layer perceptron (MLP) model is designed to produce the rainfall estimates using the geostationary satellite infrared (IR) data and low earth orbit satellite passive microwave (PMW)-based retrievals as inputs. The high-quality rainfall products from the ground radar network are used as the target labels to train this MLP model. An urban scale demonstration study over the Dallas-Fort Worth (DFW) metroplex is presented. In addition, the Climate Prediction Center morphing technique (i.e., CMORPH) is adopted for preprocessing of the satellite observations. Rainfall products from this deep learning system are evaluated using the standard CMORPH products. The results show that the proposed data fusion framework can be used for generating accurate precipitation estimates and could be considered as an alternative tool for developing future satellite retrieval algorithms.

Performance Assessment of SM2RAIN-CCI and SM2RAIN-ASCAT Precipitation Products over Pakistan

Accurate estimation of precipitation from satellite precipitation products (PPs) over the complex topography and diverse climate of Pakistan with limited rain gauges (RGs) is an arduous task. In the current study, we assessed the performance of two PPs estimated from soil moisture (SM) using the SM2RAIN algorithm, SM2RAIN-CCI and SM2RAIN-ASCAT, on the daily scale across Pakistan during the periods 2000–2015 and 2007–2015, respectively. Several statistical metrics, i.e., Bias, unbiased root mean square error (ubRMSE), Theil’s U, and the modified Kling–Gupta efficiency (KGE) score, and four categorical metrics, i.e., probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), and Bias score, were used to evaluate these two PPs against 102 RGs observations across four distinct climate regions, i.e., glacial, humid, arid and hyper-arid regions. Total mean square error (MSE) is decomposed into systematic (MSEs) and random (MSEr) error components. Moreover, the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TRMM TMPA 3B42v7) was used to assess the performance of SM2RAIN-based products at 0.25° scale during 2007–2015. Results shows that SM2RAIN-based product highly underestimated precipitation in north-east and hydraulically developed areas of the humid region. Maximum underestimation for SM2RAIN-CCI and SM2RIAN-ASCAT were 58.04% and 42.36%, respectively. Precipitation was also underestimated in mountainous areas of glacial and humid regions with maximum underestimations of 43.16% and 34.60% for SM2RAIN-CCI. Precipitation was overestimated along the coast of Arabian Sea in the hyper-arid region with maximum overestimations for SM2RAIN-CCI (SM2RAIN-ASCAT) of 59.59% (52.35%). Higher ubRMSE was observed in the vicinity of hydraulically developed areas. Theil’s U depicted higher accuracy in the arid region with values of 0.23 (SM2RAIN-CCI) and 0.15 (SM2RAIN-ASCAT). Systematic error components have larger contribution than random error components. Overall, SM2RAIN-ASCAT dominates SM2RAIN-CCI across all climate regions, with average percentage improvements in bias (27.01% in humid, 5.94% in arid, and 6.05% in hyper-arid), ubRMSE (19.61% in humid, 20.16% in arid, and 25.56% in hyper-arid), Theil’s U (9.80% in humid, 28.80% in arid, and 26.83% in hyper-arid), MSEs (24.55% in humid, 13.83% in arid, and 8.22% in hyper-arid), MSEr (19.41% in humid, 29.20% in arid, and 24.14% in hyper-arid) and KGE score (5.26% in humid, 28.12% in arid, and 24.72% in hyper-arid). Higher uncertainties were depicted in heavy and intense precipitation seasons, i.e., monsoon and pre-monsoon. Average values of statistical metrics during monsoon season for SM2RAIN-CCI (SM2RAIN-ASCAT) were 20.90% (17.82%), 10.52 mm/day (8.61 mm/day), 0.47 (0.43), and 0.47 (0.55) for bias, ubRMSE, Theil’s U, and KGE score, respectively. TMPA outperformed SM2RAIN-based products across all climate regions. SM2RAIN-based datasets are recommended for agricultural water management, irrigation scheduling, flood simulation and early flood warning system (EFWS), drought monitoring, groundwater modeling, and rainwater harvesting, and vegetation and crop monitoring in plain areas of the arid region.

