Frequency Bias Index Research Articles

Validation of the CHIRPS precipitation estimate in a Brazilian Cerrado area

Measuring and understanding precipitation over space and time is essential for several human activities. Satellite remote sensing products are presented as an alternative to the low-density network of pluviometric stations. Thus, the objective of the present study was to evaluate precipitation estimates obtained by the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) product, from 1981 to 2020, in the Rio Grande basin, Bahia state, Brazil. This watershed has about 75,000 km², is inserted in one of the most active agricultural frontiers in the world and has undergone significant changes in land use and occupation and changes in rainfall patterns. We compared data from 11 series of conventional (Hidroweb) and CHIRPS-derived surface stations on monthly and seasonal scales, using statistical metrics – relative bias (BIAS), correlation coefficient (R²), mean error (ME), and mean squared error (RMSE) – and categorical – correct proportion (PC), probability of detection (POD), frequency bias index (FBI), false alarm (FAR). Results showed that the CHIRPS precipitation estimates provided good responses compared to the data observed in conventional surface meteorological stations. Furthermore, CHIRPS products accurately detected rain with an excellent capacity to represent the space-time precipitation variation.

A D-vine copula-based quantile regression towards merging satellite precipitation products over rugged topography: a case study in the upper Tekeze–Atbara Basin

Abstract. Precipitation is a vital key element in various studies of hydrology, flood prediction, drought monitoring, and water resource management. The main challenge in conducting studies over remote regions with rugged topography is that weather stations are usually scarce and unevenly distributed. However, open-source satellite-based precipitation products (SPPs) with a suitable resolution provide alternative options in these data-scarce regions, which are typically associated with high uncertainty. To reduce the uncertainty of individual satellite products, we have proposed a D-vine copula-based quantile regression (DVQR) model to merge multiple SPPs with rain gauges (RGs). The DVQR model was employed during the 2001–2017 summer monsoon seasons and compared with two other quantile regression methods based on the multivariate linear (MLQR) and the Bayesian model averaging (BMAQ) techniques, respectively, and with two traditional merging methods – the simple modeling average (SMA) and the one-outlier-removed average (OORA) – using descriptive and categorical statistics. Four SPPs have been considered in this study, namely, Tropical Applications of Meteorology using SATellite (TAMSAT v3.1), the Climate Prediction Center MORPHing Product Climate Data Record (CMORPH-CDR), Global Precipitation Measurement (GPM) Integrated Multi-satellitE Retrievals for GPM (IMERG v06), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR). The bilinear (BIL) interpolation technique was applied to downscale SPPs from a coarse to a fine spatial resolution (1 km). The rugged-topography region of the upper Tekeze–Atbara Basin (UTAB) in Ethiopia was selected as the study area. The results indicate that the precipitation data estimates with the DVQR, MLQR, and BMAQ models and with traditional merging methods outperform the downscaled SPPs. Monthly evaluations reveal that all products perform better in July and September than in June and August due to precipitation variability. The DVQR, MLQR, and BMAQ models exhibit higher accuracy than the traditional merging methods over the UTAB. The DVQR model substantially improved all of the statistical metrics (CC = 0.80, NSE = 0.615, KGE = 0.785, MAE = 1.97 mm d−1, RMSE = 2.86 mm d−1, and PBIAS = 0.96 %) considered compared with the BMAQ and MLQR models. However, the DVQR model did not outperform the BMAQ and MLQR models with respect to the probability of detection (POD) and false-alarm ratio (FAR), although it had the best frequency bias index (FBI) and critical success index (CSI) among all of the employed models. Overall, the newly proposed merging approach improves the quality of SPPs and demonstrates the value of the proposed DVQR model in merging multiple SPPs over regions with rugged topography such as the UTAB.

