Search

To understand and improve the performance and quality of Global Precipitation Measurement (GPM) satellite-derived precipitation products during the typhoon process, with gauge observations from ground meteorological stations, this study used the successive and optimal interpolation correction methods to correct the GPM precipitation products. The performance of GPM products (Version 6) before and after the correction at different time scales are evaluated with the products during the two landfalls of Typhon"In-Fa". Results show that the original GPM precipitation products fail to describe the strong rainfall center over the high terrain area, which significantly underestimates the precipitation by approximately 45%. This is mainly due to the underestimation of the large values in the northeast region of Zhejiang province. The analysis of precipitation classification inspection suggests that the uncorrected GPM satellite-derived products have good capability for light rain, but poor for other rainfall levels. After the successive and optimal interpolation correction, significant improvement can be found in the cumulative, daily, and hourly rainfall products. The products with successive correction are better, while the product with optimal interpolation slightly overestimates the actual precipitation. The corrected GPM products can not only describe the heavy precipitation center related to the terrain but also reflect heavy rainstorms and heavy precipitation with hourly rainfall intensity greater than 23 mm·h-1.

Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

We explore impacts of model domain size on rainfall forecasts over Zhejiang Province during the Meiyu season in this paper. The existing modeling system is based on the Weather Research and Forecasting (WRF), which is referred to as the Zhejiang WRF with Advanced Data Assimilation System (ADAS) Real-time Modeling System (ZJWARMS). First, we identify the regions with large uncertainties of model forecasts using correlation analysis of different reanalysis datasets. Five key regions affecting precipitation in Zhejiang are identified by calculating the correlation-coefficients between precipitation in Zhejiang and large-scale atmospheric factors. A new WRF model domain, called ZJdomain, is then chosen to cover the key regions and to avoid the regions with large uncertainties. To evaluate the differences of precipitation forecasts in each precipitation category between using the new domain, ZJdomain, and using the current operational model domain, ZJWARMS, two sets of experiments are conducted, including two case studies and a batch test based on daily 72-h forecasts. The results suggest that the ZJdomain can better simulate the key weather systems and their associated precipitation in Zhejiang. Quantitative tests of different-category precipitation also show that the equivalent threat score (ETS) increases significantly, and the frequency of missed (FOM) and the false alarm ratio (FAR) decrease with varying degrees. In the batch test, the ETSs of the torrential rain in forecasts with the lead times of 24–48 h and 48–72 h increase by 100.3% and 62.1%, respectively. Compared with the Shanghai Meteorological Service WRF ADAS Real-time Modeling System version 2.0 (SMSWARMS), it is found that the unsuitable WRF model domain is one of the main reasons that the precipitation forecast error of ZJWARMS is always larger than that of SMSWARMS. This study demonstrates that the selection of a suitable model domain is a key to improve the forecast of regional operational numerical weather prediction systems. [ABSTRACT FROM AUTHOR]

Accurate and continuous rainfall monitoring is essential for effective water resources management, especially in arid and semi-arid regions such as the United Arab Emirates (UAE). Significant spatio-temporal precipitation variation in the UAE necessitates the use of the latest techniques to measure rainfall intensity accurately. This study investigates the consistency and applicability of four satellite precipitation products, namely PERSIANN, PERSIANN-CCS, PERSIANN-CDR, and PDIR-Now, over the UAE. Daily time series data from 2011 to 2020 were analyzed using various statistical measures and climate indices to develop the belief in the products and for their inter-comparison. The analysis revealed that the average probability of detection (POD) for PDIR and CDR was the highest, with values ranging from 0.7–0.9 and 0.6–0.9, respectively. Similarly, CDR has a better Heidke Skill Score (HSS) with an average value of 0.26. CDR outperformed its counterparts with an average correlation coefficient value of 0.70 vs. 0.65, 0.40, and 0.34 for PDIR, CCS, and PERSIANN, respectively. Precipitation indices analysis revealed that all the products overestimated the number of consecutive wet days by 15–20%, while underestimating consecutive dry days by 5–10%. The quantitative estimations indicate that all the products were matching with the gauge values during the wet months (January–April), while they showed significant overestimation during the dry months. CDR and PDIR were in close agreement with the gauge data in terms of maximum daily rainfall with an error of less than 10% for both products. As compared to others, PERSIANN-CDR provided better estimates, particularly in terms of capturing extreme rainfall events and spatial distribution of rainfall. This study provides the first comprehensive evaluation of four PERSIANN family products based on recent daily rainfall data of UAE. The findings can provide future insights into the applicability and improvement of PERSIANN products in arid and semi-arid regions.