A conceptually superior variant of Shepard's method with modified neighbourhood selection for precipitation interpolation

AbstractThe accuracy of gridded precipitation data depends on the availability of a uniformly spaced rain gauge network and an appropriate spatial interpolation method that considers the rainfall variability and other factors that influence the precipitation patterns in the region of interest. In the current study, conceptually superior variants of a widely used spatial interpolation algorithm, Shepard's method, are proposed, formulated and evaluated to overcome one of the major limitations in neighbourhood selection, that is, arbitrary selection of rain gauges. The variants provide mechanisms to objectively select the rain gauges (control points) based on correlation (variant 1), distribution similarity (variant 2) and a combination of both (variant 3). The improved variants were used in the development of gridded rainfall data at a resolution of 5 km over the Kabini River basin in south India, and in the state of Kentucky, United States. Results from multiple experiments using the original Shepard's method and its variants indicate improvements in the accuracy of precipitation estimates. Also, these variants have preserved the site‐specific statistics and distributional characteristics of the rainfall data. A variant 1 that uses a correlation‐based neighbourhood selection criterion performed better for daily and monthly data compared to others and is suitable for generation of gridded rainfall data. The variant 1 when used with information from clustering of sites for selection of the neighbours has led to improvement in gridded precipitation data estimates. The proposed variant 1 can also be used for point data estimation useful for filling missing data at any site.

A Reassessment of North American River Basin Cool‐Season Precipitation: Developments From a New Mountain Climatology Data Set

AbstractCharacterizing hydrological processes on large scales is challenging due to limitations of observational networks, remoting sensing platforms, and modeling techniques. Water balances have larger uncertainties in mountain regions, where orographic processes produce high spatial variability in precipitation patterns and snow accumulation. Recent work suggests current water budgets underestimate mountain snow water storage, perhaps indicating biases in modeled precipitation. We assess whether global hydroclimate data sets underestimate precipitation for six North American watersheds, ranging from 3–70% mountainous. By selecting a single representative year for each watershed, we compare relatively high‐resolution precipitation estimates from the Weather Research and Forecasting (WRF) regional climate model with four global products: Modern‐Era Retrospective Analysis for Research and Applications, version 2, the Global Land Data Assimilation System, the Global Precipitation Climatology Project, and the Climate Research Unit's climate data set. Comparisons to WRF precipitation suggest that observation‐based gridded data products do not produce reasonable estimates of watershed‐scale cool‐season precipitation, underestimating by 1–69%. The Global Precipitation Climatology Project and the Climate Research Unit data set have average biases of −26% and −38%, respectively. The Modern‐Era Retrospective Analysis for Research and Applications version 2 and the Global Land Data Assimilation System show smaller underestimates relative to WRF (−17% and −21%, respectively), with nearly all mean bias from the mountains (underestimated by 27% and 39%) rather than the topographically simpler lowlands (underestimated by 5% and 2%). We suggest global products fail to capture orographic enhancement of precipitation, resulting in large underestimates of precipitation, snowfall, and snow water storage in mountains of selected North American watersheds, which highlights the need for more accurate precipitation estimates to accurately assess spatiotemporal variations in the water cycle.

How do GPM IMERG precipitation estimates perform as hydrological model forcing? Evaluation for 300 catchments across Mainland China

Accurate precipitation estimates are essential for reliable hydrological simulations of fluxes and states. Satellite precipitation products have been widely used for hydrological applications, especially for ungauged regions. The Global Precipitation Measurement (GPM) product is starting to provide a new generation of precipitation estimates. This work first evaluates several GPM Integrated Multi-satellite Retrievals (IMERG) against gauge-based precipitation over Mainland China for a two-year period (2016–2017), and then explores the feasibility of using GPM to force a lumped hydrological model over 300 catchments of varying size and climate.The analysis indicates that IMERG Final run estimates (gauge-adjusted) agree well with gauge-based precipitation at daily scale, while IMERG Early run estimates (near-real time without gauge adjustments) are consistently lower than the rain gauge records. Nevertheless, both Final run and Early run estimates are slightly better than TRMM 3B42 estimates in terms of rainfall detection skill. Moreover, all three products show better skill over humid regions than over high elevation zones.When used as hydrological model forcing, IMERG Final and Early estimates provide comparable performances to gauge-based precipitation. However, TRMM 3B42 performs relatively poor in terms of hydrological simulation. Moreover, models generally perform better in humid than in arid areas for all three products. The evaluation sheds light on how IMERG-driven hydrological predictions perform in different regions across China. The findings also indicate the potential of IMERG Early run for flood forecasting and Final run for climatological/hydrological modeling in ungauged or poorly gauged basins.