Development of an Optimal Postprocessing Model Using the Microgenetic Algorithm to Improve Precipitation Forecasting in South Korea

Abstract We developed an advanced postprocessing model for precipitation forecasting using a microgenetic algorithm (MGA). The algorithm determines the optimal combination of three general circulation models: the Korean Integrated Model, the Unified Model, and the Integrated Forecast System model. To measure model accuracy, including the critical success index (CSI), probability of detection (POD), and frequency bias index, the MGA calculates optimal weights for individual models based on a fitness function that considers various indices. Our optimized multimodel yielded up to 13% and 10% improvement in CSI and POD performance compared to each individual model, respectively. Notably, when applied to an operational definition that considers precipitation thresholds from three models and averages the precipitation amount from the satisfactory models, our optimized multimodel outperformed the current operational model used by the Korea Meteorological Administration by up to 1.0% and 6.8% in terms of CSI and false alarm ratio performance, respectively. This study highlights the effectiveness of a weighted combination of global models to enhance the forecasting accuracy for regional precipitation. By utilizing the MGA for the fine-tuning of model weights, we achieved superior precipitation prediction compared to that of individual models and existing standard postprocessing operations. This approach can significantly improve the accuracy of precipitation forecasts. Significance Statement We developed an optimized multimodel for predicting precipitation occurrence using advanced techniques. By integrating various weather models with their optimized weights, our approach outperforms the method of using an arithmetic average of all models. This study underscores the potential to enhance regional precipitation forecasts, thereby facilitating more precise weather predictions for the public.

Performance Assessment of Chirps, Persiann – CDR, IMERG, and TMPA Precipitation Products across Nepal

The observation of hydrological as well as meteorological data is very essential for any kind of hydraulic and hydrological study. In Nepal, due to the significant variation of topography and climatic characteristics as well it is necessary to establish the meteorological stations densely, here only 231 meteorological stations are available and are handled by government organizations Department of Hydrology and Meteorology (DHM). Even though, due to many limitations, the satellite data is very useful for the water resources study. There are so many satellite products available but the performance of these products varies from place to place. In this study, the performance of four satellite products i.e., Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN-CDR), Integrated Multi-Satellite Retrievals for GPM (IMERG), and TRMM Multi-satellite Precipitation Analysis (TMPA) all over Nepal are evaluated with different elevation bands. The performance of each product is evaluated by Probability of Detection (POD), Critical Success Index (CSI), Frequency Bias Index (FBI), False Alarm Ratio (FAR), Root Mean Square Error (RMSE), and Percentage Bias (PBIAS). After analysis of each product, the PERSIANN-CDR data set gives a reasonable performance for all elevation bands after bias correction.

Assessment of IMERG v06 Satellite Precipitation Products in the Canadian Great Lakes Region

Abstract The Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM; IMERG) is a high-resolution gridded precipitation dataset widely used around the world. This study assessed the performance of the half-hourly IMERG v06 Early and Final Runs over a 5-yr period versus 19 high-quality surface stations in the Great Lakes region of North America. This assessment not only looked at precipitation occurrence and amount, but also studied the IMERG Quality Index (QI) and errors related to passive microwave (PMW) sources. Analysis of bias in accumulated precipitation amount and precipitation occurrence statistics suggests that IMERG presents various uncertainties with respect to time scale, meteorological season, PMW source, QI, and land surface type. Results indicate that 1) the cold season’s (November–April) larger relative bias can be mitigated via backward morphing; 2) IMERG 6-h precipitation amount scored best in the warmest season (JJA) with a consistent overestimation of the frequency bias index − 1 (FBI-1); 3) the performance of five PMW sources is affected by the season to different degrees; 4) in terms of some metrics, skills do not always enhance with increasing QI; 5) local lake effects lead to higher correlation and equitable threat score (ETS) for the stations closest to the lakes. Results of this study will be beneficial to both developers and users of IMERG precipitation products. Significance Statement The purpose of the study was to assess the performance of the gridded precipitation product from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) version 6 over the Great Lakes region of North America. The assessment performs a statistical comparison of precipitation amounts from IMERG versus surface stations as a function of time scale, season, precipitation event threshold, and input source among satellites. Interpretation of the results identifies shortcomings in the IMERG algorithms, particularly in extreme precipitation events and over ice-covered surfaces. The results also describe spatial variability in the IMERG data quality due to the complex geography of the study area and offer a clear threshold in the Quality Index (QI) flag for optimal application of the precipitation products.