Abstract Results from object‐based verification of rainfall forecasts for landfalling tropical cyclones (TCs) over China during the period 2012–2015 are presented. The sample consists of 25 landfall events and 133 operational numerical forecasts from the TC version of the Australian Community Climate and Earth System Simulator. Mean equitable threat scores, probabilities of detection and false alarm ratios for the 30 mm isohyet for the unadjusted forecasts at 0–6 hr (essentially the initialization) are (0.23, 0.55, 0.65), while the performance measures of 24 hr forecast accumulations are the best for the 0–24 hr forecast (0.37, 0.67, 0.40) and then worsen to (0.16, 0.38, 0.66) for the 48–72 hr forecast. Forecast ability also decreases with the increase in rainfall amount. The contiguous rain area (CRA) verification method is used to diagnose the source of systematic errors from the displacement, rotation, volume and pattern of the forecasted rain fields. Results show that the errors are mostly from rainfall patterns, followed by displacement errors, particularly for very heavy rain. After application of the displacement and rotation adjustments of the CRA method, averaged errors improve by about 15%. Results suggest that rainfall prediction will continue to improve with improved track prediction, but more work is needed on model initialization and the prediction of TC structure. The study has uncertainty related to the limited sample size, which could cause large variability, particularly for heavy rainfall at 6 and 72 hr. However, the results still represent a useful benchmark for future verification of landfalling TCs.

Results from object‐based verification of rainfall forecasts for landfalling tropical cyclones (TCs) over China during the period 2012–2015 are presented. The sample consists of 25 landfall events and 133 operational numerical forecasts from the TC version of the Australian Community Climate and Earth System Simulator. Mean equitable threat scores, probabilities of detection and false alarm ratios for the 30 mm isohyet for the unadjusted forecasts at 0–6 hr (essentially the initialization) are (0.23, 0.55, 0.65), while the performance measures of 24 hr forecast accumulations are the best for the 0–24 hr forecast (0.37, 0.67, 0.40) and then worsen to (0.16, 0.38, 0.66) for the 48–72 hr forecast. Forecast ability also decreases with the increase in rainfall amount. The contiguous rain area (CRA) verification method is used to diagnose the source of systematic errors from the displacement, rotation, volume and pattern of the forecasted rain fields. Results show that the errors are mostly from rainfall patterns, followed by displacement errors, particularly for very heavy rain. After application of the displacement and rotation adjustments of the CRA method, averaged errors improve by about 15%. Results suggest that rainfall prediction will continue to improve with improved track prediction, but more work is needed on model initialization and the prediction of TC structure. The study has uncertainty related to the limited sample size, which could cause large variability, particularly for heavy rainfall at 6 and 72 hr. However, the results still represent a useful benchmark for future verification of landfalling TCs. [ABSTRACT FROM AUTHOR]

This study presents the first comprehensive evaluation of the performance of the monthly TRMM rainfall product (3B43-v7) in capturing the rainfall patterns, on the geographic area of the central Mediterranean during the period 1998–2017. Monthly rainfall data from a network of ground-based stations were used to compute a series of spatiotemporal evaluation statistics for the satellite measurement accuracy. These statistics showed a satisfactory accuracy of the examined satellite products in capturing seasonal and annual rainfall patterns, having rather high correlation coefficients (0.75 to 0.96). Nevertheless, a slight overestimation of the rainfall values (with a mean value lower than 15 mm) implied by the TRMM was found at seasonal basis with the exception of winter when a small underestimation (with a mean value amounting to 8 mm) was detected. The overestimation tendency was mainly detected over the northern and the western parts of the studied domain, where the effect of the earth surface altitude seems to be important. An underestimation tendency prevails over the marine regions and seems to be associated with the rain rate (in the sense that heavy rainfall episodes are underestimated more). The overall biases of the TRMM rainfall product in respect to the control ground-based station sample found to be within ± 0.2 of the local rainfall standard deviation and nonstatistically significant almost in half of the cases in all seasons except winter. The possible causes of the detected diversity of the seasonal error statistics behavior were further explored and discussed. [ABSTRACT FROM AUTHOR]

Rainfall forecast performance was evaluated for the first time for the Hurricane Weather Research and Forecasting (HWRF) model. This study focused on HWRF performance in predicting rainfall from Hurricane Harvey in 2017. In particular, two configurations of the 2017 version of HWRF were investigated: a deterministic version of the Basin-scale HWRF (HB17) and an ensemble version of the operational HWRF (H17E). This study found that HB17 generated reasonable rainfall patterns and rain-rate distributions for Hurricane Harvey, in part due to accurate track forecasts. However, the estimated rain rates near the storm center (within 50 km) were slightly overestimated. In the rainband region (150 to 300 km), HB17 reproduced heavy rain rates and underestimated light rain rates. The accumulated rainfall pattern successfully captured Harvey’s intense outer rainband with adequate spatial displacement. In addition, the performance of H17E on probabilistic rainfall has shown that the ensemble forecasts can potentially increase the accuracy of the predicted locations for extreme rainfall. Moreover, the study also indicated the importance of high-resolution dynamical models for rainfall predictions. Although statistical models can generate the overall rainfall patterns along a track, extreme rainfall events produced from outer rainbands can only be forecasted by numerical models, such as HWRF. Accordingly, the HWRF models have the capability of simulating reasonable quantitative precipitation forecasts and providing essential rainfall guidance in order to further reduce loss of life and cost to the economy.