Improving Precipitation Estimation Using Convolutional Neural Network

AbstractPrecipitation process is generally considered to be poorly represented in numerical weather/climate models. Statistical downscaling (SD) methods, which relate precipitation with model resolved dynamics, often provide more accurate precipitation estimates compared to model's raw precipitation products. We introduce the convolutional neural network model to foster this aspect of SD for daily precipitation prediction. Specifically, we restrict the predictors to the variables that are directly resolved by discretizing the atmospheric dynamics equations. In this sense, our model works as an alternative to the existing precipitation‐related parameterization schemes for numerical precipitation estimation. We train the model to learn precipitation‐related dynamical features from the surrounding dynamical fields by optimizing a hierarchical set of spatial convolution kernels. We test the model at 14 geogrid points across the contiguous United States. Results show that provided with enough data, precipitation estimates from the convolutional neural network model outperform the reanalysis precipitation products, as well as SD products using linear regression, nearest neighbor, random forest, or fully connected deep neural network. Evaluation for the test set suggests that the improvements can be seamlessly transferred to numerical weather modeling for improving precipitation prediction. Based on the default network, we examine the impact of the network architectures on model performance. Also, we offer simple visualization and analyzing approaches to interpret the models and their results. Our study contributes to the following two aspects: First, we offer a novel approach to enhance numerical precipitation estimation; second, the proposed model provides important implications for improving precipitation‐related parameterization schemes using a data‐driven approach.

Developing an Ensemble Precipitation Algorithm from Satellite Products and Its Topographical and Seasonal Evaluations Over Pakistan

Accurate estimation of precipitation is critical for hydrological, meteorological, and climate models. This study evaluates the performance of satellite-based precipitation products (SPPs) including Global Precipitation Measurement (GPM)-based Integrated Multi-Satellite Retrievals for GPM (IMERG), Tropical Rainfall Measurement Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA 3B43-v7), Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network (PERSIANN), and PERSIANN-CDR (Climate Data Record), over Pakistan based on Surface Precipitation Gauges (SPGs) at spatial and temporal scales. A novel ensemble precipitation (EP) algorithm is developed by selecting the two best SPPs using the Paired Sample t-test and Principal Component Analysis (PCA). The SPPs and EP algorithm are evaluated over five climate zones (ranging from glacial Zone-A to hyper-arid Zone-E) based on six statistical metrics. The result indicated that IMERG outperformed all other SPPs, but still has considerable overestimation in the highly elevated zones (+20.93 mm/month in Zone-A) and relatively small underestimation in the arid zone (−2.85 mm/month in Zone-E). Based on the seasonal evaluation, IMERG and TMPA overestimated precipitation during pre-monsoon and monsoon seasons while underestimating precipitation during the post-monsoon and winter seasons. However, the developed EP algorithm significantly reduced the errors both on spatial and temporal scales. The only limitation of the EP algorithm is relatively poor performance at high elevation as compared to low elevations.

Towards combining GPM and MFG observations to monitor near real time heavy precipitation at fine scale over India and nearby oceanic regions

This is the first attempt to merge highly accurate precipitation estimates from Global Precipitation Measurement (GPM) with gap free satellite observations from Meteosat to develop a regional rainfall monitoring algorithm to estimate heavy rainfall over India and nearby oceanic regions. Rainfall signature is derived from Meteosat observations and is co-located against rainfall from GPM to establish a relationship between rainfall and signature for various rainy seasons. This relationship can be used to monitor rainfall over India and nearby oceanic regions. Performance of this technique was tested by applying it to monitor heavy precipitation over India. It is reported that our algorithm is able to detect heavy rainfall. It is also reported that present algorithm overestimates rainfall areal spread as compared to rain gauge based rainfall product. This deficiency may arise from various factors including uncertainty caused by use of different sensors from different platforms (difference in viewing geometry from MFG and GPM), poor relationship between warm rain (light rain) and IR brightness temperature, and weak characterization of orographic rain from IR signature. We validated hourly rainfall estimated from the present approach with independent observations from GPM. We also validated daily rainfall from this approach with rain gauge based product from India Meteorological Department (IMD). Present technique shows a Correlation Coefficient (CC) of 0.76, a bias of −2.72mm, a Root Mean Square Error (RMSE) of 10.82mm, Probability of Detection (POD) of 0.74, False Alarm Ratio (FAR) of 0.34 and a Skill score of 0.36 with daily rainfall from rain gauge based product of IMD at 0.25° resolution. However, FAR reduces to 0.24 for heavy rainfall events. Validation results with rain gauge observations reveal that present technique outperforms available satellite based rainfall estimates for monitoring heavy rainfall over Indian region.