Evaluation of CHIRPS and CFSR precipitation products over the Mujib Basin, Jordan

Open-source climate products provide the possibility of complementing observed data, which sometimes suffer from the scarcity and inconsistency issues. This study aims to evaluate the accuracy of two open-source climate products, Climate Hazards Group Infrared Precipitation with Station (CHIRPS 0.05) and Climate Forecast System Reanalysis (CFSR), in capturing precipitation over the Mujib Basin, Jordan, from 2002 to 2012. Both products were compared with observed data collected from ten climate stations using the point-to-pixel comparison approach at the daily, monthly, seasonal, and annual scales. The coefficient of determination (R 2 ), the root mean square error (RMSE), the mean absolute error (MAE), and the relative bias (RB) were used to evaluate the efficiency of CHIRPS and CFSR. While, categorical statistics such as the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI), Heidke skill score (HSS), and frequency bias index (FBI), were used to analyze the precipitation detection capability. Results indicated good correlations between open-source climate products and observed data in the monthly time period, where the R 2 values ranged from 0.65 (CFSR) to 0.76 (CHIRPS). Besides that, CHIRPS performed better than CFSR for the daily, monthly, and seasonal time steps, with a better ability in detecting precipitation. Therefore, CHIRPS is recommended to fill the missing gaps of observed data and to detect the drought conditions over the Mujid Basin. Keywords : CFSR, CHIRPS, Jordan, Mujib Basin, precipitation, validation

Evaluations of satellite/reanalysis rainfall and temperature products in the Bale Eco-Region (Southern Ethiopia) to enhance the quality of input data for hydro-climate studies

The sparse distribution and lack of meteorological stations due to deficit infrastructure in developing countries is one of the limiting factors for hydro-climate studies, and dependency on globally available data is often prone to various level of errors. Thus, this study aimed to evaluate the performance of satellite/reanalysis rainfall and temperature products in the Bale Eco-Region (BER) in Southern Ethiopia. This study evaluated performances of three rainfall products such as the Climate Hazards Group Infrared Precipitation with Stations, version 2.0 (CHIRPS v2.0), Tropical Applications of Meteorology using SATellite and ground-based observations, version 3.1 (TAMSAT v3.1) and Multi-Source Weighted-Ensemble Precipitation, version 2.8 (MSWEP v2.8). The two temperature products evaluated in this study are ERA5 and Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA v2). Evaluations of these satellite/reanalysis rainfall and temperature products were undertaken against observed data (1995–2014) in temperate, sub-tropical and tropical agro-ecological zones (AEZs) across multiple temporal scales ranging from the daily to annual. For assessing the performances of satellite/reanalysis rainfall and temperature products, a point-pixel evaluation approach was undertaken using five continuous evaluation scores such as correlation coefficient (R), mean absolute error (MAE), root mean square error (RMSE), percent bias (PBIAS) and Kling–Gupta efficiency (KGE). Categorical sores such as Probability of Detection (POD), False Alarm Ratio (FAR) and Frequency Bias Index (FBI) were also used for assessing the rainfall products. The findings revealed that MSWEP v2.8 has better performance than CHIRPS v2.0 and TAMSAT v3.1 in temperate and tropical AEZs at the daily, dry season and annual time steps as well as in sub-tropical AEZ in dry season and annual temporal periods, but MSWEP v2.8 displayed comparable performance with TAMSAT v3.1 in the daily time step. CHIRPS v2.0 exhibit superior performance in the monthly time scale in the three AEZs as well as in the wet season in temperate and tropical AEZs, but TAMSAT v3.1 has outperformed than CHIRPS v2.0 in the wet season in sub-tropical AEZ. The finding also indicated that the capability of MSWEP v2.8 to detect the rainy days (79–86%) and frequency of rainy days (0.99–1.79) is better than CHIRPS v2.0 and TAMSAT v3.1, but TAMSAT v3.1 has shown the best performance for identifying the non-rainy days (14–38%) than MSWEP v2.8 and CHIRPS v2.0. With regard to temperature, MERRA v2 outperformed over ERA5 in temperate and tropical AEZs for estimating both maximum and minimum temperatures from the daily to annual time scales, but ERA5 has shown superior performance than MERRA v2 in the sub-tropical AEZ. In view of the finding, we concluded that the best performing rainfall and temperature products for each AEZ can be used for data scarce regions such as the BER. The findings of this study provide important insights about the need to identify best performing rainfall and temperature products for different AEZs to enhance the quality of hydro-climate study outputs in the decision-making process.