Assessment of forecast precipitation is required before it can be used as input to hydrological models. Using radar observations in southeastern Australia, forecast rainfall from the Australian Community Climate Earth-System Simulator (ACCESS) was evaluated for 2010 and 2011. Radar rain intensities were first calibrated to gauge rainfall data from four research rainfall stations at hourly time steps. It is shown that the Australian ACCESS model (ACCESS-A) overestimated rainfall in low precipitation areas and underestimated elevated accumulations in high rainfall areas. The forecast errors were found to be dependent on the rainfall magnitude. Since the cumulative rainfall observations varied across the area and through the year, the relative error (RE) in the forecasts varied considerably with space and time, such that there was no consistent bias across the study area. Moreover, further analysis indicated that both location and magnitude errors were the main sources of forecast uncertainties on hourly accumulations, while magnitude was the dominant error on the daily time scale. Consequently, the precipitation output from ACCESS-A may not be useful for direct application in hydrological modelling, and pre-processing approaches such as bias correction or exceedance probability correction will likely be necessary for application of the numerical weather prediction (NWP) outputs. EDITOR M.C. Acreman ASSOCIATE EDITOR A. Viglione [ABSTRACT FROM AUTHOR]

Climate change impact and adaptation studies can benefit from an enhanced understanding about the performance of individual as well as ensemble simulations of climate models. Studies that evaluate downscaled simulations of General Circulation Models (GCMs) by Regional Climate Models (RCMs) for African basins are noticeably missing. Recently, the Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative has made multiple RCMs' outputs available for end users across the African continent. Before climate simulations receive applications in impact and adaptation studies, accuracy of the simulation results has to be evaluated. In this study, the rainfall accuracy of eight independent GCMs at a wide range of time scales over the Upper Blue Nile Basin (UBN) in Ethiopia is evaluated. The reference data for performance assessment was obtained from the rain gauge network of the National Meteorological Agency of Ethiopia ( www.ethiomet.gov.et/ ). The models were evaluated using a suite of statistical measures such as bias, Root Mean Squared Error (RMSE), and Coefficient of Variation (CV). Plots of observed vs. simulated rainfall amounts are also used. Findings of this study indicate that the models have some limitations in simulating light (< 1 mm) daily rainfall and heavy (> 10 mm) daily rainfall amount. The annual rainfall bias of the models varies between − 1.4% and − 50% suggesting underestimation. In many aspects the MPI-ESM-LR climate model performed best in terms of bias and RMSE. However, it was found that the performance of the models differs subject to the performance measures used for evaluation. The use of the ensemble mean rainfall simulation did not improve representation of assessed rainfall characteristics in the basin. Based on the findings in this study, we suggest to use multi-models' simulations in order to capture different aspects of the Upper Blue Nile basin rainfall. Topography and rain rate dependent bias correction algorithms should be evaluated for the basin. This study is expected to be valuable for climate change impact and adaptation studies in the UBN basin. [ABSTRACT FROM AUTHOR]

Rainfall forecast performance was evaluated for the first time for the Hurricane Weather Research and Forecasting (HWRF) model. This study focused on HWRF performance in predicting rainfall from Hurricane Harvey in 2017. In particular, two configurations of the 2017 version of HWRF were investigated: a deterministic version of the Basin-scale HWRF (HB17) and an ensemble version of the operational HWRF (H17E). This study found that HB17 generated reasonable rainfall patterns and rain-rate distributions for Hurricane Harvey, in part due to accurate track forecasts. However, the estimated rain rates near the storm center (within 50 km) were slightly overestimated. In the rainband region (150 to 300 km), HB17 reproduced heavy rain rates and underestimated light rain rates. The accumulated rainfall pattern successfully captured Harvey's intense outer rainband with adequate spatial displacement. In addition, the performance of H17E on probabilistic rainfall has shown that the ensemble forecasts can potentially increase the accuracy of the predicted locations for extreme rainfall. Moreover, the study also indicated the importance of high-resolution dynamical models for rainfall predictions. Although statistical models can generate the overall rainfall patterns along a track, extreme rainfall events produced from outer rainbands can only be forecasted by numerical models, such as HWRF. Accordingly, the HWRF models have the capability of simulating reasonable quantitative precipitation forecasts and providing essential rainfall guidance in order to further reduce loss of life and cost to the economy. [ABSTRACT FROM AUTHOR]