도시홍수 예측을 위한 레이더 적정 관측주기 분석

In the current practice, the radar scan method for precipitation estimation periodically observes regions where heavy rainfall is expected with the rotation of the radar. This is different from ground observation devices, such as rain gauge, which continuously observe cumulative precipitation. Different observation methods can cause the precipitation estimation accuracy to vary spatio-temporally. Therefore, quantitative precipitation estimation is needed to improve the accuracy of areal rainfall estimation according to the radar scan interval. For this, an error analysis has been performed according to the scan time in this study after instantaneous observation data were generated at each scan interval using the minute-unit precipitation data obtained by a ground gauge through an observation method that is similar to radar observation. The analysis result showed that there was a drastic increase in the uncertainty of rainfall estimation at scan intervals of 10 and 5 minutes compared with that at scan intervals of 3 and 2 minutes in all cases that relative error, absolute elative error, and correlation of the instantaneous estimation as to the continuous estimation. In the analysis with the actual radar observation data, the relative error tended to increase as the scan interval increased, and it tended to increase drastically with intervals equal to or larger than 3 minutes.

Kriging and Local Polynomial Methods for Blending Satellite-Derived and Gauge Precipitation Estimates to Support Hydrologic Early Warning Systems

Robust estimates of precipitation in space and time are important for efficient natural resource management and for mitigating natural hazards. This is particularly true in regions with developing infrastructure and regions that are frequently exposed to extreme events. Gauge observations of rainfall are sparse but capture the precipitation process with high fidelity. Due to its high resolution and complete spatial coverage, satellite-derived rainfall data are an attractive alternative in data-sparse regions and are often used to support hydrometeorological early warning systems. Satellite-derived precipitation data, however, tend to underrepresent extreme precipitation events. Thus, it is often desirable to blend spatially extensive satellite-derived rainfall estimates with high-fidelity rain gauge observations to obtain more accurate precipitation estimates. In this research, we use two different methods, namely, ordinary kriging and κ-nearest neighbor local polynomials, to blend rain gauge observations with the Climate Hazards Group Infrared Precipitation satellite-derived precipitation estimates in data-sparse Central America and Colombia. The utility of these methods in producing blended precipitation estimates at pentadal (five-day) and monthly time scales is demonstrated. We find that these blending methods significantly improve the satellite-derived estimates and are competitive in their ability to capture extreme precipitation.

Temporal and Spatial Assessment of Four Satellite Rainfall Estimates over French Guiana and North Brazil

Satellite precipitation products are a means of estimating rainfall, particularly in areas that are sparsely equipped with rain gauges. The Guiana Shield is a region vulnerable to high water episodes. Flood risk is enhanced by the concentration of population living along the main rivers. A good understanding of the regional hydro-climatic regime, as well as an accurate estimation of precipitation is therefore of great importance. Unfortunately, there are very few rain gauges available in the region. The objective of the study is then to compare satellite rainfall estimation products in order to complement the information available in situ and to perform a regional analysis of four operational precipitation estimates, by partitioning the whole area under study into a homogeneous hydro-climatic region. In this study, four satellite products have been tested, TRMM TMPA (Tropical Rainfall Measuring Mission Multisatellite Precipitation Analysis) V7 (Version 7) and RT (real time), CMORPH (Climate Prediction Center (CPC) MORPHing technique) and PERSIANN (Precipitation Estimation from Remotely-Sensed Information using Artificial Neural Network), for daily rain gauge data. Product performance is evaluated at daily and monthly scales based on various intensities and hydro-climatic regimes from 1 January 2001 to 30 December 2012 and using quantitative statistical criteria (coefficient correlation, bias, relative bias and root mean square error) and quantitative error metrics (probability of detection for rainy days and for no-rain days and the false alarm ratio). Over the entire study period, all products underestimate precipitation. The results obtained in terms of the hydro-climate show that for areas with intense convective precipitation, TMPA V7 shows a better performance than other products, especially in the estimation of extreme precipitation events. In regions along the Amazon, the use of PERSIANN is better. Finally, in the driest areas, TMPA V7 and PERSIANN show the same performance.