Graupel mixing ratio forecast from a cloud resolving numerical weather prediction model as a tool for lightning activity prediction

Graupel mixing ratio over Bulgaria for the warm half year of 2021 based on the AROME-BG numerical weather prediction (NWP) model, is evaluated and connected lightning data detected by the ATDnet lightning location network. Lightning data and forecasted graupel mixing ratios were considered on resolutions of 5×5 km and 10×10 km with flash rate for one and three hours, as well on a daily base using upscaling neighborhood method. Two daily model runs are considered – at 06 and 18 UTC. Commonly used skill-scores in meteorological forecasts are used as evaluation metrics – probability of detection (POD), false alarm rate (F), proportion correct index (PC), and frequency bias index (FBI). Lightning probability forecast (based on graupel mixing ratio) is evaluated at diurnal, monthly, and spatial bases. Results show that graupel mixing ratio taken from the cloud resolving NWP model AROME-BG could be used as a tool to forecast lightning probability with a relatively high performance. Decreases of forecast spatial resolution and time frequency lead to improvement of forecast probability of detection (POD) and frequency bias index (FBI) and to a slight deterioration of its false alarm rate (F) and its percent correct (PC), and the impact of forecast time frequency is more pronounced.

Evaluating Magnitude Agreement and Occurrence Consistency of CHIRPS Product with Ground-Based Observations over Medium-Sized River Basins in Nepal

High spatio-temporal resolution and accurate long-term rainfall estimates are critical in sustainable water resource planning and management, assessment of climate variability and extremes, and hydro-meteorology-related water system decisions. The recent advent of improved higher-resolution open-access satellite-based rainfall products has emerged as a viable complementary to ground-based observations that can often not capture the rainfall variability on a spatial scale. In a developing country such as Nepal, where the rain-gauge monitoring network is sparse and unevenly distributed, satellite rainfall estimates are crucial. However, substantial errors associated with such satellite rainfall estimates pose a challenge to their application, particularly in complex orographic regions such as Nepal. Therefore, these precipitation products must be validated before practical usage to check their accuracy and occurrence consistency. This study aims to assess the reliability of the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) product against ground-based observations from 1986 to 2015 in five medium-sized river basins in Nepal, namely, Babai, Bagmati, Kamala, Kankai, and the West Rapti river basin. A set of continuous evaluation metrics (correlation coefficient, root mean square error, relative bias, and Kling-Gupta efficiency) were used in analyzing the accuracy of CHIRPS and categorical metrics (probability of detection, critical success index, false alarm ratio, and frequency bias index). The Probability of Detection and Critical Success Index values were found to be considerably low (<0.4 on average), while the false alarm ratio was significant (>0.4 on average). It was found that CHIRPS showed better performance in seasonal and monthly time scales with high correlation and indicated greater consistency in non-monsoon seasons. Rainfall amount (less than 10 mm and greater than 150 mm) and rainfall frequency was underestimated by CHIRPS in all basins, while the overestimated rainfall was between 10 and 100 mm in all basins except Kamala. Additionally, CHIRPS overestimated dry days and maximum consecutive dry days in the study area. Our study suggests that CHIRPS rainfall products cannot supplant the ground-based observations but complement rain-gauge networks. However, the reliability of this product in capturing local extreme events (such as floods and droughts) seems less prominent. A high-quality rain gauge network is essential to enhance the accuracy of satellite estimations.

Evaluation of ERA5 Precipitation Accuracy Based on Various Time Scales over Iran during 2000–2018

In regional studies, reanalysis datasets can extend precipitation time series with insufficient observations. In the present study, the ERA5 precipitation dataset was compared to observational datasets from meteorological stations in nine different precipitation zones of Iran (0.125° × 0.125° grid box) for the period 2000–2018, and measurement criteria and skill detection criteria were applied to analyze the datasets. The results of the daily analysis revealed that the correlation between ERA5 and observed precipitation were larger than 0.5 at 90% of stations. Also, The daily standard relative bias indicated that precipitation was overestimated in zone 6. As detection criteria, the frequency bias index (FBI) and proportion correct (PC) showed that the ERA5 data could capture daily precipitation events. Correlation confidence comparisons between the ERA5 and observational time series at daily, monthly, and seasonal scales revealed that the correlation confidence was higher at monthly and seasonal scales. The standard relative bias results at monthly and seasonal scales followed the daily relative bias results, and most of the ERA5 underestimations during the summer belonged to zone 1 in the coastal area of the Caspian Sea with convective precipitation. In addition, some complex mountainous regions were associated with overestimated precipitation, especially in northwest Iran (zone 6) in different time scales.

Evaluation and Comparison of Daily GPM/TRMM Precipitation Products over the Tianshan Mountains in China

Based on the complex topography and climate conditions over the Tianshan Mountains (TSM) in Xinjiang, China, the new precipitation product, the Global Precipitation Measurement (GPM) (IMERG), and its predecessor, the Tropical Rainfall Measuring Mission (TRMM) 3B42 (TMPA), were evaluated and compared. The evaluation was based on daily-scale data from April 2014 to March 2015 and analyses at annual, seasonal and daily scales were performed. The results indicated that, overall, the annual precipitation in the Tianshan area tends to be greater in the north than in the south and greater in the west than in the east. Compared with the ground reference dataset, GPM and TRMM datasets represent the spatial variation of annual and seasonal precipitation over the TSM well; however, both measurements underestimate the annual precipitation. Seasonal analysis found that the spatial variability of seasonal precipitation has been underestimated. For the daily assessment, the coefficient of variation (CV), correlation coefficient (R) and relative bias (RB) were calculated. It was found that the GPM and TRMM data underestimated the larger CV. The TRMM data performed better on the daily variability of precipitation in the TSM. The R and RB data indicate that the performance of GPM is generally better than that of TRMM. The R value of GPM is generally greater than that of TRMM, and the RB value is closer to 0, indicating that it is closer to the measured value. As for the ability to detect precipitation events, the GPM products have significantly improved the probability of detection (POD) (POD values are all above 0.8, the highest is 0.979, increased by nearly 17%), and the critical success index (CSI) (increased by nearly 9% in the TSM) is also better than TRMM, although it is only slightly weaker than TRMM in terms of the false alarm ratio (FAR) and frequency bias index (FBI). Overall, GPM underestimates the low rainfall rate by 6.4% and high rainfall rate by 22.8% and overestimates middle rain rates by 29.1%. However, GPM is better than TRMM in capturing all types of rainfall events. Based on these results, GPM-IMERG presents significant improvement over its predecessor TRMM 3B42. Considering the performance of GPM in different subregions, a lot of work still needs to be done to improve the performance of the satellite before being used for research.

Evaluations on gridded precipitation products spanning more than half a century over the Tibetan Plateau and its surroundings

Long-term precipitation data play significant roles in hydrological applications and climate change studies. In this study three gridded precipitation products (Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources, APHRODITE; Climate Hazards Group InfraRed Precipitation with Station data, CHIRPS; and Precipitation Estimation from Remotely sensed Information using Artificial Neural Networks-Climate Data Record, PERSIANN-CDR) with the spatial resolution of 0.25° were evaluated using rain gauge data at the daily, monthly and annual scales over the Tibetan Plateau and its surrounding areas (TP). R2, bias, RMSE and MAE were adopted to assess precipitation amount; indices including the probability of detection (POD), false alarm ratio (FAR), critical success index (CSI) and frequency bias index (FBI) were used to evaluate gridded products’ ability to detect precipitation occurrences. We found that: (1) APHRODITE outperformed CHIRPS and PERSIANN-CDR against observations at the annual scale with the highest R2 (~0.93), the lowest MAE (~67 mm/year) and RMSE (~116 mm/year), and APHRODITE also showed better performance in distinguishing rain/no rain events with the highest POD (~0.9) and CSI (>0.6), the lowest FAR (~0.35), followed by PERSAINN-CDR and CHIRPS; (2) CHIRPS performed nearly to APHRODITE in precipitation amount with R2 around 0.8 while the lowest POD (<0.4) implied the shortage for CHIRPS in detecting rain/no rain conditions; (3) in spite of the lowest agreement with gauge measurements with the largest bias (~25%), PERSIANN-CDR had a relatively higher mean POD (>0.5), indicating that PERSIANN-CDR had better potential of detecting rainfall occurrences than CHIRPS but tended to overestimate precipitation; (4) generally, PERSIANN-CDR overestimated the precipitation greater than CHIRPS and APHRODITE data at monthly scale; and (5) the three products correlated well with each other in the interior, southern and eastern regions of TP but there were still some significant differences along the borders and surroundings of the TP by inter-comparisons. Finally, we recommended APHRODITE for climate change study for its high accuracy and long-time series, CHIRPS and PERSIANN-CDR could also be considered after correction.