Location and density of rain gauges for the estimation of spatial varying precipitation

Accurate estimation of precipitation and its spatial variability is crucial for reliable discharge simulations. Although radar and satellite based techniques are becoming increasingly widespread, quantitative precipitation estimates based on point rain gauge measurement interpolation are, and will continue to be in the foreseeable future, widely used. However, the ability to infer spatially distributed data from point measurements is strongly dependent on the number, location and reliability of measurement stations.In this study we quantitatively investigated the effect of rain gauge network configurations on the spatial interpolation by using the operational hydrometeorological sensor network in the Thur river basin in north‐eastern witzerland as a test case. Spatial precipitation based on a combination of radar and rain gauge data provided by eteowiss was assumed to represent the true precipitation values against which the precipitation interpolation from the sensor network was evaluated. The performance using scenarios with both increased and decreased station density were explored. The catchment‐average interpolation error indices significantly improve up to a density of 24 rain gauges per 1000 km2, beyond which improvements were negligible. However, a reduced rain gauge density in the higher parts of the catchment resulted in a noticeable decline of the performance indices. An evaluation based on precipitation intensity thresholds indicated a decreasing performance for higher precipitation intensities. The results of this study emphasise the benefits of dense and adequately distributed rain gauge networks.

A Bayesian kriging approach for blending satellite and ground precipitation observations

AbstractDrought and flood management practices require accurate estimates of precipitation. Gauge observations, however, are often sparse in regions with complicated terrain, clustered in valleys, and of poor quality. Consequently, the spatial extent of wet events is poorly represented. Satellite‐derived precipitation data are an attractive alternative, though they tend to underestimate the magnitude of wet events due to their dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Here we offer a Bayesian kriging approach for blending precipitation gauge data and the Climate Hazards Group Infrared Precipitation satellite‐derived precipitation estimates for Central America, Colombia, and Venezuela. First, the gauge observations are modeled as a linear function of satellite‐derived estimates and any number of other variables—for this research we include elevation. Prior distributions are defined for all model parameters and the posterior distributions are obtained simultaneously via Markov chain Monte Carlo sampling. The posterior distributions of these parameters are required for spatial estimation, and thus are obtained prior to implementing the spatial kriging model. This functional framework is applied to model parameters obtained by sampling from the posterior distributions, and the residuals of the linear model are subject to a spatial kriging model. Consequently, the posterior distributions and uncertainties of the blended precipitation estimates are obtained. We demonstrate this method by applying it to pentadal and monthly total precipitation fields during 2009. The model's performance and its inherent ability to capture wet events are investigated. We show that this blending method significantly improves upon the satellite‐derived estimates and is also competitive in its ability to represent wet events. This procedure also provides a means to estimate a full conditional distribution of the “true” observed precipitation value at each grid cell.