Performance Assessment of Sub-Daily and Daily Precipitation Estimates Derived from GPM and GSMaP Products over an Arid Environment

Precipitation is a critical variable for comprehending various climate-related research, such as water resources management, flash flood monitoring and forecasting, climatic analyses, and hydrogeological studies, etc. Here, our objective was to evaluate the rainfall estimates obtained from Global Precipitation Mission (GPM), and Global Satellite Mapping of Precipitation (GSMaP) constellation over an arid environment like the Sultanate of Oman that is characterized by a complex topography and extremely variable rainfall patterns. Global Satellite-based Precipitation Estimates (GSPEs) can provide wide coverage and high spatial and temporal resolutions, but evaluating their accuracy is a mandatory step before involving them in different hydrological applications. In this paper, the reliability of the Integrated Multi-satellitE Retrievals for the GPM (IMERG) V04 and GSMaP V06 products were evaluated using the reference in-situ rain gauges at sub-daily (e.g., 6, 12, and 18 h) and daily time scales during the period of March 2014–December 2016. A set of continuous difference statistical indices (e.g., mean absolute difference, root mean square error, mean difference, and unconditional bias), and categorical metrics (e.g., probability of detection, critical success index, false alarm ratio, and frequency bias index) were used to evaluate recorded precipitation occurrences. The results showed that the five GSPEs could generally delineate the spatial and temporal patterns of rainfall while they might have over- and under-estimations of in-situ gauge measurements. The overall quality of the GSMaP runs was superior to the IMERG products; however, it also encountered an exaggeration in case of light rain and an underestimation for heavy rain. The effects of the gauge calibration algorithm (GCA) used in the final IMERG (IMERG-F) were investigated by comparison with early and late runs. The IMERG-F V04 product did not show a significant improvement over the early (i.e., after 4 h of rainfall observations) and late (i.e., after 12 h of rainfall observations) products. The results indicated that GCA could not reduce the missed precipitation records considerably.

Evaluation of Precipitation Estimates CMORPH-CRT on Regions of Mexico with Different Climates

Satellite-based precipitation (SBP) products with global coverage have the potential to overcome the lack of information in places where there are no rain gauges to perform hydrological analyses; however, it is necessary to evaluate the reliability of the SBP products. In this study, we evaluated the performance of the Climate Prediction Center morphing technique with corrected bias (CMORPH-CRT) product in 14 sites in Mexico. The evaluation was carried out using two approaches: (1) using categorical metrics that include indicators of probability of detection (POD), false alarm rate (FAR), critical success index (CSI), and frequency bias index (FBI); and (2) through statistical indicators such as the mean absolute error (MAE), root mean square error (RMSE), relative bias (RB), and correlation coefficient (CC). The analysis was carried out with two levels of temporal aggregation: 30 min and daily. The results indicate that the CMORPH-CRT product overestimates the number of precipitation events in most cases since FBI values greater than 1 in 78.6% of analyzed stations were obtained. Also, we obtained CC values in the range of 0.018 to 0.625, which implied weak to moderate correlations, and found that in all stations, the CMORPH-CRT product overestimates the precipitation (RB &gt; 0).

Dissecting Performances of PERSIANN-CDR Precipitation Product over Huai River Basin, China

Satellite-based precipitation products, especially those with high temporal and spatial resolution, constitute a potential alternative to sparse rain gauge networks for multidisciplinary research and applications. In this study, the validation of the 30-year Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) daily precipitation dataset was conducted over the Huai River Basin (HRB) of China. Based on daily precipitation data from 182 rain gauges, several continuous and categorical validation statistics combined with bias and error decomposition techniques were employed to quantitatively dissect the PERSIANN-CDR performance on daily, monthly, and annual scales. With and without consideration of non-rainfall data, this product reproduces adequate climatologic precipitation characteristics in the HRB, such as intra-annual cycles and spatial distributions. Bias analyses show that PERSIANN-CDR overestimates daily, monthly, and annual precipitation with a regional mean percent total bias of 11%. This is related closely to the larger positive false bias on the daily scale, while the negative non-false bias comes from a large underestimation of high percentile data despite overestimating lower percentile data. The systematic sub-component (error from high precipitation), which is independent of timescale, mainly leads to the PERSIANN-CDR total Mean-Square-Error (TMSE). Moreover, the daily TMSE is attributed to non-false error. The correlation coefficient (R) and Kling–Gupta Efficiency (KGE) respectively suggest that this product can well capture the temporal variability of precipitation and has a moderate-to-high overall performance skill in reproducing precipitation. The corresponding capabilities increase from the daily to annual scale, but decrease with the specified precipitation thresholds. Overall, the PERSIANN-CDR product has good (poor) performance in detecting daily low (high) rainfall events on the basis of Probability of Detection, and it has a False Alarm Ratio of above 50% for each precipitation threshold. The Equitable Threat Score and Heidke Skill Score both suggest that PERSIANN-CDR has a certain ability to detect precipitation between the second and eighth percentiles. According to the Hanssen–Kuipers Discriminant, this product can generally discriminate rainfall events between two thresholds. The Frequency Bias Index indicates an overestimation (underestimation) of precipitation totals in thresholds below (above) the seventh percentile. Also, continuous and categorical statistics for each month show evident intra-annual fluctuations. In brief, the comprehensive dissection of PERSIANN-CDR performance reported herein facilitates a valuable reference for decision-makers seeking to mitigate the adverse impacts of water deficit in the HRB and algorithm improvements in this product.

A preliminary results of assessment of BMKG-WRF numerical model daily rainfall forecasts performance using categorical verification

Indonesia Agency for Meteorology, Climatology and Geophysics (BMKG) has been using WRF (Weather and Research Forecasting) numerical weather model in forecasting daily rainfall accumulation. The method used to compare the daily rainfall accumulation of WRF forecast results (one to three days forecast) to rain-gauge observation data from 153 meteorological stations from March 2016 to December 2017 by using categorical verification techniques. The results show that the values of Frequency Bias Index (FBI), Proportion Correct (PC), Probability of Detection (POD), False Alarm ratio (FAR), and Threat Score (TS) for one-day forecast and three-day forecast in 2016 are mostly lower than 2017. Meanwhile, for 2-days forecast in 2016 generally lower than 2017 except PC and FAR. Molucca-Papua have the highest value of PC (0.63) for one-day forecast while Kalimantan have the highest values of PC (0.67, 0.68) for 2 and 3 days ahead forecast. In contrary, Sumatera have the lowest PC value (0.55) for one-day forecast while Bali-Nusa Tenggara have the lowest PC value (0.58, 0.59) for 2 and 3 days ahead forecast, respectively. It can be concluded that the performance of BMKG WRF quite accurate in forecasting daily rainfall up to three days ahead.

Evaluation of Short-Range Precipitation Reforecasts from East Asia Regional Reanalysis

Abstract As the need for regional reanalyses emerged around the world, a short period of the East Asia Regional Reanalysis (EARR) system was recently developed based on the Unified Model (UM). In this study, the quality of the EARR is evaluated by comparing the short-range precipitation reforecasts against reforecasts of ERA-Interim (ERA-I) reanalysis and operational forecasts of the Korea Meteorological Administration (OPER). For the verification, two different periods are selected for 14 days in the summer (July 2013, denoted as 201307) and winter (February 2014, denoted as 201402). The equitable threat score (ETS) of EARR and OPER is generally greater than that of ERA-I. The frequency bias index (FBI) of EARR and OPER is overall closer to 1 than that of ERA-I for all thresholds, which indicates that EARR and OPER are much closer to the observation compared to ERA-I. For the period 201307, the ERA-I FBI is greater than 1 for lower thresholds and the probability of detection (POD) and false alarm ratio (FAR) of ERA-I are high, implying that ERA-I tends to overforecast light precipitation. In addition, using the same Weather Research and Forecasting (WRF) Model, the 6-h precipitation forecasts are integrated every 12 h (initialized from 0000/1200 UTC) for 4 months for the summer and winter season. Although the differences of ETS and FBI between EARR and ERA-I are not distinct for the summer season, overall EARR ETS is higher than ERA-I ETS, and EARR FBI is closer to 1 than ERA-I FBI. Based on several evaluations, the precipitation reforecasts of EARR are confirmed to be more accurate than those of OPER and ERA-I in East Asia.

Multi-Variable Classification Approach for the Detection of Lightning Activity Using a Low-Cost and Portable X Band Radar