이중 편파 레이더 자료를 이용한 강수 추정 알고리즘의 오차 분석

강수량의 정확한 추정은 자연 재해의 예측 및 방지를 위해 필수적이다. 최근 강우레이더는 높은 시공간 해상도로 정량적 강수 추정(quantitative precipitation estimation; QPE)을 수행하는 기술로 주목받고 있다. 특히, 레이더의 강수 추정 성능은 다양한 규모에서 여러 수문 기상 변수를 관측하는 이중 편파 기술의 도입에 의해 향상되었다. 이중 편파 레이더는 다양한 매개변수를 측정하기 때문에, 기존의 반사도-강수 관계만을 활용하는 단일 편파 레이더에 비해 강수 추정 정확도 향상이 가능하다. 본 연구의 목적은 이중 편파 레이더에 대한 대표적인 강수 추정 방법인 JPOLE(Ryzhkov et al., 2005a)와 CSU(Cifelli et al., 2011) 알고리즘의 국내 강우레이더 자료에 대한 적용성 및 불확실성을 평가하는 것이다. 이를 위해 S밴드 이중 편파 레이더인 비슬산 강우레이더의 2012년 호우사상을 대상으로 두 가지 알고리즘을 적용하여 강수를 각각 추정하고, 일별 및 시간별 강수의 시공간 분포 정확성에 대해 지상관측 강수와 비교하였다. 또한 관측지점 별 오차를 분석하여 레이더로부터의 거리 및 관측지점 고도와 레이더 정확도와의 상관성을 분석하였다. 분석 결과, JPOLE 및 CSU 알고리즘 모두 표준화된 평균절대오차(nMAE)를 기준으로 일별 강수에 대해 0.23, 시간별 강수 기준으로 0.35 이내의 비교적 정확한 강수량 추정 결과를 산출하였다. 모든 정확도 평가 지표 및 기준에 대해서 JPOLE 알고리즘이 CSU 알고리즘에 비해 우수한 정확도를 보였으나, 그 차이는 평균제곱근오차(RMSE)를 기준으로 50 mm/day 이상 일별 강수에 대해 5.5 mm(약 17%차이), 10 mm/hr 이상 시간별 강수에 대해 0.6 mm(약 8%차이)였다. 관측소 위치 별 오차 분석 결과, 불확실성이 높은 지점의 위치는 일정한 경향이 없었으나, 지상관측지점이 레이더로부터 거리 60 km, 해발고도 고도 100 m 이내의 조건에서 정확도 높은 관측지점이 존재하였다. Accurate estimation of precipitation is essential to predict and prevent natural disasters (e.g. flood, landslide, and heavy snowfall). Recently, weather radars have become a popular tool for quantitative precipitation estimation (QPE) with high spatio-temporal resolution. Especially, in the last decade, QPE performance has been improved by introduction of polarimetric technology to observe multiple hydrometeorological variables at various scales. By being able to measure variables such as the differential reflectivity, specific differential phase, and cross-correlation coefficient, the reliability of estimation has significantly improved as compared to the reflectivity-based method (Ryzhkov et al., 2005a). However, QPEs using dual polarization radar data are still subject to uncertainties resulting from rainfall conversion relationships, combination methods of different parameters, and sampling errors. In this study, we assessed the uncertainty and applicability of conventional QPE algorithms, such as JPOLE (Ryzhkov et al., 2005a) and CSU (Cifelli et al., 2011) algorithms, using the dual polarization radar at Mt. Biseul in the south-eastern part of the Korean Peninsula. Analysis results illustrated that the JPOLE algorithm outperformed the CSU algorithm slightly for daily and hourly rainfall analysis using various metrics. The higher accuracy was found in the stations located within less than 60 km from radar and 100 m in the elevation.

Evaluation of TRMM Precipitation Products over Indian Subcontinent

Abstract. Quantitatively accurate estimates of precipitation at high spatial resolutions often rely on a combination of individual satellite retrieved estimates merged using suitable techniques. Precipitation estimates from these merged data products are extensively being used by scientists and researchers for various hydro meteorological and climatological applications. The intent of this study is to evaluate the performance of gridded precipitation estimates from Tropical Rainfall Measuring Mission (TRMM) namely the 3A12 (from passive radar TRMM Microwave Imager i.e. TMI), 3A25 (from active radar Precipitation Radar i.e., PR) and 3B43 (combined data product). For this purpose, the present study utilizes 6 years of daily rainfall of spatial resolution 1° × 1°. Data period chosen is the Indian summer monsoonal months of JJAS from June 2002 to September 2007. Results indicate that over India, the TMI derived 3A12 product tends to overestimate rainfall compared to the PR derived 3A25 product for the seasonal data period. The 3A12 data product represents gridded monthly accumulations of the level 2 TMI derived 2A12 data product. The land rainfall algorithm of 2A12 is well known to be dependent on the 85 GHz high frequency channel. The present study conducts sensitivity analysis of 20 combinations of TMI frequency channels with respect to rainfall rate. The results conclude that a combination of TMI low frequency channels are better suited to model rainfall thereby questioning the usage of 85 GHz frequency channel for the generation of 2A12 rainfall estimates.