This work proposes a multi-parameter method for the detection of cloud-to-ground stroke rate (SRCG) associated to convective cells, based on the measurements of a low-cost single-polarization X-band weather radar. To train and test our procedure, we built up a multi-year dataset, collecting 1575 radar reflectivity volumes that were acquired in the pilot study area of Naples metropolitan environment matched with the LIghtning NETwork (LINET) strokes and meteorological in-situ data. Three radar-based variables are extracted simultaneously for each rain cell and properly merged together, using “ad hoc” classification methods, to produce an estimation of the expected lightning activity for each rain cell. These variables, proxies of mixed-phase particles and ice amount into a convective cell, are combined into a single label to cluster the SRCG into two categories: SRCG = 0 (no production of strokes) or SRCG &gt; 0 (stroke production), respectively. Overall, the main results are comparable with those that were obtained from more advanced radar systems, showing a Critical Success Index of 0.53, an Equitable Threat Score of 0.34, a Frequency Bias Index of 1.00, a Heidke Skill Score of 0.42, a Hanssen-Kuiper Skill Score of 0.42, and an area under the curve of probability of detection as a function of false alarm rate (usually referred as ROC curve) equal to 0.78. The developed technique, although with some limitations, outperforms those based on the use of single stroke proxy parameters.

Evaluation of Multi-Satellite Precipitation Products for Streamflow Simulations: A Case Study for the Han River Basin in the Korean Peninsula, East Asia

The accuracy and sufficiency of precipitation data play a key role in environmental research and hydrological models. They have a significant effect on the simulation results of hydrological models; therefore, reliable hydrological simulation in data-scarce areas is a challenging task. Advanced techniques can be utilized to improve the accuracy of satellite-derived rainfall data, which can be used to overcome the problem of data scarcity. Our study aims to (1) assess the accuracy of different satellite precipitation products such as Tropical Rainfall Measuring Mission (TRMM 3B42 V7), Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), PERSIANN-Climate Data Record (PERSIANN-CDR), and China Meteorological Assimilation Driving Datasets for the SWAT Model (CMADS) by comparing them with gauged rainfall data; and (2) apply them for runoff simulations for the Han River Basin in South Korea using the SWAT model. Based on the statistical measures, that is, the proportion correct (PC), the probability of detection (POD), the frequency bias index (FBI), the index of agreement (IOA), the root-mean-square-error (RMSE), the mean absolute error (MAE), the coefficient of determination (R2), and the bias, the rainfall data of the TRMM and CMADS show a better accuracy than those of PERSIANN and PERSIANN-CDR when compared to rain gauge measurements. The TRMM and CMADS data capture the spatial rainfall patterns in mountainous areas as well. The streamflow simulated by the SWAT model using ground-based rainfall data agrees well with the observed streamflow with an average Nash-Sutcliffe efficiency (NSE) of 0.68. The four satellite rainfall products were used as inputs in the SWAT model for streamflow simulation and the results were compared. The average R2, NSE, and percent bias (PBIAS) show that hydrological models using TRMM (R2 = 0.54, NSE = 0.49, PBIAS = [−52.70–28.30%]) and CMADS (R2 = 0.44, NSE = 0.42, PBIAS = [−29.30–41.80%]) data perform better than those utilizing PERSIANN (R2 = 0.29, NSE = 0.13, PBIAS = [38.10–83.20%]) and PERSIANN-CDR (R2 = 0.25, NSE = 0.16, PBIAS = [12.70–71.20%]) data. Overall, the results of this study are satisfactory, given that rainfall data obtained from TRMM and CMADS can be used to simulate the streamflow of the Han River Basin with acceptable accuracy. Based on these results, TRMM and CMADS rainfall data play important roles in hydrological simulations and water resource management in the Han River Basin and in other regions with similar climate and topographical characteristics.

Assessment of satellite-based precipitation estimates over Paraguay

Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations. Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Tropical Rainfall Measurement Mission Multi-Satellite Precipitation Analysis (TMPA V7; 3B42 V7) and the version 1.0 of the purely satellite-based product of the Climate Prediction Center Morphing Technique (CMORPH RAW) through their comparison with daily in situ precipitation measurements from 1998 to 2012 over Paraguay. The statistical assessment is conducted with several commonly used indexes. Specifically, to evaluate the accuracy of daily precipitation amounts, mean error (ME), root mean square error (RMSE), BIAS, and coefficient of determination (R2) are used, and to analyze the capability to correctly detect different precipitation intensities, false alarm ratio (FAR), frequency bias index (FBI), and probability of detection (POD) are applied to various rainfall rates (0, 0.1, 0.5, 1, 2, 5, 10, 20, 40, 60, and 80 mm/day). Results indicate that TMPA V7 has a better performance than CMORPH RAW over Paraguay. TMPA V7 has higher accuracy in the estimation of daily rainfall volumes and greater precision in the detection of wet days (> 0 mm/day). However, both satellite products show a lower ability to appropriately detect high intensity precipitation events.